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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__( _UpperCAmelCase ):
lowerCAmelCase = '''wav2vec2'''
def __init__( self : Any , __SCREAMING_SNAKE_CASE : str=32 , __SCREAMING_SNAKE_CASE : Dict=7_68 , __SCREAMING_SNAKE_CASE : int=12 , __SCREAMING_SNAKE_CASE : Tuple=12 , __SCREAMING_SNAKE_CASE : Optional[Any]=30_72 , __SCREAMING_SNAKE_CASE : List[Any]="gelu" , __SCREAMING_SNAKE_CASE : int=0.1 , __SCREAMING_SNAKE_CASE : Any=0.1 , __SCREAMING_SNAKE_CASE : Optional[int]=0.1 , __SCREAMING_SNAKE_CASE : Optional[Any]=0.0 , __SCREAMING_SNAKE_CASE : Union[str, Any]=0.0 , __SCREAMING_SNAKE_CASE : List[Any]=0.1 , __SCREAMING_SNAKE_CASE : List[Any]=0.1 , __SCREAMING_SNAKE_CASE : Union[str, Any]=0.02 , __SCREAMING_SNAKE_CASE : List[str]=1E-5 , __SCREAMING_SNAKE_CASE : Optional[int]="group" , __SCREAMING_SNAKE_CASE : str="gelu" , __SCREAMING_SNAKE_CASE : Union[str, Any]=(5_12, 5_12, 5_12, 5_12, 5_12, 5_12, 5_12) , __SCREAMING_SNAKE_CASE : Union[str, Any]=(5, 2, 2, 2, 2, 2, 2) , __SCREAMING_SNAKE_CASE : int=(10, 3, 3, 3, 3, 2, 2) , __SCREAMING_SNAKE_CASE : str=False , __SCREAMING_SNAKE_CASE : Union[str, Any]=1_28 , __SCREAMING_SNAKE_CASE : str=16 , __SCREAMING_SNAKE_CASE : str=False , __SCREAMING_SNAKE_CASE : str=True , __SCREAMING_SNAKE_CASE : List[Any]=0.05 , __SCREAMING_SNAKE_CASE : List[Any]=10 , __SCREAMING_SNAKE_CASE : Any=2 , __SCREAMING_SNAKE_CASE : Optional[int]=0.0 , __SCREAMING_SNAKE_CASE : Dict=10 , __SCREAMING_SNAKE_CASE : Optional[int]=0 , __SCREAMING_SNAKE_CASE : int=3_20 , __SCREAMING_SNAKE_CASE : Optional[int]=2 , __SCREAMING_SNAKE_CASE : Tuple=0.1 , __SCREAMING_SNAKE_CASE : Optional[Any]=1_00 , __SCREAMING_SNAKE_CASE : Tuple=2_56 , __SCREAMING_SNAKE_CASE : Dict=2_56 , __SCREAMING_SNAKE_CASE : Union[str, Any]=0.1 , __SCREAMING_SNAKE_CASE : Tuple="sum" , __SCREAMING_SNAKE_CASE : Any=False , __SCREAMING_SNAKE_CASE : List[str]=False , __SCREAMING_SNAKE_CASE : Union[str, Any]=2_56 , __SCREAMING_SNAKE_CASE : Optional[Any]=(5_12, 5_12, 5_12, 5_12, 15_00) , __SCREAMING_SNAKE_CASE : List[Any]=(5, 3, 3, 1, 1) , __SCREAMING_SNAKE_CASE : Dict=(1, 2, 3, 1, 1) , __SCREAMING_SNAKE_CASE : Tuple=5_12 , __SCREAMING_SNAKE_CASE : List[str]=0 , __SCREAMING_SNAKE_CASE : Any=1 , __SCREAMING_SNAKE_CASE : Dict=2 , __SCREAMING_SNAKE_CASE : Optional[Any]=False , __SCREAMING_SNAKE_CASE : List[Any]=3 , __SCREAMING_SNAKE_CASE : str=2 , __SCREAMING_SNAKE_CASE : Tuple=3 , __SCREAMING_SNAKE_CASE : Dict=None , __SCREAMING_SNAKE_CASE : Tuple=None , **__SCREAMING_SNAKE_CASE : Any , ) -> Union[str, Any]:
"""simple docstring"""
super().__init__(**A_ , pad_token_id=A_ , bos_token_id=A_ , eos_token_id=A_ )
__SCREAMING_SNAKE_CASE = hidden_size
__SCREAMING_SNAKE_CASE = feat_extract_norm
__SCREAMING_SNAKE_CASE = feat_extract_activation
__SCREAMING_SNAKE_CASE = list(A_ )
__SCREAMING_SNAKE_CASE = list(A_ )
__SCREAMING_SNAKE_CASE = list(A_ )
__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(A_ )
__SCREAMING_SNAKE_CASE = list(A_ )
__SCREAMING_SNAKE_CASE = list(A_ )
__SCREAMING_SNAKE_CASE = xvector_output_dim
@property
def _a ( self : Optional[Any] ) -> str:
"""simple docstring"""
return functools.reduce(operator.mul , self.conv_stride , 1 )
| 705 |
"""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 | 0 |
"""simple docstring"""
import collections
import inspect
import unittest
from transformers import FocalNetConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import (
FocalNetBackbone,
FocalNetForImageClassification,
FocalNetForMaskedImageModeling,
FocalNetModel,
)
from transformers.models.focalnet.modeling_focalnet import FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class A__:
def __init__( self : Dict , __SCREAMING_SNAKE_CASE : Tuple , __SCREAMING_SNAKE_CASE : Any=13 , __SCREAMING_SNAKE_CASE : int=32 , __SCREAMING_SNAKE_CASE : Optional[Any]=2 , __SCREAMING_SNAKE_CASE : List[Any]=3 , __SCREAMING_SNAKE_CASE : List[str]=16 , __SCREAMING_SNAKE_CASE : Union[str, Any]=[32, 64, 1_28] , __SCREAMING_SNAKE_CASE : List[Any]=[1, 2, 1] , __SCREAMING_SNAKE_CASE : Optional[int]=[2, 2, 4] , __SCREAMING_SNAKE_CASE : Dict=2 , __SCREAMING_SNAKE_CASE : List[str]=2.0 , __SCREAMING_SNAKE_CASE : Any=True , __SCREAMING_SNAKE_CASE : List[str]=0.0 , __SCREAMING_SNAKE_CASE : Optional[int]=0.0 , __SCREAMING_SNAKE_CASE : Tuple=0.1 , __SCREAMING_SNAKE_CASE : Tuple="gelu" , __SCREAMING_SNAKE_CASE : List[Any]=False , __SCREAMING_SNAKE_CASE : List[Any]=True , __SCREAMING_SNAKE_CASE : Optional[Any]=0.02 , __SCREAMING_SNAKE_CASE : Optional[int]=1E-5 , __SCREAMING_SNAKE_CASE : int=True , __SCREAMING_SNAKE_CASE : List[Any]=None , __SCREAMING_SNAKE_CASE : Optional[int]=True , __SCREAMING_SNAKE_CASE : Optional[int]=10 , __SCREAMING_SNAKE_CASE : Optional[Any]=8 , __SCREAMING_SNAKE_CASE : int=["stage1", "stage2"] , __SCREAMING_SNAKE_CASE : List[Any]=[1, 2] , ) -> List[Any]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = parent
__SCREAMING_SNAKE_CASE = batch_size
__SCREAMING_SNAKE_CASE = image_size
__SCREAMING_SNAKE_CASE = patch_size
__SCREAMING_SNAKE_CASE = num_channels
__SCREAMING_SNAKE_CASE = embed_dim
__SCREAMING_SNAKE_CASE = hidden_sizes
__SCREAMING_SNAKE_CASE = depths
__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 = patch_norm
__SCREAMING_SNAKE_CASE = layer_norm_eps
__SCREAMING_SNAKE_CASE = initializer_range
__SCREAMING_SNAKE_CASE = is_training
__SCREAMING_SNAKE_CASE = scope
__SCREAMING_SNAKE_CASE = use_labels
__SCREAMING_SNAKE_CASE = type_sequence_label_size
__SCREAMING_SNAKE_CASE = encoder_stride
__SCREAMING_SNAKE_CASE = out_features
__SCREAMING_SNAKE_CASE = out_indices
def _a ( self : Optional[Any] ) -> Dict:
"""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.type_sequence_label_size )
__SCREAMING_SNAKE_CASE = self.get_config()
return config, pixel_values, labels
def _a ( self : Tuple ) -> Optional[int]:
"""simple docstring"""
return FocalNetConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , embed_dim=self.embed_dim , hidden_sizes=self.hidden_sizes , depths=self.depths , num_heads=self.num_heads , window_size=self.window_size , mlp_ratio=self.mlp_ratio , qkv_bias=self.qkv_bias , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , drop_path_rate=self.drop_path_rate , hidden_act=self.hidden_act , use_absolute_embeddings=self.use_absolute_embeddings , path_norm=self.patch_norm , layer_norm_eps=self.layer_norm_eps , initializer_range=self.initializer_range , encoder_stride=self.encoder_stride , out_features=self.out_features , out_indices=self.out_indices , )
def _a ( self : Dict , __SCREAMING_SNAKE_CASE : Tuple , __SCREAMING_SNAKE_CASE : Union[str, Any] , __SCREAMING_SNAKE_CASE : Optional[Any] ) -> List[Any]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = FocalNetModel(config=__SCREAMING_SNAKE_CASE )
model.to(__SCREAMING_SNAKE_CASE )
model.eval()
__SCREAMING_SNAKE_CASE = model(__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths ) - 1))
__SCREAMING_SNAKE_CASE = int(config.embed_dim * 2 ** (len(config.depths ) - 1) )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, expected_seq_len, expected_dim) )
def _a ( self : Union[str, Any] , __SCREAMING_SNAKE_CASE : List[Any] , __SCREAMING_SNAKE_CASE : int , __SCREAMING_SNAKE_CASE : Optional[int] ) -> Union[str, Any]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = FocalNetBackbone(config=__SCREAMING_SNAKE_CASE )
model.to(__SCREAMING_SNAKE_CASE )
model.eval()
__SCREAMING_SNAKE_CASE = model(__SCREAMING_SNAKE_CASE )
# verify feature maps
self.parent.assertEqual(len(result.feature_maps ) , len(config.out_features ) )
self.parent.assertListEqual(list(result.feature_maps[0].shape ) , [self.batch_size, self.image_size, 8, 8] )
# verify channels
self.parent.assertEqual(len(model.channels ) , len(config.out_features ) )
self.parent.assertListEqual(model.channels , config.hidden_sizes[:-1] )
# verify backbone works with out_features=None
__SCREAMING_SNAKE_CASE = None
__SCREAMING_SNAKE_CASE = FocalNetBackbone(config=__SCREAMING_SNAKE_CASE )
model.to(__SCREAMING_SNAKE_CASE )
model.eval()
__SCREAMING_SNAKE_CASE = model(__SCREAMING_SNAKE_CASE )
# verify feature maps
self.parent.assertEqual(len(result.feature_maps ) , 1 )
self.parent.assertListEqual(list(result.feature_maps[0].shape ) , [self.batch_size, self.image_size * 2, 4, 4] )
# verify channels
self.parent.assertEqual(len(model.channels ) , 1 )
self.parent.assertListEqual(model.channels , [config.hidden_sizes[-1]] )
def _a ( self : Tuple , __SCREAMING_SNAKE_CASE : Tuple , __SCREAMING_SNAKE_CASE : int , __SCREAMING_SNAKE_CASE : str ) -> Tuple:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = FocalNetForMaskedImageModeling(config=__SCREAMING_SNAKE_CASE )
model.to(__SCREAMING_SNAKE_CASE )
model.eval()
__SCREAMING_SNAKE_CASE = model(__SCREAMING_SNAKE_CASE )
self.parent.assertEqual(
result.reconstruction.shape , (self.batch_size, self.num_channels, self.image_size, self.image_size) )
# test greyscale images
__SCREAMING_SNAKE_CASE = 1
__SCREAMING_SNAKE_CASE = FocalNetForMaskedImageModeling(__SCREAMING_SNAKE_CASE )
model.to(__SCREAMING_SNAKE_CASE )
model.eval()
__SCREAMING_SNAKE_CASE = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
__SCREAMING_SNAKE_CASE = model(__SCREAMING_SNAKE_CASE )
self.parent.assertEqual(result.reconstruction.shape , (self.batch_size, 1, self.image_size, self.image_size) )
def _a ( self : Dict , __SCREAMING_SNAKE_CASE : Optional[Any] , __SCREAMING_SNAKE_CASE : str , __SCREAMING_SNAKE_CASE : List[str] ) -> Any:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = self.type_sequence_label_size
__SCREAMING_SNAKE_CASE = FocalNetForImageClassification(__SCREAMING_SNAKE_CASE )
model.to(__SCREAMING_SNAKE_CASE )
model.eval()
__SCREAMING_SNAKE_CASE = model(__SCREAMING_SNAKE_CASE , labels=__SCREAMING_SNAKE_CASE )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
# test greyscale images
__SCREAMING_SNAKE_CASE = 1
__SCREAMING_SNAKE_CASE = FocalNetForImageClassification(__SCREAMING_SNAKE_CASE )
model.to(__SCREAMING_SNAKE_CASE )
model.eval()
__SCREAMING_SNAKE_CASE = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
__SCREAMING_SNAKE_CASE = model(__SCREAMING_SNAKE_CASE )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
def _a ( self : str ) -> List[Any]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = self.prepare_config_and_inputs()
__SCREAMING_SNAKE_CASE = config_and_inputs
__SCREAMING_SNAKE_CASE = {"pixel_values": pixel_values}
return config, inputs_dict
@require_torch
class A__( __lowercase , __lowercase , unittest.TestCase ):
lowerCAmelCase = (
(
FocalNetModel,
FocalNetForImageClassification,
FocalNetForMaskedImageModeling,
FocalNetBackbone,
)
if is_torch_available()
else ()
)
lowerCAmelCase = (
{'''feature-extraction''': FocalNetModel, '''image-classification''': FocalNetForImageClassification}
if is_torch_available()
else {}
)
lowerCAmelCase = False
lowerCAmelCase = False
lowerCAmelCase = False
lowerCAmelCase = False
lowerCAmelCase = False
def _a ( self : Tuple ) -> Tuple:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = FocalNetModelTester(self )
__SCREAMING_SNAKE_CASE = ConfigTester(self , config_class=__SCREAMING_SNAKE_CASE , embed_dim=37 , has_text_modality=__SCREAMING_SNAKE_CASE )
def _a ( self : Dict ) -> Tuple:
"""simple docstring"""
self.create_and_test_config_common_properties()
self.config_tester.create_and_test_config_to_json_string()
self.config_tester.create_and_test_config_to_json_file()
self.config_tester.create_and_test_config_from_and_save_pretrained()
self.config_tester.create_and_test_config_with_num_labels()
self.config_tester.check_config_can_be_init_without_params()
self.config_tester.check_config_arguments_init()
def _a ( self : str ) -> Any:
"""simple docstring"""
return
def _a ( self : Optional[Any] ) -> int:
"""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 : int ) -> Tuple:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_backbone(*__SCREAMING_SNAKE_CASE )
def _a ( self : int ) -> str:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_image_modeling(*__SCREAMING_SNAKE_CASE )
def _a ( self : str ) -> List[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 )
@unittest.skip(reason='''FocalNet does not use inputs_embeds''' )
def _a ( self : Any ) -> Any:
"""simple docstring"""
pass
@unittest.skip(reason='''FocalNet does not use feedforward chunking''' )
def _a ( self : str ) -> str:
"""simple docstring"""
pass
def _a ( self : Union[str, Any] ) -> str:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes[:-1]:
__SCREAMING_SNAKE_CASE = model_class(__SCREAMING_SNAKE_CASE )
self.assertIsInstance(model.get_input_embeddings() , (nn.Module) )
__SCREAMING_SNAKE_CASE = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(__SCREAMING_SNAKE_CASE , nn.Linear ) )
def _a ( self : List[Any] ) -> Any:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes[:-1]:
__SCREAMING_SNAKE_CASE = model_class(__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = inspect.signature(model.forward )
# 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 , __SCREAMING_SNAKE_CASE : Optional[Any] , __SCREAMING_SNAKE_CASE : Union[str, Any] , __SCREAMING_SNAKE_CASE : Optional[Any] , __SCREAMING_SNAKE_CASE : str ) -> List[str]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = model_class(__SCREAMING_SNAKE_CASE )
model.to(__SCREAMING_SNAKE_CASE )
model.eval()
with torch.no_grad():
__SCREAMING_SNAKE_CASE = model(**self._prepare_for_class(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) )
__SCREAMING_SNAKE_CASE = outputs.hidden_states
__SCREAMING_SNAKE_CASE = getattr(
self.model_tester , '''expected_num_hidden_layers''' , len(self.model_tester.depths ) + 1 )
self.assertEqual(len(__SCREAMING_SNAKE_CASE ) , __SCREAMING_SNAKE_CASE )
# FocalNet has a different seq_length
__SCREAMING_SNAKE_CASE = (
config.patch_size
if isinstance(config.patch_size , collections.abc.Iterable )
else (config.patch_size, config.patch_size)
)
__SCREAMING_SNAKE_CASE = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
self.assertListEqual(
list(hidden_states[0].shape[-2:] ) , [num_patches, self.model_tester.embed_dim] , )
__SCREAMING_SNAKE_CASE = outputs.reshaped_hidden_states
self.assertEqual(len(__SCREAMING_SNAKE_CASE ) , __SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = reshaped_hidden_states[0].shape
__SCREAMING_SNAKE_CASE = (
reshaped_hidden_states[0].view(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , height * width ).permute(0 , 2 , 1 )
)
self.assertListEqual(
list(reshaped_hidden_states.shape[-2:] ) , [num_patches, self.model_tester.embed_dim] , )
def _a ( self : Dict ) -> Optional[int]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs_for_common()
__SCREAMING_SNAKE_CASE = (
self.model_tester.image_size
if isinstance(self.model_tester.image_size , collections.abc.Iterable )
else (self.model_tester.image_size, self.model_tester.image_size)
)
for model_class in self.all_model_classes[:-1]:
__SCREAMING_SNAKE_CASE = True
self.check_hidden_states_output(__SCREAMING_SNAKE_CASE , __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
self.check_hidden_states_output(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
def _a ( self : List[Any] ) -> Tuple:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs_for_common()
__SCREAMING_SNAKE_CASE = 3
__SCREAMING_SNAKE_CASE = (
self.model_tester.image_size
if isinstance(self.model_tester.image_size , collections.abc.Iterable )
else (self.model_tester.image_size, self.model_tester.image_size)
)
__SCREAMING_SNAKE_CASE = (
config.patch_size
if isinstance(config.patch_size , collections.abc.Iterable )
else (config.patch_size, config.patch_size)
)
__SCREAMING_SNAKE_CASE = image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
__SCREAMING_SNAKE_CASE = image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])
for model_class in self.all_model_classes[:-1]:
__SCREAMING_SNAKE_CASE = True
self.check_hidden_states_output(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , (padded_height, padded_width) )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
__SCREAMING_SNAKE_CASE = True
self.check_hidden_states_output(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , (padded_height, padded_width) )
@slow
def _a ( self : Dict ) -> Optional[int]:
"""simple docstring"""
for model_name in FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__SCREAMING_SNAKE_CASE = FocalNetModel.from_pretrained(__SCREAMING_SNAKE_CASE )
self.assertIsNotNone(__SCREAMING_SNAKE_CASE )
def _a ( self : Tuple ) -> List[str]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs_for_common()
__SCREAMING_SNAKE_CASE = _config_zero_init(__SCREAMING_SNAKE_CASE )
for model_class in self.all_model_classes:
__SCREAMING_SNAKE_CASE = model_class(config=__SCREAMING_SNAKE_CASE )
for name, param in model.named_parameters():
if "embeddings" not in name and param.requires_grad:
self.assertIn(
((param.data.mean() * 1E9).round() / 1E9).item() , [0.0, 1.0] , msg=f"""Parameter {name} of model {model_class} seems not properly initialized""" , )
@require_vision
@require_torch
class A__( unittest.TestCase ):
@cached_property
def _a ( self : Tuple ) -> List[str]:
"""simple docstring"""
return AutoImageProcessor.from_pretrained('''microsoft/focalnet-tiny''' ) if is_vision_available() else None
@slow
def _a ( self : int ) -> Optional[Any]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = FocalNetForImageClassification.from_pretrained('''microsoft/focalnet-tiny''' ).to(__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = self.default_image_processor
__SCREAMING_SNAKE_CASE = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
__SCREAMING_SNAKE_CASE = image_processor(images=__SCREAMING_SNAKE_CASE , return_tensors='''pt''' ).to(__SCREAMING_SNAKE_CASE )
# forward pass
with torch.no_grad():
__SCREAMING_SNAKE_CASE = model(**__SCREAMING_SNAKE_CASE )
# verify the logits
__SCREAMING_SNAKE_CASE = torch.Size((1, 10_00) )
self.assertEqual(outputs.logits.shape , __SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = torch.tensor([0.21_66, -0.43_68, 0.21_91] ).to(__SCREAMING_SNAKE_CASE )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , __SCREAMING_SNAKE_CASE , atol=1E-4 ) )
self.assertTrue(outputs.logits.argmax(dim=-1 ).item() , 2_81 )
@require_torch
class A__( __lowercase , unittest.TestCase ):
lowerCAmelCase = (FocalNetBackbone,) if is_torch_available() else ()
lowerCAmelCase = FocalNetConfig
lowerCAmelCase = False
def _a ( self : Tuple ) -> Optional[int]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = FocalNetModelTester(self )
| 706 |
"""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 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
import torch
from ..models.auto import AutoModelForVisualQuestionAnswering, AutoProcessor
from ..utils import requires_backends
from .base import PipelineTool
if TYPE_CHECKING:
from PIL import Image
class A__( __magic_name__ ):
lowerCAmelCase = '''dandelin/vilt-b32-finetuned-vqa'''
lowerCAmelCase = (
'''This is a tool that answers a question about an image. It takes an input named `image` which should be the '''
'''image containing the information, as well as a `question` which should be the question in English. It '''
'''returns a text that is the answer to the question.'''
)
lowerCAmelCase = '''image_qa'''
lowerCAmelCase = AutoProcessor
lowerCAmelCase = AutoModelForVisualQuestionAnswering
lowerCAmelCase = ['''image''', '''text''']
lowerCAmelCase = ['''text''']
def __init__( self : Union[str, Any] , *__SCREAMING_SNAKE_CASE : Optional[int] , **__SCREAMING_SNAKE_CASE : List[Any] ) -> Dict:
"""simple docstring"""
requires_backends(self , ['''vision'''] )
super().__init__(*__UpperCamelCase , **__UpperCamelCase )
def _a ( self : Union[str, Any] , __SCREAMING_SNAKE_CASE : "Image" , __SCREAMING_SNAKE_CASE : str ) -> Optional[Any]:
"""simple docstring"""
return self.pre_processor(__UpperCamelCase , __UpperCamelCase , return_tensors='''pt''' )
def _a ( self : str , __SCREAMING_SNAKE_CASE : Tuple ) -> Optional[int]:
"""simple docstring"""
with torch.no_grad():
return self.model(**__UpperCamelCase ).logits
def _a ( self : Any , __SCREAMING_SNAKE_CASE : Union[str, Any] ) -> Dict:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = outputs.argmax(-1 ).item()
return self.model.config.idalabel[idx]
| 707 |
"""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 | 0 |
from typing import List, Optional, Union
import torch
from transformers import (
XLMRobertaTokenizer,
)
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import (
is_accelerate_available,
is_accelerate_version,
logging,
randn_tensor,
replace_example_docstring,
)
from .text_encoder import MultilingualCLIP
lowerCAmelCase__ =logging.get_logger(__name__) # pylint: disable=invalid-name
lowerCAmelCase__ ="\n Examples:\n ```py\n >>> from diffusers import KandinskyPipeline, KandinskyPriorPipeline\n >>> import torch\n\n >>> pipe_prior = KandinskyPriorPipeline.from_pretrained(\"kandinsky-community/Kandinsky-2-1-prior\")\n >>> pipe_prior.to(\"cuda\")\n\n >>> prompt = \"red cat, 4k photo\"\n >>> out = pipe_prior(prompt)\n >>> image_emb = out.image_embeds\n >>> negative_image_emb = out.negative_image_embeds\n\n >>> pipe = KandinskyPipeline.from_pretrained(\"kandinsky-community/kandinsky-2-1\")\n >>> pipe.to(\"cuda\")\n\n >>> image = pipe(\n ... prompt,\n ... image_embeds=image_emb,\n ... negative_image_embeds=negative_image_emb,\n ... height=768,\n ... width=768,\n ... num_inference_steps=100,\n ... ).images\n\n >>> image[0].save(\"cat.png\")\n ```\n"
def _a ( UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__=8 ) -> List[str]:
__SCREAMING_SNAKE_CASE = h // scale_factor**2
if h % scale_factor**2 != 0:
new_h += 1
__SCREAMING_SNAKE_CASE = w // scale_factor**2
if w % scale_factor**2 != 0:
new_w += 1
return new_h * scale_factor, new_w * scale_factor
class A__( A_ ):
def __init__( self : Union[str, Any] , __SCREAMING_SNAKE_CASE : Tuple , __SCREAMING_SNAKE_CASE : Dict , __SCREAMING_SNAKE_CASE : List[str] , __SCREAMING_SNAKE_CASE : Union[str, Any] , __SCREAMING_SNAKE_CASE : Optional[Any] , ) -> List[str]:
"""simple docstring"""
super().__init__()
self.register_modules(
text_encoder=__SCREAMING_SNAKE_CASE , tokenizer=__SCREAMING_SNAKE_CASE , unet=__SCREAMING_SNAKE_CASE , scheduler=__SCREAMING_SNAKE_CASE , movq=__SCREAMING_SNAKE_CASE , )
__SCREAMING_SNAKE_CASE = 2 ** (len(self.movq.config.block_out_channels ) - 1)
def _a ( self : Any , __SCREAMING_SNAKE_CASE : Tuple , __SCREAMING_SNAKE_CASE : Any , __SCREAMING_SNAKE_CASE : Optional[Any] , __SCREAMING_SNAKE_CASE : str , __SCREAMING_SNAKE_CASE : Tuple , __SCREAMING_SNAKE_CASE : Any ) -> str:
"""simple docstring"""
if latents is None:
__SCREAMING_SNAKE_CASE = randn_tensor(__SCREAMING_SNAKE_CASE , generator=__SCREAMING_SNAKE_CASE , device=__SCREAMING_SNAKE_CASE , dtype=__SCREAMING_SNAKE_CASE )
else:
if latents.shape != shape:
raise ValueError(f"""Unexpected latents shape, got {latents.shape}, expected {shape}""" )
__SCREAMING_SNAKE_CASE = latents.to(__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = latents * scheduler.init_noise_sigma
return latents
def _a ( self : Dict , __SCREAMING_SNAKE_CASE : List[Any] , __SCREAMING_SNAKE_CASE : str , __SCREAMING_SNAKE_CASE : str , __SCREAMING_SNAKE_CASE : Optional[Any] , __SCREAMING_SNAKE_CASE : Union[str, Any]=None , ) -> Dict:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = len(__SCREAMING_SNAKE_CASE ) if isinstance(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) else 1
# get prompt text embeddings
__SCREAMING_SNAKE_CASE = self.tokenizer(
__SCREAMING_SNAKE_CASE , padding='''max_length''' , truncation=__SCREAMING_SNAKE_CASE , max_length=77 , return_attention_mask=__SCREAMING_SNAKE_CASE , add_special_tokens=__SCREAMING_SNAKE_CASE , return_tensors='''pt''' , )
__SCREAMING_SNAKE_CASE = text_inputs.input_ids
__SCREAMING_SNAKE_CASE = self.tokenizer(__SCREAMING_SNAKE_CASE , padding='''longest''' , return_tensors='''pt''' ).input_ids
if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
__SCREAMING_SNAKE_CASE = self.tokenizer.batch_decode(untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1] )
logger.warning(
'''The following part of your input was truncated because CLIP can only handle sequences up to'''
f""" {self.tokenizer.model_max_length} tokens: {removed_text}""" )
__SCREAMING_SNAKE_CASE = text_input_ids.to(__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = text_inputs.attention_mask.to(__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = self.text_encoder(
input_ids=__SCREAMING_SNAKE_CASE , attention_mask=__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = prompt_embeds.repeat_interleave(__SCREAMING_SNAKE_CASE , dim=0 )
__SCREAMING_SNAKE_CASE = text_encoder_hidden_states.repeat_interleave(__SCREAMING_SNAKE_CASE , dim=0 )
__SCREAMING_SNAKE_CASE = text_mask.repeat_interleave(__SCREAMING_SNAKE_CASE , dim=0 )
if do_classifier_free_guidance:
__SCREAMING_SNAKE_CASE = 42
if negative_prompt is None:
__SCREAMING_SNAKE_CASE = [""""""] * batch_size
elif type(__SCREAMING_SNAKE_CASE ) is not type(__SCREAMING_SNAKE_CASE ):
raise TypeError(
f"""`negative_prompt` should be the same type to `prompt`, but got {type(__SCREAMING_SNAKE_CASE )} !="""
f""" {type(__SCREAMING_SNAKE_CASE )}.""" )
elif isinstance(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
__SCREAMING_SNAKE_CASE = [negative_prompt]
elif batch_size != len(__SCREAMING_SNAKE_CASE ):
raise ValueError(
f"""`negative_prompt`: {negative_prompt} has batch size {len(__SCREAMING_SNAKE_CASE )}, but `prompt`:"""
f""" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"""
''' the batch size of `prompt`.''' )
else:
__SCREAMING_SNAKE_CASE = negative_prompt
__SCREAMING_SNAKE_CASE = self.tokenizer(
__SCREAMING_SNAKE_CASE , padding='''max_length''' , max_length=77 , truncation=__SCREAMING_SNAKE_CASE , return_attention_mask=__SCREAMING_SNAKE_CASE , add_special_tokens=__SCREAMING_SNAKE_CASE , return_tensors='''pt''' , )
__SCREAMING_SNAKE_CASE = uncond_input.input_ids.to(__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = uncond_input.attention_mask.to(__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = self.text_encoder(
input_ids=__SCREAMING_SNAKE_CASE , attention_mask=__SCREAMING_SNAKE_CASE )
# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
__SCREAMING_SNAKE_CASE = negative_prompt_embeds.shape[1]
__SCREAMING_SNAKE_CASE = negative_prompt_embeds.repeat(1 , __SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = negative_prompt_embeds.view(batch_size * num_images_per_prompt , __SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = uncond_text_encoder_hidden_states.shape[1]
__SCREAMING_SNAKE_CASE = uncond_text_encoder_hidden_states.repeat(1 , __SCREAMING_SNAKE_CASE , 1 )
__SCREAMING_SNAKE_CASE = uncond_text_encoder_hidden_states.view(
batch_size * num_images_per_prompt , __SCREAMING_SNAKE_CASE , -1 )
__SCREAMING_SNAKE_CASE = uncond_text_mask.repeat_interleave(__SCREAMING_SNAKE_CASE , dim=0 )
# done duplicates
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes
__SCREAMING_SNAKE_CASE = torch.cat([negative_prompt_embeds, prompt_embeds] )
__SCREAMING_SNAKE_CASE = torch.cat([uncond_text_encoder_hidden_states, text_encoder_hidden_states] )
__SCREAMING_SNAKE_CASE = torch.cat([uncond_text_mask, text_mask] )
return prompt_embeds, text_encoder_hidden_states, text_mask
def _a ( self : Dict , __SCREAMING_SNAKE_CASE : Dict=0 ) -> List[Any]:
"""simple docstring"""
if is_accelerate_available():
from accelerate import cpu_offload
else:
raise ImportError('''Please install accelerate via `pip install accelerate`''' )
__SCREAMING_SNAKE_CASE = torch.device(f"""cuda:{gpu_id}""" )
__SCREAMING_SNAKE_CASE = [
self.unet,
self.text_encoder,
self.movq,
]
for cpu_offloaded_model in models:
if cpu_offloaded_model is not None:
cpu_offload(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
def _a ( self : Any , __SCREAMING_SNAKE_CASE : int=0 ) -> Union[str, Any]:
"""simple docstring"""
if is_accelerate_available() and is_accelerate_version('''>=''' , '''0.17.0.dev0''' ):
from accelerate import cpu_offload_with_hook
else:
raise ImportError('''`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.''' )
__SCREAMING_SNAKE_CASE = torch.device(f"""cuda:{gpu_id}""" )
if self.device.type != "cpu":
self.to('''cpu''' , silence_dtype_warnings=__SCREAMING_SNAKE_CASE )
torch.cuda.empty_cache() # otherwise we don't see the memory savings (but they probably exist)
__SCREAMING_SNAKE_CASE = None
for cpu_offloaded_model in [self.text_encoder, self.unet, self.movq]:
__SCREAMING_SNAKE_CASE = cpu_offload_with_hook(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , prev_module_hook=__SCREAMING_SNAKE_CASE )
if self.safety_checker is not None:
__SCREAMING_SNAKE_CASE = cpu_offload_with_hook(self.safety_checker , __SCREAMING_SNAKE_CASE , prev_module_hook=__SCREAMING_SNAKE_CASE )
