code
stringlengths 82
53.2k
| code_codestyle
int64 0
721
| style_context
stringlengths 91
41.9k
| style_context_codestyle
int64 0
699
| label
int64 0
1
|
|---|---|---|---|---|
from __future__ import annotations
import numpy as np
def lowerCamelCase_ ( __UpperCamelCase ):
A_ , A_ = np.shape(__UpperCamelCase )
if rows != columns:
A_ = (
'''\'table\' has to be of square shaped array but got a '''
F"{rows}x{columns} array:\n{table}"
)
raise ValueError(__UpperCamelCase )
A_ = np.zeros((rows, columns) )
A_ = np.zeros((rows, columns) )
for i in range(__UpperCamelCase ):
for j in range(__UpperCamelCase ):
A_ = sum(lower[i][k] * upper[k][j] for k in range(__UpperCamelCase ) )
if upper[j][j] == 0:
raise ArithmeticError('''No LU decomposition exists''' )
A_ = (table[i][j] - total) / upper[j][j]
A_ = 1
for j in range(__UpperCamelCase , __UpperCamelCase ):
A_ = sum(lower[i][k] * upper[k][j] for k in range(__UpperCamelCase ) )
A_ = table[i][j] - total
return lower, upper
if __name__ == "__main__":
import doctest
doctest.testmod() | 141 |
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run
import doctest
import sys
import warnings
from os.path import abspath, dirname, join
import _pytest
from transformers.testing_utils import HfDoctestModule, HfDocTestParser
# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switching between checkouts and running tests.
SCREAMING_SNAKE_CASE : Dict = abspath(join(dirname(__file__), "src"))
sys.path.insert(1, git_repo_path)
# silence FutureWarning warnings in tests since often we can't act on them until
# they become normal warnings - i.e. the tests still need to test the current functionality
warnings.simplefilter(action="ignore", category=FutureWarning)
def lowerCamelCase_ ( __UpperCamelCase ):
config.addinivalue_line(
'''markers''' , '''is_pt_tf_cross_test: mark test to run only when PT and TF interactions are tested''' )
config.addinivalue_line(
'''markers''' , '''is_pt_flax_cross_test: mark test to run only when PT and FLAX interactions are tested''' )
config.addinivalue_line('''markers''' , '''is_pipeline_test: mark test to run only when pipelines are tested''' )
config.addinivalue_line('''markers''' , '''is_staging_test: mark test to run only in the staging environment''' )
config.addinivalue_line('''markers''' , '''accelerate_tests: mark test that require accelerate''' )
config.addinivalue_line('''markers''' , '''tool_tests: mark the tool tests that are run on their specific schedule''' )
def lowerCamelCase_ ( __UpperCamelCase ):
from transformers.testing_utils import pytest_addoption_shared
pytest_addoption_shared(__UpperCamelCase )
def lowerCamelCase_ ( __UpperCamelCase ):
from transformers.testing_utils import pytest_terminal_summary_main
A_ = terminalreporter.config.getoption('''--make-reports''' )
if make_reports:
pytest_terminal_summary_main(__UpperCamelCase , id=__UpperCamelCase )
def lowerCamelCase_ ( __UpperCamelCase , __UpperCamelCase ):
# If no tests are collected, pytest exists with code 5, which makes the CI fail.
if exitstatus == 5:
A_ = 0
# Doctest custom flag to ignore output.
SCREAMING_SNAKE_CASE : Dict = doctest.register_optionflag("IGNORE_RESULT")
SCREAMING_SNAKE_CASE : Optional[int] = doctest.OutputChecker
class __lowercase ( A ):
def lowerCAmelCase_ ( self , a__ , a__ , a__ ) -> List[str]:
'''simple docstring'''
if IGNORE_RESULT & optionflags:
return True
return OutputChecker.check_output(self , a__ , a__ , a__ )
SCREAMING_SNAKE_CASE : List[str] = CustomOutputChecker
SCREAMING_SNAKE_CASE : Optional[int] = HfDoctestModule
SCREAMING_SNAKE_CASE : Any = HfDocTestParser | 141 | 1 |
"""simple docstring"""
import gc
import unittest
from diffusers import FlaxControlNetModel, FlaxStableDiffusionControlNetPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_utils import replicate
from flax.training.common_utils import shard
@slow
@require_flax
class UpperCAmelCase_ ( unittest.TestCase):
def _UpperCAmelCase ( self ) -> List[Any]:
# clean up the VRAM after each test
super().tearDown()
gc.collect()
def _UpperCAmelCase ( self ) -> Tuple:
lowercase__ , lowercase__ : str = FlaxControlNetModel.from_pretrained(
'lllyasviel/sd-controlnet-canny' , from_pt=a , dtype=jnp.bfloataa )
lowercase__ , lowercase__ : List[str] = FlaxStableDiffusionControlNetPipeline.from_pretrained(
'runwayml/stable-diffusion-v1-5' , controlnet=a , from_pt=a , dtype=jnp.bfloataa )
lowercase__ : List[Any] = controlnet_params
lowercase__ : int = 'bird'
lowercase__ : List[Any] = jax.device_count()
lowercase__ : Dict = pipe.prepare_text_inputs([prompts] * num_samples )
lowercase__ : Union[str, Any] = load_image(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png' )
lowercase__ : Optional[int] = pipe.prepare_image_inputs([canny_image] * num_samples )
lowercase__ : List[Any] = jax.random.PRNGKey(0 )
lowercase__ : Tuple = jax.random.split(a , jax.device_count() )
lowercase__ : str = replicate(a )
lowercase__ : List[str] = shard(a )
lowercase__ : Dict = shard(a )
lowercase__ : List[Any] = pipe(
prompt_ids=a , image=a , params=a , prng_seed=a , num_inference_steps=5_0 , jit=a , ).images
