code
stringlengths 81
54k
| code_codestyle
int64 0
721
| style_context
stringlengths 91
41.9k
| style_context_codestyle
int64 0
699
| label
int64 0
1
|
|---|---|---|---|---|
"""simple docstring"""
from timeit import timeit
lowerCAmelCase__ ={
"MALAYALAM": True,
"String": False,
"rotor": True,
"level": True,
"A": True,
"BB": True,
"ABC": False,
"amanaplanacanalpanama": True, # "a man a plan a canal panama"
}
# Ensure our test data is valid
assert all((key == key[::-1]) is value for key, value in test_data.items())
def _a ( UpperCAmelCase__ ) -> bool:
__SCREAMING_SNAKE_CASE = 0
__SCREAMING_SNAKE_CASE = len(UpperCAmelCase__ ) - 1
while start_i < end_i:
if s[start_i] == s[end_i]:
start_i += 1
end_i -= 1
else:
return False
return True
def _a ( UpperCAmelCase__ ) -> bool:
__SCREAMING_SNAKE_CASE = len(UpperCAmelCase__ ) // 2
__SCREAMING_SNAKE_CASE = len(UpperCAmelCase__ )
# We need to traverse till half of the length of string
# as we can get access of the i'th last element from
# i'th index.
# eg: [0,1,2,3,4,5] => 4th index can be accessed
# with the help of 1st index (i==n-i-1)
# where n is length of string
return all(s[i] == s[n - i - 1] for i in range(UpperCAmelCase__ ) )
def _a ( UpperCAmelCase__ ) -> bool:
if len(UpperCAmelCase__ ) <= 2:
return True
if s[0] == s[len(UpperCAmelCase__ ) - 1]:
return is_palindrome_recursive(s[1:-1] )
else:
return False
def _a ( UpperCAmelCase__ ) -> bool:
return s == s[::-1]
def _a ( UpperCAmelCase__ ) -> None:
__SCREAMING_SNAKE_CASE = f"""all({name}(key) is value for key, value in test_data.items())"""
__SCREAMING_SNAKE_CASE = f"""from __main__ import test_data, {name}"""
__SCREAMING_SNAKE_CASE = 50_00_00
__SCREAMING_SNAKE_CASE = timeit(stmt=UpperCAmelCase__ , setup=UpperCAmelCase__ , number=UpperCAmelCase__ )
print(f"""{name:<35} finished {number:,} runs in {result:.5f} seconds""" )
if __name__ == "__main__":
for key, value in test_data.items():
assert is_palindrome(key) is is_palindrome_recursive(key)
assert is_palindrome(key) is is_palindrome_slice(key)
print(F'''{key:21} {value}''')
print("a man a plan a canal panama")
# finished 500,000 runs in 0.46793 seconds
benchmark_function("is_palindrome_slice")
# finished 500,000 runs in 0.85234 seconds
benchmark_function("is_palindrome")
# finished 500,000 runs in 1.32028 seconds
benchmark_function("is_palindrome_recursive")
# finished 500,000 runs in 2.08679 seconds
benchmark_function("is_palindrome_traversal")
| 690 |
"""simple docstring"""
from __future__ import annotations
lowerCAmelCase__ =8.9_8_8E9 # units = N * m^s * C^-2
def _a ( UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ ) -> dict[str, float]:
__SCREAMING_SNAKE_CASE = abs(chargea * chargea )
if (force, chargea, chargea, distance).count(0 ) != 1:
raise ValueError('''One and only one argument must be 0''' )
if distance < 0:
raise ValueError('''Distance cannot be negative''' )
if force == 0:
__SCREAMING_SNAKE_CASE = COULOMBS_CONSTANT * charge_product / (distance**2)
return {"force": force}
elif chargea == 0:
__SCREAMING_SNAKE_CASE = abs(UpperCAmelCase__ ) * (distance**2) / (COULOMBS_CONSTANT * chargea)
return {"charge1": chargea}
elif chargea == 0:
__SCREAMING_SNAKE_CASE = abs(UpperCAmelCase__ ) * (distance**2) / (COULOMBS_CONSTANT * chargea)
return {"charge2": chargea}
elif distance == 0:
__SCREAMING_SNAKE_CASE = (COULOMBS_CONSTANT * charge_product / abs(UpperCAmelCase__ )) ** 0.5
return {"distance": distance}
raise ValueError('''Exactly one argument must be 0''' )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 690 | 1 |
"""simple docstring"""
import math
from typing import Dict, Iterable, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
IMAGENET_STANDARD_MEAN,
IMAGENET_STANDARD_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
get_image_size,
is_torch_available,
is_torch_tensor,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_vision_available, logging
if is_torch_available():
import torch
if is_vision_available():
import PIL
lowerCAmelCase__ =logging.get_logger(__name__)
def _a ( UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ ) -> Tuple[int, int]:
def constraint_to_multiple_of(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__=0 , UpperCAmelCase__=None ):
__SCREAMING_SNAKE_CASE = round(val / multiple ) * multiple
if max_val is not None and x > max_val:
__SCREAMING_SNAKE_CASE = math.floor(val / multiple ) * multiple
if x < min_val:
__SCREAMING_SNAKE_CASE = math.ceil(val / multiple ) * multiple
return x
__SCREAMING_SNAKE_CASE = (output_size, output_size) if isinstance(UpperCAmelCase__ , UpperCAmelCase__ ) else output_size
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = get_image_size(UpperCAmelCase__ )
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = output_size
# determine new height and width
__SCREAMING_SNAKE_CASE = output_height / input_height
__SCREAMING_SNAKE_CASE = output_width / input_width
if keep_aspect_ratio:
# scale as little as possible
if abs(1 - scale_width ) < abs(1 - scale_height ):
# fit width
__SCREAMING_SNAKE_CASE = scale_width
else:
# fit height
__SCREAMING_SNAKE_CASE = scale_height
__SCREAMING_SNAKE_CASE = constraint_to_multiple_of(scale_height * input_height , multiple=UpperCAmelCase__ )
__SCREAMING_SNAKE_CASE = constraint_to_multiple_of(scale_width * input_width , multiple=UpperCAmelCase__ )
return (new_height, new_width)
class A__( __magic_name__ ):
lowerCAmelCase = ['''pixel_values''']
def __init__( self : Tuple , __SCREAMING_SNAKE_CASE : bool = True , __SCREAMING_SNAKE_CASE : Dict[str, int] = None , __SCREAMING_SNAKE_CASE : PILImageResampling = PILImageResampling.BILINEAR , __SCREAMING_SNAKE_CASE : bool = False , __SCREAMING_SNAKE_CASE : int = 1 , __SCREAMING_SNAKE_CASE : bool = True , __SCREAMING_SNAKE_CASE : Union[int, float] = 1 / 2_55 , __SCREAMING_SNAKE_CASE : bool = True , __SCREAMING_SNAKE_CASE : Optional[Union[float, List[float]]] = None , __SCREAMING_SNAKE_CASE : Optional[Union[float, List[float]]] = None , **__SCREAMING_SNAKE_CASE : int , ) -> None:
"""simple docstring"""
super().__init__(**__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = size if size is not None else {'''height''': 3_84, '''width''': 3_84}
__SCREAMING_SNAKE_CASE = get_size_dict(__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = do_resize
__SCREAMING_SNAKE_CASE = size
__SCREAMING_SNAKE_CASE = keep_aspect_ratio
__SCREAMING_SNAKE_CASE = ensure_multiple_of
__SCREAMING_SNAKE_CASE = resample
__SCREAMING_SNAKE_CASE = do_rescale
__SCREAMING_SNAKE_CASE = rescale_factor
__SCREAMING_SNAKE_CASE = do_normalize
__SCREAMING_SNAKE_CASE = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
__SCREAMING_SNAKE_CASE = image_std if image_std is not None else IMAGENET_STANDARD_STD
def _a ( self : Optional[int] , __SCREAMING_SNAKE_CASE : np.ndarray , __SCREAMING_SNAKE_CASE : Dict[str, int] , __SCREAMING_SNAKE_CASE : bool = False , __SCREAMING_SNAKE_CASE : int = 1 , __SCREAMING_SNAKE_CASE : PILImageResampling = PILImageResampling.BICUBIC , __SCREAMING_SNAKE_CASE : Optional[Union[str, ChannelDimension]] = None , **__SCREAMING_SNAKE_CASE : Optional[Any] , ) -> np.ndarray:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = get_size_dict(__SCREAMING_SNAKE_CASE )
if "height" not in size or "width" not in size:
raise ValueError(f"""The size dictionary must contain the keys 'height' and 'width'. Got {size.keys()}""" )
__SCREAMING_SNAKE_CASE = get_resize_output_image_size(
__SCREAMING_SNAKE_CASE , output_size=(size['''height'''], size['''width''']) , keep_aspect_ratio=__SCREAMING_SNAKE_CASE , multiple=__SCREAMING_SNAKE_CASE , )
return resize(__SCREAMING_SNAKE_CASE , size=__SCREAMING_SNAKE_CASE , resample=__SCREAMING_SNAKE_CASE , data_format=__SCREAMING_SNAKE_CASE , **__SCREAMING_SNAKE_CASE )
def _a ( self : List[str] , __SCREAMING_SNAKE_CASE : np.ndarray , __SCREAMING_SNAKE_CASE : Union[int, float] , __SCREAMING_SNAKE_CASE : Optional[Union[str, ChannelDimension]] = None , **__SCREAMING_SNAKE_CASE : List[Any] , ) -> str:
"""simple docstring"""
return rescale(__SCREAMING_SNAKE_CASE , scale=__SCREAMING_SNAKE_CASE , data_format=__SCREAMING_SNAKE_CASE , **__SCREAMING_SNAKE_CASE )
def _a ( self : Tuple , __SCREAMING_SNAKE_CASE : np.ndarray , __SCREAMING_SNAKE_CASE : Union[float, List[float]] , __SCREAMING_SNAKE_CASE : Union[float, List[float]] , __SCREAMING_SNAKE_CASE : Optional[Union[str, ChannelDimension]] = None , **__SCREAMING_SNAKE_CASE : Tuple , ) -> np.ndarray:
"""simple docstring"""
return normalize(__SCREAMING_SNAKE_CASE , mean=__SCREAMING_SNAKE_CASE , std=__SCREAMING_SNAKE_CASE , data_format=__SCREAMING_SNAKE_CASE , **__SCREAMING_SNAKE_CASE )
def _a ( self : List[Any] , __SCREAMING_SNAKE_CASE : ImageInput , __SCREAMING_SNAKE_CASE : bool = None , __SCREAMING_SNAKE_CASE : int = None , __SCREAMING_SNAKE_CASE : bool = None , __SCREAMING_SNAKE_CASE : int = None , __SCREAMING_SNAKE_CASE : PILImageResampling = None , __SCREAMING_SNAKE_CASE : bool = None , __SCREAMING_SNAKE_CASE : float = None , __SCREAMING_SNAKE_CASE : bool = None , __SCREAMING_SNAKE_CASE : Optional[Union[float, List[float]]] = None , __SCREAMING_SNAKE_CASE : Optional[Union[float, List[float]]] = None , __SCREAMING_SNAKE_CASE : Optional[Union[str, TensorType]] = None , __SCREAMING_SNAKE_CASE : ChannelDimension = ChannelDimension.FIRST , **__SCREAMING_SNAKE_CASE : str , ) -> PIL.Image.Image:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = do_resize if do_resize is not None else self.do_resize
__SCREAMING_SNAKE_CASE = size if size is not None else self.size
__SCREAMING_SNAKE_CASE = get_size_dict(__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = keep_aspect_ratio if keep_aspect_ratio is not None else self.keep_aspect_ratio
__SCREAMING_SNAKE_CASE = ensure_multiple_of if ensure_multiple_of is not None else self.ensure_multiple_of
__SCREAMING_SNAKE_CASE = resample if resample is not None else self.resample
__SCREAMING_SNAKE_CASE = do_rescale if do_rescale is not None else self.do_rescale
__SCREAMING_SNAKE_CASE = rescale_factor if rescale_factor is not None else self.rescale_factor
__SCREAMING_SNAKE_CASE = do_normalize if do_normalize is not None else self.do_normalize
__SCREAMING_SNAKE_CASE = image_mean if image_mean is not None else self.image_mean
__SCREAMING_SNAKE_CASE = image_std if image_std is not None else self.image_std
__SCREAMING_SNAKE_CASE = make_list_of_images(__SCREAMING_SNAKE_CASE )
if not valid_images(__SCREAMING_SNAKE_CASE ):
raise ValueError(
'''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
'''torch.Tensor, tf.Tensor or jax.ndarray.''' )
if do_resize and size is None or resample is None:
raise ValueError('''Size and resample must be specified if do_resize is True.''' )
if do_rescale and rescale_factor is None:
raise ValueError('''Rescale factor must be specified if do_rescale is True.''' )
if do_normalize and (image_mean is None or image_std is None):
raise ValueError('''Image mean and std must be specified if do_normalize is True.''' )
# All transformations expect numpy arrays.
__SCREAMING_SNAKE_CASE = [to_numpy_array(__SCREAMING_SNAKE_CASE ) for image in images]
if do_resize:
__SCREAMING_SNAKE_CASE = [self.resize(image=__SCREAMING_SNAKE_CASE , size=__SCREAMING_SNAKE_CASE , resample=__SCREAMING_SNAKE_CASE ) for image in images]
if do_rescale:
__SCREAMING_SNAKE_CASE = [self.rescale(image=__SCREAMING_SNAKE_CASE , scale=__SCREAMING_SNAKE_CASE ) for image in images]
if do_normalize:
__SCREAMING_SNAKE_CASE = [self.normalize(image=__SCREAMING_SNAKE_CASE , mean=__SCREAMING_SNAKE_CASE , std=__SCREAMING_SNAKE_CASE ) for image in images]
__SCREAMING_SNAKE_CASE = [to_channel_dimension_format(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) for image in images]
__SCREAMING_SNAKE_CASE = {'''pixel_values''': images}
return BatchFeature(data=__SCREAMING_SNAKE_CASE , tensor_type=__SCREAMING_SNAKE_CASE )
def _a ( self : Tuple , __SCREAMING_SNAKE_CASE : int , __SCREAMING_SNAKE_CASE : List[Tuple] = None ) -> str:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = outputs.logits
# Resize logits and compute semantic segmentation maps
if target_sizes is not None:
if len(__SCREAMING_SNAKE_CASE ) != len(__SCREAMING_SNAKE_CASE ):
raise ValueError(
'''Make sure that you pass in as many target sizes as the batch dimension of the logits''' )
if is_torch_tensor(__SCREAMING_SNAKE_CASE ):
__SCREAMING_SNAKE_CASE = target_sizes.numpy()
__SCREAMING_SNAKE_CASE = []
for idx in range(len(__SCREAMING_SNAKE_CASE ) ):
__SCREAMING_SNAKE_CASE = torch.nn.functional.interpolate(
logits[idx].unsqueeze(dim=0 ) , size=target_sizes[idx] , mode='''bilinear''' , align_corners=__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = resized_logits[0].argmax(dim=0 )
semantic_segmentation.append(__SCREAMING_SNAKE_CASE )
else:
__SCREAMING_SNAKE_CASE = logits.argmax(dim=1 )
__SCREAMING_SNAKE_CASE = [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0] )]
return semantic_segmentation
| 690 |
"""simple docstring"""
import argparse
from pathlib import Path
import torch
from transformers import OPTConfig, OPTModel
from transformers.utils import logging
logging.set_verbosity_info()
lowerCAmelCase__ =logging.get_logger(__name__)
def _a ( UpperCAmelCase__ ) -> Tuple:
__SCREAMING_SNAKE_CASE = torch.load(UpperCAmelCase__ , map_location='''cpu''' )
if "model" in sd.keys():
__SCREAMING_SNAKE_CASE = torch.load(UpperCAmelCase__ , map_location='''cpu''' )['''model''']
# pop unnecessary weights
__SCREAMING_SNAKE_CASE = [
'''decoder.version''',
'''decoder.output_projection.weight''',
]
for key in keys_to_delete:
if key in sd:
sd.pop(UpperCAmelCase__ )
__SCREAMING_SNAKE_CASE = {
'''decoder.project_in_dim.weight''': '''decoder.project_in.weight''',
'''decoder.project_out_dim.weight''': '''decoder.project_out.weight''',
'''decoder.layer_norm.weight''': '''decoder.final_layer_norm.weight''',
'''decoder.layer_norm.bias''': '''decoder.final_layer_norm.bias''',
}
for old_key, new_key in keys_to_rename.items():
if old_key in sd:
__SCREAMING_SNAKE_CASE = sd.pop(UpperCAmelCase__ )
__SCREAMING_SNAKE_CASE = list(sd.keys() )
for key in keys:
if ".qkv_proj." in key:
__SCREAMING_SNAKE_CASE = sd[key]
# We split QKV in separate Q,K,V
__SCREAMING_SNAKE_CASE = key.replace('''.qkv_proj.''' , '''.q_proj.''' )
__SCREAMING_SNAKE_CASE = key.replace('''.qkv_proj.''' , '''.k_proj.''' )
__SCREAMING_SNAKE_CASE = key.replace('''.qkv_proj.''' , '''.v_proj.''' )
__SCREAMING_SNAKE_CASE = value.shape[0]
assert depth % 3 == 0
# `SequeuceParallelTransformerBlock` has QKV weight is separated in K,V,Q despite the naming:
# https://cs.github.com/facebookresearch/metaseq/blob/51871bd73cd04c038f239ea2a26db1d7f6b37927/metaseq/modules/sequence_parallel_transformer_layer.py#L97
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = torch.split(UpperCAmelCase__ , depth // 3 , dim=0 )
__SCREAMING_SNAKE_CASE = q
__SCREAMING_SNAKE_CASE = k
__SCREAMING_SNAKE_CASE = v
del sd[key]
return sd
@torch.no_grad()
def _a ( UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__=None ) -> Optional[Any]:
__SCREAMING_SNAKE_CASE = load_checkpoint(UpperCAmelCase__ )
if config is not None:
__SCREAMING_SNAKE_CASE = OPTConfig.from_pretrained(UpperCAmelCase__ )
else:
__SCREAMING_SNAKE_CASE = OPTConfig()
__SCREAMING_SNAKE_CASE = OPTModel(UpperCAmelCase__ ).half().eval()
model.load_state_dict(UpperCAmelCase__ )
# Check results
Path(UpperCAmelCase__ ).mkdir(exist_ok=UpperCAmelCase__ )
model.save_pretrained(UpperCAmelCase__ )
if __name__ == "__main__":
lowerCAmelCase__ =argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--fairseq_path",
type=str,
help=(
"path to fairseq checkpoint in correct format. You can find all checkpoints in the correct format here:"
" https://huggingface.co/models?other=opt_metasq"
),
)
parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
parser.add_argument("--hf_config", default=None, type=str, help="Define HF config.")
lowerCAmelCase__ =parser.parse_args()
convert_opt_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, config=args.hf_config)
| 690 | 1 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_rembert import RemBertTokenizer
else:
lowerCAmelCase__ =None
lowerCAmelCase__ =logging.get_logger(__name__)
lowerCAmelCase__ ={"vocab_file": "sentencepiece.model", "tokenizer_file": "tokenizer.json"}
lowerCAmelCase__ ={
"vocab_file": {
"google/rembert": "https://huggingface.co/google/rembert/resolve/main/sentencepiece.model",
},
"tokenizer_file": {
"google/rembert": "https://huggingface.co/google/rembert/resolve/main/tokenizer.json",
},
}
lowerCAmelCase__ ={
"google/rembert": 256,
}
lowerCAmelCase__ ="▁"
class A__( __magic_name__ ):
lowerCAmelCase = VOCAB_FILES_NAMES
lowerCAmelCase = PRETRAINED_VOCAB_FILES_MAP
lowerCAmelCase = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowerCAmelCase = RemBertTokenizer
def __init__( self : Optional[int] , __SCREAMING_SNAKE_CASE : Dict=None , __SCREAMING_SNAKE_CASE : Optional[Any]=None , __SCREAMING_SNAKE_CASE : Dict=True , __SCREAMING_SNAKE_CASE : Tuple=True , __SCREAMING_SNAKE_CASE : Dict=False , __SCREAMING_SNAKE_CASE : List[Any]="[CLS]" , __SCREAMING_SNAKE_CASE : Dict="[SEP]" , __SCREAMING_SNAKE_CASE : Optional[Any]="<unk>" , __SCREAMING_SNAKE_CASE : Dict="[SEP]" , __SCREAMING_SNAKE_CASE : List[Any]="<pad>" , __SCREAMING_SNAKE_CASE : List[str]="[CLS]" , __SCREAMING_SNAKE_CASE : Optional[Any]="[MASK]" , **__SCREAMING_SNAKE_CASE : Union[str, Any] , ) -> List[Any]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = AddedToken(__SCREAMING_SNAKE_CASE , lstrip=__SCREAMING_SNAKE_CASE , rstrip=__SCREAMING_SNAKE_CASE ) if isinstance(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) else mask_token
super().__init__(
__SCREAMING_SNAKE_CASE , tokenizer_file=__SCREAMING_SNAKE_CASE , do_lower_case=__SCREAMING_SNAKE_CASE , remove_space=__SCREAMING_SNAKE_CASE , keep_accents=__SCREAMING_SNAKE_CASE , bos_token=__SCREAMING_SNAKE_CASE , eos_token=__SCREAMING_SNAKE_CASE , unk_token=__SCREAMING_SNAKE_CASE , sep_token=__SCREAMING_SNAKE_CASE , pad_token=__SCREAMING_SNAKE_CASE , cls_token=__SCREAMING_SNAKE_CASE , mask_token=__SCREAMING_SNAKE_CASE , **__SCREAMING_SNAKE_CASE , )
__SCREAMING_SNAKE_CASE = do_lower_case
__SCREAMING_SNAKE_CASE = remove_space
__SCREAMING_SNAKE_CASE = keep_accents
__SCREAMING_SNAKE_CASE = vocab_file
__SCREAMING_SNAKE_CASE = False if not self.vocab_file else True
def _a ( self : Union[str, Any] , __SCREAMING_SNAKE_CASE : List[int] , __SCREAMING_SNAKE_CASE : Optional[List[int]] = None ) -> List[int]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = [self.sep_token_id]
__SCREAMING_SNAKE_CASE = [self.cls_token_id]
if token_ids_a is None:
return cls + token_ids_a + sep
return cls + token_ids_a + sep + token_ids_a + sep
def _a ( self : Tuple , __SCREAMING_SNAKE_CASE : List[int] , __SCREAMING_SNAKE_CASE : Optional[List[int]] = None , __SCREAMING_SNAKE_CASE : bool = False ) -> List[int]:
"""simple docstring"""
if already_has_special_tokens:
if token_ids_a is not None:
raise ValueError(
'''You should not supply a second sequence if the provided sequence of '''
'''ids is already formatted with special tokens for the model.''' )
return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]
if token_ids_a is not None:
return [1] + ([0] * len(__SCREAMING_SNAKE_CASE )) + [1] + ([0] * len(__SCREAMING_SNAKE_CASE )) + [1]
return [1] + ([0] * len(__SCREAMING_SNAKE_CASE )) + [1]
def _a ( self : Optional[int] , __SCREAMING_SNAKE_CASE : List[int] , __SCREAMING_SNAKE_CASE : Optional[List[int]] = None ) -> List[int]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = [self.sep_token_id]
__SCREAMING_SNAKE_CASE = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]
def _a ( self : List[str] , __SCREAMING_SNAKE_CASE : str , __SCREAMING_SNAKE_CASE : Optional[str] = None ) -> Tuple[str]:
"""simple docstring"""
if not os.path.isdir(__SCREAMING_SNAKE_CASE ):
logger.error('''Vocabulary path ({}) should be a directory'''.format(__SCREAMING_SNAKE_CASE ) )
return
__SCREAMING_SNAKE_CASE = os.path.join(
__SCREAMING_SNAKE_CASE , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(__SCREAMING_SNAKE_CASE ):
copyfile(self.vocab_file , __SCREAMING_SNAKE_CASE )
return (out_vocab_file,)
| 690 |
"""simple docstring"""
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import re
from ..models.auto import AutoProcessor
from ..models.vision_encoder_decoder import VisionEncoderDecoderModel
from ..utils import is_vision_available
from .base import PipelineTool
if is_vision_available():
from PIL import Image
class A__( __magic_name__ ):
lowerCAmelCase = '''naver-clova-ix/donut-base-finetuned-docvqa'''
lowerCAmelCase = (
'''This is a tool that answers a question about an document (pdf). It takes an input named `document` which '''
'''should be the document containing the information, as well as a `question` that is the question about the '''
'''document. It returns a text that contains the answer to the question.'''
)
lowerCAmelCase = '''document_qa'''
lowerCAmelCase = AutoProcessor
lowerCAmelCase = VisionEncoderDecoderModel
lowerCAmelCase = ['''image''', '''text''']
lowerCAmelCase = ['''text''']
def __init__( self : str , *__SCREAMING_SNAKE_CASE : List[str] , **__SCREAMING_SNAKE_CASE : List[Any] ) -> Any:
"""simple docstring"""
if not is_vision_available():
raise ValueError('''Pillow must be installed to use the DocumentQuestionAnsweringTool.''' )
super().__init__(*__SCREAMING_SNAKE_CASE , **__SCREAMING_SNAKE_CASE )
def _a ( self : Union[str, Any] , __SCREAMING_SNAKE_CASE : "Image" , __SCREAMING_SNAKE_CASE : str ) -> str:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = '''<s_docvqa><s_question>{user_input}</s_question><s_answer>'''
__SCREAMING_SNAKE_CASE = task_prompt.replace('''{user_input}''' , __SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = self.pre_processor.tokenizer(
__SCREAMING_SNAKE_CASE , add_special_tokens=__SCREAMING_SNAKE_CASE , return_tensors='''pt''' ).input_ids
__SCREAMING_SNAKE_CASE = self.pre_processor(__SCREAMING_SNAKE_CASE , return_tensors='''pt''' ).pixel_values
return {"decoder_input_ids": decoder_input_ids, "pixel_values": pixel_values}
def _a ( self : Union[str, Any] , __SCREAMING_SNAKE_CASE : Optional[Any] ) -> Tuple:
"""simple docstring"""
return self.model.generate(
inputs['''pixel_values'''].to(self.device ) , decoder_input_ids=inputs['''decoder_input_ids'''].to(self.device ) , max_length=self.model.decoder.config.max_position_embeddings , early_stopping=__SCREAMING_SNAKE_CASE , pad_token_id=self.pre_processor.tokenizer.pad_token_id , eos_token_id=self.pre_processor.tokenizer.eos_token_id , use_cache=__SCREAMING_SNAKE_CASE , num_beams=1 , bad_words_ids=[[self.pre_processor.tokenizer.unk_token_id]] , return_dict_in_generate=__SCREAMING_SNAKE_CASE , ).sequences
def _a ( self : Tuple , __SCREAMING_SNAKE_CASE : Tuple ) -> List[str]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = self.pre_processor.batch_decode(__SCREAMING_SNAKE_CASE )[0]
__SCREAMING_SNAKE_CASE = sequence.replace(self.pre_processor.tokenizer.eos_token , '''''' )
__SCREAMING_SNAKE_CASE = sequence.replace(self.pre_processor.tokenizer.pad_token , '''''' )
__SCREAMING_SNAKE_CASE = re.sub(r'''<.*?>''' , '''''' , __SCREAMING_SNAKE_CASE , count=1 ).strip() # remove first task start token
__SCREAMING_SNAKE_CASE = self.pre_processor.tokenajson(__SCREAMING_SNAKE_CASE )
return sequence["answer"]
| 690 | 1 |
"""simple docstring"""
import math
from typing import Callable, List, Optional, Union
import numpy as np
import PIL
import torch
from PIL import Image
from transformers import CLIPTextModel, CLIPTokenizer
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_upscale import StableDiffusionUpscalePipeline
from diffusers.schedulers import DDIMScheduler, DDPMScheduler, LMSDiscreteScheduler, PNDMScheduler
def _a ( UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__=[] ) -> Optional[int]:
__SCREAMING_SNAKE_CASE = size[0] - overlap_pixels * 2
__SCREAMING_SNAKE_CASE = size[1] - overlap_pixels * 2
for letter in ["l", "r"]:
if letter in remove_borders:
size_x += overlap_pixels
for letter in ["t", "b"]:
if letter in remove_borders:
size_y += overlap_pixels
__SCREAMING_SNAKE_CASE = np.ones((size_y, size_x) , dtype=np.uinta ) * 2_55
__SCREAMING_SNAKE_CASE = np.pad(UpperCAmelCase__ , mode='''linear_ramp''' , pad_width=UpperCAmelCase__ , end_values=0 )
if "l" in remove_borders:
__SCREAMING_SNAKE_CASE = mask[:, overlap_pixels : mask.shape[1]]
if "r" in remove_borders:
__SCREAMING_SNAKE_CASE = mask[:, 0 : mask.shape[1] - overlap_pixels]
if "t" in remove_borders:
__SCREAMING_SNAKE_CASE = mask[overlap_pixels : mask.shape[0], :]
if "b" in remove_borders:
__SCREAMING_SNAKE_CASE = mask[0 : mask.shape[0] - overlap_pixels, :]
return mask
def _a ( UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ ) -> Any:
return max(UpperCAmelCase__ , min(UpperCAmelCase__ , UpperCAmelCase__ ) )
def _a ( UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ ) -> List[str]:
return (
clamp(rect[0] , min[0] , max[0] ),
clamp(rect[1] , min[1] , max[1] ),
clamp(rect[2] , min[0] , max[0] ),
clamp(rect[3] , min[1] , max[1] ),
)
def _a ( UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ ) -> Union[str, Any]:
__SCREAMING_SNAKE_CASE = list(UpperCAmelCase__ )
rect[0] -= overlap
rect[1] -= overlap
rect[2] += overlap
rect[3] += overlap
__SCREAMING_SNAKE_CASE = clamp_rect(UpperCAmelCase__ , [0, 0] , [image_size[0], image_size[1]] )
return rect
def _a ( UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ ) -> List[Any]:
__SCREAMING_SNAKE_CASE = Image.new('''RGB''' , (tile.size[0] + original_slice, tile.size[1]) )
result.paste(
original_image.resize((tile.size[0], tile.size[1]) , Image.BICUBIC ).crop(
(slice_x, 0, slice_x + original_slice, tile.size[1]) ) , (0, 0) , )
result.paste(UpperCAmelCase__ , (original_slice, 0) )
return result
def _a ( UpperCAmelCase__ , UpperCAmelCase__ ) -> Optional[int]:
__SCREAMING_SNAKE_CASE = (original_image_slice * 4, 0, tile.size[0], tile.size[1])
__SCREAMING_SNAKE_CASE = tile.crop(UpperCAmelCase__ )
return tile
def _a ( UpperCAmelCase__ , UpperCAmelCase__ ) -> Tuple:
__SCREAMING_SNAKE_CASE = n % d
return n - divisor
class A__( __magic_name__ ):
def __init__( self : Optional[Any] , __SCREAMING_SNAKE_CASE : AutoencoderKL , __SCREAMING_SNAKE_CASE : CLIPTextModel , __SCREAMING_SNAKE_CASE : CLIPTokenizer , __SCREAMING_SNAKE_CASE : UNetaDConditionModel , __SCREAMING_SNAKE_CASE : DDPMScheduler , __SCREAMING_SNAKE_CASE : Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler] , __SCREAMING_SNAKE_CASE : int = 3_50 , ) -> Any:
"""simple docstring"""
super().__init__(
vae=__SCREAMING_SNAKE_CASE , text_encoder=__SCREAMING_SNAKE_CASE , tokenizer=__SCREAMING_SNAKE_CASE , unet=__SCREAMING_SNAKE_CASE , low_res_scheduler=__SCREAMING_SNAKE_CASE , scheduler=__SCREAMING_SNAKE_CASE , max_noise_level=__SCREAMING_SNAKE_CASE , )
def _a ( self : List[Any] , __SCREAMING_SNAKE_CASE : List[Any] , __SCREAMING_SNAKE_CASE : Optional[int] , __SCREAMING_SNAKE_CASE : Optional[Any] , __SCREAMING_SNAKE_CASE : Any , __SCREAMING_SNAKE_CASE : List[Any] , __SCREAMING_SNAKE_CASE : Any , __SCREAMING_SNAKE_CASE : Union[str, Any] , **__SCREAMING_SNAKE_CASE : List[str] ) -> Union[str, Any]:
"""simple docstring"""
torch.manual_seed(0 )
__SCREAMING_SNAKE_CASE = (
min(image.size[0] - (tile_size + original_image_slice) , x * tile_size ),
min(image.size[1] - (tile_size + original_image_slice) , y * tile_size ),
min(image.size[0] , (x + 1) * tile_size ),
min(image.size[1] , (y + 1) * tile_size ),
)
__SCREAMING_SNAKE_CASE = add_overlap_rect(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , image.size )
__SCREAMING_SNAKE_CASE = image.crop(__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = ((crop_rect[0] + ((crop_rect[2] - crop_rect[0]) / 2)) / image.size[0]) * tile.size[0]
__SCREAMING_SNAKE_CASE = translated_slice_x - (original_image_slice / 2)
__SCREAMING_SNAKE_CASE = max(0 , __SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = squeeze_tile(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = to_input.size
__SCREAMING_SNAKE_CASE = to_input.resize((tile_size, tile_size) , Image.BICUBIC )
__SCREAMING_SNAKE_CASE = super(__SCREAMING_SNAKE_CASE , self ).__call__(image=__SCREAMING_SNAKE_CASE , **__SCREAMING_SNAKE_CASE ).images[0]
__SCREAMING_SNAKE_CASE = upscaled_tile.resize((orig_input_size[0] * 4, orig_input_size[1] * 4) , Image.BICUBIC )
__SCREAMING_SNAKE_CASE = unsqueeze_tile(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = upscaled_tile.resize((tile.size[0] * 4, tile.size[1] * 4) , Image.BICUBIC )
__SCREAMING_SNAKE_CASE = []
if x == 0:
remove_borders.append('''l''' )
elif crop_rect[2] == image.size[0]:
remove_borders.append('''r''' )
if y == 0:
remove_borders.append('''t''' )
elif crop_rect[3] == image.size[1]:
remove_borders.append('''b''' )
__SCREAMING_SNAKE_CASE = Image.fromarray(
make_transparency_mask(
(upscaled_tile.size[0], upscaled_tile.size[1]) , tile_border * 4 , remove_borders=__SCREAMING_SNAKE_CASE ) , mode='''L''' , )
final_image.paste(
__SCREAMING_SNAKE_CASE , (crop_rect_with_overlap[0] * 4, crop_rect_with_overlap[1] * 4) , __SCREAMING_SNAKE_CASE )
@torch.no_grad()
def __call__( self : str , __SCREAMING_SNAKE_CASE : Union[str, List[str]] , __SCREAMING_SNAKE_CASE : Union[PIL.Image.Image, List[PIL.Image.Image]] , __SCREAMING_SNAKE_CASE : int = 75 , __SCREAMING_SNAKE_CASE : float = 9.0 , __SCREAMING_SNAKE_CASE : int = 50 , __SCREAMING_SNAKE_CASE : Optional[Union[str, List[str]]] = None , __SCREAMING_SNAKE_CASE : Optional[int] = 1 , __SCREAMING_SNAKE_CASE : float = 0.0 , __SCREAMING_SNAKE_CASE : Optional[torch.Generator] = None , __SCREAMING_SNAKE_CASE : Optional[torch.FloatTensor] = None , __SCREAMING_SNAKE_CASE : Optional[Callable[[int, int, torch.FloatTensor], None]] = None , __SCREAMING_SNAKE_CASE : int = 1 , __SCREAMING_SNAKE_CASE : int = 1_28 , __SCREAMING_SNAKE_CASE : int = 32 , __SCREAMING_SNAKE_CASE : int = 32 , ) -> Any:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = Image.new('''RGB''' , (image.size[0] * 4, image.size[1] * 4) )
__SCREAMING_SNAKE_CASE = math.ceil(image.size[0] / tile_size )
__SCREAMING_SNAKE_CASE = math.ceil(image.size[1] / tile_size )
__SCREAMING_SNAKE_CASE = tcx * tcy
__SCREAMING_SNAKE_CASE = 0
for y in range(__SCREAMING_SNAKE_CASE ):
for x in range(__SCREAMING_SNAKE_CASE ):
self._process_tile(
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , prompt=__SCREAMING_SNAKE_CASE , num_inference_steps=__SCREAMING_SNAKE_CASE , guidance_scale=__SCREAMING_SNAKE_CASE , noise_level=__SCREAMING_SNAKE_CASE , negative_prompt=__SCREAMING_SNAKE_CASE , num_images_per_prompt=__SCREAMING_SNAKE_CASE , eta=__SCREAMING_SNAKE_CASE , generator=__SCREAMING_SNAKE_CASE , latents=__SCREAMING_SNAKE_CASE , )
current_count += 1
if callback is not None:
callback({'''progress''': current_count / total_tile_count, '''image''': final_image} )
return final_image
def _a ( ) -> Union[str, Any]:
# Run a demo
__SCREAMING_SNAKE_CASE = '''stabilityai/stable-diffusion-x4-upscaler'''
__SCREAMING_SNAKE_CASE = StableDiffusionTiledUpscalePipeline.from_pretrained(UpperCAmelCase__ , revision='''fp16''' , torch_dtype=torch.floataa )
__SCREAMING_SNAKE_CASE = pipe.to('''cuda''' )
__SCREAMING_SNAKE_CASE = Image.open('''../../docs/source/imgs/diffusers_library.jpg''' )
def callback(UpperCAmelCase__ ):
print(f"""progress: {obj["progress"]:.4f}""" )
obj["image"].save('''diffusers_library_progress.jpg''' )
__SCREAMING_SNAKE_CASE = pipe(image=UpperCAmelCase__ , prompt='''Black font, white background, vector''' , noise_level=40 , callback=UpperCAmelCase__ )
final_image.save('''diffusers_library.jpg''' )
if __name__ == "__main__":
main()
| 690 |
"""simple docstring"""
import unittest
import numpy as np
import torch
from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class A__( unittest.TestCase ):
@property
def _a ( self : Optional[Any] ) -> Tuple:
"""simple docstring"""
torch.manual_seed(0 )
__SCREAMING_SNAKE_CASE = UNetaDModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=3 , out_channels=3 , down_block_types=('''DownBlock2D''', '''AttnDownBlock2D''') , up_block_types=('''AttnUpBlock2D''', '''UpBlock2D''') , )
return model
def _a ( self : str ) -> Any:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = self.dummy_uncond_unet
__SCREAMING_SNAKE_CASE = KarrasVeScheduler()
__SCREAMING_SNAKE_CASE = KarrasVePipeline(unet=__SCREAMING_SNAKE_CASE , scheduler=__SCREAMING_SNAKE_CASE )
pipe.to(__SCREAMING_SNAKE_CASE )
pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = torch.manual_seed(0 )
__SCREAMING_SNAKE_CASE = pipe(num_inference_steps=2 , generator=__SCREAMING_SNAKE_CASE , output_type='''numpy''' ).images
__SCREAMING_SNAKE_CASE = torch.manual_seed(0 )
__SCREAMING_SNAKE_CASE = pipe(num_inference_steps=2 , generator=__SCREAMING_SNAKE_CASE , output_type='''numpy''' , return_dict=__SCREAMING_SNAKE_CASE )[0]
__SCREAMING_SNAKE_CASE = image[0, -3:, -3:, -1]
__SCREAMING_SNAKE_CASE = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
__SCREAMING_SNAKE_CASE = np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1E-2
@slow
@require_torch
class A__( unittest.TestCase ):
def _a ( self : Any ) -> str:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = '''google/ncsnpp-celebahq-256'''
__SCREAMING_SNAKE_CASE = UNetaDModel.from_pretrained(__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = KarrasVeScheduler()
__SCREAMING_SNAKE_CASE = KarrasVePipeline(unet=__SCREAMING_SNAKE_CASE , scheduler=__SCREAMING_SNAKE_CASE )
pipe.to(__SCREAMING_SNAKE_CASE )
pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = torch.manual_seed(0 )
__SCREAMING_SNAKE_CASE = pipe(num_inference_steps=20 , generator=__SCREAMING_SNAKE_CASE , output_type='''numpy''' ).images
__SCREAMING_SNAKE_CASE = image[0, -3:, -3:, -1]
assert image.shape == (1, 2_56, 2_56, 3)
__SCREAMING_SNAKE_CASE = np.array([0.5_78, 0.58_11, 0.59_24, 0.58_09, 0.5_87, 0.58_86, 0.58_61, 0.58_02, 0.5_86] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
| 690 | 1 |
"""simple docstring"""
import importlib.metadata
from typing import Union
from packaging.version import Version, parse
from .constants import STR_OPERATION_TO_FUNC
lowerCAmelCase__ =parse(importlib.metadata.version("torch"))
def _a ( UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ ) -> Optional[int]:
if operation not in STR_OPERATION_TO_FUNC.keys():
raise ValueError(f"""`operation` must be one of {list(STR_OPERATION_TO_FUNC.keys() )}, received {operation}""" )
__SCREAMING_SNAKE_CASE = STR_OPERATION_TO_FUNC[operation]
if isinstance(UpperCAmelCase__ , UpperCAmelCase__ ):
__SCREAMING_SNAKE_CASE = parse(importlib.metadata.version(UpperCAmelCase__ ) )
return operation(UpperCAmelCase__ , parse(UpperCAmelCase__ ) )
def _a ( UpperCAmelCase__ , UpperCAmelCase__ ) -> str:
return compare_versions(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ )
| 690 |
"""simple docstring"""
import os
import re
import unicodedata
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import is_torch_available, logging
if is_torch_available():
import torch
if TYPE_CHECKING:
from transformers.pipelines.conversational import Conversation
lowerCAmelCase__ =logging.get_logger(__name__)
lowerCAmelCase__ ={"vocab_file": "spiece.model"}
lowerCAmelCase__ ={
"vocab_file": {
"AI-Sweden/gpt-sw3-126m": "https://huggingface.co/AI-Sweden/gpt-sw3-126m/resolve/main/spiece.model",
"AI-Sweden/gpt-sw3-350m": "https://huggingface.co/AI-Sweden/gpt-sw3-350m/resolve/main/spiece.model",
"AI-Sweden/gpt-sw3-1.6b": "https://huggingface.co/AI-Sweden/gpt-sw3-1.6b/resolve/main/spiece.model",
"AI-Sweden/gpt-sw3-6.7b": "https://huggingface.co/AI-Sweden/gpt-sw3-6.7b/resolve/main/spiece.model",
"AI-Sweden/gpt-sw3-20b": "https://huggingface.co/AI-Sweden/gpt-sw3-20b/resolve/main/spiece.model",
}
}
lowerCAmelCase__ ={
"AI-Sweden/gpt-sw3-126m": 2_048,
"AI-Sweden/gpt-sw3-350m": 2_048,
"AI-Sweden/gpt-sw3-1.6b": 2_048,
"AI-Sweden/gpt-sw3-6.7b": 2_048,
"AI-Sweden/gpt-sw3-20b": 2_048,
}
class A__( __magic_name__ ):
lowerCAmelCase = VOCAB_FILES_NAMES
lowerCAmelCase = PRETRAINED_VOCAB_FILES_MAP
lowerCAmelCase = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowerCAmelCase = ['''input_ids''', '''attention_mask''']
def __init__( self : int , __SCREAMING_SNAKE_CASE : int , __SCREAMING_SNAKE_CASE : str=False , __SCREAMING_SNAKE_CASE : str=False , __SCREAMING_SNAKE_CASE : Optional[int]=False , __SCREAMING_SNAKE_CASE : Union[str, Any]=None , __SCREAMING_SNAKE_CASE : List[Any]=None , __SCREAMING_SNAKE_CASE : Any=None , __SCREAMING_SNAKE_CASE : List[Any]=None , __SCREAMING_SNAKE_CASE : Optional[Dict[str, Any]] = None , **__SCREAMING_SNAKE_CASE : Dict , ) -> None:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = {} if sp_model_kwargs is None else sp_model_kwargs
__SCREAMING_SNAKE_CASE = kwargs.get('''name_or_path''' )
if name_or_path is None:
logger.warning(
'''name_or_path not provided, will work for all GPTSw3 models except gpt-sw3-7b,'''
''' you are testing the model, this can safely be ignored''' )
__SCREAMING_SNAKE_CASE = '''None'''
# Default definitions for our 2 tokenizer versions, with None-checks to enable proper testing
__SCREAMING_SNAKE_CASE = '''<|endoftext|>''' if eos_token is None else eos_token
__SCREAMING_SNAKE_CASE = '''<unk>''' if unk_token is None else unk_token
if "gpt-sw3-7b" in name_or_path:
__SCREAMING_SNAKE_CASE = unk_token if pad_token is None else pad_token
__SCREAMING_SNAKE_CASE = eos_token if bos_token is None else bos_token
else:
__SCREAMING_SNAKE_CASE = '''<pad>''' if pad_token is None else pad_token
__SCREAMING_SNAKE_CASE = '''<s>''' if bos_token is None else bos_token
super().__init__(
do_lower_case=__SCREAMING_SNAKE_CASE , remove_space=__SCREAMING_SNAKE_CASE , keep_accents=__SCREAMING_SNAKE_CASE , bos_token=__SCREAMING_SNAKE_CASE , eos_token=__SCREAMING_SNAKE_CASE , unk_token=__SCREAMING_SNAKE_CASE , pad_token=__SCREAMING_SNAKE_CASE , sp_model_kwargs=self.sp_model_kwargs , **__SCREAMING_SNAKE_CASE , )
__SCREAMING_SNAKE_CASE = do_lower_case
__SCREAMING_SNAKE_CASE = remove_space
__SCREAMING_SNAKE_CASE = keep_accents
__SCREAMING_SNAKE_CASE = vocab_file
__SCREAMING_SNAKE_CASE = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(__SCREAMING_SNAKE_CASE )
# Used for whitespace normalization in input texts
# fmt : off
__SCREAMING_SNAKE_CASE = {''' ''', ''' ''', ''' ''', ''' ''', ''' ''', ''' ''', ''' ''', ''' ''', ''' ''', ''' ''', '''''', ''''''}
# fmt : on
# Regular expression to remove non-printing characters (e.g. some unicode control chars) in preprocessing
__SCREAMING_SNAKE_CASE = re.compile(
f"""[{"".join(map(__SCREAMING_SNAKE_CASE , list(range(0 , 9 ) ) + list(range(11 , 32 ) ) + list(range(1_27 , 1_60 ) ) + [1_60, 1_73, 82_03] ) )}]""" )
def __getstate__( self : List[str] ) -> Optional[Any]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = self.__dict__.copy()
__SCREAMING_SNAKE_CASE = None
return state
def __setstate__( self : int , __SCREAMING_SNAKE_CASE : Optional[int] ) -> int:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = d
# for backward compatibility
if not hasattr(self , '''sp_model_kwargs''' ):
__SCREAMING_SNAKE_CASE = {}
__SCREAMING_SNAKE_CASE = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.vocab_file )
@property
# Copied from transformers.models.albert.tokenization_albert.AlbertTokenizer.vocab_size
def _a ( self : Optional[Any] ) -> int:
"""simple docstring"""
return len(self.sp_model )
def _a ( self : List[str] , __SCREAMING_SNAKE_CASE : str ) -> str:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = self.non_printing_characters_re.sub('''''' , __SCREAMING_SNAKE_CASE )
# Normalize whitespaces
__SCREAMING_SNAKE_CASE = ''''''.join([char if char not in self.whitespaces else ''' ''' for char in text] )
# NFC Unicode normalization
__SCREAMING_SNAKE_CASE = unicodedata.normalize('''NFC''' , __SCREAMING_SNAKE_CASE )
return text
def _a ( self : Union[str, Any] , __SCREAMING_SNAKE_CASE : str , **__SCREAMING_SNAKE_CASE : Optional[int] ) -> List[str]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = self.preprocess_text(__SCREAMING_SNAKE_CASE )
return self.sp_model.encode(__SCREAMING_SNAKE_CASE , out_type=__SCREAMING_SNAKE_CASE )
def _a ( self : Tuple , __SCREAMING_SNAKE_CASE : str ) -> int:
"""simple docstring"""
return self.sp_model.PieceToId(__SCREAMING_SNAKE_CASE )
def _a ( self : List[str] , __SCREAMING_SNAKE_CASE : int ) -> str:
"""simple docstring"""
return self.sp_model.IdToPiece(__SCREAMING_SNAKE_CASE )
@staticmethod
def _a ( __SCREAMING_SNAKE_CASE : str ) -> str:
"""simple docstring"""
return out_string
def _a ( self : Union[str, Any] , __SCREAMING_SNAKE_CASE : List[str] ) -> str:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = []
__SCREAMING_SNAKE_CASE = ''''''
__SCREAMING_SNAKE_CASE = False
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
# TODO: Check if this is needed, as it ensures that decode(encode(doc)) != doc by adding extra whitespace in the decoded document
if not prev_is_special:
out_string += " "
out_string += self.sp_model.decode(__SCREAMING_SNAKE_CASE ) + token
__SCREAMING_SNAKE_CASE = True
__SCREAMING_SNAKE_CASE = []
else:
current_sub_tokens.append(__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = False
out_string += self.sp_model.decode(__SCREAMING_SNAKE_CASE )
return out_string
def _a ( self : Union[str, Any] ) -> Dict[str, int]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = {self.convert_ids_to_tokens(__SCREAMING_SNAKE_CASE ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def _a ( self : List[Any] , __SCREAMING_SNAKE_CASE : str , __SCREAMING_SNAKE_CASE : Optional[str] = None ) -> Tuple[str]:
"""simple docstring"""
if not os.path.isdir(__SCREAMING_SNAKE_CASE ):
logger.error(f"""Vocabulary path ({save_directory}) should be a directory""" )
return
__SCREAMING_SNAKE_CASE = os.path.join(
__SCREAMING_SNAKE_CASE , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(__SCREAMING_SNAKE_CASE ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file , __SCREAMING_SNAKE_CASE )
elif not os.path.isfile(self.vocab_file ):
with open(__SCREAMING_SNAKE_CASE , '''wb''' ) as fi:
__SCREAMING_SNAKE_CASE = self.sp_model.serialized_model_proto()
fi.write(__SCREAMING_SNAKE_CASE )
return (out_vocab_file,)
def _a ( self : Optional[Any] , __SCREAMING_SNAKE_CASE : Union[str, List[str]] , __SCREAMING_SNAKE_CASE : Union[str, bool] = False ) -> Union[List[int], List[List[int]], "torch.Tensor"]:
"""simple docstring"""
if isinstance(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
__SCREAMING_SNAKE_CASE = self.preprocess_text(__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = self.sp_model.encode(__SCREAMING_SNAKE_CASE )
else:
__SCREAMING_SNAKE_CASE = [self.preprocess_text(__SCREAMING_SNAKE_CASE ) for t in text]
__SCREAMING_SNAKE_CASE = self.sp_model.encode(__SCREAMING_SNAKE_CASE )
if return_tensors is True or return_tensors == "pt":
__SCREAMING_SNAKE_CASE = torch.tensor(__SCREAMING_SNAKE_CASE )
return token_ids
def _a ( self : Any , __SCREAMING_SNAKE_CASE : Union[int, List[int]] ) -> str:
"""simple docstring"""
return self.sp_model.decode(__SCREAMING_SNAKE_CASE )
def _a ( self : List[str] , __SCREAMING_SNAKE_CASE : "Conversation" ) -> List[int]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = [f"""User: {text}""" if is_user else f"""Bot: {text}""" for is_user, text in conversation.iter_texts()]
__SCREAMING_SNAKE_CASE = (
f"""{self.eos_token}{self.bos_token}""" + f"""{self.bos_token}""".join(__SCREAMING_SNAKE_CASE ) + f"""{self.bos_token}Bot:"""
)
return self.encode(text=__SCREAMING_SNAKE_CASE )
| 690 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCAmelCase__ ={
"configuration_luke": ["LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP", "LukeConfig"],
"tokenization_luke": ["LukeTokenizer"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase__ =[
"LUKE_PRETRAINED_MODEL_ARCHIVE_LIST",
"LukeForEntityClassification",
"LukeForEntityPairClassification",
"LukeForEntitySpanClassification",
"LukeForMultipleChoice",
"LukeForQuestionAnswering",
"LukeForSequenceClassification",
"LukeForTokenClassification",
"LukeForMaskedLM",
"LukeModel",
"LukePreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_luke import LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP, LukeConfig
from .tokenization_luke import LukeTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_luke import (
LUKE_PRETRAINED_MODEL_ARCHIVE_LIST,
LukeForEntityClassification,
LukeForEntityPairClassification,
LukeForEntitySpanClassification,
LukeForMaskedLM,
LukeForMultipleChoice,
LukeForQuestionAnswering,
LukeForSequenceClassification,
LukeForTokenClassification,
LukeModel,
LukePreTrainedModel,
)
else:
import sys
lowerCAmelCase__ =_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 690 |
"""simple docstring"""
from __future__ import annotations
import json
import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent
lowerCAmelCase__ ={"UserAgent": UserAgent().random}
def _a ( UpperCAmelCase__ ) -> dict:
__SCREAMING_SNAKE_CASE = script.contents[0]
__SCREAMING_SNAKE_CASE = json.loads(data[data.find('''{"config"''' ) : -1] )
return info["entry_data"]["ProfilePage"][0]["graphql"]["user"]
class A__:
def __init__( self : Dict , __SCREAMING_SNAKE_CASE : int ) -> Optional[Any]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = f"""https://www.instagram.com/{username}/"""
__SCREAMING_SNAKE_CASE = self.get_json()
def _a ( self : List[Any] ) -> dict:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = requests.get(self.url , headers=__SCREAMING_SNAKE_CASE ).text
__SCREAMING_SNAKE_CASE = BeautifulSoup(__SCREAMING_SNAKE_CASE , '''html.parser''' ).find_all('''script''' )
try:
return extract_user_profile(scripts[4] )
except (json.decoder.JSONDecodeError, KeyError):
return extract_user_profile(scripts[3] )
def __repr__( self : Tuple ) -> str:
"""simple docstring"""
return f"""{self.__class__.__name__}('{self.username}')"""
def __str__( self : Optional[int] ) -> str:
"""simple docstring"""
return f"""{self.fullname} ({self.username}) is {self.biography}"""
@property
def _a ( self : Tuple ) -> str:
"""simple docstring"""
return self.user_data["username"]
@property
def _a ( self : List[Any] ) -> str:
"""simple docstring"""
return self.user_data["full_name"]
@property
def _a ( self : Optional[Any] ) -> str:
"""simple docstring"""
return self.user_data["biography"]
@property
def _a ( self : List[str] ) -> str:
"""simple docstring"""
return self.user_data["business_email"]
@property
def _a ( self : Any ) -> str:
"""simple docstring"""
return self.user_data["external_url"]
@property
def _a ( self : Any ) -> int:
"""simple docstring"""
return self.user_data["edge_followed_by"]["count"]
@property
def _a ( self : Dict ) -> int:
"""simple docstring"""
return self.user_data["edge_follow"]["count"]
@property
def _a ( self : str ) -> int:
"""simple docstring"""
return self.user_data["edge_owner_to_timeline_media"]["count"]
@property
def _a ( self : Union[str, Any] ) -> str:
"""simple docstring"""
return self.user_data["profile_pic_url_hd"]
@property
def _a ( self : Tuple ) -> bool:
"""simple docstring"""
return self.user_data["is_verified"]
@property
def _a ( self : Union[str, Any] ) -> bool:
"""simple docstring"""
return self.user_data["is_private"]
def _a ( UpperCAmelCase__ = "github" ) -> None:
import os
if os.environ.get('''CI''' ):
return # test failing on GitHub Actions
__SCREAMING_SNAKE_CASE = InstagramUser(UpperCAmelCase__ )
assert instagram_user.user_data
assert isinstance(instagram_user.user_data , UpperCAmelCase__ )
assert instagram_user.username == username
if username != "github":
return
assert instagram_user.fullname == "GitHub"
assert instagram_user.biography == "Built for developers."
assert instagram_user.number_of_posts > 1_50
assert instagram_user.number_of_followers > 12_00_00
assert instagram_user.number_of_followings > 15
assert instagram_user.email == "[email protected]"
assert instagram_user.website == "https://github.com/readme"
assert instagram_user.profile_picture_url.startswith('''https://instagram.''' )
assert instagram_user.is_verified is True
assert instagram_user.is_private is False
if __name__ == "__main__":
import doctest
doctest.testmod()
lowerCAmelCase__ =InstagramUser("github")
print(instagram_user)
print(F'''{instagram_user.number_of_posts = }''')
print(F'''{instagram_user.number_of_followers = }''')
print(F'''{instagram_user.number_of_followings = }''')
print(F'''{instagram_user.email = }''')
print(F'''{instagram_user.website = }''')
print(F'''{instagram_user.profile_picture_url = }''')
print(F'''{instagram_user.is_verified = }''')
print(F'''{instagram_user.is_private = }''')
| 690 | 1 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_xlnet import XLNetTokenizer
else:
lowerCAmelCase__ =None
lowerCAmelCase__ =logging.get_logger(__name__)
lowerCAmelCase__ ={"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}
lowerCAmelCase__ ={
"vocab_file": {
"xlnet-base-cased": "https://huggingface.co/xlnet-base-cased/resolve/main/spiece.model",
"xlnet-large-cased": "https://huggingface.co/xlnet-large-cased/resolve/main/spiece.model",
},
"tokenizer_file": {
"xlnet-base-cased": "https://huggingface.co/xlnet-base-cased/resolve/main/tokenizer.json",
"xlnet-large-cased": "https://huggingface.co/xlnet-large-cased/resolve/main/tokenizer.json",
},
}
lowerCAmelCase__ ={
"xlnet-base-cased": None,
"xlnet-large-cased": None,
}
lowerCAmelCase__ ="▁"
# Segments (not really needed)
lowerCAmelCase__ =0
lowerCAmelCase__ =1
lowerCAmelCase__ =2
lowerCAmelCase__ =3
lowerCAmelCase__ =4
class A__( __magic_name__ ):
lowerCAmelCase = VOCAB_FILES_NAMES
lowerCAmelCase = PRETRAINED_VOCAB_FILES_MAP
lowerCAmelCase = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowerCAmelCase = '''left'''
lowerCAmelCase = XLNetTokenizer
def __init__( self : Optional[int] , __SCREAMING_SNAKE_CASE : int=None , __SCREAMING_SNAKE_CASE : str=None , __SCREAMING_SNAKE_CASE : Dict=False , __SCREAMING_SNAKE_CASE : Union[str, Any]=True , __SCREAMING_SNAKE_CASE : Any=False , __SCREAMING_SNAKE_CASE : Tuple="<s>" , __SCREAMING_SNAKE_CASE : Union[str, Any]="</s>" , __SCREAMING_SNAKE_CASE : List[Any]="<unk>" , __SCREAMING_SNAKE_CASE : Optional[int]="<sep>" , __SCREAMING_SNAKE_CASE : str="<pad>" , __SCREAMING_SNAKE_CASE : int="<cls>" , __SCREAMING_SNAKE_CASE : int="<mask>" , __SCREAMING_SNAKE_CASE : List[Any]=["<eop>", "<eod>"] , **__SCREAMING_SNAKE_CASE : List[str] , ) -> Any:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = AddedToken(__SCREAMING_SNAKE_CASE , lstrip=__SCREAMING_SNAKE_CASE , rstrip=__SCREAMING_SNAKE_CASE ) if isinstance(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) else mask_token
super().__init__(
vocab_file=__SCREAMING_SNAKE_CASE , tokenizer_file=__SCREAMING_SNAKE_CASE , do_lower_case=__SCREAMING_SNAKE_CASE , remove_space=__SCREAMING_SNAKE_CASE , keep_accents=__SCREAMING_SNAKE_CASE , bos_token=__SCREAMING_SNAKE_CASE , eos_token=__SCREAMING_SNAKE_CASE , unk_token=__SCREAMING_SNAKE_CASE , sep_token=__SCREAMING_SNAKE_CASE , pad_token=__SCREAMING_SNAKE_CASE , cls_token=__SCREAMING_SNAKE_CASE , mask_token=__SCREAMING_SNAKE_CASE , additional_special_tokens=__SCREAMING_SNAKE_CASE , **__SCREAMING_SNAKE_CASE , )
__SCREAMING_SNAKE_CASE = 3
__SCREAMING_SNAKE_CASE = do_lower_case
__SCREAMING_SNAKE_CASE = remove_space
__SCREAMING_SNAKE_CASE = keep_accents
__SCREAMING_SNAKE_CASE = vocab_file
__SCREAMING_SNAKE_CASE = False if not self.vocab_file else True
def _a ( self : List[Any] , __SCREAMING_SNAKE_CASE : List[int] , __SCREAMING_SNAKE_CASE : Optional[List[int]] = None ) -> List[int]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = [self.sep_token_id]
__SCREAMING_SNAKE_CASE = [self.cls_token_id]
if token_ids_a is None:
return token_ids_a + sep + cls
return token_ids_a + sep + token_ids_a + sep + cls
def _a ( self : str , __SCREAMING_SNAKE_CASE : List[int] , __SCREAMING_SNAKE_CASE : Optional[List[int]] = None ) -> List[int]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = [self.sep_token_id]
__SCREAMING_SNAKE_CASE = [2]
if token_ids_a is None:
return len(token_ids_a + sep ) * [0] + cls_segment_id
return len(token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1] + cls_segment_id
def _a ( self : List[str] , __SCREAMING_SNAKE_CASE : str , __SCREAMING_SNAKE_CASE : Optional[str] = None ) -> Tuple[str]:
"""simple docstring"""
if not self.can_save_slow_tokenizer:
raise ValueError(
'''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
'''tokenizer.''' )
if not os.path.isdir(__SCREAMING_SNAKE_CASE ):
logger.error(f"""Vocabulary path ({save_directory}) should be a directory""" )
return
__SCREAMING_SNAKE_CASE = os.path.join(
__SCREAMING_SNAKE_CASE , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(__SCREAMING_SNAKE_CASE ):
copyfile(self.vocab_file , __SCREAMING_SNAKE_CASE )
return (out_vocab_file,)
| 690 |
"""simple docstring"""
from sklearn.metrics import recall_score
import datasets
lowerCAmelCase__ ="\nRecall is the fraction of the positive examples that were correctly labeled by the model as positive. It can be computed with the equation:\nRecall = TP / (TP + FN)\nWhere TP is the true positives and FN is the false negatives.\n"
lowerCAmelCase__ ="\nArgs:\n- **predictions** (`list` of `int`): The predicted labels.\n- **references** (`list` of `int`): The ground truth labels.\n- **labels** (`list` of `int`): The set of labels to include when `average` is not set to `binary`, and their order when average is `None`. Labels present in the data can be excluded in this input, for example to calculate a multiclass average ignoring a majority negative class, while labels not present in the data will result in 0 components in a macro average. For multilabel targets, labels are column indices. By default, all labels in y_true and y_pred are used in sorted order. Defaults to None.\n- **pos_label** (`int`): The class label to use as the 'positive class' when calculating the recall. Defaults to `1`.\n- **average** (`string`): This parameter is required for multiclass/multilabel targets. If None, the scores for each class are returned. Otherwise, this determines the type of averaging performed on the data. Defaults to `'binary'`.\n - `'binary'`: Only report results for the class specified by `pos_label`. This is applicable only if the target labels and predictions are binary.\n - `'micro'`: Calculate metrics globally by counting the total true positives, false negatives, and false positives.\n - `'macro'`: Calculate metrics for each label, and find their unweighted mean. This does not take label imbalance into account.\n - `'weighted'`: Calculate metrics for each label, and find their average weighted by support (the number of true instances for each label). This alters `'macro'` to account for label imbalance. Note that it can result in an F-score that is not between precision and recall.\n - `'samples'`: Calculate metrics for each instance, and find their average (only meaningful for multilabel classification).\n- **sample_weight** (`list` of `float`): Sample weights Defaults to `None`.\n- **zero_division** (): Sets the value to return when there is a zero division. Defaults to .\n - `'warn'`: If there is a zero division, the return value is `0`, but warnings are also raised.\n - `0`: If there is a zero division, the return value is `0`.\n - `1`: If there is a zero division, the return value is `1`.\n\nReturns:\n- **recall** (`float`, or `array` of `float`): Either the general recall score, or the recall scores for individual classes, depending on the values input to `labels` and `average`. Minimum possible value is 0. Maximum possible value is 1. A higher recall means that more of the positive examples have been labeled correctly. Therefore, a higher recall is generally considered better.\n\nExamples:\n\n Example 1-A simple example with some errors\n >>> recall_metric = datasets.load_metric('recall')\n >>> results = recall_metric.compute(references=[0, 0, 1, 1, 1], predictions=[0, 1, 0, 1, 1])\n >>> print(results)\n {'recall': 0.6666666666666666}\n\n Example 2-The same example as Example 1, but with `pos_label=0` instead of the default `pos_label=1`.\n >>> recall_metric = datasets.load_metric('recall')\n >>> results = recall_metric.compute(references=[0, 0, 1, 1, 1], predictions=[0, 1, 0, 1, 1], pos_label=0)\n >>> print(results)\n {'recall': 0.5}\n\n Example 3-The same example as Example 1, but with `sample_weight` included.\n >>> recall_metric = datasets.load_metric('recall')\n >>> sample_weight = [0.9, 0.2, 0.9, 0.3, 0.8]\n >>> results = recall_metric.compute(references=[0, 0, 1, 1, 1], predictions=[0, 1, 0, 1, 1], sample_weight=sample_weight)\n >>> print(results)\n {'recall': 0.55}\n\n Example 4-A multiclass example, using different averages.\n >>> recall_metric = datasets.load_metric('recall')\n >>> predictions = [0, 2, 1, 0, 0, 1]\n >>> references = [0, 1, 2, 0, 1, 2]\n >>> results = recall_metric.compute(predictions=predictions, references=references, average='macro')\n >>> print(results)\n {'recall': 0.3333333333333333}\n >>> results = recall_metric.compute(predictions=predictions, references=references, average='micro')\n >>> print(results)\n {'recall': 0.3333333333333333}\n >>> results = recall_metric.compute(predictions=predictions, references=references, average='weighted')\n >>> print(results)\n {'recall': 0.3333333333333333}\n >>> results = recall_metric.compute(predictions=predictions, references=references, average=None)\n >>> print(results)\n {'recall': array([1., 0., 0.])}\n"
lowerCAmelCase__ ="\n@article{scikit-learn, title={Scikit-learn: Machine Learning in {P}ython}, author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V. and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P. and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.}, journal={Journal of Machine Learning Research}, volume={12}, pages={2825--2830}, year={2011}\n"
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class A__( datasets.Metric ):
def _a ( self : Any ) -> int:
"""simple docstring"""
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
'''predictions''': datasets.Sequence(datasets.Value('''int32''' ) ),
'''references''': datasets.Sequence(datasets.Value('''int32''' ) ),
}
if self.config_name == '''multilabel'''
else {
'''predictions''': datasets.Value('''int32''' ),
'''references''': datasets.Value('''int32''' ),
} ) , reference_urls=['''https://scikit-learn.org/stable/modules/generated/sklearn.metrics.recall_score.html'''] , )
def _a ( self : Tuple , __SCREAMING_SNAKE_CASE : Union[str, Any] , __SCREAMING_SNAKE_CASE : Optional[Any] , __SCREAMING_SNAKE_CASE : Union[str, Any]=None , __SCREAMING_SNAKE_CASE : Optional[int]=1 , __SCREAMING_SNAKE_CASE : Optional[Any]="binary" , __SCREAMING_SNAKE_CASE : List[Any]=None , __SCREAMING_SNAKE_CASE : List[Any]="warn" , ) -> Dict:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = recall_score(
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , labels=__SCREAMING_SNAKE_CASE , pos_label=__SCREAMING_SNAKE_CASE , average=__SCREAMING_SNAKE_CASE , sample_weight=__SCREAMING_SNAKE_CASE , zero_division=__SCREAMING_SNAKE_CASE , )
return {"recall": float(__SCREAMING_SNAKE_CASE ) if score.size == 1 else score}
| 690 | 1 |
"""simple docstring"""
import fire
from utils import calculate_rouge, save_json
def _a ( UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__=None , **UpperCAmelCase__ ) -> Union[str, Any]:
__SCREAMING_SNAKE_CASE = [x.strip() for x in open(UpperCAmelCase__ ).readlines()]
__SCREAMING_SNAKE_CASE = [x.strip() for x in open(UpperCAmelCase__ ).readlines()][: len(UpperCAmelCase__ )]
__SCREAMING_SNAKE_CASE = calculate_rouge(UpperCAmelCase__ , UpperCAmelCase__ , **UpperCAmelCase__ )
if save_path is not None:
save_json(UpperCAmelCase__ , UpperCAmelCase__ , indent=UpperCAmelCase__ )
return metrics # these print nicely
if __name__ == "__main__":
fire.Fire(calculate_rouge_path)
| 690 |
"""simple docstring"""
def _a ( UpperCAmelCase__ = 10**9 ) -> int:
__SCREAMING_SNAKE_CASE = 1
__SCREAMING_SNAKE_CASE = 2
__SCREAMING_SNAKE_CASE = 0
__SCREAMING_SNAKE_CASE = 0
__SCREAMING_SNAKE_CASE = 0
while perimeter <= max_perimeter:
perimeters_sum += perimeter
prev_value += 2 * value
value += prev_value
__SCREAMING_SNAKE_CASE = 2 * value + 2 if i % 2 == 0 else 2 * value - 2
i += 1
return perimeters_sum
if __name__ == "__main__":
print(F'''{solution() = }''')
| 690 | 1 |
"""simple docstring"""
import random
import torch
from huggingface_hub import HfApi
from diffusers import UNetaDModel
lowerCAmelCase__ =HfApi()
lowerCAmelCase__ ={}
# fmt: off
lowerCAmelCase__ =torch.tensor([
-0.75_15, -1.68_83, 0.24_20, 0.03_00, 0.63_47, 1.34_33, -1.17_43, -3.74_67,
1.23_42, -2.24_85, 0.46_36, 0.80_76, -0.79_91, 0.39_69, 0.84_98, 0.91_89,
-1.88_87, -3.35_22, 0.76_39, 0.20_40, 0.62_71, -2.71_48, -1.63_16, 3.08_39,
0.31_86, 0.27_21, -0.97_59, -1.24_61, 2.62_57, 1.35_57
])
lowerCAmelCase__ =torch.tensor([
-2.36_39, -2.53_44, 0.00_54, -0.66_74, 1.59_90, 1.01_58, 0.31_24, -2.14_36,
1.87_95, -2.54_29, -0.15_66, -0.39_73, 1.24_90, 2.64_47, 1.22_83, -0.52_08,
-2.81_54, -3.51_19, 2.38_38, 1.20_33, 1.72_01, -2.12_56, -1.45_76, 2.79_48,
2.42_04, -0.97_52, -1.25_46, 0.80_27, 3.27_58, 3.13_65
])
lowerCAmelCase__ =torch.tensor([
-0.65_31, -0.68_91, -0.31_72, -0.53_75, -0.91_40, -0.53_67, -0.11_75, -0.78_69,
-0.38_08, -0.45_13, -0.20_98, -0.00_83, 0.31_83, 0.51_40, 0.22_47, -0.13_04,
-0.13_02, -0.28_02, -0.20_84, -0.20_25, -0.49_67, -0.48_73, -0.08_61, 0.69_25,
0.02_50, 0.12_90, -0.15_43, 0.63_16, 1.04_60, 1.49_43
])
lowerCAmelCase__ =torch.tensor([
0.09_11, 0.11_07, 0.01_82, 0.04_35, -0.08_05, -0.06_08, 0.03_81, 0.21_72,
-0.02_80, 0.13_27, -0.02_99, -0.02_55, -0.00_50, -0.11_70, -0.10_46, 0.03_09,
0.13_67, 0.17_28, -0.05_33, -0.07_48, -0.05_34, 0.16_24, 0.03_84, -0.18_05,
-0.07_07, 0.06_42, 0.02_20, -0.01_34, -0.13_33, -0.15_05
])
lowerCAmelCase__ =torch.tensor([
0.13_21, 0.13_37, 0.04_40, 0.06_22, -0.05_91, -0.03_70, 0.05_03, 0.21_33,
-0.01_77, 0.14_15, -0.01_16, -0.01_12, 0.00_44, -0.09_80, -0.07_89, 0.03_95,
0.15_02, 0.17_85, -0.04_88, -0.05_14, -0.04_04, 0.15_39, 0.04_54, -0.15_59,
-0.06_65, 0.06_59, 0.03_83, -0.00_05, -0.12_66, -0.13_86
])
lowerCAmelCase__ =torch.tensor([
0.11_54, 0.12_18, 0.03_07, 0.05_26, -0.07_11, -0.05_41, 0.03_66, 0.20_78,
-0.02_67, 0.13_17, -0.02_26, -0.01_93, -0.00_14, -0.10_55, -0.09_02, 0.03_30,
0.13_91, 0.17_09, -0.05_62, -0.06_93, -0.05_60, 0.14_82, 0.03_81, -0.16_83,
-0.06_81, 0.06_61, 0.03_31, -0.00_46, -0.12_68, -0.14_31
])
lowerCAmelCase__ =torch.tensor([
0.11_92, 0.12_40, 0.04_14, 0.06_06, -0.05_57, -0.04_12, 0.04_30, 0.20_42,
-0.02_00, 0.13_85, -0.01_15, -0.01_32, 0.00_17, -0.09_65, -0.08_02, 0.03_98,
0.14_33, 0.17_47, -0.04_58, -0.05_33, -0.04_07, 0.15_45, 0.04_19, -0.15_74,
-0.06_45, 0.06_26, 0.03_41, -0.00_10, -0.11_99, -0.13_90
])
lowerCAmelCase__ =torch.tensor([
0.10_75, 0.10_74, 0.02_05, 0.04_31, -0.07_74, -0.06_07, 0.02_98, 0.20_42,
-0.03_20, 0.12_67, -0.02_81, -0.02_50, -0.00_64, -0.10_91, -0.09_46, 0.02_90,
0.13_28, 0.16_50, -0.05_80, -0.07_38, -0.05_86, 0.14_40, 0.03_37, -0.17_46,
-0.07_12, 0.06_05, 0.02_50, -0.00_99, -0.13_16, -0.14_73
])
lowerCAmelCase__ =torch.tensor([
-1.45_72, -2.04_81, -0.04_14, -0.60_05, 1.41_36, 0.58_48, 0.40_28, -2.73_30,
1.22_12, -2.12_28, 0.21_55, 0.40_39, 0.76_62, 2.05_35, 0.74_77, -0.32_43,
-2.17_58, -2.76_48, 1.69_47, 0.70_26, 1.23_38, -1.60_78, -0.86_82, 2.28_10,
1.85_74, -0.57_18, -0.55_86, -0.01_86, 2.34_15, 2.12_51])
lowerCAmelCase__ =torch.tensor([
-1.36_90, -1.97_20, -0.40_90, -0.69_66, 1.46_60, 0.99_38, -0.13_85, -2.73_24,
0.77_36, -1.89_17, 0.29_23, 0.42_93, 0.16_93, 1.41_12, 1.18_87, -0.31_81,
-2.21_60, -2.63_81, 1.31_70, 0.81_63, 0.92_40, -1.65_44, -0.60_99, 2.52_59,
1.64_30, -0.90_90, -0.93_92, -0.01_26, 2.42_68, 2.32_66
])
lowerCAmelCase__ =torch.tensor([
-1.35_25, -1.96_28, -0.39_56, -0.68_60, 1.46_64, 1.00_14, -0.12_59, -2.72_12,
0.77_72, -1.88_11, 0.29_96, 0.43_88, 0.17_04, 1.40_29, 1.17_01, -0.30_27,
-2.20_53, -2.62_87, 1.33_50, 0.81_31, 0.92_74, -1.62_92, -0.60_98, 2.51_31,
1.65_05, -0.89_58, -0.92_98, -0.01_51, 2.42_57, 2.33_55
])
lowerCAmelCase__ =torch.tensor([
-2.05_85, -2.78_97, -0.28_50, -0.89_40, 1.90_52, 0.57_02, 0.63_45, -3.89_59,
1.59_32, -3.23_19, 0.19_74, 0.02_87, 1.75_66, 2.65_43, 0.83_87, -0.53_51,
-3.27_36, -4.33_75, 2.90_29, 1.63_90, 1.46_40, -2.17_01, -1.90_13, 2.93_41,
3.49_81, -0.62_55, -1.16_44, -0.15_91, 3.70_97, 3.20_66
])
lowerCAmelCase__ =torch.tensor([
-2.31_39, -2.55_94, -0.01_97, -0.67_85, 1.70_01, 1.16_06, 0.30_75, -2.17_40,
1.80_71, -2.56_30, -0.09_26, -0.38_11, 1.21_16, 2.62_46, 1.27_31, -0.53_98,
-2.81_53, -3.61_40, 2.38_93, 1.32_62, 1.62_58, -2.18_56, -1.32_67, 2.83_95,
2.37_79, -1.06_23, -1.24_68, 0.89_59, 3.33_67, 3.22_43
])
lowerCAmelCase__ =torch.tensor([
-2.06_28, -2.76_67, -0.20_89, -0.82_63, 2.05_39, 0.59_92, 0.64_95, -3.83_36,
1.60_25, -3.28_17, 0.17_21, -0.06_33, 1.75_16, 2.70_39, 0.81_00, -0.59_08,
-3.21_13, -4.43_43, 2.92_57, 1.36_32, 1.55_62, -2.14_89, -1.98_94, 3.05_60,
3.33_96, -0.73_28, -1.04_17, 0.03_83, 3.70_93, 3.23_43
])
lowerCAmelCase__ =torch.tensor([
-1.45_74, -2.05_69, -0.04_73, -0.61_17, 1.40_18, 0.57_69, 0.41_29, -2.73_44,
1.22_41, -2.13_97, 0.20_00, 0.39_37, 0.76_16, 2.04_53, 0.73_24, -0.33_91,
-2.17_46, -2.77_44, 1.69_63, 0.69_21, 1.21_87, -1.61_72, -0.88_77, 2.24_39,
1.84_71, -0.58_39, -0.56_05, -0.04_64, 2.32_50, 2.12_19
])
# fmt: on
lowerCAmelCase__ =api.list_models(filter="diffusers")
for mod in models:
if "google" in mod.author or mod.modelId == "CompVis/ldm-celebahq-256":
lowerCAmelCase__ ="/home/patrick/google_checkpoints/" + mod.modelId.split("/")[-1]
print(F'''Started running {mod.modelId}!!!''')
if mod.modelId.startswith("CompVis"):
lowerCAmelCase__ =UNetaDModel.from_pretrained(local_checkpoint, subfolder="unet")
else:
lowerCAmelCase__ =UNetaDModel.from_pretrained(local_checkpoint)
torch.manual_seed(0)
random.seed(0)
lowerCAmelCase__ =torch.randn(1, model.config.in_channels, model.config.sample_size, model.config.sample_size)
lowerCAmelCase__ =torch.tensor([10] * noise.shape[0])
with torch.no_grad():
lowerCAmelCase__ =model(noise, time_step).sample
assert torch.allclose(
logits[0, 0, 0, :30], results["_".join("_".join(mod.modelId.split("/")).split("-"))], atol=1E-3
)
print(F'''{mod.modelId} has passed successfully!!!''')
| 690 |
"""simple docstring"""
import pandas as pd
from matplotlib import pyplot as plt
from sklearn.linear_model import LinearRegression
# Splitting the dataset into the Training set and Test set
from sklearn.model_selection import train_test_split
# Fitting Polynomial Regression to the dataset
from sklearn.preprocessing import PolynomialFeatures
# Importing the dataset
lowerCAmelCase__ =pd.read_csv(
"https://s3.us-west-2.amazonaws.com/public.gamelab.fun/dataset/"
"position_salaries.csv"
)
lowerCAmelCase__ =dataset.iloc[:, 1:2].values
lowerCAmelCase__ =dataset.iloc[:, 2].values
lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ =train_test_split(X, y, test_size=0.2, random_state=0)
lowerCAmelCase__ =PolynomialFeatures(degree=4)
lowerCAmelCase__ =poly_reg.fit_transform(X)
lowerCAmelCase__ =LinearRegression()
pol_reg.fit(X_poly, y)
def _a ( ) -> List[Any]:
plt.scatter(UpperCAmelCase__ , UpperCAmelCase__ , color='''red''' )
plt.plot(UpperCAmelCase__ , pol_reg.predict(poly_reg.fit_transform(UpperCAmelCase__ ) ) , color='''blue''' )
plt.title('''Truth or Bluff (Linear Regression)''' )
plt.xlabel('''Position level''' )
plt.ylabel('''Salary''' )
plt.show()
if __name__ == "__main__":
viz_polymonial()
# Predicting a new result with Polymonial Regression
pol_reg.predict(poly_reg.fit_transform([[5.5]]))
# output should be 132148.43750003
| 690 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
lowerCAmelCase__ ={
"configuration_gpt_neo": ["GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTNeoConfig", "GPTNeoOnnxConfig"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase__ =[
"GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST",
"GPTNeoForCausalLM",
"GPTNeoForQuestionAnswering",
"GPTNeoForSequenceClassification",
"GPTNeoForTokenClassification",
"GPTNeoModel",
"GPTNeoPreTrainedModel",
"load_tf_weights_in_gpt_neo",
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase__ =[
"FlaxGPTNeoForCausalLM",
"FlaxGPTNeoModel",
"FlaxGPTNeoPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_gpt_neo import GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoConfig, GPTNeoOnnxConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_gpt_neo import (
GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST,
GPTNeoForCausalLM,
GPTNeoForQuestionAnswering,
GPTNeoForSequenceClassification,
GPTNeoForTokenClassification,
GPTNeoModel,
GPTNeoPreTrainedModel,
load_tf_weights_in_gpt_neo,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_gpt_neo import FlaxGPTNeoForCausalLM, FlaxGPTNeoModel, FlaxGPTNeoPreTrainedModel
else:
import sys
lowerCAmelCase__ =_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 690 |
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import AutoTokenizer, MBartConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import TFAutoModelForSeqaSeqLM, TFMBartForConditionalGeneration, TFMBartModel
@require_tf
class A__:
lowerCAmelCase = MBartConfig
lowerCAmelCase = {}
lowerCAmelCase = '''gelu'''
def __init__( self : List[str] , __SCREAMING_SNAKE_CASE : Optional[int] , __SCREAMING_SNAKE_CASE : Tuple=13 , __SCREAMING_SNAKE_CASE : Dict=7 , __SCREAMING_SNAKE_CASE : List[Any]=True , __SCREAMING_SNAKE_CASE : Any=False , __SCREAMING_SNAKE_CASE : Union[str, Any]=99 , __SCREAMING_SNAKE_CASE : Optional[Any]=32 , __SCREAMING_SNAKE_CASE : Optional[int]=2 , __SCREAMING_SNAKE_CASE : Any=4 , __SCREAMING_SNAKE_CASE : List[str]=37 , __SCREAMING_SNAKE_CASE : Optional[Any]=0.1 , __SCREAMING_SNAKE_CASE : Dict=0.1 , __SCREAMING_SNAKE_CASE : Any=20 , __SCREAMING_SNAKE_CASE : List[Any]=2 , __SCREAMING_SNAKE_CASE : Optional[int]=1 , __SCREAMING_SNAKE_CASE : Optional[Any]=0 , ) -> Optional[int]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = parent
__SCREAMING_SNAKE_CASE = batch_size
__SCREAMING_SNAKE_CASE = seq_length
__SCREAMING_SNAKE_CASE = is_training
__SCREAMING_SNAKE_CASE = use_labels
__SCREAMING_SNAKE_CASE = vocab_size
__SCREAMING_SNAKE_CASE = hidden_size
__SCREAMING_SNAKE_CASE = num_hidden_layers
__SCREAMING_SNAKE_CASE = num_attention_heads
__SCREAMING_SNAKE_CASE = intermediate_size
__SCREAMING_SNAKE_CASE = hidden_dropout_prob
__SCREAMING_SNAKE_CASE = attention_probs_dropout_prob
__SCREAMING_SNAKE_CASE = max_position_embeddings
__SCREAMING_SNAKE_CASE = eos_token_id
__SCREAMING_SNAKE_CASE = pad_token_id
__SCREAMING_SNAKE_CASE = bos_token_id
def _a ( self : List[str] ) -> Optional[int]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = ids_tensor([self.batch_size, self.seq_length - 1] , self.vocab_size )
__SCREAMING_SNAKE_CASE = tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size ) , 1 )
__SCREAMING_SNAKE_CASE = tf.concat([input_ids, eos_tensor] , axis=1 )
__SCREAMING_SNAKE_CASE = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
__SCREAMING_SNAKE_CASE = self.config_cls(
vocab_size=self.vocab_size , d_model=self.hidden_size , encoder_layers=self.num_hidden_layers , decoder_layers=self.num_hidden_layers , encoder_attention_heads=self.num_attention_heads , decoder_attention_heads=self.num_attention_heads , encoder_ffn_dim=self.intermediate_size , decoder_ffn_dim=self.intermediate_size , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , eos_token_ids=[2] , bos_token_id=self.bos_token_id , pad_token_id=self.pad_token_id , decoder_start_token_id=self.pad_token_id , **self.config_updates , )
__SCREAMING_SNAKE_CASE = prepare_mbart_inputs_dict(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
return config, inputs_dict
def _a ( self : Tuple , __SCREAMING_SNAKE_CASE : str , __SCREAMING_SNAKE_CASE : Optional[int] ) -> Optional[Any]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = TFMBartModel(config=__SCREAMING_SNAKE_CASE ).get_decoder()
__SCREAMING_SNAKE_CASE = inputs_dict['''input_ids''']
__SCREAMING_SNAKE_CASE = input_ids[:1, :]
__SCREAMING_SNAKE_CASE = inputs_dict['''attention_mask'''][:1, :]
__SCREAMING_SNAKE_CASE = inputs_dict['''head_mask''']
__SCREAMING_SNAKE_CASE = 1
# first forward pass
__SCREAMING_SNAKE_CASE = model(__SCREAMING_SNAKE_CASE , attention_mask=__SCREAMING_SNAKE_CASE , head_mask=__SCREAMING_SNAKE_CASE , use_cache=__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = outputs.to_tuple()
__SCREAMING_SNAKE_CASE = past_key_values[1]
def _a ( UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__=None , UpperCAmelCase__=None , UpperCAmelCase__=None , UpperCAmelCase__=None , UpperCAmelCase__=None , ) -> Optional[int]:
if attention_mask is None:
__SCREAMING_SNAKE_CASE = tf.cast(tf.math.not_equal(UpperCAmelCase__ , config.pad_token_id ) , tf.inta )
if decoder_attention_mask is None:
__SCREAMING_SNAKE_CASE = tf.concat(
[
tf.ones(decoder_input_ids[:, :1].shape , dtype=tf.inta ),
tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:] , config.pad_token_id ) , tf.inta ),
] , axis=-1 , )
if head_mask is None:
__SCREAMING_SNAKE_CASE = tf.ones((config.encoder_layers, config.encoder_attention_heads) )
if decoder_head_mask is None:
__SCREAMING_SNAKE_CASE = tf.ones((config.decoder_layers, config.decoder_attention_heads) )
if cross_attn_head_mask is None:
__SCREAMING_SNAKE_CASE = tf.ones((config.decoder_layers, config.decoder_attention_heads) )
return {
"input_ids": input_ids,
"decoder_input_ids": decoder_input_ids,
"attention_mask": attention_mask,
"decoder_attention_mask": decoder_attention_mask,
"head_mask": head_mask,
"decoder_head_mask": decoder_head_mask,
"cross_attn_head_mask": cross_attn_head_mask,
}
@require_tf
class A__( __magic_name__ , __magic_name__ , unittest.TestCase ):
lowerCAmelCase = (TFMBartForConditionalGeneration, TFMBartModel) if is_tf_available() else ()
lowerCAmelCase = (TFMBartForConditionalGeneration,) if is_tf_available() else ()
lowerCAmelCase = (
{
'''conversational''': TFMBartForConditionalGeneration,
'''feature-extraction''': TFMBartModel,
'''summarization''': TFMBartForConditionalGeneration,
'''text2text-generation''': TFMBartForConditionalGeneration,
'''translation''': TFMBartForConditionalGeneration,
}
if is_tf_available()
else {}
)
lowerCAmelCase = True
lowerCAmelCase = False
lowerCAmelCase = False
def _a ( self : Dict , __SCREAMING_SNAKE_CASE : str , __SCREAMING_SNAKE_CASE : List[str] , __SCREAMING_SNAKE_CASE : Dict , __SCREAMING_SNAKE_CASE : Optional[int] , __SCREAMING_SNAKE_CASE : List[Any] ) -> Optional[Any]:
"""simple docstring"""
if pipeline_test_casse_name != "FeatureExtractionPipelineTests":
# Exception encountered when calling layer '...'
return True
return False
def _a ( self : List[Any] ) -> Optional[int]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = TFMBartModelTester(self )
__SCREAMING_SNAKE_CASE = ConfigTester(self , config_class=__SCREAMING_SNAKE_CASE )
def _a ( self : Union[str, Any] ) -> Union[str, Any]:
"""simple docstring"""
self.config_tester.run_common_tests()
def _a ( self : int ) -> str:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.check_decoder_model_past_large_inputs(*__SCREAMING_SNAKE_CASE )
@require_sentencepiece
@require_tokenizers
@require_tf
class A__( unittest.TestCase ):
lowerCAmelCase = [
''' UN Chief Says There Is No Military Solution in Syria''',
]
lowerCAmelCase = [
'''Şeful ONU declară că nu există o soluţie militară în Siria''',
]
lowerCAmelCase = '''facebook/mbart-large-en-ro'''
@cached_property
def _a ( self : Optional[int] ) -> str:
"""simple docstring"""
return AutoTokenizer.from_pretrained(self.model_name )
@cached_property
def _a ( self : Optional[Any] ) -> List[Any]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name )
return model
def _a ( self : Any , **__SCREAMING_SNAKE_CASE : Optional[Any] ) -> Any:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = self.translate_src_text(**__SCREAMING_SNAKE_CASE )
self.assertListEqual(self.expected_text , __SCREAMING_SNAKE_CASE )
def _a ( self : Any , **__SCREAMING_SNAKE_CASE : Optional[int] ) -> Optional[Any]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = self.tokenizer(self.src_text , **__SCREAMING_SNAKE_CASE , return_tensors='''tf''' )
__SCREAMING_SNAKE_CASE = self.model.generate(
model_inputs.input_ids , attention_mask=model_inputs.attention_mask , num_beams=2 )
__SCREAMING_SNAKE_CASE = self.tokenizer.batch_decode(__SCREAMING_SNAKE_CASE , skip_special_tokens=__SCREAMING_SNAKE_CASE )
return generated_words
@slow
def _a ( self : Union[str, Any] ) -> Tuple:
"""simple docstring"""
self._assert_generated_batch_equal_expected()
| 690 | 1 |
"""simple docstring"""
from ..utils import DummyObject, requires_backends
class A__( metaclass=__magic_name__ ):
lowerCAmelCase = ['''torch''']
def __init__( self : Any , *__SCREAMING_SNAKE_CASE : List[str] , **__SCREAMING_SNAKE_CASE : List[Any] ) -> Dict:
"""simple docstring"""
requires_backends(self , ['''torch'''] )
@classmethod
def _a ( cls : Optional[int] , *__SCREAMING_SNAKE_CASE : Dict , **__SCREAMING_SNAKE_CASE : str ) -> Union[str, Any]:
"""simple docstring"""
requires_backends(cls , ['''torch'''] )
@classmethod
def _a ( cls : str , *__SCREAMING_SNAKE_CASE : int , **__SCREAMING_SNAKE_CASE : Optional[Any] ) -> Any:
"""simple docstring"""
requires_backends(cls , ['''torch'''] )
class A__( metaclass=__magic_name__ ):
lowerCAmelCase = ['''torch''']
def __init__( self : Any , *__SCREAMING_SNAKE_CASE : Tuple , **__SCREAMING_SNAKE_CASE : Union[str, Any] ) -> Tuple:
"""simple docstring"""
requires_backends(self , ['''torch'''] )
@classmethod
def _a ( cls : int , *__SCREAMING_SNAKE_CASE : Optional[int] , **__SCREAMING_SNAKE_CASE : Any ) -> int:
"""simple docstring"""
requires_backends(cls , ['''torch'''] )
@classmethod
def _a ( cls : int , *__SCREAMING_SNAKE_CASE : str , **__SCREAMING_SNAKE_CASE : Any ) -> List[str]:
"""simple docstring"""
requires_backends(cls , ['''torch'''] )
class A__( metaclass=__magic_name__ ):
lowerCAmelCase = ['''torch''']
def __init__( self : Union[str, Any] , *__SCREAMING_SNAKE_CASE : Dict , **__SCREAMING_SNAKE_CASE : Tuple ) -> List[str]:
"""simple docstring"""
requires_backends(self , ['''torch'''] )
@classmethod
def _a ( cls : str , *__SCREAMING_SNAKE_CASE : Optional[int] , **__SCREAMING_SNAKE_CASE : Optional[int] ) -> Tuple:
"""simple docstring"""
requires_backends(cls , ['''torch'''] )
@classmethod
def _a ( cls : str , *__SCREAMING_SNAKE_CASE : Union[str, Any] , **__SCREAMING_SNAKE_CASE : List[Any] ) -> Tuple:
"""simple docstring"""
requires_backends(cls , ['''torch'''] )
class A__( metaclass=__magic_name__ ):
lowerCAmelCase = ['''torch''']
def __init__( self : Optional[int] , *__SCREAMING_SNAKE_CASE : int , **__SCREAMING_SNAKE_CASE : int ) -> Any:
"""simple docstring"""
requires_backends(self , ['''torch'''] )
@classmethod
def _a ( cls : int , *__SCREAMING_SNAKE_CASE : List[Any] , **__SCREAMING_SNAKE_CASE : List[Any] ) -> List[str]:
"""simple docstring"""
requires_backends(cls , ['''torch'''] )
@classmethod
def _a ( cls : Dict , *__SCREAMING_SNAKE_CASE : Dict , **__SCREAMING_SNAKE_CASE : List[str] ) -> Any:
"""simple docstring"""
requires_backends(cls , ['''torch'''] )
class A__( metaclass=__magic_name__ ):
lowerCAmelCase = ['''torch''']
def __init__( self : Optional[Any] , *__SCREAMING_SNAKE_CASE : Tuple , **__SCREAMING_SNAKE_CASE : Tuple ) -> List[Any]:
"""simple docstring"""
requires_backends(self , ['''torch'''] )
@classmethod
def _a ( cls : List[str] , *__SCREAMING_SNAKE_CASE : List[str] , **__SCREAMING_SNAKE_CASE : Optional[int] ) -> Union[str, Any]:
"""simple docstring"""
requires_backends(cls , ['''torch'''] )
@classmethod
def _a ( cls : str , *__SCREAMING_SNAKE_CASE : List[str] , **__SCREAMING_SNAKE_CASE : Optional[Any] ) -> Union[str, Any]:
"""simple docstring"""
requires_backends(cls , ['''torch'''] )
class A__( metaclass=__magic_name__ ):
lowerCAmelCase = ['''torch''']
def __init__( self : int , *__SCREAMING_SNAKE_CASE : Optional[Any] , **__SCREAMING_SNAKE_CASE : int ) -> Optional[int]:
"""simple docstring"""
requires_backends(self , ['''torch'''] )
@classmethod
def _a ( cls : Optional[Any] , *__SCREAMING_SNAKE_CASE : List[str] , **__SCREAMING_SNAKE_CASE : Optional[Any] ) -> Optional[int]:
"""simple docstring"""
requires_backends(cls , ['''torch'''] )
@classmethod
def _a ( cls : List[Any] , *__SCREAMING_SNAKE_CASE : Union[str, Any] , **__SCREAMING_SNAKE_CASE : Tuple ) -> str:
"""simple docstring"""
requires_backends(cls , ['''torch'''] )
class A__( metaclass=__magic_name__ ):
lowerCAmelCase = ['''torch''']
def __init__( self : Any , *__SCREAMING_SNAKE_CASE : List[Any] , **__SCREAMING_SNAKE_CASE : str ) -> Union[str, Any]:
"""simple docstring"""
requires_backends(self , ['''torch'''] )
@classmethod
def _a ( cls : Dict , *__SCREAMING_SNAKE_CASE : Optional[Any] , **__SCREAMING_SNAKE_CASE : Any ) -> List[str]:
"""simple docstring"""
requires_backends(cls , ['''torch'''] )
@classmethod
def _a ( cls : Tuple , *__SCREAMING_SNAKE_CASE : Dict , **__SCREAMING_SNAKE_CASE : Optional[Any] ) -> Union[str, Any]:
"""simple docstring"""
requires_backends(cls , ['''torch'''] )
class A__( metaclass=__magic_name__ ):
lowerCAmelCase = ['''torch''']
def __init__( self : List[str] , *__SCREAMING_SNAKE_CASE : Any , **__SCREAMING_SNAKE_CASE : List[Any] ) -> List[Any]:
"""simple docstring"""
requires_backends(self , ['''torch'''] )
@classmethod
def _a ( cls : List[str] , *__SCREAMING_SNAKE_CASE : List[Any] , **__SCREAMING_SNAKE_CASE : int ) -> int:
"""simple docstring"""
requires_backends(cls , ['''torch'''] )
@classmethod
def _a ( cls : List[Any] , *__SCREAMING_SNAKE_CASE : Union[str, Any] , **__SCREAMING_SNAKE_CASE : Dict ) -> List[str]:
"""simple docstring"""
requires_backends(cls , ['''torch'''] )
class A__( metaclass=__magic_name__ ):
lowerCAmelCase = ['''torch''']
def __init__( self : str , *__SCREAMING_SNAKE_CASE : List[str] , **__SCREAMING_SNAKE_CASE : Union[str, Any] ) -> List[str]:
"""simple docstring"""
requires_backends(self , ['''torch'''] )
@classmethod
def _a ( cls : str , *__SCREAMING_SNAKE_CASE : Dict , **__SCREAMING_SNAKE_CASE : Optional[int] ) -> List[str]:
"""simple docstring"""
requires_backends(cls , ['''torch'''] )
@classmethod
def _a ( cls : Optional[int] , *__SCREAMING_SNAKE_CASE : Dict , **__SCREAMING_SNAKE_CASE : Dict ) -> List[Any]:
"""simple docstring"""
requires_backends(cls , ['''torch'''] )
class A__( metaclass=__magic_name__ ):
lowerCAmelCase = ['''torch''']
def __init__( self : str , *__SCREAMING_SNAKE_CASE : List[Any] , **__SCREAMING_SNAKE_CASE : List[Any] ) -> int:
"""simple docstring"""
requires_backends(self , ['''torch'''] )
@classmethod
def _a ( cls : Dict , *__SCREAMING_SNAKE_CASE : int , **__SCREAMING_SNAKE_CASE : str ) -> Dict:
"""simple docstring"""
requires_backends(cls , ['''torch'''] )
@classmethod
def _a ( cls : str , *__SCREAMING_SNAKE_CASE : List[Any] , **__SCREAMING_SNAKE_CASE : int ) -> Dict:
"""simple docstring"""
requires_backends(cls , ['''torch'''] )
class A__( metaclass=__magic_name__ ):
lowerCAmelCase = ['''torch''']
def __init__( self : int , *__SCREAMING_SNAKE_CASE : Optional[int] , **__SCREAMING_SNAKE_CASE : int ) -> List[str]:
"""simple docstring"""
requires_backends(self , ['''torch'''] )
@classmethod
def _a ( cls : str , *__SCREAMING_SNAKE_CASE : Optional[int] , **__SCREAMING_SNAKE_CASE : Any ) -> List[str]:
"""simple docstring"""
requires_backends(cls , ['''torch'''] )
@classmethod
def _a ( cls : List[Any] , *__SCREAMING_SNAKE_CASE : Optional[int] , **__SCREAMING_SNAKE_CASE : int ) -> List[Any]:
"""simple docstring"""
requires_backends(cls , ['''torch'''] )
def _a ( *UpperCAmelCase__ , **UpperCAmelCase__ ) -> Dict:
requires_backends(UpperCAmelCase__ , ['''torch'''] )
def _a ( *UpperCAmelCase__ , **UpperCAmelCase__ ) -> str:
requires_backends(UpperCAmelCase__ , ['''torch'''] )
def _a ( *UpperCAmelCase__ , **UpperCAmelCase__ ) -> List[str]:
requires_backends(UpperCAmelCase__ , ['''torch'''] )
def _a ( *UpperCAmelCase__ , **UpperCAmelCase__ ) -> List[str]:
requires_backends(UpperCAmelCase__ , ['''torch'''] )
def _a ( *UpperCAmelCase__ , **UpperCAmelCase__ ) -> List[Any]:
requires_backends(UpperCAmelCase__ , ['''torch'''] )
def _a ( *UpperCAmelCase__ , **UpperCAmelCase__ ) -> Dict:
requires_backends(UpperCAmelCase__ , ['''torch'''] )
def _a ( *UpperCAmelCase__ , **UpperCAmelCase__ ) -> Tuple:
requires_backends(UpperCAmelCase__ , ['''torch'''] )
class A__( metaclass=__magic_name__ ):
lowerCAmelCase = ['''torch''']
def __init__( self : List[str] , *__SCREAMING_SNAKE_CASE : List[Any] , **__SCREAMING_SNAKE_CASE : List[str] ) -> Optional[int]:
"""simple docstring"""
requires_backends(self , ['''torch'''] )
@classmethod
def _a ( cls : Tuple , *__SCREAMING_SNAKE_CASE : Optional[int] , **__SCREAMING_SNAKE_CASE : Union[str, Any] ) -> int:
"""simple docstring"""
requires_backends(cls , ['''torch'''] )
@classmethod
def _a ( cls : List[Any] , *__SCREAMING_SNAKE_CASE : Optional[int] , **__SCREAMING_SNAKE_CASE : str ) -> Any:
"""simple docstring"""
requires_backends(cls , ['''torch'''] )
class A__( metaclass=__magic_name__ ):
lowerCAmelCase = ['''torch''']
def __init__( self : Any , *__SCREAMING_SNAKE_CASE : List[Any] , **__SCREAMING_SNAKE_CASE : Optional[Any] ) -> List[Any]:
"""simple docstring"""
requires_backends(self , ['''torch'''] )
@classmethod
def _a ( cls : List[str] , *__SCREAMING_SNAKE_CASE : Optional[Any] , **__SCREAMING_SNAKE_CASE : Union[str, Any] ) -> Union[str, Any]:
"""simple docstring"""
requires_backends(cls , ['''torch'''] )
@classmethod
def _a ( cls : List[str] , *__SCREAMING_SNAKE_CASE : Optional[Any] , **__SCREAMING_SNAKE_CASE : List[Any] ) -> List[Any]:
"""simple docstring"""
requires_backends(cls , ['''torch'''] )
class A__( metaclass=__magic_name__ ):
lowerCAmelCase = ['''torch''']
def __init__( self : int , *__SCREAMING_SNAKE_CASE : Any , **__SCREAMING_SNAKE_CASE : Dict ) -> Union[str, Any]:
"""simple docstring"""
requires_backends(self , ['''torch'''] )
@classmethod
def _a ( cls : int , *__SCREAMING_SNAKE_CASE : Union[str, Any] , **__SCREAMING_SNAKE_CASE : Dict ) -> Tuple:
"""simple docstring"""
requires_backends(cls , ['''torch'''] )
@classmethod
def _a ( cls : Dict , *__SCREAMING_SNAKE_CASE : Any , **__SCREAMING_SNAKE_CASE : Optional[Any] ) -> Union[str, Any]:
"""simple docstring"""
requires_backends(cls , ['''torch'''] )
class A__( metaclass=__magic_name__ ):
lowerCAmelCase = ['''torch''']
def __init__( self : Dict , *__SCREAMING_SNAKE_CASE : Optional[Any] , **__SCREAMING_SNAKE_CASE : int ) -> List[Any]:
"""simple docstring"""
requires_backends(self , ['''torch'''] )
@classmethod
def _a ( cls : Union[str, Any] , *__SCREAMING_SNAKE_CASE : Tuple , **__SCREAMING_SNAKE_CASE : List[Any] ) -> Tuple:
"""simple docstring"""
requires_backends(cls , ['''torch'''] )
@classmethod
def _a ( cls : Optional[Any] , *__SCREAMING_SNAKE_CASE : Any , **__SCREAMING_SNAKE_CASE : Tuple ) -> int:
"""simple docstring"""
requires_backends(cls , ['''torch'''] )
class A__( metaclass=__magic_name__ ):
lowerCAmelCase = ['''torch''']
def __init__( self : List[Any] , *__SCREAMING_SNAKE_CASE : Any , **__SCREAMING_SNAKE_CASE : str ) -> Dict:
"""simple docstring"""
requires_backends(self , ['''torch'''] )
@classmethod
def _a ( cls : int , *__SCREAMING_SNAKE_CASE : Tuple , **__SCREAMING_SNAKE_CASE : List[str] ) -> int:
"""simple docstring"""
requires_backends(cls , ['''torch'''] )
@classmethod
def _a ( cls : Optional[Any] , *__SCREAMING_SNAKE_CASE : Union[str, Any] , **__SCREAMING_SNAKE_CASE : Any ) -> List[str]:
"""simple docstring"""
requires_backends(cls , ['''torch'''] )
class A__( metaclass=__magic_name__ ):
lowerCAmelCase = ['''torch''']
def __init__( self : Optional[Any] , *__SCREAMING_SNAKE_CASE : Any , **__SCREAMING_SNAKE_CASE : List[str] ) -> Any:
"""simple docstring"""
requires_backends(self , ['''torch'''] )
@classmethod
def _a ( cls : Tuple , *__SCREAMING_SNAKE_CASE : Union[str, Any] , **__SCREAMING_SNAKE_CASE : int ) -> List[Any]:
"""simple docstring"""
requires_backends(cls , ['''torch'''] )
@classmethod
def _a ( cls : Union[str, Any] , *__SCREAMING_SNAKE_CASE : List[str] , **__SCREAMING_SNAKE_CASE : List[Any] ) -> Any:
"""simple docstring"""
requires_backends(cls , ['''torch'''] )
class A__( metaclass=__magic_name__ ):
lowerCAmelCase = ['''torch''']
def __init__( self : int , *__SCREAMING_SNAKE_CASE : Union[str, Any] , **__SCREAMING_SNAKE_CASE : Optional[Any] ) -> List[str]:
"""simple docstring"""
requires_backends(self , ['''torch'''] )
@classmethod
def _a ( cls : Tuple , *__SCREAMING_SNAKE_CASE : Optional[int] , **__SCREAMING_SNAKE_CASE : Dict ) -> List[str]:
"""simple docstring"""
requires_backends(cls , ['''torch'''] )
@classmethod
def _a ( cls : Optional[int] , *__SCREAMING_SNAKE_CASE : str , **__SCREAMING_SNAKE_CASE : List[Any] ) -> Optional[int]:
"""simple docstring"""
requires_backends(cls , ['''torch'''] )
class A__( metaclass=__magic_name__ ):
lowerCAmelCase = ['''torch''']
def __init__( self : int , *__SCREAMING_SNAKE_CASE : Union[str, Any] , **__SCREAMING_SNAKE_CASE : List[Any] ) -> Optional[int]:
"""simple docstring"""
requires_backends(self , ['''torch'''] )
@classmethod
def _a ( cls : Union[str, Any] , *__SCREAMING_SNAKE_CASE : List[Any] , **__SCREAMING_SNAKE_CASE : Dict ) -> Dict:
"""simple docstring"""
requires_backends(cls , ['''torch'''] )
@classmethod
def _a ( cls : Union[str, Any] , *__SCREAMING_SNAKE_CASE : Dict , **__SCREAMING_SNAKE_CASE : List[str] ) -> Optional[Any]:
"""simple docstring"""
requires_backends(cls , ['''torch'''] )
class A__( metaclass=__magic_name__ ):
lowerCAmelCase = ['''torch''']
def __init__( self : Optional[int] , *__SCREAMING_SNAKE_CASE : Optional[int] , **__SCREAMING_SNAKE_CASE : Any ) -> Optional[Any]:
"""simple docstring"""
requires_backends(self , ['''torch'''] )
@classmethod
def _a ( cls : Dict , *__SCREAMING_SNAKE_CASE : str , **__SCREAMING_SNAKE_CASE : Any ) -> str:
"""simple docstring"""
requires_backends(cls , ['''torch'''] )
@classmethod
def _a ( cls : Any , *__SCREAMING_SNAKE_CASE : Optional[Any] , **__SCREAMING_SNAKE_CASE : Dict ) -> List[str]:
"""simple docstring"""
requires_backends(cls , ['''torch'''] )
class A__( metaclass=__magic_name__ ):
lowerCAmelCase = ['''torch''']
def __init__( self : int , *__SCREAMING_SNAKE_CASE : str , **__SCREAMING_SNAKE_CASE : Union[str, Any] ) -> Optional[Any]:
"""simple docstring"""
requires_backends(self , ['''torch'''] )
@classmethod
def _a ( cls : str , *__SCREAMING_SNAKE_CASE : Tuple , **__SCREAMING_SNAKE_CASE : Union[str, Any] ) -> Any:
"""simple docstring"""
requires_backends(cls , ['''torch'''] )
@classmethod
def _a ( cls : Any , *__SCREAMING_SNAKE_CASE : Optional[int] , **__SCREAMING_SNAKE_CASE : Optional[Any] ) -> List[Any]:
"""simple docstring"""
requires_backends(cls , ['''torch'''] )
class A__( metaclass=__magic_name__ ):
lowerCAmelCase = ['''torch''']
def __init__( self : Any , *__SCREAMING_SNAKE_CASE : Optional[int] , **__SCREAMING_SNAKE_CASE : List[Any] ) -> Union[str, Any]:
"""simple docstring"""
requires_backends(self , ['''torch'''] )
@classmethod
def _a ( cls : Dict , *__SCREAMING_SNAKE_CASE : List[str] , **__SCREAMING_SNAKE_CASE : List[Any] ) -> int:
"""simple docstring"""
requires_backends(cls , ['''torch'''] )
@classmethod
def _a ( cls : str , *__SCREAMING_SNAKE_CASE : Optional[int] , **__SCREAMING_SNAKE_CASE : List[Any] ) -> List[str]:
"""simple docstring"""
requires_backends(cls , ['''torch'''] )
class A__( metaclass=__magic_name__ ):
lowerCAmelCase = ['''torch''']
def __init__( self : str , *__SCREAMING_SNAKE_CASE : Optional[Any] , **__SCREAMING_SNAKE_CASE : int ) -> Any:
"""simple docstring"""
requires_backends(self , ['''torch'''] )
@classmethod
def _a ( cls : Optional[Any] , *__SCREAMING_SNAKE_CASE : str , **__SCREAMING_SNAKE_CASE : Union[str, Any] ) -> List[Any]:
"""simple docstring"""
requires_backends(cls , ['''torch'''] )
@classmethod
def _a ( cls : Union[str, Any] , *__SCREAMING_SNAKE_CASE : Any , **__SCREAMING_SNAKE_CASE : List[Any] ) -> int:
"""simple docstring"""
requires_backends(cls , ['''torch'''] )
class A__( metaclass=__magic_name__ ):
lowerCAmelCase = ['''torch''']
def __init__( self : Any , *__SCREAMING_SNAKE_CASE : Union[str, Any] , **__SCREAMING_SNAKE_CASE : List[str] ) -> Optional[Any]:
"""simple docstring"""
requires_backends(self , ['''torch'''] )
@classmethod
def _a ( cls : Tuple , *__SCREAMING_SNAKE_CASE : List[str] , **__SCREAMING_SNAKE_CASE : str ) -> List[str]:
"""simple docstring"""
requires_backends(cls , ['''torch'''] )
@classmethod
def _a ( cls : Optional[int] , *__SCREAMING_SNAKE_CASE : Dict , **__SCREAMING_SNAKE_CASE : Optional[int] ) -> List[Any]:
"""simple docstring"""
requires_backends(cls , ['''torch'''] )
class A__( metaclass=__magic_name__ ):
lowerCAmelCase = ['''torch''']
def __init__( self : Union[str, Any] , *__SCREAMING_SNAKE_CASE : List[Any] , **__SCREAMING_SNAKE_CASE : str ) -> Optional[Any]:
"""simple docstring"""
requires_backends(self , ['''torch'''] )
@classmethod
def _a ( cls : Union[str, Any] , *__SCREAMING_SNAKE_CASE : List[Any] , **__SCREAMING_SNAKE_CASE : Any ) -> List[Any]:
"""simple docstring"""
requires_backends(cls , ['''torch'''] )
@classmethod
def _a ( cls : Union[str, Any] , *__SCREAMING_SNAKE_CASE : Any , **__SCREAMING_SNAKE_CASE : Tuple ) -> Optional[Any]:
"""simple docstring"""
requires_backends(cls , ['''torch'''] )
class A__( metaclass=__magic_name__ ):
lowerCAmelCase = ['''torch''']
def __init__( self : Union[str, Any] , *__SCREAMING_SNAKE_CASE : Optional[int] , **__SCREAMING_SNAKE_CASE : Optional[int] ) -> Optional[Any]:
"""simple docstring"""
requires_backends(self , ['''torch'''] )
@classmethod
def _a ( cls : str , *__SCREAMING_SNAKE_CASE : Dict , **__SCREAMING_SNAKE_CASE : List[str] ) -> str:
"""simple docstring"""
requires_backends(cls , ['''torch'''] )
@classmethod
def _a ( cls : Optional[int] , *__SCREAMING_SNAKE_CASE : Optional[int] , **__SCREAMING_SNAKE_CASE : Optional[int] ) -> Union[str, Any]:
"""simple docstring"""
requires_backends(cls , ['''torch'''] )
class A__( metaclass=__magic_name__ ):
lowerCAmelCase = ['''torch''']
def __init__( self : int , *__SCREAMING_SNAKE_CASE : Union[str, Any] , **__SCREAMING_SNAKE_CASE : Tuple ) -> Optional[int]:
"""simple docstring"""
requires_backends(self , ['''torch'''] )
@classmethod
def _a ( cls : str , *__SCREAMING_SNAKE_CASE : Tuple , **__SCREAMING_SNAKE_CASE : List[Any] ) -> str:
"""simple docstring"""
requires_backends(cls , ['''torch'''] )
@classmethod
def _a ( cls : Union[str, Any] , *__SCREAMING_SNAKE_CASE : Optional[int] , **__SCREAMING_SNAKE_CASE : Dict ) -> str:
"""simple docstring"""
requires_backends(cls , ['''torch'''] )
class A__( metaclass=__magic_name__ ):
lowerCAmelCase = ['''torch''']
def __init__( self : Union[str, Any] , *__SCREAMING_SNAKE_CASE : str , **__SCREAMING_SNAKE_CASE : Union[str, Any] ) -> Optional[Any]:
"""simple docstring"""
requires_backends(self , ['''torch'''] )
@classmethod
def _a ( cls : List[str] , *__SCREAMING_SNAKE_CASE : Optional[int] , **__SCREAMING_SNAKE_CASE : Any ) -> int:
"""simple docstring"""
requires_backends(cls , ['''torch'''] )
@classmethod
def _a ( cls : Any , *__SCREAMING_SNAKE_CASE : Tuple , **__SCREAMING_SNAKE_CASE : Dict ) -> List[Any]:
"""simple docstring"""
requires_backends(cls , ['''torch'''] )
class A__( metaclass=__magic_name__ ):
lowerCAmelCase = ['''torch''']
def __init__( self : List[str] , *__SCREAMING_SNAKE_CASE : List[Any] , **__SCREAMING_SNAKE_CASE : str ) -> str:
"""simple docstring"""
requires_backends(self , ['''torch'''] )
@classmethod
def _a ( cls : List[str] , *__SCREAMING_SNAKE_CASE : str , **__SCREAMING_SNAKE_CASE : Any ) -> Optional[Any]:
"""simple docstring"""
requires_backends(cls , ['''torch'''] )
@classmethod
def _a ( cls : Tuple , *__SCREAMING_SNAKE_CASE : List[str] , **__SCREAMING_SNAKE_CASE : Optional[int] ) -> Optional[Any]:
"""simple docstring"""
requires_backends(cls , ['''torch'''] )
class A__( metaclass=__magic_name__ ):
lowerCAmelCase = ['''torch''']
def __init__( self : Optional[int] , *__SCREAMING_SNAKE_CASE : Dict , **__SCREAMING_SNAKE_CASE : List[Any] ) -> int:
"""simple docstring"""
requires_backends(self , ['''torch'''] )
@classmethod
def _a ( cls : Union[str, Any] , *__SCREAMING_SNAKE_CASE : Optional[Any] , **__SCREAMING_SNAKE_CASE : Dict ) -> List[Any]:
"""simple docstring"""
requires_backends(cls , ['''torch'''] )
@classmethod
def _a ( cls : Optional[Any] , *__SCREAMING_SNAKE_CASE : List[Any] , **__SCREAMING_SNAKE_CASE : int ) -> Optional[int]:
"""simple docstring"""
requires_backends(cls , ['''torch'''] )
class A__( metaclass=__magic_name__ ):
lowerCAmelCase = ['''torch''']
def __init__( self : Union[str, Any] , *__SCREAMING_SNAKE_CASE : str , **__SCREAMING_SNAKE_CASE : Optional[int] ) -> List[str]:
"""simple docstring"""
requires_backends(self , ['''torch'''] )
@classmethod
def _a ( cls : Union[str, Any] , *__SCREAMING_SNAKE_CASE : int , **__SCREAMING_SNAKE_CASE : List[str] ) -> Dict:
"""simple docstring"""
requires_backends(cls , ['''torch'''] )
@classmethod
def _a ( cls : List[Any] , *__SCREAMING_SNAKE_CASE : str , **__SCREAMING_SNAKE_CASE : str ) -> Tuple:
"""simple docstring"""
requires_backends(cls , ['''torch'''] )
class A__( metaclass=__magic_name__ ):
lowerCAmelCase = ['''torch''']
def __init__( self : Optional[int] , *__SCREAMING_SNAKE_CASE : Any , **__SCREAMING_SNAKE_CASE : Union[str, Any] ) -> int:
"""simple docstring"""
requires_backends(self , ['''torch'''] )
@classmethod
def _a ( cls : str , *__SCREAMING_SNAKE_CASE : int , **__SCREAMING_SNAKE_CASE : List[str] ) -> Union[str, Any]:
"""simple docstring"""
requires_backends(cls , ['''torch'''] )
@classmethod
def _a ( cls : Optional[int] , *__SCREAMING_SNAKE_CASE : Tuple , **__SCREAMING_SNAKE_CASE : Tuple ) -> Dict:
"""simple docstring"""
requires_backends(cls , ['''torch'''] )
class A__( metaclass=__magic_name__ ):
lowerCAmelCase = ['''torch''']
def __init__( self : List[Any] , *__SCREAMING_SNAKE_CASE : List[str] , **__SCREAMING_SNAKE_CASE : Union[str, Any] ) -> Optional[Any]:
"""simple docstring"""
requires_backends(self , ['''torch'''] )
@classmethod
def _a ( cls : Optional[int] , *__SCREAMING_SNAKE_CASE : Optional[int] , **__SCREAMING_SNAKE_CASE : Any ) -> List[Any]:
"""simple docstring"""
requires_backends(cls , ['''torch'''] )
@classmethod
def _a ( cls : int , *__SCREAMING_SNAKE_CASE : List[str] , **__SCREAMING_SNAKE_CASE : Optional[int] ) -> int:
"""simple docstring"""
requires_backends(cls , ['''torch'''] )
class A__( metaclass=__magic_name__ ):
lowerCAmelCase = ['''torch''']
def __init__( self : int , *__SCREAMING_SNAKE_CASE : Union[str, Any] , **__SCREAMING_SNAKE_CASE : int ) -> Optional[int]:
"""simple docstring"""
requires_backends(self , ['''torch'''] )
@classmethod
def _a ( cls : List[Any] , *__SCREAMING_SNAKE_CASE : Any , **__SCREAMING_SNAKE_CASE : List[Any] ) -> Union[str, Any]:
"""simple docstring"""
requires_backends(cls , ['''torch'''] )
@classmethod
def _a ( cls : int , *__SCREAMING_SNAKE_CASE : List[Any] , **__SCREAMING_SNAKE_CASE : List[Any] ) -> Union[str, Any]:
"""simple docstring"""
requires_backends(cls , ['''torch'''] )
class A__( metaclass=__magic_name__ ):
lowerCAmelCase = ['''torch''']
def __init__( self : Optional[Any] , *__SCREAMING_SNAKE_CASE : Any , **__SCREAMING_SNAKE_CASE : Any ) -> Dict:
"""simple docstring"""
requires_backends(self , ['''torch'''] )
@classmethod
def _a ( cls : Union[str, Any] , *__SCREAMING_SNAKE_CASE : str , **__SCREAMING_SNAKE_CASE : Optional[Any] ) -> int:
"""simple docstring"""
requires_backends(cls , ['''torch'''] )
@classmethod
def _a ( cls : Tuple , *__SCREAMING_SNAKE_CASE : Optional[int] , **__SCREAMING_SNAKE_CASE : Tuple ) -> Optional[int]:
"""simple docstring"""
requires_backends(cls , ['''torch'''] )
class A__( metaclass=__magic_name__ ):
lowerCAmelCase = ['''torch''']
def __init__( self : Optional[Any] , *__SCREAMING_SNAKE_CASE : List[Any] , **__SCREAMING_SNAKE_CASE : Tuple ) -> Optional[int]:
"""simple docstring"""
requires_backends(self , ['''torch'''] )
@classmethod
def _a ( cls : Tuple , *__SCREAMING_SNAKE_CASE : Union[str, Any] , **__SCREAMING_SNAKE_CASE : str ) -> Any:
"""simple docstring"""
requires_backends(cls , ['''torch'''] )
@classmethod
def _a ( cls : Optional[Any] , *__SCREAMING_SNAKE_CASE : Optional[Any] , **__SCREAMING_SNAKE_CASE : List[str] ) -> Dict:
"""simple docstring"""
requires_backends(cls , ['''torch'''] )
class A__( metaclass=__magic_name__ ):
lowerCAmelCase = ['''torch''']
def __init__( self : str , *__SCREAMING_SNAKE_CASE : Optional[int] , **__SCREAMING_SNAKE_CASE : Tuple ) -> Any:
"""simple docstring"""
requires_backends(self , ['''torch'''] )
@classmethod
def _a ( cls : int , *__SCREAMING_SNAKE_CASE : Optional[int] , **__SCREAMING_SNAKE_CASE : List[str] ) -> Tuple:
"""simple docstring"""
requires_backends(cls , ['''torch'''] )
@classmethod
def _a ( cls : List[str] , *__SCREAMING_SNAKE_CASE : Optional[Any] , **__SCREAMING_SNAKE_CASE : Tuple ) -> int:
"""simple docstring"""
requires_backends(cls , ['''torch'''] )
class A__( metaclass=__magic_name__ ):
lowerCAmelCase = ['''torch''']
def __init__( self : List[Any] , *__SCREAMING_SNAKE_CASE : Any , **__SCREAMING_SNAKE_CASE : Tuple ) -> int:
"""simple docstring"""
requires_backends(self , ['''torch'''] )
@classmethod
def _a ( cls : int , *__SCREAMING_SNAKE_CASE : Optional[Any] , **__SCREAMING_SNAKE_CASE : List[Any] ) -> Tuple:
"""simple docstring"""
requires_backends(cls , ['''torch'''] )
@classmethod
def _a ( cls : List[str] , *__SCREAMING_SNAKE_CASE : Union[str, Any] , **__SCREAMING_SNAKE_CASE : Dict ) -> List[Any]:
"""simple docstring"""
requires_backends(cls , ['''torch'''] )
class A__( metaclass=__magic_name__ ):
lowerCAmelCase = ['''torch''']
def __init__( self : int , *__SCREAMING_SNAKE_CASE : List[str] , **__SCREAMING_SNAKE_CASE : int ) -> int:
"""simple docstring"""
requires_backends(self , ['''torch'''] )
@classmethod
def _a ( cls : int , *__SCREAMING_SNAKE_CASE : Any , **__SCREAMING_SNAKE_CASE : Optional[int] ) -> int:
"""simple docstring"""
requires_backends(cls , ['''torch'''] )
@classmethod
def _a ( cls : Any , *__SCREAMING_SNAKE_CASE : Tuple , **__SCREAMING_SNAKE_CASE : int ) -> str:
"""simple docstring"""
requires_backends(cls , ['''torch'''] )
class A__( metaclass=__magic_name__ ):
lowerCAmelCase = ['''torch''']
def __init__( self : Optional[Any] , *__SCREAMING_SNAKE_CASE : str , **__SCREAMING_SNAKE_CASE : str ) -> Union[str, Any]:
"""simple docstring"""
requires_backends(self , ['''torch'''] )
@classmethod
def _a ( cls : List[str] , *__SCREAMING_SNAKE_CASE : Tuple , **__SCREAMING_SNAKE_CASE : Dict ) -> int:
"""simple docstring"""
requires_backends(cls , ['''torch'''] )
@classmethod
def _a ( cls : List[str] , *__SCREAMING_SNAKE_CASE : Tuple , **__SCREAMING_SNAKE_CASE : List[Any] ) -> Tuple:
"""simple docstring"""
requires_backends(cls , ['''torch'''] )
class A__( metaclass=__magic_name__ ):
lowerCAmelCase = ['''torch''']
def __init__( self : Dict , *__SCREAMING_SNAKE_CASE : Optional[int] , **__SCREAMING_SNAKE_CASE : int ) -> Union[str, Any]:
"""simple docstring"""
requires_backends(self , ['''torch'''] )
@classmethod
def _a ( cls : List[str] , *__SCREAMING_SNAKE_CASE : Optional[int] , **__SCREAMING_SNAKE_CASE : str ) -> Union[str, Any]:
"""simple docstring"""
requires_backends(cls , ['''torch'''] )
@classmethod
def _a ( cls : int , *__SCREAMING_SNAKE_CASE : Optional[Any] , **__SCREAMING_SNAKE_CASE : Any ) -> str:
"""simple docstring"""
requires_backends(cls , ['''torch'''] )
class A__( metaclass=__magic_name__ ):
lowerCAmelCase = ['''torch''']
def __init__( self : str , *__SCREAMING_SNAKE_CASE : Optional[int] , **__SCREAMING_SNAKE_CASE : Union[str, Any] ) -> Optional[int]:
"""simple docstring"""
requires_backends(self , ['''torch'''] )
@classmethod
def _a ( cls : str , *__SCREAMING_SNAKE_CASE : List[Any] , **__SCREAMING_SNAKE_CASE : str ) -> List[Any]:
"""simple docstring"""
requires_backends(cls , ['''torch'''] )
@classmethod
def _a ( cls : Union[str, Any] , *__SCREAMING_SNAKE_CASE : Any , **__SCREAMING_SNAKE_CASE : Tuple ) -> Any:
"""simple docstring"""
requires_backends(cls , ['''torch'''] )
class A__( metaclass=__magic_name__ ):
lowerCAmelCase = ['''torch''']
def __init__( self : Union[str, Any] , *__SCREAMING_SNAKE_CASE : List[Any] , **__SCREAMING_SNAKE_CASE : Union[str, Any] ) -> List[Any]:
"""simple docstring"""
requires_backends(self , ['''torch'''] )
@classmethod
def _a ( cls : int , *__SCREAMING_SNAKE_CASE : int , **__SCREAMING_SNAKE_CASE : List[str] ) -> List[Any]:
"""simple docstring"""
requires_backends(cls , ['''torch'''] )
@classmethod
def _a ( cls : Optional[int] , *__SCREAMING_SNAKE_CASE : Dict , **__SCREAMING_SNAKE_CASE : str ) -> Optional[int]:
"""simple docstring"""
requires_backends(cls , ['''torch'''] )
class A__( metaclass=__magic_name__ ):
lowerCAmelCase = ['''torch''']
def __init__( self : Union[str, Any] , *__SCREAMING_SNAKE_CASE : Union[str, Any] , **__SCREAMING_SNAKE_CASE : Dict ) -> Optional[int]:
"""simple docstring"""
requires_backends(self , ['''torch'''] )
@classmethod
def _a ( cls : Any , *__SCREAMING_SNAKE_CASE : Dict , **__SCREAMING_SNAKE_CASE : str ) -> List[Any]:
"""simple docstring"""
requires_backends(cls , ['''torch'''] )
@classmethod
def _a ( cls : Dict , *__SCREAMING_SNAKE_CASE : str , **__SCREAMING_SNAKE_CASE : List[str] ) -> List[Any]:
"""simple docstring"""
requires_backends(cls , ['''torch'''] )
class A__( metaclass=__magic_name__ ):
lowerCAmelCase = ['''torch''']
def __init__( self : Optional[int] , *__SCREAMING_SNAKE_CASE : Optional[int] , **__SCREAMING_SNAKE_CASE : List[str] ) -> str:
"""simple docstring"""
requires_backends(self , ['''torch'''] )
@classmethod
def _a ( cls : Dict , *__SCREAMING_SNAKE_CASE : Any , **__SCREAMING_SNAKE_CASE : Optional[int] ) -> int:
"""simple docstring"""
requires_backends(cls , ['''torch'''] )
@classmethod
def _a ( cls : Dict , *__SCREAMING_SNAKE_CASE : List[Any] , **__SCREAMING_SNAKE_CASE : List[str] ) -> Optional[int]:
"""simple docstring"""
requires_backends(cls , ['''torch'''] )
class A__( metaclass=__magic_name__ ):
lowerCAmelCase = ['''torch''']
def __init__( self : Union[str, Any] , *__SCREAMING_SNAKE_CASE : Tuple , **__SCREAMING_SNAKE_CASE : Any ) -> Optional[int]:
"""simple docstring"""
requires_backends(self , ['''torch'''] )
@classmethod
def _a ( cls : str , *__SCREAMING_SNAKE_CASE : str , **__SCREAMING_SNAKE_CASE : Optional[Any] ) -> str:
"""simple docstring"""
requires_backends(cls , ['''torch'''] )
@classmethod
def _a ( cls : Any , *__SCREAMING_SNAKE_CASE : int , **__SCREAMING_SNAKE_CASE : str ) -> Tuple:
"""simple docstring"""
requires_backends(cls , ['''torch'''] )
class A__( metaclass=__magic_name__ ):
lowerCAmelCase = ['''torch''']
def __init__( self : List[Any] , *__SCREAMING_SNAKE_CASE : str , **__SCREAMING_SNAKE_CASE : Optional[int] ) -> Dict:
"""simple docstring"""
requires_backends(self , ['''torch'''] )
@classmethod
def _a ( cls : Dict , *__SCREAMING_SNAKE_CASE : Optional[Any] , **__SCREAMING_SNAKE_CASE : Union[str, Any] ) -> Any:
"""simple docstring"""
requires_backends(cls , ['''torch'''] )
@classmethod
def _a ( cls : Union[str, Any] , *__SCREAMING_SNAKE_CASE : Any , **__SCREAMING_SNAKE_CASE : Union[str, Any] ) -> List[Any]:
"""simple docstring"""
requires_backends(cls , ['''torch'''] )
class A__( metaclass=__magic_name__ ):
lowerCAmelCase = ['''torch''']
def __init__( self : Dict , *__SCREAMING_SNAKE_CASE : Tuple , **__SCREAMING_SNAKE_CASE : Optional[int] ) -> List[Any]:
"""simple docstring"""
requires_backends(self , ['''torch'''] )
@classmethod
def _a ( cls : Any , *__SCREAMING_SNAKE_CASE : Optional[Any] , **__SCREAMING_SNAKE_CASE : int ) -> List[Any]:
"""simple docstring"""
requires_backends(cls , ['''torch'''] )
@classmethod
def _a ( cls : Optional[Any] , *__SCREAMING_SNAKE_CASE : Optional[Any] , **__SCREAMING_SNAKE_CASE : Any ) -> Tuple:
"""simple docstring"""
requires_backends(cls , ['''torch'''] )
class A__( metaclass=__magic_name__ ):
lowerCAmelCase = ['''torch''']
def __init__( self : Any , *__SCREAMING_SNAKE_CASE : List[Any] , **__SCREAMING_SNAKE_CASE : str ) -> Optional[int]:
"""simple docstring"""
requires_backends(self , ['''torch'''] )
@classmethod
def _a ( cls : int , *__SCREAMING_SNAKE_CASE : int , **__SCREAMING_SNAKE_CASE : List[Any] ) -> Any:
"""simple docstring"""
requires_backends(cls , ['''torch'''] )
@classmethod
def _a ( cls : Union[str, Any] , *__SCREAMING_SNAKE_CASE : str , **__SCREAMING_SNAKE_CASE : Union[str, Any] ) -> Optional[int]:
"""simple docstring"""
requires_backends(cls , ['''torch'''] )
class A__( metaclass=__magic_name__ ):
lowerCAmelCase = ['''torch''']
def __init__( self : Optional[Any] , *__SCREAMING_SNAKE_CASE : Union[str, Any] , **__SCREAMING_SNAKE_CASE : int ) -> Tuple:
"""simple docstring"""
requires_backends(self , ['''torch'''] )
@classmethod
def _a ( cls : Optional[Any] , *__SCREAMING_SNAKE_CASE : Optional[int] , **__SCREAMING_SNAKE_CASE : Tuple ) -> Tuple:
"""simple docstring"""
requires_backends(cls , ['''torch'''] )
@classmethod
def _a ( cls : Optional[Any] , *__SCREAMING_SNAKE_CASE : Optional[int] , **__SCREAMING_SNAKE_CASE : Union[str, Any] ) -> Dict:
"""simple docstring"""
requires_backends(cls , ['''torch'''] )
| 690 |
"""simple docstring"""
from ....configuration_utils import PretrainedConfig
from ....utils import logging
lowerCAmelCase__ =logging.get_logger(__name__)
lowerCAmelCase__ ={
"Visual-Attention-Network/van-base": (
"https://huggingface.co/Visual-Attention-Network/van-base/blob/main/config.json"
),
}
class A__( __magic_name__ ):
lowerCAmelCase = '''van'''
def __init__( self : int , __SCREAMING_SNAKE_CASE : Optional[Any]=2_24 , __SCREAMING_SNAKE_CASE : Union[str, Any]=3 , __SCREAMING_SNAKE_CASE : Tuple=[7, 3, 3, 3] , __SCREAMING_SNAKE_CASE : Optional[int]=[4, 2, 2, 2] , __SCREAMING_SNAKE_CASE : str=[64, 1_28, 3_20, 5_12] , __SCREAMING_SNAKE_CASE : Optional[Any]=[3, 3, 12, 3] , __SCREAMING_SNAKE_CASE : Dict=[8, 8, 4, 4] , __SCREAMING_SNAKE_CASE : Any="gelu" , __SCREAMING_SNAKE_CASE : Tuple=0.02 , __SCREAMING_SNAKE_CASE : Dict=1E-6 , __SCREAMING_SNAKE_CASE : Any=1E-2 , __SCREAMING_SNAKE_CASE : str=0.0 , __SCREAMING_SNAKE_CASE : Union[str, Any]=0.0 , **__SCREAMING_SNAKE_CASE : str , ) -> List[str]:
"""simple docstring"""
super().__init__(**__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = image_size
__SCREAMING_SNAKE_CASE = num_channels
__SCREAMING_SNAKE_CASE = patch_sizes
__SCREAMING_SNAKE_CASE = strides
__SCREAMING_SNAKE_CASE = hidden_sizes
__SCREAMING_SNAKE_CASE = depths
__SCREAMING_SNAKE_CASE = mlp_ratios
__SCREAMING_SNAKE_CASE = hidden_act
__SCREAMING_SNAKE_CASE = initializer_range
__SCREAMING_SNAKE_CASE = layer_norm_eps
__SCREAMING_SNAKE_CASE = layer_scale_init_value
__SCREAMING_SNAKE_CASE = drop_path_rate
__SCREAMING_SNAKE_CASE = dropout_rate
| 690 | 1 |
"""simple docstring"""
from sklearn.metrics import recall_score
import datasets
lowerCAmelCase__ ="\nRecall is the fraction of the positive examples that were correctly labeled by the model as positive. It can be computed with the equation:\nRecall = TP / (TP + FN)\nWhere TP is the true positives and FN is the false negatives.\n"
lowerCAmelCase__ ="\nArgs:\n- **predictions** (`list` of `int`): The predicted labels.\n- **references** (`list` of `int`): The ground truth labels.\n- **labels** (`list` of `int`): The set of labels to include when `average` is not set to `binary`, and their order when average is `None`. Labels present in the data can be excluded in this input, for example to calculate a multiclass average ignoring a majority negative class, while labels not present in the data will result in 0 components in a macro average. For multilabel targets, labels are column indices. By default, all labels in y_true and y_pred are used in sorted order. Defaults to None.\n- **pos_label** (`int`): The class label to use as the 'positive class' when calculating the recall. Defaults to `1`.\n- **average** (`string`): This parameter is required for multiclass/multilabel targets. If None, the scores for each class are returned. Otherwise, this determines the type of averaging performed on the data. Defaults to `'binary'`.\n - `'binary'`: Only report results for the class specified by `pos_label`. This is applicable only if the target labels and predictions are binary.\n - `'micro'`: Calculate metrics globally by counting the total true positives, false negatives, and false positives.\n - `'macro'`: Calculate metrics for each label, and find their unweighted mean. This does not take label imbalance into account.\n - `'weighted'`: Calculate metrics for each label, and find their average weighted by support (the number of true instances for each label). This alters `'macro'` to account for label imbalance. Note that it can result in an F-score that is not between precision and recall.\n - `'samples'`: Calculate metrics for each instance, and find their average (only meaningful for multilabel classification).\n- **sample_weight** (`list` of `float`): Sample weights Defaults to `None`.\n- **zero_division** (): Sets the value to return when there is a zero division. Defaults to .\n - `'warn'`: If there is a zero division, the return value is `0`, but warnings are also raised.\n - `0`: If there is a zero division, the return value is `0`.\n - `1`: If there is a zero division, the return value is `1`.\n\nReturns:\n- **recall** (`float`, or `array` of `float`): Either the general recall score, or the recall scores for individual classes, depending on the values input to `labels` and `average`. Minimum possible value is 0. Maximum possible value is 1. A higher recall means that more of the positive examples have been labeled correctly. Therefore, a higher recall is generally considered better.\n\nExamples:\n\n Example 1-A simple example with some errors\n >>> recall_metric = datasets.load_metric('recall')\n >>> results = recall_metric.compute(references=[0, 0, 1, 1, 1], predictions=[0, 1, 0, 1, 1])\n >>> print(results)\n {'recall': 0.6666666666666666}\n\n Example 2-The same example as Example 1, but with `pos_label=0` instead of the default `pos_label=1`.\n >>> recall_metric = datasets.load_metric('recall')\n >>> results = recall_metric.compute(references=[0, 0, 1, 1, 1], predictions=[0, 1, 0, 1, 1], pos_label=0)\n >>> print(results)\n {'recall': 0.5}\n\n Example 3-The same example as Example 1, but with `sample_weight` included.\n >>> recall_metric = datasets.load_metric('recall')\n >>> sample_weight = [0.9, 0.2, 0.9, 0.3, 0.8]\n >>> results = recall_metric.compute(references=[0, 0, 1, 1, 1], predictions=[0, 1, 0, 1, 1], sample_weight=sample_weight)\n >>> print(results)\n {'recall': 0.55}\n\n Example 4-A multiclass example, using different averages.\n >>> recall_metric = datasets.load_metric('recall')\n >>> predictions = [0, 2, 1, 0, 0, 1]\n >>> references = [0, 1, 2, 0, 1, 2]\n >>> results = recall_metric.compute(predictions=predictions, references=references, average='macro')\n >>> print(results)\n {'recall': 0.3333333333333333}\n >>> results = recall_metric.compute(predictions=predictions, references=references, average='micro')\n >>> print(results)\n {'recall': 0.3333333333333333}\n >>> results = recall_metric.compute(predictions=predictions, references=references, average='weighted')\n >>> print(results)\n {'recall': 0.3333333333333333}\n >>> results = recall_metric.compute(predictions=predictions, references=references, average=None)\n >>> print(results)\n {'recall': array([1., 0., 0.])}\n"
lowerCAmelCase__ ="\n@article{scikit-learn, title={Scikit-learn: Machine Learning in {P}ython}, author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V. and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P. and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.}, journal={Journal of Machine Learning Research}, volume={12}, pages={2825--2830}, year={2011}\n"
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class A__( datasets.Metric ):
def _a ( self : Any ) -> int:
"""simple docstring"""
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
'''predictions''': datasets.Sequence(datasets.Value('''int32''' ) ),
'''references''': datasets.Sequence(datasets.Value('''int32''' ) ),
}
if self.config_name == '''multilabel'''
else {
'''predictions''': datasets.Value('''int32''' ),
'''references''': datasets.Value('''int32''' ),
} ) , reference_urls=['''https://scikit-learn.org/stable/modules/generated/sklearn.metrics.recall_score.html'''] , )
def _a ( self : Tuple , __SCREAMING_SNAKE_CASE : Union[str, Any] , __SCREAMING_SNAKE_CASE : Optional[Any] , __SCREAMING_SNAKE_CASE : Union[str, Any]=None , __SCREAMING_SNAKE_CASE : Optional[int]=1 , __SCREAMING_SNAKE_CASE : Optional[Any]="binary" , __SCREAMING_SNAKE_CASE : List[Any]=None , __SCREAMING_SNAKE_CASE : List[Any]="warn" , ) -> Dict:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = recall_score(
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , labels=__SCREAMING_SNAKE_CASE , pos_label=__SCREAMING_SNAKE_CASE , average=__SCREAMING_SNAKE_CASE , sample_weight=__SCREAMING_SNAKE_CASE , zero_division=__SCREAMING_SNAKE_CASE , )
return {"recall": float(__SCREAMING_SNAKE_CASE ) if score.size == 1 else score}
| 690 |
"""simple docstring"""
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCAmelCase__ ={"configuration_timm_backbone": ["TimmBackboneConfig"]}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase__ =["TimmBackbone"]
if TYPE_CHECKING:
from .configuration_timm_backbone import TimmBackboneConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_timm_backbone import TimmBackbone
else:
import sys
lowerCAmelCase__ =_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 690 | 1 |
"""simple docstring"""
def _a ( UpperCAmelCase__ ) -> list[list[int]]:
__SCREAMING_SNAKE_CASE = []
if len(UpperCAmelCase__ ) == 1:
return [nums.copy()]
for _ in range(len(UpperCAmelCase__ ) ):
__SCREAMING_SNAKE_CASE = nums.pop(0 )
__SCREAMING_SNAKE_CASE = permute(UpperCAmelCase__ )
for perm in permutations:
perm.append(UpperCAmelCase__ )
result.extend(UpperCAmelCase__ )
nums.append(UpperCAmelCase__ )
return result
def _a ( UpperCAmelCase__ ) -> Dict:
def backtrack(UpperCAmelCase__ ):
if start == len(UpperCAmelCase__ ) - 1:
output.append(nums[:] )
else:
for i in range(UpperCAmelCase__ , len(UpperCAmelCase__ ) ):
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = nums[i], nums[start]
backtrack(start + 1 )
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = nums[i], nums[start] # backtrack
__SCREAMING_SNAKE_CASE = []
backtrack(0 )
return output
if __name__ == "__main__":
import doctest
# use res to print the data in permute2 function
lowerCAmelCase__ =permutea([1, 2, 3])
print(res)
doctest.testmod()
| 690 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
lowerCAmelCase__ ={
"configuration_altclip": [
"ALTCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP",
"AltCLIPConfig",
"AltCLIPTextConfig",
"AltCLIPVisionConfig",
],
"processing_altclip": ["AltCLIPProcessor"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase__ =[
"ALTCLIP_PRETRAINED_MODEL_ARCHIVE_LIST",
"AltCLIPPreTrainedModel",
"AltCLIPModel",
"AltCLIPTextModel",
"AltCLIPVisionModel",
]
if TYPE_CHECKING:
from .configuration_altclip import (
ALTCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
AltCLIPConfig,
AltCLIPTextConfig,
AltCLIPVisionConfig,
)
from .processing_altclip import AltCLIPProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_altclip import (
ALTCLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
AltCLIPModel,
AltCLIPPreTrainedModel,
AltCLIPTextModel,
AltCLIPVisionModel,
)
else:
import sys
lowerCAmelCase__ =_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 690 | 1 |
"""simple docstring"""
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
if is_torch_available():
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
@require_torch
@require_sentencepiece
@require_tokenizers
class A__( unittest.TestCase ):
@slow
def _a ( self : Optional[Any] ) -> str:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = AutoModelForSeqaSeqLM.from_pretrained('''google/mt5-small''' , return_dict=__SCREAMING_SNAKE_CASE ).to(__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = AutoTokenizer.from_pretrained('''google/mt5-small''' )
__SCREAMING_SNAKE_CASE = tokenizer('''Hello there''' , return_tensors='''pt''' ).input_ids
__SCREAMING_SNAKE_CASE = tokenizer('''Hi I am''' , return_tensors='''pt''' ).input_ids
__SCREAMING_SNAKE_CASE = model(input_ids.to(__SCREAMING_SNAKE_CASE ) , labels=labels.to(__SCREAMING_SNAKE_CASE ) ).loss
__SCREAMING_SNAKE_CASE = -(labels.shape[-1] * loss.item())
__SCREAMING_SNAKE_CASE = -84.91_27
self.assertTrue(abs(mtf_score - EXPECTED_SCORE ) < 1E-4 )
| 690 |
"""simple docstring"""
import json
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
from transformers import (
CONFIG_MAPPING,
IMAGE_PROCESSOR_MAPPING,
AutoConfig,
AutoImageProcessor,
CLIPConfig,
CLIPImageProcessor,
)
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER
sys.path.append(str(Path(__file__).parent.parent.parent.parent / "utils"))
from test_module.custom_configuration import CustomConfig # noqa E402
from test_module.custom_image_processing import CustomImageProcessor # noqa E402
class A__( unittest.TestCase ):
def _a ( self : int ) -> Union[str, Any]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = 0
def _a ( self : Tuple ) -> Tuple:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = AutoImageProcessor.from_pretrained('''openai/clip-vit-base-patch32''' )
self.assertIsInstance(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
def _a ( self : str ) -> Optional[int]:
"""simple docstring"""
with tempfile.TemporaryDirectory() as tmpdirname:
__SCREAMING_SNAKE_CASE = Path(__SCREAMING_SNAKE_CASE ) / '''preprocessor_config.json'''
__SCREAMING_SNAKE_CASE = Path(__SCREAMING_SNAKE_CASE ) / '''config.json'''
json.dump(
{'''image_processor_type''': '''CLIPImageProcessor''', '''processor_class''': '''CLIPProcessor'''} , open(__SCREAMING_SNAKE_CASE , '''w''' ) , )
json.dump({'''model_type''': '''clip'''} , open(__SCREAMING_SNAKE_CASE , '''w''' ) )
__SCREAMING_SNAKE_CASE = AutoImageProcessor.from_pretrained(__SCREAMING_SNAKE_CASE )
self.assertIsInstance(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
def _a ( self : List[str] ) -> Optional[int]:
"""simple docstring"""
with tempfile.TemporaryDirectory() as tmpdirname:
__SCREAMING_SNAKE_CASE = Path(__SCREAMING_SNAKE_CASE ) / '''preprocessor_config.json'''
__SCREAMING_SNAKE_CASE = Path(__SCREAMING_SNAKE_CASE ) / '''config.json'''
json.dump(
{'''feature_extractor_type''': '''CLIPFeatureExtractor''', '''processor_class''': '''CLIPProcessor'''} , open(__SCREAMING_SNAKE_CASE , '''w''' ) , )
json.dump({'''model_type''': '''clip'''} , open(__SCREAMING_SNAKE_CASE , '''w''' ) )
__SCREAMING_SNAKE_CASE = AutoImageProcessor.from_pretrained(__SCREAMING_SNAKE_CASE )
self.assertIsInstance(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
def _a ( self : str ) -> int:
"""simple docstring"""
with tempfile.TemporaryDirectory() as tmpdirname:
__SCREAMING_SNAKE_CASE = CLIPConfig()
# Create a dummy config file with image_proceesor_type
__SCREAMING_SNAKE_CASE = Path(__SCREAMING_SNAKE_CASE ) / '''preprocessor_config.json'''
__SCREAMING_SNAKE_CASE = Path(__SCREAMING_SNAKE_CASE ) / '''config.json'''
json.dump(
{'''image_processor_type''': '''CLIPImageProcessor''', '''processor_class''': '''CLIPProcessor'''} , open(__SCREAMING_SNAKE_CASE , '''w''' ) , )
json.dump({'''model_type''': '''clip'''} , open(__SCREAMING_SNAKE_CASE , '''w''' ) )
# remove image_processor_type to make sure config.json alone is enough to load image processor locally
__SCREAMING_SNAKE_CASE = AutoImageProcessor.from_pretrained(__SCREAMING_SNAKE_CASE ).to_dict()
config_dict.pop('''image_processor_type''' )
__SCREAMING_SNAKE_CASE = CLIPImageProcessor(**__SCREAMING_SNAKE_CASE )
# save in new folder
model_config.save_pretrained(__SCREAMING_SNAKE_CASE )
config.save_pretrained(__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = AutoImageProcessor.from_pretrained(__SCREAMING_SNAKE_CASE )
# make sure private variable is not incorrectly saved
__SCREAMING_SNAKE_CASE = json.loads(config.to_json_string() )
self.assertTrue('''_processor_class''' not in dict_as_saved )
self.assertIsInstance(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
def _a ( self : Optional[int] ) -> Tuple:
"""simple docstring"""
with tempfile.TemporaryDirectory() as tmpdirname:
__SCREAMING_SNAKE_CASE = Path(__SCREAMING_SNAKE_CASE ) / '''preprocessor_config.json'''
json.dump(
{'''image_processor_type''': '''CLIPImageProcessor''', '''processor_class''': '''CLIPProcessor'''} , open(__SCREAMING_SNAKE_CASE , '''w''' ) , )
__SCREAMING_SNAKE_CASE = AutoImageProcessor.from_pretrained(__SCREAMING_SNAKE_CASE )
self.assertIsInstance(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
def _a ( self : List[Any] ) -> str:
"""simple docstring"""
with self.assertRaisesRegex(
__SCREAMING_SNAKE_CASE , '''clip-base is not a local folder and is not a valid model identifier''' ):
__SCREAMING_SNAKE_CASE = AutoImageProcessor.from_pretrained('''clip-base''' )
def _a ( self : Any ) -> Optional[Any]:
"""simple docstring"""
with self.assertRaisesRegex(
__SCREAMING_SNAKE_CASE , r'''aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)''' ):
__SCREAMING_SNAKE_CASE = AutoImageProcessor.from_pretrained(__SCREAMING_SNAKE_CASE , revision='''aaaaaa''' )
def _a ( self : Dict ) -> Dict:
"""simple docstring"""
with self.assertRaisesRegex(
__SCREAMING_SNAKE_CASE , '''hf-internal-testing/config-no-model does not appear to have a file named preprocessor_config.json.''' , ):
__SCREAMING_SNAKE_CASE = AutoImageProcessor.from_pretrained('''hf-internal-testing/config-no-model''' )
def _a ( self : int ) -> Any:
"""simple docstring"""
with self.assertRaises(__SCREAMING_SNAKE_CASE ):