# We'll offload the last model manually.
__SCREAMING_SNAKE_CASE = hook
@property
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
def _a ( self : List[str] ) -> int:
"""simple docstring"""
if not hasattr(self.unet , '''_hf_hook''' ):
return self.device
for module in self.unet.modules():
if (
hasattr(__SCREAMING_SNAKE_CASE , '''_hf_hook''' )
and hasattr(module._hf_hook , '''execution_device''' )
and module._hf_hook.execution_device is not None
):
return torch.device(module._hf_hook.execution_device )
return self.device
@torch.no_grad()
@replace_example_docstring(__SCREAMING_SNAKE_CASE )
def __call__( self : Union[str, Any] , __SCREAMING_SNAKE_CASE : Any , __SCREAMING_SNAKE_CASE : List[str] , __SCREAMING_SNAKE_CASE : Union[str, Any] , __SCREAMING_SNAKE_CASE : Tuple = None , __SCREAMING_SNAKE_CASE : Optional[Any] = 5_12 , __SCREAMING_SNAKE_CASE : str = 5_12 , __SCREAMING_SNAKE_CASE : Dict = 1_00 , __SCREAMING_SNAKE_CASE : Optional[int] = 4.0 , __SCREAMING_SNAKE_CASE : Tuple = 1 , __SCREAMING_SNAKE_CASE : Dict = None , __SCREAMING_SNAKE_CASE : Dict = None , __SCREAMING_SNAKE_CASE : Optional[Any] = "pil" , __SCREAMING_SNAKE_CASE : str = True , ) -> str:
"""simple docstring"""
if isinstance(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
__SCREAMING_SNAKE_CASE = 1
elif isinstance(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
__SCREAMING_SNAKE_CASE = len(__SCREAMING_SNAKE_CASE )
else:
raise ValueError(f"""`prompt` has to be of type `str` or `list` but is {type(__SCREAMING_SNAKE_CASE )}""" )
__SCREAMING_SNAKE_CASE = self._execution_device
__SCREAMING_SNAKE_CASE = batch_size * num_images_per_prompt
__SCREAMING_SNAKE_CASE = guidance_scale > 1.0
__SCREAMING_SNAKE_CASE = self._encode_prompt(
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
if isinstance(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
__SCREAMING_SNAKE_CASE = torch.cat(__SCREAMING_SNAKE_CASE , dim=0 )
if isinstance(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
__SCREAMING_SNAKE_CASE = torch.cat(__SCREAMING_SNAKE_CASE , dim=0 )
if do_classifier_free_guidance:
__SCREAMING_SNAKE_CASE = image_embeds.repeat_interleave(__SCREAMING_SNAKE_CASE , dim=0 )
__SCREAMING_SNAKE_CASE = negative_image_embeds.repeat_interleave(__SCREAMING_SNAKE_CASE , dim=0 )
__SCREAMING_SNAKE_CASE = torch.cat([negative_image_embeds, image_embeds] , dim=0 ).to(
dtype=prompt_embeds.dtype , device=__SCREAMING_SNAKE_CASE )
self.scheduler.set_timesteps(__SCREAMING_SNAKE_CASE , device=__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = self.scheduler.timesteps
__SCREAMING_SNAKE_CASE = self.unet.config.in_channels
__SCREAMING_SNAKE_CASE = get_new_h_w(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , self.movq_scale_factor )
# create initial latent
__SCREAMING_SNAKE_CASE = self.prepare_latents(
(batch_size, num_channels_latents, height, width) , text_encoder_hidden_states.dtype , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , self.scheduler , )
for i, t in enumerate(self.progress_bar(__SCREAMING_SNAKE_CASE ) ):
# expand the latents if we are doing classifier free guidance
__SCREAMING_SNAKE_CASE = torch.cat([latents] * 2 ) if do_classifier_free_guidance else latents
__SCREAMING_SNAKE_CASE = {"""text_embeds""": prompt_embeds, """image_embeds""": image_embeds}
__SCREAMING_SNAKE_CASE = self.unet(
sample=__SCREAMING_SNAKE_CASE , timestep=__SCREAMING_SNAKE_CASE , encoder_hidden_states=__SCREAMING_SNAKE_CASE , added_cond_kwargs=__SCREAMING_SNAKE_CASE , return_dict=__SCREAMING_SNAKE_CASE , )[0]
if do_classifier_free_guidance:
__SCREAMING_SNAKE_CASE = noise_pred.split(latents.shape[1] , dim=1 )
__SCREAMING_SNAKE_CASE = noise_pred.chunk(2 )
__SCREAMING_SNAKE_CASE = variance_pred.chunk(2 )
__SCREAMING_SNAKE_CASE = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
__SCREAMING_SNAKE_CASE = torch.cat([noise_pred, variance_pred_text] , dim=1 )
if not (
hasattr(self.scheduler.config , '''variance_type''' )
and self.scheduler.config.variance_type in ["learned", "learned_range"]
):
__SCREAMING_SNAKE_CASE = noise_pred.split(latents.shape[1] , dim=1 )
# compute the previous noisy sample x_t -> x_t-1
__SCREAMING_SNAKE_CASE = self.scheduler.step(
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , generator=__SCREAMING_SNAKE_CASE , ).prev_sample
# post-processing
__SCREAMING_SNAKE_CASE = self.movq.decode(__SCREAMING_SNAKE_CASE , force_not_quantize=__SCREAMING_SNAKE_CASE )["""sample"""]
if output_type not in ["pt", "np", "pil"]:
raise ValueError(f"""Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}""" )
if output_type in ["np", "pil"]:
__SCREAMING_SNAKE_CASE = image * 0.5 + 0.5
__SCREAMING_SNAKE_CASE = image.clamp(0 , 1 )
__SCREAMING_SNAKE_CASE = image.cpu().permute(0 , 2 , 3 , 1 ).float().numpy()
if output_type == "pil":
__SCREAMING_SNAKE_CASE = self.numpy_to_pil(__SCREAMING_SNAKE_CASE )
if not return_dict:
return (image,)
return ImagePipelineOutput(images=__SCREAMING_SNAKE_CASE )
| 708 |
"""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 | 0 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
lowerCAmelCase__ =logging.get_logger(__name__)
lowerCAmelCase__ ={
"shi-labs/nat-mini-in1k-224": "https://huggingface.co/shi-labs/nat-mini-in1k-224/resolve/main/config.json",
# See all Nat models at https://huggingface.co/models?filter=nat
}
class A__( __A , __A ):
'''simple docstring'''
lowerCAmelCase = """nat"""
lowerCAmelCase = {
"""num_attention_heads""": """num_heads""",
"""num_hidden_layers""": """num_layers""",
}
def __init__( self : Tuple , __SCREAMING_SNAKE_CASE : Dict=4 , __SCREAMING_SNAKE_CASE : Any=3 , __SCREAMING_SNAKE_CASE : List[Any]=64 , __SCREAMING_SNAKE_CASE : Union[str, Any]=[3, 4, 6, 5] , __SCREAMING_SNAKE_CASE : List[Any]=[2, 4, 8, 16] , __SCREAMING_SNAKE_CASE : Any=7 , __SCREAMING_SNAKE_CASE : Optional[Any]=3.0 , __SCREAMING_SNAKE_CASE : Tuple=True , __SCREAMING_SNAKE_CASE : List[str]=0.0 , __SCREAMING_SNAKE_CASE : int=0.0 , __SCREAMING_SNAKE_CASE : List[Any]=0.1 , __SCREAMING_SNAKE_CASE : Tuple="gelu" , __SCREAMING_SNAKE_CASE : List[str]=0.02 , __SCREAMING_SNAKE_CASE : Tuple=1E-5 , __SCREAMING_SNAKE_CASE : List[str]=0.0 , __SCREAMING_SNAKE_CASE : Union[str, Any]=None , __SCREAMING_SNAKE_CASE : Dict=None , **__SCREAMING_SNAKE_CASE : List[str] , ) -> str:
"""simple docstring"""
super().__init__(**UpperCamelCase__ )
__SCREAMING_SNAKE_CASE = patch_size
__SCREAMING_SNAKE_CASE = num_channels
__SCREAMING_SNAKE_CASE = embed_dim
__SCREAMING_SNAKE_CASE = depths
__SCREAMING_SNAKE_CASE = len(UpperCamelCase__ )
__SCREAMING_SNAKE_CASE = num_heads
__SCREAMING_SNAKE_CASE = kernel_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 = layer_norm_eps
__SCREAMING_SNAKE_CASE = initializer_range
# we set the hidden_size attribute in order to make Nat work with VisionEncoderDecoderModel
# this indicates the channel dimension after the last stage of the model
__SCREAMING_SNAKE_CASE = int(embed_dim * 2 ** (len(UpperCamelCase__ ) - 1) )
__SCREAMING_SNAKE_CASE = layer_scale_init_value
__SCREAMING_SNAKE_CASE = ['stem'] + [f"""stage{idx}""" for idx in range(1 , len(UpperCamelCase__ ) + 1 )]
__SCREAMING_SNAKE_CASE = get_aligned_output_features_output_indices(
out_features=UpperCamelCase__ , out_indices=UpperCamelCase__ , stage_names=self.stage_names )
| 709 |
"""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 | 0 |
"""simple docstring"""
import argparse
import json
from pathlib import Path
import torch
import torchaudio
from datasets import load_dataset
from huggingface_hub import hf_hub_download
from transformers import ASTConfig, ASTFeatureExtractor, ASTForAudioClassification
from transformers.utils import logging
logging.set_verbosity_info()
lowerCAmelCase__ =logging.get_logger(__name__)
def _a ( UpperCAmelCase__ ) -> Dict:
__SCREAMING_SNAKE_CASE = ASTConfig()
if "10-10" in model_name:
pass
elif "speech-commands" in model_name:
__SCREAMING_SNAKE_CASE = 1_28
elif "12-12" in model_name:
__SCREAMING_SNAKE_CASE = 12
__SCREAMING_SNAKE_CASE = 12
elif "14-14" in model_name:
__SCREAMING_SNAKE_CASE = 14
__SCREAMING_SNAKE_CASE = 14
elif "16-16" in model_name:
__SCREAMING_SNAKE_CASE = 16
__SCREAMING_SNAKE_CASE = 16
else:
raise ValueError('''Model not supported''' )
__SCREAMING_SNAKE_CASE = '''huggingface/label-files'''
if "speech-commands" in model_name:
__SCREAMING_SNAKE_CASE = 35
__SCREAMING_SNAKE_CASE = '''speech-commands-v2-id2label.json'''
else:
__SCREAMING_SNAKE_CASE = 5_27
__SCREAMING_SNAKE_CASE = '''audioset-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 = idalabel
__SCREAMING_SNAKE_CASE = {v: k for k, v in idalabel.items()}
return config
def _a ( UpperCAmelCase__ ) -> str:
if "module.v" in name:
__SCREAMING_SNAKE_CASE = name.replace('''module.v''' , '''audio_spectrogram_transformer''' )
if "cls_token" in name:
__SCREAMING_SNAKE_CASE = name.replace('''cls_token''' , '''embeddings.cls_token''' )
if "dist_token" in name:
__SCREAMING_SNAKE_CASE = name.replace('''dist_token''' , '''embeddings.distillation_token''' )
if "pos_embed" in name:
__SCREAMING_SNAKE_CASE = name.replace('''pos_embed''' , '''embeddings.position_embeddings''' )
if "patch_embed.proj" in name:
__SCREAMING_SNAKE_CASE = name.replace('''patch_embed.proj''' , '''embeddings.patch_embeddings.projection''' )
# transformer blocks
if "blocks" in name:
__SCREAMING_SNAKE_CASE = name.replace('''blocks''' , '''encoder.layer''' )
if "attn.proj" in name:
__SCREAMING_SNAKE_CASE = name.replace('''attn.proj''' , '''attention.output.dense''' )
if "attn" in name:
__SCREAMING_SNAKE_CASE = name.replace('''attn''' , '''attention.self''' )
if "norm1" in name:
__SCREAMING_SNAKE_CASE = name.replace('''norm1''' , '''layernorm_before''' )
if "norm2" in name:
__SCREAMING_SNAKE_CASE = name.replace('''norm2''' , '''layernorm_after''' )
if "mlp.fc1" in name:
__SCREAMING_SNAKE_CASE = name.replace('''mlp.fc1''' , '''intermediate.dense''' )
if "mlp.fc2" in name:
__SCREAMING_SNAKE_CASE = name.replace('''mlp.fc2''' , '''output.dense''' )
# final layernorm
if "audio_spectrogram_transformer.norm" in name:
__SCREAMING_SNAKE_CASE = name.replace('''audio_spectrogram_transformer.norm''' , '''audio_spectrogram_transformer.layernorm''' )
# classifier head
if "module.mlp_head.0" in name:
__SCREAMING_SNAKE_CASE = name.replace('''module.mlp_head.0''' , '''classifier.layernorm''' )
if "module.mlp_head.1" in name:
__SCREAMING_SNAKE_CASE = name.replace('''module.mlp_head.1''' , '''classifier.dense''' )
return name
def _a ( UpperCAmelCase__ , UpperCAmelCase__ ) -> str:
for key in orig_state_dict.copy().keys():
__SCREAMING_SNAKE_CASE = orig_state_dict.pop(UpperCAmelCase__ )
if "qkv" in key:
__SCREAMING_SNAKE_CASE = key.split('''.''' )
__SCREAMING_SNAKE_CASE = int(key_split[3] )
__SCREAMING_SNAKE_CASE = config.hidden_size
if "weight" in key:
__SCREAMING_SNAKE_CASE = val[:dim, :]
__SCREAMING_SNAKE_CASE = val[dim : dim * 2, :]
__SCREAMING_SNAKE_CASE = val[-dim:, :]
else:
__SCREAMING_SNAKE_CASE = val[:dim]
__SCREAMING_SNAKE_CASE = val[dim : dim * 2]
__SCREAMING_SNAKE_CASE = val[-dim:]
else:
__SCREAMING_SNAKE_CASE = val
return orig_state_dict
def _a ( UpperCAmelCase__ ) -> Optional[int]:
__SCREAMING_SNAKE_CASE = [
'''module.v.head.weight''',
'''module.v.head.bias''',
'''module.v.head_dist.weight''',
'''module.v.head_dist.bias''',
]
for k in ignore_keys:
state_dict.pop(UpperCAmelCase__ , UpperCAmelCase__ )
@torch.no_grad()
def _a ( UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__=False ) -> Tuple:
__SCREAMING_SNAKE_CASE = get_audio_spectrogram_transformer_config(UpperCAmelCase__ )
__SCREAMING_SNAKE_CASE = {
'''ast-finetuned-audioset-10-10-0.4593''': (
'''https://www.dropbox.com/s/ca0b1v2nlxzyeb4/audioset_10_10_0.4593.pth?dl=1'''
),
'''ast-finetuned-audioset-10-10-0.450''': (
'''https://www.dropbox.com/s/1tv0hovue1bxupk/audioset_10_10_0.4495.pth?dl=1'''
),
'''ast-finetuned-audioset-10-10-0.448''': (
'''https://www.dropbox.com/s/6u5sikl4b9wo4u5/audioset_10_10_0.4483.pth?dl=1'''
),
'''ast-finetuned-audioset-10-10-0.448-v2''': (
'''https://www.dropbox.com/s/kt6i0v9fvfm1mbq/audioset_10_10_0.4475.pth?dl=1'''
),
'''ast-finetuned-audioset-12-12-0.447''': (
'''https://www.dropbox.com/s/snfhx3tizr4nuc8/audioset_12_12_0.4467.pth?dl=1'''
),
'''ast-finetuned-audioset-14-14-0.443''': (
'''https://www.dropbox.com/s/z18s6pemtnxm4k7/audioset_14_14_0.4431.pth?dl=1'''
),
'''ast-finetuned-audioset-16-16-0.442''': (
'''https://www.dropbox.com/s/mdsa4t1xmcimia6/audioset_16_16_0.4422.pth?dl=1'''
),
'''ast-finetuned-speech-commands-v2''': (
'''https://www.dropbox.com/s/q0tbqpwv44pquwy/speechcommands_10_10_0.9812.pth?dl=1'''
),
}
# load original state_dict
__SCREAMING_SNAKE_CASE = model_name_to_url[model_name]
__SCREAMING_SNAKE_CASE = torch.hub.load_state_dict_from_url(UpperCAmelCase__ , map_location='''cpu''' )
# remove some keys
remove_keys(UpperCAmelCase__ )
# rename some keys
__SCREAMING_SNAKE_CASE = convert_state_dict(UpperCAmelCase__ , UpperCAmelCase__ )
# load 🤗 model
__SCREAMING_SNAKE_CASE = ASTForAudioClassification(UpperCAmelCase__ )
model.eval()
model.load_state_dict(UpperCAmelCase__ )
# verify outputs on dummy input
# source: https://github.com/YuanGongND/ast/blob/79e873b8a54d0a3b330dd522584ff2b9926cd581/src/run.py#L62
__SCREAMING_SNAKE_CASE = -4.2677393 if '''speech-commands''' not in model_name else -6.845978
__SCREAMING_SNAKE_CASE = 4.5689974 if '''speech-commands''' not in model_name else 5.5654526
__SCREAMING_SNAKE_CASE = 10_24 if '''speech-commands''' not in model_name else 1_28
__SCREAMING_SNAKE_CASE = ASTFeatureExtractor(mean=UpperCAmelCase__ , std=UpperCAmelCase__ , max_length=UpperCAmelCase__ )
if "speech-commands" in model_name:
__SCREAMING_SNAKE_CASE = load_dataset('''speech_commands''' , '''v0.02''' , split='''validation''' )
__SCREAMING_SNAKE_CASE = dataset[0]['''audio''']['''array''']
else:
__SCREAMING_SNAKE_CASE = hf_hub_download(
repo_id='''nielsr/audio-spectogram-transformer-checkpoint''' , filename='''sample_audio.flac''' , repo_type='''dataset''' , )
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = torchaudio.load(UpperCAmelCase__ )
__SCREAMING_SNAKE_CASE = waveform.squeeze().numpy()
__SCREAMING_SNAKE_CASE = feature_extractor(UpperCAmelCase__ , sampling_rate=1_60_00 , return_tensors='''pt''' )
# forward pass
__SCREAMING_SNAKE_CASE = model(**UpperCAmelCase__ )
__SCREAMING_SNAKE_CASE = outputs.logits
if model_name == "ast-finetuned-audioset-10-10-0.4593":
__SCREAMING_SNAKE_CASE = torch.tensor([-0.8760, -7.0042, -8.6602] )
elif model_name == "ast-finetuned-audioset-10-10-0.450":
__SCREAMING_SNAKE_CASE = torch.tensor([-1.1986, -7.0903, -8.2718] )
elif model_name == "ast-finetuned-audioset-10-10-0.448":
__SCREAMING_SNAKE_CASE = torch.tensor([-2.6128, -8.0080, -9.4344] )
elif model_name == "ast-finetuned-audioset-10-10-0.448-v2":
__SCREAMING_SNAKE_CASE = torch.tensor([-1.5080, -7.4534, -8.8917] )
elif model_name == "ast-finetuned-audioset-12-12-0.447":
__SCREAMING_SNAKE_CASE = torch.tensor([-0.5050, -6.5833, -8.0843] )
elif model_name == "ast-finetuned-audioset-14-14-0.443":
__SCREAMING_SNAKE_CASE = torch.tensor([-0.3826, -7.0336, -8.2413] )
elif model_name == "ast-finetuned-audioset-16-16-0.442":
__SCREAMING_SNAKE_CASE = torch.tensor([-1.2113, -6.9101, -8.3470] )
elif model_name == "ast-finetuned-speech-commands-v2":
__SCREAMING_SNAKE_CASE = torch.tensor([6.1589, -8.0566, -8.7984] )
else:
raise ValueError('''Unknown model name''' )
if not torch.allclose(logits[0, :3] , UpperCAmelCase__ , atol=1E-4 ):
raise ValueError('''Logits don\'t match''' )
print('''Looks ok!''' )
if pytorch_dump_folder_path is not None:
Path(UpperCAmelCase__ ).mkdir(exist_ok=UpperCAmelCase__ )
print(f"""Saving model {model_name} to {pytorch_dump_folder_path}""" )
model.save_pretrained(UpperCAmelCase__ )
print(f"""Saving feature extractor to {pytorch_dump_folder_path}""" )
feature_extractor.save_pretrained(UpperCAmelCase__ )
if push_to_hub:
print('''Pushing model and feature extractor to the hub...''' )
model.push_to_hub(f"""MIT/{model_name}""" )
feature_extractor.push_to_hub(f"""MIT/{model_name}""" )
if __name__ == "__main__":
lowerCAmelCase__ =argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--model_name",
default="ast-finetuned-audioset-10-10-0.4593",
type=str,
help="Name of the Audio Spectrogram Transformer 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_audio_spectrogram_transformer_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
| 710 |
"""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 | 0 |
"""simple docstring"""
# DISCLAIMER: This file is strongly influenced by https://github.com/ermongroup/ddim
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import flax
import jax
import jax.numpy as jnp
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import (
CommonSchedulerState,
FlaxKarrasDiffusionSchedulers,
FlaxSchedulerMixin,
FlaxSchedulerOutput,
add_noise_common,
get_velocity_common,
)
@flax.struct.dataclass
class A__:
lowerCAmelCase = 42
# setable values
lowerCAmelCase = 42
lowerCAmelCase = 42
lowerCAmelCase = None
@classmethod
def _a ( cls : Optional[int] , __SCREAMING_SNAKE_CASE : CommonSchedulerState , __SCREAMING_SNAKE_CASE : jnp.ndarray , __SCREAMING_SNAKE_CASE : jnp.ndarray ) -> List[Any]:
"""simple docstring"""
return cls(common=UpperCamelCase__ , init_noise_sigma=UpperCamelCase__ , timesteps=UpperCamelCase__ )
@dataclass
class A__( lowercase_ ):
lowerCAmelCase = 42
class A__( lowercase_ , lowercase_ ):
lowerCAmelCase = [e.name for e in FlaxKarrasDiffusionSchedulers]
lowerCAmelCase = 42
@property
def _a ( self : Dict ) -> List[Any]:
"""simple docstring"""
return True
@register_to_config
def __init__( self : int , __SCREAMING_SNAKE_CASE : int = 10_00 , __SCREAMING_SNAKE_CASE : float = 0.00_01 , __SCREAMING_SNAKE_CASE : float = 0.02 , __SCREAMING_SNAKE_CASE : str = "linear" , __SCREAMING_SNAKE_CASE : Optional[jnp.ndarray] = None , __SCREAMING_SNAKE_CASE : str = "fixed_small" , __SCREAMING_SNAKE_CASE : bool = True , __SCREAMING_SNAKE_CASE : str = "epsilon" , __SCREAMING_SNAKE_CASE : jnp.dtype = jnp.floataa , ) -> Dict:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = dtype
def _a ( self : Tuple , __SCREAMING_SNAKE_CASE : Optional[CommonSchedulerState] = None ) -> Optional[int]:
"""simple docstring"""
if common is None:
__SCREAMING_SNAKE_CASE = CommonSchedulerState.create(self )
# standard deviation of the initial noise distribution
__SCREAMING_SNAKE_CASE = jnp.array(1.0 , dtype=self.dtype )
__SCREAMING_SNAKE_CASE = jnp.arange(0 , self.config.num_train_timesteps ).round()[::-1]
return DDPMSchedulerState.create(
common=UpperCamelCase__ , init_noise_sigma=UpperCamelCase__ , timesteps=UpperCamelCase__ , )
def _a ( self : str , __SCREAMING_SNAKE_CASE : DDPMSchedulerState , __SCREAMING_SNAKE_CASE : jnp.ndarray , __SCREAMING_SNAKE_CASE : Optional[int] = None ) -> Optional[Any]:
"""simple docstring"""
return sample
def _a ( self : Tuple , __SCREAMING_SNAKE_CASE : DDPMSchedulerState , __SCREAMING_SNAKE_CASE : int , __SCREAMING_SNAKE_CASE : Tuple = () ) -> int:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = self.config.num_train_timesteps // num_inference_steps
# creates integer timesteps by multiplying by ratio
# rounding to avoid issues when num_inference_step is power of 3
__SCREAMING_SNAKE_CASE = (jnp.arange(0 , UpperCamelCase__ ) * step_ratio).round()[::-1]
return state.replace(
num_inference_steps=UpperCamelCase__ , timesteps=UpperCamelCase__ , )
def _a ( self : Dict , __SCREAMING_SNAKE_CASE : DDPMSchedulerState , __SCREAMING_SNAKE_CASE : Optional[Any] , __SCREAMING_SNAKE_CASE : List[Any]=None , __SCREAMING_SNAKE_CASE : List[str]=None ) -> Union[str, Any]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = state.common.alphas_cumprod[t]
__SCREAMING_SNAKE_CASE = jnp.where(t > 0 , state.common.alphas_cumprod[t - 1] , jnp.array(1.0 , dtype=self.dtype ) )
# For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf)
# and sample from it to get previous sample
# x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample
__SCREAMING_SNAKE_CASE = (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * state.common.betas[t]
if variance_type is None:
__SCREAMING_SNAKE_CASE = self.config.variance_type
# hacks - were probably added for training stability
if variance_type == "fixed_small":
__SCREAMING_SNAKE_CASE = jnp.clip(UpperCamelCase__ , a_min=1E-2_0 )
# for rl-diffuser https://arxiv.org/abs/2205.09991
elif variance_type == "fixed_small_log":
__SCREAMING_SNAKE_CASE = jnp.log(jnp.clip(UpperCamelCase__ , a_min=1E-2_0 ) )
elif variance_type == "fixed_large":
__SCREAMING_SNAKE_CASE = state.common.betas[t]
elif variance_type == "fixed_large_log":
# Glide max_log
__SCREAMING_SNAKE_CASE = jnp.log(state.common.betas[t] )
elif variance_type == "learned":
return predicted_variance
elif variance_type == "learned_range":
__SCREAMING_SNAKE_CASE = variance
__SCREAMING_SNAKE_CASE = state.common.betas[t]
__SCREAMING_SNAKE_CASE = (predicted_variance + 1) / 2
__SCREAMING_SNAKE_CASE = frac * max_log + (1 - frac) * min_log
return variance
def _a ( self : str , __SCREAMING_SNAKE_CASE : DDPMSchedulerState , __SCREAMING_SNAKE_CASE : jnp.ndarray , __SCREAMING_SNAKE_CASE : int , __SCREAMING_SNAKE_CASE : jnp.ndarray , __SCREAMING_SNAKE_CASE : Optional[jax.random.KeyArray] = None , __SCREAMING_SNAKE_CASE : bool = True , ) -> int:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = timestep
if key is None:
__SCREAMING_SNAKE_CASE = jax.random.PRNGKey(0 )
if model_output.shape[1] == sample.shape[1] * 2 and self.config.variance_type in ["learned", "learned_range"]:
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = jnp.split(UpperCamelCase__ , sample.shape[1] , axis=1 )
else:
__SCREAMING_SNAKE_CASE = None
# 1. compute alphas, betas
__SCREAMING_SNAKE_CASE = state.common.alphas_cumprod[t]
__SCREAMING_SNAKE_CASE = jnp.where(t > 0 , state.common.alphas_cumprod[t - 1] , jnp.array(1.0 , dtype=self.dtype ) )
__SCREAMING_SNAKE_CASE = 1 - alpha_prod_t
__SCREAMING_SNAKE_CASE = 1 - alpha_prod_t_prev
# 2. compute predicted original sample from predicted noise also called
# "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
if self.config.prediction_type == "epsilon":
__SCREAMING_SNAKE_CASE = (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
elif self.config.prediction_type == "sample":
__SCREAMING_SNAKE_CASE = model_output
elif self.config.prediction_type == "v_prediction":
__SCREAMING_SNAKE_CASE = (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
else:
raise ValueError(
f"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample` """
''' for the FlaxDDPMScheduler.''' )
# 3. Clip "predicted x_0"
if self.config.clip_sample:
__SCREAMING_SNAKE_CASE = jnp.clip(UpperCamelCase__ , -1 , 1 )
# 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
__SCREAMING_SNAKE_CASE = (alpha_prod_t_prev ** 0.5 * state.common.betas[t]) / beta_prod_t
__SCREAMING_SNAKE_CASE = state.common.alphas[t] ** 0.5 * beta_prod_t_prev / beta_prod_t
# 5. Compute predicted previous sample µ_t
# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
__SCREAMING_SNAKE_CASE = pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample
# 6. Add noise
def random_variance():
__SCREAMING_SNAKE_CASE = jax.random.split(UpperCamelCase__ , num=1 )
__SCREAMING_SNAKE_CASE = jax.random.normal(UpperCamelCase__ , shape=model_output.shape , dtype=self.dtype )
return (self._get_variance(UpperCamelCase__ , UpperCamelCase__ , predicted_variance=UpperCamelCase__ ) ** 0.5) * noise
__SCREAMING_SNAKE_CASE = jnp.where(t > 0 , random_variance() , jnp.zeros(model_output.shape , dtype=self.dtype ) )
__SCREAMING_SNAKE_CASE = pred_prev_sample + variance
if not return_dict:
return (pred_prev_sample, state)
return FlaxDDPMSchedulerOutput(prev_sample=UpperCamelCase__ , state=UpperCamelCase__ )
def _a ( self : List[Any] , __SCREAMING_SNAKE_CASE : DDPMSchedulerState , __SCREAMING_SNAKE_CASE : jnp.ndarray , __SCREAMING_SNAKE_CASE : jnp.ndarray , __SCREAMING_SNAKE_CASE : jnp.ndarray , ) -> Tuple:
"""simple docstring"""
return add_noise_common(state.common , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
def _a ( self : List[str] , __SCREAMING_SNAKE_CASE : DDPMSchedulerState , __SCREAMING_SNAKE_CASE : jnp.ndarray , __SCREAMING_SNAKE_CASE : jnp.ndarray , __SCREAMING_SNAKE_CASE : jnp.ndarray , ) -> List[Any]:
"""simple docstring"""
return get_velocity_common(state.common , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
def __len__( self : Any ) -> List[Any]:
"""simple docstring"""
return self.config.num_train_timesteps
| 711 |
"""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 | 0 |
"""simple docstring"""
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class A__( __UpperCAmelCase ):
lowerCAmelCase = ['''image_processor''', '''tokenizer''']
lowerCAmelCase = '''BridgeTowerImageProcessor'''
lowerCAmelCase = ('''RobertaTokenizer''', '''RobertaTokenizerFast''')
def __init__( self : Dict , __SCREAMING_SNAKE_CASE : Tuple , __SCREAMING_SNAKE_CASE : str ) -> List[Any]:
"""simple docstring"""
super().__init__(lowerCAmelCase_ , lowerCAmelCase_ )
def __call__( self : str , __SCREAMING_SNAKE_CASE : Optional[int] , __SCREAMING_SNAKE_CASE : Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None , __SCREAMING_SNAKE_CASE : bool = True , __SCREAMING_SNAKE_CASE : Union[bool, str, PaddingStrategy] = False , __SCREAMING_SNAKE_CASE : Union[bool, str, TruncationStrategy] = None , __SCREAMING_SNAKE_CASE : Optional[int] = None , __SCREAMING_SNAKE_CASE : int = 0 , __SCREAMING_SNAKE_CASE : Optional[int] = None , __SCREAMING_SNAKE_CASE : Optional[bool] = None , __SCREAMING_SNAKE_CASE : Optional[bool] = None , __SCREAMING_SNAKE_CASE : bool = False , __SCREAMING_SNAKE_CASE : bool = False , __SCREAMING_SNAKE_CASE : bool = False , __SCREAMING_SNAKE_CASE : bool = False , __SCREAMING_SNAKE_CASE : bool = True , __SCREAMING_SNAKE_CASE : Optional[Union[str, TensorType]] = None , **__SCREAMING_SNAKE_CASE : str , ) -> List[str]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = self.tokenizer(
text=lowerCAmelCase_ , add_special_tokens=lowerCAmelCase_ , padding=lowerCAmelCase_ , truncation=lowerCAmelCase_ , max_length=lowerCAmelCase_ , stride=lowerCAmelCase_ , pad_to_multiple_of=lowerCAmelCase_ , return_token_type_ids=lowerCAmelCase_ , return_attention_mask=lowerCAmelCase_ , return_overflowing_tokens=lowerCAmelCase_ , return_special_tokens_mask=lowerCAmelCase_ , return_offsets_mapping=lowerCAmelCase_ , return_length=lowerCAmelCase_ , verbose=lowerCAmelCase_ , return_tensors=lowerCAmelCase_ , **lowerCAmelCase_ , )
# add pixel_values + pixel_mask
__SCREAMING_SNAKE_CASE = self.image_processor(
lowerCAmelCase_ , return_tensors=lowerCAmelCase_ , do_normalize=lowerCAmelCase_ , do_center_crop=lowerCAmelCase_ , **lowerCAmelCase_ )
encoding.update(lowerCAmelCase_ )
return encoding
def _a ( self : Any , *__SCREAMING_SNAKE_CASE : Optional[int] , **__SCREAMING_SNAKE_CASE : Dict ) -> Union[str, Any]:
"""simple docstring"""
return self.tokenizer.batch_decode(*lowerCAmelCase_ , **lowerCAmelCase_ )
def _a ( self : Tuple , *__SCREAMING_SNAKE_CASE : List[str] , **__SCREAMING_SNAKE_CASE : str ) -> str:
"""simple docstring"""
return self.tokenizer.decode(*lowerCAmelCase_ , **lowerCAmelCase_ )
@property
def _a ( self : Tuple ) -> Any:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = self.tokenizer.model_input_names
__SCREAMING_SNAKE_CASE = self.image_processor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )
| 712 |
"""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 | 0 |
"""simple docstring"""
import multiprocessing
import time
from arguments import PretokenizationArguments
from datasets import load_dataset
from transformers import AutoTokenizer, HfArgumentParser
def _a ( UpperCAmelCase__ ) -> List[str]:
__SCREAMING_SNAKE_CASE = {}
__SCREAMING_SNAKE_CASE = tokenizer(example['''content'''] , truncation=a_ )['''input_ids''']
__SCREAMING_SNAKE_CASE = len(example['''content'''] ) / len(output['''input_ids'''] )
return output
lowerCAmelCase__ =HfArgumentParser(PretokenizationArguments)
lowerCAmelCase__ =parser.parse_args()
if args.num_workers is None:
lowerCAmelCase__ =multiprocessing.cpu_count()
lowerCAmelCase__ =AutoTokenizer.from_pretrained(args.tokenizer_dir)
lowerCAmelCase__ =time.time()
lowerCAmelCase__ =load_dataset(args.dataset_name, split="train")
print(F'''Dataset loaded in {time.time()-t_start:.2f}s''')
lowerCAmelCase__ =time.time()
lowerCAmelCase__ =ds.map(
tokenize,
num_proc=args.num_workers,
remove_columns=[
"repo_name",
"path",
"copies",
"size",
"content",
"license",
"hash",
"line_mean",
"line_max",
"alpha_frac",
"autogenerated",
],
)
print(F'''Dataset tokenized in {time.time()-t_start:.2f}s''')
lowerCAmelCase__ =time.time()
ds.push_to_hub(args.tokenized_data_repo)
print(F'''Data pushed to the hub in {time.time()-t_start:.2f}s''')
| 713 |
"""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 | 0 |
"""simple docstring"""
import os
import posixpath
import uuid
from dataclasses import dataclass
from typing import TYPE_CHECKING, Iterable, List, Optional, Tuple, Union
import numpy as np
import pyarrow as pa
import datasets
from datasets.arrow_writer import ArrowWriter, ParquetWriter
from datasets.config import MAX_SHARD_SIZE
from datasets.filesystems import (
is_remote_filesystem,
rename,
)
from datasets.iterable_dataset import _BaseExamplesIterable
from datasets.utils.py_utils import convert_file_size_to_int
lowerCAmelCase__ =datasets.utils.logging.get_logger(__name__)
if TYPE_CHECKING:
import pyspark
@dataclass
class A__( datasets.BuilderConfig ):
lowerCAmelCase = None
def _a ( UpperCAmelCase__ , UpperCAmelCase__ , ) -> Optional[Any]:
import pyspark
def generate_fn():
__SCREAMING_SNAKE_CASE = df.select('''*''' , pyspark.sql.functions.spark_partition_id().alias('''part_id''' ) )
for partition_id in partition_order:
__SCREAMING_SNAKE_CASE = df_with_partition_id.select('''*''' ).where(f"""part_id = {partition_id}""" ).drop('''part_id''' )
__SCREAMING_SNAKE_CASE = partition_df.collect()
__SCREAMING_SNAKE_CASE = 0
for row in rows:
yield f"""{partition_id}_{row_id}""", row.asDict()
row_id += 1
return generate_fn
class A__( _BaseExamplesIterable ):
def __init__( self : Any , __SCREAMING_SNAKE_CASE : Dict , __SCREAMING_SNAKE_CASE : List[Any]=None , ) -> List[Any]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = df
__SCREAMING_SNAKE_CASE = partition_order or range(self.df.rdd.getNumPartitions() )
__SCREAMING_SNAKE_CASE = _generate_iterable_examples(self.df , self.partition_order )
def __iter__( self : Optional[int] ) -> List[str]:
"""simple docstring"""
yield from self.generate_examples_fn()
def _a ( self : Tuple , __SCREAMING_SNAKE_CASE : Tuple ) -> Optional[int]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = list(range(self.df.rdd.getNumPartitions() ) )
generator.shuffle(_A )
return SparkExamplesIterable(self.df , partition_order=_A )
def _a ( self : int , __SCREAMING_SNAKE_CASE : Any , __SCREAMING_SNAKE_CASE : Optional[Any] ) -> int:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = self.split_shard_indices_by_worker(_A , _A )
return SparkExamplesIterable(self.df , partition_order=_A )
@property
def _a ( self : List[str] ) -> List[Any]:
"""simple docstring"""
return len(self.partition_order )
class A__( datasets.DatasetBuilder ):
lowerCAmelCase = SparkConfig
def __init__( self : int , __SCREAMING_SNAKE_CASE : Tuple , __SCREAMING_SNAKE_CASE : Optional[int] = None , __SCREAMING_SNAKE_CASE : List[Any] = None , **__SCREAMING_SNAKE_CASE : List[Any] , ) -> List[Any]:
"""simple docstring"""
import pyspark
__SCREAMING_SNAKE_CASE = pyspark.sql.SparkSession.builder.getOrCreate()
__SCREAMING_SNAKE_CASE = df
__SCREAMING_SNAKE_CASE = working_dir
super().__init__(
cache_dir=_A , config_name=str(self.df.semanticHash() ) , **_A , )
def _a ( self : Dict ) -> List[str]:
"""simple docstring"""
def create_cache_and_write_probe(__SCREAMING_SNAKE_CASE : Any ):
# makedirs with exist_ok will recursively create the directory. It will not throw an error if directories
# already exist.
os.makedirs(self._cache_dir , exist_ok=_A )
__SCREAMING_SNAKE_CASE = os.path.join(self._cache_dir , '''fs_test''' + uuid.uuida().hex )
# Opening the file in append mode will create a new file unless it already exists, in which case it will not
# change the file contents.
open(_A , '''a''' )
return [probe_file]
if self._spark.conf.get('''spark.master''' , '''''' ).startswith('''local''' ):