assert images.shape == (jax.device_count(), 1, 7_6_8, 5_1_2, 3)
lowercase__ : Any = images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:] )
lowercase__ : Tuple = images[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]
lowercase__ : int = jnp.asarray(jax.device_get(image_slice.flatten() ) )
lowercase__ : Optional[Any] = jnp.array(
[0.167_969, 0.116_699, 0.081_543, 0.154_297, 0.132_812, 0.108_887, 0.169_922, 0.169_922, 0.205_078] )
print(f"""output_slice: {output_slice}""" )
assert jnp.abs(output_slice - expected_slice ).max() < 1e-2
def _UpperCAmelCase ( self ) -> List[str]:
lowercase__ , lowercase__ : int = FlaxControlNetModel.from_pretrained(
'lllyasviel/sd-controlnet-openpose' , from_pt=a , dtype=jnp.bfloataa )
lowercase__ , lowercase__ : Optional[Any] = FlaxStableDiffusionControlNetPipeline.from_pretrained(
'runwayml/stable-diffusion-v1-5' , controlnet=a , from_pt=a , dtype=jnp.bfloataa )
lowercase__ : Optional[Any] = controlnet_params
lowercase__ : List[Any] = 'Chef in the kitchen'
lowercase__ : List[str] = jax.device_count()
lowercase__ : Dict = pipe.prepare_text_inputs([prompts] * num_samples )
lowercase__ : Optional[Any] = load_image(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/pose.png' )
lowercase__ : Optional[int] = pipe.prepare_image_inputs([pose_image] * num_samples )
lowercase__ : List[str] = jax.random.PRNGKey(0 )
lowercase__ : str = jax.random.split(a , jax.device_count() )
lowercase__ : Optional[Any] = replicate(a )
lowercase__ : Optional[Any] = shard(a )
lowercase__ : List[Any] = shard(a )
lowercase__ : List[Any] = pipe(
prompt_ids=a , image=a , params=a , prng_seed=a , num_inference_steps=5_0 , jit=a , ).images
assert images.shape == (jax.device_count(), 1, 7_6_8, 5_1_2, 3)
lowercase__ : Union[str, Any] = images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:] )
lowercase__ : List[str] = images[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]
lowercase__ : Optional[int] = jnp.asarray(jax.device_get(image_slice.flatten() ) )
lowercase__ : str = jnp.array(
[[0.271_484, 0.261_719, 0.275_391, 0.277_344, 0.279_297, 0.291_016, 0.294_922, 0.302_734, 0.302_734]] )
print(f"""output_slice: {output_slice}""" )
assert jnp.abs(output_slice - expected_slice ).max() < 1e-2
| 645 | """simple docstring"""
import copy
import inspect
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers import VideoMAEConfig
from transformers.models.auto import get_values
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_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import (
MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING,
VideoMAEForPreTraining,
VideoMAEForVideoClassification,
VideoMAEModel,
)
from transformers.models.videomae.modeling_videomae import VIDEOMAE_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from transformers import VideoMAEImageProcessor
class UpperCAmelCase_ :
def __init__( self , a , a=1_3 , a=1_0 , a=3 , a=2 , a=2 , a=2 , a=True , a=True , a=3_2 , a=5 , a=4 , a=3_7 , a="gelu" , a=0.1 , a=0.1 , a=1_0 , a=0.02 , a=0.9 , a=None , ) -> Optional[Any]:
lowercase__ : str = parent
lowercase__ : int = batch_size
lowercase__ : Union[str, Any] = image_size
lowercase__ : Optional[Any] = num_channels
lowercase__ : Dict = patch_size
lowercase__ : Tuple = tubelet_size
lowercase__ : Optional[int] = num_frames
lowercase__ : Optional[int] = is_training
lowercase__ : int = use_labels
lowercase__ : Optional[int] = hidden_size
lowercase__ : Union[str, Any] = num_hidden_layers
lowercase__ : Optional[int] = num_attention_heads
lowercase__ : Any = intermediate_size
lowercase__ : str = hidden_act
lowercase__ : List[Any] = hidden_dropout_prob
lowercase__ : str = attention_probs_dropout_prob
lowercase__ : Union[str, Any] = type_sequence_label_size
lowercase__ : List[Any] = initializer_range
lowercase__ : str = mask_ratio
lowercase__ : Optional[Any] = scope
# in VideoMAE, the number of tokens equals num_frames/tubelet_size * num_patches per frame
lowercase__ : Optional[Any] = (image_size // patch_size) ** 2
lowercase__ : str = (num_frames // tubelet_size) * self.num_patches_per_frame
# use this variable to define bool_masked_pos
lowercase__ : str = int(mask_ratio * self.seq_length )
def _UpperCAmelCase ( self ) -> Tuple:
lowercase__ : int = floats_tensor(
[self.batch_size, self.num_frames, self.num_channels, self.image_size, self.image_size] )
lowercase__ : int = None
if self.use_labels:
lowercase__ : Optional[Any] = ids_tensor([self.batch_size] , self.type_sequence_label_size )
lowercase__ : Dict = self.get_config()
return config, pixel_values, labels
def _UpperCAmelCase ( self ) -> Tuple:
return VideoMAEConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , num_frames=self.num_frames , tubelet_size=self.tubelet_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , is_decoder=a , initializer_range=self.initializer_range , )
def _UpperCAmelCase ( self , a , a , a ) -> Optional[int]:
lowercase__ : Dict = VideoMAEModel(config=a )
model.to(a )
model.eval()