__SCREAMING_SNAKE_CASE = AutoImageProcessor.from_pretrained('''hf-internal-testing/test_dynamic_image_processor''' )
# If remote code is disabled, we can't load this config.
with self.assertRaises(__SCREAMING_SNAKE_CASE ):
__SCREAMING_SNAKE_CASE = AutoImageProcessor.from_pretrained(
'''hf-internal-testing/test_dynamic_image_processor''' , trust_remote_code=__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = AutoImageProcessor.from_pretrained(
'''hf-internal-testing/test_dynamic_image_processor''' , trust_remote_code=__SCREAMING_SNAKE_CASE )
self.assertEqual(image_processor.__class__.__name__ , '''NewImageProcessor''' )
# Test image processor can be reloaded.
with tempfile.TemporaryDirectory() as tmp_dir:
image_processor.save_pretrained(__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = AutoImageProcessor.from_pretrained(__SCREAMING_SNAKE_CASE , trust_remote_code=__SCREAMING_SNAKE_CASE )
self.assertEqual(reloaded_image_processor.__class__.__name__ , '''NewImageProcessor''' )
def _a ( self : Optional[Any] ) -> str:
"""simple docstring"""
try:
AutoConfig.register('''custom''' , __SCREAMING_SNAKE_CASE )
AutoImageProcessor.register(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
# Trying to register something existing in the Transformers library will raise an error
with self.assertRaises(__SCREAMING_SNAKE_CASE ):
AutoImageProcessor.register(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
with tempfile.TemporaryDirectory() as tmpdirname:
__SCREAMING_SNAKE_CASE = Path(__SCREAMING_SNAKE_CASE ) / '''preprocessor_config.json'''
__SCREAMING_SNAKE_CASE = Path(__SCREAMING_SNAKE_CASE ) / '''config.json'''
json.dump(
{'''feature_extractor_type''': '''CLIPFeatureExtractor''', '''processor_class''': '''CLIPProcessor'''} , open(__SCREAMING_SNAKE_CASE , '''w''' ) , )
json.dump({'''model_type''': '''clip'''} , open(__SCREAMING_SNAKE_CASE , '''w''' ) )
__SCREAMING_SNAKE_CASE = CustomImageProcessor.from_pretrained(__SCREAMING_SNAKE_CASE )
# Now that the config is registered, it can be used as any other config with the auto-API
with tempfile.TemporaryDirectory() as tmp_dir:
image_processor.save_pretrained(__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = AutoImageProcessor.from_pretrained(__SCREAMING_SNAKE_CASE )
self.assertIsInstance(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
finally:
if "custom" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["custom"]
if CustomConfig in IMAGE_PROCESSOR_MAPPING._extra_content:
del IMAGE_PROCESSOR_MAPPING._extra_content[CustomConfig]
def _a ( self : int ) -> List[Any]:
"""simple docstring"""
class A__( __magic_name__ ):
lowerCAmelCase = True
try:
AutoConfig.register('''custom''' , __SCREAMING_SNAKE_CASE )
AutoImageProcessor.register(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
# If remote code is not set, the default is to use local
__SCREAMING_SNAKE_CASE = AutoImageProcessor.from_pretrained('''hf-internal-testing/test_dynamic_image_processor''' )
self.assertEqual(image_processor.__class__.__name__ , '''NewImageProcessor''' )
self.assertTrue(image_processor.is_local )
# If remote code is disabled, we load the local one.
__SCREAMING_SNAKE_CASE = AutoImageProcessor.from_pretrained(
'''hf-internal-testing/test_dynamic_image_processor''' , trust_remote_code=__SCREAMING_SNAKE_CASE )
self.assertEqual(image_processor.__class__.__name__ , '''NewImageProcessor''' )
self.assertTrue(image_processor.is_local )
# If remote is enabled, we load from the Hub
__SCREAMING_SNAKE_CASE = AutoImageProcessor.from_pretrained(
'''hf-internal-testing/test_dynamic_image_processor''' , trust_remote_code=__SCREAMING_SNAKE_CASE )
self.assertEqual(image_processor.__class__.__name__ , '''NewImageProcessor''' )
self.assertTrue(not hasattr(__SCREAMING_SNAKE_CASE , '''is_local''' ) )
finally:
if "custom" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["custom"]
if CustomConfig in IMAGE_PROCESSOR_MAPPING._extra_content:
del IMAGE_PROCESSOR_MAPPING._extra_content[CustomConfig]
| 690 | 1 |
"""simple docstring"""
import os
import shutil
import sys
import tempfile
import unittest
from pathlib import Path
import pytest
import transformers
from transformers import (
BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
AutoTokenizer,
BertConfig,
BertTokenizer,
BertTokenizerFast,
CTRLTokenizer,
GPTaTokenizer,
GPTaTokenizerFast,
PreTrainedTokenizerFast,
RobertaTokenizer,
RobertaTokenizerFast,
is_tokenizers_available,
)
from transformers.models.auto.configuration_auto import CONFIG_MAPPING, AutoConfig
from transformers.models.auto.tokenization_auto import (
TOKENIZER_MAPPING,
get_tokenizer_config,
tokenizer_class_from_name,
)
from transformers.models.roberta.configuration_roberta import RobertaConfig
from transformers.testing_utils import (
DUMMY_DIFF_TOKENIZER_IDENTIFIER,
DUMMY_UNKNOWN_IDENTIFIER,
SMALL_MODEL_IDENTIFIER,
RequestCounter,
require_tokenizers,
slow,
)
sys.path.append(str(Path(__file__).parent.parent.parent.parent / "utils"))
from test_module.custom_configuration import CustomConfig # noqa E402
from test_module.custom_tokenization import CustomTokenizer # noqa E402
if is_tokenizers_available():
from test_module.custom_tokenization_fast import CustomTokenizerFast
class A__( unittest.TestCase ):
def _a ( self : Union[str, Any] ) -> str:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = 0
@slow
def _a ( self : Optional[int] ) -> Optional[Any]:
"""simple docstring"""
for model_name in (x for x in BERT_PRETRAINED_CONFIG_ARCHIVE_MAP.keys() if "japanese" not in x):
__SCREAMING_SNAKE_CASE = AutoTokenizer.from_pretrained(__SCREAMING_SNAKE_CASE )
self.assertIsNotNone(__SCREAMING_SNAKE_CASE )
self.assertIsInstance(__SCREAMING_SNAKE_CASE , (BertTokenizer, BertTokenizerFast) )
self.assertGreater(len(__SCREAMING_SNAKE_CASE ) , 0 )
for model_name in GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP.keys():
__SCREAMING_SNAKE_CASE = AutoTokenizer.from_pretrained(__SCREAMING_SNAKE_CASE )
self.assertIsNotNone(__SCREAMING_SNAKE_CASE )
self.assertIsInstance(__SCREAMING_SNAKE_CASE , (GPTaTokenizer, GPTaTokenizerFast) )
self.assertGreater(len(__SCREAMING_SNAKE_CASE ) , 0 )
def _a ( self : Dict ) -> Dict:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = AutoTokenizer.from_pretrained(__SCREAMING_SNAKE_CASE )
self.assertIsInstance(__SCREAMING_SNAKE_CASE , (BertTokenizer, BertTokenizerFast) )
self.assertEqual(tokenizer.vocab_size , 12 )
def _a ( self : int ) -> Tuple:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = AutoTokenizer.from_pretrained(__SCREAMING_SNAKE_CASE )
self.assertIsInstance(__SCREAMING_SNAKE_CASE , (RobertaTokenizer, RobertaTokenizerFast) )
self.assertEqual(tokenizer.vocab_size , 20 )
def _a ( self : List[Any] ) -> List[Any]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = AutoConfig.from_pretrained(__SCREAMING_SNAKE_CASE )
self.assertIsInstance(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
# Check that tokenizer_type ≠ model_type
__SCREAMING_SNAKE_CASE = AutoTokenizer.from_pretrained(__SCREAMING_SNAKE_CASE , config=__SCREAMING_SNAKE_CASE )
self.assertIsInstance(__SCREAMING_SNAKE_CASE , (BertTokenizer, BertTokenizerFast) )
self.assertEqual(tokenizer.vocab_size , 12 )
def _a ( self : int ) -> Union[str, Any]:
"""simple docstring"""
with tempfile.TemporaryDirectory() as tmp_dir:
shutil.copy('''./tests/fixtures/vocab.txt''' , os.path.join(__SCREAMING_SNAKE_CASE , '''vocab.txt''' ) )
__SCREAMING_SNAKE_CASE = AutoTokenizer.from_pretrained(__SCREAMING_SNAKE_CASE , tokenizer_type='''bert''' , use_fast=__SCREAMING_SNAKE_CASE )
self.assertIsInstance(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
with tempfile.TemporaryDirectory() as tmp_dir:
shutil.copy('''./tests/fixtures/vocab.json''' , os.path.join(__SCREAMING_SNAKE_CASE , '''vocab.json''' ) )
shutil.copy('''./tests/fixtures/merges.txt''' , os.path.join(__SCREAMING_SNAKE_CASE , '''merges.txt''' ) )
__SCREAMING_SNAKE_CASE = AutoTokenizer.from_pretrained(__SCREAMING_SNAKE_CASE , tokenizer_type='''gpt2''' , use_fast=__SCREAMING_SNAKE_CASE )
self.assertIsInstance(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
@require_tokenizers
def _a ( self : List[str] ) -> Any:
"""simple docstring"""
with tempfile.TemporaryDirectory() as tmp_dir:
shutil.copy('''./tests/fixtures/vocab.txt''' , os.path.join(__SCREAMING_SNAKE_CASE , '''vocab.txt''' ) )
__SCREAMING_SNAKE_CASE = AutoTokenizer.from_pretrained(__SCREAMING_SNAKE_CASE , tokenizer_type='''bert''' )
self.assertIsInstance(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
with tempfile.TemporaryDirectory() as tmp_dir:
shutil.copy('''./tests/fixtures/vocab.json''' , os.path.join(__SCREAMING_SNAKE_CASE , '''vocab.json''' ) )
shutil.copy('''./tests/fixtures/merges.txt''' , os.path.join(__SCREAMING_SNAKE_CASE , '''merges.txt''' ) )
__SCREAMING_SNAKE_CASE = AutoTokenizer.from_pretrained(__SCREAMING_SNAKE_CASE , tokenizer_type='''gpt2''' )
self.assertIsInstance(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
def _a ( self : str ) -> Optional[int]:
"""simple docstring"""
with pytest.raises(__SCREAMING_SNAKE_CASE ):
AutoTokenizer.from_pretrained('''./''' , tokenizer_type='''xxx''' )
@require_tokenizers
def _a ( self : List[Any] ) -> Optional[Any]:
"""simple docstring"""
for tokenizer_class in [BertTokenizer, BertTokenizerFast, AutoTokenizer]:
__SCREAMING_SNAKE_CASE = tokenizer_class.from_pretrained('''wietsedv/bert-base-dutch-cased''' )
self.assertIsInstance(__SCREAMING_SNAKE_CASE , (BertTokenizer, BertTokenizerFast) )
if isinstance(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
self.assertEqual(tokenizer.basic_tokenizer.do_lower_case , __SCREAMING_SNAKE_CASE )
else:
self.assertEqual(tokenizer.do_lower_case , __SCREAMING_SNAKE_CASE )
self.assertEqual(tokenizer.model_max_length , 5_12 )
@require_tokenizers
def _a ( self : int ) -> List[Any]:
"""simple docstring"""
for tokenizer_class in [BertTokenizer, BertTokenizerFast, AutoTokenizer]:
with self.assertRaisesRegex(
__SCREAMING_SNAKE_CASE , '''julien-c/herlolip-not-exists is not a local folder and is not a valid model identifier''' , ):
__SCREAMING_SNAKE_CASE = tokenizer_class.from_pretrained('''julien-c/herlolip-not-exists''' )
def _a ( self : Optional[Any] ) -> Any:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = TOKENIZER_MAPPING.values()
__SCREAMING_SNAKE_CASE = []
for slow_tok, fast_tok in tokenizers:
if slow_tok is not None:
tokenizer_names.append(slow_tok.__name__ )
if fast_tok is not None:
tokenizer_names.append(fast_tok.__name__ )
for tokenizer_name in tokenizer_names:
# must find the right class
tokenizer_class_from_name(__SCREAMING_SNAKE_CASE )
@require_tokenizers
def _a ( self : Optional[int] ) -> Dict:
"""simple docstring"""
self.assertIsInstance(AutoTokenizer.from_pretrained('''bert-base-cased''' , use_fast=__SCREAMING_SNAKE_CASE ) , __SCREAMING_SNAKE_CASE )
self.assertIsInstance(AutoTokenizer.from_pretrained('''bert-base-cased''' ) , __SCREAMING_SNAKE_CASE )
@require_tokenizers
def _a ( self : List[str] ) -> Any:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = AutoTokenizer.from_pretrained('''distilbert-base-uncased''' , do_lower_case=__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = '''Hello, world. How are you?'''
__SCREAMING_SNAKE_CASE = tokenizer.tokenize(__SCREAMING_SNAKE_CASE )
self.assertEqual('''[UNK]''' , tokens[0] )
__SCREAMING_SNAKE_CASE = AutoTokenizer.from_pretrained('''microsoft/mpnet-base''' , do_lower_case=__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = tokenizer.tokenize(__SCREAMING_SNAKE_CASE )
self.assertEqual('''[UNK]''' , tokens[0] )
@require_tokenizers
def _a ( self : Tuple ) -> Optional[int]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = AutoTokenizer.from_pretrained('''robot-test/dummy-tokenizer-fast-with-model-config''' )
self.assertEqual(type(__SCREAMING_SNAKE_CASE ) , __SCREAMING_SNAKE_CASE )
self.assertEqual(tokenizer.model_max_length , 5_12 )
self.assertEqual(tokenizer.vocab_size , 3_00_00 )
self.assertEqual(tokenizer.unk_token , '''[UNK]''' )
self.assertEqual(tokenizer.padding_side , '''right''' )
self.assertEqual(tokenizer.truncation_side , '''right''' )
def _a ( self : str ) -> List[Any]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = AutoTokenizer.from_pretrained(__SCREAMING_SNAKE_CASE )
self.assertIsInstance(__SCREAMING_SNAKE_CASE , (BertTokenizer, BertTokenizerFast) )
with tempfile.TemporaryDirectory() as tmp_dir:
tokenizer.save_pretrained(__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = AutoTokenizer.from_pretrained(__SCREAMING_SNAKE_CASE )
self.assertIsInstance(__SCREAMING_SNAKE_CASE , tokenizer.__class__ )
self.assertEqual(tokenizera.vocab_size , 12 )
def _a ( self : str ) -> Union[str, Any]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = AutoTokenizer.from_pretrained('''ctrl''' )
# There is no fast CTRL so this always gives us a slow tokenizer.
self.assertIsInstance(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
def _a ( self : Dict ) -> Union[str, Any]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = get_tokenizer_config('''bert-base-cased''' )
__SCREAMING_SNAKE_CASE = config.pop('''_commit_hash''' , __SCREAMING_SNAKE_CASE )
# If we ever update bert-base-cased tokenizer config, this dict here will need to be updated.
self.assertEqual(__SCREAMING_SNAKE_CASE , {'''do_lower_case''': False} )
# This model does not have a tokenizer_config so we get back an empty dict.
__SCREAMING_SNAKE_CASE = get_tokenizer_config(__SCREAMING_SNAKE_CASE )
self.assertDictEqual(__SCREAMING_SNAKE_CASE , {} )
# A tokenizer saved with `save_pretrained` always creates a tokenizer config.
__SCREAMING_SNAKE_CASE = AutoTokenizer.from_pretrained(__SCREAMING_SNAKE_CASE )
with tempfile.TemporaryDirectory() as tmp_dir:
tokenizer.save_pretrained(__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = get_tokenizer_config(__SCREAMING_SNAKE_CASE )
# Check the class of the tokenizer was properly saved (note that it always saves the slow class).
self.assertEqual(config['''tokenizer_class'''] , '''BertTokenizer''' )
def _a ( self : str ) -> Optional[Any]:
"""simple docstring"""
try:
AutoConfig.register('''custom''' , __SCREAMING_SNAKE_CASE )
AutoTokenizer.register(__SCREAMING_SNAKE_CASE , slow_tokenizer_class=__SCREAMING_SNAKE_CASE )
# Trying to register something existing in the Transformers library will raise an error
with self.assertRaises(__SCREAMING_SNAKE_CASE ):
AutoTokenizer.register(__SCREAMING_SNAKE_CASE , slow_tokenizer_class=__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = CustomTokenizer.from_pretrained(__SCREAMING_SNAKE_CASE )
with tempfile.TemporaryDirectory() as tmp_dir:
tokenizer.save_pretrained(__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = AutoTokenizer.from_pretrained(__SCREAMING_SNAKE_CASE )
self.assertIsInstance(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
finally:
if "custom" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["custom"]
if CustomConfig in TOKENIZER_MAPPING._extra_content:
del TOKENIZER_MAPPING._extra_content[CustomConfig]
@require_tokenizers
def _a ( self : List[Any] ) -> Optional[Any]:
"""simple docstring"""
try:
AutoConfig.register('''custom''' , __SCREAMING_SNAKE_CASE )
# Can register in two steps
AutoTokenizer.register(__SCREAMING_SNAKE_CASE , slow_tokenizer_class=__SCREAMING_SNAKE_CASE )
self.assertEqual(TOKENIZER_MAPPING[CustomConfig] , (CustomTokenizer, None) )
AutoTokenizer.register(__SCREAMING_SNAKE_CASE , fast_tokenizer_class=__SCREAMING_SNAKE_CASE )
self.assertEqual(TOKENIZER_MAPPING[CustomConfig] , (CustomTokenizer, CustomTokenizerFast) )
del TOKENIZER_MAPPING._extra_content[CustomConfig]
# Can register in one step
AutoTokenizer.register(
__SCREAMING_SNAKE_CASE , slow_tokenizer_class=__SCREAMING_SNAKE_CASE , fast_tokenizer_class=__SCREAMING_SNAKE_CASE )
self.assertEqual(TOKENIZER_MAPPING[CustomConfig] , (CustomTokenizer, CustomTokenizerFast) )
# Trying to register something existing in the Transformers library will raise an error
with self.assertRaises(__SCREAMING_SNAKE_CASE ):
AutoTokenizer.register(__SCREAMING_SNAKE_CASE , fast_tokenizer_class=__SCREAMING_SNAKE_CASE )
# We pass through a bert tokenizer fast cause there is no converter slow to fast for our new toknizer
# and that model does not have a tokenizer.json
with tempfile.TemporaryDirectory() as tmp_dir:
__SCREAMING_SNAKE_CASE = BertTokenizerFast.from_pretrained(__SCREAMING_SNAKE_CASE )
bert_tokenizer.save_pretrained(__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = CustomTokenizerFast.from_pretrained(__SCREAMING_SNAKE_CASE )
with tempfile.TemporaryDirectory() as tmp_dir:
tokenizer.save_pretrained(__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = AutoTokenizer.from_pretrained(__SCREAMING_SNAKE_CASE )
self.assertIsInstance(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = AutoTokenizer.from_pretrained(__SCREAMING_SNAKE_CASE , use_fast=__SCREAMING_SNAKE_CASE )
self.assertIsInstance(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
finally:
if "custom" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["custom"]
if CustomConfig in TOKENIZER_MAPPING._extra_content:
del TOKENIZER_MAPPING._extra_content[CustomConfig]
def _a ( self : Union[str, Any] ) -> Any:
"""simple docstring"""
with self.assertRaises(__SCREAMING_SNAKE_CASE ):
__SCREAMING_SNAKE_CASE = AutoTokenizer.from_pretrained('''hf-internal-testing/test_dynamic_tokenizer''' )
# If remote code is disabled, we can't load this config.
with self.assertRaises(__SCREAMING_SNAKE_CASE ):
__SCREAMING_SNAKE_CASE = AutoTokenizer.from_pretrained(
'''hf-internal-testing/test_dynamic_tokenizer''' , trust_remote_code=__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = AutoTokenizer.from_pretrained('''hf-internal-testing/test_dynamic_tokenizer''' , trust_remote_code=__SCREAMING_SNAKE_CASE )
self.assertTrue(tokenizer.special_attribute_present )
# Test tokenizer can be reloaded.
with tempfile.TemporaryDirectory() as tmp_dir:
tokenizer.save_pretrained(__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = AutoTokenizer.from_pretrained(__SCREAMING_SNAKE_CASE , trust_remote_code=__SCREAMING_SNAKE_CASE )
self.assertTrue(reloaded_tokenizer.special_attribute_present )
if is_tokenizers_available():
self.assertEqual(tokenizer.__class__.__name__ , '''NewTokenizerFast''' )
self.assertEqual(reloaded_tokenizer.__class__.__name__ , '''NewTokenizerFast''' )
# Test we can also load the slow version
__SCREAMING_SNAKE_CASE = AutoTokenizer.from_pretrained(
'''hf-internal-testing/test_dynamic_tokenizer''' , trust_remote_code=__SCREAMING_SNAKE_CASE , use_fast=__SCREAMING_SNAKE_CASE )
self.assertTrue(tokenizer.special_attribute_present )
self.assertEqual(tokenizer.__class__.__name__ , '''NewTokenizer''' )
# Test tokenizer can be reloaded.
with tempfile.TemporaryDirectory() as tmp_dir:
tokenizer.save_pretrained(__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = AutoTokenizer.from_pretrained(__SCREAMING_SNAKE_CASE , trust_remote_code=__SCREAMING_SNAKE_CASE , use_fast=__SCREAMING_SNAKE_CASE )
self.assertEqual(reloaded_tokenizer.__class__.__name__ , '''NewTokenizer''' )
self.assertTrue(reloaded_tokenizer.special_attribute_present )
else:
self.assertEqual(tokenizer.__class__.__name__ , '''NewTokenizer''' )
self.assertEqual(reloaded_tokenizer.__class__.__name__ , '''NewTokenizer''' )
@require_tokenizers
def _a ( self : Tuple ) -> Any:
"""simple docstring"""
class A__( __magic_name__ ):
lowerCAmelCase = False
class A__( __magic_name__ ):
lowerCAmelCase = NewTokenizer
lowerCAmelCase = False
try:
AutoConfig.register('''custom''' , __SCREAMING_SNAKE_CASE )
AutoTokenizer.register(__SCREAMING_SNAKE_CASE , slow_tokenizer_class=__SCREAMING_SNAKE_CASE )
AutoTokenizer.register(__SCREAMING_SNAKE_CASE , fast_tokenizer_class=__SCREAMING_SNAKE_CASE )
# If remote code is not set, the default is to use local
__SCREAMING_SNAKE_CASE = AutoTokenizer.from_pretrained('''hf-internal-testing/test_dynamic_tokenizer''' )
self.assertEqual(tokenizer.__class__.__name__ , '''NewTokenizerFast''' )
self.assertFalse(tokenizer.special_attribute_present )
__SCREAMING_SNAKE_CASE = AutoTokenizer.from_pretrained('''hf-internal-testing/test_dynamic_tokenizer''' , use_fast=__SCREAMING_SNAKE_CASE )
self.assertEqual(tokenizer.__class__.__name__ , '''NewTokenizer''' )
self.assertFalse(tokenizer.special_attribute_present )
# If remote code is disabled, we load the local one.
__SCREAMING_SNAKE_CASE = AutoTokenizer.from_pretrained(
'''hf-internal-testing/test_dynamic_tokenizer''' , trust_remote_code=__SCREAMING_SNAKE_CASE )
self.assertEqual(tokenizer.__class__.__name__ , '''NewTokenizerFast''' )
self.assertFalse(tokenizer.special_attribute_present )
__SCREAMING_SNAKE_CASE = AutoTokenizer.from_pretrained(
'''hf-internal-testing/test_dynamic_tokenizer''' , trust_remote_code=__SCREAMING_SNAKE_CASE , use_fast=__SCREAMING_SNAKE_CASE )
self.assertEqual(tokenizer.__class__.__name__ , '''NewTokenizer''' )
self.assertFalse(tokenizer.special_attribute_present )
# If remote is enabled, we load from the Hub
__SCREAMING_SNAKE_CASE = AutoTokenizer.from_pretrained(
'''hf-internal-testing/test_dynamic_tokenizer''' , trust_remote_code=__SCREAMING_SNAKE_CASE )
self.assertEqual(tokenizer.__class__.__name__ , '''NewTokenizerFast''' )
self.assertTrue(tokenizer.special_attribute_present )
__SCREAMING_SNAKE_CASE = AutoTokenizer.from_pretrained(
'''hf-internal-testing/test_dynamic_tokenizer''' , trust_remote_code=__SCREAMING_SNAKE_CASE , use_fast=__SCREAMING_SNAKE_CASE )
self.assertEqual(tokenizer.__class__.__name__ , '''NewTokenizer''' )
self.assertTrue(tokenizer.special_attribute_present )
finally:
if "custom" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["custom"]
if CustomConfig in TOKENIZER_MAPPING._extra_content:
del TOKENIZER_MAPPING._extra_content[CustomConfig]
def _a ( self : Tuple ) -> str:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = AutoTokenizer.from_pretrained(
'''hf-internal-testing/test_dynamic_tokenizer_legacy''' , trust_remote_code=__SCREAMING_SNAKE_CASE )
self.assertTrue(tokenizer.special_attribute_present )
if is_tokenizers_available():
self.assertEqual(tokenizer.__class__.__name__ , '''NewTokenizerFast''' )
# Test we can also load the slow version
__SCREAMING_SNAKE_CASE = AutoTokenizer.from_pretrained(
'''hf-internal-testing/test_dynamic_tokenizer_legacy''' , trust_remote_code=__SCREAMING_SNAKE_CASE , use_fast=__SCREAMING_SNAKE_CASE )
self.assertTrue(tokenizer.special_attribute_present )
self.assertEqual(tokenizer.__class__.__name__ , '''NewTokenizer''' )
else:
self.assertEqual(tokenizer.__class__.__name__ , '''NewTokenizer''' )
def _a ( self : Dict ) -> Union[str, Any]:
"""simple docstring"""
with self.assertRaisesRegex(
__SCREAMING_SNAKE_CASE , '''bert-base is not a local folder and is not a valid model identifier''' ):
__SCREAMING_SNAKE_CASE = AutoTokenizer.from_pretrained('''bert-base''' )
def _a ( self : Tuple ) -> Optional[Any]:
"""simple docstring"""
with self.assertRaisesRegex(
__SCREAMING_SNAKE_CASE , r'''aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)''' ):
__SCREAMING_SNAKE_CASE = AutoTokenizer.from_pretrained(__SCREAMING_SNAKE_CASE , revision='''aaaaaa''' )
def _a ( self : Optional[int] ) -> Optional[Any]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-bert''' )
with RequestCounter() as counter:
__SCREAMING_SNAKE_CASE = AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-bert''' )
self.assertEqual(counter.get_request_count , 0 )
self.assertEqual(counter.head_request_count , 1 )
self.assertEqual(counter.other_request_count , 0 )
| 690 |
"""simple docstring"""
import math
lowerCAmelCase__ =10
lowerCAmelCase__ =7
lowerCAmelCase__ =BALLS_PER_COLOUR * NUM_COLOURS
def _a ( UpperCAmelCase__ = 20 ) -> str:
__SCREAMING_SNAKE_CASE = math.comb(UpperCAmelCase__ , UpperCAmelCase__ )
__SCREAMING_SNAKE_CASE = math.comb(NUM_BALLS - BALLS_PER_COLOUR , UpperCAmelCase__ )
__SCREAMING_SNAKE_CASE = NUM_COLOURS * (1 - missing_colour / total)
return f"""{result:.9f}"""
if __name__ == "__main__":
print(solution(20))
| 690 | 1 |
"""simple docstring"""
from .imports import is_tqdm_available
if is_tqdm_available():
from tqdm.auto import tqdm as _tqdm
from ..state import PartialState
def _a ( UpperCAmelCase__ = True , *UpperCAmelCase__ , **UpperCAmelCase__ ) -> Optional[Any]:
if not is_tqdm_available():
raise ImportError('''Accelerate\'s `tqdm` module requires `tqdm` to be installed. Please run `pip install tqdm`.''' )
__SCREAMING_SNAKE_CASE = False
if main_process_only:
__SCREAMING_SNAKE_CASE = PartialState().local_process_index == 0
return _tqdm(*UpperCAmelCase__ , **UpperCAmelCase__ , disable=UpperCAmelCase__ )
| 690 |
"""simple docstring"""
from collections import UserDict
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_torch_available():
from ..models.auto.modeling_auto import MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING
if is_tf_available():
from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING
from ..tf_utils import stable_softmax
lowerCAmelCase__ =logging.get_logger(__name__)
@add_end_docstrings(__magic_name__ )
class A__( __magic_name__ ):
def __init__( self : Optional[Any] , **__SCREAMING_SNAKE_CASE : str ) -> Optional[Any]:
"""simple docstring"""
super().__init__(**__SCREAMING_SNAKE_CASE )
requires_backends(self , '''vision''' )
self.check_model_type(
TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING
if self.framework == '''tf'''
else MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING )
def __call__( self : Optional[Any] , __SCREAMING_SNAKE_CASE : Union[str, List[str], "Image", List["Image"]] , **__SCREAMING_SNAKE_CASE : Union[str, Any] ) -> Tuple:
"""simple docstring"""
return super().__call__(__SCREAMING_SNAKE_CASE , **__SCREAMING_SNAKE_CASE )
def _a ( self : int , **__SCREAMING_SNAKE_CASE : int ) -> Any:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = {}
if "candidate_labels" in kwargs:
__SCREAMING_SNAKE_CASE = kwargs['''candidate_labels''']
if "hypothesis_template" in kwargs:
__SCREAMING_SNAKE_CASE = kwargs['''hypothesis_template''']
return preprocess_params, {}, {}
def _a ( self : Any , __SCREAMING_SNAKE_CASE : Any , __SCREAMING_SNAKE_CASE : Union[str, Any]=None , __SCREAMING_SNAKE_CASE : Optional[int]="This is a photo of {}." ) -> Union[str, Any]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = load_image(__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = self.image_processor(images=[image] , return_tensors=self.framework )
__SCREAMING_SNAKE_CASE = candidate_labels
__SCREAMING_SNAKE_CASE = [hypothesis_template.format(__SCREAMING_SNAKE_CASE ) for x in candidate_labels]
__SCREAMING_SNAKE_CASE = self.tokenizer(__SCREAMING_SNAKE_CASE , return_tensors=self.framework , padding=__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = [text_inputs]
return inputs
def _a ( self : Dict , __SCREAMING_SNAKE_CASE : List[Any] ) -> int:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = model_inputs.pop('''candidate_labels''' )
__SCREAMING_SNAKE_CASE = model_inputs.pop('''text_inputs''' )
if isinstance(text_inputs[0] , __SCREAMING_SNAKE_CASE ):
__SCREAMING_SNAKE_CASE = text_inputs[0]
else:
# Batching case.
__SCREAMING_SNAKE_CASE = text_inputs[0][0]
__SCREAMING_SNAKE_CASE = self.model(**__SCREAMING_SNAKE_CASE , **__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = {
'''candidate_labels''': candidate_labels,
'''logits''': outputs.logits_per_image,
}
return model_outputs
def _a ( self : Any , __SCREAMING_SNAKE_CASE : List[str] ) -> Optional[int]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = model_outputs.pop('''candidate_labels''' )
__SCREAMING_SNAKE_CASE = model_outputs['''logits'''][0]
if self.framework == "pt":
__SCREAMING_SNAKE_CASE = logits.softmax(dim=-1 ).squeeze(-1 )
__SCREAMING_SNAKE_CASE = probs.tolist()
if not isinstance(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
__SCREAMING_SNAKE_CASE = [scores]
elif self.framework == "tf":
__SCREAMING_SNAKE_CASE = stable_softmax(__SCREAMING_SNAKE_CASE , axis=-1 )
__SCREAMING_SNAKE_CASE = probs.numpy().tolist()
else:
raise ValueError(f"""Unsupported framework: {self.framework}""" )
__SCREAMING_SNAKE_CASE = [
{'''score''': score, '''label''': candidate_label}
for score, candidate_label in sorted(zip(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) , key=lambda __SCREAMING_SNAKE_CASE : -x[0] )
]
return result
| 690 | 1 |
"""simple docstring"""
def _a ( UpperCAmelCase__ = 50 ) -> int:
__SCREAMING_SNAKE_CASE = [1] * (length + 1)
for row_length in range(length + 1 ):
for tile_length in range(2 , 5 ):
for tile_start in range(row_length - tile_length + 1 ):
ways_number[row_length] += ways_number[
row_length - tile_start - tile_length
]
return ways_number[length]
if __name__ == "__main__":
print(F'''{solution() = }''')
| 690 |
"""simple docstring"""
from __future__ import annotations
from collections.abc import Callable
lowerCAmelCase__ =list[list[float | int]]
def _a ( UpperCAmelCase__ , UpperCAmelCase__ ) -> Matrix:
__SCREAMING_SNAKE_CASE = len(UpperCAmelCase__ )
__SCREAMING_SNAKE_CASE = [[0 for _ in range(size + 1 )] for _ in range(UpperCAmelCase__ )]
__SCREAMING_SNAKE_CASE = 42
__SCREAMING_SNAKE_CASE = 42
__SCREAMING_SNAKE_CASE = 42
__SCREAMING_SNAKE_CASE = 42
__SCREAMING_SNAKE_CASE = 42
__SCREAMING_SNAKE_CASE = 42
for row in range(UpperCAmelCase__ ):
for col in range(UpperCAmelCase__ ):
__SCREAMING_SNAKE_CASE = matrix[row][col]
__SCREAMING_SNAKE_CASE = vector[row][0]
__SCREAMING_SNAKE_CASE = 0
__SCREAMING_SNAKE_CASE = 0
while row < size and col < size:
# pivoting
__SCREAMING_SNAKE_CASE = max((abs(augmented[rowa][col] ), rowa) for rowa in range(UpperCAmelCase__ , UpperCAmelCase__ ) )[
1
]
if augmented[pivot_row][col] == 0:
col += 1
continue
else:
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = augmented[pivot_row], augmented[row]
for rowa in range(row + 1 , UpperCAmelCase__ ):
__SCREAMING_SNAKE_CASE = augmented[rowa][col] / augmented[row][col]
__SCREAMING_SNAKE_CASE = 0
for cola in range(col + 1 , size + 1 ):
augmented[rowa][cola] -= augmented[row][cola] * ratio
row += 1
col += 1
# back substitution
for col in range(1 , UpperCAmelCase__ ):
for row in range(UpperCAmelCase__ ):
__SCREAMING_SNAKE_CASE = augmented[row][col] / augmented[col][col]
for cola in range(UpperCAmelCase__ , size + 1 ):
augmented[row][cola] -= augmented[col][cola] * ratio
# round to get rid of numbers like 2.000000000000004
return [
[round(augmented[row][size] / augmented[row][row] , 10 )] for row in range(UpperCAmelCase__ )
]
def _a ( UpperCAmelCase__ ) -> Callable[[int], int]:
__SCREAMING_SNAKE_CASE = len(UpperCAmelCase__ )
__SCREAMING_SNAKE_CASE = [[0 for _ in range(UpperCAmelCase__ )] for _ in range(UpperCAmelCase__ )]
__SCREAMING_SNAKE_CASE = [[0] for _ in range(UpperCAmelCase__ )]
__SCREAMING_SNAKE_CASE = 42
__SCREAMING_SNAKE_CASE = 42
__SCREAMING_SNAKE_CASE = 42
__SCREAMING_SNAKE_CASE = 42
for x_val, y_val in enumerate(UpperCAmelCase__ ):
for col in range(UpperCAmelCase__ ):
__SCREAMING_SNAKE_CASE = (x_val + 1) ** (size - col - 1)
__SCREAMING_SNAKE_CASE = y_val
__SCREAMING_SNAKE_CASE = solve(UpperCAmelCase__ , UpperCAmelCase__ )
def interpolated_func(UpperCAmelCase__ ) -> int:
return sum(
round(coeffs[x_val][0] ) * (var ** (size - x_val - 1))
for x_val in range(UpperCAmelCase__ ) )
return interpolated_func
def _a ( UpperCAmelCase__ ) -> int:
return (
1
- variable
+ variable**2
- variable**3
+ variable**4
- variable**5
+ variable**6
- variable**7
+ variable**8
- variable**9
+ variable**10
)
def _a ( UpperCAmelCase__ = question_function , UpperCAmelCase__ = 10 ) -> int:
__SCREAMING_SNAKE_CASE = [func(UpperCAmelCase__ ) for x_val in range(1 , order + 1 )]
__SCREAMING_SNAKE_CASE = [
interpolate(data_points[:max_coeff] ) for max_coeff in range(1 , order + 1 )
]
__SCREAMING_SNAKE_CASE = 0
__SCREAMING_SNAKE_CASE = 42
__SCREAMING_SNAKE_CASE = 42
for poly in polynomials:
__SCREAMING_SNAKE_CASE = 1
while func(UpperCAmelCase__ ) == poly(UpperCAmelCase__ ):
x_val += 1
ret += poly(UpperCAmelCase__ )
return ret
if __name__ == "__main__":
print(F'''{solution() = }''')
| 690 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCAmelCase__ ={
"configuration_blip_2": [
"BLIP_2_PRETRAINED_CONFIG_ARCHIVE_MAP",
"Blip2Config",
"Blip2QFormerConfig",
"Blip2VisionConfig",
],
"processing_blip_2": ["Blip2Processor"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase__ =[
"BLIP_2_PRETRAINED_MODEL_ARCHIVE_LIST",
"Blip2Model",
"Blip2QFormerModel",
"Blip2PreTrainedModel",
"Blip2ForConditionalGeneration",
"Blip2VisionModel",
]
if TYPE_CHECKING:
from .configuration_blip_a import (
BLIP_2_PRETRAINED_CONFIG_ARCHIVE_MAP,
BlipaConfig,
BlipaQFormerConfig,
BlipaVisionConfig,
)
from .processing_blip_a import BlipaProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_blip_a import (
BLIP_2_PRETRAINED_MODEL_ARCHIVE_LIST,
BlipaForConditionalGeneration,
BlipaModel,
BlipaPreTrainedModel,
BlipaQFormerModel,
BlipaVisionModel,
)
else:
import sys
lowerCAmelCase__ =_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 690 |
"""simple docstring"""
from json import JSONDecodeError # Workaround for requests.exceptions.JSONDecodeError
import requests
def _a ( UpperCAmelCase__ = "isbn/0140328726" ) -> dict:
__SCREAMING_SNAKE_CASE = olid.strip().strip('''/''' ) # Remove leading/trailing whitespace & slashes
if new_olid.count('''/''' ) != 1:
__SCREAMING_SNAKE_CASE = f"""{olid} is not a valid Open Library olid"""
raise ValueError(UpperCAmelCase__ )
return requests.get(f"""https://openlibrary.org/{new_olid}.json""" ).json()
def _a ( UpperCAmelCase__ ) -> dict:
__SCREAMING_SNAKE_CASE = {
'''title''': '''Title''',
'''publish_date''': '''Publish date''',
'''authors''': '''Authors''',
'''number_of_pages''': '''Number of pages:''',
'''first_sentence''': '''First sentence''',
'''isbn_10''': '''ISBN (10)''',
'''isbn_13''': '''ISBN (13)''',
}
__SCREAMING_SNAKE_CASE = {better_key: ol_book_data[key] for key, better_key in desired_keys.items()}
__SCREAMING_SNAKE_CASE = [
get_openlibrary_data(author['''key'''] )['''name'''] for author in data['''Authors''']
]
__SCREAMING_SNAKE_CASE = data['''First sentence''']['''value''']
for key, value in data.items():
if isinstance(UpperCAmelCase__ , UpperCAmelCase__ ):
__SCREAMING_SNAKE_CASE = ''', '''.join(UpperCAmelCase__ )
return data
if __name__ == "__main__":
import doctest
doctest.testmod()
while True:
lowerCAmelCase__ =input("\nEnter the ISBN code to search (or 'quit' to stop): ").strip()
if isbn.lower() in ("", "q", "quit", "exit", "stop"):
break
if len(isbn) not in (10, 13) or not isbn.isdigit():
print(F'''Sorry, {isbn} is not a valid ISBN. Please, input a valid ISBN.''')
continue
print(F'''\nSearching Open Library for ISBN: {isbn}...\n''')
try:
lowerCAmelCase__ =summarize_book(get_openlibrary_data(F'''isbn/{isbn}'''))
print("\n".join(F'''{key}: {value}''' for key, value in book_summary.items()))
except JSONDecodeError: # Workaround for requests.exceptions.RequestException:
print(F'''Sorry, there are no results for ISBN: {isbn}.''')
| 690 | 1 |
"""simple docstring"""
from __future__ import annotations
def _a ( UpperCAmelCase__ , UpperCAmelCase__ ) -> bool:
__SCREAMING_SNAKE_CASE = get_failure_array(UpperCAmelCase__ )
# 2) Step through text searching for pattern
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = 0, 0 # index into text, pattern
while i < len(UpperCAmelCase__ ):
if pattern[j] == text[i]:
if j == (len(UpperCAmelCase__ ) - 1):
return True
j += 1
# if this is a prefix in our pattern
# just go back far enough to continue
elif j > 0:
__SCREAMING_SNAKE_CASE = failure[j - 1]
continue
i += 1
return False
def _a ( UpperCAmelCase__ ) -> list[int]:
__SCREAMING_SNAKE_CASE = [0]
__SCREAMING_SNAKE_CASE = 0
__SCREAMING_SNAKE_CASE = 1
while j < len(UpperCAmelCase__ ):
if pattern[i] == pattern[j]:
i += 1
elif i > 0:
__SCREAMING_SNAKE_CASE = failure[i - 1]
continue
j += 1
failure.append(UpperCAmelCase__ )
return failure
if __name__ == "__main__":
# Test 1)
lowerCAmelCase__ ="abc1abc12"
lowerCAmelCase__ ="alskfjaldsabc1abc1abc12k23adsfabcabc"
lowerCAmelCase__ ="alskfjaldsk23adsfabcabc"
assert kmp(pattern, texta) and not kmp(pattern, texta)
# Test 2)
lowerCAmelCase__ ="ABABX"
lowerCAmelCase__ ="ABABZABABYABABX"
assert kmp(pattern, text)
# Test 3)
lowerCAmelCase__ ="AAAB"
lowerCAmelCase__ ="ABAAAAAB"
assert kmp(pattern, text)
# Test 4)
lowerCAmelCase__ ="abcdabcy"
lowerCAmelCase__ ="abcxabcdabxabcdabcdabcy"
assert kmp(pattern, text)
# Test 5)
lowerCAmelCase__ ="aabaabaaa"
assert get_failure_array(pattern) == [0, 1, 0, 1, 2, 3, 4, 5, 2]
| 690 |
"""simple docstring"""
from math import ceil
from typing import List, Optional, Union
import numpy as np
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import BatchFeature, SequenceFeatureExtractor
from ...utils import TensorType, logging
lowerCAmelCase__ =logging.get_logger(__name__)
class A__( __magic_name__ ):
lowerCAmelCase = ['''audio_values''', '''audio_mask''']
def __init__( self : Dict , __SCREAMING_SNAKE_CASE : Optional[Any]=20_48 , __SCREAMING_SNAKE_CASE : str=1 , __SCREAMING_SNAKE_CASE : List[Any]=[16, 16] , __SCREAMING_SNAKE_CASE : Union[str, Any]=1_28 , __SCREAMING_SNAKE_CASE : int=4_41_00 , __SCREAMING_SNAKE_CASE : Union[str, Any]=86 , __SCREAMING_SNAKE_CASE : str=20_48 , __SCREAMING_SNAKE_CASE : Optional[Any]=0.0 , **__SCREAMING_SNAKE_CASE : Optional[int] , ) -> Any:
"""simple docstring"""
super().__init__(
feature_size=__SCREAMING_SNAKE_CASE , sampling_rate=__SCREAMING_SNAKE_CASE , padding_value=__SCREAMING_SNAKE_CASE , **__SCREAMING_SNAKE_CASE , )
__SCREAMING_SNAKE_CASE = spectrogram_length
__SCREAMING_SNAKE_CASE = num_channels
__SCREAMING_SNAKE_CASE = patch_size
__SCREAMING_SNAKE_CASE = feature_size // self.patch_size[1]
__SCREAMING_SNAKE_CASE = n_fft
__SCREAMING_SNAKE_CASE = sampling_rate // hop_length_to_sampling_rate
__SCREAMING_SNAKE_CASE = sampling_rate
__SCREAMING_SNAKE_CASE = padding_value
__SCREAMING_SNAKE_CASE = mel_filter_bank(
num_frequency_bins=1 + n_fft // 2 , num_mel_filters=__SCREAMING_SNAKE_CASE , min_frequency=0.0 , max_frequency=2_20_50.0 , sampling_rate=__SCREAMING_SNAKE_CASE , norm='''slaney''' , mel_scale='''slaney''' , ).T
def _a ( self : str , __SCREAMING_SNAKE_CASE : np.array ) -> np.ndarray:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = spectrogram(
__SCREAMING_SNAKE_CASE , window_function(self.n_fft , '''hann''' ) , frame_length=self.n_fft , hop_length=self.hop_length , power=2.0 , mel_filters=self.mel_filters.T , log_mel='''dB''' , db_range=80.0 , )
__SCREAMING_SNAKE_CASE = log_spec[:, :-1]
__SCREAMING_SNAKE_CASE = log_spec - 20.0
__SCREAMING_SNAKE_CASE = np.clip(log_spec / 40.0 , -2.0 , 0.0 ) + 1.0
return log_spec
def __call__( self : str , __SCREAMING_SNAKE_CASE : Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]] , __SCREAMING_SNAKE_CASE : Optional[Union[str, TensorType]] = None , __SCREAMING_SNAKE_CASE : Optional[bool] = True , __SCREAMING_SNAKE_CASE : Optional[int] = None , __SCREAMING_SNAKE_CASE : bool = False , __SCREAMING_SNAKE_CASE : bool = False , **__SCREAMING_SNAKE_CASE : Tuple , ) -> BatchFeature:
"""simple docstring"""
if sampling_rate is not None:
if sampling_rate != self.sampling_rate:
raise ValueError(
'''This feature extractor is set to support sampling rate'''
f""" of {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled"""
f""" with {self.sampling_rate} and not {sampling_rate}.""" )
else:
logger.warning(
'''It is strongly recommended to pass the `sampling_rate` argument to this function. '''
'''Failing to do so can result in silent errors that might be hard to debug.''' )
__SCREAMING_SNAKE_CASE = isinstance(__SCREAMING_SNAKE_CASE , np.ndarray ) and len(raw_speech.shape ) > 1
if is_batched_numpy and len(raw_speech.shape ) > 2:
raise ValueError(f"""Only mono-channel audio is supported for input to {self}""" )
__SCREAMING_SNAKE_CASE = is_batched_numpy or (
isinstance(__SCREAMING_SNAKE_CASE , (list, tuple) ) and (isinstance(raw_speech[0] , (np.ndarray, tuple, list) ))
)
if is_batched:
__SCREAMING_SNAKE_CASE = [np.asarray([speech] , dtype=np.floataa ).T for speech in raw_speech]
elif not is_batched and not isinstance(__SCREAMING_SNAKE_CASE , np.ndarray ):
__SCREAMING_SNAKE_CASE = np.asarray(__SCREAMING_SNAKE_CASE , dtype=np.floataa )
elif isinstance(__SCREAMING_SNAKE_CASE , np.ndarray ) and raw_speech.dtype is np.dtype(np.floataa ):
__SCREAMING_SNAKE_CASE = raw_speech.astype(np.floataa )
# always return batch
if not is_batched:
__SCREAMING_SNAKE_CASE = [np.asarray([raw_speech] ).T]
# Convert audio signals to log mel spectrograms, truncate by time axis
__SCREAMING_SNAKE_CASE = [
self._np_extract_fbank_features(waveform.squeeze() ).T[: self.spectrogram_length] for waveform in raw_speech
]
if isinstance(audio_features[0] , __SCREAMING_SNAKE_CASE ):
__SCREAMING_SNAKE_CASE = [np.asarray(__SCREAMING_SNAKE_CASE , dtype=np.floataa ) for feature in audio_features]
# Create audio attention mask
__SCREAMING_SNAKE_CASE = max(
[ceil(feature.shape[0] / self.patch_size[0] ) * self.freq_len for feature in audio_features] ) # The maximum number of audio patches in a batch
if return_attention_mask:
__SCREAMING_SNAKE_CASE = [
(ceil(feature.shape[0] / self.patch_size[0] ) * self.freq_len) * [1]
+ (max_patch_len - ceil(feature.shape[0] / self.patch_size[0] ) * self.freq_len) * [0]
for feature in audio_features
]
__SCREAMING_SNAKE_CASE = np.array(__SCREAMING_SNAKE_CASE ).astype(np.floataa )
# convert into correct format for padding
__SCREAMING_SNAKE_CASE = max_patch_len // self.freq_len * self.patch_size[0] # The maximum audio size in a batch
__SCREAMING_SNAKE_CASE = np.ones([len(__SCREAMING_SNAKE_CASE ), 1, max_time_len, self.feature_size] ).astype(np.floataa )
__SCREAMING_SNAKE_CASE = padded_audio_features * self.padding_value
for i in range(len(__SCREAMING_SNAKE_CASE ) ):
__SCREAMING_SNAKE_CASE = audio_features[i]
__SCREAMING_SNAKE_CASE = feature
# return as BatchFeature
if return_attention_mask:
__SCREAMING_SNAKE_CASE = {'''audio_values''': padded_audio_features, '''audio_mask''': audio_mask}
else:
__SCREAMING_SNAKE_CASE = {'''audio_values''': padded_audio_features}
__SCREAMING_SNAKE_CASE = BatchFeature(data=__SCREAMING_SNAKE_CASE , tensor_type=__SCREAMING_SNAKE_CASE )
return encoded_inputs
| 690 | 1 |
"""simple docstring"""
def _a ( UpperCAmelCase__ , UpperCAmelCase__ ) -> Optional[int]:
__SCREAMING_SNAKE_CASE = [1]
for i in range(2 , UpperCAmelCase__ ):
factorials.append(factorials[-1] * i )
assert 0 <= k < factorials[-1] * n, "k out of bounds"
__SCREAMING_SNAKE_CASE = []
__SCREAMING_SNAKE_CASE = list(range(UpperCAmelCase__ ) )
# Find permutation
while factorials:
__SCREAMING_SNAKE_CASE = factorials.pop()
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = divmod(UpperCAmelCase__ , UpperCAmelCase__ )
permutation.append(elements[number] )
elements.remove(elements[number] )
permutation.append(elements[0] )
return permutation
if __name__ == "__main__":
import doctest
doctest.testmod()
| 690 |
"""simple docstring"""
def _a ( UpperCAmelCase__ ) -> str:
__SCREAMING_SNAKE_CASE = ''''''
for ch in key:
if ch == " " or ch not in key_no_dups and ch.isalpha():
key_no_dups += ch
return key_no_dups
def _a ( UpperCAmelCase__ ) -> dict[str, str]:
__SCREAMING_SNAKE_CASE = [chr(i + 65 ) for i in range(26 )]
# Remove duplicate characters from key
__SCREAMING_SNAKE_CASE = remove_duplicates(key.upper() )
__SCREAMING_SNAKE_CASE = len(UpperCAmelCase__ )
# First fill cipher with key characters
__SCREAMING_SNAKE_CASE = {alphabet[i]: char for i, char in enumerate(UpperCAmelCase__ )}
# Then map remaining characters in alphabet to
# the alphabet from the beginning
for i in range(len(UpperCAmelCase__ ) , 26 ):
__SCREAMING_SNAKE_CASE = alphabet[i - offset]
# Ensure we are not mapping letters to letters previously mapped
while char in key:
offset -= 1
__SCREAMING_SNAKE_CASE = alphabet[i - offset]
__SCREAMING_SNAKE_CASE = char
return cipher_alphabet
def _a ( UpperCAmelCase__ , UpperCAmelCase__ ) -> str:
return "".join(cipher_map.get(UpperCAmelCase__ , UpperCAmelCase__ ) for ch in message.upper() )
def _a ( UpperCAmelCase__ , UpperCAmelCase__ ) -> str:
__SCREAMING_SNAKE_CASE = {v: k for k, v in cipher_map.items()}
return "".join(rev_cipher_map.get(UpperCAmelCase__ , UpperCAmelCase__ ) for ch in message.upper() )
def _a ( ) -> None:
__SCREAMING_SNAKE_CASE = input('''Enter message to encode or decode: ''' ).strip()
__SCREAMING_SNAKE_CASE = input('''Enter keyword: ''' ).strip()
__SCREAMING_SNAKE_CASE = input('''Encipher or decipher? E/D:''' ).strip()[0].lower()
try:
__SCREAMING_SNAKE_CASE = {'''e''': encipher, '''d''': decipher}[option]
except KeyError:
raise KeyError('''invalid input option''' )
__SCREAMING_SNAKE_CASE = create_cipher_map(UpperCAmelCase__ )
print(func(UpperCAmelCase__ , UpperCAmelCase__ ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| 690 | 1 |
"""simple docstring"""
from __future__ import annotations
lowerCAmelCase__ =10
def _a ( UpperCAmelCase__ ) -> list[int]:
__SCREAMING_SNAKE_CASE = 1
__SCREAMING_SNAKE_CASE = max(UpperCAmelCase__ )
while placement <= max_digit:
# declare and initialize empty buckets
__SCREAMING_SNAKE_CASE = [[] for _ in range(UpperCAmelCase__ )]
# split list_of_ints between the buckets
for i in list_of_ints:
__SCREAMING_SNAKE_CASE = int((i / placement) % RADIX )
buckets[tmp].append(UpperCAmelCase__ )
# put each buckets' contents into list_of_ints
__SCREAMING_SNAKE_CASE = 0
for b in range(UpperCAmelCase__ ):
for i in buckets[b]:
__SCREAMING_SNAKE_CASE = i
a += 1
# move to next
placement *= RADIX
return list_of_ints
if __name__ == "__main__":
import doctest
doctest.testmod()
| 690 |
"""simple docstring"""
from __future__ import annotations
import inspect
import unittest
from typing import List, Tuple
from transformers import RegNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST, TFRegNetForImageClassification, TFRegNetModel
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class A__:
def __init__( self : Optional[int] , __SCREAMING_SNAKE_CASE : Dict , __SCREAMING_SNAKE_CASE : List[str]=3 , __SCREAMING_SNAKE_CASE : Dict=32 , __SCREAMING_SNAKE_CASE : Optional[Any]=3 , __SCREAMING_SNAKE_CASE : Union[str, Any]=10 , __SCREAMING_SNAKE_CASE : str=[10, 20, 30, 40] , __SCREAMING_SNAKE_CASE : Optional[int]=[1, 1, 2, 1] , __SCREAMING_SNAKE_CASE : int=True , __SCREAMING_SNAKE_CASE : int=True , __SCREAMING_SNAKE_CASE : Optional[Any]="relu" , __SCREAMING_SNAKE_CASE : List[str]=3 , __SCREAMING_SNAKE_CASE : Union[str, Any]=None , ) -> Any:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = parent
__SCREAMING_SNAKE_CASE = batch_size
__SCREAMING_SNAKE_CASE = image_size
__SCREAMING_SNAKE_CASE = num_channels
__SCREAMING_SNAKE_CASE = embeddings_size
__SCREAMING_SNAKE_CASE = hidden_sizes
__SCREAMING_SNAKE_CASE = depths
__SCREAMING_SNAKE_CASE = is_training
__SCREAMING_SNAKE_CASE = use_labels
__SCREAMING_SNAKE_CASE = hidden_act
__SCREAMING_SNAKE_CASE = num_labels
__SCREAMING_SNAKE_CASE = scope
__SCREAMING_SNAKE_CASE = len(__SCREAMING_SNAKE_CASE )
def _a ( self : List[Any] ) -> List[str]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
__SCREAMING_SNAKE_CASE = None
if self.use_labels:
__SCREAMING_SNAKE_CASE = ids_tensor([self.batch_size] , self.num_labels )
__SCREAMING_SNAKE_CASE = self.get_config()
return config, pixel_values, labels
def _a ( self : Union[str, Any] ) -> List[str]:
"""simple docstring"""
return RegNetConfig(
num_channels=self.num_channels , embeddings_size=self.embeddings_size , hidden_sizes=self.hidden_sizes , depths=self.depths , hidden_act=self.hidden_act , num_labels=self.num_labels , )
def _a ( self : str , __SCREAMING_SNAKE_CASE : Any , __SCREAMING_SNAKE_CASE : str , __SCREAMING_SNAKE_CASE : Optional[Any] ) -> Dict:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = TFRegNetModel(config=__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = model(__SCREAMING_SNAKE_CASE , training=__SCREAMING_SNAKE_CASE )
# expected last hidden states: B, C, H // 32, W // 32
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32) , )
def _a ( self : int , __SCREAMING_SNAKE_CASE : Union[str, Any] , __SCREAMING_SNAKE_CASE : str , __SCREAMING_SNAKE_CASE : Optional[Any] ) -> Union[str, Any]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = self.num_labels
__SCREAMING_SNAKE_CASE = TFRegNetForImageClassification(__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = model(__SCREAMING_SNAKE_CASE , labels=__SCREAMING_SNAKE_CASE , training=__SCREAMING_SNAKE_CASE )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def _a ( self : Optional[Any] ) -> Any:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = self.prepare_config_and_inputs()
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = config_and_inputs
__SCREAMING_SNAKE_CASE = {'''pixel_values''': pixel_values}
return config, inputs_dict
@require_tf
class A__( __magic_name__ , __magic_name__ , unittest.TestCase ):
lowerCAmelCase = (TFRegNetModel, TFRegNetForImageClassification) if is_tf_available() else ()
lowerCAmelCase = (
{'''feature-extraction''': TFRegNetModel, '''image-classification''': TFRegNetForImageClassification}
if is_tf_available()
else {}
)
lowerCAmelCase = False
lowerCAmelCase = False
lowerCAmelCase = False
lowerCAmelCase = False
lowerCAmelCase = False
def _a ( self : Optional[Any] ) -> Union[str, Any]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = TFRegNetModelTester(self )
__SCREAMING_SNAKE_CASE = ConfigTester(self , config_class=__SCREAMING_SNAKE_CASE , has_text_modality=__SCREAMING_SNAKE_CASE )
def _a ( self : Tuple ) -> Optional[Any]:
"""simple docstring"""
return
@unittest.skip(reason='''RegNet does not use inputs_embeds''' )
def _a ( self : Any ) -> Optional[Any]:
"""simple docstring"""
pass
@unittest.skipIf(
not is_tf_available() or len(tf.config.list_physical_devices('''GPU''' ) ) == 0 , reason='''TF does not support backprop for grouped convolutions on CPU.''' , )
@slow
def _a ( self : Dict ) -> List[Any]:
"""simple docstring"""
super().test_keras_fit()
@unittest.skip(reason='''RegNet does not support input and output embeddings''' )
def _a ( self : Dict ) -> Union[str, Any]:
"""simple docstring"""
pass
def _a ( self : List[Any] ) -> List[Any]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__SCREAMING_SNAKE_CASE = model_class(__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = inspect.signature(model.call )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
__SCREAMING_SNAKE_CASE = [*signature.parameters.keys()]
__SCREAMING_SNAKE_CASE = ['''pixel_values''']
self.assertListEqual(arg_names[:1] , __SCREAMING_SNAKE_CASE )
def _a ( self : Any ) -> Union[str, Any]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__SCREAMING_SNAKE_CASE )
def _a ( self : List[str] ) -> Tuple:
"""simple docstring"""
def check_hidden_states_output(__SCREAMING_SNAKE_CASE : Optional[Any] , __SCREAMING_SNAKE_CASE : Optional[Any] , __SCREAMING_SNAKE_CASE : Any ):
__SCREAMING_SNAKE_CASE = model_class(__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = model(**self._prepare_for_class(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) , training=__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
__SCREAMING_SNAKE_CASE = self.model_tester.num_stages
self.assertEqual(len(__SCREAMING_SNAKE_CASE ) , expected_num_stages + 1 )
# RegNet's feature maps are of shape (batch_size, num_channels, height, width)
self.assertListEqual(
list(hidden_states[0].shape[-2:] ) , [self.model_tester.image_size // 2, self.model_tester.image_size // 2] , )
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs_for_common()
__SCREAMING_SNAKE_CASE = ['''basic''', '''bottleneck''']
for model_class in self.all_model_classes:
for layer_type in layers_type:
__SCREAMING_SNAKE_CASE = layer_type
__SCREAMING_SNAKE_CASE = True
check_hidden_states_output(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
__SCREAMING_SNAKE_CASE = True
check_hidden_states_output(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
def _a ( self : Union[str, Any] ) -> List[str]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs_for_common()
def check_equivalence(__SCREAMING_SNAKE_CASE : Tuple , __SCREAMING_SNAKE_CASE : Optional[Any] , __SCREAMING_SNAKE_CASE : Tuple , __SCREAMING_SNAKE_CASE : Union[str, Any]={} ):
__SCREAMING_SNAKE_CASE = model(__SCREAMING_SNAKE_CASE , return_dict=__SCREAMING_SNAKE_CASE , **__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = model(__SCREAMING_SNAKE_CASE , return_dict=__SCREAMING_SNAKE_CASE , **__SCREAMING_SNAKE_CASE ).to_tuple()
def recursive_check(__SCREAMING_SNAKE_CASE : int , __SCREAMING_SNAKE_CASE : Dict ):
if isinstance(__SCREAMING_SNAKE_CASE , (List, Tuple) ):
for tuple_iterable_value, dict_iterable_value in zip(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
recursive_check(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
elif tuple_object is None:
return
else:
self.assertTrue(
all(tf.equal(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) ) , msg=(
'''Tuple and dict output are not equal. Difference:'''
f""" {tf.math.reduce_max(tf.abs(tuple_object - dict_object ) )}"""
) , )
recursive_check(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
for model_class in self.all_model_classes:
__SCREAMING_SNAKE_CASE = model_class(__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = self._prepare_for_class(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = self._prepare_for_class(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
check_equivalence(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = self._prepare_for_class(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , return_labels=__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = self._prepare_for_class(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , return_labels=__SCREAMING_SNAKE_CASE )
check_equivalence(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = self._prepare_for_class(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = self._prepare_for_class(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
check_equivalence(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , {'''output_hidden_states''': True} )
__SCREAMING_SNAKE_CASE = self._prepare_for_class(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , return_labels=__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = self._prepare_for_class(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , return_labels=__SCREAMING_SNAKE_CASE )
check_equivalence(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , {'''output_hidden_states''': True} )
def _a ( self : str ) -> str:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*__SCREAMING_SNAKE_CASE )
@slow
def _a ( self : Union[str, Any] ) -> Union[str, Any]:
"""simple docstring"""
for model_name in TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__SCREAMING_SNAKE_CASE = TFRegNetModel.from_pretrained(__SCREAMING_SNAKE_CASE )
self.assertIsNotNone(__SCREAMING_SNAKE_CASE )
def _a ( ) -> Dict:
__SCREAMING_SNAKE_CASE = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
return image
@require_tf
@require_vision
class A__( unittest.TestCase ):
@cached_property
def _a ( self : List[Any] ) -> str:
"""simple docstring"""
return (
AutoImageProcessor.from_pretrained(TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0] )
if is_vision_available()
else None
)
@slow
def _a ( self : List[str] ) -> Any:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = TFRegNetForImageClassification.from_pretrained(TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0] )
__SCREAMING_SNAKE_CASE = self.default_image_processor
__SCREAMING_SNAKE_CASE = prepare_img()
__SCREAMING_SNAKE_CASE = image_processor(images=__SCREAMING_SNAKE_CASE , return_tensors='''tf''' )
# forward pass
__SCREAMING_SNAKE_CASE = model(**__SCREAMING_SNAKE_CASE , training=__SCREAMING_SNAKE_CASE )
# verify the logits
__SCREAMING_SNAKE_CASE = tf.TensorShape((1, 10_00) )
self.assertEqual(outputs.logits.shape , __SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = tf.constant([-0.41_80, -1.50_51, -3.48_36] )
tf.debugging.assert_near(outputs.logits[0, :3] , __SCREAMING_SNAKE_CASE , atol=1E-4 )
| 690 | 1 |
"""simple docstring"""
from __future__ import annotations
import os
from collections.abc import Mapping
lowerCAmelCase__ =tuple[int, int]
class A__:
def __init__( self : Optional[int] , __SCREAMING_SNAKE_CASE : set[int] , __SCREAMING_SNAKE_CASE : Mapping[EdgeT, int] ) -> None:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = vertices
__SCREAMING_SNAKE_CASE = {
(min(__SCREAMING_SNAKE_CASE ), max(__SCREAMING_SNAKE_CASE )): weight for edge, weight in edges.items()
}
def _a ( self : List[Any] , __SCREAMING_SNAKE_CASE : EdgeT , __SCREAMING_SNAKE_CASE : int ) -> None:
"""simple docstring"""
self.vertices.add(edge[0] )
self.vertices.add(edge[1] )
__SCREAMING_SNAKE_CASE = weight
def _a ( self : List[str] ) -> Graph:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = Graph({min(self.vertices )} , {} )
__SCREAMING_SNAKE_CASE = 42
__SCREAMING_SNAKE_CASE = 42
__SCREAMING_SNAKE_CASE = 42
__SCREAMING_SNAKE_CASE = 42
while len(subgraph.vertices ) < len(self.vertices ):
__SCREAMING_SNAKE_CASE = max(self.edges.values() ) + 1
for edge, weight in self.edges.items():
if (edge[0] in subgraph.vertices) ^ (edge[1] in subgraph.vertices):
if weight < min_weight:
__SCREAMING_SNAKE_CASE = edge
__SCREAMING_SNAKE_CASE = weight
subgraph.add_edge(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
return subgraph
def _a ( UpperCAmelCase__ = "p107_network.txt" ) -> int:
__SCREAMING_SNAKE_CASE = os.path.abspath(os.path.dirname(UpperCAmelCase__ ) )
__SCREAMING_SNAKE_CASE = os.path.join(UpperCAmelCase__ , UpperCAmelCase__ )
__SCREAMING_SNAKE_CASE = {}
__SCREAMING_SNAKE_CASE = 42
__SCREAMING_SNAKE_CASE = 42
__SCREAMING_SNAKE_CASE = 42
with open(UpperCAmelCase__ ) as f:
__SCREAMING_SNAKE_CASE = f.read().strip().split('''\n''' )
__SCREAMING_SNAKE_CASE = [line.split(''',''' ) for line in data]
for edgea in range(1 , len(UpperCAmelCase__ ) ):
for edgea in range(UpperCAmelCase__ ):
if adjaceny_matrix[edgea][edgea] != "-":
__SCREAMING_SNAKE_CASE = int(adjaceny_matrix[edgea][edgea] )
__SCREAMING_SNAKE_CASE = Graph(set(range(len(UpperCAmelCase__ ) ) ) , UpperCAmelCase__ )
__SCREAMING_SNAKE_CASE = graph.prims_algorithm()
__SCREAMING_SNAKE_CASE = sum(graph.edges.values() )
__SCREAMING_SNAKE_CASE = sum(subgraph.edges.values() )
return initial_total - optimal_total
if __name__ == "__main__":
print(F'''{solution() = }''')
| 690 |
"""simple docstring"""
import pickle
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, XLMRobertaTokenizer, XLMRobertaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
lowerCAmelCase__ =get_tests_dir("fixtures/test_sentencepiece.model")
@require_sentencepiece
@require_tokenizers
class A__( __magic_name__ , unittest.TestCase ):
lowerCAmelCase = XLMRobertaTokenizer
lowerCAmelCase = XLMRobertaTokenizerFast
lowerCAmelCase = True
lowerCAmelCase = True
def _a ( self : Optional[int] ) -> List[Any]:
"""simple docstring"""
super().setUp()
# We have a SentencePiece fixture for testing
__SCREAMING_SNAKE_CASE = XLMRobertaTokenizer(__SCREAMING_SNAKE_CASE , keep_accents=__SCREAMING_SNAKE_CASE )
tokenizer.save_pretrained(self.tmpdirname )
def _a ( self : str ) -> Union[str, Any]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = '''<pad>'''
__SCREAMING_SNAKE_CASE = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(__SCREAMING_SNAKE_CASE ) , __SCREAMING_SNAKE_CASE )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(__SCREAMING_SNAKE_CASE ) , __SCREAMING_SNAKE_CASE )
def _a ( self : int ) -> List[Any]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , '''<s>''' )
self.assertEqual(vocab_keys[1] , '''<pad>''' )
self.assertEqual(vocab_keys[-1] , '''<mask>''' )
self.assertEqual(len(__SCREAMING_SNAKE_CASE ) , 10_02 )
def _a ( self : Tuple ) -> Optional[int]:
"""simple docstring"""
self.assertEqual(self.get_tokenizer().vocab_size , 10_02 )
def _a ( self : int ) -> int:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = XLMRobertaTokenizer(__SCREAMING_SNAKE_CASE , keep_accents=__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = tokenizer.tokenize('''This is a test''' )
self.assertListEqual(__SCREAMING_SNAKE_CASE , ['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est'''] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(__SCREAMING_SNAKE_CASE ) , [value + tokenizer.fairseq_offset for value in [2_85, 46, 10, 1_70, 3_82]] , )
__SCREAMING_SNAKE_CASE = tokenizer.tokenize('''I was born in 92000, and this is falsé.''' )
self.assertListEqual(
__SCREAMING_SNAKE_CASE , [
SPIECE_UNDERLINE + '''I''',
SPIECE_UNDERLINE + '''was''',
SPIECE_UNDERLINE + '''b''',
'''or''',
'''n''',
SPIECE_UNDERLINE + '''in''',
SPIECE_UNDERLINE + '''''',
'''9''',
'''2''',
'''0''',
'''0''',
'''0''',
''',''',
SPIECE_UNDERLINE + '''and''',
SPIECE_UNDERLINE + '''this''',
SPIECE_UNDERLINE + '''is''',
SPIECE_UNDERLINE + '''f''',
'''al''',
'''s''',
'''é''',
'''.''',
] , )
__SCREAMING_SNAKE_CASE = tokenizer.convert_tokens_to_ids(__SCREAMING_SNAKE_CASE )
self.assertListEqual(
__SCREAMING_SNAKE_CASE , [
value + tokenizer.fairseq_offset
for value in [8, 21, 84, 55, 24, 19, 7, 2, 6_02, 3_47, 3_47, 3_47, 3, 12, 66, 46, 72, 80, 6, 2, 4]
# ^ unk: 2 + 1 = 3 unk: 2 + 1 = 3 ^
] , )
__SCREAMING_SNAKE_CASE = tokenizer.convert_ids_to_tokens(__SCREAMING_SNAKE_CASE )
self.assertListEqual(
__SCREAMING_SNAKE_CASE , [
SPIECE_UNDERLINE + '''I''',
SPIECE_UNDERLINE + '''was''',
SPIECE_UNDERLINE + '''b''',
'''or''',
'''n''',
SPIECE_UNDERLINE + '''in''',
SPIECE_UNDERLINE + '''''',
'''<unk>''',
'''2''',
'''0''',
'''0''',
'''0''',
''',''',
SPIECE_UNDERLINE + '''and''',
SPIECE_UNDERLINE + '''this''',
SPIECE_UNDERLINE + '''is''',
SPIECE_UNDERLINE + '''f''',
'''al''',
'''s''',
'''<unk>''',
'''.''',
] , )
def _a ( self : int ) -> Tuple:
"""simple docstring"""
if not self.test_slow_tokenizer:
# as we don't have a slow version, we can't compare the outputs between slow and fast versions
return
__SCREAMING_SNAKE_CASE = (self.rust_tokenizer_class, '''hf-internal-testing/tiny-xlm-roberta''', {})
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})""" ):
__SCREAMING_SNAKE_CASE = self.rust_tokenizer_class.from_pretrained(__SCREAMING_SNAKE_CASE , **__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = self.tokenizer_class.from_pretrained(__SCREAMING_SNAKE_CASE , **__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = tempfile.mkdtemp()
__SCREAMING_SNAKE_CASE = tokenizer_r.save_pretrained(__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = tokenizer_p.save_pretrained(__SCREAMING_SNAKE_CASE )
# Checks it save with the same files + the tokenizer.json file for the fast one
self.assertTrue(any('''tokenizer.json''' in f for f in tokenizer_r_files ) )
__SCREAMING_SNAKE_CASE = tuple(f for f in tokenizer_r_files if '''tokenizer.json''' not in f )
self.assertSequenceEqual(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
# Checks everything loads correctly in the same way
__SCREAMING_SNAKE_CASE = tokenizer_r.from_pretrained(__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = tokenizer_p.from_pretrained(__SCREAMING_SNAKE_CASE )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) )
# self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
# self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))
shutil.rmtree(__SCREAMING_SNAKE_CASE )
# Save tokenizer rust, legacy_format=True
__SCREAMING_SNAKE_CASE = tempfile.mkdtemp()
__SCREAMING_SNAKE_CASE = tokenizer_r.save_pretrained(__SCREAMING_SNAKE_CASE , legacy_format=__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = tokenizer_p.save_pretrained(__SCREAMING_SNAKE_CASE )
# Checks it save with the same files
self.assertSequenceEqual(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
# Checks everything loads correctly in the same way
__SCREAMING_SNAKE_CASE = tokenizer_r.from_pretrained(__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = tokenizer_p.from_pretrained(__SCREAMING_SNAKE_CASE )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) )
shutil.rmtree(__SCREAMING_SNAKE_CASE )
# Save tokenizer rust, legacy_format=False
__SCREAMING_SNAKE_CASE = tempfile.mkdtemp()
__SCREAMING_SNAKE_CASE = tokenizer_r.save_pretrained(__SCREAMING_SNAKE_CASE , legacy_format=__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = tokenizer_p.save_pretrained(__SCREAMING_SNAKE_CASE )
# Checks it saved the tokenizer.json file
self.assertTrue(any('''tokenizer.json''' in f for f in tokenizer_r_files ) )
# Checks everything loads correctly in the same way
__SCREAMING_SNAKE_CASE = tokenizer_r.from_pretrained(__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = tokenizer_p.from_pretrained(__SCREAMING_SNAKE_CASE )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) )
shutil.rmtree(__SCREAMING_SNAKE_CASE )
@cached_property
def _a ( self : Union[str, Any] ) -> List[str]:
"""simple docstring"""
return XLMRobertaTokenizer.from_pretrained('''xlm-roberta-base''' )
def _a ( self : Optional[Any] ) -> Union[str, Any]:
"""simple docstring"""
with tempfile.NamedTemporaryFile() as f:
shutil.copyfile(__SCREAMING_SNAKE_CASE , f.name )
__SCREAMING_SNAKE_CASE = XLMRobertaTokenizer(f.name , keep_accents=__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = pickle.dumps(__SCREAMING_SNAKE_CASE )
pickle.loads(__SCREAMING_SNAKE_CASE )
def _a ( self : Union[str, Any] ) -> List[Any]:
"""simple docstring"""
if not self.test_rust_tokenizer:
return
__SCREAMING_SNAKE_CASE = self.get_tokenizer()
__SCREAMING_SNAKE_CASE = self.get_rust_tokenizer()
__SCREAMING_SNAKE_CASE = '''I was born in 92000, and this is falsé.'''
__SCREAMING_SNAKE_CASE = tokenizer.tokenize(__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = rust_tokenizer.tokenize(__SCREAMING_SNAKE_CASE )
self.assertListEqual(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = tokenizer.encode(__SCREAMING_SNAKE_CASE , add_special_tokens=__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = rust_tokenizer.encode(__SCREAMING_SNAKE_CASE , add_special_tokens=__SCREAMING_SNAKE_CASE )
self.assertListEqual(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = self.get_rust_tokenizer()
__SCREAMING_SNAKE_CASE = tokenizer.encode(__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = rust_tokenizer.encode(__SCREAMING_SNAKE_CASE )
self.assertListEqual(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
@slow
def _a ( self : Any ) -> Dict:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = '''Hello World!'''
__SCREAMING_SNAKE_CASE = [0, 3_53_78, 66_61, 38, 2]
# xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base') # xlmr.large has same tokenizer
# xlmr.eval()
# xlmr.encode(symbols)
self.assertListEqual(__SCREAMING_SNAKE_CASE , self.big_tokenizer.encode(__SCREAMING_SNAKE_CASE ) )
@slow
def _a ( self : Optional[Any] ) -> Union[str, Any]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = (
'''This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) " [ ] ! : - . Also we will'''
''' add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth'''
)
__SCREAMING_SNAKE_CASE = [
0,
32_93,
83,
10,
45_52,
49_89,
79_86,
6_78,
10,
59_15,
1_11,
17_94_59,
12_48_50,
4,
60_44,
2_37,
12,
6,
5,
6,
4,
67_80,
7_05,
15,
13_88,
44,
3_78,
1_01_14,
7_11,
1_52,
20,
6,
5,
2_23_76,
6_42,
12_21,
1_51_90,
3_41_53,
4_50,
56_08,
9_59,
11_19,
5_77_02,
1_36,
1_86,
47,
10_98,
2_93_67,
47,
# 4426, # What fairseq tokenizes from "<unk>": "_<"
# 3678, # What fairseq tokenizes from "<unk>": "unk"
# 2740, # What fairseq tokenizes from "<unk>": ">"
3, # What we tokenize from "<unk>": "<unk>"
6, # Residue from the tokenization: an extra sentencepiece underline
4,
60_44,
2_37,
62_84,
5_09_01,
5_28,
31,
90,
34,
9_27,
2,
]
# xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base') # xlmr.large has same tokenizer
# xlmr.eval()
# xlmr.encode(symbols)
self.assertListEqual(__SCREAMING_SNAKE_CASE , self.big_tokenizer.encode(__SCREAMING_SNAKE_CASE ) )
@slow
def _a ( self : Optional[int] ) -> Dict:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = {'''input_ids''': [[0, 1_10_62, 8_27_72, 7, 15, 8_27_72, 5_38, 5_15_29, 2_37, 1_71_98, 12_90, 2_06, 9, 21_51_75, 13_14, 1_36, 1_71_98, 12_90, 2_06, 9, 5_63_59, 42, 12_20_09, 9, 1_64_66, 16, 8_73_44, 45_37, 9, 47_17, 7_83_81, 6, 15_99_58, 7, 15, 2_44_80, 6_18, 4, 5_27, 2_26_93, 54_28, 4, 27_77, 2_44_80, 98_74, 4, 4_35_23, 5_94, 4, 8_03, 1_83_92, 3_31_89, 18, 4, 4_35_23, 2_44_47, 1_23_99, 1_00, 2_49_55, 8_36_58, 96_26, 14_40_57, 15, 8_39, 2_23_35, 16, 1_36, 2_49_55, 8_36_58, 8_34_79, 15, 3_91_02, 7_24, 16, 6_78, 6_45, 27_89, 13_28, 45_89, 42, 12_20_09, 11_57_74, 23, 8_05, 13_28, 4_68_76, 7, 1_36, 5_38_94, 19_40, 4_22_27, 4_11_59, 1_77_21, 8_23, 4_25, 4, 2_75_12, 9_87_22, 2_06, 1_36, 55_31, 49_70, 9_19, 1_73_36, 5, 2], [0, 2_00_80, 6_18, 83, 8_27_75, 47, 4_79, 9, 15_17, 73, 5_38_94, 3_33, 8_05_81, 11_01_17, 1_88_11, 52_56, 12_95, 51, 15_25_26, 2_97, 79_86, 3_90, 12_44_16, 5_38, 3_54_31, 2_14, 98, 1_50_44, 2_57_37, 1_36, 71_08, 4_37_01, 23, 7_56, 13_53_55, 7, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 5_81, 6_37_73, 11_94_55, 6, 14_77_97, 8_82_03, 7, 6_45, 70, 21, 32_85, 1_02_69, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], '''attention_mask''': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # noqa: E501
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=__SCREAMING_SNAKE_CASE , model_name='''xlm-roberta-base''' , revision='''d9d8a8ea5eb94b1c6654ae9249df7793cd2933d3''' , )
| 690 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
lowerCAmelCase__ ={
"configuration_tapas": ["TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP", "TapasConfig"],
"tokenization_tapas": ["TapasTokenizer"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase__ =[
"TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST",
"TapasForMaskedLM",
"TapasForQuestionAnswering",
"TapasForSequenceClassification",
"TapasModel",
"TapasPreTrainedModel",
"load_tf_weights_in_tapas",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase__ =[
"TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST",
"TFTapasForMaskedLM",
"TFTapasForQuestionAnswering",
"TFTapasForSequenceClassification",
"TFTapasModel",
"TFTapasPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_tapas import TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP, TapasConfig
from .tokenization_tapas import TapasTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tapas import (
TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
TapasForMaskedLM,
TapasForQuestionAnswering,
TapasForSequenceClassification,
TapasModel,
TapasPreTrainedModel,
load_tf_weights_in_tapas,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_tapas import (
TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
TFTapasForMaskedLM,
TFTapasForQuestionAnswering,
TFTapasForSequenceClassification,
TFTapasModel,
TFTapasPreTrainedModel,
)
else:
import sys
lowerCAmelCase__ =_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 690 |
"""simple docstring"""
from __future__ import annotations
lowerCAmelCase__ =8.9_8_8E9 # units = N * m^s * C^-2
def _a ( UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ ) -> dict[str, float]:
__SCREAMING_SNAKE_CASE = abs(chargea * chargea )
if (force, chargea, chargea, distance).count(0 ) != 1:
raise ValueError('''One and only one argument must be 0''' )
if distance < 0:
raise ValueError('''Distance cannot be negative''' )
if force == 0:
__SCREAMING_SNAKE_CASE = COULOMBS_CONSTANT * charge_product / (distance**2)
return {"force": force}
elif chargea == 0:
__SCREAMING_SNAKE_CASE = abs(UpperCAmelCase__ ) * (distance**2) / (COULOMBS_CONSTANT * chargea)
return {"charge1": chargea}
elif chargea == 0:
__SCREAMING_SNAKE_CASE = abs(UpperCAmelCase__ ) * (distance**2) / (COULOMBS_CONSTANT * chargea)
return {"charge2": chargea}
elif distance == 0:
__SCREAMING_SNAKE_CASE = (COULOMBS_CONSTANT * charge_product / abs(UpperCAmelCase__ )) ** 0.5
return {"distance": distance}
raise ValueError('''Exactly one argument must be 0''' )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 690 | 1 |
"""simple docstring"""
from manim import *
class A__( __magic_name__ ):
def _a ( self : Union[str, Any] ) -> Optional[int]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = Rectangle(height=0.5 , width=0.5 )
__SCREAMING_SNAKE_CASE = Rectangle(height=0.46 , width=0.46 ).set_stroke(width=0 )
__SCREAMING_SNAKE_CASE = [mem.copy() for i in range(6 )]
__SCREAMING_SNAKE_CASE = [mem.copy() for i in range(6 )]
__SCREAMING_SNAKE_CASE = VGroup(*__SCREAMING_SNAKE_CASE ).arrange(__SCREAMING_SNAKE_CASE , buff=0 )
__SCREAMING_SNAKE_CASE = VGroup(*__SCREAMING_SNAKE_CASE ).arrange(__SCREAMING_SNAKE_CASE , buff=0 )
__SCREAMING_SNAKE_CASE = VGroup(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ).arrange(__SCREAMING_SNAKE_CASE , buff=0 )
__SCREAMING_SNAKE_CASE = Text('''CPU''' , font_size=24 )
__SCREAMING_SNAKE_CASE = Group(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ).arrange(__SCREAMING_SNAKE_CASE , buff=0.5 , aligned_edge=__SCREAMING_SNAKE_CASE )
cpu.move_to([-2.5, -0.5, 0] )
self.add(__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = [mem.copy() for i in range(1 )]
__SCREAMING_SNAKE_CASE = VGroup(*__SCREAMING_SNAKE_CASE ).arrange(__SCREAMING_SNAKE_CASE , buff=0 )
__SCREAMING_SNAKE_CASE = Text('''GPU''' , font_size=24 )
__SCREAMING_SNAKE_CASE = Group(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ).arrange(__SCREAMING_SNAKE_CASE , buff=0.5 , aligned_edge=__SCREAMING_SNAKE_CASE )
gpu.align_to(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
gpu.set_x(gpu.get_x() - 1 )
self.add(__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = [mem.copy() for i in range(6 )]
__SCREAMING_SNAKE_CASE = VGroup(*__SCREAMING_SNAKE_CASE ).arrange(__SCREAMING_SNAKE_CASE , buff=0 )
__SCREAMING_SNAKE_CASE = Text('''Model''' , font_size=24 )
__SCREAMING_SNAKE_CASE = Group(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ).arrange(__SCREAMING_SNAKE_CASE , buff=0.5 , aligned_edge=__SCREAMING_SNAKE_CASE )
model.move_to([3, -1.0, 0] )
self.play(
Create(__SCREAMING_SNAKE_CASE , run_time=1 ) , Create(__SCREAMING_SNAKE_CASE , run_time=1 ) , Create(__SCREAMING_SNAKE_CASE , run_time=1 ) , )
__SCREAMING_SNAKE_CASE = MarkupText(
f"""First, an empty model skeleton is loaded\ninto <span fgcolor='{YELLOW}'>memory</span> without using much RAM.""" , font_size=24 , )
__SCREAMING_SNAKE_CASE = Square(side_length=2.2 )
key.move_to([-5, 2, 0] )
__SCREAMING_SNAKE_CASE = MarkupText(
f"""<b>Key:</b>\n\n<span fgcolor='{YELLOW}'>●</span> Empty Model""" , font_size=18 , )
key_text.move_to([-5, 2.4, 0] )
step_a.move_to([2, 2, 0] )
self.play(Write(__SCREAMING_SNAKE_CASE , run_time=2.5 ) , Write(__SCREAMING_SNAKE_CASE ) , Write(__SCREAMING_SNAKE_CASE ) )
self.add(__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = []
__SCREAMING_SNAKE_CASE = []
__SCREAMING_SNAKE_CASE = []
for i, rect in enumerate(__SCREAMING_SNAKE_CASE ):
__SCREAMING_SNAKE_CASE = Rectangle(height=0.46 , width=0.46 ).set_stroke(width=0.0 ).set_fill(__SCREAMING_SNAKE_CASE , opacity=0.7 )
cpu_target.move_to(__SCREAMING_SNAKE_CASE )
cpu_target.generate_target()
__SCREAMING_SNAKE_CASE = 0.46 / 4
__SCREAMING_SNAKE_CASE = 0.46 / 3
if i == 0:
cpu_target.target.next_to(cpu_left_col_base[0].get_corner(DOWN + LEFT ) , buff=0.02 , direction=__SCREAMING_SNAKE_CASE )
cpu_target.target.set_x(cpu_target.target.get_x() + 0.1 )
elif i == 3:
cpu_target.target.next_to(cpu_targs[0].target , direction=__SCREAMING_SNAKE_CASE , buff=0.0 )
else:
cpu_target.target.next_to(cpu_targs[i - 1].target , direction=__SCREAMING_SNAKE_CASE , buff=0.0 )
cpu_targs.append(__SCREAMING_SNAKE_CASE )
first_animations.append(rect.animate(run_time=0.5 ).set_stroke(__SCREAMING_SNAKE_CASE ) )
second_animations.append(MoveToTarget(__SCREAMING_SNAKE_CASE , run_time=1.5 ) )
self.play(*__SCREAMING_SNAKE_CASE )
self.play(*__SCREAMING_SNAKE_CASE )
self.wait()
| 690 |
"""simple docstring"""
import argparse
from pathlib import Path
import torch
from transformers import OPTConfig, OPTModel
from transformers.utils import logging
logging.set_verbosity_info()
lowerCAmelCase__ =logging.get_logger(__name__)
def _a ( UpperCAmelCase__ ) -> Tuple:
__SCREAMING_SNAKE_CASE = torch.load(UpperCAmelCase__ , map_location='''cpu''' )
if "model" in sd.keys():
__SCREAMING_SNAKE_CASE = torch.load(UpperCAmelCase__ , map_location='''cpu''' )['''model''']
# pop unnecessary weights
__SCREAMING_SNAKE_CASE = [
'''decoder.version''',
'''decoder.output_projection.weight''',
]
for key in keys_to_delete:
if key in sd:
sd.pop(UpperCAmelCase__ )
__SCREAMING_SNAKE_CASE = {
'''decoder.project_in_dim.weight''': '''decoder.project_in.weight''',
'''decoder.project_out_dim.weight''': '''decoder.project_out.weight''',
'''decoder.layer_norm.weight''': '''decoder.final_layer_norm.weight''',
'''decoder.layer_norm.bias''': '''decoder.final_layer_norm.bias''',
}
for old_key, new_key in keys_to_rename.items():
if old_key in sd:
__SCREAMING_SNAKE_CASE = sd.pop(UpperCAmelCase__ )
__SCREAMING_SNAKE_CASE = list(sd.keys() )
for key in keys:
if ".qkv_proj." in key:
__SCREAMING_SNAKE_CASE = sd[key]
# We split QKV in separate Q,K,V
__SCREAMING_SNAKE_CASE = key.replace('''.qkv_proj.''' , '''.q_proj.''' )
__SCREAMING_SNAKE_CASE = key.replace('''.qkv_proj.''' , '''.k_proj.''' )
__SCREAMING_SNAKE_CASE = key.replace('''.qkv_proj.''' , '''.v_proj.''' )
__SCREAMING_SNAKE_CASE = value.shape[0]
assert depth % 3 == 0
# `SequeuceParallelTransformerBlock` has QKV weight is separated in K,V,Q despite the naming:
# https://cs.github.com/facebookresearch/metaseq/blob/51871bd73cd04c038f239ea2a26db1d7f6b37927/metaseq/modules/sequence_parallel_transformer_layer.py#L97
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = torch.split(UpperCAmelCase__ , depth // 3 , dim=0 )
__SCREAMING_SNAKE_CASE = q
__SCREAMING_SNAKE_CASE = k
__SCREAMING_SNAKE_CASE = v
del sd[key]
return sd
@torch.no_grad()
def _a ( UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__=None ) -> Optional[Any]:
__SCREAMING_SNAKE_CASE = load_checkpoint(UpperCAmelCase__ )
if config is not None:
__SCREAMING_SNAKE_CASE = OPTConfig.from_pretrained(UpperCAmelCase__ )
else:
__SCREAMING_SNAKE_CASE = OPTConfig()
__SCREAMING_SNAKE_CASE = OPTModel(UpperCAmelCase__ ).half().eval()
model.load_state_dict(UpperCAmelCase__ )
# Check results
Path(UpperCAmelCase__ ).mkdir(exist_ok=UpperCAmelCase__ )
model.save_pretrained(UpperCAmelCase__ )
if __name__ == "__main__":
lowerCAmelCase__ =argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--fairseq_path",
type=str,
help=(
"path to fairseq checkpoint in correct format. You can find all checkpoints in the correct format here:"
" https://huggingface.co/models?other=opt_metasq"
),
)
parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
parser.add_argument("--hf_config", default=None, type=str, help="Define HF config.")
lowerCAmelCase__ =parser.parse_args()
convert_opt_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, config=args.hf_config)
| 690 | 1 |
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
import diffusers
from diffusers import (
AutoencoderKL,
EulerDiscreteScheduler,
StableDiffusionLatentUpscalePipeline,
StableDiffusionPipeline,
UNetaDConditionModel,
)
from diffusers.schedulers import KarrasDiffusionSchedulers
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin
enable_full_determinism()
def _a ( UpperCAmelCase__ ) -> Dict:
__SCREAMING_SNAKE_CASE = [tensor.shape for tensor in tensor_list]
return all(shape == shapes[0] for shape in shapes[1:] )
class A__( __magic_name__ , __magic_name__ , __magic_name__ , unittest.TestCase ):
lowerCAmelCase = StableDiffusionLatentUpscalePipeline
lowerCAmelCase = TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
'''height''',
'''width''',
'''cross_attention_kwargs''',
'''negative_prompt_embeds''',
'''prompt_embeds''',
}
lowerCAmelCase = PipelineTesterMixin.required_optional_params - {'''num_images_per_prompt'''}
lowerCAmelCase = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
lowerCAmelCase = frozenset(
[] ) # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
lowerCAmelCase = frozenset([] )
lowerCAmelCase = True
@property
def _a ( self : List[Any] ) -> Tuple:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = 1
__SCREAMING_SNAKE_CASE = 4
__SCREAMING_SNAKE_CASE = (16, 16)
__SCREAMING_SNAKE_CASE = floats_tensor((batch_size, num_channels) + sizes , rng=random.Random(0 ) ).to(__SCREAMING_SNAKE_CASE )
return image
def _a ( self : Dict ) -> Optional[int]:
"""simple docstring"""
torch.manual_seed(0 )
__SCREAMING_SNAKE_CASE = UNetaDConditionModel(
act_fn='''gelu''' , attention_head_dim=8 , norm_num_groups=__SCREAMING_SNAKE_CASE , block_out_channels=[32, 32, 64, 64] , time_cond_proj_dim=1_60 , conv_in_kernel=1 , conv_out_kernel=1 , cross_attention_dim=32 , down_block_types=(
'''KDownBlock2D''',
'''KCrossAttnDownBlock2D''',
'''KCrossAttnDownBlock2D''',
'''KCrossAttnDownBlock2D''',
) , in_channels=8 , mid_block_type=__SCREAMING_SNAKE_CASE , only_cross_attention=__SCREAMING_SNAKE_CASE , out_channels=5 , resnet_time_scale_shift='''scale_shift''' , time_embedding_type='''fourier''' , timestep_post_act='''gelu''' , up_block_types=('''KCrossAttnUpBlock2D''', '''KCrossAttnUpBlock2D''', '''KCrossAttnUpBlock2D''', '''KUpBlock2D''') , )
__SCREAMING_SNAKE_CASE = AutoencoderKL(
block_out_channels=[32, 32, 64, 64] , in_channels=3 , out_channels=3 , down_block_types=[
'''DownEncoderBlock2D''',
'''DownEncoderBlock2D''',
'''DownEncoderBlock2D''',
'''DownEncoderBlock2D''',
] , up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''', '''UpDecoderBlock2D''', '''UpDecoderBlock2D'''] , latent_channels=4 , )
__SCREAMING_SNAKE_CASE = EulerDiscreteScheduler(prediction_type='''sample''' )
__SCREAMING_SNAKE_CASE = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=32 , intermediate_size=37 , layer_norm_eps=1E-0_5 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=10_00 , hidden_act='''quick_gelu''' , projection_dim=5_12 , )
__SCREAMING_SNAKE_CASE = CLIPTextModel(__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''' )
__SCREAMING_SNAKE_CASE = {
'''unet''': model.eval(),
'''vae''': vae.eval(),
'''scheduler''': scheduler,
'''text_encoder''': text_encoder,
'''tokenizer''': tokenizer,
}
return components
def _a ( self : Union[str, Any] , __SCREAMING_SNAKE_CASE : Any , __SCREAMING_SNAKE_CASE : Dict=0 ) -> int:
"""simple docstring"""
if str(__SCREAMING_SNAKE_CASE ).startswith('''mps''' ):
__SCREAMING_SNAKE_CASE = torch.manual_seed(__SCREAMING_SNAKE_CASE )
else:
__SCREAMING_SNAKE_CASE = torch.Generator(device=__SCREAMING_SNAKE_CASE ).manual_seed(__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = {
'''prompt''': '''A painting of a squirrel eating a burger''',
'''image''': self.dummy_image.cpu(),
'''generator''': generator,
'''num_inference_steps''': 2,
'''output_type''': '''numpy''',
}
return inputs
def _a ( self : List[Any] ) -> int:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = '''cpu'''
__SCREAMING_SNAKE_CASE = self.get_dummy_components()
__SCREAMING_SNAKE_CASE = self.pipeline_class(**__SCREAMING_SNAKE_CASE )
pipe.to(__SCREAMING_SNAKE_CASE )
pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = self.get_dummy_inputs(__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = pipe(**__SCREAMING_SNAKE_CASE ).images
__SCREAMING_SNAKE_CASE = image[0, -3:, -3:, -1]
self.assertEqual(image.shape , (1, 2_56, 2_56, 3) )
__SCREAMING_SNAKE_CASE = np.array(
[0.47_22_24_12, 0.41_92_16_33, 0.44_71_74_34, 0.46_87_41_92, 0.42_58_82_58, 0.46_15_07_26, 0.4_67_75_34, 0.45_58_38_32, 0.48_57_90_55] )
__SCREAMING_SNAKE_CASE = np.abs(image_slice.flatten() - expected_slice ).max()
self.assertLessEqual(__SCREAMING_SNAKE_CASE , 1E-3 )
def _a ( self : Dict ) -> List[str]:
"""simple docstring"""
super().test_attention_slicing_forward_pass(expected_max_diff=7E-3 )
def _a ( self : List[str] ) -> Union[str, Any]:
"""simple docstring"""
super().test_cpu_offload_forward_pass(expected_max_diff=3E-3 )
def _a ( self : Union[str, Any] ) -> List[str]:
"""simple docstring"""
super().test_dict_tuple_outputs_equivalent(expected_max_difference=3E-3 )
def _a ( self : Tuple ) -> Union[str, Any]:
"""simple docstring"""
super().test_inference_batch_single_identical(expected_max_diff=7E-3 )
def _a ( self : Optional[int] ) -> str:
"""simple docstring"""
super().test_pt_np_pil_outputs_equivalent(expected_max_diff=3E-3 )
def _a ( self : Tuple ) -> Optional[Any]:
"""simple docstring"""
super().test_save_load_local(expected_max_difference=3E-3 )
def _a ( self : int ) -> Union[str, Any]:
"""simple docstring"""
super().test_save_load_optional_components(expected_max_difference=3E-3 )
def _a ( self : Optional[int] ) -> Optional[Any]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = [
'''DDIMScheduler''',
'''DDPMScheduler''',
'''PNDMScheduler''',
'''HeunDiscreteScheduler''',
'''EulerAncestralDiscreteScheduler''',
'''KDPM2DiscreteScheduler''',
'''KDPM2AncestralDiscreteScheduler''',
'''DPMSolverSDEScheduler''',
]
__SCREAMING_SNAKE_CASE = self.get_dummy_components()
__SCREAMING_SNAKE_CASE = self.pipeline_class(**__SCREAMING_SNAKE_CASE )
# make sure that PNDM does not need warm-up
pipe.scheduler.register_to_config(skip_prk_steps=__SCREAMING_SNAKE_CASE )
pipe.to(__SCREAMING_SNAKE_CASE )
pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = self.get_dummy_inputs(__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = 2
__SCREAMING_SNAKE_CASE = []
for scheduler_enum in KarrasDiffusionSchedulers:
if scheduler_enum.name in skip_schedulers:
# no sigma schedulers are not supported
# no schedulers
continue
__SCREAMING_SNAKE_CASE = getattr(__SCREAMING_SNAKE_CASE , scheduler_enum.name )
__SCREAMING_SNAKE_CASE = scheduler_cls.from_config(pipe.scheduler.config )
__SCREAMING_SNAKE_CASE = pipe(**__SCREAMING_SNAKE_CASE )[0]
outputs.append(__SCREAMING_SNAKE_CASE )
assert check_same_shape(__SCREAMING_SNAKE_CASE )
@require_torch_gpu
@slow
class A__( unittest.TestCase ):
def _a ( self : Tuple ) -> Any:
"""simple docstring"""
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def _a ( self : List[Any] ) -> Optional[Any]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = torch.manual_seed(33 )
__SCREAMING_SNAKE_CASE = StableDiffusionPipeline.from_pretrained('''CompVis/stable-diffusion-v1-4''' , torch_dtype=torch.floataa )
pipe.to('''cuda''' )
__SCREAMING_SNAKE_CASE = StableDiffusionLatentUpscalePipeline.from_pretrained(
'''stabilityai/sd-x2-latent-upscaler''' , torch_dtype=torch.floataa )
upscaler.to('''cuda''' )
__SCREAMING_SNAKE_CASE = '''a photo of an astronaut high resolution, unreal engine, ultra realistic'''
__SCREAMING_SNAKE_CASE = pipe(__SCREAMING_SNAKE_CASE , generator=__SCREAMING_SNAKE_CASE , output_type='''latent''' ).images
__SCREAMING_SNAKE_CASE = upscaler(
prompt=__SCREAMING_SNAKE_CASE , image=__SCREAMING_SNAKE_CASE , num_inference_steps=20 , guidance_scale=0 , generator=__SCREAMING_SNAKE_CASE , output_type='''np''' , ).images[0]
__SCREAMING_SNAKE_CASE = load_numpy(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/astronaut_1024.npy''' )
assert np.abs((expected_image - image).mean() ) < 5E-2
def _a ( self : List[str] ) -> Any:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = torch.manual_seed(33 )
__SCREAMING_SNAKE_CASE = StableDiffusionLatentUpscalePipeline.from_pretrained(
'''stabilityai/sd-x2-latent-upscaler''' , torch_dtype=torch.floataa )
upscaler.to('''cuda''' )
__SCREAMING_SNAKE_CASE = '''the temple of fire by Ross Tran and Gerardo Dottori, oil on canvas'''
__SCREAMING_SNAKE_CASE = load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/fire_temple_512.png''' )
__SCREAMING_SNAKE_CASE = upscaler(
prompt=__SCREAMING_SNAKE_CASE , image=__SCREAMING_SNAKE_CASE , num_inference_steps=20 , guidance_scale=0 , generator=__SCREAMING_SNAKE_CASE , output_type='''np''' , ).images[0]
__SCREAMING_SNAKE_CASE = load_numpy(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/fire_temple_1024.npy''' )
assert np.abs((expected_image - image).max() ) < 5E-2
| 690 |
"""simple docstring"""
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import re
from ..models.auto import AutoProcessor
from ..models.vision_encoder_decoder import VisionEncoderDecoderModel
from ..utils import is_vision_available
from .base import PipelineTool
if is_vision_available():
from PIL import Image
class A__( __magic_name__ ):
lowerCAmelCase = '''naver-clova-ix/donut-base-finetuned-docvqa'''
lowerCAmelCase = (
'''This is a tool that answers a question about an document (pdf). It takes an input named `document` which '''
'''should be the document containing the information, as well as a `question` that is the question about the '''
'''document. It returns a text that contains the answer to the question.'''