return
# If the cluster is multi-node, make sure that the user provided a cache_dir and that it is on an NFS
# accessible to the driver.
# TODO: Stream batches to the driver using ArrowCollectSerializer instead of throwing an error.
if self._cache_dir:
__SCREAMING_SNAKE_CASE = (
self._spark.sparkContext.parallelize(range(1 ) , 1 ).mapPartitions(_A ).collect()
)
if os.path.isfile(probe[0] ):
return
raise ValueError(
'''When using Dataset.from_spark on a multi-node cluster, the driver and all workers should be able to access cache_dir''' )
def _a ( self : Any ) -> List[Any]:
"""simple docstring"""
return datasets.DatasetInfo(features=self.config.features )
def _a ( self : str , __SCREAMING_SNAKE_CASE : Dict ) -> List[Any]:
"""simple docstring"""
return [datasets.SplitGenerator(name=datasets.Split.TRAIN )]
def _a ( self : Optional[Any] , __SCREAMING_SNAKE_CASE : Optional[Any] ) -> Dict:
"""simple docstring"""
import pyspark
def get_arrow_batch_size(__SCREAMING_SNAKE_CASE : int ):
for batch in it:
yield pa.RecordBatch.from_pydict({'''batch_bytes''': [batch.nbytes]} )
__SCREAMING_SNAKE_CASE = self.df.count()
__SCREAMING_SNAKE_CASE = df_num_rows if df_num_rows <= 1_00 else 1_00
# Approximate the size of each row (in Arrow format) by averaging over a max-100-row sample.
__SCREAMING_SNAKE_CASE = (
self.df.limit(_A )
.repartition(1 )
.mapInArrow(_A , '''batch_bytes: long''' )
.agg(pyspark.sql.functions.sum('''batch_bytes''' ).alias('''sample_bytes''' ) )
.collect()[0]
.sample_bytes
/ sample_num_rows
)
__SCREAMING_SNAKE_CASE = approx_bytes_per_row * df_num_rows
if approx_total_size > max_shard_size:
# Make sure there is at least one row per partition.
__SCREAMING_SNAKE_CASE = min(_A , int(approx_total_size / max_shard_size ) )
__SCREAMING_SNAKE_CASE = self.df.repartition(_A )
def _a ( self : str , __SCREAMING_SNAKE_CASE : Union[str, Any] , __SCREAMING_SNAKE_CASE : Any , __SCREAMING_SNAKE_CASE : Dict , ) -> Optional[int]:
"""simple docstring"""
import pyspark
__SCREAMING_SNAKE_CASE = ParquetWriter if file_format == '''parquet''' else ArrowWriter
__SCREAMING_SNAKE_CASE = os.path.join(self._working_dir , os.path.basename(_A ) ) if self._working_dir else fpath
__SCREAMING_SNAKE_CASE = file_format == '''parquet'''
# Define these so that we don't reference self in write_arrow, which will result in a pickling error due to
# pickling the SparkContext.
__SCREAMING_SNAKE_CASE = self.config.features
__SCREAMING_SNAKE_CASE = self._writer_batch_size
__SCREAMING_SNAKE_CASE = self._fs.storage_options
def write_arrow(__SCREAMING_SNAKE_CASE : str ):
# Within the same SparkContext, no two task attempts will share the same attempt ID.
__SCREAMING_SNAKE_CASE = pyspark.TaskContext().taskAttemptId()
__SCREAMING_SNAKE_CASE = next(_A , _A )
if first_batch is None:
# Some partitions might not receive any data.
return pa.RecordBatch.from_arrays(
[[task_id], [0], [0]] , names=['''task_id''', '''num_examples''', '''num_bytes'''] , )
__SCREAMING_SNAKE_CASE = 0
__SCREAMING_SNAKE_CASE = writer_class(
features=_A , path=working_fpath.replace('''SSSSS''' , f"""{shard_id:05d}""" ).replace('''TTTTT''' , f"""{task_id:05d}""" ) , writer_batch_size=_A , storage_options=_A , embed_local_files=_A , )
__SCREAMING_SNAKE_CASE = pa.Table.from_batches([first_batch] )
writer.write_table(_A )
for batch in it:
if max_shard_size is not None and writer._num_bytes >= max_shard_size:
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = writer.finalize()
writer.close()
yield pa.RecordBatch.from_arrays(
[[task_id], [num_examples], [num_bytes]] , names=['''task_id''', '''num_examples''', '''num_bytes'''] , )
shard_id += 1
__SCREAMING_SNAKE_CASE = writer_class(
features=writer._features , path=working_fpath.replace('''SSSSS''' , f"""{shard_id:05d}""" ).replace('''TTTTT''' , f"""{task_id:05d}""" ) , writer_batch_size=_A , storage_options=_A , embed_local_files=_A , )
__SCREAMING_SNAKE_CASE = pa.Table.from_batches([batch] )
writer.write_table(_A )
if writer._num_bytes > 0:
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = writer.finalize()
writer.close()
yield pa.RecordBatch.from_arrays(
[[task_id], [num_examples], [num_bytes]] , names=['''task_id''', '''num_examples''', '''num_bytes'''] , )
if working_fpath != fpath:
for file in os.listdir(os.path.dirname(_A ) ):
__SCREAMING_SNAKE_CASE = os.path.join(os.path.dirname(_A ) , os.path.basename(_A ) )
shutil.move(_A , _A )
__SCREAMING_SNAKE_CASE = (
self.df.mapInArrow(_A , '''task_id: long, num_examples: long, num_bytes: long''' )
.groupBy('''task_id''' )
.agg(
pyspark.sql.functions.sum('''num_examples''' ).alias('''total_num_examples''' ) , pyspark.sql.functions.sum('''num_bytes''' ).alias('''total_num_bytes''' ) , pyspark.sql.functions.count('''num_bytes''' ).alias('''num_shards''' ) , pyspark.sql.functions.collect_list('''num_examples''' ).alias('''shard_lengths''' ) , )
.collect()
)
for row in stats:
yield row.task_id, (row.total_num_examples, row.total_num_bytes, row.num_shards, row.shard_lengths)
def _a ( self : List[str] , __SCREAMING_SNAKE_CASE : Dict , __SCREAMING_SNAKE_CASE : str = "arrow" , __SCREAMING_SNAKE_CASE : int = None , __SCREAMING_SNAKE_CASE : int = None , **__SCREAMING_SNAKE_CASE : int , ) -> Optional[int]:
"""simple docstring"""
self._validate_cache_dir()
__SCREAMING_SNAKE_CASE = convert_file_size_to_int(max_shard_size or MAX_SHARD_SIZE )
self._repartition_df_if_needed(_A )
__SCREAMING_SNAKE_CASE = not is_remote_filesystem(self._fs )
__SCREAMING_SNAKE_CASE = os.path.join if is_local else posixpath.join
__SCREAMING_SNAKE_CASE = '''-TTTTT-SSSSS-of-NNNNN'''
__SCREAMING_SNAKE_CASE = f"""{self.name}-{split_generator.name}{SUFFIX}.{file_format}"""
__SCREAMING_SNAKE_CASE = path_join(self._output_dir , _A )
__SCREAMING_SNAKE_CASE = 0
__SCREAMING_SNAKE_CASE = 0
__SCREAMING_SNAKE_CASE = 0
__SCREAMING_SNAKE_CASE = []
__SCREAMING_SNAKE_CASE = []
for task_id, content in self._prepare_split_single(_A , _A , _A ):
(
(
__SCREAMING_SNAKE_CASE
) , (
__SCREAMING_SNAKE_CASE
) , (
__SCREAMING_SNAKE_CASE
) , (
__SCREAMING_SNAKE_CASE
) ,
) = content
if num_bytes > 0:
total_num_examples += num_examples
total_num_bytes += num_bytes
total_shards += num_shards
task_id_and_num_shards.append((task_id, num_shards) )
all_shard_lengths.extend(_A )
__SCREAMING_SNAKE_CASE = total_num_examples
__SCREAMING_SNAKE_CASE = total_num_bytes
# should rename everything at the end
logger.debug(f"""Renaming {total_shards} shards.""" )
if total_shards > 1:
__SCREAMING_SNAKE_CASE = all_shard_lengths
# Define fs outside of _rename_shard so that we don't reference self in the function, which will result in a
# pickling error due to pickling the SparkContext.
__SCREAMING_SNAKE_CASE = self._fs
# use the -SSSSS-of-NNNNN pattern
def _rename_shard(
__SCREAMING_SNAKE_CASE : Tuple , __SCREAMING_SNAKE_CASE : Tuple , __SCREAMING_SNAKE_CASE : Dict , ):
rename(
_A , fpath.replace('''SSSSS''' , f"""{shard_id:05d}""" ).replace('''TTTTT''' , f"""{task_id:05d}""" ) , fpath.replace('''TTTTT-SSSSS''' , f"""{global_shard_id:05d}""" ).replace('''NNNNN''' , f"""{total_shards:05d}""" ) , )
__SCREAMING_SNAKE_CASE = []
__SCREAMING_SNAKE_CASE = 0
for i in range(len(_A ) ):
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = task_id_and_num_shards[i]
for shard_id in range(_A ):
args.append([task_id, shard_id, global_shard_id] )
global_shard_id += 1
self._spark.sparkContext.parallelize(_A , len(_A ) ).map(lambda __SCREAMING_SNAKE_CASE : _rename_shard(*_A ) ).collect()
else:
# don't use any pattern
__SCREAMING_SNAKE_CASE = 0
__SCREAMING_SNAKE_CASE = task_id_and_num_shards[0][0]
self._rename(
fpath.replace('''SSSSS''' , f"""{shard_id:05d}""" ).replace('''TTTTT''' , f"""{task_id:05d}""" ) , fpath.replace(_A , '''''' ) , )
def _a ( self : Optional[Any] , __SCREAMING_SNAKE_CASE : Optional[int] , ) -> str:
"""simple docstring"""
return SparkExamplesIterable(self.df )
| 714 |
"""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 | 0 |
"""simple docstring"""
import html
from ...feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from ...utils import is_bsa_available, logging, requires_backends
if is_bsa_available():
import bsa
from bsa import BeautifulSoup
lowerCAmelCase__ =logging.get_logger(__name__)
class A__( __magic_name__ ):
def __init__( self : Optional[Any] , **__SCREAMING_SNAKE_CASE : Any ) -> List[Any]:
"""simple docstring"""
requires_backends(self , ['''bs4'''] )
super().__init__(**__UpperCamelCase )
def _a ( self : Any , __SCREAMING_SNAKE_CASE : Tuple ) -> Tuple:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = []
__SCREAMING_SNAKE_CASE = []
__SCREAMING_SNAKE_CASE = element if element.name else element.parent
for parent in child.parents: # type: bs4.element.Tag
__SCREAMING_SNAKE_CASE = parent.find_all(child.name , recursive=__UpperCamelCase )
xpath_tags.append(child.name )
xpath_subscripts.append(
0 if 1 == len(__UpperCamelCase ) else next(i for i, s in enumerate(__UpperCamelCase , 1 ) if s is child ) )
__SCREAMING_SNAKE_CASE = parent
xpath_tags.reverse()
xpath_subscripts.reverse()
return xpath_tags, xpath_subscripts
def _a ( self : Tuple , __SCREAMING_SNAKE_CASE : str ) -> Optional[Any]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = BeautifulSoup(__UpperCamelCase , '''html.parser''' )
__SCREAMING_SNAKE_CASE = []
__SCREAMING_SNAKE_CASE = []
__SCREAMING_SNAKE_CASE = []
for element in html_code.descendants:
if type(__UpperCamelCase ) == bsa.element.NavigableString:
if type(element.parent ) != bsa.element.Tag:
continue
__SCREAMING_SNAKE_CASE = html.unescape(__UpperCamelCase ).strip()
if not text_in_this_tag:
continue
all_doc_strings.append(__UpperCamelCase )
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = self.xpath_soup(__UpperCamelCase )
stringaxtag_seq.append(__UpperCamelCase )
stringaxsubs_seq.append(__UpperCamelCase )
if len(__UpperCamelCase ) != len(__UpperCamelCase ):
raise ValueError('''Number of doc strings and xtags does not correspond''' )
if len(__UpperCamelCase ) != len(__UpperCamelCase ):
raise ValueError('''Number of doc strings and xsubs does not correspond''' )
return all_doc_strings, stringaxtag_seq, stringaxsubs_seq
def _a ( self : str , __SCREAMING_SNAKE_CASE : Optional[Any] , __SCREAMING_SNAKE_CASE : int ) -> str:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = ''''''
for tagname, subs in zip(__UpperCamelCase , __UpperCamelCase ):
xpath += f"""/{tagname}"""
if subs != 0:
xpath += f"""[{subs}]"""
return xpath
def __call__( self : Any , __SCREAMING_SNAKE_CASE : int ) -> Dict:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = False
# Check that strings has a valid type
if isinstance(__UpperCamelCase , __UpperCamelCase ):
__SCREAMING_SNAKE_CASE = True
elif isinstance(__UpperCamelCase , (list, tuple) ):
if len(__UpperCamelCase ) == 0 or isinstance(html_strings[0] , __UpperCamelCase ):
__SCREAMING_SNAKE_CASE = True
if not valid_strings:
raise ValueError(
'''HTML strings must of type `str`, `List[str]` (batch of examples), '''
f"""but is of type {type(__UpperCamelCase )}.""" )
__SCREAMING_SNAKE_CASE = bool(isinstance(__UpperCamelCase , (list, tuple) ) and (isinstance(html_strings[0] , __UpperCamelCase )) )
if not is_batched:
__SCREAMING_SNAKE_CASE = [html_strings]
# Get nodes + xpaths
__SCREAMING_SNAKE_CASE = []
__SCREAMING_SNAKE_CASE = []
for html_string in html_strings:
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = self.get_three_from_single(__UpperCamelCase )
nodes.append(__UpperCamelCase )
__SCREAMING_SNAKE_CASE = []
for node, tag_list, sub_list in zip(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
__SCREAMING_SNAKE_CASE = self.construct_xpath(__UpperCamelCase , __UpperCamelCase )
xpath_strings.append(__UpperCamelCase )
xpaths.append(__UpperCamelCase )
# return as Dict
__SCREAMING_SNAKE_CASE = {'''nodes''': nodes, '''xpaths''': xpaths}
__SCREAMING_SNAKE_CASE = BatchFeature(data=__UpperCamelCase , tensor_type=__UpperCamelCase )
return encoded_inputs
| 715 |
"""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 | 0 |
"""simple docstring"""
def _a ( UpperCAmelCase__ ) -> Dict:
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = [], []
while len(_UpperCamelCase ) > 1:
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = min(_UpperCamelCase ), max(_UpperCamelCase )
start.append(_UpperCamelCase )
end.append(_UpperCamelCase )
collection.remove(_UpperCamelCase )
collection.remove(_UpperCamelCase )
end.reverse()
return start + collection + end
if __name__ == "__main__":
lowerCAmelCase__ =input("Enter numbers separated by a comma:\n").strip()
lowerCAmelCase__ =[int(item) for item in user_input.split(",")]
print(*merge_sort(unsorted), sep=",")
| 716 |
"""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 | 0 |
"""simple docstring"""
from collections import defaultdict
class A__:
def __init__( self : Optional[Any] , __SCREAMING_SNAKE_CASE : Optional[int] , __SCREAMING_SNAKE_CASE : Optional[Any] ) -> str:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = total # total no of tasks (N)
# DP table will have a dimension of (2^M)*N
# initially all values are set to -1
__SCREAMING_SNAKE_CASE = [
[-1 for i in range(total + 1 )] for j in range(2 ** len(__SCREAMING_SNAKE_CASE ) )
]
__SCREAMING_SNAKE_CASE = defaultdict(__SCREAMING_SNAKE_CASE ) # stores the list of persons for each task
# final_mask is used to check if all persons are included by setting all bits
# to 1
__SCREAMING_SNAKE_CASE = (1 << len(__SCREAMING_SNAKE_CASE )) - 1
def _a ( self : Tuple , __SCREAMING_SNAKE_CASE : int , __SCREAMING_SNAKE_CASE : Union[str, Any] ) -> Union[str, Any]:
"""simple docstring"""
if mask == self.final_mask:
return 1
# if not everyone gets the task and no more tasks are available, return 0
if task_no > self.total_tasks:
return 0
# if case already considered
if self.dp[mask][task_no] != -1:
return self.dp[mask][task_no]
# Number of ways when we don't this task in the arrangement
__SCREAMING_SNAKE_CASE = self.count_ways_until(__SCREAMING_SNAKE_CASE , task_no + 1 )
# now assign the tasks one by one to all possible persons and recursively
# assign for the remaining tasks.
if task_no in self.task:
for p in self.task[task_no]:
# if p is already given a task
if mask & (1 << p):
continue
# assign this task to p and change the mask value. And recursively
# assign tasks with the new mask value.
total_ways_util += self.count_ways_until(mask | (1 << p) , task_no + 1 )
# save the value.
__SCREAMING_SNAKE_CASE = total_ways_util
return self.dp[mask][task_no]
def _a ( self : Optional[int] , __SCREAMING_SNAKE_CASE : Any ) -> Any:
"""simple docstring"""
for i in range(len(__SCREAMING_SNAKE_CASE ) ):
for j in task_performed[i]:
self.task[j].append(__SCREAMING_SNAKE_CASE )
# call the function to fill the DP table, final answer is stored in dp[0][1]
return self.count_ways_until(0 , 1 )
if __name__ == "__main__":
lowerCAmelCase__ =5 # total no of tasks (the value of N)
# the list of tasks that can be done by M persons.
lowerCAmelCase__ =[[1, 3, 4], [1, 2, 5], [3, 4]]
print(
AssignmentUsingBitmask(task_performed, total_tasks).count_no_of_ways(
task_performed
)
)
| 717 |
"""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 | 0 |
import unittest
from transformers.testing_utils import CaptureStdout
from transformers.tools.python_interpreter import evaluate
def _a ( UpperCAmelCase__ ) -> Dict:
return x + 2
class A__( unittest.TestCase ):
def _a ( self : Tuple ) -> str:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = '''x = 3'''
__SCREAMING_SNAKE_CASE = {}
__SCREAMING_SNAKE_CASE = evaluate(lowerCamelCase__ , {} , state=lowerCamelCase__ )
assert result == 3
self.assertDictEqual(lowerCamelCase__ , {'''x''': 3} )
__SCREAMING_SNAKE_CASE = '''x = y'''
__SCREAMING_SNAKE_CASE = {'''y''': 5}
__SCREAMING_SNAKE_CASE = evaluate(lowerCamelCase__ , {} , state=lowerCamelCase__ )
# evaluate returns the value of the last assignment.
assert result == 5
self.assertDictEqual(lowerCamelCase__ , {'''x''': 5, '''y''': 5} )
def _a ( self : int ) -> List[Any]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = '''y = add_two(x)'''
__SCREAMING_SNAKE_CASE = {'''x''': 3}
__SCREAMING_SNAKE_CASE = evaluate(lowerCamelCase__ , {'''add_two''': add_two} , state=lowerCamelCase__ )
assert result == 5
self.assertDictEqual(lowerCamelCase__ , {'''x''': 3, '''y''': 5} )
# Won't work without the tool
with CaptureStdout() as out:
__SCREAMING_SNAKE_CASE = evaluate(lowerCamelCase__ , {} , state=lowerCamelCase__ )
assert result is None
assert "tried to execute add_two" in out.out
def _a ( self : Tuple ) -> List[Any]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = '''x = 3'''
__SCREAMING_SNAKE_CASE = {}
__SCREAMING_SNAKE_CASE = evaluate(lowerCamelCase__ , {} , state=lowerCamelCase__ )
assert result == 3
self.assertDictEqual(lowerCamelCase__ , {'''x''': 3} )
def _a ( self : Any ) -> Dict:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = '''test_dict = {\'x\': x, \'y\': add_two(x)}'''
__SCREAMING_SNAKE_CASE = {'''x''': 3}
__SCREAMING_SNAKE_CASE = evaluate(lowerCamelCase__ , {'''add_two''': add_two} , state=lowerCamelCase__ )
self.assertDictEqual(lowerCamelCase__ , {'''x''': 3, '''y''': 5} )
self.assertDictEqual(lowerCamelCase__ , {'''x''': 3, '''test_dict''': {'''x''': 3, '''y''': 5}} )
def _a ( self : Tuple ) -> str:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = '''x = 3\ny = 5'''
__SCREAMING_SNAKE_CASE = {}
__SCREAMING_SNAKE_CASE = evaluate(lowerCamelCase__ , {} , state=lowerCamelCase__ )
# evaluate returns the value of the last assignment.
assert result == 5
self.assertDictEqual(lowerCamelCase__ , {'''x''': 3, '''y''': 5} )
def _a ( self : Tuple ) -> Dict:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = '''text = f\'This is x: {x}.\''''
__SCREAMING_SNAKE_CASE = {'''x''': 3}
__SCREAMING_SNAKE_CASE = evaluate(lowerCamelCase__ , {} , state=lowerCamelCase__ )
# evaluate returns the value of the last assignment.
assert result == "This is x: 3."
self.assertDictEqual(lowerCamelCase__ , {'''x''': 3, '''text''': '''This is x: 3.'''} )
def _a ( self : List[Any] ) -> List[str]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = '''if x <= 3:\n y = 2\nelse:\n y = 5'''
__SCREAMING_SNAKE_CASE = {'''x''': 3}
__SCREAMING_SNAKE_CASE = evaluate(lowerCamelCase__ , {} , state=lowerCamelCase__ )
# evaluate returns the value of the last assignment.
assert result == 2
self.assertDictEqual(lowerCamelCase__ , {'''x''': 3, '''y''': 2} )
__SCREAMING_SNAKE_CASE = {'''x''': 8}
__SCREAMING_SNAKE_CASE = evaluate(lowerCamelCase__ , {} , state=lowerCamelCase__ )
# evaluate returns the value of the last assignment.
assert result == 5
self.assertDictEqual(lowerCamelCase__ , {'''x''': 8, '''y''': 5} )
def _a ( self : List[Any] ) -> Tuple:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = '''test_list = [x, add_two(x)]'''
__SCREAMING_SNAKE_CASE = {'''x''': 3}
__SCREAMING_SNAKE_CASE = evaluate(lowerCamelCase__ , {'''add_two''': add_two} , state=lowerCamelCase__ )
self.assertListEqual(lowerCamelCase__ , [3, 5] )
self.assertDictEqual(lowerCamelCase__ , {'''x''': 3, '''test_list''': [3, 5]} )
def _a ( self : List[Any] ) -> Optional[int]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = '''y = x'''
__SCREAMING_SNAKE_CASE = {'''x''': 3}
__SCREAMING_SNAKE_CASE = evaluate(lowerCamelCase__ , {} , state=lowerCamelCase__ )
assert result == 3
self.assertDictEqual(lowerCamelCase__ , {'''x''': 3, '''y''': 3} )
def _a ( self : Dict ) -> Optional[int]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = '''test_list = [x, add_two(x)]\ntest_list[1]'''
__SCREAMING_SNAKE_CASE = {'''x''': 3}
__SCREAMING_SNAKE_CASE = evaluate(lowerCamelCase__ , {'''add_two''': add_two} , state=lowerCamelCase__ )
assert result == 5
self.assertDictEqual(lowerCamelCase__ , {'''x''': 3, '''test_list''': [3, 5]} )
__SCREAMING_SNAKE_CASE = '''test_dict = {\'x\': x, \'y\': add_two(x)}\ntest_dict[\'y\']'''
__SCREAMING_SNAKE_CASE = {'''x''': 3}
__SCREAMING_SNAKE_CASE = evaluate(lowerCamelCase__ , {'''add_two''': add_two} , state=lowerCamelCase__ )
assert result == 5
self.assertDictEqual(lowerCamelCase__ , {'''x''': 3, '''test_dict''': {'''x''': 3, '''y''': 5}} )
def _a ( self : Optional[int] ) -> str:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = '''x = 0\nfor i in range(3):\n x = i'''
__SCREAMING_SNAKE_CASE = {}
__SCREAMING_SNAKE_CASE = evaluate(lowerCamelCase__ , {'''range''': range} , state=lowerCamelCase__ )
assert result == 2
self.assertDictEqual(lowerCamelCase__ , {'''x''': 2, '''i''': 2} )
| 718 |
"""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 | 0 |
"""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__( UpperCamelCase_ ):
lowerCAmelCase = ['''audio_values''', '''audio_mask''']
def __init__( self : List[Any] , __SCREAMING_SNAKE_CASE : Optional[int]=20_48 , __SCREAMING_SNAKE_CASE : str=1 , __SCREAMING_SNAKE_CASE : Any=[16, 16] , __SCREAMING_SNAKE_CASE : int=1_28 , __SCREAMING_SNAKE_CASE : Union[str, Any]=4_41_00 , __SCREAMING_SNAKE_CASE : Optional[Any]=86 , __SCREAMING_SNAKE_CASE : int=20_48 , __SCREAMING_SNAKE_CASE : str=0.0 , **__SCREAMING_SNAKE_CASE : 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 : Optional[Any] , __SCREAMING_SNAKE_CASE : Tuple ) -> 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 : Union[str, Any] , __SCREAMING_SNAKE_CASE : int , __SCREAMING_SNAKE_CASE : Optional[int] = None , __SCREAMING_SNAKE_CASE : Optional[Any] = True , __SCREAMING_SNAKE_CASE : Tuple = None , __SCREAMING_SNAKE_CASE : Optional[Any] = False , __SCREAMING_SNAKE_CASE : Optional[Any] = False , **__SCREAMING_SNAKE_CASE : str , ) -> 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
| 719 |
"""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 | 0 |
"""simple docstring"""
import socket
def _a ( ) -> Optional[Any]:
__SCREAMING_SNAKE_CASE = socket.socket(socket.AF_INET , socket.SOCK_STREAM )
__SCREAMING_SNAKE_CASE = socket.gethostname()
__SCREAMING_SNAKE_CASE = 1_23_12
sock.connect((host, port) )
sock.send(B'''Hello server!''' )
with open('''Received_file''' , '''wb''' ) as out_file:
print('''File opened''' )
print('''Receiving data...''' )
while True:
__SCREAMING_SNAKE_CASE = sock.recv(10_24 )
if not data:
break
out_file.write(UpperCamelCase__ )
print('''Successfully received the file''' )
sock.close()
print('''Connection closed''' )
if __name__ == "__main__":
main()
| 720 |
"""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 | 0 |
"""simple docstring"""
from collections import Counter
from pathlib import Path
from typing import Optional, Tuple
import yaml
class A__( yaml.SafeLoader ):
def _a ( self : Optional[Any] , __SCREAMING_SNAKE_CASE : Optional[int] ) -> Any:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = [self.constructed_objects[key_node] for key_node, _ in node.value]
__SCREAMING_SNAKE_CASE = [tuple(__SCREAMING_SNAKE_CASE ) if isinstance(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) else key for key in keys]
__SCREAMING_SNAKE_CASE = Counter(__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = [key for key in counter if counter[key] > 1]
if duplicate_keys:
raise TypeError(f"""Got duplicate yaml keys: {duplicate_keys}""" )
def _a ( self : Any , __SCREAMING_SNAKE_CASE : Union[str, Any] , __SCREAMING_SNAKE_CASE : Any=False ) -> Tuple:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = super().construct_mapping(__SCREAMING_SNAKE_CASE , deep=__SCREAMING_SNAKE_CASE )
self._check_no_duplicates_on_constructed_node(__SCREAMING_SNAKE_CASE )
return mapping
def _a ( UpperCAmelCase__ ) -> Tuple[Optional[str], str]:
__SCREAMING_SNAKE_CASE = list(readme_content.splitlines() )
if full_content and full_content[0] == "---" and "---" in full_content[1:]:
__SCREAMING_SNAKE_CASE = full_content[1:].index('''---''' ) + 1
__SCREAMING_SNAKE_CASE = '''\n'''.join(full_content[1:sep_idx] )
return yamlblock, "\n".join(full_content[sep_idx + 1 :] )
return None, "\n".join(lowerCamelCase__ )
class A__( __magic_name__ ):
# class attributes
lowerCAmelCase = {"""train_eval_index"""} # train-eval-index in the YAML metadata
@classmethod
def _a ( cls : Dict , __SCREAMING_SNAKE_CASE : Path ) -> "DatasetMetadata":
"""simple docstring"""
with open(__SCREAMING_SNAKE_CASE , encoding='''utf-8''' ) as readme_file:
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = _split_yaml_from_readme(readme_file.read() )
if yaml_string is not None:
return cls.from_yaml_string(__SCREAMING_SNAKE_CASE )
else:
return cls()
def _a ( self : Any , __SCREAMING_SNAKE_CASE : Path ) -> Union[str, Any]:
"""simple docstring"""
if path.exists():
with open(__SCREAMING_SNAKE_CASE , encoding='''utf-8''' ) as readme_file:
__SCREAMING_SNAKE_CASE = readme_file.read()
else:
__SCREAMING_SNAKE_CASE = None
__SCREAMING_SNAKE_CASE = self._to_readme(__SCREAMING_SNAKE_CASE )
with open(__SCREAMING_SNAKE_CASE , '''w''' , encoding='''utf-8''' ) as readme_file:
readme_file.write(__SCREAMING_SNAKE_CASE )
def _a ( self : Tuple , __SCREAMING_SNAKE_CASE : Optional[str] = None ) -> str:
"""simple docstring"""
if readme_content is not None:
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = _split_yaml_from_readme(__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = '''---\n''' + self.to_yaml_string() + '''---\n''' + content
else:
__SCREAMING_SNAKE_CASE = '''---\n''' + self.to_yaml_string() + '''---\n'''
return full_content
@classmethod
def _a ( cls : Optional[int] , __SCREAMING_SNAKE_CASE : str ) -> "DatasetMetadata":
"""simple docstring"""
__SCREAMING_SNAKE_CASE = yaml.load(__SCREAMING_SNAKE_CASE , Loader=_NoDuplicateSafeLoader ) or {}
# Convert the YAML keys to DatasetMetadata fields
__SCREAMING_SNAKE_CASE = {
(key.replace('''-''' , '''_''' ) if key.replace('''-''' , '''_''' ) in cls._FIELDS_WITH_DASHES else key): value
for key, value in metadata_dict.items()
}
return cls(**__SCREAMING_SNAKE_CASE )
def _a ( self : Optional[Any] ) -> str:
"""simple docstring"""
return yaml.safe_dump(
{
(key.replace('''_''' , '''-''' ) if key in self._FIELDS_WITH_DASHES else key): value
for key, value in self.items()
} , sort_keys=__SCREAMING_SNAKE_CASE , allow_unicode=__SCREAMING_SNAKE_CASE , encoding='''utf-8''' , ).decode('''utf-8''' )
lowerCAmelCase__ ={
"image-classification": [],
"translation": [],
"image-segmentation": [],
"fill-mask": [],
"automatic-speech-recognition": [],
"token-classification": [],
"sentence-similarity": [],
"audio-classification": [],
"question-answering": [],
"summarization": [],
"zero-shot-classification": [],
"table-to-text": [],
"feature-extraction": [],
"other": [],
"multiple-choice": [],
"text-classification": [],
"text-to-image": [],
"text2text-generation": [],
"zero-shot-image-classification": [],
"tabular-classification": [],
"tabular-regression": [],
"image-to-image": [],
"tabular-to-text": [],
"unconditional-image-generation": [],
"text-retrieval": [],
"text-to-speech": [],
"object-detection": [],
"audio-to-audio": [],
"text-generation": [],
"conversational": [],
"table-question-answering": [],
"visual-question-answering": [],
"image-to-text": [],
"reinforcement-learning": [],
"voice-activity-detection": [],
"time-series-forecasting": [],
"document-question-answering": [],
}
if __name__ == "__main__":
from argparse import ArgumentParser
lowerCAmelCase__ =ArgumentParser(usage="Validate the yaml metadata block of a README.md file.")