lowercase__ : Tuple = model(a )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def _UpperCAmelCase ( self , a , a , a ) -> Union[str, Any]:
lowercase__ : str = VideoMAEForPreTraining(a )
model.to(a )
model.eval()
# important: each video needs to have the same number of masked patches
# hence we define a single mask, which we then repeat for each example in the batch
lowercase__ : Any = torch.ones((self.num_masks,) )
lowercase__ : str = torch.cat([mask, torch.zeros(self.seq_length - mask.size(0 ) )] )
lowercase__ : Optional[int] = mask.expand(self.batch_size , -1 ).bool()
lowercase__ : str = model(a , a )
# model only returns predictions for masked patches
lowercase__ : str = mask.sum().item()
lowercase__ : int = 3 * self.tubelet_size * self.patch_size**2
self.parent.assertEqual(result.logits.shape , (self.batch_size, num_masked_patches, decoder_num_labels) )
def _UpperCAmelCase ( self ) -> str:
lowercase__ : Dict = self.prepare_config_and_inputs()
lowercase__ , lowercase__ , lowercase__ : Union[str, Any] = config_and_inputs
lowercase__ : List[str] = {'pixel_values': pixel_values}
return config, inputs_dict
@require_torch
class UpperCAmelCase_ ( _a , _a , unittest.TestCase):
lowerCamelCase__ : Tuple = (
(VideoMAEModel, VideoMAEForPreTraining, VideoMAEForVideoClassification) if is_torch_available() else ()
)
lowerCamelCase__ : Optional[int] = (
{"feature-extraction": VideoMAEModel, "video-classification": VideoMAEForVideoClassification}
if is_torch_available()
else {}
)
lowerCamelCase__ : Any = False
lowerCamelCase__ : Any = False
lowerCamelCase__ : Union[str, Any] = False
lowerCamelCase__ : str = False
def _UpperCAmelCase ( self ) -> Tuple:
lowercase__ : Optional[Any] = VideoMAEModelTester(self )
lowercase__ : Optional[Any] = ConfigTester(self , config_class=a , has_text_modality=a , hidden_size=3_7 )
def _UpperCAmelCase ( self , a , a , a=False ) -> Optional[int]:
lowercase__ : Union[str, Any] = copy.deepcopy(a )
if model_class == VideoMAEForPreTraining:
# important: each video needs to have the same number of masked patches
# hence we define a single mask, which we then repeat for each example in the batch
lowercase__ : Optional[Any] = torch.ones((self.model_tester.num_masks,) )
lowercase__ : Any = torch.cat([mask, torch.zeros(self.model_tester.seq_length - mask.size(0 ) )] )
lowercase__ : Any = mask.expand(self.model_tester.batch_size , -1 ).bool()
lowercase__ : Union[str, Any] = bool_masked_pos.to(a )
if return_labels:
if model_class in [
*get_values(a ),
]:
lowercase__ : Dict = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=a )
return inputs_dict
def _UpperCAmelCase ( self ) -> Tuple:
self.config_tester.run_common_tests()
@unittest.skip(reason='VideoMAE does not use inputs_embeds' )
def _UpperCAmelCase ( self ) -> Dict:
pass
def _UpperCAmelCase ( self ) -> List[Any]:
lowercase__ , lowercase__ : Optional[int] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowercase__ : int = model_class(a )
self.assertIsInstance(model.get_input_embeddings() , (nn.Module) )
lowercase__ : int = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(a , nn.Linear ) )
def _UpperCAmelCase ( self ) -> Optional[int]:
lowercase__ , lowercase__ : Any = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowercase__ : List[str] = model_class(a )
lowercase__ : int = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
lowercase__ : Optional[Any] = [*signature.parameters.keys()]
lowercase__ : int = ['pixel_values']
self.assertListEqual(arg_names[:1] , a )
def _UpperCAmelCase ( self ) -> Optional[Any]:
lowercase__ : List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*a )
def _UpperCAmelCase ( self ) -> Tuple:
lowercase__ : Union[str, Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_pretraining(*a )
@slow
def _UpperCAmelCase ( self ) -> str:
for model_name in VIDEOMAE_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowercase__ : List[Any] = VideoMAEModel.from_pretrained(a )
self.assertIsNotNone(a )
def _UpperCAmelCase ( self ) -> Optional[Any]:
if not self.has_attentions:
pass
else:
lowercase__ , lowercase__ : Union[str, Any] = self.model_tester.prepare_config_and_inputs_for_common()
lowercase__ : str = True
for model_class in self.all_model_classes:
lowercase__ : Union[str, Any] = self.model_tester.seq_length - self.model_tester.num_masks
lowercase__ : Any = (
num_visible_patches if model_class == VideoMAEForPreTraining else self.model_tester.seq_length
)
lowercase__ : Optional[Any] = True
lowercase__ : int = False
lowercase__ : Any = True
lowercase__ : List[str] = model_class(a )
model.to(a )
model.eval()
with torch.no_grad():
lowercase__ : Optional[int] = model(**self._prepare_for_class(a , a ) )
lowercase__ : Dict = outputs.attentions
self.assertEqual(len(a ) , self.model_tester.num_hidden_layers )
# check that output_attentions also work using config
del inputs_dict["output_attentions"]
lowercase__ : str = True
lowercase__ : List[str] = model_class(a )
model.to(a )
model.eval()
with torch.no_grad():
lowercase__ : List[Any] = model(**self._prepare_for_class(a , a ) )
lowercase__ : Optional[Any] = outputs.attentions