)
lowerCAmelCase = '''document_qa'''
lowerCAmelCase = AutoProcessor
lowerCAmelCase = VisionEncoderDecoderModel
lowerCAmelCase = ['''image''', '''text''']
lowerCAmelCase = ['''text''']
def __init__( self : str , *__SCREAMING_SNAKE_CASE : List[str] , **__SCREAMING_SNAKE_CASE : List[Any] ) -> Any:
"""simple docstring"""
if not is_vision_available():
raise ValueError('''Pillow must be installed to use the DocumentQuestionAnsweringTool.''' )
super().__init__(*__SCREAMING_SNAKE_CASE , **__SCREAMING_SNAKE_CASE )
def _a ( self : Union[str, Any] , __SCREAMING_SNAKE_CASE : "Image" , __SCREAMING_SNAKE_CASE : str ) -> str:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = '''<s_docvqa><s_question>{user_input}</s_question><s_answer>'''
__SCREAMING_SNAKE_CASE = task_prompt.replace('''{user_input}''' , __SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = self.pre_processor.tokenizer(
__SCREAMING_SNAKE_CASE , add_special_tokens=__SCREAMING_SNAKE_CASE , return_tensors='''pt''' ).input_ids
__SCREAMING_SNAKE_CASE = self.pre_processor(__SCREAMING_SNAKE_CASE , return_tensors='''pt''' ).pixel_values
return {"decoder_input_ids": decoder_input_ids, "pixel_values": pixel_values}
def _a ( self : Union[str, Any] , __SCREAMING_SNAKE_CASE : Optional[Any] ) -> Tuple:
"""simple docstring"""
return self.model.generate(
inputs['''pixel_values'''].to(self.device ) , decoder_input_ids=inputs['''decoder_input_ids'''].to(self.device ) , max_length=self.model.decoder.config.max_position_embeddings , early_stopping=__SCREAMING_SNAKE_CASE , pad_token_id=self.pre_processor.tokenizer.pad_token_id , eos_token_id=self.pre_processor.tokenizer.eos_token_id , use_cache=__SCREAMING_SNAKE_CASE , num_beams=1 , bad_words_ids=[[self.pre_processor.tokenizer.unk_token_id]] , return_dict_in_generate=__SCREAMING_SNAKE_CASE , ).sequences
def _a ( self : Tuple , __SCREAMING_SNAKE_CASE : Tuple ) -> List[str]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = self.pre_processor.batch_decode(__SCREAMING_SNAKE_CASE )[0]
__SCREAMING_SNAKE_CASE = sequence.replace(self.pre_processor.tokenizer.eos_token , '''''' )
__SCREAMING_SNAKE_CASE = sequence.replace(self.pre_processor.tokenizer.pad_token , '''''' )
__SCREAMING_SNAKE_CASE = re.sub(r'''<.*?>''' , '''''' , __SCREAMING_SNAKE_CASE , count=1 ).strip() # remove first task start token
__SCREAMING_SNAKE_CASE = self.pre_processor.tokenajson(__SCREAMING_SNAKE_CASE )
return sequence["answer"]
| 690 | 1 |
"""simple docstring"""
import numpy as np
import torch
from torch.utils.data import Dataset, IterableDataset
from ..utils.generic import ModelOutput
class A__( __magic_name__ ):
def __init__( self : Optional[Any] , __SCREAMING_SNAKE_CASE : Dict , __SCREAMING_SNAKE_CASE : List[Any] , __SCREAMING_SNAKE_CASE : List[Any] ) -> List[Any]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = dataset
__SCREAMING_SNAKE_CASE = process
__SCREAMING_SNAKE_CASE = params
def __len__( self : Optional[int] ) -> str:
"""simple docstring"""
return len(self.dataset )
def __getitem__( self : Tuple , __SCREAMING_SNAKE_CASE : Any ) -> int:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = self.dataset[i]
__SCREAMING_SNAKE_CASE = self.process(__SCREAMING_SNAKE_CASE , **self.params )
return processed
class A__( __magic_name__ ):
def __init__( self : Dict , __SCREAMING_SNAKE_CASE : str , __SCREAMING_SNAKE_CASE : Tuple , __SCREAMING_SNAKE_CASE : Optional[int] , __SCREAMING_SNAKE_CASE : Dict=None ) -> Optional[int]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = loader
__SCREAMING_SNAKE_CASE = infer
__SCREAMING_SNAKE_CASE = params
if loader_batch_size == 1:
# Let's spare some time by deactivating altogether
__SCREAMING_SNAKE_CASE = None
__SCREAMING_SNAKE_CASE = loader_batch_size
# Internal bookkeeping
__SCREAMING_SNAKE_CASE = None
__SCREAMING_SNAKE_CASE = None
def __len__( self : Optional[Any] ) -> int:
"""simple docstring"""
return len(self.loader )
def __iter__( self : Tuple ) -> str:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = iter(self.loader )
return self
def _a ( self : Any ) -> Any:
"""simple docstring"""
if isinstance(self._loader_batch_data , torch.Tensor ):
# Batch data is simple tensor, just fetch the slice
__SCREAMING_SNAKE_CASE = self._loader_batch_data[self._loader_batch_index]
else:
# Batch data is assumed to be BaseModelOutput (or dict)
__SCREAMING_SNAKE_CASE = {}
for k, element in self._loader_batch_data.items():
if isinstance(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
# Convert ModelOutput to tuple first
__SCREAMING_SNAKE_CASE = element.to_tuple()
if isinstance(element[0] , torch.Tensor ):
__SCREAMING_SNAKE_CASE = tuple(el[self._loader_batch_index].unsqueeze(0 ) for el in element )
elif isinstance(element[0] , np.ndarray ):
__SCREAMING_SNAKE_CASE = tuple(np.expand_dims(el[self._loader_batch_index] , 0 ) for el in element )
continue
if k in {"hidden_states", "past_key_values", "attentions"} and isinstance(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
# Those are stored as lists of tensors so need specific unbatching.
if isinstance(element[0] , torch.Tensor ):
__SCREAMING_SNAKE_CASE = tuple(el[self._loader_batch_index].unsqueeze(0 ) for el in element )
elif isinstance(element[0] , np.ndarray ):
__SCREAMING_SNAKE_CASE = tuple(np.expand_dims(el[self._loader_batch_index] , 0 ) for el in element )
continue
if element is None:
# This can happen for optional data that get passed around
__SCREAMING_SNAKE_CASE = None
elif isinstance(element[self._loader_batch_index] , torch.Tensor ):
# Take correct batch data, but make it looked like batch_size=1
# For compatibility with other methods within transformers
__SCREAMING_SNAKE_CASE = element[self._loader_batch_index].unsqueeze(0 )
elif isinstance(element[self._loader_batch_index] , np.ndarray ):
# Take correct batch data, but make it looked like batch_size=1
# For compatibility with other methods within transformers
__SCREAMING_SNAKE_CASE = np.expand_dims(element[self._loader_batch_index] , 0 )
else:
# This is typically a list, so no need to `unsqueeze`.
__SCREAMING_SNAKE_CASE = element[self._loader_batch_index]
# Recreate the element by reusing the original class to make it look
# batch_size=1
__SCREAMING_SNAKE_CASE = self._loader_batch_data.__class__(__SCREAMING_SNAKE_CASE )
self._loader_batch_index += 1
return result
def _a ( self : Union[str, Any] ) -> Tuple:
"""simple docstring"""
if self._loader_batch_index is not None and self._loader_batch_index < self.loader_batch_size:
# We are currently unrolling a batch so we just need to return
# the current item within a batch
return self.loader_batch_item()
# We're out of items within a batch
__SCREAMING_SNAKE_CASE = next(self.iterator )
__SCREAMING_SNAKE_CASE = self.infer(__SCREAMING_SNAKE_CASE , **self.params )
# We now have a batch of "inferred things".
if self.loader_batch_size is not None:
# Try to infer the size of the batch
if isinstance(__SCREAMING_SNAKE_CASE , torch.Tensor ):
__SCREAMING_SNAKE_CASE = processed
else:
__SCREAMING_SNAKE_CASE = list(processed.keys() )[0]
__SCREAMING_SNAKE_CASE = processed[key]
if isinstance(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
__SCREAMING_SNAKE_CASE = len(__SCREAMING_SNAKE_CASE )
else:
__SCREAMING_SNAKE_CASE = first_tensor.shape[0]
if 0 < observed_batch_size < self.loader_batch_size:
# could be last batch so we can't unroll as many
# elements.
__SCREAMING_SNAKE_CASE = observed_batch_size
# Setting internal index to unwrap the batch
__SCREAMING_SNAKE_CASE = processed
__SCREAMING_SNAKE_CASE = 0
return self.loader_batch_item()
else:
# We're not unrolling batches
return processed
class A__( __magic_name__ ):
def __init__( self : Optional[int] , __SCREAMING_SNAKE_CASE : Dict , __SCREAMING_SNAKE_CASE : Tuple , __SCREAMING_SNAKE_CASE : Union[str, Any] , __SCREAMING_SNAKE_CASE : Any=None ) -> Optional[int]:
"""simple docstring"""
super().__init__(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
def __iter__( self : Dict ) -> Tuple:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = iter(self.loader )
__SCREAMING_SNAKE_CASE = None
return self
def _a ( self : List[str] ) -> Union[str, Any]:
"""simple docstring"""
if self.subiterator is None:
__SCREAMING_SNAKE_CASE = self.infer(next(self.iterator ) , **self.params )
try:
# Try to return next item
__SCREAMING_SNAKE_CASE = next(self.subiterator )
except StopIteration:
# When a preprocess iterator ends, we can start lookig at the next item
# ChunkIterator will keep feeding until ALL elements of iterator
# all have created their subiterator and have been iterating against.
#
# Another way to look at it, is we're basically flattening lists of lists
# into a single list, but with generators
__SCREAMING_SNAKE_CASE = self.infer(next(self.iterator ) , **self.params )
__SCREAMING_SNAKE_CASE = next(self.subiterator )
return processed
class A__( __magic_name__ ):
def __iter__( self : Tuple ) -> Optional[Any]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = iter(self.loader )
return self
def _a ( self : List[str] ) -> Any:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = False
__SCREAMING_SNAKE_CASE = []
if self._loader_batch_index is not None and self._loader_batch_index < self.loader_batch_size:
while self._loader_batch_index < self.loader_batch_size:
__SCREAMING_SNAKE_CASE = self.loader_batch_item()
__SCREAMING_SNAKE_CASE = item.pop('''is_last''' )
accumulator.append(__SCREAMING_SNAKE_CASE )
if is_last:
return accumulator
while not is_last:
__SCREAMING_SNAKE_CASE = self.infer(next(self.iterator ) , **self.params )
if self.loader_batch_size is not None:
if isinstance(__SCREAMING_SNAKE_CASE , torch.Tensor ):
__SCREAMING_SNAKE_CASE = processed
else:
__SCREAMING_SNAKE_CASE = list(processed.keys() )[0]
__SCREAMING_SNAKE_CASE = processed[key]
if isinstance(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
__SCREAMING_SNAKE_CASE = len(__SCREAMING_SNAKE_CASE )
else:
__SCREAMING_SNAKE_CASE = first_tensor.shape[0]
if 0 < observed_batch_size < self.loader_batch_size:
# could be last batch so we can't unroll as many
# elements.
__SCREAMING_SNAKE_CASE = observed_batch_size
__SCREAMING_SNAKE_CASE = processed
__SCREAMING_SNAKE_CASE = 0
while self._loader_batch_index < self.loader_batch_size:
__SCREAMING_SNAKE_CASE = self.loader_batch_item()
__SCREAMING_SNAKE_CASE = item.pop('''is_last''' )
accumulator.append(__SCREAMING_SNAKE_CASE )
if is_last:
return accumulator
else:
__SCREAMING_SNAKE_CASE = processed
__SCREAMING_SNAKE_CASE = item.pop('''is_last''' )
accumulator.append(__SCREAMING_SNAKE_CASE )
return accumulator
class A__( __magic_name__ ):
def __init__( self : str , __SCREAMING_SNAKE_CASE : Dataset , __SCREAMING_SNAKE_CASE : str ) -> Any:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = dataset
__SCREAMING_SNAKE_CASE = key
def __len__( self : List[Any] ) -> Any:
"""simple docstring"""
return len(self.dataset )
def __getitem__( self : Optional[Any] , __SCREAMING_SNAKE_CASE : Union[str, Any] ) -> str:
"""simple docstring"""
return self.dataset[i][self.key]
class A__( __magic_name__ ):
def __init__( self : str , __SCREAMING_SNAKE_CASE : Dataset , __SCREAMING_SNAKE_CASE : str , __SCREAMING_SNAKE_CASE : str ) -> List[Any]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = dataset
__SCREAMING_SNAKE_CASE = keya
__SCREAMING_SNAKE_CASE = keya
def __len__( self : Optional[int] ) -> str:
"""simple docstring"""
return len(self.dataset )
def __getitem__( self : List[str] , __SCREAMING_SNAKE_CASE : Any ) -> Any:
"""simple docstring"""
return {"text": self.dataset[i][self.keya], "text_pair": self.dataset[i][self.keya]}
| 690 |
"""simple docstring"""
import unittest
import numpy as np
import torch
from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class A__( unittest.TestCase ):
@property
def _a ( self : Optional[Any] ) -> Tuple:
"""simple docstring"""
torch.manual_seed(0 )
__SCREAMING_SNAKE_CASE = UNetaDModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=3 , out_channels=3 , down_block_types=('''DownBlock2D''', '''AttnDownBlock2D''') , up_block_types=('''AttnUpBlock2D''', '''UpBlock2D''') , )
return model
def _a ( self : str ) -> Any:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = self.dummy_uncond_unet
__SCREAMING_SNAKE_CASE = KarrasVeScheduler()
__SCREAMING_SNAKE_CASE = KarrasVePipeline(unet=__SCREAMING_SNAKE_CASE , scheduler=__SCREAMING_SNAKE_CASE )
pipe.to(__SCREAMING_SNAKE_CASE )
pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = torch.manual_seed(0 )
__SCREAMING_SNAKE_CASE = pipe(num_inference_steps=2 , generator=__SCREAMING_SNAKE_CASE , output_type='''numpy''' ).images
__SCREAMING_SNAKE_CASE = torch.manual_seed(0 )
__SCREAMING_SNAKE_CASE = pipe(num_inference_steps=2 , generator=__SCREAMING_SNAKE_CASE , output_type='''numpy''' , return_dict=__SCREAMING_SNAKE_CASE )[0]
__SCREAMING_SNAKE_CASE = image[0, -3:, -3:, -1]
__SCREAMING_SNAKE_CASE = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
__SCREAMING_SNAKE_CASE = np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1E-2
@slow
@require_torch
class A__( unittest.TestCase ):
def _a ( self : Any ) -> str:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = '''google/ncsnpp-celebahq-256'''
__SCREAMING_SNAKE_CASE = UNetaDModel.from_pretrained(__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = KarrasVeScheduler()
__SCREAMING_SNAKE_CASE = KarrasVePipeline(unet=__SCREAMING_SNAKE_CASE , scheduler=__SCREAMING_SNAKE_CASE )
pipe.to(__SCREAMING_SNAKE_CASE )
pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = torch.manual_seed(0 )
__SCREAMING_SNAKE_CASE = pipe(num_inference_steps=20 , generator=__SCREAMING_SNAKE_CASE , output_type='''numpy''' ).images
__SCREAMING_SNAKE_CASE = image[0, -3:, -3:, -1]
assert image.shape == (1, 2_56, 2_56, 3)
__SCREAMING_SNAKE_CASE = np.array([0.5_78, 0.58_11, 0.59_24, 0.58_09, 0.5_87, 0.58_86, 0.58_61, 0.58_02, 0.5_86] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
| 690 | 1 |
"""simple docstring"""
from ....configuration_utils import PretrainedConfig
from ....utils import logging
lowerCAmelCase__ =logging.get_logger(__name__)
lowerCAmelCase__ ={
"Visual-Attention-Network/van-base": (
"https://huggingface.co/Visual-Attention-Network/van-base/blob/main/config.json"
),
}
class A__( __magic_name__ ):
lowerCAmelCase = '''van'''
def __init__( self : int , __SCREAMING_SNAKE_CASE : Optional[Any]=2_24 , __SCREAMING_SNAKE_CASE : Union[str, Any]=3 , __SCREAMING_SNAKE_CASE : Tuple=[7, 3, 3, 3] , __SCREAMING_SNAKE_CASE : Optional[int]=[4, 2, 2, 2] , __SCREAMING_SNAKE_CASE : str=[64, 1_28, 3_20, 5_12] , __SCREAMING_SNAKE_CASE : Optional[Any]=[3, 3, 12, 3] , __SCREAMING_SNAKE_CASE : Dict=[8, 8, 4, 4] , __SCREAMING_SNAKE_CASE : Any="gelu" , __SCREAMING_SNAKE_CASE : Tuple=0.02 , __SCREAMING_SNAKE_CASE : Dict=1E-6 , __SCREAMING_SNAKE_CASE : Any=1E-2 , __SCREAMING_SNAKE_CASE : str=0.0 , __SCREAMING_SNAKE_CASE : Union[str, Any]=0.0 , **__SCREAMING_SNAKE_CASE : str , ) -> List[str]:
"""simple docstring"""
super().__init__(**__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = image_size
__SCREAMING_SNAKE_CASE = num_channels
__SCREAMING_SNAKE_CASE = patch_sizes
__SCREAMING_SNAKE_CASE = strides
__SCREAMING_SNAKE_CASE = hidden_sizes
__SCREAMING_SNAKE_CASE = depths
__SCREAMING_SNAKE_CASE = mlp_ratios
__SCREAMING_SNAKE_CASE = hidden_act
__SCREAMING_SNAKE_CASE = initializer_range
__SCREAMING_SNAKE_CASE = layer_norm_eps
__SCREAMING_SNAKE_CASE = layer_scale_init_value
__SCREAMING_SNAKE_CASE = drop_path_rate
__SCREAMING_SNAKE_CASE = dropout_rate
| 690 |
"""simple docstring"""
import os
import re
import unicodedata
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import is_torch_available, logging
if is_torch_available():
import torch
if TYPE_CHECKING:
from transformers.pipelines.conversational import Conversation
lowerCAmelCase__ =logging.get_logger(__name__)
lowerCAmelCase__ ={"vocab_file": "spiece.model"}
lowerCAmelCase__ ={
"vocab_file": {
"AI-Sweden/gpt-sw3-126m": "https://huggingface.co/AI-Sweden/gpt-sw3-126m/resolve/main/spiece.model",
"AI-Sweden/gpt-sw3-350m": "https://huggingface.co/AI-Sweden/gpt-sw3-350m/resolve/main/spiece.model",
"AI-Sweden/gpt-sw3-1.6b": "https://huggingface.co/AI-Sweden/gpt-sw3-1.6b/resolve/main/spiece.model",
"AI-Sweden/gpt-sw3-6.7b": "https://huggingface.co/AI-Sweden/gpt-sw3-6.7b/resolve/main/spiece.model",
"AI-Sweden/gpt-sw3-20b": "https://huggingface.co/AI-Sweden/gpt-sw3-20b/resolve/main/spiece.model",
}
}
lowerCAmelCase__ ={
"AI-Sweden/gpt-sw3-126m": 2_048,
"AI-Sweden/gpt-sw3-350m": 2_048,
"AI-Sweden/gpt-sw3-1.6b": 2_048,
"AI-Sweden/gpt-sw3-6.7b": 2_048,
"AI-Sweden/gpt-sw3-20b": 2_048,
}
class A__( __magic_name__ ):
lowerCAmelCase = VOCAB_FILES_NAMES
lowerCAmelCase = PRETRAINED_VOCAB_FILES_MAP
lowerCAmelCase = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowerCAmelCase = ['''input_ids''', '''attention_mask''']
def __init__( self : int , __SCREAMING_SNAKE_CASE : int , __SCREAMING_SNAKE_CASE : str=False , __SCREAMING_SNAKE_CASE : str=False , __SCREAMING_SNAKE_CASE : Optional[int]=False , __SCREAMING_SNAKE_CASE : Union[str, Any]=None , __SCREAMING_SNAKE_CASE : List[Any]=None , __SCREAMING_SNAKE_CASE : Any=None , __SCREAMING_SNAKE_CASE : List[Any]=None , __SCREAMING_SNAKE_CASE : Optional[Dict[str, Any]] = None , **__SCREAMING_SNAKE_CASE : Dict , ) -> None:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = {} if sp_model_kwargs is None else sp_model_kwargs
__SCREAMING_SNAKE_CASE = kwargs.get('''name_or_path''' )
if name_or_path is None:
logger.warning(
'''name_or_path not provided, will work for all GPTSw3 models except gpt-sw3-7b,'''
''' you are testing the model, this can safely be ignored''' )
__SCREAMING_SNAKE_CASE = '''None'''
# Default definitions for our 2 tokenizer versions, with None-checks to enable proper testing
__SCREAMING_SNAKE_CASE = '''<|endoftext|>''' if eos_token is None else eos_token
__SCREAMING_SNAKE_CASE = '''<unk>''' if unk_token is None else unk_token
if "gpt-sw3-7b" in name_or_path:
__SCREAMING_SNAKE_CASE = unk_token if pad_token is None else pad_token
__SCREAMING_SNAKE_CASE = eos_token if bos_token is None else bos_token
else:
__SCREAMING_SNAKE_CASE = '''<pad>''' if pad_token is None else pad_token
__SCREAMING_SNAKE_CASE = '''<s>''' if bos_token is None else bos_token
super().__init__(
do_lower_case=__SCREAMING_SNAKE_CASE , remove_space=__SCREAMING_SNAKE_CASE , keep_accents=__SCREAMING_SNAKE_CASE , bos_token=__SCREAMING_SNAKE_CASE , eos_token=__SCREAMING_SNAKE_CASE , unk_token=__SCREAMING_SNAKE_CASE , pad_token=__SCREAMING_SNAKE_CASE , sp_model_kwargs=self.sp_model_kwargs , **__SCREAMING_SNAKE_CASE , )
__SCREAMING_SNAKE_CASE = do_lower_case
__SCREAMING_SNAKE_CASE = remove_space
__SCREAMING_SNAKE_CASE = keep_accents
__SCREAMING_SNAKE_CASE = vocab_file
__SCREAMING_SNAKE_CASE = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(__SCREAMING_SNAKE_CASE )
# Used for whitespace normalization in input texts
# fmt : off
__SCREAMING_SNAKE_CASE = {''' ''', ''' ''', ''' ''', ''' ''', ''' ''', ''' ''', ''' ''', ''' ''', ''' ''', ''' ''', '''''', ''''''}
# fmt : on
# Regular expression to remove non-printing characters (e.g. some unicode control chars) in preprocessing
__SCREAMING_SNAKE_CASE = re.compile(
f"""[{"".join(map(__SCREAMING_SNAKE_CASE , list(range(0 , 9 ) ) + list(range(11 , 32 ) ) + list(range(1_27 , 1_60 ) ) + [1_60, 1_73, 82_03] ) )}]""" )
def __getstate__( self : List[str] ) -> Optional[Any]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = self.__dict__.copy()
__SCREAMING_SNAKE_CASE = None
return state
def __setstate__( self : int , __SCREAMING_SNAKE_CASE : Optional[int] ) -> int:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = d
# for backward compatibility
if not hasattr(self , '''sp_model_kwargs''' ):
__SCREAMING_SNAKE_CASE = {}
__SCREAMING_SNAKE_CASE = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.vocab_file )
@property
# Copied from transformers.models.albert.tokenization_albert.AlbertTokenizer.vocab_size
def _a ( self : Optional[Any] ) -> int:
"""simple docstring"""
return len(self.sp_model )
def _a ( self : List[str] , __SCREAMING_SNAKE_CASE : str ) -> str:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = self.non_printing_characters_re.sub('''''' , __SCREAMING_SNAKE_CASE )
# Normalize whitespaces
__SCREAMING_SNAKE_CASE = ''''''.join([char if char not in self.whitespaces else ''' ''' for char in text] )
# NFC Unicode normalization
__SCREAMING_SNAKE_CASE = unicodedata.normalize('''NFC''' , __SCREAMING_SNAKE_CASE )
return text
def _a ( self : Union[str, Any] , __SCREAMING_SNAKE_CASE : str , **__SCREAMING_SNAKE_CASE : Optional[int] ) -> List[str]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = self.preprocess_text(__SCREAMING_SNAKE_CASE )
return self.sp_model.encode(__SCREAMING_SNAKE_CASE , out_type=__SCREAMING_SNAKE_CASE )
def _a ( self : Tuple , __SCREAMING_SNAKE_CASE : str ) -> int:
"""simple docstring"""
return self.sp_model.PieceToId(__SCREAMING_SNAKE_CASE )
def _a ( self : List[str] , __SCREAMING_SNAKE_CASE : int ) -> str:
"""simple docstring"""
return self.sp_model.IdToPiece(__SCREAMING_SNAKE_CASE )
@staticmethod
def _a ( __SCREAMING_SNAKE_CASE : str ) -> str:
"""simple docstring"""
return out_string
def _a ( self : Union[str, Any] , __SCREAMING_SNAKE_CASE : List[str] ) -> str:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = []
__SCREAMING_SNAKE_CASE = ''''''
__SCREAMING_SNAKE_CASE = False
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
# TODO: Check if this is needed, as it ensures that decode(encode(doc)) != doc by adding extra whitespace in the decoded document
if not prev_is_special:
out_string += " "
out_string += self.sp_model.decode(__SCREAMING_SNAKE_CASE ) + token
__SCREAMING_SNAKE_CASE = True
__SCREAMING_SNAKE_CASE = []
else:
current_sub_tokens.append(__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = False
out_string += self.sp_model.decode(__SCREAMING_SNAKE_CASE )
return out_string
def _a ( self : Union[str, Any] ) -> Dict[str, int]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = {self.convert_ids_to_tokens(__SCREAMING_SNAKE_CASE ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def _a ( self : List[Any] , __SCREAMING_SNAKE_CASE : str , __SCREAMING_SNAKE_CASE : Optional[str] = None ) -> Tuple[str]:
"""simple docstring"""
if not os.path.isdir(__SCREAMING_SNAKE_CASE ):
logger.error(f"""Vocabulary path ({save_directory}) should be a directory""" )
return
__SCREAMING_SNAKE_CASE = os.path.join(
__SCREAMING_SNAKE_CASE , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(__SCREAMING_SNAKE_CASE ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file , __SCREAMING_SNAKE_CASE )
elif not os.path.isfile(self.vocab_file ):
with open(__SCREAMING_SNAKE_CASE , '''wb''' ) as fi:
__SCREAMING_SNAKE_CASE = self.sp_model.serialized_model_proto()
fi.write(__SCREAMING_SNAKE_CASE )
return (out_vocab_file,)
def _a ( self : Optional[Any] , __SCREAMING_SNAKE_CASE : Union[str, List[str]] , __SCREAMING_SNAKE_CASE : Union[str, bool] = False ) -> Union[List[int], List[List[int]], "torch.Tensor"]:
"""simple docstring"""
if isinstance(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
__SCREAMING_SNAKE_CASE = self.preprocess_text(__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = self.sp_model.encode(__SCREAMING_SNAKE_CASE )
else:
__SCREAMING_SNAKE_CASE = [self.preprocess_text(__SCREAMING_SNAKE_CASE ) for t in text]
__SCREAMING_SNAKE_CASE = self.sp_model.encode(__SCREAMING_SNAKE_CASE )
if return_tensors is True or return_tensors == "pt":
__SCREAMING_SNAKE_CASE = torch.tensor(__SCREAMING_SNAKE_CASE )
return token_ids
def _a ( self : Any , __SCREAMING_SNAKE_CASE : Union[int, List[int]] ) -> str:
"""simple docstring"""
return self.sp_model.decode(__SCREAMING_SNAKE_CASE )
def _a ( self : List[str] , __SCREAMING_SNAKE_CASE : "Conversation" ) -> List[int]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = [f"""User: {text}""" if is_user else f"""Bot: {text}""" for is_user, text in conversation.iter_texts()]
__SCREAMING_SNAKE_CASE = (
f"""{self.eos_token}{self.bos_token}""" + f"""{self.bos_token}""".join(__SCREAMING_SNAKE_CASE ) + f"""{self.bos_token}Bot:"""
)
return self.encode(text=__SCREAMING_SNAKE_CASE )
| 690 | 1 |
"""simple docstring"""
import unittest
from transformers import DebertaVaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
DebertaVaForMaskedLM,
DebertaVaForMultipleChoice,
DebertaVaForQuestionAnswering,
DebertaVaForSequenceClassification,
DebertaVaForTokenClassification,
DebertaVaModel,
)
from transformers.models.deberta_va.modeling_deberta_va import DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST
class A__( __magic_name__ ):
def __init__( self : Dict , __SCREAMING_SNAKE_CASE : int , __SCREAMING_SNAKE_CASE : Union[str, Any]=13 , __SCREAMING_SNAKE_CASE : Tuple=7 , __SCREAMING_SNAKE_CASE : int=True , __SCREAMING_SNAKE_CASE : Union[str, Any]=True , __SCREAMING_SNAKE_CASE : Dict=True , __SCREAMING_SNAKE_CASE : List[Any]=True , __SCREAMING_SNAKE_CASE : str=99 , __SCREAMING_SNAKE_CASE : Tuple=32 , __SCREAMING_SNAKE_CASE : Tuple=5 , __SCREAMING_SNAKE_CASE : List[Any]=4 , __SCREAMING_SNAKE_CASE : Union[str, Any]=37 , __SCREAMING_SNAKE_CASE : Dict="gelu" , __SCREAMING_SNAKE_CASE : List[str]=0.1 , __SCREAMING_SNAKE_CASE : List[str]=0.1 , __SCREAMING_SNAKE_CASE : List[str]=5_12 , __SCREAMING_SNAKE_CASE : Optional[int]=16 , __SCREAMING_SNAKE_CASE : str=2 , __SCREAMING_SNAKE_CASE : List[Any]=0.02 , __SCREAMING_SNAKE_CASE : int=False , __SCREAMING_SNAKE_CASE : str=True , __SCREAMING_SNAKE_CASE : Any="None" , __SCREAMING_SNAKE_CASE : Dict=3 , __SCREAMING_SNAKE_CASE : Tuple=4 , __SCREAMING_SNAKE_CASE : Any=None , ) -> Tuple:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = parent
__SCREAMING_SNAKE_CASE = batch_size
__SCREAMING_SNAKE_CASE = seq_length
__SCREAMING_SNAKE_CASE = is_training
__SCREAMING_SNAKE_CASE = use_input_mask
__SCREAMING_SNAKE_CASE = use_token_type_ids
__SCREAMING_SNAKE_CASE = use_labels
__SCREAMING_SNAKE_CASE = vocab_size
__SCREAMING_SNAKE_CASE = hidden_size
__SCREAMING_SNAKE_CASE = num_hidden_layers
__SCREAMING_SNAKE_CASE = num_attention_heads
__SCREAMING_SNAKE_CASE = intermediate_size
__SCREAMING_SNAKE_CASE = hidden_act
__SCREAMING_SNAKE_CASE = hidden_dropout_prob
__SCREAMING_SNAKE_CASE = attention_probs_dropout_prob
__SCREAMING_SNAKE_CASE = max_position_embeddings
__SCREAMING_SNAKE_CASE = type_vocab_size
__SCREAMING_SNAKE_CASE = type_sequence_label_size
__SCREAMING_SNAKE_CASE = initializer_range
__SCREAMING_SNAKE_CASE = num_labels
__SCREAMING_SNAKE_CASE = num_choices
__SCREAMING_SNAKE_CASE = relative_attention
__SCREAMING_SNAKE_CASE = position_biased_input
__SCREAMING_SNAKE_CASE = pos_att_type
__SCREAMING_SNAKE_CASE = scope
def _a ( self : Union[str, Any] ) -> Dict:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
__SCREAMING_SNAKE_CASE = None
if self.use_input_mask:
__SCREAMING_SNAKE_CASE = ids_tensor([self.batch_size, self.seq_length] , vocab_size=2 )
__SCREAMING_SNAKE_CASE = None
if self.use_token_type_ids:
__SCREAMING_SNAKE_CASE = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
__SCREAMING_SNAKE_CASE = None
__SCREAMING_SNAKE_CASE = None
__SCREAMING_SNAKE_CASE = None
if self.use_labels:
__SCREAMING_SNAKE_CASE = ids_tensor([self.batch_size] , self.type_sequence_label_size )
__SCREAMING_SNAKE_CASE = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
__SCREAMING_SNAKE_CASE = ids_tensor([self.batch_size] , self.num_choices )
__SCREAMING_SNAKE_CASE = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def _a ( self : str ) -> List[Any]:
"""simple docstring"""
return DebertaVaConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , initializer_range=self.initializer_range , relative_attention=self.relative_attention , position_biased_input=self.position_biased_input , pos_att_type=self.pos_att_type , )
def _a ( self : Optional[int] , __SCREAMING_SNAKE_CASE : Any ) -> str:
"""simple docstring"""
self.parent.assertListEqual(list(result.loss.size() ) , [] )
def _a ( self : Optional[Any] , __SCREAMING_SNAKE_CASE : int , __SCREAMING_SNAKE_CASE : int , __SCREAMING_SNAKE_CASE : Any , __SCREAMING_SNAKE_CASE : int , __SCREAMING_SNAKE_CASE : Any , __SCREAMING_SNAKE_CASE : Dict , __SCREAMING_SNAKE_CASE : List[str] ) -> List[str]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = DebertaVaModel(config=__SCREAMING_SNAKE_CASE )
model.to(__SCREAMING_SNAKE_CASE )
model.eval()
__SCREAMING_SNAKE_CASE = model(__SCREAMING_SNAKE_CASE , attention_mask=__SCREAMING_SNAKE_CASE , token_type_ids=__SCREAMING_SNAKE_CASE )[0]
__SCREAMING_SNAKE_CASE = model(__SCREAMING_SNAKE_CASE , token_type_ids=__SCREAMING_SNAKE_CASE )[0]
__SCREAMING_SNAKE_CASE = model(__SCREAMING_SNAKE_CASE )[0]
self.parent.assertListEqual(list(sequence_output.size() ) , [self.batch_size, self.seq_length, self.hidden_size] )
def _a ( self : Optional[Any] , __SCREAMING_SNAKE_CASE : Any , __SCREAMING_SNAKE_CASE : List[Any] , __SCREAMING_SNAKE_CASE : Optional[Any] , __SCREAMING_SNAKE_CASE : Optional[int] , __SCREAMING_SNAKE_CASE : str , __SCREAMING_SNAKE_CASE : Optional[int] , __SCREAMING_SNAKE_CASE : Union[str, Any] ) -> str:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = DebertaVaForMaskedLM(config=__SCREAMING_SNAKE_CASE )
model.to(__SCREAMING_SNAKE_CASE )
model.eval()
__SCREAMING_SNAKE_CASE = model(__SCREAMING_SNAKE_CASE , attention_mask=__SCREAMING_SNAKE_CASE , token_type_ids=__SCREAMING_SNAKE_CASE , labels=__SCREAMING_SNAKE_CASE )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def _a ( self : Optional[Any] , __SCREAMING_SNAKE_CASE : Optional[Any] , __SCREAMING_SNAKE_CASE : Dict , __SCREAMING_SNAKE_CASE : Any , __SCREAMING_SNAKE_CASE : Any , __SCREAMING_SNAKE_CASE : Any , __SCREAMING_SNAKE_CASE : Tuple , __SCREAMING_SNAKE_CASE : List[str] ) -> Optional[int]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = self.num_labels
__SCREAMING_SNAKE_CASE = DebertaVaForSequenceClassification(__SCREAMING_SNAKE_CASE )
model.to(__SCREAMING_SNAKE_CASE )
model.eval()
__SCREAMING_SNAKE_CASE = model(__SCREAMING_SNAKE_CASE , attention_mask=__SCREAMING_SNAKE_CASE , token_type_ids=__SCREAMING_SNAKE_CASE , labels=__SCREAMING_SNAKE_CASE )
self.parent.assertListEqual(list(result.logits.size() ) , [self.batch_size, self.num_labels] )
self.check_loss_output(__SCREAMING_SNAKE_CASE )
def _a ( self : int , __SCREAMING_SNAKE_CASE : Tuple , __SCREAMING_SNAKE_CASE : List[str] , __SCREAMING_SNAKE_CASE : List[Any] , __SCREAMING_SNAKE_CASE : List[str] , __SCREAMING_SNAKE_CASE : Union[str, Any] , __SCREAMING_SNAKE_CASE : Optional[int] , __SCREAMING_SNAKE_CASE : List[str] ) -> Dict:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = self.num_labels
__SCREAMING_SNAKE_CASE = DebertaVaForTokenClassification(config=__SCREAMING_SNAKE_CASE )
model.to(__SCREAMING_SNAKE_CASE )
model.eval()
__SCREAMING_SNAKE_CASE = model(__SCREAMING_SNAKE_CASE , attention_mask=__SCREAMING_SNAKE_CASE , token_type_ids=__SCREAMING_SNAKE_CASE , labels=__SCREAMING_SNAKE_CASE )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def _a ( self : int , __SCREAMING_SNAKE_CASE : List[str] , __SCREAMING_SNAKE_CASE : Optional[int] , __SCREAMING_SNAKE_CASE : List[str] , __SCREAMING_SNAKE_CASE : List[str] , __SCREAMING_SNAKE_CASE : Optional[Any] , __SCREAMING_SNAKE_CASE : Optional[int] , __SCREAMING_SNAKE_CASE : List[str] ) -> str:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = DebertaVaForQuestionAnswering(config=__SCREAMING_SNAKE_CASE )
model.to(__SCREAMING_SNAKE_CASE )
model.eval()
__SCREAMING_SNAKE_CASE = model(
__SCREAMING_SNAKE_CASE , attention_mask=__SCREAMING_SNAKE_CASE , token_type_ids=__SCREAMING_SNAKE_CASE , start_positions=__SCREAMING_SNAKE_CASE , end_positions=__SCREAMING_SNAKE_CASE , )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def _a ( self : int , __SCREAMING_SNAKE_CASE : Tuple , __SCREAMING_SNAKE_CASE : Dict , __SCREAMING_SNAKE_CASE : Union[str, Any] , __SCREAMING_SNAKE_CASE : str , __SCREAMING_SNAKE_CASE : str , __SCREAMING_SNAKE_CASE : Union[str, Any] , __SCREAMING_SNAKE_CASE : Any ) -> List[str]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = DebertaVaForMultipleChoice(config=__SCREAMING_SNAKE_CASE )
model.to(__SCREAMING_SNAKE_CASE )
model.eval()
__SCREAMING_SNAKE_CASE = input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
__SCREAMING_SNAKE_CASE = token_type_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
__SCREAMING_SNAKE_CASE = input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
__SCREAMING_SNAKE_CASE = model(
__SCREAMING_SNAKE_CASE , attention_mask=__SCREAMING_SNAKE_CASE , token_type_ids=__SCREAMING_SNAKE_CASE , labels=__SCREAMING_SNAKE_CASE , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def _a ( self : Optional[int] ) -> Union[str, Any]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = self.prepare_config_and_inputs()
(
(
__SCREAMING_SNAKE_CASE
) , (
__SCREAMING_SNAKE_CASE
) , (
__SCREAMING_SNAKE_CASE
) , (
__SCREAMING_SNAKE_CASE
) , (
__SCREAMING_SNAKE_CASE
) , (
__SCREAMING_SNAKE_CASE
) , (
__SCREAMING_SNAKE_CASE
) ,
) = config_and_inputs
__SCREAMING_SNAKE_CASE = {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
return config, inputs_dict
@require_torch
class A__( __magic_name__ , __magic_name__ , unittest.TestCase ):
lowerCAmelCase = (
(
DebertaVaModel,
DebertaVaForMaskedLM,
DebertaVaForSequenceClassification,
DebertaVaForTokenClassification,
DebertaVaForQuestionAnswering,
DebertaVaForMultipleChoice,
)
if is_torch_available()
else ()
)
lowerCAmelCase = (
{
'''feature-extraction''': DebertaVaModel,
'''fill-mask''': DebertaVaForMaskedLM,
'''question-answering''': DebertaVaForQuestionAnswering,
'''text-classification''': DebertaVaForSequenceClassification,
'''token-classification''': DebertaVaForTokenClassification,
'''zero-shot''': DebertaVaForSequenceClassification,
}
if is_torch_available()
else {}
)
lowerCAmelCase = True
lowerCAmelCase = False
lowerCAmelCase = False
lowerCAmelCase = False
lowerCAmelCase = False
def _a ( self : Optional[int] ) -> Union[str, Any]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = DebertaVaModelTester(self )
__SCREAMING_SNAKE_CASE = ConfigTester(self , config_class=__SCREAMING_SNAKE_CASE , hidden_size=37 )
def _a ( self : Optional[int] ) -> Any:
"""simple docstring"""
self.config_tester.run_common_tests()
def _a ( self : int ) -> List[str]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_deberta_model(*__SCREAMING_SNAKE_CASE )
def _a ( self : Dict ) -> Tuple:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_deberta_for_sequence_classification(*__SCREAMING_SNAKE_CASE )
def _a ( self : int ) -> Dict:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_deberta_for_masked_lm(*__SCREAMING_SNAKE_CASE )
def _a ( self : Tuple ) -> Dict:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_deberta_for_question_answering(*__SCREAMING_SNAKE_CASE )
def _a ( self : Optional[Any] ) -> Tuple:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_deberta_for_token_classification(*__SCREAMING_SNAKE_CASE )
def _a ( self : str ) -> str:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_deberta_for_multiple_choice(*__SCREAMING_SNAKE_CASE )
@slow
def _a ( self : int ) -> Tuple:
"""simple docstring"""
for model_name in DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__SCREAMING_SNAKE_CASE = DebertaVaModel.from_pretrained(__SCREAMING_SNAKE_CASE )
self.assertIsNotNone(__SCREAMING_SNAKE_CASE )
@require_torch
@require_sentencepiece
@require_tokenizers
class A__( unittest.TestCase ):
@unittest.skip(reason='''Model not available yet''' )
def _a ( self : str ) -> str:
"""simple docstring"""
pass
@slow
def _a ( self : Any ) -> List[Any]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = DebertaVaModel.from_pretrained('''microsoft/deberta-v2-xlarge''' )
__SCREAMING_SNAKE_CASE = torch.tensor([[0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69, 4_60_78, 15_88, 2]] )
__SCREAMING_SNAKE_CASE = torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]] )
with torch.no_grad():
__SCREAMING_SNAKE_CASE = model(__SCREAMING_SNAKE_CASE , attention_mask=__SCREAMING_SNAKE_CASE )[0]
# compare the actual values for a slice.
__SCREAMING_SNAKE_CASE = torch.tensor(
[[[0.23_56, 0.19_48, 0.03_69], [-0.10_63, 0.35_86, -0.51_52], [-0.63_99, -0.02_59, -0.25_25]]] )
self.assertTrue(torch.allclose(output[:, 1:4, 1:4] , __SCREAMING_SNAKE_CASE , atol=1E-4 ) , f"""{output[:, 1:4, 1:4]}""" )
| 690 |
"""simple docstring"""
from __future__ import annotations
import json
import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent
lowerCAmelCase__ ={"UserAgent": UserAgent().random}
def _a ( UpperCAmelCase__ ) -> dict:
__SCREAMING_SNAKE_CASE = script.contents[0]
__SCREAMING_SNAKE_CASE = json.loads(data[data.find('''{"config"''' ) : -1] )
return info["entry_data"]["ProfilePage"][0]["graphql"]["user"]
class A__:
def __init__( self : Dict , __SCREAMING_SNAKE_CASE : int ) -> Optional[Any]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = f"""https://www.instagram.com/{username}/"""
__SCREAMING_SNAKE_CASE = self.get_json()
def _a ( self : List[Any] ) -> dict:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = requests.get(self.url , headers=__SCREAMING_SNAKE_CASE ).text
__SCREAMING_SNAKE_CASE = BeautifulSoup(__SCREAMING_SNAKE_CASE , '''html.parser''' ).find_all('''script''' )
try:
return extract_user_profile(scripts[4] )
except (json.decoder.JSONDecodeError, KeyError):
return extract_user_profile(scripts[3] )
def __repr__( self : Tuple ) -> str:
"""simple docstring"""
return f"""{self.__class__.__name__}('{self.username}')"""
def __str__( self : Optional[int] ) -> str:
"""simple docstring"""
return f"""{self.fullname} ({self.username}) is {self.biography}"""
@property
def _a ( self : Tuple ) -> str:
"""simple docstring"""
return self.user_data["username"]
@property
def _a ( self : List[Any] ) -> str:
"""simple docstring"""
return self.user_data["full_name"]
@property
def _a ( self : Optional[Any] ) -> str:
"""simple docstring"""
return self.user_data["biography"]
@property
def _a ( self : List[str] ) -> str:
"""simple docstring"""
return self.user_data["business_email"]
@property
def _a ( self : Any ) -> str:
"""simple docstring"""
return self.user_data["external_url"]
@property
def _a ( self : Any ) -> int:
"""simple docstring"""
return self.user_data["edge_followed_by"]["count"]
@property
def _a ( self : Dict ) -> int:
"""simple docstring"""
return self.user_data["edge_follow"]["count"]
@property
def _a ( self : str ) -> int:
"""simple docstring"""
return self.user_data["edge_owner_to_timeline_media"]["count"]
@property
def _a ( self : Union[str, Any] ) -> str:
"""simple docstring"""
return self.user_data["profile_pic_url_hd"]
@property
def _a ( self : Tuple ) -> bool:
"""simple docstring"""
return self.user_data["is_verified"]
@property
def _a ( self : Union[str, Any] ) -> bool:
"""simple docstring"""
return self.user_data["is_private"]
def _a ( UpperCAmelCase__ = "github" ) -> None:
import os
if os.environ.get('''CI''' ):
return # test failing on GitHub Actions
__SCREAMING_SNAKE_CASE = InstagramUser(UpperCAmelCase__ )
assert instagram_user.user_data
assert isinstance(instagram_user.user_data , UpperCAmelCase__ )
assert instagram_user.username == username
if username != "github":
return
assert instagram_user.fullname == "GitHub"
assert instagram_user.biography == "Built for developers."
assert instagram_user.number_of_posts > 1_50
assert instagram_user.number_of_followers > 12_00_00
assert instagram_user.number_of_followings > 15
assert instagram_user.email == "[email protected]"
assert instagram_user.website == "https://github.com/readme"
assert instagram_user.profile_picture_url.startswith('''https://instagram.''' )
assert instagram_user.is_verified is True
assert instagram_user.is_private is False
if __name__ == "__main__":
import doctest
doctest.testmod()
lowerCAmelCase__ =InstagramUser("github")
print(instagram_user)
print(F'''{instagram_user.number_of_posts = }''')
print(F'''{instagram_user.number_of_followers = }''')
print(F'''{instagram_user.number_of_followings = }''')
print(F'''{instagram_user.email = }''')
print(F'''{instagram_user.website = }''')
print(F'''{instagram_user.profile_picture_url = }''')
print(F'''{instagram_user.is_verified = }''')
print(F'''{instagram_user.is_private = }''')
| 690 | 1 |
"""simple docstring"""
import json
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_vision_available():
from transformers import OneFormerImageProcessor
from transformers.models.oneformer.image_processing_oneformer import binary_mask_to_rle
from transformers.models.oneformer.modeling_oneformer import OneFormerForUniversalSegmentationOutput
if is_vision_available():
from PIL import Image
def _a ( UpperCAmelCase__ , UpperCAmelCase__="shi-labs/oneformer_demo" ) -> List[str]:
with open(hf_hub_download(UpperCAmelCase__ , UpperCAmelCase__ , repo_type='''dataset''' ) , '''r''' ) as f:
__SCREAMING_SNAKE_CASE = json.load(UpperCAmelCase__ )
__SCREAMING_SNAKE_CASE = {}
__SCREAMING_SNAKE_CASE = []
__SCREAMING_SNAKE_CASE = []
for key, info in class_info.items():
__SCREAMING_SNAKE_CASE = info['''name''']
class_names.append(info['''name'''] )
if info["isthing"]:
thing_ids.append(int(UpperCAmelCase__ ) )
__SCREAMING_SNAKE_CASE = thing_ids
__SCREAMING_SNAKE_CASE = class_names
return metadata
class A__( unittest.TestCase ):
def __init__( self : List[Any] , __SCREAMING_SNAKE_CASE : Optional[Any] , __SCREAMING_SNAKE_CASE : List[str]=7 , __SCREAMING_SNAKE_CASE : str=3 , __SCREAMING_SNAKE_CASE : str=30 , __SCREAMING_SNAKE_CASE : Any=4_00 , __SCREAMING_SNAKE_CASE : str=None , __SCREAMING_SNAKE_CASE : List[str]=True , __SCREAMING_SNAKE_CASE : int=True , __SCREAMING_SNAKE_CASE : Union[str, Any]=[0.5, 0.5, 0.5] , __SCREAMING_SNAKE_CASE : Any=[0.5, 0.5, 0.5] , __SCREAMING_SNAKE_CASE : Union[str, Any]=10 , __SCREAMING_SNAKE_CASE : List[Any]=False , __SCREAMING_SNAKE_CASE : int=2_55 , __SCREAMING_SNAKE_CASE : Dict="shi-labs/oneformer_demo" , __SCREAMING_SNAKE_CASE : List[str]="ade20k_panoptic.json" , __SCREAMING_SNAKE_CASE : str=10 , ) -> int:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = parent
__SCREAMING_SNAKE_CASE = batch_size
__SCREAMING_SNAKE_CASE = num_channels
__SCREAMING_SNAKE_CASE = min_resolution
__SCREAMING_SNAKE_CASE = max_resolution
__SCREAMING_SNAKE_CASE = do_resize
__SCREAMING_SNAKE_CASE = {'''shortest_edge''': 32, '''longest_edge''': 13_33} if size is None else size
__SCREAMING_SNAKE_CASE = do_normalize
__SCREAMING_SNAKE_CASE = image_mean
__SCREAMING_SNAKE_CASE = image_std
__SCREAMING_SNAKE_CASE = class_info_file
__SCREAMING_SNAKE_CASE = prepare_metadata(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = num_text
__SCREAMING_SNAKE_CASE = repo_path
# for the post_process_functions
__SCREAMING_SNAKE_CASE = 2
__SCREAMING_SNAKE_CASE = 10
__SCREAMING_SNAKE_CASE = 10
__SCREAMING_SNAKE_CASE = 3
__SCREAMING_SNAKE_CASE = 4
__SCREAMING_SNAKE_CASE = num_labels
__SCREAMING_SNAKE_CASE = do_reduce_labels
__SCREAMING_SNAKE_CASE = ignore_index
def _a ( self : Tuple ) -> str:
"""simple docstring"""
return {
"do_resize": self.do_resize,
"size": self.size,
"do_normalize": self.do_normalize,
"image_mean": self.image_mean,
"image_std": self.image_std,
"num_labels": self.num_labels,
"do_reduce_labels": self.do_reduce_labels,
"ignore_index": self.ignore_index,
"class_info_file": self.class_info_file,
"metadata": self.metadata,
"num_text": self.num_text,
}
def _a ( self : str , __SCREAMING_SNAKE_CASE : str , __SCREAMING_SNAKE_CASE : List[Any]=False ) -> Tuple:
"""simple docstring"""
if not batched:
__SCREAMING_SNAKE_CASE = image_inputs[0]
if isinstance(__SCREAMING_SNAKE_CASE , Image.Image ):
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = image.size
else:
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = image.shape[1], image.shape[2]
if w < h:
__SCREAMING_SNAKE_CASE = int(self.size['''shortest_edge'''] * h / w )
__SCREAMING_SNAKE_CASE = self.size['''shortest_edge''']
elif w > h:
__SCREAMING_SNAKE_CASE = self.size['''shortest_edge''']
__SCREAMING_SNAKE_CASE = int(self.size['''shortest_edge'''] * w / h )
else:
__SCREAMING_SNAKE_CASE = self.size['''shortest_edge''']
__SCREAMING_SNAKE_CASE = self.size['''shortest_edge''']
else:
__SCREAMING_SNAKE_CASE = []
for image in image_inputs:
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = self.get_expected_values([image] )
expected_values.append((expected_height, expected_width) )
__SCREAMING_SNAKE_CASE = max(__SCREAMING_SNAKE_CASE , key=lambda __SCREAMING_SNAKE_CASE : item[0] )[0]
__SCREAMING_SNAKE_CASE = max(__SCREAMING_SNAKE_CASE , key=lambda __SCREAMING_SNAKE_CASE : item[1] )[1]
return expected_height, expected_width
def _a ( self : Optional[int] ) -> int:
"""simple docstring"""
return OneFormerForUniversalSegmentationOutput(
# +1 for null class
class_queries_logits=torch.randn((self.batch_size, self.num_queries, self.num_classes + 1) ) , masks_queries_logits=torch.randn((self.batch_size, self.num_queries, self.height, self.width) ) , )
@require_torch
@require_vision
class A__( __magic_name__ , unittest.TestCase ):
lowerCAmelCase = OneFormerImageProcessor if (is_vision_available() and is_torch_available()) else None
# only for test_image_processing_common.test_image_proc_to_json_string
lowerCAmelCase = image_processing_class
def _a ( self : int ) -> Optional[Any]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = OneFormerImageProcessorTester(self )
@property
def _a ( self : Optional[int] ) -> Optional[Any]:
"""simple docstring"""
return self.image_processing_tester.prepare_image_processor_dict()
def _a ( self : Union[str, Any] ) -> Optional[int]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE , '''image_mean''' ) )
self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE , '''image_std''' ) )
self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE , '''do_normalize''' ) )
self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE , '''do_resize''' ) )
self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE , '''size''' ) )
self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE , '''ignore_index''' ) )
self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE , '''class_info_file''' ) )
self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE , '''num_text''' ) )
self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE , '''repo_path''' ) )
self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE , '''metadata''' ) )
self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE , '''do_reduce_labels''' ) )
def _a ( self : Tuple ) -> Tuple:
"""simple docstring"""
pass
def _a ( self : Optional[int] ) -> int:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
__SCREAMING_SNAKE_CASE = prepare_image_inputs(self.image_processing_tester , equal_resolution=__SCREAMING_SNAKE_CASE )
for image in image_inputs:
self.assertIsInstance(__SCREAMING_SNAKE_CASE , Image.Image )
# Test not batched input
__SCREAMING_SNAKE_CASE = image_processor(image_inputs[0] , ['''semantic'''] , return_tensors='''pt''' ).pixel_values
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = self.image_processing_tester.get_expected_values(__SCREAMING_SNAKE_CASE )
self.assertEqual(
encoded_images.shape , (1, self.image_processing_tester.num_channels, expected_height, expected_width) , )
# Test batched
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = self.image_processing_tester.get_expected_values(__SCREAMING_SNAKE_CASE , batched=__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = image_processor(
__SCREAMING_SNAKE_CASE , ['''semantic'''] * len(__SCREAMING_SNAKE_CASE ) , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processing_tester.batch_size,
self.image_processing_tester.num_channels,
expected_height,
expected_width,
) , )
def _a ( self : List[Any] ) -> str:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
__SCREAMING_SNAKE_CASE = prepare_image_inputs(self.image_processing_tester , equal_resolution=__SCREAMING_SNAKE_CASE , numpify=__SCREAMING_SNAKE_CASE )
for image in image_inputs:
self.assertIsInstance(__SCREAMING_SNAKE_CASE , np.ndarray )
# Test not batched input
__SCREAMING_SNAKE_CASE = image_processor(image_inputs[0] , ['''semantic'''] , return_tensors='''pt''' ).pixel_values
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = self.image_processing_tester.get_expected_values(__SCREAMING_SNAKE_CASE )
self.assertEqual(
encoded_images.shape , (1, self.image_processing_tester.num_channels, expected_height, expected_width) , )
# Test batched
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = self.image_processing_tester.get_expected_values(__SCREAMING_SNAKE_CASE , batched=__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = image_processor(
__SCREAMING_SNAKE_CASE , ['''semantic'''] * len(__SCREAMING_SNAKE_CASE ) , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processing_tester.batch_size,
self.image_processing_tester.num_channels,
expected_height,
expected_width,
) , )
def _a ( self : Union[str, Any] ) -> Dict:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
__SCREAMING_SNAKE_CASE = prepare_image_inputs(self.image_processing_tester , equal_resolution=__SCREAMING_SNAKE_CASE , torchify=__SCREAMING_SNAKE_CASE )
for image in image_inputs:
self.assertIsInstance(__SCREAMING_SNAKE_CASE , torch.Tensor )
# Test not batched input
__SCREAMING_SNAKE_CASE = image_processor(image_inputs[0] , ['''semantic'''] , return_tensors='''pt''' ).pixel_values
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = self.image_processing_tester.get_expected_values(__SCREAMING_SNAKE_CASE )
self.assertEqual(
encoded_images.shape , (1, self.image_processing_tester.num_channels, expected_height, expected_width) , )
# Test batched
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = self.image_processing_tester.get_expected_values(__SCREAMING_SNAKE_CASE , batched=__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = image_processor(
__SCREAMING_SNAKE_CASE , ['''semantic'''] * len(__SCREAMING_SNAKE_CASE ) , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processing_tester.batch_size,
self.image_processing_tester.num_channels,
expected_height,
expected_width,
) , )
def _a ( self : int , __SCREAMING_SNAKE_CASE : Union[str, Any]=False , __SCREAMING_SNAKE_CASE : List[Any]=False , __SCREAMING_SNAKE_CASE : List[Any]="np" ) -> List[str]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = self.image_processing_class(**self.image_processor_dict )
# prepare image and target
__SCREAMING_SNAKE_CASE = self.image_processing_tester.num_labels
__SCREAMING_SNAKE_CASE = None
__SCREAMING_SNAKE_CASE = None
__SCREAMING_SNAKE_CASE = prepare_image_inputs(self.image_processing_tester , equal_resolution=__SCREAMING_SNAKE_CASE )
if with_segmentation_maps:
__SCREAMING_SNAKE_CASE = num_labels
if is_instance_map:
__SCREAMING_SNAKE_CASE = list(range(__SCREAMING_SNAKE_CASE ) ) * 2
__SCREAMING_SNAKE_CASE = dict(enumerate(__SCREAMING_SNAKE_CASE ) )
__SCREAMING_SNAKE_CASE = [
np.random.randint(0 , high * 2 , (img.size[1], img.size[0]) ).astype(np.uinta ) for img in image_inputs
]
if segmentation_type == "pil":
__SCREAMING_SNAKE_CASE = [Image.fromarray(__SCREAMING_SNAKE_CASE ) for annotation in annotations]
__SCREAMING_SNAKE_CASE = image_processor(
__SCREAMING_SNAKE_CASE , ['''semantic'''] * len(__SCREAMING_SNAKE_CASE ) , __SCREAMING_SNAKE_CASE , return_tensors='''pt''' , instance_id_to_semantic_id=__SCREAMING_SNAKE_CASE , pad_and_return_pixel_mask=__SCREAMING_SNAKE_CASE , )
return inputs
def _a ( self : Tuple ) -> Optional[Any]:
"""simple docstring"""
pass
def _a ( self : Optional[int] ) -> Union[str, Any]:
"""simple docstring"""
def common(__SCREAMING_SNAKE_CASE : Any=False , __SCREAMING_SNAKE_CASE : Any=None ):
__SCREAMING_SNAKE_CASE = self.comm_get_image_processor_inputs(
with_segmentation_maps=__SCREAMING_SNAKE_CASE , is_instance_map=__SCREAMING_SNAKE_CASE , segmentation_type=__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = inputs['''mask_labels''']
__SCREAMING_SNAKE_CASE = inputs['''class_labels''']
__SCREAMING_SNAKE_CASE = inputs['''pixel_values''']
__SCREAMING_SNAKE_CASE = inputs['''text_inputs''']
# check the batch_size
for mask_label, class_label, text_input in zip(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
self.assertEqual(mask_label.shape[0] , class_label.shape[0] )
# this ensure padding has happened
self.assertEqual(mask_label.shape[1:] , pixel_values.shape[2:] )
self.assertEqual(len(__SCREAMING_SNAKE_CASE ) , self.image_processing_tester.num_text )
common()
common(is_instance_map=__SCREAMING_SNAKE_CASE )
common(is_instance_map=__SCREAMING_SNAKE_CASE , segmentation_type='''pil''' )
common(is_instance_map=__SCREAMING_SNAKE_CASE , segmentation_type='''pil''' )
def _a ( self : List[str] ) -> int:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = np.zeros((20, 50) )
__SCREAMING_SNAKE_CASE = 1
__SCREAMING_SNAKE_CASE = 1
__SCREAMING_SNAKE_CASE = 1
__SCREAMING_SNAKE_CASE = binary_mask_to_rle(__SCREAMING_SNAKE_CASE )
self.assertEqual(len(__SCREAMING_SNAKE_CASE ) , 4 )
self.assertEqual(rle[0] , 21 )
self.assertEqual(rle[1] , 45 )
def _a ( self : int ) -> Optional[int]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = self.image_processing_class(
num_labels=self.image_processing_tester.num_classes , max_seq_length=77 , task_seq_length=77 , class_info_file='''ade20k_panoptic.json''' , num_text=self.image_processing_tester.num_text , repo_path='''shi-labs/oneformer_demo''' , )
__SCREAMING_SNAKE_CASE = self.image_processing_tester.get_fake_oneformer_outputs()
__SCREAMING_SNAKE_CASE = fature_extractor.post_process_semantic_segmentation(__SCREAMING_SNAKE_CASE )
self.assertEqual(len(__SCREAMING_SNAKE_CASE ) , self.image_processing_tester.batch_size )
self.assertEqual(
segmentation[0].shape , (
self.image_processing_tester.height,
self.image_processing_tester.width,
) , )
__SCREAMING_SNAKE_CASE = [(1, 4) for i in range(self.image_processing_tester.batch_size )]
__SCREAMING_SNAKE_CASE = fature_extractor.post_process_semantic_segmentation(__SCREAMING_SNAKE_CASE , target_sizes=__SCREAMING_SNAKE_CASE )
self.assertEqual(segmentation[0].shape , target_sizes[0] )
def _a ( self : Optional[int] ) -> str:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = self.image_processing_class(
num_labels=self.image_processing_tester.num_classes , max_seq_length=77 , task_seq_length=77 , class_info_file='''ade20k_panoptic.json''' , num_text=self.image_processing_tester.num_text , repo_path='''shi-labs/oneformer_demo''' , )
__SCREAMING_SNAKE_CASE = self.image_processing_tester.get_fake_oneformer_outputs()
__SCREAMING_SNAKE_CASE = image_processor.post_process_instance_segmentation(__SCREAMING_SNAKE_CASE , threshold=0 )
self.assertTrue(len(__SCREAMING_SNAKE_CASE ) == self.image_processing_tester.batch_size )
for el in segmentation:
self.assertTrue('''segmentation''' in el )
self.assertTrue('''segments_info''' in el )
self.assertEqual(type(el['''segments_info'''] ) , __SCREAMING_SNAKE_CASE )
self.assertEqual(
el['''segmentation'''].shape , (self.image_processing_tester.height, self.image_processing_tester.width) )
def _a ( self : Any ) -> Any:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = self.image_processing_class(
num_labels=self.image_processing_tester.num_classes , max_seq_length=77 , task_seq_length=77 , class_info_file='''ade20k_panoptic.json''' , num_text=self.image_processing_tester.num_text , repo_path='''shi-labs/oneformer_demo''' , )
__SCREAMING_SNAKE_CASE = self.image_processing_tester.get_fake_oneformer_outputs()
__SCREAMING_SNAKE_CASE = image_processor.post_process_panoptic_segmentation(__SCREAMING_SNAKE_CASE , threshold=0 )
self.assertTrue(len(__SCREAMING_SNAKE_CASE ) == self.image_processing_tester.batch_size )
for el in segmentation:
self.assertTrue('''segmentation''' in el )
self.assertTrue('''segments_info''' in el )
self.assertEqual(type(el['''segments_info'''] ) , __SCREAMING_SNAKE_CASE )
self.assertEqual(
el['''segmentation'''].shape , (self.image_processing_tester.height, self.image_processing_tester.width) )
| 690 |
"""simple docstring"""
from sklearn.metrics import recall_score
import datasets
lowerCAmelCase__ ="\nRecall is the fraction of the positive examples that were correctly labeled by the model as positive. It can be computed with the equation:\nRecall = TP / (TP + FN)\nWhere TP is the true positives and FN is the false negatives.\n"
lowerCAmelCase__ ="\nArgs:\n- **predictions** (`list` of `int`): The predicted labels.\n- **references** (`list` of `int`): The ground truth labels.\n- **labels** (`list` of `int`): The set of labels to include when `average` is not set to `binary`, and their order when average is `None`. Labels present in the data can be excluded in this input, for example to calculate a multiclass average ignoring a majority negative class, while labels not present in the data will result in 0 components in a macro average. For multilabel targets, labels are column indices. By default, all labels in y_true and y_pred are used in sorted order. Defaults to None.\n- **pos_label** (`int`): The class label to use as the 'positive class' when calculating the recall. Defaults to `1`.\n- **average** (`string`): This parameter is required for multiclass/multilabel targets. If None, the scores for each class are returned. Otherwise, this determines the type of averaging performed on the data. Defaults to `'binary'`.\n - `'binary'`: Only report results for the class specified by `pos_label`. This is applicable only if the target labels and predictions are binary.\n - `'micro'`: Calculate metrics globally by counting the total true positives, false negatives, and false positives.\n - `'macro'`: Calculate metrics for each label, and find their unweighted mean. This does not take label imbalance into account.\n - `'weighted'`: Calculate metrics for each label, and find their average weighted by support (the number of true instances for each label). This alters `'macro'` to account for label imbalance. Note that it can result in an F-score that is not between precision and recall.\n - `'samples'`: Calculate metrics for each instance, and find their average (only meaningful for multilabel classification).\n- **sample_weight** (`list` of `float`): Sample weights Defaults to `None`.\n- **zero_division** (): Sets the value to return when there is a zero division. Defaults to .\n - `'warn'`: If there is a zero division, the return value is `0`, but warnings are also raised.\n - `0`: If there is a zero division, the return value is `0`.\n - `1`: If there is a zero division, the return value is `1`.\n\nReturns:\n- **recall** (`float`, or `array` of `float`): Either the general recall score, or the recall scores for individual classes, depending on the values input to `labels` and `average`. Minimum possible value is 0. Maximum possible value is 1. A higher recall means that more of the positive examples have been labeled correctly. Therefore, a higher recall is generally considered better.\n\nExamples:\n\n Example 1-A simple example with some errors\n >>> recall_metric = datasets.load_metric('recall')\n >>> results = recall_metric.compute(references=[0, 0, 1, 1, 1], predictions=[0, 1, 0, 1, 1])\n >>> print(results)\n {'recall': 0.6666666666666666}\n\n Example 2-The same example as Example 1, but with `pos_label=0` instead of the default `pos_label=1`.\n >>> recall_metric = datasets.load_metric('recall')\n >>> results = recall_metric.compute(references=[0, 0, 1, 1, 1], predictions=[0, 1, 0, 1, 1], pos_label=0)\n >>> print(results)\n {'recall': 0.5}\n\n Example 3-The same example as Example 1, but with `sample_weight` included.\n >>> recall_metric = datasets.load_metric('recall')\n >>> sample_weight = [0.9, 0.2, 0.9, 0.3, 0.8]\n >>> results = recall_metric.compute(references=[0, 0, 1, 1, 1], predictions=[0, 1, 0, 1, 1], sample_weight=sample_weight)\n >>> print(results)\n {'recall': 0.55}\n\n Example 4-A multiclass example, using different averages.\n >>> recall_metric = datasets.load_metric('recall')\n >>> predictions = [0, 2, 1, 0, 0, 1]\n >>> references = [0, 1, 2, 0, 1, 2]\n >>> results = recall_metric.compute(predictions=predictions, references=references, average='macro')\n >>> print(results)\n {'recall': 0.3333333333333333}\n >>> results = recall_metric.compute(predictions=predictions, references=references, average='micro')\n >>> print(results)\n {'recall': 0.3333333333333333}\n >>> results = recall_metric.compute(predictions=predictions, references=references, average='weighted')\n >>> print(results)\n {'recall': 0.3333333333333333}\n >>> results = recall_metric.compute(predictions=predictions, references=references, average=None)\n >>> print(results)\n {'recall': array([1., 0., 0.])}\n"
lowerCAmelCase__ ="\n@article{scikit-learn, title={Scikit-learn: Machine Learning in {P}ython}, author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V. and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P. and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.}, journal={Journal of Machine Learning Research}, volume={12}, pages={2825--2830}, year={2011}\n"
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class A__( datasets.Metric ):
def _a ( self : Any ) -> int:
"""simple docstring"""
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
'''predictions''': datasets.Sequence(datasets.Value('''int32''' ) ),
'''references''': datasets.Sequence(datasets.Value('''int32''' ) ),
}
if self.config_name == '''multilabel'''
else {
'''predictions''': datasets.Value('''int32''' ),
'''references''': datasets.Value('''int32''' ),
} ) , reference_urls=['''https://scikit-learn.org/stable/modules/generated/sklearn.metrics.recall_score.html'''] , )
def _a ( self : Tuple , __SCREAMING_SNAKE_CASE : Union[str, Any] , __SCREAMING_SNAKE_CASE : Optional[Any] , __SCREAMING_SNAKE_CASE : Union[str, Any]=None , __SCREAMING_SNAKE_CASE : Optional[int]=1 , __SCREAMING_SNAKE_CASE : Optional[Any]="binary" , __SCREAMING_SNAKE_CASE : List[Any]=None , __SCREAMING_SNAKE_CASE : List[Any]="warn" , ) -> Dict:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = recall_score(
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , labels=__SCREAMING_SNAKE_CASE , pos_label=__SCREAMING_SNAKE_CASE , average=__SCREAMING_SNAKE_CASE , sample_weight=__SCREAMING_SNAKE_CASE , zero_division=__SCREAMING_SNAKE_CASE , )
return {"recall": float(__SCREAMING_SNAKE_CASE ) if score.size == 1 else score}
| 690 | 1 |
"""simple docstring"""
import argparse
import collections
import json
from pathlib import Path
import requests
import torch
import yaml
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
MobileViTImageProcessor,
MobileViTVaConfig,
MobileViTVaForImageClassification,
MobileViTVaForSemanticSegmentation,
)
from transformers.utils import logging
logging.set_verbosity_info()
lowerCAmelCase__ =logging.get_logger(__name__)
def _a ( UpperCAmelCase__ ) -> List[str]:
print('''Loading config file...''' )
def flatten_yaml_as_dict(UpperCAmelCase__ , UpperCAmelCase__="" , UpperCAmelCase__="." ):
__SCREAMING_SNAKE_CASE = []
for k, v in d.items():
__SCREAMING_SNAKE_CASE = parent_key + sep + k if parent_key else k
if isinstance(UpperCAmelCase__ , collections.abc.MutableMapping ):
items.extend(flatten_yaml_as_dict(UpperCAmelCase__ , UpperCAmelCase__ , sep=UpperCAmelCase__ ).items() )
else:
items.append((new_key, v) )
return dict(UpperCAmelCase__ )
__SCREAMING_SNAKE_CASE = argparse.Namespace()
with open(UpperCAmelCase__ , '''r''' ) as yaml_file:
try:
__SCREAMING_SNAKE_CASE = yaml.load(UpperCAmelCase__ , Loader=yaml.FullLoader )
__SCREAMING_SNAKE_CASE = flatten_yaml_as_dict(UpperCAmelCase__ )
for k, v in flat_cfg.items():
setattr(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ )
except yaml.YAMLError as exc:
logger.error('''Error while loading config file: {}. Error message: {}'''.format(UpperCAmelCase__ , str(UpperCAmelCase__ ) ) )
return config
def _a ( UpperCAmelCase__ , UpperCAmelCase__ ) -> str:
__SCREAMING_SNAKE_CASE = MobileViTVaConfig()
__SCREAMING_SNAKE_CASE = False
# dataset
if task_name.startswith('''imagenet1k_''' ):
__SCREAMING_SNAKE_CASE = 10_00
if int(task_name.strip().split('''_''' )[-1] ) == 3_84:
__SCREAMING_SNAKE_CASE = 3_84
else:
__SCREAMING_SNAKE_CASE = 2_56
__SCREAMING_SNAKE_CASE = '''imagenet-1k-id2label.json'''
elif task_name.startswith('''imagenet21k_to_1k_''' ):
__SCREAMING_SNAKE_CASE = 2_10_00
if int(task_name.strip().split('''_''' )[-1] ) == 3_84:
__SCREAMING_SNAKE_CASE = 3_84
else:
__SCREAMING_SNAKE_CASE = 2_56
__SCREAMING_SNAKE_CASE = '''imagenet-22k-id2label.json'''
elif task_name.startswith('''ade20k_''' ):
__SCREAMING_SNAKE_CASE = 1_51
__SCREAMING_SNAKE_CASE = 5_12
__SCREAMING_SNAKE_CASE = '''ade20k-id2label.json'''
__SCREAMING_SNAKE_CASE = True
elif task_name.startswith('''voc_''' ):
__SCREAMING_SNAKE_CASE = 21
__SCREAMING_SNAKE_CASE = 5_12
__SCREAMING_SNAKE_CASE = '''pascal-voc-id2label.json'''
__SCREAMING_SNAKE_CASE = True
# orig_config
__SCREAMING_SNAKE_CASE = load_orig_config_file(UpperCAmelCase__ )
assert getattr(UpperCAmelCase__ , '''model.classification.name''' , -1 ) == "mobilevit_v2", "Invalid model"
__SCREAMING_SNAKE_CASE = getattr(UpperCAmelCase__ , '''model.classification.mitv2.width_multiplier''' , 1.0 )
assert (
getattr(UpperCAmelCase__ , '''model.classification.mitv2.attn_norm_layer''' , -1 ) == "layer_norm_2d"
), "Norm layers other than layer_norm_2d is not supported"
__SCREAMING_SNAKE_CASE = getattr(UpperCAmelCase__ , '''model.classification.activation.name''' , '''swish''' )
# config.image_size == getattr(orig_config, 'sampler.bs.crop_size_width', 256)
if is_segmentation_model:
__SCREAMING_SNAKE_CASE = getattr(UpperCAmelCase__ , '''model.segmentation.output_stride''' , 16 )
if "_deeplabv3" in task_name:
__SCREAMING_SNAKE_CASE = getattr(UpperCAmelCase__ , '''model.segmentation.deeplabv3.aspp_rates''' , [12, 24, 36] )
__SCREAMING_SNAKE_CASE = getattr(UpperCAmelCase__ , '''model.segmentation.deeplabv3.aspp_out_channels''' , 5_12 )
__SCREAMING_SNAKE_CASE = getattr(UpperCAmelCase__ , '''model.segmentation.deeplabv3.aspp_dropout''' , 0.1 )
# id2label
__SCREAMING_SNAKE_CASE = '''huggingface/label-files'''
__SCREAMING_SNAKE_CASE = json.load(open(hf_hub_download(UpperCAmelCase__ , UpperCAmelCase__ , repo_type='''dataset''' ) , '''r''' ) )
__SCREAMING_SNAKE_CASE = {int(UpperCAmelCase__ ): v for k, v in idalabel.items()}
__SCREAMING_SNAKE_CASE = idalabel
__SCREAMING_SNAKE_CASE = {v: k for k, v in idalabel.items()}
return config
def _a ( UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ ) -> Union[str, Any]:
__SCREAMING_SNAKE_CASE = dct.pop(UpperCAmelCase__ )
__SCREAMING_SNAKE_CASE = val
def _a ( UpperCAmelCase__ , UpperCAmelCase__=False ) -> List[str]:
if base_model:
__SCREAMING_SNAKE_CASE = ''''''
else:
__SCREAMING_SNAKE_CASE = '''mobilevitv2.'''
__SCREAMING_SNAKE_CASE = []
for k in state_dict.keys():
if k[:8] == "encoder.":
__SCREAMING_SNAKE_CASE = k[8:]
else:
__SCREAMING_SNAKE_CASE = k
if ".block." in k:
__SCREAMING_SNAKE_CASE = k_new.replace('''.block.''' , '''.''' )
if ".conv." in k:
__SCREAMING_SNAKE_CASE = k_new.replace('''.conv.''' , '''.convolution.''' )
if ".norm." in k:
__SCREAMING_SNAKE_CASE = k_new.replace('''.norm.''' , '''.normalization.''' )
if "conv_1." in k:
__SCREAMING_SNAKE_CASE = k_new.replace('''conv_1.''' , f"""{model_prefix}conv_stem.""" )
for i in [1, 2]:
if f"""layer_{i}.""" in k:
__SCREAMING_SNAKE_CASE = k_new.replace(f"""layer_{i}.""" , f"""{model_prefix}encoder.layer.{i-1}.layer.""" )
if ".exp_1x1." in k:
__SCREAMING_SNAKE_CASE = k_new.replace('''.exp_1x1.''' , '''.expand_1x1.''' )
if ".red_1x1." in k:
__SCREAMING_SNAKE_CASE = k_new.replace('''.red_1x1.''' , '''.reduce_1x1.''' )
for i in [3, 4, 5]:
if f"""layer_{i}.0.""" in k:
__SCREAMING_SNAKE_CASE = k_new.replace(f"""layer_{i}.0.""" , f"""{model_prefix}encoder.layer.{i-1}.downsampling_layer.""" )
if f"""layer_{i}.1.local_rep.0.""" in k:
__SCREAMING_SNAKE_CASE = k_new.replace(f"""layer_{i}.1.local_rep.0.""" , f"""{model_prefix}encoder.layer.{i-1}.conv_kxk.""" )
if f"""layer_{i}.1.local_rep.1.""" in k:
__SCREAMING_SNAKE_CASE = k_new.replace(f"""layer_{i}.1.local_rep.1.""" , f"""{model_prefix}encoder.layer.{i-1}.conv_1x1.""" )
for i in [3, 4, 5]:
if i == 3:
__SCREAMING_SNAKE_CASE = [0, 1]
elif i == 4:
__SCREAMING_SNAKE_CASE = [0, 1, 2, 3]
elif i == 5:
__SCREAMING_SNAKE_CASE = [0, 1, 2]
for j in j_in:
if f"""layer_{i}.1.global_rep.{j}.""" in k:
__SCREAMING_SNAKE_CASE = k_new.replace(
f"""layer_{i}.1.global_rep.{j}.""" , f"""{model_prefix}encoder.layer.{i-1}.transformer.layer.{j}.""" )
if f"""layer_{i}.1.global_rep.{j+1}.""" in k:
__SCREAMING_SNAKE_CASE = k_new.replace(
f"""layer_{i}.1.global_rep.{j+1}.""" , f"""{model_prefix}encoder.layer.{i-1}.layernorm.""" )
if f"""layer_{i}.1.conv_proj.""" in k:
__SCREAMING_SNAKE_CASE = k_new.replace(f"""layer_{i}.1.conv_proj.""" , f"""{model_prefix}encoder.layer.{i-1}.conv_projection.""" )
if "pre_norm_attn.0." in k:
__SCREAMING_SNAKE_CASE = k_new.replace('''pre_norm_attn.0.''' , '''layernorm_before.''' )
if "pre_norm_attn.1." in k:
__SCREAMING_SNAKE_CASE = k_new.replace('''pre_norm_attn.1.''' , '''attention.''' )
if "pre_norm_ffn.0." in k:
__SCREAMING_SNAKE_CASE = k_new.replace('''pre_norm_ffn.0.''' , '''layernorm_after.''' )
if "pre_norm_ffn.1." in k:
__SCREAMING_SNAKE_CASE = k_new.replace('''pre_norm_ffn.1.''' , '''ffn.conv1.''' )
if "pre_norm_ffn.3." in k:
__SCREAMING_SNAKE_CASE = k_new.replace('''pre_norm_ffn.3.''' , '''ffn.conv2.''' )
if "classifier.1." in k:
__SCREAMING_SNAKE_CASE = k_new.replace('''classifier.1.''' , '''classifier.''' )
if "seg_head." in k:
__SCREAMING_SNAKE_CASE = k_new.replace('''seg_head.''' , '''segmentation_head.''' )
if ".aspp_layer." in k:
__SCREAMING_SNAKE_CASE = k_new.replace('''.aspp_layer.''' , '''.''' )
if ".aspp_pool." in k:
__SCREAMING_SNAKE_CASE = k_new.replace('''.aspp_pool.''' , '''.''' )
rename_keys.append((k, k_new) )
return rename_keys
def _a ( UpperCAmelCase__ ) -> int:
__SCREAMING_SNAKE_CASE = []
for k in state_dict.keys():
if k.startswith('''seg_head.aux_head.''' ):
keys_to_ignore.append(UpperCAmelCase__ )
for k in keys_to_ignore:
state_dict.pop(UpperCAmelCase__ , UpperCAmelCase__ )
def _a ( ) -> Dict:
__SCREAMING_SNAKE_CASE = '''http://images.cocodataset.org/val2017/000000039769.jpg'''
# url = "https://cdn.britannica.com/86/141086-050-9D7C75EE/Gulfstream-G450-business-jet-passengers.jpg"
__SCREAMING_SNAKE_CASE = Image.open(requests.get(UpperCAmelCase__ , stream=UpperCAmelCase__ ).raw )
return im
@torch.no_grad()
def _a ( UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ ) -> Dict:
__SCREAMING_SNAKE_CASE = get_mobilevitva_config(UpperCAmelCase__ , UpperCAmelCase__ )
# load original state_dict
__SCREAMING_SNAKE_CASE = torch.load(UpperCAmelCase__ , map_location='''cpu''' )
# load huggingface model
if task_name.startswith('''ade20k_''' ) or task_name.startswith('''voc_''' ):
__SCREAMING_SNAKE_CASE = MobileViTVaForSemanticSegmentation(UpperCAmelCase__ ).eval()
__SCREAMING_SNAKE_CASE = False
else:
__SCREAMING_SNAKE_CASE = MobileViTVaForImageClassification(UpperCAmelCase__ ).eval()
__SCREAMING_SNAKE_CASE = False
# remove and rename some keys of load the original model
__SCREAMING_SNAKE_CASE = checkpoint
remove_unused_keys(UpperCAmelCase__ )
__SCREAMING_SNAKE_CASE = create_rename_keys(UpperCAmelCase__ , base_model=UpperCAmelCase__ )
for rename_key_src, rename_key_dest in rename_keys:
rename_key(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ )
# load modified state_dict
model.load_state_dict(UpperCAmelCase__ )
# Check outputs on an image, prepared by MobileViTImageProcessor
__SCREAMING_SNAKE_CASE = MobileViTImageProcessor(crop_size=config.image_size , size=config.image_size + 32 )
__SCREAMING_SNAKE_CASE = image_processor(images=prepare_img() , return_tensors='''pt''' )
__SCREAMING_SNAKE_CASE = model(**UpperCAmelCase__ )
# verify classification model
if task_name.startswith('''imagenet''' ):
__SCREAMING_SNAKE_CASE = outputs.logits
__SCREAMING_SNAKE_CASE = logits.argmax(-1 ).item()
print('''Predicted class:''' , model.config.idalabel[predicted_class_idx] )
if task_name.startswith('''imagenet1k_256''' ) and config.width_multiplier == 1.0:
# expected_logits for base variant
__SCREAMING_SNAKE_CASE = torch.tensor([-1.6336E00, -7.3204E-02, -5.1883E-01] )
assert torch.allclose(logits[0, :3] , UpperCAmelCase__ , atol=1E-4 )
Path(UpperCAmelCase__ ).mkdir(exist_ok=UpperCAmelCase__ )
print(f"""Saving model {task_name} to {pytorch_dump_folder_path}""" )
model.save_pretrained(UpperCAmelCase__ )
print(f"""Saving image processor to {pytorch_dump_folder_path}""" )
image_processor.save_pretrained(UpperCAmelCase__ )
if __name__ == "__main__":
lowerCAmelCase__ =argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--task",
default="imagenet1k_256",
type=str,
help=(
"Name of the task for which the MobileViTV2 model you'd like to convert is trained on . "
"\n Classification (ImageNet-1k)\n - MobileViTV2 (256x256) : imagenet1k_256\n - MobileViTV2 (Trained on 256x256 and Finetuned on 384x384) : imagenet1k_384\n - MobileViTV2 (Trained on ImageNet-21k and Finetuned on ImageNet-1k 256x256) :\n imagenet21k_to_1k_256\n - MobileViTV2 (Trained on ImageNet-21k, Finetuned on ImageNet-1k 256x256, and Finetuned on\n ImageNet-1k 384x384) : imagenet21k_to_1k_384\n Segmentation\n - ADE20K Dataset : ade20k_deeplabv3\n - Pascal VOC 2012 Dataset: voc_deeplabv3\n "
),
choices=[
"imagenet1k_256",
"imagenet1k_384",
"imagenet21k_to_1k_256",
"imagenet21k_to_1k_384",
"ade20k_deeplabv3",
"voc_deeplabv3",
],
)
parser.add_argument(
"--orig_checkpoint_path", required=True, type=str, help="Path to the original state dict (.pt file)."
)
parser.add_argument("--orig_config_path", required=True, type=str, help="Path to the original config file.")
parser.add_argument(
"--pytorch_dump_folder_path", required=True, type=str, help="Path to the output PyTorch model directory."
)
lowerCAmelCase__ =parser.parse_args()
convert_mobilevitva_checkpoint(
args.task, args.orig_checkpoint_path, args.orig_config_path, args.pytorch_dump_folder_path
)
| 690 |
"""simple docstring"""
def _a ( UpperCAmelCase__ = 10**9 ) -> int:
__SCREAMING_SNAKE_CASE = 1
__SCREAMING_SNAKE_CASE = 2
__SCREAMING_SNAKE_CASE = 0
__SCREAMING_SNAKE_CASE = 0
__SCREAMING_SNAKE_CASE = 0
while perimeter <= max_perimeter:
perimeters_sum += perimeter
prev_value += 2 * value
value += prev_value
__SCREAMING_SNAKE_CASE = 2 * value + 2 if i % 2 == 0 else 2 * value - 2
i += 1
return perimeters_sum
if __name__ == "__main__":
print(F'''{solution() = }''')
| 690 | 1 |
"""simple docstring"""
import copy
import os
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Dict, Mapping, Optional, Union
if TYPE_CHECKING:
from ...processing_utils import ProcessorMixin
from ...utils import TensorType
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowerCAmelCase__ =logging.get_logger(__name__)
lowerCAmelCase__ ={
"google/owlvit-base-patch32": "https://huggingface.co/google/owlvit-base-patch32/resolve/main/config.json",
"google/owlvit-base-patch16": "https://huggingface.co/google/owlvit-base-patch16/resolve/main/config.json",
"google/owlvit-large-patch14": "https://huggingface.co/google/owlvit-large-patch14/resolve/main/config.json",
}
class A__( __magic_name__ ):
lowerCAmelCase = '''owlvit_text_model'''
def __init__( self : Any , __SCREAMING_SNAKE_CASE : List[str]=4_94_08 , __SCREAMING_SNAKE_CASE : List[str]=5_12 , __SCREAMING_SNAKE_CASE : Optional[Any]=20_48 , __SCREAMING_SNAKE_CASE : Dict=12 , __SCREAMING_SNAKE_CASE : Union[str, Any]=8 , __SCREAMING_SNAKE_CASE : str=16 , __SCREAMING_SNAKE_CASE : Optional[Any]="quick_gelu" , __SCREAMING_SNAKE_CASE : Dict=1E-5 , __SCREAMING_SNAKE_CASE : int=0.0 , __SCREAMING_SNAKE_CASE : Tuple=0.02 , __SCREAMING_SNAKE_CASE : Optional[Any]=1.0 , __SCREAMING_SNAKE_CASE : str=0 , __SCREAMING_SNAKE_CASE : Union[str, Any]=4_94_06 , __SCREAMING_SNAKE_CASE : int=4_94_07 , **__SCREAMING_SNAKE_CASE : Tuple , ) -> Tuple:
"""simple docstring"""
super().__init__(pad_token_id=__SCREAMING_SNAKE_CASE , bos_token_id=__SCREAMING_SNAKE_CASE , eos_token_id=__SCREAMING_SNAKE_CASE , **__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = vocab_size
__SCREAMING_SNAKE_CASE = hidden_size
__SCREAMING_SNAKE_CASE = intermediate_size
__SCREAMING_SNAKE_CASE = num_hidden_layers
__SCREAMING_SNAKE_CASE = num_attention_heads
__SCREAMING_SNAKE_CASE = max_position_embeddings
__SCREAMING_SNAKE_CASE = hidden_act
__SCREAMING_SNAKE_CASE = layer_norm_eps
__SCREAMING_SNAKE_CASE = attention_dropout
__SCREAMING_SNAKE_CASE = initializer_range
__SCREAMING_SNAKE_CASE = initializer_factor
@classmethod
def _a ( cls : Union[str, Any] , __SCREAMING_SNAKE_CASE : Union[str, os.PathLike] , **__SCREAMING_SNAKE_CASE : Union[str, Any] ) -> "PretrainedConfig":
"""simple docstring"""
cls._set_token_in_kwargs(__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = cls.get_config_dict(__SCREAMING_SNAKE_CASE , **__SCREAMING_SNAKE_CASE )
# get the text config dict if we are loading from OwlViTConfig
if config_dict.get('''model_type''' ) == "owlvit":
__SCREAMING_SNAKE_CASE = config_dict['''text_config''']
if "model_type" in config_dict and hasattr(cls , '''model_type''' ) and config_dict["model_type"] != cls.model_type:
logger.warning(
f"""You are using a model of type {config_dict["model_type"]} to instantiate a model of type """
f"""{cls.model_type}. This is not supported for all configurations of models and can yield errors.""" )
return cls.from_dict(__SCREAMING_SNAKE_CASE , **__SCREAMING_SNAKE_CASE )
class A__( __magic_name__ ):
lowerCAmelCase = '''owlvit_vision_model'''
def __init__( self : Optional[Any] , __SCREAMING_SNAKE_CASE : int=7_68 , __SCREAMING_SNAKE_CASE : Any=30_72 , __SCREAMING_SNAKE_CASE : Dict=12 , __SCREAMING_SNAKE_CASE : Dict=12 , __SCREAMING_SNAKE_CASE : Any=3 , __SCREAMING_SNAKE_CASE : int=7_68 , __SCREAMING_SNAKE_CASE : Optional[int]=32 , __SCREAMING_SNAKE_CASE : List[Any]="quick_gelu" , __SCREAMING_SNAKE_CASE : Union[str, Any]=1E-5 , __SCREAMING_SNAKE_CASE : List[Any]=0.0 , __SCREAMING_SNAKE_CASE : List[Any]=0.02 , __SCREAMING_SNAKE_CASE : Optional[int]=1.0 , **__SCREAMING_SNAKE_CASE : Union[str, Any] , ) -> int:
"""simple docstring"""
super().__init__(**__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = hidden_size
__SCREAMING_SNAKE_CASE = intermediate_size
__SCREAMING_SNAKE_CASE = num_hidden_layers
__SCREAMING_SNAKE_CASE = num_attention_heads
__SCREAMING_SNAKE_CASE = num_channels
__SCREAMING_SNAKE_CASE = image_size
__SCREAMING_SNAKE_CASE = patch_size
__SCREAMING_SNAKE_CASE = hidden_act
__SCREAMING_SNAKE_CASE = layer_norm_eps
__SCREAMING_SNAKE_CASE = attention_dropout
__SCREAMING_SNAKE_CASE = initializer_range
__SCREAMING_SNAKE_CASE = initializer_factor
@classmethod
def _a ( cls : str , __SCREAMING_SNAKE_CASE : Union[str, os.PathLike] , **__SCREAMING_SNAKE_CASE : Tuple ) -> "PretrainedConfig":
"""simple docstring"""
cls._set_token_in_kwargs(__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = cls.get_config_dict(__SCREAMING_SNAKE_CASE , **__SCREAMING_SNAKE_CASE )
# get the vision config dict if we are loading from OwlViTConfig
if config_dict.get('''model_type''' ) == "owlvit":
__SCREAMING_SNAKE_CASE = config_dict['''vision_config''']
if "model_type" in config_dict and hasattr(cls , '''model_type''' ) and config_dict["model_type"] != cls.model_type:
logger.warning(
f"""You are using a model of type {config_dict["model_type"]} to instantiate a model of type """
f"""{cls.model_type}. This is not supported for all configurations of models and can yield errors.""" )
return cls.from_dict(__SCREAMING_SNAKE_CASE , **__SCREAMING_SNAKE_CASE )
class A__( __magic_name__ ):
lowerCAmelCase = '''owlvit'''
lowerCAmelCase = True
def __init__( self : Dict , __SCREAMING_SNAKE_CASE : str=None , __SCREAMING_SNAKE_CASE : Optional[int]=None , __SCREAMING_SNAKE_CASE : Optional[Any]=5_12 , __SCREAMING_SNAKE_CASE : Tuple=2.65_92 , __SCREAMING_SNAKE_CASE : Dict=True , **__SCREAMING_SNAKE_CASE : Any , ) -> int:
"""simple docstring"""
super().__init__(**__SCREAMING_SNAKE_CASE )
if text_config is None:
__SCREAMING_SNAKE_CASE = {}
logger.info('''text_config is None. Initializing the OwlViTTextConfig with default values.''' )
if vision_config is None:
__SCREAMING_SNAKE_CASE = {}
logger.info('''vision_config is None. initializing the OwlViTVisionConfig with default values.''' )
__SCREAMING_SNAKE_CASE = OwlViTTextConfig(**__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = OwlViTVisionConfig(**__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = projection_dim
__SCREAMING_SNAKE_CASE = logit_scale_init_value
__SCREAMING_SNAKE_CASE = return_dict
__SCREAMING_SNAKE_CASE = 1.0
@classmethod
def _a ( cls : Optional[int] , __SCREAMING_SNAKE_CASE : Union[str, os.PathLike] , **__SCREAMING_SNAKE_CASE : Any ) -> "PretrainedConfig":
"""simple docstring"""
cls._set_token_in_kwargs(__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = cls.get_config_dict(__SCREAMING_SNAKE_CASE , **__SCREAMING_SNAKE_CASE )
if "model_type" in config_dict and hasattr(cls , '''model_type''' ) and config_dict["model_type"] != cls.model_type:
logger.warning(
f"""You are using a model of type {config_dict["model_type"]} to instantiate a model of type """
f"""{cls.model_type}. This is not supported for all configurations of models and can yield errors.""" )
return cls.from_dict(__SCREAMING_SNAKE_CASE , **__SCREAMING_SNAKE_CASE )
@classmethod
def _a ( cls : Dict , __SCREAMING_SNAKE_CASE : Dict , __SCREAMING_SNAKE_CASE : Dict , **__SCREAMING_SNAKE_CASE : Optional[Any] ) -> Any:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = {}
__SCREAMING_SNAKE_CASE = text_config
__SCREAMING_SNAKE_CASE = vision_config
return cls.from_dict(__SCREAMING_SNAKE_CASE , **__SCREAMING_SNAKE_CASE )
def _a ( self : List[Any] ) -> Optional[Any]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = copy.deepcopy(self.__dict__ )
__SCREAMING_SNAKE_CASE = self.text_config.to_dict()
__SCREAMING_SNAKE_CASE = self.vision_config.to_dict()
__SCREAMING_SNAKE_CASE = self.__class__.model_type
return output
class A__( __magic_name__ ):
@property
def _a ( self : Dict ) -> Mapping[str, Mapping[int, str]]:
"""simple docstring"""
return OrderedDict(
[
('''input_ids''', {0: '''batch''', 1: '''sequence'''}),
('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
('''attention_mask''', {0: '''batch''', 1: '''sequence'''}),
] )
@property
def _a ( self : Optional[Any] ) -> Mapping[str, Mapping[int, str]]:
"""simple docstring"""
return OrderedDict(
[
('''logits_per_image''', {0: '''batch'''}),
('''logits_per_text''', {0: '''batch'''}),
('''text_embeds''', {0: '''batch'''}),
('''image_embeds''', {0: '''batch'''}),
] )
@property
def _a ( self : List[Any] ) -> float:
"""simple docstring"""
return 1E-4
def _a ( self : List[Any] , __SCREAMING_SNAKE_CASE : "ProcessorMixin" , __SCREAMING_SNAKE_CASE : int = -1 , __SCREAMING_SNAKE_CASE : int = -1 , __SCREAMING_SNAKE_CASE : Optional["TensorType"] = None , ) -> Mapping[str, Any]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = super().generate_dummy_inputs(
processor.tokenizer , batch_size=__SCREAMING_SNAKE_CASE , seq_length=__SCREAMING_SNAKE_CASE , framework=__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = super().generate_dummy_inputs(
processor.image_processor , batch_size=__SCREAMING_SNAKE_CASE , framework=__SCREAMING_SNAKE_CASE )
return {**text_input_dict, **image_input_dict}
@property
def _a ( self : Optional[Any] ) -> int:
"""simple docstring"""
return 14
| 690 |
"""simple docstring"""
import pandas as pd
from matplotlib import pyplot as plt
from sklearn.linear_model import LinearRegression
# Splitting the dataset into the Training set and Test set
from sklearn.model_selection import train_test_split
# Fitting Polynomial Regression to the dataset
from sklearn.preprocessing import PolynomialFeatures
# Importing the dataset
lowerCAmelCase__ =pd.read_csv(
"https://s3.us-west-2.amazonaws.com/public.gamelab.fun/dataset/"
"position_salaries.csv"
)
lowerCAmelCase__ =dataset.iloc[:, 1:2].values
lowerCAmelCase__ =dataset.iloc[:, 2].values
lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ =train_test_split(X, y, test_size=0.2, random_state=0)
lowerCAmelCase__ =PolynomialFeatures(degree=4)
lowerCAmelCase__ =poly_reg.fit_transform(X)
lowerCAmelCase__ =LinearRegression()
pol_reg.fit(X_poly, y)
def _a ( ) -> List[Any]:
plt.scatter(UpperCAmelCase__ , UpperCAmelCase__ , color='''red''' )
plt.plot(UpperCAmelCase__ , pol_reg.predict(poly_reg.fit_transform(UpperCAmelCase__ ) ) , color='''blue''' )
plt.title('''Truth or Bluff (Linear Regression)''' )
plt.xlabel('''Position level''' )
plt.ylabel('''Salary''' )
plt.show()
if __name__ == "__main__":
viz_polymonial()
# Predicting a new result with Polymonial Regression
pol_reg.predict(poly_reg.fit_transform([[5.5]]))
# output should be 132148.43750003
| 690 | 1 |
"""simple docstring"""
def _a ( UpperCAmelCase__ ) -> str:
__SCREAMING_SNAKE_CASE = ''''''
for ch in key:
if ch == " " or ch not in key_no_dups and ch.isalpha():
key_no_dups += ch
return key_no_dups
def _a ( UpperCAmelCase__ ) -> dict[str, str]:
__SCREAMING_SNAKE_CASE = [chr(i + 65 ) for i in range(26 )]
# Remove duplicate characters from key
__SCREAMING_SNAKE_CASE = remove_duplicates(key.upper() )
__SCREAMING_SNAKE_CASE = len(UpperCAmelCase__ )
# First fill cipher with key characters
__SCREAMING_SNAKE_CASE = {alphabet[i]: char for i, char in enumerate(UpperCAmelCase__ )}
# Then map remaining characters in alphabet to
# the alphabet from the beginning
for i in range(len(UpperCAmelCase__ ) , 26 ):
__SCREAMING_SNAKE_CASE = alphabet[i - offset]
# Ensure we are not mapping letters to letters previously mapped
while char in key:
offset -= 1
__SCREAMING_SNAKE_CASE = alphabet[i - offset]
__SCREAMING_SNAKE_CASE = char
return cipher_alphabet
def _a ( UpperCAmelCase__ , UpperCAmelCase__ ) -> str:
return "".join(cipher_map.get(UpperCAmelCase__ , UpperCAmelCase__ ) for ch in message.upper() )
def _a ( UpperCAmelCase__ , UpperCAmelCase__ ) -> str:
__SCREAMING_SNAKE_CASE = {v: k for k, v in cipher_map.items()}
return "".join(rev_cipher_map.get(UpperCAmelCase__ , UpperCAmelCase__ ) for ch in message.upper() )
def _a ( ) -> None:
__SCREAMING_SNAKE_CASE = input('''Enter message to encode or decode: ''' ).strip()
__SCREAMING_SNAKE_CASE = input('''Enter keyword: ''' ).strip()
__SCREAMING_SNAKE_CASE = input('''Encipher or decipher? E/D:''' ).strip()[0].lower()
try:
__SCREAMING_SNAKE_CASE = {'''e''': encipher, '''d''': decipher}[option]
except KeyError:
raise KeyError('''invalid input option''' )
__SCREAMING_SNAKE_CASE = create_cipher_map(UpperCAmelCase__ )
print(func(UpperCAmelCase__ , UpperCAmelCase__ ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| 690 |
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import AutoTokenizer, MBartConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import TFAutoModelForSeqaSeqLM, TFMBartForConditionalGeneration, TFMBartModel
@require_tf
class A__:
lowerCAmelCase = MBartConfig
lowerCAmelCase = {}
lowerCAmelCase = '''gelu'''
def __init__( self : List[str] , __SCREAMING_SNAKE_CASE : Optional[int] , __SCREAMING_SNAKE_CASE : Tuple=13 , __SCREAMING_SNAKE_CASE : Dict=7 , __SCREAMING_SNAKE_CASE : List[Any]=True , __SCREAMING_SNAKE_CASE : Any=False , __SCREAMING_SNAKE_CASE : Union[str, Any]=99 , __SCREAMING_SNAKE_CASE : Optional[Any]=32 , __SCREAMING_SNAKE_CASE : Optional[int]=2 , __SCREAMING_SNAKE_CASE : Any=4 , __SCREAMING_SNAKE_CASE : List[str]=37 , __SCREAMING_SNAKE_CASE : Optional[Any]=0.1 , __SCREAMING_SNAKE_CASE : Dict=0.1 , __SCREAMING_SNAKE_CASE : Any=20 , __SCREAMING_SNAKE_CASE : List[Any]=2 , __SCREAMING_SNAKE_CASE : Optional[int]=1 , __SCREAMING_SNAKE_CASE : Optional[Any]=0 , ) -> Optional[int]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = parent
__SCREAMING_SNAKE_CASE = batch_size
__SCREAMING_SNAKE_CASE = seq_length
__SCREAMING_SNAKE_CASE = is_training
__SCREAMING_SNAKE_CASE = use_labels
__SCREAMING_SNAKE_CASE = vocab_size
__SCREAMING_SNAKE_CASE = hidden_size
__SCREAMING_SNAKE_CASE = num_hidden_layers
__SCREAMING_SNAKE_CASE = num_attention_heads
__SCREAMING_SNAKE_CASE = intermediate_size
__SCREAMING_SNAKE_CASE = hidden_dropout_prob
__SCREAMING_SNAKE_CASE = attention_probs_dropout_prob
__SCREAMING_SNAKE_CASE = max_position_embeddings
__SCREAMING_SNAKE_CASE = eos_token_id
__SCREAMING_SNAKE_CASE = pad_token_id
__SCREAMING_SNAKE_CASE = bos_token_id
def _a ( self : List[str] ) -> Optional[int]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = ids_tensor([self.batch_size, self.seq_length - 1] , self.vocab_size )
__SCREAMING_SNAKE_CASE = tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size ) , 1 )
__SCREAMING_SNAKE_CASE = tf.concat([input_ids, eos_tensor] , axis=1 )
__SCREAMING_SNAKE_CASE = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
__SCREAMING_SNAKE_CASE = self.config_cls(
vocab_size=self.vocab_size , d_model=self.hidden_size , encoder_layers=self.num_hidden_layers , decoder_layers=self.num_hidden_layers , encoder_attention_heads=self.num_attention_heads , decoder_attention_heads=self.num_attention_heads , encoder_ffn_dim=self.intermediate_size , decoder_ffn_dim=self.intermediate_size , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , eos_token_ids=[2] , bos_token_id=self.bos_token_id , pad_token_id=self.pad_token_id , decoder_start_token_id=self.pad_token_id , **self.config_updates , )
__SCREAMING_SNAKE_CASE = prepare_mbart_inputs_dict(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
return config, inputs_dict
def _a ( self : Tuple , __SCREAMING_SNAKE_CASE : str , __SCREAMING_SNAKE_CASE : Optional[int] ) -> Optional[Any]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = TFMBartModel(config=__SCREAMING_SNAKE_CASE ).get_decoder()
__SCREAMING_SNAKE_CASE = inputs_dict['''input_ids''']
__SCREAMING_SNAKE_CASE = input_ids[:1, :]
__SCREAMING_SNAKE_CASE = inputs_dict['''attention_mask'''][:1, :]
__SCREAMING_SNAKE_CASE = inputs_dict['''head_mask''']
__SCREAMING_SNAKE_CASE = 1
# first forward pass
__SCREAMING_SNAKE_CASE = model(__SCREAMING_SNAKE_CASE , attention_mask=__SCREAMING_SNAKE_CASE , head_mask=__SCREAMING_SNAKE_CASE , use_cache=__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = outputs.to_tuple()
__SCREAMING_SNAKE_CASE = past_key_values[1]
def _a ( UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__=None , UpperCAmelCase__=None , UpperCAmelCase__=None , UpperCAmelCase__=None , UpperCAmelCase__=None , ) -> Optional[int]:
if attention_mask is None:
__SCREAMING_SNAKE_CASE = tf.cast(tf.math.not_equal(UpperCAmelCase__ , config.pad_token_id ) , tf.inta )
if decoder_attention_mask is None:
__SCREAMING_SNAKE_CASE = tf.concat(
[
tf.ones(decoder_input_ids[:, :1].shape , dtype=tf.inta ),
tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:] , config.pad_token_id ) , tf.inta ),
] , axis=-1 , )
if head_mask is None:
__SCREAMING_SNAKE_CASE = tf.ones((config.encoder_layers, config.encoder_attention_heads) )
if decoder_head_mask is None:
__SCREAMING_SNAKE_CASE = tf.ones((config.decoder_layers, config.decoder_attention_heads) )
if cross_attn_head_mask is None:
__SCREAMING_SNAKE_CASE = tf.ones((config.decoder_layers, config.decoder_attention_heads) )
return {
"input_ids": input_ids,
"decoder_input_ids": decoder_input_ids,
"attention_mask": attention_mask,
"decoder_attention_mask": decoder_attention_mask,
"head_mask": head_mask,
"decoder_head_mask": decoder_head_mask,
"cross_attn_head_mask": cross_attn_head_mask,
}
@require_tf
class A__( __magic_name__ , __magic_name__ , unittest.TestCase ):
lowerCAmelCase = (TFMBartForConditionalGeneration, TFMBartModel) if is_tf_available() else ()
lowerCAmelCase = (TFMBartForConditionalGeneration,) if is_tf_available() else ()
lowerCAmelCase = (
{
'''conversational''': TFMBartForConditionalGeneration,
'''feature-extraction''': TFMBartModel,
'''summarization''': TFMBartForConditionalGeneration,
'''text2text-generation''': TFMBartForConditionalGeneration,
'''translation''': TFMBartForConditionalGeneration,
}
if is_tf_available()
else {}
)
lowerCAmelCase = True
lowerCAmelCase = False
lowerCAmelCase = False
def _a ( self : Dict , __SCREAMING_SNAKE_CASE : str , __SCREAMING_SNAKE_CASE : List[str] , __SCREAMING_SNAKE_CASE : Dict , __SCREAMING_SNAKE_CASE : Optional[int] , __SCREAMING_SNAKE_CASE : List[Any] ) -> Optional[Any]:
"""simple docstring"""
if pipeline_test_casse_name != "FeatureExtractionPipelineTests":
# Exception encountered when calling layer '...'
return True
return False
def _a ( self : List[Any] ) -> Optional[int]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = TFMBartModelTester(self )
__SCREAMING_SNAKE_CASE = ConfigTester(self , config_class=__SCREAMING_SNAKE_CASE )
def _a ( self : Union[str, Any] ) -> Union[str, Any]:
"""simple docstring"""
self.config_tester.run_common_tests()
def _a ( self : int ) -> str:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.check_decoder_model_past_large_inputs(*__SCREAMING_SNAKE_CASE )
@require_sentencepiece
@require_tokenizers
@require_tf
class A__( unittest.TestCase ):
lowerCAmelCase = [
''' UN Chief Says There Is No Military Solution in Syria''',
]
lowerCAmelCase = [
'''Şeful ONU declară că nu există o soluţie militară în Siria''',
]
lowerCAmelCase = '''facebook/mbart-large-en-ro'''
@cached_property
def _a ( self : Optional[int] ) -> str:
"""simple docstring"""
return AutoTokenizer.from_pretrained(self.model_name )
@cached_property
def _a ( self : Optional[Any] ) -> List[Any]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name )
return model
def _a ( self : Any , **__SCREAMING_SNAKE_CASE : Optional[Any] ) -> Any:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = self.translate_src_text(**__SCREAMING_SNAKE_CASE )
self.assertListEqual(self.expected_text , __SCREAMING_SNAKE_CASE )
def _a ( self : Any , **__SCREAMING_SNAKE_CASE : Optional[int] ) -> Optional[Any]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = self.tokenizer(self.src_text , **__SCREAMING_SNAKE_CASE , return_tensors='''tf''' )
__SCREAMING_SNAKE_CASE = self.model.generate(
model_inputs.input_ids , attention_mask=model_inputs.attention_mask , num_beams=2 )
__SCREAMING_SNAKE_CASE = self.tokenizer.batch_decode(__SCREAMING_SNAKE_CASE , skip_special_tokens=__SCREAMING_SNAKE_CASE )
return generated_words
@slow
def _a ( self : Union[str, Any] ) -> Tuple:
"""simple docstring"""
self._assert_generated_batch_equal_expected()
| 690 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
lowerCAmelCase__ ={
"configuration_convnext": ["CONVNEXT_PRETRAINED_CONFIG_ARCHIVE_MAP", "ConvNextConfig", "ConvNextOnnxConfig"]
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase__ =["ConvNextFeatureExtractor"]
lowerCAmelCase__ =["ConvNextImageProcessor"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase__ =[
"CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST",
"ConvNextForImageClassification",
"ConvNextModel",
"ConvNextPreTrainedModel",
"ConvNextBackbone",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase__ =[
"TFConvNextForImageClassification",
"TFConvNextModel",
"TFConvNextPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_convnext import CONVNEXT_PRETRAINED_CONFIG_ARCHIVE_MAP, ConvNextConfig, ConvNextOnnxConfig
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_convnext import ConvNextFeatureExtractor
from .image_processing_convnext import ConvNextImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_convnext import (
CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
ConvNextBackbone,
ConvNextForImageClassification,
ConvNextModel,
ConvNextPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_convnext import TFConvNextForImageClassification, TFConvNextModel, TFConvNextPreTrainedModel
else:
import sys
lowerCAmelCase__ =_LazyModule(__name__, globals()["__file__"], _import_structure)
| 690 |
"""simple docstring"""
from ....configuration_utils import PretrainedConfig
from ....utils import logging
lowerCAmelCase__ =logging.get_logger(__name__)
lowerCAmelCase__ ={
"Visual-Attention-Network/van-base": (
"https://huggingface.co/Visual-Attention-Network/van-base/blob/main/config.json"
),
}
class A__( __magic_name__ ):
lowerCAmelCase = '''van'''
def __init__( self : int , __SCREAMING_SNAKE_CASE : Optional[Any]=2_24 , __SCREAMING_SNAKE_CASE : Union[str, Any]=3 , __SCREAMING_SNAKE_CASE : Tuple=[7, 3, 3, 3] , __SCREAMING_SNAKE_CASE : Optional[int]=[4, 2, 2, 2] , __SCREAMING_SNAKE_CASE : str=[64, 1_28, 3_20, 5_12] , __SCREAMING_SNAKE_CASE : Optional[Any]=[3, 3, 12, 3] , __SCREAMING_SNAKE_CASE : Dict=[8, 8, 4, 4] , __SCREAMING_SNAKE_CASE : Any="gelu" , __SCREAMING_SNAKE_CASE : Tuple=0.02 , __SCREAMING_SNAKE_CASE : Dict=1E-6 , __SCREAMING_SNAKE_CASE : Any=1E-2 , __SCREAMING_SNAKE_CASE : str=0.0 , __SCREAMING_SNAKE_CASE : Union[str, Any]=0.0 , **__SCREAMING_SNAKE_CASE : str , ) -> List[str]:
"""simple docstring"""
super().__init__(**__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = image_size
__SCREAMING_SNAKE_CASE = num_channels
__SCREAMING_SNAKE_CASE = patch_sizes
__SCREAMING_SNAKE_CASE = strides
__SCREAMING_SNAKE_CASE = hidden_sizes
__SCREAMING_SNAKE_CASE = depths
__SCREAMING_SNAKE_CASE = mlp_ratios
__SCREAMING_SNAKE_CASE = hidden_act
__SCREAMING_SNAKE_CASE = initializer_range
__SCREAMING_SNAKE_CASE = layer_norm_eps
__SCREAMING_SNAKE_CASE = layer_scale_init_value
__SCREAMING_SNAKE_CASE = drop_path_rate
__SCREAMING_SNAKE_CASE = dropout_rate
| 690 | 1 |
"""simple docstring"""
from __future__ import annotations
from collections.abc import Callable
lowerCAmelCase__ =list[list[float | int]]
def _a ( UpperCAmelCase__ , UpperCAmelCase__ ) -> Matrix:
__SCREAMING_SNAKE_CASE = len(UpperCAmelCase__ )
__SCREAMING_SNAKE_CASE = [[0 for _ in range(size + 1 )] for _ in range(UpperCAmelCase__ )]
__SCREAMING_SNAKE_CASE = 42
__SCREAMING_SNAKE_CASE = 42
__SCREAMING_SNAKE_CASE = 42
__SCREAMING_SNAKE_CASE = 42
__SCREAMING_SNAKE_CASE = 42
__SCREAMING_SNAKE_CASE = 42
for row in range(UpperCAmelCase__ ):
for col in range(UpperCAmelCase__ ):
__SCREAMING_SNAKE_CASE = matrix[row][col]
__SCREAMING_SNAKE_CASE = vector[row][0]
__SCREAMING_SNAKE_CASE = 0
__SCREAMING_SNAKE_CASE = 0
while row < size and col < size:
# pivoting
__SCREAMING_SNAKE_CASE = max((abs(augmented[rowa][col] ), rowa) for rowa in range(UpperCAmelCase__ , UpperCAmelCase__ ) )[
1
]
if augmented[pivot_row][col] == 0:
col += 1
continue
else:
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = augmented[pivot_row], augmented[row]
for rowa in range(row + 1 , UpperCAmelCase__ ):
__SCREAMING_SNAKE_CASE = augmented[rowa][col] / augmented[row][col]
__SCREAMING_SNAKE_CASE = 0
for cola in range(col + 1 , size + 1 ):
augmented[rowa][cola] -= augmented[row][cola] * ratio
row += 1
col += 1
# back substitution
for col in range(1 , UpperCAmelCase__ ):
for row in range(UpperCAmelCase__ ):
__SCREAMING_SNAKE_CASE = augmented[row][col] / augmented[col][col]
for cola in range(UpperCAmelCase__ , size + 1 ):
augmented[row][cola] -= augmented[col][cola] * ratio
# round to get rid of numbers like 2.000000000000004
return [
[round(augmented[row][size] / augmented[row][row] , 10 )] for row in range(UpperCAmelCase__ )
]
def _a ( UpperCAmelCase__ ) -> Callable[[int], int]:
__SCREAMING_SNAKE_CASE = len(UpperCAmelCase__ )
__SCREAMING_SNAKE_CASE = [[0 for _ in range(UpperCAmelCase__ )] for _ in range(UpperCAmelCase__ )]
__SCREAMING_SNAKE_CASE = [[0] for _ in range(UpperCAmelCase__ )]
__SCREAMING_SNAKE_CASE = 42
__SCREAMING_SNAKE_CASE = 42
__SCREAMING_SNAKE_CASE = 42
__SCREAMING_SNAKE_CASE = 42
for x_val, y_val in enumerate(UpperCAmelCase__ ):
for col in range(UpperCAmelCase__ ):
__SCREAMING_SNAKE_CASE = (x_val + 1) ** (size - col - 1)
__SCREAMING_SNAKE_CASE = y_val
__SCREAMING_SNAKE_CASE = solve(UpperCAmelCase__ , UpperCAmelCase__ )
def interpolated_func(UpperCAmelCase__ ) -> int:
return sum(
round(coeffs[x_val][0] ) * (var ** (size - x_val - 1))
for x_val in range(UpperCAmelCase__ ) )
return interpolated_func
def _a ( UpperCAmelCase__ ) -> int:
return (
1
- variable
+ variable**2
- variable**3
+ variable**4
- variable**5
+ variable**6
- variable**7
+ variable**8
- variable**9
+ variable**10
)
def _a ( UpperCAmelCase__ = question_function , UpperCAmelCase__ = 10 ) -> int:
__SCREAMING_SNAKE_CASE = [func(UpperCAmelCase__ ) for x_val in range(1 , order + 1 )]
__SCREAMING_SNAKE_CASE = [
interpolate(data_points[:max_coeff] ) for max_coeff in range(1 , order + 1 )
]
__SCREAMING_SNAKE_CASE = 0
__SCREAMING_SNAKE_CASE = 42
__SCREAMING_SNAKE_CASE = 42
for poly in polynomials:
__SCREAMING_SNAKE_CASE = 1
while func(UpperCAmelCase__ ) == poly(UpperCAmelCase__ ):
x_val += 1
ret += poly(UpperCAmelCase__ )
return ret
if __name__ == "__main__":
print(F'''{solution() = }''')
| 690 |
"""simple docstring"""
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCAmelCase__ ={"configuration_timm_backbone": ["TimmBackboneConfig"]}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase__ =["TimmBackbone"]
if TYPE_CHECKING:
from .configuration_timm_backbone import TimmBackboneConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_timm_backbone import TimmBackbone
else:
import sys
lowerCAmelCase__ =_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 690 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
lowerCAmelCase__ ={
"configuration_nezha": ["NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP", "NezhaConfig"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase__ =[
"NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST",
"NezhaForNextSentencePrediction",
"NezhaForMaskedLM",
"NezhaForPreTraining",
"NezhaForMultipleChoice",
"NezhaForQuestionAnswering",
"NezhaForSequenceClassification",
"NezhaForTokenClassification",
"NezhaModel",
"NezhaPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_nezha import NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP, NezhaConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_nezha import (
NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST,
NezhaForMaskedLM,
NezhaForMultipleChoice,
NezhaForNextSentencePrediction,
NezhaForPreTraining,
NezhaForQuestionAnswering,
NezhaForSequenceClassification,
NezhaForTokenClassification,
NezhaModel,
NezhaPreTrainedModel,
)
else:
import sys
lowerCAmelCase__ =_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 690 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
lowerCAmelCase__ ={
"configuration_altclip": [
"ALTCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP",
"AltCLIPConfig",
"AltCLIPTextConfig",
"AltCLIPVisionConfig",
],
"processing_altclip": ["AltCLIPProcessor"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase__ =[
"ALTCLIP_PRETRAINED_MODEL_ARCHIVE_LIST",
"AltCLIPPreTrainedModel",
"AltCLIPModel",
"AltCLIPTextModel",
"AltCLIPVisionModel",
]
if TYPE_CHECKING:
from .configuration_altclip import (
ALTCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
AltCLIPConfig,
AltCLIPTextConfig,
AltCLIPVisionConfig,
)
from .processing_altclip import AltCLIPProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_altclip import (
ALTCLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
AltCLIPModel,
AltCLIPPreTrainedModel,
AltCLIPTextModel,
AltCLIPVisionModel,
)
else:
import sys
lowerCAmelCase__ =_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 690 | 1 |
"""simple docstring"""
import json
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
from transformers import (
CONFIG_MAPPING,
IMAGE_PROCESSOR_MAPPING,
AutoConfig,
AutoImageProcessor,
CLIPConfig,
CLIPImageProcessor,
)
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER
sys.path.append(str(Path(__file__).parent.parent.parent.parent / "utils"))
from test_module.custom_configuration import CustomConfig # noqa E402
from test_module.custom_image_processing import CustomImageProcessor # noqa E402
class A__( unittest.TestCase ):
def _a ( self : int ) -> Union[str, Any]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = 0
def _a ( self : Tuple ) -> Tuple:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = AutoImageProcessor.from_pretrained('''openai/clip-vit-base-patch32''' )
self.assertIsInstance(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
def _a ( self : str ) -> Optional[int]:
"""simple docstring"""
with tempfile.TemporaryDirectory() as tmpdirname:
__SCREAMING_SNAKE_CASE = Path(__SCREAMING_SNAKE_CASE ) / '''preprocessor_config.json'''
__SCREAMING_SNAKE_CASE = Path(__SCREAMING_SNAKE_CASE ) / '''config.json'''
json.dump(
{'''image_processor_type''': '''CLIPImageProcessor''', '''processor_class''': '''CLIPProcessor'''} , open(__SCREAMING_SNAKE_CASE , '''w''' ) , )
json.dump({'''model_type''': '''clip'''} , open(__SCREAMING_SNAKE_CASE , '''w''' ) )
__SCREAMING_SNAKE_CASE = AutoImageProcessor.from_pretrained(__SCREAMING_SNAKE_CASE )
self.assertIsInstance(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
def _a ( self : List[str] ) -> Optional[int]:
"""simple docstring"""
with tempfile.TemporaryDirectory() as tmpdirname:
__SCREAMING_SNAKE_CASE = Path(__SCREAMING_SNAKE_CASE ) / '''preprocessor_config.json'''
__SCREAMING_SNAKE_CASE = Path(__SCREAMING_SNAKE_CASE ) / '''config.json'''
json.dump(
{'''feature_extractor_type''': '''CLIPFeatureExtractor''', '''processor_class''': '''CLIPProcessor'''} , open(__SCREAMING_SNAKE_CASE , '''w''' ) , )
json.dump({'''model_type''': '''clip'''} , open(__SCREAMING_SNAKE_CASE , '''w''' ) )
__SCREAMING_SNAKE_CASE = AutoImageProcessor.from_pretrained(__SCREAMING_SNAKE_CASE )
self.assertIsInstance(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
def _a ( self : str ) -> int:
"""simple docstring"""
with tempfile.TemporaryDirectory() as tmpdirname:
__SCREAMING_SNAKE_CASE = CLIPConfig()
# Create a dummy config file with image_proceesor_type
__SCREAMING_SNAKE_CASE = Path(__SCREAMING_SNAKE_CASE ) / '''preprocessor_config.json'''
__SCREAMING_SNAKE_CASE = Path(__SCREAMING_SNAKE_CASE ) / '''config.json'''
json.dump(
{'''image_processor_type''': '''CLIPImageProcessor''', '''processor_class''': '''CLIPProcessor'''} , open(__SCREAMING_SNAKE_CASE , '''w''' ) , )
json.dump({'''model_type''': '''clip'''} , open(__SCREAMING_SNAKE_CASE , '''w''' ) )
# remove image_processor_type to make sure config.json alone is enough to load image processor locally
__SCREAMING_SNAKE_CASE = AutoImageProcessor.from_pretrained(__SCREAMING_SNAKE_CASE ).to_dict()
config_dict.pop('''image_processor_type''' )
__SCREAMING_SNAKE_CASE = CLIPImageProcessor(**__SCREAMING_SNAKE_CASE )
# save in new folder
model_config.save_pretrained(__SCREAMING_SNAKE_CASE )
config.save_pretrained(__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = AutoImageProcessor.from_pretrained(__SCREAMING_SNAKE_CASE )
# make sure private variable is not incorrectly saved
__SCREAMING_SNAKE_CASE = json.loads(config.to_json_string() )
self.assertTrue('''_processor_class''' not in dict_as_saved )
self.assertIsInstance(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
def _a ( self : Optional[int] ) -> Tuple:
"""simple docstring"""
with tempfile.TemporaryDirectory() as tmpdirname:
__SCREAMING_SNAKE_CASE = Path(__SCREAMING_SNAKE_CASE ) / '''preprocessor_config.json'''
json.dump(
{'''image_processor_type''': '''CLIPImageProcessor''', '''processor_class''': '''CLIPProcessor'''} , open(__SCREAMING_SNAKE_CASE , '''w''' ) , )
__SCREAMING_SNAKE_CASE = AutoImageProcessor.from_pretrained(__SCREAMING_SNAKE_CASE )
self.assertIsInstance(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
def _a ( self : List[Any] ) -> str:
"""simple docstring"""
with self.assertRaisesRegex(
__SCREAMING_SNAKE_CASE , '''clip-base is not a local folder and is not a valid model identifier''' ):
__SCREAMING_SNAKE_CASE = AutoImageProcessor.from_pretrained('''clip-base''' )
def _a ( self : Any ) -> Optional[Any]:
"""simple docstring"""
with self.assertRaisesRegex(
__SCREAMING_SNAKE_CASE , r'''aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)''' ):
__SCREAMING_SNAKE_CASE = AutoImageProcessor.from_pretrained(__SCREAMING_SNAKE_CASE , revision='''aaaaaa''' )
def _a ( self : Dict ) -> Dict:
"""simple docstring"""
with self.assertRaisesRegex(
__SCREAMING_SNAKE_CASE , '''hf-internal-testing/config-no-model does not appear to have a file named preprocessor_config.json.''' , ):
__SCREAMING_SNAKE_CASE = AutoImageProcessor.from_pretrained('''hf-internal-testing/config-no-model''' )
def _a ( self : int ) -> Any:
"""simple docstring"""
with self.assertRaises(__SCREAMING_SNAKE_CASE ):
__SCREAMING_SNAKE_CASE = AutoImageProcessor.from_pretrained('''hf-internal-testing/test_dynamic_image_processor''' )
# If remote code is disabled, we can't load this config.
with self.assertRaises(__SCREAMING_SNAKE_CASE ):
__SCREAMING_SNAKE_CASE = AutoImageProcessor.from_pretrained(
'''hf-internal-testing/test_dynamic_image_processor''' , trust_remote_code=__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = AutoImageProcessor.from_pretrained(
'''hf-internal-testing/test_dynamic_image_processor''' , trust_remote_code=__SCREAMING_SNAKE_CASE )
self.assertEqual(image_processor.__class__.__name__ , '''NewImageProcessor''' )
# Test image processor can be reloaded.
with tempfile.TemporaryDirectory() as tmp_dir:
image_processor.save_pretrained(__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = AutoImageProcessor.from_pretrained(__SCREAMING_SNAKE_CASE , trust_remote_code=__SCREAMING_SNAKE_CASE )
self.assertEqual(reloaded_image_processor.__class__.__name__ , '''NewImageProcessor''' )
def _a ( self : Optional[Any] ) -> str:
"""simple docstring"""
try:
AutoConfig.register('''custom''' , __SCREAMING_SNAKE_CASE )
AutoImageProcessor.register(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
# Trying to register something existing in the Transformers library will raise an error
with self.assertRaises(__SCREAMING_SNAKE_CASE ):
AutoImageProcessor.register(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
with tempfile.TemporaryDirectory() as tmpdirname:
__SCREAMING_SNAKE_CASE = Path(__SCREAMING_SNAKE_CASE ) / '''preprocessor_config.json'''
__SCREAMING_SNAKE_CASE = Path(__SCREAMING_SNAKE_CASE ) / '''config.json'''
json.dump(
{'''feature_extractor_type''': '''CLIPFeatureExtractor''', '''processor_class''': '''CLIPProcessor'''} , open(__SCREAMING_SNAKE_CASE , '''w''' ) , )
json.dump({'''model_type''': '''clip'''} , open(__SCREAMING_SNAKE_CASE , '''w''' ) )
__SCREAMING_SNAKE_CASE = CustomImageProcessor.from_pretrained(__SCREAMING_SNAKE_CASE )
# Now that the config is registered, it can be used as any other config with the auto-API
with tempfile.TemporaryDirectory() as tmp_dir:
image_processor.save_pretrained(__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = AutoImageProcessor.from_pretrained(__SCREAMING_SNAKE_CASE )
self.assertIsInstance(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
finally:
if "custom" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["custom"]
if CustomConfig in IMAGE_PROCESSOR_MAPPING._extra_content:
del IMAGE_PROCESSOR_MAPPING._extra_content[CustomConfig]
def _a ( self : int ) -> List[Any]:
"""simple docstring"""
class A__( __magic_name__ ):
lowerCAmelCase = True
try:
AutoConfig.register('''custom''' , __SCREAMING_SNAKE_CASE )
AutoImageProcessor.register(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
# If remote code is not set, the default is to use local
__SCREAMING_SNAKE_CASE = AutoImageProcessor.from_pretrained('''hf-internal-testing/test_dynamic_image_processor''' )
self.assertEqual(image_processor.__class__.__name__ , '''NewImageProcessor''' )
self.assertTrue(image_processor.is_local )
# If remote code is disabled, we load the local one.
__SCREAMING_SNAKE_CASE = AutoImageProcessor.from_pretrained(
'''hf-internal-testing/test_dynamic_image_processor''' , trust_remote_code=__SCREAMING_SNAKE_CASE )
self.assertEqual(image_processor.__class__.__name__ , '''NewImageProcessor''' )
self.assertTrue(image_processor.is_local )
# If remote is enabled, we load from the Hub
__SCREAMING_SNAKE_CASE = AutoImageProcessor.from_pretrained(
'''hf-internal-testing/test_dynamic_image_processor''' , trust_remote_code=__SCREAMING_SNAKE_CASE )
self.assertEqual(image_processor.__class__.__name__ , '''NewImageProcessor''' )
self.assertTrue(not hasattr(__SCREAMING_SNAKE_CASE , '''is_local''' ) )
finally:
if "custom" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["custom"]
if CustomConfig in IMAGE_PROCESSOR_MAPPING._extra_content:
del IMAGE_PROCESSOR_MAPPING._extra_content[CustomConfig]
| 690 |
"""simple docstring"""
import json
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
from transformers import (
CONFIG_MAPPING,
IMAGE_PROCESSOR_MAPPING,
AutoConfig,
AutoImageProcessor,
CLIPConfig,
CLIPImageProcessor,
)
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER
sys.path.append(str(Path(__file__).parent.parent.parent.parent / "utils"))
from test_module.custom_configuration import CustomConfig # noqa E402
from test_module.custom_image_processing import CustomImageProcessor # noqa E402
class A__( unittest.TestCase ):
def _a ( self : int ) -> Union[str, Any]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = 0
def _a ( self : Tuple ) -> Tuple:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = AutoImageProcessor.from_pretrained('''openai/clip-vit-base-patch32''' )
self.assertIsInstance(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
def _a ( self : str ) -> Optional[int]:
"""simple docstring"""
with tempfile.TemporaryDirectory() as tmpdirname:
__SCREAMING_SNAKE_CASE = Path(__SCREAMING_SNAKE_CASE ) / '''preprocessor_config.json'''
__SCREAMING_SNAKE_CASE = Path(__SCREAMING_SNAKE_CASE ) / '''config.json'''
json.dump(
{'''image_processor_type''': '''CLIPImageProcessor''', '''processor_class''': '''CLIPProcessor'''} , open(__SCREAMING_SNAKE_CASE , '''w''' ) , )
json.dump({'''model_type''': '''clip'''} , open(__SCREAMING_SNAKE_CASE , '''w''' ) )
__SCREAMING_SNAKE_CASE = AutoImageProcessor.from_pretrained(__SCREAMING_SNAKE_CASE )
self.assertIsInstance(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
def _a ( self : List[str] ) -> Optional[int]:
"""simple docstring"""
with tempfile.TemporaryDirectory() as tmpdirname:
__SCREAMING_SNAKE_CASE = Path(__SCREAMING_SNAKE_CASE ) / '''preprocessor_config.json'''
__SCREAMING_SNAKE_CASE = Path(__SCREAMING_SNAKE_CASE ) / '''config.json'''
json.dump(
{'''feature_extractor_type''': '''CLIPFeatureExtractor''', '''processor_class''': '''CLIPProcessor'''} , open(__SCREAMING_SNAKE_CASE , '''w''' ) , )
json.dump({'''model_type''': '''clip'''} , open(__SCREAMING_SNAKE_CASE , '''w''' ) )
__SCREAMING_SNAKE_CASE = AutoImageProcessor.from_pretrained(__SCREAMING_SNAKE_CASE )
self.assertIsInstance(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
def _a ( self : str ) -> int:
"""simple docstring"""
with tempfile.TemporaryDirectory() as tmpdirname:
__SCREAMING_SNAKE_CASE = CLIPConfig()
# Create a dummy config file with image_proceesor_type
__SCREAMING_SNAKE_CASE = Path(__SCREAMING_SNAKE_CASE ) / '''preprocessor_config.json'''
__SCREAMING_SNAKE_CASE = Path(__SCREAMING_SNAKE_CASE ) / '''config.json'''
json.dump(
{'''image_processor_type''': '''CLIPImageProcessor''', '''processor_class''': '''CLIPProcessor'''} , open(__SCREAMING_SNAKE_CASE , '''w''' ) , )
json.dump({'''model_type''': '''clip'''} , open(__SCREAMING_SNAKE_CASE , '''w''' ) )
# remove image_processor_type to make sure config.json alone is enough to load image processor locally
__SCREAMING_SNAKE_CASE = AutoImageProcessor.from_pretrained(__SCREAMING_SNAKE_CASE ).to_dict()
config_dict.pop('''image_processor_type''' )
__SCREAMING_SNAKE_CASE = CLIPImageProcessor(**__SCREAMING_SNAKE_CASE )
# save in new folder
model_config.save_pretrained(__SCREAMING_SNAKE_CASE )
config.save_pretrained(__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = AutoImageProcessor.from_pretrained(__SCREAMING_SNAKE_CASE )
# make sure private variable is not incorrectly saved
__SCREAMING_SNAKE_CASE = json.loads(config.to_json_string() )
self.assertTrue('''_processor_class''' not in dict_as_saved )
self.assertIsInstance(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
def _a ( self : Optional[int] ) -> Tuple:
"""simple docstring"""
with tempfile.TemporaryDirectory() as tmpdirname:
__SCREAMING_SNAKE_CASE = Path(__SCREAMING_SNAKE_CASE ) / '''preprocessor_config.json'''
json.dump(
{'''image_processor_type''': '''CLIPImageProcessor''', '''processor_class''': '''CLIPProcessor'''} , open(__SCREAMING_SNAKE_CASE , '''w''' ) , )
__SCREAMING_SNAKE_CASE = AutoImageProcessor.from_pretrained(__SCREAMING_SNAKE_CASE )
self.assertIsInstance(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
def _a ( self : List[Any] ) -> str:
"""simple docstring"""
with self.assertRaisesRegex(
__SCREAMING_SNAKE_CASE , '''clip-base is not a local folder and is not a valid model identifier''' ):
__SCREAMING_SNAKE_CASE = AutoImageProcessor.from_pretrained('''clip-base''' )
def _a ( self : Any ) -> Optional[Any]:
"""simple docstring"""
with self.assertRaisesRegex(
__SCREAMING_SNAKE_CASE , r'''aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)''' ):
__SCREAMING_SNAKE_CASE = AutoImageProcessor.from_pretrained(__SCREAMING_SNAKE_CASE , revision='''aaaaaa''' )
def _a ( self : Dict ) -> Dict:
"""simple docstring"""
with self.assertRaisesRegex(
__SCREAMING_SNAKE_CASE , '''hf-internal-testing/config-no-model does not appear to have a file named preprocessor_config.json.''' , ):
__SCREAMING_SNAKE_CASE = AutoImageProcessor.from_pretrained('''hf-internal-testing/config-no-model''' )
def _a ( self : int ) -> Any:
"""simple docstring"""
with self.assertRaises(__SCREAMING_SNAKE_CASE ):
__SCREAMING_SNAKE_CASE = AutoImageProcessor.from_pretrained('''hf-internal-testing/test_dynamic_image_processor''' )
# If remote code is disabled, we can't load this config.
with self.assertRaises(__SCREAMING_SNAKE_CASE ):
__SCREAMING_SNAKE_CASE = AutoImageProcessor.from_pretrained(
'''hf-internal-testing/test_dynamic_image_processor''' , trust_remote_code=__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = AutoImageProcessor.from_pretrained(
'''hf-internal-testing/test_dynamic_image_processor''' , trust_remote_code=__SCREAMING_SNAKE_CASE )
self.assertEqual(image_processor.__class__.__name__ , '''NewImageProcessor''' )
# Test image processor can be reloaded.
with tempfile.TemporaryDirectory() as tmp_dir:
image_processor.save_pretrained(__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = AutoImageProcessor.from_pretrained(__SCREAMING_SNAKE_CASE , trust_remote_code=__SCREAMING_SNAKE_CASE )
self.assertEqual(reloaded_image_processor.__class__.__name__ , '''NewImageProcessor''' )
def _a ( self : Optional[Any] ) -> str:
"""simple docstring"""
try:
AutoConfig.register('''custom''' , __SCREAMING_SNAKE_CASE )
AutoImageProcessor.register(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
# Trying to register something existing in the Transformers library will raise an error
with self.assertRaises(__SCREAMING_SNAKE_CASE ):
AutoImageProcessor.register(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
with tempfile.TemporaryDirectory() as tmpdirname:
__SCREAMING_SNAKE_CASE = Path(__SCREAMING_SNAKE_CASE ) / '''preprocessor_config.json'''
__SCREAMING_SNAKE_CASE = Path(__SCREAMING_SNAKE_CASE ) / '''config.json'''
json.dump(
{'''feature_extractor_type''': '''CLIPFeatureExtractor''', '''processor_class''': '''CLIPProcessor'''} , open(__SCREAMING_SNAKE_CASE , '''w''' ) , )
json.dump({'''model_type''': '''clip'''} , open(__SCREAMING_SNAKE_CASE , '''w''' ) )
__SCREAMING_SNAKE_CASE = CustomImageProcessor.from_pretrained(__SCREAMING_SNAKE_CASE )
# Now that the config is registered, it can be used as any other config with the auto-API
with tempfile.TemporaryDirectory() as tmp_dir:
image_processor.save_pretrained(__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = AutoImageProcessor.from_pretrained(__SCREAMING_SNAKE_CASE )
self.assertIsInstance(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
finally:
if "custom" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["custom"]
if CustomConfig in IMAGE_PROCESSOR_MAPPING._extra_content:
del IMAGE_PROCESSOR_MAPPING._extra_content[CustomConfig]
def _a ( self : int ) -> List[Any]:
"""simple docstring"""
class A__( __magic_name__ ):
lowerCAmelCase = True
try:
AutoConfig.register('''custom''' , __SCREAMING_SNAKE_CASE )
AutoImageProcessor.register(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
# If remote code is not set, the default is to use local
__SCREAMING_SNAKE_CASE = AutoImageProcessor.from_pretrained('''hf-internal-testing/test_dynamic_image_processor''' )
self.assertEqual(image_processor.__class__.__name__ , '''NewImageProcessor''' )
self.assertTrue(image_processor.is_local )
# If remote code is disabled, we load the local one.
__SCREAMING_SNAKE_CASE = AutoImageProcessor.from_pretrained(
'''hf-internal-testing/test_dynamic_image_processor''' , trust_remote_code=__SCREAMING_SNAKE_CASE )
self.assertEqual(image_processor.__class__.__name__ , '''NewImageProcessor''' )
self.assertTrue(image_processor.is_local )
# If remote is enabled, we load from the Hub
__SCREAMING_SNAKE_CASE = AutoImageProcessor.from_pretrained(
'''hf-internal-testing/test_dynamic_image_processor''' , trust_remote_code=__SCREAMING_SNAKE_CASE )
self.assertEqual(image_processor.__class__.__name__ , '''NewImageProcessor''' )
self.assertTrue(not hasattr(__SCREAMING_SNAKE_CASE , '''is_local''' ) )
finally:
if "custom" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["custom"]
if CustomConfig in IMAGE_PROCESSOR_MAPPING._extra_content:
del IMAGE_PROCESSOR_MAPPING._extra_content[CustomConfig]
| 690 | 1 |
"""simple docstring"""
import os
import socket
from contextlib import contextmanager
import torch
from ..commands.config.default import write_basic_config # noqa: F401
from ..state import PartialState
from .dataclasses import DistributedType
from .imports import is_deepspeed_available, is_tpu_available
from .transformer_engine import convert_model
from .versions import is_torch_version
if is_deepspeed_available():
from deepspeed import DeepSpeedEngine
if is_tpu_available(check_device=False):
import torch_xla.core.xla_model as xm
def _a ( UpperCAmelCase__ ) -> Tuple:
if is_torch_version('''<''' , '''2.0.0''' ) or not hasattr(UpperCAmelCase__ , '''_dynamo''' ):
return False
return isinstance(UpperCAmelCase__ , torch._dynamo.eval_frame.OptimizedModule )
def _a ( UpperCAmelCase__ , UpperCAmelCase__ = True ) -> Dict:
__SCREAMING_SNAKE_CASE = (torch.nn.parallel.DistributedDataParallel, torch.nn.DataParallel)
__SCREAMING_SNAKE_CASE = is_compiled_module(UpperCAmelCase__ )
if is_compiled:
__SCREAMING_SNAKE_CASE = model
__SCREAMING_SNAKE_CASE = model._orig_mod
if is_deepspeed_available():
options += (DeepSpeedEngine,)
while isinstance(UpperCAmelCase__ , UpperCAmelCase__ ):
__SCREAMING_SNAKE_CASE = model.module
if not keep_fpaa_wrapper:
__SCREAMING_SNAKE_CASE = getattr(UpperCAmelCase__ , '''forward''' )
__SCREAMING_SNAKE_CASE = model.__dict__.pop('''_original_forward''' , UpperCAmelCase__ )
if original_forward is not None:
while hasattr(UpperCAmelCase__ , '''__wrapped__''' ):
__SCREAMING_SNAKE_CASE = forward.__wrapped__
if forward == original_forward:
break
__SCREAMING_SNAKE_CASE = forward
if getattr(UpperCAmelCase__ , '''_converted_to_transformer_engine''' , UpperCAmelCase__ ):
convert_model(UpperCAmelCase__ , to_transformer_engine=UpperCAmelCase__ )
if is_compiled:
__SCREAMING_SNAKE_CASE = model
__SCREAMING_SNAKE_CASE = compiled_model
return model
def _a ( ) -> Dict:
PartialState().wait_for_everyone()
def _a ( UpperCAmelCase__ , UpperCAmelCase__ ) -> str:
if PartialState().distributed_type == DistributedType.TPU:
xm.save(UpperCAmelCase__ , UpperCAmelCase__ )
elif PartialState().local_process_index == 0:
torch.save(UpperCAmelCase__ , UpperCAmelCase__ )
@contextmanager
def _a ( **UpperCAmelCase__ ) -> List[Any]:
for key, value in kwargs.items():
__SCREAMING_SNAKE_CASE = str(UpperCAmelCase__ )
yield
for key in kwargs:
if key.upper() in os.environ:
del os.environ[key.upper()]
def _a ( UpperCAmelCase__ ) -> Tuple:
if not hasattr(UpperCAmelCase__ , '''__qualname__''' ) and not hasattr(UpperCAmelCase__ , '''__name__''' ):
__SCREAMING_SNAKE_CASE = getattr(UpperCAmelCase__ , '''__class__''' , UpperCAmelCase__ )
if hasattr(UpperCAmelCase__ , '''__qualname__''' ):
return obj.__qualname__
if hasattr(UpperCAmelCase__ , '''__name__''' ):
return obj.__name__
return str(UpperCAmelCase__ )
def _a ( UpperCAmelCase__ , UpperCAmelCase__ ) -> Union[str, Any]:
for key, value in source.items():
if isinstance(UpperCAmelCase__ , UpperCAmelCase__ ):
__SCREAMING_SNAKE_CASE = destination.setdefault(UpperCAmelCase__ , {} )
merge_dicts(UpperCAmelCase__ , UpperCAmelCase__ )
else:
__SCREAMING_SNAKE_CASE = value
return destination
def _a ( UpperCAmelCase__ = None ) -> bool:
if port is None:
__SCREAMING_SNAKE_CASE = 2_95_00
with socket.socket(socket.AF_INET , socket.SOCK_STREAM ) as s:
return s.connect_ex(('''localhost''', port) ) == 0
| 690 |
"""simple docstring"""
import math
lowerCAmelCase__ =10
lowerCAmelCase__ =7
lowerCAmelCase__ =BALLS_PER_COLOUR * NUM_COLOURS
def _a ( UpperCAmelCase__ = 20 ) -> str:
__SCREAMING_SNAKE_CASE = math.comb(UpperCAmelCase__ , UpperCAmelCase__ )
__SCREAMING_SNAKE_CASE = math.comb(NUM_BALLS - BALLS_PER_COLOUR , UpperCAmelCase__ )
__SCREAMING_SNAKE_CASE = NUM_COLOURS * (1 - missing_colour / total)
return f"""{result:.9f}"""
if __name__ == "__main__":
print(solution(20))
| 690 | 1 |
"""simple docstring"""
from typing import List, Optional, Union
import numpy as np
import tensorflow as tf
from .utils import logging
lowerCAmelCase__ =logging.get_logger(__name__)
def _a ( UpperCAmelCase__ ) -> List[int]:
if isinstance(UpperCAmelCase__ , np.ndarray ):
return list(tensor.shape )
__SCREAMING_SNAKE_CASE = tf.shape(UpperCAmelCase__ )
if tensor.shape == tf.TensorShape(UpperCAmelCase__ ):
return dynamic
__SCREAMING_SNAKE_CASE = tensor.shape.as_list()
return [dynamic[i] if s is None else s for i, s in enumerate(UpperCAmelCase__ )]
def _a ( UpperCAmelCase__ , UpperCAmelCase__ = None , UpperCAmelCase__ = None ) -> tf.Tensor:
return tf.nn.softmax(logits=logits + 1E-9 , axis=UpperCAmelCase__ , name=UpperCAmelCase__ )
def _a ( UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__=1E-5 , UpperCAmelCase__=-1 ) -> List[str]:
# This is a very simplified functional layernorm, designed to duplicate
# the functionality of PyTorch nn.functional.layer_norm when this is needed to port
# models in Transformers.
if weight.shape.rank != 1 or bias.shape.rank != 1 or not isinstance(UpperCAmelCase__ , UpperCAmelCase__ ):
raise NotImplementedError('''Only 1D weight and bias tensors are supported for now, with only a single axis.''' )
# Get mean and variance on the axis to be normalized
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = tf.nn.moments(UpperCAmelCase__ , axes=[axis] , keepdims=UpperCAmelCase__ )
if axis != -1:
# Reshape scale and weight to have the same rank as inputs, but with 1 dimensions
# on every dimension except axis
__SCREAMING_SNAKE_CASE = [1] * inputs.shape.rank
__SCREAMING_SNAKE_CASE = shape_list(UpperCAmelCase__ )[axis]
__SCREAMING_SNAKE_CASE = tf.reshape(UpperCAmelCase__ , UpperCAmelCase__ )
__SCREAMING_SNAKE_CASE = tf.reshape(UpperCAmelCase__ , UpperCAmelCase__ )
# Compute layer normalization using the batch_normalization
# function.
__SCREAMING_SNAKE_CASE = tf.nn.batch_normalization(
UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , offset=UpperCAmelCase__ , scale=UpperCAmelCase__ , variance_epsilon=UpperCAmelCase__ , )
return outputs
def _a ( UpperCAmelCase__ , UpperCAmelCase__=0 , UpperCAmelCase__=-1 ) -> List[str]:
# Replicates the behavior of torch.flatten in TF
# If end_dim or start_dim is negative, count them from the end
if end_dim < 0:
end_dim += input.shape.rank
if start_dim < 0:
start_dim += input.shape.rank
if start_dim == end_dim:
return input
__SCREAMING_SNAKE_CASE = tf.shape(UpperCAmelCase__ )
__SCREAMING_SNAKE_CASE = tf.math.reduce_prod(in_shape[start_dim : end_dim + 1] )
__SCREAMING_SNAKE_CASE = tf.concat([in_shape[:start_dim], [flattened_dim], in_shape[end_dim + 1 :]] , axis=0 )
return tf.reshape(UpperCAmelCase__ , UpperCAmelCase__ )
def _a ( UpperCAmelCase__ ) -> tf.Tensor:
if not isinstance(UpperCAmelCase__ , tf.Tensor ):
__SCREAMING_SNAKE_CASE = tf.convert_to_tensor(UpperCAmelCase__ ) # Catches stray NumPy inputs
if encoder_attention_mask.shape.rank == 3:
__SCREAMING_SNAKE_CASE = encoder_attention_mask[:, None, :, :]
if encoder_attention_mask.shape.rank == 2:
__SCREAMING_SNAKE_CASE = encoder_attention_mask[:, None, None, :]
# T5 has a mask that can compare sequence ids, we can simulate this here with this transposition
# Cf. https://github.com/tensorflow/mesh/blob/8d2465e9bc93129b913b5ccc6a59aa97abd96ec6/mesh_tensorflow
# /transformer/transformer_layers.py#L270
# encoder_extended_attention_mask = (encoder_extended_attention_mask ==
# encoder_extended_attention_mask.transpose(-1, -2))
__SCREAMING_SNAKE_CASE = (
tf.cast(1 , encoder_attention_mask.dtype ) - encoder_extended_attention_mask
) * encoder_extended_attention_mask.dtype.min
return encoder_extended_attention_mask
def _a ( UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ = "input_ids" ) -> None:
tf.debugging.assert_less(
UpperCAmelCase__ , tf.cast(UpperCAmelCase__ , dtype=tensor.dtype ) , message=(
f"""The maximum value of {tensor_name} ({tf.math.reduce_max(UpperCAmelCase__ )}) must be smaller than the embedding """
f"""layer's input dimension ({embed_dim}). The likely cause is some problem at tokenization time."""