ap.add_argument("readme_filepath")
lowerCAmelCase__ =ap.parse_args()
lowerCAmelCase__ =Path(args.readme_filepath)
lowerCAmelCase__ =DatasetMetadata.from_readme(readme_filepath)
print(dataset_metadata)
dataset_metadata.to_readme(readme_filepath)
| 721 |
"""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 | 0 |
"""simple docstring"""
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__)
| 700 |
"""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 | 0 |
"""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
| 701 |
"""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 | 0 |
"""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''' , )
| 702 |
"""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 | 0 |
"""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() = }''')
| 703 |
"""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 | 0 |
"""simple docstring"""
import math
import sys
import cva
import numpy as np
def _a ( UpperCAmelCase__ , UpperCAmelCase__ ) -> np.ndarray:
# For applying gaussian function for each element in matrix.
__SCREAMING_SNAKE_CASE = math.sqrt(UpperCAmelCase__ )
__SCREAMING_SNAKE_CASE = 1 / (sigma * math.sqrt(2 * math.pi ))
return cons * np.exp(-((img / sigma) ** 2) * 0.5 )
def _a ( UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ ) -> np.ndarray:
__SCREAMING_SNAKE_CASE = kernel_size // 2
return img[x - half : x + half + 1, y - half : y + half + 1]
def _a ( UpperCAmelCase__ , UpperCAmelCase__ ) -> np.ndarray:
# Creates a gaussian kernel of given dimension.
__SCREAMING_SNAKE_CASE = np.zeros((kernel_size, kernel_size) )
for i in range(0 , UpperCAmelCase__ ):
for j in range(0 , UpperCAmelCase__ ):
__SCREAMING_SNAKE_CASE = math.sqrt(
abs(i - kernel_size // 2 ) ** 2 + abs(j - kernel_size // 2 ) ** 2 )
return vec_gaussian(UpperCAmelCase__ , UpperCAmelCase__ )
def _a ( UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , ) -> np.ndarray:
__SCREAMING_SNAKE_CASE = np.zeros(img.shape )
__SCREAMING_SNAKE_CASE = get_gauss_kernel(UpperCAmelCase__ , UpperCAmelCase__ )
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = img.shape
for i in range(kernel_size // 2 , size_x - kernel_size // 2 ):
for j in range(kernel_size // 2 , size_y - kernel_size // 2 ):
__SCREAMING_SNAKE_CASE = get_slice(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ )
__SCREAMING_SNAKE_CASE = img_s - img_s[kernel_size // 2, kernel_size // 2]
__SCREAMING_SNAKE_CASE = vec_gaussian(UpperCAmelCase__ , UpperCAmelCase__ )
__SCREAMING_SNAKE_CASE = np.multiply(UpperCAmelCase__ , UpperCAmelCase__ )
__SCREAMING_SNAKE_CASE = np.multiply(UpperCAmelCase__ , UpperCAmelCase__ )
__SCREAMING_SNAKE_CASE = np.sum(UpperCAmelCase__ ) / np.sum(UpperCAmelCase__ )
__SCREAMING_SNAKE_CASE = val
return imga
def _a ( UpperCAmelCase__ ) -> tuple:
__SCREAMING_SNAKE_CASE = args[1] if args[1:] else '''../image_data/lena.jpg'''
__SCREAMING_SNAKE_CASE = float(args[2] ) if args[2:] else 1.0
__SCREAMING_SNAKE_CASE = float(args[3] ) if args[3:] else 1.0
if args[4:]:
__SCREAMING_SNAKE_CASE = int(args[4] )
__SCREAMING_SNAKE_CASE = kernel_size + abs(kernel_size % 2 - 1 )
else:
__SCREAMING_SNAKE_CASE = 5
return filename, spatial_variance, intensity_variance, kernel_size
if __name__ == "__main__":
lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ =parse_args(sys.argv)
lowerCAmelCase__ =cva.imread(filename, 0)
cva.imshow("input image", img)
lowerCAmelCase__ =img / 255
lowerCAmelCase__ =out.astype("float32")
lowerCAmelCase__ =bilateral_filter(out, spatial_variance, intensity_variance, kernel_size)
lowerCAmelCase__ =out * 255
lowerCAmelCase__ =np.uinta(out)
cva.imshow("output image", out)
cva.waitKey(0)
cva.destroyAllWindows()
| 704 |
"""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 | 0 |
from __future__ import annotations
import random
# Maximum size of the population. Bigger could be faster but is more memory expensive.
lowerCAmelCase__ =200
# Number of elements selected in every generation of evolution. The selection takes
# place from best to worst of that generation and must be smaller than N_POPULATION.
lowerCAmelCase__ =50
# Probability that an element of a generation can mutate, changing one of its genes.
# This will guarantee that all genes will be used during evolution.
lowerCAmelCase__ =0.4
# Just a seed to improve randomness required by the algorithm.
random.seed(random.randint(0, 1_000))
def _a ( UpperCAmelCase__ , UpperCAmelCase__ ) -> tuple[str, float]:
__SCREAMING_SNAKE_CASE = len([g for position, g in enumerate(UpperCAmelCase__ ) if g == main_target[position]] )
return (item, float(UpperCAmelCase__ ))
def _a ( UpperCAmelCase__ , UpperCAmelCase__ ) -> tuple[str, str]:
__SCREAMING_SNAKE_CASE = random.randint(0 , len(UpperCAmelCase__ ) - 1 )
__SCREAMING_SNAKE_CASE = parent_a[:random_slice] + parent_a[random_slice:]
__SCREAMING_SNAKE_CASE = parent_a[:random_slice] + parent_a[random_slice:]
return (child_a, child_a)
def _a ( UpperCAmelCase__ , UpperCAmelCase__ ) -> str:
__SCREAMING_SNAKE_CASE = list(UpperCAmelCase__ )
if random.uniform(0 , 1 ) < MUTATION_PROBABILITY:
__SCREAMING_SNAKE_CASE = random.choice(UpperCAmelCase__ )
return "".join(UpperCAmelCase__ )
def _a ( UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , ) -> list[str]:
__SCREAMING_SNAKE_CASE = []
# Generate more children proportionally to the fitness score.
__SCREAMING_SNAKE_CASE = int(parent_a[1] * 1_00 ) + 1
__SCREAMING_SNAKE_CASE = 10 if child_n >= 10 else child_n
for _ in range(UpperCAmelCase__ ):
__SCREAMING_SNAKE_CASE = population_score[random.randint(0 , UpperCAmelCase__ )][0]
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = crossover(parent_a[0] , UpperCAmelCase__ )
# Append new string to the population list.
pop.append(mutate(UpperCAmelCase__ , UpperCAmelCase__ ) )
pop.append(mutate(UpperCAmelCase__ , UpperCAmelCase__ ) )
return pop
def _a ( UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ = True ) -> tuple[int, int, str]:
# Verify if N_POPULATION is bigger than N_SELECTED
if N_POPULATION < N_SELECTED:
__SCREAMING_SNAKE_CASE = f"""{N_POPULATION} must be bigger than {N_SELECTED}"""
raise ValueError(UpperCAmelCase__ )
# Verify that the target contains no genes besides the ones inside genes variable.
__SCREAMING_SNAKE_CASE = sorted({c for c in target if c not in genes} )
if not_in_genes_list:
__SCREAMING_SNAKE_CASE = f"""{not_in_genes_list} is not in genes list, evolution cannot converge"""
raise ValueError(UpperCAmelCase__ )
# Generate random starting population.
__SCREAMING_SNAKE_CASE = []
for _ in range(UpperCAmelCase__ ):
population.append(''''''.join([random.choice(UpperCAmelCase__ ) for i in range(len(UpperCAmelCase__ ) )] ) )
# Just some logs to know what the algorithms is doing.
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = 0, 0
# This loop will end when we find a perfect match for our target.
while True:
generation += 1
total_population += len(UpperCAmelCase__ )
# Random population created. Now it's time to evaluate.
# Adding a bit of concurrency can make everything faster,
#
# import concurrent.futures
# population_score: list[tuple[str, float]] = []
# with concurrent.futures.ThreadPoolExecutor(
# max_workers=NUM_WORKERS) as executor:
# futures = {executor.submit(evaluate, item) for item in population}
# concurrent.futures.wait(futures)
# population_score = [item.result() for item in futures]
#
# but with a simple algorithm like this, it will probably be slower.
# We just need to call evaluate for every item inside the population.
__SCREAMING_SNAKE_CASE = [evaluate(UpperCAmelCase__ , UpperCAmelCase__ ) for item in population]
# Check if there is a matching evolution.
__SCREAMING_SNAKE_CASE = sorted(UpperCAmelCase__ , key=lambda UpperCAmelCase__ : x[1] , reverse=UpperCAmelCase__ )
if population_score[0][0] == target:
return (generation, total_population, population_score[0][0])
# Print the best result every 10 generation.
# Just to know that the algorithm is working.
if debug and generation % 10 == 0:
print(
f"""\nGeneration: {generation}"""
f"""\nTotal Population:{total_population}"""
f"""\nBest score: {population_score[0][1]}"""
f"""\nBest string: {population_score[0][0]}""" )
# Flush the old population, keeping some of the best evolutions.
# Keeping this avoid regression of evolution.
__SCREAMING_SNAKE_CASE = population[: int(N_POPULATION / 3 )]
population.clear()
population.extend(UpperCAmelCase__ )
# Normalize population score to be between 0 and 1.
__SCREAMING_SNAKE_CASE = [
(item, score / len(UpperCAmelCase__ )) for item, score in population_score
]
# This is selection
for i in range(UpperCAmelCase__ ):
population.extend(select(population_score[int(UpperCAmelCase__ )] , UpperCAmelCase__ , UpperCAmelCase__ ) )
# Check if the population has already reached the maximum value and if so,
# break the cycle. If this check is disabled, the algorithm will take
# forever to compute large strings, but will also calculate small strings in
# a far fewer generations.
if len(UpperCAmelCase__ ) > N_POPULATION:
break
if __name__ == "__main__":
lowerCAmelCase__ =(
"This is a genetic algorithm to evaluate, combine, evolve, and mutate a string!"
)
lowerCAmelCase__ =list(
" ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklm"
"nopqrstuvwxyz.,;!?+-*#@^'èéòà€ù=)(&%$£/\\"
)
lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ =basic(target_str, genes_list)
print(
F'''\nGeneration: {generation}\nTotal Population: {population}\nTarget: {target}'''
)
| 705 |
"""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 | 0 |
"""simple docstring"""
import json
from typing import List, Optional, Tuple
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_mvp import MvpTokenizer
lowerCAmelCase__ =logging.get_logger(__name__)
lowerCAmelCase__ ={"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}
# See all MVP models at https://huggingface.co/models?filter=mvp
lowerCAmelCase__ ={
"vocab_file": {
"RUCAIBox/mvp": "https://huggingface.co/RUCAIBox/mvp/resolve/main/vocab.json",
},
"added_tokens.json": {
"RUCAIBox/mvp": "https://huggingface.co/RUCAIBox/mvp/resolve/main/added_tokens.json",
},
"merges_file": {
"RUCAIBox/mvp": "https://huggingface.co/RUCAIBox/mvp/resolve/main/merges.txt",
},
"tokenizer_file": {
"RUCAIBox/mvp": "https://huggingface.co/RUCAIBox/mvp/resolve/main/tokenizer.json",
},
}
lowerCAmelCase__ ={
"RUCAIBox/mvp": 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''']
lowerCAmelCase = MvpTokenizer
def __init__( self : Tuple , __SCREAMING_SNAKE_CASE : List[Any]=None , __SCREAMING_SNAKE_CASE : Optional[int]=None , __SCREAMING_SNAKE_CASE : List[str]=None , __SCREAMING_SNAKE_CASE : Tuple="replace" , __SCREAMING_SNAKE_CASE : Any="<s>" , __SCREAMING_SNAKE_CASE : List[str]="</s>" , __SCREAMING_SNAKE_CASE : List[Any]="</s>" , __SCREAMING_SNAKE_CASE : Optional[Any]="<s>" , __SCREAMING_SNAKE_CASE : str="<unk>" , __SCREAMING_SNAKE_CASE : str="<pad>" , __SCREAMING_SNAKE_CASE : Optional[Any]="<mask>" , __SCREAMING_SNAKE_CASE : str=False , __SCREAMING_SNAKE_CASE : Dict=True , **__SCREAMING_SNAKE_CASE : Any , ) -> int:
"""simple docstring"""
super().__init__(
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , tokenizer_file=__SCREAMING_SNAKE_CASE , errors=__SCREAMING_SNAKE_CASE , bos_token=__SCREAMING_SNAKE_CASE , eos_token=__SCREAMING_SNAKE_CASE , sep_token=__SCREAMING_SNAKE_CASE , cls_token=__SCREAMING_SNAKE_CASE , unk_token=__SCREAMING_SNAKE_CASE , pad_token=__SCREAMING_SNAKE_CASE , mask_token=__SCREAMING_SNAKE_CASE , add_prefix_space=__SCREAMING_SNAKE_CASE , trim_offsets=__SCREAMING_SNAKE_CASE , **__SCREAMING_SNAKE_CASE , )
__SCREAMING_SNAKE_CASE = json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__() )
if pre_tok_state.get('''add_prefix_space''' , __SCREAMING_SNAKE_CASE ) != add_prefix_space:
__SCREAMING_SNAKE_CASE = getattr(__SCREAMING_SNAKE_CASE , pre_tok_state.pop('''type''' ) )
__SCREAMING_SNAKE_CASE = add_prefix_space
__SCREAMING_SNAKE_CASE = pre_tok_class(**__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = add_prefix_space
# the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
__SCREAMING_SNAKE_CASE = '''post_processor'''
__SCREAMING_SNAKE_CASE = getattr(self.backend_tokenizer , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
if tokenizer_component_instance:
__SCREAMING_SNAKE_CASE = json.loads(tokenizer_component_instance.__getstate__() )
# The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
if "sep" in state:
__SCREAMING_SNAKE_CASE = tuple(state['''sep'''] )
if "cls" in state:
__SCREAMING_SNAKE_CASE = tuple(state['''cls'''] )
__SCREAMING_SNAKE_CASE = False
if state.get('''add_prefix_space''' , __SCREAMING_SNAKE_CASE ) != add_prefix_space:
__SCREAMING_SNAKE_CASE = add_prefix_space
__SCREAMING_SNAKE_CASE = True
if state.get('''trim_offsets''' , __SCREAMING_SNAKE_CASE ) != trim_offsets:
__SCREAMING_SNAKE_CASE = trim_offsets
__SCREAMING_SNAKE_CASE = True
if changes_to_apply:
__SCREAMING_SNAKE_CASE = getattr(__SCREAMING_SNAKE_CASE , state.pop('''type''' ) )
__SCREAMING_SNAKE_CASE = component_class(**__SCREAMING_SNAKE_CASE )
setattr(self.backend_tokenizer , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
@property
def _a ( self : Tuple ) -> str:
"""simple docstring"""
if self._mask_token is None:
if self.verbose:
logger.error('''Using mask_token, but it is not set yet.''' )
return None
return str(self._mask_token )
@mask_token.setter
def _a ( self : Dict , __SCREAMING_SNAKE_CASE : Any ) -> List[str]:
"""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 value
__SCREAMING_SNAKE_CASE = value
def _a ( self : str , *__SCREAMING_SNAKE_CASE : Optional[int] , **__SCREAMING_SNAKE_CASE : str ) -> BatchEncoding:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = kwargs.get('''is_split_into_words''' , __SCREAMING_SNAKE_CASE )
if is_split_into_words and not self.add_prefix_space:
raise ValueError(
f"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
'''to use it with pretokenized inputs.''' )
return super()._batch_encode_plus(*__SCREAMING_SNAKE_CASE , **__SCREAMING_SNAKE_CASE )
def _a ( self : Dict , *__SCREAMING_SNAKE_CASE : List[Any] , **__SCREAMING_SNAKE_CASE : Any ) -> BatchEncoding:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = kwargs.get('''is_split_into_words''' , __SCREAMING_SNAKE_CASE )
if is_split_into_words and not self.add_prefix_space:
raise ValueError(
f"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
'''to use it with pretokenized inputs.''' )
return super()._encode_plus(*__SCREAMING_SNAKE_CASE , **__SCREAMING_SNAKE_CASE )
def _a ( self : int , __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 )
def _a ( self : Optional[Any] , __SCREAMING_SNAKE_CASE : List[Any] , __SCREAMING_SNAKE_CASE : Any=None ) -> Optional[Any]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = [self.bos_token_id] + token_ids_a + [self.eos_token_id]
if token_ids_a is None:
return output
return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]
def _a ( self : Optional[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]
| 706 |
"""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 | 0 |
"""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
| 707 |
"""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 | 0 |
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 )
| 708 |
"""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 | 0 |
"""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__)
| 709 |
"""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 | 0 |
"""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__)
| 710 |
"""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 | 0 |
"""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,)
| 711 |
"""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 | 0 |
"""simple docstring"""
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available
if is_vision_available():
from PIL import Image
from transformers import AlignProcessor, EfficientNetImageProcessor
@require_vision
class A__( unittest.TestCase ):
def _a ( self : List[str] ) -> str:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = tempfile.mkdtemp()
__SCREAMING_SNAKE_CASE = [
'''[UNK]''',
'''[CLS]''',
'''[SEP]''',
'''[PAD]''',
'''[MASK]''',
'''want''',
'''##want''',
'''##ed''',
'''wa''',
'''un''',
'''runn''',
'''##ing''',
''',''',
'''low''',
'''lowest''',
]
__SCREAMING_SNAKE_CASE = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['''vocab_file'''] )
with open(self.vocab_file , '''w''' , encoding='''utf-8''' ) as vocab_writer:
vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens] ) )
__SCREAMING_SNAKE_CASE = {
'''do_resize''': True,
'''size''': 20,
'''do_center_crop''': True,
'''crop_size''': 18,
'''do_normalize''': True,
'''image_mean''': [0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73],
'''image_std''': [0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11],
}
__SCREAMING_SNAKE_CASE = os.path.join(self.tmpdirname , __SCREAMING_SNAKE_CASE )
with open(self.image_processor_file , '''w''' , encoding='''utf-8''' ) as fp:
json.dump(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
def _a ( self : List[str] , **__SCREAMING_SNAKE_CASE : Tuple ) -> Union[str, Any]:
"""simple docstring"""
return BertTokenizer.from_pretrained(self.tmpdirname , **__SCREAMING_SNAKE_CASE )
def _a ( self : List[str] , **__SCREAMING_SNAKE_CASE : str ) -> Optional[int]:
"""simple docstring"""
return BertTokenizerFast.from_pretrained(self.tmpdirname , **__SCREAMING_SNAKE_CASE )
def _a ( self : List[str] , **__SCREAMING_SNAKE_CASE : Tuple ) -> Any:
"""simple docstring"""
return EfficientNetImageProcessor.from_pretrained(self.tmpdirname , **__SCREAMING_SNAKE_CASE )
def _a ( self : Optional[int] ) -> Optional[int]:
"""simple docstring"""
shutil.rmtree(self.tmpdirname )
def _a ( self : Optional[int] ) -> Optional[Any]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = [np.random.randint(2_55 , size=(3, 30, 4_00) , dtype=np.uinta )]
__SCREAMING_SNAKE_CASE = [Image.fromarray(np.moveaxis(__SCREAMING_SNAKE_CASE , 0 , -1 ) ) for x in image_inputs]
return image_inputs
def _a ( self : Any ) -> Optional[int]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = self.get_tokenizer()
__SCREAMING_SNAKE_CASE = self.get_rust_tokenizer()
__SCREAMING_SNAKE_CASE = self.get_image_processor()
__SCREAMING_SNAKE_CASE = AlignProcessor(tokenizer=__SCREAMING_SNAKE_CASE , image_processor=__SCREAMING_SNAKE_CASE )
processor_slow.save_pretrained(self.tmpdirname )
__SCREAMING_SNAKE_CASE = AlignProcessor.from_pretrained(self.tmpdirname , use_fast=__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = AlignProcessor(tokenizer=__SCREAMING_SNAKE_CASE , image_processor=__SCREAMING_SNAKE_CASE )
processor_fast.save_pretrained(self.tmpdirname )
__SCREAMING_SNAKE_CASE = AlignProcessor.from_pretrained(self.tmpdirname )
self.assertEqual(processor_slow.tokenizer.get_vocab() , tokenizer_slow.get_vocab() )
self.assertEqual(processor_fast.tokenizer.get_vocab() , tokenizer_fast.get_vocab() )
self.assertEqual(tokenizer_slow.get_vocab() , tokenizer_fast.get_vocab() )
self.assertIsInstance(processor_slow.tokenizer , __SCREAMING_SNAKE_CASE )
self.assertIsInstance(processor_fast.tokenizer , __SCREAMING_SNAKE_CASE )
self.assertEqual(processor_slow.image_processor.to_json_string() , image_processor.to_json_string() )
self.assertEqual(processor_fast.image_processor.to_json_string() , image_processor.to_json_string() )
self.assertIsInstance(processor_slow.image_processor , __SCREAMING_SNAKE_CASE )
self.assertIsInstance(processor_fast.image_processor , __SCREAMING_SNAKE_CASE )
def _a ( self : Union[str, Any] ) -> Tuple:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = AlignProcessor(tokenizer=self.get_tokenizer() , image_processor=self.get_image_processor() )
processor.save_pretrained(self.tmpdirname )
__SCREAMING_SNAKE_CASE = self.get_tokenizer(bos_token='''(BOS)''' , eos_token='''(EOS)''' )
__SCREAMING_SNAKE_CASE = self.get_image_processor(do_normalize=__SCREAMING_SNAKE_CASE , padding_value=1.0 )
__SCREAMING_SNAKE_CASE = AlignProcessor.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.image_processor.to_json_string() , image_processor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.image_processor , __SCREAMING_SNAKE_CASE )
def _a ( self : Tuple ) -> Any:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = self.get_image_processor()
__SCREAMING_SNAKE_CASE = self.get_tokenizer()
__SCREAMING_SNAKE_CASE = AlignProcessor(tokenizer=__SCREAMING_SNAKE_CASE , image_processor=__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = self.prepare_image_inputs()
__SCREAMING_SNAKE_CASE = image_processor(__SCREAMING_SNAKE_CASE , return_tensors='''np''' )
__SCREAMING_SNAKE_CASE = processor(images=__SCREAMING_SNAKE_CASE , return_tensors='''np''' )
for key in input_image_proc.keys():
self.assertAlmostEqual(input_image_proc[key].sum() , input_processor[key].sum() , delta=1E-2 )
def _a ( self : List[Any] ) -> List[str]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = self.get_image_processor()
__SCREAMING_SNAKE_CASE = self.get_tokenizer()
__SCREAMING_SNAKE_CASE = AlignProcessor(tokenizer=__SCREAMING_SNAKE_CASE , image_processor=__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = '''lower newer'''
__SCREAMING_SNAKE_CASE = processor(text=__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = tokenizer(__SCREAMING_SNAKE_CASE , padding='''max_length''' , max_length=64 )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key] )
def _a ( self : int ) -> Dict:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = self.get_image_processor()
__SCREAMING_SNAKE_CASE = self.get_tokenizer()
__SCREAMING_SNAKE_CASE = AlignProcessor(tokenizer=__SCREAMING_SNAKE_CASE , image_processor=__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = '''lower newer'''
__SCREAMING_SNAKE_CASE = self.prepare_image_inputs()
__SCREAMING_SNAKE_CASE = processor(text=__SCREAMING_SNAKE_CASE , images=__SCREAMING_SNAKE_CASE )
self.assertListEqual(list(inputs.keys() ) , ['''input_ids''', '''token_type_ids''', '''attention_mask''', '''pixel_values'''] )
# test if it raises when no input is passed
with pytest.raises(__SCREAMING_SNAKE_CASE ):
processor()
def _a ( self : List[Any] ) -> List[Any]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = self.get_image_processor()
__SCREAMING_SNAKE_CASE = self.get_tokenizer()
__SCREAMING_SNAKE_CASE = AlignProcessor(tokenizer=__SCREAMING_SNAKE_CASE , image_processor=__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 : int ) -> List[str]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = self.get_image_processor()
__SCREAMING_SNAKE_CASE = self.get_tokenizer()
__SCREAMING_SNAKE_CASE = AlignProcessor(tokenizer=__SCREAMING_SNAKE_CASE , image_processor=__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = '''lower newer'''
__SCREAMING_SNAKE_CASE = self.prepare_image_inputs()
__SCREAMING_SNAKE_CASE = processor(text=__SCREAMING_SNAKE_CASE , images=__SCREAMING_SNAKE_CASE )
self.assertListEqual(list(inputs.keys() ) , processor.model_input_names )
| 712 |
"""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 | 0 |
"""simple docstring"""
import enum
import warnings
from .. import MODEL_FOR_CAUSAL_LM_MAPPING, TF_MODEL_FOR_CAUSAL_LM_MAPPING
from ..utils import add_end_docstrings, is_tf_available
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_tf_available():
import tensorflow as tf
class A__( enum.Enum ):
lowerCAmelCase = 0
lowerCAmelCase = 1
lowerCAmelCase = 2
@add_end_docstrings(__magic_name__ )
class A__( __magic_name__ ):
lowerCAmelCase = '''
In 1991, the remains of Russian Tsar Nicholas II and his family (except for Alexei and Maria) are discovered. The
voice of Nicholas\'s young son, Tsarevich Alexei Nikolaevich, narrates the remainder of the story. 1883 Western
Siberia, a young Grigori Rasputin is asked by his father and a group of men to perform magic. Rasputin has a vision
and denounces one of the men as a horse thief. Although his father initially slaps him for making such an
accusation, Rasputin watches as the man is chased outside and beaten. Twenty years later, Rasputin sees a vision of
the Virgin Mary, prompting him to become a priest. Rasputin quickly becomes famous, with people, even a bishop,
begging for his blessing. <eod> </s> <eos>
'''
def __init__( self : Union[str, Any] , *__SCREAMING_SNAKE_CASE : str , **__SCREAMING_SNAKE_CASE : List[str] ) -> Optional[Any]:
"""simple docstring"""
super().__init__(*__SCREAMING_SNAKE_CASE , **__SCREAMING_SNAKE_CASE )
self.check_model_type(
TF_MODEL_FOR_CAUSAL_LM_MAPPING if self.framework == '''tf''' else MODEL_FOR_CAUSAL_LM_MAPPING )
if "prefix" not in self._preprocess_params:
# This is very specific. The logic is quite complex and needs to be done
# as a "default".
# It also defines both some preprocess_kwargs and generate_kwargs
# which is why we cannot put them in their respective methods.
__SCREAMING_SNAKE_CASE = None
if self.model.config.prefix is not None:
__SCREAMING_SNAKE_CASE = self.model.config.prefix
if prefix is None and self.model.__class__.__name__ in [
"XLNetLMHeadModel",
"TransfoXLLMHeadModel",
"TFXLNetLMHeadModel",
"TFTransfoXLLMHeadModel",
]:
# For XLNet and TransformerXL we add an article to the prompt to give more state to the model.
__SCREAMING_SNAKE_CASE = self.XL_PREFIX
if prefix is not None:
# Recalculate some generate_kwargs linked to prefix.
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = self._sanitize_parameters(prefix=__SCREAMING_SNAKE_CASE , **self._forward_params )
__SCREAMING_SNAKE_CASE = {**self._preprocess_params, **preprocess_params}
__SCREAMING_SNAKE_CASE = {**self._forward_params, **forward_params}
def _a ( self : Any , __SCREAMING_SNAKE_CASE : int=None , __SCREAMING_SNAKE_CASE : List[str]=None , __SCREAMING_SNAKE_CASE : List[Any]=None , __SCREAMING_SNAKE_CASE : str=None , __SCREAMING_SNAKE_CASE : int=None , __SCREAMING_SNAKE_CASE : Dict=None , __SCREAMING_SNAKE_CASE : Dict=None , __SCREAMING_SNAKE_CASE : Any=None , **__SCREAMING_SNAKE_CASE : Union[str, Any] , ) -> Any:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = {}
if prefix is not None:
__SCREAMING_SNAKE_CASE = prefix
if prefix:
__SCREAMING_SNAKE_CASE = self.tokenizer(
__SCREAMING_SNAKE_CASE , padding=__SCREAMING_SNAKE_CASE , add_special_tokens=__SCREAMING_SNAKE_CASE , return_tensors=self.framework )
__SCREAMING_SNAKE_CASE = prefix_inputs['''input_ids'''].shape[-1]
if handle_long_generation is not None:
if handle_long_generation not in {"hole"}:
raise ValueError(
f"""{handle_long_generation} is not a valid value for `handle_long_generation` parameter expected"""
''' [None, \'hole\']''' )
__SCREAMING_SNAKE_CASE = handle_long_generation
preprocess_params.update(__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = generate_kwargs
__SCREAMING_SNAKE_CASE = {}
if return_full_text is not None and return_type is None:
if return_text is not None:
raise ValueError('''`return_text` is mutually exclusive with `return_full_text`''' )
if return_tensors is not None:
raise ValueError('''`return_full_text` is mutually exclusive with `return_tensors`''' )
__SCREAMING_SNAKE_CASE = ReturnType.FULL_TEXT if return_full_text else ReturnType.NEW_TEXT
if return_tensors is not None and return_type is None:
if return_text is not None:
raise ValueError('''`return_text` is mutually exclusive with `return_tensors`''' )
__SCREAMING_SNAKE_CASE = ReturnType.TENSORS
if return_type is not None:
__SCREAMING_SNAKE_CASE = return_type
if clean_up_tokenization_spaces is not None:
__SCREAMING_SNAKE_CASE = clean_up_tokenization_spaces
if stop_sequence is not None:
__SCREAMING_SNAKE_CASE = self.tokenizer.encode(__SCREAMING_SNAKE_CASE , add_special_tokens=__SCREAMING_SNAKE_CASE )
if len(__SCREAMING_SNAKE_CASE ) > 1:
warnings.warn(
'''Stopping on a multiple token sequence is not yet supported on transformers. The first token of'''
''' the stop sequence will be used as the stop sequence string in the interim.''' )
__SCREAMING_SNAKE_CASE = stop_sequence_ids[0]
return preprocess_params, forward_params, postprocess_params
def _a ( self : Union[str, Any] , *__SCREAMING_SNAKE_CASE : List[str] , **__SCREAMING_SNAKE_CASE : Optional[Any] ) -> int:
"""simple docstring"""
if self.model.__class__.__name__ in ["TransfoXLLMHeadModel"]:
kwargs.update({'''add_space_before_punct_symbol''': True} )
return super()._parse_and_tokenize(*__SCREAMING_SNAKE_CASE , **__SCREAMING_SNAKE_CASE )
def __call__( self : Dict , __SCREAMING_SNAKE_CASE : Dict , **__SCREAMING_SNAKE_CASE : Tuple ) -> Union[str, Any]:
"""simple docstring"""
return super().__call__(__SCREAMING_SNAKE_CASE , **__SCREAMING_SNAKE_CASE )
def _a ( self : Optional[Any] , __SCREAMING_SNAKE_CASE : Union[str, Any] , __SCREAMING_SNAKE_CASE : List[str]="" , __SCREAMING_SNAKE_CASE : Optional[int]=None , **__SCREAMING_SNAKE_CASE : Optional[Any] ) -> List[str]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = self.tokenizer(
prefix + prompt_text , padding=__SCREAMING_SNAKE_CASE , add_special_tokens=__SCREAMING_SNAKE_CASE , return_tensors=self.framework )
__SCREAMING_SNAKE_CASE = prompt_text
if handle_long_generation == "hole":
__SCREAMING_SNAKE_CASE = inputs['''input_ids'''].shape[-1]
if "max_new_tokens" in generate_kwargs:
__SCREAMING_SNAKE_CASE = generate_kwargs['''max_new_tokens''']
else:
__SCREAMING_SNAKE_CASE = generate_kwargs.get('''max_length''' , self.model.config.max_length ) - cur_len
if new_tokens < 0:
raise ValueError('''We cannot infer how many new tokens are expected''' )
if cur_len + new_tokens > self.tokenizer.model_max_length:
__SCREAMING_SNAKE_CASE = self.tokenizer.model_max_length - new_tokens
if keep_length <= 0:
raise ValueError(
'''We cannot use `hole` to handle this generation the number of desired tokens exceeds the'''
''' models max length''' )
__SCREAMING_SNAKE_CASE = inputs['''input_ids'''][:, -keep_length:]
if "attention_mask" in inputs:
__SCREAMING_SNAKE_CASE = inputs['''attention_mask'''][:, -keep_length:]
return inputs
def _a ( self : int , __SCREAMING_SNAKE_CASE : Tuple , **__SCREAMING_SNAKE_CASE : List[Any] ) -> Any:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = model_inputs['''input_ids''']
__SCREAMING_SNAKE_CASE = model_inputs.get('''attention_mask''' , __SCREAMING_SNAKE_CASE )
# Allow empty prompts
if input_ids.shape[1] == 0:
__SCREAMING_SNAKE_CASE = None
__SCREAMING_SNAKE_CASE = None
__SCREAMING_SNAKE_CASE = 1
else:
__SCREAMING_SNAKE_CASE = input_ids.shape[0]
__SCREAMING_SNAKE_CASE = model_inputs.pop('''prompt_text''' )
# If there is a prefix, we may need to adjust the generation length. Do so without permanently modifying
# generate_kwargs, as some of the parameterization may come from the initialization of the pipeline.
__SCREAMING_SNAKE_CASE = generate_kwargs.pop('''prefix_length''' , 0 )
if prefix_length > 0:
__SCREAMING_SNAKE_CASE = '''max_new_tokens''' in generate_kwargs or (
'''generation_config''' in generate_kwargs
and generate_kwargs['''generation_config'''].max_new_tokens is not None
)
if not has_max_new_tokens:
__SCREAMING_SNAKE_CASE = generate_kwargs.get('''max_length''' ) or self.model.config.max_length
generate_kwargs["max_length"] += prefix_length
__SCREAMING_SNAKE_CASE = '''min_new_tokens''' in generate_kwargs or (
'''generation_config''' in generate_kwargs
and generate_kwargs['''generation_config'''].min_new_tokens is not None
)
if not has_min_new_tokens and "min_length" in generate_kwargs:
generate_kwargs["min_length"] += prefix_length
# BS x SL
__SCREAMING_SNAKE_CASE = self.model.generate(input_ids=__SCREAMING_SNAKE_CASE , attention_mask=__SCREAMING_SNAKE_CASE , **__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = generated_sequence.shape[0]
if self.framework == "pt":
__SCREAMING_SNAKE_CASE = generated_sequence.reshape(__SCREAMING_SNAKE_CASE , out_b // in_b , *generated_sequence.shape[1:] )
elif self.framework == "tf":
__SCREAMING_SNAKE_CASE = tf.reshape(__SCREAMING_SNAKE_CASE , (in_b, out_b // in_b, *generated_sequence.shape[1:]) )
return {"generated_sequence": generated_sequence, "input_ids": input_ids, "prompt_text": prompt_text}
def _a ( self : int , __SCREAMING_SNAKE_CASE : Union[str, Any] , __SCREAMING_SNAKE_CASE : Union[str, Any]=ReturnType.FULL_TEXT , __SCREAMING_SNAKE_CASE : List[str]=True ) -> Tuple:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = model_outputs['''generated_sequence'''][0]
__SCREAMING_SNAKE_CASE = model_outputs['''input_ids''']
__SCREAMING_SNAKE_CASE = model_outputs['''prompt_text''']
__SCREAMING_SNAKE_CASE = generated_sequence.numpy().tolist()
__SCREAMING_SNAKE_CASE = []
for sequence in generated_sequence:
if return_type == ReturnType.TENSORS:
__SCREAMING_SNAKE_CASE = {'''generated_token_ids''': sequence}
elif return_type in {ReturnType.NEW_TEXT, ReturnType.FULL_TEXT}:
# Decode text
__SCREAMING_SNAKE_CASE = self.tokenizer.decode(
__SCREAMING_SNAKE_CASE , skip_special_tokens=__SCREAMING_SNAKE_CASE , clean_up_tokenization_spaces=__SCREAMING_SNAKE_CASE , )
# Remove PADDING prompt of the sequence if XLNet or Transfo-XL model is used
if input_ids is None:
__SCREAMING_SNAKE_CASE = 0
else:
__SCREAMING_SNAKE_CASE = len(
self.tokenizer.decode(
input_ids[0] , skip_special_tokens=__SCREAMING_SNAKE_CASE , clean_up_tokenization_spaces=__SCREAMING_SNAKE_CASE , ) )
if return_type == ReturnType.FULL_TEXT:
__SCREAMING_SNAKE_CASE = prompt_text + text[prompt_length:]
else:
__SCREAMING_SNAKE_CASE = text[prompt_length:]
__SCREAMING_SNAKE_CASE = {'''generated_text''': all_text}
records.append(__SCREAMING_SNAKE_CASE )
return records
| 713 |
"""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 | 0 |
"""simple docstring"""
import argparse
from collections import defaultdict
import yaml
lowerCAmelCase__ ="docs/source/en/_toctree.yml"
def _a ( UpperCAmelCase__ ) -> int:
__SCREAMING_SNAKE_CASE = defaultdict(UpperCAmelCase__ )
__SCREAMING_SNAKE_CASE = []
__SCREAMING_SNAKE_CASE = []
for doc in doc_list:
if "local" in doc:
counts[doc["local"]] += 1
if doc["title"].lower() == "overview":
overview_doc.append({'''local''': doc['''local'''], '''title''': doc['''title''']} )
else:
new_doc_list.append(UpperCAmelCase__ )
__SCREAMING_SNAKE_CASE = new_doc_list
__SCREAMING_SNAKE_CASE = [key for key, value in counts.items() if value > 1]
__SCREAMING_SNAKE_CASE = []
for duplicate_key in duplicates:
__SCREAMING_SNAKE_CASE = list({doc['''title'''] for doc in doc_list if doc['''local'''] == duplicate_key} )
if len(UpperCAmelCase__ ) > 1:
raise ValueError(
f"""{duplicate_key} is present several times in the documentation table of content at """
'''`docs/source/en/_toctree.yml` with different *Title* values. Choose one of those and remove the '''
'''others.''' )
# Only add this once
new_doc.append({'''local''': duplicate_key, '''title''': titles[0]} )
# Add none duplicate-keys
new_doc.extend([doc for doc in doc_list if '''local''' not in counts or counts[doc['''local''']] == 1] )
__SCREAMING_SNAKE_CASE = sorted(UpperCAmelCase__ , key=lambda UpperCAmelCase__ : s["title"].lower() )
# "overview" gets special treatment and is always first
if len(UpperCAmelCase__ ) > 1:
raise ValueError('''{doc_list} has two \'overview\' docs which is not allowed.''' )
overview_doc.extend(UpperCAmelCase__ )
# Sort
return overview_doc
def _a ( UpperCAmelCase__=False ) -> Dict:
with open(UpperCAmelCase__ , encoding='''utf-8''' ) as f:
__SCREAMING_SNAKE_CASE = yaml.safe_load(f.read() )
# Get to the API doc
__SCREAMING_SNAKE_CASE = 0
while content[api_idx]["title"] != "API":
api_idx += 1
__SCREAMING_SNAKE_CASE = content[api_idx]['''sections''']
# Then to the model doc
__SCREAMING_SNAKE_CASE = 0
while api_doc[scheduler_idx]["title"] != "Schedulers":
scheduler_idx += 1
__SCREAMING_SNAKE_CASE = api_doc[scheduler_idx]['''sections''']
__SCREAMING_SNAKE_CASE = clean_doc_toc(UpperCAmelCase__ )
__SCREAMING_SNAKE_CASE = False
if new_scheduler_doc != scheduler_doc:
__SCREAMING_SNAKE_CASE = True
if overwrite:
__SCREAMING_SNAKE_CASE = new_scheduler_doc
if diff:
if overwrite:
__SCREAMING_SNAKE_CASE = api_doc
with open(UpperCAmelCase__ , '''w''' , encoding='''utf-8''' ) as f:
f.write(yaml.dump(UpperCAmelCase__ , allow_unicode=UpperCAmelCase__ ) )
else:
raise ValueError(
'''The model doc part of the table of content is not properly sorted, run `make style` to fix this.''' )
def _a ( UpperCAmelCase__=False ) -> List[str]:
with open(UpperCAmelCase__ , encoding='''utf-8''' ) as f:
__SCREAMING_SNAKE_CASE = yaml.safe_load(f.read() )
# Get to the API doc
__SCREAMING_SNAKE_CASE = 0
while content[api_idx]["title"] != "API":
api_idx += 1
__SCREAMING_SNAKE_CASE = content[api_idx]['''sections''']
# Then to the model doc
__SCREAMING_SNAKE_CASE = 0
while api_doc[pipeline_idx]["title"] != "Pipelines":
pipeline_idx += 1
__SCREAMING_SNAKE_CASE = False
__SCREAMING_SNAKE_CASE = api_doc[pipeline_idx]['''sections''']
__SCREAMING_SNAKE_CASE = []
# sort sub pipeline docs
for pipeline_doc in pipeline_docs:
if "section" in pipeline_doc:
__SCREAMING_SNAKE_CASE = pipeline_doc['''section''']
__SCREAMING_SNAKE_CASE = clean_doc_toc(UpperCAmelCase__ )
if overwrite:
__SCREAMING_SNAKE_CASE = new_sub_pipeline_doc
new_pipeline_docs.append(UpperCAmelCase__ )
# sort overall pipeline doc
__SCREAMING_SNAKE_CASE = clean_doc_toc(UpperCAmelCase__ )
if new_pipeline_docs != pipeline_docs:
__SCREAMING_SNAKE_CASE = True
if overwrite:
__SCREAMING_SNAKE_CASE = new_pipeline_docs
if diff:
if overwrite:
__SCREAMING_SNAKE_CASE = api_doc
with open(UpperCAmelCase__ , '''w''' , encoding='''utf-8''' ) as f:
f.write(yaml.dump(UpperCAmelCase__ , allow_unicode=UpperCAmelCase__ ) )
else:
raise ValueError(
'''The model doc part of the table of content is not properly sorted, run `make style` to fix this.''' )
if __name__ == "__main__":
lowerCAmelCase__ =argparse.ArgumentParser()
parser.add_argument("--fix_and_overwrite", action="store_true", help="Whether to fix inconsistencies.")