self.assertEqual(len(a ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, seq_len, seq_len] , )
lowercase__ : List[str] = len(a )
# Check attention is always last and order is fine
lowercase__ : Optional[int] = True
lowercase__ : List[str] = True
lowercase__ : int = model_class(a )
model.to(a )
model.eval()
with torch.no_grad():
lowercase__ : List[str] = model(**self._prepare_for_class(a , a ) )
self.assertEqual(out_len + 1 , len(a ) )
lowercase__ : int = outputs.attentions
self.assertEqual(len(a ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(self_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, seq_len, seq_len] , )
def _UpperCAmelCase ( self ) -> Optional[int]:
def check_hidden_states_output(a , a , a ):
lowercase__ : Optional[int] = model_class(a )
model.to(a )
model.eval()
with torch.no_grad():
lowercase__ : Optional[Any] = model(**self._prepare_for_class(a , a ) )
lowercase__ : Optional[int] = outputs.hidden_states
lowercase__ : List[Any] = self.model_tester.num_hidden_layers + 1
self.assertEqual(len(a ) , a )
lowercase__ : Optional[Any] = self.model_tester.seq_length - self.model_tester.num_masks
lowercase__ : Union[str, Any] = num_visible_patches if model_class == VideoMAEForPreTraining else self.model_tester.seq_length
self.assertListEqual(
list(hidden_states[0].shape[-2:] ) , [seq_length, self.model_tester.hidden_size] , )
lowercase__ , lowercase__ : List[Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowercase__ : Tuple = True
check_hidden_states_output(a , a , a )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
lowercase__ : Union[str, Any] = True
check_hidden_states_output(a , a , a )
@unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.' )
def _UpperCAmelCase ( self ) -> List[Any]:
pass
def a_ ( ):
'''simple docstring'''
lowercase__ : int = hf_hub_download(
repo_id='hf-internal-testing/spaghetti-video' , filename='eating_spaghetti.npy' , repo_type='dataset' )
lowercase__ : str = np.load(_lowerCAmelCase )
return list(_lowerCAmelCase )
@require_torch
@require_vision
class UpperCAmelCase_ ( unittest.TestCase):
@cached_property
def _UpperCAmelCase ( self ) -> Optional[Any]:
# logits were tested with a different mean and std, so we use the same here
return (
VideoMAEImageProcessor(image_mean=[0.5, 0.5, 0.5] , image_std=[0.5, 0.5, 0.5] )
if is_vision_available()
else None
)
@slow
def _UpperCAmelCase ( self ) -> int:
lowercase__ : Dict = VideoMAEForVideoClassification.from_pretrained('MCG-NJU/videomae-base-finetuned-kinetics' ).to(
a )
lowercase__ : str = self.default_image_processor
lowercase__ : List[str] = prepare_video()
lowercase__ : int = image_processor(a , return_tensors='pt' ).to(a )
# forward pass
with torch.no_grad():
lowercase__ : Union[str, Any] = model(**a )
# verify the logits
lowercase__ : str = torch.Size((1, 4_0_0) )
self.assertEqual(outputs.logits.shape , a )
lowercase__ : List[Any] = torch.tensor([0.3_669, -0.0_688, -0.2_421] ).to(a )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , a , atol=1e-4 ) )
@slow
def _UpperCAmelCase ( self ) -> List[str]:
lowercase__ : Optional[int] = VideoMAEForPreTraining.from_pretrained('MCG-NJU/videomae-base-short' ).to(a )
lowercase__ : Optional[Any] = self.default_image_processor
lowercase__ : List[str] = prepare_video()
lowercase__ : str = image_processor(a , return_tensors='pt' ).to(a )
# add boolean mask, indicating which patches to mask
lowercase__ : Union[str, Any] = hf_hub_download(repo_id='hf-internal-testing/bool-masked-pos' , filename='bool_masked_pos.pt' )
lowercase__ : str = torch.load(a )
# forward pass
with torch.no_grad():
lowercase__ : List[Any] = model(**a )
# verify the logits
lowercase__ : Dict = torch.Size([1, 1_4_0_8, 1_5_3_6] )
lowercase__ : List[str] = torch.tensor(
[[0.7_994, 0.9_612, 0.8_508], [0.7_401, 0.8_958, 0.8_302], [0.5_862, 0.7_468, 0.7_325]] , device=a )
self.assertEqual(outputs.logits.shape , a )
self.assertTrue(torch.allclose(outputs.logits[0, :3, :3] , a , atol=1e-4 ) )
# verify the loss (`config.norm_pix_loss` = `True`)
lowercase__ : List[Any] = torch.tensor([0.5_142] , device=a )
self.assertTrue(torch.allclose(outputs.loss , a , atol=1e-4 ) )
# verify the loss (`config.norm_pix_loss` = `False`)
lowercase__ : Tuple = VideoMAEForPreTraining.from_pretrained('MCG-NJU/videomae-base-short' , norm_pix_loss=a ).to(
a )
with torch.no_grad():
lowercase__ : Any = model(**a )
lowercase__ : List[Any] = torch.tensor(torch.tensor([0.6_469] ) , device=a )
self.assertTrue(torch.allclose(outputs.loss , a , atol=1e-4 ) )
| 645 | 1 |
import numpy as np
import qiskit
def _lowercase ( __lowerCamelCase : Tuple = 8 ,__lowerCamelCase : List[str] = None ) -> str:
'''simple docstring'''
UpperCamelCase__ : Tuple = np.random.default_rng(seed=_lowerCAmelCase )
# Roughly 25% of the qubits will contribute to the key.
# So we take more than we need.
UpperCamelCase__ : List[str] = 6 * key_len
# Measurement basis for Alice's qubits.
UpperCamelCase__ : List[Any] = rng.integers(2 ,size=_lowerCAmelCase )
# The set of states Alice will prepare.
UpperCamelCase__ : List[Any] = rng.integers(2 ,size=_lowerCAmelCase )