) , )
def _a ( UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ ) -> Optional[int]:
__SCREAMING_SNAKE_CASE = 6_45_12
# Check that no item in `data` is larger than `HDF5_OBJECT_HEADER_LIMIT`
# because in that case even chunking the array would not make the saving
# possible.
__SCREAMING_SNAKE_CASE = [x for x in data if len(UpperCAmelCase__ ) > HDF5_OBJECT_HEADER_LIMIT]
# Expecting this to never be true.
if bad_attributes:
raise RuntimeError(
'''The following attributes cannot be saved to HDF5 file because '''
f"""they are larger than {HDF5_OBJECT_HEADER_LIMIT} """
f"""bytes: {bad_attributes}""" )
__SCREAMING_SNAKE_CASE = np.asarray(UpperCAmelCase__ )
__SCREAMING_SNAKE_CASE = 1
__SCREAMING_SNAKE_CASE = np.array_split(UpperCAmelCase__ , UpperCAmelCase__ )
# This will never loop forever thanks to the test above.
while any(x.nbytes > HDF5_OBJECT_HEADER_LIMIT for x in chunked_data ):
num_chunks += 1
__SCREAMING_SNAKE_CASE = np.array_split(UpperCAmelCase__ , UpperCAmelCase__ )
if num_chunks > 1:
for chunk_id, chunk_data in enumerate(UpperCAmelCase__ ):
__SCREAMING_SNAKE_CASE = chunk_data
else:
__SCREAMING_SNAKE_CASE = data
def _a ( UpperCAmelCase__ , UpperCAmelCase__ ) -> int:
if name in group.attrs:
__SCREAMING_SNAKE_CASE = [n.decode('''utf8''' ) if hasattr(UpperCAmelCase__ , '''decode''' ) else n for n in group.attrs[name]]
else:
__SCREAMING_SNAKE_CASE = []
__SCREAMING_SNAKE_CASE = 0
while "%s%d" % (name, chunk_id) in group.attrs:
data.extend(
[n.decode('''utf8''' ) if hasattr(UpperCAmelCase__ , '''decode''' ) else n for n in group.attrs['''%s%d''' % (name, chunk_id)]] )
chunk_id += 1
return data
def _a ( UpperCAmelCase__ ) -> Union[str, Any]:
def _expand_single_ad_tensor(UpperCAmelCase__ ):
if isinstance(UpperCAmelCase__ , tf.Tensor ) and t.shape.rank == 1:
return tf.expand_dims(UpperCAmelCase__ , axis=-1 )
return t
return tf.nest.map_structure(_expand_single_ad_tensor , UpperCAmelCase__ )
| 690 |
"""simple docstring"""
from collections import UserDict
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_torch_available():
from ..models.auto.modeling_auto import MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING
if is_tf_available():
from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING
from ..tf_utils import stable_softmax
lowerCAmelCase__ =logging.get_logger(__name__)
@add_end_docstrings(__magic_name__ )
class A__( __magic_name__ ):
def __init__( self : Optional[Any] , **__SCREAMING_SNAKE_CASE : str ) -> Optional[Any]:
"""simple docstring"""
super().__init__(**__SCREAMING_SNAKE_CASE )
requires_backends(self , '''vision''' )
self.check_model_type(
TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING
if self.framework == '''tf'''
else MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING )
def __call__( self : Optional[Any] , __SCREAMING_SNAKE_CASE : Union[str, List[str], "Image", List["Image"]] , **__SCREAMING_SNAKE_CASE : Union[str, Any] ) -> Tuple:
"""simple docstring"""
return super().__call__(__SCREAMING_SNAKE_CASE , **__SCREAMING_SNAKE_CASE )
def _a ( self : int , **__SCREAMING_SNAKE_CASE : int ) -> Any:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = {}
if "candidate_labels" in kwargs:
__SCREAMING_SNAKE_CASE = kwargs['''candidate_labels''']
if "hypothesis_template" in kwargs:
__SCREAMING_SNAKE_CASE = kwargs['''hypothesis_template''']
return preprocess_params, {}, {}
def _a ( self : Any , __SCREAMING_SNAKE_CASE : Any , __SCREAMING_SNAKE_CASE : Union[str, Any]=None , __SCREAMING_SNAKE_CASE : Optional[int]="This is a photo of {}." ) -> Union[str, Any]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = load_image(__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = self.image_processor(images=[image] , return_tensors=self.framework )
__SCREAMING_SNAKE_CASE = candidate_labels
__SCREAMING_SNAKE_CASE = [hypothesis_template.format(__SCREAMING_SNAKE_CASE ) for x in candidate_labels]
__SCREAMING_SNAKE_CASE = self.tokenizer(__SCREAMING_SNAKE_CASE , return_tensors=self.framework , padding=__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = [text_inputs]
return inputs
def _a ( self : Dict , __SCREAMING_SNAKE_CASE : List[Any] ) -> int:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = model_inputs.pop('''candidate_labels''' )
__SCREAMING_SNAKE_CASE = model_inputs.pop('''text_inputs''' )
if isinstance(text_inputs[0] , __SCREAMING_SNAKE_CASE ):
__SCREAMING_SNAKE_CASE = text_inputs[0]
else:
# Batching case.
__SCREAMING_SNAKE_CASE = text_inputs[0][0]
__SCREAMING_SNAKE_CASE = self.model(**__SCREAMING_SNAKE_CASE , **__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = {
'''candidate_labels''': candidate_labels,
'''logits''': outputs.logits_per_image,
}
return model_outputs
def _a ( self : Any , __SCREAMING_SNAKE_CASE : List[str] ) -> Optional[int]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = model_outputs.pop('''candidate_labels''' )
__SCREAMING_SNAKE_CASE = model_outputs['''logits'''][0]
if self.framework == "pt":
__SCREAMING_SNAKE_CASE = logits.softmax(dim=-1 ).squeeze(-1 )
__SCREAMING_SNAKE_CASE = probs.tolist()
if not isinstance(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
__SCREAMING_SNAKE_CASE = [scores]
elif self.framework == "tf":
__SCREAMING_SNAKE_CASE = stable_softmax(__SCREAMING_SNAKE_CASE , axis=-1 )
__SCREAMING_SNAKE_CASE = probs.numpy().tolist()
else:
raise ValueError(f"""Unsupported framework: {self.framework}""" )
__SCREAMING_SNAKE_CASE = [
{'''score''': score, '''label''': candidate_label}
for score, candidate_label in sorted(zip(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) , key=lambda __SCREAMING_SNAKE_CASE : -x[0] )
]
return result
| 690 | 1 |
"""simple docstring"""
import torch
from torch import nn
from transformers import CLIPPreTrainedModel, CLIPVisionModel
from ...models.attention import BasicTransformerBlock
from ...utils import logging
lowerCAmelCase__ =logging.get_logger(__name__) # pylint: disable=invalid-name
class A__( __magic_name__ ):
def __init__( self : List[Any] , __SCREAMING_SNAKE_CASE : Optional[int] , __SCREAMING_SNAKE_CASE : Optional[int]=7_68 ) -> List[str]:
"""simple docstring"""
super().__init__(__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = proj_size
__SCREAMING_SNAKE_CASE = CLIPVisionModel(__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = PaintByExampleMapper(__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = nn.LayerNorm(config.hidden_size )
__SCREAMING_SNAKE_CASE = nn.Linear(config.hidden_size , self.proj_size )
# uncondition for scaling
__SCREAMING_SNAKE_CASE = nn.Parameter(torch.randn((1, 1, self.proj_size) ) )
def _a ( self : int , __SCREAMING_SNAKE_CASE : Optional[int] , __SCREAMING_SNAKE_CASE : List[str]=False ) -> Union[str, Any]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = self.model(pixel_values=__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = clip_output.pooler_output
__SCREAMING_SNAKE_CASE = self.mapper(latent_states[:, None] )
__SCREAMING_SNAKE_CASE = self.final_layer_norm(__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = self.proj_out(__SCREAMING_SNAKE_CASE )
if return_uncond_vector:
return latent_states, self.uncond_vector
return latent_states
class A__( nn.Module ):
def __init__( self : Optional[Any] , __SCREAMING_SNAKE_CASE : List[Any] ) -> Optional[Any]:
"""simple docstring"""
super().__init__()
__SCREAMING_SNAKE_CASE = (config.num_hidden_layers + 1) // 5
__SCREAMING_SNAKE_CASE = config.hidden_size
__SCREAMING_SNAKE_CASE = 1
__SCREAMING_SNAKE_CASE = nn.ModuleList(
[
BasicTransformerBlock(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , activation_fn='''gelu''' , attention_bias=__SCREAMING_SNAKE_CASE )
for _ in range(__SCREAMING_SNAKE_CASE )
] )
def _a ( self : Tuple , __SCREAMING_SNAKE_CASE : List[str] ) -> Any:
"""simple docstring"""
for block in self.blocks:
__SCREAMING_SNAKE_CASE = block(__SCREAMING_SNAKE_CASE )
return hidden_states
| 690 |
"""simple docstring"""
from __future__ import annotations
from collections.abc import Callable
lowerCAmelCase__ =list[list[float | int]]
def _a ( UpperCAmelCase__ , UpperCAmelCase__ ) -> Matrix:
__SCREAMING_SNAKE_CASE = len(UpperCAmelCase__ )
__SCREAMING_SNAKE_CASE = [[0 for _ in range(size + 1 )] for _ in range(UpperCAmelCase__ )]
__SCREAMING_SNAKE_CASE = 42
__SCREAMING_SNAKE_CASE = 42
__SCREAMING_SNAKE_CASE = 42
__SCREAMING_SNAKE_CASE = 42
__SCREAMING_SNAKE_CASE = 42
__SCREAMING_SNAKE_CASE = 42
for row in range(UpperCAmelCase__ ):
for col in range(UpperCAmelCase__ ):
__SCREAMING_SNAKE_CASE = matrix[row][col]
__SCREAMING_SNAKE_CASE = vector[row][0]
__SCREAMING_SNAKE_CASE = 0
__SCREAMING_SNAKE_CASE = 0
while row < size and col < size:
# pivoting
__SCREAMING_SNAKE_CASE = max((abs(augmented[rowa][col] ), rowa) for rowa in range(UpperCAmelCase__ , UpperCAmelCase__ ) )[
1
]
if augmented[pivot_row][col] == 0:
col += 1
continue
else:
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = augmented[pivot_row], augmented[row]
for rowa in range(row + 1 , UpperCAmelCase__ ):
__SCREAMING_SNAKE_CASE = augmented[rowa][col] / augmented[row][col]
__SCREAMING_SNAKE_CASE = 0
for cola in range(col + 1 , size + 1 ):
augmented[rowa][cola] -= augmented[row][cola] * ratio
row += 1
col += 1
# back substitution
for col in range(1 , UpperCAmelCase__ ):
for row in range(UpperCAmelCase__ ):
__SCREAMING_SNAKE_CASE = augmented[row][col] / augmented[col][col]
for cola in range(UpperCAmelCase__ , size + 1 ):
augmented[row][cola] -= augmented[col][cola] * ratio
# round to get rid of numbers like 2.000000000000004
return [
[round(augmented[row][size] / augmented[row][row] , 10 )] for row in range(UpperCAmelCase__ )
]
def _a ( UpperCAmelCase__ ) -> Callable[[int], int]:
__SCREAMING_SNAKE_CASE = len(UpperCAmelCase__ )
__SCREAMING_SNAKE_CASE = [[0 for _ in range(UpperCAmelCase__ )] for _ in range(UpperCAmelCase__ )]
__SCREAMING_SNAKE_CASE = [[0] for _ in range(UpperCAmelCase__ )]
__SCREAMING_SNAKE_CASE = 42
__SCREAMING_SNAKE_CASE = 42
__SCREAMING_SNAKE_CASE = 42
__SCREAMING_SNAKE_CASE = 42
for x_val, y_val in enumerate(UpperCAmelCase__ ):
for col in range(UpperCAmelCase__ ):
__SCREAMING_SNAKE_CASE = (x_val + 1) ** (size - col - 1)
__SCREAMING_SNAKE_CASE = y_val
__SCREAMING_SNAKE_CASE = solve(UpperCAmelCase__ , UpperCAmelCase__ )
def interpolated_func(UpperCAmelCase__ ) -> int:
return sum(
round(coeffs[x_val][0] ) * (var ** (size - x_val - 1))
for x_val in range(UpperCAmelCase__ ) )
return interpolated_func
def _a ( UpperCAmelCase__ ) -> int:
return (
1
- variable
+ variable**2
- variable**3
+ variable**4
- variable**5
+ variable**6
- variable**7
+ variable**8
- variable**9
+ variable**10
)
def _a ( UpperCAmelCase__ = question_function , UpperCAmelCase__ = 10 ) -> int:
__SCREAMING_SNAKE_CASE = [func(UpperCAmelCase__ ) for x_val in range(1 , order + 1 )]
__SCREAMING_SNAKE_CASE = [
interpolate(data_points[:max_coeff] ) for max_coeff in range(1 , order + 1 )
]
__SCREAMING_SNAKE_CASE = 0
__SCREAMING_SNAKE_CASE = 42
__SCREAMING_SNAKE_CASE = 42
for poly in polynomials:
__SCREAMING_SNAKE_CASE = 1
while func(UpperCAmelCase__ ) == poly(UpperCAmelCase__ ):
x_val += 1
ret += poly(UpperCAmelCase__ )
return ret
if __name__ == "__main__":
print(F'''{solution() = }''')
| 690 | 1 |
"""simple docstring"""
import os
import sys
import unittest
lowerCAmelCase__ =os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, "utils"))
import check_dummies # noqa: E402
from check_dummies import create_dummy_files, create_dummy_object, find_backend, read_init # noqa: E402
# Align TRANSFORMERS_PATH in check_dummies with the current path
lowerCAmelCase__ =os.path.join(git_repo_path, "src", "diffusers")
class A__( unittest.TestCase ):
def _a ( self : Dict ) -> Union[str, Any]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = find_backend(''' if not is_torch_available():''' )
self.assertEqual(__SCREAMING_SNAKE_CASE , '''torch''' )
# backend_with_underscore = find_backend(" if not is_tensorflow_text_available():")
# self.assertEqual(backend_with_underscore, "tensorflow_text")
__SCREAMING_SNAKE_CASE = find_backend(''' if not (is_torch_available() and is_transformers_available()):''' )
self.assertEqual(__SCREAMING_SNAKE_CASE , '''torch_and_transformers''' )
# double_backend_with_underscore = find_backend(
# " if not (is_sentencepiece_available() and is_tensorflow_text_available()):"
# )
# self.assertEqual(double_backend_with_underscore, "sentencepiece_and_tensorflow_text")
__SCREAMING_SNAKE_CASE = find_backend(
''' if not (is_torch_available() and is_transformers_available() and is_onnx_available()):''' )
self.assertEqual(__SCREAMING_SNAKE_CASE , '''torch_and_transformers_and_onnx''' )
def _a ( self : int ) -> Tuple:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = read_init()
# We don't assert on the exact list of keys to allow for smooth grow of backend-specific objects
self.assertIn('''torch''' , __SCREAMING_SNAKE_CASE )
self.assertIn('''torch_and_transformers''' , __SCREAMING_SNAKE_CASE )
self.assertIn('''flax_and_transformers''' , __SCREAMING_SNAKE_CASE )
self.assertIn('''torch_and_transformers_and_onnx''' , __SCREAMING_SNAKE_CASE )
# Likewise, we can't assert on the exact content of a key
self.assertIn('''UNet2DModel''' , objects['''torch'''] )
self.assertIn('''FlaxUNet2DConditionModel''' , objects['''flax'''] )
self.assertIn('''StableDiffusionPipeline''' , objects['''torch_and_transformers'''] )
self.assertIn('''FlaxStableDiffusionPipeline''' , objects['''flax_and_transformers'''] )
self.assertIn('''LMSDiscreteScheduler''' , objects['''torch_and_scipy'''] )
self.assertIn('''OnnxStableDiffusionPipeline''' , objects['''torch_and_transformers_and_onnx'''] )
def _a ( self : List[str] ) -> Tuple:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = create_dummy_object('''CONSTANT''' , '''\'torch\'''' )
self.assertEqual(__SCREAMING_SNAKE_CASE , '''\nCONSTANT = None\n''' )
__SCREAMING_SNAKE_CASE = create_dummy_object('''function''' , '''\'torch\'''' )
self.assertEqual(
__SCREAMING_SNAKE_CASE , '''\ndef function(*args, **kwargs):\n requires_backends(function, \'torch\')\n''' )
__SCREAMING_SNAKE_CASE = '''
class FakeClass(metaclass=DummyObject):
_backends = \'torch\'
def __init__(self, *args, **kwargs):
requires_backends(self, \'torch\')
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, \'torch\')
@classmethod
def from_pretrained(cls, *args, **kwargs):
requires_backends(cls, \'torch\')
'''
__SCREAMING_SNAKE_CASE = create_dummy_object('''FakeClass''' , '''\'torch\'''' )
self.assertEqual(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
def _a ( self : Tuple ) -> List[str]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = '''# This file is autogenerated by the command `make fix-copies`, do not edit.
from ..utils import DummyObject, requires_backends
CONSTANT = None
def function(*args, **kwargs):
requires_backends(function, ["torch"])
class FakeClass(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["torch"])
@classmethod
def from_pretrained(cls, *args, **kwargs):
requires_backends(cls, ["torch"])
'''
__SCREAMING_SNAKE_CASE = create_dummy_files({'''torch''': ['''CONSTANT''', '''function''', '''FakeClass''']} )
self.assertEqual(dummy_files['''torch'''] , __SCREAMING_SNAKE_CASE )
| 690 |
"""simple docstring"""
from json import JSONDecodeError # Workaround for requests.exceptions.JSONDecodeError
import requests
def _a ( UpperCAmelCase__ = "isbn/0140328726" ) -> dict:
__SCREAMING_SNAKE_CASE = olid.strip().strip('''/''' ) # Remove leading/trailing whitespace & slashes
if new_olid.count('''/''' ) != 1:
__SCREAMING_SNAKE_CASE = f"""{olid} is not a valid Open Library olid"""
raise ValueError(UpperCAmelCase__ )
return requests.get(f"""https://openlibrary.org/{new_olid}.json""" ).json()
def _a ( UpperCAmelCase__ ) -> dict:
__SCREAMING_SNAKE_CASE = {
'''title''': '''Title''',
'''publish_date''': '''Publish date''',
'''authors''': '''Authors''',
'''number_of_pages''': '''Number of pages:''',
'''first_sentence''': '''First sentence''',
'''isbn_10''': '''ISBN (10)''',
'''isbn_13''': '''ISBN (13)''',
}
__SCREAMING_SNAKE_CASE = {better_key: ol_book_data[key] for key, better_key in desired_keys.items()}
__SCREAMING_SNAKE_CASE = [
get_openlibrary_data(author['''key'''] )['''name'''] for author in data['''Authors''']
]
__SCREAMING_SNAKE_CASE = data['''First sentence''']['''value''']
for key, value in data.items():
if isinstance(UpperCAmelCase__ , UpperCAmelCase__ ):
__SCREAMING_SNAKE_CASE = ''', '''.join(UpperCAmelCase__ )
return data
if __name__ == "__main__":
import doctest
doctest.testmod()
while True:
lowerCAmelCase__ =input("\nEnter the ISBN code to search (or 'quit' to stop): ").strip()
if isbn.lower() in ("", "q", "quit", "exit", "stop"):
break
if len(isbn) not in (10, 13) or not isbn.isdigit():
print(F'''Sorry, {isbn} is not a valid ISBN. Please, input a valid ISBN.''')
continue
print(F'''\nSearching Open Library for ISBN: {isbn}...\n''')
try:
lowerCAmelCase__ =summarize_book(get_openlibrary_data(F'''isbn/{isbn}'''))
print("\n".join(F'''{key}: {value}''' for key, value in book_summary.items()))
except JSONDecodeError: # Workaround for requests.exceptions.RequestException:
print(F'''Sorry, there are no results for ISBN: {isbn}.''')
| 690 | 1 |
"""simple docstring"""
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
import evaluate
import numpy as np
import torch
from datasets import load_dataset
from PIL import Image
from torchvision.transforms import (
CenterCrop,
Compose,
Normalize,
RandomHorizontalFlip,
RandomResizedCrop,
Resize,
ToTensor,
)
import transformers
from transformers import (
MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
AutoConfig,
AutoImageProcessor,
AutoModelForImageClassification,
HfArgumentParser,
Trainer,
TrainingArguments,
set_seed,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version
lowerCAmelCase__ =logging.getLogger(__name__)
# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version("4.31.0")
require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/image-classification/requirements.txt")
lowerCAmelCase__ =list(MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING.keys())
lowerCAmelCase__ =tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
def _a ( UpperCAmelCase__ ) -> str:
with open(UpperCAmelCase__ , '''rb''' ) as f:
__SCREAMING_SNAKE_CASE = Image.open(UpperCAmelCase__ )
return im.convert('''RGB''' )
@dataclass
class A__:
lowerCAmelCase = field(
default=__magic_name__ , metadata={
'''help''': '''Name of a dataset from the hub (could be your own, possibly private dataset hosted on the hub).'''
} , )
lowerCAmelCase = field(
default=__magic_name__ , metadata={'''help''': '''The configuration name of the dataset to use (via the datasets library).'''} )
lowerCAmelCase = field(default=__magic_name__ , metadata={'''help''': '''A folder containing the training data.'''} )
lowerCAmelCase = field(default=__magic_name__ , metadata={'''help''': '''A folder containing the validation data.'''} )
lowerCAmelCase = field(
default=0.15 , metadata={'''help''': '''Percent to split off of train for validation.'''} )
lowerCAmelCase = field(
default=__magic_name__ , metadata={
'''help''': (
'''For debugging purposes or quicker training, truncate the number of training examples to this '''
'''value if set.'''
)
} , )
lowerCAmelCase = field(
default=__magic_name__ , metadata={
'''help''': (
'''For debugging purposes or quicker training, truncate the number of evaluation examples to this '''
'''value if set.'''
)
} , )
def _a ( self : List[str] ) -> Union[str, Any]:
"""simple docstring"""
if self.dataset_name is None and (self.train_dir is None and self.validation_dir is None):
raise ValueError(
'''You must specify either a dataset name from the hub or a train and/or validation directory.''' )
@dataclass
class A__:
lowerCAmelCase = field(
default='''google/vit-base-patch16-224-in21k''' , metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''} , )
lowerCAmelCase = field(
default=__magic_name__ , metadata={'''help''': '''If training from scratch, pass a model type from the list: ''' + ''', '''.join(__magic_name__ )} , )
lowerCAmelCase = field(
default=__magic_name__ , metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''} )
lowerCAmelCase = field(
default=__magic_name__ , metadata={'''help''': '''Where do you want to store the pretrained models downloaded from s3'''} )
lowerCAmelCase = field(
default='''main''' , metadata={'''help''': '''The specific model version to use (can be a branch name, tag name or commit id).'''} , )
lowerCAmelCase = field(default=__magic_name__ , metadata={'''help''': '''Name or path of preprocessor config.'''} )
lowerCAmelCase = field(
default=__magic_name__ , metadata={
'''help''': (
'''Will use the token generated when running `huggingface-cli login` (necessary to use this script '''
'''with private models).'''
)
} , )
lowerCAmelCase = field(
default=__magic_name__ , metadata={'''help''': '''Will enable to load a pretrained model whose head dimensions are different.'''} , )
def _a ( UpperCAmelCase__ ) -> Dict:
__SCREAMING_SNAKE_CASE = torch.stack([example['''pixel_values'''] for example in examples] )
__SCREAMING_SNAKE_CASE = torch.tensor([example['''labels'''] for example in examples] )
return {"pixel_values": pixel_values, "labels": labels}
def _a ( ) -> int:
# See all possible arguments in src/transformers/training_args.py
# or by passing the --help flag to this script.
# We now keep distinct sets of args, for a cleaner separation of concerns.
__SCREAMING_SNAKE_CASE = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
if len(sys.argv ) == 2 and sys.argv[1].endswith('''.json''' ):
# If we pass only one argument to the script and it's the path to a json file,
# let's parse it to get our arguments.
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
else:
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = parser.parse_args_into_dataclasses()
# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
# information sent is the one passed as arguments along with your Python/PyTorch versions.
send_example_telemetry('''run_image_classification''' , UpperCAmelCase__ , UpperCAmelCase__ )
# Setup logging
logging.basicConfig(
format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''' , datefmt='''%m/%d/%Y %H:%M:%S''' , handlers=[logging.StreamHandler(sys.stdout )] , )
if training_args.should_log:
# The default of training_args.log_level is passive, so we set log level at info here to have that default.
transformers.utils.logging.set_verbosity_info()
__SCREAMING_SNAKE_CASE = training_args.get_process_log_level()
logger.setLevel(UpperCAmelCase__ )
transformers.utils.logging.set_verbosity(UpperCAmelCase__ )
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
# Log on each process the small summary:
logger.warning(
f"""Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"""
+ f"""distributed training: {bool(training_args.local_rank != -1 )}, 16-bits training: {training_args.fpaa}""" )
logger.info(f"""Training/evaluation parameters {training_args}""" )
# Detecting last checkpoint.
__SCREAMING_SNAKE_CASE = None
if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
__SCREAMING_SNAKE_CASE = get_last_checkpoint(training_args.output_dir )
if last_checkpoint is None and len(os.listdir(training_args.output_dir ) ) > 0:
raise ValueError(
f"""Output directory ({training_args.output_dir}) already exists and is not empty. """
'''Use --overwrite_output_dir to overcome.''' )
elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
logger.info(
f"""Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change """
'''the `--output_dir` or add `--overwrite_output_dir` to train from scratch.''' )
# Set seed before initializing model.
set_seed(training_args.seed )
# Initialize our dataset and prepare it for the 'image-classification' task.
if data_args.dataset_name is not None:
__SCREAMING_SNAKE_CASE = load_dataset(
data_args.dataset_name , data_args.dataset_config_name , cache_dir=model_args.cache_dir , task='''image-classification''' , use_auth_token=True if model_args.use_auth_token else None , )
else:
__SCREAMING_SNAKE_CASE = {}
if data_args.train_dir is not None:
__SCREAMING_SNAKE_CASE = os.path.join(data_args.train_dir , '''**''' )
if data_args.validation_dir is not None:
__SCREAMING_SNAKE_CASE = os.path.join(data_args.validation_dir , '''**''' )
__SCREAMING_SNAKE_CASE = load_dataset(
'''imagefolder''' , data_files=UpperCAmelCase__ , cache_dir=model_args.cache_dir , task='''image-classification''' , )
# If we don't have a validation split, split off a percentage of train as validation.
__SCREAMING_SNAKE_CASE = None if '''validation''' in dataset.keys() else data_args.train_val_split
if isinstance(data_args.train_val_split , UpperCAmelCase__ ) and data_args.train_val_split > 0.0:
__SCREAMING_SNAKE_CASE = dataset['''train'''].train_test_split(data_args.train_val_split )
__SCREAMING_SNAKE_CASE = split['''train''']
__SCREAMING_SNAKE_CASE = split['''test''']
# Prepare label mappings.
# We'll include these in the model's config to get human readable labels in the Inference API.
__SCREAMING_SNAKE_CASE = dataset['''train'''].features['''labels'''].names
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = {}, {}
for i, label in enumerate(UpperCAmelCase__ ):
__SCREAMING_SNAKE_CASE = str(UpperCAmelCase__ )
__SCREAMING_SNAKE_CASE = label
# Load the accuracy metric from the datasets package
__SCREAMING_SNAKE_CASE = evaluate.load('''accuracy''' )
# Define our compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with a
# predictions and label_ids field) and has to return a dictionary string to float.
def compute_metrics(UpperCAmelCase__ ):
return metric.compute(predictions=np.argmax(p.predictions , axis=1 ) , references=p.label_ids )
__SCREAMING_SNAKE_CASE = AutoConfig.from_pretrained(
model_args.config_name or model_args.model_name_or_path , num_labels=len(UpperCAmelCase__ ) , labelaid=UpperCAmelCase__ , idalabel=UpperCAmelCase__ , finetuning_task='''image-classification''' , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
__SCREAMING_SNAKE_CASE = AutoModelForImageClassification.from_pretrained(
model_args.model_name_or_path , from_tf=bool('''.ckpt''' in model_args.model_name_or_path ) , config=UpperCAmelCase__ , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , ignore_mismatched_sizes=model_args.ignore_mismatched_sizes , )
__SCREAMING_SNAKE_CASE = AutoImageProcessor.from_pretrained(
model_args.image_processor_name or model_args.model_name_or_path , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
# Define torchvision transforms to be applied to each image.
if "shortest_edge" in image_processor.size:
__SCREAMING_SNAKE_CASE = image_processor.size['''shortest_edge''']
else:
__SCREAMING_SNAKE_CASE = (image_processor.size['''height'''], image_processor.size['''width'''])
__SCREAMING_SNAKE_CASE = Normalize(mean=image_processor.image_mean , std=image_processor.image_std )
__SCREAMING_SNAKE_CASE = Compose(
[
RandomResizedCrop(UpperCAmelCase__ ),
RandomHorizontalFlip(),
ToTensor(),
normalize,
] )
__SCREAMING_SNAKE_CASE = Compose(
[
Resize(UpperCAmelCase__ ),
CenterCrop(UpperCAmelCase__ ),
ToTensor(),
normalize,
] )
def train_transforms(UpperCAmelCase__ ):
__SCREAMING_SNAKE_CASE = [
_train_transforms(pil_img.convert('''RGB''' ) ) for pil_img in example_batch['''image''']
]
return example_batch
def val_transforms(UpperCAmelCase__ ):
__SCREAMING_SNAKE_CASE = [_val_transforms(pil_img.convert('''RGB''' ) ) for pil_img in example_batch['''image''']]
return example_batch
if training_args.do_train:
if "train" not in dataset:
raise ValueError('''--do_train requires a train dataset''' )
if data_args.max_train_samples is not None:
__SCREAMING_SNAKE_CASE = (
dataset['''train'''].shuffle(seed=training_args.seed ).select(range(data_args.max_train_samples ) )
)
# Set the training transforms
dataset["train"].set_transform(UpperCAmelCase__ )
if training_args.do_eval:
if "validation" not in dataset:
raise ValueError('''--do_eval requires a validation dataset''' )
if data_args.max_eval_samples is not None:
__SCREAMING_SNAKE_CASE = (
dataset['''validation'''].shuffle(seed=training_args.seed ).select(range(data_args.max_eval_samples ) )
)
# Set the validation transforms
dataset["validation"].set_transform(UpperCAmelCase__ )
# Initalize our trainer
__SCREAMING_SNAKE_CASE = Trainer(
model=UpperCAmelCase__ , args=UpperCAmelCase__ , train_dataset=dataset['''train'''] if training_args.do_train else None , eval_dataset=dataset['''validation'''] if training_args.do_eval else None , compute_metrics=UpperCAmelCase__ , tokenizer=UpperCAmelCase__ , data_collator=UpperCAmelCase__ , )
# Training
if training_args.do_train:
__SCREAMING_SNAKE_CASE = None
if training_args.resume_from_checkpoint is not None:
__SCREAMING_SNAKE_CASE = training_args.resume_from_checkpoint
elif last_checkpoint is not None:
__SCREAMING_SNAKE_CASE = last_checkpoint
__SCREAMING_SNAKE_CASE = trainer.train(resume_from_checkpoint=UpperCAmelCase__ )
trainer.save_model()
trainer.log_metrics('''train''' , train_result.metrics )
trainer.save_metrics('''train''' , train_result.metrics )
trainer.save_state()
# Evaluation
if training_args.do_eval:
__SCREAMING_SNAKE_CASE = trainer.evaluate()
trainer.log_metrics('''eval''' , UpperCAmelCase__ )
trainer.save_metrics('''eval''' , UpperCAmelCase__ )
# Write model card and (optionally) push to hub
__SCREAMING_SNAKE_CASE = {
'''finetuned_from''': model_args.model_name_or_path,
'''tasks''': '''image-classification''',
'''dataset''': data_args.dataset_name,
'''tags''': ['''image-classification''', '''vision'''],
}
if training_args.push_to_hub:
trainer.push_to_hub(**UpperCAmelCase__ )
else:
trainer.create_model_card(**UpperCAmelCase__ )
if __name__ == "__main__":
main()
| 690 |
"""simple docstring"""
from math import ceil
from typing import List, Optional, Union
import numpy as np
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import BatchFeature, SequenceFeatureExtractor
from ...utils import TensorType, logging
lowerCAmelCase__ =logging.get_logger(__name__)
class A__( __magic_name__ ):
lowerCAmelCase = ['''audio_values''', '''audio_mask''']
def __init__( self : Dict , __SCREAMING_SNAKE_CASE : Optional[Any]=20_48 , __SCREAMING_SNAKE_CASE : str=1 , __SCREAMING_SNAKE_CASE : List[Any]=[16, 16] , __SCREAMING_SNAKE_CASE : Union[str, Any]=1_28 , __SCREAMING_SNAKE_CASE : int=4_41_00 , __SCREAMING_SNAKE_CASE : Union[str, Any]=86 , __SCREAMING_SNAKE_CASE : str=20_48 , __SCREAMING_SNAKE_CASE : Optional[Any]=0.0 , **__SCREAMING_SNAKE_CASE : Optional[int] , ) -> Any:
"""simple docstring"""
super().__init__(
feature_size=__SCREAMING_SNAKE_CASE , sampling_rate=__SCREAMING_SNAKE_CASE , padding_value=__SCREAMING_SNAKE_CASE , **__SCREAMING_SNAKE_CASE , )
__SCREAMING_SNAKE_CASE = spectrogram_length
__SCREAMING_SNAKE_CASE = num_channels
__SCREAMING_SNAKE_CASE = patch_size
__SCREAMING_SNAKE_CASE = feature_size // self.patch_size[1]
__SCREAMING_SNAKE_CASE = n_fft
__SCREAMING_SNAKE_CASE = sampling_rate // hop_length_to_sampling_rate
__SCREAMING_SNAKE_CASE = sampling_rate
__SCREAMING_SNAKE_CASE = padding_value
__SCREAMING_SNAKE_CASE = mel_filter_bank(
num_frequency_bins=1 + n_fft // 2 , num_mel_filters=__SCREAMING_SNAKE_CASE , min_frequency=0.0 , max_frequency=2_20_50.0 , sampling_rate=__SCREAMING_SNAKE_CASE , norm='''slaney''' , mel_scale='''slaney''' , ).T
def _a ( self : str , __SCREAMING_SNAKE_CASE : np.array ) -> np.ndarray:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = spectrogram(
__SCREAMING_SNAKE_CASE , window_function(self.n_fft , '''hann''' ) , frame_length=self.n_fft , hop_length=self.hop_length , power=2.0 , mel_filters=self.mel_filters.T , log_mel='''dB''' , db_range=80.0 , )
__SCREAMING_SNAKE_CASE = log_spec[:, :-1]
__SCREAMING_SNAKE_CASE = log_spec - 20.0
__SCREAMING_SNAKE_CASE = np.clip(log_spec / 40.0 , -2.0 , 0.0 ) + 1.0
return log_spec
def __call__( self : str , __SCREAMING_SNAKE_CASE : Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]] , __SCREAMING_SNAKE_CASE : Optional[Union[str, TensorType]] = None , __SCREAMING_SNAKE_CASE : Optional[bool] = True , __SCREAMING_SNAKE_CASE : Optional[int] = None , __SCREAMING_SNAKE_CASE : bool = False , __SCREAMING_SNAKE_CASE : bool = False , **__SCREAMING_SNAKE_CASE : Tuple , ) -> BatchFeature:
"""simple docstring"""
if sampling_rate is not None:
if sampling_rate != self.sampling_rate:
raise ValueError(
'''This feature extractor is set to support sampling rate'''
f""" of {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled"""
f""" with {self.sampling_rate} and not {sampling_rate}.""" )
else:
logger.warning(
'''It is strongly recommended to pass the `sampling_rate` argument to this function. '''
'''Failing to do so can result in silent errors that might be hard to debug.''' )
__SCREAMING_SNAKE_CASE = isinstance(__SCREAMING_SNAKE_CASE , np.ndarray ) and len(raw_speech.shape ) > 1
if is_batched_numpy and len(raw_speech.shape ) > 2:
raise ValueError(f"""Only mono-channel audio is supported for input to {self}""" )
__SCREAMING_SNAKE_CASE = is_batched_numpy or (
isinstance(__SCREAMING_SNAKE_CASE , (list, tuple) ) and (isinstance(raw_speech[0] , (np.ndarray, tuple, list) ))
)
if is_batched:
__SCREAMING_SNAKE_CASE = [np.asarray([speech] , dtype=np.floataa ).T for speech in raw_speech]
elif not is_batched and not isinstance(__SCREAMING_SNAKE_CASE , np.ndarray ):
__SCREAMING_SNAKE_CASE = np.asarray(__SCREAMING_SNAKE_CASE , dtype=np.floataa )
elif isinstance(__SCREAMING_SNAKE_CASE , np.ndarray ) and raw_speech.dtype is np.dtype(np.floataa ):
__SCREAMING_SNAKE_CASE = raw_speech.astype(np.floataa )
# always return batch
if not is_batched:
__SCREAMING_SNAKE_CASE = [np.asarray([raw_speech] ).T]
# Convert audio signals to log mel spectrograms, truncate by time axis
__SCREAMING_SNAKE_CASE = [
self._np_extract_fbank_features(waveform.squeeze() ).T[: self.spectrogram_length] for waveform in raw_speech
]
if isinstance(audio_features[0] , __SCREAMING_SNAKE_CASE ):
__SCREAMING_SNAKE_CASE = [np.asarray(__SCREAMING_SNAKE_CASE , dtype=np.floataa ) for feature in audio_features]
# Create audio attention mask
__SCREAMING_SNAKE_CASE = max(
[ceil(feature.shape[0] / self.patch_size[0] ) * self.freq_len for feature in audio_features] ) # The maximum number of audio patches in a batch
if return_attention_mask:
__SCREAMING_SNAKE_CASE = [
(ceil(feature.shape[0] / self.patch_size[0] ) * self.freq_len) * [1]
+ (max_patch_len - ceil(feature.shape[0] / self.patch_size[0] ) * self.freq_len) * [0]
for feature in audio_features
]
__SCREAMING_SNAKE_CASE = np.array(__SCREAMING_SNAKE_CASE ).astype(np.floataa )
# convert into correct format for padding
__SCREAMING_SNAKE_CASE = max_patch_len // self.freq_len * self.patch_size[0] # The maximum audio size in a batch
__SCREAMING_SNAKE_CASE = np.ones([len(__SCREAMING_SNAKE_CASE ), 1, max_time_len, self.feature_size] ).astype(np.floataa )
__SCREAMING_SNAKE_CASE = padded_audio_features * self.padding_value
for i in range(len(__SCREAMING_SNAKE_CASE ) ):
__SCREAMING_SNAKE_CASE = audio_features[i]
__SCREAMING_SNAKE_CASE = feature
# return as BatchFeature
if return_attention_mask:
__SCREAMING_SNAKE_CASE = {'''audio_values''': padded_audio_features, '''audio_mask''': audio_mask}
else:
__SCREAMING_SNAKE_CASE = {'''audio_values''': padded_audio_features}
__SCREAMING_SNAKE_CASE = BatchFeature(data=__SCREAMING_SNAKE_CASE , tensor_type=__SCREAMING_SNAKE_CASE )
return encoded_inputs
| 690 | 1 |
"""simple docstring"""
import argparse
import shutil
import time
from json import JSONDecodeError
from logging import getLogger
from pathlib import Path
from typing import Dict, List
import torch
from torch.utils.data import DataLoader
from tqdm import tqdm
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils import (
SeqaSeqDataset,
calculate_bleu,
calculate_rouge,
chunks,
lmap,
load_json,
parse_numeric_n_bool_cl_kwargs,
save_json,
use_task_specific_params,
write_txt_file,
)
lowerCAmelCase__ =getLogger(__name__)
def _a ( UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ = 8 , UpperCAmelCase__ = 10_24 , UpperCAmelCase__="val" , UpperCAmelCase__=None , UpperCAmelCase__=False , UpperCAmelCase__="summarization" , UpperCAmelCase__=None , UpperCAmelCase__=1 , UpperCAmelCase__ = None , UpperCAmelCase__="" , **UpperCAmelCase__ , ) -> Dict:
__SCREAMING_SNAKE_CASE = str(UpperCAmelCase__ )
assert local_rank is not None
torch.distributed.init_process_group(backend='''nccl''' , rank=UpperCAmelCase__ )
__SCREAMING_SNAKE_CASE = Path(UpperCAmelCase__ )
__SCREAMING_SNAKE_CASE = save_dir.joinpath(f"""rank_{local_rank}_output.json""" )
torch.cuda.set_device(UpperCAmelCase__ )
__SCREAMING_SNAKE_CASE = AutoModelForSeqaSeqLM.from_pretrained(UpperCAmelCase__ ).cuda()
if fpaa:
__SCREAMING_SNAKE_CASE = model.half()
# determine if we need to increase num_beams
use_task_specific_params(UpperCAmelCase__ , UpperCAmelCase__ ) # update config with task specific params
__SCREAMING_SNAKE_CASE = generate_kwargs.pop('''num_beams''' , model.config.num_beams ) # AttributeError risk?
if num_return_sequences > num_beams:
__SCREAMING_SNAKE_CASE = num_return_sequences
__SCREAMING_SNAKE_CASE = AutoTokenizer.from_pretrained(UpperCAmelCase__ )
logger.info(f"""Inferred tokenizer type: {tokenizer.__class__}""" ) # if this is wrong, check config.model_type.
if max_source_length is None:
__SCREAMING_SNAKE_CASE = tokenizer.model_max_length
if prefix is None:
__SCREAMING_SNAKE_CASE = prefix or getattr(model.config , '''prefix''' , '''''' ) or ''''''
__SCREAMING_SNAKE_CASE = SeqaSeqDataset(
UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , max_target_length=10_24 , type_path=UpperCAmelCase__ , n_obs=UpperCAmelCase__ , prefix=UpperCAmelCase__ , **UpperCAmelCase__ , )
# I set shuffle=True for a more accurate progress bar.
# If all the longest samples are first, the prog bar estimate is too high at the beginning.
__SCREAMING_SNAKE_CASE = ds.make_sortish_sampler(UpperCAmelCase__ , distributed=UpperCAmelCase__ , add_extra_examples=UpperCAmelCase__ , shuffle=UpperCAmelCase__ )
__SCREAMING_SNAKE_CASE = DataLoader(UpperCAmelCase__ , sampler=UpperCAmelCase__ , batch_size=UpperCAmelCase__ , collate_fn=ds.collate_fn )
__SCREAMING_SNAKE_CASE = []
for batch in tqdm(UpperCAmelCase__ ):
__SCREAMING_SNAKE_CASE = model.generate(
input_ids=batch['''input_ids'''].to(model.device ) , attention_mask=batch['''attention_mask'''].to(model.device ) , num_return_sequences=UpperCAmelCase__ , num_beams=UpperCAmelCase__ , **UpperCAmelCase__ , )
__SCREAMING_SNAKE_CASE = tokenizer.batch_decode(UpperCAmelCase__ , skip_special_tokens=UpperCAmelCase__ , clean_up_tokenization_spaces=UpperCAmelCase__ )
__SCREAMING_SNAKE_CASE = batch['''ids''']
if num_return_sequences > 1:
__SCREAMING_SNAKE_CASE = chunks(UpperCAmelCase__ , UpperCAmelCase__ ) # batch size chunks, each of size num_return_seq
for i, pred in enumerate(UpperCAmelCase__ ):
results.append({'''pred''': pred, '''id''': ids[i].item()} )
save_json(UpperCAmelCase__ , UpperCAmelCase__ )
return results, sampler.num_replicas
def _a ( ) -> str:
__SCREAMING_SNAKE_CASE = argparse.ArgumentParser(
epilog='''Unspecified args like --num_beams=2 --decoder_start_token_id=4 are passed to model.generate''' )
parser.add_argument('''--data_dir''' , type=UpperCAmelCase__ , help='''like cnn_dm/test.source''' )
parser.add_argument(
'''--model_name''' , type=UpperCAmelCase__ , help='''like facebook/bart-large-cnn,t5-base, etc.''' , default='''sshleifer/distilbart-xsum-12-3''' , )
parser.add_argument('''--save_dir''' , type=UpperCAmelCase__ , help='''where to save''' , default='''tmp_gen''' )
parser.add_argument('''--max_source_length''' , type=UpperCAmelCase__ , default=UpperCAmelCase__ )
parser.add_argument(
'''--type_path''' , type=UpperCAmelCase__ , default='''test''' , help='''which subset to evaluate typically train/val/test''' )
parser.add_argument('''--task''' , type=UpperCAmelCase__ , default='''summarization''' , help='''used for task_specific_params + metrics''' )
parser.add_argument('''--bs''' , type=UpperCAmelCase__ , default=8 , required=UpperCAmelCase__ , help='''batch size''' )
parser.add_argument(
'''--local_rank''' , type=UpperCAmelCase__ , default=-1 , required=UpperCAmelCase__ , help='''should be passed by distributed.launch''' )
parser.add_argument(
'''--n_obs''' , type=UpperCAmelCase__ , default=UpperCAmelCase__ , required=UpperCAmelCase__ , help='''How many observations. Defaults to all.''' )
parser.add_argument(
'''--num_return_sequences''' , type=UpperCAmelCase__ , default=1 , required=UpperCAmelCase__ , help='''How many sequences to return''' )
parser.add_argument(
'''--sync_timeout''' , type=UpperCAmelCase__ , default=6_00 , required=UpperCAmelCase__ , help='''How long should master process wait for other processes to finish.''' , )
parser.add_argument('''--src_lang''' , type=UpperCAmelCase__ , default=UpperCAmelCase__ , required=UpperCAmelCase__ )
parser.add_argument('''--tgt_lang''' , type=UpperCAmelCase__ , default=UpperCAmelCase__ , required=UpperCAmelCase__ )
parser.add_argument(
'''--prefix''' , type=UpperCAmelCase__ , required=UpperCAmelCase__ , default=UpperCAmelCase__ , help='''will be added to the begininng of src examples''' )
parser.add_argument('''--fp16''' , action='''store_true''' )
parser.add_argument('''--debug''' , action='''store_true''' )
__SCREAMING_SNAKE_CASE = time.time()
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = parser.parse_known_args()
__SCREAMING_SNAKE_CASE = parse_numeric_n_bool_cl_kwargs(UpperCAmelCase__ )
if generate_kwargs and args.local_rank <= 0:
print(f"""parsed the following generate kwargs: {generate_kwargs}""" )
__SCREAMING_SNAKE_CASE = Path(args.save_dir + '''_tmp''' )
Path(UpperCAmelCase__ ).mkdir(exist_ok=UpperCAmelCase__ ) # this handles locking.
__SCREAMING_SNAKE_CASE = list(json_save_dir.glob('''rank_*.json''' ) )
if intermediate_files:
raise ValueError(f"""Found files at {json_save_dir} please move or remove them.""" )
# In theory, a node could finish and save before another node hits this. If this happens, we can address later.
__SCREAMING_SNAKE_CASE = {}
if args.src_lang is not None:
__SCREAMING_SNAKE_CASE = args.src_lang
if args.tgt_lang is not None:
__SCREAMING_SNAKE_CASE = args.tgt_lang
Path(args.save_dir ).mkdir(exist_ok=UpperCAmelCase__ )
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = eval_data_dir(
args.data_dir , UpperCAmelCase__ , args.model_name , type_path=args.type_path , bs=args.bs , fpaa=args.fpaa , task=args.task , local_rank=args.local_rank , n_obs=args.n_obs , max_source_length=args.max_source_length , num_return_sequences=args.num_return_sequences , prefix=args.prefix , dataset_kwargs=UpperCAmelCase__ , **UpperCAmelCase__ , )
if args.local_rank <= 0:
__SCREAMING_SNAKE_CASE = Path(args.save_dir )
save_dir.mkdir(exist_ok=UpperCAmelCase__ )
__SCREAMING_SNAKE_CASE = gather_results_from_each_node(UpperCAmelCase__ , UpperCAmelCase__ , args.sync_timeout )
__SCREAMING_SNAKE_CASE = combine_partial_results(UpperCAmelCase__ )
if args.num_return_sequences > 1:
__SCREAMING_SNAKE_CASE = save_dir.joinpath('''pseudolabel_results.json''' )
print(f"""Saving aggregated results at {save_path}, intermediate in {json_save_dir}/""" )
save_json(UpperCAmelCase__ , UpperCAmelCase__ )
return
__SCREAMING_SNAKE_CASE = Path(args.data_dir ).joinpath(args.type_path + '''.target''' )
with open(UpperCAmelCase__ ) as f:
__SCREAMING_SNAKE_CASE = [x.rstrip() for x in f.readlines()][: len(UpperCAmelCase__ )]
# Calculate metrics, save metrics, and save _generations.txt
__SCREAMING_SNAKE_CASE = '''translation''' in args.task
__SCREAMING_SNAKE_CASE = calculate_bleu if calc_bleu else calculate_rouge
__SCREAMING_SNAKE_CASE = '''bleu''' if calc_bleu else '''rouge'''
__SCREAMING_SNAKE_CASE = score_fn(UpperCAmelCase__ , UpperCAmelCase__ )
__SCREAMING_SNAKE_CASE = len(UpperCAmelCase__ )
__SCREAMING_SNAKE_CASE = time.time() - start_time
__SCREAMING_SNAKE_CASE = round(runtime / metrics['''n_obs'''] , 4 )
__SCREAMING_SNAKE_CASE = num_replicas
# TODO(@stas00): add whatever metadata to metrics
__SCREAMING_SNAKE_CASE = save_dir.joinpath(f"""{args.type_path}_{metric_name}.json""" )
save_json(UpperCAmelCase__ , UpperCAmelCase__ , indent=UpperCAmelCase__ )
print(UpperCAmelCase__ )
write_txt_file(UpperCAmelCase__ , save_dir.joinpath(f"""{args.type_path}_generations.txt""" ) )
if args.debug:
write_txt_file(UpperCAmelCase__ , save_dir.joinpath(f"""{args.type_path}.target""" ) )
else:
shutil.rmtree(UpperCAmelCase__ )
def _a ( UpperCAmelCase__ ) -> List:
__SCREAMING_SNAKE_CASE = []
for partial_result in partial_results:
records.extend(UpperCAmelCase__ )
__SCREAMING_SNAKE_CASE = sorted(UpperCAmelCase__ , key=lambda UpperCAmelCase__ : x["id"] )
__SCREAMING_SNAKE_CASE = [x['''pred'''] for x in records]
return preds
def _a ( UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ ) -> List[Dict[str, List]]:
# WAIT FOR lots of .json files
__SCREAMING_SNAKE_CASE = time.time()
logger.info('''waiting for all nodes to finish''' )
__SCREAMING_SNAKE_CASE = None
while (time.time() - start_wait) < timeout:
__SCREAMING_SNAKE_CASE = list(save_dir.glob('''rank_*.json''' ) )
if len(UpperCAmelCase__ ) < num_replicas:
continue
try:
# make sure all json files are fully saved
__SCREAMING_SNAKE_CASE = lmap(UpperCAmelCase__ , UpperCAmelCase__ )
return json_data
except JSONDecodeError:
continue
else:
raise TimeoutError('''Rank 0 gave up on waiting for other processes''' )
# Unreachable
if __name__ == "__main__":
# Usage for MT:
run_generate()
| 690 |
"""simple docstring"""
def _a ( UpperCAmelCase__ ) -> str:
__SCREAMING_SNAKE_CASE = ''''''
for ch in key:
if ch == " " or ch not in key_no_dups and ch.isalpha():
key_no_dups += ch
return key_no_dups
def _a ( UpperCAmelCase__ ) -> dict[str, str]:
__SCREAMING_SNAKE_CASE = [chr(i + 65 ) for i in range(26 )]
# Remove duplicate characters from key
__SCREAMING_SNAKE_CASE = remove_duplicates(key.upper() )
__SCREAMING_SNAKE_CASE = len(UpperCAmelCase__ )
# First fill cipher with key characters
__SCREAMING_SNAKE_CASE = {alphabet[i]: char for i, char in enumerate(UpperCAmelCase__ )}
# Then map remaining characters in alphabet to
# the alphabet from the beginning
for i in range(len(UpperCAmelCase__ ) , 26 ):
__SCREAMING_SNAKE_CASE = alphabet[i - offset]
# Ensure we are not mapping letters to letters previously mapped
while char in key:
offset -= 1
__SCREAMING_SNAKE_CASE = alphabet[i - offset]
__SCREAMING_SNAKE_CASE = char
return cipher_alphabet
def _a ( UpperCAmelCase__ , UpperCAmelCase__ ) -> str:
return "".join(cipher_map.get(UpperCAmelCase__ , UpperCAmelCase__ ) for ch in message.upper() )
def _a ( UpperCAmelCase__ , UpperCAmelCase__ ) -> str:
__SCREAMING_SNAKE_CASE = {v: k for k, v in cipher_map.items()}
return "".join(rev_cipher_map.get(UpperCAmelCase__ , UpperCAmelCase__ ) for ch in message.upper() )
def _a ( ) -> None:
__SCREAMING_SNAKE_CASE = input('''Enter message to encode or decode: ''' ).strip()
__SCREAMING_SNAKE_CASE = input('''Enter keyword: ''' ).strip()
__SCREAMING_SNAKE_CASE = input('''Encipher or decipher? E/D:''' ).strip()[0].lower()
try:
__SCREAMING_SNAKE_CASE = {'''e''': encipher, '''d''': decipher}[option]
except KeyError:
raise KeyError('''invalid input option''' )
__SCREAMING_SNAKE_CASE = create_cipher_map(UpperCAmelCase__ )
print(func(UpperCAmelCase__ , UpperCAmelCase__ ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| 690 | 1 |
"""simple docstring"""
import shutil
import tempfile
import unittest
from transformers import ClapFeatureExtractor, ClapProcessor, RobertaTokenizer, RobertaTokenizerFast
from transformers.testing_utils import require_sentencepiece, require_torchaudio
from .test_feature_extraction_clap import floats_list
@require_torchaudio
@require_sentencepiece
class A__( unittest.TestCase ):
def _a ( self : Dict ) -> Union[str, Any]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = '''laion/clap-htsat-unfused'''
__SCREAMING_SNAKE_CASE = tempfile.mkdtemp()
def _a ( self : int , **__SCREAMING_SNAKE_CASE : List[Any] ) -> Tuple:
"""simple docstring"""
return RobertaTokenizer.from_pretrained(self.checkpoint , **__SCREAMING_SNAKE_CASE )
def _a ( self : Optional[int] , **__SCREAMING_SNAKE_CASE : str ) -> Union[str, Any]:
"""simple docstring"""
return ClapFeatureExtractor.from_pretrained(self.checkpoint , **__SCREAMING_SNAKE_CASE )
def _a ( self : List[Any] ) -> List[str]:
"""simple docstring"""
shutil.rmtree(self.tmpdirname )
def _a ( self : Any ) -> Dict:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = self.get_tokenizer()
__SCREAMING_SNAKE_CASE = self.get_feature_extractor()
__SCREAMING_SNAKE_CASE = ClapProcessor(tokenizer=__SCREAMING_SNAKE_CASE , feature_extractor=__SCREAMING_SNAKE_CASE )
processor.save_pretrained(self.tmpdirname )
__SCREAMING_SNAKE_CASE = ClapProcessor.from_pretrained(self.tmpdirname )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer.get_vocab() )
self.assertIsInstance(processor.tokenizer , __SCREAMING_SNAKE_CASE )
self.assertEqual(processor.feature_extractor.to_json_string() , feature_extractor.to_json_string() )
self.assertIsInstance(processor.feature_extractor , __SCREAMING_SNAKE_CASE )
def _a ( self : str ) -> int:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = ClapProcessor(tokenizer=self.get_tokenizer() , feature_extractor=self.get_feature_extractor() )
processor.save_pretrained(self.tmpdirname )
__SCREAMING_SNAKE_CASE = self.get_tokenizer(bos_token='''(BOS)''' , eos_token='''(EOS)''' )
__SCREAMING_SNAKE_CASE = self.get_feature_extractor(do_normalize=__SCREAMING_SNAKE_CASE , padding_value=1.0 )
__SCREAMING_SNAKE_CASE = ClapProcessor.from_pretrained(
self.tmpdirname , bos_token='''(BOS)''' , eos_token='''(EOS)''' , do_normalize=__SCREAMING_SNAKE_CASE , padding_value=1.0 )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() )
self.assertIsInstance(processor.tokenizer , __SCREAMING_SNAKE_CASE )
self.assertEqual(processor.feature_extractor.to_json_string() , feature_extractor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.feature_extractor , __SCREAMING_SNAKE_CASE )
def _a ( self : Dict ) -> int:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = self.get_feature_extractor()
__SCREAMING_SNAKE_CASE = self.get_tokenizer()
__SCREAMING_SNAKE_CASE = ClapProcessor(tokenizer=__SCREAMING_SNAKE_CASE , feature_extractor=__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = floats_list((3, 10_00) )
__SCREAMING_SNAKE_CASE = feature_extractor(__SCREAMING_SNAKE_CASE , return_tensors='''np''' )
__SCREAMING_SNAKE_CASE = processor(audios=__SCREAMING_SNAKE_CASE , return_tensors='''np''' )
for key in input_feat_extract.keys():
self.assertAlmostEqual(input_feat_extract[key].sum() , input_processor[key].sum() , delta=1E-2 )
def _a ( self : Union[str, Any] ) -> Tuple:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = self.get_feature_extractor()
__SCREAMING_SNAKE_CASE = self.get_tokenizer()
__SCREAMING_SNAKE_CASE = ClapProcessor(tokenizer=__SCREAMING_SNAKE_CASE , feature_extractor=__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = '''This is a test string'''
__SCREAMING_SNAKE_CASE = processor(text=__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = tokenizer(__SCREAMING_SNAKE_CASE )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key] )
def _a ( self : Optional[int] ) -> Tuple:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = self.get_feature_extractor()
__SCREAMING_SNAKE_CASE = self.get_tokenizer()
__SCREAMING_SNAKE_CASE = ClapProcessor(tokenizer=__SCREAMING_SNAKE_CASE , feature_extractor=__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
__SCREAMING_SNAKE_CASE = processor.batch_decode(__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = tokenizer.batch_decode(__SCREAMING_SNAKE_CASE )
self.assertListEqual(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
def _a ( self : Union[str, Any] ) -> Tuple:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = self.get_feature_extractor()
__SCREAMING_SNAKE_CASE = self.get_tokenizer()
__SCREAMING_SNAKE_CASE = ClapProcessor(tokenizer=__SCREAMING_SNAKE_CASE , feature_extractor=__SCREAMING_SNAKE_CASE )
self.assertListEqual(
processor.model_input_names[2:] , feature_extractor.model_input_names , msg='''`processor` and `feature_extractor` model input names do not match''' , )
| 690 |
"""simple docstring"""
from __future__ import annotations
import inspect
import unittest
from typing import List, Tuple
from transformers import RegNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST, TFRegNetForImageClassification, TFRegNetModel
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class A__:
def __init__( self : Optional[int] , __SCREAMING_SNAKE_CASE : Dict , __SCREAMING_SNAKE_CASE : List[str]=3 , __SCREAMING_SNAKE_CASE : Dict=32 , __SCREAMING_SNAKE_CASE : Optional[Any]=3 , __SCREAMING_SNAKE_CASE : Union[str, Any]=10 , __SCREAMING_SNAKE_CASE : str=[10, 20, 30, 40] , __SCREAMING_SNAKE_CASE : Optional[int]=[1, 1, 2, 1] , __SCREAMING_SNAKE_CASE : int=True , __SCREAMING_SNAKE_CASE : int=True , __SCREAMING_SNAKE_CASE : Optional[Any]="relu" , __SCREAMING_SNAKE_CASE : List[str]=3 , __SCREAMING_SNAKE_CASE : Union[str, Any]=None , ) -> Any:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = parent
__SCREAMING_SNAKE_CASE = batch_size
__SCREAMING_SNAKE_CASE = image_size
__SCREAMING_SNAKE_CASE = num_channels
__SCREAMING_SNAKE_CASE = embeddings_size
__SCREAMING_SNAKE_CASE = hidden_sizes
__SCREAMING_SNAKE_CASE = depths
__SCREAMING_SNAKE_CASE = is_training
__SCREAMING_SNAKE_CASE = use_labels
__SCREAMING_SNAKE_CASE = hidden_act
__SCREAMING_SNAKE_CASE = num_labels
__SCREAMING_SNAKE_CASE = scope
__SCREAMING_SNAKE_CASE = len(__SCREAMING_SNAKE_CASE )
def _a ( self : List[Any] ) -> List[str]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
__SCREAMING_SNAKE_CASE = None
if self.use_labels:
__SCREAMING_SNAKE_CASE = ids_tensor([self.batch_size] , self.num_labels )
__SCREAMING_SNAKE_CASE = self.get_config()
return config, pixel_values, labels
def _a ( self : Union[str, Any] ) -> List[str]:
"""simple docstring"""
return RegNetConfig(
num_channels=self.num_channels , embeddings_size=self.embeddings_size , hidden_sizes=self.hidden_sizes , depths=self.depths , hidden_act=self.hidden_act , num_labels=self.num_labels , )
def _a ( self : str , __SCREAMING_SNAKE_CASE : Any , __SCREAMING_SNAKE_CASE : str , __SCREAMING_SNAKE_CASE : Optional[Any] ) -> Dict:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = TFRegNetModel(config=__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = model(__SCREAMING_SNAKE_CASE , training=__SCREAMING_SNAKE_CASE )
# expected last hidden states: B, C, H // 32, W // 32
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32) , )
def _a ( self : int , __SCREAMING_SNAKE_CASE : Union[str, Any] , __SCREAMING_SNAKE_CASE : str , __SCREAMING_SNAKE_CASE : Optional[Any] ) -> Union[str, Any]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = self.num_labels
__SCREAMING_SNAKE_CASE = TFRegNetForImageClassification(__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = model(__SCREAMING_SNAKE_CASE , labels=__SCREAMING_SNAKE_CASE , training=__SCREAMING_SNAKE_CASE )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def _a ( self : Optional[Any] ) -> Any:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = self.prepare_config_and_inputs()
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = config_and_inputs
__SCREAMING_SNAKE_CASE = {'''pixel_values''': pixel_values}
return config, inputs_dict
@require_tf
class A__( __magic_name__ , __magic_name__ , unittest.TestCase ):
lowerCAmelCase = (TFRegNetModel, TFRegNetForImageClassification) if is_tf_available() else ()
lowerCAmelCase = (
{'''feature-extraction''': TFRegNetModel, '''image-classification''': TFRegNetForImageClassification}
if is_tf_available()
else {}
)
lowerCAmelCase = False
lowerCAmelCase = False
lowerCAmelCase = False
lowerCAmelCase = False
lowerCAmelCase = False
def _a ( self : Optional[Any] ) -> Union[str, Any]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = TFRegNetModelTester(self )
__SCREAMING_SNAKE_CASE = ConfigTester(self , config_class=__SCREAMING_SNAKE_CASE , has_text_modality=__SCREAMING_SNAKE_CASE )
def _a ( self : Tuple ) -> Optional[Any]:
"""simple docstring"""
return
@unittest.skip(reason='''RegNet does not use inputs_embeds''' )
def _a ( self : Any ) -> Optional[Any]:
"""simple docstring"""
pass
@unittest.skipIf(
not is_tf_available() or len(tf.config.list_physical_devices('''GPU''' ) ) == 0 , reason='''TF does not support backprop for grouped convolutions on CPU.''' , )
@slow
def _a ( self : Dict ) -> List[Any]:
"""simple docstring"""
super().test_keras_fit()
@unittest.skip(reason='''RegNet does not support input and output embeddings''' )
def _a ( self : Dict ) -> Union[str, Any]:
"""simple docstring"""
pass
def _a ( self : List[Any] ) -> List[Any]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__SCREAMING_SNAKE_CASE = model_class(__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = inspect.signature(model.call )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
__SCREAMING_SNAKE_CASE = [*signature.parameters.keys()]
__SCREAMING_SNAKE_CASE = ['''pixel_values''']
self.assertListEqual(arg_names[:1] , __SCREAMING_SNAKE_CASE )
def _a ( self : Any ) -> Union[str, Any]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__SCREAMING_SNAKE_CASE )
def _a ( self : List[str] ) -> Tuple:
"""simple docstring"""
def check_hidden_states_output(__SCREAMING_SNAKE_CASE : Optional[Any] , __SCREAMING_SNAKE_CASE : Optional[Any] , __SCREAMING_SNAKE_CASE : Any ):
__SCREAMING_SNAKE_CASE = model_class(__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = model(**self._prepare_for_class(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) , training=__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
__SCREAMING_SNAKE_CASE = self.model_tester.num_stages
self.assertEqual(len(__SCREAMING_SNAKE_CASE ) , expected_num_stages + 1 )
# RegNet's feature maps are of shape (batch_size, num_channels, height, width)
self.assertListEqual(
list(hidden_states[0].shape[-2:] ) , [self.model_tester.image_size // 2, self.model_tester.image_size // 2] , )
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs_for_common()
__SCREAMING_SNAKE_CASE = ['''basic''', '''bottleneck''']
for model_class in self.all_model_classes:
for layer_type in layers_type:
__SCREAMING_SNAKE_CASE = layer_type
__SCREAMING_SNAKE_CASE = True
check_hidden_states_output(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
__SCREAMING_SNAKE_CASE = True
check_hidden_states_output(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
def _a ( self : Union[str, Any] ) -> List[str]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs_for_common()
def check_equivalence(__SCREAMING_SNAKE_CASE : Tuple , __SCREAMING_SNAKE_CASE : Optional[Any] , __SCREAMING_SNAKE_CASE : Tuple , __SCREAMING_SNAKE_CASE : Union[str, Any]={} ):
__SCREAMING_SNAKE_CASE = model(__SCREAMING_SNAKE_CASE , return_dict=__SCREAMING_SNAKE_CASE , **__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = model(__SCREAMING_SNAKE_CASE , return_dict=__SCREAMING_SNAKE_CASE , **__SCREAMING_SNAKE_CASE ).to_tuple()
def recursive_check(__SCREAMING_SNAKE_CASE : int , __SCREAMING_SNAKE_CASE : Dict ):
if isinstance(__SCREAMING_SNAKE_CASE , (List, Tuple) ):
for tuple_iterable_value, dict_iterable_value in zip(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
recursive_check(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
elif tuple_object is None:
return
else:
self.assertTrue(
all(tf.equal(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) ) , msg=(
'''Tuple and dict output are not equal. Difference:'''
f""" {tf.math.reduce_max(tf.abs(tuple_object - dict_object ) )}"""
) , )
recursive_check(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
for model_class in self.all_model_classes:
__SCREAMING_SNAKE_CASE = model_class(__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = self._prepare_for_class(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = self._prepare_for_class(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
check_equivalence(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = self._prepare_for_class(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , return_labels=__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = self._prepare_for_class(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , return_labels=__SCREAMING_SNAKE_CASE )
check_equivalence(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = self._prepare_for_class(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = self._prepare_for_class(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
check_equivalence(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , {'''output_hidden_states''': True} )
__SCREAMING_SNAKE_CASE = self._prepare_for_class(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , return_labels=__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = self._prepare_for_class(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , return_labels=__SCREAMING_SNAKE_CASE )
check_equivalence(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , {'''output_hidden_states''': True} )
def _a ( self : str ) -> str:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*__SCREAMING_SNAKE_CASE )
@slow
def _a ( self : Union[str, Any] ) -> Union[str, Any]:
"""simple docstring"""
for model_name in TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__SCREAMING_SNAKE_CASE = TFRegNetModel.from_pretrained(__SCREAMING_SNAKE_CASE )
self.assertIsNotNone(__SCREAMING_SNAKE_CASE )
def _a ( ) -> Dict:
__SCREAMING_SNAKE_CASE = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
return image
@require_tf
@require_vision
class A__( unittest.TestCase ):
@cached_property
def _a ( self : List[Any] ) -> str:
"""simple docstring"""
return (
AutoImageProcessor.from_pretrained(TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0] )
if is_vision_available()
else None
)
@slow
def _a ( self : List[str] ) -> Any:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = TFRegNetForImageClassification.from_pretrained(TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0] )
__SCREAMING_SNAKE_CASE = self.default_image_processor
__SCREAMING_SNAKE_CASE = prepare_img()
__SCREAMING_SNAKE_CASE = image_processor(images=__SCREAMING_SNAKE_CASE , return_tensors='''tf''' )
# forward pass
__SCREAMING_SNAKE_CASE = model(**__SCREAMING_SNAKE_CASE , training=__SCREAMING_SNAKE_CASE )
# verify the logits
__SCREAMING_SNAKE_CASE = tf.TensorShape((1, 10_00) )
self.assertEqual(outputs.logits.shape , __SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = tf.constant([-0.41_80, -1.50_51, -3.48_36] )
tf.debugging.assert_near(outputs.logits[0, :3] , __SCREAMING_SNAKE_CASE , atol=1E-4 )
| 690 | 1 |
"""simple docstring"""
import argparse
import os
import numpy as np
import tensorflow as tf
import torch
from transformers import BertModel
def _a ( UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ ) -> Optional[int]:
__SCREAMING_SNAKE_CASE = ('''dense.weight''', '''attention.self.query''', '''attention.self.key''', '''attention.self.value''')
__SCREAMING_SNAKE_CASE = (
('''layer.''', '''layer_'''),
('''word_embeddings.weight''', '''word_embeddings'''),
('''position_embeddings.weight''', '''position_embeddings'''),
('''token_type_embeddings.weight''', '''token_type_embeddings'''),
('''.''', '''/'''),
('''LayerNorm/weight''', '''LayerNorm/gamma'''),
('''LayerNorm/bias''', '''LayerNorm/beta'''),
('''weight''', '''kernel'''),
)
if not os.path.isdir(UpperCAmelCase__ ):
os.makedirs(UpperCAmelCase__ )
__SCREAMING_SNAKE_CASE = model.state_dict()
def to_tf_var_name(UpperCAmelCase__ ):
for patt, repl in iter(UpperCAmelCase__ ):
__SCREAMING_SNAKE_CASE = name.replace(UpperCAmelCase__ , UpperCAmelCase__ )
return f"""bert/{name}"""
def create_tf_var(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ ):
__SCREAMING_SNAKE_CASE = tf.dtypes.as_dtype(tensor.dtype )
__SCREAMING_SNAKE_CASE = tf.get_variable(dtype=UpperCAmelCase__ , shape=tensor.shape , name=UpperCAmelCase__ , initializer=tf.zeros_initializer() )
session.run(tf.variables_initializer([tf_var] ) )
session.run(UpperCAmelCase__ )
return tf_var
tf.reset_default_graph()
with tf.Session() as session:
for var_name in state_dict:
__SCREAMING_SNAKE_CASE = to_tf_var_name(UpperCAmelCase__ )
__SCREAMING_SNAKE_CASE = state_dict[var_name].numpy()
if any(x in var_name for x in tensors_to_transpose ):
__SCREAMING_SNAKE_CASE = torch_tensor.T
__SCREAMING_SNAKE_CASE = create_tf_var(tensor=UpperCAmelCase__ , name=UpperCAmelCase__ , session=UpperCAmelCase__ )
tf.keras.backend.set_value(UpperCAmelCase__ , UpperCAmelCase__ )
__SCREAMING_SNAKE_CASE = session.run(UpperCAmelCase__ )
print(f"""Successfully created {tf_name}: {np.allclose(UpperCAmelCase__ , UpperCAmelCase__ )}""" )
__SCREAMING_SNAKE_CASE = tf.train.Saver(tf.trainable_variables() )
saver.save(UpperCAmelCase__ , os.path.join(UpperCAmelCase__ , model_name.replace('''-''' , '''_''' ) + '''.ckpt''' ) )
def _a ( UpperCAmelCase__=None ) -> List[str]:
__SCREAMING_SNAKE_CASE = argparse.ArgumentParser()
parser.add_argument('''--model_name''' , type=UpperCAmelCase__ , required=UpperCAmelCase__ , help='''model name e.g. bert-base-uncased''' )
parser.add_argument(
'''--cache_dir''' , type=UpperCAmelCase__ , default=UpperCAmelCase__ , required=UpperCAmelCase__ , help='''Directory containing pytorch model''' )
parser.add_argument('''--pytorch_model_path''' , type=UpperCAmelCase__ , required=UpperCAmelCase__ , help='''/path/to/<pytorch-model-name>.bin''' )
parser.add_argument('''--tf_cache_dir''' , type=UpperCAmelCase__ , required=UpperCAmelCase__ , help='''Directory in which to save tensorflow model''' )
__SCREAMING_SNAKE_CASE = parser.parse_args(UpperCAmelCase__ )
__SCREAMING_SNAKE_CASE = BertModel.from_pretrained(
pretrained_model_name_or_path=args.model_name , state_dict=torch.load(args.pytorch_model_path ) , cache_dir=args.cache_dir , )
convert_pytorch_checkpoint_to_tf(model=UpperCAmelCase__ , ckpt_dir=args.tf_cache_dir , model_name=args.model_name )
if __name__ == "__main__":
main()
| 690 |
"""simple docstring"""
import pickle
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, XLMRobertaTokenizer, XLMRobertaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
lowerCAmelCase__ =get_tests_dir("fixtures/test_sentencepiece.model")
@require_sentencepiece
@require_tokenizers
class A__( __magic_name__ , unittest.TestCase ):
lowerCAmelCase = XLMRobertaTokenizer
lowerCAmelCase = XLMRobertaTokenizerFast
lowerCAmelCase = True
lowerCAmelCase = True
def _a ( self : Optional[int] ) -> List[Any]:
"""simple docstring"""
super().setUp()
# We have a SentencePiece fixture for testing
__SCREAMING_SNAKE_CASE = XLMRobertaTokenizer(__SCREAMING_SNAKE_CASE , keep_accents=__SCREAMING_SNAKE_CASE )
tokenizer.save_pretrained(self.tmpdirname )
def _a ( self : str ) -> Union[str, Any]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = '''<pad>'''
__SCREAMING_SNAKE_CASE = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(__SCREAMING_SNAKE_CASE ) , __SCREAMING_SNAKE_CASE )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(__SCREAMING_SNAKE_CASE ) , __SCREAMING_SNAKE_CASE )
def _a ( self : int ) -> List[Any]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , '''<s>''' )
self.assertEqual(vocab_keys[1] , '''<pad>''' )
self.assertEqual(vocab_keys[-1] , '''<mask>''' )
self.assertEqual(len(__SCREAMING_SNAKE_CASE ) , 10_02 )
def _a ( self : Tuple ) -> Optional[int]:
"""simple docstring"""
self.assertEqual(self.get_tokenizer().vocab_size , 10_02 )
def _a ( self : int ) -> int:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = XLMRobertaTokenizer(__SCREAMING_SNAKE_CASE , keep_accents=__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = tokenizer.tokenize('''This is a test''' )
self.assertListEqual(__SCREAMING_SNAKE_CASE , ['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est'''] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(__SCREAMING_SNAKE_CASE ) , [value + tokenizer.fairseq_offset for value in [2_85, 46, 10, 1_70, 3_82]] , )
__SCREAMING_SNAKE_CASE = tokenizer.tokenize('''I was born in 92000, and this is falsé.''' )
self.assertListEqual(
__SCREAMING_SNAKE_CASE , [
SPIECE_UNDERLINE + '''I''',
SPIECE_UNDERLINE + '''was''',
SPIECE_UNDERLINE + '''b''',
'''or''',
'''n''',
SPIECE_UNDERLINE + '''in''',
SPIECE_UNDERLINE + '''''',
'''9''',
'''2''',
'''0''',
'''0''',
'''0''',
''',''',
SPIECE_UNDERLINE + '''and''',
SPIECE_UNDERLINE + '''this''',
SPIECE_UNDERLINE + '''is''',
SPIECE_UNDERLINE + '''f''',
'''al''',
'''s''',
'''é''',
'''.''',
] , )
__SCREAMING_SNAKE_CASE = tokenizer.convert_tokens_to_ids(__SCREAMING_SNAKE_CASE )
self.assertListEqual(
__SCREAMING_SNAKE_CASE , [
value + tokenizer.fairseq_offset
for value in [8, 21, 84, 55, 24, 19, 7, 2, 6_02, 3_47, 3_47, 3_47, 3, 12, 66, 46, 72, 80, 6, 2, 4]
# ^ unk: 2 + 1 = 3 unk: 2 + 1 = 3 ^
] , )
__SCREAMING_SNAKE_CASE = tokenizer.convert_ids_to_tokens(__SCREAMING_SNAKE_CASE )
self.assertListEqual(
__SCREAMING_SNAKE_CASE , [
SPIECE_UNDERLINE + '''I''',
SPIECE_UNDERLINE + '''was''',
SPIECE_UNDERLINE + '''b''',
'''or''',
'''n''',
SPIECE_UNDERLINE + '''in''',
SPIECE_UNDERLINE + '''''',
'''<unk>''',
'''2''',
'''0''',
'''0''',
'''0''',
''',''',
SPIECE_UNDERLINE + '''and''',
SPIECE_UNDERLINE + '''this''',
SPIECE_UNDERLINE + '''is''',
SPIECE_UNDERLINE + '''f''',
'''al''',
'''s''',
'''<unk>''',
'''.''',
] , )
def _a ( self : int ) -> Tuple:
"""simple docstring"""
if not self.test_slow_tokenizer:
# as we don't have a slow version, we can't compare the outputs between slow and fast versions
return
__SCREAMING_SNAKE_CASE = (self.rust_tokenizer_class, '''hf-internal-testing/tiny-xlm-roberta''', {})
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})""" ):
__SCREAMING_SNAKE_CASE = self.rust_tokenizer_class.from_pretrained(__SCREAMING_SNAKE_CASE , **__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = self.tokenizer_class.from_pretrained(__SCREAMING_SNAKE_CASE , **__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = tempfile.mkdtemp()
__SCREAMING_SNAKE_CASE = tokenizer_r.save_pretrained(__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = tokenizer_p.save_pretrained(__SCREAMING_SNAKE_CASE )
# Checks it save with the same files + the tokenizer.json file for the fast one
self.assertTrue(any('''tokenizer.json''' in f for f in tokenizer_r_files ) )
__SCREAMING_SNAKE_CASE = tuple(f for f in tokenizer_r_files if '''tokenizer.json''' not in f )
self.assertSequenceEqual(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
# Checks everything loads correctly in the same way
__SCREAMING_SNAKE_CASE = tokenizer_r.from_pretrained(__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = tokenizer_p.from_pretrained(__SCREAMING_SNAKE_CASE )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) )
# self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
# self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))
shutil.rmtree(__SCREAMING_SNAKE_CASE )
# Save tokenizer rust, legacy_format=True
__SCREAMING_SNAKE_CASE = tempfile.mkdtemp()
__SCREAMING_SNAKE_CASE = tokenizer_r.save_pretrained(__SCREAMING_SNAKE_CASE , legacy_format=__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = tokenizer_p.save_pretrained(__SCREAMING_SNAKE_CASE )
# Checks it save with the same files
self.assertSequenceEqual(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
# Checks everything loads correctly in the same way
__SCREAMING_SNAKE_CASE = tokenizer_r.from_pretrained(__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = tokenizer_p.from_pretrained(__SCREAMING_SNAKE_CASE )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) )
shutil.rmtree(__SCREAMING_SNAKE_CASE )
# Save tokenizer rust, legacy_format=False
__SCREAMING_SNAKE_CASE = tempfile.mkdtemp()
__SCREAMING_SNAKE_CASE = tokenizer_r.save_pretrained(__SCREAMING_SNAKE_CASE , legacy_format=__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = tokenizer_p.save_pretrained(__SCREAMING_SNAKE_CASE )
# Checks it saved the tokenizer.json file
self.assertTrue(any('''tokenizer.json''' in f for f in tokenizer_r_files ) )
# Checks everything loads correctly in the same way
__SCREAMING_SNAKE_CASE = tokenizer_r.from_pretrained(__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = tokenizer_p.from_pretrained(__SCREAMING_SNAKE_CASE )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) )
shutil.rmtree(__SCREAMING_SNAKE_CASE )
@cached_property
def _a ( self : Union[str, Any] ) -> List[str]:
"""simple docstring"""
return XLMRobertaTokenizer.from_pretrained('''xlm-roberta-base''' )
def _a ( self : Optional[Any] ) -> Union[str, Any]:
"""simple docstring"""
with tempfile.NamedTemporaryFile() as f:
shutil.copyfile(__SCREAMING_SNAKE_CASE , f.name )
__SCREAMING_SNAKE_CASE = XLMRobertaTokenizer(f.name , keep_accents=__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = pickle.dumps(__SCREAMING_SNAKE_CASE )
pickle.loads(__SCREAMING_SNAKE_CASE )
def _a ( self : Union[str, Any] ) -> List[Any]:
"""simple docstring"""
if not self.test_rust_tokenizer:
return
__SCREAMING_SNAKE_CASE = self.get_tokenizer()
__SCREAMING_SNAKE_CASE = self.get_rust_tokenizer()
__SCREAMING_SNAKE_CASE = '''I was born in 92000, and this is falsé.'''
__SCREAMING_SNAKE_CASE = tokenizer.tokenize(__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = rust_tokenizer.tokenize(__SCREAMING_SNAKE_CASE )
self.assertListEqual(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = tokenizer.encode(__SCREAMING_SNAKE_CASE , add_special_tokens=__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = rust_tokenizer.encode(__SCREAMING_SNAKE_CASE , add_special_tokens=__SCREAMING_SNAKE_CASE )
self.assertListEqual(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = self.get_rust_tokenizer()
__SCREAMING_SNAKE_CASE = tokenizer.encode(__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = rust_tokenizer.encode(__SCREAMING_SNAKE_CASE )
self.assertListEqual(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
@slow
def _a ( self : Any ) -> Dict:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = '''Hello World!'''
__SCREAMING_SNAKE_CASE = [0, 3_53_78, 66_61, 38, 2]
# xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base') # xlmr.large has same tokenizer
# xlmr.eval()
# xlmr.encode(symbols)
self.assertListEqual(__SCREAMING_SNAKE_CASE , self.big_tokenizer.encode(__SCREAMING_SNAKE_CASE ) )
@slow
def _a ( self : Optional[Any] ) -> Union[str, Any]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = (
'''This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) " [ ] ! : - . Also we will'''
''' add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth'''
)
__SCREAMING_SNAKE_CASE = [
0,
32_93,
83,
10,
45_52,
49_89,
79_86,
6_78,
10,
59_15,
1_11,
17_94_59,
12_48_50,
4,
60_44,
2_37,
12,
6,
5,
6,
4,
67_80,
7_05,
15,
13_88,
44,
3_78,
1_01_14,
7_11,
1_52,
20,
6,
5,
2_23_76,
6_42,
12_21,
1_51_90,
3_41_53,
4_50,
56_08,
9_59,
11_19,
5_77_02,
1_36,
1_86,
47,
10_98,
2_93_67,
47,
# 4426, # What fairseq tokenizes from "<unk>": "_<"
# 3678, # What fairseq tokenizes from "<unk>": "unk"
# 2740, # What fairseq tokenizes from "<unk>": ">"
3, # What we tokenize from "<unk>": "<unk>"
6, # Residue from the tokenization: an extra sentencepiece underline
4,
60_44,
2_37,
62_84,
5_09_01,
5_28,
31,
90,
34,
9_27,
2,
]
# xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base') # xlmr.large has same tokenizer
# xlmr.eval()
# xlmr.encode(symbols)
self.assertListEqual(__SCREAMING_SNAKE_CASE , self.big_tokenizer.encode(__SCREAMING_SNAKE_CASE ) )
@slow
def _a ( self : Optional[int] ) -> Dict:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = {'''input_ids''': [[0, 1_10_62, 8_27_72, 7, 15, 8_27_72, 5_38, 5_15_29, 2_37, 1_71_98, 12_90, 2_06, 9, 21_51_75, 13_14, 1_36, 1_71_98, 12_90, 2_06, 9, 5_63_59, 42, 12_20_09, 9, 1_64_66, 16, 8_73_44, 45_37, 9, 47_17, 7_83_81, 6, 15_99_58, 7, 15, 2_44_80, 6_18, 4, 5_27, 2_26_93, 54_28, 4, 27_77, 2_44_80, 98_74, 4, 4_35_23, 5_94, 4, 8_03, 1_83_92, 3_31_89, 18, 4, 4_35_23, 2_44_47, 1_23_99, 1_00, 2_49_55, 8_36_58, 96_26, 14_40_57, 15, 8_39, 2_23_35, 16, 1_36, 2_49_55, 8_36_58, 8_34_79, 15, 3_91_02, 7_24, 16, 6_78, 6_45, 27_89, 13_28, 45_89, 42, 12_20_09, 11_57_74, 23, 8_05, 13_28, 4_68_76, 7, 1_36, 5_38_94, 19_40, 4_22_27, 4_11_59, 1_77_21, 8_23, 4_25, 4, 2_75_12, 9_87_22, 2_06, 1_36, 55_31, 49_70, 9_19, 1_73_36, 5, 2], [0, 2_00_80, 6_18, 83, 8_27_75, 47, 4_79, 9, 15_17, 73, 5_38_94, 3_33, 8_05_81, 11_01_17, 1_88_11, 52_56, 12_95, 51, 15_25_26, 2_97, 79_86, 3_90, 12_44_16, 5_38, 3_54_31, 2_14, 98, 1_50_44, 2_57_37, 1_36, 71_08, 4_37_01, 23, 7_56, 13_53_55, 7, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 5_81, 6_37_73, 11_94_55, 6, 14_77_97, 8_82_03, 7, 6_45, 70, 21, 32_85, 1_02_69, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], '''attention_mask''': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # noqa: E501
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=__SCREAMING_SNAKE_CASE , model_name='''xlm-roberta-base''' , revision='''d9d8a8ea5eb94b1c6654ae9249df7793cd2933d3''' , )
| 690 | 1 |
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from diffusers import DDIMScheduler, KandinskyVaaPipeline, KandinskyVaaPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.utils import floats_tensor, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
enable_full_determinism()
class A__( __magic_name__ , unittest.TestCase ):
lowerCAmelCase = KandinskyVaaPipeline
lowerCAmelCase = [
'''image_embeds''',
'''negative_image_embeds''',
]
lowerCAmelCase = ['''image_embeds''', '''negative_image_embeds''']
lowerCAmelCase = [
'''generator''',
'''height''',
'''width''',
'''latents''',
'''guidance_scale''',
'''num_inference_steps''',
'''return_dict''',
'''guidance_scale''',
'''num_images_per_prompt''',
'''output_type''',
'''return_dict''',
]
lowerCAmelCase = False
@property
def _a ( self : str ) -> Union[str, Any]:
"""simple docstring"""
return 32
@property
def _a ( self : List[str] ) -> Dict:
"""simple docstring"""
return 32
@property
def _a ( self : int ) -> Optional[Any]:
"""simple docstring"""
return self.time_input_dim
@property
def _a ( self : Dict ) -> Any:
"""simple docstring"""
return self.time_input_dim * 4
@property
def _a ( self : List[str] ) -> Any:
"""simple docstring"""
return 1_00
@property
def _a ( self : Optional[Any] ) -> Tuple:
"""simple docstring"""
torch.manual_seed(0 )
__SCREAMING_SNAKE_CASE = {
'''in_channels''': 4,
# Out channels is double in channels because predicts mean and variance
'''out_channels''': 8,
'''addition_embed_type''': '''image''',
'''down_block_types''': ('''ResnetDownsampleBlock2D''', '''SimpleCrossAttnDownBlock2D'''),
'''up_block_types''': ('''SimpleCrossAttnUpBlock2D''', '''ResnetUpsampleBlock2D'''),
'''mid_block_type''': '''UNetMidBlock2DSimpleCrossAttn''',
'''block_out_channels''': (self.block_out_channels_a, self.block_out_channels_a * 2),
'''layers_per_block''': 1,
'''encoder_hid_dim''': self.text_embedder_hidden_size,
'''encoder_hid_dim_type''': '''image_proj''',
'''cross_attention_dim''': self.cross_attention_dim,
'''attention_head_dim''': 4,
'''resnet_time_scale_shift''': '''scale_shift''',
'''class_embed_type''': None,
}
__SCREAMING_SNAKE_CASE = UNetaDConditionModel(**__SCREAMING_SNAKE_CASE )
return model
@property
def _a ( self : Dict ) -> str:
"""simple docstring"""
return {
"block_out_channels": [32, 64],
"down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
"in_channels": 3,
"latent_channels": 4,
"layers_per_block": 1,
"norm_num_groups": 8,
"norm_type": "spatial",
"num_vq_embeddings": 12,
"out_channels": 3,
"up_block_types": [
"AttnUpDecoderBlock2D",
"UpDecoderBlock2D",
],
"vq_embed_dim": 4,
}
@property
def _a ( self : Dict ) -> Tuple:
"""simple docstring"""
torch.manual_seed(0 )
__SCREAMING_SNAKE_CASE = VQModel(**self.dummy_movq_kwargs )
return model
def _a ( self : Dict ) -> Dict:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = self.dummy_unet
__SCREAMING_SNAKE_CASE = self.dummy_movq
__SCREAMING_SNAKE_CASE = DDIMScheduler(
num_train_timesteps=10_00 , beta_schedule='''linear''' , beta_start=0.0_00_85 , beta_end=0.0_12 , clip_sample=__SCREAMING_SNAKE_CASE , set_alpha_to_one=__SCREAMING_SNAKE_CASE , steps_offset=1 , prediction_type='''epsilon''' , thresholding=__SCREAMING_SNAKE_CASE , )
__SCREAMING_SNAKE_CASE = {
'''unet''': unet,
'''scheduler''': scheduler,
'''movq''': movq,
}
return components
def _a ( self : int , __SCREAMING_SNAKE_CASE : Optional[int] , __SCREAMING_SNAKE_CASE : Any=0 ) -> Dict:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = floats_tensor((1, self.text_embedder_hidden_size) , rng=random.Random(__SCREAMING_SNAKE_CASE ) ).to(__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = floats_tensor((1, self.text_embedder_hidden_size) , rng=random.Random(seed + 1 ) ).to(
__SCREAMING_SNAKE_CASE )
if str(__SCREAMING_SNAKE_CASE ).startswith('''mps''' ):
__SCREAMING_SNAKE_CASE = torch.manual_seed(__SCREAMING_SNAKE_CASE )
else:
__SCREAMING_SNAKE_CASE = torch.Generator(device=__SCREAMING_SNAKE_CASE ).manual_seed(__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = {
'''image_embeds''': image_embeds,
'''negative_image_embeds''': negative_image_embeds,
'''generator''': generator,
'''height''': 64,
'''width''': 64,
'''guidance_scale''': 4.0,
'''num_inference_steps''': 2,
'''output_type''': '''np''',
}
return inputs
def _a ( self : int ) -> List[Any]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = '''cpu'''
__SCREAMING_SNAKE_CASE = self.get_dummy_components()
__SCREAMING_SNAKE_CASE = self.pipeline_class(**__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = pipe.to(__SCREAMING_SNAKE_CASE )
pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = pipe(**self.get_dummy_inputs(__SCREAMING_SNAKE_CASE ) )
__SCREAMING_SNAKE_CASE = output.images
__SCREAMING_SNAKE_CASE = pipe(
**self.get_dummy_inputs(__SCREAMING_SNAKE_CASE ) , return_dict=__SCREAMING_SNAKE_CASE , )[0]
__SCREAMING_SNAKE_CASE = image[0, -3:, -3:, -1]
__SCREAMING_SNAKE_CASE = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
__SCREAMING_SNAKE_CASE = np.array(
[0.6_23_79_76, 1.0, 0.36_44_13_32, 1.0, 0.70_63_96_34, 0.29_87_71_86, 0.85_65_21_25, 0.5_21_68_43, 0.54_45_40_46] )
assert (
np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
), f""" expected_slice {expected_slice}, but got {image_slice.flatten()}"""
assert (
np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1E-2
), f""" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"""
@slow
@require_torch_gpu
class A__( unittest.TestCase ):
def _a ( self : Optional[int] ) -> Any:
"""simple docstring"""
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def _a ( self : Optional[Any] ) -> List[str]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = load_numpy(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
'''/kandinskyv22/kandinskyv22_text2img_cat_fp16.npy''' )
__SCREAMING_SNAKE_CASE = KandinskyVaaPriorPipeline.from_pretrained(
'''kandinsky-community/kandinsky-2-2-prior''' , torch_dtype=torch.floataa )
pipe_prior.to(__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = KandinskyVaaPipeline.from_pretrained(
'''kandinsky-community/kandinsky-2-2-decoder''' , torch_dtype=torch.floataa )
__SCREAMING_SNAKE_CASE = pipeline.to(__SCREAMING_SNAKE_CASE )
pipeline.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = '''red cat, 4k photo'''
__SCREAMING_SNAKE_CASE = torch.Generator(device='''cuda''' ).manual_seed(0 )
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = pipe_prior(
__SCREAMING_SNAKE_CASE , generator=__SCREAMING_SNAKE_CASE , num_inference_steps=5 , negative_prompt='''''' , ).to_tuple()
__SCREAMING_SNAKE_CASE = torch.Generator(device='''cuda''' ).manual_seed(0 )
__SCREAMING_SNAKE_CASE = pipeline(
image_embeds=__SCREAMING_SNAKE_CASE , negative_image_embeds=__SCREAMING_SNAKE_CASE , generator=__SCREAMING_SNAKE_CASE , num_inference_steps=1_00 , output_type='''np''' , )
__SCREAMING_SNAKE_CASE = output.images[0]
assert image.shape == (5_12, 5_12, 3)
assert_mean_pixel_difference(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
| 690 |
"""simple docstring"""
from __future__ import annotations
lowerCAmelCase__ =8.9_8_8E9 # units = N * m^s * C^-2
def _a ( UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ ) -> dict[str, float]:
__SCREAMING_SNAKE_CASE = abs(chargea * chargea )
if (force, chargea, chargea, distance).count(0 ) != 1:
raise ValueError('''One and only one argument must be 0''' )
if distance < 0:
raise ValueError('''Distance cannot be negative''' )
if force == 0:
__SCREAMING_SNAKE_CASE = COULOMBS_CONSTANT * charge_product / (distance**2)
return {"force": force}
elif chargea == 0:
__SCREAMING_SNAKE_CASE = abs(UpperCAmelCase__ ) * (distance**2) / (COULOMBS_CONSTANT * chargea)
return {"charge1": chargea}
elif chargea == 0:
__SCREAMING_SNAKE_CASE = abs(UpperCAmelCase__ ) * (distance**2) / (COULOMBS_CONSTANT * chargea)
return {"charge2": chargea}
elif distance == 0:
__SCREAMING_SNAKE_CASE = (COULOMBS_CONSTANT * charge_product / abs(UpperCAmelCase__ )) ** 0.5
return {"distance": distance}
raise ValueError('''Exactly one argument must be 0''' )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 690 | 1 |
"""simple docstring"""
import importlib
import json
import os
from collections import OrderedDict
from typing import Dict, Optional, Union
# Build the list of all image processors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module, resolve_trust_remote_code
from ...image_processing_utils import ImageProcessingMixin
from ...utils import CONFIG_NAME, IMAGE_PROCESSOR_NAME, get_file_from_repo, logging
from .auto_factory import _LazyAutoMapping
from .configuration_auto import (
CONFIG_MAPPING_NAMES,
AutoConfig,
model_type_to_module_name,
replace_list_option_in_docstrings,
)
lowerCAmelCase__ =logging.get_logger(__name__)
lowerCAmelCase__ =OrderedDict(
[
("align", "EfficientNetImageProcessor"),
("beit", "BeitImageProcessor"),
("bit", "BitImageProcessor"),
("blip", "BlipImageProcessor"),
("blip-2", "BlipImageProcessor"),
("bridgetower", "BridgeTowerImageProcessor"),
("chinese_clip", "ChineseCLIPImageProcessor"),
("clip", "CLIPImageProcessor"),
("clipseg", "ViTImageProcessor"),
("conditional_detr", "ConditionalDetrImageProcessor"),
("convnext", "ConvNextImageProcessor"),
("convnextv2", "ConvNextImageProcessor"),
("cvt", "ConvNextImageProcessor"),
("data2vec-vision", "BeitImageProcessor"),
("deformable_detr", "DeformableDetrImageProcessor"),
("deit", "DeiTImageProcessor"),
("deta", "DetaImageProcessor"),
("detr", "DetrImageProcessor"),
("dinat", "ViTImageProcessor"),
("donut-swin", "DonutImageProcessor"),
("dpt", "DPTImageProcessor"),
("efficientformer", "EfficientFormerImageProcessor"),
("efficientnet", "EfficientNetImageProcessor"),
("flava", "FlavaImageProcessor"),
("focalnet", "BitImageProcessor"),
("git", "CLIPImageProcessor"),
("glpn", "GLPNImageProcessor"),
("groupvit", "CLIPImageProcessor"),
("imagegpt", "ImageGPTImageProcessor"),
("instructblip", "BlipImageProcessor"),
("layoutlmv2", "LayoutLMv2ImageProcessor"),
("layoutlmv3", "LayoutLMv3ImageProcessor"),
("levit", "LevitImageProcessor"),
("mask2former", "Mask2FormerImageProcessor"),
("maskformer", "MaskFormerImageProcessor"),
("mgp-str", "ViTImageProcessor"),
("mobilenet_v1", "MobileNetV1ImageProcessor"),
("mobilenet_v2", "MobileNetV2ImageProcessor"),
("mobilevit", "MobileViTImageProcessor"),
("mobilevit", "MobileViTImageProcessor"),
("mobilevitv2", "MobileViTImageProcessor"),
("nat", "ViTImageProcessor"),
("oneformer", "OneFormerImageProcessor"),
("owlvit", "OwlViTImageProcessor"),
("perceiver", "PerceiverImageProcessor"),
("pix2struct", "Pix2StructImageProcessor"),
("poolformer", "PoolFormerImageProcessor"),
("regnet", "ConvNextImageProcessor"),
("resnet", "ConvNextImageProcessor"),
("sam", "SamImageProcessor"),
("segformer", "SegformerImageProcessor"),
("swiftformer", "ViTImageProcessor"),
("swin", "ViTImageProcessor"),
("swin2sr", "Swin2SRImageProcessor"),
("swinv2", "ViTImageProcessor"),
("table-transformer", "DetrImageProcessor"),
("timesformer", "VideoMAEImageProcessor"),
("tvlt", "TvltImageProcessor"),
("upernet", "SegformerImageProcessor"),
("van", "ConvNextImageProcessor"),
("videomae", "VideoMAEImageProcessor"),
("vilt", "ViltImageProcessor"),
("vit", "ViTImageProcessor"),
("vit_hybrid", "ViTHybridImageProcessor"),
("vit_mae", "ViTImageProcessor"),
("vit_msn", "ViTImageProcessor"),
("xclip", "CLIPImageProcessor"),
("yolos", "YolosImageProcessor"),
]
)
lowerCAmelCase__ =_LazyAutoMapping(CONFIG_MAPPING_NAMES, IMAGE_PROCESSOR_MAPPING_NAMES)
def _a ( UpperCAmelCase__ ) -> Dict:
for module_name, extractors in IMAGE_PROCESSOR_MAPPING_NAMES.items():
if class_name in extractors:
__SCREAMING_SNAKE_CASE = model_type_to_module_name(UpperCAmelCase__ )
__SCREAMING_SNAKE_CASE = importlib.import_module(f""".{module_name}""" , '''transformers.models''' )
try:
return getattr(UpperCAmelCase__ , UpperCAmelCase__ )
except AttributeError:
continue
for _, extractor in IMAGE_PROCESSOR_MAPPING._extra_content.items():
if getattr(UpperCAmelCase__ , '''__name__''' , UpperCAmelCase__ ) == class_name:
return extractor
# We did not fine the class, but maybe it's because a dep is missing. In that case, the class will be in the main
# init and we return the proper dummy to get an appropriate error message.
__SCREAMING_SNAKE_CASE = importlib.import_module('''transformers''' )
if hasattr(UpperCAmelCase__ , UpperCAmelCase__ ):
return getattr(UpperCAmelCase__ , UpperCAmelCase__ )
return None
def _a ( UpperCAmelCase__ , UpperCAmelCase__ = None , UpperCAmelCase__ = False , UpperCAmelCase__ = False , UpperCAmelCase__ = None , UpperCAmelCase__ = None , UpperCAmelCase__ = None , UpperCAmelCase__ = False , **UpperCAmelCase__ , ) -> Dict:
__SCREAMING_SNAKE_CASE = get_file_from_repo(
UpperCAmelCase__ , UpperCAmelCase__ , cache_dir=UpperCAmelCase__ , force_download=UpperCAmelCase__ , resume_download=UpperCAmelCase__ , proxies=UpperCAmelCase__ , use_auth_token=UpperCAmelCase__ , revision=UpperCAmelCase__ , local_files_only=UpperCAmelCase__ , )
if resolved_config_file is None:
logger.info(
'''Could not locate the image processor configuration file, will try to use the model config instead.''' )
return {}
with open(UpperCAmelCase__ , encoding='''utf-8''' ) as reader:
return json.load(UpperCAmelCase__ )
class A__:
def __init__( self : Tuple ) -> Optional[int]:
"""simple docstring"""
raise EnvironmentError(
'''AutoImageProcessor is designed to be instantiated '''
'''using the `AutoImageProcessor.from_pretrained(pretrained_model_name_or_path)` method.''' )
@classmethod
@replace_list_option_in_docstrings(__SCREAMING_SNAKE_CASE )
def _a ( cls : Union[str, Any] , __SCREAMING_SNAKE_CASE : Dict , **__SCREAMING_SNAKE_CASE : Dict ) -> List[str]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = kwargs.pop('''config''' , __SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = kwargs.pop('''trust_remote_code''' , __SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = True
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = ImageProcessingMixin.get_image_processor_dict(__SCREAMING_SNAKE_CASE , **__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = config_dict.get('''image_processor_type''' , __SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = None
if "AutoImageProcessor" in config_dict.get('''auto_map''' , {} ):
__SCREAMING_SNAKE_CASE = config_dict['''auto_map''']['''AutoImageProcessor''']
# If we still don't have the image processor class, check if we're loading from a previous feature extractor config
# and if so, infer the image processor class from there.
if image_processor_class is None and image_processor_auto_map is None:
__SCREAMING_SNAKE_CASE = config_dict.pop('''feature_extractor_type''' , __SCREAMING_SNAKE_CASE )
if feature_extractor_class is not None:
logger.warning(
'''Could not find image processor class in the image processor config or the model config. Loading'''
''' based on pattern matching with the model\'s feature extractor configuration.''' )
__SCREAMING_SNAKE_CASE = feature_extractor_class.replace('''FeatureExtractor''' , '''ImageProcessor''' )
if "AutoFeatureExtractor" in config_dict.get('''auto_map''' , {} ):
__SCREAMING_SNAKE_CASE = config_dict['''auto_map''']['''AutoFeatureExtractor''']
__SCREAMING_SNAKE_CASE = feature_extractor_auto_map.replace('''FeatureExtractor''' , '''ImageProcessor''' )
logger.warning(
'''Could not find image processor auto map in the image processor config or the model config.'''
''' Loading based on pattern matching with the model\'s feature extractor configuration.''' )
# If we don't find the image processor class in the image processor config, let's try the model config.
if image_processor_class is None and image_processor_auto_map is None:
if not isinstance(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
__SCREAMING_SNAKE_CASE = AutoConfig.from_pretrained(__SCREAMING_SNAKE_CASE , **__SCREAMING_SNAKE_CASE )
# It could be in `config.image_processor_type``
__SCREAMING_SNAKE_CASE = getattr(__SCREAMING_SNAKE_CASE , '''image_processor_type''' , __SCREAMING_SNAKE_CASE )
if hasattr(__SCREAMING_SNAKE_CASE , '''auto_map''' ) and "AutoImageProcessor" in config.auto_map:
__SCREAMING_SNAKE_CASE = config.auto_map['''AutoImageProcessor''']
if image_processor_class is not None:
__SCREAMING_SNAKE_CASE = image_processor_class_from_name(__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = image_processor_auto_map is not None
__SCREAMING_SNAKE_CASE = image_processor_class is not None or type(__SCREAMING_SNAKE_CASE ) in IMAGE_PROCESSOR_MAPPING
__SCREAMING_SNAKE_CASE = resolve_trust_remote_code(
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
if has_remote_code and trust_remote_code:
__SCREAMING_SNAKE_CASE = get_class_from_dynamic_module(
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , **__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = kwargs.pop('''code_revision''' , __SCREAMING_SNAKE_CASE )
if os.path.isdir(__SCREAMING_SNAKE_CASE ):
image_processor_class.register_for_auto_class()
return image_processor_class.from_dict(__SCREAMING_SNAKE_CASE , **__SCREAMING_SNAKE_CASE )
elif image_processor_class is not None:
return image_processor_class.from_dict(__SCREAMING_SNAKE_CASE , **__SCREAMING_SNAKE_CASE )
# Last try: we use the IMAGE_PROCESSOR_MAPPING.
elif type(__SCREAMING_SNAKE_CASE ) in IMAGE_PROCESSOR_MAPPING:
__SCREAMING_SNAKE_CASE = IMAGE_PROCESSOR_MAPPING[type(__SCREAMING_SNAKE_CASE )]
return image_processor_class.from_dict(__SCREAMING_SNAKE_CASE , **__SCREAMING_SNAKE_CASE )
raise ValueError(
f"""Unrecognized image processor in {pretrained_model_name_or_path}. Should have a """
f"""`image_processor_type` key in its {IMAGE_PROCESSOR_NAME} of {CONFIG_NAME}, or one of the following """
f"""`model_type` keys in its {CONFIG_NAME}: {", ".join(c for c in IMAGE_PROCESSOR_MAPPING_NAMES.keys() )}""" )
@staticmethod
def _a ( __SCREAMING_SNAKE_CASE : Tuple , __SCREAMING_SNAKE_CASE : List[Any] ) -> Optional[int]:
"""simple docstring"""
IMAGE_PROCESSOR_MAPPING.register(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
| 690 |
"""simple docstring"""
import argparse
from pathlib import Path
import torch
from transformers import OPTConfig, OPTModel
from transformers.utils import logging
logging.set_verbosity_info()
lowerCAmelCase__ =logging.get_logger(__name__)
def _a ( UpperCAmelCase__ ) -> Tuple:
__SCREAMING_SNAKE_CASE = torch.load(UpperCAmelCase__ , map_location='''cpu''' )
if "model" in sd.keys():
__SCREAMING_SNAKE_CASE = torch.load(UpperCAmelCase__ , map_location='''cpu''' )['''model''']
# pop unnecessary weights
__SCREAMING_SNAKE_CASE = [
'''decoder.version''',
'''decoder.output_projection.weight''',
]
for key in keys_to_delete:
if key in sd:
sd.pop(UpperCAmelCase__ )
__SCREAMING_SNAKE_CASE = {
'''decoder.project_in_dim.weight''': '''decoder.project_in.weight''',
'''decoder.project_out_dim.weight''': '''decoder.project_out.weight''',
'''decoder.layer_norm.weight''': '''decoder.final_layer_norm.weight''',
'''decoder.layer_norm.bias''': '''decoder.final_layer_norm.bias''',
}
for old_key, new_key in keys_to_rename.items():
if old_key in sd:
__SCREAMING_SNAKE_CASE = sd.pop(UpperCAmelCase__ )
__SCREAMING_SNAKE_CASE = list(sd.keys() )
for key in keys:
if ".qkv_proj." in key:
__SCREAMING_SNAKE_CASE = sd[key]
# We split QKV in separate Q,K,V
__SCREAMING_SNAKE_CASE = key.replace('''.qkv_proj.''' , '''.q_proj.''' )
__SCREAMING_SNAKE_CASE = key.replace('''.qkv_proj.''' , '''.k_proj.''' )
__SCREAMING_SNAKE_CASE = key.replace('''.qkv_proj.''' , '''.v_proj.''' )
__SCREAMING_SNAKE_CASE = value.shape[0]
assert depth % 3 == 0
# `SequeuceParallelTransformerBlock` has QKV weight is separated in K,V,Q despite the naming:
# https://cs.github.com/facebookresearch/metaseq/blob/51871bd73cd04c038f239ea2a26db1d7f6b37927/metaseq/modules/sequence_parallel_transformer_layer.py#L97
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = torch.split(UpperCAmelCase__ , depth // 3 , dim=0 )
__SCREAMING_SNAKE_CASE = q
__SCREAMING_SNAKE_CASE = k
__SCREAMING_SNAKE_CASE = v
del sd[key]
return sd
@torch.no_grad()
def _a ( UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__=None ) -> Optional[Any]:
__SCREAMING_SNAKE_CASE = load_checkpoint(UpperCAmelCase__ )
if config is not None:
__SCREAMING_SNAKE_CASE = OPTConfig.from_pretrained(UpperCAmelCase__ )
else:
__SCREAMING_SNAKE_CASE = OPTConfig()
__SCREAMING_SNAKE_CASE = OPTModel(UpperCAmelCase__ ).half().eval()
model.load_state_dict(UpperCAmelCase__ )
# Check results
Path(UpperCAmelCase__ ).mkdir(exist_ok=UpperCAmelCase__ )
model.save_pretrained(UpperCAmelCase__ )
if __name__ == "__main__":
lowerCAmelCase__ =argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--fairseq_path",
type=str,
help=(
"path to fairseq checkpoint in correct format. You can find all checkpoints in the correct format here:"
" https://huggingface.co/models?other=opt_metasq"
),
)
parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
parser.add_argument("--hf_config", default=None, type=str, help="Define HF config.")
lowerCAmelCase__ =parser.parse_args()
convert_opt_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, config=args.hf_config)
| 690 | 1 |
"""simple docstring"""
def _a ( UpperCAmelCase__ ) -> bool:
__SCREAMING_SNAKE_CASE = (1 + 24 * n) ** 0.5
return ((1 + root) / 6) % 1 == 0
def _a ( UpperCAmelCase__ = 50_00 ) -> int:
__SCREAMING_SNAKE_CASE = [(i * (3 * i - 1)) // 2 for i in range(1 , UpperCAmelCase__ )]
for i, pentagonal_i in enumerate(UpperCAmelCase__ ):
for j in range(UpperCAmelCase__ , len(UpperCAmelCase__ ) ):
__SCREAMING_SNAKE_CASE = pentagonal_nums[j]
__SCREAMING_SNAKE_CASE = pentagonal_i + pentagonal_j
__SCREAMING_SNAKE_CASE = pentagonal_j - pentagonal_i
if is_pentagonal(UpperCAmelCase__ ) and is_pentagonal(UpperCAmelCase__ ):
return b
return -1
if __name__ == "__main__":
print(F'''{solution() = }''')
| 690 |
"""simple docstring"""
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import re
from ..models.auto import AutoProcessor
from ..models.vision_encoder_decoder import VisionEncoderDecoderModel
from ..utils import is_vision_available
from .base import PipelineTool
if is_vision_available():
from PIL import Image
class A__( __magic_name__ ):
lowerCAmelCase = '''naver-clova-ix/donut-base-finetuned-docvqa'''
lowerCAmelCase = (
'''This is a tool that answers a question about an document (pdf). It takes an input named `document` which '''
'''should be the document containing the information, as well as a `question` that is the question about the '''
'''document. It returns a text that contains the answer to the question.'''