lowerCAmelCase__ =parser.parse_args()
check_scheduler_doc(args.fix_and_overwrite)
check_pipeline_doc(args.fix_and_overwrite)
| 714 |
"""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 | 0 |
"""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__)
| 715 |
"""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 | 0 |
"""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()
| 716 |
"""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 | 0 |
"""simple docstring"""
from ...utils import logging
from ..ta.modeling_tf_ta import TFTaEncoderModel, TFTaForConditionalGeneration, TFTaModel
from .configuration_mta import MTaConfig
lowerCAmelCase__ =logging.get_logger(__name__)
lowerCAmelCase__ ="T5Config"
class A__( __magic_name__ ):
lowerCAmelCase = '''mt5'''
lowerCAmelCase = MTaConfig
class A__( __magic_name__ ):
lowerCAmelCase = '''mt5'''
lowerCAmelCase = MTaConfig
class A__( __magic_name__ ):
lowerCAmelCase = '''mt5'''
lowerCAmelCase = MTaConfig
| 717 |
"""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 | 0 |
import os
import re
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
lowerCAmelCase__ =logging.get_logger(__name__)
lowerCAmelCase__ ={
"vocab_file": "vocab.txt",
"merges_file": "bpe.codes",
}
lowerCAmelCase__ ={
"vocab_file": {
"vinai/phobert-base": "https://huggingface.co/vinai/phobert-base/resolve/main/vocab.txt",
"vinai/phobert-large": "https://huggingface.co/vinai/phobert-large/resolve/main/vocab.txt",
},
"merges_file": {
"vinai/phobert-base": "https://huggingface.co/vinai/phobert-base/resolve/main/bpe.codes",
"vinai/phobert-large": "https://huggingface.co/vinai/phobert-large/resolve/main/bpe.codes",
},
}
lowerCAmelCase__ ={
"vinai/phobert-base": 256,
"vinai/phobert-large": 256,
}
def _a ( UpperCAmelCase__ ) -> List[Any]:
__SCREAMING_SNAKE_CASE = set()
__SCREAMING_SNAKE_CASE = word[0]
for char in word[1:]:
pairs.add((prev_char, char) )
__SCREAMING_SNAKE_CASE = char
__SCREAMING_SNAKE_CASE = set(UpperCAmelCase__ )
return pairs
class A__( __magic_name__ ):
lowerCAmelCase = VOCAB_FILES_NAMES
lowerCAmelCase = PRETRAINED_VOCAB_FILES_MAP
lowerCAmelCase = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
def __init__( self : Optional[int] , __SCREAMING_SNAKE_CASE : List[str] , __SCREAMING_SNAKE_CASE : int , __SCREAMING_SNAKE_CASE : Union[str, Any]="<s>" , __SCREAMING_SNAKE_CASE : Any="</s>" , __SCREAMING_SNAKE_CASE : Any="</s>" , __SCREAMING_SNAKE_CASE : Any="<s>" , __SCREAMING_SNAKE_CASE : Union[str, Any]="<unk>" , __SCREAMING_SNAKE_CASE : List[Any]="<pad>" , __SCREAMING_SNAKE_CASE : Any="<mask>" , **__SCREAMING_SNAKE_CASE : int , ) -> Tuple:
"""simple docstring"""
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 , **__SCREAMING_SNAKE_CASE , )
__SCREAMING_SNAKE_CASE = vocab_file
__SCREAMING_SNAKE_CASE = merges_file
__SCREAMING_SNAKE_CASE = {}
__SCREAMING_SNAKE_CASE = 0
__SCREAMING_SNAKE_CASE = 1
__SCREAMING_SNAKE_CASE = 2
__SCREAMING_SNAKE_CASE = 3
self.add_from_file(__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = {v: k for k, v in self.encoder.items()}
with open(__SCREAMING_SNAKE_CASE , encoding='''utf-8''' ) as merges_handle:
__SCREAMING_SNAKE_CASE = merges_handle.read().split('''\n''' )[:-1]
__SCREAMING_SNAKE_CASE = [tuple(merge.split()[:-1] ) for merge in merges]
__SCREAMING_SNAKE_CASE = dict(zip(__SCREAMING_SNAKE_CASE , range(len(__SCREAMING_SNAKE_CASE ) ) ) )
__SCREAMING_SNAKE_CASE = {}
def _a ( self : 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 : List[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 : 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 + sep + token_ids_a + sep ) * [0]
@property
def _a ( self : Dict ) -> Optional[Any]:
"""simple docstring"""
return len(self.encoder )
def _a ( self : Optional[Any] ) -> List[Any]:
"""simple docstring"""
return dict(self.encoder , **self.added_tokens_encoder )
def _a ( self : List[Any] , __SCREAMING_SNAKE_CASE : int ) -> Any:
"""simple docstring"""
if token in self.cache:
return self.cache[token]
__SCREAMING_SNAKE_CASE = tuple(__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = tuple(list(word[:-1] ) + [word[-1] + '''</w>'''] )
__SCREAMING_SNAKE_CASE = get_pairs(__SCREAMING_SNAKE_CASE )
if not pairs:
return token
while True:
__SCREAMING_SNAKE_CASE = min(__SCREAMING_SNAKE_CASE , key=lambda __SCREAMING_SNAKE_CASE : self.bpe_ranks.get(__SCREAMING_SNAKE_CASE , float('''inf''' ) ) )
if bigram not in self.bpe_ranks:
break
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = bigram
__SCREAMING_SNAKE_CASE = []
__SCREAMING_SNAKE_CASE = 0
while i < len(__SCREAMING_SNAKE_CASE ):
try:
__SCREAMING_SNAKE_CASE = word.index(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
except ValueError:
new_word.extend(word[i:] )
break
else:
new_word.extend(word[i:j] )
__SCREAMING_SNAKE_CASE = j
if word[i] == first and i < len(__SCREAMING_SNAKE_CASE ) - 1 and word[i + 1] == second:
new_word.append(first + second )
i += 2
else:
new_word.append(word[i] )
i += 1
__SCREAMING_SNAKE_CASE = tuple(__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = new_word
if len(__SCREAMING_SNAKE_CASE ) == 1:
break
else:
__SCREAMING_SNAKE_CASE = get_pairs(__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = '''@@ '''.join(__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = word[:-4]
__SCREAMING_SNAKE_CASE = word
return word
def _a ( self : List[str] , __SCREAMING_SNAKE_CASE : Any ) -> Optional[int]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = []
__SCREAMING_SNAKE_CASE = re.findall(r'''\S+\n?''' , __SCREAMING_SNAKE_CASE )
for token in words:
split_tokens.extend(list(self.bpe(__SCREAMING_SNAKE_CASE ).split(''' ''' ) ) )
return split_tokens
def _a ( self : Any , __SCREAMING_SNAKE_CASE : int ) -> Any:
"""simple docstring"""
return self.encoder.get(__SCREAMING_SNAKE_CASE , self.encoder.get(self.unk_token ) )
def _a ( self : str , __SCREAMING_SNAKE_CASE : List[Any] ) -> Optional[Any]:
"""simple docstring"""
return self.decoder.get(__SCREAMING_SNAKE_CASE , self.unk_token )
def _a ( self : Optional[int] , __SCREAMING_SNAKE_CASE : List[str] ) -> Optional[Any]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = ''' '''.join(__SCREAMING_SNAKE_CASE ).replace('''@@ ''' , '''''' ).strip()
return out_string
def _a ( self : int , __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['''merges_file'''] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(__SCREAMING_SNAKE_CASE ):
copyfile(self.vocab_file , __SCREAMING_SNAKE_CASE )
if os.path.abspath(self.merges_file ) != os.path.abspath(__SCREAMING_SNAKE_CASE ):
copyfile(self.merges_file , __SCREAMING_SNAKE_CASE )
return out_vocab_file, out_merge_file
def _a ( self : Union[str, Any] , __SCREAMING_SNAKE_CASE : Union[str, Any] ) -> Any:
"""simple docstring"""
if isinstance(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
try:
with open(__SCREAMING_SNAKE_CASE , '''r''' , encoding='''utf-8''' ) as fd:
self.add_from_file(__SCREAMING_SNAKE_CASE )
except FileNotFoundError as fnfe:
raise fnfe
except UnicodeError:
raise Exception(f"""Incorrect encoding detected in {f}, please rebuild the dataset""" )
return
__SCREAMING_SNAKE_CASE = f.readlines()
for lineTmp in lines:
__SCREAMING_SNAKE_CASE = lineTmp.strip()
__SCREAMING_SNAKE_CASE = line.rfind(''' ''' )
if idx == -1:
raise ValueError('''Incorrect dictionary format, expected \'<token> <cnt>\'''' )
__SCREAMING_SNAKE_CASE = line[:idx]
__SCREAMING_SNAKE_CASE = len(self.encoder )
| 718 |
"""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 | 0 |
"""simple docstring"""
import copy
import tempfile
import unittest
from huggingface_hub import HfFolder, delete_repo
from parameterized import parameterized
from requests.exceptions import HTTPError
from transformers import AutoConfig, GenerationConfig
from transformers.testing_utils import TOKEN, USER, is_staging_test
class A__( unittest.TestCase ):
@parameterized.expand([(None,), ('''foo.json''',)] )
def _a ( self : List[str] , __SCREAMING_SNAKE_CASE : Optional[int] ) -> Any:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = GenerationConfig(
do_sample=__SCREAMING_SNAKE_CASE , temperature=0.7 , length_penalty=1.0 , bad_words_ids=[[1, 2, 3], [4, 5]] , )
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(__SCREAMING_SNAKE_CASE , config_name=__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = GenerationConfig.from_pretrained(__SCREAMING_SNAKE_CASE , config_name=__SCREAMING_SNAKE_CASE )
# Checks parameters that were specified
self.assertEqual(loaded_config.do_sample , __SCREAMING_SNAKE_CASE )
self.assertEqual(loaded_config.temperature , 0.7 )
self.assertEqual(loaded_config.length_penalty , 1.0 )
self.assertEqual(loaded_config.bad_words_ids , [[1, 2, 3], [4, 5]] )
# Checks parameters that were not specified (defaults)
self.assertEqual(loaded_config.top_k , 50 )
self.assertEqual(loaded_config.max_length , 20 )
self.assertEqual(loaded_config.max_time , __SCREAMING_SNAKE_CASE )
def _a ( self : Dict ) -> str:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = AutoConfig.from_pretrained('''gpt2''' )
__SCREAMING_SNAKE_CASE = GenerationConfig.from_model_config(__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = GenerationConfig()
# The generation config has loaded a few non-default parameters from the model config
self.assertNotEqual(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
# One of those parameters is eos_token_id -- check if it matches
self.assertNotEqual(generation_config_from_model.eos_token_id , default_generation_config.eos_token_id )
self.assertEqual(generation_config_from_model.eos_token_id , model_config.eos_token_id )
def _a ( self : Optional[int] ) -> Union[str, Any]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = GenerationConfig()
__SCREAMING_SNAKE_CASE = {
'''max_new_tokens''': 10_24,
'''foo''': '''bar''',
}
__SCREAMING_SNAKE_CASE = copy.deepcopy(__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = generation_config.update(**__SCREAMING_SNAKE_CASE )
# update_kwargs was not modified (no side effects)
self.assertEqual(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
# update_kwargs was used to update the config on valid attributes
self.assertEqual(generation_config.max_new_tokens , 10_24 )
# `.update()` returns a dictionary of unused kwargs
self.assertEqual(__SCREAMING_SNAKE_CASE , {'''foo''': '''bar'''} )
def _a ( self : Dict ) -> Optional[int]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = GenerationConfig()
__SCREAMING_SNAKE_CASE = '''bar'''
with tempfile.TemporaryDirectory('''test-generation-config''' ) as tmp_dir:
generation_config.save_pretrained(__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = GenerationConfig.from_pretrained(__SCREAMING_SNAKE_CASE )
# update_kwargs was used to update the config on valid attributes
self.assertEqual(new_config.foo , '''bar''' )
__SCREAMING_SNAKE_CASE = GenerationConfig.from_model_config(__SCREAMING_SNAKE_CASE )
assert not hasattr(__SCREAMING_SNAKE_CASE , '''foo''' ) # no new kwargs should be initialized if from config
def _a ( self : Optional[Any] ) -> Dict:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = GenerationConfig()
self.assertEqual(default_config.temperature , 1.0 )
self.assertEqual(default_config.do_sample , __SCREAMING_SNAKE_CASE )
self.assertEqual(default_config.num_beams , 1 )
__SCREAMING_SNAKE_CASE = GenerationConfig(
do_sample=__SCREAMING_SNAKE_CASE , temperature=0.7 , length_penalty=1.0 , bad_words_ids=[[1, 2, 3], [4, 5]] , )
self.assertEqual(config.temperature , 0.7 )
self.assertEqual(config.do_sample , __SCREAMING_SNAKE_CASE )
self.assertEqual(config.num_beams , 1 )
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = GenerationConfig.from_pretrained(__SCREAMING_SNAKE_CASE , temperature=1.0 )
self.assertEqual(loaded_config.temperature , 1.0 )
self.assertEqual(loaded_config.do_sample , __SCREAMING_SNAKE_CASE )
self.assertEqual(loaded_config.num_beams , 1 ) # default value
@is_staging_test
class A__( unittest.TestCase ):
@classmethod
def _a ( cls : List[str] ) -> Any:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = TOKEN
HfFolder.save_token(__SCREAMING_SNAKE_CASE )
@classmethod
def _a ( cls : str ) -> Union[str, Any]:
"""simple docstring"""
try:
delete_repo(token=cls._token , repo_id='''test-generation-config''' )
except HTTPError:
pass
try:
delete_repo(token=cls._token , repo_id='''valid_org/test-generation-config-org''' )
except HTTPError:
pass
def _a ( self : Optional[Any] ) -> Dict:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = GenerationConfig(
do_sample=__SCREAMING_SNAKE_CASE , temperature=0.7 , length_penalty=1.0 , )
config.push_to_hub('''test-generation-config''' , use_auth_token=self._token )
__SCREAMING_SNAKE_CASE = GenerationConfig.from_pretrained(f"""{USER}/test-generation-config""" )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(__SCREAMING_SNAKE_CASE , getattr(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) )
# Reset repo
delete_repo(token=self._token , repo_id='''test-generation-config''' )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(
__SCREAMING_SNAKE_CASE , repo_id='''test-generation-config''' , push_to_hub=__SCREAMING_SNAKE_CASE , use_auth_token=self._token )
__SCREAMING_SNAKE_CASE = GenerationConfig.from_pretrained(f"""{USER}/test-generation-config""" )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(__SCREAMING_SNAKE_CASE , getattr(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) )
def _a ( self : Any ) -> Optional[Any]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = GenerationConfig(
do_sample=__SCREAMING_SNAKE_CASE , temperature=0.7 , length_penalty=1.0 , )
config.push_to_hub('''valid_org/test-generation-config-org''' , use_auth_token=self._token )
__SCREAMING_SNAKE_CASE = GenerationConfig.from_pretrained('''valid_org/test-generation-config-org''' )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(__SCREAMING_SNAKE_CASE , getattr(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) )
# Reset repo
delete_repo(token=self._token , repo_id='''valid_org/test-generation-config-org''' )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(
__SCREAMING_SNAKE_CASE , repo_id='''valid_org/test-generation-config-org''' , push_to_hub=__SCREAMING_SNAKE_CASE , use_auth_token=self._token )
__SCREAMING_SNAKE_CASE = GenerationConfig.from_pretrained('''valid_org/test-generation-config-org''' )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(__SCREAMING_SNAKE_CASE , getattr(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) )
| 719 |
"""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 | 0 |
"""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
| 720 |
"""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 | 0 |
"""simple docstring"""
from typing import Any, Dict, Optional
import torch
import torch.nn.functional as F
from torch import nn
from ..utils import maybe_allow_in_graph
from .activations import get_activation
from .attention_processor import Attention
from .embeddings import CombinedTimestepLabelEmbeddings
@maybe_allow_in_graph
class A__( nn.Module ):
def __init__( self : List[str] , __SCREAMING_SNAKE_CASE : int , __SCREAMING_SNAKE_CASE : int , __SCREAMING_SNAKE_CASE : int , __SCREAMING_SNAKE_CASE : str=0.0 , __SCREAMING_SNAKE_CASE : Optional[int] = None , __SCREAMING_SNAKE_CASE : str = "geglu" , __SCREAMING_SNAKE_CASE : Optional[int] = None , __SCREAMING_SNAKE_CASE : bool = False , __SCREAMING_SNAKE_CASE : bool = False , __SCREAMING_SNAKE_CASE : bool = False , __SCREAMING_SNAKE_CASE : bool = False , __SCREAMING_SNAKE_CASE : bool = True , __SCREAMING_SNAKE_CASE : str = "layer_norm" , __SCREAMING_SNAKE_CASE : bool = False , ) -> Optional[int]:
"""simple docstring"""
super().__init__()
__SCREAMING_SNAKE_CASE = only_cross_attention
__SCREAMING_SNAKE_CASE = (num_embeds_ada_norm is not None) and norm_type == '''ada_norm_zero'''
__SCREAMING_SNAKE_CASE = (num_embeds_ada_norm is not None) and norm_type == '''ada_norm'''
if norm_type in ("ada_norm", "ada_norm_zero") and num_embeds_ada_norm is None:
raise ValueError(
f"""`norm_type` is set to {norm_type}, but `num_embeds_ada_norm` is not defined. Please make sure to"""
f""" define `num_embeds_ada_norm` if setting `norm_type` to {norm_type}.""" )
# Define 3 blocks. Each block has its own normalization layer.
# 1. Self-Attn
if self.use_ada_layer_norm:
__SCREAMING_SNAKE_CASE = AdaLayerNorm(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
elif self.use_ada_layer_norm_zero:
__SCREAMING_SNAKE_CASE = AdaLayerNormZero(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
else:
__SCREAMING_SNAKE_CASE = nn.LayerNorm(__SCREAMING_SNAKE_CASE , elementwise_affine=__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = Attention(
query_dim=__SCREAMING_SNAKE_CASE , heads=__SCREAMING_SNAKE_CASE , dim_head=__SCREAMING_SNAKE_CASE , dropout=__SCREAMING_SNAKE_CASE , bias=__SCREAMING_SNAKE_CASE , cross_attention_dim=cross_attention_dim if only_cross_attention else None , upcast_attention=__SCREAMING_SNAKE_CASE , )
# 2. Cross-Attn
if cross_attention_dim is not None or double_self_attention:
# We currently only use AdaLayerNormZero for self attention where there will only be one attention block.
# I.e. the number of returned modulation chunks from AdaLayerZero would not make sense if returned during
# the second cross attention block.
__SCREAMING_SNAKE_CASE = (
AdaLayerNorm(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
if self.use_ada_layer_norm
else nn.LayerNorm(__SCREAMING_SNAKE_CASE , elementwise_affine=__SCREAMING_SNAKE_CASE )
)
__SCREAMING_SNAKE_CASE = Attention(
query_dim=__SCREAMING_SNAKE_CASE , cross_attention_dim=cross_attention_dim if not double_self_attention else None , heads=__SCREAMING_SNAKE_CASE , dim_head=__SCREAMING_SNAKE_CASE , dropout=__SCREAMING_SNAKE_CASE , bias=__SCREAMING_SNAKE_CASE , upcast_attention=__SCREAMING_SNAKE_CASE , ) # is self-attn if encoder_hidden_states is none
else:
__SCREAMING_SNAKE_CASE = None
__SCREAMING_SNAKE_CASE = None
# 3. Feed-forward
__SCREAMING_SNAKE_CASE = nn.LayerNorm(__SCREAMING_SNAKE_CASE , elementwise_affine=__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = FeedForward(__SCREAMING_SNAKE_CASE , dropout=__SCREAMING_SNAKE_CASE , activation_fn=__SCREAMING_SNAKE_CASE , final_dropout=__SCREAMING_SNAKE_CASE )
# let chunk size default to None
__SCREAMING_SNAKE_CASE = None
__SCREAMING_SNAKE_CASE = 0
def _a ( self : Optional[Any] , __SCREAMING_SNAKE_CASE : Optional[int] , __SCREAMING_SNAKE_CASE : int ) -> Optional[int]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = chunk_size
__SCREAMING_SNAKE_CASE = dim
def _a ( self : int , __SCREAMING_SNAKE_CASE : torch.FloatTensor , __SCREAMING_SNAKE_CASE : Optional[torch.FloatTensor] = None , __SCREAMING_SNAKE_CASE : Optional[torch.FloatTensor] = None , __SCREAMING_SNAKE_CASE : Optional[torch.FloatTensor] = None , __SCREAMING_SNAKE_CASE : Optional[torch.LongTensor] = None , __SCREAMING_SNAKE_CASE : Dict[str, Any] = None , __SCREAMING_SNAKE_CASE : Optional[torch.LongTensor] = None , ) -> Tuple:
"""simple docstring"""
if self.use_ada_layer_norm:
__SCREAMING_SNAKE_CASE = self.norma(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
elif self.use_ada_layer_norm_zero:
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = self.norma(
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , hidden_dtype=hidden_states.dtype )
else:
__SCREAMING_SNAKE_CASE = self.norma(__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = cross_attention_kwargs if cross_attention_kwargs is not None else {}
__SCREAMING_SNAKE_CASE = self.attna(
__SCREAMING_SNAKE_CASE , encoder_hidden_states=encoder_hidden_states if self.only_cross_attention else None , attention_mask=__SCREAMING_SNAKE_CASE , **__SCREAMING_SNAKE_CASE , )
if self.use_ada_layer_norm_zero:
__SCREAMING_SNAKE_CASE = gate_msa.unsqueeze(1 ) * attn_output
__SCREAMING_SNAKE_CASE = attn_output + hidden_states
# 2. Cross-Attention
if self.attna is not None:
__SCREAMING_SNAKE_CASE = (
self.norma(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) if self.use_ada_layer_norm else self.norma(__SCREAMING_SNAKE_CASE )
)
__SCREAMING_SNAKE_CASE = self.attna(
__SCREAMING_SNAKE_CASE , encoder_hidden_states=__SCREAMING_SNAKE_CASE , attention_mask=__SCREAMING_SNAKE_CASE , **__SCREAMING_SNAKE_CASE , )
__SCREAMING_SNAKE_CASE = attn_output + hidden_states
# 3. Feed-forward
__SCREAMING_SNAKE_CASE = self.norma(__SCREAMING_SNAKE_CASE )
if self.use_ada_layer_norm_zero:
__SCREAMING_SNAKE_CASE = norm_hidden_states * (1 + scale_mlp[:, None]) + shift_mlp[:, None]
if self._chunk_size is not None:
# "feed_forward_chunk_size" can be used to save memory
if norm_hidden_states.shape[self._chunk_dim] % self._chunk_size != 0:
raise ValueError(
f"""`hidden_states` dimension to be chunked: {norm_hidden_states.shape[self._chunk_dim]} has to be divisible by chunk size: {self._chunk_size}. Make sure to set an appropriate `chunk_size` when calling `unet.enable_forward_chunking`.""" )
__SCREAMING_SNAKE_CASE = norm_hidden_states.shape[self._chunk_dim] // self._chunk_size
__SCREAMING_SNAKE_CASE = torch.cat(
[self.ff(__SCREAMING_SNAKE_CASE ) for hid_slice in norm_hidden_states.chunk(__SCREAMING_SNAKE_CASE , dim=self._chunk_dim )] , dim=self._chunk_dim , )
else:
__SCREAMING_SNAKE_CASE = self.ff(__SCREAMING_SNAKE_CASE )
if self.use_ada_layer_norm_zero:
__SCREAMING_SNAKE_CASE = gate_mlp.unsqueeze(1 ) * ff_output
__SCREAMING_SNAKE_CASE = ff_output + hidden_states
return hidden_states
class A__( nn.Module ):
def __init__( self : Tuple , __SCREAMING_SNAKE_CASE : int , __SCREAMING_SNAKE_CASE : Optional[int] = None , __SCREAMING_SNAKE_CASE : int = 4 , __SCREAMING_SNAKE_CASE : float = 0.0 , __SCREAMING_SNAKE_CASE : str = "geglu" , __SCREAMING_SNAKE_CASE : bool = False , ) -> int:
"""simple docstring"""
super().__init__()
__SCREAMING_SNAKE_CASE = int(dim * mult )
__SCREAMING_SNAKE_CASE = dim_out if dim_out is not None else dim
if activation_fn == "gelu":
__SCREAMING_SNAKE_CASE = GELU(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
if activation_fn == "gelu-approximate":
__SCREAMING_SNAKE_CASE = GELU(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , approximate='''tanh''' )
elif activation_fn == "geglu":
__SCREAMING_SNAKE_CASE = GEGLU(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
elif activation_fn == "geglu-approximate":
__SCREAMING_SNAKE_CASE = ApproximateGELU(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = nn.ModuleList([] )
# project in
self.net.append(__SCREAMING_SNAKE_CASE )
# project dropout
self.net.append(nn.Dropout(__SCREAMING_SNAKE_CASE ) )
# project out
self.net.append(nn.Linear(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) )
# FF as used in Vision Transformer, MLP-Mixer, etc. have a final dropout
if final_dropout:
self.net.append(nn.Dropout(__SCREAMING_SNAKE_CASE ) )
def _a ( self : int , __SCREAMING_SNAKE_CASE : Any ) -> Union[str, Any]:
"""simple docstring"""
for module in self.net:
__SCREAMING_SNAKE_CASE = module(__SCREAMING_SNAKE_CASE )
return hidden_states
class A__( nn.Module ):
def __init__( self : Any , __SCREAMING_SNAKE_CASE : int , __SCREAMING_SNAKE_CASE : int , __SCREAMING_SNAKE_CASE : str = "none" ) -> str:
"""simple docstring"""
super().__init__()
__SCREAMING_SNAKE_CASE = nn.Linear(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = approximate
def _a ( self : Dict , __SCREAMING_SNAKE_CASE : Any ) -> List[str]:
"""simple docstring"""
if gate.device.type != "mps":
return F.gelu(__SCREAMING_SNAKE_CASE , approximate=self.approximate )
# mps: gelu is not implemented for float16
return F.gelu(gate.to(dtype=torch.floataa ) , approximate=self.approximate ).to(dtype=gate.dtype )
def _a ( self : Tuple , __SCREAMING_SNAKE_CASE : Tuple ) -> str:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = self.proj(__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = self.gelu(__SCREAMING_SNAKE_CASE )
return hidden_states
class A__( nn.Module ):
def __init__( self : int , __SCREAMING_SNAKE_CASE : int , __SCREAMING_SNAKE_CASE : int ) -> str:
"""simple docstring"""
super().__init__()
__SCREAMING_SNAKE_CASE = nn.Linear(__SCREAMING_SNAKE_CASE , dim_out * 2 )
def _a ( self : Union[str, Any] , __SCREAMING_SNAKE_CASE : Tuple ) -> Optional[Any]:
"""simple docstring"""
if gate.device.type != "mps":
return F.gelu(__SCREAMING_SNAKE_CASE )
# mps: gelu is not implemented for float16
return F.gelu(gate.to(dtype=torch.floataa ) ).to(dtype=gate.dtype )
def _a ( self : Dict , __SCREAMING_SNAKE_CASE : int ) -> List[str]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = self.proj(__SCREAMING_SNAKE_CASE ).chunk(2 , dim=-1 )
return hidden_states * self.gelu(__SCREAMING_SNAKE_CASE )
class A__( nn.Module ):
def __init__( self : int , __SCREAMING_SNAKE_CASE : int , __SCREAMING_SNAKE_CASE : int ) -> Any:
"""simple docstring"""
super().__init__()
__SCREAMING_SNAKE_CASE = nn.Linear(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
def _a ( self : List[str] , __SCREAMING_SNAKE_CASE : str ) -> Optional[Any]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = self.proj(__SCREAMING_SNAKE_CASE )
return x * torch.sigmoid(1.7_02 * x )
class A__( nn.Module ):
def __init__( self : str , __SCREAMING_SNAKE_CASE : Optional[int] , __SCREAMING_SNAKE_CASE : List[Any] ) -> int:
"""simple docstring"""
super().__init__()
__SCREAMING_SNAKE_CASE = nn.Embedding(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = nn.SiLU()
__SCREAMING_SNAKE_CASE = nn.Linear(__SCREAMING_SNAKE_CASE , embedding_dim * 2 )
__SCREAMING_SNAKE_CASE = nn.LayerNorm(__SCREAMING_SNAKE_CASE , elementwise_affine=__SCREAMING_SNAKE_CASE )
def _a ( self : Any , __SCREAMING_SNAKE_CASE : List[str] , __SCREAMING_SNAKE_CASE : int ) -> int:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = self.linear(self.silu(self.emb(__SCREAMING_SNAKE_CASE ) ) )
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = torch.chunk(__SCREAMING_SNAKE_CASE , 2 )
__SCREAMING_SNAKE_CASE = self.norm(__SCREAMING_SNAKE_CASE ) * (1 + scale) + shift
return x
class A__( nn.Module ):
def __init__( self : int , __SCREAMING_SNAKE_CASE : List[Any] , __SCREAMING_SNAKE_CASE : Dict ) -> Optional[int]:
"""simple docstring"""
super().__init__()
__SCREAMING_SNAKE_CASE = CombinedTimestepLabelEmbeddings(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = nn.SiLU()
__SCREAMING_SNAKE_CASE = nn.Linear(__SCREAMING_SNAKE_CASE , 6 * embedding_dim , bias=__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = nn.LayerNorm(__SCREAMING_SNAKE_CASE , elementwise_affine=__SCREAMING_SNAKE_CASE , eps=1E-6 )
def _a ( self : Union[str, Any] , __SCREAMING_SNAKE_CASE : str , __SCREAMING_SNAKE_CASE : List[Any] , __SCREAMING_SNAKE_CASE : List[Any] , __SCREAMING_SNAKE_CASE : Optional[int]=None ) -> Any:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = self.linear(self.silu(self.emb(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , hidden_dtype=__SCREAMING_SNAKE_CASE ) ) )
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = emb.chunk(6 , dim=1 )
__SCREAMING_SNAKE_CASE = self.norm(__SCREAMING_SNAKE_CASE ) * (1 + scale_msa[:, None]) + shift_msa[:, None]
return x, gate_msa, shift_mlp, scale_mlp, gate_mlp
class A__( nn.Module ):
def __init__( self : List[Any] , __SCREAMING_SNAKE_CASE : int , __SCREAMING_SNAKE_CASE : int , __SCREAMING_SNAKE_CASE : int , __SCREAMING_SNAKE_CASE : Optional[str] = None , __SCREAMING_SNAKE_CASE : float = 1E-5 ) -> List[Any]:
"""simple docstring"""
super().__init__()
__SCREAMING_SNAKE_CASE = num_groups
__SCREAMING_SNAKE_CASE = eps
if act_fn is None:
__SCREAMING_SNAKE_CASE = None
else:
__SCREAMING_SNAKE_CASE = get_activation(__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = nn.Linear(__SCREAMING_SNAKE_CASE , out_dim * 2 )
def _a ( self : List[Any] , __SCREAMING_SNAKE_CASE : Optional[int] , __SCREAMING_SNAKE_CASE : Dict ) -> Optional[int]:
"""simple docstring"""
if self.act:
__SCREAMING_SNAKE_CASE = self.act(__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = self.linear(__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = emb[:, :, None, None]
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = emb.chunk(2 , dim=1 )
__SCREAMING_SNAKE_CASE = F.group_norm(__SCREAMING_SNAKE_CASE , self.num_groups , eps=self.eps )
__SCREAMING_SNAKE_CASE = x * (1 + scale) + shift
return x
| 721 |
"""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 | 0 |
"""simple docstring"""
from packaging import version
from .import_utils import is_accelerate_available
if is_accelerate_available():
import accelerate
def _a ( UpperCAmelCase__ : Union[str, Any] ) -> List[Any]:
if not is_accelerate_available():
return method
__SCREAMING_SNAKE_CASE = version.parse(accelerate.__version__ ).base_version
if version.parse(UpperCAmelCase__ ) < version.parse('''0.17.0''' ):
return method
def wrapper(self : Optional[Any] , *UpperCAmelCase__ : Optional[int] , **UpperCAmelCase__ : Dict ):
if hasattr(self , '''_hf_hook''' ) and hasattr(self._hf_hook , '''pre_forward''' ):
self._hf_hook.pre_forward(self )
return method(self , *UpperCAmelCase__ , **UpperCAmelCase__ )
return wrapper
| 700 |
"""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 | 0 |
"""simple docstring"""
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_mobilebert import MobileBertTokenizer
lowerCAmelCase__ =logging.get_logger(__name__)
lowerCAmelCase__ ={"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}
lowerCAmelCase__ ={
"vocab_file": {"mobilebert-uncased": "https://huggingface.co/google/mobilebert-uncased/resolve/main/vocab.txt"},
"tokenizer_file": {
"mobilebert-uncased": "https://huggingface.co/google/mobilebert-uncased/resolve/main/tokenizer.json"
},
}
lowerCAmelCase__ ={"mobilebert-uncased": 512}
lowerCAmelCase__ ={}
class A__( __magic_name__ ):
lowerCAmelCase = VOCAB_FILES_NAMES
lowerCAmelCase = PRETRAINED_VOCAB_FILES_MAP
lowerCAmelCase = PRETRAINED_INIT_CONFIGURATION
lowerCAmelCase = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowerCAmelCase = MobileBertTokenizer
def __init__( self : Optional[int] , __SCREAMING_SNAKE_CASE : Union[str, Any]=None , __SCREAMING_SNAKE_CASE : List[Any]=None , __SCREAMING_SNAKE_CASE : Optional[int]=True , __SCREAMING_SNAKE_CASE : Union[str, Any]="[UNK]" , __SCREAMING_SNAKE_CASE : Tuple="[SEP]" , __SCREAMING_SNAKE_CASE : List[str]="[PAD]" , __SCREAMING_SNAKE_CASE : Optional[Any]="[CLS]" , __SCREAMING_SNAKE_CASE : Dict="[MASK]" , __SCREAMING_SNAKE_CASE : str=True , __SCREAMING_SNAKE_CASE : Any=None , **__SCREAMING_SNAKE_CASE : List[Any] , ) -> Dict:
"""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 : Any , __SCREAMING_SNAKE_CASE : Optional[int] , __SCREAMING_SNAKE_CASE : Dict=None ) -> Optional[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 : 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 : Optional[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 )
| 701 |
"""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 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
lowerCAmelCase__ ={"configuration_glpn": ["GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP", "GLPNConfig"]}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase__ =["GLPNFeatureExtractor"]
lowerCAmelCase__ =["GLPNImageProcessor"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase__ =[
"GLPN_PRETRAINED_MODEL_ARCHIVE_LIST",
"GLPNForDepthEstimation",
"GLPNLayer",
"GLPNModel",
"GLPNPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_glpn import GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP, GLPNConfig
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_glpn import GLPNFeatureExtractor
from .image_processing_glpn import GLPNImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_glpn import (
GLPN_PRETRAINED_MODEL_ARCHIVE_LIST,
GLPNForDepthEstimation,
GLPNLayer,
GLPNModel,
GLPNPreTrainedModel,
)
else:
import sys
lowerCAmelCase__ =_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 702 |
"""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 | 0 |
"""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()
| 703 |
"""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 | 0 |
"""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''' ) , )
| 704 |
"""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 | 0 |
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
| 705 |
"""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 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowerCAmelCase__ ={"configuration_xglm": ["XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "XGLMConfig"]}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase__ =["XGLMTokenizer"]
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase__ =["XGLMTokenizerFast"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase__ =[
"XGLM_PRETRAINED_MODEL_ARCHIVE_LIST",
"XGLMForCausalLM",
"XGLMModel",
"XGLMPreTrainedModel",
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase__ =[
"FlaxXGLMForCausalLM",
"FlaxXGLMModel",
"FlaxXGLMPreTrainedModel",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase__ =[
"TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST",
"TFXGLMForCausalLM",
"TFXGLMModel",
"TFXGLMPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_xglm import XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XGLMConfig
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_xglm import XGLMTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_xglm_fast import XGLMTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_xglm import XGLM_PRETRAINED_MODEL_ARCHIVE_LIST, XGLMForCausalLM, XGLMModel, XGLMPreTrainedModel
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_xglm import FlaxXGLMForCausalLM, FlaxXGLMModel, FlaxXGLMPreTrainedModel
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_xglm import (
TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST,
TFXGLMForCausalLM,
TFXGLMModel,
TFXGLMPreTrainedModel,
)
else:
import sys
lowerCAmelCase__ =_LazyModule(__name__, globals()["__file__"], _import_structure)
| 706 |
"""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 | 0 |
"""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__ )
| 707 |
"""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 | 0 |
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__)
| 708 |
"""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 | 0 |
"""simple docstring"""
from typing import Optional, Tuple, Union
import tensorflow as tf
from ...activations_tf import ACTaFN
from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
from ...modeling_tf_outputs import (
TFBaseModelOutputWithNoAttention,
TFBaseModelOutputWithPoolingAndNoAttention,
TFSequenceClassifierOutput,
)
from ...modeling_tf_utils import TFPreTrainedModel, TFSequenceClassificationLoss, keras_serializable, unpack_inputs
from ...tf_utils import shape_list
from ...utils import logging
from .configuration_regnet import RegNetConfig
lowerCAmelCase__ =logging.get_logger(__name__)
# General docstring
lowerCAmelCase__ ="RegNetConfig"
# Base docstring
lowerCAmelCase__ ="facebook/regnet-y-040"
lowerCAmelCase__ =[1, 1_088, 7, 7]
# Image classification docstring
lowerCAmelCase__ ="facebook/regnet-y-040"
lowerCAmelCase__ ="tabby, tabby cat"
lowerCAmelCase__ =[
"facebook/regnet-y-040",
# See all regnet models at https://huggingface.co/models?filter=regnet
]
class A__( tf.keras.layers.Layer ):
'''simple docstring'''
def __init__( self : List[str] , __SCREAMING_SNAKE_CASE : int , __SCREAMING_SNAKE_CASE : int = 3 , __SCREAMING_SNAKE_CASE : int = 1 , __SCREAMING_SNAKE_CASE : int = 1 , __SCREAMING_SNAKE_CASE : Optional[str] = "relu" , **__SCREAMING_SNAKE_CASE : Union[str, Any] , ) -> Any:
"""simple docstring"""
super().__init__(**__SCREAMING_SNAKE_CASE )
# The padding and conv has been verified in
# https://colab.research.google.com/gist/sayakpaul/854bc10eeaf21c9ee2119e0b9f3841a7/scratchpad.ipynb
__SCREAMING_SNAKE_CASE = tf.keras.layers.ZeroPaddingaD(padding=kernel_size // 2 )
__SCREAMING_SNAKE_CASE = tf.keras.layers.ConvaD(
filters=__SCREAMING_SNAKE_CASE , kernel_size=__SCREAMING_SNAKE_CASE , strides=__SCREAMING_SNAKE_CASE , padding='''VALID''' , groups=__SCREAMING_SNAKE_CASE , use_bias=__SCREAMING_SNAKE_CASE , name='''convolution''' , )
__SCREAMING_SNAKE_CASE = tf.keras.layers.BatchNormalization(epsilon=1E-5 , momentum=0.9 , name='''normalization''' )
__SCREAMING_SNAKE_CASE = ACTaFN[activation] if activation is not None else tf.identity
def _a ( self : Optional[Any] , __SCREAMING_SNAKE_CASE : Tuple ) -> str:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = self.convolution(self.padding(__SCREAMING_SNAKE_CASE ) )
__SCREAMING_SNAKE_CASE = self.normalization(__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = self.activation(__SCREAMING_SNAKE_CASE )
return hidden_state
class A__( tf.keras.layers.Layer ):
'''simple docstring'''
def __init__( self : Optional[Any] , __SCREAMING_SNAKE_CASE : RegNetConfig , **__SCREAMING_SNAKE_CASE : int ) -> Optional[Any]:
"""simple docstring"""
super().__init__(**__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = config.num_channels
__SCREAMING_SNAKE_CASE = TFRegNetConvLayer(
out_channels=config.embedding_size , kernel_size=3 , stride=2 , activation=config.hidden_act , name='''embedder''' , )
def _a ( self : List[Any] , __SCREAMING_SNAKE_CASE : List[str] ) -> Tuple:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = shape_list(__SCREAMING_SNAKE_CASE )[1]
if tf.executing_eagerly() and num_channels != self.num_channels:
raise ValueError(
'''Make sure that the channel dimension of the pixel values match with the one set in the configuration.''' )