# Measurement basis for Bob's qubits.
UpperCamelCase__ : Optional[int] = rng.integers(2 ,size=_lowerCAmelCase )
# Quantum Circuit to simulate BB84
UpperCamelCase__ : List[Any] = qiskit.QuantumCircuit(_lowerCAmelCase ,name='''BB84''' )
# Alice prepares her qubits according to rules above.
for index, _ in enumerate(_lowerCAmelCase ):
if alice_state[index] == 1:
bbaa_circ.x(_lowerCAmelCase )
if alice_basis[index] == 1:
bbaa_circ.h(_lowerCAmelCase )
bbaa_circ.barrier()
# Bob measures the received qubits according to rules above.
for index, _ in enumerate(_lowerCAmelCase ):
if bob_basis[index] == 1:
bbaa_circ.h(_lowerCAmelCase )
bbaa_circ.barrier()
bbaa_circ.measure_all()
# Simulate the quantum circuit.
UpperCamelCase__ : Union[str, Any] = qiskit.Aer.get_backend('''aer_simulator''' )
# We only need to run one shot because the key is unique.
# Multiple shots will produce the same key.
UpperCamelCase__ : Tuple = qiskit.execute(_lowerCAmelCase ,_lowerCAmelCase ,shots=1 ,seed_simulator=_lowerCAmelCase )
# Returns the result of measurement.
UpperCamelCase__ : Optional[Any] = job.result().get_counts(_lowerCAmelCase ).most_frequent()
# Extracting the generated key from the simulation results.
# Only keep measurement results where Alice and Bob chose the same basis.
UpperCamelCase__ : Tuple = "".join(
[
result_bit
for alice_basis_bit, bob_basis_bit, result_bit in zip(
_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase )
if alice_basis_bit == bob_basis_bit
] )
# Get final key. Pad with 0 if too short, otherwise truncate.
UpperCamelCase__ : Tuple = gen_key[:key_len] if len(_lowerCAmelCase ) >= key_len else gen_key.ljust(_lowerCAmelCase ,'''0''' )
return key
if __name__ == "__main__":
print(F'The generated key is : {bbaa(8, seed=0)}')
from doctest import testmod
testmod()
| 344 |
from typing import TYPE_CHECKING
from ...utils import _LazyModule
__lowerCamelCase : str = {"""tokenization_byt5""": ["""ByT5Tokenizer"""]}
if TYPE_CHECKING:
from .tokenization_byta import ByTaTokenizer
else:
import sys
__lowerCamelCase : List[str] = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 629 | 0 |
import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput
def __lowerCamelCase ( A__ : Dict , A__ : Union[str, Any]=0.999 , A__ : List[Any]="cosine" , ) -> Optional[Any]:
if alpha_transform_type == "cosine":
def alpha_bar_fn(A__ : Tuple ):
return math.cos((t + 0.008) / 1.008 * math.pi / 2 ) ** 2
elif alpha_transform_type == "exp":
def alpha_bar_fn(A__ : Any ):
return math.exp(t * -12.0 )
else:
raise ValueError(f'''Unsupported alpha_tranform_type: {alpha_transform_type}''' )
lowerCamelCase_ : Tuple = []
for i in range(A__ ):
lowerCamelCase_ : List[Any] = i / num_diffusion_timesteps
lowerCamelCase_ : Optional[Any] = (i + 1) / num_diffusion_timesteps
betas.append(min(1 - alpha_bar_fn(A__ ) / alpha_bar_fn(A__ ) , A__ ) )
return torch.tensor(A__ , dtype=torch.floataa )
class SCREAMING_SNAKE_CASE_ (a__ , a__ ):
'''simple docstring'''
_a = [e.name for e in KarrasDiffusionSchedulers]
_a = 2
@register_to_config
def __init__( self : Tuple , __a : int = 1_000 , __a : float = 0.00_085 , __a : float = 0.012 , __a : str = "linear" , __a : Optional[Union[np.ndarray, List[float]]] = None , __a : str = "epsilon" , __a : str = "linspace" , __a : int = 0 , ) ->Union[str, Any]:
if trained_betas is not None:
lowerCamelCase_ : Tuple = torch.tensor(__a , dtype=torch.floataa )
elif beta_schedule == "linear":
lowerCamelCase_ : str = torch.linspace(__a , __a , __a , dtype=torch.floataa )
elif beta_schedule == "scaled_linear":