)
lowerCAmelCase = '''document_qa'''
lowerCAmelCase = AutoProcessor
lowerCAmelCase = VisionEncoderDecoderModel
lowerCAmelCase = ['''image''', '''text''']
lowerCAmelCase = ['''text''']
def __init__( self : str , *__SCREAMING_SNAKE_CASE : List[str] , **__SCREAMING_SNAKE_CASE : List[Any] ) -> Any:
"""simple docstring"""
if not is_vision_available():
raise ValueError('''Pillow must be installed to use the DocumentQuestionAnsweringTool.''' )
super().__init__(*__SCREAMING_SNAKE_CASE , **__SCREAMING_SNAKE_CASE )
def _a ( self : Union[str, Any] , __SCREAMING_SNAKE_CASE : "Image" , __SCREAMING_SNAKE_CASE : str ) -> str:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = '''<s_docvqa><s_question>{user_input}</s_question><s_answer>'''
__SCREAMING_SNAKE_CASE = task_prompt.replace('''{user_input}''' , __SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = self.pre_processor.tokenizer(
__SCREAMING_SNAKE_CASE , add_special_tokens=__SCREAMING_SNAKE_CASE , return_tensors='''pt''' ).input_ids
__SCREAMING_SNAKE_CASE = self.pre_processor(__SCREAMING_SNAKE_CASE , return_tensors='''pt''' ).pixel_values
return {"decoder_input_ids": decoder_input_ids, "pixel_values": pixel_values}
def _a ( self : Union[str, Any] , __SCREAMING_SNAKE_CASE : Optional[Any] ) -> Tuple:
"""simple docstring"""
return self.model.generate(
inputs['''pixel_values'''].to(self.device ) , decoder_input_ids=inputs['''decoder_input_ids'''].to(self.device ) , max_length=self.model.decoder.config.max_position_embeddings , early_stopping=__SCREAMING_SNAKE_CASE , pad_token_id=self.pre_processor.tokenizer.pad_token_id , eos_token_id=self.pre_processor.tokenizer.eos_token_id , use_cache=__SCREAMING_SNAKE_CASE , num_beams=1 , bad_words_ids=[[self.pre_processor.tokenizer.unk_token_id]] , return_dict_in_generate=__SCREAMING_SNAKE_CASE , ).sequences
def _a ( self : Tuple , __SCREAMING_SNAKE_CASE : Tuple ) -> List[str]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = self.pre_processor.batch_decode(__SCREAMING_SNAKE_CASE )[0]
__SCREAMING_SNAKE_CASE = sequence.replace(self.pre_processor.tokenizer.eos_token , '''''' )
__SCREAMING_SNAKE_CASE = sequence.replace(self.pre_processor.tokenizer.pad_token , '''''' )
__SCREAMING_SNAKE_CASE = re.sub(r'''<.*?>''' , '''''' , __SCREAMING_SNAKE_CASE , count=1 ).strip() # remove first task start token
__SCREAMING_SNAKE_CASE = self.pre_processor.tokenajson(__SCREAMING_SNAKE_CASE )
return sequence["answer"]
| 690 | 1 |
"""simple docstring"""
from typing import Any
def _a ( UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , ) -> list:
_validation(
UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , )
# Creates data structures and fill initial step
__SCREAMING_SNAKE_CASE = {}
__SCREAMING_SNAKE_CASE = {}
for state in states_space:
__SCREAMING_SNAKE_CASE = observations_space[0]
__SCREAMING_SNAKE_CASE = (
initial_probabilities[state] * emission_probabilities[state][observation]
)
__SCREAMING_SNAKE_CASE = None
# Fills the data structure with the probabilities of
# different transitions and pointers to previous states
for o in range(1 , len(UpperCAmelCase__ ) ):
__SCREAMING_SNAKE_CASE = observations_space[o]
__SCREAMING_SNAKE_CASE = observations_space[o - 1]
for state in states_space:
# Calculates the argmax for probability function
__SCREAMING_SNAKE_CASE = ''''''
__SCREAMING_SNAKE_CASE = -1
for k_state in states_space:
__SCREAMING_SNAKE_CASE = (
probabilities[(k_state, prior_observation)]
* transition_probabilities[k_state][state]
* emission_probabilities[state][observation]
)
if probability > max_probability:
__SCREAMING_SNAKE_CASE = probability
__SCREAMING_SNAKE_CASE = k_state
# Update probabilities and pointers dicts
__SCREAMING_SNAKE_CASE = (
probabilities[(arg_max, prior_observation)]
* transition_probabilities[arg_max][state]
* emission_probabilities[state][observation]
)
__SCREAMING_SNAKE_CASE = arg_max
# The final observation
__SCREAMING_SNAKE_CASE = observations_space[len(UpperCAmelCase__ ) - 1]
# argmax for given final observation
__SCREAMING_SNAKE_CASE = ''''''
__SCREAMING_SNAKE_CASE = -1
for k_state in states_space:
__SCREAMING_SNAKE_CASE = probabilities[(k_state, final_observation)]
if probability > max_probability:
__SCREAMING_SNAKE_CASE = probability
__SCREAMING_SNAKE_CASE = k_state
__SCREAMING_SNAKE_CASE = arg_max
# Process pointers backwards
__SCREAMING_SNAKE_CASE = last_state
__SCREAMING_SNAKE_CASE = []
for o in range(len(UpperCAmelCase__ ) - 1 , -1 , -1 ):
result.append(UpperCAmelCase__ )
__SCREAMING_SNAKE_CASE = pointers[previous, observations_space[o]]
result.reverse()
return result
def _a ( UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , ) -> None:
_validate_not_empty(
UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , )
_validate_lists(UpperCAmelCase__ , UpperCAmelCase__ )
_validate_dicts(
UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ )
def _a ( UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , ) -> None:
if not all(
[
observations_space,
states_space,
initial_probabilities,
transition_probabilities,
emission_probabilities,
] ):
raise ValueError('''There\'s an empty parameter''' )
def _a ( UpperCAmelCase__ , UpperCAmelCase__ ) -> None:
_validate_list(UpperCAmelCase__ , '''observations_space''' )
_validate_list(UpperCAmelCase__ , '''states_space''' )
def _a ( UpperCAmelCase__ , UpperCAmelCase__ ) -> None:
if not isinstance(_object , UpperCAmelCase__ ):
__SCREAMING_SNAKE_CASE = f"""{var_name} must be a list"""
raise ValueError(UpperCAmelCase__ )
else:
for x in _object:
if not isinstance(UpperCAmelCase__ , UpperCAmelCase__ ):
__SCREAMING_SNAKE_CASE = f"""{var_name} must be a list of strings"""
raise ValueError(UpperCAmelCase__ )
def _a ( UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , ) -> None:
_validate_dict(UpperCAmelCase__ , '''initial_probabilities''' , UpperCAmelCase__ )
_validate_nested_dict(UpperCAmelCase__ , '''transition_probabilities''' )
_validate_nested_dict(UpperCAmelCase__ , '''emission_probabilities''' )
def _a ( UpperCAmelCase__ , UpperCAmelCase__ ) -> None:
_validate_dict(_object , UpperCAmelCase__ , UpperCAmelCase__ )
for x in _object.values():
_validate_dict(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ )
def _a ( UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ = False ) -> None:
if not isinstance(_object , UpperCAmelCase__ ):
__SCREAMING_SNAKE_CASE = f"""{var_name} must be a dict"""
raise ValueError(UpperCAmelCase__ )
if not all(isinstance(UpperCAmelCase__ , UpperCAmelCase__ ) for x in _object ):
__SCREAMING_SNAKE_CASE = f"""{var_name} all keys must be strings"""
raise ValueError(UpperCAmelCase__ )
if not all(isinstance(UpperCAmelCase__ , UpperCAmelCase__ ) for x in _object.values() ):
__SCREAMING_SNAKE_CASE = '''nested dictionary ''' if nested else ''''''
__SCREAMING_SNAKE_CASE = f"""{var_name} {nested_text}all values must be {value_type.__name__}"""
raise ValueError(UpperCAmelCase__ )
if __name__ == "__main__":
from doctest import testmod
testmod()
| 690 |
"""simple docstring"""
import unittest
import numpy as np
import torch
from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class A__( unittest.TestCase ):
@property
def _a ( self : Optional[Any] ) -> Tuple:
"""simple docstring"""
torch.manual_seed(0 )
__SCREAMING_SNAKE_CASE = UNetaDModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=3 , out_channels=3 , down_block_types=('''DownBlock2D''', '''AttnDownBlock2D''') , up_block_types=('''AttnUpBlock2D''', '''UpBlock2D''') , )
return model
def _a ( self : str ) -> Any:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = self.dummy_uncond_unet
__SCREAMING_SNAKE_CASE = KarrasVeScheduler()
__SCREAMING_SNAKE_CASE = KarrasVePipeline(unet=__SCREAMING_SNAKE_CASE , scheduler=__SCREAMING_SNAKE_CASE )
pipe.to(__SCREAMING_SNAKE_CASE )
pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = torch.manual_seed(0 )
__SCREAMING_SNAKE_CASE = pipe(num_inference_steps=2 , generator=__SCREAMING_SNAKE_CASE , output_type='''numpy''' ).images
__SCREAMING_SNAKE_CASE = torch.manual_seed(0 )
__SCREAMING_SNAKE_CASE = pipe(num_inference_steps=2 , generator=__SCREAMING_SNAKE_CASE , output_type='''numpy''' , return_dict=__SCREAMING_SNAKE_CASE )[0]
__SCREAMING_SNAKE_CASE = image[0, -3:, -3:, -1]
__SCREAMING_SNAKE_CASE = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
__SCREAMING_SNAKE_CASE = np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1E-2
@slow
@require_torch
class A__( unittest.TestCase ):
def _a ( self : Any ) -> str:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = '''google/ncsnpp-celebahq-256'''
__SCREAMING_SNAKE_CASE = UNetaDModel.from_pretrained(__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = KarrasVeScheduler()
__SCREAMING_SNAKE_CASE = KarrasVePipeline(unet=__SCREAMING_SNAKE_CASE , scheduler=__SCREAMING_SNAKE_CASE )
pipe.to(__SCREAMING_SNAKE_CASE )
pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = torch.manual_seed(0 )
__SCREAMING_SNAKE_CASE = pipe(num_inference_steps=20 , generator=__SCREAMING_SNAKE_CASE , output_type='''numpy''' ).images
__SCREAMING_SNAKE_CASE = image[0, -3:, -3:, -1]
assert image.shape == (1, 2_56, 2_56, 3)
__SCREAMING_SNAKE_CASE = np.array([0.5_78, 0.58_11, 0.59_24, 0.58_09, 0.5_87, 0.58_86, 0.58_61, 0.58_02, 0.5_86] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
| 690 | 1 |
"""simple docstring"""
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
from .tokenization_realm import RealmTokenizer
lowerCAmelCase__ =logging.get_logger(__name__)
lowerCAmelCase__ ={"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}
lowerCAmelCase__ ={
"vocab_file": {
"google/realm-cc-news-pretrained-embedder": (
"https://huggingface.co/google/realm-cc-news-pretrained-embedder/resolve/main/vocab.txt"
),
"google/realm-cc-news-pretrained-encoder": (
"https://huggingface.co/google/realm-cc-news-pretrained-encoder/resolve/main/vocab.txt"
),
"google/realm-cc-news-pretrained-scorer": (
"https://huggingface.co/google/realm-cc-news-pretrained-scorer/resolve/main/vocab.txt"
),
"google/realm-cc-news-pretrained-openqa": (
"https://huggingface.co/google/realm-cc-news-pretrained-openqa/aresolve/main/vocab.txt"
),
"google/realm-orqa-nq-openqa": "https://huggingface.co/google/realm-orqa-nq-openqa/resolve/main/vocab.txt",
"google/realm-orqa-nq-reader": "https://huggingface.co/google/realm-orqa-nq-reader/resolve/main/vocab.txt",
"google/realm-orqa-wq-openqa": "https://huggingface.co/google/realm-orqa-wq-openqa/resolve/main/vocab.txt",
"google/realm-orqa-wq-reader": "https://huggingface.co/google/realm-orqa-wq-reader/resolve/main/vocab.txt",
},
"tokenizer_file": {
"google/realm-cc-news-pretrained-embedder": (
"https://huggingface.co/google/realm-cc-news-pretrained-embedder/resolve/main/tokenizer.jsont"
),
"google/realm-cc-news-pretrained-encoder": (
"https://huggingface.co/google/realm-cc-news-pretrained-encoder/resolve/main/tokenizer.json"
),
"google/realm-cc-news-pretrained-scorer": (
"https://huggingface.co/google/realm-cc-news-pretrained-scorer/resolve/main/tokenizer.json"
),
"google/realm-cc-news-pretrained-openqa": (
"https://huggingface.co/google/realm-cc-news-pretrained-openqa/aresolve/main/tokenizer.json"
),
"google/realm-orqa-nq-openqa": (
"https://huggingface.co/google/realm-orqa-nq-openqa/resolve/main/tokenizer.json"
),
"google/realm-orqa-nq-reader": (
"https://huggingface.co/google/realm-orqa-nq-reader/resolve/main/tokenizer.json"
),
"google/realm-orqa-wq-openqa": (
"https://huggingface.co/google/realm-orqa-wq-openqa/resolve/main/tokenizer.json"
),
"google/realm-orqa-wq-reader": (
"https://huggingface.co/google/realm-orqa-wq-reader/resolve/main/tokenizer.json"
),
},
}
lowerCAmelCase__ ={
"google/realm-cc-news-pretrained-embedder": 512,
"google/realm-cc-news-pretrained-encoder": 512,
"google/realm-cc-news-pretrained-scorer": 512,
"google/realm-cc-news-pretrained-openqa": 512,
"google/realm-orqa-nq-openqa": 512,
"google/realm-orqa-nq-reader": 512,
"google/realm-orqa-wq-openqa": 512,
"google/realm-orqa-wq-reader": 512,
}
lowerCAmelCase__ ={
"google/realm-cc-news-pretrained-embedder": {"do_lower_case": True},
"google/realm-cc-news-pretrained-encoder": {"do_lower_case": True},
"google/realm-cc-news-pretrained-scorer": {"do_lower_case": True},
"google/realm-cc-news-pretrained-openqa": {"do_lower_case": True},
"google/realm-orqa-nq-openqa": {"do_lower_case": True},
"google/realm-orqa-nq-reader": {"do_lower_case": True},
"google/realm-orqa-wq-openqa": {"do_lower_case": True},
"google/realm-orqa-wq-reader": {"do_lower_case": True},
}
class A__( __magic_name__ ):
lowerCAmelCase = VOCAB_FILES_NAMES
lowerCAmelCase = PRETRAINED_VOCAB_FILES_MAP
lowerCAmelCase = PRETRAINED_INIT_CONFIGURATION
lowerCAmelCase = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowerCAmelCase = RealmTokenizer
def __init__( self : Union[str, Any] , __SCREAMING_SNAKE_CASE : Optional[int]=None , __SCREAMING_SNAKE_CASE : Union[str, Any]=None , __SCREAMING_SNAKE_CASE : Dict=True , __SCREAMING_SNAKE_CASE : Union[str, Any]="[UNK]" , __SCREAMING_SNAKE_CASE : Dict="[SEP]" , __SCREAMING_SNAKE_CASE : Optional[Any]="[PAD]" , __SCREAMING_SNAKE_CASE : Tuple="[CLS]" , __SCREAMING_SNAKE_CASE : List[Any]="[MASK]" , __SCREAMING_SNAKE_CASE : Tuple=True , __SCREAMING_SNAKE_CASE : Union[str, Any]=None , **__SCREAMING_SNAKE_CASE : Dict , ) -> str:
"""simple docstring"""
super().__init__(
__SCREAMING_SNAKE_CASE , tokenizer_file=__SCREAMING_SNAKE_CASE , do_lower_case=__SCREAMING_SNAKE_CASE , unk_token=__SCREAMING_SNAKE_CASE , sep_token=__SCREAMING_SNAKE_CASE , pad_token=__SCREAMING_SNAKE_CASE , cls_token=__SCREAMING_SNAKE_CASE , mask_token=__SCREAMING_SNAKE_CASE , tokenize_chinese_chars=__SCREAMING_SNAKE_CASE , strip_accents=__SCREAMING_SNAKE_CASE , **__SCREAMING_SNAKE_CASE , )
__SCREAMING_SNAKE_CASE = json.loads(self.backend_tokenizer.normalizer.__getstate__() )
if (
normalizer_state.get('''lowercase''' , __SCREAMING_SNAKE_CASE ) != do_lower_case
or normalizer_state.get('''strip_accents''' , __SCREAMING_SNAKE_CASE ) != strip_accents
or normalizer_state.get('''handle_chinese_chars''' , __SCREAMING_SNAKE_CASE ) != tokenize_chinese_chars
):
__SCREAMING_SNAKE_CASE = getattr(__SCREAMING_SNAKE_CASE , normalizer_state.pop('''type''' ) )
__SCREAMING_SNAKE_CASE = do_lower_case
__SCREAMING_SNAKE_CASE = strip_accents
__SCREAMING_SNAKE_CASE = tokenize_chinese_chars
__SCREAMING_SNAKE_CASE = normalizer_class(**__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = do_lower_case
def _a ( self : Optional[Any] , __SCREAMING_SNAKE_CASE : Optional[int] , **__SCREAMING_SNAKE_CASE : Dict ) -> List[str]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = PaddingStrategy.MAX_LENGTH
__SCREAMING_SNAKE_CASE = text
__SCREAMING_SNAKE_CASE = kwargs.pop('''text_pair''' , __SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = kwargs.pop('''return_tensors''' , __SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = {
'''input_ids''': [],
'''attention_mask''': [],
'''token_type_ids''': [],
}
for idx, candidate_text in enumerate(__SCREAMING_SNAKE_CASE ):
if batch_text_pair is not None:
__SCREAMING_SNAKE_CASE = batch_text_pair[idx]
else:
__SCREAMING_SNAKE_CASE = None
__SCREAMING_SNAKE_CASE = super().__call__(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , return_tensors=__SCREAMING_SNAKE_CASE , **__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = encoded_candidates.get('''input_ids''' )
__SCREAMING_SNAKE_CASE = encoded_candidates.get('''attention_mask''' )
__SCREAMING_SNAKE_CASE = encoded_candidates.get('''token_type_ids''' )
if encoded_input_ids is not None:
output_data["input_ids"].append(__SCREAMING_SNAKE_CASE )
if encoded_attention_mask is not None:
output_data["attention_mask"].append(__SCREAMING_SNAKE_CASE )
if encoded_token_type_ids is not None:
output_data["token_type_ids"].append(__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = {key: item for key, item in output_data.items() if len(__SCREAMING_SNAKE_CASE ) != 0}
return BatchEncoding(__SCREAMING_SNAKE_CASE , tensor_type=__SCREAMING_SNAKE_CASE )
def _a ( self : Tuple , __SCREAMING_SNAKE_CASE : int , __SCREAMING_SNAKE_CASE : str=None ) -> List[Any]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = [self.cls_token_id] + token_ids_a + [self.sep_token_id]
if token_ids_a:
output += token_ids_a + [self.sep_token_id]
return output
def _a ( self : Union[str, Any] , __SCREAMING_SNAKE_CASE : List[int] , __SCREAMING_SNAKE_CASE : Optional[List[int]] = None ) -> List[int]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = [self.sep_token_id]
__SCREAMING_SNAKE_CASE = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]
def _a ( self : Union[str, Any] , __SCREAMING_SNAKE_CASE : str , __SCREAMING_SNAKE_CASE : Optional[str] = None ) -> Tuple[str]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = self._tokenizer.model.save(__SCREAMING_SNAKE_CASE , name=__SCREAMING_SNAKE_CASE )
return tuple(__SCREAMING_SNAKE_CASE )
| 690 |
"""simple docstring"""
import os
import re
import unicodedata
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import is_torch_available, logging
if is_torch_available():
import torch
if TYPE_CHECKING:
from transformers.pipelines.conversational import Conversation
lowerCAmelCase__ =logging.get_logger(__name__)
lowerCAmelCase__ ={"vocab_file": "spiece.model"}
lowerCAmelCase__ ={
"vocab_file": {
"AI-Sweden/gpt-sw3-126m": "https://huggingface.co/AI-Sweden/gpt-sw3-126m/resolve/main/spiece.model",
"AI-Sweden/gpt-sw3-350m": "https://huggingface.co/AI-Sweden/gpt-sw3-350m/resolve/main/spiece.model",
"AI-Sweden/gpt-sw3-1.6b": "https://huggingface.co/AI-Sweden/gpt-sw3-1.6b/resolve/main/spiece.model",
"AI-Sweden/gpt-sw3-6.7b": "https://huggingface.co/AI-Sweden/gpt-sw3-6.7b/resolve/main/spiece.model",
"AI-Sweden/gpt-sw3-20b": "https://huggingface.co/AI-Sweden/gpt-sw3-20b/resolve/main/spiece.model",
}
}
lowerCAmelCase__ ={
"AI-Sweden/gpt-sw3-126m": 2_048,
"AI-Sweden/gpt-sw3-350m": 2_048,
"AI-Sweden/gpt-sw3-1.6b": 2_048,
"AI-Sweden/gpt-sw3-6.7b": 2_048,
"AI-Sweden/gpt-sw3-20b": 2_048,
}
class A__( __magic_name__ ):
lowerCAmelCase = VOCAB_FILES_NAMES
lowerCAmelCase = PRETRAINED_VOCAB_FILES_MAP
lowerCAmelCase = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowerCAmelCase = ['''input_ids''', '''attention_mask''']
def __init__( self : int , __SCREAMING_SNAKE_CASE : int , __SCREAMING_SNAKE_CASE : str=False , __SCREAMING_SNAKE_CASE : str=False , __SCREAMING_SNAKE_CASE : Optional[int]=False , __SCREAMING_SNAKE_CASE : Union[str, Any]=None , __SCREAMING_SNAKE_CASE : List[Any]=None , __SCREAMING_SNAKE_CASE : Any=None , __SCREAMING_SNAKE_CASE : List[Any]=None , __SCREAMING_SNAKE_CASE : Optional[Dict[str, Any]] = None , **__SCREAMING_SNAKE_CASE : Dict , ) -> None:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = {} if sp_model_kwargs is None else sp_model_kwargs
__SCREAMING_SNAKE_CASE = kwargs.get('''name_or_path''' )
if name_or_path is None:
logger.warning(
'''name_or_path not provided, will work for all GPTSw3 models except gpt-sw3-7b,'''
''' you are testing the model, this can safely be ignored''' )
__SCREAMING_SNAKE_CASE = '''None'''
# Default definitions for our 2 tokenizer versions, with None-checks to enable proper testing
__SCREAMING_SNAKE_CASE = '''<|endoftext|>''' if eos_token is None else eos_token
__SCREAMING_SNAKE_CASE = '''<unk>''' if unk_token is None else unk_token
if "gpt-sw3-7b" in name_or_path:
__SCREAMING_SNAKE_CASE = unk_token if pad_token is None else pad_token
__SCREAMING_SNAKE_CASE = eos_token if bos_token is None else bos_token
else:
__SCREAMING_SNAKE_CASE = '''<pad>''' if pad_token is None else pad_token
__SCREAMING_SNAKE_CASE = '''<s>''' if bos_token is None else bos_token
super().__init__(
do_lower_case=__SCREAMING_SNAKE_CASE , remove_space=__SCREAMING_SNAKE_CASE , keep_accents=__SCREAMING_SNAKE_CASE , bos_token=__SCREAMING_SNAKE_CASE , eos_token=__SCREAMING_SNAKE_CASE , unk_token=__SCREAMING_SNAKE_CASE , pad_token=__SCREAMING_SNAKE_CASE , sp_model_kwargs=self.sp_model_kwargs , **__SCREAMING_SNAKE_CASE , )
__SCREAMING_SNAKE_CASE = do_lower_case
__SCREAMING_SNAKE_CASE = remove_space
__SCREAMING_SNAKE_CASE = keep_accents
__SCREAMING_SNAKE_CASE = vocab_file
__SCREAMING_SNAKE_CASE = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(__SCREAMING_SNAKE_CASE )
# Used for whitespace normalization in input texts
# fmt : off
__SCREAMING_SNAKE_CASE = {''' ''', ''' ''', ''' ''', ''' ''', ''' ''', ''' ''', ''' ''', ''' ''', ''' ''', ''' ''', '''''', ''''''}
# fmt : on
# Regular expression to remove non-printing characters (e.g. some unicode control chars) in preprocessing
__SCREAMING_SNAKE_CASE = re.compile(
f"""[{"".join(map(__SCREAMING_SNAKE_CASE , list(range(0 , 9 ) ) + list(range(11 , 32 ) ) + list(range(1_27 , 1_60 ) ) + [1_60, 1_73, 82_03] ) )}]""" )
def __getstate__( self : List[str] ) -> Optional[Any]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = self.__dict__.copy()
__SCREAMING_SNAKE_CASE = None
return state
def __setstate__( self : int , __SCREAMING_SNAKE_CASE : Optional[int] ) -> int:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = d
# for backward compatibility
if not hasattr(self , '''sp_model_kwargs''' ):
__SCREAMING_SNAKE_CASE = {}
__SCREAMING_SNAKE_CASE = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.vocab_file )
@property
# Copied from transformers.models.albert.tokenization_albert.AlbertTokenizer.vocab_size
def _a ( self : Optional[Any] ) -> int:
"""simple docstring"""
return len(self.sp_model )
def _a ( self : List[str] , __SCREAMING_SNAKE_CASE : str ) -> str:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = self.non_printing_characters_re.sub('''''' , __SCREAMING_SNAKE_CASE )
# Normalize whitespaces
__SCREAMING_SNAKE_CASE = ''''''.join([char if char not in self.whitespaces else ''' ''' for char in text] )
# NFC Unicode normalization
__SCREAMING_SNAKE_CASE = unicodedata.normalize('''NFC''' , __SCREAMING_SNAKE_CASE )
return text
def _a ( self : Union[str, Any] , __SCREAMING_SNAKE_CASE : str , **__SCREAMING_SNAKE_CASE : Optional[int] ) -> List[str]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = self.preprocess_text(__SCREAMING_SNAKE_CASE )
return self.sp_model.encode(__SCREAMING_SNAKE_CASE , out_type=__SCREAMING_SNAKE_CASE )
def _a ( self : Tuple , __SCREAMING_SNAKE_CASE : str ) -> int:
"""simple docstring"""
return self.sp_model.PieceToId(__SCREAMING_SNAKE_CASE )
def _a ( self : List[str] , __SCREAMING_SNAKE_CASE : int ) -> str:
"""simple docstring"""
return self.sp_model.IdToPiece(__SCREAMING_SNAKE_CASE )
@staticmethod
def _a ( __SCREAMING_SNAKE_CASE : str ) -> str:
"""simple docstring"""
return out_string
def _a ( self : Union[str, Any] , __SCREAMING_SNAKE_CASE : List[str] ) -> str:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = []
__SCREAMING_SNAKE_CASE = ''''''
__SCREAMING_SNAKE_CASE = False
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
# TODO: Check if this is needed, as it ensures that decode(encode(doc)) != doc by adding extra whitespace in the decoded document
if not prev_is_special:
out_string += " "
out_string += self.sp_model.decode(__SCREAMING_SNAKE_CASE ) + token
__SCREAMING_SNAKE_CASE = True
__SCREAMING_SNAKE_CASE = []
else:
current_sub_tokens.append(__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = False
out_string += self.sp_model.decode(__SCREAMING_SNAKE_CASE )
return out_string
def _a ( self : Union[str, Any] ) -> Dict[str, int]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = {self.convert_ids_to_tokens(__SCREAMING_SNAKE_CASE ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def _a ( self : List[Any] , __SCREAMING_SNAKE_CASE : str , __SCREAMING_SNAKE_CASE : Optional[str] = None ) -> Tuple[str]:
"""simple docstring"""
if not os.path.isdir(__SCREAMING_SNAKE_CASE ):
logger.error(f"""Vocabulary path ({save_directory}) should be a directory""" )
return
__SCREAMING_SNAKE_CASE = os.path.join(
__SCREAMING_SNAKE_CASE , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(__SCREAMING_SNAKE_CASE ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file , __SCREAMING_SNAKE_CASE )
elif not os.path.isfile(self.vocab_file ):
with open(__SCREAMING_SNAKE_CASE , '''wb''' ) as fi:
__SCREAMING_SNAKE_CASE = self.sp_model.serialized_model_proto()
fi.write(__SCREAMING_SNAKE_CASE )
return (out_vocab_file,)
def _a ( self : Optional[Any] , __SCREAMING_SNAKE_CASE : Union[str, List[str]] , __SCREAMING_SNAKE_CASE : Union[str, bool] = False ) -> Union[List[int], List[List[int]], "torch.Tensor"]:
"""simple docstring"""
if isinstance(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
__SCREAMING_SNAKE_CASE = self.preprocess_text(__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = self.sp_model.encode(__SCREAMING_SNAKE_CASE )
else:
__SCREAMING_SNAKE_CASE = [self.preprocess_text(__SCREAMING_SNAKE_CASE ) for t in text]
__SCREAMING_SNAKE_CASE = self.sp_model.encode(__SCREAMING_SNAKE_CASE )
if return_tensors is True or return_tensors == "pt":
__SCREAMING_SNAKE_CASE = torch.tensor(__SCREAMING_SNAKE_CASE )
return token_ids
def _a ( self : Any , __SCREAMING_SNAKE_CASE : Union[int, List[int]] ) -> str:
"""simple docstring"""
return self.sp_model.decode(__SCREAMING_SNAKE_CASE )
def _a ( self : List[str] , __SCREAMING_SNAKE_CASE : "Conversation" ) -> List[int]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = [f"""User: {text}""" if is_user else f"""Bot: {text}""" for is_user, text in conversation.iter_texts()]
__SCREAMING_SNAKE_CASE = (
f"""{self.eos_token}{self.bos_token}""" + f"""{self.bos_token}""".join(__SCREAMING_SNAKE_CASE ) + f"""{self.bos_token}Bot:"""
)
return self.encode(text=__SCREAMING_SNAKE_CASE )
| 690 | 1 |
"""simple docstring"""
import torch
from transformers import CamembertForMaskedLM, CamembertTokenizer
def _a ( UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__=5 ) -> Union[str, Any]:
# Adapted from https://github.com/pytorch/fairseq/blob/master/fairseq/models/roberta/hub_interface.py
assert masked_input.count('''<mask>''' ) == 1
__SCREAMING_SNAKE_CASE = torch.tensor(tokenizer.encode(UpperCAmelCase__ , add_special_tokens=UpperCAmelCase__ ) ).unsqueeze(0 ) # Batch size 1
__SCREAMING_SNAKE_CASE = model(UpperCAmelCase__ )[0] # The last hidden-state is the first element of the output tuple
__SCREAMING_SNAKE_CASE = (input_ids.squeeze() == tokenizer.mask_token_id).nonzero().item()
__SCREAMING_SNAKE_CASE = logits[0, masked_index, :]
__SCREAMING_SNAKE_CASE = logits.softmax(dim=0 )
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = prob.topk(k=UpperCAmelCase__ , dim=0 )
__SCREAMING_SNAKE_CASE = ''' '''.join(
[tokenizer.convert_ids_to_tokens(indices[i].item() ) for i in range(len(UpperCAmelCase__ ) )] )
__SCREAMING_SNAKE_CASE = tokenizer.mask_token
__SCREAMING_SNAKE_CASE = []
for index, predicted_token_bpe in enumerate(topk_predicted_token_bpe.split(''' ''' ) ):
__SCREAMING_SNAKE_CASE = predicted_token_bpe.replace('''\u2581''' , ''' ''' )
if " {0}".format(UpperCAmelCase__ ) in masked_input:
topk_filled_outputs.append(
(
masked_input.replace(''' {0}'''.format(UpperCAmelCase__ ) , UpperCAmelCase__ ),
values[index].item(),
predicted_token,
) )
else:
topk_filled_outputs.append(
(
masked_input.replace(UpperCAmelCase__ , UpperCAmelCase__ ),
values[index].item(),
predicted_token,
) )
return topk_filled_outputs
lowerCAmelCase__ =CamembertTokenizer.from_pretrained("camembert-base")
lowerCAmelCase__ =CamembertForMaskedLM.from_pretrained("camembert-base")
model.eval()
lowerCAmelCase__ ="Le camembert est <mask> :)"
print(fill_mask(masked_input, model, tokenizer, topk=3))
| 690 |
"""simple docstring"""
from __future__ import annotations
import json
import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent
lowerCAmelCase__ ={"UserAgent": UserAgent().random}
def _a ( UpperCAmelCase__ ) -> dict:
__SCREAMING_SNAKE_CASE = script.contents[0]
__SCREAMING_SNAKE_CASE = json.loads(data[data.find('''{"config"''' ) : -1] )
return info["entry_data"]["ProfilePage"][0]["graphql"]["user"]
class A__:
def __init__( self : Dict , __SCREAMING_SNAKE_CASE : int ) -> Optional[Any]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = f"""https://www.instagram.com/{username}/"""
__SCREAMING_SNAKE_CASE = self.get_json()
def _a ( self : List[Any] ) -> dict:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = requests.get(self.url , headers=__SCREAMING_SNAKE_CASE ).text
__SCREAMING_SNAKE_CASE = BeautifulSoup(__SCREAMING_SNAKE_CASE , '''html.parser''' ).find_all('''script''' )
try:
return extract_user_profile(scripts[4] )
except (json.decoder.JSONDecodeError, KeyError):
return extract_user_profile(scripts[3] )
def __repr__( self : Tuple ) -> str:
"""simple docstring"""
return f"""{self.__class__.__name__}('{self.username}')"""
def __str__( self : Optional[int] ) -> str:
"""simple docstring"""
return f"""{self.fullname} ({self.username}) is {self.biography}"""
@property
def _a ( self : Tuple ) -> str:
"""simple docstring"""
return self.user_data["username"]
@property
def _a ( self : List[Any] ) -> str:
"""simple docstring"""
return self.user_data["full_name"]
@property
def _a ( self : Optional[Any] ) -> str:
"""simple docstring"""
return self.user_data["biography"]
@property
def _a ( self : List[str] ) -> str:
"""simple docstring"""
return self.user_data["business_email"]
@property
def _a ( self : Any ) -> str:
"""simple docstring"""
return self.user_data["external_url"]
@property
def _a ( self : Any ) -> int:
"""simple docstring"""
return self.user_data["edge_followed_by"]["count"]
@property
def _a ( self : Dict ) -> int:
"""simple docstring"""
return self.user_data["edge_follow"]["count"]
@property
def _a ( self : str ) -> int:
"""simple docstring"""
return self.user_data["edge_owner_to_timeline_media"]["count"]
@property
def _a ( self : Union[str, Any] ) -> str:
"""simple docstring"""
return self.user_data["profile_pic_url_hd"]
@property
def _a ( self : Tuple ) -> bool:
"""simple docstring"""
return self.user_data["is_verified"]
@property
def _a ( self : Union[str, Any] ) -> bool:
"""simple docstring"""
return self.user_data["is_private"]
def _a ( UpperCAmelCase__ = "github" ) -> None:
import os
if os.environ.get('''CI''' ):
return # test failing on GitHub Actions
__SCREAMING_SNAKE_CASE = InstagramUser(UpperCAmelCase__ )
assert instagram_user.user_data
assert isinstance(instagram_user.user_data , UpperCAmelCase__ )
assert instagram_user.username == username
if username != "github":
return
assert instagram_user.fullname == "GitHub"
assert instagram_user.biography == "Built for developers."
assert instagram_user.number_of_posts > 1_50
assert instagram_user.number_of_followers > 12_00_00
assert instagram_user.number_of_followings > 15
assert instagram_user.email == "[email protected]"
assert instagram_user.website == "https://github.com/readme"
assert instagram_user.profile_picture_url.startswith('''https://instagram.''' )
assert instagram_user.is_verified is True
assert instagram_user.is_private is False
if __name__ == "__main__":
import doctest
doctest.testmod()
lowerCAmelCase__ =InstagramUser("github")
print(instagram_user)
print(F'''{instagram_user.number_of_posts = }''')
print(F'''{instagram_user.number_of_followers = }''')
print(F'''{instagram_user.number_of_followings = }''')
print(F'''{instagram_user.email = }''')
print(F'''{instagram_user.website = }''')
print(F'''{instagram_user.profile_picture_url = }''')
print(F'''{instagram_user.is_verified = }''')
print(F'''{instagram_user.is_private = }''')
| 690 | 1 |
"""simple docstring"""
import argparse
from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging
logging.set_verbosity_info()
def _a ( UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ ) -> Optional[int]:
# Initialise PyTorch model
__SCREAMING_SNAKE_CASE = TaConfig.from_json_file(UpperCAmelCase__ )
print(f"""Building PyTorch model from configuration: {config}""" )
__SCREAMING_SNAKE_CASE = TaForConditionalGeneration(UpperCAmelCase__ )
# Load weights from tf checkpoint
load_tf_weights_in_ta(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ )
# Save pytorch-model
print(f"""Save PyTorch model to {pytorch_dump_path}""" )
model.save_pretrained(UpperCAmelCase__ )
if __name__ == "__main__":
lowerCAmelCase__ =argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--tf_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
)
parser.add_argument(
"--config_file",
default=None,
type=str,
required=True,
help=(
"The config json file corresponding to the pre-trained T5 model. \nThis specifies the model architecture."
),
)
parser.add_argument(
"--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
)
lowerCAmelCase__ =parser.parse_args()
convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)
| 690 |
"""simple docstring"""
from sklearn.metrics import recall_score
import datasets
lowerCAmelCase__ ="\nRecall is the fraction of the positive examples that were correctly labeled by the model as positive. It can be computed with the equation:\nRecall = TP / (TP + FN)\nWhere TP is the true positives and FN is the false negatives.\n"
lowerCAmelCase__ ="\nArgs:\n- **predictions** (`list` of `int`): The predicted labels.\n- **references** (`list` of `int`): The ground truth labels.\n- **labels** (`list` of `int`): The set of labels to include when `average` is not set to `binary`, and their order when average is `None`. Labels present in the data can be excluded in this input, for example to calculate a multiclass average ignoring a majority negative class, while labels not present in the data will result in 0 components in a macro average. For multilabel targets, labels are column indices. By default, all labels in y_true and y_pred are used in sorted order. Defaults to None.\n- **pos_label** (`int`): The class label to use as the 'positive class' when calculating the recall. Defaults to `1`.\n- **average** (`string`): This parameter is required for multiclass/multilabel targets. If None, the scores for each class are returned. Otherwise, this determines the type of averaging performed on the data. Defaults to `'binary'`.\n - `'binary'`: Only report results for the class specified by `pos_label`. This is applicable only if the target labels and predictions are binary.\n - `'micro'`: Calculate metrics globally by counting the total true positives, false negatives, and false positives.\n - `'macro'`: Calculate metrics for each label, and find their unweighted mean. This does not take label imbalance into account.\n - `'weighted'`: Calculate metrics for each label, and find their average weighted by support (the number of true instances for each label). This alters `'macro'` to account for label imbalance. Note that it can result in an F-score that is not between precision and recall.\n - `'samples'`: Calculate metrics for each instance, and find their average (only meaningful for multilabel classification).\n- **sample_weight** (`list` of `float`): Sample weights Defaults to `None`.\n- **zero_division** (): Sets the value to return when there is a zero division. Defaults to .\n - `'warn'`: If there is a zero division, the return value is `0`, but warnings are also raised.\n - `0`: If there is a zero division, the return value is `0`.\n - `1`: If there is a zero division, the return value is `1`.\n\nReturns:\n- **recall** (`float`, or `array` of `float`): Either the general recall score, or the recall scores for individual classes, depending on the values input to `labels` and `average`. Minimum possible value is 0. Maximum possible value is 1. A higher recall means that more of the positive examples have been labeled correctly. Therefore, a higher recall is generally considered better.\n\nExamples:\n\n Example 1-A simple example with some errors\n >>> recall_metric = datasets.load_metric('recall')\n >>> results = recall_metric.compute(references=[0, 0, 1, 1, 1], predictions=[0, 1, 0, 1, 1])\n >>> print(results)\n {'recall': 0.6666666666666666}\n\n Example 2-The same example as Example 1, but with `pos_label=0` instead of the default `pos_label=1`.\n >>> recall_metric = datasets.load_metric('recall')\n >>> results = recall_metric.compute(references=[0, 0, 1, 1, 1], predictions=[0, 1, 0, 1, 1], pos_label=0)\n >>> print(results)\n {'recall': 0.5}\n\n Example 3-The same example as Example 1, but with `sample_weight` included.\n >>> recall_metric = datasets.load_metric('recall')\n >>> sample_weight = [0.9, 0.2, 0.9, 0.3, 0.8]\n >>> results = recall_metric.compute(references=[0, 0, 1, 1, 1], predictions=[0, 1, 0, 1, 1], sample_weight=sample_weight)\n >>> print(results)\n {'recall': 0.55}\n\n Example 4-A multiclass example, using different averages.\n >>> recall_metric = datasets.load_metric('recall')\n >>> predictions = [0, 2, 1, 0, 0, 1]\n >>> references = [0, 1, 2, 0, 1, 2]\n >>> results = recall_metric.compute(predictions=predictions, references=references, average='macro')\n >>> print(results)\n {'recall': 0.3333333333333333}\n >>> results = recall_metric.compute(predictions=predictions, references=references, average='micro')\n >>> print(results)\n {'recall': 0.3333333333333333}\n >>> results = recall_metric.compute(predictions=predictions, references=references, average='weighted')\n >>> print(results)\n {'recall': 0.3333333333333333}\n >>> results = recall_metric.compute(predictions=predictions, references=references, average=None)\n >>> print(results)\n {'recall': array([1., 0., 0.])}\n"
lowerCAmelCase__ ="\n@article{scikit-learn, title={Scikit-learn: Machine Learning in {P}ython}, author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V. and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P. and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.}, journal={Journal of Machine Learning Research}, volume={12}, pages={2825--2830}, year={2011}\n"
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class A__( datasets.Metric ):
def _a ( self : Any ) -> int:
"""simple docstring"""
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
'''predictions''': datasets.Sequence(datasets.Value('''int32''' ) ),
'''references''': datasets.Sequence(datasets.Value('''int32''' ) ),
}
if self.config_name == '''multilabel'''
else {
'''predictions''': datasets.Value('''int32''' ),
'''references''': datasets.Value('''int32''' ),
} ) , reference_urls=['''https://scikit-learn.org/stable/modules/generated/sklearn.metrics.recall_score.html'''] , )
def _a ( self : Tuple , __SCREAMING_SNAKE_CASE : Union[str, Any] , __SCREAMING_SNAKE_CASE : Optional[Any] , __SCREAMING_SNAKE_CASE : Union[str, Any]=None , __SCREAMING_SNAKE_CASE : Optional[int]=1 , __SCREAMING_SNAKE_CASE : Optional[Any]="binary" , __SCREAMING_SNAKE_CASE : List[Any]=None , __SCREAMING_SNAKE_CASE : List[Any]="warn" , ) -> Dict:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = recall_score(
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , labels=__SCREAMING_SNAKE_CASE , pos_label=__SCREAMING_SNAKE_CASE , average=__SCREAMING_SNAKE_CASE , sample_weight=__SCREAMING_SNAKE_CASE , zero_division=__SCREAMING_SNAKE_CASE , )
return {"recall": float(__SCREAMING_SNAKE_CASE ) if score.size == 1 else score}
| 690 | 1 |
"""simple docstring"""
import argparse
import os
# New Code #
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, DistributedType
from accelerate.utils import find_executable_batch_size
########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to ensure out-of-memory errors never
# interrupt training, and builds off the `nlp_example.py` script.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
# - single CPU or single GPU
# - multi GPUS (using PyTorch distributed mode)
# - (multi) TPUs
# - fp16 (mixed-precision) or fp32 (normal precision)
#
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################
lowerCAmelCase__ =16
lowerCAmelCase__ =32
def _a ( UpperCAmelCase__ , UpperCAmelCase__ = 16 ) -> List[str]:
__SCREAMING_SNAKE_CASE = AutoTokenizer.from_pretrained('''bert-base-cased''' )
__SCREAMING_SNAKE_CASE = load_dataset('''glue''' , '''mrpc''' )
def tokenize_function(UpperCAmelCase__ ):
# max_length=None => use the model max length (it's actually the default)
__SCREAMING_SNAKE_CASE = tokenizer(examples['''sentence1'''] , examples['''sentence2'''] , truncation=UpperCAmelCase__ , max_length=UpperCAmelCase__ )
return outputs
# Apply the method we just defined to all the examples in all the splits of the dataset
# starting with the main process first:
with accelerator.main_process_first():
__SCREAMING_SNAKE_CASE = datasets.map(
UpperCAmelCase__ , batched=UpperCAmelCase__ , remove_columns=['''idx''', '''sentence1''', '''sentence2'''] , )
# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
# transformers library
__SCREAMING_SNAKE_CASE = tokenized_datasets.rename_column('''label''' , '''labels''' )
def collate_fn(UpperCAmelCase__ ):
# On TPU it's best to pad everything to the same length or training will be very slow.
__SCREAMING_SNAKE_CASE = 1_28 if accelerator.distributed_type == DistributedType.TPU else None
# When using mixed precision we want round multiples of 8/16
if accelerator.mixed_precision == "fp8":
__SCREAMING_SNAKE_CASE = 16
elif accelerator.mixed_precision != "no":
__SCREAMING_SNAKE_CASE = 8
else:
__SCREAMING_SNAKE_CASE = None
return tokenizer.pad(
UpperCAmelCase__ , padding='''longest''' , max_length=UpperCAmelCase__ , pad_to_multiple_of=UpperCAmelCase__ , return_tensors='''pt''' , )
# Instantiate dataloaders.
__SCREAMING_SNAKE_CASE = DataLoader(
tokenized_datasets['''train'''] , shuffle=UpperCAmelCase__ , collate_fn=UpperCAmelCase__ , batch_size=UpperCAmelCase__ )
__SCREAMING_SNAKE_CASE = DataLoader(
tokenized_datasets['''validation'''] , shuffle=UpperCAmelCase__ , collate_fn=UpperCAmelCase__ , batch_size=UpperCAmelCase__ )
return train_dataloader, eval_dataloader
# For testing only
if os.environ.get("TESTING_MOCKED_DATALOADERS", None) == "1":
from accelerate.test_utils.training import mocked_dataloaders
lowerCAmelCase__ =mocked_dataloaders # noqa: F811
def _a ( UpperCAmelCase__ , UpperCAmelCase__ ) -> Tuple:
# For testing only
if os.environ.get('''TESTING_MOCKED_DATALOADERS''' , UpperCAmelCase__ ) == "1":
__SCREAMING_SNAKE_CASE = 2
# Initialize accelerator
__SCREAMING_SNAKE_CASE = Accelerator(cpu=args.cpu , mixed_precision=args.mixed_precision )
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
__SCREAMING_SNAKE_CASE = config['''lr''']
__SCREAMING_SNAKE_CASE = int(config['''num_epochs'''] )
__SCREAMING_SNAKE_CASE = int(config['''seed'''] )
__SCREAMING_SNAKE_CASE = int(config['''batch_size'''] )
__SCREAMING_SNAKE_CASE = evaluate.load('''glue''' , '''mrpc''' )
# New Code #
# We now can define an inner training loop function. It should take a batch size as the only parameter,
# and build the dataloaders in there.
# It also gets our decorator
@find_executable_batch_size(starting_batch_size=UpperCAmelCase__ )
def inner_training_loop(UpperCAmelCase__ ):
# And now just move everything below under this function
# We need to bring in the Accelerator object from earlier
nonlocal accelerator
# And reset all of its attributes that could hold onto any memory:
accelerator.free_memory()
# Then we can declare the model, optimizer, and everything else:
set_seed(UpperCAmelCase__ )
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
__SCREAMING_SNAKE_CASE = AutoModelForSequenceClassification.from_pretrained('''bert-base-cased''' , return_dict=UpperCAmelCase__ )
# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
__SCREAMING_SNAKE_CASE = model.to(accelerator.device )
# Instantiate optimizer
__SCREAMING_SNAKE_CASE = AdamW(params=model.parameters() , lr=UpperCAmelCase__ )
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = get_dataloaders(UpperCAmelCase__ , UpperCAmelCase__ )
# Instantiate scheduler
__SCREAMING_SNAKE_CASE = get_linear_schedule_with_warmup(
optimizer=UpperCAmelCase__ , num_warmup_steps=1_00 , num_training_steps=(len(UpperCAmelCase__ ) * num_epochs) , )
# Prepare everything
# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
# prepare method.
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = accelerator.prepare(
UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ )
# Now we train the model
for epoch in range(UpperCAmelCase__ ):
model.train()
for step, batch in enumerate(UpperCAmelCase__ ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
__SCREAMING_SNAKE_CASE = model(**UpperCAmelCase__ )
__SCREAMING_SNAKE_CASE = outputs.loss
accelerator.backward(UpperCAmelCase__ )
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
model.eval()
for step, batch in enumerate(UpperCAmelCase__ ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
with torch.no_grad():
__SCREAMING_SNAKE_CASE = model(**UpperCAmelCase__ )
__SCREAMING_SNAKE_CASE = outputs.logits.argmax(dim=-1 )
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = accelerator.gather_for_metrics((predictions, batch['''labels''']) )
metric.add_batch(
predictions=UpperCAmelCase__ , references=UpperCAmelCase__ , )
__SCREAMING_SNAKE_CASE = metric.compute()
# Use accelerator.print to print only on the main process.
accelerator.print(f"""epoch {epoch}:""" , UpperCAmelCase__ )
# New Code #
# And call it at the end with no arguments
# Note: You could also refactor this outside of your training loop function
inner_training_loop()
def _a ( ) -> Dict:
__SCREAMING_SNAKE_CASE = argparse.ArgumentParser(description='''Simple example of training script.''' )
parser.add_argument(
'''--mixed_precision''' , type=UpperCAmelCase__ , default=UpperCAmelCase__ , choices=['''no''', '''fp16''', '''bf16''', '''fp8'''] , help='''Whether to use mixed precision. Choose'''
'''between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'''
'''and an Nvidia Ampere GPU.''' , )
parser.add_argument('''--cpu''' , action='''store_true''' , help='''If passed, will train on the CPU.''' )
__SCREAMING_SNAKE_CASE = parser.parse_args()
__SCREAMING_SNAKE_CASE = {'''lr''': 2E-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
training_function(UpperCAmelCase__ , UpperCAmelCase__ )
if __name__ == "__main__":
main()
| 690 |
"""simple docstring"""
def _a ( UpperCAmelCase__ = 10**9 ) -> int:
__SCREAMING_SNAKE_CASE = 1
__SCREAMING_SNAKE_CASE = 2
__SCREAMING_SNAKE_CASE = 0
__SCREAMING_SNAKE_CASE = 0
__SCREAMING_SNAKE_CASE = 0
while perimeter <= max_perimeter:
perimeters_sum += perimeter
prev_value += 2 * value
value += prev_value
__SCREAMING_SNAKE_CASE = 2 * value + 2 if i % 2 == 0 else 2 * value - 2
i += 1
return perimeters_sum
if __name__ == "__main__":
print(F'''{solution() = }''')
| 690 | 1 |
"""simple docstring"""
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
WavaVecaConformerConfig,
WavaVecaConformerForCTC,
WavaVecaConformerForPreTraining,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaProcessor,
logging,
)
logging.set_verbosity_info()
lowerCAmelCase__ =logging.get_logger(__name__)
lowerCAmelCase__ ={
"post_extract_proj": "feature_projection.projection",
"encoder.pos_conv.0": "encoder.pos_conv_embed.conv",
"self_attn.linear_k": "encoder.layers.*.self_attn.linear_k",
"self_attn.linear_v": "encoder.layers.*.self_attn.linear_v",
"self_attn.linear_q": "encoder.layers.*.self_attn.linear_q",
"self_attn.pos_bias_u": "encoder.layers.*.self_attn.pos_bias_u",
"self_attn.pos_bias_v": "encoder.layers.*.self_attn.pos_bias_v",
"self_attn.linear_out": "encoder.layers.*.self_attn.linear_out",
"self_attn.linear_pos": "encoder.layers.*.self_attn.linear_pos",
"self_attn.rotary_emb": "encoder.embed_positions",
"self_attn_layer_norm": "encoder.layers.*.self_attn_layer_norm",
"conv_module.pointwise_conv1": "encoder.layers.*.conv_module.pointwise_conv1",
"conv_module.pointwise_conv2": "encoder.layers.*.conv_module.pointwise_conv2",
"conv_module.depthwise_conv": "encoder.layers.*.conv_module.depthwise_conv",
"conv_module.batch_norm": "encoder.layers.*.conv_module.batch_norm",
"conv_module.layer_norm": "encoder.layers.*.conv_module.layer_norm",
"ffn1.w_1": "encoder.layers.*.ffn1.intermediate_dense",
"ffn1.w_2": "encoder.layers.*.ffn1.output_dense",
"ffn1.layer_norm": "encoder.layers.*.ffn1_layer_norm",
"ffn2.w_1": "encoder.layers.*.ffn2.intermediate_dense",
"ffn2.w_2": "encoder.layers.*.ffn2.output_dense",
"ffn2.layer_norm": "encoder.layers.*.ffn2_layer_norm",
"final_layer_norm": "encoder.layers.*.final_layer_norm",
"encoder.layer_norm": "encoder.layer_norm",
"w2v_model.layer_norm": "feature_projection.layer_norm",
"quantizer.weight_proj": "quantizer.weight_proj",
"quantizer.vars": "quantizer.codevectors",
"project_q": "project_q",
"final_proj": "project_hid",
"w2v_encoder.proj": "lm_head",
"mask_emb": "masked_spec_embed",
}
lowerCAmelCase__ =[
"lm_head",
"quantizer.weight_proj",
"quantizer.codevectors",
"project_q",
"project_hid",
]
def _a ( UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ ) -> List[str]:
for attribute in key.split('''.''' ):
__SCREAMING_SNAKE_CASE = getattr(UpperCAmelCase__ , UpperCAmelCase__ )
if weight_type is not None:
__SCREAMING_SNAKE_CASE = getattr(UpperCAmelCase__ , UpperCAmelCase__ ).shape
else:
__SCREAMING_SNAKE_CASE = hf_pointer.shape
if hf_shape != value.shape:
raise ValueError(
f"""Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be"""
f""" {value.shape} for {full_name}""" )
if weight_type == "weight":
__SCREAMING_SNAKE_CASE = value
elif weight_type == "weight_g":
__SCREAMING_SNAKE_CASE = value
elif weight_type == "weight_v":
__SCREAMING_SNAKE_CASE = value
elif weight_type == "bias":
__SCREAMING_SNAKE_CASE = value
elif weight_type == "running_mean":
__SCREAMING_SNAKE_CASE = value
elif weight_type == "running_var":
__SCREAMING_SNAKE_CASE = value
elif weight_type == "num_batches_tracked":
__SCREAMING_SNAKE_CASE = value
elif weight_type == "inv_freq":
__SCREAMING_SNAKE_CASE = value
else:
__SCREAMING_SNAKE_CASE = value
logger.info(f"""{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.""" )
def _a ( UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ ) -> Any:
__SCREAMING_SNAKE_CASE = []
__SCREAMING_SNAKE_CASE = fairseq_model.state_dict()
__SCREAMING_SNAKE_CASE = hf_model.wavaveca_conformer.feature_extractor
for name, value in fairseq_dict.items():
__SCREAMING_SNAKE_CASE = False
if "conv_layers" in name:
load_conv_layer(
UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , hf_model.config.feat_extract_norm == '''group''' , )
__SCREAMING_SNAKE_CASE = True
else:
for key, mapped_key in MAPPING.items():
__SCREAMING_SNAKE_CASE = '''wav2vec2_conformer.''' + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
if key in name or key.split('''w2v_model.''' )[-1] == name.split('''.''' )[0]:
__SCREAMING_SNAKE_CASE = True
if "*" in mapped_key:
__SCREAMING_SNAKE_CASE = name.split(UpperCAmelCase__ )[0].split('''.''' )[-2]
__SCREAMING_SNAKE_CASE = mapped_key.replace('''*''' , UpperCAmelCase__ )
if "pos_bias_u" in name:
__SCREAMING_SNAKE_CASE = None
elif "pos_bias_v" in name:
__SCREAMING_SNAKE_CASE = None
elif "weight_g" in name:
__SCREAMING_SNAKE_CASE = '''weight_g'''
elif "weight_v" in name:
__SCREAMING_SNAKE_CASE = '''weight_v'''
elif "bias" in name:
__SCREAMING_SNAKE_CASE = '''bias'''
elif "weight" in name:
# TODO: don't match quantizer.weight_proj
__SCREAMING_SNAKE_CASE = '''weight'''
elif "running_mean" in name:
__SCREAMING_SNAKE_CASE = '''running_mean'''
elif "inv_freq" in name:
__SCREAMING_SNAKE_CASE = '''inv_freq'''
elif "running_var" in name:
__SCREAMING_SNAKE_CASE = '''running_var'''
elif "num_batches_tracked" in name:
__SCREAMING_SNAKE_CASE = '''num_batches_tracked'''
else:
__SCREAMING_SNAKE_CASE = None
set_recursively(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ )
continue
if not is_used:
unused_weights.append(UpperCAmelCase__ )
logger.warning(f"""Unused weights: {unused_weights}""" )
def _a ( UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ ) -> Optional[Any]:
__SCREAMING_SNAKE_CASE = full_name.split('''conv_layers.''' )[-1]
__SCREAMING_SNAKE_CASE = name.split('''.''' )
__SCREAMING_SNAKE_CASE = int(items[0] )
__SCREAMING_SNAKE_CASE = int(items[1] )
if type_id == 0:
if "bias" in name:
if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
raise ValueError(
f"""{full_name} has size {value.shape}, but"""
f""" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.""" )
__SCREAMING_SNAKE_CASE = value
logger.info(f"""Feat extract conv layer {layer_id} was initialized from {full_name}.""" )
elif "weight" in name:
if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
raise ValueError(
f"""{full_name} has size {value.shape}, but"""
f""" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.""" )
__SCREAMING_SNAKE_CASE = value
logger.info(f"""Feat extract conv layer {layer_id} was initialized from {full_name}.""" )
elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
if "bias" in name:
if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
raise ValueError(
f"""{full_name} has size {value.shape}, but"""
f""" {feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape} was found.""" )
__SCREAMING_SNAKE_CASE = value
logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.""" )
elif "weight" in name:
if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
raise ValueError(
f"""{full_name} has size {value.shape}, but"""
f""" {feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape} was found.""" )
__SCREAMING_SNAKE_CASE = value
logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.""" )
else:
unused_weights.append(UpperCAmelCase__ )
@torch.no_grad()
def _a ( UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__=None , UpperCAmelCase__=None , UpperCAmelCase__=True ) -> Dict:
if config_path is not None:
__SCREAMING_SNAKE_CASE = WavaVecaConformerConfig.from_pretrained(UpperCAmelCase__ , hidden_act='''swish''' )
else:
__SCREAMING_SNAKE_CASE = WavaVecaConformerConfig()
if "rope" in checkpoint_path:
__SCREAMING_SNAKE_CASE = '''rotary'''
if is_finetuned:
if dict_path:
__SCREAMING_SNAKE_CASE = Dictionary.load(UpperCAmelCase__ )
# important change bos & pad token id since CTC symbol is <pad> and
# not <s> as in fairseq
__SCREAMING_SNAKE_CASE = target_dict.pad_index
__SCREAMING_SNAKE_CASE = target_dict.bos_index
__SCREAMING_SNAKE_CASE = target_dict.eos_index
__SCREAMING_SNAKE_CASE = len(target_dict.symbols )
__SCREAMING_SNAKE_CASE = os.path.join(UpperCAmelCase__ , '''vocab.json''' )
if not os.path.isdir(UpperCAmelCase__ ):
logger.error('''--pytorch_dump_folder_path ({}) should be a directory'''.format(UpperCAmelCase__ ) )
return
os.makedirs(UpperCAmelCase__ , exist_ok=UpperCAmelCase__ )
__SCREAMING_SNAKE_CASE = target_dict.indices
# fairseq has the <pad> and <s> switched
__SCREAMING_SNAKE_CASE = 0
__SCREAMING_SNAKE_CASE = 1
with open(UpperCAmelCase__ , '''w''' , encoding='''utf-8''' ) as vocab_handle:
json.dump(UpperCAmelCase__ , UpperCAmelCase__ )
__SCREAMING_SNAKE_CASE = WavaVecaCTCTokenizer(
UpperCAmelCase__ , unk_token=target_dict.unk_word , pad_token=target_dict.pad_word , bos_token=target_dict.bos_word , eos_token=target_dict.eos_word , word_delimiter_token='''|''' , do_lower_case=UpperCAmelCase__ , )
__SCREAMING_SNAKE_CASE = True if config.feat_extract_norm == '''layer''' else False
__SCREAMING_SNAKE_CASE = WavaVecaFeatureExtractor(
feature_size=1 , sampling_rate=1_60_00 , padding_value=0 , do_normalize=UpperCAmelCase__ , return_attention_mask=UpperCAmelCase__ , )
__SCREAMING_SNAKE_CASE = WavaVecaProcessor(feature_extractor=UpperCAmelCase__ , tokenizer=UpperCAmelCase__ )
processor.save_pretrained(UpperCAmelCase__ )
__SCREAMING_SNAKE_CASE = WavaVecaConformerForCTC(UpperCAmelCase__ )
else:
__SCREAMING_SNAKE_CASE = WavaVecaConformerForPreTraining(UpperCAmelCase__ )
if is_finetuned:
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = fairseq.checkpoint_utils.load_model_ensemble_and_task(
[checkpoint_path] , arg_overrides={'''data''': '''/'''.join(dict_path.split('''/''' )[:-1] )} )
else:
__SCREAMING_SNAKE_CASE = argparse.Namespace(task='''audio_pretraining''' )
__SCREAMING_SNAKE_CASE = fairseq.tasks.setup_task(UpperCAmelCase__ )
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path] , task=UpperCAmelCase__ )
__SCREAMING_SNAKE_CASE = model[0].eval()
recursively_load_weights(UpperCAmelCase__ , UpperCAmelCase__ , not is_finetuned )
hf_wavavec.save_pretrained(UpperCAmelCase__ )
if __name__ == "__main__":
lowerCAmelCase__ =argparse.ArgumentParser()
parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to fairseq checkpoint")
parser.add_argument("--dict_path", default=None, type=str, help="Path to dict of fine-tuned model")
parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
parser.add_argument(
"--not_finetuned", action="store_true", help="Whether the model to convert is a fine-tuned model or not"
)
lowerCAmelCase__ =parser.parse_args()
convert_wavaveca_conformer_checkpoint(
args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
)
| 690 |
"""simple docstring"""
import pandas as pd
from matplotlib import pyplot as plt
from sklearn.linear_model import LinearRegression
# Splitting the dataset into the Training set and Test set
from sklearn.model_selection import train_test_split
# Fitting Polynomial Regression to the dataset
from sklearn.preprocessing import PolynomialFeatures
# Importing the dataset
lowerCAmelCase__ =pd.read_csv(
"https://s3.us-west-2.amazonaws.com/public.gamelab.fun/dataset/"
"position_salaries.csv"
)
lowerCAmelCase__ =dataset.iloc[:, 1:2].values
lowerCAmelCase__ =dataset.iloc[:, 2].values
lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ =train_test_split(X, y, test_size=0.2, random_state=0)
lowerCAmelCase__ =PolynomialFeatures(degree=4)
lowerCAmelCase__ =poly_reg.fit_transform(X)
lowerCAmelCase__ =LinearRegression()
pol_reg.fit(X_poly, y)
def _a ( ) -> List[Any]:
plt.scatter(UpperCAmelCase__ , UpperCAmelCase__ , color='''red''' )
plt.plot(UpperCAmelCase__ , pol_reg.predict(poly_reg.fit_transform(UpperCAmelCase__ ) ) , color='''blue''' )
plt.title('''Truth or Bluff (Linear Regression)''' )
plt.xlabel('''Position level''' )
plt.ylabel('''Salary''' )
plt.show()
if __name__ == "__main__":
viz_polymonial()
# Predicting a new result with Polymonial Regression
pol_reg.predict(poly_reg.fit_transform([[5.5]]))
# output should be 132148.43750003
| 690 | 1 |
"""simple docstring"""
import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List
import git
import torch
from torch.utils.data import Dataset
from transformers import BartTokenizer, RagTokenizer, TaTokenizer
def _a ( UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__=True , UpperCAmelCase__="pt" ) -> str:
__SCREAMING_SNAKE_CASE = {'''add_prefix_space''': True} if isinstance(UpperCAmelCase__ , UpperCAmelCase__ ) and not line.startswith(''' ''' ) else {}
__SCREAMING_SNAKE_CASE = padding_side
return tokenizer(
[line] , max_length=UpperCAmelCase__ , padding='''max_length''' if pad_to_max_length else None , truncation=UpperCAmelCase__ , return_tensors=UpperCAmelCase__ , add_special_tokens=UpperCAmelCase__ , **UpperCAmelCase__ , )
def _a ( UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__=None , ) -> Optional[Any]:
__SCREAMING_SNAKE_CASE = input_ids.ne(UpperCAmelCase__ ).any(dim=0 )
if attention_mask is None:
return input_ids[:, keep_column_mask]
else:
return (input_ids[:, keep_column_mask], attention_mask[:, keep_column_mask])
class A__( __magic_name__ ):
def __init__( self : Dict , __SCREAMING_SNAKE_CASE : List[str] , __SCREAMING_SNAKE_CASE : Any , __SCREAMING_SNAKE_CASE : Union[str, Any] , __SCREAMING_SNAKE_CASE : Dict , __SCREAMING_SNAKE_CASE : Tuple="train" , __SCREAMING_SNAKE_CASE : List[Any]=None , __SCREAMING_SNAKE_CASE : List[str]=None , __SCREAMING_SNAKE_CASE : Optional[int]=None , __SCREAMING_SNAKE_CASE : str="" , ) -> Union[str, Any]:
"""simple docstring"""
super().__init__()
__SCREAMING_SNAKE_CASE = Path(__SCREAMING_SNAKE_CASE ).joinpath(type_path + '''.source''' )
__SCREAMING_SNAKE_CASE = Path(__SCREAMING_SNAKE_CASE ).joinpath(type_path + '''.target''' )
__SCREAMING_SNAKE_CASE = self.get_char_lens(self.src_file )
__SCREAMING_SNAKE_CASE = max_source_length
__SCREAMING_SNAKE_CASE = max_target_length
assert min(self.src_lens ) > 0, f"""found empty line in {self.src_file}"""
__SCREAMING_SNAKE_CASE = tokenizer
__SCREAMING_SNAKE_CASE = prefix
if n_obs is not None:
__SCREAMING_SNAKE_CASE = self.src_lens[:n_obs]
__SCREAMING_SNAKE_CASE = src_lang
__SCREAMING_SNAKE_CASE = tgt_lang
def __len__( self : List[str] ) -> Optional[Any]:
"""simple docstring"""
return len(self.src_lens )
def __getitem__( self : Dict , __SCREAMING_SNAKE_CASE : List[Any] ) -> Dict[str, torch.Tensor]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = index + 1 # linecache starts at 1
__SCREAMING_SNAKE_CASE = self.prefix + linecache.getline(str(self.src_file ) , __SCREAMING_SNAKE_CASE ).rstrip('''\n''' )
__SCREAMING_SNAKE_CASE = linecache.getline(str(self.tgt_file ) , __SCREAMING_SNAKE_CASE ).rstrip('''\n''' )
assert source_line, f"""empty source line for index {index}"""
assert tgt_line, f"""empty tgt line for index {index}"""
# Need to add eos token manually for T5
if isinstance(self.tokenizer , __SCREAMING_SNAKE_CASE ):
source_line += self.tokenizer.eos_token
tgt_line += self.tokenizer.eos_token
# Pad source and target to the right
__SCREAMING_SNAKE_CASE = (
self.tokenizer.question_encoder if isinstance(self.tokenizer , __SCREAMING_SNAKE_CASE ) else self.tokenizer
)
__SCREAMING_SNAKE_CASE = self.tokenizer.generator if isinstance(self.tokenizer , __SCREAMING_SNAKE_CASE ) else self.tokenizer
__SCREAMING_SNAKE_CASE = encode_line(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , self.max_source_length , '''right''' )
__SCREAMING_SNAKE_CASE = encode_line(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , self.max_target_length , '''right''' )
__SCREAMING_SNAKE_CASE = source_inputs['''input_ids'''].squeeze()
__SCREAMING_SNAKE_CASE = target_inputs['''input_ids'''].squeeze()
__SCREAMING_SNAKE_CASE = source_inputs['''attention_mask'''].squeeze()
return {
"input_ids": source_ids,
"attention_mask": src_mask,
"decoder_input_ids": target_ids,
}
@staticmethod
def _a ( __SCREAMING_SNAKE_CASE : Optional[int] ) -> Union[str, Any]:
"""simple docstring"""
return [len(__SCREAMING_SNAKE_CASE ) for x in Path(__SCREAMING_SNAKE_CASE ).open().readlines()]
def _a ( self : Optional[int] , __SCREAMING_SNAKE_CASE : int ) -> Dict[str, torch.Tensor]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = torch.stack([x['''input_ids'''] for x in batch] )
__SCREAMING_SNAKE_CASE = torch.stack([x['''attention_mask'''] for x in batch] )
__SCREAMING_SNAKE_CASE = torch.stack([x['''decoder_input_ids'''] for x in batch] )
__SCREAMING_SNAKE_CASE = (
self.tokenizer.generator.pad_token_id
if isinstance(self.tokenizer , __SCREAMING_SNAKE_CASE )
else self.tokenizer.pad_token_id
)
__SCREAMING_SNAKE_CASE = (
self.tokenizer.question_encoder.pad_token_id
if isinstance(self.tokenizer , __SCREAMING_SNAKE_CASE )
else self.tokenizer.pad_token_id
)
__SCREAMING_SNAKE_CASE = trim_batch(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = trim_batch(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , attention_mask=__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = {
'''input_ids''': source_ids,
'''attention_mask''': source_mask,
'''decoder_input_ids''': y,
}
return batch
lowerCAmelCase__ =getLogger(__name__)
def _a ( UpperCAmelCase__ ) -> List[str]:
return list(itertools.chain.from_iterable(UpperCAmelCase__ ) )
def _a ( UpperCAmelCase__ ) -> None:
__SCREAMING_SNAKE_CASE = get_git_info()
save_json(UpperCAmelCase__ , os.path.join(UpperCAmelCase__ , '''git_log.json''' ) )
def _a ( UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__=4 , **UpperCAmelCase__ ) -> str:
with open(UpperCAmelCase__ , '''w''' ) as f:
json.dump(UpperCAmelCase__ , UpperCAmelCase__ , indent=UpperCAmelCase__ , **UpperCAmelCase__ )
def _a ( UpperCAmelCase__ ) -> List[Any]:
with open(UpperCAmelCase__ ) as f:
return json.load(UpperCAmelCase__ )
def _a ( ) -> Optional[int]:
__SCREAMING_SNAKE_CASE = git.Repo(search_parent_directories=UpperCAmelCase__ )
__SCREAMING_SNAKE_CASE = {
'''repo_id''': str(UpperCAmelCase__ ),
'''repo_sha''': str(repo.head.object.hexsha ),
'''repo_branch''': str(repo.active_branch ),
'''hostname''': str(socket.gethostname() ),
}
return repo_infos
def _a ( UpperCAmelCase__ , UpperCAmelCase__ ) -> List:
return list(map(UpperCAmelCase__ , UpperCAmelCase__ ) )
def _a ( UpperCAmelCase__ , UpperCAmelCase__ ) -> Union[str, Any]:
with open(UpperCAmelCase__ , '''wb''' ) as f:
return pickle.dump(UpperCAmelCase__ , UpperCAmelCase__ )
def _a ( UpperCAmelCase__ ) -> List[str]:
def remove_articles(UpperCAmelCase__ ):
return re.sub(r'''\b(a|an|the)\b''' , ''' ''' , UpperCAmelCase__ )
def white_space_fix(UpperCAmelCase__ ):
return " ".join(text.split() )
def remove_punc(UpperCAmelCase__ ):
__SCREAMING_SNAKE_CASE = set(string.punctuation )
return "".join(ch for ch in text if ch not in exclude )
def lower(UpperCAmelCase__ ):
return text.lower()
return white_space_fix(remove_articles(remove_punc(lower(UpperCAmelCase__ ) ) ) )
def _a ( UpperCAmelCase__ , UpperCAmelCase__ ) -> Optional[Any]:
__SCREAMING_SNAKE_CASE = normalize_answer(UpperCAmelCase__ ).split()
__SCREAMING_SNAKE_CASE = normalize_answer(UpperCAmelCase__ ).split()
__SCREAMING_SNAKE_CASE = Counter(UpperCAmelCase__ ) & Counter(UpperCAmelCase__ )
__SCREAMING_SNAKE_CASE = sum(common.values() )
if num_same == 0:
return 0
__SCREAMING_SNAKE_CASE = 1.0 * num_same / len(UpperCAmelCase__ )
__SCREAMING_SNAKE_CASE = 1.0 * num_same / len(UpperCAmelCase__ )
__SCREAMING_SNAKE_CASE = (2 * precision * recall) / (precision + recall)
return fa
def _a ( UpperCAmelCase__ , UpperCAmelCase__ ) -> Union[str, Any]:
return normalize_answer(UpperCAmelCase__ ) == normalize_answer(UpperCAmelCase__ )
def _a ( UpperCAmelCase__ , UpperCAmelCase__ ) -> Dict:
assert len(UpperCAmelCase__ ) == len(UpperCAmelCase__ )
__SCREAMING_SNAKE_CASE = 0
for hypo, pred in zip(UpperCAmelCase__ , UpperCAmelCase__ ):
em += exact_match_score(UpperCAmelCase__ , UpperCAmelCase__ )
if len(UpperCAmelCase__ ) > 0:
em /= len(UpperCAmelCase__ )
return {"em": em}
def _a ( UpperCAmelCase__ ) -> Optional[Any]:
return model_prefix.startswith('''rag''' )
def _a ( UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ ) -> Union[str, Any]:
__SCREAMING_SNAKE_CASE = {p: p for p in extra_params}
# T5 models don't have `dropout` param, they have `dropout_rate` instead
__SCREAMING_SNAKE_CASE = '''dropout_rate'''
for p in extra_params:
if getattr(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ ):
if not hasattr(UpperCAmelCase__ , UpperCAmelCase__ ) and not hasattr(UpperCAmelCase__ , equivalent_param[p] ):
logger.info('''config doesn\'t have a `{}` attribute'''.format(UpperCAmelCase__ ) )
delattr(UpperCAmelCase__ , UpperCAmelCase__ )
continue
__SCREAMING_SNAKE_CASE = p if hasattr(UpperCAmelCase__ , UpperCAmelCase__ ) else equivalent_param[p]
setattr(UpperCAmelCase__ , UpperCAmelCase__ , getattr(UpperCAmelCase__ , UpperCAmelCase__ ) )
delattr(UpperCAmelCase__ , UpperCAmelCase__ )
return hparams, config
| 690 |
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import AutoTokenizer, MBartConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import TFAutoModelForSeqaSeqLM, TFMBartForConditionalGeneration, TFMBartModel
@require_tf
class A__:
lowerCAmelCase = MBartConfig
lowerCAmelCase = {}
lowerCAmelCase = '''gelu'''
def __init__( self : List[str] , __SCREAMING_SNAKE_CASE : Optional[int] , __SCREAMING_SNAKE_CASE : Tuple=13 , __SCREAMING_SNAKE_CASE : Dict=7 , __SCREAMING_SNAKE_CASE : List[Any]=True , __SCREAMING_SNAKE_CASE : Any=False , __SCREAMING_SNAKE_CASE : Union[str, Any]=99 , __SCREAMING_SNAKE_CASE : Optional[Any]=32 , __SCREAMING_SNAKE_CASE : Optional[int]=2 , __SCREAMING_SNAKE_CASE : Any=4 , __SCREAMING_SNAKE_CASE : List[str]=37 , __SCREAMING_SNAKE_CASE : Optional[Any]=0.1 , __SCREAMING_SNAKE_CASE : Dict=0.1 , __SCREAMING_SNAKE_CASE : Any=20 , __SCREAMING_SNAKE_CASE : List[Any]=2 , __SCREAMING_SNAKE_CASE : Optional[int]=1 , __SCREAMING_SNAKE_CASE : Optional[Any]=0 , ) -> Optional[int]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = parent
__SCREAMING_SNAKE_CASE = batch_size
__SCREAMING_SNAKE_CASE = seq_length
__SCREAMING_SNAKE_CASE = is_training
__SCREAMING_SNAKE_CASE = use_labels
__SCREAMING_SNAKE_CASE = vocab_size
__SCREAMING_SNAKE_CASE = hidden_size
__SCREAMING_SNAKE_CASE = num_hidden_layers
__SCREAMING_SNAKE_CASE = num_attention_heads
__SCREAMING_SNAKE_CASE = intermediate_size
__SCREAMING_SNAKE_CASE = hidden_dropout_prob
__SCREAMING_SNAKE_CASE = attention_probs_dropout_prob
__SCREAMING_SNAKE_CASE = max_position_embeddings
__SCREAMING_SNAKE_CASE = eos_token_id
__SCREAMING_SNAKE_CASE = pad_token_id
__SCREAMING_SNAKE_CASE = bos_token_id
def _a ( self : List[str] ) -> Optional[int]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = ids_tensor([self.batch_size, self.seq_length - 1] , self.vocab_size )
__SCREAMING_SNAKE_CASE = tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size ) , 1 )
__SCREAMING_SNAKE_CASE = tf.concat([input_ids, eos_tensor] , axis=1 )
__SCREAMING_SNAKE_CASE = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
__SCREAMING_SNAKE_CASE = self.config_cls(
vocab_size=self.vocab_size , d_model=self.hidden_size , encoder_layers=self.num_hidden_layers , decoder_layers=self.num_hidden_layers , encoder_attention_heads=self.num_attention_heads , decoder_attention_heads=self.num_attention_heads , encoder_ffn_dim=self.intermediate_size , decoder_ffn_dim=self.intermediate_size , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , eos_token_ids=[2] , bos_token_id=self.bos_token_id , pad_token_id=self.pad_token_id , decoder_start_token_id=self.pad_token_id , **self.config_updates , )
__SCREAMING_SNAKE_CASE = prepare_mbart_inputs_dict(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
return config, inputs_dict
def _a ( self : Tuple , __SCREAMING_SNAKE_CASE : str , __SCREAMING_SNAKE_CASE : Optional[int] ) -> Optional[Any]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = TFMBartModel(config=__SCREAMING_SNAKE_CASE ).get_decoder()
__SCREAMING_SNAKE_CASE = inputs_dict['''input_ids''']
__SCREAMING_SNAKE_CASE = input_ids[:1, :]
__SCREAMING_SNAKE_CASE = inputs_dict['''attention_mask'''][:1, :]
__SCREAMING_SNAKE_CASE = inputs_dict['''head_mask''']
__SCREAMING_SNAKE_CASE = 1
# first forward pass
__SCREAMING_SNAKE_CASE = model(__SCREAMING_SNAKE_CASE , attention_mask=__SCREAMING_SNAKE_CASE , head_mask=__SCREAMING_SNAKE_CASE , use_cache=__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = outputs.to_tuple()
__SCREAMING_SNAKE_CASE = past_key_values[1]
def _a ( UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__=None , UpperCAmelCase__=None , UpperCAmelCase__=None , UpperCAmelCase__=None , UpperCAmelCase__=None , ) -> Optional[int]:
if attention_mask is None:
__SCREAMING_SNAKE_CASE = tf.cast(tf.math.not_equal(UpperCAmelCase__ , config.pad_token_id ) , tf.inta )
if decoder_attention_mask is None:
__SCREAMING_SNAKE_CASE = tf.concat(
[
tf.ones(decoder_input_ids[:, :1].shape , dtype=tf.inta ),
tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:] , config.pad_token_id ) , tf.inta ),
] , axis=-1 , )
if head_mask is None:
__SCREAMING_SNAKE_CASE = tf.ones((config.encoder_layers, config.encoder_attention_heads) )
if decoder_head_mask is None:
__SCREAMING_SNAKE_CASE = tf.ones((config.decoder_layers, config.decoder_attention_heads) )
if cross_attn_head_mask is None:
__SCREAMING_SNAKE_CASE = tf.ones((config.decoder_layers, config.decoder_attention_heads) )
return {
"input_ids": input_ids,
"decoder_input_ids": decoder_input_ids,
"attention_mask": attention_mask,
"decoder_attention_mask": decoder_attention_mask,
"head_mask": head_mask,
"decoder_head_mask": decoder_head_mask,
"cross_attn_head_mask": cross_attn_head_mask,
}
@require_tf
class A__( __magic_name__ , __magic_name__ , unittest.TestCase ):
lowerCAmelCase = (TFMBartForConditionalGeneration, TFMBartModel) if is_tf_available() else ()
lowerCAmelCase = (TFMBartForConditionalGeneration,) if is_tf_available() else ()
lowerCAmelCase = (
{
'''conversational''': TFMBartForConditionalGeneration,
'''feature-extraction''': TFMBartModel,
'''summarization''': TFMBartForConditionalGeneration,
'''text2text-generation''': TFMBartForConditionalGeneration,
'''translation''': TFMBartForConditionalGeneration,
}
if is_tf_available()
else {}
)
lowerCAmelCase = True
lowerCAmelCase = False
lowerCAmelCase = False
def _a ( self : Dict , __SCREAMING_SNAKE_CASE : str , __SCREAMING_SNAKE_CASE : List[str] , __SCREAMING_SNAKE_CASE : Dict , __SCREAMING_SNAKE_CASE : Optional[int] , __SCREAMING_SNAKE_CASE : List[Any] ) -> Optional[Any]:
"""simple docstring"""
if pipeline_test_casse_name != "FeatureExtractionPipelineTests":
# Exception encountered when calling layer '...'
return True
return False
def _a ( self : List[Any] ) -> Optional[int]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = TFMBartModelTester(self )
__SCREAMING_SNAKE_CASE = ConfigTester(self , config_class=__SCREAMING_SNAKE_CASE )
def _a ( self : Union[str, Any] ) -> Union[str, Any]:
"""simple docstring"""
self.config_tester.run_common_tests()
def _a ( self : int ) -> str:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.check_decoder_model_past_large_inputs(*__SCREAMING_SNAKE_CASE )
@require_sentencepiece
@require_tokenizers
@require_tf
class A__( unittest.TestCase ):
lowerCAmelCase = [
''' UN Chief Says There Is No Military Solution in Syria''',
]
lowerCAmelCase = [
'''Şeful ONU declară că nu există o soluţie militară în Siria''',
]
lowerCAmelCase = '''facebook/mbart-large-en-ro'''
@cached_property
def _a ( self : Optional[int] ) -> str:
"""simple docstring"""
return AutoTokenizer.from_pretrained(self.model_name )
@cached_property
def _a ( self : Optional[Any] ) -> List[Any]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name )
return model
def _a ( self : Any , **__SCREAMING_SNAKE_CASE : Optional[Any] ) -> Any:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = self.translate_src_text(**__SCREAMING_SNAKE_CASE )
self.assertListEqual(self.expected_text , __SCREAMING_SNAKE_CASE )
def _a ( self : Any , **__SCREAMING_SNAKE_CASE : Optional[int] ) -> Optional[Any]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = self.tokenizer(self.src_text , **__SCREAMING_SNAKE_CASE , return_tensors='''tf''' )
__SCREAMING_SNAKE_CASE = self.model.generate(
model_inputs.input_ids , attention_mask=model_inputs.attention_mask , num_beams=2 )
__SCREAMING_SNAKE_CASE = self.tokenizer.batch_decode(__SCREAMING_SNAKE_CASE , skip_special_tokens=__SCREAMING_SNAKE_CASE )
return generated_words
@slow
def _a ( self : Union[str, Any] ) -> Tuple:
"""simple docstring"""
self._assert_generated_batch_equal_expected()
| 690 | 1 |
"""simple docstring"""
from multiprocessing import Lock, Pipe, Process
# lock used to ensure that two processes do not access a pipe at the same time
lowerCAmelCase__ =Lock()
def _a ( UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ ) -> Union[str, Any]:
global process_lock
# we perform n swaps since after n swaps we know we are sorted
# we *could* stop early if we are sorted already, but it takes as long to
# find out we are sorted as it does to sort the list with this algorithm
for i in range(0 , 10 ):
if (i + position) % 2 == 0 and r_send is not None:
# send your value to your right neighbor
process_lock.acquire()
r_send[1].send(UpperCAmelCase__ )
process_lock.release()
# receive your right neighbor's value
process_lock.acquire()
__SCREAMING_SNAKE_CASE = rr_cv[0].recv()
process_lock.release()
# take the lower value since you are on the left
__SCREAMING_SNAKE_CASE = min(UpperCAmelCase__ , UpperCAmelCase__ )
elif (i + position) % 2 != 0 and l_send is not None:
# send your value to your left neighbor
process_lock.acquire()
l_send[1].send(UpperCAmelCase__ )
process_lock.release()
# receive your left neighbor's value
process_lock.acquire()
__SCREAMING_SNAKE_CASE = lr_cv[0].recv()
process_lock.release()
# take the higher value since you are on the right
__SCREAMING_SNAKE_CASE = max(UpperCAmelCase__ , UpperCAmelCase__ )
# after all swaps are performed, send the values back to main
result_pipe[1].send(UpperCAmelCase__ )
def _a ( UpperCAmelCase__ ) -> Optional[Any]:
__SCREAMING_SNAKE_CASE = []
__SCREAMING_SNAKE_CASE = []
# initialize the list of pipes where the values will be retrieved
for _ in arr:
result_pipe.append(Pipe() )
# creates the processes
# the first and last process only have one neighbor so they are made outside
# of the loop
__SCREAMING_SNAKE_CASE = Pipe()
__SCREAMING_SNAKE_CASE = Pipe()
process_array_.append(
Process(
target=UpperCAmelCase__ , args=(0, arr[0], None, temp_rs, None, temp_rr, result_pipe[0]) , ) )
__SCREAMING_SNAKE_CASE = temp_rs
__SCREAMING_SNAKE_CASE = temp_rr
for i in range(1 , len(UpperCAmelCase__ ) - 1 ):
__SCREAMING_SNAKE_CASE = Pipe()
__SCREAMING_SNAKE_CASE = Pipe()
process_array_.append(
Process(
target=UpperCAmelCase__ , args=(i, arr[i], temp_ls, temp_rs, temp_lr, temp_rr, result_pipe[i]) , ) )
__SCREAMING_SNAKE_CASE = temp_rs
__SCREAMING_SNAKE_CASE = temp_rr
process_array_.append(
Process(
target=UpperCAmelCase__ , args=(
len(UpperCAmelCase__ ) - 1,
arr[len(UpperCAmelCase__ ) - 1],
temp_ls,
None,
temp_lr,
None,
result_pipe[len(UpperCAmelCase__ ) - 1],
) , ) )
# start the processes
for p in process_array_:
p.start()
# wait for the processes to end and write their values to the list
for p in range(0 , len(UpperCAmelCase__ ) ):
__SCREAMING_SNAKE_CASE = result_pipe[p][0].recv()
process_array_[p].join()
return arr
def _a ( ) -> List[str]:
__SCREAMING_SNAKE_CASE = list(range(10 , 0 , -1 ) )
print('''Initial List''' )
print(*UpperCAmelCase__ )
__SCREAMING_SNAKE_CASE = odd_even_transposition(UpperCAmelCase__ )
print('''Sorted List\n''' )
print(*UpperCAmelCase__ )
if __name__ == "__main__":
main()
| 690 |
"""simple docstring"""
from ....configuration_utils import PretrainedConfig
from ....utils import logging
lowerCAmelCase__ =logging.get_logger(__name__)
lowerCAmelCase__ ={
"Visual-Attention-Network/van-base": (
"https://huggingface.co/Visual-Attention-Network/van-base/blob/main/config.json"
),
}
class A__( __magic_name__ ):
lowerCAmelCase = '''van'''
def __init__( self : int , __SCREAMING_SNAKE_CASE : Optional[Any]=2_24 , __SCREAMING_SNAKE_CASE : Union[str, Any]=3 , __SCREAMING_SNAKE_CASE : Tuple=[7, 3, 3, 3] , __SCREAMING_SNAKE_CASE : Optional[int]=[4, 2, 2, 2] , __SCREAMING_SNAKE_CASE : str=[64, 1_28, 3_20, 5_12] , __SCREAMING_SNAKE_CASE : Optional[Any]=[3, 3, 12, 3] , __SCREAMING_SNAKE_CASE : Dict=[8, 8, 4, 4] , __SCREAMING_SNAKE_CASE : Any="gelu" , __SCREAMING_SNAKE_CASE : Tuple=0.02 , __SCREAMING_SNAKE_CASE : Dict=1E-6 , __SCREAMING_SNAKE_CASE : Any=1E-2 , __SCREAMING_SNAKE_CASE : str=0.0 , __SCREAMING_SNAKE_CASE : Union[str, Any]=0.0 , **__SCREAMING_SNAKE_CASE : str , ) -> List[str]:
"""simple docstring"""
super().__init__(**__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = image_size
__SCREAMING_SNAKE_CASE = num_channels
__SCREAMING_SNAKE_CASE = patch_sizes
__SCREAMING_SNAKE_CASE = strides
__SCREAMING_SNAKE_CASE = hidden_sizes
__SCREAMING_SNAKE_CASE = depths
__SCREAMING_SNAKE_CASE = mlp_ratios
__SCREAMING_SNAKE_CASE = hidden_act
__SCREAMING_SNAKE_CASE = initializer_range
__SCREAMING_SNAKE_CASE = layer_norm_eps
__SCREAMING_SNAKE_CASE = layer_scale_init_value
__SCREAMING_SNAKE_CASE = drop_path_rate
__SCREAMING_SNAKE_CASE = dropout_rate
| 690 | 1 |
"""simple docstring"""
from __future__ import annotations
from collections.abc import Iterator
from typing import Generic, TypeVar
lowerCAmelCase__ =TypeVar("T")
class A__( Generic[T] ):
def __init__( self : str , __SCREAMING_SNAKE_CASE : T ) -> List[Any]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = data
__SCREAMING_SNAKE_CASE = None
def __str__( self : int ) -> str:
"""simple docstring"""
return f"""{self.data}"""
class A__( Generic[T] ):
def __init__( self : Any ) -> None:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = None
def __iter__( self : Optional[int] ) -> Iterator[T]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = self.top
while node:
yield node.data
__SCREAMING_SNAKE_CASE = node.next
def __str__( self : Dict ) -> str:
"""simple docstring"""
return "->".join([str(__SCREAMING_SNAKE_CASE ) for item in self] )
def __len__( self : List[Any] ) -> int:
"""simple docstring"""
return len(tuple(iter(self ) ) )
def _a ( self : Union[str, Any] ) -> bool:
"""simple docstring"""
return self.top is None
def _a ( self : Dict , __SCREAMING_SNAKE_CASE : T ) -> None:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = Node(__SCREAMING_SNAKE_CASE )
if not self.is_empty():
__SCREAMING_SNAKE_CASE = self.top
__SCREAMING_SNAKE_CASE = node
def _a ( self : Optional[int] ) -> T:
"""simple docstring"""
if self.is_empty():
raise IndexError('''pop from empty stack''' )
assert isinstance(self.top , __SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = self.top
__SCREAMING_SNAKE_CASE = self.top.next
return pop_node.data
def _a ( self : Any ) -> T:
"""simple docstring"""
if self.is_empty():
raise IndexError('''peek from empty stack''' )
assert self.top is not None
return self.top.data
def _a ( self : Union[str, Any] ) -> None:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = None
if __name__ == "__main__":
from doctest import testmod
testmod()
| 690 |
"""simple docstring"""
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCAmelCase__ ={"configuration_timm_backbone": ["TimmBackboneConfig"]}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase__ =["TimmBackbone"]
if TYPE_CHECKING:
from .configuration_timm_backbone import TimmBackboneConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_timm_backbone import TimmBackbone
else:
import sys
lowerCAmelCase__ =_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 690 | 1 |
"""simple docstring"""
def _a ( UpperCAmelCase__ ) -> int:
assert (
isinstance(UpperCAmelCase__ , UpperCAmelCase__ ) and number_of_steps > 0
), f"""number_of_steps needs to be positive integer, your input {number_of_steps}"""
if number_of_steps == 1:
return 1
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = 1, 1
for _ in range(number_of_steps - 1 ):
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = current + previous, current
return current
if __name__ == "__main__":
import doctest
doctest.testmod()
| 690 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
lowerCAmelCase__ ={
"configuration_altclip": [
"ALTCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP",
"AltCLIPConfig",
"AltCLIPTextConfig",
"AltCLIPVisionConfig",
],
"processing_altclip": ["AltCLIPProcessor"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase__ =[
"ALTCLIP_PRETRAINED_MODEL_ARCHIVE_LIST",
"AltCLIPPreTrainedModel",
"AltCLIPModel",
"AltCLIPTextModel",
"AltCLIPVisionModel",
]
if TYPE_CHECKING:
from .configuration_altclip import (
ALTCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
AltCLIPConfig,
AltCLIPTextConfig,
AltCLIPVisionConfig,
)
from .processing_altclip import AltCLIPProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_altclip import (
ALTCLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
AltCLIPModel,
AltCLIPPreTrainedModel,
AltCLIPTextModel,
AltCLIPVisionModel,
)
else:
import sys
lowerCAmelCase__ =_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 690 | 1 |
"""simple docstring"""
import json
import logging
import os
import sys
from time import time
from unittest.mock import patch
from transformers.testing_utils import TestCasePlus, require_torch_tpu
logging.basicConfig(level=logging.DEBUG)
lowerCAmelCase__ =logging.getLogger()
def _a ( UpperCAmelCase__ ) -> Dict:
__SCREAMING_SNAKE_CASE = {}
__SCREAMING_SNAKE_CASE = os.path.join(UpperCAmelCase__ , '''all_results.json''' )
if os.path.exists(UpperCAmelCase__ ):
with open(UpperCAmelCase__ , '''r''' ) as f:
__SCREAMING_SNAKE_CASE = json.load(UpperCAmelCase__ )
else:
raise ValueError(f"""can't find {path}""" )
return results
lowerCAmelCase__ =logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
@require_torch_tpu
class A__( __magic_name__ ):
def _a ( self : Optional[Any] ) -> str:
"""simple docstring"""
import xla_spawn
__SCREAMING_SNAKE_CASE = self.get_auto_remove_tmp_dir()
__SCREAMING_SNAKE_CASE = f"""
./examples/pytorch/text-classification/run_glue.py
--num_cores=8
./examples/pytorch/text-classification/run_glue.py
--model_name_or_path distilbert-base-uncased
--output_dir {tmp_dir}
--overwrite_output_dir
--train_file ./tests/fixtures/tests_samples/MRPC/train.csv
--validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv
--do_train
--do_eval
--debug tpu_metrics_debug
--per_device_train_batch_size=2
--per_device_eval_batch_size=1
--learning_rate=1e-4
--max_steps=10
--warmup_steps=2
--seed=42
--max_seq_length=128
""".split()
with patch.object(__SCREAMING_SNAKE_CASE , '''argv''' , __SCREAMING_SNAKE_CASE ):
__SCREAMING_SNAKE_CASE = time()
xla_spawn.main()
__SCREAMING_SNAKE_CASE = time()
__SCREAMING_SNAKE_CASE = get_results(__SCREAMING_SNAKE_CASE )
self.assertGreaterEqual(result['''eval_accuracy'''] , 0.75 )
# Assert that the script takes less than 500 seconds to make sure it doesn't hang.
self.assertLess(end - start , 5_00 )
def _a ( self : Union[str, Any] ) -> Dict:
"""simple docstring"""
import xla_spawn
__SCREAMING_SNAKE_CASE = '''
./tests/test_trainer_tpu.py
--num_cores=8
./tests/test_trainer_tpu.py
'''.split()
with patch.object(__SCREAMING_SNAKE_CASE , '''argv''' , __SCREAMING_SNAKE_CASE ):
xla_spawn.main()
| 690 |
"""simple docstring"""
import json
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
from transformers import (
CONFIG_MAPPING,
IMAGE_PROCESSOR_MAPPING,
AutoConfig,
AutoImageProcessor,
CLIPConfig,
CLIPImageProcessor,
)
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER
sys.path.append(str(Path(__file__).parent.parent.parent.parent / "utils"))
from test_module.custom_configuration import CustomConfig # noqa E402
from test_module.custom_image_processing import CustomImageProcessor # noqa E402
class A__( unittest.TestCase ):
def _a ( self : int ) -> Union[str, Any]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = 0
def _a ( self : Tuple ) -> Tuple:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = AutoImageProcessor.from_pretrained('''openai/clip-vit-base-patch32''' )
self.assertIsInstance(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
def _a ( self : str ) -> Optional[int]:
"""simple docstring"""
with tempfile.TemporaryDirectory() as tmpdirname:
__SCREAMING_SNAKE_CASE = Path(__SCREAMING_SNAKE_CASE ) / '''preprocessor_config.json'''
__SCREAMING_SNAKE_CASE = Path(__SCREAMING_SNAKE_CASE ) / '''config.json'''
json.dump(
{'''image_processor_type''': '''CLIPImageProcessor''', '''processor_class''': '''CLIPProcessor'''} , open(__SCREAMING_SNAKE_CASE , '''w''' ) , )
json.dump({'''model_type''': '''clip'''} , open(__SCREAMING_SNAKE_CASE , '''w''' ) )
__SCREAMING_SNAKE_CASE = AutoImageProcessor.from_pretrained(__SCREAMING_SNAKE_CASE )
self.assertIsInstance(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
def _a ( self : List[str] ) -> Optional[int]:
"""simple docstring"""
with tempfile.TemporaryDirectory() as tmpdirname:
__SCREAMING_SNAKE_CASE = Path(__SCREAMING_SNAKE_CASE ) / '''preprocessor_config.json'''
__SCREAMING_SNAKE_CASE = Path(__SCREAMING_SNAKE_CASE ) / '''config.json'''
json.dump(
{'''feature_extractor_type''': '''CLIPFeatureExtractor''', '''processor_class''': '''CLIPProcessor'''} , open(__SCREAMING_SNAKE_CASE , '''w''' ) , )
json.dump({'''model_type''': '''clip'''} , open(__SCREAMING_SNAKE_CASE , '''w''' ) )
__SCREAMING_SNAKE_CASE = AutoImageProcessor.from_pretrained(__SCREAMING_SNAKE_CASE )
self.assertIsInstance(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
def _a ( self : str ) -> int:
"""simple docstring"""
with tempfile.TemporaryDirectory() as tmpdirname:
__SCREAMING_SNAKE_CASE = CLIPConfig()
# Create a dummy config file with image_proceesor_type
__SCREAMING_SNAKE_CASE = Path(__SCREAMING_SNAKE_CASE ) / '''preprocessor_config.json'''
__SCREAMING_SNAKE_CASE = Path(__SCREAMING_SNAKE_CASE ) / '''config.json'''
json.dump(
{'''image_processor_type''': '''CLIPImageProcessor''', '''processor_class''': '''CLIPProcessor'''} , open(__SCREAMING_SNAKE_CASE , '''w''' ) , )
json.dump({'''model_type''': '''clip'''} , open(__SCREAMING_SNAKE_CASE , '''w''' ) )
# remove image_processor_type to make sure config.json alone is enough to load image processor locally
__SCREAMING_SNAKE_CASE = AutoImageProcessor.from_pretrained(__SCREAMING_SNAKE_CASE ).to_dict()
config_dict.pop('''image_processor_type''' )
__SCREAMING_SNAKE_CASE = CLIPImageProcessor(**__SCREAMING_SNAKE_CASE )
# save in new folder
model_config.save_pretrained(__SCREAMING_SNAKE_CASE )
config.save_pretrained(__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = AutoImageProcessor.from_pretrained(__SCREAMING_SNAKE_CASE )
# make sure private variable is not incorrectly saved
__SCREAMING_SNAKE_CASE = json.loads(config.to_json_string() )
self.assertTrue('''_processor_class''' not in dict_as_saved )
self.assertIsInstance(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
def _a ( self : Optional[int] ) -> Tuple:
"""simple docstring"""
with tempfile.TemporaryDirectory() as tmpdirname:
__SCREAMING_SNAKE_CASE = Path(__SCREAMING_SNAKE_CASE ) / '''preprocessor_config.json'''
json.dump(
{'''image_processor_type''': '''CLIPImageProcessor''', '''processor_class''': '''CLIPProcessor'''} , open(__SCREAMING_SNAKE_CASE , '''w''' ) , )
__SCREAMING_SNAKE_CASE = AutoImageProcessor.from_pretrained(__SCREAMING_SNAKE_CASE )
self.assertIsInstance(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
def _a ( self : List[Any] ) -> str:
"""simple docstring"""
with self.assertRaisesRegex(
__SCREAMING_SNAKE_CASE , '''clip-base is not a local folder and is not a valid model identifier''' ):
__SCREAMING_SNAKE_CASE = AutoImageProcessor.from_pretrained('''clip-base''' )
def _a ( self : Any ) -> Optional[Any]:
"""simple docstring"""
with self.assertRaisesRegex(
__SCREAMING_SNAKE_CASE , r'''aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)''' ):
__SCREAMING_SNAKE_CASE = AutoImageProcessor.from_pretrained(__SCREAMING_SNAKE_CASE , revision='''aaaaaa''' )
def _a ( self : Dict ) -> Dict:
"""simple docstring"""
with self.assertRaisesRegex(
__SCREAMING_SNAKE_CASE , '''hf-internal-testing/config-no-model does not appear to have a file named preprocessor_config.json.''' , ):
__SCREAMING_SNAKE_CASE = AutoImageProcessor.from_pretrained('''hf-internal-testing/config-no-model''' )
def _a ( self : int ) -> Any:
"""simple docstring"""
with self.assertRaises(__SCREAMING_SNAKE_CASE ):
__SCREAMING_SNAKE_CASE = AutoImageProcessor.from_pretrained('''hf-internal-testing/test_dynamic_image_processor''' )
# If remote code is disabled, we can't load this config.
with self.assertRaises(__SCREAMING_SNAKE_CASE ):
__SCREAMING_SNAKE_CASE = AutoImageProcessor.from_pretrained(
'''hf-internal-testing/test_dynamic_image_processor''' , trust_remote_code=__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = AutoImageProcessor.from_pretrained(
'''hf-internal-testing/test_dynamic_image_processor''' , trust_remote_code=__SCREAMING_SNAKE_CASE )
self.assertEqual(image_processor.__class__.__name__ , '''NewImageProcessor''' )
# Test image processor can be reloaded.
with tempfile.TemporaryDirectory() as tmp_dir:
image_processor.save_pretrained(__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = AutoImageProcessor.from_pretrained(__SCREAMING_SNAKE_CASE , trust_remote_code=__SCREAMING_SNAKE_CASE )
self.assertEqual(reloaded_image_processor.__class__.__name__ , '''NewImageProcessor''' )
def _a ( self : Optional[Any] ) -> str:
"""simple docstring"""
try:
AutoConfig.register('''custom''' , __SCREAMING_SNAKE_CASE )
AutoImageProcessor.register(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
# Trying to register something existing in the Transformers library will raise an error
with self.assertRaises(__SCREAMING_SNAKE_CASE ):
AutoImageProcessor.register(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
with tempfile.TemporaryDirectory() as tmpdirname:
__SCREAMING_SNAKE_CASE = Path(__SCREAMING_SNAKE_CASE ) / '''preprocessor_config.json'''
__SCREAMING_SNAKE_CASE = Path(__SCREAMING_SNAKE_CASE ) / '''config.json'''
json.dump(
{'''feature_extractor_type''': '''CLIPFeatureExtractor''', '''processor_class''': '''CLIPProcessor'''} , open(__SCREAMING_SNAKE_CASE , '''w''' ) , )
json.dump({'''model_type''': '''clip'''} , open(__SCREAMING_SNAKE_CASE , '''w''' ) )
__SCREAMING_SNAKE_CASE = CustomImageProcessor.from_pretrained(__SCREAMING_SNAKE_CASE )
# Now that the config is registered, it can be used as any other config with the auto-API
with tempfile.TemporaryDirectory() as tmp_dir:
image_processor.save_pretrained(__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = AutoImageProcessor.from_pretrained(__SCREAMING_SNAKE_CASE )
self.assertIsInstance(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
finally:
if "custom" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["custom"]
if CustomConfig in IMAGE_PROCESSOR_MAPPING._extra_content:
del IMAGE_PROCESSOR_MAPPING._extra_content[CustomConfig]
def _a ( self : int ) -> List[Any]:
"""simple docstring"""
class A__( __magic_name__ ):
lowerCAmelCase = True
try:
AutoConfig.register('''custom''' , __SCREAMING_SNAKE_CASE )
AutoImageProcessor.register(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
# If remote code is not set, the default is to use local
__SCREAMING_SNAKE_CASE = AutoImageProcessor.from_pretrained('''hf-internal-testing/test_dynamic_image_processor''' )
self.assertEqual(image_processor.__class__.__name__ , '''NewImageProcessor''' )
self.assertTrue(image_processor.is_local )
# If remote code is disabled, we load the local one.
__SCREAMING_SNAKE_CASE = AutoImageProcessor.from_pretrained(
'''hf-internal-testing/test_dynamic_image_processor''' , trust_remote_code=__SCREAMING_SNAKE_CASE )
self.assertEqual(image_processor.__class__.__name__ , '''NewImageProcessor''' )
self.assertTrue(image_processor.is_local )
# If remote is enabled, we load from the Hub
__SCREAMING_SNAKE_CASE = AutoImageProcessor.from_pretrained(
'''hf-internal-testing/test_dynamic_image_processor''' , trust_remote_code=__SCREAMING_SNAKE_CASE )
self.assertEqual(image_processor.__class__.__name__ , '''NewImageProcessor''' )
self.assertTrue(not hasattr(__SCREAMING_SNAKE_CASE , '''is_local''' ) )
finally:
if "custom" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["custom"]
if CustomConfig in IMAGE_PROCESSOR_MAPPING._extra_content:
del IMAGE_PROCESSOR_MAPPING._extra_content[CustomConfig]
| 690 | 1 |
"""simple docstring"""
import torch
from diffusers import DDIMParallelScheduler
from .test_schedulers import SchedulerCommonTest
class A__( __magic_name__ ):
lowerCAmelCase = (DDIMParallelScheduler,)
lowerCAmelCase = (('''eta''', 0.0), ('''num_inference_steps''', 50))
def _a ( self : str , **__SCREAMING_SNAKE_CASE : Optional[Any] ) -> int:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = {
'''num_train_timesteps''': 10_00,
'''beta_start''': 0.00_01,
'''beta_end''': 0.02,
'''beta_schedule''': '''linear''',
'''clip_sample''': True,
}
config.update(**__SCREAMING_SNAKE_CASE )
return config
def _a ( self : Any , **__SCREAMING_SNAKE_CASE : List[str] ) -> Union[str, Any]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = self.scheduler_classes[0]
__SCREAMING_SNAKE_CASE = self.get_scheduler_config(**__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = scheduler_class(**__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = 10, 0.0
__SCREAMING_SNAKE_CASE = self.dummy_model()
__SCREAMING_SNAKE_CASE = self.dummy_sample_deter
scheduler.set_timesteps(__SCREAMING_SNAKE_CASE )
for t in scheduler.timesteps:
__SCREAMING_SNAKE_CASE = model(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = scheduler.step(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ).prev_sample
return sample
def _a ( self : List[str] ) -> Any:
"""simple docstring"""
for timesteps in [1_00, 5_00, 10_00]:
self.check_over_configs(num_train_timesteps=__SCREAMING_SNAKE_CASE )
def _a ( self : List[str] ) -> int:
"""simple docstring"""
for steps_offset in [0, 1]:
self.check_over_configs(steps_offset=__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = self.scheduler_classes[0]
__SCREAMING_SNAKE_CASE = self.get_scheduler_config(steps_offset=1 )
__SCREAMING_SNAKE_CASE = scheduler_class(**__SCREAMING_SNAKE_CASE )
scheduler.set_timesteps(5 )
assert torch.equal(scheduler.timesteps , torch.LongTensor([8_01, 6_01, 4_01, 2_01, 1] ) )
def _a ( self : Any ) -> List[Any]:
"""simple docstring"""
for beta_start, beta_end in zip([0.00_01, 0.0_01, 0.01, 0.1] , [0.0_02, 0.02, 0.2, 2] ):
self.check_over_configs(beta_start=__SCREAMING_SNAKE_CASE , beta_end=__SCREAMING_SNAKE_CASE )
def _a ( self : Union[str, Any] ) -> Dict:
"""simple docstring"""
for schedule in ["linear", "squaredcos_cap_v2"]:
self.check_over_configs(beta_schedule=__SCREAMING_SNAKE_CASE )
def _a ( self : str ) -> str:
"""simple docstring"""
for prediction_type in ["epsilon", "v_prediction"]:
self.check_over_configs(prediction_type=__SCREAMING_SNAKE_CASE )
def _a ( self : Any ) -> List[Any]:
"""simple docstring"""
for clip_sample in [True, False]:
self.check_over_configs(clip_sample=__SCREAMING_SNAKE_CASE )
def _a ( self : Union[str, Any] ) -> Optional[Any]:
"""simple docstring"""
for timestep_spacing in ["trailing", "leading"]:
self.check_over_configs(timestep_spacing=__SCREAMING_SNAKE_CASE )
def _a ( self : List[str] ) -> Optional[int]:
"""simple docstring"""
for rescale_betas_zero_snr in [True, False]:
self.check_over_configs(rescale_betas_zero_snr=__SCREAMING_SNAKE_CASE )
def _a ( self : List[str] ) -> Union[str, Any]:
"""simple docstring"""
self.check_over_configs(thresholding=__SCREAMING_SNAKE_CASE )
for threshold in [0.5, 1.0, 2.0]:
for prediction_type in ["epsilon", "v_prediction"]:
self.check_over_configs(
thresholding=__SCREAMING_SNAKE_CASE , prediction_type=__SCREAMING_SNAKE_CASE , sample_max_value=__SCREAMING_SNAKE_CASE , )
def _a ( self : List[Any] ) -> List[str]:
"""simple docstring"""
for t in [1, 10, 49]:
self.check_over_forward(time_step=__SCREAMING_SNAKE_CASE )
def _a ( self : Any ) -> Union[str, Any]:
"""simple docstring"""
for t, num_inference_steps in zip([1, 10, 50] , [10, 50, 5_00] ):
self.check_over_forward(time_step=__SCREAMING_SNAKE_CASE , num_inference_steps=__SCREAMING_SNAKE_CASE )
def _a ( self : Any ) -> Tuple:
"""simple docstring"""
for t, eta in zip([1, 10, 49] , [0.0, 0.5, 1.0] ):
self.check_over_forward(time_step=__SCREAMING_SNAKE_CASE , eta=__SCREAMING_SNAKE_CASE )
def _a ( self : Dict ) -> Optional[int]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = self.scheduler_classes[0]
__SCREAMING_SNAKE_CASE = self.get_scheduler_config()
__SCREAMING_SNAKE_CASE = scheduler_class(**__SCREAMING_SNAKE_CASE )
assert torch.sum(torch.abs(scheduler._get_variance(0 , 0 ) - 0.0 ) ) < 1E-5
assert torch.sum(torch.abs(scheduler._get_variance(4_20 , 4_00 ) - 0.1_47_71 ) ) < 1E-5
assert torch.sum(torch.abs(scheduler._get_variance(9_80 , 9_60 ) - 0.3_24_60 ) ) < 1E-5
assert torch.sum(torch.abs(scheduler._get_variance(0 , 0 ) - 0.0 ) ) < 1E-5
assert torch.sum(torch.abs(scheduler._get_variance(4_87 , 4_86 ) - 0.0_09_79 ) ) < 1E-5
assert torch.sum(torch.abs(scheduler._get_variance(9_99 , 9_98 ) - 0.02 ) ) < 1E-5
def _a ( self : List[Any] ) -> Optional[Any]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = self.scheduler_classes[0]
__SCREAMING_SNAKE_CASE = self.get_scheduler_config()
__SCREAMING_SNAKE_CASE = scheduler_class(**__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = 10, 0.0
scheduler.set_timesteps(__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = self.dummy_model()
__SCREAMING_SNAKE_CASE = self.dummy_sample_deter
__SCREAMING_SNAKE_CASE = self.dummy_sample_deter + 0.1
__SCREAMING_SNAKE_CASE = self.dummy_sample_deter - 0.1
__SCREAMING_SNAKE_CASE = samplea.shape[0]
__SCREAMING_SNAKE_CASE = torch.stack([samplea, samplea, samplea] , dim=0 )
__SCREAMING_SNAKE_CASE = torch.arange(__SCREAMING_SNAKE_CASE )[0:3, None].repeat(1 , __SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = model(samples.flatten(0 , 1 ) , timesteps.flatten(0 , 1 ) )
__SCREAMING_SNAKE_CASE = scheduler.batch_step_no_noise(__SCREAMING_SNAKE_CASE , timesteps.flatten(0 , 1 ) , samples.flatten(0 , 1 ) , __SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = torch.sum(torch.abs(__SCREAMING_SNAKE_CASE ) )
__SCREAMING_SNAKE_CASE = torch.mean(torch.abs(__SCREAMING_SNAKE_CASE ) )
assert abs(result_sum.item() - 11_47.79_04 ) < 1E-2
assert abs(result_mean.item() - 0.49_82 ) < 1E-3
def _a ( self : Union[str, Any] ) -> Dict:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = self.full_loop()
__SCREAMING_SNAKE_CASE = torch.sum(torch.abs(__SCREAMING_SNAKE_CASE ) )
__SCREAMING_SNAKE_CASE = torch.mean(torch.abs(__SCREAMING_SNAKE_CASE ) )
assert abs(result_sum.item() - 1_72.00_67 ) < 1E-2
assert abs(result_mean.item() - 0.22_39_67 ) < 1E-3
def _a ( self : Any ) -> List[str]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = self.full_loop(prediction_type='''v_prediction''' )
__SCREAMING_SNAKE_CASE = torch.sum(torch.abs(__SCREAMING_SNAKE_CASE ) )
__SCREAMING_SNAKE_CASE = torch.mean(torch.abs(__SCREAMING_SNAKE_CASE ) )
assert abs(result_sum.item() - 52.53_02 ) < 1E-2
assert abs(result_mean.item() - 0.06_84 ) < 1E-3
def _a ( self : Optional[int] ) -> Dict:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = self.full_loop(set_alpha_to_one=__SCREAMING_SNAKE_CASE , beta_start=0.01 )
__SCREAMING_SNAKE_CASE = torch.sum(torch.abs(__SCREAMING_SNAKE_CASE ) )
__SCREAMING_SNAKE_CASE = torch.mean(torch.abs(__SCREAMING_SNAKE_CASE ) )
assert abs(result_sum.item() - 1_49.82_95 ) < 1E-2
assert abs(result_mean.item() - 0.19_51 ) < 1E-3
def _a ( self : Union[str, Any] ) -> Optional[int]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = self.full_loop(set_alpha_to_one=__SCREAMING_SNAKE_CASE , beta_start=0.01 )
__SCREAMING_SNAKE_CASE = torch.sum(torch.abs(__SCREAMING_SNAKE_CASE ) )
__SCREAMING_SNAKE_CASE = torch.mean(torch.abs(__SCREAMING_SNAKE_CASE ) )
assert abs(result_sum.item() - 1_49.07_84 ) < 1E-2
assert abs(result_mean.item() - 0.19_41 ) < 1E-3
| 690 |
"""simple docstring"""
import math
lowerCAmelCase__ =10
lowerCAmelCase__ =7
lowerCAmelCase__ =BALLS_PER_COLOUR * NUM_COLOURS
def _a ( UpperCAmelCase__ = 20 ) -> str:
__SCREAMING_SNAKE_CASE = math.comb(UpperCAmelCase__ , UpperCAmelCase__ )
__SCREAMING_SNAKE_CASE = math.comb(NUM_BALLS - BALLS_PER_COLOUR , UpperCAmelCase__ )
__SCREAMING_SNAKE_CASE = NUM_COLOURS * (1 - missing_colour / total)
return f"""{result:.9f}"""
if __name__ == "__main__":
print(solution(20))
| 690 | 1 |
"""simple docstring"""
from __future__ import annotations
lowerCAmelCase__ =[]
def _a ( UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ ) -> bool:
for i in range(len(UpperCAmelCase__ ) ):
if board[row][i] == 1:
return False
for i in range(len(UpperCAmelCase__ ) ):
if board[i][column] == 1:
return False
for i, j in zip(range(UpperCAmelCase__ , -1 , -1 ) , range(UpperCAmelCase__ , -1 , -1 ) ):
if board[i][j] == 1:
return False
for i, j in zip(range(UpperCAmelCase__ , -1 , -1 ) , range(UpperCAmelCase__ , len(UpperCAmelCase__ ) ) ):
if board[i][j] == 1:
return False
return True
def _a ( UpperCAmelCase__ , UpperCAmelCase__ ) -> bool:
if row >= len(UpperCAmelCase__ ):
solution.append(UpperCAmelCase__ )
printboard(UpperCAmelCase__ )
print()
return True
for i in range(len(UpperCAmelCase__ ) ):
if is_safe(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ ):
__SCREAMING_SNAKE_CASE = 1
solve(UpperCAmelCase__ , row + 1 )
__SCREAMING_SNAKE_CASE = 0
return False
def _a ( UpperCAmelCase__ ) -> None:
for i in range(len(UpperCAmelCase__ ) ):
for j in range(len(UpperCAmelCase__ ) ):
if board[i][j] == 1:
print('''Q''' , end=''' ''' )
else:
print('''.''' , end=''' ''' )
print()
# n=int(input("The no. of queens"))
lowerCAmelCase__ =8
lowerCAmelCase__ =[[0 for i in range(n)] for j in range(n)]
solve(board, 0)
print("The total no. of solutions are :", len(solution))
| 690 |
"""simple docstring"""
from collections import UserDict
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_torch_available():
from ..models.auto.modeling_auto import MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING
if is_tf_available():
from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING
from ..tf_utils import stable_softmax
lowerCAmelCase__ =logging.get_logger(__name__)
@add_end_docstrings(__magic_name__ )
class A__( __magic_name__ ):
def __init__( self : Optional[Any] , **__SCREAMING_SNAKE_CASE : str ) -> Optional[Any]:
"""simple docstring"""
super().__init__(**__SCREAMING_SNAKE_CASE )
requires_backends(self , '''vision''' )
self.check_model_type(
TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING
if self.framework == '''tf'''
else MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING )
def __call__( self : Optional[Any] , __SCREAMING_SNAKE_CASE : Union[str, List[str], "Image", List["Image"]] , **__SCREAMING_SNAKE_CASE : Union[str, Any] ) -> Tuple:
"""simple docstring"""
return super().__call__(__SCREAMING_SNAKE_CASE , **__SCREAMING_SNAKE_CASE )
def _a ( self : int , **__SCREAMING_SNAKE_CASE : int ) -> Any:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = {}
if "candidate_labels" in kwargs:
__SCREAMING_SNAKE_CASE = kwargs['''candidate_labels''']
if "hypothesis_template" in kwargs:
__SCREAMING_SNAKE_CASE = kwargs['''hypothesis_template''']
return preprocess_params, {}, {}
def _a ( self : Any , __SCREAMING_SNAKE_CASE : Any , __SCREAMING_SNAKE_CASE : Union[str, Any]=None , __SCREAMING_SNAKE_CASE : Optional[int]="This is a photo of {}." ) -> Union[str, Any]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = load_image(__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = self.image_processor(images=[image] , return_tensors=self.framework )
__SCREAMING_SNAKE_CASE = candidate_labels
__SCREAMING_SNAKE_CASE = [hypothesis_template.format(__SCREAMING_SNAKE_CASE ) for x in candidate_labels]
__SCREAMING_SNAKE_CASE = self.tokenizer(__SCREAMING_SNAKE_CASE , return_tensors=self.framework , padding=__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = [text_inputs]
return inputs
def _a ( self : Dict , __SCREAMING_SNAKE_CASE : List[Any] ) -> int:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = model_inputs.pop('''candidate_labels''' )
__SCREAMING_SNAKE_CASE = model_inputs.pop('''text_inputs''' )
if isinstance(text_inputs[0] , __SCREAMING_SNAKE_CASE ):
__SCREAMING_SNAKE_CASE = text_inputs[0]
else:
# Batching case.
__SCREAMING_SNAKE_CASE = text_inputs[0][0]
__SCREAMING_SNAKE_CASE = self.model(**__SCREAMING_SNAKE_CASE , **__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = {
'''candidate_labels''': candidate_labels,
'''logits''': outputs.logits_per_image,
}
return model_outputs
def _a ( self : Any , __SCREAMING_SNAKE_CASE : List[str] ) -> Optional[int]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = model_outputs.pop('''candidate_labels''' )
__SCREAMING_SNAKE_CASE = model_outputs['''logits'''][0]
if self.framework == "pt":
__SCREAMING_SNAKE_CASE = logits.softmax(dim=-1 ).squeeze(-1 )
__SCREAMING_SNAKE_CASE = probs.tolist()
if not isinstance(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
__SCREAMING_SNAKE_CASE = [scores]
elif self.framework == "tf":
__SCREAMING_SNAKE_CASE = stable_softmax(__SCREAMING_SNAKE_CASE , axis=-1 )
__SCREAMING_SNAKE_CASE = probs.numpy().tolist()
else:
raise ValueError(f"""Unsupported framework: {self.framework}""" )
__SCREAMING_SNAKE_CASE = [
{'''score''': score, '''label''': candidate_label}
for score, candidate_label in sorted(zip(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) , key=lambda __SCREAMING_SNAKE_CASE : -x[0] )
]
return result
| 690 | 1 |
"""simple docstring"""
import torch
from diffusers import DDPMParallelScheduler
from .test_schedulers import SchedulerCommonTest
class A__( __magic_name__ ):
lowerCAmelCase = (DDPMParallelScheduler,)
def _a ( self : Tuple , **__SCREAMING_SNAKE_CASE : Dict ) -> Dict:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = {
'''num_train_timesteps''': 10_00,
'''beta_start''': 0.00_01,
'''beta_end''': 0.02,
'''beta_schedule''': '''linear''',
'''variance_type''': '''fixed_small''',
'''clip_sample''': True,
}
config.update(**__SCREAMING_SNAKE_CASE )
return config
def _a ( self : Any ) -> str:
"""simple docstring"""
for timesteps in [1, 5, 1_00, 10_00]:
self.check_over_configs(num_train_timesteps=__SCREAMING_SNAKE_CASE )
def _a ( self : Any ) -> Optional[Any]:
"""simple docstring"""
for beta_start, beta_end in zip([0.00_01, 0.0_01, 0.01, 0.1] , [0.0_02, 0.02, 0.2, 2] ):
self.check_over_configs(beta_start=__SCREAMING_SNAKE_CASE , beta_end=__SCREAMING_SNAKE_CASE )
def _a ( self : Tuple ) -> Tuple:
"""simple docstring"""
for schedule in ["linear", "squaredcos_cap_v2"]:
self.check_over_configs(beta_schedule=__SCREAMING_SNAKE_CASE )
def _a ( self : Union[str, Any] ) -> Any:
"""simple docstring"""
for variance in ["fixed_small", "fixed_large", "other"]:
self.check_over_configs(variance_type=__SCREAMING_SNAKE_CASE )
def _a ( self : int ) -> Dict:
"""simple docstring"""
for clip_sample in [True, False]:
self.check_over_configs(clip_sample=__SCREAMING_SNAKE_CASE )
def _a ( self : Union[str, Any] ) -> Optional[int]:
"""simple docstring"""
self.check_over_configs(thresholding=__SCREAMING_SNAKE_CASE )
for threshold in [0.5, 1.0, 2.0]:
for prediction_type in ["epsilon", "sample", "v_prediction"]:
self.check_over_configs(
thresholding=__SCREAMING_SNAKE_CASE , prediction_type=__SCREAMING_SNAKE_CASE , sample_max_value=__SCREAMING_SNAKE_CASE , )
def _a ( self : Optional[Any] ) -> List[str]:
"""simple docstring"""
for prediction_type in ["epsilon", "sample", "v_prediction"]:
self.check_over_configs(prediction_type=__SCREAMING_SNAKE_CASE )
def _a ( self : Optional[int] ) -> Dict:
"""simple docstring"""
for t in [0, 5_00, 9_99]:
self.check_over_forward(time_step=__SCREAMING_SNAKE_CASE )
def _a ( self : Dict ) -> List[str]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = self.scheduler_classes[0]
__SCREAMING_SNAKE_CASE = self.get_scheduler_config()
__SCREAMING_SNAKE_CASE = scheduler_class(**__SCREAMING_SNAKE_CASE )
assert torch.sum(torch.abs(scheduler._get_variance(0 ) - 0.0 ) ) < 1E-5
assert torch.sum(torch.abs(scheduler._get_variance(4_87 ) - 0.0_09_79 ) ) < 1E-5
assert torch.sum(torch.abs(scheduler._get_variance(9_99 ) - 0.02 ) ) < 1E-5
def _a ( self : str ) -> List[str]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = self.scheduler_classes[0]
__SCREAMING_SNAKE_CASE = self.get_scheduler_config()
__SCREAMING_SNAKE_CASE = scheduler_class(**__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = len(__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = self.dummy_model()
__SCREAMING_SNAKE_CASE = self.dummy_sample_deter
__SCREAMING_SNAKE_CASE = self.dummy_sample_deter + 0.1
__SCREAMING_SNAKE_CASE = self.dummy_sample_deter - 0.1
__SCREAMING_SNAKE_CASE = samplea.shape[0]
__SCREAMING_SNAKE_CASE = torch.stack([samplea, samplea, samplea] , dim=0 )
__SCREAMING_SNAKE_CASE = torch.arange(__SCREAMING_SNAKE_CASE )[0:3, None].repeat(1 , __SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = model(samples.flatten(0 , 1 ) , timesteps.flatten(0 , 1 ) )
__SCREAMING_SNAKE_CASE = scheduler.batch_step_no_noise(__SCREAMING_SNAKE_CASE , timesteps.flatten(0 , 1 ) , samples.flatten(0 , 1 ) )
__SCREAMING_SNAKE_CASE = torch.sum(torch.abs(__SCREAMING_SNAKE_CASE ) )
__SCREAMING_SNAKE_CASE = torch.mean(torch.abs(__SCREAMING_SNAKE_CASE ) )
assert abs(result_sum.item() - 11_53.18_33 ) < 1E-2
assert abs(result_mean.item() - 0.50_05 ) < 1E-3
def _a ( self : Optional[Any] ) -> List[str]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = self.scheduler_classes[0]
__SCREAMING_SNAKE_CASE = self.get_scheduler_config()
__SCREAMING_SNAKE_CASE = scheduler_class(**__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = len(__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = self.dummy_model()
__SCREAMING_SNAKE_CASE = self.dummy_sample_deter
__SCREAMING_SNAKE_CASE = torch.manual_seed(0 )
for t in reversed(range(__SCREAMING_SNAKE_CASE ) ):
# 1. predict noise residual
__SCREAMING_SNAKE_CASE = model(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
# 2. predict previous mean of sample x_t-1
__SCREAMING_SNAKE_CASE = scheduler.step(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , generator=__SCREAMING_SNAKE_CASE ).prev_sample
__SCREAMING_SNAKE_CASE = pred_prev_sample
__SCREAMING_SNAKE_CASE = torch.sum(torch.abs(__SCREAMING_SNAKE_CASE ) )
__SCREAMING_SNAKE_CASE = torch.mean(torch.abs(__SCREAMING_SNAKE_CASE ) )
assert abs(result_sum.item() - 2_58.96_06 ) < 1E-2
assert abs(result_mean.item() - 0.33_72 ) < 1E-3
def _a ( self : List[str] ) -> Union[str, Any]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = self.scheduler_classes[0]
__SCREAMING_SNAKE_CASE = self.get_scheduler_config(prediction_type='''v_prediction''' )
__SCREAMING_SNAKE_CASE = scheduler_class(**__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = len(__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = self.dummy_model()
__SCREAMING_SNAKE_CASE = self.dummy_sample_deter
__SCREAMING_SNAKE_CASE = torch.manual_seed(0 )
for t in reversed(range(__SCREAMING_SNAKE_CASE ) ):
# 1. predict noise residual
__SCREAMING_SNAKE_CASE = model(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
# 2. predict previous mean of sample x_t-1
__SCREAMING_SNAKE_CASE = scheduler.step(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , generator=__SCREAMING_SNAKE_CASE ).prev_sample
__SCREAMING_SNAKE_CASE = pred_prev_sample
__SCREAMING_SNAKE_CASE = torch.sum(torch.abs(__SCREAMING_SNAKE_CASE ) )
__SCREAMING_SNAKE_CASE = torch.mean(torch.abs(__SCREAMING_SNAKE_CASE ) )
assert abs(result_sum.item() - 2_02.02_96 ) < 1E-2
assert abs(result_mean.item() - 0.26_31 ) < 1E-3
def _a ( self : Dict ) -> Any:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = self.scheduler_classes[0]
__SCREAMING_SNAKE_CASE = self.get_scheduler_config()
__SCREAMING_SNAKE_CASE = scheduler_class(**__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = [1_00, 87, 50, 1, 0]
scheduler.set_timesteps(timesteps=__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = scheduler.timesteps
for i, timestep in enumerate(__SCREAMING_SNAKE_CASE ):
if i == len(__SCREAMING_SNAKE_CASE ) - 1:
__SCREAMING_SNAKE_CASE = -1
else:
__SCREAMING_SNAKE_CASE = timesteps[i + 1]
__SCREAMING_SNAKE_CASE = scheduler.previous_timestep(__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = prev_t.item()
self.assertEqual(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
def _a ( self : List[Any] ) -> Union[str, Any]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = self.scheduler_classes[0]
__SCREAMING_SNAKE_CASE = self.get_scheduler_config()
__SCREAMING_SNAKE_CASE = scheduler_class(**__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = [1_00, 87, 50, 51, 0]
with self.assertRaises(__SCREAMING_SNAKE_CASE , msg='''`custom_timesteps` must be in descending order.''' ):
scheduler.set_timesteps(timesteps=__SCREAMING_SNAKE_CASE )
def _a ( self : Optional[Any] ) -> Tuple:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = self.scheduler_classes[0]
__SCREAMING_SNAKE_CASE = self.get_scheduler_config()
__SCREAMING_SNAKE_CASE = scheduler_class(**__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = [1_00, 87, 50, 1, 0]
__SCREAMING_SNAKE_CASE = len(__SCREAMING_SNAKE_CASE )
with self.assertRaises(__SCREAMING_SNAKE_CASE , msg='''Can only pass one of `num_inference_steps` or `custom_timesteps`.''' ):
scheduler.set_timesteps(num_inference_steps=__SCREAMING_SNAKE_CASE , timesteps=__SCREAMING_SNAKE_CASE )
def _a ( self : Tuple ) -> Optional[Any]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = self.scheduler_classes[0]
__SCREAMING_SNAKE_CASE = self.get_scheduler_config()
__SCREAMING_SNAKE_CASE = scheduler_class(**__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = [scheduler.config.num_train_timesteps]
with self.assertRaises(
__SCREAMING_SNAKE_CASE , msg='''`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}''' , ):
scheduler.set_timesteps(timesteps=__SCREAMING_SNAKE_CASE )
| 690 |
"""simple docstring"""
from __future__ import annotations
from collections.abc import Callable
lowerCAmelCase__ =list[list[float | int]]
def _a ( UpperCAmelCase__ , UpperCAmelCase__ ) -> Matrix:
__SCREAMING_SNAKE_CASE = len(UpperCAmelCase__ )
__SCREAMING_SNAKE_CASE = [[0 for _ in range(size + 1 )] for _ in range(UpperCAmelCase__ )]
__SCREAMING_SNAKE_CASE = 42
__SCREAMING_SNAKE_CASE = 42
__SCREAMING_SNAKE_CASE = 42
__SCREAMING_SNAKE_CASE = 42
__SCREAMING_SNAKE_CASE = 42
__SCREAMING_SNAKE_CASE = 42
for row in range(UpperCAmelCase__ ):
for col in range(UpperCAmelCase__ ):
__SCREAMING_SNAKE_CASE = matrix[row][col]
__SCREAMING_SNAKE_CASE = vector[row][0]
__SCREAMING_SNAKE_CASE = 0
__SCREAMING_SNAKE_CASE = 0
while row < size and col < size:
# pivoting
__SCREAMING_SNAKE_CASE = max((abs(augmented[rowa][col] ), rowa) for rowa in range(UpperCAmelCase__ , UpperCAmelCase__ ) )[
1
]
if augmented[pivot_row][col] == 0:
col += 1
continue
else:
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = augmented[pivot_row], augmented[row]
for rowa in range(row + 1 , UpperCAmelCase__ ):
__SCREAMING_SNAKE_CASE = augmented[rowa][col] / augmented[row][col]
__SCREAMING_SNAKE_CASE = 0
for cola in range(col + 1 , size + 1 ):
augmented[rowa][cola] -= augmented[row][cola] * ratio
row += 1
col += 1
# back substitution
for col in range(1 , UpperCAmelCase__ ):
for row in range(UpperCAmelCase__ ):
__SCREAMING_SNAKE_CASE = augmented[row][col] / augmented[col][col]
for cola in range(UpperCAmelCase__ , size + 1 ):
augmented[row][cola] -= augmented[col][cola] * ratio
# round to get rid of numbers like 2.000000000000004
return [
[round(augmented[row][size] / augmented[row][row] , 10 )] for row in range(UpperCAmelCase__ )
]
def _a ( UpperCAmelCase__ ) -> Callable[[int], int]:
__SCREAMING_SNAKE_CASE = len(UpperCAmelCase__ )
__SCREAMING_SNAKE_CASE = [[0 for _ in range(UpperCAmelCase__ )] for _ in range(UpperCAmelCase__ )]
__SCREAMING_SNAKE_CASE = [[0] for _ in range(UpperCAmelCase__ )]
__SCREAMING_SNAKE_CASE = 42
__SCREAMING_SNAKE_CASE = 42
__SCREAMING_SNAKE_CASE = 42
__SCREAMING_SNAKE_CASE = 42
for x_val, y_val in enumerate(UpperCAmelCase__ ):
for col in range(UpperCAmelCase__ ):
__SCREAMING_SNAKE_CASE = (x_val + 1) ** (size - col - 1)
__SCREAMING_SNAKE_CASE = y_val
__SCREAMING_SNAKE_CASE = solve(UpperCAmelCase__ , UpperCAmelCase__ )
def interpolated_func(UpperCAmelCase__ ) -> int:
return sum(
round(coeffs[x_val][0] ) * (var ** (size - x_val - 1))
for x_val in range(UpperCAmelCase__ ) )
return interpolated_func
def _a ( UpperCAmelCase__ ) -> int:
return (
1
- variable
+ variable**2
- variable**3
+ variable**4
- variable**5
+ variable**6
- variable**7
+ variable**8
- variable**9
+ variable**10
)
def _a ( UpperCAmelCase__ = question_function , UpperCAmelCase__ = 10 ) -> int:
__SCREAMING_SNAKE_CASE = [func(UpperCAmelCase__ ) for x_val in range(1 , order + 1 )]
__SCREAMING_SNAKE_CASE = [
interpolate(data_points[:max_coeff] ) for max_coeff in range(1 , order + 1 )
]
__SCREAMING_SNAKE_CASE = 0
__SCREAMING_SNAKE_CASE = 42
__SCREAMING_SNAKE_CASE = 42
for poly in polynomials:
__SCREAMING_SNAKE_CASE = 1
while func(UpperCAmelCase__ ) == poly(UpperCAmelCase__ ):
x_val += 1
ret += poly(UpperCAmelCase__ )
return ret
if __name__ == "__main__":
print(F'''{solution() = }''')
| 690 | 1 |
"""simple docstring"""
import re
import jax.numpy as jnp
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey
from ..utils import logging
lowerCAmelCase__ =logging.get_logger(__name__)
def _a ( UpperCAmelCase__ ) -> str:
__SCREAMING_SNAKE_CASE = r'''\w+[.]\d+'''
__SCREAMING_SNAKE_CASE = re.findall(UpperCAmelCase__ , UpperCAmelCase__ )
for pat in pats:
__SCREAMING_SNAKE_CASE = key.replace(UpperCAmelCase__ , '''_'''.join(pat.split('''.''' ) ) )
return key
def _a ( UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ ) -> Union[str, Any]:
__SCREAMING_SNAKE_CASE = pt_tuple_key[:-1] + ('''scale''',)
if (
any('''norm''' in str_ for str_ in pt_tuple_key )
and (pt_tuple_key[-1] == "bias")
and (pt_tuple_key[:-1] + ("bias",) not in random_flax_state_dict)
and (pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict)
):
__SCREAMING_SNAKE_CASE = pt_tuple_key[:-1] + ('''scale''',)
return renamed_pt_tuple_key, pt_tensor
elif pt_tuple_key[-1] in ["weight", "gamma"] and pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict:
__SCREAMING_SNAKE_CASE = pt_tuple_key[:-1] + ('''scale''',)
return renamed_pt_tuple_key, pt_tensor
# embedding
if pt_tuple_key[-1] == "weight" and pt_tuple_key[:-1] + ("embedding",) in random_flax_state_dict:
__SCREAMING_SNAKE_CASE = pt_tuple_key[:-1] + ('''embedding''',)
return renamed_pt_tuple_key, pt_tensor
# conv layer
__SCREAMING_SNAKE_CASE = pt_tuple_key[:-1] + ('''kernel''',)
if pt_tuple_key[-1] == "weight" and pt_tensor.ndim == 4:
__SCREAMING_SNAKE_CASE = pt_tensor.transpose(2 , 3 , 1 , 0 )
return renamed_pt_tuple_key, pt_tensor
# linear layer
__SCREAMING_SNAKE_CASE = pt_tuple_key[:-1] + ('''kernel''',)
if pt_tuple_key[-1] == "weight":
__SCREAMING_SNAKE_CASE = pt_tensor.T
return renamed_pt_tuple_key, pt_tensor
# old PyTorch layer norm weight
__SCREAMING_SNAKE_CASE = pt_tuple_key[:-1] + ('''weight''',)
if pt_tuple_key[-1] == "gamma":
return renamed_pt_tuple_key, pt_tensor
# old PyTorch layer norm bias
__SCREAMING_SNAKE_CASE = pt_tuple_key[:-1] + ('''bias''',)
if pt_tuple_key[-1] == "beta":
return renamed_pt_tuple_key, pt_tensor
return pt_tuple_key, pt_tensor
def _a ( UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__=42 ) -> Tuple:
# Step 1: Convert pytorch tensor to numpy
__SCREAMING_SNAKE_CASE = {k: v.numpy() for k, v in pt_state_dict.items()}
# Step 2: Since the model is stateless, get random Flax params
__SCREAMING_SNAKE_CASE = flax_model.init_weights(PRNGKey(UpperCAmelCase__ ) )
__SCREAMING_SNAKE_CASE = flatten_dict(UpperCAmelCase__ )
__SCREAMING_SNAKE_CASE = {}
# Need to change some parameters name to match Flax names
for pt_key, pt_tensor in pt_state_dict.items():
__SCREAMING_SNAKE_CASE = rename_key(UpperCAmelCase__ )
__SCREAMING_SNAKE_CASE = tuple(renamed_pt_key.split('''.''' ) )
# Correctly rename weight parameters
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = rename_key_and_reshape_tensor(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ )
if flax_key in random_flax_state_dict:
if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
raise ValueError(
f"""PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape """
f"""{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}.""" )
# also add unexpected weight so that warning is thrown
__SCREAMING_SNAKE_CASE = jnp.asarray(UpperCAmelCase__ )
return unflatten_dict(UpperCAmelCase__ )
| 690 |
"""simple docstring"""
from json import JSONDecodeError # Workaround for requests.exceptions.JSONDecodeError
import requests
def _a ( UpperCAmelCase__ = "isbn/0140328726" ) -> dict:
__SCREAMING_SNAKE_CASE = olid.strip().strip('''/''' ) # Remove leading/trailing whitespace & slashes
if new_olid.count('''/''' ) != 1:
__SCREAMING_SNAKE_CASE = f"""{olid} is not a valid Open Library olid"""
raise ValueError(UpperCAmelCase__ )
return requests.get(f"""https://openlibrary.org/{new_olid}.json""" ).json()
def _a ( UpperCAmelCase__ ) -> dict:
__SCREAMING_SNAKE_CASE = {
'''title''': '''Title''',
'''publish_date''': '''Publish date''',
'''authors''': '''Authors''',
'''number_of_pages''': '''Number of pages:''',
'''first_sentence''': '''First sentence''',
'''isbn_10''': '''ISBN (10)''',
'''isbn_13''': '''ISBN (13)''',
}
__SCREAMING_SNAKE_CASE = {better_key: ol_book_data[key] for key, better_key in desired_keys.items()}
__SCREAMING_SNAKE_CASE = [
get_openlibrary_data(author['''key'''] )['''name'''] for author in data['''Authors''']
]
__SCREAMING_SNAKE_CASE = data['''First sentence''']['''value''']
for key, value in data.items():
if isinstance(UpperCAmelCase__ , UpperCAmelCase__ ):
__SCREAMING_SNAKE_CASE = ''', '''.join(UpperCAmelCase__ )
return data
if __name__ == "__main__":
import doctest
doctest.testmod()
while True:
lowerCAmelCase__ =input("\nEnter the ISBN code to search (or 'quit' to stop): ").strip()
if isbn.lower() in ("", "q", "quit", "exit", "stop"):
break
if len(isbn) not in (10, 13) or not isbn.isdigit():
print(F'''Sorry, {isbn} is not a valid ISBN. Please, input a valid ISBN.''')