# When running on CPU, `tf.keras.layers.Conv2D` doesn't support `NCHW` format.
# So change the input format from `NCHW` to `NHWC`.
# shape = (batch_size, in_height, in_width, in_channels=num_channels)
__SCREAMING_SNAKE_CASE = tf.transpose(__SCREAMING_SNAKE_CASE , perm=(0, 2, 3, 1) )
__SCREAMING_SNAKE_CASE = self.embedder(__SCREAMING_SNAKE_CASE )
return hidden_state
class A__( tf.keras.layers.Layer ):
'''simple docstring'''
def __init__( self : Tuple , __SCREAMING_SNAKE_CASE : int , __SCREAMING_SNAKE_CASE : int = 2 , **__SCREAMING_SNAKE_CASE : int ) -> int:
"""simple docstring"""
super().__init__(**__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = tf.keras.layers.ConvaD(
filters=__SCREAMING_SNAKE_CASE , kernel_size=1 , strides=__SCREAMING_SNAKE_CASE , use_bias=__SCREAMING_SNAKE_CASE , name='''convolution''' )
__SCREAMING_SNAKE_CASE = tf.keras.layers.BatchNormalization(epsilon=1E-5 , momentum=0.9 , name='''normalization''' )
def _a ( self : List[str] , __SCREAMING_SNAKE_CASE : tf.Tensor , __SCREAMING_SNAKE_CASE : bool = False ) -> tf.Tensor:
"""simple docstring"""
return self.normalization(self.convolution(__SCREAMING_SNAKE_CASE ) , training=__SCREAMING_SNAKE_CASE )
class A__( tf.keras.layers.Layer ):
'''simple docstring'''
def __init__( self : str , __SCREAMING_SNAKE_CASE : int , __SCREAMING_SNAKE_CASE : int , **__SCREAMING_SNAKE_CASE : Any ) -> str:
"""simple docstring"""
super().__init__(**__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = tf.keras.layers.GlobalAveragePoolingaD(keepdims=__SCREAMING_SNAKE_CASE , name='''pooler''' )
__SCREAMING_SNAKE_CASE = [
tf.keras.layers.ConvaD(filters=__SCREAMING_SNAKE_CASE , kernel_size=1 , activation='''relu''' , name='''attention.0''' ),
tf.keras.layers.ConvaD(filters=__SCREAMING_SNAKE_CASE , kernel_size=1 , activation='''sigmoid''' , name='''attention.2''' ),
]
def _a ( self : Optional[Any] , __SCREAMING_SNAKE_CASE : List[str] ) -> Optional[int]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = self.pooler(__SCREAMING_SNAKE_CASE )
for layer_module in self.attention:
__SCREAMING_SNAKE_CASE = layer_module(__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = hidden_state * pooled
return hidden_state
class A__( tf.keras.layers.Layer ):
'''simple docstring'''
def __init__( self : List[str] , __SCREAMING_SNAKE_CASE : RegNetConfig , __SCREAMING_SNAKE_CASE : int , __SCREAMING_SNAKE_CASE : int , __SCREAMING_SNAKE_CASE : int = 1 , **__SCREAMING_SNAKE_CASE : Optional[Any] ) -> str:
"""simple docstring"""
super().__init__(**__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = in_channels != out_channels or stride != 1
__SCREAMING_SNAKE_CASE = max(1 , out_channels // config.groups_width )
__SCREAMING_SNAKE_CASE = (
TFRegNetShortCut(__SCREAMING_SNAKE_CASE , stride=__SCREAMING_SNAKE_CASE , name='''shortcut''' )
if should_apply_shortcut
else tf.keras.layers.Activation('''linear''' , name='''shortcut''' )
)
# `self.layers` instead of `self.layer` because that is a reserved argument.
__SCREAMING_SNAKE_CASE = [
TFRegNetConvLayer(__SCREAMING_SNAKE_CASE , kernel_size=1 , activation=config.hidden_act , name='''layer.0''' ),
TFRegNetConvLayer(
__SCREAMING_SNAKE_CASE , stride=__SCREAMING_SNAKE_CASE , groups=__SCREAMING_SNAKE_CASE , activation=config.hidden_act , name='''layer.1''' ),
TFRegNetConvLayer(__SCREAMING_SNAKE_CASE , kernel_size=1 , activation=__SCREAMING_SNAKE_CASE , name='''layer.2''' ),
]
__SCREAMING_SNAKE_CASE = ACTaFN[config.hidden_act]
def _a ( self : List[Any] , __SCREAMING_SNAKE_CASE : str ) -> Optional[Any]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = hidden_state
for layer_module in self.layers:
__SCREAMING_SNAKE_CASE = layer_module(__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = self.shortcut(__SCREAMING_SNAKE_CASE )
hidden_state += residual
__SCREAMING_SNAKE_CASE = self.activation(__SCREAMING_SNAKE_CASE )
return hidden_state
class A__( tf.keras.layers.Layer ):
'''simple docstring'''
def __init__( self : Optional[int] , __SCREAMING_SNAKE_CASE : RegNetConfig , __SCREAMING_SNAKE_CASE : int , __SCREAMING_SNAKE_CASE : int , __SCREAMING_SNAKE_CASE : int = 1 , **__SCREAMING_SNAKE_CASE : Optional[int] ) -> List[Any]:
"""simple docstring"""
super().__init__(**__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = in_channels != out_channels or stride != 1
__SCREAMING_SNAKE_CASE = max(1 , out_channels // config.groups_width )
__SCREAMING_SNAKE_CASE = (
TFRegNetShortCut(__SCREAMING_SNAKE_CASE , stride=__SCREAMING_SNAKE_CASE , name='''shortcut''' )
if should_apply_shortcut
else tf.keras.layers.Activation('''linear''' , name='''shortcut''' )
)
__SCREAMING_SNAKE_CASE = [
TFRegNetConvLayer(__SCREAMING_SNAKE_CASE , kernel_size=1 , activation=config.hidden_act , name='''layer.0''' ),
TFRegNetConvLayer(
__SCREAMING_SNAKE_CASE , stride=__SCREAMING_SNAKE_CASE , groups=__SCREAMING_SNAKE_CASE , activation=config.hidden_act , name='''layer.1''' ),
TFRegNetSELayer(__SCREAMING_SNAKE_CASE , reduced_channels=int(round(in_channels / 4 ) ) , name='''layer.2''' ),
TFRegNetConvLayer(__SCREAMING_SNAKE_CASE , kernel_size=1 , activation=__SCREAMING_SNAKE_CASE , name='''layer.3''' ),
]
__SCREAMING_SNAKE_CASE = ACTaFN[config.hidden_act]
def _a ( self : Tuple , __SCREAMING_SNAKE_CASE : List[Any] ) -> Tuple:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = hidden_state
for layer_module in self.layers:
__SCREAMING_SNAKE_CASE = layer_module(__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = self.shortcut(__SCREAMING_SNAKE_CASE )
hidden_state += residual
__SCREAMING_SNAKE_CASE = self.activation(__SCREAMING_SNAKE_CASE )
return hidden_state
class A__( tf.keras.layers.Layer ):
'''simple docstring'''
def __init__( self : Any , __SCREAMING_SNAKE_CASE : RegNetConfig , __SCREAMING_SNAKE_CASE : int , __SCREAMING_SNAKE_CASE : int , __SCREAMING_SNAKE_CASE : int = 2 , __SCREAMING_SNAKE_CASE : int = 2 , **__SCREAMING_SNAKE_CASE : Union[str, Any] ) -> str:
"""simple docstring"""
super().__init__(**__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = TFRegNetXLayer if config.layer_type == '''x''' else TFRegNetYLayer
__SCREAMING_SNAKE_CASE = [
# downsampling is done in the first layer with stride of 2
layer(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , stride=__SCREAMING_SNAKE_CASE , name='''layers.0''' ),
*[layer(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , name=f"""layers.{i+1}""" ) for i in range(depth - 1 )],
]
def _a ( self : Optional[Any] , __SCREAMING_SNAKE_CASE : Any ) -> int:
"""simple docstring"""
for layer_module in self.layers:
__SCREAMING_SNAKE_CASE = layer_module(__SCREAMING_SNAKE_CASE )
return hidden_state
class A__( tf.keras.layers.Layer ):
'''simple docstring'''
def __init__( self : int , __SCREAMING_SNAKE_CASE : RegNetConfig , **__SCREAMING_SNAKE_CASE : Any ) -> Optional[int]:
"""simple docstring"""
super().__init__(**__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = []
# based on `downsample_in_first_stage`, the first layer of the first stage may or may not downsample the input
self.stages.append(
TFRegNetStage(
__SCREAMING_SNAKE_CASE , config.embedding_size , config.hidden_sizes[0] , stride=2 if config.downsample_in_first_stage else 1 , depth=config.depths[0] , name='''stages.0''' , ) )
__SCREAMING_SNAKE_CASE = zip(config.hidden_sizes , config.hidden_sizes[1:] )
for i, ((in_channels, out_channels), depth) in enumerate(zip(__SCREAMING_SNAKE_CASE , config.depths[1:] ) ):
self.stages.append(TFRegNetStage(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , depth=__SCREAMING_SNAKE_CASE , name=f"""stages.{i+1}""" ) )
def _a ( self : Dict , __SCREAMING_SNAKE_CASE : tf.Tensor , __SCREAMING_SNAKE_CASE : bool = False , __SCREAMING_SNAKE_CASE : bool = True ) -> TFBaseModelOutputWithNoAttention:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = () if output_hidden_states else None
for stage_module in self.stages:
if output_hidden_states:
__SCREAMING_SNAKE_CASE = hidden_states + (hidden_state,)
__SCREAMING_SNAKE_CASE = stage_module(__SCREAMING_SNAKE_CASE )
if output_hidden_states:
__SCREAMING_SNAKE_CASE = hidden_states + (hidden_state,)
if not return_dict:
return tuple(v for v in [hidden_state, hidden_states] if v is not None )
return TFBaseModelOutputWithNoAttention(last_hidden_state=__SCREAMING_SNAKE_CASE , hidden_states=__SCREAMING_SNAKE_CASE )
@keras_serializable
class A__( tf.keras.layers.Layer ):
'''simple docstring'''
lowerCAmelCase = RegNetConfig
def __init__( self : Any , __SCREAMING_SNAKE_CASE : Dict , **__SCREAMING_SNAKE_CASE : Optional[int] ) -> str:
"""simple docstring"""
super().__init__(**__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = config
__SCREAMING_SNAKE_CASE = TFRegNetEmbeddings(__SCREAMING_SNAKE_CASE , name='''embedder''' )
__SCREAMING_SNAKE_CASE = TFRegNetEncoder(__SCREAMING_SNAKE_CASE , name='''encoder''' )
__SCREAMING_SNAKE_CASE = tf.keras.layers.GlobalAveragePoolingaD(keepdims=__SCREAMING_SNAKE_CASE , name='''pooler''' )
@unpack_inputs
def _a ( self : str , __SCREAMING_SNAKE_CASE : tf.Tensor , __SCREAMING_SNAKE_CASE : Optional[bool] = None , __SCREAMING_SNAKE_CASE : Optional[bool] = None , __SCREAMING_SNAKE_CASE : bool = False , ) -> TFBaseModelOutputWithPoolingAndNoAttention:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
__SCREAMING_SNAKE_CASE = return_dict if return_dict is not None else self.config.use_return_dict
__SCREAMING_SNAKE_CASE = self.embedder(__SCREAMING_SNAKE_CASE , training=__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = self.encoder(
__SCREAMING_SNAKE_CASE , output_hidden_states=__SCREAMING_SNAKE_CASE , return_dict=__SCREAMING_SNAKE_CASE , training=__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = encoder_outputs[0]
__SCREAMING_SNAKE_CASE = self.pooler(__SCREAMING_SNAKE_CASE )
# Change to NCHW output format have uniformity in the modules
__SCREAMING_SNAKE_CASE = tf.transpose(__SCREAMING_SNAKE_CASE , perm=(0, 3, 1, 2) )
__SCREAMING_SNAKE_CASE = tf.transpose(__SCREAMING_SNAKE_CASE , perm=(0, 3, 1, 2) )
# Change the other hidden state outputs to NCHW as well
if output_hidden_states:
__SCREAMING_SNAKE_CASE = tuple([tf.transpose(__SCREAMING_SNAKE_CASE , perm=(0, 3, 1, 2) ) for h in encoder_outputs[1]] )
if not return_dict:
return (last_hidden_state, pooled_output) + encoder_outputs[1:]
return TFBaseModelOutputWithPoolingAndNoAttention(
last_hidden_state=__SCREAMING_SNAKE_CASE , pooler_output=__SCREAMING_SNAKE_CASE , hidden_states=hidden_states if output_hidden_states else encoder_outputs.hidden_states , )
class A__( __magic_name__ ):
'''simple docstring'''
lowerCAmelCase = RegNetConfig
lowerCAmelCase = '''regnet'''
lowerCAmelCase = '''pixel_values'''
@property
def _a ( self : List[Any] ) -> str:
"""simple docstring"""
return {"pixel_values": tf.TensorSpec(shape=(None, self.config.num_channels, 2_24, 2_24) , dtype=tf.floataa )}
lowerCAmelCase__ =r"\n Parameters:\n This model is a Tensorflow\n [tf.keras.layers.Layer](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Layer) sub-class. Use it as a\n regular Tensorflow Module and refer to the Tensorflow documentation for all matter related to general usage and\n behavior.\n config ([`RegNetConfig`]): Model configuration class with all the parameters of the model.\n Initializing with a config file does not load the weights associated with the model, only the\n configuration. Check out the [`~TFPreTrainedModel.from_pretrained`] method to load the model weights.\n"
lowerCAmelCase__ =r"\n Args:\n pixel_values (`tf.Tensor` of shape `(batch_size, num_channels, height, width)`):\n Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See\n [`ConveNextImageProcessor.__call__`] for details.\n output_hidden_states (`bool`, *optional*):\n Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for\n more detail.\n return_dict (`bool`, *optional*):\n Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.\n"
@add_start_docstrings(
'''The bare RegNet model outputting raw features without any specific head on top.''' , __magic_name__ , )
class A__( __magic_name__ ):
'''simple docstring'''
def __init__( self : Union[str, Any] , __SCREAMING_SNAKE_CASE : RegNetConfig , *__SCREAMING_SNAKE_CASE : List[str] , **__SCREAMING_SNAKE_CASE : Optional[Any] ) -> Dict:
"""simple docstring"""
super().__init__(__SCREAMING_SNAKE_CASE , *__SCREAMING_SNAKE_CASE , **__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = TFRegNetMainLayer(__SCREAMING_SNAKE_CASE , name='''regnet''' )
@unpack_inputs
@add_start_docstrings_to_model_forward(__SCREAMING_SNAKE_CASE )
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC , output_type=__SCREAMING_SNAKE_CASE , config_class=_CONFIG_FOR_DOC , modality='''vision''' , expected_output=_EXPECTED_OUTPUT_SHAPE , )
def _a ( self : str , __SCREAMING_SNAKE_CASE : tf.Tensor , __SCREAMING_SNAKE_CASE : Optional[bool] = None , __SCREAMING_SNAKE_CASE : Optional[bool] = None , __SCREAMING_SNAKE_CASE : Any=False , ) -> Union[TFBaseModelOutputWithPoolingAndNoAttention, Tuple[tf.Tensor]]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
__SCREAMING_SNAKE_CASE = return_dict if return_dict is not None else self.config.use_return_dict
__SCREAMING_SNAKE_CASE = self.regnet(
pixel_values=__SCREAMING_SNAKE_CASE , output_hidden_states=__SCREAMING_SNAKE_CASE , return_dict=__SCREAMING_SNAKE_CASE , training=__SCREAMING_SNAKE_CASE , )
if not return_dict:
return (outputs[0],) + outputs[1:]
return TFBaseModelOutputWithPoolingAndNoAttention(
last_hidden_state=outputs.last_hidden_state , pooler_output=outputs.pooler_output , hidden_states=outputs.hidden_states , )
@add_start_docstrings(
'''
RegNet Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for
ImageNet.
''' , __magic_name__ , )
class A__( __magic_name__ , __magic_name__ ):
'''simple docstring'''
def __init__( self : Dict , __SCREAMING_SNAKE_CASE : RegNetConfig , *__SCREAMING_SNAKE_CASE : List[Any] , **__SCREAMING_SNAKE_CASE : Optional[Any] ) -> Optional[int]:
"""simple docstring"""
super().__init__(__SCREAMING_SNAKE_CASE , *__SCREAMING_SNAKE_CASE , **__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = config.num_labels
__SCREAMING_SNAKE_CASE = TFRegNetMainLayer(__SCREAMING_SNAKE_CASE , name='''regnet''' )
# classification head
__SCREAMING_SNAKE_CASE = [
tf.keras.layers.Flatten(),
tf.keras.layers.Dense(config.num_labels , name='''classifier.1''' ) if config.num_labels > 0 else tf.identity,
]
@unpack_inputs
@add_start_docstrings_to_model_forward(__SCREAMING_SNAKE_CASE )
@add_code_sample_docstrings(
checkpoint=_IMAGE_CLASS_CHECKPOINT , output_type=__SCREAMING_SNAKE_CASE , config_class=_CONFIG_FOR_DOC , expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT , )
def _a ( self : Optional[int] , __SCREAMING_SNAKE_CASE : tf.Tensor = None , __SCREAMING_SNAKE_CASE : tf.Tensor = None , __SCREAMING_SNAKE_CASE : bool = None , __SCREAMING_SNAKE_CASE : bool = None , __SCREAMING_SNAKE_CASE : str=False , ) -> Union[TFSequenceClassifierOutput, Tuple[tf.Tensor]]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
__SCREAMING_SNAKE_CASE = return_dict if return_dict is not None else self.config.use_return_dict
__SCREAMING_SNAKE_CASE = self.regnet(
__SCREAMING_SNAKE_CASE , output_hidden_states=__SCREAMING_SNAKE_CASE , return_dict=__SCREAMING_SNAKE_CASE , training=__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = outputs.pooler_output if return_dict else outputs[1]
__SCREAMING_SNAKE_CASE = self.classifier[0](__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = self.classifier[1](__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = None if labels is None else self.hf_compute_loss(labels=__SCREAMING_SNAKE_CASE , logits=__SCREAMING_SNAKE_CASE )
if not return_dict:
__SCREAMING_SNAKE_CASE = (logits,) + outputs[2:]
return ((loss,) + output) if loss is not None else output
return TFSequenceClassifierOutput(loss=__SCREAMING_SNAKE_CASE , logits=__SCREAMING_SNAKE_CASE , hidden_states=outputs.hidden_states )
| 709 |
"""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 | 0 |
"""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,)
| 710 |
"""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 | 0 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase__ =logging.get_logger(__name__)
lowerCAmelCase__ ={
"uw-madison/mra-base-512-4": "https://huggingface.co/uw-madison/mra-base-512-4/resolve/main/config.json",
}
class A__( __magic_name__ ):
lowerCAmelCase = '''mra'''
def __init__( self : Optional[Any] , __SCREAMING_SNAKE_CASE : List[str]=5_02_65 , __SCREAMING_SNAKE_CASE : Union[str, Any]=7_68 , __SCREAMING_SNAKE_CASE : List[Any]=12 , __SCREAMING_SNAKE_CASE : Optional[int]=12 , __SCREAMING_SNAKE_CASE : Any=30_72 , __SCREAMING_SNAKE_CASE : Optional[Any]="gelu" , __SCREAMING_SNAKE_CASE : Optional[int]=0.1 , __SCREAMING_SNAKE_CASE : Any=0.1 , __SCREAMING_SNAKE_CASE : Optional[Any]=5_12 , __SCREAMING_SNAKE_CASE : Any=1 , __SCREAMING_SNAKE_CASE : str=0.02 , __SCREAMING_SNAKE_CASE : Union[str, Any]=1E-5 , __SCREAMING_SNAKE_CASE : Optional[int]="absolute" , __SCREAMING_SNAKE_CASE : Optional[int]=4 , __SCREAMING_SNAKE_CASE : int="full" , __SCREAMING_SNAKE_CASE : Optional[Any]=0 , __SCREAMING_SNAKE_CASE : Optional[int]=0 , __SCREAMING_SNAKE_CASE : List[Any]=1 , __SCREAMING_SNAKE_CASE : Dict=0 , __SCREAMING_SNAKE_CASE : Tuple=2 , **__SCREAMING_SNAKE_CASE : Dict , ) -> Union[str, Any]:
"""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 = max_position_embeddings
__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 = initializer_range
__SCREAMING_SNAKE_CASE = type_vocab_size
__SCREAMING_SNAKE_CASE = layer_norm_eps
__SCREAMING_SNAKE_CASE = position_embedding_type
__SCREAMING_SNAKE_CASE = block_per_row
__SCREAMING_SNAKE_CASE = approx_mode
__SCREAMING_SNAKE_CASE = initial_prior_first_n_blocks
__SCREAMING_SNAKE_CASE = initial_prior_diagonal_n_blocks
| 711 |
"""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 | 0 |
"""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.""" )
| 712 |
"""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 | 0 |
"""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__ )
| 713 |
"""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 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
lowerCAmelCase__ ={
"configuration_rag": ["RagConfig"],
"retrieval_rag": ["RagRetriever"],
"tokenization_rag": ["RagTokenizer"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase__ =[
"RagModel",
"RagPreTrainedModel",
"RagSequenceForGeneration",
"RagTokenForGeneration",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase__ =[
"TFRagModel",
"TFRagPreTrainedModel",
"TFRagSequenceForGeneration",
"TFRagTokenForGeneration",
]
if TYPE_CHECKING:
from .configuration_rag import RagConfig
from .retrieval_rag import RagRetriever
from .tokenization_rag import RagTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_rag import RagModel, RagPreTrainedModel, RagSequenceForGeneration, RagTokenForGeneration
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_rag import (
TFRagModel,
TFRagPreTrainedModel,
TFRagSequenceForGeneration,
TFRagTokenForGeneration,
)
else:
import sys
lowerCAmelCase__ =_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 714 |
"""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 | 0 |
"""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")
| 715 |
"""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 | 0 |
"""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()
| 716 |
"""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 | 0 |
"""simple docstring"""
import importlib
import inspect
import os
import re
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
lowerCAmelCase__ ="src/transformers"
# This is to make sure the transformers module imported is the one in the repo.
lowerCAmelCase__ =importlib.util.spec_from_file_location(
"transformers",
os.path.join(PATH_TO_TRANSFORMERS, "__init__.py"),
submodule_search_locations=[PATH_TO_TRANSFORMERS],
)
lowerCAmelCase__ =spec.loader.load_module()
lowerCAmelCase__ =transformers.models.auto.configuration_auto.CONFIG_MAPPING
# Regex pattern used to find the checkpoint mentioned in the docstring of `config_class`.
# For example, `[bert-base-uncased](https://huggingface.co/bert-base-uncased)`
lowerCAmelCase__ =re.compile("\[(.+?)\]\((https://huggingface\.co/.+?)\)")
lowerCAmelCase__ ={
"CLIPConfigMixin",
"DecisionTransformerConfigMixin",
"EncoderDecoderConfigMixin",
"RagConfigMixin",
"SpeechEncoderDecoderConfigMixin",
"VisionEncoderDecoderConfigMixin",
"VisionTextDualEncoderConfigMixin",
}
def _a ( ) -> Union[str, Any]:
__SCREAMING_SNAKE_CASE = []
for config_class in list(CONFIG_MAPPING.values() ):
__SCREAMING_SNAKE_CASE = False
# source code of `config_class`
__SCREAMING_SNAKE_CASE = inspect.getsource(UpperCAmelCase__ )
__SCREAMING_SNAKE_CASE = _re_checkpoint.findall(UpperCAmelCase__ )
for checkpoint in checkpoints:
# Each `checkpoint` is a tuple of a checkpoint name and a checkpoint link.
# For example, `('bert-base-uncased', 'https://huggingface.co/bert-base-uncased')`
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = checkpoint
# verify the checkpoint name corresponds to the checkpoint link
__SCREAMING_SNAKE_CASE = f"""https://huggingface.co/{ckpt_name}"""
if ckpt_link == ckpt_link_from_name:
__SCREAMING_SNAKE_CASE = True
break
__SCREAMING_SNAKE_CASE = config_class.__name__
if not checkpoint_found and name not in CONFIG_CLASSES_TO_IGNORE_FOR_DOCSTRING_CHECKPOINT_CHECK:
configs_without_checkpoint.append(UpperCAmelCase__ )
if len(UpperCAmelCase__ ) > 0:
__SCREAMING_SNAKE_CASE = '''\n'''.join(sorted(UpperCAmelCase__ ) )
raise ValueError(f"""The following configurations don't contain any valid checkpoint:\n{message}""" )
if __name__ == "__main__":
check_config_docstrings_have_checkpoints()
| 717 |
"""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 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowerCAmelCase__ ={
"configuration_blenderbot_small": [
"BLENDERBOT_SMALL_PRETRAINED_CONFIG_ARCHIVE_MAP",
"BlenderbotSmallConfig",
"BlenderbotSmallOnnxConfig",
],
"tokenization_blenderbot_small": ["BlenderbotSmallTokenizer"],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase__ =["BlenderbotSmallTokenizerFast"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase__ =[
"BLENDERBOT_SMALL_PRETRAINED_MODEL_ARCHIVE_LIST",
"BlenderbotSmallForCausalLM",
"BlenderbotSmallForConditionalGeneration",
"BlenderbotSmallModel",
"BlenderbotSmallPreTrainedModel",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase__ =[
"TFBlenderbotSmallForConditionalGeneration",
"TFBlenderbotSmallModel",
"TFBlenderbotSmallPreTrainedModel",
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase__ =[
"FlaxBlenderbotSmallForConditionalGeneration",
"FlaxBlenderbotSmallModel",
"FlaxBlenderbotSmallPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_blenderbot_small import (
BLENDERBOT_SMALL_PRETRAINED_CONFIG_ARCHIVE_MAP,
BlenderbotSmallConfig,
BlenderbotSmallOnnxConfig,
)
from .tokenization_blenderbot_small import BlenderbotSmallTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_blenderbot_small_fast import BlenderbotSmallTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_blenderbot_small import (
BLENDERBOT_SMALL_PRETRAINED_MODEL_ARCHIVE_LIST,
BlenderbotSmallForCausalLM,
BlenderbotSmallForConditionalGeneration,
BlenderbotSmallModel,
BlenderbotSmallPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_blenderbot_small import (
TFBlenderbotSmallForConditionalGeneration,
TFBlenderbotSmallModel,
TFBlenderbotSmallPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_blenderbot_small import (
FlaxBlenderbotSmallForConditionalGeneration,
FlaxBlenderbotSmallModel,
FlaxBlenderbotSmallPreTrainedModel,
)
else:
import sys
lowerCAmelCase__ =_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 718 |
"""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 | 0 |
"""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''' , )
| 719 |
"""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 | 0 |
"""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
| 720 |
"""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 | 0 |
"""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()
| 721 |
"""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 | 0 |
"""simple docstring"""
import string
from math import logaa
def _a ( UpperCAmelCase__ : Union[str, Any] , UpperCAmelCase__ : Optional[int] ) -> 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__ : Any , UpperCAmelCase__ : Optional[Any] ) -> 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__ : str , UpperCAmelCase__ : Optional[Any] , UpperCAmelCase__ : Optional[int]=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__ : Union[str, Any] , UpperCAmelCase__ : Optional[int] ) -> float:
return round(tf * idf , 3 )
| 700 |
"""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 | 0 |
"""simple docstring"""
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"]
| 701 |
"""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 | 0 |
"""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()
| 702 |
"""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 | 0 |
"""simple docstring"""
import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional
import datasets
import evaluate
import numpy as np
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
AutoModelForSequenceClassification,
AutoTokenizer,
DataCollatorWithPadding,
EvalPrediction,
HfArgumentParser,
Trainer,
TrainingArguments,
default_data_collator,
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
# 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/text-classification/requirements.txt''')
lowerCAmelCase__ =logging.getLogger(__name__)
@dataclass
class A__:
lowerCAmelCase = field(
default=1_28 , metadata={
'''help''': (
'''The maximum total input sequence length after tokenization. Sequences longer '''
'''than this will be truncated, sequences shorter will be padded.'''