# this schedule is very specific to the latent diffusion model.
lowerCamelCase_ : Tuple = (
torch.linspace(beta_start**0.5 , beta_end**0.5 , __a , dtype=torch.floataa ) ** 2
)
elif beta_schedule == "squaredcos_cap_v2":
# Glide cosine schedule
lowerCamelCase_ : Any = betas_for_alpha_bar(__a )
else:
raise NotImplementedError(F'''{beta_schedule} does is not implemented for {self.__class__}''' )
lowerCamelCase_ : Dict = 1.0 - self.betas
lowerCamelCase_ : List[Any] = torch.cumprod(self.alphas , dim=0 )
# set all values
self.set_timesteps(__a , __a , __a )
def _lowerCAmelCase ( self : Tuple , __a : Optional[int] , __a : Optional[int]=None ) ->Union[str, Any]:
if schedule_timesteps is None:
lowerCamelCase_ : Optional[Any] = self.timesteps
lowerCamelCase_ : Tuple = (schedule_timesteps == timestep).nonzero()
# The sigma index that is taken for the **very** first `step`
# is always the second index (or the last index if there is only 1)
# This way we can ensure we don't accidentally skip a sigma in
# case we start in the middle of the denoising schedule (e.g. for image-to-image)
if len(self._index_counter ) == 0:
lowerCamelCase_ : Optional[Any] = 1 if len(__a ) > 1 else 0
else:
lowerCamelCase_ : List[str] = timestep.cpu().item() if torch.is_tensor(__a ) else timestep
lowerCamelCase_ : int = self._index_counter[timestep_int]
return indices[pos].item()
@property
def _lowerCAmelCase ( self : Union[str, Any] ) ->List[str]:
# standard deviation of the initial noise distribution
if self.config.timestep_spacing in ["linspace", "trailing"]:
return self.sigmas.max()
return (self.sigmas.max() ** 2 + 1) ** 0.5
def _lowerCAmelCase ( self : Dict , __a : torch.FloatTensor , __a : Union[float, torch.FloatTensor] , ) ->torch.FloatTensor:
lowerCamelCase_ : Any = self.index_for_timestep(__a )
if self.state_in_first_order:
lowerCamelCase_ : Any = self.sigmas[step_index]
else:
lowerCamelCase_ : Optional[Any] = self.sigmas_interpol[step_index]
lowerCamelCase_ : Any = sample / ((sigma**2 + 1) ** 0.5)
return sample
def _lowerCAmelCase ( self : List[str] , __a : int , __a : Union[str, torch.device] = None , __a : Optional[int] = None , ) ->List[str]:
lowerCamelCase_ : Optional[Any] = num_inference_steps
lowerCamelCase_ : int = num_train_timesteps or self.config.num_train_timesteps
# "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
if self.config.timestep_spacing == "linspace":
lowerCamelCase_ : Optional[Any] = np.linspace(0 , num_train_timesteps - 1 , __a , dtype=__a )[::-1].copy()
elif self.config.timestep_spacing == "leading":
lowerCamelCase_ : Union[str, Any] = num_train_timesteps // self.num_inference_steps
# creates integer timesteps by multiplying by ratio
# casting to int to avoid issues when num_inference_step is power of 3
lowerCamelCase_ : str = (np.arange(0 , __a ) * step_ratio).round()[::-1].copy().astype(__a )
timesteps += self.config.steps_offset
elif self.config.timestep_spacing == "trailing":
lowerCamelCase_ : Optional[Any] = num_train_timesteps / self.num_inference_steps
# creates integer timesteps by multiplying by ratio
# casting to int to avoid issues when num_inference_step is power of 3
lowerCamelCase_ : str = (np.arange(__a , 0 , -step_ratio )).round().copy().astype(__a )
timesteps -= 1
else:
raise ValueError(
F'''{self.config.timestep_spacing} is not supported. Please make sure to choose one of \'linspace\', \'leading\' or \'trailing\'.''' )
lowerCamelCase_ : List[Any] = np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5 )
lowerCamelCase_ : Optional[int] = torch.from_numpy(np.log(__a ) ).to(__a )
lowerCamelCase_ : Tuple = np.interp(__a , np.arange(0 , len(__a ) ) , __a )
lowerCamelCase_ : Any = np.concatenate([sigmas, [0.0]] ).astype(np.floataa )
lowerCamelCase_ : Dict = torch.from_numpy(__a ).to(device=__a )
# interpolate sigmas
lowerCamelCase_ : List[str] = sigmas.log().lerp(sigmas.roll(1 ).log() , 0.5 ).exp()
lowerCamelCase_ : Tuple = torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2 ), sigmas[-1:]] )
lowerCamelCase_ : Dict = torch.cat(
[sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2 ), sigmas_interpol[-1:]] )
if str(__a ).startswith("""mps""" ):
# mps does not support float64
lowerCamelCase_ : Optional[int] = torch.from_numpy(__a ).to(__a , dtype=torch.floataa )
else:
lowerCamelCase_ : List[str] = torch.from_numpy(__a ).to(__a )
# interpolate timesteps
lowerCamelCase_ : Optional[int] = self.sigma_to_t(__a ).to(__a , dtype=timesteps.dtype )
lowerCamelCase_ : Dict = torch.stack((timesteps_interpol[1:-1, None], timesteps[1:, None]) , dim=-1 ).flatten()
lowerCamelCase_ : Union[str, Any] = torch.cat([timesteps[:1], interleaved_timesteps] )
lowerCamelCase_ : List[str] = None
# for exp beta schedules, such as the one for `pipeline_shap_e.py`
# we need an index counter
lowerCamelCase_ : str = defaultdict(__a )
def _lowerCAmelCase ( self : Any , __a : Dict ) ->str:
# get log sigma
lowerCamelCase_ : List[str] = sigma.log()
# get distribution
lowerCamelCase_ : Any = log_sigma - self.log_sigmas[:, None]
# get sigmas range
lowerCamelCase_ : Optional[int] = dists.ge(0 ).cumsum(dim=0 ).argmax(dim=0 ).clamp(max=self.log_sigmas.shape[0] - 2 )
lowerCamelCase_ : Any = low_idx + 1
lowerCamelCase_ : Optional[int] = self.log_sigmas[low_idx]
lowerCamelCase_ : List[Any] = self.log_sigmas[high_idx]
# interpolate sigmas
lowerCamelCase_ : List[str] = (low - log_sigma) / (low - high)
lowerCamelCase_ : Optional[Any] = w.clamp(0 , 1 )
# transform interpolation to time range
lowerCamelCase_ : Optional[Any] = (1 - w) * low_idx + w * high_idx
lowerCamelCase_ : Tuple = t.view(sigma.shape )
return t
@property
def _lowerCAmelCase ( self : Tuple ) ->Optional[Any]:
return self.sample is None
def _lowerCAmelCase ( self : Optional[Any] , __a : Union[torch.FloatTensor, np.ndarray] , __a : Union[float, torch.FloatTensor] , __a : Union[torch.FloatTensor, np.ndarray] , __a : bool = True , ) ->Union[SchedulerOutput, Tuple]:
lowerCamelCase_ : int = self.index_for_timestep(__a )
# advance index counter by 1
lowerCamelCase_ : Tuple = timestep.cpu().item() if torch.is_tensor(__a ) else timestep
self._index_counter[timestep_int] += 1
if self.state_in_first_order:
lowerCamelCase_ : Optional[int] = self.sigmas[step_index]
lowerCamelCase_ : Union[str, Any] = self.sigmas_interpol[step_index + 1]
lowerCamelCase_ : Dict = self.sigmas[step_index + 1]
else:
# 2nd order / KDPM2's method
lowerCamelCase_ : Union[str, Any] = self.sigmas[step_index - 1]
lowerCamelCase_ : Optional[Any] = self.sigmas_interpol[step_index]
lowerCamelCase_ : Tuple = self.sigmas[step_index]