continue
print(F'''\nSearching Open Library for ISBN: {isbn}...\n''')
try:
lowerCAmelCase__ =summarize_book(get_openlibrary_data(F'''isbn/{isbn}'''))
print("\n".join(F'''{key}: {value}''' for key, value in book_summary.items()))
except JSONDecodeError: # Workaround for requests.exceptions.RequestException:
print(F'''Sorry, there are no results for ISBN: {isbn}.''')
| 690 | 1 |
"""simple docstring"""
import re
from filelock import FileLock
try:
import nltk
lowerCAmelCase__ =True
except (ImportError, ModuleNotFoundError):
lowerCAmelCase__ =False
if NLTK_AVAILABLE:
with FileLock(".lock") as lock:
nltk.download("punkt", quiet=True)
def _a ( UpperCAmelCase__ ) -> str:
re.sub('''<n>''' , '''''' , UpperCAmelCase__ ) # remove pegasus newline char
assert NLTK_AVAILABLE, "nltk must be installed to separate newlines between sentences. (pip install nltk)"
return "\n".join(nltk.sent_tokenize(UpperCAmelCase__ ) )
| 690 |
"""simple docstring"""
from math import ceil
from typing import List, Optional, Union
import numpy as np
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import BatchFeature, SequenceFeatureExtractor
from ...utils import TensorType, logging
lowerCAmelCase__ =logging.get_logger(__name__)
class A__( __magic_name__ ):
lowerCAmelCase = ['''audio_values''', '''audio_mask''']
def __init__( self : Dict , __SCREAMING_SNAKE_CASE : Optional[Any]=20_48 , __SCREAMING_SNAKE_CASE : str=1 , __SCREAMING_SNAKE_CASE : List[Any]=[16, 16] , __SCREAMING_SNAKE_CASE : Union[str, Any]=1_28 , __SCREAMING_SNAKE_CASE : int=4_41_00 , __SCREAMING_SNAKE_CASE : Union[str, Any]=86 , __SCREAMING_SNAKE_CASE : str=20_48 , __SCREAMING_SNAKE_CASE : Optional[Any]=0.0 , **__SCREAMING_SNAKE_CASE : Optional[int] , ) -> Any:
"""simple docstring"""
super().__init__(
feature_size=__SCREAMING_SNAKE_CASE , sampling_rate=__SCREAMING_SNAKE_CASE , padding_value=__SCREAMING_SNAKE_CASE , **__SCREAMING_SNAKE_CASE , )
__SCREAMING_SNAKE_CASE = spectrogram_length
__SCREAMING_SNAKE_CASE = num_channels
__SCREAMING_SNAKE_CASE = patch_size
__SCREAMING_SNAKE_CASE = feature_size // self.patch_size[1]
__SCREAMING_SNAKE_CASE = n_fft
__SCREAMING_SNAKE_CASE = sampling_rate // hop_length_to_sampling_rate
__SCREAMING_SNAKE_CASE = sampling_rate
__SCREAMING_SNAKE_CASE = padding_value
__SCREAMING_SNAKE_CASE = mel_filter_bank(
num_frequency_bins=1 + n_fft // 2 , num_mel_filters=__SCREAMING_SNAKE_CASE , min_frequency=0.0 , max_frequency=2_20_50.0 , sampling_rate=__SCREAMING_SNAKE_CASE , norm='''slaney''' , mel_scale='''slaney''' , ).T
def _a ( self : str , __SCREAMING_SNAKE_CASE : np.array ) -> np.ndarray:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = spectrogram(
__SCREAMING_SNAKE_CASE , window_function(self.n_fft , '''hann''' ) , frame_length=self.n_fft , hop_length=self.hop_length , power=2.0 , mel_filters=self.mel_filters.T , log_mel='''dB''' , db_range=80.0 , )
__SCREAMING_SNAKE_CASE = log_spec[:, :-1]
__SCREAMING_SNAKE_CASE = log_spec - 20.0
__SCREAMING_SNAKE_CASE = np.clip(log_spec / 40.0 , -2.0 , 0.0 ) + 1.0
return log_spec
def __call__( self : str , __SCREAMING_SNAKE_CASE : Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]] , __SCREAMING_SNAKE_CASE : Optional[Union[str, TensorType]] = None , __SCREAMING_SNAKE_CASE : Optional[bool] = True , __SCREAMING_SNAKE_CASE : Optional[int] = None , __SCREAMING_SNAKE_CASE : bool = False , __SCREAMING_SNAKE_CASE : bool = False , **__SCREAMING_SNAKE_CASE : Tuple , ) -> BatchFeature:
"""simple docstring"""
if sampling_rate is not None:
if sampling_rate != self.sampling_rate:
raise ValueError(
'''This feature extractor is set to support sampling rate'''
f""" of {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled"""
f""" with {self.sampling_rate} and not {sampling_rate}.""" )
else:
logger.warning(
'''It is strongly recommended to pass the `sampling_rate` argument to this function. '''
'''Failing to do so can result in silent errors that might be hard to debug.''' )
__SCREAMING_SNAKE_CASE = isinstance(__SCREAMING_SNAKE_CASE , np.ndarray ) and len(raw_speech.shape ) > 1
if is_batched_numpy and len(raw_speech.shape ) > 2:
raise ValueError(f"""Only mono-channel audio is supported for input to {self}""" )
__SCREAMING_SNAKE_CASE = is_batched_numpy or (
isinstance(__SCREAMING_SNAKE_CASE , (list, tuple) ) and (isinstance(raw_speech[0] , (np.ndarray, tuple, list) ))
)
if is_batched:
__SCREAMING_SNAKE_CASE = [np.asarray([speech] , dtype=np.floataa ).T for speech in raw_speech]
elif not is_batched and not isinstance(__SCREAMING_SNAKE_CASE , np.ndarray ):
__SCREAMING_SNAKE_CASE = np.asarray(__SCREAMING_SNAKE_CASE , dtype=np.floataa )
elif isinstance(__SCREAMING_SNAKE_CASE , np.ndarray ) and raw_speech.dtype is np.dtype(np.floataa ):
__SCREAMING_SNAKE_CASE = raw_speech.astype(np.floataa )
# always return batch
if not is_batched:
__SCREAMING_SNAKE_CASE = [np.asarray([raw_speech] ).T]
# Convert audio signals to log mel spectrograms, truncate by time axis
__SCREAMING_SNAKE_CASE = [
self._np_extract_fbank_features(waveform.squeeze() ).T[: self.spectrogram_length] for waveform in raw_speech
]
if isinstance(audio_features[0] , __SCREAMING_SNAKE_CASE ):
__SCREAMING_SNAKE_CASE = [np.asarray(__SCREAMING_SNAKE_CASE , dtype=np.floataa ) for feature in audio_features]
# Create audio attention mask
__SCREAMING_SNAKE_CASE = max(
[ceil(feature.shape[0] / self.patch_size[0] ) * self.freq_len for feature in audio_features] ) # The maximum number of audio patches in a batch
if return_attention_mask:
__SCREAMING_SNAKE_CASE = [
(ceil(feature.shape[0] / self.patch_size[0] ) * self.freq_len) * [1]
+ (max_patch_len - ceil(feature.shape[0] / self.patch_size[0] ) * self.freq_len) * [0]
for feature in audio_features
]
__SCREAMING_SNAKE_CASE = np.array(__SCREAMING_SNAKE_CASE ).astype(np.floataa )
# convert into correct format for padding
__SCREAMING_SNAKE_CASE = max_patch_len // self.freq_len * self.patch_size[0] # The maximum audio size in a batch
__SCREAMING_SNAKE_CASE = np.ones([len(__SCREAMING_SNAKE_CASE ), 1, max_time_len, self.feature_size] ).astype(np.floataa )
__SCREAMING_SNAKE_CASE = padded_audio_features * self.padding_value
for i in range(len(__SCREAMING_SNAKE_CASE ) ):
__SCREAMING_SNAKE_CASE = audio_features[i]
__SCREAMING_SNAKE_CASE = feature
# return as BatchFeature
if return_attention_mask:
__SCREAMING_SNAKE_CASE = {'''audio_values''': padded_audio_features, '''audio_mask''': audio_mask}
else:
__SCREAMING_SNAKE_CASE = {'''audio_values''': padded_audio_features}
__SCREAMING_SNAKE_CASE = BatchFeature(data=__SCREAMING_SNAKE_CASE , tensor_type=__SCREAMING_SNAKE_CASE )
return encoded_inputs
| 690 | 1 |
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import AutoTokenizer, MBartConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import TFAutoModelForSeqaSeqLM, TFMBartForConditionalGeneration, TFMBartModel
@require_tf
class A__:
lowerCAmelCase = MBartConfig
lowerCAmelCase = {}
lowerCAmelCase = '''gelu'''
def __init__( self : List[str] , __SCREAMING_SNAKE_CASE : Optional[int] , __SCREAMING_SNAKE_CASE : Tuple=13 , __SCREAMING_SNAKE_CASE : Dict=7 , __SCREAMING_SNAKE_CASE : List[Any]=True , __SCREAMING_SNAKE_CASE : Any=False , __SCREAMING_SNAKE_CASE : Union[str, Any]=99 , __SCREAMING_SNAKE_CASE : Optional[Any]=32 , __SCREAMING_SNAKE_CASE : Optional[int]=2 , __SCREAMING_SNAKE_CASE : Any=4 , __SCREAMING_SNAKE_CASE : List[str]=37 , __SCREAMING_SNAKE_CASE : Optional[Any]=0.1 , __SCREAMING_SNAKE_CASE : Dict=0.1 , __SCREAMING_SNAKE_CASE : Any=20 , __SCREAMING_SNAKE_CASE : List[Any]=2 , __SCREAMING_SNAKE_CASE : Optional[int]=1 , __SCREAMING_SNAKE_CASE : Optional[Any]=0 , ) -> Optional[int]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = parent
__SCREAMING_SNAKE_CASE = batch_size
__SCREAMING_SNAKE_CASE = seq_length
__SCREAMING_SNAKE_CASE = is_training
__SCREAMING_SNAKE_CASE = use_labels
__SCREAMING_SNAKE_CASE = vocab_size
__SCREAMING_SNAKE_CASE = hidden_size
__SCREAMING_SNAKE_CASE = num_hidden_layers
__SCREAMING_SNAKE_CASE = num_attention_heads
__SCREAMING_SNAKE_CASE = intermediate_size
__SCREAMING_SNAKE_CASE = hidden_dropout_prob
__SCREAMING_SNAKE_CASE = attention_probs_dropout_prob
__SCREAMING_SNAKE_CASE = max_position_embeddings
__SCREAMING_SNAKE_CASE = eos_token_id
__SCREAMING_SNAKE_CASE = pad_token_id
__SCREAMING_SNAKE_CASE = bos_token_id
def _a ( self : List[str] ) -> Optional[int]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = ids_tensor([self.batch_size, self.seq_length - 1] , self.vocab_size )
__SCREAMING_SNAKE_CASE = tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size ) , 1 )
__SCREAMING_SNAKE_CASE = tf.concat([input_ids, eos_tensor] , axis=1 )
__SCREAMING_SNAKE_CASE = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
__SCREAMING_SNAKE_CASE = self.config_cls(
vocab_size=self.vocab_size , d_model=self.hidden_size , encoder_layers=self.num_hidden_layers , decoder_layers=self.num_hidden_layers , encoder_attention_heads=self.num_attention_heads , decoder_attention_heads=self.num_attention_heads , encoder_ffn_dim=self.intermediate_size , decoder_ffn_dim=self.intermediate_size , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , eos_token_ids=[2] , bos_token_id=self.bos_token_id , pad_token_id=self.pad_token_id , decoder_start_token_id=self.pad_token_id , **self.config_updates , )
__SCREAMING_SNAKE_CASE = prepare_mbart_inputs_dict(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
return config, inputs_dict
def _a ( self : Tuple , __SCREAMING_SNAKE_CASE : str , __SCREAMING_SNAKE_CASE : Optional[int] ) -> Optional[Any]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = TFMBartModel(config=__SCREAMING_SNAKE_CASE ).get_decoder()
__SCREAMING_SNAKE_CASE = inputs_dict['''input_ids''']
__SCREAMING_SNAKE_CASE = input_ids[:1, :]
__SCREAMING_SNAKE_CASE = inputs_dict['''attention_mask'''][:1, :]
__SCREAMING_SNAKE_CASE = inputs_dict['''head_mask''']
__SCREAMING_SNAKE_CASE = 1
# first forward pass
__SCREAMING_SNAKE_CASE = model(__SCREAMING_SNAKE_CASE , attention_mask=__SCREAMING_SNAKE_CASE , head_mask=__SCREAMING_SNAKE_CASE , use_cache=__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = outputs.to_tuple()
__SCREAMING_SNAKE_CASE = past_key_values[1]
def _a ( UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__=None , UpperCAmelCase__=None , UpperCAmelCase__=None , UpperCAmelCase__=None , UpperCAmelCase__=None , ) -> Optional[int]:
if attention_mask is None:
__SCREAMING_SNAKE_CASE = tf.cast(tf.math.not_equal(UpperCAmelCase__ , config.pad_token_id ) , tf.inta )
if decoder_attention_mask is None:
__SCREAMING_SNAKE_CASE = tf.concat(
[
tf.ones(decoder_input_ids[:, :1].shape , dtype=tf.inta ),
tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:] , config.pad_token_id ) , tf.inta ),
] , axis=-1 , )
if head_mask is None:
__SCREAMING_SNAKE_CASE = tf.ones((config.encoder_layers, config.encoder_attention_heads) )
if decoder_head_mask is None:
__SCREAMING_SNAKE_CASE = tf.ones((config.decoder_layers, config.decoder_attention_heads) )
if cross_attn_head_mask is None:
__SCREAMING_SNAKE_CASE = tf.ones((config.decoder_layers, config.decoder_attention_heads) )
return {
"input_ids": input_ids,
"decoder_input_ids": decoder_input_ids,
"attention_mask": attention_mask,
"decoder_attention_mask": decoder_attention_mask,
"head_mask": head_mask,
"decoder_head_mask": decoder_head_mask,
"cross_attn_head_mask": cross_attn_head_mask,
}
@require_tf
class A__( __magic_name__ , __magic_name__ , unittest.TestCase ):
lowerCAmelCase = (TFMBartForConditionalGeneration, TFMBartModel) if is_tf_available() else ()
lowerCAmelCase = (TFMBartForConditionalGeneration,) if is_tf_available() else ()
lowerCAmelCase = (
{
'''conversational''': TFMBartForConditionalGeneration,
'''feature-extraction''': TFMBartModel,
'''summarization''': TFMBartForConditionalGeneration,
'''text2text-generation''': TFMBartForConditionalGeneration,
'''translation''': TFMBartForConditionalGeneration,
}
if is_tf_available()
else {}
)
lowerCAmelCase = True
lowerCAmelCase = False
lowerCAmelCase = False
def _a ( self : Dict , __SCREAMING_SNAKE_CASE : str , __SCREAMING_SNAKE_CASE : List[str] , __SCREAMING_SNAKE_CASE : Dict , __SCREAMING_SNAKE_CASE : Optional[int] , __SCREAMING_SNAKE_CASE : List[Any] ) -> Optional[Any]:
"""simple docstring"""
if pipeline_test_casse_name != "FeatureExtractionPipelineTests":
# Exception encountered when calling layer '...'
return True
return False
def _a ( self : List[Any] ) -> Optional[int]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = TFMBartModelTester(self )
__SCREAMING_SNAKE_CASE = ConfigTester(self , config_class=__SCREAMING_SNAKE_CASE )
def _a ( self : Union[str, Any] ) -> Union[str, Any]:
"""simple docstring"""
self.config_tester.run_common_tests()
def _a ( self : int ) -> str:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.check_decoder_model_past_large_inputs(*__SCREAMING_SNAKE_CASE )
@require_sentencepiece
@require_tokenizers
@require_tf
class A__( unittest.TestCase ):
lowerCAmelCase = [
''' UN Chief Says There Is No Military Solution in Syria''',
]
lowerCAmelCase = [
'''Şeful ONU declară că nu există o soluţie militară în Siria''',
]
lowerCAmelCase = '''facebook/mbart-large-en-ro'''
@cached_property
def _a ( self : Optional[int] ) -> str:
"""simple docstring"""
return AutoTokenizer.from_pretrained(self.model_name )
@cached_property
def _a ( self : Optional[Any] ) -> List[Any]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name )
return model
def _a ( self : Any , **__SCREAMING_SNAKE_CASE : Optional[Any] ) -> Any:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = self.translate_src_text(**__SCREAMING_SNAKE_CASE )
self.assertListEqual(self.expected_text , __SCREAMING_SNAKE_CASE )
def _a ( self : Any , **__SCREAMING_SNAKE_CASE : Optional[int] ) -> Optional[Any]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = self.tokenizer(self.src_text , **__SCREAMING_SNAKE_CASE , return_tensors='''tf''' )
__SCREAMING_SNAKE_CASE = self.model.generate(
model_inputs.input_ids , attention_mask=model_inputs.attention_mask , num_beams=2 )
__SCREAMING_SNAKE_CASE = self.tokenizer.batch_decode(__SCREAMING_SNAKE_CASE , skip_special_tokens=__SCREAMING_SNAKE_CASE )
return generated_words
@slow
def _a ( self : Union[str, Any] ) -> Tuple:
"""simple docstring"""
self._assert_generated_batch_equal_expected()
| 690 |
"""simple docstring"""
def _a ( UpperCAmelCase__ ) -> str:
__SCREAMING_SNAKE_CASE = ''''''
for ch in key:
if ch == " " or ch not in key_no_dups and ch.isalpha():
key_no_dups += ch
return key_no_dups
def _a ( UpperCAmelCase__ ) -> dict[str, str]:
__SCREAMING_SNAKE_CASE = [chr(i + 65 ) for i in range(26 )]
# Remove duplicate characters from key
__SCREAMING_SNAKE_CASE = remove_duplicates(key.upper() )
__SCREAMING_SNAKE_CASE = len(UpperCAmelCase__ )
# First fill cipher with key characters
__SCREAMING_SNAKE_CASE = {alphabet[i]: char for i, char in enumerate(UpperCAmelCase__ )}
# Then map remaining characters in alphabet to
# the alphabet from the beginning
for i in range(len(UpperCAmelCase__ ) , 26 ):
__SCREAMING_SNAKE_CASE = alphabet[i - offset]
# Ensure we are not mapping letters to letters previously mapped
while char in key:
offset -= 1
__SCREAMING_SNAKE_CASE = alphabet[i - offset]
__SCREAMING_SNAKE_CASE = char
return cipher_alphabet
def _a ( UpperCAmelCase__ , UpperCAmelCase__ ) -> str:
return "".join(cipher_map.get(UpperCAmelCase__ , UpperCAmelCase__ ) for ch in message.upper() )
def _a ( UpperCAmelCase__ , UpperCAmelCase__ ) -> str:
__SCREAMING_SNAKE_CASE = {v: k for k, v in cipher_map.items()}
return "".join(rev_cipher_map.get(UpperCAmelCase__ , UpperCAmelCase__ ) for ch in message.upper() )
def _a ( ) -> None:
__SCREAMING_SNAKE_CASE = input('''Enter message to encode or decode: ''' ).strip()
__SCREAMING_SNAKE_CASE = input('''Enter keyword: ''' ).strip()
__SCREAMING_SNAKE_CASE = input('''Encipher or decipher? E/D:''' ).strip()[0].lower()
try:
__SCREAMING_SNAKE_CASE = {'''e''': encipher, '''d''': decipher}[option]
except KeyError:
raise KeyError('''invalid input option''' )
__SCREAMING_SNAKE_CASE = create_cipher_map(UpperCAmelCase__ )
print(func(UpperCAmelCase__ , UpperCAmelCase__ ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| 690 | 1 |
"""simple docstring"""
import unittest
from datasets import load_dataset
from transformers.pipelines import pipeline
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_torch, slow
@is_pipeline_test
@require_torch
class A__( unittest.TestCase ):
@require_torch
def _a ( self : int ) -> Tuple:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = pipeline(
task='''zero-shot-audio-classification''' , model='''hf-internal-testing/tiny-clap-htsat-unfused''' )
__SCREAMING_SNAKE_CASE = load_dataset('''ashraq/esc50''' )
__SCREAMING_SNAKE_CASE = dataset['''train''']['''audio'''][-1]['''array''']
__SCREAMING_SNAKE_CASE = audio_classifier(__SCREAMING_SNAKE_CASE , candidate_labels=['''Sound of a dog''', '''Sound of vaccum cleaner'''] )
self.assertEqual(
nested_simplify(__SCREAMING_SNAKE_CASE ) , [{'''score''': 0.5_01, '''label''': '''Sound of a dog'''}, {'''score''': 0.4_99, '''label''': '''Sound of vaccum cleaner'''}] , )
@unittest.skip('''No models are available in TF''' )
def _a ( self : Any ) -> List[Any]:
"""simple docstring"""
pass
@slow
@require_torch
def _a ( self : Dict ) -> List[Any]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = pipeline(
task='''zero-shot-audio-classification''' , model='''laion/clap-htsat-unfused''' , )
# This is an audio of a dog
__SCREAMING_SNAKE_CASE = load_dataset('''ashraq/esc50''' )
__SCREAMING_SNAKE_CASE = dataset['''train''']['''audio'''][-1]['''array''']
__SCREAMING_SNAKE_CASE = audio_classifier(__SCREAMING_SNAKE_CASE , candidate_labels=['''Sound of a dog''', '''Sound of vaccum cleaner'''] )
self.assertEqual(
nested_simplify(__SCREAMING_SNAKE_CASE ) , [
{'''score''': 0.9_99, '''label''': '''Sound of a dog'''},
{'''score''': 0.0_01, '''label''': '''Sound of vaccum cleaner'''},
] , )
__SCREAMING_SNAKE_CASE = audio_classifier([audio] * 5 , candidate_labels=['''Sound of a dog''', '''Sound of vaccum cleaner'''] )
self.assertEqual(
nested_simplify(__SCREAMING_SNAKE_CASE ) , [
[
{'''score''': 0.9_99, '''label''': '''Sound of a dog'''},
{'''score''': 0.0_01, '''label''': '''Sound of vaccum cleaner'''},
],
]
* 5 , )
__SCREAMING_SNAKE_CASE = audio_classifier(
[audio] * 5 , candidate_labels=['''Sound of a dog''', '''Sound of vaccum cleaner'''] , batch_size=5 )
self.assertEqual(
nested_simplify(__SCREAMING_SNAKE_CASE ) , [
[
{'''score''': 0.9_99, '''label''': '''Sound of a dog'''},
{'''score''': 0.0_01, '''label''': '''Sound of vaccum cleaner'''},
],
]
* 5 , )
@unittest.skip('''No models are available in TF''' )
def _a ( self : Optional[Any] ) -> Any:
"""simple docstring"""
pass
| 690 |
"""simple docstring"""
from __future__ import annotations
import inspect
import unittest
from typing import List, Tuple
from transformers import RegNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST, TFRegNetForImageClassification, TFRegNetModel
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class A__:
def __init__( self : Optional[int] , __SCREAMING_SNAKE_CASE : Dict , __SCREAMING_SNAKE_CASE : List[str]=3 , __SCREAMING_SNAKE_CASE : Dict=32 , __SCREAMING_SNAKE_CASE : Optional[Any]=3 , __SCREAMING_SNAKE_CASE : Union[str, Any]=10 , __SCREAMING_SNAKE_CASE : str=[10, 20, 30, 40] , __SCREAMING_SNAKE_CASE : Optional[int]=[1, 1, 2, 1] , __SCREAMING_SNAKE_CASE : int=True , __SCREAMING_SNAKE_CASE : int=True , __SCREAMING_SNAKE_CASE : Optional[Any]="relu" , __SCREAMING_SNAKE_CASE : List[str]=3 , __SCREAMING_SNAKE_CASE : Union[str, Any]=None , ) -> Any:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = parent
__SCREAMING_SNAKE_CASE = batch_size
__SCREAMING_SNAKE_CASE = image_size
__SCREAMING_SNAKE_CASE = num_channels
__SCREAMING_SNAKE_CASE = embeddings_size
__SCREAMING_SNAKE_CASE = hidden_sizes
__SCREAMING_SNAKE_CASE = depths
__SCREAMING_SNAKE_CASE = is_training
__SCREAMING_SNAKE_CASE = use_labels
__SCREAMING_SNAKE_CASE = hidden_act
__SCREAMING_SNAKE_CASE = num_labels
__SCREAMING_SNAKE_CASE = scope
__SCREAMING_SNAKE_CASE = len(__SCREAMING_SNAKE_CASE )
def _a ( self : List[Any] ) -> List[str]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
__SCREAMING_SNAKE_CASE = None
if self.use_labels:
__SCREAMING_SNAKE_CASE = ids_tensor([self.batch_size] , self.num_labels )
__SCREAMING_SNAKE_CASE = self.get_config()
return config, pixel_values, labels
def _a ( self : Union[str, Any] ) -> List[str]:
"""simple docstring"""
return RegNetConfig(
num_channels=self.num_channels , embeddings_size=self.embeddings_size , hidden_sizes=self.hidden_sizes , depths=self.depths , hidden_act=self.hidden_act , num_labels=self.num_labels , )
def _a ( self : str , __SCREAMING_SNAKE_CASE : Any , __SCREAMING_SNAKE_CASE : str , __SCREAMING_SNAKE_CASE : Optional[Any] ) -> Dict:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = TFRegNetModel(config=__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = model(__SCREAMING_SNAKE_CASE , training=__SCREAMING_SNAKE_CASE )
# expected last hidden states: B, C, H // 32, W // 32
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32) , )
def _a ( self : int , __SCREAMING_SNAKE_CASE : Union[str, Any] , __SCREAMING_SNAKE_CASE : str , __SCREAMING_SNAKE_CASE : Optional[Any] ) -> Union[str, Any]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = self.num_labels
__SCREAMING_SNAKE_CASE = TFRegNetForImageClassification(__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = model(__SCREAMING_SNAKE_CASE , labels=__SCREAMING_SNAKE_CASE , training=__SCREAMING_SNAKE_CASE )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def _a ( self : Optional[Any] ) -> Any:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = self.prepare_config_and_inputs()
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = config_and_inputs
__SCREAMING_SNAKE_CASE = {'''pixel_values''': pixel_values}
return config, inputs_dict
@require_tf
class A__( __magic_name__ , __magic_name__ , unittest.TestCase ):
lowerCAmelCase = (TFRegNetModel, TFRegNetForImageClassification) if is_tf_available() else ()
lowerCAmelCase = (
{'''feature-extraction''': TFRegNetModel, '''image-classification''': TFRegNetForImageClassification}
if is_tf_available()
else {}
)
lowerCAmelCase = False
lowerCAmelCase = False
lowerCAmelCase = False
lowerCAmelCase = False
lowerCAmelCase = False
def _a ( self : Optional[Any] ) -> Union[str, Any]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = TFRegNetModelTester(self )
__SCREAMING_SNAKE_CASE = ConfigTester(self , config_class=__SCREAMING_SNAKE_CASE , has_text_modality=__SCREAMING_SNAKE_CASE )
def _a ( self : Tuple ) -> Optional[Any]:
"""simple docstring"""
return
@unittest.skip(reason='''RegNet does not use inputs_embeds''' )
def _a ( self : Any ) -> Optional[Any]:
"""simple docstring"""
pass
@unittest.skipIf(
not is_tf_available() or len(tf.config.list_physical_devices('''GPU''' ) ) == 0 , reason='''TF does not support backprop for grouped convolutions on CPU.''' , )
@slow
def _a ( self : Dict ) -> List[Any]:
"""simple docstring"""
super().test_keras_fit()
@unittest.skip(reason='''RegNet does not support input and output embeddings''' )
def _a ( self : Dict ) -> Union[str, Any]:
"""simple docstring"""
pass
def _a ( self : List[Any] ) -> List[Any]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__SCREAMING_SNAKE_CASE = model_class(__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = inspect.signature(model.call )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
__SCREAMING_SNAKE_CASE = [*signature.parameters.keys()]
__SCREAMING_SNAKE_CASE = ['''pixel_values''']
self.assertListEqual(arg_names[:1] , __SCREAMING_SNAKE_CASE )
def _a ( self : Any ) -> Union[str, Any]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__SCREAMING_SNAKE_CASE )
def _a ( self : List[str] ) -> Tuple:
"""simple docstring"""
def check_hidden_states_output(__SCREAMING_SNAKE_CASE : Optional[Any] , __SCREAMING_SNAKE_CASE : Optional[Any] , __SCREAMING_SNAKE_CASE : Any ):
__SCREAMING_SNAKE_CASE = model_class(__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = model(**self._prepare_for_class(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) , training=__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
__SCREAMING_SNAKE_CASE = self.model_tester.num_stages
self.assertEqual(len(__SCREAMING_SNAKE_CASE ) , expected_num_stages + 1 )
# RegNet's feature maps are of shape (batch_size, num_channels, height, width)
self.assertListEqual(
list(hidden_states[0].shape[-2:] ) , [self.model_tester.image_size // 2, self.model_tester.image_size // 2] , )
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs_for_common()
__SCREAMING_SNAKE_CASE = ['''basic''', '''bottleneck''']
for model_class in self.all_model_classes:
for layer_type in layers_type:
__SCREAMING_SNAKE_CASE = layer_type
__SCREAMING_SNAKE_CASE = True
check_hidden_states_output(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
__SCREAMING_SNAKE_CASE = True
check_hidden_states_output(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
def _a ( self : Union[str, Any] ) -> List[str]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs_for_common()
def check_equivalence(__SCREAMING_SNAKE_CASE : Tuple , __SCREAMING_SNAKE_CASE : Optional[Any] , __SCREAMING_SNAKE_CASE : Tuple , __SCREAMING_SNAKE_CASE : Union[str, Any]={} ):
__SCREAMING_SNAKE_CASE = model(__SCREAMING_SNAKE_CASE , return_dict=__SCREAMING_SNAKE_CASE , **__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = model(__SCREAMING_SNAKE_CASE , return_dict=__SCREAMING_SNAKE_CASE , **__SCREAMING_SNAKE_CASE ).to_tuple()
def recursive_check(__SCREAMING_SNAKE_CASE : int , __SCREAMING_SNAKE_CASE : Dict ):
if isinstance(__SCREAMING_SNAKE_CASE , (List, Tuple) ):
for tuple_iterable_value, dict_iterable_value in zip(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
recursive_check(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
elif tuple_object is None:
return
else:
self.assertTrue(
all(tf.equal(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) ) , msg=(
'''Tuple and dict output are not equal. Difference:'''
f""" {tf.math.reduce_max(tf.abs(tuple_object - dict_object ) )}"""
) , )
recursive_check(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
for model_class in self.all_model_classes:
__SCREAMING_SNAKE_CASE = model_class(__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = self._prepare_for_class(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = self._prepare_for_class(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
check_equivalence(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = self._prepare_for_class(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , return_labels=__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = self._prepare_for_class(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , return_labels=__SCREAMING_SNAKE_CASE )
check_equivalence(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = self._prepare_for_class(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = self._prepare_for_class(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
check_equivalence(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , {'''output_hidden_states''': True} )
__SCREAMING_SNAKE_CASE = self._prepare_for_class(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , return_labels=__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = self._prepare_for_class(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , return_labels=__SCREAMING_SNAKE_CASE )
check_equivalence(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , {'''output_hidden_states''': True} )
def _a ( self : str ) -> str:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*__SCREAMING_SNAKE_CASE )
@slow
def _a ( self : Union[str, Any] ) -> Union[str, Any]:
"""simple docstring"""
for model_name in TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__SCREAMING_SNAKE_CASE = TFRegNetModel.from_pretrained(__SCREAMING_SNAKE_CASE )
self.assertIsNotNone(__SCREAMING_SNAKE_CASE )
def _a ( ) -> Dict:
__SCREAMING_SNAKE_CASE = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
return image
@require_tf
@require_vision
class A__( unittest.TestCase ):
@cached_property
def _a ( self : List[Any] ) -> str:
"""simple docstring"""
return (
AutoImageProcessor.from_pretrained(TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0] )
if is_vision_available()
else None
)
@slow
def _a ( self : List[str] ) -> Any:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = TFRegNetForImageClassification.from_pretrained(TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0] )
__SCREAMING_SNAKE_CASE = self.default_image_processor
__SCREAMING_SNAKE_CASE = prepare_img()
__SCREAMING_SNAKE_CASE = image_processor(images=__SCREAMING_SNAKE_CASE , return_tensors='''tf''' )
# forward pass
__SCREAMING_SNAKE_CASE = model(**__SCREAMING_SNAKE_CASE , training=__SCREAMING_SNAKE_CASE )
# verify the logits
__SCREAMING_SNAKE_CASE = tf.TensorShape((1, 10_00) )
self.assertEqual(outputs.logits.shape , __SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = tf.constant([-0.41_80, -1.50_51, -3.48_36] )
tf.debugging.assert_near(outputs.logits[0, :3] , __SCREAMING_SNAKE_CASE , atol=1E-4 )
| 690 | 1 |
"""simple docstring"""
import tempfile
import numpy as np
import torch
from transformers import AutoTokenizer, TaEncoderModel
from diffusers import DDPMScheduler, UNetaDConditionModel
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.pipelines.deepfloyd_if import IFWatermarker
from diffusers.utils.testing_utils import torch_device
from ..test_pipelines_common import to_np
class A__:
def _a ( self : str ) -> Tuple:
"""simple docstring"""
torch.manual_seed(0 )
__SCREAMING_SNAKE_CASE = TaEncoderModel.from_pretrained('''hf-internal-testing/tiny-random-t5''' )
torch.manual_seed(0 )
__SCREAMING_SNAKE_CASE = AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-t5''' )
torch.manual_seed(0 )
__SCREAMING_SNAKE_CASE = UNetaDConditionModel(
sample_size=32 , layers_per_block=1 , block_out_channels=[32, 64] , down_block_types=[
'''ResnetDownsampleBlock2D''',
'''SimpleCrossAttnDownBlock2D''',
] , mid_block_type='''UNetMidBlock2DSimpleCrossAttn''' , up_block_types=['''SimpleCrossAttnUpBlock2D''', '''ResnetUpsampleBlock2D'''] , in_channels=3 , out_channels=6 , cross_attention_dim=32 , encoder_hid_dim=32 , attention_head_dim=8 , addition_embed_type='''text''' , addition_embed_type_num_heads=2 , cross_attention_norm='''group_norm''' , resnet_time_scale_shift='''scale_shift''' , act_fn='''gelu''' , )
unet.set_attn_processor(AttnAddedKVProcessor() ) # For reproducibility tests
torch.manual_seed(0 )
__SCREAMING_SNAKE_CASE = DDPMScheduler(
num_train_timesteps=10_00 , beta_schedule='''squaredcos_cap_v2''' , beta_start=0.00_01 , beta_end=0.02 , thresholding=__SCREAMING_SNAKE_CASE , dynamic_thresholding_ratio=0.95 , sample_max_value=1.0 , prediction_type='''epsilon''' , variance_type='''learned_range''' , )
torch.manual_seed(0 )
__SCREAMING_SNAKE_CASE = IFWatermarker()
return {
"text_encoder": text_encoder,
"tokenizer": tokenizer,
"unet": unet,
"scheduler": scheduler,
"watermarker": watermarker,
"safety_checker": None,
"feature_extractor": None,
}
def _a ( self : str ) -> List[Any]:
"""simple docstring"""
torch.manual_seed(0 )
__SCREAMING_SNAKE_CASE = TaEncoderModel.from_pretrained('''hf-internal-testing/tiny-random-t5''' )
torch.manual_seed(0 )
__SCREAMING_SNAKE_CASE = AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-t5''' )
torch.manual_seed(0 )
__SCREAMING_SNAKE_CASE = UNetaDConditionModel(
sample_size=32 , layers_per_block=[1, 2] , block_out_channels=[32, 64] , down_block_types=[
'''ResnetDownsampleBlock2D''',
'''SimpleCrossAttnDownBlock2D''',
] , mid_block_type='''UNetMidBlock2DSimpleCrossAttn''' , up_block_types=['''SimpleCrossAttnUpBlock2D''', '''ResnetUpsampleBlock2D'''] , in_channels=6 , out_channels=6 , cross_attention_dim=32 , encoder_hid_dim=32 , attention_head_dim=8 , addition_embed_type='''text''' , addition_embed_type_num_heads=2 , cross_attention_norm='''group_norm''' , resnet_time_scale_shift='''scale_shift''' , act_fn='''gelu''' , class_embed_type='''timestep''' , mid_block_scale_factor=1.4_14 , time_embedding_act_fn='''gelu''' , time_embedding_dim=32 , )
unet.set_attn_processor(AttnAddedKVProcessor() ) # For reproducibility tests
torch.manual_seed(0 )
__SCREAMING_SNAKE_CASE = DDPMScheduler(
num_train_timesteps=10_00 , beta_schedule='''squaredcos_cap_v2''' , beta_start=0.00_01 , beta_end=0.02 , thresholding=__SCREAMING_SNAKE_CASE , dynamic_thresholding_ratio=0.95 , sample_max_value=1.0 , prediction_type='''epsilon''' , variance_type='''learned_range''' , )
torch.manual_seed(0 )
__SCREAMING_SNAKE_CASE = DDPMScheduler(
num_train_timesteps=10_00 , beta_schedule='''squaredcos_cap_v2''' , beta_start=0.00_01 , beta_end=0.02 , )
torch.manual_seed(0 )
__SCREAMING_SNAKE_CASE = IFWatermarker()
return {
"text_encoder": text_encoder,
"tokenizer": tokenizer,
"unet": unet,
"scheduler": scheduler,
"image_noising_scheduler": image_noising_scheduler,
"watermarker": watermarker,
"safety_checker": None,
"feature_extractor": None,
}
def _a ( self : Optional[Any] ) -> Optional[int]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = self.get_dummy_components()
__SCREAMING_SNAKE_CASE = self.pipeline_class(**__SCREAMING_SNAKE_CASE )
pipe.to(__SCREAMING_SNAKE_CASE )
pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = self.get_dummy_inputs(__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = inputs['''prompt''']
__SCREAMING_SNAKE_CASE = inputs['''generator''']
__SCREAMING_SNAKE_CASE = inputs['''num_inference_steps''']
__SCREAMING_SNAKE_CASE = inputs['''output_type''']
if "image" in inputs:
__SCREAMING_SNAKE_CASE = inputs['''image''']
else:
__SCREAMING_SNAKE_CASE = None
if "mask_image" in inputs:
__SCREAMING_SNAKE_CASE = inputs['''mask_image''']
else:
__SCREAMING_SNAKE_CASE = None
if "original_image" in inputs:
__SCREAMING_SNAKE_CASE = inputs['''original_image''']
else:
__SCREAMING_SNAKE_CASE = None
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = pipe.encode_prompt(__SCREAMING_SNAKE_CASE )
# inputs with prompt converted to embeddings
__SCREAMING_SNAKE_CASE = {
'''prompt_embeds''': prompt_embeds,
'''negative_prompt_embeds''': negative_prompt_embeds,
'''generator''': generator,
'''num_inference_steps''': num_inference_steps,
'''output_type''': output_type,
}
if image is not None:
__SCREAMING_SNAKE_CASE = image
if mask_image is not None:
__SCREAMING_SNAKE_CASE = mask_image
if original_image is not None:
__SCREAMING_SNAKE_CASE = original_image
# set all optional components to None
for optional_component in pipe._optional_components:
setattr(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = pipe(**__SCREAMING_SNAKE_CASE )[0]
with tempfile.TemporaryDirectory() as tmpdir:
pipe.save_pretrained(__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = self.pipeline_class.from_pretrained(__SCREAMING_SNAKE_CASE )
pipe_loaded.to(__SCREAMING_SNAKE_CASE )
pipe_loaded.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE )
pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor() ) # For reproducibility tests
for optional_component in pipe._optional_components:
self.assertTrue(
getattr(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) is None , f"""`{optional_component}` did not stay set to None after loading.""" , )
__SCREAMING_SNAKE_CASE = self.get_dummy_inputs(__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = inputs['''generator''']
__SCREAMING_SNAKE_CASE = inputs['''num_inference_steps''']
__SCREAMING_SNAKE_CASE = inputs['''output_type''']
# inputs with prompt converted to embeddings
__SCREAMING_SNAKE_CASE = {
'''prompt_embeds''': prompt_embeds,
'''negative_prompt_embeds''': negative_prompt_embeds,
'''generator''': generator,
'''num_inference_steps''': num_inference_steps,
'''output_type''': output_type,
}
if image is not None:
__SCREAMING_SNAKE_CASE = image
if mask_image is not None:
__SCREAMING_SNAKE_CASE = mask_image
if original_image is not None:
__SCREAMING_SNAKE_CASE = original_image
__SCREAMING_SNAKE_CASE = pipe_loaded(**__SCREAMING_SNAKE_CASE )[0]
__SCREAMING_SNAKE_CASE = np.abs(to_np(__SCREAMING_SNAKE_CASE ) - to_np(__SCREAMING_SNAKE_CASE ) ).max()
self.assertLess(__SCREAMING_SNAKE_CASE , 1E-4 )
def _a ( self : Dict ) -> Tuple:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = self.get_dummy_components()
__SCREAMING_SNAKE_CASE = self.pipeline_class(**__SCREAMING_SNAKE_CASE )
pipe.to(__SCREAMING_SNAKE_CASE )
pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = self.get_dummy_inputs(__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = pipe(**__SCREAMING_SNAKE_CASE )[0]
with tempfile.TemporaryDirectory() as tmpdir:
pipe.save_pretrained(__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = self.pipeline_class.from_pretrained(__SCREAMING_SNAKE_CASE )
pipe_loaded.to(__SCREAMING_SNAKE_CASE )
pipe_loaded.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE )
pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor() ) # For reproducibility tests
__SCREAMING_SNAKE_CASE = self.get_dummy_inputs(__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = pipe_loaded(**__SCREAMING_SNAKE_CASE )[0]
__SCREAMING_SNAKE_CASE = np.abs(to_np(__SCREAMING_SNAKE_CASE ) - to_np(__SCREAMING_SNAKE_CASE ) ).max()
self.assertLess(__SCREAMING_SNAKE_CASE , 1E-4 )
| 690 |
"""simple docstring"""
import pickle
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, XLMRobertaTokenizer, XLMRobertaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
lowerCAmelCase__ =get_tests_dir("fixtures/test_sentencepiece.model")
@require_sentencepiece
@require_tokenizers
class A__( __magic_name__ , unittest.TestCase ):
lowerCAmelCase = XLMRobertaTokenizer
lowerCAmelCase = XLMRobertaTokenizerFast
lowerCAmelCase = True
lowerCAmelCase = True
def _a ( self : Optional[int] ) -> List[Any]:
"""simple docstring"""
super().setUp()
# We have a SentencePiece fixture for testing
__SCREAMING_SNAKE_CASE = XLMRobertaTokenizer(__SCREAMING_SNAKE_CASE , keep_accents=__SCREAMING_SNAKE_CASE )
tokenizer.save_pretrained(self.tmpdirname )
def _a ( self : str ) -> Union[str, Any]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = '''<pad>'''
__SCREAMING_SNAKE_CASE = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(__SCREAMING_SNAKE_CASE ) , __SCREAMING_SNAKE_CASE )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(__SCREAMING_SNAKE_CASE ) , __SCREAMING_SNAKE_CASE )
def _a ( self : int ) -> List[Any]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , '''<s>''' )
self.assertEqual(vocab_keys[1] , '''<pad>''' )
self.assertEqual(vocab_keys[-1] , '''<mask>''' )
self.assertEqual(len(__SCREAMING_SNAKE_CASE ) , 10_02 )
def _a ( self : Tuple ) -> Optional[int]:
"""simple docstring"""
self.assertEqual(self.get_tokenizer().vocab_size , 10_02 )
def _a ( self : int ) -> int:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = XLMRobertaTokenizer(__SCREAMING_SNAKE_CASE , keep_accents=__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = tokenizer.tokenize('''This is a test''' )
self.assertListEqual(__SCREAMING_SNAKE_CASE , ['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est'''] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(__SCREAMING_SNAKE_CASE ) , [value + tokenizer.fairseq_offset for value in [2_85, 46, 10, 1_70, 3_82]] , )
__SCREAMING_SNAKE_CASE = tokenizer.tokenize('''I was born in 92000, and this is falsé.''' )
self.assertListEqual(
__SCREAMING_SNAKE_CASE , [
SPIECE_UNDERLINE + '''I''',
SPIECE_UNDERLINE + '''was''',
SPIECE_UNDERLINE + '''b''',
'''or''',
'''n''',
SPIECE_UNDERLINE + '''in''',
SPIECE_UNDERLINE + '''''',
'''9''',
'''2''',
'''0''',
'''0''',
'''0''',
''',''',
SPIECE_UNDERLINE + '''and''',
SPIECE_UNDERLINE + '''this''',
SPIECE_UNDERLINE + '''is''',
SPIECE_UNDERLINE + '''f''',
'''al''',
'''s''',
'''é''',
'''.''',
] , )
__SCREAMING_SNAKE_CASE = tokenizer.convert_tokens_to_ids(__SCREAMING_SNAKE_CASE )
self.assertListEqual(
__SCREAMING_SNAKE_CASE , [
value + tokenizer.fairseq_offset
for value in [8, 21, 84, 55, 24, 19, 7, 2, 6_02, 3_47, 3_47, 3_47, 3, 12, 66, 46, 72, 80, 6, 2, 4]
# ^ unk: 2 + 1 = 3 unk: 2 + 1 = 3 ^
] , )
__SCREAMING_SNAKE_CASE = tokenizer.convert_ids_to_tokens(__SCREAMING_SNAKE_CASE )
self.assertListEqual(
__SCREAMING_SNAKE_CASE , [
SPIECE_UNDERLINE + '''I''',
SPIECE_UNDERLINE + '''was''',
SPIECE_UNDERLINE + '''b''',
'''or''',
'''n''',
SPIECE_UNDERLINE + '''in''',
SPIECE_UNDERLINE + '''''',
'''<unk>''',
'''2''',
'''0''',
'''0''',
'''0''',
''',''',
SPIECE_UNDERLINE + '''and''',
SPIECE_UNDERLINE + '''this''',
SPIECE_UNDERLINE + '''is''',
SPIECE_UNDERLINE + '''f''',
'''al''',
'''s''',
'''<unk>''',
'''.''',
] , )
def _a ( self : int ) -> Tuple:
"""simple docstring"""
if not self.test_slow_tokenizer:
# as we don't have a slow version, we can't compare the outputs between slow and fast versions
return
__SCREAMING_SNAKE_CASE = (self.rust_tokenizer_class, '''hf-internal-testing/tiny-xlm-roberta''', {})
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})""" ):
__SCREAMING_SNAKE_CASE = self.rust_tokenizer_class.from_pretrained(__SCREAMING_SNAKE_CASE , **__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = self.tokenizer_class.from_pretrained(__SCREAMING_SNAKE_CASE , **__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = tempfile.mkdtemp()
__SCREAMING_SNAKE_CASE = tokenizer_r.save_pretrained(__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = tokenizer_p.save_pretrained(__SCREAMING_SNAKE_CASE )
# Checks it save with the same files + the tokenizer.json file for the fast one
self.assertTrue(any('''tokenizer.json''' in f for f in tokenizer_r_files ) )
__SCREAMING_SNAKE_CASE = tuple(f for f in tokenizer_r_files if '''tokenizer.json''' not in f )
self.assertSequenceEqual(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
# Checks everything loads correctly in the same way
__SCREAMING_SNAKE_CASE = tokenizer_r.from_pretrained(__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = tokenizer_p.from_pretrained(__SCREAMING_SNAKE_CASE )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) )
# self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
# self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))
shutil.rmtree(__SCREAMING_SNAKE_CASE )
# Save tokenizer rust, legacy_format=True
__SCREAMING_SNAKE_CASE = tempfile.mkdtemp()
__SCREAMING_SNAKE_CASE = tokenizer_r.save_pretrained(__SCREAMING_SNAKE_CASE , legacy_format=__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = tokenizer_p.save_pretrained(__SCREAMING_SNAKE_CASE )
# Checks it save with the same files
self.assertSequenceEqual(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
# Checks everything loads correctly in the same way
__SCREAMING_SNAKE_CASE = tokenizer_r.from_pretrained(__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = tokenizer_p.from_pretrained(__SCREAMING_SNAKE_CASE )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) )
shutil.rmtree(__SCREAMING_SNAKE_CASE )
# Save tokenizer rust, legacy_format=False
__SCREAMING_SNAKE_CASE = tempfile.mkdtemp()
__SCREAMING_SNAKE_CASE = tokenizer_r.save_pretrained(__SCREAMING_SNAKE_CASE , legacy_format=__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = tokenizer_p.save_pretrained(__SCREAMING_SNAKE_CASE )
# Checks it saved the tokenizer.json file
self.assertTrue(any('''tokenizer.json''' in f for f in tokenizer_r_files ) )
# Checks everything loads correctly in the same way
__SCREAMING_SNAKE_CASE = tokenizer_r.from_pretrained(__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = tokenizer_p.from_pretrained(__SCREAMING_SNAKE_CASE )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) )
shutil.rmtree(__SCREAMING_SNAKE_CASE )
@cached_property
def _a ( self : Union[str, Any] ) -> List[str]:
"""simple docstring"""
return XLMRobertaTokenizer.from_pretrained('''xlm-roberta-base''' )
def _a ( self : Optional[Any] ) -> Union[str, Any]:
"""simple docstring"""
with tempfile.NamedTemporaryFile() as f:
shutil.copyfile(__SCREAMING_SNAKE_CASE , f.name )
__SCREAMING_SNAKE_CASE = XLMRobertaTokenizer(f.name , keep_accents=__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = pickle.dumps(__SCREAMING_SNAKE_CASE )
pickle.loads(__SCREAMING_SNAKE_CASE )
def _a ( self : Union[str, Any] ) -> List[Any]:
"""simple docstring"""
if not self.test_rust_tokenizer:
return
__SCREAMING_SNAKE_CASE = self.get_tokenizer()
__SCREAMING_SNAKE_CASE = self.get_rust_tokenizer()
__SCREAMING_SNAKE_CASE = '''I was born in 92000, and this is falsé.'''
__SCREAMING_SNAKE_CASE = tokenizer.tokenize(__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = rust_tokenizer.tokenize(__SCREAMING_SNAKE_CASE )
self.assertListEqual(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = tokenizer.encode(__SCREAMING_SNAKE_CASE , add_special_tokens=__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = rust_tokenizer.encode(__SCREAMING_SNAKE_CASE , add_special_tokens=__SCREAMING_SNAKE_CASE )
self.assertListEqual(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = self.get_rust_tokenizer()
__SCREAMING_SNAKE_CASE = tokenizer.encode(__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = rust_tokenizer.encode(__SCREAMING_SNAKE_CASE )
self.assertListEqual(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
@slow
def _a ( self : Any ) -> Dict:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = '''Hello World!'''
__SCREAMING_SNAKE_CASE = [0, 3_53_78, 66_61, 38, 2]
# xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base') # xlmr.large has same tokenizer
# xlmr.eval()
# xlmr.encode(symbols)
self.assertListEqual(__SCREAMING_SNAKE_CASE , self.big_tokenizer.encode(__SCREAMING_SNAKE_CASE ) )
@slow
def _a ( self : Optional[Any] ) -> Union[str, Any]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = (
'''This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) " [ ] ! : - . Also we will'''
''' add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth'''
)
__SCREAMING_SNAKE_CASE = [
0,
32_93,
83,
10,
45_52,
49_89,
79_86,
6_78,
10,
59_15,
1_11,
17_94_59,
12_48_50,
4,
60_44,
2_37,
12,
6,
5,
6,
4,
67_80,
7_05,
15,
13_88,
44,
3_78,
1_01_14,
7_11,
1_52,
20,
6,
5,
2_23_76,
6_42,
12_21,
1_51_90,
3_41_53,
4_50,
56_08,
9_59,
11_19,
5_77_02,
1_36,
1_86,
47,
10_98,
2_93_67,
47,
# 4426, # What fairseq tokenizes from "<unk>": "_<"
# 3678, # What fairseq tokenizes from "<unk>": "unk"
# 2740, # What fairseq tokenizes from "<unk>": ">"
3, # What we tokenize from "<unk>": "<unk>"
6, # Residue from the tokenization: an extra sentencepiece underline
4,
60_44,
2_37,
62_84,
5_09_01,
5_28,
31,
90,
34,
9_27,
2,
]
# xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base') # xlmr.large has same tokenizer
# xlmr.eval()
# xlmr.encode(symbols)
self.assertListEqual(__SCREAMING_SNAKE_CASE , self.big_tokenizer.encode(__SCREAMING_SNAKE_CASE ) )
@slow
def _a ( self : Optional[int] ) -> Dict:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = {'''input_ids''': [[0, 1_10_62, 8_27_72, 7, 15, 8_27_72, 5_38, 5_15_29, 2_37, 1_71_98, 12_90, 2_06, 9, 21_51_75, 13_14, 1_36, 1_71_98, 12_90, 2_06, 9, 5_63_59, 42, 12_20_09, 9, 1_64_66, 16, 8_73_44, 45_37, 9, 47_17, 7_83_81, 6, 15_99_58, 7, 15, 2_44_80, 6_18, 4, 5_27, 2_26_93, 54_28, 4, 27_77, 2_44_80, 98_74, 4, 4_35_23, 5_94, 4, 8_03, 1_83_92, 3_31_89, 18, 4, 4_35_23, 2_44_47, 1_23_99, 1_00, 2_49_55, 8_36_58, 96_26, 14_40_57, 15, 8_39, 2_23_35, 16, 1_36, 2_49_55, 8_36_58, 8_34_79, 15, 3_91_02, 7_24, 16, 6_78, 6_45, 27_89, 13_28, 45_89, 42, 12_20_09, 11_57_74, 23, 8_05, 13_28, 4_68_76, 7, 1_36, 5_38_94, 19_40, 4_22_27, 4_11_59, 1_77_21, 8_23, 4_25, 4, 2_75_12, 9_87_22, 2_06, 1_36, 55_31, 49_70, 9_19, 1_73_36, 5, 2], [0, 2_00_80, 6_18, 83, 8_27_75, 47, 4_79, 9, 15_17, 73, 5_38_94, 3_33, 8_05_81, 11_01_17, 1_88_11, 52_56, 12_95, 51, 15_25_26, 2_97, 79_86, 3_90, 12_44_16, 5_38, 3_54_31, 2_14, 98, 1_50_44, 2_57_37, 1_36, 71_08, 4_37_01, 23, 7_56, 13_53_55, 7, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 5_81, 6_37_73, 11_94_55, 6, 14_77_97, 8_82_03, 7, 6_45, 70, 21, 32_85, 1_02_69, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], '''attention_mask''': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # noqa: E501
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=__SCREAMING_SNAKE_CASE , model_name='''xlm-roberta-base''' , revision='''d9d8a8ea5eb94b1c6654ae9249df7793cd2933d3''' , )
| 690 | 1 |
"""simple docstring"""
import importlib.util
import json
import os
import warnings
from dataclasses import dataclass, field
import torch
from ..training_args import TrainingArguments
from ..utils import cached_property, is_sagemaker_dp_enabled, logging
lowerCAmelCase__ =logging.get_logger(__name__)
def _a ( ) -> Any:
# Get the sagemaker specific mp parameters from smp_options variable.
__SCREAMING_SNAKE_CASE = os.getenv('''SM_HP_MP_PARAMETERS''' , '''{}''' )
try:
# Parse it and check the field "partitions" is included, it is required for model parallel.
__SCREAMING_SNAKE_CASE = json.loads(UpperCAmelCase__ )
if "partitions" not in smp_options:
return False
except json.JSONDecodeError:
return False
# Get the sagemaker specific framework parameters from mpi_options variable.
__SCREAMING_SNAKE_CASE = os.getenv('''SM_FRAMEWORK_PARAMS''' , '''{}''' )
try:
# Parse it and check the field "sagemaker_distributed_dataparallel_enabled".
__SCREAMING_SNAKE_CASE = json.loads(UpperCAmelCase__ )
if not mpi_options.get('''sagemaker_mpi_enabled''' , UpperCAmelCase__ ):
return False
except json.JSONDecodeError:
return False
# Lastly, check if the `smdistributed` module is present.
return importlib.util.find_spec('''smdistributed''' ) is not None
if is_sagemaker_model_parallel_available():
import smdistributed.modelparallel.torch as smp
smp.init()
@dataclass
class A__( __magic_name__ ):
lowerCAmelCase = field(
default='''''' , metadata={'''help''': '''Used by the SageMaker launcher to send mp-specific args. Ignored in SageMakerTrainer'''} , )
def _a ( self : str ) -> int:
"""simple docstring"""
super().__post_init__()
warnings.warn(
'''`SageMakerTrainingArguments` is deprecated and will be removed in v5 of Transformers. You can use '''
'''`TrainingArguments` instead.''' , __SCREAMING_SNAKE_CASE , )
@cached_property
def _a ( self : int ) -> "torch.device":
"""simple docstring"""
logger.info('''PyTorch: setting up devices''' )
if torch.distributed.is_available() and torch.distributed.is_initialized() and self.local_rank == -1:
logger.warning(
'''torch.distributed process group is initialized, but local_rank == -1. '''
'''In order to use Torch DDP, launch your script with `python -m torch.distributed.launch''' )
if self.no_cuda:
__SCREAMING_SNAKE_CASE = torch.device('''cpu''' )
__SCREAMING_SNAKE_CASE = 0
elif is_sagemaker_model_parallel_available():
__SCREAMING_SNAKE_CASE = smp.local_rank()
__SCREAMING_SNAKE_CASE = torch.device('''cuda''' , __SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = 1
elif is_sagemaker_dp_enabled():
import smdistributed.dataparallel.torch.torch_smddp # noqa: F401
torch.distributed.init_process_group(backend='''smddp''' , timeout=self.ddp_timeout_delta )
__SCREAMING_SNAKE_CASE = int(os.getenv('''SMDATAPARALLEL_LOCAL_RANK''' ) )
__SCREAMING_SNAKE_CASE = torch.device('''cuda''' , self.local_rank )
__SCREAMING_SNAKE_CASE = 1
elif self.local_rank == -1:
# if n_gpu is > 1 we'll use nn.DataParallel.
# If you only want to use a specific subset of GPUs use `CUDA_VISIBLE_DEVICES=0`
# Explicitly set CUDA to the first (index 0) CUDA device, otherwise `set_device` will
# trigger an error that a device index is missing. Index 0 takes into account the
# GPUs available in the environment, so `CUDA_VISIBLE_DEVICES=1,2` with `cuda:0`
# will use the first GPU in that env, i.e. GPU#1
__SCREAMING_SNAKE_CASE = torch.device('''cuda:0''' if torch.cuda.is_available() else '''cpu''' )
# Sometimes the line in the postinit has not been run before we end up here, so just checking we're not at
# the default value.
__SCREAMING_SNAKE_CASE = torch.cuda.device_count()
else:
# Here, we'll use torch.distributed.
# Initializes the distributed backend which will take care of synchronizing nodes/GPUs
if not torch.distributed.is_initialized():
torch.distributed.init_process_group(backend='''nccl''' , timeout=self.ddp_timeout_delta )
__SCREAMING_SNAKE_CASE = torch.device('''cuda''' , self.local_rank )
__SCREAMING_SNAKE_CASE = 1
if device.type == "cuda":
torch.cuda.set_device(__SCREAMING_SNAKE_CASE )
return device
@property
def _a ( self : List[str] ) -> Optional[Any]:
"""simple docstring"""
if is_sagemaker_model_parallel_available():
return smp.dp_size()
return super().world_size
@property
def _a ( self : Optional[int] ) -> Union[str, Any]:
"""simple docstring"""
return not is_sagemaker_model_parallel_available()
@property
def _a ( self : Optional[int] ) -> Optional[Any]:
"""simple docstring"""
return False
| 690 |
"""simple docstring"""
from __future__ import annotations
lowerCAmelCase__ =8.9_8_8E9 # units = N * m^s * C^-2
def _a ( UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ ) -> dict[str, float]:
__SCREAMING_SNAKE_CASE = abs(chargea * chargea )
if (force, chargea, chargea, distance).count(0 ) != 1:
raise ValueError('''One and only one argument must be 0''' )
if distance < 0:
raise ValueError('''Distance cannot be negative''' )
if force == 0:
__SCREAMING_SNAKE_CASE = COULOMBS_CONSTANT * charge_product / (distance**2)
return {"force": force}
elif chargea == 0:
__SCREAMING_SNAKE_CASE = abs(UpperCAmelCase__ ) * (distance**2) / (COULOMBS_CONSTANT * chargea)
return {"charge1": chargea}
elif chargea == 0:
__SCREAMING_SNAKE_CASE = abs(UpperCAmelCase__ ) * (distance**2) / (COULOMBS_CONSTANT * chargea)
return {"charge2": chargea}
elif distance == 0:
__SCREAMING_SNAKE_CASE = (COULOMBS_CONSTANT * charge_product / abs(UpperCAmelCase__ )) ** 0.5
return {"distance": distance}
raise ValueError('''Exactly one argument must be 0''' )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 690 | 1 |
"""simple docstring"""
import logging
from pathlib import Path
import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only
from utils_rag import save_json
def _a ( UpperCAmelCase__ ) -> str:
__SCREAMING_SNAKE_CASE = filter(lambda UpperCAmelCase__ : p.requires_grad , model.parameters() )
__SCREAMING_SNAKE_CASE = sum([np.prod(p.size() ) for p in model_parameters] )
return params
lowerCAmelCase__ =logging.getLogger(__name__)
def _a ( UpperCAmelCase__ , UpperCAmelCase__ ) -> Optional[int]:
if metric == "rouge2":
__SCREAMING_SNAKE_CASE = '''{val_avg_rouge2:.4f}-{step_count}'''
elif metric == "bleu":
__SCREAMING_SNAKE_CASE = '''{val_avg_bleu:.4f}-{step_count}'''
elif metric == "em":
__SCREAMING_SNAKE_CASE = '''{val_avg_em:.4f}-{step_count}'''
elif metric == "loss":
__SCREAMING_SNAKE_CASE = '''{val_avg_loss:.4f}-{step_count}'''
else:
raise NotImplementedError(
f"""seq2seq callbacks only support rouge2 and bleu, got {metric}, You can make your own by adding to this"""
''' function.''' )
__SCREAMING_SNAKE_CASE = ModelCheckpoint(
dirpath=UpperCAmelCase__ , filename=UpperCAmelCase__ , monitor=f"""val_{metric}""" , mode='''max''' , save_top_k=1 , every_n_epochs=1 , )
return checkpoint_callback
def _a ( UpperCAmelCase__ , UpperCAmelCase__ ) -> List[str]:
return EarlyStopping(
monitor=f"""val_{metric}""" , mode='''min''' if '''loss''' in metric else '''max''' , patience=UpperCAmelCase__ , verbose=UpperCAmelCase__ , )
class A__( pl.Callback ):
def _a ( self : List[Any] , __SCREAMING_SNAKE_CASE : Optional[int] , __SCREAMING_SNAKE_CASE : Dict ) -> Dict:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = {f"""lr_group_{i}""": param['''lr'''] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups )}
pl_module.logger.log_metrics(__SCREAMING_SNAKE_CASE )
@rank_zero_only
def _a ( self : Any , __SCREAMING_SNAKE_CASE : pl.Trainer , __SCREAMING_SNAKE_CASE : pl.LightningModule , __SCREAMING_SNAKE_CASE : str , __SCREAMING_SNAKE_CASE : Optional[Any]=True ) -> None:
"""simple docstring"""
logger.info(f"""***** {type_path} results at step {trainer.global_step:05d} *****""" )
__SCREAMING_SNAKE_CASE = trainer.callback_metrics
trainer.logger.log_metrics({k: v for k, v in metrics.items() if k not in ['''log''', '''progress_bar''', '''preds''']} )
# Log results
__SCREAMING_SNAKE_CASE = Path(pl_module.hparams.output_dir )
if type_path == "test":
__SCREAMING_SNAKE_CASE = od / '''test_results.txt'''
__SCREAMING_SNAKE_CASE = od / '''test_generations.txt'''
else:
# this never gets hit. I prefer not to save intermediate generations, and results are in metrics.json
# If people want this it will be easy enough to add back.
__SCREAMING_SNAKE_CASE = od / f"""{type_path}_results/{trainer.global_step:05d}.txt"""
__SCREAMING_SNAKE_CASE = od / f"""{type_path}_generations/{trainer.global_step:05d}.txt"""
results_file.parent.mkdir(exist_ok=__SCREAMING_SNAKE_CASE )
generations_file.parent.mkdir(exist_ok=__SCREAMING_SNAKE_CASE )
with open(__SCREAMING_SNAKE_CASE , '''a+''' ) as writer:
for key in sorted(__SCREAMING_SNAKE_CASE ):
if key in ["log", "progress_bar", "preds"]:
continue
__SCREAMING_SNAKE_CASE = metrics[key]
if isinstance(__SCREAMING_SNAKE_CASE , torch.Tensor ):
__SCREAMING_SNAKE_CASE = val.item()
__SCREAMING_SNAKE_CASE = f"""{key}: {val:.6f}\n"""
writer.write(__SCREAMING_SNAKE_CASE )
if not save_generations:
return
if "preds" in metrics:
__SCREAMING_SNAKE_CASE = '''\n'''.join(metrics['''preds'''] )
generations_file.open('''w+''' ).write(__SCREAMING_SNAKE_CASE )
@rank_zero_only
def _a ( self : int , __SCREAMING_SNAKE_CASE : Optional[int] , __SCREAMING_SNAKE_CASE : Tuple ) -> List[Any]:
"""simple docstring"""
try:
__SCREAMING_SNAKE_CASE = pl_module.model.model.num_parameters()
except AttributeError:
__SCREAMING_SNAKE_CASE = pl_module.model.num_parameters()
__SCREAMING_SNAKE_CASE = count_trainable_parameters(__SCREAMING_SNAKE_CASE )
# mp stands for million parameters
trainer.logger.log_metrics({'''n_params''': npars, '''mp''': npars / 1E6, '''grad_mp''': n_trainable_pars / 1E6} )
@rank_zero_only
def _a ( self : int , __SCREAMING_SNAKE_CASE : pl.Trainer , __SCREAMING_SNAKE_CASE : pl.LightningModule ) -> Any:
"""simple docstring"""
save_json(pl_module.metrics , pl_module.metrics_save_path )
return self._write_logs(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , '''test''' )
@rank_zero_only
def _a ( self : Union[str, Any] , __SCREAMING_SNAKE_CASE : pl.Trainer , __SCREAMING_SNAKE_CASE : List[Any] ) -> List[Any]:
"""simple docstring"""
save_json(pl_module.metrics , pl_module.metrics_save_path )
# Uncommenting this will save val generations
# return self._write_logs(trainer, pl_module, "valid")
| 690 |
"""simple docstring"""
import argparse
from pathlib import Path
import torch
from transformers import OPTConfig, OPTModel
from transformers.utils import logging
logging.set_verbosity_info()
lowerCAmelCase__ =logging.get_logger(__name__)
def _a ( UpperCAmelCase__ ) -> Tuple:
__SCREAMING_SNAKE_CASE = torch.load(UpperCAmelCase__ , map_location='''cpu''' )
if "model" in sd.keys():
__SCREAMING_SNAKE_CASE = torch.load(UpperCAmelCase__ , map_location='''cpu''' )['''model''']
# pop unnecessary weights
__SCREAMING_SNAKE_CASE = [
'''decoder.version''',
'''decoder.output_projection.weight''',
]
for key in keys_to_delete:
if key in sd:
sd.pop(UpperCAmelCase__ )
__SCREAMING_SNAKE_CASE = {
'''decoder.project_in_dim.weight''': '''decoder.project_in.weight''',
'''decoder.project_out_dim.weight''': '''decoder.project_out.weight''',
'''decoder.layer_norm.weight''': '''decoder.final_layer_norm.weight''',
'''decoder.layer_norm.bias''': '''decoder.final_layer_norm.bias''',
}
for old_key, new_key in keys_to_rename.items():
if old_key in sd:
__SCREAMING_SNAKE_CASE = sd.pop(UpperCAmelCase__ )
__SCREAMING_SNAKE_CASE = list(sd.keys() )
for key in keys:
if ".qkv_proj." in key:
__SCREAMING_SNAKE_CASE = sd[key]
# We split QKV in separate Q,K,V
__SCREAMING_SNAKE_CASE = key.replace('''.qkv_proj.''' , '''.q_proj.''' )
__SCREAMING_SNAKE_CASE = key.replace('''.qkv_proj.''' , '''.k_proj.''' )
__SCREAMING_SNAKE_CASE = key.replace('''.qkv_proj.''' , '''.v_proj.''' )
__SCREAMING_SNAKE_CASE = value.shape[0]
assert depth % 3 == 0
# `SequeuceParallelTransformerBlock` has QKV weight is separated in K,V,Q despite the naming:
# https://cs.github.com/facebookresearch/metaseq/blob/51871bd73cd04c038f239ea2a26db1d7f6b37927/metaseq/modules/sequence_parallel_transformer_layer.py#L97
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = torch.split(UpperCAmelCase__ , depth // 3 , dim=0 )
__SCREAMING_SNAKE_CASE = q
__SCREAMING_SNAKE_CASE = k
__SCREAMING_SNAKE_CASE = v
del sd[key]
return sd
@torch.no_grad()
def _a ( UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__=None ) -> Optional[Any]:
__SCREAMING_SNAKE_CASE = load_checkpoint(UpperCAmelCase__ )
if config is not None:
__SCREAMING_SNAKE_CASE = OPTConfig.from_pretrained(UpperCAmelCase__ )
else:
__SCREAMING_SNAKE_CASE = OPTConfig()
__SCREAMING_SNAKE_CASE = OPTModel(UpperCAmelCase__ ).half().eval()
model.load_state_dict(UpperCAmelCase__ )
# Check results
Path(UpperCAmelCase__ ).mkdir(exist_ok=UpperCAmelCase__ )
model.save_pretrained(UpperCAmelCase__ )
if __name__ == "__main__":
lowerCAmelCase__ =argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--fairseq_path",
type=str,
help=(
"path to fairseq checkpoint in correct format. You can find all checkpoints in the correct format here:"
" https://huggingface.co/models?other=opt_metasq"
),
)
parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
parser.add_argument("--hf_config", default=None, type=str, help="Define HF config.")
lowerCAmelCase__ =parser.parse_args()
convert_opt_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, config=args.hf_config)
| 690 | 1 |
"""simple docstring"""
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import SegformerImageProcessor, SwinConfig, UperNetConfig, UperNetForSemanticSegmentation
def _a ( UpperCAmelCase__ ) -> Tuple:
__SCREAMING_SNAKE_CASE = 3_84
__SCREAMING_SNAKE_CASE = 7
if "tiny" in model_name:
__SCREAMING_SNAKE_CASE = 96
__SCREAMING_SNAKE_CASE = (2, 2, 6, 2)
__SCREAMING_SNAKE_CASE = (3, 6, 12, 24)
elif "small" in model_name:
__SCREAMING_SNAKE_CASE = 96
__SCREAMING_SNAKE_CASE = (2, 2, 18, 2)
__SCREAMING_SNAKE_CASE = (3, 6, 12, 24)
elif "base" in model_name:
__SCREAMING_SNAKE_CASE = 1_28
__SCREAMING_SNAKE_CASE = (2, 2, 18, 2)
__SCREAMING_SNAKE_CASE = (4, 8, 16, 32)
__SCREAMING_SNAKE_CASE = 12
__SCREAMING_SNAKE_CASE = 5_12
elif "large" in model_name:
__SCREAMING_SNAKE_CASE = 1_92
__SCREAMING_SNAKE_CASE = (2, 2, 18, 2)
__SCREAMING_SNAKE_CASE = (6, 12, 24, 48)
__SCREAMING_SNAKE_CASE = 12
__SCREAMING_SNAKE_CASE = 7_68
# set label information
__SCREAMING_SNAKE_CASE = 1_50
__SCREAMING_SNAKE_CASE = '''huggingface/label-files'''
__SCREAMING_SNAKE_CASE = '''ade20k-id2label.json'''
__SCREAMING_SNAKE_CASE = json.load(open(hf_hub_download(UpperCAmelCase__ , UpperCAmelCase__ , repo_type='''dataset''' ) , '''r''' ) )
__SCREAMING_SNAKE_CASE = {int(UpperCAmelCase__ ): v for k, v in idalabel.items()}
__SCREAMING_SNAKE_CASE = {v: k for k, v in idalabel.items()}
__SCREAMING_SNAKE_CASE = SwinConfig(
embed_dim=UpperCAmelCase__ , depths=UpperCAmelCase__ , num_heads=UpperCAmelCase__ , window_size=UpperCAmelCase__ , out_features=['''stage1''', '''stage2''', '''stage3''', '''stage4'''] , )
__SCREAMING_SNAKE_CASE = UperNetConfig(
backbone_config=UpperCAmelCase__ , auxiliary_in_channels=UpperCAmelCase__ , num_labels=UpperCAmelCase__ , idalabel=UpperCAmelCase__ , labelaid=UpperCAmelCase__ , )
return config
def _a ( UpperCAmelCase__ ) -> Any:
__SCREAMING_SNAKE_CASE = []
# fmt: off
# stem
rename_keys.append(('''backbone.patch_embed.projection.weight''', '''backbone.embeddings.patch_embeddings.projection.weight''') )
rename_keys.append(('''backbone.patch_embed.projection.bias''', '''backbone.embeddings.patch_embeddings.projection.bias''') )
rename_keys.append(('''backbone.patch_embed.norm.weight''', '''backbone.embeddings.norm.weight''') )
rename_keys.append(('''backbone.patch_embed.norm.bias''', '''backbone.embeddings.norm.bias''') )
# stages
for i in range(len(config.backbone_config.depths ) ):
for j in range(config.backbone_config.depths[i] ):
rename_keys.append((f"""backbone.stages.{i}.blocks.{j}.norm1.weight""", f"""backbone.encoder.layers.{i}.blocks.{j}.layernorm_before.weight""") )
rename_keys.append((f"""backbone.stages.{i}.blocks.{j}.norm1.bias""", f"""backbone.encoder.layers.{i}.blocks.{j}.layernorm_before.bias""") )
rename_keys.append((f"""backbone.stages.{i}.blocks.{j}.attn.w_msa.relative_position_bias_table""", f"""backbone.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table""") )
rename_keys.append((f"""backbone.stages.{i}.blocks.{j}.attn.w_msa.relative_position_index""", f"""backbone.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index""") )
rename_keys.append((f"""backbone.stages.{i}.blocks.{j}.attn.w_msa.proj.weight""", f"""backbone.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight""") )
rename_keys.append((f"""backbone.stages.{i}.blocks.{j}.attn.w_msa.proj.bias""", f"""backbone.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias""") )
rename_keys.append((f"""backbone.stages.{i}.blocks.{j}.norm2.weight""", f"""backbone.encoder.layers.{i}.blocks.{j}.layernorm_after.weight""") )
rename_keys.append((f"""backbone.stages.{i}.blocks.{j}.norm2.bias""", f"""backbone.encoder.layers.{i}.blocks.{j}.layernorm_after.bias""") )
rename_keys.append((f"""backbone.stages.{i}.blocks.{j}.ffn.layers.0.0.weight""", f"""backbone.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight""") )
rename_keys.append((f"""backbone.stages.{i}.blocks.{j}.ffn.layers.0.0.bias""", f"""backbone.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias""") )
rename_keys.append((f"""backbone.stages.{i}.blocks.{j}.ffn.layers.1.weight""", f"""backbone.encoder.layers.{i}.blocks.{j}.output.dense.weight""") )
rename_keys.append((f"""backbone.stages.{i}.blocks.{j}.ffn.layers.1.bias""", f"""backbone.encoder.layers.{i}.blocks.{j}.output.dense.bias""") )
if i < 3:
rename_keys.append((f"""backbone.stages.{i}.downsample.reduction.weight""", f"""backbone.encoder.layers.{i}.downsample.reduction.weight""") )
rename_keys.append((f"""backbone.stages.{i}.downsample.norm.weight""", f"""backbone.encoder.layers.{i}.downsample.norm.weight""") )
rename_keys.append((f"""backbone.stages.{i}.downsample.norm.bias""", f"""backbone.encoder.layers.{i}.downsample.norm.bias""") )
rename_keys.append((f"""backbone.norm{i}.weight""", f"""backbone.hidden_states_norms.stage{i+1}.weight""") )
rename_keys.append((f"""backbone.norm{i}.bias""", f"""backbone.hidden_states_norms.stage{i+1}.bias""") )
# decode head
rename_keys.extend(
[
('''decode_head.conv_seg.weight''', '''decode_head.classifier.weight'''),
('''decode_head.conv_seg.bias''', '''decode_head.classifier.bias'''),
('''auxiliary_head.conv_seg.weight''', '''auxiliary_head.classifier.weight'''),
('''auxiliary_head.conv_seg.bias''', '''auxiliary_head.classifier.bias'''),
] )
# fmt: on
return rename_keys
def _a ( UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ ) -> Optional[int]:
__SCREAMING_SNAKE_CASE = dct.pop(UpperCAmelCase__ )
__SCREAMING_SNAKE_CASE = val
def _a ( UpperCAmelCase__ , UpperCAmelCase__ ) -> List[str]:
__SCREAMING_SNAKE_CASE = [int(backbone_config.embed_dim * 2**i ) for i in range(len(backbone_config.depths ) )]
for i in range(len(backbone_config.depths ) ):
__SCREAMING_SNAKE_CASE = num_features[i]
for j in range(backbone_config.depths[i] ):
# fmt: off
# read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias)
__SCREAMING_SNAKE_CASE = state_dict.pop(f"""backbone.stages.{i}.blocks.{j}.attn.w_msa.qkv.weight""" )
__SCREAMING_SNAKE_CASE = state_dict.pop(f"""backbone.stages.{i}.blocks.{j}.attn.w_msa.qkv.bias""" )
# next, add query, keys and values (in that order) to the state dict
__SCREAMING_SNAKE_CASE = in_proj_weight[:dim, :]
__SCREAMING_SNAKE_CASE = in_proj_bias[: dim]
__SCREAMING_SNAKE_CASE = in_proj_weight[
dim : dim * 2, :
]
__SCREAMING_SNAKE_CASE = in_proj_bias[
dim : dim * 2
]
__SCREAMING_SNAKE_CASE = in_proj_weight[
-dim :, :
]
__SCREAMING_SNAKE_CASE = in_proj_bias[-dim :]
# fmt: on
def _a ( UpperCAmelCase__ ) -> Dict:
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = x.shape
__SCREAMING_SNAKE_CASE = x.reshape(UpperCAmelCase__ , 4 , in_channel // 4 )
__SCREAMING_SNAKE_CASE = x[:, [0, 2, 1, 3], :].transpose(1 , 2 ).reshape(UpperCAmelCase__ , UpperCAmelCase__ )
return x
def _a ( UpperCAmelCase__ ) -> Any:
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = x.shape
__SCREAMING_SNAKE_CASE = x.reshape(UpperCAmelCase__ , in_channel // 4 , 4 )
__SCREAMING_SNAKE_CASE = x[:, :, [0, 2, 1, 3]].transpose(1 , 2 ).reshape(UpperCAmelCase__ , UpperCAmelCase__ )
return x
def _a ( UpperCAmelCase__ ) -> Optional[Any]:
__SCREAMING_SNAKE_CASE = x.shape[0]
__SCREAMING_SNAKE_CASE = x.reshape(4 , in_channel // 4 )
__SCREAMING_SNAKE_CASE = x[[0, 2, 1, 3], :].transpose(0 , 1 ).reshape(UpperCAmelCase__ )
return x
def _a ( UpperCAmelCase__ ) -> List[Any]:
__SCREAMING_SNAKE_CASE = x.shape[0]
__SCREAMING_SNAKE_CASE = x.reshape(in_channel // 4 , 4 )
__SCREAMING_SNAKE_CASE = x[:, [0, 2, 1, 3]].transpose(0 , 1 ).reshape(UpperCAmelCase__ )
return x
def _a ( UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ ) -> str:
__SCREAMING_SNAKE_CASE = {
'''upernet-swin-tiny''': '''https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_tiny_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K/upernet_swin_tiny_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K_20210531_112542-e380ad3e.pth''',
'''upernet-swin-small''': '''https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_small_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K/upernet_swin_small_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K_20210526_192015-ee2fff1c.pth''',
'''upernet-swin-base''': '''https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_22K/upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_22K_20210531_125459-429057bf.pth''',
'''upernet-swin-large''': '''https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_large_patch4_window12_512x512_pretrain_384x384_22K_160k_ade20k/upernet_swin_large_patch4_window12_512x512_pretrain_384x384_22K_160k_ade20k_20220318_091743-9ba68901.pth''',
}
__SCREAMING_SNAKE_CASE = model_name_to_url[model_name]
__SCREAMING_SNAKE_CASE = torch.hub.load_state_dict_from_url(UpperCAmelCase__ , map_location='''cpu''' , file_name=UpperCAmelCase__ )[
'''state_dict'''
]
for name, param in state_dict.items():
print(UpperCAmelCase__ , param.shape )
__SCREAMING_SNAKE_CASE = get_upernet_config(UpperCAmelCase__ )
__SCREAMING_SNAKE_CASE = UperNetForSemanticSegmentation(UpperCAmelCase__ )
model.eval()
# replace "bn" => "batch_norm"
for key in state_dict.copy().keys():
__SCREAMING_SNAKE_CASE = state_dict.pop(UpperCAmelCase__ )
if "bn" in key:
__SCREAMING_SNAKE_CASE = key.replace('''bn''' , '''batch_norm''' )
__SCREAMING_SNAKE_CASE = val
# rename keys
__SCREAMING_SNAKE_CASE = create_rename_keys(UpperCAmelCase__ )
for src, dest in rename_keys:
rename_key(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ )
read_in_q_k_v(UpperCAmelCase__ , config.backbone_config )
# fix downsample parameters
for key, value in state_dict.items():
if "downsample" in key:
if "reduction" in key:
__SCREAMING_SNAKE_CASE = reverse_correct_unfold_reduction_order(UpperCAmelCase__ )
if "norm" in key:
__SCREAMING_SNAKE_CASE = reverse_correct_unfold_norm_order(UpperCAmelCase__ )
model.load_state_dict(UpperCAmelCase__ )
# verify on image
__SCREAMING_SNAKE_CASE = '''https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000001.jpg'''
__SCREAMING_SNAKE_CASE = Image.open(requests.get(UpperCAmelCase__ , stream=UpperCAmelCase__ ).raw ).convert('''RGB''' )
__SCREAMING_SNAKE_CASE = SegformerImageProcessor()
__SCREAMING_SNAKE_CASE = processor(UpperCAmelCase__ , return_tensors='''pt''' ).pixel_values
with torch.no_grad():
__SCREAMING_SNAKE_CASE = model(UpperCAmelCase__ )
__SCREAMING_SNAKE_CASE = outputs.logits
print(logits.shape )
print('''First values of logits:''' , logits[0, 0, :3, :3] )
# assert values
if model_name == "upernet-swin-tiny":
__SCREAMING_SNAKE_CASE = torch.tensor(
[[-7.5958, -7.5958, -7.4302], [-7.5958, -7.5958, -7.4302], [-7.4797, -7.4797, -7.3068]] )
elif model_name == "upernet-swin-small":
__SCREAMING_SNAKE_CASE = torch.tensor(
[[-7.1921, -7.1921, -6.9532], [-7.1921, -7.1921, -6.9532], [-7.0908, -7.0908, -6.8534]] )
elif model_name == "upernet-swin-base":
__SCREAMING_SNAKE_CASE = torch.tensor(
[[-6.5851, -6.5851, -6.4330], [-6.5851, -6.5851, -6.4330], [-6.4763, -6.4763, -6.3254]] )
elif model_name == "upernet-swin-large":
__SCREAMING_SNAKE_CASE = torch.tensor(
[[-7.5297, -7.5297, -7.3802], [-7.5297, -7.5297, -7.3802], [-7.4044, -7.4044, -7.2586]] )
print('''Logits:''' , outputs.logits[0, 0, :3, :3] )
assert torch.allclose(outputs.logits[0, 0, :3, :3] , UpperCAmelCase__ , atol=1E-4 )
print('''Looks ok!''' )
if pytorch_dump_folder_path is not None:
print(f"""Saving model {model_name} to {pytorch_dump_folder_path}""" )
model.save_pretrained(UpperCAmelCase__ )
print(f"""Saving processor to {pytorch_dump_folder_path}""" )
processor.save_pretrained(UpperCAmelCase__ )
if push_to_hub:
print(f"""Pushing model and processor for {model_name} to hub""" )
model.push_to_hub(f"""openmmlab/{model_name}""" )
processor.push_to_hub(f"""openmmlab/{model_name}""" )
if __name__ == "__main__":
lowerCAmelCase__ =argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--model_name",
default="upernet-swin-tiny",
type=str,
choices=[F'''upernet-swin-{size}''' for size in ["tiny", "small", "base", "large"]],
help="Name of the Swin + UperNet model you'd like to convert.",
)
parser.add_argument(
"--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
)
parser.add_argument(
"--push_to_hub", action="store_true", help="Whether or not to push the converted model to the 🤗 hub."
)
lowerCAmelCase__ =parser.parse_args()
convert_upernet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
| 690 |
"""simple docstring"""
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import re
from ..models.auto import AutoProcessor
from ..models.vision_encoder_decoder import VisionEncoderDecoderModel
from ..utils import is_vision_available
from .base import PipelineTool
if is_vision_available():
from PIL import Image
class A__( __magic_name__ ):
lowerCAmelCase = '''naver-clova-ix/donut-base-finetuned-docvqa'''
lowerCAmelCase = (
'''This is a tool that answers a question about an document (pdf). It takes an input named `document` which '''
'''should be the document containing the information, as well as a `question` that is the question about the '''
'''document. It returns a text that contains the answer to the question.'''
)
lowerCAmelCase = '''document_qa'''
lowerCAmelCase = AutoProcessor
lowerCAmelCase = VisionEncoderDecoderModel
lowerCAmelCase = ['''image''', '''text''']
lowerCAmelCase = ['''text''']
def __init__( self : str , *__SCREAMING_SNAKE_CASE : List[str] , **__SCREAMING_SNAKE_CASE : List[Any] ) -> Any:
"""simple docstring"""
if not is_vision_available():
raise ValueError('''Pillow must be installed to use the DocumentQuestionAnsweringTool.''' )
super().__init__(*__SCREAMING_SNAKE_CASE , **__SCREAMING_SNAKE_CASE )
def _a ( self : Union[str, Any] , __SCREAMING_SNAKE_CASE : "Image" , __SCREAMING_SNAKE_CASE : str ) -> str:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = '''<s_docvqa><s_question>{user_input}</s_question><s_answer>'''
__SCREAMING_SNAKE_CASE = task_prompt.replace('''{user_input}''' , __SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = self.pre_processor.tokenizer(
__SCREAMING_SNAKE_CASE , add_special_tokens=__SCREAMING_SNAKE_CASE , return_tensors='''pt''' ).input_ids
__SCREAMING_SNAKE_CASE = self.pre_processor(__SCREAMING_SNAKE_CASE , return_tensors='''pt''' ).pixel_values
return {"decoder_input_ids": decoder_input_ids, "pixel_values": pixel_values}
def _a ( self : Union[str, Any] , __SCREAMING_SNAKE_CASE : Optional[Any] ) -> Tuple:
"""simple docstring"""
return self.model.generate(
inputs['''pixel_values'''].to(self.device ) , decoder_input_ids=inputs['''decoder_input_ids'''].to(self.device ) , max_length=self.model.decoder.config.max_position_embeddings , early_stopping=__SCREAMING_SNAKE_CASE , pad_token_id=self.pre_processor.tokenizer.pad_token_id , eos_token_id=self.pre_processor.tokenizer.eos_token_id , use_cache=__SCREAMING_SNAKE_CASE , num_beams=1 , bad_words_ids=[[self.pre_processor.tokenizer.unk_token_id]] , return_dict_in_generate=__SCREAMING_SNAKE_CASE , ).sequences
def _a ( self : Tuple , __SCREAMING_SNAKE_CASE : Tuple ) -> List[str]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = self.pre_processor.batch_decode(__SCREAMING_SNAKE_CASE )[0]
__SCREAMING_SNAKE_CASE = sequence.replace(self.pre_processor.tokenizer.eos_token , '''''' )
__SCREAMING_SNAKE_CASE = sequence.replace(self.pre_processor.tokenizer.pad_token , '''''' )
__SCREAMING_SNAKE_CASE = re.sub(r'''<.*?>''' , '''''' , __SCREAMING_SNAKE_CASE , count=1 ).strip() # remove first task start token
__SCREAMING_SNAKE_CASE = self.pre_processor.tokenajson(__SCREAMING_SNAKE_CASE )
return sequence["answer"]
| 690 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
lowerCAmelCase__ ={
"configuration_owlvit": [
"OWLVIT_PRETRAINED_CONFIG_ARCHIVE_MAP",
"OwlViTConfig",
"OwlViTOnnxConfig",
"OwlViTTextConfig",
"OwlViTVisionConfig",
],
"processing_owlvit": ["OwlViTProcessor"],
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase__ =["OwlViTFeatureExtractor"]
lowerCAmelCase__ =["OwlViTImageProcessor"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase__ =[
"OWLVIT_PRETRAINED_MODEL_ARCHIVE_LIST",
"OwlViTModel",
"OwlViTPreTrainedModel",
"OwlViTTextModel",
"OwlViTVisionModel",
"OwlViTForObjectDetection",
]
if TYPE_CHECKING:
from .configuration_owlvit import (
OWLVIT_PRETRAINED_CONFIG_ARCHIVE_MAP,
OwlViTConfig,
OwlViTOnnxConfig,
OwlViTTextConfig,
OwlViTVisionConfig,
)
from .processing_owlvit import OwlViTProcessor
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_owlvit import OwlViTFeatureExtractor
from .image_processing_owlvit import OwlViTImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_owlvit import (
OWLVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
OwlViTForObjectDetection,
OwlViTModel,
OwlViTPreTrainedModel,
OwlViTTextModel,
OwlViTVisionModel,
)
else:
import sys
lowerCAmelCase__ =_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 690 |
"""simple docstring"""
import unittest
import numpy as np
import torch
from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class A__( unittest.TestCase ):
@property
def _a ( self : Optional[Any] ) -> Tuple:
"""simple docstring"""
torch.manual_seed(0 )
__SCREAMING_SNAKE_CASE = UNetaDModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=3 , out_channels=3 , down_block_types=('''DownBlock2D''', '''AttnDownBlock2D''') , up_block_types=('''AttnUpBlock2D''', '''UpBlock2D''') , )
return model
def _a ( self : str ) -> Any:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = self.dummy_uncond_unet
__SCREAMING_SNAKE_CASE = KarrasVeScheduler()
__SCREAMING_SNAKE_CASE = KarrasVePipeline(unet=__SCREAMING_SNAKE_CASE , scheduler=__SCREAMING_SNAKE_CASE )
pipe.to(__SCREAMING_SNAKE_CASE )
pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = torch.manual_seed(0 )
__SCREAMING_SNAKE_CASE = pipe(num_inference_steps=2 , generator=__SCREAMING_SNAKE_CASE , output_type='''numpy''' ).images
__SCREAMING_SNAKE_CASE = torch.manual_seed(0 )
__SCREAMING_SNAKE_CASE = pipe(num_inference_steps=2 , generator=__SCREAMING_SNAKE_CASE , output_type='''numpy''' , return_dict=__SCREAMING_SNAKE_CASE )[0]
__SCREAMING_SNAKE_CASE = image[0, -3:, -3:, -1]
__SCREAMING_SNAKE_CASE = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
__SCREAMING_SNAKE_CASE = np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1E-2
@slow
@require_torch
class A__( unittest.TestCase ):
def _a ( self : Any ) -> str:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = '''google/ncsnpp-celebahq-256'''
__SCREAMING_SNAKE_CASE = UNetaDModel.from_pretrained(__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = KarrasVeScheduler()
__SCREAMING_SNAKE_CASE = KarrasVePipeline(unet=__SCREAMING_SNAKE_CASE , scheduler=__SCREAMING_SNAKE_CASE )
pipe.to(__SCREAMING_SNAKE_CASE )
pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = torch.manual_seed(0 )
__SCREAMING_SNAKE_CASE = pipe(num_inference_steps=20 , generator=__SCREAMING_SNAKE_CASE , output_type='''numpy''' ).images
__SCREAMING_SNAKE_CASE = image[0, -3:, -3:, -1]
assert image.shape == (1, 2_56, 2_56, 3)
__SCREAMING_SNAKE_CASE = np.array([0.5_78, 0.58_11, 0.59_24, 0.58_09, 0.5_87, 0.58_86, 0.58_61, 0.58_02, 0.5_86] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
| 690 | 1 |
"""simple docstring"""
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase__ =logging.get_logger(__name__)
lowerCAmelCase__ ={
"facebook/wav2vec2-base-960h": "https://huggingface.co/facebook/wav2vec2-base-960h/resolve/main/config.json",
# See all Wav2Vec2 models at https://huggingface.co/models?filter=wav2vec2
}
class A__( __magic_name__ ):
lowerCAmelCase = '''wav2vec2'''
def __init__( self : Tuple , __SCREAMING_SNAKE_CASE : Optional[Any]=32 , __SCREAMING_SNAKE_CASE : List[Any]=7_68 , __SCREAMING_SNAKE_CASE : Any=12 , __SCREAMING_SNAKE_CASE : Optional[Any]=12 , __SCREAMING_SNAKE_CASE : str=30_72 , __SCREAMING_SNAKE_CASE : Dict="gelu" , __SCREAMING_SNAKE_CASE : Optional[int]=0.1 , __SCREAMING_SNAKE_CASE : List[str]=0.1 , __SCREAMING_SNAKE_CASE : str=0.1 , __SCREAMING_SNAKE_CASE : Tuple=0.0 , __SCREAMING_SNAKE_CASE : str=0.0 , __SCREAMING_SNAKE_CASE : int=0.1 , __SCREAMING_SNAKE_CASE : Dict=0.1 , __SCREAMING_SNAKE_CASE : List[Any]=0.02 , __SCREAMING_SNAKE_CASE : List[str]=1E-5 , __SCREAMING_SNAKE_CASE : List[str]="group" , __SCREAMING_SNAKE_CASE : Optional[int]="gelu" , __SCREAMING_SNAKE_CASE : Union[str, Any]=(5_12, 5_12, 5_12, 5_12, 5_12, 5_12, 5_12) , __SCREAMING_SNAKE_CASE : List[str]=(5, 2, 2, 2, 2, 2, 2) , __SCREAMING_SNAKE_CASE : Optional[int]=(10, 3, 3, 3, 3, 2, 2) , __SCREAMING_SNAKE_CASE : Tuple=False , __SCREAMING_SNAKE_CASE : int=1_28 , __SCREAMING_SNAKE_CASE : int=16 , __SCREAMING_SNAKE_CASE : Optional[int]=False , __SCREAMING_SNAKE_CASE : Any=True , __SCREAMING_SNAKE_CASE : List[str]=0.05 , __SCREAMING_SNAKE_CASE : Union[str, Any]=10 , __SCREAMING_SNAKE_CASE : Optional[int]=2 , __SCREAMING_SNAKE_CASE : Optional[Any]=0.0 , __SCREAMING_SNAKE_CASE : str=10 , __SCREAMING_SNAKE_CASE : Union[str, Any]=0 , __SCREAMING_SNAKE_CASE : Optional[Any]=3_20 , __SCREAMING_SNAKE_CASE : Optional[int]=2 , __SCREAMING_SNAKE_CASE : int=0.1 , __SCREAMING_SNAKE_CASE : Union[str, Any]=1_00 , __SCREAMING_SNAKE_CASE : Any=2_56 , __SCREAMING_SNAKE_CASE : str=2_56 , __SCREAMING_SNAKE_CASE : Any=0.1 , __SCREAMING_SNAKE_CASE : Tuple="sum" , __SCREAMING_SNAKE_CASE : Union[str, Any]=False , __SCREAMING_SNAKE_CASE : Tuple=False , __SCREAMING_SNAKE_CASE : Any=2_56 , __SCREAMING_SNAKE_CASE : str=(5_12, 5_12, 5_12, 5_12, 15_00) , __SCREAMING_SNAKE_CASE : Tuple=(5, 3, 3, 1, 1) , __SCREAMING_SNAKE_CASE : str=(1, 2, 3, 1, 1) , __SCREAMING_SNAKE_CASE : str=5_12 , __SCREAMING_SNAKE_CASE : List[Any]=0 , __SCREAMING_SNAKE_CASE : Optional[Any]=1 , __SCREAMING_SNAKE_CASE : Tuple=2 , __SCREAMING_SNAKE_CASE : Union[str, Any]=False , __SCREAMING_SNAKE_CASE : int=3 , __SCREAMING_SNAKE_CASE : Any=2 , __SCREAMING_SNAKE_CASE : Any=3 , __SCREAMING_SNAKE_CASE : int=None , __SCREAMING_SNAKE_CASE : List[str]=None , **__SCREAMING_SNAKE_CASE : Optional[Any] , ) -> int:
"""simple docstring"""
super().__init__(**__SCREAMING_SNAKE_CASE , pad_token_id=__SCREAMING_SNAKE_CASE , bos_token_id=__SCREAMING_SNAKE_CASE , eos_token_id=__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = hidden_size
__SCREAMING_SNAKE_CASE = feat_extract_norm
__SCREAMING_SNAKE_CASE = feat_extract_activation
__SCREAMING_SNAKE_CASE = list(__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = list(__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = list(__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = conv_bias
__SCREAMING_SNAKE_CASE = num_conv_pos_embeddings
__SCREAMING_SNAKE_CASE = num_conv_pos_embedding_groups
__SCREAMING_SNAKE_CASE = len(self.conv_dim )
__SCREAMING_SNAKE_CASE = num_hidden_layers
__SCREAMING_SNAKE_CASE = intermediate_size
__SCREAMING_SNAKE_CASE = hidden_act
__SCREAMING_SNAKE_CASE = num_attention_heads
__SCREAMING_SNAKE_CASE = hidden_dropout
__SCREAMING_SNAKE_CASE = attention_dropout
__SCREAMING_SNAKE_CASE = activation_dropout
__SCREAMING_SNAKE_CASE = feat_proj_dropout
__SCREAMING_SNAKE_CASE = final_dropout
__SCREAMING_SNAKE_CASE = layerdrop
__SCREAMING_SNAKE_CASE = layer_norm_eps
__SCREAMING_SNAKE_CASE = initializer_range
__SCREAMING_SNAKE_CASE = vocab_size
__SCREAMING_SNAKE_CASE = do_stable_layer_norm
__SCREAMING_SNAKE_CASE = use_weighted_layer_sum
if (
(len(self.conv_stride ) != self.num_feat_extract_layers)
or (len(self.conv_kernel ) != self.num_feat_extract_layers)
or (len(self.conv_dim ) != self.num_feat_extract_layers)
):
raise ValueError(
'''Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =='''
''' `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ='''
f""" {len(self.conv_dim )}`, `len(config.conv_stride) = {len(self.conv_stride )}`,"""
f""" `len(config.conv_kernel) = {len(self.conv_kernel )}`.""" )
# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
__SCREAMING_SNAKE_CASE = apply_spec_augment
__SCREAMING_SNAKE_CASE = mask_time_prob
__SCREAMING_SNAKE_CASE = mask_time_length
__SCREAMING_SNAKE_CASE = mask_time_min_masks
__SCREAMING_SNAKE_CASE = mask_feature_prob
__SCREAMING_SNAKE_CASE = mask_feature_length
__SCREAMING_SNAKE_CASE = mask_feature_min_masks
# parameters for pretraining with codevector quantized representations
__SCREAMING_SNAKE_CASE = num_codevectors_per_group
__SCREAMING_SNAKE_CASE = num_codevector_groups
__SCREAMING_SNAKE_CASE = contrastive_logits_temperature
__SCREAMING_SNAKE_CASE = feat_quantizer_dropout
__SCREAMING_SNAKE_CASE = num_negatives
__SCREAMING_SNAKE_CASE = codevector_dim
__SCREAMING_SNAKE_CASE = proj_codevector_dim
__SCREAMING_SNAKE_CASE = diversity_loss_weight
# ctc loss
__SCREAMING_SNAKE_CASE = ctc_loss_reduction
__SCREAMING_SNAKE_CASE = ctc_zero_infinity
# adapter
__SCREAMING_SNAKE_CASE = add_adapter
__SCREAMING_SNAKE_CASE = adapter_kernel_size
__SCREAMING_SNAKE_CASE = adapter_stride
__SCREAMING_SNAKE_CASE = num_adapter_layers
__SCREAMING_SNAKE_CASE = output_hidden_size or hidden_size
__SCREAMING_SNAKE_CASE = adapter_attn_dim
# SequenceClassification-specific parameter. Feel free to ignore for other classes.
__SCREAMING_SNAKE_CASE = classifier_proj_size
# XVector-specific parameters. Feel free to ignore for other classes.
__SCREAMING_SNAKE_CASE = list(__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = list(__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = list(__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = xvector_output_dim
@property
def _a ( self : Optional[Any] ) -> int:
"""simple docstring"""
return functools.reduce(operator.mul , self.conv_stride , 1 )
| 690 |
"""simple docstring"""
import os
import re
import unicodedata
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import is_torch_available, logging
if is_torch_available():
import torch
if TYPE_CHECKING:
from transformers.pipelines.conversational import Conversation
lowerCAmelCase__ =logging.get_logger(__name__)
lowerCAmelCase__ ={"vocab_file": "spiece.model"}
lowerCAmelCase__ ={
"vocab_file": {
"AI-Sweden/gpt-sw3-126m": "https://huggingface.co/AI-Sweden/gpt-sw3-126m/resolve/main/spiece.model",
"AI-Sweden/gpt-sw3-350m": "https://huggingface.co/AI-Sweden/gpt-sw3-350m/resolve/main/spiece.model",
"AI-Sweden/gpt-sw3-1.6b": "https://huggingface.co/AI-Sweden/gpt-sw3-1.6b/resolve/main/spiece.model",
"AI-Sweden/gpt-sw3-6.7b": "https://huggingface.co/AI-Sweden/gpt-sw3-6.7b/resolve/main/spiece.model",
"AI-Sweden/gpt-sw3-20b": "https://huggingface.co/AI-Sweden/gpt-sw3-20b/resolve/main/spiece.model",
}
}
lowerCAmelCase__ ={
"AI-Sweden/gpt-sw3-126m": 2_048,
"AI-Sweden/gpt-sw3-350m": 2_048,
"AI-Sweden/gpt-sw3-1.6b": 2_048,
"AI-Sweden/gpt-sw3-6.7b": 2_048,
"AI-Sweden/gpt-sw3-20b": 2_048,
}
class A__( __magic_name__ ):
lowerCAmelCase = VOCAB_FILES_NAMES
lowerCAmelCase = PRETRAINED_VOCAB_FILES_MAP
lowerCAmelCase = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowerCAmelCase = ['''input_ids''', '''attention_mask''']
def __init__( self : int , __SCREAMING_SNAKE_CASE : int , __SCREAMING_SNAKE_CASE : str=False , __SCREAMING_SNAKE_CASE : str=False , __SCREAMING_SNAKE_CASE : Optional[int]=False , __SCREAMING_SNAKE_CASE : Union[str, Any]=None , __SCREAMING_SNAKE_CASE : List[Any]=None , __SCREAMING_SNAKE_CASE : Any=None , __SCREAMING_SNAKE_CASE : List[Any]=None , __SCREAMING_SNAKE_CASE : Optional[Dict[str, Any]] = None , **__SCREAMING_SNAKE_CASE : Dict , ) -> None:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = {} if sp_model_kwargs is None else sp_model_kwargs
__SCREAMING_SNAKE_CASE = kwargs.get('''name_or_path''' )
if name_or_path is None:
logger.warning(
'''name_or_path not provided, will work for all GPTSw3 models except gpt-sw3-7b,'''
''' you are testing the model, this can safely be ignored''' )
__SCREAMING_SNAKE_CASE = '''None'''
# Default definitions for our 2 tokenizer versions, with None-checks to enable proper testing
__SCREAMING_SNAKE_CASE = '''<|endoftext|>''' if eos_token is None else eos_token
__SCREAMING_SNAKE_CASE = '''<unk>''' if unk_token is None else unk_token
if "gpt-sw3-7b" in name_or_path:
__SCREAMING_SNAKE_CASE = unk_token if pad_token is None else pad_token
__SCREAMING_SNAKE_CASE = eos_token if bos_token is None else bos_token
else:
__SCREAMING_SNAKE_CASE = '''<pad>''' if pad_token is None else pad_token
__SCREAMING_SNAKE_CASE = '''<s>''' if bos_token is None else bos_token
super().__init__(
do_lower_case=__SCREAMING_SNAKE_CASE , remove_space=__SCREAMING_SNAKE_CASE , keep_accents=__SCREAMING_SNAKE_CASE , bos_token=__SCREAMING_SNAKE_CASE , eos_token=__SCREAMING_SNAKE_CASE , unk_token=__SCREAMING_SNAKE_CASE , pad_token=__SCREAMING_SNAKE_CASE , sp_model_kwargs=self.sp_model_kwargs , **__SCREAMING_SNAKE_CASE , )
__SCREAMING_SNAKE_CASE = do_lower_case
__SCREAMING_SNAKE_CASE = remove_space
__SCREAMING_SNAKE_CASE = keep_accents
__SCREAMING_SNAKE_CASE = vocab_file
__SCREAMING_SNAKE_CASE = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(__SCREAMING_SNAKE_CASE )
# Used for whitespace normalization in input texts
# fmt : off
__SCREAMING_SNAKE_CASE = {''' ''', ''' ''', ''' ''', ''' ''', ''' ''', ''' ''', ''' ''', ''' ''', ''' ''', ''' ''', '''''', ''''''}
# fmt : on
# Regular expression to remove non-printing characters (e.g. some unicode control chars) in preprocessing
__SCREAMING_SNAKE_CASE = re.compile(
f"""[{"".join(map(__SCREAMING_SNAKE_CASE , list(range(0 , 9 ) ) + list(range(11 , 32 ) ) + list(range(1_27 , 1_60 ) ) + [1_60, 1_73, 82_03] ) )}]""" )
def __getstate__( self : List[str] ) -> Optional[Any]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = self.__dict__.copy()
__SCREAMING_SNAKE_CASE = None
return state
def __setstate__( self : int , __SCREAMING_SNAKE_CASE : Optional[int] ) -> int:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = d
# for backward compatibility
if not hasattr(self , '''sp_model_kwargs''' ):
__SCREAMING_SNAKE_CASE = {}
__SCREAMING_SNAKE_CASE = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.vocab_file )
@property
# Copied from transformers.models.albert.tokenization_albert.AlbertTokenizer.vocab_size
def _a ( self : Optional[Any] ) -> int:
"""simple docstring"""
return len(self.sp_model )
def _a ( self : List[str] , __SCREAMING_SNAKE_CASE : str ) -> str:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = self.non_printing_characters_re.sub('''''' , __SCREAMING_SNAKE_CASE )
# Normalize whitespaces
__SCREAMING_SNAKE_CASE = ''''''.join([char if char not in self.whitespaces else ''' ''' for char in text] )
# NFC Unicode normalization
__SCREAMING_SNAKE_CASE = unicodedata.normalize('''NFC''' , __SCREAMING_SNAKE_CASE )
return text
def _a ( self : Union[str, Any] , __SCREAMING_SNAKE_CASE : str , **__SCREAMING_SNAKE_CASE : Optional[int] ) -> List[str]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = self.preprocess_text(__SCREAMING_SNAKE_CASE )
return self.sp_model.encode(__SCREAMING_SNAKE_CASE , out_type=__SCREAMING_SNAKE_CASE )
def _a ( self : Tuple , __SCREAMING_SNAKE_CASE : str ) -> int:
"""simple docstring"""
return self.sp_model.PieceToId(__SCREAMING_SNAKE_CASE )
def _a ( self : List[str] , __SCREAMING_SNAKE_CASE : int ) -> str:
"""simple docstring"""
return self.sp_model.IdToPiece(__SCREAMING_SNAKE_CASE )
@staticmethod
def _a ( __SCREAMING_SNAKE_CASE : str ) -> str:
"""simple docstring"""
return out_string
def _a ( self : Union[str, Any] , __SCREAMING_SNAKE_CASE : List[str] ) -> str:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = []
__SCREAMING_SNAKE_CASE = ''''''
__SCREAMING_SNAKE_CASE = False
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
# TODO: Check if this is needed, as it ensures that decode(encode(doc)) != doc by adding extra whitespace in the decoded document
if not prev_is_special:
out_string += " "
out_string += self.sp_model.decode(__SCREAMING_SNAKE_CASE ) + token
__SCREAMING_SNAKE_CASE = True
__SCREAMING_SNAKE_CASE = []
else:
current_sub_tokens.append(__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = False
out_string += self.sp_model.decode(__SCREAMING_SNAKE_CASE )
return out_string
def _a ( self : Union[str, Any] ) -> Dict[str, int]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = {self.convert_ids_to_tokens(__SCREAMING_SNAKE_CASE ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def _a ( self : List[Any] , __SCREAMING_SNAKE_CASE : str , __SCREAMING_SNAKE_CASE : Optional[str] = None ) -> Tuple[str]:
"""simple docstring"""
if not os.path.isdir(__SCREAMING_SNAKE_CASE ):
logger.error(f"""Vocabulary path ({save_directory}) should be a directory""" )
return
__SCREAMING_SNAKE_CASE = os.path.join(
__SCREAMING_SNAKE_CASE , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(__SCREAMING_SNAKE_CASE ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file , __SCREAMING_SNAKE_CASE )
elif not os.path.isfile(self.vocab_file ):
with open(__SCREAMING_SNAKE_CASE , '''wb''' ) as fi:
__SCREAMING_SNAKE_CASE = self.sp_model.serialized_model_proto()
fi.write(__SCREAMING_SNAKE_CASE )
return (out_vocab_file,)
def _a ( self : Optional[Any] , __SCREAMING_SNAKE_CASE : Union[str, List[str]] , __SCREAMING_SNAKE_CASE : Union[str, bool] = False ) -> Union[List[int], List[List[int]], "torch.Tensor"]:
"""simple docstring"""
if isinstance(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
__SCREAMING_SNAKE_CASE = self.preprocess_text(__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = self.sp_model.encode(__SCREAMING_SNAKE_CASE )
else:
__SCREAMING_SNAKE_CASE = [self.preprocess_text(__SCREAMING_SNAKE_CASE ) for t in text]
__SCREAMING_SNAKE_CASE = self.sp_model.encode(__SCREAMING_SNAKE_CASE )
if return_tensors is True or return_tensors == "pt":
__SCREAMING_SNAKE_CASE = torch.tensor(__SCREAMING_SNAKE_CASE )
return token_ids
def _a ( self : Any , __SCREAMING_SNAKE_CASE : Union[int, List[int]] ) -> str:
"""simple docstring"""
return self.sp_model.decode(__SCREAMING_SNAKE_CASE )
def _a ( self : List[str] , __SCREAMING_SNAKE_CASE : "Conversation" ) -> List[int]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = [f"""User: {text}""" if is_user else f"""Bot: {text}""" for is_user, text in conversation.iter_texts()]
__SCREAMING_SNAKE_CASE = (
f"""{self.eos_token}{self.bos_token}""" + f"""{self.bos_token}""".join(__SCREAMING_SNAKE_CASE ) + f"""{self.bos_token}Bot:"""
)
return self.encode(text=__SCREAMING_SNAKE_CASE )
| 690 | 1 |
"""simple docstring"""
import argparse
import os
import shutil
from pathlib import Path
import onnx
import torch
from packaging import version
from torch.onnx import export
from diffusers import OnnxRuntimeModel, OnnxStableDiffusionPipeline, StableDiffusionPipeline
lowerCAmelCase__ =version.parse(version.parse(torch.__version__).base_version) < version.parse("1.11")
def _a ( UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__=False , ) -> Optional[Any]:
output_path.parent.mkdir(parents=UpperCAmelCase__ , exist_ok=UpperCAmelCase__ )
# PyTorch deprecated the `enable_onnx_checker` and `use_external_data_format` arguments in v1.11,
# so we check the torch version for backwards compatibility
if is_torch_less_than_1_11:
export(
UpperCAmelCase__ , UpperCAmelCase__ , f=output_path.as_posix() , input_names=UpperCAmelCase__ , output_names=UpperCAmelCase__ , dynamic_axes=UpperCAmelCase__ , do_constant_folding=UpperCAmelCase__ , use_external_data_format=UpperCAmelCase__ , enable_onnx_checker=UpperCAmelCase__ , opset_version=UpperCAmelCase__ , )
else:
export(
UpperCAmelCase__ , UpperCAmelCase__ , f=output_path.as_posix() , input_names=UpperCAmelCase__ , output_names=UpperCAmelCase__ , dynamic_axes=UpperCAmelCase__ , do_constant_folding=UpperCAmelCase__ , opset_version=UpperCAmelCase__ , )
@torch.no_grad()
def _a ( UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ = False ) -> str:
__SCREAMING_SNAKE_CASE = torch.floataa if fpaa else torch.floataa
if fpaa and torch.cuda.is_available():
__SCREAMING_SNAKE_CASE = '''cuda'''
elif fpaa and not torch.cuda.is_available():
raise ValueError('''`float16` model export is only supported on GPUs with CUDA''' )
else:
__SCREAMING_SNAKE_CASE = '''cpu'''
__SCREAMING_SNAKE_CASE = StableDiffusionPipeline.from_pretrained(UpperCAmelCase__ , torch_dtype=UpperCAmelCase__ ).to(UpperCAmelCase__ )
__SCREAMING_SNAKE_CASE = Path(UpperCAmelCase__ )
# TEXT ENCODER
__SCREAMING_SNAKE_CASE = pipeline.text_encoder.config.max_position_embeddings
__SCREAMING_SNAKE_CASE = pipeline.text_encoder.config.hidden_size
__SCREAMING_SNAKE_CASE = pipeline.tokenizer(
'''A sample prompt''' , padding='''max_length''' , max_length=pipeline.tokenizer.model_max_length , truncation=UpperCAmelCase__ , return_tensors='''pt''' , )
onnx_export(
pipeline.text_encoder , model_args=(text_input.input_ids.to(device=UpperCAmelCase__ , dtype=torch.intaa )) , output_path=output_path / '''text_encoder''' / '''model.onnx''' , ordered_input_names=['''input_ids'''] , output_names=['''last_hidden_state''', '''pooler_output'''] , dynamic_axes={
'''input_ids''': {0: '''batch''', 1: '''sequence'''},
} , opset=UpperCAmelCase__ , )
del pipeline.text_encoder
# UNET
__SCREAMING_SNAKE_CASE = pipeline.unet.config.in_channels
__SCREAMING_SNAKE_CASE = pipeline.unet.config.sample_size
__SCREAMING_SNAKE_CASE = output_path / '''unet''' / '''model.onnx'''
onnx_export(
pipeline.unet , model_args=(
torch.randn(2 , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ ).to(device=UpperCAmelCase__ , dtype=UpperCAmelCase__ ),
torch.randn(2 ).to(device=UpperCAmelCase__ , dtype=UpperCAmelCase__ ),
torch.randn(2 , UpperCAmelCase__ , UpperCAmelCase__ ).to(device=UpperCAmelCase__ , dtype=UpperCAmelCase__ ),
False,
) , output_path=UpperCAmelCase__ , ordered_input_names=['''sample''', '''timestep''', '''encoder_hidden_states''', '''return_dict'''] , output_names=['''out_sample'''] , dynamic_axes={
'''sample''': {0: '''batch''', 1: '''channels''', 2: '''height''', 3: '''width'''},
'''timestep''': {0: '''batch'''},
'''encoder_hidden_states''': {0: '''batch''', 1: '''sequence'''},
} , opset=UpperCAmelCase__ , use_external_data_format=UpperCAmelCase__ , )
__SCREAMING_SNAKE_CASE = str(unet_path.absolute().as_posix() )
__SCREAMING_SNAKE_CASE = os.path.dirname(UpperCAmelCase__ )
__SCREAMING_SNAKE_CASE = onnx.load(UpperCAmelCase__ )
# clean up existing tensor files
shutil.rmtree(UpperCAmelCase__ )
os.mkdir(UpperCAmelCase__ )
# collate external tensor files into one
onnx.save_model(
UpperCAmelCase__ , UpperCAmelCase__ , save_as_external_data=UpperCAmelCase__ , all_tensors_to_one_file=UpperCAmelCase__ , location='''weights.pb''' , convert_attribute=UpperCAmelCase__ , )
del pipeline.unet
# VAE ENCODER
__SCREAMING_SNAKE_CASE = pipeline.vae
__SCREAMING_SNAKE_CASE = vae_encoder.config.in_channels
__SCREAMING_SNAKE_CASE = vae_encoder.config.sample_size
# need to get the raw tensor output (sample) from the encoder
__SCREAMING_SNAKE_CASE = lambda UpperCAmelCase__ , UpperCAmelCase__ : vae_encoder.encode(UpperCAmelCase__ , UpperCAmelCase__ )[0].sample()
onnx_export(
UpperCAmelCase__ , model_args=(
torch.randn(1 , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ ).to(device=UpperCAmelCase__ , dtype=UpperCAmelCase__ ),
False,
) , output_path=output_path / '''vae_encoder''' / '''model.onnx''' , ordered_input_names=['''sample''', '''return_dict'''] , output_names=['''latent_sample'''] , dynamic_axes={
'''sample''': {0: '''batch''', 1: '''channels''', 2: '''height''', 3: '''width'''},
} , opset=UpperCAmelCase__ , )
# VAE DECODER
__SCREAMING_SNAKE_CASE = pipeline.vae
__SCREAMING_SNAKE_CASE = vae_decoder.config.latent_channels
__SCREAMING_SNAKE_CASE = vae_decoder.config.out_channels
# forward only through the decoder part
__SCREAMING_SNAKE_CASE = vae_encoder.decode
onnx_export(
UpperCAmelCase__ , model_args=(
torch.randn(1 , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ ).to(device=UpperCAmelCase__ , dtype=UpperCAmelCase__ ),
False,
) , output_path=output_path / '''vae_decoder''' / '''model.onnx''' , ordered_input_names=['''latent_sample''', '''return_dict'''] , output_names=['''sample'''] , dynamic_axes={
'''latent_sample''': {0: '''batch''', 1: '''channels''', 2: '''height''', 3: '''width'''},
} , opset=UpperCAmelCase__ , )
del pipeline.vae
# SAFETY CHECKER
if pipeline.safety_checker is not None:
__SCREAMING_SNAKE_CASE = pipeline.safety_checker
__SCREAMING_SNAKE_CASE = safety_checker.config.vision_config.num_channels
__SCREAMING_SNAKE_CASE = safety_checker.config.vision_config.image_size
__SCREAMING_SNAKE_CASE = safety_checker.forward_onnx
onnx_export(
pipeline.safety_checker , model_args=(
torch.randn(
1 , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , ).to(device=UpperCAmelCase__ , dtype=UpperCAmelCase__ ),
torch.randn(1 , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ ).to(device=UpperCAmelCase__ , dtype=UpperCAmelCase__ ),
) , output_path=output_path / '''safety_checker''' / '''model.onnx''' , ordered_input_names=['''clip_input''', '''images'''] , output_names=['''out_images''', '''has_nsfw_concepts'''] , dynamic_axes={
'''clip_input''': {0: '''batch''', 1: '''channels''', 2: '''height''', 3: '''width'''},
'''images''': {0: '''batch''', 1: '''height''', 2: '''width''', 3: '''channels'''},
} , opset=UpperCAmelCase__ , )
del pipeline.safety_checker
__SCREAMING_SNAKE_CASE = OnnxRuntimeModel.from_pretrained(output_path / '''safety_checker''' )
__SCREAMING_SNAKE_CASE = pipeline.feature_extractor
else:
__SCREAMING_SNAKE_CASE = None
__SCREAMING_SNAKE_CASE = None
__SCREAMING_SNAKE_CASE = OnnxStableDiffusionPipeline(
vae_encoder=OnnxRuntimeModel.from_pretrained(output_path / '''vae_encoder''' ) , vae_decoder=OnnxRuntimeModel.from_pretrained(output_path / '''vae_decoder''' ) , text_encoder=OnnxRuntimeModel.from_pretrained(output_path / '''text_encoder''' ) , tokenizer=pipeline.tokenizer , unet=OnnxRuntimeModel.from_pretrained(output_path / '''unet''' ) , scheduler=pipeline.scheduler , safety_checker=UpperCAmelCase__ , feature_extractor=UpperCAmelCase__ , requires_safety_checker=safety_checker is not None , )
onnx_pipeline.save_pretrained(UpperCAmelCase__ )
print('''ONNX pipeline saved to''' , UpperCAmelCase__ )
del pipeline
del onnx_pipeline
__SCREAMING_SNAKE_CASE = OnnxStableDiffusionPipeline.from_pretrained(UpperCAmelCase__ , provider='''CPUExecutionProvider''' )
print('''ONNX pipeline is loadable''' )
if __name__ == "__main__":
lowerCAmelCase__ =argparse.ArgumentParser()
parser.add_argument(
"--model_path",
type=str,
required=True,
help="Path to the `diffusers` checkpoint to convert (either a local directory or on the Hub).",
)
parser.add_argument("--output_path", type=str, required=True, help="Path to the output model.")
parser.add_argument(
"--opset",
default=14,
type=int,
help="The version of the ONNX operator set to use.",
)
parser.add_argument("--fp16", action="store_true", default=False, help="Export the models in `float16` mode")
lowerCAmelCase__ =parser.parse_args()
convert_models(args.model_path, args.output_path, args.opset, args.fpaa)
| 690 |
"""simple docstring"""
from __future__ import annotations
import json
import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent
lowerCAmelCase__ ={"UserAgent": UserAgent().random}
def _a ( UpperCAmelCase__ ) -> dict:
__SCREAMING_SNAKE_CASE = script.contents[0]
__SCREAMING_SNAKE_CASE = json.loads(data[data.find('''{"config"''' ) : -1] )
return info["entry_data"]["ProfilePage"][0]["graphql"]["user"]
class A__:
def __init__( self : Dict , __SCREAMING_SNAKE_CASE : int ) -> Optional[Any]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = f"""https://www.instagram.com/{username}/"""
__SCREAMING_SNAKE_CASE = self.get_json()
def _a ( self : List[Any] ) -> dict:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = requests.get(self.url , headers=__SCREAMING_SNAKE_CASE ).text
__SCREAMING_SNAKE_CASE = BeautifulSoup(__SCREAMING_SNAKE_CASE , '''html.parser''' ).find_all('''script''' )
try:
return extract_user_profile(scripts[4] )
except (json.decoder.JSONDecodeError, KeyError):
return extract_user_profile(scripts[3] )
def __repr__( self : Tuple ) -> str:
"""simple docstring"""
return f"""{self.__class__.__name__}('{self.username}')"""
def __str__( self : Optional[int] ) -> str:
"""simple docstring"""
return f"""{self.fullname} ({self.username}) is {self.biography}"""
@property
def _a ( self : Tuple ) -> str:
"""simple docstring"""
return self.user_data["username"]
@property
def _a ( self : List[Any] ) -> str:
"""simple docstring"""
return self.user_data["full_name"]
@property
def _a ( self : Optional[Any] ) -> str:
"""simple docstring"""
return self.user_data["biography"]
@property
def _a ( self : List[str] ) -> str:
"""simple docstring"""
return self.user_data["business_email"]
@property
def _a ( self : Any ) -> str:
"""simple docstring"""
return self.user_data["external_url"]
@property
def _a ( self : Any ) -> int:
"""simple docstring"""
return self.user_data["edge_followed_by"]["count"]
@property
def _a ( self : Dict ) -> int:
"""simple docstring"""
return self.user_data["edge_follow"]["count"]
@property
def _a ( self : str ) -> int:
"""simple docstring"""
return self.user_data["edge_owner_to_timeline_media"]["count"]
@property
def _a ( self : Union[str, Any] ) -> str:
"""simple docstring"""
return self.user_data["profile_pic_url_hd"]
@property
def _a ( self : Tuple ) -> bool:
"""simple docstring"""
return self.user_data["is_verified"]
@property
def _a ( self : Union[str, Any] ) -> bool:
"""simple docstring"""
return self.user_data["is_private"]
def _a ( UpperCAmelCase__ = "github" ) -> None:
import os
if os.environ.get('''CI''' ):
return # test failing on GitHub Actions
__SCREAMING_SNAKE_CASE = InstagramUser(UpperCAmelCase__ )
assert instagram_user.user_data
assert isinstance(instagram_user.user_data , UpperCAmelCase__ )
assert instagram_user.username == username
if username != "github":
return
assert instagram_user.fullname == "GitHub"
assert instagram_user.biography == "Built for developers."
assert instagram_user.number_of_posts > 1_50
assert instagram_user.number_of_followers > 12_00_00
assert instagram_user.number_of_followings > 15
assert instagram_user.email == "[email protected]"
assert instagram_user.website == "https://github.com/readme"
assert instagram_user.profile_picture_url.startswith('''https://instagram.''' )
assert instagram_user.is_verified is True
assert instagram_user.is_private is False
if __name__ == "__main__":
import doctest
doctest.testmod()
lowerCAmelCase__ =InstagramUser("github")
print(instagram_user)
print(F'''{instagram_user.number_of_posts = }''')
print(F'''{instagram_user.number_of_followers = }''')
print(F'''{instagram_user.number_of_followings = }''')
print(F'''{instagram_user.email = }''')
print(F'''{instagram_user.website = }''')
print(F'''{instagram_user.profile_picture_url = }''')
print(F'''{instagram_user.is_verified = }''')
print(F'''{instagram_user.is_private = }''')
| 690 | 1 |
"""simple docstring"""
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import torch
from ..models.auto import AutoModelForSequenceClassification, AutoTokenizer
from .base import PipelineTool
class A__( __magic_name__ ):
lowerCAmelCase = '''facebook/bart-large-mnli'''
lowerCAmelCase = (
'''This is a tool that classifies an English text using provided labels. It takes two inputs: `text`, which '''
'''should be the text to classify, and `labels`, which should be the list of labels to use for classification. '''
'''It returns the most likely label in the list of provided `labels` for the input text.'''