)
} , )
lowerCAmelCase = field(
default=__magic_name__ , metadata={'''help''': '''Overwrite the cached preprocessed datasets or not.'''} )
lowerCAmelCase = field(
default=__magic_name__ , metadata={
'''help''': (
'''Whether to pad all samples to `max_seq_length`. '''
'''If False, will pad the samples dynamically when batching to the maximum length in the batch.'''
)
} , )
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.'''
)
} , )
lowerCAmelCase = field(
default=__magic_name__ , metadata={
'''help''': (
'''For debugging purposes or quicker training, truncate the number of prediction examples to this '''
'''value if set.'''
)
} , )
@dataclass
class A__:
lowerCAmelCase = field(
default=__magic_name__ , metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''} )
lowerCAmelCase = field(
default=__magic_name__ , metadata={'''help''': '''Evaluation language. Also train language if `train_language` is set to None.'''} )
lowerCAmelCase = field(
default=__magic_name__ , metadata={'''help''': '''Train language if it is different from the evaluation language.'''} )
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''': '''Pretrained tokenizer 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 huggingface.co'''} , )
lowerCAmelCase = field(
default=__magic_name__ , metadata={'''help''': '''arg to indicate if tokenizer should do lower case in AutoTokenizer.from_pretrained()'''} , )
lowerCAmelCase = field(
default=__magic_name__ , metadata={'''help''': '''Whether to use one of the fast tokenizer (backed by the tokenizers library) or not.'''} , )
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''': (
'''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 ( ) -> Tuple:
# 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) )
__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_xnli''' , 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__ )
datasets.utils.logging.set_verbosity(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:
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 )
# In distributed training, the load_dataset function guarantees that only one local process can concurrently
# download the dataset.
# Downloading and loading xnli dataset from the hub.
if training_args.do_train:
if model_args.train_language is None:
__SCREAMING_SNAKE_CASE = load_dataset(
'''xnli''' , model_args.language , split='''train''' , cache_dir=model_args.cache_dir , use_auth_token=True if model_args.use_auth_token else None , )
else:
__SCREAMING_SNAKE_CASE = load_dataset(
'''xnli''' , model_args.train_language , split='''train''' , cache_dir=model_args.cache_dir , use_auth_token=True if model_args.use_auth_token else None , )
__SCREAMING_SNAKE_CASE = train_dataset.features['''label'''].names
if training_args.do_eval:
__SCREAMING_SNAKE_CASE = load_dataset(
'''xnli''' , model_args.language , split='''validation''' , cache_dir=model_args.cache_dir , use_auth_token=True if model_args.use_auth_token else None , )
__SCREAMING_SNAKE_CASE = eval_dataset.features['''label'''].names
if training_args.do_predict:
__SCREAMING_SNAKE_CASE = load_dataset(
'''xnli''' , model_args.language , split='''test''' , cache_dir=model_args.cache_dir , use_auth_token=True if model_args.use_auth_token else None , )
__SCREAMING_SNAKE_CASE = predict_dataset.features['''label'''].names
# Labels
__SCREAMING_SNAKE_CASE = len(UpperCAmelCase__ )
# Load pretrained model and tokenizer
# In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
__SCREAMING_SNAKE_CASE = AutoConfig.from_pretrained(
model_args.config_name if model_args.config_name else model_args.model_name_or_path , num_labels=UpperCAmelCase__ , idalabel={str(UpperCAmelCase__ ): label for i, label in enumerate(UpperCAmelCase__ )} , labelaid={label: i for i, label in enumerate(UpperCAmelCase__ )} , finetuning_task='''xnli''' , 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 = AutoTokenizer.from_pretrained(
model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path , do_lower_case=model_args.do_lower_case , cache_dir=model_args.cache_dir , use_fast=model_args.use_fast_tokenizer , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
__SCREAMING_SNAKE_CASE = AutoModelForSequenceClassification.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 , )
# Preprocessing the datasets
# Padding strategy
if data_args.pad_to_max_length:
__SCREAMING_SNAKE_CASE = '''max_length'''
else:
# We will pad later, dynamically at batch creation, to the max sequence length in each batch
__SCREAMING_SNAKE_CASE = False
def preprocess_function(UpperCAmelCase__ ):
# Tokenize the texts
return tokenizer(
examples['''premise'''] , examples['''hypothesis'''] , padding=UpperCAmelCase__ , max_length=data_args.max_seq_length , truncation=UpperCAmelCase__ , )
if training_args.do_train:
if data_args.max_train_samples is not None:
__SCREAMING_SNAKE_CASE = min(len(UpperCAmelCase__ ) , data_args.max_train_samples )
__SCREAMING_SNAKE_CASE = train_dataset.select(range(UpperCAmelCase__ ) )
with training_args.main_process_first(desc='''train dataset map pre-processing''' ):
__SCREAMING_SNAKE_CASE = train_dataset.map(
UpperCAmelCase__ , batched=UpperCAmelCase__ , load_from_cache_file=not data_args.overwrite_cache , desc='''Running tokenizer on train dataset''' , )
# Log a few random samples from the training set:
for index in random.sample(range(len(UpperCAmelCase__ ) ) , 3 ):
logger.info(f"""Sample {index} of the training set: {train_dataset[index]}.""" )
if training_args.do_eval:
if data_args.max_eval_samples is not None:
__SCREAMING_SNAKE_CASE = min(len(UpperCAmelCase__ ) , data_args.max_eval_samples )
__SCREAMING_SNAKE_CASE = eval_dataset.select(range(UpperCAmelCase__ ) )
with training_args.main_process_first(desc='''validation dataset map pre-processing''' ):
__SCREAMING_SNAKE_CASE = eval_dataset.map(
UpperCAmelCase__ , batched=UpperCAmelCase__ , load_from_cache_file=not data_args.overwrite_cache , desc='''Running tokenizer on validation dataset''' , )
if training_args.do_predict:
if data_args.max_predict_samples is not None:
__SCREAMING_SNAKE_CASE = min(len(UpperCAmelCase__ ) , data_args.max_predict_samples )
__SCREAMING_SNAKE_CASE = predict_dataset.select(range(UpperCAmelCase__ ) )
with training_args.main_process_first(desc='''prediction dataset map pre-processing''' ):
__SCREAMING_SNAKE_CASE = predict_dataset.map(
UpperCAmelCase__ , batched=UpperCAmelCase__ , load_from_cache_file=not data_args.overwrite_cache , desc='''Running tokenizer on prediction dataset''' , )
# Get the metric function
__SCREAMING_SNAKE_CASE = evaluate.load('''xnli''' )
# You can define your custom 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__ ):
__SCREAMING_SNAKE_CASE = p.predictions[0] if isinstance(p.predictions , UpperCAmelCase__ ) else p.predictions
__SCREAMING_SNAKE_CASE = np.argmax(UpperCAmelCase__ , axis=1 )
return metric.compute(predictions=UpperCAmelCase__ , references=p.label_ids )
# Data collator will default to DataCollatorWithPadding, so we change it if we already did the padding.
if data_args.pad_to_max_length:
__SCREAMING_SNAKE_CASE = default_data_collator
elif training_args.fpaa:
__SCREAMING_SNAKE_CASE = DataCollatorWithPadding(UpperCAmelCase__ , pad_to_multiple_of=8 )
else:
__SCREAMING_SNAKE_CASE = None
# Initialize our Trainer
__SCREAMING_SNAKE_CASE = Trainer(
model=UpperCAmelCase__ , args=UpperCAmelCase__ , train_dataset=train_dataset if training_args.do_train else None , eval_dataset=eval_dataset 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__ )
__SCREAMING_SNAKE_CASE = train_result.metrics
__SCREAMING_SNAKE_CASE = (
data_args.max_train_samples if data_args.max_train_samples is not None else len(UpperCAmelCase__ )
)
__SCREAMING_SNAKE_CASE = min(UpperCAmelCase__ , len(UpperCAmelCase__ ) )
trainer.save_model() # Saves the tokenizer too for easy upload
trainer.log_metrics('''train''' , UpperCAmelCase__ )
trainer.save_metrics('''train''' , UpperCAmelCase__ )
trainer.save_state()
# Evaluation
if training_args.do_eval:
logger.info('''*** Evaluate ***''' )
__SCREAMING_SNAKE_CASE = trainer.evaluate(eval_dataset=UpperCAmelCase__ )
__SCREAMING_SNAKE_CASE = data_args.max_eval_samples if data_args.max_eval_samples is not None else len(UpperCAmelCase__ )
__SCREAMING_SNAKE_CASE = min(UpperCAmelCase__ , len(UpperCAmelCase__ ) )
trainer.log_metrics('''eval''' , UpperCAmelCase__ )
trainer.save_metrics('''eval''' , UpperCAmelCase__ )
# Prediction
if training_args.do_predict:
logger.info('''*** Predict ***''' )
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = trainer.predict(UpperCAmelCase__ , metric_key_prefix='''predict''' )
__SCREAMING_SNAKE_CASE = (
data_args.max_predict_samples if data_args.max_predict_samples is not None else len(UpperCAmelCase__ )
)
__SCREAMING_SNAKE_CASE = min(UpperCAmelCase__ , len(UpperCAmelCase__ ) )
trainer.log_metrics('''predict''' , UpperCAmelCase__ )
trainer.save_metrics('''predict''' , UpperCAmelCase__ )
__SCREAMING_SNAKE_CASE = np.argmax(UpperCAmelCase__ , axis=1 )
__SCREAMING_SNAKE_CASE = os.path.join(training_args.output_dir , '''predictions.txt''' )
if trainer.is_world_process_zero():
with open(UpperCAmelCase__ , '''w''' ) as writer:
writer.write('''index\tprediction\n''' )
for index, item in enumerate(UpperCAmelCase__ ):
__SCREAMING_SNAKE_CASE = label_list[item]
writer.write(f"""{index}\t{item}\n""" )
if __name__ == "__main__":
main()
| 703 |
"""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 | 0 |
"""simple docstring"""
from __future__ import annotations
import numpy as np
from numpy import floataa
from numpy.typing import NDArray
def _a ( UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , ) -> list[float]:
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = coefficient_matrix.shape
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = constant_matrix.shape
if rowsa != colsa:
__SCREAMING_SNAKE_CASE = f"""Coefficient matrix dimensions must be nxn but received {rowsa}x{colsa}"""
raise ValueError(UpperCAmelCase__ )
if colsa != 1:
__SCREAMING_SNAKE_CASE = f"""Constant matrix must be nx1 but received {rowsa}x{colsa}"""
raise ValueError(UpperCAmelCase__ )
if rowsa != rowsa:
__SCREAMING_SNAKE_CASE = (
'''Coefficient and constant matrices dimensions must be nxn and nx1 but '''
f"""received {rowsa}x{colsa} and {rowsa}x{colsa}"""
)
raise ValueError(UpperCAmelCase__ )
if len(UpperCAmelCase__ ) != rowsa:
__SCREAMING_SNAKE_CASE = (
'''Number of initial values must be equal to number of rows in coefficient '''
f"""matrix but received {len(UpperCAmelCase__ )} and {rowsa}"""
)
raise ValueError(UpperCAmelCase__ )
if iterations <= 0:
raise ValueError('''Iterations must be at least 1''' )
__SCREAMING_SNAKE_CASE = np.concatenate(
(coefficient_matrix, constant_matrix) , axis=1 )
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = table.shape
strictly_diagonally_dominant(UpperCAmelCase__ )
# Iterates the whole matrix for given number of times
for _ in range(UpperCAmelCase__ ):
__SCREAMING_SNAKE_CASE = []
for row in range(UpperCAmelCase__ ):
__SCREAMING_SNAKE_CASE = 0
for col in range(UpperCAmelCase__ ):
if col == row:
__SCREAMING_SNAKE_CASE = table[row][col]
elif col == cols - 1:
__SCREAMING_SNAKE_CASE = table[row][col]
else:
temp += (-1) * table[row][col] * init_val[col]
__SCREAMING_SNAKE_CASE = (temp + val) / denom
new_val.append(UpperCAmelCase__ )
__SCREAMING_SNAKE_CASE = new_val
return [float(UpperCAmelCase__ ) for i in new_val]
def _a ( UpperCAmelCase__ ) -> bool:
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = table.shape
__SCREAMING_SNAKE_CASE = True
for i in range(0 , UpperCAmelCase__ ):
__SCREAMING_SNAKE_CASE = 0
for j in range(0 , cols - 1 ):
if i == j:
continue
else:
total += table[i][j]
if table[i][i] <= total:
raise ValueError('''Coefficient matrix is not strictly diagonally dominant''' )
return is_diagonally_dominant
# Test Cases
if __name__ == "__main__":
import doctest
doctest.testmod()
| 704 |
"""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 | 0 |
from __future__ import annotations
from typing import Any
def _a ( UpperCAmelCase__ ) -> int:
if not postfix_notation:
return 0
__SCREAMING_SNAKE_CASE = {'''+''', '''-''', '''*''', '''/'''}
__SCREAMING_SNAKE_CASE = []
for token in postfix_notation:
if token in operations:
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = stack.pop(), stack.pop()
if token == "+":
stack.append(a + b )
elif token == "-":
stack.append(a - b )
elif token == "*":
stack.append(a * b )
else:
if a * b < 0 and a % b != 0:
stack.append(a // b + 1 )
else:
stack.append(a // b )
else:
stack.append(int(UpperCAmelCase__ ) )
return stack.pop()
if __name__ == "__main__":
import doctest
doctest.testmod()
| 705 |
"""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 | 0 |
"""simple docstring"""
import math
class A__:
def _a ( self : int , __SCREAMING_SNAKE_CASE : list[list[float]] , __SCREAMING_SNAKE_CASE : list[int] ) -> int:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = 0.0
__SCREAMING_SNAKE_CASE = 0.0
for i in range(len(__SCREAMING_SNAKE_CASE ) ):
da += math.pow((sample[i] - weights[0][i]) , 2 )
da += math.pow((sample[i] - weights[1][i]) , 2 )
return 0 if da > da else 1
return 0
def _a ( self : Optional[int] , __SCREAMING_SNAKE_CASE : list[list[int | float]] , __SCREAMING_SNAKE_CASE : list[int] , __SCREAMING_SNAKE_CASE : int , __SCREAMING_SNAKE_CASE : float ) -> list[list[int | float]]:
"""simple docstring"""
for i in range(len(__SCREAMING_SNAKE_CASE ) ):
weights[j][i] += alpha * (sample[i] - weights[j][i])
return weights
def SCREAMING_SNAKE_CASE ( ) -> None:
# Training Examples ( m, n )
__SCREAMING_SNAKE_CASE = [[1, 1, 0, 0], [0, 0, 0, 1], [1, 0, 0, 0], [0, 0, 1, 1]]
# weight initialization ( n, C )
__SCREAMING_SNAKE_CASE = [[0.2, 0.6, 0.5, 0.9], [0.8, 0.4, 0.7, 0.3]]
# training
__SCREAMING_SNAKE_CASE = SelfOrganizingMap()
__SCREAMING_SNAKE_CASE = 3
__SCREAMING_SNAKE_CASE = 0.5
for _ in range(UpperCAmelCase__ ):
for j in range(len(UpperCAmelCase__ ) ):
# training sample
__SCREAMING_SNAKE_CASE = training_samples[j]
# Compute the winning vector
__SCREAMING_SNAKE_CASE = self_organizing_map.get_winner(UpperCAmelCase__ , UpperCAmelCase__ )
# Update the winning vector
__SCREAMING_SNAKE_CASE = self_organizing_map.update(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ )
# classify test sample
__SCREAMING_SNAKE_CASE = [0, 0, 0, 1]
__SCREAMING_SNAKE_CASE = self_organizing_map.get_winner(UpperCAmelCase__ , UpperCAmelCase__ )
# results
print(f"""Clusters that the test sample belongs to : {winner}""" )
print(f"""Weights that have been trained : {weights}""" )
# running the main() function
if __name__ == "__main__":
main()
| 706 |
"""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 | 0 |
"""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()
| 707 |
"""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 | 0 |
def _a ( ) -> Optional[Any]:
__SCREAMING_SNAKE_CASE = []
__SCREAMING_SNAKE_CASE = 1
while len(UpperCAmelCase__ ) < 1E6:
constant.append(str(UpperCAmelCase__ ) )
i += 1
__SCREAMING_SNAKE_CASE = ''''''.join(UpperCAmelCase__ )
return (
int(constant[0] )
* int(constant[9] )
* int(constant[99] )
* int(constant[9_99] )
* int(constant[99_99] )
* int(constant[9_99_99] )
* int(constant[99_99_99] )
)
if __name__ == "__main__":
print(solution())
| 708 |
"""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 | 0 |
"""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()
| 709 |
"""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 | 0 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowerCAmelCase__ =logging.get_logger(__name__)
lowerCAmelCase__ ={
"facebook/data2vec-text-base": "https://huggingface.co/data2vec/resolve/main/config.json",
}
class A__( __magic_name__ ):
lowerCAmelCase = '''data2vec-text'''
def __init__( self : List[Any] , __SCREAMING_SNAKE_CASE : Union[str, Any]=3_05_22 , __SCREAMING_SNAKE_CASE : int=7_68 , __SCREAMING_SNAKE_CASE : Optional[int]=12 , __SCREAMING_SNAKE_CASE : Tuple=12 , __SCREAMING_SNAKE_CASE : Any=30_72 , __SCREAMING_SNAKE_CASE : int="gelu" , __SCREAMING_SNAKE_CASE : Optional[int]=0.1 , __SCREAMING_SNAKE_CASE : List[Any]=0.1 , __SCREAMING_SNAKE_CASE : str=5_12 , __SCREAMING_SNAKE_CASE : Optional[int]=2 , __SCREAMING_SNAKE_CASE : List[Any]=0.02 , __SCREAMING_SNAKE_CASE : Optional[Any]=1E-1_2 , __SCREAMING_SNAKE_CASE : Optional[int]=1 , __SCREAMING_SNAKE_CASE : Optional[Any]=0 , __SCREAMING_SNAKE_CASE : List[Any]=2 , __SCREAMING_SNAKE_CASE : Optional[Any]="absolute" , __SCREAMING_SNAKE_CASE : str=True , __SCREAMING_SNAKE_CASE : Dict=None , **__SCREAMING_SNAKE_CASE : Tuple , ) -> List[Any]:
"""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 = num_hidden_layers
__SCREAMING_SNAKE_CASE = num_attention_heads
__SCREAMING_SNAKE_CASE = hidden_act
__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 = type_vocab_size
__SCREAMING_SNAKE_CASE = initializer_range
__SCREAMING_SNAKE_CASE = layer_norm_eps
__SCREAMING_SNAKE_CASE = position_embedding_type
__SCREAMING_SNAKE_CASE = use_cache
__SCREAMING_SNAKE_CASE = classifier_dropout
class A__( __magic_name__ ):
@property
def _a ( self : str ) -> Mapping[str, Mapping[int, str]]:
"""simple docstring"""
if self.task == "multiple-choice":
__SCREAMING_SNAKE_CASE = {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
else:
__SCREAMING_SNAKE_CASE = {0: '''batch''', 1: '''sequence'''}
return OrderedDict(
[
('''input_ids''', dynamic_axis),
('''attention_mask''', dynamic_axis),
] )
| 710 |
"""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 | 0 |
"""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]
| 711 |
"""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 | 0 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowerCAmelCase__ =logging.get_logger(__name__)
lowerCAmelCase__ ={
"YituTech/conv-bert-base": "https://huggingface.co/YituTech/conv-bert-base/resolve/main/config.json",
"YituTech/conv-bert-medium-small": (
"https://huggingface.co/YituTech/conv-bert-medium-small/resolve/main/config.json"
),
"YituTech/conv-bert-small": "https://huggingface.co/YituTech/conv-bert-small/resolve/main/config.json",
# See all ConvBERT models at https://huggingface.co/models?filter=convbert
}
class A__( __magic_name__ ):
lowerCAmelCase = '''convbert'''
def __init__( self : int , __SCREAMING_SNAKE_CASE : List[str]=3_05_22 , __SCREAMING_SNAKE_CASE : Dict=7_68 , __SCREAMING_SNAKE_CASE : Optional[Any]=12 , __SCREAMING_SNAKE_CASE : Tuple=12 , __SCREAMING_SNAKE_CASE : Dict=30_72 , __SCREAMING_SNAKE_CASE : int="gelu" , __SCREAMING_SNAKE_CASE : int=0.1 , __SCREAMING_SNAKE_CASE : List[str]=0.1 , __SCREAMING_SNAKE_CASE : List[str]=5_12 , __SCREAMING_SNAKE_CASE : Dict=2 , __SCREAMING_SNAKE_CASE : Dict=0.02 , __SCREAMING_SNAKE_CASE : str=1E-1_2 , __SCREAMING_SNAKE_CASE : Dict=1 , __SCREAMING_SNAKE_CASE : List[str]=0 , __SCREAMING_SNAKE_CASE : int=2 , __SCREAMING_SNAKE_CASE : Any=7_68 , __SCREAMING_SNAKE_CASE : Optional[Any]=2 , __SCREAMING_SNAKE_CASE : List[Any]=9 , __SCREAMING_SNAKE_CASE : Any=1 , __SCREAMING_SNAKE_CASE : Union[str, Any]=None , **__SCREAMING_SNAKE_CASE : int , ) -> Union[str, Any]:
"""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 = 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 = initializer_range
__SCREAMING_SNAKE_CASE = layer_norm_eps
__SCREAMING_SNAKE_CASE = embedding_size
__SCREAMING_SNAKE_CASE = head_ratio
__SCREAMING_SNAKE_CASE = conv_kernel_size
__SCREAMING_SNAKE_CASE = num_groups
__SCREAMING_SNAKE_CASE = classifier_dropout
class A__( __magic_name__ ):
@property
def _a ( self : Dict ) -> Mapping[str, Mapping[int, str]]:
"""simple docstring"""
if self.task == "multiple-choice":
__SCREAMING_SNAKE_CASE = {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
else:
__SCREAMING_SNAKE_CASE = {0: '''batch''', 1: '''sequence'''}
return OrderedDict(
[
('''input_ids''', dynamic_axis),
('''attention_mask''', dynamic_axis),
('''token_type_ids''', dynamic_axis),
] )
| 712 |
"""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 | 0 |
"""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()
| 713 |
"""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 | 0 |
"""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()
| 714 |
"""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 | 0 |
"""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
)
| 715 |
"""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 | 0 |
"""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__)
| 716 |
"""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 | 0 |
"""simple docstring"""
import unittest
import numpy as np
import timeout_decorator # noqa
from transformers import BlenderbotConfig, is_flax_available
from transformers.testing_utils import jax_device, require_flax, slow
from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor
if is_flax_available():
import os
# The slow tests are often failing with OOM error on GPU
# This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
# but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
lowerCAmelCase__ ="platform"
import jax
import jax.numpy as jnp
from transformers import BlenderbotTokenizer
from transformers.models.blenderbot.modeling_flax_blenderbot import (
FlaxBlenderbotForConditionalGeneration,
FlaxBlenderbotModel,
shift_tokens_right,
)
def _a ( UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__=None , UpperCAmelCase__=None , UpperCAmelCase__=None , UpperCAmelCase__=None , UpperCAmelCase__=None , UpperCAmelCase__=None , ) -> Tuple:
if attention_mask is None:
__SCREAMING_SNAKE_CASE = np.where(input_ids != config.pad_token_id , 1 , 0 )
if decoder_attention_mask is None:
__SCREAMING_SNAKE_CASE = np.where(decoder_input_ids != config.pad_token_id , 1 , 0 )
if head_mask is None:
__SCREAMING_SNAKE_CASE = np.ones((config.encoder_layers, config.encoder_attention_heads) )
if decoder_head_mask is None:
__SCREAMING_SNAKE_CASE = np.ones((config.decoder_layers, config.decoder_attention_heads) )
if cross_attn_head_mask is None:
__SCREAMING_SNAKE_CASE = np.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": attention_mask,
}
class A__:
def __init__( self : List[str] , __SCREAMING_SNAKE_CASE : Union[str, Any] , __SCREAMING_SNAKE_CASE : Optional[int]=13 , __SCREAMING_SNAKE_CASE : str=7 , __SCREAMING_SNAKE_CASE : Optional[Any]=True , __SCREAMING_SNAKE_CASE : List[str]=False , __SCREAMING_SNAKE_CASE : Optional[int]=99 , __SCREAMING_SNAKE_CASE : Optional[int]=16 , __SCREAMING_SNAKE_CASE : Tuple=2 , __SCREAMING_SNAKE_CASE : Optional[Any]=4 , __SCREAMING_SNAKE_CASE : Any=4 , __SCREAMING_SNAKE_CASE : Dict="gelu" , __SCREAMING_SNAKE_CASE : int=0.1 , __SCREAMING_SNAKE_CASE : int=0.1 , __SCREAMING_SNAKE_CASE : Any=32 , __SCREAMING_SNAKE_CASE : Optional[int]=2 , __SCREAMING_SNAKE_CASE : Any=1 , __SCREAMING_SNAKE_CASE : Dict=0 , __SCREAMING_SNAKE_CASE : Optional[Any]=0.02 , ) -> List[Any]:
"""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_act
__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
__SCREAMING_SNAKE_CASE = initializer_range
def _a ( self : Optional[Any] ) -> Dict:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = np.clip(ids_tensor([self.batch_size, self.seq_length - 1] , self.vocab_size ) , 3 , self.vocab_size )
__SCREAMING_SNAKE_CASE = np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1) , dtype=np.intaa )) , -1 )
__SCREAMING_SNAKE_CASE = shift_tokens_right(__SCREAMING_SNAKE_CASE , 1 , 2 )
__SCREAMING_SNAKE_CASE = BlenderbotConfig(
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_id=self.eos_token_id , bos_token_id=self.bos_token_id , pad_token_id=self.pad_token_id , initializer_range=self.initializer_range , use_cache=__SCREAMING_SNAKE_CASE , )
__SCREAMING_SNAKE_CASE = prepare_blenderbot_inputs_dict(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
return config, inputs_dict
def _a ( self : Union[str, Any] ) -> str:
"""simple docstring"""
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = self.prepare_config_and_inputs()
return config, inputs_dict
def _a ( self : Optional[Any] , __SCREAMING_SNAKE_CASE : Optional[Any] , __SCREAMING_SNAKE_CASE : List[Any] , __SCREAMING_SNAKE_CASE : int ) -> Any:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = 20
__SCREAMING_SNAKE_CASE = model_class_name(__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = model.encode(inputs_dict['''input_ids'''] )
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = (
inputs_dict['''decoder_input_ids'''],
inputs_dict['''decoder_attention_mask'''],
)
__SCREAMING_SNAKE_CASE = model.init_cache(decoder_input_ids.shape[0] , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = jnp.ones((decoder_input_ids.shape[0], max_decoder_length) , dtype='''i4''' )
__SCREAMING_SNAKE_CASE = jnp.broadcast_to(
jnp.arange(decoder_input_ids.shape[-1] - 1 )[None, :] , (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1) , )
__SCREAMING_SNAKE_CASE = model.decode(
decoder_input_ids[:, :-1] , __SCREAMING_SNAKE_CASE , decoder_attention_mask=__SCREAMING_SNAKE_CASE , past_key_values=__SCREAMING_SNAKE_CASE , decoder_position_ids=__SCREAMING_SNAKE_CASE , )
__SCREAMING_SNAKE_CASE = jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]] , dtype='''i4''' )
__SCREAMING_SNAKE_CASE = model.decode(
decoder_input_ids[:, -1:] , __SCREAMING_SNAKE_CASE , decoder_attention_mask=__SCREAMING_SNAKE_CASE , past_key_values=outputs_cache.past_key_values , decoder_position_ids=__SCREAMING_SNAKE_CASE , )
__SCREAMING_SNAKE_CASE = model.decode(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5]) ) )
self.parent.assertTrue(diff < 1E-3 , msg=f"""Max diff is {diff}""" )
def _a ( self : List[Any] , __SCREAMING_SNAKE_CASE : Optional[Any] , __SCREAMING_SNAKE_CASE : List[Any] , __SCREAMING_SNAKE_CASE : Tuple ) -> Tuple:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = 20
__SCREAMING_SNAKE_CASE = model_class_name(__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = model.encode(inputs_dict['''input_ids'''] )
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = (
inputs_dict['''decoder_input_ids'''],
inputs_dict['''decoder_attention_mask'''],
)
__SCREAMING_SNAKE_CASE = jnp.concatenate(
[
decoder_attention_mask,
jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1]) ),
] , axis=-1 , )
__SCREAMING_SNAKE_CASE = model.init_cache(decoder_input_ids.shape[0] , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = jnp.broadcast_to(
jnp.arange(decoder_input_ids.shape[-1] - 1 )[None, :] , (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1) , )
__SCREAMING_SNAKE_CASE = model.decode(
decoder_input_ids[:, :-1] , __SCREAMING_SNAKE_CASE , decoder_attention_mask=__SCREAMING_SNAKE_CASE , past_key_values=__SCREAMING_SNAKE_CASE , decoder_position_ids=__SCREAMING_SNAKE_CASE , )
__SCREAMING_SNAKE_CASE = jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]] , dtype='''i4''' )
__SCREAMING_SNAKE_CASE = model.decode(
decoder_input_ids[:, -1:] , __SCREAMING_SNAKE_CASE , past_key_values=outputs_cache.past_key_values , decoder_attention_mask=__SCREAMING_SNAKE_CASE , decoder_position_ids=__SCREAMING_SNAKE_CASE , )
__SCREAMING_SNAKE_CASE = model.decode(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , decoder_attention_mask=__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5]) ) )
self.parent.assertTrue(diff < 1E-3 , msg=f"""Max diff is {diff}""" )
@require_flax
class A__( unittest.TestCase ):
lowerCAmelCase = 99
def _a ( self : List[Any] ) -> int:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = np.array(
[
[71, 82, 18, 33, 46, 91, 2],
[68, 34, 26, 58, 30, 82, 2],
[5, 97, 17, 39, 94, 40, 2],
[76, 83, 94, 25, 70, 78, 2],
[87, 59, 41, 35, 48, 66, 2],
[55, 13, 16, 58, 5, 2, 1], # note padding
[64, 27, 31, 51, 12, 75, 2],
[52, 64, 86, 17, 83, 39, 2],
[48, 61, 9, 24, 71, 82, 2],
[26, 1, 60, 48, 22, 13, 2],
[21, 5, 62, 28, 14, 76, 2],
[45, 98, 37, 86, 59, 48, 2],
[70, 70, 50, 9, 28, 0, 2],
] , dtype=np.intaa , )
__SCREAMING_SNAKE_CASE = input_ids.shape[0]
__SCREAMING_SNAKE_CASE = BlenderbotConfig(
vocab_size=self.vocab_size , d_model=24 , encoder_layers=2 , decoder_layers=2 , encoder_attention_heads=2 , decoder_attention_heads=2 , encoder_ffn_dim=32 , decoder_ffn_dim=32 , max_position_embeddings=48 , eos_token_id=2 , pad_token_id=1 , bos_token_id=0 , )
return config, input_ids, batch_size
def _a ( self : Any ) -> List[str]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = self._get_config_and_data()
__SCREAMING_SNAKE_CASE = FlaxBlenderbotForConditionalGeneration(__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = lm_model(input_ids=__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = (batch_size, input_ids.shape[1], config.vocab_size)
self.assertEqual(outputs['''logits'''].shape , __SCREAMING_SNAKE_CASE )
def _a ( self : Tuple ) -> List[str]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = BlenderbotConfig(
vocab_size=self.vocab_size , d_model=14 , encoder_layers=2 , decoder_layers=2 , encoder_attention_heads=2 , decoder_attention_heads=2 , encoder_ffn_dim=8 , decoder_ffn_dim=8 , max_position_embeddings=48 , )
__SCREAMING_SNAKE_CASE = FlaxBlenderbotForConditionalGeneration(__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]] , dtype=np.intaa )
__SCREAMING_SNAKE_CASE = np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]] , dtype=np.intaa )
__SCREAMING_SNAKE_CASE = lm_model(input_ids=__SCREAMING_SNAKE_CASE , decoder_input_ids=__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = (*summary.shape, config.vocab_size)
self.assertEqual(outputs['''logits'''].shape , __SCREAMING_SNAKE_CASE )
def _a ( self : Optional[int] ) -> str:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]] , dtype=np.intaa )
__SCREAMING_SNAKE_CASE = shift_tokens_right(__SCREAMING_SNAKE_CASE , 1 , 2 )
__SCREAMING_SNAKE_CASE = np.equal(__SCREAMING_SNAKE_CASE , 1 ).astype(np.floataa ).sum()
__SCREAMING_SNAKE_CASE = np.equal(__SCREAMING_SNAKE_CASE , 1 ).astype(np.floataa ).sum()
self.assertEqual(shifted.shape , input_ids.shape )
self.assertEqual(__SCREAMING_SNAKE_CASE , n_pad_before - 1 )
self.assertTrue(np.equal(shifted[:, 0] , 2 ).all() )
@require_flax
class A__( __magic_name__ , unittest.TestCase , __magic_name__ ):
lowerCAmelCase = True
lowerCAmelCase = (
(
FlaxBlenderbotModel,
FlaxBlenderbotForConditionalGeneration,
)
if is_flax_available()
else ()
)
lowerCAmelCase = (FlaxBlenderbotForConditionalGeneration,) if is_flax_available() else ()
def _a ( self : List[str] ) -> Tuple:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = FlaxBlenderbotModelTester(self )
def _a ( self : Dict ) -> Dict:
"""simple docstring"""
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs()
for model_class in self.all_model_classes:
self.model_tester.check_use_cache_forward(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
def _a ( self : Union[str, Any] ) -> Tuple:
"""simple docstring"""
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs()
for model_class in self.all_model_classes:
self.model_tester.check_use_cache_forward_with_attn_mask(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
def _a ( self : List[str] ) -> 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:
with self.subTest(model_class.__name__ ):
__SCREAMING_SNAKE_CASE = self._prepare_for_class(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = model_class(__SCREAMING_SNAKE_CASE )
@jax.jit
def encode_jitted(__SCREAMING_SNAKE_CASE : Union[str, Any] , __SCREAMING_SNAKE_CASE : Dict=None , **__SCREAMING_SNAKE_CASE : Optional[int] ):
return model.encode(input_ids=__SCREAMING_SNAKE_CASE , attention_mask=__SCREAMING_SNAKE_CASE )
with self.subTest('''JIT Enabled''' ):
__SCREAMING_SNAKE_CASE = encode_jitted(**__SCREAMING_SNAKE_CASE ).to_tuple()
with self.subTest('''JIT Disabled''' ):
with jax.disable_jit():
__SCREAMING_SNAKE_CASE = encode_jitted(**__SCREAMING_SNAKE_CASE ).to_tuple()
self.assertEqual(len(__SCREAMING_SNAKE_CASE ) , len(__SCREAMING_SNAKE_CASE ) )
for jitted_output, output in zip(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
self.assertEqual(jitted_output.shape , output.shape )
def _a ( self : List[str] ) -> 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:
with self.subTest(model_class.__name__ ):
__SCREAMING_SNAKE_CASE = model_class(__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = model.encode(inputs_dict['''input_ids'''] , inputs_dict['''attention_mask'''] )
__SCREAMING_SNAKE_CASE = {
'''decoder_input_ids''': inputs_dict['''decoder_input_ids'''],
'''decoder_attention_mask''': inputs_dict['''decoder_attention_mask'''],
'''encoder_outputs''': encoder_outputs,
}
@jax.jit
def decode_jitted(__SCREAMING_SNAKE_CASE : List[str] , __SCREAMING_SNAKE_CASE : Union[str, Any] , __SCREAMING_SNAKE_CASE : Optional[Any] ):
return model.decode(
decoder_input_ids=__SCREAMING_SNAKE_CASE , decoder_attention_mask=__SCREAMING_SNAKE_CASE , encoder_outputs=__SCREAMING_SNAKE_CASE , )
with self.subTest('''JIT Enabled''' ):
__SCREAMING_SNAKE_CASE = decode_jitted(**__SCREAMING_SNAKE_CASE ).to_tuple()
with self.subTest('''JIT Disabled''' ):
with jax.disable_jit():
__SCREAMING_SNAKE_CASE = decode_jitted(**__SCREAMING_SNAKE_CASE ).to_tuple()
self.assertEqual(len(__SCREAMING_SNAKE_CASE ) , len(__SCREAMING_SNAKE_CASE ) )
for jitted_output, output in zip(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
self.assertEqual(jitted_output.shape , output.shape )
@slow
def _a ( self : List[str] ) -> Union[str, Any]:
"""simple docstring"""
for model_class_name in self.all_model_classes:
__SCREAMING_SNAKE_CASE = model_class_name.from_pretrained('''facebook/blenderbot-400M-distill''' )
# FlaxBlenderbotForSequenceClassification expects eos token in input_ids
__SCREAMING_SNAKE_CASE = np.ones((1, 1) ) * model.config.eos_token_id
__SCREAMING_SNAKE_CASE = model(__SCREAMING_SNAKE_CASE )
self.assertIsNotNone(__SCREAMING_SNAKE_CASE )
@unittest.skipUnless(jax_device != '''cpu''' , '''3B test too slow on CPU.''' )
@slow
def _a ( self : Tuple ) -> int:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = {'''num_beams''': 1, '''early_stopping''': True, '''min_length''': 15, '''max_length''': 25}
__SCREAMING_SNAKE_CASE = {'''skip_special_tokens''': True, '''clean_up_tokenization_spaces''': True}
__SCREAMING_SNAKE_CASE = FlaxBlenderbotForConditionalGeneration.from_pretrained('''facebook/blenderbot-3B''' , from_pt=__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = BlenderbotTokenizer.from_pretrained('''facebook/blenderbot-3B''' )
__SCREAMING_SNAKE_CASE = ['''Sam''']
__SCREAMING_SNAKE_CASE = tokenizer(__SCREAMING_SNAKE_CASE , return_tensors='''jax''' )
__SCREAMING_SNAKE_CASE = model.generate(**__SCREAMING_SNAKE_CASE , **__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = '''Sam is a great name. It means "sun" in Gaelic.'''