# currently only gamma=0 is supported. This usually works best anyways.
# We can support gamma in the future but then need to scale the timestep before
# passing it to the model which requires a change in API
lowerCamelCase_ : List[Any] = 0
lowerCamelCase_ : Dict = sigma * (gamma + 1) # Note: sigma_hat == sigma for now
# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
if self.config.prediction_type == "epsilon":
lowerCamelCase_ : List[Any] = sigma_hat if self.state_in_first_order else sigma_interpol
lowerCamelCase_ : Tuple = sample - sigma_input * model_output
elif self.config.prediction_type == "v_prediction":
lowerCamelCase_ : Optional[int] = sigma_hat if self.state_in_first_order else sigma_interpol
lowerCamelCase_ : Dict = model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
sample / (sigma_input**2 + 1)
)
elif self.config.prediction_type == "sample":
raise NotImplementedError("""prediction_type not implemented yet: sample""" )
else:
raise ValueError(
F'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`''' )
if self.state_in_first_order:
# 2. Convert to an ODE derivative for 1st order
lowerCamelCase_ : Optional[int] = (sample - pred_original_sample) / sigma_hat
# 3. delta timestep
lowerCamelCase_ : str = sigma_interpol - sigma_hat
# store for 2nd order step
lowerCamelCase_ : Any = sample
else:
# DPM-Solver-2
# 2. Convert to an ODE derivative for 2nd order
lowerCamelCase_ : Tuple = (sample - pred_original_sample) / sigma_interpol
# 3. delta timestep
lowerCamelCase_ : Dict = sigma_next - sigma_hat
lowerCamelCase_ : str = self.sample
lowerCamelCase_ : int = None
lowerCamelCase_ : str = sample + derivative * dt
if not return_dict:
return (prev_sample,)
return SchedulerOutput(prev_sample=__a )
def _lowerCAmelCase ( self : Dict , __a : torch.FloatTensor , __a : torch.FloatTensor , __a : torch.FloatTensor , ) ->torch.FloatTensor:
# Make sure sigmas and timesteps have the same device and dtype as original_samples
lowerCamelCase_ : List[str] = self.sigmas.to(device=original_samples.device , dtype=original_samples.dtype )
if original_samples.device.type == "mps" and torch.is_floating_point(__a ):
# mps does not support float64
lowerCamelCase_ : Optional[int] = self.timesteps.to(original_samples.device , dtype=torch.floataa )
lowerCamelCase_ : int = timesteps.to(original_samples.device , dtype=torch.floataa )
else:
lowerCamelCase_ : List[Any] = self.timesteps.to(original_samples.device )
lowerCamelCase_ : Optional[Any] = timesteps.to(original_samples.device )
lowerCamelCase_ : Any = [self.index_for_timestep(__a , __a ) for t in timesteps]
lowerCamelCase_ : str = sigmas[step_indices].flatten()
while len(sigma.shape ) < len(original_samples.shape ):
lowerCamelCase_ : Optional[Any] = sigma.unsqueeze(-1 )
lowerCamelCase_ : Tuple = original_samples + noise * sigma
return noisy_samples
def __len__( self : Optional[Any] ) ->int:
return self.config.num_train_timesteps
| 171 |
from maths.is_square_free import is_square_free
from maths.prime_factors import prime_factors
def __lowerCamelCase ( A__ : int ) -> int:
lowerCamelCase_ : Union[str, Any] = prime_factors(A__ )
if is_square_free(A__ ):
return -1 if len(A__ ) % 2 else 1
return 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 171 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
_lowerCAmelCase = {
"""configuration_mvp""": ["""MVP_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MvpConfig""", """MvpOnnxConfig"""],
"""tokenization_mvp""": ["""MvpTokenizer"""],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowerCAmelCase = ["""MvpTokenizerFast"""]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowerCAmelCase = [
"""MVP_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""MvpForCausalLM""",
"""MvpForConditionalGeneration""",
"""MvpForQuestionAnswering""",
"""MvpForSequenceClassification""",
"""MvpModel""",
"""MvpPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_mvp import MVP_PRETRAINED_CONFIG_ARCHIVE_MAP, MvpConfig, MvpOnnxConfig
from .tokenization_mvp import MvpTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_mvp_fast import MvpTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mvp import (
MVP_PRETRAINED_MODEL_ARCHIVE_LIST,
MvpForCausalLM,
MvpForConditionalGeneration,
MvpForQuestionAnswering,
MvpForSequenceClassification,
MvpModel,
MvpPreTrainedModel,
)
else:
import sys
_lowerCAmelCase = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 432 |
from typing import Callable, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCamelCase : int = logging.get_logger(__name__)
_lowerCamelCase : Union[str, Any] = {
"""microsoft/xprophetnet-large-wiki100-cased""": (
"""https://huggingface.co/microsoft/xprophetnet-large-wiki100-cased/resolve/main/config.json"""
),