)
lowerCAmelCase = '''text_classifier'''
lowerCAmelCase = AutoTokenizer
lowerCAmelCase = AutoModelForSequenceClassification
lowerCAmelCase = ['''text''', ['''text''']]
lowerCAmelCase = ['''text''']
def _a ( self : Optional[Any] ) -> Optional[int]:
"""simple docstring"""
super().setup()
__SCREAMING_SNAKE_CASE = self.model.config
__SCREAMING_SNAKE_CASE = -1
for idx, label in config.idalabel.items():
if label.lower().startswith('''entail''' ):
__SCREAMING_SNAKE_CASE = int(__SCREAMING_SNAKE_CASE )
if self.entailment_id == -1:
raise ValueError('''Could not determine the entailment ID from the model config, please pass it at init.''' )
def _a ( self : Any , __SCREAMING_SNAKE_CASE : Dict , __SCREAMING_SNAKE_CASE : Tuple ) -> List[str]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = labels
return self.pre_processor(
[text] * len(__SCREAMING_SNAKE_CASE ) , [f"""This example is {label}""" for label in labels] , return_tensors='''pt''' , padding='''max_length''' , )
def _a ( self : int , __SCREAMING_SNAKE_CASE : List[str] ) -> Any:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = outputs.logits
__SCREAMING_SNAKE_CASE = torch.argmax(logits[:, 2] ).item()
return self._labels[label_id]
| 690 |
"""simple docstring"""
from sklearn.metrics import recall_score
import datasets
lowerCAmelCase__ ="\nRecall is the fraction of the positive examples that were correctly labeled by the model as positive. It can be computed with the equation:\nRecall = TP / (TP + FN)\nWhere TP is the true positives and FN is the false negatives.\n"
lowerCAmelCase__ ="\nArgs:\n- **predictions** (`list` of `int`): The predicted labels.\n- **references** (`list` of `int`): The ground truth labels.\n- **labels** (`list` of `int`): The set of labels to include when `average` is not set to `binary`, and their order when average is `None`. Labels present in the data can be excluded in this input, for example to calculate a multiclass average ignoring a majority negative class, while labels not present in the data will result in 0 components in a macro average. For multilabel targets, labels are column indices. By default, all labels in y_true and y_pred are used in sorted order. Defaults to None.\n- **pos_label** (`int`): The class label to use as the 'positive class' when calculating the recall. Defaults to `1`.\n- **average** (`string`): This parameter is required for multiclass/multilabel targets. If None, the scores for each class are returned. Otherwise, this determines the type of averaging performed on the data. Defaults to `'binary'`.\n - `'binary'`: Only report results for the class specified by `pos_label`. This is applicable only if the target labels and predictions are binary.\n - `'micro'`: Calculate metrics globally by counting the total true positives, false negatives, and false positives.\n - `'macro'`: Calculate metrics for each label, and find their unweighted mean. This does not take label imbalance into account.\n - `'weighted'`: Calculate metrics for each label, and find their average weighted by support (the number of true instances for each label). This alters `'macro'` to account for label imbalance. Note that it can result in an F-score that is not between precision and recall.\n - `'samples'`: Calculate metrics for each instance, and find their average (only meaningful for multilabel classification).\n- **sample_weight** (`list` of `float`): Sample weights Defaults to `None`.\n- **zero_division** (): Sets the value to return when there is a zero division. Defaults to .\n - `'warn'`: If there is a zero division, the return value is `0`, but warnings are also raised.\n - `0`: If there is a zero division, the return value is `0`.\n - `1`: If there is a zero division, the return value is `1`.\n\nReturns:\n- **recall** (`float`, or `array` of `float`): Either the general recall score, or the recall scores for individual classes, depending on the values input to `labels` and `average`. Minimum possible value is 0. Maximum possible value is 1. A higher recall means that more of the positive examples have been labeled correctly. Therefore, a higher recall is generally considered better.\n\nExamples:\n\n Example 1-A simple example with some errors\n >>> recall_metric = datasets.load_metric('recall')\n >>> results = recall_metric.compute(references=[0, 0, 1, 1, 1], predictions=[0, 1, 0, 1, 1])\n >>> print(results)\n {'recall': 0.6666666666666666}\n\n Example 2-The same example as Example 1, but with `pos_label=0` instead of the default `pos_label=1`.\n >>> recall_metric = datasets.load_metric('recall')\n >>> results = recall_metric.compute(references=[0, 0, 1, 1, 1], predictions=[0, 1, 0, 1, 1], pos_label=0)\n >>> print(results)\n {'recall': 0.5}\n\n Example 3-The same example as Example 1, but with `sample_weight` included.\n >>> recall_metric = datasets.load_metric('recall')\n >>> sample_weight = [0.9, 0.2, 0.9, 0.3, 0.8]\n >>> results = recall_metric.compute(references=[0, 0, 1, 1, 1], predictions=[0, 1, 0, 1, 1], sample_weight=sample_weight)\n >>> print(results)\n {'recall': 0.55}\n\n Example 4-A multiclass example, using different averages.\n >>> recall_metric = datasets.load_metric('recall')\n >>> predictions = [0, 2, 1, 0, 0, 1]\n >>> references = [0, 1, 2, 0, 1, 2]\n >>> results = recall_metric.compute(predictions=predictions, references=references, average='macro')\n >>> print(results)\n {'recall': 0.3333333333333333}\n >>> results = recall_metric.compute(predictions=predictions, references=references, average='micro')\n >>> print(results)\n {'recall': 0.3333333333333333}\n >>> results = recall_metric.compute(predictions=predictions, references=references, average='weighted')\n >>> print(results)\n {'recall': 0.3333333333333333}\n >>> results = recall_metric.compute(predictions=predictions, references=references, average=None)\n >>> print(results)\n {'recall': array([1., 0., 0.])}\n"
lowerCAmelCase__ ="\n@article{scikit-learn, title={Scikit-learn: Machine Learning in {P}ython}, author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V. and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P. and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.}, journal={Journal of Machine Learning Research}, volume={12}, pages={2825--2830}, year={2011}\n"
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class A__( datasets.Metric ):
def _a ( self : Any ) -> int:
"""simple docstring"""
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
'''predictions''': datasets.Sequence(datasets.Value('''int32''' ) ),
'''references''': datasets.Sequence(datasets.Value('''int32''' ) ),
}
if self.config_name == '''multilabel'''
else {
'''predictions''': datasets.Value('''int32''' ),
'''references''': datasets.Value('''int32''' ),
} ) , reference_urls=['''https://scikit-learn.org/stable/modules/generated/sklearn.metrics.recall_score.html'''] , )
def _a ( self : Tuple , __SCREAMING_SNAKE_CASE : Union[str, Any] , __SCREAMING_SNAKE_CASE : Optional[Any] , __SCREAMING_SNAKE_CASE : Union[str, Any]=None , __SCREAMING_SNAKE_CASE : Optional[int]=1 , __SCREAMING_SNAKE_CASE : Optional[Any]="binary" , __SCREAMING_SNAKE_CASE : List[Any]=None , __SCREAMING_SNAKE_CASE : List[Any]="warn" , ) -> Dict:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = recall_score(
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , labels=__SCREAMING_SNAKE_CASE , pos_label=__SCREAMING_SNAKE_CASE , average=__SCREAMING_SNAKE_CASE , sample_weight=__SCREAMING_SNAKE_CASE , zero_division=__SCREAMING_SNAKE_CASE , )
return {"recall": float(__SCREAMING_SNAKE_CASE ) if score.size == 1 else score}
| 690 | 1 |
"""simple docstring"""
from __future__ import annotations
from PIL import Image
# Define glider example
lowerCAmelCase__ =[
[0, 1, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0, 0],
[1, 1, 1, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
]
# Define blinker example
lowerCAmelCase__ =[[0, 1, 0], [0, 1, 0], [0, 1, 0]]
def _a ( UpperCAmelCase__ ) -> list[list[int]]:
__SCREAMING_SNAKE_CASE = []
for i in range(len(UpperCAmelCase__ ) ):
__SCREAMING_SNAKE_CASE = []
for j in range(len(cells[i] ) ):
# Get the number of live neighbours
__SCREAMING_SNAKE_CASE = 0
if i > 0 and j > 0:
neighbour_count += cells[i - 1][j - 1]
if i > 0:
neighbour_count += cells[i - 1][j]
if i > 0 and j < len(cells[i] ) - 1:
neighbour_count += cells[i - 1][j + 1]
if j > 0:
neighbour_count += cells[i][j - 1]
if j < len(cells[i] ) - 1:
neighbour_count += cells[i][j + 1]
if i < len(UpperCAmelCase__ ) - 1 and j > 0:
neighbour_count += cells[i + 1][j - 1]
if i < len(UpperCAmelCase__ ) - 1:
neighbour_count += cells[i + 1][j]
if i < len(UpperCAmelCase__ ) - 1 and j < len(cells[i] ) - 1:
neighbour_count += cells[i + 1][j + 1]
# Rules of the game of life (excerpt from Wikipedia):
# 1. Any live cell with two or three live neighbours survives.
# 2. Any dead cell with three live neighbours becomes a live cell.
# 3. All other live cells die in the next generation.
# Similarly, all other dead cells stay dead.
__SCREAMING_SNAKE_CASE = cells[i][j] == 1
if (
(alive and 2 <= neighbour_count <= 3)
or not alive
and neighbour_count == 3
):
next_generation_row.append(1 )
else:
next_generation_row.append(0 )
next_generation.append(UpperCAmelCase__ )
return next_generation
def _a ( UpperCAmelCase__ , UpperCAmelCase__ ) -> list[Image.Image]:
__SCREAMING_SNAKE_CASE = []
for _ in range(UpperCAmelCase__ ):
# Create output image
__SCREAMING_SNAKE_CASE = Image.new('''RGB''' , (len(cells[0] ), len(UpperCAmelCase__ )) )
__SCREAMING_SNAKE_CASE = img.load()
# Save cells to image
for x in range(len(UpperCAmelCase__ ) ):
for y in range(len(cells[0] ) ):
__SCREAMING_SNAKE_CASE = 2_55 - cells[y][x] * 2_55
__SCREAMING_SNAKE_CASE = (colour, colour, colour)
# Save image
images.append(UpperCAmelCase__ )
__SCREAMING_SNAKE_CASE = new_generation(UpperCAmelCase__ )
return images
if __name__ == "__main__":
lowerCAmelCase__ =generate_images(GLIDER, 16)
images[0].save("out.gif", save_all=True, append_images=images[1:])
| 690 |
"""simple docstring"""
def _a ( UpperCAmelCase__ = 10**9 ) -> int:
__SCREAMING_SNAKE_CASE = 1
__SCREAMING_SNAKE_CASE = 2
__SCREAMING_SNAKE_CASE = 0
__SCREAMING_SNAKE_CASE = 0
__SCREAMING_SNAKE_CASE = 0
while perimeter <= max_perimeter:
perimeters_sum += perimeter
prev_value += 2 * value
value += prev_value
__SCREAMING_SNAKE_CASE = 2 * value + 2 if i % 2 == 0 else 2 * value - 2
i += 1
return perimeters_sum
if __name__ == "__main__":
print(F'''{solution() = }''')
| 690 | 1 |
"""simple docstring"""
from timeit import timeit
def _a ( UpperCAmelCase__ ) -> int:
if number < 0:
raise ValueError('''the value of input must not be negative''' )
__SCREAMING_SNAKE_CASE = 0
while number:
number &= number - 1
result += 1
return result
def _a ( UpperCAmelCase__ ) -> int:
if number < 0:
raise ValueError('''the value of input must not be negative''' )
__SCREAMING_SNAKE_CASE = 0
while number:
if number % 2 == 1:
result += 1
number >>= 1
return result
def _a ( ) -> None:
def do_benchmark(UpperCAmelCase__ ) -> None:
__SCREAMING_SNAKE_CASE = '''import __main__ as z'''
print(f"""Benchmark when {number = }:""" )
print(f"""{get_set_bits_count_using_modulo_operator(UpperCAmelCase__ ) = }""" )
__SCREAMING_SNAKE_CASE = timeit('''z.get_set_bits_count_using_modulo_operator(25)''' , setup=UpperCAmelCase__ )
print(f"""timeit() runs in {timing} seconds""" )
print(f"""{get_set_bits_count_using_brian_kernighans_algorithm(UpperCAmelCase__ ) = }""" )
__SCREAMING_SNAKE_CASE = timeit(
'''z.get_set_bits_count_using_brian_kernighans_algorithm(25)''' , setup=UpperCAmelCase__ , )
print(f"""timeit() runs in {timing} seconds""" )
for number in (25, 37, 58, 0):
do_benchmark(UpperCAmelCase__ )
print()
if __name__ == "__main__":
import doctest
doctest.testmod()
benchmark()
| 690 |
"""simple docstring"""
import pandas as pd
from matplotlib import pyplot as plt
from sklearn.linear_model import LinearRegression
# Splitting the dataset into the Training set and Test set
from sklearn.model_selection import train_test_split
# Fitting Polynomial Regression to the dataset
from sklearn.preprocessing import PolynomialFeatures
# Importing the dataset
lowerCAmelCase__ =pd.read_csv(
"https://s3.us-west-2.amazonaws.com/public.gamelab.fun/dataset/"
"position_salaries.csv"
)
lowerCAmelCase__ =dataset.iloc[:, 1:2].values
lowerCAmelCase__ =dataset.iloc[:, 2].values
lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ =train_test_split(X, y, test_size=0.2, random_state=0)
lowerCAmelCase__ =PolynomialFeatures(degree=4)
lowerCAmelCase__ =poly_reg.fit_transform(X)
lowerCAmelCase__ =LinearRegression()
pol_reg.fit(X_poly, y)
def _a ( ) -> List[Any]:
plt.scatter(UpperCAmelCase__ , UpperCAmelCase__ , color='''red''' )
plt.plot(UpperCAmelCase__ , pol_reg.predict(poly_reg.fit_transform(UpperCAmelCase__ ) ) , color='''blue''' )
plt.title('''Truth or Bluff (Linear Regression)''' )
plt.xlabel('''Position level''' )
plt.ylabel('''Salary''' )
plt.show()
if __name__ == "__main__":
viz_polymonial()
# Predicting a new result with Polymonial Regression
pol_reg.predict(poly_reg.fit_transform([[5.5]]))
# output should be 132148.43750003
| 690 | 1 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
lowerCAmelCase__ =logging.get_logger(__name__)
lowerCAmelCase__ ={"vocab_file": "spiece.model"}
lowerCAmelCase__ ={
"vocab_file": {
"bert_for_seq_generation": (
"https://huggingface.co/google/bert_for_seq_generation_L-24_bbc_encoder/resolve/main/spiece.model"
),
}
}
lowerCAmelCase__ ={"bert_for_seq_generation": 512}
class A__( __magic_name__ ):
lowerCAmelCase = VOCAB_FILES_NAMES
lowerCAmelCase = PRETRAINED_VOCAB_FILES_MAP
lowerCAmelCase = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowerCAmelCase = []
lowerCAmelCase = ['''input_ids''', '''attention_mask''']
def __init__( self : Optional[Any] , __SCREAMING_SNAKE_CASE : str , __SCREAMING_SNAKE_CASE : List[str]="<s>" , __SCREAMING_SNAKE_CASE : Optional[int]="</s>" , __SCREAMING_SNAKE_CASE : int="<unk>" , __SCREAMING_SNAKE_CASE : Dict="<pad>" , __SCREAMING_SNAKE_CASE : Any="<::::>" , __SCREAMING_SNAKE_CASE : Optional[Dict[str, Any]] = None , **__SCREAMING_SNAKE_CASE : str , ) -> None:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = {} if sp_model_kwargs is None else sp_model_kwargs
# Add extra_ids to the special token list
super().__init__(
bos_token=__SCREAMING_SNAKE_CASE , eos_token=__SCREAMING_SNAKE_CASE , unk_token=__SCREAMING_SNAKE_CASE , pad_token=__SCREAMING_SNAKE_CASE , sep_token=__SCREAMING_SNAKE_CASE , sp_model_kwargs=self.sp_model_kwargs , **__SCREAMING_SNAKE_CASE , )
__SCREAMING_SNAKE_CASE = vocab_file
__SCREAMING_SNAKE_CASE = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(__SCREAMING_SNAKE_CASE )
@property
def _a ( self : Optional[Any] ) -> Union[str, Any]:
"""simple docstring"""
return self.sp_model.get_piece_size()
def _a ( self : str ) -> List[Any]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = {self.convert_ids_to_tokens(__SCREAMING_SNAKE_CASE ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def __getstate__( self : Tuple ) -> Optional[int]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = self.__dict__.copy()
__SCREAMING_SNAKE_CASE = None
return state
def __setstate__( self : Union[str, Any] , __SCREAMING_SNAKE_CASE : int ) -> int:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = d
# for backward compatibility
if not hasattr(self , '''sp_model_kwargs''' ):
__SCREAMING_SNAKE_CASE = {}
__SCREAMING_SNAKE_CASE = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.vocab_file )
def _a ( self : Optional[int] , __SCREAMING_SNAKE_CASE : str ) -> List[str]:
"""simple docstring"""
return self.sp_model.encode(__SCREAMING_SNAKE_CASE , out_type=__SCREAMING_SNAKE_CASE )
def _a ( self : Optional[int] , __SCREAMING_SNAKE_CASE : List[str] ) -> int:
"""simple docstring"""
return self.sp_model.piece_to_id(__SCREAMING_SNAKE_CASE )
def _a ( self : Tuple , __SCREAMING_SNAKE_CASE : int ) -> List[str]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = self.sp_model.IdToPiece(__SCREAMING_SNAKE_CASE )
return token
def _a ( self : Tuple , __SCREAMING_SNAKE_CASE : List[str] ) -> List[Any]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = []
__SCREAMING_SNAKE_CASE = ''''''
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
out_string += self.sp_model.decode(__SCREAMING_SNAKE_CASE ) + token
__SCREAMING_SNAKE_CASE = []
else:
current_sub_tokens.append(__SCREAMING_SNAKE_CASE )
out_string += self.sp_model.decode(__SCREAMING_SNAKE_CASE )
return out_string.strip()
def _a ( self : Tuple , __SCREAMING_SNAKE_CASE : str , __SCREAMING_SNAKE_CASE : Optional[str] = None ) -> Tuple[str]:
"""simple docstring"""
if not os.path.isdir(__SCREAMING_SNAKE_CASE ):
logger.error(f"""Vocabulary path ({save_directory}) should be a directory""" )
return
__SCREAMING_SNAKE_CASE = os.path.join(
__SCREAMING_SNAKE_CASE , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(__SCREAMING_SNAKE_CASE ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file , __SCREAMING_SNAKE_CASE )
elif not os.path.isfile(self.vocab_file ):
with open(__SCREAMING_SNAKE_CASE , '''wb''' ) as fi:
__SCREAMING_SNAKE_CASE = self.sp_model.serialized_model_proto()
fi.write(__SCREAMING_SNAKE_CASE )
return (out_vocab_file,)
| 690 |
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import AutoTokenizer, MBartConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import TFAutoModelForSeqaSeqLM, TFMBartForConditionalGeneration, TFMBartModel
@require_tf
class A__:
lowerCAmelCase = MBartConfig
lowerCAmelCase = {}
lowerCAmelCase = '''gelu'''
def __init__( self : List[str] , __SCREAMING_SNAKE_CASE : Optional[int] , __SCREAMING_SNAKE_CASE : Tuple=13 , __SCREAMING_SNAKE_CASE : Dict=7 , __SCREAMING_SNAKE_CASE : List[Any]=True , __SCREAMING_SNAKE_CASE : Any=False , __SCREAMING_SNAKE_CASE : Union[str, Any]=99 , __SCREAMING_SNAKE_CASE : Optional[Any]=32 , __SCREAMING_SNAKE_CASE : Optional[int]=2 , __SCREAMING_SNAKE_CASE : Any=4 , __SCREAMING_SNAKE_CASE : List[str]=37 , __SCREAMING_SNAKE_CASE : Optional[Any]=0.1 , __SCREAMING_SNAKE_CASE : Dict=0.1 , __SCREAMING_SNAKE_CASE : Any=20 , __SCREAMING_SNAKE_CASE : List[Any]=2 , __SCREAMING_SNAKE_CASE : Optional[int]=1 , __SCREAMING_SNAKE_CASE : Optional[Any]=0 , ) -> Optional[int]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = parent
__SCREAMING_SNAKE_CASE = batch_size
__SCREAMING_SNAKE_CASE = seq_length
__SCREAMING_SNAKE_CASE = is_training
__SCREAMING_SNAKE_CASE = use_labels
__SCREAMING_SNAKE_CASE = vocab_size
__SCREAMING_SNAKE_CASE = hidden_size
__SCREAMING_SNAKE_CASE = num_hidden_layers
__SCREAMING_SNAKE_CASE = num_attention_heads
__SCREAMING_SNAKE_CASE = intermediate_size
__SCREAMING_SNAKE_CASE = hidden_dropout_prob
__SCREAMING_SNAKE_CASE = attention_probs_dropout_prob
__SCREAMING_SNAKE_CASE = max_position_embeddings
__SCREAMING_SNAKE_CASE = eos_token_id
__SCREAMING_SNAKE_CASE = pad_token_id
__SCREAMING_SNAKE_CASE = bos_token_id
def _a ( self : List[str] ) -> Optional[int]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = ids_tensor([self.batch_size, self.seq_length - 1] , self.vocab_size )
__SCREAMING_SNAKE_CASE = tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size ) , 1 )
__SCREAMING_SNAKE_CASE = tf.concat([input_ids, eos_tensor] , axis=1 )
__SCREAMING_SNAKE_CASE = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
__SCREAMING_SNAKE_CASE = self.config_cls(
vocab_size=self.vocab_size , d_model=self.hidden_size , encoder_layers=self.num_hidden_layers , decoder_layers=self.num_hidden_layers , encoder_attention_heads=self.num_attention_heads , decoder_attention_heads=self.num_attention_heads , encoder_ffn_dim=self.intermediate_size , decoder_ffn_dim=self.intermediate_size , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , eos_token_ids=[2] , bos_token_id=self.bos_token_id , pad_token_id=self.pad_token_id , decoder_start_token_id=self.pad_token_id , **self.config_updates , )
__SCREAMING_SNAKE_CASE = prepare_mbart_inputs_dict(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
return config, inputs_dict
def _a ( self : Tuple , __SCREAMING_SNAKE_CASE : str , __SCREAMING_SNAKE_CASE : Optional[int] ) -> Optional[Any]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = TFMBartModel(config=__SCREAMING_SNAKE_CASE ).get_decoder()
__SCREAMING_SNAKE_CASE = inputs_dict['''input_ids''']
__SCREAMING_SNAKE_CASE = input_ids[:1, :]
__SCREAMING_SNAKE_CASE = inputs_dict['''attention_mask'''][:1, :]
__SCREAMING_SNAKE_CASE = inputs_dict['''head_mask''']
__SCREAMING_SNAKE_CASE = 1
# first forward pass
__SCREAMING_SNAKE_CASE = model(__SCREAMING_SNAKE_CASE , attention_mask=__SCREAMING_SNAKE_CASE , head_mask=__SCREAMING_SNAKE_CASE , use_cache=__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = outputs.to_tuple()
__SCREAMING_SNAKE_CASE = past_key_values[1]
def _a ( UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__=None , UpperCAmelCase__=None , UpperCAmelCase__=None , UpperCAmelCase__=None , UpperCAmelCase__=None , ) -> Optional[int]:
if attention_mask is None:
__SCREAMING_SNAKE_CASE = tf.cast(tf.math.not_equal(UpperCAmelCase__ , config.pad_token_id ) , tf.inta )
if decoder_attention_mask is None:
__SCREAMING_SNAKE_CASE = tf.concat(
[
tf.ones(decoder_input_ids[:, :1].shape , dtype=tf.inta ),
tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:] , config.pad_token_id ) , tf.inta ),
] , axis=-1 , )
if head_mask is None:
__SCREAMING_SNAKE_CASE = tf.ones((config.encoder_layers, config.encoder_attention_heads) )
if decoder_head_mask is None:
__SCREAMING_SNAKE_CASE = tf.ones((config.decoder_layers, config.decoder_attention_heads) )
if cross_attn_head_mask is None:
__SCREAMING_SNAKE_CASE = tf.ones((config.decoder_layers, config.decoder_attention_heads) )
return {
"input_ids": input_ids,
"decoder_input_ids": decoder_input_ids,
"attention_mask": attention_mask,
"decoder_attention_mask": decoder_attention_mask,
"head_mask": head_mask,
"decoder_head_mask": decoder_head_mask,
"cross_attn_head_mask": cross_attn_head_mask,
}
@require_tf
class A__( __magic_name__ , __magic_name__ , unittest.TestCase ):
lowerCAmelCase = (TFMBartForConditionalGeneration, TFMBartModel) if is_tf_available() else ()
lowerCAmelCase = (TFMBartForConditionalGeneration,) if is_tf_available() else ()
lowerCAmelCase = (
{
'''conversational''': TFMBartForConditionalGeneration,
'''feature-extraction''': TFMBartModel,
'''summarization''': TFMBartForConditionalGeneration,
'''text2text-generation''': TFMBartForConditionalGeneration,
'''translation''': TFMBartForConditionalGeneration,
}
if is_tf_available()
else {}
)
lowerCAmelCase = True
lowerCAmelCase = False
lowerCAmelCase = False
def _a ( self : Dict , __SCREAMING_SNAKE_CASE : str , __SCREAMING_SNAKE_CASE : List[str] , __SCREAMING_SNAKE_CASE : Dict , __SCREAMING_SNAKE_CASE : Optional[int] , __SCREAMING_SNAKE_CASE : List[Any] ) -> Optional[Any]:
"""simple docstring"""
if pipeline_test_casse_name != "FeatureExtractionPipelineTests":
# Exception encountered when calling layer '...'
return True
return False
def _a ( self : List[Any] ) -> Optional[int]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = TFMBartModelTester(self )
__SCREAMING_SNAKE_CASE = ConfigTester(self , config_class=__SCREAMING_SNAKE_CASE )
def _a ( self : Union[str, Any] ) -> Union[str, Any]:
"""simple docstring"""
self.config_tester.run_common_tests()
def _a ( self : int ) -> str:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.check_decoder_model_past_large_inputs(*__SCREAMING_SNAKE_CASE )
@require_sentencepiece
@require_tokenizers
@require_tf
class A__( unittest.TestCase ):
lowerCAmelCase = [
''' UN Chief Says There Is No Military Solution in Syria''',
]
lowerCAmelCase = [
'''Şeful ONU declară că nu există o soluţie militară în Siria''',
]
lowerCAmelCase = '''facebook/mbart-large-en-ro'''
@cached_property
def _a ( self : Optional[int] ) -> str:
"""simple docstring"""
return AutoTokenizer.from_pretrained(self.model_name )
@cached_property
def _a ( self : Optional[Any] ) -> List[Any]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name )
return model
def _a ( self : Any , **__SCREAMING_SNAKE_CASE : Optional[Any] ) -> Any:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = self.translate_src_text(**__SCREAMING_SNAKE_CASE )
self.assertListEqual(self.expected_text , __SCREAMING_SNAKE_CASE )
def _a ( self : Any , **__SCREAMING_SNAKE_CASE : Optional[int] ) -> Optional[Any]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = self.tokenizer(self.src_text , **__SCREAMING_SNAKE_CASE , return_tensors='''tf''' )
__SCREAMING_SNAKE_CASE = self.model.generate(
model_inputs.input_ids , attention_mask=model_inputs.attention_mask , num_beams=2 )
__SCREAMING_SNAKE_CASE = self.tokenizer.batch_decode(__SCREAMING_SNAKE_CASE , skip_special_tokens=__SCREAMING_SNAKE_CASE )
return generated_words
@slow
def _a ( self : Union[str, Any] ) -> Tuple:
"""simple docstring"""
self._assert_generated_batch_equal_expected()
| 690 | 1 |
"""simple docstring"""
import math
lowerCAmelCase__ =10
lowerCAmelCase__ =7
lowerCAmelCase__ =BALLS_PER_COLOUR * NUM_COLOURS
def _a ( UpperCAmelCase__ = 20 ) -> str:
__SCREAMING_SNAKE_CASE = math.comb(UpperCAmelCase__ , UpperCAmelCase__ )
__SCREAMING_SNAKE_CASE = math.comb(NUM_BALLS - BALLS_PER_COLOUR , UpperCAmelCase__ )
__SCREAMING_SNAKE_CASE = NUM_COLOURS * (1 - missing_colour / total)
return f"""{result:.9f}"""
if __name__ == "__main__":
print(solution(20))
| 690 |
"""simple docstring"""
from ....configuration_utils import PretrainedConfig
from ....utils import logging
lowerCAmelCase__ =logging.get_logger(__name__)
lowerCAmelCase__ ={
"Visual-Attention-Network/van-base": (
"https://huggingface.co/Visual-Attention-Network/van-base/blob/main/config.json"
),
}
class A__( __magic_name__ ):
lowerCAmelCase = '''van'''
def __init__( self : int , __SCREAMING_SNAKE_CASE : Optional[Any]=2_24 , __SCREAMING_SNAKE_CASE : Union[str, Any]=3 , __SCREAMING_SNAKE_CASE : Tuple=[7, 3, 3, 3] , __SCREAMING_SNAKE_CASE : Optional[int]=[4, 2, 2, 2] , __SCREAMING_SNAKE_CASE : str=[64, 1_28, 3_20, 5_12] , __SCREAMING_SNAKE_CASE : Optional[Any]=[3, 3, 12, 3] , __SCREAMING_SNAKE_CASE : Dict=[8, 8, 4, 4] , __SCREAMING_SNAKE_CASE : Any="gelu" , __SCREAMING_SNAKE_CASE : Tuple=0.02 , __SCREAMING_SNAKE_CASE : Dict=1E-6 , __SCREAMING_SNAKE_CASE : Any=1E-2 , __SCREAMING_SNAKE_CASE : str=0.0 , __SCREAMING_SNAKE_CASE : Union[str, Any]=0.0 , **__SCREAMING_SNAKE_CASE : str , ) -> List[str]:
"""simple docstring"""
super().__init__(**__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = image_size
__SCREAMING_SNAKE_CASE = num_channels
__SCREAMING_SNAKE_CASE = patch_sizes
__SCREAMING_SNAKE_CASE = strides
__SCREAMING_SNAKE_CASE = hidden_sizes
__SCREAMING_SNAKE_CASE = depths
__SCREAMING_SNAKE_CASE = mlp_ratios
__SCREAMING_SNAKE_CASE = hidden_act
__SCREAMING_SNAKE_CASE = initializer_range
__SCREAMING_SNAKE_CASE = layer_norm_eps
__SCREAMING_SNAKE_CASE = layer_scale_init_value
__SCREAMING_SNAKE_CASE = drop_path_rate
__SCREAMING_SNAKE_CASE = dropout_rate
| 690 | 1 |
"""simple docstring"""
import argparse
import dataclasses
import json
import logging
import os
import shutil
from typing import List, Optional
import datasets
from accelerate import Accelerator
from datasets import load_dataset
from finetuning import finetune
from tqdm.auto import tqdm
import transformers
from transformers import AutoConfig, set_seed
from transformers.trainer_utils import IntervalStrategy
lowerCAmelCase__ =logging.getLogger(__name__)
lowerCAmelCase__ ="pytorch_model.bin"
@dataclasses.dataclass
class A__:
lowerCAmelCase = dataclasses.field(
metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models.'''} )
lowerCAmelCase = dataclasses.field(
default=__magic_name__ , metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co.'''} , )
@dataclasses.dataclass
class A__:
lowerCAmelCase = dataclasses.field(metadata={'''help''': '''A csv or a json file containing the training data.'''} )
lowerCAmelCase = dataclasses.field(metadata={'''help''': '''A csv or a json file containing the data to predict on.'''} )
lowerCAmelCase = dataclasses.field(
default=__magic_name__ , metadata={'''help''': '''A csv or a json file containing the validation data.'''} )
lowerCAmelCase = dataclasses.field(
default=__magic_name__ , metadata={'''help''': '''The name of the task to train on.'''} , )
lowerCAmelCase = dataclasses.field(
default=__magic_name__ , metadata={'''help''': '''The list of labels for the task.'''} )
@dataclasses.dataclass
class A__:
lowerCAmelCase = dataclasses.field(
metadata={'''help''': '''The output directory where the model predictions and checkpoints will be written.'''} )
lowerCAmelCase = dataclasses.field(
default='''accuracy''' , metadata={'''help''': '''The evaluation metric used for the task.'''} )
lowerCAmelCase = dataclasses.field(
default='''no''' , metadata={
'''help''': '''The evaluation strategy to adopt during training. Possible values are: ["no", "step", "epoch]'''
} , )
lowerCAmelCase = dataclasses.field(
default=10 , metadata={'''help''': '''Number of evaluation calls with no improvement after which training will be stopped.'''} , )
lowerCAmelCase = dataclasses.field(
default=0.0 , metadata={
'''help''': '''How much the specified evaluation metric must improve to satisfy early stopping conditions.'''
} , )
lowerCAmelCase = dataclasses.field(
default=__magic_name__ , metadata={'''help''': '''Whether to filter the pseudo-labeled data based on the confidence score.'''} , )
lowerCAmelCase = dataclasses.field(
default=__magic_name__ , metadata={'''help''': '''Whether to filter the pseudo-labeled data based on the validation performance.'''} , )
lowerCAmelCase = dataclasses.field(
default=__magic_name__ , metadata={'''help''': '''Whether to fine-tune on labeled data after pseudo training.'''} , )
lowerCAmelCase = dataclasses.field(
default=0.0 , metadata={'''help''': '''Confidence threshold for pseudo-labeled data filtering.'''} , )
lowerCAmelCase = dataclasses.field(
default=1_00 , metadata={'''help''': '''Number of evaluation calls with no improvement after which training will be stopped.'''} , )
lowerCAmelCase = dataclasses.field(
default=__magic_name__ , metadata={'''help''': '''Random seed for initialization.'''} , )
def _a ( UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ ) -> List[str]:
__SCREAMING_SNAKE_CASE = datasets.concatenate_datasets([infer_input, infer_output] , axis=1 )
if args.do_filter_by_confidence:
__SCREAMING_SNAKE_CASE = dataset.filter(lambda UpperCAmelCase__ : example["probability"] > args.confidence_threshold )
if args.do_filter_by_val_performance:
assert eval_result >= 0.0 and eval_result <= 1.0
__SCREAMING_SNAKE_CASE = int(eval_result * len(UpperCAmelCase__ ) )
print(UpperCAmelCase__ )
__SCREAMING_SNAKE_CASE = dataset.sort('''probability''' , reverse=UpperCAmelCase__ )
__SCREAMING_SNAKE_CASE = dataset.select(range(UpperCAmelCase__ ) )
__SCREAMING_SNAKE_CASE = dataset.remove_columns(['''label''', '''probability'''] )
__SCREAMING_SNAKE_CASE = dataset.rename_column('''prediction''' , '''label''' )
__SCREAMING_SNAKE_CASE = dataset.map(lambda UpperCAmelCase__ : {"label": idalabel[example["label"]]} )
__SCREAMING_SNAKE_CASE = dataset.shuffle(seed=args.seed )
__SCREAMING_SNAKE_CASE = os.path.join(UpperCAmelCase__ , f"""train_pseudo.{args.data_file_extension}""" )
if args.data_file_extension == "csv":
dataset.to_csv(UpperCAmelCase__ , index=UpperCAmelCase__ )
else:
dataset.to_json(UpperCAmelCase__ )
def _a ( UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , **UpperCAmelCase__ ) -> List[Any]:
__SCREAMING_SNAKE_CASE = Accelerator()
# Make one log on every process with the configuration for debugging.
logging.basicConfig(
format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''' , datefmt='''%m/%d/%Y %H:%M:%S''' , level=logging.INFO , )
logger.info(accelerator.state )
# Setup logging, we only want one process per machine to log things on the
# screen. accelerator.is_local_main_process is only True for one process per
# machine.
logger.setLevel(logging.INFO if accelerator.is_local_main_process else logging.ERROR )
if accelerator.is_local_main_process:
datasets.utils.logging.set_verbosity_warning()
transformers.utils.logging.set_verbosity_info()
else:
datasets.utils.logging.set_verbosity_error()
transformers.utils.logging.set_verbosity_error()
__SCREAMING_SNAKE_CASE = STModelArguments(model_name_or_path=UpperCAmelCase__ )
__SCREAMING_SNAKE_CASE = STDataArguments(train_file=UpperCAmelCase__ , infer_file=UpperCAmelCase__ )
__SCREAMING_SNAKE_CASE = STTrainingArguments(output_dir=UpperCAmelCase__ )
__SCREAMING_SNAKE_CASE = argparse.Namespace()
for arg_class in (model_args, data_args, training_args):
for key, value in vars(UpperCAmelCase__ ).items():
setattr(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ )
for key, value in kwargs.items():
if hasattr(UpperCAmelCase__ , UpperCAmelCase__ ):
setattr(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ )
# Sanity checks
__SCREAMING_SNAKE_CASE = {}
__SCREAMING_SNAKE_CASE = None
# You need to provide the training data and the data to predict on
assert args.train_file is not None
assert args.infer_file is not None
__SCREAMING_SNAKE_CASE = args.train_file
__SCREAMING_SNAKE_CASE = args.infer_file
if args.evaluation_strategy != IntervalStrategy.NO.value:
assert args.eval_file is not None
__SCREAMING_SNAKE_CASE = args.eval_file
for key in data_files:
__SCREAMING_SNAKE_CASE = data_files[key].split('''.''' )[-1]
assert extension in ["csv", "json"], f"""`{key}_file` should be a csv or a json file."""
if args.data_file_extension is None:
__SCREAMING_SNAKE_CASE = extension
else:
assert extension == args.data_file_extension, f"""`{key}_file` should be a {args.data_file_extension} file`."""
assert (
args.eval_metric in datasets.list_metrics()
), f"""{args.eval_metric} not in the list of supported metrics {datasets.list_metrics()}."""
# If passed along, set the training seed now.
if args.seed is not None:
set_seed(args.seed )
logger.info('''Creating the initial data directory for self-training...''' )
__SCREAMING_SNAKE_CASE = f"""{args.output_dir}/self-train_iter-{{}}""".format
__SCREAMING_SNAKE_CASE = data_dir_format(0 )
if accelerator.is_main_process:
if args.output_dir is not None:
os.makedirs(args.output_dir , exist_ok=UpperCAmelCase__ )
os.makedirs(UpperCAmelCase__ , exist_ok=UpperCAmelCase__ )
accelerator.wait_for_everyone()
__SCREAMING_SNAKE_CASE = None
__SCREAMING_SNAKE_CASE = None
__SCREAMING_SNAKE_CASE = 0
__SCREAMING_SNAKE_CASE = False
# Show the progress bar
__SCREAMING_SNAKE_CASE = tqdm(range(args.max_selftrain_iterations ) , disable=not accelerator.is_local_main_process )
# Self-train
for iteration in range(0 , int(args.max_selftrain_iterations ) ):
__SCREAMING_SNAKE_CASE = data_dir_format(UpperCAmelCase__ )
assert os.path.exists(UpperCAmelCase__ )
# Stage 1: initial fine-tuning for iteration = 0 or pseudo-training for
# iteration > 0
__SCREAMING_SNAKE_CASE = os.path.join(UpperCAmelCase__ , '''stage-1''' )
__SCREAMING_SNAKE_CASE = {
'''accelerator''': accelerator,
'''model_name_or_path''': args.model_name_or_path,
'''cache_dir''': args.cache_dir,
'''do_train''': True,
'''train_file''': data_files['''train'''] if iteration == 0 else data_files['''train_pseudo'''],
'''do_eval''': True if args.eval_file is not None else False,
'''eval_file''': data_files['''eval'''],
'''do_predict''': True,
'''infer_file''': data_files['''infer'''],
'''task_name''': args.task_name,
'''label_list''': args.label_list,
'''output_dir''': current_output_dir,
'''eval_metric''': args.eval_metric,
'''evaluation_strategy''': args.evaluation_strategy,
'''early_stopping_patience''': args.early_stopping_patience,
'''early_stopping_threshold''': args.early_stopping_threshold,
'''seed''': args.seed,
}
# Add additional training arguments
for key, value in kwargs.items():
if key not in arguments_dict and not hasattr(UpperCAmelCase__ , UpperCAmelCase__ ):
arguments_dict.update({key: value} )
__SCREAMING_SNAKE_CASE = os.path.join(UpperCAmelCase__ , '''best-checkpoint''' , UpperCAmelCase__ )
if os.path.exists(UpperCAmelCase__ ):
logger.info(
'''Found existing model checkpoint at %s. Skipping self-training: iteration: %d, stage: 1.''' , UpperCAmelCase__ , UpperCAmelCase__ , )
else:
logger.info('''***** Running self-training: iteration: %d, stage: 1 *****''' , UpperCAmelCase__ )
finetune(**UpperCAmelCase__ )
accelerator.wait_for_everyone()
assert os.path.exists(UpperCAmelCase__ )
logger.info('''Self-training job completed: iteration: %d, stage: 1.''' , UpperCAmelCase__ )
if iteration > 0 and args.finetune_on_labeled_data:
# Stage 2 (optional): fine-tuning on the original labeled data
__SCREAMING_SNAKE_CASE = os.path.join(UpperCAmelCase__ , '''best-checkpoint''' )
__SCREAMING_SNAKE_CASE = os.path.join(UpperCAmelCase__ , '''stage-2''' )
# Update arguments_dict
__SCREAMING_SNAKE_CASE = model_path
__SCREAMING_SNAKE_CASE = data_files['''train''']
__SCREAMING_SNAKE_CASE = current_output_dir
__SCREAMING_SNAKE_CASE = os.path.join(UpperCAmelCase__ , '''best-checkpoint''' , UpperCAmelCase__ )
if os.path.exists(UpperCAmelCase__ ):
logger.info(
'''Found existing model checkpoint at %s. Skipping self-training: iteration: %d, stage: 2.''' , UpperCAmelCase__ , UpperCAmelCase__ , )
else:
logger.info('''***** Running self-training: iteration: %d, stage: 2 *****''' , UpperCAmelCase__ )
finetune(**UpperCAmelCase__ )
accelerator.wait_for_everyone()
assert os.path.exists(UpperCAmelCase__ )
logger.info('''Self-training job completed: iteration: %d, stage: 2.''' , UpperCAmelCase__ )
__SCREAMING_SNAKE_CASE = iteration
__SCREAMING_SNAKE_CASE = data_dir_format(iteration + 1 )
__SCREAMING_SNAKE_CASE = AutoConfig.from_pretrained(os.path.join(UpperCAmelCase__ , '''best-checkpoint''' ) )
__SCREAMING_SNAKE_CASE = config.idalabel
__SCREAMING_SNAKE_CASE = os.path.join(UpperCAmelCase__ , '''eval_results_best-checkpoint.json''' )
__SCREAMING_SNAKE_CASE = os.path.join(UpperCAmelCase__ , '''test_results_best-checkpoint.json''' )
assert os.path.exists(UpperCAmelCase__ )
with open(UpperCAmelCase__ , '''r''' ) as f:
__SCREAMING_SNAKE_CASE = float(json.load(UpperCAmelCase__ )[args.eval_metric] )
__SCREAMING_SNAKE_CASE = os.path.join(UpperCAmelCase__ , '''infer_output_best-checkpoint.csv''' )
assert os.path.exists(UpperCAmelCase__ )
# Loading the dataset from local csv or json files.
__SCREAMING_SNAKE_CASE = load_dataset(args.data_file_extension , data_files={'''data''': data_files['''infer''']} )['''data''']
__SCREAMING_SNAKE_CASE = load_dataset('''csv''' , data_files={'''data''': infer_output_file} )['''data''']
if accelerator.is_main_process:
os.makedirs(UpperCAmelCase__ , exist_ok=UpperCAmelCase__ )
shutil.copy(UpperCAmelCase__ , os.path.join(UpperCAmelCase__ , f"""eval_results_iter-{iteration}.json""" ) )
if os.path.exists(UpperCAmelCase__ ):
shutil.copy(UpperCAmelCase__ , os.path.join(UpperCAmelCase__ , f"""test_results_iter-{iteration}.json""" ) )
create_pseudo_labeled_data(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ )
accelerator.wait_for_everyone()
__SCREAMING_SNAKE_CASE = os.path.join(UpperCAmelCase__ , f"""train_pseudo.{args.data_file_extension}""" )
if args.evaluation_strategy != IntervalStrategy.NO.value:
__SCREAMING_SNAKE_CASE = eval_result
if best_iteration is None:
__SCREAMING_SNAKE_CASE = new_iteration
__SCREAMING_SNAKE_CASE = new_eval_result
else:
if new_eval_result - best_eval_result > args.early_stopping_threshold:
__SCREAMING_SNAKE_CASE = new_iteration
__SCREAMING_SNAKE_CASE = new_eval_result
__SCREAMING_SNAKE_CASE = 0
else:
if new_eval_result == best_eval_result:
__SCREAMING_SNAKE_CASE = new_iteration
__SCREAMING_SNAKE_CASE = new_eval_result
early_stopping_patience_counter += 1
if early_stopping_patience_counter >= args.early_stopping_patience:
__SCREAMING_SNAKE_CASE = True
progress_bar.update(1 )
if should_training_stop:
break
if best_iteration is not None:
# Save the best iteration
logger.info('''Best iteration: %d''' , UpperCAmelCase__ )
logger.info('''Best evaluation result: %s = %f''' , args.eval_metric , UpperCAmelCase__ )
accelerator.wait_for_everyone()
if accelerator.is_main_process:
shutil.copy(
os.path.join(UpperCAmelCase__ , f"""eval_results_iter-{iteration}.json""" ) , os.path.join(UpperCAmelCase__ , '''eval_results_best-iteration.json''' ) , )
else:
# Assume that the last iteration is the best
logger.info('''Best iteration: %d''' , args.max_selftrain_iterations - 1 )
logger.info('''Best evaluation result: %s = %f''' , args.eval_metric , UpperCAmelCase__ )
accelerator.wait_for_everyone()
if accelerator.is_main_process:
shutil.copy(
os.path.join(UpperCAmelCase__ , f"""eval_results_iter-{args.max_selftrain_iterations - 1}.json""" ) , os.path.join(UpperCAmelCase__ , '''eval_results_best-iteration.json''' ) , )
| 690 |
"""simple docstring"""
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCAmelCase__ ={"configuration_timm_backbone": ["TimmBackboneConfig"]}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase__ =["TimmBackbone"]
if TYPE_CHECKING:
from .configuration_timm_backbone import TimmBackboneConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_timm_backbone import TimmBackbone
else:
import sys
lowerCAmelCase__ =_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 690 | 1 |
"""simple docstring"""
from __future__ import annotations
def _a ( UpperCAmelCase__ ) -> list[int]: # This function is recursive
__SCREAMING_SNAKE_CASE = len(UpperCAmelCase__ )
# If the array contains only one element, we return it (it's the stop condition of
# recursion)
if array_length <= 1:
return array
# Else
__SCREAMING_SNAKE_CASE = array[0]
__SCREAMING_SNAKE_CASE = False
__SCREAMING_SNAKE_CASE = 1
__SCREAMING_SNAKE_CASE = []
while not is_found and i < array_length:
if array[i] < pivot:
__SCREAMING_SNAKE_CASE = True
__SCREAMING_SNAKE_CASE = [element for element in array[i:] if element >= array[i]]
__SCREAMING_SNAKE_CASE = longest_subsequence(UpperCAmelCase__ )
if len(UpperCAmelCase__ ) > len(UpperCAmelCase__ ):
__SCREAMING_SNAKE_CASE = temp_array
else:
i += 1
__SCREAMING_SNAKE_CASE = [element for element in array[1:] if element >= pivot]
__SCREAMING_SNAKE_CASE = [pivot, *longest_subsequence(UpperCAmelCase__ )]
if len(UpperCAmelCase__ ) > len(UpperCAmelCase__ ):
return temp_array
else:
return longest_subseq
if __name__ == "__main__":
import doctest
doctest.testmod()
| 690 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
lowerCAmelCase__ ={
"configuration_altclip": [
"ALTCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP",
"AltCLIPConfig",
"AltCLIPTextConfig",
"AltCLIPVisionConfig",
],
"processing_altclip": ["AltCLIPProcessor"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase__ =[
"ALTCLIP_PRETRAINED_MODEL_ARCHIVE_LIST",
"AltCLIPPreTrainedModel",
"AltCLIPModel",
"AltCLIPTextModel",
"AltCLIPVisionModel",
]
if TYPE_CHECKING:
from .configuration_altclip import (
ALTCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
AltCLIPConfig,
AltCLIPTextConfig,
AltCLIPVisionConfig,
)
from .processing_altclip import AltCLIPProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_altclip import (
ALTCLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
AltCLIPModel,
AltCLIPPreTrainedModel,
AltCLIPTextModel,
AltCLIPVisionModel,
)
else:
import sys
lowerCAmelCase__ =_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 690 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
lowerCAmelCase__ ={
"configuration_transfo_xl": ["TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP", "TransfoXLConfig"],
"tokenization_transfo_xl": ["TransfoXLCorpus", "TransfoXLTokenizer"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase__ =[
"TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST",
"AdaptiveEmbedding",
"TransfoXLForSequenceClassification",
"TransfoXLLMHeadModel",
"TransfoXLModel",
"TransfoXLPreTrainedModel",
"load_tf_weights_in_transfo_xl",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase__ =[
"TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST",
"TFAdaptiveEmbedding",
"TFTransfoXLForSequenceClassification",
"TFTransfoXLLMHeadModel",
"TFTransfoXLMainLayer",
"TFTransfoXLModel",
"TFTransfoXLPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_transfo_xl import TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP, TransfoXLConfig
from .tokenization_transfo_xl import TransfoXLCorpus, TransfoXLTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_transfo_xl import (
TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
AdaptiveEmbedding,
TransfoXLForSequenceClassification,
TransfoXLLMHeadModel,
TransfoXLModel,
TransfoXLPreTrainedModel,
load_tf_weights_in_transfo_xl,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_transfo_xl import (
TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
TFAdaptiveEmbedding,
TFTransfoXLForSequenceClassification,
TFTransfoXLLMHeadModel,
TFTransfoXLMainLayer,
TFTransfoXLModel,
TFTransfoXLPreTrainedModel,
)
else:
import sys
lowerCAmelCase__ =_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 690 |
"""simple docstring"""
import json
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
from transformers import (
CONFIG_MAPPING,
IMAGE_PROCESSOR_MAPPING,
AutoConfig,
AutoImageProcessor,
CLIPConfig,
CLIPImageProcessor,
)
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER
sys.path.append(str(Path(__file__).parent.parent.parent.parent / "utils"))
from test_module.custom_configuration import CustomConfig # noqa E402
from test_module.custom_image_processing import CustomImageProcessor # noqa E402
class A__( unittest.TestCase ):
def _a ( self : int ) -> Union[str, Any]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = 0
def _a ( self : Tuple ) -> Tuple:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = AutoImageProcessor.from_pretrained('''openai/clip-vit-base-patch32''' )
self.assertIsInstance(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
def _a ( self : str ) -> Optional[int]:
"""simple docstring"""
with tempfile.TemporaryDirectory() as tmpdirname:
__SCREAMING_SNAKE_CASE = Path(__SCREAMING_SNAKE_CASE ) / '''preprocessor_config.json'''
__SCREAMING_SNAKE_CASE = Path(__SCREAMING_SNAKE_CASE ) / '''config.json'''
json.dump(
{'''image_processor_type''': '''CLIPImageProcessor''', '''processor_class''': '''CLIPProcessor'''} , open(__SCREAMING_SNAKE_CASE , '''w''' ) , )
json.dump({'''model_type''': '''clip'''} , open(__SCREAMING_SNAKE_CASE , '''w''' ) )
__SCREAMING_SNAKE_CASE = AutoImageProcessor.from_pretrained(__SCREAMING_SNAKE_CASE )
self.assertIsInstance(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
def _a ( self : List[str] ) -> Optional[int]:
"""simple docstring"""
with tempfile.TemporaryDirectory() as tmpdirname:
__SCREAMING_SNAKE_CASE = Path(__SCREAMING_SNAKE_CASE ) / '''preprocessor_config.json'''
__SCREAMING_SNAKE_CASE = Path(__SCREAMING_SNAKE_CASE ) / '''config.json'''
json.dump(
{'''feature_extractor_type''': '''CLIPFeatureExtractor''', '''processor_class''': '''CLIPProcessor'''} , open(__SCREAMING_SNAKE_CASE , '''w''' ) , )
json.dump({'''model_type''': '''clip'''} , open(__SCREAMING_SNAKE_CASE , '''w''' ) )
__SCREAMING_SNAKE_CASE = AutoImageProcessor.from_pretrained(__SCREAMING_SNAKE_CASE )
self.assertIsInstance(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
def _a ( self : str ) -> int:
"""simple docstring"""
with tempfile.TemporaryDirectory() as tmpdirname:
__SCREAMING_SNAKE_CASE = CLIPConfig()
# Create a dummy config file with image_proceesor_type
__SCREAMING_SNAKE_CASE = Path(__SCREAMING_SNAKE_CASE ) / '''preprocessor_config.json'''
__SCREAMING_SNAKE_CASE = Path(__SCREAMING_SNAKE_CASE ) / '''config.json'''
json.dump(
{'''image_processor_type''': '''CLIPImageProcessor''', '''processor_class''': '''CLIPProcessor'''} , open(__SCREAMING_SNAKE_CASE , '''w''' ) , )
json.dump({'''model_type''': '''clip'''} , open(__SCREAMING_SNAKE_CASE , '''w''' ) )
# remove image_processor_type to make sure config.json alone is enough to load image processor locally
__SCREAMING_SNAKE_CASE = AutoImageProcessor.from_pretrained(__SCREAMING_SNAKE_CASE ).to_dict()
config_dict.pop('''image_processor_type''' )
__SCREAMING_SNAKE_CASE = CLIPImageProcessor(**__SCREAMING_SNAKE_CASE )
# save in new folder
model_config.save_pretrained(__SCREAMING_SNAKE_CASE )
config.save_pretrained(__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = AutoImageProcessor.from_pretrained(__SCREAMING_SNAKE_CASE )
# make sure private variable is not incorrectly saved
__SCREAMING_SNAKE_CASE = json.loads(config.to_json_string() )
self.assertTrue('''_processor_class''' not in dict_as_saved )
self.assertIsInstance(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
def _a ( self : Optional[int] ) -> Tuple:
"""simple docstring"""
with tempfile.TemporaryDirectory() as tmpdirname:
__SCREAMING_SNAKE_CASE = Path(__SCREAMING_SNAKE_CASE ) / '''preprocessor_config.json'''
json.dump(
{'''image_processor_type''': '''CLIPImageProcessor''', '''processor_class''': '''CLIPProcessor'''} , open(__SCREAMING_SNAKE_CASE , '''w''' ) , )
__SCREAMING_SNAKE_CASE = AutoImageProcessor.from_pretrained(__SCREAMING_SNAKE_CASE )
self.assertIsInstance(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
def _a ( self : List[Any] ) -> str:
"""simple docstring"""
with self.assertRaisesRegex(
__SCREAMING_SNAKE_CASE , '''clip-base is not a local folder and is not a valid model identifier''' ):
__SCREAMING_SNAKE_CASE = AutoImageProcessor.from_pretrained('''clip-base''' )
def _a ( self : Any ) -> Optional[Any]:
"""simple docstring"""
with self.assertRaisesRegex(
__SCREAMING_SNAKE_CASE , r'''aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)''' ):
__SCREAMING_SNAKE_CASE = AutoImageProcessor.from_pretrained(__SCREAMING_SNAKE_CASE , revision='''aaaaaa''' )
def _a ( self : Dict ) -> Dict:
"""simple docstring"""
with self.assertRaisesRegex(
__SCREAMING_SNAKE_CASE , '''hf-internal-testing/config-no-model does not appear to have a file named preprocessor_config.json.''' , ):
__SCREAMING_SNAKE_CASE = AutoImageProcessor.from_pretrained('''hf-internal-testing/config-no-model''' )
def _a ( self : int ) -> Any:
"""simple docstring"""
with self.assertRaises(__SCREAMING_SNAKE_CASE ):
__SCREAMING_SNAKE_CASE = AutoImageProcessor.from_pretrained('''hf-internal-testing/test_dynamic_image_processor''' )
# If remote code is disabled, we can't load this config.
with self.assertRaises(__SCREAMING_SNAKE_CASE ):
__SCREAMING_SNAKE_CASE = AutoImageProcessor.from_pretrained(
'''hf-internal-testing/test_dynamic_image_processor''' , trust_remote_code=__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = AutoImageProcessor.from_pretrained(
'''hf-internal-testing/test_dynamic_image_processor''' , trust_remote_code=__SCREAMING_SNAKE_CASE )
self.assertEqual(image_processor.__class__.__name__ , '''NewImageProcessor''' )
# Test image processor can be reloaded.
with tempfile.TemporaryDirectory() as tmp_dir:
image_processor.save_pretrained(__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = AutoImageProcessor.from_pretrained(__SCREAMING_SNAKE_CASE , trust_remote_code=__SCREAMING_SNAKE_CASE )
self.assertEqual(reloaded_image_processor.__class__.__name__ , '''NewImageProcessor''' )
def _a ( self : Optional[Any] ) -> str:
"""simple docstring"""
try:
AutoConfig.register('''custom''' , __SCREAMING_SNAKE_CASE )
AutoImageProcessor.register(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
# Trying to register something existing in the Transformers library will raise an error
with self.assertRaises(__SCREAMING_SNAKE_CASE ):
AutoImageProcessor.register(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
with tempfile.TemporaryDirectory() as tmpdirname:
__SCREAMING_SNAKE_CASE = Path(__SCREAMING_SNAKE_CASE ) / '''preprocessor_config.json'''
__SCREAMING_SNAKE_CASE = Path(__SCREAMING_SNAKE_CASE ) / '''config.json'''
json.dump(
{'''feature_extractor_type''': '''CLIPFeatureExtractor''', '''processor_class''': '''CLIPProcessor'''} , open(__SCREAMING_SNAKE_CASE , '''w''' ) , )
json.dump({'''model_type''': '''clip'''} , open(__SCREAMING_SNAKE_CASE , '''w''' ) )
__SCREAMING_SNAKE_CASE = CustomImageProcessor.from_pretrained(__SCREAMING_SNAKE_CASE )
# Now that the config is registered, it can be used as any other config with the auto-API
with tempfile.TemporaryDirectory() as tmp_dir:
image_processor.save_pretrained(__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = AutoImageProcessor.from_pretrained(__SCREAMING_SNAKE_CASE )
self.assertIsInstance(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
finally:
if "custom" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["custom"]
if CustomConfig in IMAGE_PROCESSOR_MAPPING._extra_content:
del IMAGE_PROCESSOR_MAPPING._extra_content[CustomConfig]
def _a ( self : int ) -> List[Any]:
"""simple docstring"""
class A__( __magic_name__ ):
lowerCAmelCase = True
try:
AutoConfig.register('''custom''' , __SCREAMING_SNAKE_CASE )
AutoImageProcessor.register(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
# If remote code is not set, the default is to use local
__SCREAMING_SNAKE_CASE = AutoImageProcessor.from_pretrained('''hf-internal-testing/test_dynamic_image_processor''' )
self.assertEqual(image_processor.__class__.__name__ , '''NewImageProcessor''' )
self.assertTrue(image_processor.is_local )
# If remote code is disabled, we load the local one.
__SCREAMING_SNAKE_CASE = AutoImageProcessor.from_pretrained(
'''hf-internal-testing/test_dynamic_image_processor''' , trust_remote_code=__SCREAMING_SNAKE_CASE )
self.assertEqual(image_processor.__class__.__name__ , '''NewImageProcessor''' )
self.assertTrue(image_processor.is_local )
# If remote is enabled, we load from the Hub
__SCREAMING_SNAKE_CASE = AutoImageProcessor.from_pretrained(
'''hf-internal-testing/test_dynamic_image_processor''' , trust_remote_code=__SCREAMING_SNAKE_CASE )
self.assertEqual(image_processor.__class__.__name__ , '''NewImageProcessor''' )
self.assertTrue(not hasattr(__SCREAMING_SNAKE_CASE , '''is_local''' ) )
finally:
if "custom" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["custom"]
if CustomConfig in IMAGE_PROCESSOR_MAPPING._extra_content:
del IMAGE_PROCESSOR_MAPPING._extra_content[CustomConfig]
| 690 | 1 |
"""simple docstring"""
# Copyright (c) 2021-, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
####################################################################################################
#
# Note: If when running this conversion script you're getting an exception:
# ModuleNotFoundError: No module named 'megatron.model.enums'
# you need to tell python where to find the clone of Megatron-LM, e.g.:
#
# cd /tmp
# git clone https://github.com/NVIDIA/Megatron-LM
# PYTHONPATH=/tmp/Megatron-LM python src/transformers/models/megatron_gpt2/convert_megatron_gpt2_checkpoint.py ...
#
# if you already have it cloned elsewhere, simply adjust the path to the existing path
#
# If the training was done using a Megatron-LM fork, e.g.,
# https://github.com/microsoft/Megatron-DeepSpeed/ then chances are that you need to have that one
# in your path, i.e., /path/to/Megatron-DeepSpeed/
#
import argparse
import os
import re
import zipfile
import torch
from transformers import AutoTokenizer, GPTaConfig
def _a ( UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__=0 ) -> Optional[Any]:
# Format the message.
if name is None:
__SCREAMING_SNAKE_CASE = None
else:
__SCREAMING_SNAKE_CASE = '''.''' * max(0 , spaces - 2 ) + '''# {:''' + str(50 - spaces ) + '''s}'''
__SCREAMING_SNAKE_CASE = fmt.format(UpperCAmelCase__ )
# Print and recurse (if needed).
if isinstance(UpperCAmelCase__ , UpperCAmelCase__ ):
if msg is not None:
print(UpperCAmelCase__ )
for k in val.keys():
recursive_print(UpperCAmelCase__ , val[k] , spaces + 2 )
elif isinstance(UpperCAmelCase__ , torch.Tensor ):
print(UpperCAmelCase__ , ''':''' , val.size() )
else:
print(UpperCAmelCase__ , ''':''' , UpperCAmelCase__ )
def _a ( UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ ) -> Any:
# Permutes layout of param tensor to [num_splits * num_heads * hidden_size, :]
# for compatibility with later versions of NVIDIA Megatron-LM.
# The inverse operation is performed inside Megatron-LM to read checkpoints:
# https://github.com/NVIDIA/Megatron-LM/blob/v2.4/megatron/checkpointing.py#L209
# If param is the weight tensor of the self-attention block, the returned tensor
# will have to be transposed one more time to be read by HuggingFace GPT2.
__SCREAMING_SNAKE_CASE = param.size()
if checkpoint_version == 1.0:
# version 1.0 stores [num_heads * hidden_size * num_splits, :]
__SCREAMING_SNAKE_CASE = (num_heads, hidden_size, num_splits) + input_shape[1:]
__SCREAMING_SNAKE_CASE = param.view(*UpperCAmelCase__ )
__SCREAMING_SNAKE_CASE = param.transpose(0 , 2 )
__SCREAMING_SNAKE_CASE = param.transpose(1 , 2 ).contiguous()
elif checkpoint_version >= 2.0:
# other versions store [num_heads * num_splits * hidden_size, :]
__SCREAMING_SNAKE_CASE = (num_heads, num_splits, hidden_size) + input_shape[1:]
__SCREAMING_SNAKE_CASE = param.view(*UpperCAmelCase__ )
__SCREAMING_SNAKE_CASE = param.transpose(0 , 1 ).contiguous()
__SCREAMING_SNAKE_CASE = param.view(*UpperCAmelCase__ )
return param
def _a ( UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ ) -> str:
# The converted output model.
__SCREAMING_SNAKE_CASE = {}
# old versions did not store training args
__SCREAMING_SNAKE_CASE = input_state_dict.get('''args''' , UpperCAmelCase__ )
if ds_args is not None:
# do not make the user write a config file when the exact dimensions/sizes are already in the checkpoint
# from pprint import pprint
# pprint(vars(ds_args))
__SCREAMING_SNAKE_CASE = ds_args.padded_vocab_size
__SCREAMING_SNAKE_CASE = ds_args.max_position_embeddings
__SCREAMING_SNAKE_CASE = ds_args.hidden_size
__SCREAMING_SNAKE_CASE = ds_args.num_layers
__SCREAMING_SNAKE_CASE = ds_args.num_attention_heads
__SCREAMING_SNAKE_CASE = ds_args.ffn_hidden_size
# pprint(config)
# The number of heads.
__SCREAMING_SNAKE_CASE = config.n_head
# The hidden_size per head.
__SCREAMING_SNAKE_CASE = config.n_embd // config.n_head
# Megatron-LM checkpoint version
if "checkpoint_version" in input_state_dict.keys():
__SCREAMING_SNAKE_CASE = input_state_dict['''checkpoint_version''']
else:
__SCREAMING_SNAKE_CASE = 0.0
# The model.
__SCREAMING_SNAKE_CASE = input_state_dict['''model''']
# The language model.
__SCREAMING_SNAKE_CASE = model['''language_model''']
# The embeddings.
__SCREAMING_SNAKE_CASE = lm['''embedding''']
# The word embeddings.
__SCREAMING_SNAKE_CASE = embeddings['''word_embeddings''']['''weight''']
# Truncate the embedding table to vocab_size rows.
__SCREAMING_SNAKE_CASE = word_embeddings[: config.vocab_size, :]
__SCREAMING_SNAKE_CASE = word_embeddings
# The position embeddings.
__SCREAMING_SNAKE_CASE = embeddings['''position_embeddings''']['''weight''']
# Read the causal mask dimension (seqlen). [max_sequence_length, hidden_size]
__SCREAMING_SNAKE_CASE = pos_embeddings.size(0 )
if n_positions != config.n_positions:
raise ValueError(
f"""pos_embeddings.max_sequence_length={n_positions} and config.n_positions={config.n_positions} don't match""" )
# Store the position embeddings.
__SCREAMING_SNAKE_CASE = pos_embeddings
# The transformer.
__SCREAMING_SNAKE_CASE = lm['''transformer'''] if '''transformer''' in lm.keys() else lm['''encoder''']
# The regex to extract layer names.
__SCREAMING_SNAKE_CASE = re.compile(r'''layers\.(\d+)\.([a-z0-9_.]+)\.([a-z]+)''' )
# The simple map of names for "automated" rules.
__SCREAMING_SNAKE_CASE = {
'''attention.dense''': '''.attn.c_proj.''',
'''self_attention.dense''': '''.attn.c_proj.''',
'''mlp.dense_h_to_4h''': '''.mlp.c_fc.''',
'''mlp.dense_4h_to_h''': '''.mlp.c_proj.''',
}
# Extract the layers.
for key, val in transformer.items():
# Match the name.
__SCREAMING_SNAKE_CASE = layer_re.match(UpperCAmelCase__ )
# Stop if that's not a layer
if m is None:
break
# The index of the layer.
__SCREAMING_SNAKE_CASE = int(m.group(1 ) )
# The name of the operation.
__SCREAMING_SNAKE_CASE = m.group(2 )
# Is it a weight or a bias?
__SCREAMING_SNAKE_CASE = m.group(3 )
# The name of the layer.
__SCREAMING_SNAKE_CASE = f"""transformer.h.{layer_idx}"""
# For layernorm(s), simply store the layer norm.
if op_name.endswith('''layernorm''' ):
__SCREAMING_SNAKE_CASE = '''ln_1''' if op_name.startswith('''input''' ) else '''ln_2'''
__SCREAMING_SNAKE_CASE = val
# Transpose the QKV matrix.
elif (
op_name == "attention.query_key_value" or op_name == "self_attention.query_key_value"
) and weight_or_bias == "weight":
# Insert a tensor of 1x1xDxD bias.
__SCREAMING_SNAKE_CASE = torch.tril(torch.ones((n_positions, n_positions) , dtype=torch.floataa ) ).view(
1 , 1 , UpperCAmelCase__ , UpperCAmelCase__ )
__SCREAMING_SNAKE_CASE = causal_mask
# Insert a "dummy" tensor for masked_bias.
__SCREAMING_SNAKE_CASE = torch.tensor(-1E4 , dtype=torch.floataa )
__SCREAMING_SNAKE_CASE = masked_bias
__SCREAMING_SNAKE_CASE = fix_query_key_value_ordering(UpperCAmelCase__ , UpperCAmelCase__ , 3 , UpperCAmelCase__ , UpperCAmelCase__ )
# Megatron stores (3*D) x D but transformers-GPT2 expects D x 3*D.
__SCREAMING_SNAKE_CASE = out_val.transpose(0 , 1 ).contiguous()
# Store.
__SCREAMING_SNAKE_CASE = out_val
# Transpose the bias.
elif (
op_name == "attention.query_key_value" or op_name == "self_attention.query_key_value"
) and weight_or_bias == "bias":
__SCREAMING_SNAKE_CASE = fix_query_key_value_ordering(UpperCAmelCase__ , UpperCAmelCase__ , 3 , UpperCAmelCase__ , UpperCAmelCase__ )
# Store. No change of shape.
__SCREAMING_SNAKE_CASE = out_val
# Transpose the weights.
elif weight_or_bias == "weight":
__SCREAMING_SNAKE_CASE = megatron_to_transformers[op_name]
__SCREAMING_SNAKE_CASE = val.transpose(0 , 1 )
# Copy the bias.
elif weight_or_bias == "bias":
__SCREAMING_SNAKE_CASE = megatron_to_transformers[op_name]
__SCREAMING_SNAKE_CASE = val
# DEBUG.
assert config.n_layer == layer_idx + 1
# The final layernorm.
__SCREAMING_SNAKE_CASE = transformer['''final_layernorm.weight''']
__SCREAMING_SNAKE_CASE = transformer['''final_layernorm.bias''']
# For LM head, transformers' wants the matrix to weight embeddings.
__SCREAMING_SNAKE_CASE = word_embeddings
# It should be done!
return output_state_dict
def _a ( ) -> int:
# Create the argument parser.
__SCREAMING_SNAKE_CASE = argparse.ArgumentParser()
parser.add_argument('''--print-checkpoint-structure''' , action='''store_true''' )
parser.add_argument(
'''path_to_checkpoint''' , type=UpperCAmelCase__ , help='''Path to the checkpoint file (.zip archive or direct .pt file)''' , )
parser.add_argument(
'''--config_file''' , default='''''' , type=UpperCAmelCase__ , help='''An optional config json file describing the pre-trained model.''' , )
__SCREAMING_SNAKE_CASE = parser.parse_args()
# Extract the basename.
__SCREAMING_SNAKE_CASE = os.path.dirname(args.path_to_checkpoint )
# Load the model.
# the .zip is very optional, let's keep it for backward compatibility
print(f"""Extracting PyTorch state dictionary from {args.path_to_checkpoint}""" )
if args.path_to_checkpoint.endswith('''.zip''' ):
with zipfile.ZipFile(args.path_to_checkpoint , '''r''' ) as checkpoint:
with checkpoint.open('''release/mp_rank_00/model_optim_rng.pt''' ) as pytorch_dict:
__SCREAMING_SNAKE_CASE = torch.load(UpperCAmelCase__ , map_location='''cpu''' )
else:
__SCREAMING_SNAKE_CASE = torch.load(args.path_to_checkpoint , map_location='''cpu''' )
__SCREAMING_SNAKE_CASE = input_state_dict.get('''args''' , UpperCAmelCase__ )
# Read the config, or default to the model released by NVIDIA.
if args.config_file == "":
if ds_args is not None:
if ds_args.bias_gelu_fusion:
__SCREAMING_SNAKE_CASE = '''gelu_fast'''
elif ds_args.openai_gelu:
__SCREAMING_SNAKE_CASE = '''gelu_new'''
else:
__SCREAMING_SNAKE_CASE = '''gelu'''
else:
# in the very early days this used to be "gelu_new"
__SCREAMING_SNAKE_CASE = '''gelu_new'''
# Spell out all parameters in case the defaults change.
__SCREAMING_SNAKE_CASE = GPTaConfig(
vocab_size=5_02_57 , n_positions=10_24 , n_embd=10_24 , n_layer=24 , n_head=16 , n_inner=40_96 , activation_function=UpperCAmelCase__ , resid_pdrop=0.1 , embd_pdrop=0.1 , attn_pdrop=0.1 , layer_norm_epsilon=1E-5 , initializer_range=0.02 , summary_type='''cls_index''' , summary_use_proj=UpperCAmelCase__ , summary_activation=UpperCAmelCase__ , summary_proj_to_labels=UpperCAmelCase__ , summary_first_dropout=0.1 , scale_attn_weights=UpperCAmelCase__ , use_cache=UpperCAmelCase__ , bos_token_id=5_02_56 , eos_token_id=5_02_56 , )
else:
__SCREAMING_SNAKE_CASE = GPTaConfig.from_json_file(args.config_file )
__SCREAMING_SNAKE_CASE = ['''GPT2LMHeadModel''']
# Convert.
print('''Converting''' )
__SCREAMING_SNAKE_CASE = convert_megatron_checkpoint(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ )
# Print the structure of converted state dict.
if args.print_checkpoint_structure:
recursive_print(UpperCAmelCase__ , UpperCAmelCase__ )
# Add tokenizer class info to config
# see https://github.com/huggingface/transformers/issues/13906)
if ds_args is not None:
__SCREAMING_SNAKE_CASE = ds_args.tokenizer_type
if tokenizer_type == "GPT2BPETokenizer":
__SCREAMING_SNAKE_CASE = '''gpt2'''
elif tokenizer_type == "PretrainedFromHF":
__SCREAMING_SNAKE_CASE = ds_args.tokenizer_name_or_path
else:
raise ValueError(f"""Unrecognized tokenizer_type {tokenizer_type}""" )
else:
__SCREAMING_SNAKE_CASE = '''gpt2'''
__SCREAMING_SNAKE_CASE = AutoTokenizer.from_pretrained(UpperCAmelCase__ )
__SCREAMING_SNAKE_CASE = type(UpperCAmelCase__ ).__name__
__SCREAMING_SNAKE_CASE = tokenizer_class
# Store the config to file.
print('''Saving config''' )
config.save_pretrained(UpperCAmelCase__ )
# Save tokenizer based on args
print(f"""Adding {tokenizer_class} tokenizer files""" )
tokenizer.save_pretrained(UpperCAmelCase__ )
# Store the state_dict to file.
__SCREAMING_SNAKE_CASE = os.path.join(UpperCAmelCase__ , '''pytorch_model.bin''' )
print(f"""Saving checkpoint to \"{output_checkpoint_file}\"""" )
torch.save(UpperCAmelCase__ , UpperCAmelCase__ )
####################################################################################################
if __name__ == "__main__":
main()
####################################################################################################
| 690 |
"""simple docstring"""
import math
lowerCAmelCase__ =10
lowerCAmelCase__ =7
lowerCAmelCase__ =BALLS_PER_COLOUR * NUM_COLOURS
def _a ( UpperCAmelCase__ = 20 ) -> str:
__SCREAMING_SNAKE_CASE = math.comb(UpperCAmelCase__ , UpperCAmelCase__ )
__SCREAMING_SNAKE_CASE = math.comb(NUM_BALLS - BALLS_PER_COLOUR , UpperCAmelCase__ )
__SCREAMING_SNAKE_CASE = NUM_COLOURS * (1 - missing_colour / total)
return f"""{result:.9f}"""
if __name__ == "__main__":
print(solution(20))
| 690 | 1 |
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast
from diffusers import DDIMScheduler, KandinskyImgaImgPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
enable_full_determinism()
class A__( __magic_name__ , unittest.TestCase ):
lowerCAmelCase = KandinskyImgaImgPipeline
lowerCAmelCase = ['''prompt''', '''image_embeds''', '''negative_image_embeds''', '''image''']
lowerCAmelCase = [
'''prompt''',
'''negative_prompt''',
'''image_embeds''',
'''negative_image_embeds''',
'''image''',
]
lowerCAmelCase = [
'''generator''',
'''height''',
'''width''',
'''strength''',
'''guidance_scale''',
'''negative_prompt''',
'''num_inference_steps''',
'''return_dict''',
'''guidance_scale''',
'''num_images_per_prompt''',
'''output_type''',
'''return_dict''',
]
lowerCAmelCase = False
@property
def _a ( self : int ) -> Any:
"""simple docstring"""
return 32
@property
def _a ( self : str ) -> Tuple:
"""simple docstring"""
return 32
@property
def _a ( self : Union[str, Any] ) -> Dict:
"""simple docstring"""
return self.time_input_dim
@property
def _a ( self : Any ) -> Union[str, Any]:
"""simple docstring"""
return self.time_input_dim * 4
@property
def _a ( self : List[str] ) -> Union[str, Any]:
"""simple docstring"""
return 1_00
@property
def _a ( self : Union[str, Any] ) -> Dict:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = XLMRobertaTokenizerFast.from_pretrained('''YiYiXu/tiny-random-mclip-base''' )
return tokenizer
@property
def _a ( self : Optional[int] ) -> List[str]:
"""simple docstring"""
torch.manual_seed(0 )
__SCREAMING_SNAKE_CASE = MCLIPConfig(
numDims=self.cross_attention_dim , transformerDimensions=self.text_embedder_hidden_size , hidden_size=self.text_embedder_hidden_size , intermediate_size=37 , num_attention_heads=4 , num_hidden_layers=5 , vocab_size=10_05 , )
__SCREAMING_SNAKE_CASE = MultilingualCLIP(__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = text_encoder.eval()
return text_encoder
@property
def _a ( self : Tuple ) -> Dict:
"""simple docstring"""
torch.manual_seed(0 )
__SCREAMING_SNAKE_CASE = {
'''in_channels''': 4,
# Out channels is double in channels because predicts mean and variance
'''out_channels''': 8,
'''addition_embed_type''': '''text_image''',
'''down_block_types''': ('''ResnetDownsampleBlock2D''', '''SimpleCrossAttnDownBlock2D'''),
'''up_block_types''': ('''SimpleCrossAttnUpBlock2D''', '''ResnetUpsampleBlock2D'''),
'''mid_block_type''': '''UNetMidBlock2DSimpleCrossAttn''',
'''block_out_channels''': (self.block_out_channels_a, self.block_out_channels_a * 2),
'''layers_per_block''': 1,
'''encoder_hid_dim''': self.text_embedder_hidden_size,
'''encoder_hid_dim_type''': '''text_image_proj''',
'''cross_attention_dim''': self.cross_attention_dim,
'''attention_head_dim''': 4,
'''resnet_time_scale_shift''': '''scale_shift''',
'''class_embed_type''': None,
}
__SCREAMING_SNAKE_CASE = UNetaDConditionModel(**__SCREAMING_SNAKE_CASE )
return model
@property
def _a ( self : Union[str, Any] ) -> List[Any]:
"""simple docstring"""
return {
"block_out_channels": [32, 64],
"down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
"in_channels": 3,
"latent_channels": 4,
"layers_per_block": 1,
"norm_num_groups": 8,
"norm_type": "spatial",
"num_vq_embeddings": 12,
"out_channels": 3,
"up_block_types": [
"AttnUpDecoderBlock2D",
"UpDecoderBlock2D",
],
"vq_embed_dim": 4,
}
@property
def _a ( self : List[Any] ) -> str:
"""simple docstring"""
torch.manual_seed(0 )
__SCREAMING_SNAKE_CASE = VQModel(**self.dummy_movq_kwargs )
return model
def _a ( self : Union[str, Any] ) -> List[Any]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = self.dummy_text_encoder
__SCREAMING_SNAKE_CASE = self.dummy_tokenizer
__SCREAMING_SNAKE_CASE = self.dummy_unet
__SCREAMING_SNAKE_CASE = self.dummy_movq
__SCREAMING_SNAKE_CASE = {
'''num_train_timesteps''': 10_00,
'''beta_schedule''': '''linear''',
'''beta_start''': 0.0_00_85,
'''beta_end''': 0.0_12,
'''clip_sample''': False,
'''set_alpha_to_one''': False,
'''steps_offset''': 0,
'''prediction_type''': '''epsilon''',
'''thresholding''': False,
}
__SCREAMING_SNAKE_CASE = DDIMScheduler(**__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = {
'''text_encoder''': text_encoder,
'''tokenizer''': tokenizer,
'''unet''': unet,
'''scheduler''': scheduler,
'''movq''': movq,
}
return components
def _a ( self : Tuple , __SCREAMING_SNAKE_CASE : Optional[Any] , __SCREAMING_SNAKE_CASE : int=0 ) -> Any:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = floats_tensor((1, self.cross_attention_dim) , rng=random.Random(__SCREAMING_SNAKE_CASE ) ).to(__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = floats_tensor((1, self.cross_attention_dim) , rng=random.Random(seed + 1 ) ).to(__SCREAMING_SNAKE_CASE )
# create init_image
__SCREAMING_SNAKE_CASE = floats_tensor((1, 3, 64, 64) , rng=random.Random(__SCREAMING_SNAKE_CASE ) ).to(__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = image.cpu().permute(0 , 2 , 3 , 1 )[0]
__SCREAMING_SNAKE_CASE = Image.fromarray(np.uinta(__SCREAMING_SNAKE_CASE ) ).convert('''RGB''' ).resize((2_56, 2_56) )
if str(__SCREAMING_SNAKE_CASE ).startswith('''mps''' ):
__SCREAMING_SNAKE_CASE = torch.manual_seed(__SCREAMING_SNAKE_CASE )
else:
__SCREAMING_SNAKE_CASE = torch.Generator(device=__SCREAMING_SNAKE_CASE ).manual_seed(__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = {
'''prompt''': '''horse''',
'''image''': init_image,
'''image_embeds''': image_embeds,
'''negative_image_embeds''': negative_image_embeds,
'''generator''': generator,
'''height''': 64,
'''width''': 64,
'''num_inference_steps''': 10,
'''guidance_scale''': 7.0,
'''strength''': 0.2,
'''output_type''': '''np''',
}
return inputs
def _a ( self : Optional[int] ) -> Dict:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = '''cpu'''
__SCREAMING_SNAKE_CASE = self.get_dummy_components()
__SCREAMING_SNAKE_CASE = self.pipeline_class(**__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = pipe.to(__SCREAMING_SNAKE_CASE )
pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = pipe(**self.get_dummy_inputs(__SCREAMING_SNAKE_CASE ) )
__SCREAMING_SNAKE_CASE = output.images
__SCREAMING_SNAKE_CASE = pipe(
**self.get_dummy_inputs(__SCREAMING_SNAKE_CASE ) , return_dict=__SCREAMING_SNAKE_CASE , )[0]
__SCREAMING_SNAKE_CASE = image[0, -3:, -3:, -1]
__SCREAMING_SNAKE_CASE = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
__SCREAMING_SNAKE_CASE = np.array(
[0.61_47_49_43, 0.6_07_35_39, 0.43_30_85_44, 0.5_92_82_69, 0.47_49_35_95, 0.46_75_59_73, 0.4_61_38_38, 0.45_36_87_97, 0.50_11_92_33] )
assert (
np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
), f""" expected_slice {expected_slice}, but got {image_slice.flatten()}"""
assert (
np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1E-2
), f""" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"""
@slow
@require_torch_gpu
class A__( unittest.TestCase ):
def _a ( self : List[str] ) -> Dict:
"""simple docstring"""
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def _a ( self : str ) -> Tuple:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = load_numpy(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
'''/kandinsky/kandinsky_img2img_frog.npy''' )
__SCREAMING_SNAKE_CASE = load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/kandinsky/cat.png''' )
__SCREAMING_SNAKE_CASE = '''A red cartoon frog, 4k'''
__SCREAMING_SNAKE_CASE = KandinskyPriorPipeline.from_pretrained(
'''kandinsky-community/kandinsky-2-1-prior''' , torch_dtype=torch.floataa )
pipe_prior.to(__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = KandinskyImgaImgPipeline.from_pretrained(
'''kandinsky-community/kandinsky-2-1''' , torch_dtype=torch.floataa )
__SCREAMING_SNAKE_CASE = pipeline.to(__SCREAMING_SNAKE_CASE )
pipeline.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = torch.Generator(device='''cpu''' ).manual_seed(0 )
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = pipe_prior(
__SCREAMING_SNAKE_CASE , generator=__SCREAMING_SNAKE_CASE , num_inference_steps=5 , negative_prompt='''''' , ).to_tuple()
__SCREAMING_SNAKE_CASE = pipeline(
__SCREAMING_SNAKE_CASE , image=__SCREAMING_SNAKE_CASE , image_embeds=__SCREAMING_SNAKE_CASE , negative_image_embeds=__SCREAMING_SNAKE_CASE , generator=__SCREAMING_SNAKE_CASE , num_inference_steps=1_00 , height=7_68 , width=7_68 , strength=0.2 , output_type='''np''' , )
__SCREAMING_SNAKE_CASE = output.images[0]
assert image.shape == (7_68, 7_68, 3)
assert_mean_pixel_difference(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
| 690 |
"""simple docstring"""
from collections import UserDict
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_torch_available():
from ..models.auto.modeling_auto import MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING
if is_tf_available():
from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING
from ..tf_utils import stable_softmax
lowerCAmelCase__ =logging.get_logger(__name__)
@add_end_docstrings(__magic_name__ )
class A__( __magic_name__ ):
def __init__( self : Optional[Any] , **__SCREAMING_SNAKE_CASE : str ) -> Optional[Any]:
"""simple docstring"""
super().__init__(**__SCREAMING_SNAKE_CASE )
requires_backends(self , '''vision''' )
self.check_model_type(
TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING
if self.framework == '''tf'''
else MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING )
def __call__( self : Optional[Any] , __SCREAMING_SNAKE_CASE : Union[str, List[str], "Image", List["Image"]] , **__SCREAMING_SNAKE_CASE : Union[str, Any] ) -> Tuple:
"""simple docstring"""
return super().__call__(__SCREAMING_SNAKE_CASE , **__SCREAMING_SNAKE_CASE )
def _a ( self : int , **__SCREAMING_SNAKE_CASE : int ) -> Any:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = {}
if "candidate_labels" in kwargs:
__SCREAMING_SNAKE_CASE = kwargs['''candidate_labels''']
if "hypothesis_template" in kwargs:
__SCREAMING_SNAKE_CASE = kwargs['''hypothesis_template''']
return preprocess_params, {}, {}
def _a ( self : Any , __SCREAMING_SNAKE_CASE : Any , __SCREAMING_SNAKE_CASE : Union[str, Any]=None , __SCREAMING_SNAKE_CASE : Optional[int]="This is a photo of {}." ) -> Union[str, Any]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = load_image(__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = self.image_processor(images=[image] , return_tensors=self.framework )
__SCREAMING_SNAKE_CASE = candidate_labels
__SCREAMING_SNAKE_CASE = [hypothesis_template.format(__SCREAMING_SNAKE_CASE ) for x in candidate_labels]
__SCREAMING_SNAKE_CASE = self.tokenizer(__SCREAMING_SNAKE_CASE , return_tensors=self.framework , padding=__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = [text_inputs]
return inputs
def _a ( self : Dict , __SCREAMING_SNAKE_CASE : List[Any] ) -> int:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = model_inputs.pop('''candidate_labels''' )
__SCREAMING_SNAKE_CASE = model_inputs.pop('''text_inputs''' )
if isinstance(text_inputs[0] , __SCREAMING_SNAKE_CASE ):
__SCREAMING_SNAKE_CASE = text_inputs[0]
else:
# Batching case.
__SCREAMING_SNAKE_CASE = text_inputs[0][0]
__SCREAMING_SNAKE_CASE = self.model(**__SCREAMING_SNAKE_CASE , **__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = {
'''candidate_labels''': candidate_labels,
'''logits''': outputs.logits_per_image,
}
return model_outputs
def _a ( self : Any , __SCREAMING_SNAKE_CASE : List[str] ) -> Optional[int]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = model_outputs.pop('''candidate_labels''' )
__SCREAMING_SNAKE_CASE = model_outputs['''logits'''][0]
if self.framework == "pt":
__SCREAMING_SNAKE_CASE = logits.softmax(dim=-1 ).squeeze(-1 )
__SCREAMING_SNAKE_CASE = probs.tolist()
if not isinstance(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
__SCREAMING_SNAKE_CASE = [scores]
elif self.framework == "tf":
__SCREAMING_SNAKE_CASE = stable_softmax(__SCREAMING_SNAKE_CASE , axis=-1 )
__SCREAMING_SNAKE_CASE = probs.numpy().tolist()
else:
raise ValueError(f"""Unsupported framework: {self.framework}""" )
__SCREAMING_SNAKE_CASE = [
{'''score''': score, '''label''': candidate_label}
for score, candidate_label in sorted(zip(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) , key=lambda __SCREAMING_SNAKE_CASE : -x[0] )
]
return result
| 690 | 1 |
"""simple docstring"""
import string
from math import logaa
def _a ( UpperCAmelCase__ , UpperCAmelCase__ ) -> int:
__SCREAMING_SNAKE_CASE = document.translate(
str.maketrans('''''' , '''''' , string.punctuation ) ).replace('''\n''' , '''''' )
__SCREAMING_SNAKE_CASE = document_without_punctuation.split(''' ''' ) # word tokenization
return len([word for word in tokenize_document if word.lower() == term.lower()] )
def _a ( UpperCAmelCase__ , UpperCAmelCase__ ) -> tuple[int, int]:
__SCREAMING_SNAKE_CASE = corpus.lower().translate(
str.maketrans('''''' , '''''' , string.punctuation ) ) # strip all punctuation and replace it with ''
__SCREAMING_SNAKE_CASE = corpus_without_punctuation.split('''\n''' )
__SCREAMING_SNAKE_CASE = term.lower()
return (len([doc for doc in docs if term in doc] ), len(UpperCAmelCase__ ))
def _a ( UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__=False ) -> float:
if smoothing:
if n == 0:
raise ValueError('''log10(0) is undefined.''' )
return round(1 + logaa(n / (1 + df) ) , 3 )
if df == 0:
raise ZeroDivisionError('''df must be > 0''' )
elif n == 0:
raise ValueError('''log10(0) is undefined.''' )
return round(logaa(n / df ) , 3 )
def _a ( UpperCAmelCase__ , UpperCAmelCase__ ) -> float:
return round(tf * idf , 3 )
| 690 |
"""simple docstring"""
from __future__ import annotations
from collections.abc import Callable
lowerCAmelCase__ =list[list[float | int]]
def _a ( UpperCAmelCase__ , UpperCAmelCase__ ) -> Matrix:
__SCREAMING_SNAKE_CASE = len(UpperCAmelCase__ )
__SCREAMING_SNAKE_CASE = [[0 for _ in range(size + 1 )] for _ in range(UpperCAmelCase__ )]
__SCREAMING_SNAKE_CASE = 42
__SCREAMING_SNAKE_CASE = 42
__SCREAMING_SNAKE_CASE = 42
__SCREAMING_SNAKE_CASE = 42
__SCREAMING_SNAKE_CASE = 42
__SCREAMING_SNAKE_CASE = 42
for row in range(UpperCAmelCase__ ):
for col in range(UpperCAmelCase__ ):
__SCREAMING_SNAKE_CASE = matrix[row][col]
__SCREAMING_SNAKE_CASE = vector[row][0]
__SCREAMING_SNAKE_CASE = 0
__SCREAMING_SNAKE_CASE = 0
while row < size and col < size:
# pivoting
__SCREAMING_SNAKE_CASE = max((abs(augmented[rowa][col] ), rowa) for rowa in range(UpperCAmelCase__ , UpperCAmelCase__ ) )[
1
]
if augmented[pivot_row][col] == 0:
col += 1
continue
else:
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = augmented[pivot_row], augmented[row]
for rowa in range(row + 1 , UpperCAmelCase__ ):
__SCREAMING_SNAKE_CASE = augmented[rowa][col] / augmented[row][col]
__SCREAMING_SNAKE_CASE = 0
for cola in range(col + 1 , size + 1 ):
augmented[rowa][cola] -= augmented[row][cola] * ratio
row += 1
col += 1
# back substitution
for col in range(1 , UpperCAmelCase__ ):
for row in range(UpperCAmelCase__ ):
__SCREAMING_SNAKE_CASE = augmented[row][col] / augmented[col][col]
for cola in range(UpperCAmelCase__ , size + 1 ):
augmented[row][cola] -= augmented[col][cola] * ratio
# round to get rid of numbers like 2.000000000000004
return [
[round(augmented[row][size] / augmented[row][row] , 10 )] for row in range(UpperCAmelCase__ )
]
def _a ( UpperCAmelCase__ ) -> Callable[[int], int]:
__SCREAMING_SNAKE_CASE = len(UpperCAmelCase__ )
__SCREAMING_SNAKE_CASE = [[0 for _ in range(UpperCAmelCase__ )] for _ in range(UpperCAmelCase__ )]
__SCREAMING_SNAKE_CASE = [[0] for _ in range(UpperCAmelCase__ )]
__SCREAMING_SNAKE_CASE = 42
__SCREAMING_SNAKE_CASE = 42
__SCREAMING_SNAKE_CASE = 42
__SCREAMING_SNAKE_CASE = 42
for x_val, y_val in enumerate(UpperCAmelCase__ ):
for col in range(UpperCAmelCase__ ):
__SCREAMING_SNAKE_CASE = (x_val + 1) ** (size - col - 1)
__SCREAMING_SNAKE_CASE = y_val
__SCREAMING_SNAKE_CASE = solve(UpperCAmelCase__ , UpperCAmelCase__ )
def interpolated_func(UpperCAmelCase__ ) -> int:
return sum(
round(coeffs[x_val][0] ) * (var ** (size - x_val - 1))
for x_val in range(UpperCAmelCase__ ) )
return interpolated_func
def _a ( UpperCAmelCase__ ) -> int:
return (
1
- variable
+ variable**2
- variable**3
+ variable**4
- variable**5
+ variable**6
- variable**7
+ variable**8
- variable**9
+ variable**10
)
def _a ( UpperCAmelCase__ = question_function , UpperCAmelCase__ = 10 ) -> int:
__SCREAMING_SNAKE_CASE = [func(UpperCAmelCase__ ) for x_val in range(1 , order + 1 )]
__SCREAMING_SNAKE_CASE = [
interpolate(data_points[:max_coeff] ) for max_coeff in range(1 , order + 1 )
]
__SCREAMING_SNAKE_CASE = 0
__SCREAMING_SNAKE_CASE = 42
__SCREAMING_SNAKE_CASE = 42
for poly in polynomials:
__SCREAMING_SNAKE_CASE = 1
while func(UpperCAmelCase__ ) == poly(UpperCAmelCase__ ):
x_val += 1
ret += poly(UpperCAmelCase__ )
return ret
if __name__ == "__main__":
print(F'''{solution() = }''')
| 690 | 1 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
lowerCAmelCase__ =logging.get_logger(__name__)
lowerCAmelCase__ ={
"microsoft/swin-tiny-patch4-window7-224": (
"https://huggingface.co/microsoft/swin-tiny-patch4-window7-224/resolve/main/config.json"
),
# See all Swin models at https://huggingface.co/models?filter=swin
}
class A__( __magic_name__ , __magic_name__ ):
lowerCAmelCase = '''swin'''
lowerCAmelCase = {
'''num_attention_heads''': '''num_heads''',
'''num_hidden_layers''': '''num_layers''',
}
def __init__( self : Tuple , __SCREAMING_SNAKE_CASE : List[Any]=2_24 , __SCREAMING_SNAKE_CASE : Any=4 , __SCREAMING_SNAKE_CASE : Union[str, Any]=3 , __SCREAMING_SNAKE_CASE : Optional[Any]=96 , __SCREAMING_SNAKE_CASE : Union[str, Any]=[2, 2, 6, 2] , __SCREAMING_SNAKE_CASE : int=[3, 6, 12, 24] , __SCREAMING_SNAKE_CASE : List[Any]=7 , __SCREAMING_SNAKE_CASE : Any=4.0 , __SCREAMING_SNAKE_CASE : str=True , __SCREAMING_SNAKE_CASE : int=0.0 , __SCREAMING_SNAKE_CASE : Optional[Any]=0.0 , __SCREAMING_SNAKE_CASE : Any=0.1 , __SCREAMING_SNAKE_CASE : Dict="gelu" , __SCREAMING_SNAKE_CASE : Dict=False , __SCREAMING_SNAKE_CASE : Union[str, Any]=0.02 , __SCREAMING_SNAKE_CASE : Any=1E-5 , __SCREAMING_SNAKE_CASE : Union[str, Any]=32 , __SCREAMING_SNAKE_CASE : str=None , __SCREAMING_SNAKE_CASE : Union[str, Any]=None , **__SCREAMING_SNAKE_CASE : Optional[Any] , ) -> Tuple:
"""simple docstring"""
super().__init__(**__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = image_size
__SCREAMING_SNAKE_CASE = patch_size
__SCREAMING_SNAKE_CASE = num_channels
__SCREAMING_SNAKE_CASE = embed_dim
__SCREAMING_SNAKE_CASE = depths
__SCREAMING_SNAKE_CASE = len(__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = num_heads
__SCREAMING_SNAKE_CASE = window_size
__SCREAMING_SNAKE_CASE = mlp_ratio
__SCREAMING_SNAKE_CASE = qkv_bias
__SCREAMING_SNAKE_CASE = hidden_dropout_prob
__SCREAMING_SNAKE_CASE = attention_probs_dropout_prob
__SCREAMING_SNAKE_CASE = drop_path_rate
__SCREAMING_SNAKE_CASE = hidden_act
__SCREAMING_SNAKE_CASE = use_absolute_embeddings
__SCREAMING_SNAKE_CASE = layer_norm_eps
__SCREAMING_SNAKE_CASE = initializer_range
__SCREAMING_SNAKE_CASE = encoder_stride
# we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
# this indicates the channel dimension after the last stage of the model
__SCREAMING_SNAKE_CASE = int(embed_dim * 2 ** (len(__SCREAMING_SNAKE_CASE ) - 1) )
__SCREAMING_SNAKE_CASE = ['''stem'''] + [f"""stage{idx}""" for idx in range(1 , len(__SCREAMING_SNAKE_CASE ) + 1 )]
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = get_aligned_output_features_output_indices(
out_features=__SCREAMING_SNAKE_CASE , out_indices=__SCREAMING_SNAKE_CASE , stage_names=self.stage_names )
class A__( __magic_name__ ):
lowerCAmelCase = version.parse('''1.11''' )
@property
def _a ( self : str ) -> Mapping[str, Mapping[int, str]]:
"""simple docstring"""
return OrderedDict(
[
('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
] )
@property
def _a ( self : Any ) -> float:
"""simple docstring"""
return 1E-4
| 690 |
"""simple docstring"""
from json import JSONDecodeError # Workaround for requests.exceptions.JSONDecodeError
import requests
def _a ( UpperCAmelCase__ = "isbn/0140328726" ) -> dict:
__SCREAMING_SNAKE_CASE = olid.strip().strip('''/''' ) # Remove leading/trailing whitespace & slashes
if new_olid.count('''/''' ) != 1:
__SCREAMING_SNAKE_CASE = f"""{olid} is not a valid Open Library olid"""
raise ValueError(UpperCAmelCase__ )
return requests.get(f"""https://openlibrary.org/{new_olid}.json""" ).json()
def _a ( UpperCAmelCase__ ) -> dict:
__SCREAMING_SNAKE_CASE = {
'''title''': '''Title''',
'''publish_date''': '''Publish date''',
'''authors''': '''Authors''',
'''number_of_pages''': '''Number of pages:''',
'''first_sentence''': '''First sentence''',
'''isbn_10''': '''ISBN (10)''',
'''isbn_13''': '''ISBN (13)''',
}
__SCREAMING_SNAKE_CASE = {better_key: ol_book_data[key] for key, better_key in desired_keys.items()}
__SCREAMING_SNAKE_CASE = [
get_openlibrary_data(author['''key'''] )['''name'''] for author in data['''Authors''']
]
__SCREAMING_SNAKE_CASE = data['''First sentence''']['''value''']
for key, value in data.items():
if isinstance(UpperCAmelCase__ , UpperCAmelCase__ ):
__SCREAMING_SNAKE_CASE = ''', '''.join(UpperCAmelCase__ )
return data
if __name__ == "__main__":
import doctest
doctest.testmod()
while True:
lowerCAmelCase__ =input("\nEnter the ISBN code to search (or 'quit' to stop): ").strip()
if isbn.lower() in ("", "q", "quit", "exit", "stop"):
break
if len(isbn) not in (10, 13) or not isbn.isdigit():
print(F'''Sorry, {isbn} is not a valid ISBN. Please, input a valid ISBN.''')
continue
print(F'''\nSearching Open Library for ISBN: {isbn}...\n''')
try:
lowerCAmelCase__ =summarize_book(get_openlibrary_data(F'''isbn/{isbn}'''))
print("\n".join(F'''{key}: {value}''' for key, value in book_summary.items()))
except JSONDecodeError: # Workaround for requests.exceptions.RequestException:
print(F'''Sorry, there are no results for ISBN: {isbn}.''')
| 690 | 1 |
"""simple docstring"""
import unittest
from transformers import SPIECE_UNDERLINE, ReformerTokenizer, ReformerTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
lowerCAmelCase__ =get_tests_dir("fixtures/test_sentencepiece.model")
@require_sentencepiece
@require_tokenizers
class A__( __magic_name__ , unittest.TestCase ):
lowerCAmelCase = ReformerTokenizer
lowerCAmelCase = ReformerTokenizerFast
lowerCAmelCase = True
lowerCAmelCase = False
lowerCAmelCase = True
def _a ( self : Dict ) -> Optional[Any]:
"""simple docstring"""
super().setUp()
__SCREAMING_SNAKE_CASE = ReformerTokenizer(__SCREAMING_SNAKE_CASE , keep_accents=__SCREAMING_SNAKE_CASE )
tokenizer.save_pretrained(self.tmpdirname )
def _a ( self : int ) -> Union[str, Any]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = '''<s>'''
__SCREAMING_SNAKE_CASE = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(__SCREAMING_SNAKE_CASE ) , __SCREAMING_SNAKE_CASE )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(__SCREAMING_SNAKE_CASE ) , __SCREAMING_SNAKE_CASE )
def _a ( self : int ) -> List[Any]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , '''<unk>''' )
self.assertEqual(vocab_keys[1] , '''<s>''' )
self.assertEqual(vocab_keys[-1] , '''j''' )
self.assertEqual(len(__SCREAMING_SNAKE_CASE ) , 10_00 )
def _a ( self : Optional[int] ) -> Tuple:
"""simple docstring"""
self.assertEqual(self.get_tokenizer().vocab_size , 10_00 )
def _a ( self : Any ) -> str:
"""simple docstring"""
if not self.test_rust_tokenizer:
return
__SCREAMING_SNAKE_CASE = self.get_tokenizer()
__SCREAMING_SNAKE_CASE = self.get_rust_tokenizer()
__SCREAMING_SNAKE_CASE = '''I was born in 92000, and this is falsé.'''
__SCREAMING_SNAKE_CASE = tokenizer.tokenize(__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = rust_tokenizer.tokenize(__SCREAMING_SNAKE_CASE )
self.assertListEqual(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = tokenizer.encode(__SCREAMING_SNAKE_CASE , add_special_tokens=__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = rust_tokenizer.encode(__SCREAMING_SNAKE_CASE , add_special_tokens=__SCREAMING_SNAKE_CASE )
self.assertListEqual(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = self.get_rust_tokenizer()
__SCREAMING_SNAKE_CASE = tokenizer.encode(__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = rust_tokenizer.encode(__SCREAMING_SNAKE_CASE )
self.assertListEqual(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
def _a ( self : Optional[int] , __SCREAMING_SNAKE_CASE : List[str]=15 ) -> List[str]:
"""simple docstring"""
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})""" ):
__SCREAMING_SNAKE_CASE = self.rust_tokenizer_class.from_pretrained(__SCREAMING_SNAKE_CASE , **__SCREAMING_SNAKE_CASE )
# Simple input
__SCREAMING_SNAKE_CASE = '''This is a simple input'''
__SCREAMING_SNAKE_CASE = ['''This is a simple input 1''', '''This is a simple input 2''']
__SCREAMING_SNAKE_CASE = ('''This is a simple input''', '''This is a pair''')
__SCREAMING_SNAKE_CASE = [
('''This is a simple input 1''', '''This is a simple input 2'''),
('''This is a simple pair 1''', '''This is a simple pair 2'''),
]
# Simple input tests
self.assertRaises(__SCREAMING_SNAKE_CASE , tokenizer_r.encode , __SCREAMING_SNAKE_CASE , max_length=__SCREAMING_SNAKE_CASE , padding='''max_length''' )
# Simple input
self.assertRaises(__SCREAMING_SNAKE_CASE , tokenizer_r.encode_plus , __SCREAMING_SNAKE_CASE , max_length=__SCREAMING_SNAKE_CASE , padding='''max_length''' )
# Simple input
self.assertRaises(
__SCREAMING_SNAKE_CASE , tokenizer_r.batch_encode_plus , __SCREAMING_SNAKE_CASE , max_length=__SCREAMING_SNAKE_CASE , padding='''max_length''' , )
# Pair input
self.assertRaises(__SCREAMING_SNAKE_CASE , tokenizer_r.encode , __SCREAMING_SNAKE_CASE , max_length=__SCREAMING_SNAKE_CASE , padding='''max_length''' )
# Pair input
self.assertRaises(__SCREAMING_SNAKE_CASE , tokenizer_r.encode_plus , __SCREAMING_SNAKE_CASE , max_length=__SCREAMING_SNAKE_CASE , padding='''max_length''' )
# Pair input
self.assertRaises(
__SCREAMING_SNAKE_CASE , tokenizer_r.batch_encode_plus , __SCREAMING_SNAKE_CASE , max_length=__SCREAMING_SNAKE_CASE , padding='''max_length''' , )
def _a ( self : List[str] ) -> List[Any]:
"""simple docstring"""
pass
def _a ( self : Any ) -> List[str]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = ReformerTokenizer(__SCREAMING_SNAKE_CASE , keep_accents=__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = tokenizer.tokenize('''This is a test''' )
self.assertListEqual(__SCREAMING_SNAKE_CASE , ['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est'''] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(__SCREAMING_SNAKE_CASE ) , [2_85, 46, 10, 1_70, 3_82] , )
__SCREAMING_SNAKE_CASE = tokenizer.tokenize('''I was born in 92000, and this is falsé.''' )
self.assertListEqual(
__SCREAMING_SNAKE_CASE , [
SPIECE_UNDERLINE + '''I''',
SPIECE_UNDERLINE + '''was''',
SPIECE_UNDERLINE + '''b''',
'''or''',
'''n''',
SPIECE_UNDERLINE + '''in''',
SPIECE_UNDERLINE + '''''',
'''9''',
'''2''',
'''0''',
'''0''',
'''0''',
''',''',
SPIECE_UNDERLINE + '''and''',
SPIECE_UNDERLINE + '''this''',
SPIECE_UNDERLINE + '''is''',
SPIECE_UNDERLINE + '''f''',
'''al''',
'''s''',
'''é''',
'''.''',
] , )
__SCREAMING_SNAKE_CASE = tokenizer.convert_tokens_to_ids(__SCREAMING_SNAKE_CASE )
self.assertListEqual(
__SCREAMING_SNAKE_CASE , [8, 21, 84, 55, 24, 19, 7, 0, 6_02, 3_47, 3_47, 3_47, 3, 12, 66, 46, 72, 80, 6, 0, 4] , )
__SCREAMING_SNAKE_CASE = tokenizer.convert_ids_to_tokens(__SCREAMING_SNAKE_CASE )
self.assertListEqual(
__SCREAMING_SNAKE_CASE , [
SPIECE_UNDERLINE + '''I''',
SPIECE_UNDERLINE + '''was''',
SPIECE_UNDERLINE + '''b''',
'''or''',
'''n''',
SPIECE_UNDERLINE + '''in''',
SPIECE_UNDERLINE + '''''',
'''<unk>''',
'''2''',
'''0''',
'''0''',
'''0''',
''',''',
SPIECE_UNDERLINE + '''and''',
SPIECE_UNDERLINE + '''this''',
SPIECE_UNDERLINE + '''is''',
SPIECE_UNDERLINE + '''f''',
'''al''',
'''s''',
'''<unk>''',
'''.''',
] , )
@cached_property
def _a ( self : Union[str, Any] ) -> int:
"""simple docstring"""
return ReformerTokenizer.from_pretrained('''google/reformer-crime-and-punishment''' )
@slow
def _a ( self : List[Any] ) -> List[Any]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = '''Hello World!'''