__SCREAMING_SNAKE_CASE = tokenizer.batch_decode(__SCREAMING_SNAKE_CASE , **__SCREAMING_SNAKE_CASE )
assert generated_txt[0].strip() == tgt_text
| 717 |
"""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 | 0 |
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__ )
| 718 |
"""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 | 0 |
"""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
| 719 |
"""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 | 0 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowerCAmelCase__ =logging.get_logger(__name__)
lowerCAmelCase__ ={
"junnyu/roformer_chinese_small": "https://huggingface.co/junnyu/roformer_chinese_small/resolve/main/config.json",
"junnyu/roformer_chinese_base": "https://huggingface.co/junnyu/roformer_chinese_base/resolve/main/config.json",
"junnyu/roformer_chinese_char_small": (
"https://huggingface.co/junnyu/roformer_chinese_char_small/resolve/main/config.json"
),
"junnyu/roformer_chinese_char_base": (
"https://huggingface.co/junnyu/roformer_chinese_char_base/resolve/main/config.json"
),
"junnyu/roformer_small_discriminator": (
"https://huggingface.co/junnyu/roformer_small_discriminator/resolve/main/config.json"
),
"junnyu/roformer_small_generator": (
"https://huggingface.co/junnyu/roformer_small_generator/resolve/main/config.json"
),
# See all RoFormer models at https://huggingface.co/models?filter=roformer
}
class A__( __magic_name__ ):
lowerCAmelCase = '''roformer'''
def __init__( self : Dict , __SCREAMING_SNAKE_CASE : Optional[int]=5_00_00 , __SCREAMING_SNAKE_CASE : List[str]=None , __SCREAMING_SNAKE_CASE : Dict=7_68 , __SCREAMING_SNAKE_CASE : Dict=12 , __SCREAMING_SNAKE_CASE : List[Any]=12 , __SCREAMING_SNAKE_CASE : int=30_72 , __SCREAMING_SNAKE_CASE : Union[str, Any]="gelu" , __SCREAMING_SNAKE_CASE : Any=0.1 , __SCREAMING_SNAKE_CASE : Optional[Any]=0.1 , __SCREAMING_SNAKE_CASE : Tuple=15_36 , __SCREAMING_SNAKE_CASE : Union[str, Any]=2 , __SCREAMING_SNAKE_CASE : Tuple=0.02 , __SCREAMING_SNAKE_CASE : Tuple=1E-1_2 , __SCREAMING_SNAKE_CASE : Dict=0 , __SCREAMING_SNAKE_CASE : List[str]=False , __SCREAMING_SNAKE_CASE : Optional[int]=True , **__SCREAMING_SNAKE_CASE : List[Any] , ) -> List[Any]:
"""simple docstring"""
super().__init__(pad_token_id=__SCREAMING_SNAKE_CASE , **__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = vocab_size
__SCREAMING_SNAKE_CASE = hidden_size if embedding_size is None else embedding_size
__SCREAMING_SNAKE_CASE = hidden_size
__SCREAMING_SNAKE_CASE = num_hidden_layers
__SCREAMING_SNAKE_CASE = num_attention_heads
__SCREAMING_SNAKE_CASE = hidden_act
__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 = type_vocab_size
__SCREAMING_SNAKE_CASE = initializer_range
__SCREAMING_SNAKE_CASE = layer_norm_eps
__SCREAMING_SNAKE_CASE = rotary_value
__SCREAMING_SNAKE_CASE = use_cache
class A__( __magic_name__ ):
@property
def _a ( self : Optional[int] ) -> Mapping[str, Mapping[int, str]]:
"""simple docstring"""
if self.task == "multiple-choice":
__SCREAMING_SNAKE_CASE = {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
else:
__SCREAMING_SNAKE_CASE = {0: '''batch''', 1: '''sequence'''}
__SCREAMING_SNAKE_CASE = {0: '''batch''', 1: '''sequence'''}
return OrderedDict(
[
('''input_ids''', dynamic_axis),
('''attention_mask''', dynamic_axis),
('''token_type_ids''', dynamic_axis),
] )
| 720 |
"""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 | 0 |
"""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) )
| 721 |
"""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 | 0 |
"""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}
| 700 |
"""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 | 0 |
"""simple docstring"""
from __future__ import annotations
lowerCAmelCase__ =[
[-1, 0], # left
[0, -1], # down
[1, 0], # right
[0, 1], # up
]
def _a ( UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , ) -> tuple[list[list[int]], list[list[int]]]:
__SCREAMING_SNAKE_CASE = [
[0 for col in range(len(grid[0] ) )] for row in range(len(UpperCAmelCase__ ) )
] # the reference grid
__SCREAMING_SNAKE_CASE = 1
__SCREAMING_SNAKE_CASE = [
[0 for col in range(len(grid[0] ) )] for row in range(len(UpperCAmelCase__ ) )
] # the action grid
__SCREAMING_SNAKE_CASE = init[0]
__SCREAMING_SNAKE_CASE = init[1]
__SCREAMING_SNAKE_CASE = 0
__SCREAMING_SNAKE_CASE = g + heuristic[x][y] # cost from starting cell to destination cell
__SCREAMING_SNAKE_CASE = [[f, g, x, y]]
__SCREAMING_SNAKE_CASE = False # flag that is set when search is complete
__SCREAMING_SNAKE_CASE = False # flag set if we can't find expand
while not found and not resign:
if len(UpperCAmelCase__ ) == 0:
raise ValueError('''Algorithm is unable to find solution''' )
else: # to choose the least costliest action so as to move closer to the goal
cell.sort()
cell.reverse()
__SCREAMING_SNAKE_CASE = cell.pop()
__SCREAMING_SNAKE_CASE = next_cell[2]
__SCREAMING_SNAKE_CASE = next_cell[3]
__SCREAMING_SNAKE_CASE = next_cell[1]
if x == goal[0] and y == goal[1]:
__SCREAMING_SNAKE_CASE = True
else:
for i in range(len(UpperCAmelCase__ ) ): # to try out different valid actions
__SCREAMING_SNAKE_CASE = x + DIRECTIONS[i][0]
__SCREAMING_SNAKE_CASE = y + DIRECTIONS[i][1]
if xa >= 0 and xa < len(UpperCAmelCase__ ) and ya >= 0 and ya < len(grid[0] ):
if closed[xa][ya] == 0 and grid[xa][ya] == 0:
__SCREAMING_SNAKE_CASE = g + cost
__SCREAMING_SNAKE_CASE = ga + heuristic[xa][ya]
cell.append([fa, ga, xa, ya] )
__SCREAMING_SNAKE_CASE = 1
__SCREAMING_SNAKE_CASE = i
__SCREAMING_SNAKE_CASE = []
__SCREAMING_SNAKE_CASE = goal[0]
__SCREAMING_SNAKE_CASE = goal[1]
invpath.append([x, y] ) # we get the reverse path from here
while x != init[0] or y != init[1]:
__SCREAMING_SNAKE_CASE = x - DIRECTIONS[action[x][y]][0]
__SCREAMING_SNAKE_CASE = y - DIRECTIONS[action[x][y]][1]
__SCREAMING_SNAKE_CASE = xa
__SCREAMING_SNAKE_CASE = ya
invpath.append([x, y] )
__SCREAMING_SNAKE_CASE = []
for i in range(len(UpperCAmelCase__ ) ):
path.append(invpath[len(UpperCAmelCase__ ) - 1 - i] )
return path, action
if __name__ == "__main__":
lowerCAmelCase__ =[
[0, 1, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0], # 0 are free path whereas 1's are obstacles
[0, 1, 0, 0, 0, 0],
[0, 1, 0, 0, 1, 0],
[0, 0, 0, 0, 1, 0],
]
lowerCAmelCase__ =[0, 0]
# all coordinates are given in format [y,x]
lowerCAmelCase__ =[len(grid) - 1, len(grid[0]) - 1]
lowerCAmelCase__ =1
# the cost map which pushes the path closer to the goal
lowerCAmelCase__ =[[0 for row in range(len(grid[0]))] for col in range(len(grid))]
for i in range(len(grid)):
for j in range(len(grid[0])):
lowerCAmelCase__ =abs(i - goal[0]) + abs(j - goal[1])
if grid[i][j] == 1:
# added extra penalty in the heuristic map
lowerCAmelCase__ =99
lowerCAmelCase__ , lowerCAmelCase__ =search(grid, init, goal, cost, heuristic)
print("ACTION MAP")
for i in range(len(action)):
print(action[i])
for i in range(len(path)):
print(path[i])
| 701 |
"""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 | 0 |
"""simple docstring"""
import logging
import random
import ray
from transformers import RagConfig, RagRetriever, RagTokenizer
from transformers.models.rag.retrieval_rag import CustomHFIndex
lowerCAmelCase__ =logging.getLogger(__name__)
class A__:
def __init__( self : Dict ) -> Dict:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = False
def _a ( self : int , __SCREAMING_SNAKE_CASE : str , __SCREAMING_SNAKE_CASE : Optional[int] , __SCREAMING_SNAKE_CASE : int , __SCREAMING_SNAKE_CASE : List[Any] ) -> List[str]:
"""simple docstring"""
if not self.initialized:
__SCREAMING_SNAKE_CASE = RagRetriever(
__SCREAMING_SNAKE_CASE , question_encoder_tokenizer=__SCREAMING_SNAKE_CASE , generator_tokenizer=__SCREAMING_SNAKE_CASE , index=__SCREAMING_SNAKE_CASE , init_retrieval=__SCREAMING_SNAKE_CASE , )
__SCREAMING_SNAKE_CASE = True
def _a ( self : Tuple ) -> Union[str, Any]:
"""simple docstring"""
self.retriever.index.init_index()
def _a ( self : int , __SCREAMING_SNAKE_CASE : Optional[Any] , __SCREAMING_SNAKE_CASE : Union[str, Any] ) -> Union[str, Any]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = self.retriever._main_retrieve(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
return doc_ids, retrieved_doc_embeds
class A__( __magic_name__ ):
def __init__( self : Union[str, Any] , __SCREAMING_SNAKE_CASE : Union[str, Any] , __SCREAMING_SNAKE_CASE : Optional[int] , __SCREAMING_SNAKE_CASE : Optional[int] , __SCREAMING_SNAKE_CASE : Dict , __SCREAMING_SNAKE_CASE : str=None ) -> Optional[Any]:
"""simple docstring"""
if index is not None and index.is_initialized() and len(__SCREAMING_SNAKE_CASE ) > 0:
raise ValueError(
'''When using Ray for distributed fine-tuning, '''
'''you\'ll need to provide the paths instead, '''
'''as the dataset and the index are loaded '''
'''separately. More info in examples/rag/use_own_knowledge_dataset.py ''' )
super().__init__(
__SCREAMING_SNAKE_CASE , question_encoder_tokenizer=__SCREAMING_SNAKE_CASE , generator_tokenizer=__SCREAMING_SNAKE_CASE , index=__SCREAMING_SNAKE_CASE , init_retrieval=__SCREAMING_SNAKE_CASE , )
__SCREAMING_SNAKE_CASE = retrieval_workers
if len(self.retrieval_workers ) > 0:
ray.get(
[
worker.create_rag_retriever.remote(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
for worker in self.retrieval_workers
] )
def _a ( self : Any ) -> Dict:
"""simple docstring"""
logger.info('''initializing retrieval''' )
if len(self.retrieval_workers ) > 0:
ray.get([worker.init_retrieval.remote() for worker in self.retrieval_workers] )
else:
# Non-distributed training. Load index into this same process.
self.index.init_index()
def _a ( self : Union[str, Any] , __SCREAMING_SNAKE_CASE : Tuple , __SCREAMING_SNAKE_CASE : Optional[Any] ) -> Union[str, Any]:
"""simple docstring"""
if len(self.retrieval_workers ) > 0:
# Select a random retrieval actor.
__SCREAMING_SNAKE_CASE = self.retrieval_workers[random.randint(0 , len(self.retrieval_workers ) - 1 )]
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = ray.get(random_worker.retrieve.remote(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) )
else:
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = self._main_retrieve(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
return retrieved_doc_embeds, doc_ids, self.index.get_doc_dicts(__SCREAMING_SNAKE_CASE )
@classmethod
def _a ( cls : Optional[Any] , __SCREAMING_SNAKE_CASE : Tuple , __SCREAMING_SNAKE_CASE : List[str]=None , **__SCREAMING_SNAKE_CASE : int ) -> List[Any]:
"""simple docstring"""
return super(__SCREAMING_SNAKE_CASE , cls ).get_tokenizers(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , **__SCREAMING_SNAKE_CASE )
@classmethod
def _a ( cls : int , __SCREAMING_SNAKE_CASE : Optional[Any] , __SCREAMING_SNAKE_CASE : str , __SCREAMING_SNAKE_CASE : Union[str, Any]=None , **__SCREAMING_SNAKE_CASE : List[Any] ) -> Union[str, Any]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = kwargs.pop('''config''' , __SCREAMING_SNAKE_CASE ) or RagConfig.from_pretrained(__SCREAMING_SNAKE_CASE , **__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = RagTokenizer.from_pretrained(__SCREAMING_SNAKE_CASE , config=__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = rag_tokenizer.question_encoder
__SCREAMING_SNAKE_CASE = rag_tokenizer.generator
if indexed_dataset is not None:
__SCREAMING_SNAKE_CASE = '''custom'''
__SCREAMING_SNAKE_CASE = CustomHFIndex(config.retrieval_vector_size , __SCREAMING_SNAKE_CASE )
else:
__SCREAMING_SNAKE_CASE = cls._build_index(__SCREAMING_SNAKE_CASE )
return cls(
__SCREAMING_SNAKE_CASE , question_encoder_tokenizer=__SCREAMING_SNAKE_CASE , generator_tokenizer=__SCREAMING_SNAKE_CASE , retrieval_workers=__SCREAMING_SNAKE_CASE , index=__SCREAMING_SNAKE_CASE , )
| 702 |
"""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 | 0 |
"""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
| 703 |
"""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 | 0 |
"""simple docstring"""
import argparse
from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection
from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline
if __name__ == "__main__":
lowerCAmelCase__ =argparse.ArgumentParser()
parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output model.")
parser.add_argument(
"--txt2img_unclip",
default="kakaobrain/karlo-v1-alpha",
type=str,
required=False,
help="The pretrained txt2img unclip.",
)
lowerCAmelCase__ =parser.parse_args()
lowerCAmelCase__ =UnCLIPPipeline.from_pretrained(args.txtaimg_unclip)
lowerCAmelCase__ =CLIPImageProcessor()
lowerCAmelCase__ =CLIPVisionModelWithProjection.from_pretrained("openai/clip-vit-large-patch14")
lowerCAmelCase__ =UnCLIPImageVariationPipeline(
decoder=txtaimg.decoder,
text_encoder=txtaimg.text_encoder,
tokenizer=txtaimg.tokenizer,
text_proj=txtaimg.text_proj,
feature_extractor=feature_extractor,
image_encoder=image_encoder,
super_res_first=txtaimg.super_res_first,
super_res_last=txtaimg.super_res_last,
decoder_scheduler=txtaimg.decoder_scheduler,
super_res_scheduler=txtaimg.super_res_scheduler,
)
imgaimg.save_pretrained(args.dump_path)
| 704 |
"""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 | 0 |
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()
| 705 |
"""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 | 0 |
"""simple docstring"""
def SCREAMING_SNAKE_CASE ( 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 SCREAMING_SNAKE_CASE ( 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()
| 706 |
"""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 | 0 |
"""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:])
| 707 |
"""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 | 0 |
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]}
| 708 |
"""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 | 0 |
"""simple docstring"""
import inspect
import unittest
from transformers import RegNetConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from transformers.utils import cached_property, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor
if is_flax_available():
import jax
import jax.numpy as jnp
from transformers.models.regnet.modeling_flax_regnet import FlaxRegNetForImageClassification, FlaxRegNetModel
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class A__( unittest.TestCase ):
'''simple docstring'''
def __init__( self : Union[str, Any] , __SCREAMING_SNAKE_CASE : int , __SCREAMING_SNAKE_CASE : Union[str, Any]=3 , __SCREAMING_SNAKE_CASE : List[Any]=32 , __SCREAMING_SNAKE_CASE : str=3 , __SCREAMING_SNAKE_CASE : Union[str, Any]=10 , __SCREAMING_SNAKE_CASE : Any=[10, 20, 30, 40] , __SCREAMING_SNAKE_CASE : Tuple=[1, 1, 2, 1] , __SCREAMING_SNAKE_CASE : List[Any]=True , __SCREAMING_SNAKE_CASE : Tuple=True , __SCREAMING_SNAKE_CASE : List[str]="relu" , __SCREAMING_SNAKE_CASE : Optional[int]=3 , __SCREAMING_SNAKE_CASE : int=None , ) -> Optional[int]:
"""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 : Dict ) -> List[Any]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
__SCREAMING_SNAKE_CASE = self.get_config()
return config, pixel_values
def _a ( self : int ) -> Union[str, Any]:
"""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 , image_size=self.image_size , )
def _a ( self : Optional[int] , __SCREAMING_SNAKE_CASE : Dict , __SCREAMING_SNAKE_CASE : Tuple ) -> Optional[int]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = FlaxRegNetModel(config=__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = model(__SCREAMING_SNAKE_CASE )
# Output shape (b, c, h, w)
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 : Optional[int] , __SCREAMING_SNAKE_CASE : int , __SCREAMING_SNAKE_CASE : Tuple ) -> List[str]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = self.num_labels
__SCREAMING_SNAKE_CASE = FlaxRegNetForImageClassification(config=__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = model(__SCREAMING_SNAKE_CASE )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def _a ( self : Optional[Any] ) -> Optional[Any]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = self.prepare_config_and_inputs()
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = config_and_inputs
__SCREAMING_SNAKE_CASE = {'''pixel_values''': pixel_values}
return config, inputs_dict
@require_flax
class A__( __magic_name__ , unittest.TestCase ):
'''simple docstring'''
lowerCAmelCase = (FlaxRegNetModel, FlaxRegNetForImageClassification) if is_flax_available() else ()
lowerCAmelCase = False
lowerCAmelCase = False
lowerCAmelCase = False
def _a ( self : Tuple ) -> None:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = FlaxRegNetModelTester(self )
__SCREAMING_SNAKE_CASE = ConfigTester(self , config_class=__SCREAMING_SNAKE_CASE , has_text_modality=__SCREAMING_SNAKE_CASE )
def _a ( self : Tuple ) -> int:
"""simple docstring"""
self.create_and_test_config_common_properties()
self.config_tester.create_and_test_config_to_json_string()
self.config_tester.create_and_test_config_to_json_file()
self.config_tester.create_and_test_config_from_and_save_pretrained()
self.config_tester.create_and_test_config_with_num_labels()
self.config_tester.check_config_can_be_init_without_params()
self.config_tester.check_config_arguments_init()
def _a ( self : Optional[Any] ) -> Optional[Any]:
"""simple docstring"""
return
def _a ( self : Dict ) -> str:
"""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] ) -> 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 )
@unittest.skip(reason='''RegNet does not use inputs_embeds''' )
def _a ( self : List[str] ) -> List[str]:
"""simple docstring"""
pass
@unittest.skip(reason='''RegNet does not support input and output embeddings''' )
def _a ( self : List[Any] ) -> int:
"""simple docstring"""
pass
def _a ( self : int ) -> List[str]:
"""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 : Tuple ) -> Optional[Any]:
"""simple docstring"""
def check_hidden_states_output(__SCREAMING_SNAKE_CASE : Dict , __SCREAMING_SNAKE_CASE : Optional[Any] , __SCREAMING_SNAKE_CASE : List[str] ):
__SCREAMING_SNAKE_CASE = model_class(__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = model(**self._prepare_for_class(__SCREAMING_SNAKE_CASE , __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 )
__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 = 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 : List[Any] ) -> Tuple:
"""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:
with self.subTest(model_class.__name__ ):
__SCREAMING_SNAKE_CASE = self._prepare_for_class(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = model_class(__SCREAMING_SNAKE_CASE )
@jax.jit
def model_jitted(__SCREAMING_SNAKE_CASE : Dict , **__SCREAMING_SNAKE_CASE : Dict ):
return model(pixel_values=__SCREAMING_SNAKE_CASE , **__SCREAMING_SNAKE_CASE )
with self.subTest('''JIT Enabled''' ):
__SCREAMING_SNAKE_CASE = model_jitted(**__SCREAMING_SNAKE_CASE ).to_tuple()
with self.subTest('''JIT Disabled''' ):
with jax.disable_jit():
__SCREAMING_SNAKE_CASE = model_jitted(**__SCREAMING_SNAKE_CASE ).to_tuple()
self.assertEqual(len(__SCREAMING_SNAKE_CASE ) , len(__SCREAMING_SNAKE_CASE ) )
for jitted_output, output in zip(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
self.assertEqual(jitted_output.shape , output.shape )
def _a ( ) -> List[str]:
__SCREAMING_SNAKE_CASE = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
return image
@require_flax
class A__( unittest.TestCase ):
'''simple docstring'''
@cached_property
def _a ( self : Optional[Any] ) -> List[str]:
"""simple docstring"""
return AutoImageProcessor.from_pretrained('''facebook/regnet-y-040''' ) if is_vision_available() else None
@slow
def _a ( self : Optional[Any] ) -> Union[str, Any]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = FlaxRegNetForImageClassification.from_pretrained('''facebook/regnet-y-040''' )
__SCREAMING_SNAKE_CASE = self.default_image_processor
__SCREAMING_SNAKE_CASE = prepare_img()
__SCREAMING_SNAKE_CASE = image_processor(images=__SCREAMING_SNAKE_CASE , return_tensors='''np''' )
__SCREAMING_SNAKE_CASE = model(**__SCREAMING_SNAKE_CASE )
# verify the logits
__SCREAMING_SNAKE_CASE = (1, 10_00)
self.assertEqual(outputs.logits.shape , __SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = jnp.array([-0.41_80, -1.50_51, -3.48_36] )
self.assertTrue(jnp.allclose(outputs.logits[0, :3] , __SCREAMING_SNAKE_CASE , atol=1E-4 ) )
| 709 |
"""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 | 0 |
"""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
| 710 |
"""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 | 0 |
"""simple docstring"""
from __future__ import annotations
from math import pi
def _a ( UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ ) -> dict[str, float]:
if (inductance, frequency, reactance).count(0 ) != 1:
raise ValueError('''One and only one argument must be 0''' )
if inductance < 0:
raise ValueError('''Inductance cannot be negative''' )
if frequency < 0:
raise ValueError('''Frequency cannot be negative''' )
if reactance < 0:
raise ValueError('''Inductive reactance cannot be negative''' )
if inductance == 0:
return {"inductance": reactance / (2 * pi * frequency)}
elif frequency == 0:
return {"frequency": reactance / (2 * pi * inductance)}
elif reactance == 0:
return {"reactance": 2 * pi * frequency * inductance}
else:
raise ValueError('''Exactly one argument must be 0''' )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 711 |
"""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 | 0 |
"""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)
| 712 |
"""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 | 0 |
"""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()
| 713 |
"""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 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCAmelCase__ ={"configuration_focalnet": ["FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "FocalNetConfig"]}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase__ =[
"FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST",
"FocalNetForImageClassification",
"FocalNetForMaskedImageModeling",
"FocalNetBackbone",
"FocalNetModel",
"FocalNetPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_focalnet import FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP, FocalNetConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_focalnet import (
FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST,
FocalNetBackbone,
FocalNetForImageClassification,
FocalNetForMaskedImageModeling,
FocalNetModel,
FocalNetPreTrainedModel,
)
else:
import sys
lowerCAmelCase__ =_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 714 |
"""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 | 0 |
"""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() = }''')
| 715 |
"""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 | 0 |
"""simple docstring"""
import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional
import numpy as np
from utils_multiple_choice import MultipleChoiceDataset, Split, processors
import transformers
from transformers import (
AutoConfig,
AutoModelForMultipleChoice,
AutoTokenizer,
DataCollatorWithPadding,
EvalPrediction,
HfArgumentParser,
Trainer,
TrainingArguments,
set_seed,
)
from transformers.trainer_utils import is_main_process
lowerCAmelCase__ =logging.getLogger(__name__)
def _a ( UpperCAmelCase__ , UpperCAmelCase__ ) -> Union[str, Any]:
return (preds == labels).mean()
@dataclass
class A__:
lowerCAmelCase = field(
metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''} )
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''': '''Pretrained tokenizer 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 huggingface.co'''} , )
@dataclass
class A__:
lowerCAmelCase = field(metadata={'''help''': '''The name of the task to train on: ''' + ''', '''.join(processors.keys() )} )
lowerCAmelCase = field(metadata={'''help''': '''Should contain the data files for the task.'''} )
lowerCAmelCase = field(
default=1_28 , metadata={
'''help''': (
'''The maximum total input sequence length after tokenization. Sequences longer '''
'''than this will be truncated, sequences shorter will be padded.'''
)
} , )
lowerCAmelCase = field(
default=__magic_name__ , metadata={'''help''': '''Overwrite the cached training and evaluation sets'''} )
def _a ( ) -> Tuple:
# 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) )
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = parser.parse_args_into_dataclasses()
if (
os.path.exists(training_args.output_dir )
and os.listdir(training_args.output_dir )
and training_args.do_train
and not training_args.overwrite_output_dir
):
raise ValueError(
f"""Output directory ({training_args.output_dir}) already exists and is not empty. Use"""
''' --overwrite_output_dir to overcome.''' )
# Setup logging
logging.basicConfig(
format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''' , datefmt='''%m/%d/%Y %H:%M:%S''' , level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN , )
logger.warning(
'''Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s''' , training_args.local_rank , training_args.device , training_args.n_gpu , bool(training_args.local_rank != -1 ) , training_args.fpaa , )
# Set the verbosity to info of the Transformers logger (on main process only):
if is_main_process(training_args.local_rank ):
transformers.utils.logging.set_verbosity_info()
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
logger.info('''Training/evaluation parameters %s''' , UpperCAmelCase__ )
# Set seed
set_seed(training_args.seed )
try:
__SCREAMING_SNAKE_CASE = processors[data_args.task_name]()
__SCREAMING_SNAKE_CASE = processor.get_labels()
__SCREAMING_SNAKE_CASE = len(UpperCAmelCase__ )
except KeyError:
raise ValueError('''Task not found: %s''' % (data_args.task_name) )
# Load pretrained model and tokenizer
#
# Distributed training:
# The .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
__SCREAMING_SNAKE_CASE = AutoConfig.from_pretrained(
model_args.config_name if model_args.config_name else model_args.model_name_or_path , num_labels=UpperCAmelCase__ , finetuning_task=data_args.task_name , cache_dir=model_args.cache_dir , )
__SCREAMING_SNAKE_CASE = AutoTokenizer.from_pretrained(
model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path , cache_dir=model_args.cache_dir , )
__SCREAMING_SNAKE_CASE = AutoModelForMultipleChoice.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 , )
# Get datasets
__SCREAMING_SNAKE_CASE = (
MultipleChoiceDataset(
data_dir=data_args.data_dir , tokenizer=UpperCAmelCase__ , task=data_args.task_name , max_seq_length=data_args.max_seq_length , overwrite_cache=data_args.overwrite_cache , mode=Split.train , )
if training_args.do_train
else None
)
__SCREAMING_SNAKE_CASE = (
MultipleChoiceDataset(
data_dir=data_args.data_dir , tokenizer=UpperCAmelCase__ , task=data_args.task_name , max_seq_length=data_args.max_seq_length , overwrite_cache=data_args.overwrite_cache , mode=Split.dev , )
if training_args.do_eval
else None
)
def compute_metrics(UpperCAmelCase__ ) -> Dict:
__SCREAMING_SNAKE_CASE = np.argmax(p.predictions , axis=1 )
return {"acc": simple_accuracy(UpperCAmelCase__ , p.label_ids )}
# Data collator
__SCREAMING_SNAKE_CASE = DataCollatorWithPadding(UpperCAmelCase__ , pad_to_multiple_of=8 ) if training_args.fpaa else None
# Initialize our Trainer
__SCREAMING_SNAKE_CASE = Trainer(
model=UpperCAmelCase__ , args=UpperCAmelCase__ , train_dataset=UpperCAmelCase__ , eval_dataset=UpperCAmelCase__ , compute_metrics=UpperCAmelCase__ , data_collator=UpperCAmelCase__ , )
# Training
if training_args.do_train:
trainer.train(
model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path ) else None )
trainer.save_model()
# For convenience, we also re-save the tokenizer to the same directory,
# so that you can share your model easily on huggingface.co/models =)
if trainer.is_world_master():
tokenizer.save_pretrained(training_args.output_dir )
# Evaluation
__SCREAMING_SNAKE_CASE = {}
if training_args.do_eval:
logger.info('''*** Evaluate ***''' )
__SCREAMING_SNAKE_CASE = trainer.evaluate()
__SCREAMING_SNAKE_CASE = os.path.join(training_args.output_dir , '''eval_results.txt''' )
if trainer.is_world_master():
with open(UpperCAmelCase__ , '''w''' ) as writer:
logger.info('''***** Eval results *****''' )
for key, value in result.items():
logger.info(''' %s = %s''' , UpperCAmelCase__ , UpperCAmelCase__ )
writer.write('''%s = %s\n''' % (key, value) )
results.update(UpperCAmelCase__ )
return results
def _a ( UpperCAmelCase__ ) -> Optional[Any]:
# For xla_spawn (TPUs)
main()
if __name__ == "__main__":
main()
| 716 |
"""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 | 0 |
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