__SCREAMING_SNAKE_CASE = [1_26, 32, 2_62, 1_52, 38, 72, 2_87]
self.assertListEqual(__SCREAMING_SNAKE_CASE , self.big_tokenizer.encode(__SCREAMING_SNAKE_CASE ) )
@slow
def _a ( self : Tuple ) -> int:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = (
'''This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) " [ ] ! : - . Also we will'''
''' add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth'''
)
__SCREAMING_SNAKE_CASE = [
1_08,
2_65,
24,
1_11,
4,
2_58,
1_56,
35,
28,
2_75,
3,
2_59,
2_97,
2_60,
84,
4,
35,
1_10,
44,
8,
2_59,
91,
2_68,
21,
11,
2_09,
2_74,
1_09,
2_66,
2_77,
1_17,
86,
93,
3_15,
2_58,
2_78,
2_58,
2_77,
2_58,
0,
2_58,
2_88,
2_58,
3_19,
2_58,
0,
2_58,
0,
2_58,
0,
2_58,
0,
2_58,
2_87,
2_58,
3_15,
2_58,
2_89,
2_58,
2_78,
99,
2_69,
2_66,
2_62,
8,
2_59,
2_41,
4,
2_17,
2_30,
2_68,
2_66,
55,
1_68,
1_06,
75,
1_93,
2_66,
2_23,
27,
49,
26,
2_82,
25,
2_64,
2_99,
19,
26,
0,
2_58,
2_77,
1_17,
86,
93,
1_76,
1_83,
2_70,
11,
2_62,
42,
61,
2_65,
]
self.assertListEqual(__SCREAMING_SNAKE_CASE , self.big_tokenizer.encode(__SCREAMING_SNAKE_CASE ) )
@require_torch
@slow
def _a ( self : str ) -> Any:
"""simple docstring"""
import torch
from transformers import ReformerConfig, ReformerModel
# Build sequence
__SCREAMING_SNAKE_CASE = list(self.big_tokenizer.get_vocab().keys() )[:10]
__SCREAMING_SNAKE_CASE = ''' '''.join(__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = self.big_tokenizer.encode_plus(__SCREAMING_SNAKE_CASE , return_tensors='''pt''' )
__SCREAMING_SNAKE_CASE = self.big_tokenizer.batch_encode_plus([sequence, sequence] , return_tensors='''pt''' )
__SCREAMING_SNAKE_CASE = ReformerConfig()
# The input gets padded during training so adjust the axial position encodings from the pretrained model value of (512, 1024)
__SCREAMING_SNAKE_CASE = encoded_sequence['''input_ids'''].shape
__SCREAMING_SNAKE_CASE = ReformerModel(__SCREAMING_SNAKE_CASE )
# Reformer has config.vocab_size == tokenizer.vocab_size == len(tokenizer) - 1 = 320; len(tokenizer) is 321 (including a pad token with id 320)
assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size
with torch.no_grad():
model(**__SCREAMING_SNAKE_CASE )
model(**__SCREAMING_SNAKE_CASE )
@slow
def _a ( self : Any ) -> Any:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = {'''input_ids''': [[1_08, 2_65, 24, 1_11, 4, 2_58, 1_56, 7, 51, 2_79, 58, 7, 76, 25, 69, 2_78], [1_40, 2_43, 2_64, 1_34, 17, 2_67, 77, 2_63, 22, 2_62, 2_97, 2_58, 3_04, 1_77, 2_79, 2_66, 14, 89, 13, 35, 2_61, 2_99, 2_72, 1_37, 2_75, 2_78]], '''attention_mask''': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]} # noqa: E501
# fmt: on
# This tokenizer does not know some characters like ")".
# That is the reason why we use very simple texts here.
# Also see https://github.com/huggingface/transformers/pull/11737#issuecomment-850769064
__SCREAMING_SNAKE_CASE = [
'''This is a very simple sentence.''',
'''The quick brown fox jumps over the lazy dog.''',
]
self.tokenizer_integration_test_util(
expected_encoding=__SCREAMING_SNAKE_CASE , model_name='''google/reformer-crime-and-punishment''' , revision='''0e6c3decb8211d49bf881013425dc8b0448b3f5a''' , padding=__SCREAMING_SNAKE_CASE , sequences=__SCREAMING_SNAKE_CASE , )
| 690 |
"""simple docstring"""
from math import ceil
from typing import List, Optional, Union
import numpy as np
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import BatchFeature, SequenceFeatureExtractor
from ...utils import TensorType, logging
lowerCAmelCase__ =logging.get_logger(__name__)
class A__( __magic_name__ ):
lowerCAmelCase = ['''audio_values''', '''audio_mask''']
def __init__( self : Dict , __SCREAMING_SNAKE_CASE : Optional[Any]=20_48 , __SCREAMING_SNAKE_CASE : str=1 , __SCREAMING_SNAKE_CASE : List[Any]=[16, 16] , __SCREAMING_SNAKE_CASE : Union[str, Any]=1_28 , __SCREAMING_SNAKE_CASE : int=4_41_00 , __SCREAMING_SNAKE_CASE : Union[str, Any]=86 , __SCREAMING_SNAKE_CASE : str=20_48 , __SCREAMING_SNAKE_CASE : Optional[Any]=0.0 , **__SCREAMING_SNAKE_CASE : Optional[int] , ) -> Any:
"""simple docstring"""
super().__init__(
feature_size=__SCREAMING_SNAKE_CASE , sampling_rate=__SCREAMING_SNAKE_CASE , padding_value=__SCREAMING_SNAKE_CASE , **__SCREAMING_SNAKE_CASE , )
__SCREAMING_SNAKE_CASE = spectrogram_length
__SCREAMING_SNAKE_CASE = num_channels
__SCREAMING_SNAKE_CASE = patch_size
__SCREAMING_SNAKE_CASE = feature_size // self.patch_size[1]
__SCREAMING_SNAKE_CASE = n_fft
__SCREAMING_SNAKE_CASE = sampling_rate // hop_length_to_sampling_rate
__SCREAMING_SNAKE_CASE = sampling_rate
__SCREAMING_SNAKE_CASE = padding_value
__SCREAMING_SNAKE_CASE = mel_filter_bank(
num_frequency_bins=1 + n_fft // 2 , num_mel_filters=__SCREAMING_SNAKE_CASE , min_frequency=0.0 , max_frequency=2_20_50.0 , sampling_rate=__SCREAMING_SNAKE_CASE , norm='''slaney''' , mel_scale='''slaney''' , ).T
def _a ( self : str , __SCREAMING_SNAKE_CASE : np.array ) -> np.ndarray:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = spectrogram(
__SCREAMING_SNAKE_CASE , window_function(self.n_fft , '''hann''' ) , frame_length=self.n_fft , hop_length=self.hop_length , power=2.0 , mel_filters=self.mel_filters.T , log_mel='''dB''' , db_range=80.0 , )
__SCREAMING_SNAKE_CASE = log_spec[:, :-1]
__SCREAMING_SNAKE_CASE = log_spec - 20.0
__SCREAMING_SNAKE_CASE = np.clip(log_spec / 40.0 , -2.0 , 0.0 ) + 1.0
return log_spec
def __call__( self : str , __SCREAMING_SNAKE_CASE : Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]] , __SCREAMING_SNAKE_CASE : Optional[Union[str, TensorType]] = None , __SCREAMING_SNAKE_CASE : Optional[bool] = True , __SCREAMING_SNAKE_CASE : Optional[int] = None , __SCREAMING_SNAKE_CASE : bool = False , __SCREAMING_SNAKE_CASE : bool = False , **__SCREAMING_SNAKE_CASE : Tuple , ) -> BatchFeature:
"""simple docstring"""
if sampling_rate is not None:
if sampling_rate != self.sampling_rate:
raise ValueError(
'''This feature extractor is set to support sampling rate'''
f""" of {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled"""
f""" with {self.sampling_rate} and not {sampling_rate}.""" )
else:
logger.warning(
'''It is strongly recommended to pass the `sampling_rate` argument to this function. '''
'''Failing to do so can result in silent errors that might be hard to debug.''' )
__SCREAMING_SNAKE_CASE = isinstance(__SCREAMING_SNAKE_CASE , np.ndarray ) and len(raw_speech.shape ) > 1
if is_batched_numpy and len(raw_speech.shape ) > 2:
raise ValueError(f"""Only mono-channel audio is supported for input to {self}""" )
__SCREAMING_SNAKE_CASE = is_batched_numpy or (
isinstance(__SCREAMING_SNAKE_CASE , (list, tuple) ) and (isinstance(raw_speech[0] , (np.ndarray, tuple, list) ))
)
if is_batched:
__SCREAMING_SNAKE_CASE = [np.asarray([speech] , dtype=np.floataa ).T for speech in raw_speech]
elif not is_batched and not isinstance(__SCREAMING_SNAKE_CASE , np.ndarray ):
__SCREAMING_SNAKE_CASE = np.asarray(__SCREAMING_SNAKE_CASE , dtype=np.floataa )
elif isinstance(__SCREAMING_SNAKE_CASE , np.ndarray ) and raw_speech.dtype is np.dtype(np.floataa ):
__SCREAMING_SNAKE_CASE = raw_speech.astype(np.floataa )
# always return batch
if not is_batched:
__SCREAMING_SNAKE_CASE = [np.asarray([raw_speech] ).T]
# Convert audio signals to log mel spectrograms, truncate by time axis
__SCREAMING_SNAKE_CASE = [
self._np_extract_fbank_features(waveform.squeeze() ).T[: self.spectrogram_length] for waveform in raw_speech
]
if isinstance(audio_features[0] , __SCREAMING_SNAKE_CASE ):
__SCREAMING_SNAKE_CASE = [np.asarray(__SCREAMING_SNAKE_CASE , dtype=np.floataa ) for feature in audio_features]
# Create audio attention mask
__SCREAMING_SNAKE_CASE = max(
[ceil(feature.shape[0] / self.patch_size[0] ) * self.freq_len for feature in audio_features] ) # The maximum number of audio patches in a batch
if return_attention_mask:
__SCREAMING_SNAKE_CASE = [
(ceil(feature.shape[0] / self.patch_size[0] ) * self.freq_len) * [1]
+ (max_patch_len - ceil(feature.shape[0] / self.patch_size[0] ) * self.freq_len) * [0]
for feature in audio_features
]
__SCREAMING_SNAKE_CASE = np.array(__SCREAMING_SNAKE_CASE ).astype(np.floataa )
# convert into correct format for padding
__SCREAMING_SNAKE_CASE = max_patch_len // self.freq_len * self.patch_size[0] # The maximum audio size in a batch
__SCREAMING_SNAKE_CASE = np.ones([len(__SCREAMING_SNAKE_CASE ), 1, max_time_len, self.feature_size] ).astype(np.floataa )
__SCREAMING_SNAKE_CASE = padded_audio_features * self.padding_value
for i in range(len(__SCREAMING_SNAKE_CASE ) ):
__SCREAMING_SNAKE_CASE = audio_features[i]
__SCREAMING_SNAKE_CASE = feature
# return as BatchFeature
if return_attention_mask:
__SCREAMING_SNAKE_CASE = {'''audio_values''': padded_audio_features, '''audio_mask''': audio_mask}
else:
__SCREAMING_SNAKE_CASE = {'''audio_values''': padded_audio_features}
__SCREAMING_SNAKE_CASE = BatchFeature(data=__SCREAMING_SNAKE_CASE , tensor_type=__SCREAMING_SNAKE_CASE )
return encoded_inputs
| 690 | 1 |
"""simple docstring"""
import os
import tempfile
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from torch import nn
from transformers import (
Adafactor,
AdamW,
get_constant_schedule,
get_constant_schedule_with_warmup,
get_cosine_schedule_with_warmup,
get_cosine_with_hard_restarts_schedule_with_warmup,
get_inverse_sqrt_schedule,
get_linear_schedule_with_warmup,
get_polynomial_decay_schedule_with_warmup,
)
def _a ( UpperCAmelCase__ , UpperCAmelCase__=10 ) -> Any:
__SCREAMING_SNAKE_CASE = []
for _ in range(UpperCAmelCase__ ):
lrs.append(scheduler.get_lr()[0] )
scheduler.step()
return lrs
def _a ( UpperCAmelCase__ , UpperCAmelCase__=10 ) -> Tuple:
__SCREAMING_SNAKE_CASE = []
for step in range(UpperCAmelCase__ ):
lrs.append(scheduler.get_lr()[0] )
scheduler.step()
if step == num_steps // 2:
with tempfile.TemporaryDirectory() as tmpdirname:
__SCREAMING_SNAKE_CASE = os.path.join(UpperCAmelCase__ , '''schedule.bin''' )
torch.save(scheduler.state_dict() , UpperCAmelCase__ )
__SCREAMING_SNAKE_CASE = torch.load(UpperCAmelCase__ )
scheduler.load_state_dict(UpperCAmelCase__ )
return lrs
@require_torch
class A__( unittest.TestCase ):
def _a ( self : Tuple , __SCREAMING_SNAKE_CASE : int , __SCREAMING_SNAKE_CASE : int , __SCREAMING_SNAKE_CASE : int ) -> Any:
"""simple docstring"""
self.assertEqual(len(__SCREAMING_SNAKE_CASE ) , len(__SCREAMING_SNAKE_CASE ) )
for a, b in zip(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
self.assertAlmostEqual(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , delta=__SCREAMING_SNAKE_CASE )
def _a ( self : Optional[Any] ) -> Optional[Any]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = torch.tensor([0.1, -0.2, -0.1] , requires_grad=__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = torch.tensor([0.4, 0.2, -0.5] )
__SCREAMING_SNAKE_CASE = nn.MSELoss()
# No warmup, constant schedule, no gradient clipping
__SCREAMING_SNAKE_CASE = AdamW(params=[w] , lr=2E-1 , weight_decay=0.0 )
for _ in range(1_00 ):
__SCREAMING_SNAKE_CASE = criterion(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
loss.backward()
optimizer.step()
w.grad.detach_() # No zero_grad() function on simple tensors. we do it ourselves.
w.grad.zero_()
self.assertListAlmostEqual(w.tolist() , [0.4, 0.2, -0.5] , tol=1E-2 )
def _a ( self : Tuple ) -> Tuple:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = torch.tensor([0.1, -0.2, -0.1] , requires_grad=__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = torch.tensor([0.4, 0.2, -0.5] )
__SCREAMING_SNAKE_CASE = nn.MSELoss()
# No warmup, constant schedule, no gradient clipping
__SCREAMING_SNAKE_CASE = Adafactor(
params=[w] , lr=1E-2 , eps=(1E-3_0, 1E-3) , clip_threshold=1.0 , decay_rate=-0.8 , betaa=__SCREAMING_SNAKE_CASE , weight_decay=0.0 , relative_step=__SCREAMING_SNAKE_CASE , scale_parameter=__SCREAMING_SNAKE_CASE , warmup_init=__SCREAMING_SNAKE_CASE , )
for _ in range(10_00 ):
__SCREAMING_SNAKE_CASE = criterion(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
loss.backward()
optimizer.step()
w.grad.detach_() # No zero_grad() function on simple tensors. we do it ourselves.
w.grad.zero_()
self.assertListAlmostEqual(w.tolist() , [0.4, 0.2, -0.5] , tol=1E-2 )
@require_torch
class A__( unittest.TestCase ):
lowerCAmelCase = nn.Linear(50 , 50 ) if is_torch_available() else None
lowerCAmelCase = AdamW(m.parameters() , lr=10.0 ) if is_torch_available() else None
lowerCAmelCase = 10
def _a ( self : List[str] , __SCREAMING_SNAKE_CASE : List[str] , __SCREAMING_SNAKE_CASE : str , __SCREAMING_SNAKE_CASE : List[Any] , __SCREAMING_SNAKE_CASE : str=None ) -> Dict:
"""simple docstring"""
self.assertEqual(len(__SCREAMING_SNAKE_CASE ) , len(__SCREAMING_SNAKE_CASE ) )
for a, b in zip(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
self.assertAlmostEqual(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , delta=__SCREAMING_SNAKE_CASE , msg=__SCREAMING_SNAKE_CASE )
def _a ( self : Union[str, Any] ) -> int:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = {'''num_warmup_steps''': 2, '''num_training_steps''': 10}
# schedulers doct format
# function: (sched_args_dict, expected_learning_rates)
__SCREAMING_SNAKE_CASE = {
get_constant_schedule: ({}, [10.0] * self.num_steps),
get_constant_schedule_with_warmup: (
{'''num_warmup_steps''': 4},
[0.0, 2.5, 5.0, 7.5, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0],
),
get_linear_schedule_with_warmup: (
{**common_kwargs},
[0.0, 5.0, 10.0, 8.75, 7.5, 6.25, 5.0, 3.75, 2.5, 1.25],
),
get_cosine_schedule_with_warmup: (
{**common_kwargs},
[0.0, 5.0, 10.0, 9.61, 8.53, 6.91, 5.0, 3.08, 1.46, 0.38],
),
get_cosine_with_hard_restarts_schedule_with_warmup: (
{**common_kwargs, '''num_cycles''': 2},
[0.0, 5.0, 10.0, 8.53, 5.0, 1.46, 10.0, 8.53, 5.0, 1.46],
),
get_polynomial_decay_schedule_with_warmup: (
{**common_kwargs, '''power''': 2.0, '''lr_end''': 1E-7},
[0.0, 5.0, 10.0, 7.6_56, 5.6_25, 3.9_06, 2.5, 1.4_06, 0.6_25, 0.1_56],
),
get_inverse_sqrt_schedule: (
{'''num_warmup_steps''': 2},
[0.0, 5.0, 10.0, 8.1_65, 7.0_71, 6.3_25, 5.7_74, 5.3_45, 5.0, 4.7_14],
),
}
for scheduler_func, data in scheds.items():
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = data
__SCREAMING_SNAKE_CASE = scheduler_func(self.optimizer , **__SCREAMING_SNAKE_CASE )
self.assertEqual(len([scheduler.get_lr()[0]] ) , 1 )
__SCREAMING_SNAKE_CASE = unwrap_schedule(__SCREAMING_SNAKE_CASE , self.num_steps )
self.assertListAlmostEqual(
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , tol=1E-2 , msg=f"""failed for {scheduler_func} in normal scheduler""" , )
__SCREAMING_SNAKE_CASE = scheduler_func(self.optimizer , **__SCREAMING_SNAKE_CASE )
if scheduler_func.__name__ != "get_constant_schedule":
LambdaScheduleWrapper.wrap_scheduler(__SCREAMING_SNAKE_CASE ) # wrap to test picklability of the schedule
__SCREAMING_SNAKE_CASE = unwrap_and_save_reload_schedule(__SCREAMING_SNAKE_CASE , self.num_steps )
self.assertListEqual(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , msg=f"""failed for {scheduler_func} in save and reload""" )
class A__:
def __init__( self : List[str] , __SCREAMING_SNAKE_CASE : List[Any] ) -> Union[str, Any]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = fn
def __call__( self : Union[str, Any] , *__SCREAMING_SNAKE_CASE : Optional[int] , **__SCREAMING_SNAKE_CASE : Dict ) -> Tuple:
"""simple docstring"""
return self.fn(*__SCREAMING_SNAKE_CASE , **__SCREAMING_SNAKE_CASE )
@classmethod
def _a ( self : List[str] , __SCREAMING_SNAKE_CASE : List[str] ) -> int:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = list(map(self , scheduler.lr_lambdas ) )
| 690 |
"""simple docstring"""
def _a ( UpperCAmelCase__ ) -> str:
__SCREAMING_SNAKE_CASE = ''''''
for ch in key:
if ch == " " or ch not in key_no_dups and ch.isalpha():
key_no_dups += ch
return key_no_dups
def _a ( UpperCAmelCase__ ) -> dict[str, str]:
__SCREAMING_SNAKE_CASE = [chr(i + 65 ) for i in range(26 )]
# Remove duplicate characters from key
__SCREAMING_SNAKE_CASE = remove_duplicates(key.upper() )
__SCREAMING_SNAKE_CASE = len(UpperCAmelCase__ )
# First fill cipher with key characters
__SCREAMING_SNAKE_CASE = {alphabet[i]: char for i, char in enumerate(UpperCAmelCase__ )}
# Then map remaining characters in alphabet to
# the alphabet from the beginning
for i in range(len(UpperCAmelCase__ ) , 26 ):
__SCREAMING_SNAKE_CASE = alphabet[i - offset]
# Ensure we are not mapping letters to letters previously mapped
while char in key:
offset -= 1
__SCREAMING_SNAKE_CASE = alphabet[i - offset]
__SCREAMING_SNAKE_CASE = char
return cipher_alphabet
def _a ( UpperCAmelCase__ , UpperCAmelCase__ ) -> str:
return "".join(cipher_map.get(UpperCAmelCase__ , UpperCAmelCase__ ) for ch in message.upper() )
def _a ( UpperCAmelCase__ , UpperCAmelCase__ ) -> str:
__SCREAMING_SNAKE_CASE = {v: k for k, v in cipher_map.items()}
return "".join(rev_cipher_map.get(UpperCAmelCase__ , UpperCAmelCase__ ) for ch in message.upper() )
def _a ( ) -> None:
__SCREAMING_SNAKE_CASE = input('''Enter message to encode or decode: ''' ).strip()
__SCREAMING_SNAKE_CASE = input('''Enter keyword: ''' ).strip()
__SCREAMING_SNAKE_CASE = input('''Encipher or decipher? E/D:''' ).strip()[0].lower()
try:
__SCREAMING_SNAKE_CASE = {'''e''': encipher, '''d''': decipher}[option]
except KeyError:
raise KeyError('''invalid input option''' )
__SCREAMING_SNAKE_CASE = create_cipher_map(UpperCAmelCase__ )
print(func(UpperCAmelCase__ , UpperCAmelCase__ ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| 690 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
lowerCAmelCase__ ={"configuration_speech_encoder_decoder": ["SpeechEncoderDecoderConfig"]}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase__ =["SpeechEncoderDecoderModel"]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase__ =["FlaxSpeechEncoderDecoderModel"]
if TYPE_CHECKING:
from .configuration_speech_encoder_decoder import SpeechEncoderDecoderConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_speech_encoder_decoder import SpeechEncoderDecoderModel
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_speech_encoder_decoder import FlaxSpeechEncoderDecoderModel
else:
import sys
lowerCAmelCase__ =_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 690 |
"""simple docstring"""
from __future__ import annotations
import inspect
import unittest
from typing import List, Tuple
from transformers import RegNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST, TFRegNetForImageClassification, TFRegNetModel
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class A__:
def __init__( self : Optional[int] , __SCREAMING_SNAKE_CASE : Dict , __SCREAMING_SNAKE_CASE : List[str]=3 , __SCREAMING_SNAKE_CASE : Dict=32 , __SCREAMING_SNAKE_CASE : Optional[Any]=3 , __SCREAMING_SNAKE_CASE : Union[str, Any]=10 , __SCREAMING_SNAKE_CASE : str=[10, 20, 30, 40] , __SCREAMING_SNAKE_CASE : Optional[int]=[1, 1, 2, 1] , __SCREAMING_SNAKE_CASE : int=True , __SCREAMING_SNAKE_CASE : int=True , __SCREAMING_SNAKE_CASE : Optional[Any]="relu" , __SCREAMING_SNAKE_CASE : List[str]=3 , __SCREAMING_SNAKE_CASE : Union[str, Any]=None , ) -> Any:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = parent
__SCREAMING_SNAKE_CASE = batch_size
__SCREAMING_SNAKE_CASE = image_size
__SCREAMING_SNAKE_CASE = num_channels
__SCREAMING_SNAKE_CASE = embeddings_size
__SCREAMING_SNAKE_CASE = hidden_sizes
__SCREAMING_SNAKE_CASE = depths
__SCREAMING_SNAKE_CASE = is_training
__SCREAMING_SNAKE_CASE = use_labels
__SCREAMING_SNAKE_CASE = hidden_act
__SCREAMING_SNAKE_CASE = num_labels
__SCREAMING_SNAKE_CASE = scope
__SCREAMING_SNAKE_CASE = len(__SCREAMING_SNAKE_CASE )
def _a ( self : List[Any] ) -> List[str]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
__SCREAMING_SNAKE_CASE = None
if self.use_labels:
__SCREAMING_SNAKE_CASE = ids_tensor([self.batch_size] , self.num_labels )
__SCREAMING_SNAKE_CASE = self.get_config()
return config, pixel_values, labels
def _a ( self : Union[str, Any] ) -> List[str]:
"""simple docstring"""
return RegNetConfig(
num_channels=self.num_channels , embeddings_size=self.embeddings_size , hidden_sizes=self.hidden_sizes , depths=self.depths , hidden_act=self.hidden_act , num_labels=self.num_labels , )
def _a ( self : str , __SCREAMING_SNAKE_CASE : Any , __SCREAMING_SNAKE_CASE : str , __SCREAMING_SNAKE_CASE : Optional[Any] ) -> Dict:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = TFRegNetModel(config=__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = model(__SCREAMING_SNAKE_CASE , training=__SCREAMING_SNAKE_CASE )
# expected last hidden states: B, C, H // 32, W // 32
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32) , )
def _a ( self : int , __SCREAMING_SNAKE_CASE : Union[str, Any] , __SCREAMING_SNAKE_CASE : str , __SCREAMING_SNAKE_CASE : Optional[Any] ) -> Union[str, Any]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = self.num_labels
__SCREAMING_SNAKE_CASE = TFRegNetForImageClassification(__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = model(__SCREAMING_SNAKE_CASE , labels=__SCREAMING_SNAKE_CASE , training=__SCREAMING_SNAKE_CASE )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def _a ( self : Optional[Any] ) -> Any:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = self.prepare_config_and_inputs()
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = config_and_inputs
__SCREAMING_SNAKE_CASE = {'''pixel_values''': pixel_values}
return config, inputs_dict
@require_tf
class A__( __magic_name__ , __magic_name__ , unittest.TestCase ):
lowerCAmelCase = (TFRegNetModel, TFRegNetForImageClassification) if is_tf_available() else ()
lowerCAmelCase = (
{'''feature-extraction''': TFRegNetModel, '''image-classification''': TFRegNetForImageClassification}
if is_tf_available()
else {}
)
lowerCAmelCase = False
lowerCAmelCase = False
lowerCAmelCase = False
lowerCAmelCase = False
lowerCAmelCase = False
def _a ( self : Optional[Any] ) -> Union[str, Any]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = TFRegNetModelTester(self )
__SCREAMING_SNAKE_CASE = ConfigTester(self , config_class=__SCREAMING_SNAKE_CASE , has_text_modality=__SCREAMING_SNAKE_CASE )
def _a ( self : Tuple ) -> Optional[Any]:
"""simple docstring"""
return
@unittest.skip(reason='''RegNet does not use inputs_embeds''' )
def _a ( self : Any ) -> Optional[Any]:
"""simple docstring"""
pass
@unittest.skipIf(
not is_tf_available() or len(tf.config.list_physical_devices('''GPU''' ) ) == 0 , reason='''TF does not support backprop for grouped convolutions on CPU.''' , )
@slow
def _a ( self : Dict ) -> List[Any]:
"""simple docstring"""
super().test_keras_fit()
@unittest.skip(reason='''RegNet does not support input and output embeddings''' )
def _a ( self : Dict ) -> Union[str, Any]:
"""simple docstring"""
pass
def _a ( self : List[Any] ) -> List[Any]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__SCREAMING_SNAKE_CASE = model_class(__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = inspect.signature(model.call )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
__SCREAMING_SNAKE_CASE = [*signature.parameters.keys()]
__SCREAMING_SNAKE_CASE = ['''pixel_values''']
self.assertListEqual(arg_names[:1] , __SCREAMING_SNAKE_CASE )
def _a ( self : Any ) -> Union[str, Any]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__SCREAMING_SNAKE_CASE )
def _a ( self : List[str] ) -> Tuple:
"""simple docstring"""
def check_hidden_states_output(__SCREAMING_SNAKE_CASE : Optional[Any] , __SCREAMING_SNAKE_CASE : Optional[Any] , __SCREAMING_SNAKE_CASE : Any ):
__SCREAMING_SNAKE_CASE = model_class(__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = model(**self._prepare_for_class(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) , training=__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
__SCREAMING_SNAKE_CASE = self.model_tester.num_stages
self.assertEqual(len(__SCREAMING_SNAKE_CASE ) , expected_num_stages + 1 )
# RegNet's feature maps are of shape (batch_size, num_channels, height, width)
self.assertListEqual(
list(hidden_states[0].shape[-2:] ) , [self.model_tester.image_size // 2, self.model_tester.image_size // 2] , )
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs_for_common()
__SCREAMING_SNAKE_CASE = ['''basic''', '''bottleneck''']
for model_class in self.all_model_classes:
for layer_type in layers_type:
__SCREAMING_SNAKE_CASE = layer_type
__SCREAMING_SNAKE_CASE = True
check_hidden_states_output(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
__SCREAMING_SNAKE_CASE = True
check_hidden_states_output(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
def _a ( self : Union[str, Any] ) -> List[str]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs_for_common()
def check_equivalence(__SCREAMING_SNAKE_CASE : Tuple , __SCREAMING_SNAKE_CASE : Optional[Any] , __SCREAMING_SNAKE_CASE : Tuple , __SCREAMING_SNAKE_CASE : Union[str, Any]={} ):
__SCREAMING_SNAKE_CASE = model(__SCREAMING_SNAKE_CASE , return_dict=__SCREAMING_SNAKE_CASE , **__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = model(__SCREAMING_SNAKE_CASE , return_dict=__SCREAMING_SNAKE_CASE , **__SCREAMING_SNAKE_CASE ).to_tuple()
def recursive_check(__SCREAMING_SNAKE_CASE : int , __SCREAMING_SNAKE_CASE : Dict ):
if isinstance(__SCREAMING_SNAKE_CASE , (List, Tuple) ):
for tuple_iterable_value, dict_iterable_value in zip(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
recursive_check(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
elif tuple_object is None:
return
else:
self.assertTrue(
all(tf.equal(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) ) , msg=(
'''Tuple and dict output are not equal. Difference:'''
f""" {tf.math.reduce_max(tf.abs(tuple_object - dict_object ) )}"""
) , )
recursive_check(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
for model_class in self.all_model_classes:
__SCREAMING_SNAKE_CASE = model_class(__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = self._prepare_for_class(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = self._prepare_for_class(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
check_equivalence(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = self._prepare_for_class(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , return_labels=__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = self._prepare_for_class(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , return_labels=__SCREAMING_SNAKE_CASE )
check_equivalence(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = self._prepare_for_class(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = self._prepare_for_class(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
check_equivalence(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , {'''output_hidden_states''': True} )
__SCREAMING_SNAKE_CASE = self._prepare_for_class(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , return_labels=__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = self._prepare_for_class(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , return_labels=__SCREAMING_SNAKE_CASE )
check_equivalence(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , {'''output_hidden_states''': True} )
def _a ( self : str ) -> str:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*__SCREAMING_SNAKE_CASE )
@slow
def _a ( self : Union[str, Any] ) -> Union[str, Any]:
"""simple docstring"""
for model_name in TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__SCREAMING_SNAKE_CASE = TFRegNetModel.from_pretrained(__SCREAMING_SNAKE_CASE )
self.assertIsNotNone(__SCREAMING_SNAKE_CASE )
def _a ( ) -> Dict:
__SCREAMING_SNAKE_CASE = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
return image
@require_tf
@require_vision
class A__( unittest.TestCase ):
@cached_property
def _a ( self : List[Any] ) -> str:
"""simple docstring"""
return (
AutoImageProcessor.from_pretrained(TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0] )
if is_vision_available()
else None
)
@slow
def _a ( self : List[str] ) -> Any:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = TFRegNetForImageClassification.from_pretrained(TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0] )
__SCREAMING_SNAKE_CASE = self.default_image_processor
__SCREAMING_SNAKE_CASE = prepare_img()
__SCREAMING_SNAKE_CASE = image_processor(images=__SCREAMING_SNAKE_CASE , return_tensors='''tf''' )
# forward pass
__SCREAMING_SNAKE_CASE = model(**__SCREAMING_SNAKE_CASE , training=__SCREAMING_SNAKE_CASE )
# verify the logits
__SCREAMING_SNAKE_CASE = tf.TensorShape((1, 10_00) )
self.assertEqual(outputs.logits.shape , __SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = tf.constant([-0.41_80, -1.50_51, -3.48_36] )
tf.debugging.assert_near(outputs.logits[0, :3] , __SCREAMING_SNAKE_CASE , atol=1E-4 )
| 690 | 1 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase__ =logging.get_logger(__name__)
lowerCAmelCase__ ={
"RWKV/rwkv-4-169m-pile": "https://huggingface.co/RWKV/rwkv-4-169m-pile/resolve/main/config.json",
"RWKV/rwkv-4-430m-pile": "https://huggingface.co/RWKV/rwkv-4-430m-pile/resolve/main/config.json",
"RWKV/rwkv-4-1b5-pile": "https://huggingface.co/RWKV/rwkv-4-1b5-pile/resolve/main/config.json",
"RWKV/rwkv-4-3b-pile": "https://huggingface.co/RWKV/rwkv-4-3b-pile/resolve/main/config.json",
"RWKV/rwkv-4-7b-pile": "https://huggingface.co/RWKV/rwkv-4-7b-pile/resolve/main/config.json",
"RWKV/rwkv-4-14b-pile": "https://huggingface.co/RWKV/rwkv-4-14b-pile/resolve/main/config.json",
"RWKV/rwkv-raven-1b5": "https://huggingface.co/RWKV/rwkv-raven-1b5/resolve/main/config.json",
"RWKV/rwkv-raven-3b": "https://huggingface.co/RWKV/rwkv-raven-3b/resolve/main/config.json",
"RWKV/rwkv-raven-7b": "https://huggingface.co/RWKV/rwkv-raven-7b/resolve/main/config.json",
"RWKV/rwkv-raven-14b": "https://huggingface.co/RWKV/rwkv-raven-14b/resolve/main/config.json",
}
class A__( __magic_name__ ):
lowerCAmelCase = '''rwkv'''
lowerCAmelCase = {'''max_position_embeddings''': '''context_length'''}
def __init__( self : Dict , __SCREAMING_SNAKE_CASE : Tuple=5_02_77 , __SCREAMING_SNAKE_CASE : Union[str, Any]=10_24 , __SCREAMING_SNAKE_CASE : Tuple=40_96 , __SCREAMING_SNAKE_CASE : int=32 , __SCREAMING_SNAKE_CASE : Tuple=None , __SCREAMING_SNAKE_CASE : str=None , __SCREAMING_SNAKE_CASE : List[Any]=1E-5 , __SCREAMING_SNAKE_CASE : Union[str, Any]=0 , __SCREAMING_SNAKE_CASE : Any=0 , __SCREAMING_SNAKE_CASE : List[Any]=6 , __SCREAMING_SNAKE_CASE : Union[str, Any]=False , __SCREAMING_SNAKE_CASE : List[str]=True , **__SCREAMING_SNAKE_CASE : List[Any] , ) -> List[str]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = vocab_size
__SCREAMING_SNAKE_CASE = context_length
__SCREAMING_SNAKE_CASE = hidden_size
__SCREAMING_SNAKE_CASE = num_hidden_layers
__SCREAMING_SNAKE_CASE = attention_hidden_size if attention_hidden_size is not None else hidden_size
__SCREAMING_SNAKE_CASE = intermediate_size if intermediate_size is not None else 4 * hidden_size
__SCREAMING_SNAKE_CASE = layer_norm_epsilon
__SCREAMING_SNAKE_CASE = rescale_every
__SCREAMING_SNAKE_CASE = use_cache
__SCREAMING_SNAKE_CASE = bos_token_id
__SCREAMING_SNAKE_CASE = eos_token_id
super().__init__(
tie_word_embeddings=__SCREAMING_SNAKE_CASE , bos_token_id=__SCREAMING_SNAKE_CASE , eos_token_id=__SCREAMING_SNAKE_CASE , **__SCREAMING_SNAKE_CASE )
| 690 |
"""simple docstring"""
import pickle
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, XLMRobertaTokenizer, XLMRobertaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
lowerCAmelCase__ =get_tests_dir("fixtures/test_sentencepiece.model")
@require_sentencepiece
@require_tokenizers
class A__( __magic_name__ , unittest.TestCase ):
lowerCAmelCase = XLMRobertaTokenizer
lowerCAmelCase = XLMRobertaTokenizerFast
lowerCAmelCase = True
lowerCAmelCase = True
def _a ( self : Optional[int] ) -> List[Any]:
"""simple docstring"""
super().setUp()
# We have a SentencePiece fixture for testing
__SCREAMING_SNAKE_CASE = XLMRobertaTokenizer(__SCREAMING_SNAKE_CASE , keep_accents=__SCREAMING_SNAKE_CASE )
tokenizer.save_pretrained(self.tmpdirname )
def _a ( self : str ) -> Union[str, Any]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = '''<pad>'''
__SCREAMING_SNAKE_CASE = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(__SCREAMING_SNAKE_CASE ) , __SCREAMING_SNAKE_CASE )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(__SCREAMING_SNAKE_CASE ) , __SCREAMING_SNAKE_CASE )
def _a ( self : int ) -> List[Any]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , '''<s>''' )
self.assertEqual(vocab_keys[1] , '''<pad>''' )
self.assertEqual(vocab_keys[-1] , '''<mask>''' )
self.assertEqual(len(__SCREAMING_SNAKE_CASE ) , 10_02 )
def _a ( self : Tuple ) -> Optional[int]:
"""simple docstring"""
self.assertEqual(self.get_tokenizer().vocab_size , 10_02 )
def _a ( self : int ) -> int:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = XLMRobertaTokenizer(__SCREAMING_SNAKE_CASE , keep_accents=__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = tokenizer.tokenize('''This is a test''' )
self.assertListEqual(__SCREAMING_SNAKE_CASE , ['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est'''] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(__SCREAMING_SNAKE_CASE ) , [value + tokenizer.fairseq_offset for value in [2_85, 46, 10, 1_70, 3_82]] , )
__SCREAMING_SNAKE_CASE = tokenizer.tokenize('''I was born in 92000, and this is falsé.''' )
self.assertListEqual(
__SCREAMING_SNAKE_CASE , [
SPIECE_UNDERLINE + '''I''',
SPIECE_UNDERLINE + '''was''',
SPIECE_UNDERLINE + '''b''',
'''or''',
'''n''',
SPIECE_UNDERLINE + '''in''',
SPIECE_UNDERLINE + '''''',
'''9''',
'''2''',
'''0''',
'''0''',
'''0''',
''',''',
SPIECE_UNDERLINE + '''and''',
SPIECE_UNDERLINE + '''this''',
SPIECE_UNDERLINE + '''is''',
SPIECE_UNDERLINE + '''f''',
'''al''',
'''s''',
'''é''',
'''.''',
] , )
__SCREAMING_SNAKE_CASE = tokenizer.convert_tokens_to_ids(__SCREAMING_SNAKE_CASE )
self.assertListEqual(
__SCREAMING_SNAKE_CASE , [
value + tokenizer.fairseq_offset
for value in [8, 21, 84, 55, 24, 19, 7, 2, 6_02, 3_47, 3_47, 3_47, 3, 12, 66, 46, 72, 80, 6, 2, 4]
# ^ unk: 2 + 1 = 3 unk: 2 + 1 = 3 ^
] , )
__SCREAMING_SNAKE_CASE = tokenizer.convert_ids_to_tokens(__SCREAMING_SNAKE_CASE )
self.assertListEqual(
__SCREAMING_SNAKE_CASE , [
SPIECE_UNDERLINE + '''I''',
SPIECE_UNDERLINE + '''was''',
SPIECE_UNDERLINE + '''b''',
'''or''',
'''n''',
SPIECE_UNDERLINE + '''in''',
SPIECE_UNDERLINE + '''''',
'''<unk>''',
'''2''',
'''0''',
'''0''',
'''0''',
''',''',
SPIECE_UNDERLINE + '''and''',
SPIECE_UNDERLINE + '''this''',
SPIECE_UNDERLINE + '''is''',
SPIECE_UNDERLINE + '''f''',
'''al''',
'''s''',
'''<unk>''',
'''.''',
] , )
def _a ( self : int ) -> Tuple:
"""simple docstring"""
if not self.test_slow_tokenizer:
# as we don't have a slow version, we can't compare the outputs between slow and fast versions
return
__SCREAMING_SNAKE_CASE = (self.rust_tokenizer_class, '''hf-internal-testing/tiny-xlm-roberta''', {})
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})""" ):
__SCREAMING_SNAKE_CASE = self.rust_tokenizer_class.from_pretrained(__SCREAMING_SNAKE_CASE , **__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = self.tokenizer_class.from_pretrained(__SCREAMING_SNAKE_CASE , **__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = tempfile.mkdtemp()
__SCREAMING_SNAKE_CASE = tokenizer_r.save_pretrained(__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = tokenizer_p.save_pretrained(__SCREAMING_SNAKE_CASE )
# Checks it save with the same files + the tokenizer.json file for the fast one
self.assertTrue(any('''tokenizer.json''' in f for f in tokenizer_r_files ) )
__SCREAMING_SNAKE_CASE = tuple(f for f in tokenizer_r_files if '''tokenizer.json''' not in f )
self.assertSequenceEqual(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
# Checks everything loads correctly in the same way
__SCREAMING_SNAKE_CASE = tokenizer_r.from_pretrained(__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = tokenizer_p.from_pretrained(__SCREAMING_SNAKE_CASE )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) )
# self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
# self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))
shutil.rmtree(__SCREAMING_SNAKE_CASE )
# Save tokenizer rust, legacy_format=True
__SCREAMING_SNAKE_CASE = tempfile.mkdtemp()
__SCREAMING_SNAKE_CASE = tokenizer_r.save_pretrained(__SCREAMING_SNAKE_CASE , legacy_format=__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = tokenizer_p.save_pretrained(__SCREAMING_SNAKE_CASE )
# Checks it save with the same files
self.assertSequenceEqual(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
# Checks everything loads correctly in the same way
__SCREAMING_SNAKE_CASE = tokenizer_r.from_pretrained(__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = tokenizer_p.from_pretrained(__SCREAMING_SNAKE_CASE )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) )
shutil.rmtree(__SCREAMING_SNAKE_CASE )
# Save tokenizer rust, legacy_format=False
__SCREAMING_SNAKE_CASE = tempfile.mkdtemp()
__SCREAMING_SNAKE_CASE = tokenizer_r.save_pretrained(__SCREAMING_SNAKE_CASE , legacy_format=__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = tokenizer_p.save_pretrained(__SCREAMING_SNAKE_CASE )
# Checks it saved the tokenizer.json file
self.assertTrue(any('''tokenizer.json''' in f for f in tokenizer_r_files ) )
# Checks everything loads correctly in the same way
__SCREAMING_SNAKE_CASE = tokenizer_r.from_pretrained(__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = tokenizer_p.from_pretrained(__SCREAMING_SNAKE_CASE )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) )
shutil.rmtree(__SCREAMING_SNAKE_CASE )
@cached_property
def _a ( self : Union[str, Any] ) -> List[str]:
"""simple docstring"""
return XLMRobertaTokenizer.from_pretrained('''xlm-roberta-base''' )
def _a ( self : Optional[Any] ) -> Union[str, Any]:
"""simple docstring"""
with tempfile.NamedTemporaryFile() as f:
shutil.copyfile(__SCREAMING_SNAKE_CASE , f.name )
__SCREAMING_SNAKE_CASE = XLMRobertaTokenizer(f.name , keep_accents=__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = pickle.dumps(__SCREAMING_SNAKE_CASE )
pickle.loads(__SCREAMING_SNAKE_CASE )
def _a ( self : Union[str, Any] ) -> List[Any]:
"""simple docstring"""
if not self.test_rust_tokenizer:
return
__SCREAMING_SNAKE_CASE = self.get_tokenizer()
__SCREAMING_SNAKE_CASE = self.get_rust_tokenizer()
__SCREAMING_SNAKE_CASE = '''I was born in 92000, and this is falsé.'''
__SCREAMING_SNAKE_CASE = tokenizer.tokenize(__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = rust_tokenizer.tokenize(__SCREAMING_SNAKE_CASE )
self.assertListEqual(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = tokenizer.encode(__SCREAMING_SNAKE_CASE , add_special_tokens=__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = rust_tokenizer.encode(__SCREAMING_SNAKE_CASE , add_special_tokens=__SCREAMING_SNAKE_CASE )
self.assertListEqual(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = self.get_rust_tokenizer()
__SCREAMING_SNAKE_CASE = tokenizer.encode(__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = rust_tokenizer.encode(__SCREAMING_SNAKE_CASE )
self.assertListEqual(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
@slow
def _a ( self : Any ) -> Dict:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = '''Hello World!'''
__SCREAMING_SNAKE_CASE = [0, 3_53_78, 66_61, 38, 2]
# xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base') # xlmr.large has same tokenizer
# xlmr.eval()
# xlmr.encode(symbols)
self.assertListEqual(__SCREAMING_SNAKE_CASE , self.big_tokenizer.encode(__SCREAMING_SNAKE_CASE ) )
@slow
def _a ( self : Optional[Any] ) -> Union[str, Any]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = (
'''This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) " [ ] ! : - . Also we will'''
''' add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth'''
)
__SCREAMING_SNAKE_CASE = [
0,
32_93,
83,
10,
45_52,
49_89,
79_86,
6_78,
10,
59_15,
1_11,
17_94_59,
12_48_50,
4,
60_44,
2_37,
12,
6,
5,
6,
4,
67_80,
7_05,
15,
13_88,
44,
3_78,
1_01_14,
7_11,
1_52,
20,
6,
5,
2_23_76,
6_42,
12_21,
1_51_90,
3_41_53,
4_50,
56_08,
9_59,
11_19,
5_77_02,
1_36,
1_86,
47,
10_98,
2_93_67,
47,
# 4426, # What fairseq tokenizes from "<unk>": "_<"
# 3678, # What fairseq tokenizes from "<unk>": "unk"
# 2740, # What fairseq tokenizes from "<unk>": ">"
3, # What we tokenize from "<unk>": "<unk>"
6, # Residue from the tokenization: an extra sentencepiece underline
4,
60_44,
2_37,
62_84,
5_09_01,
5_28,
31,
90,
34,
9_27,
2,
]
# xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base') # xlmr.large has same tokenizer
# xlmr.eval()
# xlmr.encode(symbols)
self.assertListEqual(__SCREAMING_SNAKE_CASE , self.big_tokenizer.encode(__SCREAMING_SNAKE_CASE ) )
@slow
def _a ( self : Optional[int] ) -> Dict:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = {'''input_ids''': [[0, 1_10_62, 8_27_72, 7, 15, 8_27_72, 5_38, 5_15_29, 2_37, 1_71_98, 12_90, 2_06, 9, 21_51_75, 13_14, 1_36, 1_71_98, 12_90, 2_06, 9, 5_63_59, 42, 12_20_09, 9, 1_64_66, 16, 8_73_44, 45_37, 9, 47_17, 7_83_81, 6, 15_99_58, 7, 15, 2_44_80, 6_18, 4, 5_27, 2_26_93, 54_28, 4, 27_77, 2_44_80, 98_74, 4, 4_35_23, 5_94, 4, 8_03, 1_83_92, 3_31_89, 18, 4, 4_35_23, 2_44_47, 1_23_99, 1_00, 2_49_55, 8_36_58, 96_26, 14_40_57, 15, 8_39, 2_23_35, 16, 1_36, 2_49_55, 8_36_58, 8_34_79, 15, 3_91_02, 7_24, 16, 6_78, 6_45, 27_89, 13_28, 45_89, 42, 12_20_09, 11_57_74, 23, 8_05, 13_28, 4_68_76, 7, 1_36, 5_38_94, 19_40, 4_22_27, 4_11_59, 1_77_21, 8_23, 4_25, 4, 2_75_12, 9_87_22, 2_06, 1_36, 55_31, 49_70, 9_19, 1_73_36, 5, 2], [0, 2_00_80, 6_18, 83, 8_27_75, 47, 4_79, 9, 15_17, 73, 5_38_94, 3_33, 8_05_81, 11_01_17, 1_88_11, 52_56, 12_95, 51, 15_25_26, 2_97, 79_86, 3_90, 12_44_16, 5_38, 3_54_31, 2_14, 98, 1_50_44, 2_57_37, 1_36, 71_08, 4_37_01, 23, 7_56, 13_53_55, 7, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 5_81, 6_37_73, 11_94_55, 6, 14_77_97, 8_82_03, 7, 6_45, 70, 21, 32_85, 1_02_69, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], '''attention_mask''': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # noqa: E501
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=__SCREAMING_SNAKE_CASE , model_name='''xlm-roberta-base''' , revision='''d9d8a8ea5eb94b1c6654ae9249df7793cd2933d3''' , )
| 690 | 1 |
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, DDIMScheduler, LDMTextToImagePipeline, UNetaDConditionModel
from diffusers.utils.testing_utils import (
enable_full_determinism,
load_numpy,
nightly,
require_torch_gpu,
slow,
torch_device,
)
from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
enable_full_determinism()
class A__( __magic_name__ , unittest.TestCase ):
lowerCAmelCase = LDMTextToImagePipeline
lowerCAmelCase = TEXT_TO_IMAGE_PARAMS - {
'''negative_prompt''',
'''negative_prompt_embeds''',
'''cross_attention_kwargs''',
'''prompt_embeds''',
}
lowerCAmelCase = PipelineTesterMixin.required_optional_params - {
'''num_images_per_prompt''',
'''callback''',
'''callback_steps''',
}
lowerCAmelCase = TEXT_TO_IMAGE_BATCH_PARAMS
lowerCAmelCase = False
def _a ( self : Dict ) -> str:
"""simple docstring"""
torch.manual_seed(0 )
__SCREAMING_SNAKE_CASE = UNetaDConditionModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=4 , out_channels=4 , down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''') , up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''') , cross_attention_dim=32 , )
__SCREAMING_SNAKE_CASE = DDIMScheduler(
beta_start=0.0_00_85 , beta_end=0.0_12 , beta_schedule='''scaled_linear''' , clip_sample=__SCREAMING_SNAKE_CASE , set_alpha_to_one=__SCREAMING_SNAKE_CASE , )
torch.manual_seed(0 )
__SCREAMING_SNAKE_CASE = AutoencoderKL(
block_out_channels=(32, 64) , in_channels=3 , out_channels=3 , down_block_types=('''DownEncoderBlock2D''', '''DownEncoderBlock2D''') , up_block_types=('''UpDecoderBlock2D''', '''UpDecoderBlock2D''') , latent_channels=4 , )
torch.manual_seed(0 )
__SCREAMING_SNAKE_CASE = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=32 , intermediate_size=37 , layer_norm_eps=1E-0_5 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=10_00 , )
__SCREAMING_SNAKE_CASE = CLIPTextModel(__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''' )
__SCREAMING_SNAKE_CASE = {
'''unet''': unet,
'''scheduler''': scheduler,
'''vqvae''': vae,
'''bert''': text_encoder,
'''tokenizer''': tokenizer,
}
return components
def _a ( self : Any , __SCREAMING_SNAKE_CASE : Any , __SCREAMING_SNAKE_CASE : Union[str, Any]=0 ) -> List[str]:
"""simple docstring"""
if str(__SCREAMING_SNAKE_CASE ).startswith('''mps''' ):
__SCREAMING_SNAKE_CASE = torch.manual_seed(__SCREAMING_SNAKE_CASE )
else:
__SCREAMING_SNAKE_CASE = torch.Generator(device=__SCREAMING_SNAKE_CASE ).manual_seed(__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = {
'''prompt''': '''A painting of a squirrel eating a burger''',
'''generator''': generator,
'''num_inference_steps''': 2,
'''guidance_scale''': 6.0,
'''output_type''': '''numpy''',
}
return inputs
def _a ( self : List[str] ) -> Union[str, Any]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = '''cpu''' # ensure determinism for the device-dependent torch.Generator
__SCREAMING_SNAKE_CASE = self.get_dummy_components()
__SCREAMING_SNAKE_CASE = LDMTextToImagePipeline(**__SCREAMING_SNAKE_CASE )
pipe.to(__SCREAMING_SNAKE_CASE )
pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = self.get_dummy_inputs(__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = pipe(**__SCREAMING_SNAKE_CASE ).images
__SCREAMING_SNAKE_CASE = image[0, -3:, -3:, -1]
assert image.shape == (1, 16, 16, 3)
__SCREAMING_SNAKE_CASE = np.array([0.61_01, 0.61_56, 0.56_22, 0.48_95, 0.66_61, 0.38_04, 0.57_48, 0.61_36, 0.50_14] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-3
@slow
@require_torch_gpu
class A__( unittest.TestCase ):
def _a ( self : Union[str, Any] ) -> str:
"""simple docstring"""
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def _a ( self : Optional[Any] , __SCREAMING_SNAKE_CASE : Tuple , __SCREAMING_SNAKE_CASE : Optional[int]=torch.floataa , __SCREAMING_SNAKE_CASE : Dict=0 ) -> Tuple:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = torch.manual_seed(__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = np.random.RandomState(__SCREAMING_SNAKE_CASE ).standard_normal((1, 4, 32, 32) )
__SCREAMING_SNAKE_CASE = torch.from_numpy(__SCREAMING_SNAKE_CASE ).to(device=__SCREAMING_SNAKE_CASE , dtype=__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = {
'''prompt''': '''A painting of a squirrel eating a burger''',
'''latents''': latents,
'''generator''': generator,
'''num_inference_steps''': 3,
'''guidance_scale''': 6.0,
'''output_type''': '''numpy''',
}
return inputs
def _a ( self : Any ) -> List[Any]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = LDMTextToImagePipeline.from_pretrained('''CompVis/ldm-text2im-large-256''' ).to(__SCREAMING_SNAKE_CASE )
pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = self.get_inputs(__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = pipe(**__SCREAMING_SNAKE_CASE ).images
__SCREAMING_SNAKE_CASE = image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 2_56, 2_56, 3)
__SCREAMING_SNAKE_CASE = np.array([0.5_18_25, 0.5_28_50, 0.5_25_43, 0.5_42_58, 0.5_23_04, 0.5_25_69, 0.5_43_63, 0.5_52_76, 0.5_68_78] )
__SCREAMING_SNAKE_CASE = np.abs(expected_slice - image_slice ).max()
assert max_diff < 1E-3
@nightly
@require_torch_gpu
class A__( unittest.TestCase ):
def _a ( self : List[str] ) -> List[str]:
"""simple docstring"""
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def _a ( self : Optional[Any] , __SCREAMING_SNAKE_CASE : List[Any] , __SCREAMING_SNAKE_CASE : List[Any]=torch.floataa , __SCREAMING_SNAKE_CASE : Optional[Any]=0 ) -> List[Any]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = torch.manual_seed(__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = np.random.RandomState(__SCREAMING_SNAKE_CASE ).standard_normal((1, 4, 32, 32) )
__SCREAMING_SNAKE_CASE = torch.from_numpy(__SCREAMING_SNAKE_CASE ).to(device=__SCREAMING_SNAKE_CASE , dtype=__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = {
'''prompt''': '''A painting of a squirrel eating a burger''',
'''latents''': latents,
'''generator''': generator,
'''num_inference_steps''': 50,
'''guidance_scale''': 6.0,
'''output_type''': '''numpy''',
}
return inputs
def _a ( self : Tuple ) -> int:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = LDMTextToImagePipeline.from_pretrained('''CompVis/ldm-text2im-large-256''' ).to(__SCREAMING_SNAKE_CASE )
pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = self.get_inputs(__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = pipe(**__SCREAMING_SNAKE_CASE ).images[0]
__SCREAMING_SNAKE_CASE = load_numpy(
'''https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/ldm_text2img/ldm_large_256_ddim.npy''' )
__SCREAMING_SNAKE_CASE = np.abs(expected_image - image ).max()
assert max_diff < 1E-3
| 690 |
"""simple docstring"""
from __future__ import annotations
lowerCAmelCase__ =8.9_8_8E9 # units = N * m^s * C^-2
def _a ( UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ ) -> dict[str, float]:
__SCREAMING_SNAKE_CASE = abs(chargea * chargea )
if (force, chargea, chargea, distance).count(0 ) != 1:
raise ValueError('''One and only one argument must be 0''' )
if distance < 0:
raise ValueError('''Distance cannot be negative''' )
if force == 0:
__SCREAMING_SNAKE_CASE = COULOMBS_CONSTANT * charge_product / (distance**2)
return {"force": force}
elif chargea == 0:
__SCREAMING_SNAKE_CASE = abs(UpperCAmelCase__ ) * (distance**2) / (COULOMBS_CONSTANT * chargea)
return {"charge1": chargea}
elif chargea == 0:
__SCREAMING_SNAKE_CASE = abs(UpperCAmelCase__ ) * (distance**2) / (COULOMBS_CONSTANT * chargea)
return {"charge2": chargea}
elif distance == 0:
__SCREAMING_SNAKE_CASE = (COULOMBS_CONSTANT * charge_product / abs(UpperCAmelCase__ )) ** 0.5
return {"distance": distance}
raise ValueError('''Exactly one argument must be 0''' )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 690 | 1 |
"""simple docstring"""
import os
import tempfile
import unittest
import numpy as np
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax, slow
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_utils import replicate
from flax.training.common_utils import shard
from diffusers import FlaxDDIMScheduler, FlaxDiffusionPipeline, FlaxStableDiffusionPipeline
@require_flax
class A__( unittest.TestCase ):
def _a ( self : Tuple ) -> List[Any]:
"""simple docstring"""
with tempfile.TemporaryDirectory() as tmpdirname:
# pipeline has Flax weights
__SCREAMING_SNAKE_CASE = FlaxDiffusionPipeline.from_pretrained(
'''hf-internal-testing/tiny-stable-diffusion-pipe''' , safety_checker=__SCREAMING_SNAKE_CASE , cache_dir=__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = [t[-1] for t in os.walk(os.path.join(__SCREAMING_SNAKE_CASE , os.listdir(__SCREAMING_SNAKE_CASE )[0] , '''snapshots''' ) )]
__SCREAMING_SNAKE_CASE = [item for sublist in all_root_files for item in sublist]
# None of the downloaded files should be a PyTorch file even if we have some here:
# https://huggingface.co/hf-internal-testing/tiny-stable-diffusion-pipe/blob/main/unet/diffusion_pytorch_model.bin
assert not any(f.endswith('''.bin''' ) for f in files )
@slow
@require_flax
class A__( unittest.TestCase ):
def _a ( self : int ) -> Dict:
"""simple docstring"""
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = FlaxStableDiffusionPipeline.from_pretrained(
'''hf-internal-testing/tiny-stable-diffusion-pipe''' , safety_checker=__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = (
'''A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of'''
''' field, close up, split lighting, cinematic'''
)
__SCREAMING_SNAKE_CASE = jax.random.PRNGKey(0 )
__SCREAMING_SNAKE_CASE = 4
__SCREAMING_SNAKE_CASE = jax.device_count()
__SCREAMING_SNAKE_CASE = num_samples * [prompt]
__SCREAMING_SNAKE_CASE = pipeline.prepare_inputs(__SCREAMING_SNAKE_CASE )
# shard inputs and rng
__SCREAMING_SNAKE_CASE = replicate(__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = jax.random.split(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = shard(__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = pipeline(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , jit=__SCREAMING_SNAKE_CASE ).images
assert images.shape == (num_samples, 1, 64, 64, 3)
if jax.device_count() == 8:
assert np.abs(np.abs(images[0, 0, :2, :2, -2:] , dtype=np.floataa ).sum() - 4.1_51_47_45 ) < 1E-3
assert np.abs(np.abs(__SCREAMING_SNAKE_CASE , dtype=np.floataa ).sum() - 4_99_47.8_75 ) < 5E-1
__SCREAMING_SNAKE_CASE = pipeline.numpy_to_pil(np.asarray(images.reshape((num_samples,) + images.shape[-3:] ) ) )
assert len(__SCREAMING_SNAKE_CASE ) == num_samples
def _a ( self : Tuple ) -> Union[str, Any]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = FlaxStableDiffusionPipeline.from_pretrained(
'''CompVis/stable-diffusion-v1-4''' , revision='''flax''' , safety_checker=__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = (
'''A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of'''
''' field, close up, split lighting, cinematic'''
)
__SCREAMING_SNAKE_CASE = jax.random.PRNGKey(0 )
__SCREAMING_SNAKE_CASE = 50
__SCREAMING_SNAKE_CASE = jax.device_count()
__SCREAMING_SNAKE_CASE = num_samples * [prompt]
__SCREAMING_SNAKE_CASE = pipeline.prepare_inputs(__SCREAMING_SNAKE_CASE )
# shard inputs and rng
__SCREAMING_SNAKE_CASE = replicate(__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = jax.random.split(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = shard(__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = pipeline(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , jit=__SCREAMING_SNAKE_CASE ).images
assert images.shape == (num_samples, 1, 5_12, 5_12, 3)
if jax.device_count() == 8:
assert np.abs((np.abs(images[0, 0, :2, :2, -2:] , dtype=np.floataa ).sum() - 0.05_65_24_01) ) < 1E-3
assert np.abs((np.abs(__SCREAMING_SNAKE_CASE , dtype=np.floataa ).sum() - 2_38_38_08.2) ) < 5E-1
def _a ( self : int ) -> Any:
"""simple docstring"""
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = FlaxStableDiffusionPipeline.from_pretrained(
'''CompVis/stable-diffusion-v1-4''' , revision='''bf16''' , dtype=jnp.bfloataa , safety_checker=__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = (
'''A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of'''
''' field, close up, split lighting, cinematic'''
)
__SCREAMING_SNAKE_CASE = jax.random.PRNGKey(0 )
__SCREAMING_SNAKE_CASE = 50
__SCREAMING_SNAKE_CASE = jax.device_count()
__SCREAMING_SNAKE_CASE = num_samples * [prompt]
__SCREAMING_SNAKE_CASE = pipeline.prepare_inputs(__SCREAMING_SNAKE_CASE )
# shard inputs and rng
__SCREAMING_SNAKE_CASE = replicate(__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = jax.random.split(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = shard(__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = pipeline(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , jit=__SCREAMING_SNAKE_CASE ).images
assert images.shape == (num_samples, 1, 5_12, 5_12, 3)
if jax.device_count() == 8:
assert np.abs((np.abs(images[0, 0, :2, :2, -2:] , dtype=np.floataa ).sum() - 0.04_00_39_06) ) < 1E-3
assert np.abs((np.abs(__SCREAMING_SNAKE_CASE , dtype=np.floataa ).sum() - 2_37_35_16.75) ) < 5E-1
def _a ( self : Any ) -> Tuple:
"""simple docstring"""
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = FlaxStableDiffusionPipeline.from_pretrained(
'''CompVis/stable-diffusion-v1-4''' , revision='''bf16''' , dtype=jnp.bfloataa )
__SCREAMING_SNAKE_CASE = (
'''A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of'''
''' field, close up, split lighting, cinematic'''
)
__SCREAMING_SNAKE_CASE = jax.random.PRNGKey(0 )
__SCREAMING_SNAKE_CASE = 50
__SCREAMING_SNAKE_CASE = jax.device_count()
__SCREAMING_SNAKE_CASE = num_samples * [prompt]
__SCREAMING_SNAKE_CASE = pipeline.prepare_inputs(__SCREAMING_SNAKE_CASE )
# shard inputs and rng
__SCREAMING_SNAKE_CASE = replicate(__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = jax.random.split(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = shard(__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = pipeline(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , jit=__SCREAMING_SNAKE_CASE ).images
assert images.shape == (num_samples, 1, 5_12, 5_12, 3)
if jax.device_count() == 8:
assert np.abs((np.abs(images[0, 0, :2, :2, -2:] , dtype=np.floataa ).sum() - 0.04_00_39_06) ) < 1E-3
assert np.abs((np.abs(__SCREAMING_SNAKE_CASE , dtype=np.floataa ).sum() - 2_37_35_16.75) ) < 5E-1
def _a ( self : Any ) -> int:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = FlaxDDIMScheduler(
beta_start=0.0_00_85 , beta_end=0.0_12 , beta_schedule='''scaled_linear''' , set_alpha_to_one=__SCREAMING_SNAKE_CASE , steps_offset=1 , )
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = FlaxStableDiffusionPipeline.from_pretrained(
'''CompVis/stable-diffusion-v1-4''' , revision='''bf16''' , dtype=jnp.bfloataa , scheduler=__SCREAMING_SNAKE_CASE , safety_checker=__SCREAMING_SNAKE_CASE , )
__SCREAMING_SNAKE_CASE = scheduler.create_state()
__SCREAMING_SNAKE_CASE = scheduler_state
__SCREAMING_SNAKE_CASE = (
'''A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of'''
''' field, close up, split lighting, cinematic'''
)
__SCREAMING_SNAKE_CASE = jax.random.PRNGKey(0 )
__SCREAMING_SNAKE_CASE = 50
__SCREAMING_SNAKE_CASE = jax.device_count()
__SCREAMING_SNAKE_CASE = num_samples * [prompt]
__SCREAMING_SNAKE_CASE = pipeline.prepare_inputs(__SCREAMING_SNAKE_CASE )
# shard inputs and rng
__SCREAMING_SNAKE_CASE = replicate(__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = jax.random.split(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = shard(__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = pipeline(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , jit=__SCREAMING_SNAKE_CASE ).images
assert images.shape == (num_samples, 1, 5_12, 5_12, 3)
if jax.device_count() == 8:
assert np.abs((np.abs(images[0, 0, :2, :2, -2:] , dtype=np.floataa ).sum() - 0.0_45_04_39_45) ) < 1E-3
assert np.abs((np.abs(__SCREAMING_SNAKE_CASE , dtype=np.floataa ).sum() - 2_34_76_93.5) ) < 5E-1
def _a ( self : Optional[int] ) -> List[Any]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = (
'''A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of'''
''' field, close up, split lighting, cinematic'''
)
__SCREAMING_SNAKE_CASE = jax.device_count()
__SCREAMING_SNAKE_CASE = num_samples * [prompt]
__SCREAMING_SNAKE_CASE = jax.random.split(jax.random.PRNGKey(0 ) , __SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = FlaxStableDiffusionPipeline.from_pretrained(
'''CompVis/stable-diffusion-v1-4''' , revision='''bf16''' , dtype=jnp.bfloataa , safety_checker=__SCREAMING_SNAKE_CASE , )
__SCREAMING_SNAKE_CASE = replicate(__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = pipeline.prepare_inputs(__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = shard(__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = pipeline(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , jit=__SCREAMING_SNAKE_CASE ).images
assert images.shape == (num_samples, 1, 5_12, 5_12, 3)
__SCREAMING_SNAKE_CASE = images[2, 0, 2_56, 10:17, 1]
# With memory efficient attention
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = FlaxStableDiffusionPipeline.from_pretrained(
'''CompVis/stable-diffusion-v1-4''' , revision='''bf16''' , dtype=jnp.bfloataa , safety_checker=__SCREAMING_SNAKE_CASE , use_memory_efficient_attention=__SCREAMING_SNAKE_CASE , )
__SCREAMING_SNAKE_CASE = replicate(__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = pipeline.prepare_inputs(__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = shard(__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = pipeline(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , jit=__SCREAMING_SNAKE_CASE ).images
assert images_eff.shape == (num_samples, 1, 5_12, 5_12, 3)
__SCREAMING_SNAKE_CASE = images[2, 0, 2_56, 10:17, 1]
# I checked the results visually and they are very similar. However, I saw that the max diff is `1` and the `sum`
# over the 8 images is exactly `256`, which is very suspicious. Testing a random slice for now.
assert abs(slice_eff - slice ).max() < 1E-2
| 690 |
"""simple docstring"""
import argparse
from pathlib import Path
import torch
from transformers import OPTConfig, OPTModel
from transformers.utils import logging
logging.set_verbosity_info()
lowerCAmelCase__ =logging.get_logger(__name__)
def _a ( UpperCAmelCase__ ) -> Tuple:
__SCREAMING_SNAKE_CASE = torch.load(UpperCAmelCase__ , map_location='''cpu''' )
if "model" in sd.keys():
__SCREAMING_SNAKE_CASE = torch.load(UpperCAmelCase__ , map_location='''cpu''' )['''model''']
# pop unnecessary weights
__SCREAMING_SNAKE_CASE = [
'''decoder.version''',
'''decoder.output_projection.weight''',
]
for key in keys_to_delete:
if key in sd:
sd.pop(UpperCAmelCase__ )
__SCREAMING_SNAKE_CASE = {
'''decoder.project_in_dim.weight''': '''decoder.project_in.weight''',
'''decoder.project_out_dim.weight''': '''decoder.project_out.weight''',
'''decoder.layer_norm.weight''': '''decoder.final_layer_norm.weight''',
'''decoder.layer_norm.bias''': '''decoder.final_layer_norm.bias''',
}
for old_key, new_key in keys_to_rename.items():
if old_key in sd:
__SCREAMING_SNAKE_CASE = sd.pop(UpperCAmelCase__ )
__SCREAMING_SNAKE_CASE = list(sd.keys() )
for key in keys:
if ".qkv_proj." in key:
__SCREAMING_SNAKE_CASE = sd[key]
# We split QKV in separate Q,K,V
__SCREAMING_SNAKE_CASE = key.replace('''.qkv_proj.''' , '''.q_proj.''' )
__SCREAMING_SNAKE_CASE = key.replace('''.qkv_proj.''' , '''.k_proj.''' )
__SCREAMING_SNAKE_CASE = key.replace('''.qkv_proj.''' , '''.v_proj.''' )
__SCREAMING_SNAKE_CASE = value.shape[0]
assert depth % 3 == 0
# `SequeuceParallelTransformerBlock` has QKV weight is separated in K,V,Q despite the naming:
# https://cs.github.com/facebookresearch/metaseq/blob/51871bd73cd04c038f239ea2a26db1d7f6b37927/metaseq/modules/sequence_parallel_transformer_layer.py#L97
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = torch.split(UpperCAmelCase__ , depth // 3 , dim=0 )
__SCREAMING_SNAKE_CASE = q
__SCREAMING_SNAKE_CASE = k
__SCREAMING_SNAKE_CASE = v
del sd[key]
return sd
@torch.no_grad()
def _a ( UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__=None ) -> Optional[Any]:
__SCREAMING_SNAKE_CASE = load_checkpoint(UpperCAmelCase__ )
if config is not None:
__SCREAMING_SNAKE_CASE = OPTConfig.from_pretrained(UpperCAmelCase__ )
else:
__SCREAMING_SNAKE_CASE = OPTConfig()
__SCREAMING_SNAKE_CASE = OPTModel(UpperCAmelCase__ ).half().eval()
model.load_state_dict(UpperCAmelCase__ )
# Check results
Path(UpperCAmelCase__ ).mkdir(exist_ok=UpperCAmelCase__ )
model.save_pretrained(UpperCAmelCase__ )
if __name__ == "__main__":
lowerCAmelCase__ =argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--fairseq_path",
type=str,
help=(
"path to fairseq checkpoint in correct format. You can find all checkpoints in the correct format here:"
" https://huggingface.co/models?other=opt_metasq"
),
)
parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
parser.add_argument("--hf_config", default=None, type=str, help="Define HF config.")
lowerCAmelCase__ =parser.parse_args()
convert_opt_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, config=args.hf_config)
| 690 | 1 |
"""simple docstring"""
import warnings
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase__ =logging.get_logger(__name__)
lowerCAmelCase__ ={
"xlnet-base-cased": "https://huggingface.co/xlnet-base-cased/resolve/main/config.json",
"xlnet-large-cased": "https://huggingface.co/xlnet-large-cased/resolve/main/config.json",
}
class A__( __magic_name__ ):
lowerCAmelCase = '''xlnet'''
lowerCAmelCase = ['''mems''']
lowerCAmelCase = {
'''n_token''': '''vocab_size''', # Backward compatibility
'''hidden_size''': '''d_model''',
'''num_attention_heads''': '''n_head''',
'''num_hidden_layers''': '''n_layer''',
}
def __init__( self : Tuple , __SCREAMING_SNAKE_CASE : Any=3_20_00 , __SCREAMING_SNAKE_CASE : Tuple=10_24 , __SCREAMING_SNAKE_CASE : Optional[int]=24 , __SCREAMING_SNAKE_CASE : Dict=16 , __SCREAMING_SNAKE_CASE : str=40_96 , __SCREAMING_SNAKE_CASE : List[str]="gelu" , __SCREAMING_SNAKE_CASE : Optional[Any]=True , __SCREAMING_SNAKE_CASE : int="bi" , __SCREAMING_SNAKE_CASE : str=0.02 , __SCREAMING_SNAKE_CASE : Tuple=1E-1_2 , __SCREAMING_SNAKE_CASE : int=0.1 , __SCREAMING_SNAKE_CASE : Tuple=5_12 , __SCREAMING_SNAKE_CASE : Any=None , __SCREAMING_SNAKE_CASE : Tuple=True , __SCREAMING_SNAKE_CASE : str=False , __SCREAMING_SNAKE_CASE : Dict=False , __SCREAMING_SNAKE_CASE : Union[str, Any]=-1 , __SCREAMING_SNAKE_CASE : Any=False , __SCREAMING_SNAKE_CASE : Tuple="last" , __SCREAMING_SNAKE_CASE : Dict=True , __SCREAMING_SNAKE_CASE : int="tanh" , __SCREAMING_SNAKE_CASE : Tuple=0.1 , __SCREAMING_SNAKE_CASE : Union[str, Any]=5 , __SCREAMING_SNAKE_CASE : str=5 , __SCREAMING_SNAKE_CASE : List[Any]=5 , __SCREAMING_SNAKE_CASE : Dict=1 , __SCREAMING_SNAKE_CASE : Any=2 , **__SCREAMING_SNAKE_CASE : Dict , ) -> Union[str, Any]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = vocab_size
__SCREAMING_SNAKE_CASE = d_model
__SCREAMING_SNAKE_CASE = n_layer
__SCREAMING_SNAKE_CASE = n_head
if d_model % n_head != 0:
raise ValueError(f"""'d_model % n_head' ({d_model % n_head}) should be equal to 0""" )
if "d_head" in kwargs:
if kwargs["d_head"] != d_model // n_head:
raise ValueError(
f"""`d_head` ({kwargs["d_head"]}) should be equal to `d_model // n_head` ({d_model // n_head})""" )
__SCREAMING_SNAKE_CASE = d_model // n_head
__SCREAMING_SNAKE_CASE = ff_activation
__SCREAMING_SNAKE_CASE = d_inner
__SCREAMING_SNAKE_CASE = untie_r
__SCREAMING_SNAKE_CASE = attn_type
__SCREAMING_SNAKE_CASE = initializer_range
__SCREAMING_SNAKE_CASE = layer_norm_eps
__SCREAMING_SNAKE_CASE = dropout
__SCREAMING_SNAKE_CASE = mem_len
__SCREAMING_SNAKE_CASE = reuse_len
__SCREAMING_SNAKE_CASE = bi_data
__SCREAMING_SNAKE_CASE = clamp_len
__SCREAMING_SNAKE_CASE = same_length
__SCREAMING_SNAKE_CASE = summary_type
__SCREAMING_SNAKE_CASE = summary_use_proj
__SCREAMING_SNAKE_CASE = summary_activation
__SCREAMING_SNAKE_CASE = summary_last_dropout
__SCREAMING_SNAKE_CASE = start_n_top
__SCREAMING_SNAKE_CASE = end_n_top
__SCREAMING_SNAKE_CASE = bos_token_id
__SCREAMING_SNAKE_CASE = pad_token_id
__SCREAMING_SNAKE_CASE = eos_token_id
if "use_cache" in kwargs:
warnings.warn(
'''The `use_cache` argument is deprecated and will be removed in a future version, use `use_mems_eval`'''
''' instead.''' , __SCREAMING_SNAKE_CASE , )
__SCREAMING_SNAKE_CASE = kwargs['''use_cache''']
__SCREAMING_SNAKE_CASE = use_mems_eval
__SCREAMING_SNAKE_CASE = use_mems_train
super().__init__(pad_token_id=__SCREAMING_SNAKE_CASE , bos_token_id=__SCREAMING_SNAKE_CASE , eos_token_id=__SCREAMING_SNAKE_CASE , **__SCREAMING_SNAKE_CASE )
@property
def _a ( self : Dict ) -> Dict:
"""simple docstring"""
logger.info(f"""The model {self.model_type} is one of the few models that has no sequence length limit.""" )
return -1
@max_position_embeddings.setter
def _a ( self : Optional[int] , __SCREAMING_SNAKE_CASE : Tuple ) -> Any:
"""simple docstring"""
raise NotImplementedError(
f"""The model {self.model_type} is one of the few models that has no sequence length limit.""" )
| 690 |
"""simple docstring"""
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import re
from ..models.auto import AutoProcessor
from ..models.vision_encoder_decoder import VisionEncoderDecoderModel
from ..utils import is_vision_available
from .base import PipelineTool
if is_vision_available():
from PIL import Image
class A__( __magic_name__ ):
lowerCAmelCase = '''naver-clova-ix/donut-base-finetuned-docvqa'''
lowerCAmelCase = (
'''This is a tool that answers a question about an document (pdf). It takes an input named `document` which '''
'''should be the document containing the information, as well as a `question` that is the question about the '''
'''document. It returns a text that contains the answer to the question.'''
)
lowerCAmelCase = '''document_qa'''
lowerCAmelCase = AutoProcessor
lowerCAmelCase = VisionEncoderDecoderModel
lowerCAmelCase = ['''image''', '''text''']
lowerCAmelCase = ['''text''']
def __init__( self : str , *__SCREAMING_SNAKE_CASE : List[str] , **__SCREAMING_SNAKE_CASE : List[Any] ) -> Any:
"""simple docstring"""
if not is_vision_available():
raise ValueError('''Pillow must be installed to use the DocumentQuestionAnsweringTool.''' )
super().__init__(*__SCREAMING_SNAKE_CASE , **__SCREAMING_SNAKE_CASE )
def _a ( self : Union[str, Any] , __SCREAMING_SNAKE_CASE : "Image" , __SCREAMING_SNAKE_CASE : str ) -> str:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = '''<s_docvqa><s_question>{user_input}</s_question><s_answer>'''
__SCREAMING_SNAKE_CASE = task_prompt.replace('''{user_input}''' , __SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = self.pre_processor.tokenizer(
__SCREAMING_SNAKE_CASE , add_special_tokens=__SCREAMING_SNAKE_CASE , return_tensors='''pt''' ).input_ids
__SCREAMING_SNAKE_CASE = self.pre_processor(__SCREAMING_SNAKE_CASE , return_tensors='''pt''' ).pixel_values
return {"decoder_input_ids": decoder_input_ids, "pixel_values": pixel_values}
def _a ( self : Union[str, Any] , __SCREAMING_SNAKE_CASE : Optional[Any] ) -> Tuple:
"""simple docstring"""
return self.model.generate(
inputs['''pixel_values'''].to(self.device ) , decoder_input_ids=inputs['''decoder_input_ids'''].to(self.device ) , max_length=self.model.decoder.config.max_position_embeddings , early_stopping=__SCREAMING_SNAKE_CASE , pad_token_id=self.pre_processor.tokenizer.pad_token_id , eos_token_id=self.pre_processor.tokenizer.eos_token_id , use_cache=__SCREAMING_SNAKE_CASE , num_beams=1 , bad_words_ids=[[self.pre_processor.tokenizer.unk_token_id]] , return_dict_in_generate=__SCREAMING_SNAKE_CASE , ).sequences
def _a ( self : Tuple , __SCREAMING_SNAKE_CASE : Tuple ) -> List[str]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = self.pre_processor.batch_decode(__SCREAMING_SNAKE_CASE )[0]
__SCREAMING_SNAKE_CASE = sequence.replace(self.pre_processor.tokenizer.eos_token , '''''' )
__SCREAMING_SNAKE_CASE = sequence.replace(self.pre_processor.tokenizer.pad_token , '''''' )
__SCREAMING_SNAKE_CASE = re.sub(r'''<.*?>''' , '''''' , __SCREAMING_SNAKE_CASE , count=1 ).strip() # remove first task start token
__SCREAMING_SNAKE_CASE = self.pre_processor.tokenajson(__SCREAMING_SNAKE_CASE )
return sequence["answer"]
| 690 | 1 |
"""simple docstring"""
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Features, Sequence, Value
from .base import TaskTemplate
@dataclass(frozen=__magic_name__ )
class A__( __magic_name__ ):
# `task` is not a ClassVar since we want it to be part of the `asdict` output for JSON serialization
lowerCAmelCase = field(default='''question-answering-extractive''' , metadata={'''include_in_asdict_even_if_is_default''': True} )
lowerCAmelCase = Features({'''question''': Value('''string''' ), '''context''': Value('''string''' )} )
lowerCAmelCase = Features(
{
'''answers''': Sequence(
{
'''text''': Value('''string''' ),
'''answer_start''': Value('''int32''' ),
} )
} )
lowerCAmelCase = "question"
lowerCAmelCase = "context"
lowerCAmelCase = "answers"
@property
def _a ( self : Union[str, Any] ) -> Dict[str, str]:
"""simple docstring"""
return {self.question_column: "question", self.context_column: "context", self.answers_column: "answers"}
| 690 |
"""simple docstring"""
import unittest
import numpy as np
import torch
from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class A__( unittest.TestCase ):
@property
def _a ( self : Optional[Any] ) -> Tuple:
"""simple docstring"""
torch.manual_seed(0 )
__SCREAMING_SNAKE_CASE = UNetaDModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=3 , out_channels=3 , down_block_types=('''DownBlock2D''', '''AttnDownBlock2D''') , up_block_types=('''AttnUpBlock2D''', '''UpBlock2D''') , )
return model
def _a ( self : str ) -> Any:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = self.dummy_uncond_unet
__SCREAMING_SNAKE_CASE = KarrasVeScheduler()
__SCREAMING_SNAKE_CASE = KarrasVePipeline(unet=__SCREAMING_SNAKE_CASE , scheduler=__SCREAMING_SNAKE_CASE )
pipe.to(__SCREAMING_SNAKE_CASE )
pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = torch.manual_seed(0 )
__SCREAMING_SNAKE_CASE = pipe(num_inference_steps=2 , generator=__SCREAMING_SNAKE_CASE , output_type='''numpy''' ).images
__SCREAMING_SNAKE_CASE = torch.manual_seed(0 )
__SCREAMING_SNAKE_CASE = pipe(num_inference_steps=2 , generator=__SCREAMING_SNAKE_CASE , output_type='''numpy''' , return_dict=__SCREAMING_SNAKE_CASE )[0]
__SCREAMING_SNAKE_CASE = image[0, -3:, -3:, -1]
__SCREAMING_SNAKE_CASE = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
__SCREAMING_SNAKE_CASE = np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1E-2
@slow
@require_torch
class A__( unittest.TestCase ):
def _a ( self : Any ) -> str:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = '''google/ncsnpp-celebahq-256'''
__SCREAMING_SNAKE_CASE = UNetaDModel.from_pretrained(__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = KarrasVeScheduler()
__SCREAMING_SNAKE_CASE = KarrasVePipeline(unet=__SCREAMING_SNAKE_CASE , scheduler=__SCREAMING_SNAKE_CASE )
pipe.to(__SCREAMING_SNAKE_CASE )
pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = torch.manual_seed(0 )
__SCREAMING_SNAKE_CASE = pipe(num_inference_steps=20 , generator=__SCREAMING_SNAKE_CASE , output_type='''numpy''' ).images
__SCREAMING_SNAKE_CASE = image[0, -3:, -3:, -1]
assert image.shape == (1, 2_56, 2_56, 3)
__SCREAMING_SNAKE_CASE = np.array([0.5_78, 0.58_11, 0.59_24, 0.58_09, 0.5_87, 0.58_86, 0.58_61, 0.58_02, 0.5_86] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
| 690 | 1 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
lowerCAmelCase__ =logging.get_logger(__name__)
lowerCAmelCase__ ="▁"
lowerCAmelCase__ ={"vocab_file": "sentencepiece.bpe.model", "monolingual_vocab_file": "dict.txt"}
lowerCAmelCase__ ={
"vocab_file": {
"vinai/bartpho-syllable": "https://huggingface.co/vinai/bartpho-syllable/resolve/main/sentencepiece.bpe.model",
},
"monolingual_vocab_file": {
"vinai/bartpho-syllable": "https://huggingface.co/vinai/bartpho-syllable/resolve/main/dict.txt",
},
}
lowerCAmelCase__ ={"vinai/bartpho-syllable": 1_024}
class A__( __magic_name__ ):
lowerCAmelCase = VOCAB_FILES_NAMES
lowerCAmelCase = PRETRAINED_VOCAB_FILES_MAP
lowerCAmelCase = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowerCAmelCase = ['''input_ids''', '''attention_mask''']
def __init__( self : int , __SCREAMING_SNAKE_CASE : Any , __SCREAMING_SNAKE_CASE : Dict , __SCREAMING_SNAKE_CASE : Tuple="<s>" , __SCREAMING_SNAKE_CASE : Optional[Any]="</s>" , __SCREAMING_SNAKE_CASE : Optional[Any]="</s>" , __SCREAMING_SNAKE_CASE : Optional[int]="<s>" , __SCREAMING_SNAKE_CASE : Tuple="<unk>" , __SCREAMING_SNAKE_CASE : str="<pad>" , __SCREAMING_SNAKE_CASE : Any="<mask>" , __SCREAMING_SNAKE_CASE : Optional[Dict[str, Any]] = None , **__SCREAMING_SNAKE_CASE : List[str] , ) -> None:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = AddedToken(__SCREAMING_SNAKE_CASE , lstrip=__SCREAMING_SNAKE_CASE , rstrip=__SCREAMING_SNAKE_CASE ) if isinstance(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) else mask_token
__SCREAMING_SNAKE_CASE = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
bos_token=__SCREAMING_SNAKE_CASE , eos_token=__SCREAMING_SNAKE_CASE , unk_token=__SCREAMING_SNAKE_CASE , sep_token=__SCREAMING_SNAKE_CASE , cls_token=__SCREAMING_SNAKE_CASE , pad_token=__SCREAMING_SNAKE_CASE , mask_token=__SCREAMING_SNAKE_CASE , sp_model_kwargs=self.sp_model_kwargs , **__SCREAMING_SNAKE_CASE , )
__SCREAMING_SNAKE_CASE = vocab_file
__SCREAMING_SNAKE_CASE = monolingual_vocab_file
__SCREAMING_SNAKE_CASE = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(str(__SCREAMING_SNAKE_CASE ) )
# Load the reduced vocab
# Keep order of special tokens for backward compatibility
__SCREAMING_SNAKE_CASE = {}
__SCREAMING_SNAKE_CASE = 0
for token in [bos_token, pad_token, eos_token, unk_token, sep_token, cls_token]:
if str(__SCREAMING_SNAKE_CASE ) not in self.fairseq_tokens_to_ids:
__SCREAMING_SNAKE_CASE = cnt
cnt += 1
with open(__SCREAMING_SNAKE_CASE , '''r''' , encoding='''utf-8''' ) as f:
for line in f.readlines():
__SCREAMING_SNAKE_CASE = line.strip().split()[0]
__SCREAMING_SNAKE_CASE = len(self.fairseq_tokens_to_ids )
if str(__SCREAMING_SNAKE_CASE ) not in self.fairseq_tokens_to_ids:
__SCREAMING_SNAKE_CASE = len(self.fairseq_tokens_to_ids )
__SCREAMING_SNAKE_CASE = {v: k for k, v in self.fairseq_tokens_to_ids.items()}
def __getstate__( self : str ) -> Union[str, Any]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = self.__dict__.copy()
__SCREAMING_SNAKE_CASE = None
__SCREAMING_SNAKE_CASE = self.sp_model.serialized_model_proto()
return state
def __setstate__( self : Optional[int] , __SCREAMING_SNAKE_CASE : Tuple ) -> List[Any]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = d
# for backward compatibility
if not hasattr(self , '''sp_model_kwargs''' ):
__SCREAMING_SNAKE_CASE = {}
__SCREAMING_SNAKE_CASE = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.LoadFromSerializedProto(self.sp_model_proto )
def _a ( self : Optional[int] , __SCREAMING_SNAKE_CASE : List[int] , __SCREAMING_SNAKE_CASE : Optional[List[int]] = None ) -> List[int]:
"""simple docstring"""
if token_ids_a is None:
return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
__SCREAMING_SNAKE_CASE = [self.cls_token_id]
__SCREAMING_SNAKE_CASE = [self.sep_token_id]
return cls + token_ids_a + sep + sep + token_ids_a + sep
def _a ( self : Any , __SCREAMING_SNAKE_CASE : List[int] , __SCREAMING_SNAKE_CASE : Optional[List[int]] = None , __SCREAMING_SNAKE_CASE : bool = False ) -> List[int]:
"""simple docstring"""
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=__SCREAMING_SNAKE_CASE , token_ids_a=__SCREAMING_SNAKE_CASE , already_has_special_tokens=__SCREAMING_SNAKE_CASE )
if token_ids_a is None:
return [1] + ([0] * len(__SCREAMING_SNAKE_CASE )) + [1]
return [1] + ([0] * len(__SCREAMING_SNAKE_CASE )) + [1, 1] + ([0] * len(__SCREAMING_SNAKE_CASE )) + [1]
def _a ( self : Union[str, Any] , __SCREAMING_SNAKE_CASE : List[int] , __SCREAMING_SNAKE_CASE : Optional[List[int]] = None ) -> List[int]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = [self.sep_token_id]
__SCREAMING_SNAKE_CASE = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]
@property
def _a ( self : Tuple ) -> int:
"""simple docstring"""
return len(self.fairseq_ids_to_tokens )
def _a ( self : List[str] ) -> List[Any]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = {self.convert_ids_to_tokens(__SCREAMING_SNAKE_CASE ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def _a ( self : Union[str, Any] , __SCREAMING_SNAKE_CASE : str ) -> List[str]:
"""simple docstring"""
return self.sp_model.encode(__SCREAMING_SNAKE_CASE , out_type=__SCREAMING_SNAKE_CASE )
def _a ( self : Optional[int] , __SCREAMING_SNAKE_CASE : List[Any] ) -> Dict:
"""simple docstring"""
if token in self.fairseq_tokens_to_ids:
return self.fairseq_tokens_to_ids[token]
else:
return self.unk_token_id
def _a ( self : Union[str, Any] , __SCREAMING_SNAKE_CASE : Any ) -> Any:
"""simple docstring"""
return self.fairseq_ids_to_tokens[index]
def _a ( self : Union[str, Any] , __SCREAMING_SNAKE_CASE : List[str] ) -> int:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = ''''''.join(__SCREAMING_SNAKE_CASE ).replace(__SCREAMING_SNAKE_CASE , ''' ''' ).strip()
return out_string
def _a ( self : str , __SCREAMING_SNAKE_CASE : str , __SCREAMING_SNAKE_CASE : Optional[str] = None ) -> Tuple[str]:
"""simple docstring"""
if not os.path.isdir(__SCREAMING_SNAKE_CASE ):
logger.error(f"""Vocabulary path ({save_directory}) should be a directory""" )
return
__SCREAMING_SNAKE_CASE = os.path.join(
__SCREAMING_SNAKE_CASE , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )
__SCREAMING_SNAKE_CASE = os.path.join(
__SCREAMING_SNAKE_CASE , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''monolingual_vocab_file'''] , )
if os.path.abspath(self.vocab_file ) != os.path.abspath(__SCREAMING_SNAKE_CASE ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file , __SCREAMING_SNAKE_CASE )
elif not os.path.isfile(self.vocab_file ):
with open(__SCREAMING_SNAKE_CASE , '''wb''' ) as fi:
__SCREAMING_SNAKE_CASE = self.sp_model.serialized_model_proto()
fi.write(__SCREAMING_SNAKE_CASE )
if os.path.abspath(self.monolingual_vocab_file ) != os.path.abspath(
__SCREAMING_SNAKE_CASE ) and os.path.isfile(self.monolingual_vocab_file ):
copyfile(self.monolingual_vocab_file , __SCREAMING_SNAKE_CASE )
elif not os.path.isfile(self.monolingual_vocab_file ):
with open(__SCREAMING_SNAKE_CASE , '''w''' , encoding='''utf-8''' ) as fp:
for token in self.fairseq_tokens_to_ids:
if token not in self.all_special_tokens:
fp.write(f"""{str(__SCREAMING_SNAKE_CASE )} \n""" )
return out_vocab_file, out_monolingual_vocab_file
| 690 |
"""simple docstring"""
import os
import re
import unicodedata
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import is_torch_available, logging
if is_torch_available():
import torch
if TYPE_CHECKING:
from transformers.pipelines.conversational import Conversation
lowerCAmelCase__ =logging.get_logger(__name__)
lowerCAmelCase__ ={"vocab_file": "spiece.model"}
lowerCAmelCase__ ={
"vocab_file": {
"AI-Sweden/gpt-sw3-126m": "https://huggingface.co/AI-Sweden/gpt-sw3-126m/resolve/main/spiece.model",
"AI-Sweden/gpt-sw3-350m": "https://huggingface.co/AI-Sweden/gpt-sw3-350m/resolve/main/spiece.model",
"AI-Sweden/gpt-sw3-1.6b": "https://huggingface.co/AI-Sweden/gpt-sw3-1.6b/resolve/main/spiece.model",
"AI-Sweden/gpt-sw3-6.7b": "https://huggingface.co/AI-Sweden/gpt-sw3-6.7b/resolve/main/spiece.model",
"AI-Sweden/gpt-sw3-20b": "https://huggingface.co/AI-Sweden/gpt-sw3-20b/resolve/main/spiece.model",
}
}
lowerCAmelCase__ ={
"AI-Sweden/gpt-sw3-126m": 2_048,
"AI-Sweden/gpt-sw3-350m": 2_048,
"AI-Sweden/gpt-sw3-1.6b": 2_048,
"AI-Sweden/gpt-sw3-6.7b": 2_048,
"AI-Sweden/gpt-sw3-20b": 2_048,
}
class A__( __magic_name__ ):
lowerCAmelCase = VOCAB_FILES_NAMES
lowerCAmelCase = PRETRAINED_VOCAB_FILES_MAP
lowerCAmelCase = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowerCAmelCase = ['''input_ids''', '''attention_mask''']
def __init__( self : int , __SCREAMING_SNAKE_CASE : int , __SCREAMING_SNAKE_CASE : str=False , __SCREAMING_SNAKE_CASE : str=False , __SCREAMING_SNAKE_CASE : Optional[int]=False , __SCREAMING_SNAKE_CASE : Union[str, Any]=None , __SCREAMING_SNAKE_CASE : List[Any]=None , __SCREAMING_SNAKE_CASE : Any=None , __SCREAMING_SNAKE_CASE : List[Any]=None , __SCREAMING_SNAKE_CASE : Optional[Dict[str, Any]] = None , **__SCREAMING_SNAKE_CASE : Dict , ) -> None:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = {} if sp_model_kwargs is None else sp_model_kwargs
__SCREAMING_SNAKE_CASE = kwargs.get('''name_or_path''' )
if name_or_path is None:
logger.warning(
'''name_or_path not provided, will work for all GPTSw3 models except gpt-sw3-7b,'''
''' you are testing the model, this can safely be ignored''' )
__SCREAMING_SNAKE_CASE = '''None'''
# Default definitions for our 2 tokenizer versions, with None-checks to enable proper testing
__SCREAMING_SNAKE_CASE = '''<|endoftext|>''' if eos_token is None else eos_token
__SCREAMING_SNAKE_CASE = '''<unk>''' if unk_token is None else unk_token
if "gpt-sw3-7b" in name_or_path:
__SCREAMING_SNAKE_CASE = unk_token if pad_token is None else pad_token
__SCREAMING_SNAKE_CASE = eos_token if bos_token is None else bos_token
else:
__SCREAMING_SNAKE_CASE = '''<pad>''' if pad_token is None else pad_token
__SCREAMING_SNAKE_CASE = '''<s>''' if bos_token is None else bos_token
super().__init__(
do_lower_case=__SCREAMING_SNAKE_CASE , remove_space=__SCREAMING_SNAKE_CASE , keep_accents=__SCREAMING_SNAKE_CASE , bos_token=__SCREAMING_SNAKE_CASE , eos_token=__SCREAMING_SNAKE_CASE , unk_token=__SCREAMING_SNAKE_CASE , pad_token=__SCREAMING_SNAKE_CASE , sp_model_kwargs=self.sp_model_kwargs , **__SCREAMING_SNAKE_CASE , )
__SCREAMING_SNAKE_CASE = do_lower_case
__SCREAMING_SNAKE_CASE = remove_space
__SCREAMING_SNAKE_CASE = keep_accents
__SCREAMING_SNAKE_CASE = vocab_file
__SCREAMING_SNAKE_CASE = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(__SCREAMING_SNAKE_CASE )
# Used for whitespace normalization in input texts
# fmt : off
__SCREAMING_SNAKE_CASE = {''' ''', ''' ''', ''' ''', ''' ''', ''' ''', ''' ''', ''' ''', ''' ''', ''' ''', ''' ''', '''''', ''''''}
# fmt : on
# Regular expression to remove non-printing characters (e.g. some unicode control chars) in preprocessing
__SCREAMING_SNAKE_CASE = re.compile(
f"""[{"".join(map(__SCREAMING_SNAKE_CASE , list(range(0 , 9 ) ) + list(range(11 , 32 ) ) + list(range(1_27 , 1_60 ) ) + [1_60, 1_73, 82_03] ) )}]""" )
def __getstate__( self : List[str] ) -> Optional[Any]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = self.__dict__.copy()
__SCREAMING_SNAKE_CASE = None
return state
def __setstate__( self : int , __SCREAMING_SNAKE_CASE : Optional[int] ) -> int:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = d
# for backward compatibility
if not hasattr(self , '''sp_model_kwargs''' ):
__SCREAMING_SNAKE_CASE = {}
__SCREAMING_SNAKE_CASE = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.vocab_file )
@property
# Copied from transformers.models.albert.tokenization_albert.AlbertTokenizer.vocab_size
def _a ( self : Optional[Any] ) -> int:
"""simple docstring"""
return len(self.sp_model )
def _a ( self : List[str] , __SCREAMING_SNAKE_CASE : str ) -> str:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = self.non_printing_characters_re.sub('''''' , __SCREAMING_SNAKE_CASE )
# Normalize whitespaces
__SCREAMING_SNAKE_CASE = ''''''.join([char if char not in self.whitespaces else ''' ''' for char in text] )
# NFC Unicode normalization
__SCREAMING_SNAKE_CASE = unicodedata.normalize('''NFC''' , __SCREAMING_SNAKE_CASE )
return text
def _a ( self : Union[str, Any] , __SCREAMING_SNAKE_CASE : str , **__SCREAMING_SNAKE_CASE : Optional[int] ) -> List[str]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = self.preprocess_text(__SCREAMING_SNAKE_CASE )
return self.sp_model.encode(__SCREAMING_SNAKE_CASE , out_type=__SCREAMING_SNAKE_CASE )
def _a ( self : Tuple , __SCREAMING_SNAKE_CASE : str ) -> int:
"""simple docstring"""
return self.sp_model.PieceToId(__SCREAMING_SNAKE_CASE )
def _a ( self : List[str] , __SCREAMING_SNAKE_CASE : int ) -> str:
"""simple docstring"""
return self.sp_model.IdToPiece(__SCREAMING_SNAKE_CASE )
@staticmethod
def _a ( __SCREAMING_SNAKE_CASE : str ) -> str:
"""simple docstring"""
return out_string
def _a ( self : Union[str, Any] , __SCREAMING_SNAKE_CASE : List[str] ) -> str:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = []
__SCREAMING_SNAKE_CASE = ''''''
__SCREAMING_SNAKE_CASE = False
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
# TODO: Check if this is needed, as it ensures that decode(encode(doc)) != doc by adding extra whitespace in the decoded document
if not prev_is_special:
out_string += " "
out_string += self.sp_model.decode(__SCREAMING_SNAKE_CASE ) + token
__SCREAMING_SNAKE_CASE = True
__SCREAMING_SNAKE_CASE = []
else:
current_sub_tokens.append(__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = False
out_string += self.sp_model.decode(__SCREAMING_SNAKE_CASE )
return out_string
def _a ( self : Union[str, Any] ) -> Dict[str, int]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = {self.convert_ids_to_tokens(__SCREAMING_SNAKE_CASE ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def _a ( self : List[Any] , __SCREAMING_SNAKE_CASE : str , __SCREAMING_SNAKE_CASE : Optional[str] = None ) -> Tuple[str]:
"""simple docstring"""
if not os.path.isdir(__SCREAMING_SNAKE_CASE ):
logger.error(f"""Vocabulary path ({save_directory}) should be a directory""" )
return
__SCREAMING_SNAKE_CASE = os.path.join(
__SCREAMING_SNAKE_CASE , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(__SCREAMING_SNAKE_CASE ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file , __SCREAMING_SNAKE_CASE )
elif not os.path.isfile(self.vocab_file ):
with open(__SCREAMING_SNAKE_CASE , '''wb''' ) as fi:
__SCREAMING_SNAKE_CASE = self.sp_model.serialized_model_proto()
fi.write(__SCREAMING_SNAKE_CASE )
return (out_vocab_file,)
def _a ( self : Optional[Any] , __SCREAMING_SNAKE_CASE : Union[str, List[str]] , __SCREAMING_SNAKE_CASE : Union[str, bool] = False ) -> Union[List[int], List[List[int]], "torch.Tensor"]:
"""simple docstring"""
if isinstance(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
__SCREAMING_SNAKE_CASE = self.preprocess_text(__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = self.sp_model.encode(__SCREAMING_SNAKE_CASE )
else:
__SCREAMING_SNAKE_CASE = [self.preprocess_text(__SCREAMING_SNAKE_CASE ) for t in text]
__SCREAMING_SNAKE_CASE = self.sp_model.encode(__SCREAMING_SNAKE_CASE )
if return_tensors is True or return_tensors == "pt":
__SCREAMING_SNAKE_CASE = torch.tensor(__SCREAMING_SNAKE_CASE )
return token_ids
def _a ( self : Any , __SCREAMING_SNAKE_CASE : Union[int, List[int]] ) -> str:
"""simple docstring"""
return self.sp_model.decode(__SCREAMING_SNAKE_CASE )
def _a ( self : List[str] , __SCREAMING_SNAKE_CASE : "Conversation" ) -> List[int]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = [f"""User: {text}""" if is_user else f"""Bot: {text}""" for is_user, text in conversation.iter_texts()]
__SCREAMING_SNAKE_CASE = (
f"""{self.eos_token}{self.bos_token}""" + f"""{self.bos_token}""".join(__SCREAMING_SNAKE_CASE ) + f"""{self.bos_token}Bot:"""
)
return self.encode(text=__SCREAMING_SNAKE_CASE )
| 690 | 1 |
"""simple docstring"""
from __future__ import annotations
import inspect
import unittest
from typing import List, Tuple
from transformers import RegNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST, TFRegNetForImageClassification, TFRegNetModel
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class A__:
def __init__( self : Optional[int] , __SCREAMING_SNAKE_CASE : Dict , __SCREAMING_SNAKE_CASE : List[str]=3 , __SCREAMING_SNAKE_CASE : Dict=32 , __SCREAMING_SNAKE_CASE : Optional[Any]=3 , __SCREAMING_SNAKE_CASE : Union[str, Any]=10 , __SCREAMING_SNAKE_CASE : str=[10, 20, 30, 40] , __SCREAMING_SNAKE_CASE : Optional[int]=[1, 1, 2, 1] , __SCREAMING_SNAKE_CASE : int=True , __SCREAMING_SNAKE_CASE : int=True , __SCREAMING_SNAKE_CASE : Optional[Any]="relu" , __SCREAMING_SNAKE_CASE : List[str]=3 , __SCREAMING_SNAKE_CASE : Union[str, Any]=None , ) -> Any:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = parent
__SCREAMING_SNAKE_CASE = batch_size
__SCREAMING_SNAKE_CASE = image_size
__SCREAMING_SNAKE_CASE = num_channels
__SCREAMING_SNAKE_CASE = embeddings_size
__SCREAMING_SNAKE_CASE = hidden_sizes
__SCREAMING_SNAKE_CASE = depths
__SCREAMING_SNAKE_CASE = is_training
__SCREAMING_SNAKE_CASE = use_labels
__SCREAMING_SNAKE_CASE = hidden_act
__SCREAMING_SNAKE_CASE = num_labels
__SCREAMING_SNAKE_CASE = scope
__SCREAMING_SNAKE_CASE = len(__SCREAMING_SNAKE_CASE )
def _a ( self : List[Any] ) -> List[str]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
__SCREAMING_SNAKE_CASE = None
if self.use_labels:
__SCREAMING_SNAKE_CASE = ids_tensor([self.batch_size] , self.num_labels )
__SCREAMING_SNAKE_CASE = self.get_config()
return config, pixel_values, labels
def _a ( self : Union[str, Any] ) -> List[str]:
"""simple docstring"""
return RegNetConfig(
num_channels=self.num_channels , embeddings_size=self.embeddings_size , hidden_sizes=self.hidden_sizes , depths=self.depths , hidden_act=self.hidden_act , num_labels=self.num_labels , )
def _a ( self : str , __SCREAMING_SNAKE_CASE : Any , __SCREAMING_SNAKE_CASE : str , __SCREAMING_SNAKE_CASE : Optional[Any] ) -> Dict:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = TFRegNetModel(config=__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = model(__SCREAMING_SNAKE_CASE , training=__SCREAMING_SNAKE_CASE )
# expected last hidden states: B, C, H // 32, W // 32
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32) , )
def _a ( self : int , __SCREAMING_SNAKE_CASE : Union[str, Any] , __SCREAMING_SNAKE_CASE : str , __SCREAMING_SNAKE_CASE : Optional[Any] ) -> Union[str, Any]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = self.num_labels
__SCREAMING_SNAKE_CASE = TFRegNetForImageClassification(__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = model(__SCREAMING_SNAKE_CASE , labels=__SCREAMING_SNAKE_CASE , training=__SCREAMING_SNAKE_CASE )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def _a ( self : Optional[Any] ) -> Any:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = self.prepare_config_and_inputs()
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = config_and_inputs
__SCREAMING_SNAKE_CASE = {'''pixel_values''': pixel_values}
return config, inputs_dict
@require_tf
class A__( __magic_name__ , __magic_name__ , unittest.TestCase ):
lowerCAmelCase = (TFRegNetModel, TFRegNetForImageClassification) if is_tf_available() else ()
lowerCAmelCase = (
{'''feature-extraction''': TFRegNetModel, '''image-classification''': TFRegNetForImageClassification}
if is_tf_available()
else {}
)
lowerCAmelCase = False
lowerCAmelCase = False
lowerCAmelCase = False
lowerCAmelCase = False
lowerCAmelCase = False
def _a ( self : Optional[Any] ) -> Union[str, Any]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = TFRegNetModelTester(self )
__SCREAMING_SNAKE_CASE = ConfigTester(self , config_class=__SCREAMING_SNAKE_CASE , has_text_modality=__SCREAMING_SNAKE_CASE )
def _a ( self : Tuple ) -> Optional[Any]:
"""simple docstring"""
return
@unittest.skip(reason='''RegNet does not use inputs_embeds''' )
def _a ( self : Any ) -> Optional[Any]:
"""simple docstring"""
pass
@unittest.skipIf(
not is_tf_available() or len(tf.config.list_physical_devices('''GPU''' ) ) == 0 , reason='''TF does not support backprop for grouped convolutions on CPU.''' , )
@slow
def _a ( self : Dict ) -> List[Any]:
"""simple docstring"""
super().test_keras_fit()
@unittest.skip(reason='''RegNet does not support input and output embeddings''' )
def _a ( self : Dict ) -> Union[str, Any]:
"""simple docstring"""
pass
def _a ( self : List[Any] ) -> List[Any]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__SCREAMING_SNAKE_CASE = model_class(__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = inspect.signature(model.call )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
__SCREAMING_SNAKE_CASE = [*signature.parameters.keys()]
__SCREAMING_SNAKE_CASE = ['''pixel_values''']
self.assertListEqual(arg_names[:1] , __SCREAMING_SNAKE_CASE )
def _a ( self : Any ) -> Union[str, Any]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__SCREAMING_SNAKE_CASE )
def _a ( self : List[str] ) -> Tuple:
"""simple docstring"""
def check_hidden_states_output(__SCREAMING_SNAKE_CASE : Optional[Any] , __SCREAMING_SNAKE_CASE : Optional[Any] , __SCREAMING_SNAKE_CASE : Any ):
__SCREAMING_SNAKE_CASE = model_class(__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = model(**self._prepare_for_class(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) , training=__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
__SCREAMING_SNAKE_CASE = self.model_tester.num_stages
self.assertEqual(len(__SCREAMING_SNAKE_CASE ) , expected_num_stages + 1 )
# RegNet's feature maps are of shape (batch_size, num_channels, height, width)
self.assertListEqual(
list(hidden_states[0].shape[-2:] ) , [self.model_tester.image_size // 2, self.model_tester.image_size // 2] , )
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs_for_common()
__SCREAMING_SNAKE_CASE = ['''basic''', '''bottleneck''']
for model_class in self.all_model_classes:
for layer_type in layers_type:
__SCREAMING_SNAKE_CASE = layer_type
__SCREAMING_SNAKE_CASE = True
check_hidden_states_output(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
__SCREAMING_SNAKE_CASE = True
check_hidden_states_output(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
def _a ( self : Union[str, Any] ) -> List[str]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs_for_common()
def check_equivalence(__SCREAMING_SNAKE_CASE : Tuple , __SCREAMING_SNAKE_CASE : Optional[Any] , __SCREAMING_SNAKE_CASE : Tuple , __SCREAMING_SNAKE_CASE : Union[str, Any]={} ):
__SCREAMING_SNAKE_CASE = model(__SCREAMING_SNAKE_CASE , return_dict=__SCREAMING_SNAKE_CASE , **__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = model(__SCREAMING_SNAKE_CASE , return_dict=__SCREAMING_SNAKE_CASE , **__SCREAMING_SNAKE_CASE ).to_tuple()
def recursive_check(__SCREAMING_SNAKE_CASE : int , __SCREAMING_SNAKE_CASE : Dict ):
if isinstance(__SCREAMING_SNAKE_CASE , (List, Tuple) ):
for tuple_iterable_value, dict_iterable_value in zip(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
recursive_check(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
elif tuple_object is None:
return
else:
self.assertTrue(
all(tf.equal(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) ) , msg=(
'''Tuple and dict output are not equal. Difference:'''
f""" {tf.math.reduce_max(tf.abs(tuple_object - dict_object ) )}"""
) , )
recursive_check(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
for model_class in self.all_model_classes:
__SCREAMING_SNAKE_CASE = model_class(__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = self._prepare_for_class(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = self._prepare_for_class(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
check_equivalence(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = self._prepare_for_class(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , return_labels=__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = self._prepare_for_class(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , return_labels=__SCREAMING_SNAKE_CASE )
check_equivalence(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = self._prepare_for_class(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = self._prepare_for_class(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
check_equivalence(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , {'''output_hidden_states''': True} )
__SCREAMING_SNAKE_CASE = self._prepare_for_class(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , return_labels=__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = self._prepare_for_class(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , return_labels=__SCREAMING_SNAKE_CASE )
check_equivalence(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , {'''output_hidden_states''': True} )
def _a ( self : str ) -> str:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*__SCREAMING_SNAKE_CASE )
@slow
def _a ( self : Union[str, Any] ) -> Union[str, Any]:
"""simple docstring"""
for model_name in TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__SCREAMING_SNAKE_CASE = TFRegNetModel.from_pretrained(__SCREAMING_SNAKE_CASE )
self.assertIsNotNone(__SCREAMING_SNAKE_CASE )
def _a ( ) -> Dict:
__SCREAMING_SNAKE_CASE = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
return image
@require_tf
@require_vision
class A__( unittest.TestCase ):
@cached_property
def _a ( self : List[Any] ) -> str:
"""simple docstring"""
return (
AutoImageProcessor.from_pretrained(TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0] )
if is_vision_available()
else None
)
@slow
def _a ( self : List[str] ) -> Any:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = TFRegNetForImageClassification.from_pretrained(TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0] )
__SCREAMING_SNAKE_CASE = self.default_image_processor
__SCREAMING_SNAKE_CASE = prepare_img()
__SCREAMING_SNAKE_CASE = image_processor(images=__SCREAMING_SNAKE_CASE , return_tensors='''tf''' )
# forward pass
__SCREAMING_SNAKE_CASE = model(**__SCREAMING_SNAKE_CASE , training=__SCREAMING_SNAKE_CASE )
# verify the logits
__SCREAMING_SNAKE_CASE = tf.TensorShape((1, 10_00) )
self.assertEqual(outputs.logits.shape , __SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = tf.constant([-0.41_80, -1.50_51, -3.48_36] )
tf.debugging.assert_near(outputs.logits[0, :3] , __SCREAMING_SNAKE_CASE , atol=1E-4 )
| 690 |
"""simple docstring"""
from __future__ import annotations
import json
import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent
lowerCAmelCase__ ={"UserAgent": UserAgent().random}
def _a ( UpperCAmelCase__ ) -> dict:
__SCREAMING_SNAKE_CASE = script.contents[0]
__SCREAMING_SNAKE_CASE = json.loads(data[data.find('''{"config"''' ) : -1] )
return info["entry_data"]["ProfilePage"][0]["graphql"]["user"]
class A__:
def __init__( self : Dict , __SCREAMING_SNAKE_CASE : int ) -> Optional[Any]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = f"""https://www.instagram.com/{username}/"""
__SCREAMING_SNAKE_CASE = self.get_json()
def _a ( self : List[Any] ) -> dict:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = requests.get(self.url , headers=__SCREAMING_SNAKE_CASE ).text
__SCREAMING_SNAKE_CASE = BeautifulSoup(__SCREAMING_SNAKE_CASE , '''html.parser''' ).find_all('''script''' )
try:
return extract_user_profile(scripts[4] )
except (json.decoder.JSONDecodeError, KeyError):
return extract_user_profile(scripts[3] )
def __repr__( self : Tuple ) -> str:
"""simple docstring"""
return f"""{self.__class__.__name__}('{self.username}')"""
def __str__( self : Optional[int] ) -> str:
"""simple docstring"""
return f"""{self.fullname} ({self.username}) is {self.biography}"""
@property
def _a ( self : Tuple ) -> str:
"""simple docstring"""
return self.user_data["username"]
@property
def _a ( self : List[Any] ) -> str:
"""simple docstring"""
return self.user_data["full_name"]
@property
def _a ( self : Optional[Any] ) -> str:
"""simple docstring"""
return self.user_data["biography"]
@property
def _a ( self : List[str] ) -> str:
"""simple docstring"""
return self.user_data["business_email"]
@property
def _a ( self : Any ) -> str:
"""simple docstring"""
return self.user_data["external_url"]
@property
def _a ( self : Any ) -> int:
"""simple docstring"""
return self.user_data["edge_followed_by"]["count"]
@property
def _a ( self : Dict ) -> int:
"""simple docstring"""
return self.user_data["edge_follow"]["count"]
@property
def _a ( self : str ) -> int:
"""simple docstring"""
return self.user_data["edge_owner_to_timeline_media"]["count"]
@property
def _a ( self : Union[str, Any] ) -> str:
"""simple docstring"""
return self.user_data["profile_pic_url_hd"]
@property
def _a ( self : Tuple ) -> bool:
"""simple docstring"""
return self.user_data["is_verified"]
@property
def _a ( self : Union[str, Any] ) -> bool:
"""simple docstring"""
return self.user_data["is_private"]
def _a ( UpperCAmelCase__ = "github" ) -> None:
import os
if os.environ.get('''CI''' ):
return # test failing on GitHub Actions
__SCREAMING_SNAKE_CASE = InstagramUser(UpperCAmelCase__ )
assert instagram_user.user_data
assert isinstance(instagram_user.user_data , UpperCAmelCase__ )
assert instagram_user.username == username
if username != "github":
return
assert instagram_user.fullname == "GitHub"
assert instagram_user.biography == "Built for developers."
assert instagram_user.number_of_posts > 1_50
assert instagram_user.number_of_followers > 12_00_00
assert instagram_user.number_of_followings > 15
assert instagram_user.email == "[email protected]"
assert instagram_user.website == "https://github.com/readme"
assert instagram_user.profile_picture_url.startswith('''https://instagram.''' )
assert instagram_user.is_verified is True
assert instagram_user.is_private is False
if __name__ == "__main__":
import doctest
doctest.testmod()
lowerCAmelCase__ =InstagramUser("github")
print(instagram_user)
print(F'''{instagram_user.number_of_posts = }''')
print(F'''{instagram_user.number_of_followers = }''')
print(F'''{instagram_user.number_of_followings = }''')
print(F'''{instagram_user.email = }''')
print(F'''{instagram_user.website = }''')
print(F'''{instagram_user.profile_picture_url = }''')
print(F'''{instagram_user.is_verified = }''')
print(F'''{instagram_user.is_private = }''')
| 690 | 1 |
"""simple docstring"""
import pickle
import unittest
import torch
from accelerate import Accelerator
from accelerate.state import AcceleratorState
from accelerate.test_utils import require_cpu
@require_cpu
class A__( unittest.TestCase ):
def _a ( self : int ) -> List[Any]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = torch.nn.Linear(10 , 10 )
__SCREAMING_SNAKE_CASE = torch.optim.SGD(model.parameters() , 0.1 )
__SCREAMING_SNAKE_CASE = Accelerator()
__SCREAMING_SNAKE_CASE = accelerator.prepare(__SCREAMING_SNAKE_CASE )
try:
pickle.loads(pickle.dumps(__SCREAMING_SNAKE_CASE ) )
except Exception as e:
self.fail(f"""Accelerated optimizer pickling failed with {e}""" )
AcceleratorState._reset_state()
| 690 |
"""simple docstring"""
from sklearn.metrics import recall_score
import datasets
lowerCAmelCase__ ="\nRecall is the fraction of the positive examples that were correctly labeled by the model as positive. It can be computed with the equation:\nRecall = TP / (TP + FN)\nWhere TP is the true positives and FN is the false negatives.\n"
lowerCAmelCase__ ="\nArgs:\n- **predictions** (`list` of `int`): The predicted labels.\n- **references** (`list` of `int`): The ground truth labels.\n- **labels** (`list` of `int`): The set of labels to include when `average` is not set to `binary`, and their order when average is `None`. Labels present in the data can be excluded in this input, for example to calculate a multiclass average ignoring a majority negative class, while labels not present in the data will result in 0 components in a macro average. For multilabel targets, labels are column indices. By default, all labels in y_true and y_pred are used in sorted order. Defaults to None.\n- **pos_label** (`int`): The class label to use as the 'positive class' when calculating the recall. Defaults to `1`.\n- **average** (`string`): This parameter is required for multiclass/multilabel targets. If None, the scores for each class are returned. Otherwise, this determines the type of averaging performed on the data. Defaults to `'binary'`.\n - `'binary'`: Only report results for the class specified by `pos_label`. This is applicable only if the target labels and predictions are binary.\n - `'micro'`: Calculate metrics globally by counting the total true positives, false negatives, and false positives.\n - `'macro'`: Calculate metrics for each label, and find their unweighted mean. This does not take label imbalance into account.\n - `'weighted'`: Calculate metrics for each label, and find their average weighted by support (the number of true instances for each label). This alters `'macro'` to account for label imbalance. Note that it can result in an F-score that is not between precision and recall.\n - `'samples'`: Calculate metrics for each instance, and find their average (only meaningful for multilabel classification).\n- **sample_weight** (`list` of `float`): Sample weights Defaults to `None`.\n- **zero_division** (): Sets the value to return when there is a zero division. Defaults to .\n - `'warn'`: If there is a zero division, the return value is `0`, but warnings are also raised.\n - `0`: If there is a zero division, the return value is `0`.\n - `1`: If there is a zero division, the return value is `1`.\n\nReturns:\n- **recall** (`float`, or `array` of `float`): Either the general recall score, or the recall scores for individual classes, depending on the values input to `labels` and `average`. Minimum possible value is 0. Maximum possible value is 1. A higher recall means that more of the positive examples have been labeled correctly. Therefore, a higher recall is generally considered better.\n\nExamples:\n\n Example 1-A simple example with some errors\n >>> recall_metric = datasets.load_metric('recall')\n >>> results = recall_metric.compute(references=[0, 0, 1, 1, 1], predictions=[0, 1, 0, 1, 1])\n >>> print(results)\n {'recall': 0.6666666666666666}\n\n Example 2-The same example as Example 1, but with `pos_label=0` instead of the default `pos_label=1`.\n >>> recall_metric = datasets.load_metric('recall')\n >>> results = recall_metric.compute(references=[0, 0, 1, 1, 1], predictions=[0, 1, 0, 1, 1], pos_label=0)\n >>> print(results)\n {'recall': 0.5}\n\n Example 3-The same example as Example 1, but with `sample_weight` included.\n >>> recall_metric = datasets.load_metric('recall')\n >>> sample_weight = [0.9, 0.2, 0.9, 0.3, 0.8]\n >>> results = recall_metric.compute(references=[0, 0, 1, 1, 1], predictions=[0, 1, 0, 1, 1], sample_weight=sample_weight)\n >>> print(results)\n {'recall': 0.55}\n\n Example 4-A multiclass example, using different averages.\n >>> recall_metric = datasets.load_metric('recall')\n >>> predictions = [0, 2, 1, 0, 0, 1]\n >>> references = [0, 1, 2, 0, 1, 2]\n >>> results = recall_metric.compute(predictions=predictions, references=references, average='macro')\n >>> print(results)\n {'recall': 0.3333333333333333}\n >>> results = recall_metric.compute(predictions=predictions, references=references, average='micro')\n >>> print(results)\n {'recall': 0.3333333333333333}\n >>> results = recall_metric.compute(predictions=predictions, references=references, average='weighted')\n >>> print(results)\n {'recall': 0.3333333333333333}\n >>> results = recall_metric.compute(predictions=predictions, references=references, average=None)\n >>> print(results)\n {'recall': array([1., 0., 0.])}\n"
lowerCAmelCase__ ="\n@article{scikit-learn, title={Scikit-learn: Machine Learning in {P}ython}, author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V. and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P. and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.}, journal={Journal of Machine Learning Research}, volume={12}, pages={2825--2830}, year={2011}\n"
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class A__( datasets.Metric ):
def _a ( self : Any ) -> int:
"""simple docstring"""
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
'''predictions''': datasets.Sequence(datasets.Value('''int32''' ) ),
'''references''': datasets.Sequence(datasets.Value('''int32''' ) ),
}
if self.config_name == '''multilabel'''
else {
'''predictions''': datasets.Value('''int32''' ),
'''references''': datasets.Value('''int32''' ),
} ) , reference_urls=['''https://scikit-learn.org/stable/modules/generated/sklearn.metrics.recall_score.html'''] , )
def _a ( self : Tuple , __SCREAMING_SNAKE_CASE : Union[str, Any] , __SCREAMING_SNAKE_CASE : Optional[Any] , __SCREAMING_SNAKE_CASE : Union[str, Any]=None , __SCREAMING_SNAKE_CASE : Optional[int]=1 , __SCREAMING_SNAKE_CASE : Optional[Any]="binary" , __SCREAMING_SNAKE_CASE : List[Any]=None , __SCREAMING_SNAKE_CASE : List[Any]="warn" , ) -> Dict:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = recall_score(
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , labels=__SCREAMING_SNAKE_CASE , pos_label=__SCREAMING_SNAKE_CASE , average=__SCREAMING_SNAKE_CASE , sample_weight=__SCREAMING_SNAKE_CASE , zero_division=__SCREAMING_SNAKE_CASE , )
return {"recall": float(__SCREAMING_SNAKE_CASE ) if score.size == 1 else score}
| 690 | 1 |
"""simple docstring"""
from __future__ import annotations
import typing
from collections.abc import Iterable
import numpy as np
lowerCAmelCase__ =typing.Union[Iterable[float], Iterable[int], np.ndarray] # noqa: UP007
lowerCAmelCase__ =typing.Union[np.floataa, int, float] # noqa: UP007
def _a ( UpperCAmelCase__ , UpperCAmelCase__ ) -> VectorOut:
return np.sqrt(np.sum((np.asarray(UpperCAmelCase__ ) - np.asarray(UpperCAmelCase__ )) ** 2 ) )
def _a ( UpperCAmelCase__ , UpperCAmelCase__ ) -> VectorOut:
return sum((va - va) ** 2 for va, va in zip(UpperCAmelCase__ , UpperCAmelCase__ ) ) ** (1 / 2)
if __name__ == "__main__":
def _a ( ) -> None:
from timeit import timeit
print('''Without Numpy''' )
print(
timeit(
'''euclidean_distance_no_np([1, 2, 3], [4, 5, 6])''' , number=1_00_00 , globals=globals() , ) )
print('''With Numpy''' )
print(
timeit(
'''euclidean_distance([1, 2, 3], [4, 5, 6])''' , number=1_00_00 , globals=globals() , ) )
benchmark()
| 690 |
"""simple docstring"""
def _a ( UpperCAmelCase__ = 10**9 ) -> int:
__SCREAMING_SNAKE_CASE = 1
__SCREAMING_SNAKE_CASE = 2
__SCREAMING_SNAKE_CASE = 0
__SCREAMING_SNAKE_CASE = 0
__SCREAMING_SNAKE_CASE = 0
while perimeter <= max_perimeter:
perimeters_sum += perimeter
prev_value += 2 * value
value += prev_value
__SCREAMING_SNAKE_CASE = 2 * value + 2 if i % 2 == 0 else 2 * value - 2
i += 1
return perimeters_sum
if __name__ == "__main__":
print(F'''{solution() = }''')
| 690 | 1 |
"""simple docstring"""
import string
def _a ( UpperCAmelCase__ ) -> None:
for key in range(len(string.ascii_uppercase ) ):
__SCREAMING_SNAKE_CASE = ''''''
for symbol in message:
if symbol in string.ascii_uppercase:
__SCREAMING_SNAKE_CASE = string.ascii_uppercase.find(UpperCAmelCase__ )
__SCREAMING_SNAKE_CASE = num - key
if num < 0:
__SCREAMING_SNAKE_CASE = num + len(string.ascii_uppercase )
__SCREAMING_SNAKE_CASE = translated + string.ascii_uppercase[num]
else:
__SCREAMING_SNAKE_CASE = translated + symbol
print(f"""Decryption using Key #{key}: {translated}""" )
def _a ( ) -> None:
__SCREAMING_SNAKE_CASE = input('''Encrypted message: ''' )
__SCREAMING_SNAKE_CASE = message.upper()
decrypt(UpperCAmelCase__ )
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| 690 |
"""simple docstring"""
import pandas as pd
from matplotlib import pyplot as plt
from sklearn.linear_model import LinearRegression
# Splitting the dataset into the Training set and Test set
from sklearn.model_selection import train_test_split
# Fitting Polynomial Regression to the dataset
from sklearn.preprocessing import PolynomialFeatures
# Importing the dataset
lowerCAmelCase__ =pd.read_csv(
"https://s3.us-west-2.amazonaws.com/public.gamelab.fun/dataset/"
"position_salaries.csv"
)
lowerCAmelCase__ =dataset.iloc[:, 1:2].values
lowerCAmelCase__ =dataset.iloc[:, 2].values
lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ =train_test_split(X, y, test_size=0.2, random_state=0)
lowerCAmelCase__ =PolynomialFeatures(degree=4)
lowerCAmelCase__ =poly_reg.fit_transform(X)
lowerCAmelCase__ =LinearRegression()
pol_reg.fit(X_poly, y)
def _a ( ) -> List[Any]:
plt.scatter(UpperCAmelCase__ , UpperCAmelCase__ , color='''red''' )
plt.plot(UpperCAmelCase__ , pol_reg.predict(poly_reg.fit_transform(UpperCAmelCase__ ) ) , color='''blue''' )
plt.title('''Truth or Bluff (Linear Regression)''' )
plt.xlabel('''Position level''' )
plt.ylabel('''Salary''' )
plt.show()
if __name__ == "__main__":
viz_polymonial()
# Predicting a new result with Polymonial Regression
pol_reg.predict(poly_reg.fit_transform([[5.5]]))
# output should be 132148.43750003
| 690 | 1 |
"""simple docstring"""
# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny model through reduction of a normal pre-trained model, but keeping the
# full vocab, merges file, and thus also resulting in a larger model due to a large vocab size.
# This gives ~3MB in total for all files.
#
# If you want a 50 times smaller than this see `fsmt-make-super-tiny-model.py`, which is slightly more complicated
#
#
# It will be used then as "stas/tiny-wmt19-en-de"
# Build
from transformers import FSMTTokenizer, FSMTConfig, FSMTForConditionalGeneration
lowerCAmelCase__ ="facebook/wmt19-en-de"
lowerCAmelCase__ =FSMTTokenizer.from_pretrained(mname)
# get the correct vocab sizes, etc. from the master model
lowerCAmelCase__ =FSMTConfig.from_pretrained(mname)
config.update(
dict(
d_model=4,
encoder_layers=1,
decoder_layers=1,
encoder_ffn_dim=4,
decoder_ffn_dim=4,
encoder_attention_heads=1,
decoder_attention_heads=1,
)
)
lowerCAmelCase__ =FSMTForConditionalGeneration(config)
print(F'''num of params {tiny_model.num_parameters()}''')
# Test
lowerCAmelCase__ =tokenizer(["Making tiny model"], return_tensors="pt")
lowerCAmelCase__ =tiny_model(**batch)
print("test output:", len(outputs.logits[0]))
# Save
lowerCAmelCase__ ="tiny-wmt19-en-de"
tiny_model.half() # makes it smaller
tiny_model.save_pretrained(mname_tiny)
tokenizer.save_pretrained(mname_tiny)
print(F'''Generated {mname_tiny}''')
# Upload
# transformers-cli upload tiny-wmt19-en-de
| 690 |
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import AutoTokenizer, MBartConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import TFAutoModelForSeqaSeqLM, TFMBartForConditionalGeneration, TFMBartModel
@require_tf
class A__:
lowerCAmelCase = MBartConfig
lowerCAmelCase = {}
lowerCAmelCase = '''gelu'''
def __init__( self : List[str] , __SCREAMING_SNAKE_CASE : Optional[int] , __SCREAMING_SNAKE_CASE : Tuple=13 , __SCREAMING_SNAKE_CASE : Dict=7 , __SCREAMING_SNAKE_CASE : List[Any]=True , __SCREAMING_SNAKE_CASE : Any=False , __SCREAMING_SNAKE_CASE : Union[str, Any]=99 , __SCREAMING_SNAKE_CASE : Optional[Any]=32 , __SCREAMING_SNAKE_CASE : Optional[int]=2 , __SCREAMING_SNAKE_CASE : Any=4 , __SCREAMING_SNAKE_CASE : List[str]=37 , __SCREAMING_SNAKE_CASE : Optional[Any]=0.1 , __SCREAMING_SNAKE_CASE : Dict=0.1 , __SCREAMING_SNAKE_CASE : Any=20 , __SCREAMING_SNAKE_CASE : List[Any]=2 , __SCREAMING_SNAKE_CASE : Optional[int]=1 , __SCREAMING_SNAKE_CASE : Optional[Any]=0 , ) -> Optional[int]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = parent
__SCREAMING_SNAKE_CASE = batch_size
__SCREAMING_SNAKE_CASE = seq_length
__SCREAMING_SNAKE_CASE = is_training
__SCREAMING_SNAKE_CASE = use_labels
__SCREAMING_SNAKE_CASE = vocab_size
__SCREAMING_SNAKE_CASE = hidden_size
__SCREAMING_SNAKE_CASE = num_hidden_layers
__SCREAMING_SNAKE_CASE = num_attention_heads
__SCREAMING_SNAKE_CASE = intermediate_size
__SCREAMING_SNAKE_CASE = hidden_dropout_prob
__SCREAMING_SNAKE_CASE = attention_probs_dropout_prob
__SCREAMING_SNAKE_CASE = max_position_embeddings
__SCREAMING_SNAKE_CASE = eos_token_id
__SCREAMING_SNAKE_CASE = pad_token_id
__SCREAMING_SNAKE_CASE = bos_token_id
def _a ( self : List[str] ) -> Optional[int]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = ids_tensor([self.batch_size, self.seq_length - 1] , self.vocab_size )
__SCREAMING_SNAKE_CASE = tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size ) , 1 )
__SCREAMING_SNAKE_CASE = tf.concat([input_ids, eos_tensor] , axis=1 )
__SCREAMING_SNAKE_CASE = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
__SCREAMING_SNAKE_CASE = self.config_cls(
vocab_size=self.vocab_size , d_model=self.hidden_size , encoder_layers=self.num_hidden_layers , decoder_layers=self.num_hidden_layers , encoder_attention_heads=self.num_attention_heads , decoder_attention_heads=self.num_attention_heads , encoder_ffn_dim=self.intermediate_size , decoder_ffn_dim=self.intermediate_size , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , eos_token_ids=[2] , bos_token_id=self.bos_token_id , pad_token_id=self.pad_token_id , decoder_start_token_id=self.pad_token_id , **self.config_updates , )
__SCREAMING_SNAKE_CASE = prepare_mbart_inputs_dict(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
return config, inputs_dict
def _a ( self : Tuple , __SCREAMING_SNAKE_CASE : str , __SCREAMING_SNAKE_CASE : Optional[int] ) -> Optional[Any]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = TFMBartModel(config=__SCREAMING_SNAKE_CASE ).get_decoder()
__SCREAMING_SNAKE_CASE = inputs_dict['''input_ids''']
__SCREAMING_SNAKE_CASE = input_ids[:1, :]
__SCREAMING_SNAKE_CASE = inputs_dict['''attention_mask'''][:1, :]
__SCREAMING_SNAKE_CASE = inputs_dict['''head_mask''']
__SCREAMING_SNAKE_CASE = 1
# first forward pass
__SCREAMING_SNAKE_CASE = model(__SCREAMING_SNAKE_CASE , attention_mask=__SCREAMING_SNAKE_CASE , head_mask=__SCREAMING_SNAKE_CASE , use_cache=__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = outputs.to_tuple()
__SCREAMING_SNAKE_CASE = past_key_values[1]
def _a ( UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__=None , UpperCAmelCase__=None , UpperCAmelCase__=None , UpperCAmelCase__=None , UpperCAmelCase__=None , ) -> Optional[int]:
if attention_mask is None:
__SCREAMING_SNAKE_CASE = tf.cast(tf.math.not_equal(UpperCAmelCase__ , config.pad_token_id ) , tf.inta )
if decoder_attention_mask is None:
__SCREAMING_SNAKE_CASE = tf.concat(
[
tf.ones(decoder_input_ids[:, :1].shape , dtype=tf.inta ),
tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:] , config.pad_token_id ) , tf.inta ),
] , axis=-1 , )
if head_mask is None:
__SCREAMING_SNAKE_CASE = tf.ones((config.encoder_layers, config.encoder_attention_heads) )
if decoder_head_mask is None:
__SCREAMING_SNAKE_CASE = tf.ones((config.decoder_layers, config.decoder_attention_heads) )
if cross_attn_head_mask is None:
__SCREAMING_SNAKE_CASE = tf.ones((config.decoder_layers, config.decoder_attention_heads) )
return {
"input_ids": input_ids,
"decoder_input_ids": decoder_input_ids,
"attention_mask": attention_mask,
"decoder_attention_mask": decoder_attention_mask,
"head_mask": head_mask,
"decoder_head_mask": decoder_head_mask,
"cross_attn_head_mask": cross_attn_head_mask,
}
@require_tf
class A__( __magic_name__ , __magic_name__ , unittest.TestCase ):
lowerCAmelCase = (TFMBartForConditionalGeneration, TFMBartModel) if is_tf_available() else ()
lowerCAmelCase = (TFMBartForConditionalGeneration,) if is_tf_available() else ()
lowerCAmelCase = (
{
'''conversational''': TFMBartForConditionalGeneration,
'''feature-extraction''': TFMBartModel,
'''summarization''': TFMBartForConditionalGeneration,
'''text2text-generation''': TFMBartForConditionalGeneration,
'''translation''': TFMBartForConditionalGeneration,
}
if is_tf_available()
else {}
)
lowerCAmelCase = True
lowerCAmelCase = False
lowerCAmelCase = False
def _a ( self : Dict , __SCREAMING_SNAKE_CASE : str , __SCREAMING_SNAKE_CASE : List[str] , __SCREAMING_SNAKE_CASE : Dict , __SCREAMING_SNAKE_CASE : Optional[int] , __SCREAMING_SNAKE_CASE : List[Any] ) -> Optional[Any]:
"""simple docstring"""
if pipeline_test_casse_name != "FeatureExtractionPipelineTests":
# Exception encountered when calling layer '...'
return True
return False
def _a ( self : List[Any] ) -> Optional[int]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = TFMBartModelTester(self )
__SCREAMING_SNAKE_CASE = ConfigTester(self , config_class=__SCREAMING_SNAKE_CASE )
def _a ( self : Union[str, Any] ) -> Union[str, Any]:
"""simple docstring"""
self.config_tester.run_common_tests()
def _a ( self : int ) -> str:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.check_decoder_model_past_large_inputs(*__SCREAMING_SNAKE_CASE )
@require_sentencepiece
@require_tokenizers
@require_tf
class A__( unittest.TestCase ):
lowerCAmelCase = [
''' UN Chief Says There Is No Military Solution in Syria''',
]
lowerCAmelCase = [
'''Şeful ONU declară că nu există o soluţie militară în Siria''',
]
lowerCAmelCase = '''facebook/mbart-large-en-ro'''
@cached_property
def _a ( self : Optional[int] ) -> str:
"""simple docstring"""
return AutoTokenizer.from_pretrained(self.model_name )
@cached_property
def _a ( self : Optional[Any] ) -> List[Any]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name )
return model
def _a ( self : Any , **__SCREAMING_SNAKE_CASE : Optional[Any] ) -> Any:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = self.translate_src_text(**__SCREAMING_SNAKE_CASE )
self.assertListEqual(self.expected_text , __SCREAMING_SNAKE_CASE )
def _a ( self : Any , **__SCREAMING_SNAKE_CASE : Optional[int] ) -> Optional[Any]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = self.tokenizer(self.src_text , **__SCREAMING_SNAKE_CASE , return_tensors='''tf''' )
__SCREAMING_SNAKE_CASE = self.model.generate(
model_inputs.input_ids , attention_mask=model_inputs.attention_mask , num_beams=2 )
__SCREAMING_SNAKE_CASE = self.tokenizer.batch_decode(__SCREAMING_SNAKE_CASE , skip_special_tokens=__SCREAMING_SNAKE_CASE )
return generated_words
@slow
def _a ( self : Union[str, Any] ) -> Tuple:
"""simple docstring"""
self._assert_generated_batch_equal_expected()
| 690 | 1 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase__ =logging.get_logger(__name__)
lowerCAmelCase__ ={
"google/realm-cc-news-pretrained-embedder": (
"https://huggingface.co/google/realm-cc-news-pretrained-embedder/resolve/main/config.json"
),
"google/realm-cc-news-pretrained-encoder": (
"https://huggingface.co/google/realm-cc-news-pretrained-encoder/resolve/main/config.json"
),
"google/realm-cc-news-pretrained-scorer": (
"https://huggingface.co/google/realm-cc-news-pretrained-scorer/resolve/main/config.json"
),
"google/realm-cc-news-pretrained-openqa": (
"https://huggingface.co/google/realm-cc-news-pretrained-openqa/aresolve/main/config.json"
),
"google/realm-orqa-nq-openqa": "https://huggingface.co/google/realm-orqa-nq-openqa/resolve/main/config.json",
"google/realm-orqa-nq-reader": "https://huggingface.co/google/realm-orqa-nq-reader/resolve/main/config.json",
"google/realm-orqa-wq-openqa": "https://huggingface.co/google/realm-orqa-wq-openqa/resolve/main/config.json",
"google/realm-orqa-wq-reader": "https://huggingface.co/google/realm-orqa-wq-reader/resolve/main/config.json",
# See all REALM models at https://huggingface.co/models?filter=realm
}
class A__( __magic_name__ ):
lowerCAmelCase = '''realm'''
def __init__( self : str , __SCREAMING_SNAKE_CASE : Optional[int]=3_05_22 , __SCREAMING_SNAKE_CASE : Optional[int]=7_68 , __SCREAMING_SNAKE_CASE : Tuple=1_28 , __SCREAMING_SNAKE_CASE : str=12 , __SCREAMING_SNAKE_CASE : Any=12 , __SCREAMING_SNAKE_CASE : Any=8 , __SCREAMING_SNAKE_CASE : Any=30_72 , __SCREAMING_SNAKE_CASE : List[Any]="gelu_new" , __SCREAMING_SNAKE_CASE : List[Any]=0.1 , __SCREAMING_SNAKE_CASE : Tuple=0.1 , __SCREAMING_SNAKE_CASE : Optional[int]=5_12 , __SCREAMING_SNAKE_CASE : List[str]=2 , __SCREAMING_SNAKE_CASE : str=0.02 , __SCREAMING_SNAKE_CASE : str=1E-1_2 , __SCREAMING_SNAKE_CASE : Optional[int]=2_56 , __SCREAMING_SNAKE_CASE : Optional[int]=10 , __SCREAMING_SNAKE_CASE : Any=1E-3 , __SCREAMING_SNAKE_CASE : Optional[Any]=5 , __SCREAMING_SNAKE_CASE : int=3_20 , __SCREAMING_SNAKE_CASE : Union[str, Any]=13_35_37_18 , __SCREAMING_SNAKE_CASE : List[str]=50_00 , __SCREAMING_SNAKE_CASE : Optional[Any]=1 , __SCREAMING_SNAKE_CASE : str=0 , __SCREAMING_SNAKE_CASE : Optional[int]=2 , **__SCREAMING_SNAKE_CASE : Tuple , ) -> List[str]:
"""simple docstring"""
super().__init__(pad_token_id=__SCREAMING_SNAKE_CASE , bos_token_id=__SCREAMING_SNAKE_CASE , eos_token_id=__SCREAMING_SNAKE_CASE , **__SCREAMING_SNAKE_CASE )
# Common config
__SCREAMING_SNAKE_CASE = vocab_size
__SCREAMING_SNAKE_CASE = max_position_embeddings
__SCREAMING_SNAKE_CASE = hidden_size
__SCREAMING_SNAKE_CASE = retriever_proj_size
__SCREAMING_SNAKE_CASE = num_hidden_layers
__SCREAMING_SNAKE_CASE = num_attention_heads
__SCREAMING_SNAKE_CASE = num_candidates
__SCREAMING_SNAKE_CASE = intermediate_size
__SCREAMING_SNAKE_CASE = hidden_act
__SCREAMING_SNAKE_CASE = hidden_dropout_prob
__SCREAMING_SNAKE_CASE = attention_probs_dropout_prob
__SCREAMING_SNAKE_CASE = initializer_range
__SCREAMING_SNAKE_CASE = type_vocab_size
__SCREAMING_SNAKE_CASE = layer_norm_eps
# Reader config
__SCREAMING_SNAKE_CASE = span_hidden_size
__SCREAMING_SNAKE_CASE = max_span_width
__SCREAMING_SNAKE_CASE = reader_layer_norm_eps
__SCREAMING_SNAKE_CASE = reader_beam_size
__SCREAMING_SNAKE_CASE = reader_seq_len
# Retrieval config
__SCREAMING_SNAKE_CASE = num_block_records
__SCREAMING_SNAKE_CASE = searcher_beam_size
| 690 |
"""simple docstring"""
from ....configuration_utils import PretrainedConfig
from ....utils import logging
lowerCAmelCase__ =logging.get_logger(__name__)
lowerCAmelCase__ ={
"Visual-Attention-Network/van-base": (
"https://huggingface.co/Visual-Attention-Network/van-base/blob/main/config.json"
),
}
class A__( __magic_name__ ):
lowerCAmelCase = '''van'''
def __init__( self : int , __SCREAMING_SNAKE_CASE : Optional[Any]=2_24 , __SCREAMING_SNAKE_CASE : Union[str, Any]=3 , __SCREAMING_SNAKE_CASE : Tuple=[7, 3, 3, 3] , __SCREAMING_SNAKE_CASE : Optional[int]=[4, 2, 2, 2] , __SCREAMING_SNAKE_CASE : str=[64, 1_28, 3_20, 5_12] , __SCREAMING_SNAKE_CASE : Optional[Any]=[3, 3, 12, 3] , __SCREAMING_SNAKE_CASE : Dict=[8, 8, 4, 4] , __SCREAMING_SNAKE_CASE : Any="gelu" , __SCREAMING_SNAKE_CASE : Tuple=0.02 , __SCREAMING_SNAKE_CASE : Dict=1E-6 , __SCREAMING_SNAKE_CASE : Any=1E-2 , __SCREAMING_SNAKE_CASE : str=0.0 , __SCREAMING_SNAKE_CASE : Union[str, Any]=0.0 , **__SCREAMING_SNAKE_CASE : str , ) -> List[str]:
"""simple docstring"""
super().__init__(**__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = image_size
__SCREAMING_SNAKE_CASE = num_channels
__SCREAMING_SNAKE_CASE = patch_sizes
__SCREAMING_SNAKE_CASE = strides
__SCREAMING_SNAKE_CASE = hidden_sizes
__SCREAMING_SNAKE_CASE = depths
__SCREAMING_SNAKE_CASE = mlp_ratios
__SCREAMING_SNAKE_CASE = hidden_act
__SCREAMING_SNAKE_CASE = initializer_range
__SCREAMING_SNAKE_CASE = layer_norm_eps
__SCREAMING_SNAKE_CASE = layer_scale_init_value
__SCREAMING_SNAKE_CASE = drop_path_rate
__SCREAMING_SNAKE_CASE = dropout_rate
| 690 | 1 |
"""simple docstring"""
from collections import defaultdict
def _a ( UpperCAmelCase__ , UpperCAmelCase__ ) -> bool:
__SCREAMING_SNAKE_CASE = first_str.lower().strip()
__SCREAMING_SNAKE_CASE = second_str.lower().strip()
# Remove whitespace
__SCREAMING_SNAKE_CASE = first_str.replace(''' ''' , '''''' )
__SCREAMING_SNAKE_CASE = second_str.replace(''' ''' , '''''' )
# Strings of different lengths are not anagrams
if len(UpperCAmelCase__ ) != len(UpperCAmelCase__ ):
return False
# Default values for count should be 0
__SCREAMING_SNAKE_CASE = defaultdict(UpperCAmelCase__ )
# For each character in input strings,
# increment count in the corresponding
for i in range(len(UpperCAmelCase__ ) ):
count[first_str[i]] += 1
count[second_str[i]] -= 1
return all(_count == 0 for _count in count.values() )
if __name__ == "__main__":
from doctest import testmod
testmod()
lowerCAmelCase__ =input("Enter the first string ").strip()
lowerCAmelCase__ =input("Enter the second string ").strip()
lowerCAmelCase__ =check_anagrams(input_a, input_b)
print(F'''{input_a} and {input_b} are {'' if status else 'not '}anagrams.''')
| 690 |
"""simple docstring"""
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCAmelCase__ ={"configuration_timm_backbone": ["TimmBackboneConfig"]}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase__ =["TimmBackbone"]
if TYPE_CHECKING:
from .configuration_timm_backbone import TimmBackboneConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_timm_backbone import TimmBackbone
else:
import sys
lowerCAmelCase__ =_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 690 | 1 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.