}
class lowercase ( SCREAMING_SNAKE_CASE_):
'''simple docstring'''
UpperCAmelCase : Tuple = 'xlm-prophetnet'
UpperCAmelCase : int = ['past_key_values']
UpperCAmelCase : List[Any] = {
'num_attention_heads': 'num_encoder_attention_heads',
}
def __init__( self : List[Any] , snake_case : Optional[float] = 0.1 , snake_case : Optional[Union[str, Callable]] = "gelu" , snake_case : Optional[int] = 30522 , snake_case : Optional[int] = 1024 , snake_case : Optional[int] = 4096 , snake_case : Optional[int] = 12 , snake_case : Optional[int] = 16 , snake_case : Optional[int] = 4096 , snake_case : Optional[int] = 12 , snake_case : Optional[int] = 16 , snake_case : Optional[float] = 0.1 , snake_case : Optional[float] = 0.1 , snake_case : Optional[int] = 512 , snake_case : Optional[float] = 0.02 , snake_case : Optional[bool] = True , snake_case : Optional[bool] = True , snake_case : Optional[int] = 0 , snake_case : Optional[int] = 2 , snake_case : Optional[int] = 32 , snake_case : Optional[int] = 128 , snake_case : Optional[bool] = False , snake_case : Optional[float] = 0.0 , snake_case : Optional[bool] = True , snake_case : Optional[int] = 0 , snake_case : Optional[int] = 1 , snake_case : Optional[int] = 2 , **snake_case : Optional[int] , ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Union[str, Any] = vocab_size
SCREAMING_SNAKE_CASE : int = hidden_size
SCREAMING_SNAKE_CASE : Dict = encoder_ffn_dim
SCREAMING_SNAKE_CASE : Optional[Any] = num_encoder_layers
SCREAMING_SNAKE_CASE : str = num_encoder_attention_heads
SCREAMING_SNAKE_CASE : Optional[Any] = decoder_ffn_dim
SCREAMING_SNAKE_CASE : Any = num_decoder_layers
SCREAMING_SNAKE_CASE : List[str] = num_decoder_attention_heads
SCREAMING_SNAKE_CASE : Optional[Any] = max_position_embeddings
SCREAMING_SNAKE_CASE : Any = init_std # Normal(0, this parameter)
SCREAMING_SNAKE_CASE : List[str] = activation_function
# parameters for xlmprophetnet
SCREAMING_SNAKE_CASE : Tuple = ngram
SCREAMING_SNAKE_CASE : Optional[int] = num_buckets
SCREAMING_SNAKE_CASE : Any = relative_max_distance
SCREAMING_SNAKE_CASE : Optional[int] = disable_ngram_loss
SCREAMING_SNAKE_CASE : List[str] = eps
# 3 Types of Dropout
SCREAMING_SNAKE_CASE : Optional[Any] = attention_dropout
SCREAMING_SNAKE_CASE : Any = activation_dropout
SCREAMING_SNAKE_CASE : Optional[int] = dropout
SCREAMING_SNAKE_CASE : Tuple = use_cache
super().__init__(
pad_token_id=snake_case , bos_token_id=snake_case , eos_token_id=snake_case , is_encoder_decoder=snake_case , add_cross_attention=snake_case , decoder_start_token_id=snake_case , **snake_case , )
@property
def lowerCamelCase_ ( self : Optional[Any] ):
'''simple docstring'''
return self.num_encoder_layers + self.num_decoder_layers
@num_hidden_layers.setter
def lowerCamelCase_ ( self : List[Any] , snake_case : Optional[int] ):
'''simple docstring'''
raise NotImplementedError(
'This model does not support the setting of `num_hidden_layers`. Please set `num_encoder_layers` and'
' `num_decoder_layers`.' ) | 352 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
__lowerCAmelCase : str ={
'configuration_gpt_bigcode': ['GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GPTBigCodeConfig'],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowerCAmelCase : Optional[int] =[
'GPT_BIGCODE_PRETRAINED_MODEL_ARCHIVE_LIST',
'GPTBigCodeForSequenceClassification',
'GPTBigCodeForTokenClassification',
'GPTBigCodeForCausalLM',
'GPTBigCodeModel',
'GPTBigCodePreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_gpt_bigcode import GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTBigCodeConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_gpt_bigcode import (
GPT_BIGCODE_PRETRAINED_MODEL_ARCHIVE_LIST,
GPTBigCodeForCausalLM,
GPTBigCodeForSequenceClassification,
GPTBigCodeForTokenClassification,
GPTBigCodeModel,
GPTBigCodePreTrainedModel,
)
else:
import sys
__lowerCAmelCase : int =_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 709 |
from __future__ import annotations
def _UpperCamelCase ( lowercase__ , lowercase__ ):
__SCREAMING_SNAKE_CASE : list[list[int]] = []
__SCREAMING_SNAKE_CASE : list[int] = []
__SCREAMING_SNAKE_CASE : Union[str, Any] = 0
__SCREAMING_SNAKE_CASE : Any = sum(lowercase__ )
create_state_space_tree(lowercase__ , lowercase__ , lowercase__ , lowercase__ , lowercase__ , lowercase__ )
return result
def _UpperCamelCase ( lowercase__ , lowercase__ , lowercase__ , lowercase__ , lowercase__ , lowercase__ , ):
if sum(lowercase__ ) > max_sum or (remaining_nums_sum + sum(lowercase__ )) < max_sum:
return
if sum(lowercase__ ) == max_sum:
result.append(lowercase__ )
return
for index in range(lowercase__ , len(lowercase__ ) ):
create_state_space_tree(
lowercase__ , lowercase__ , index + 1 , [*path, nums[index]] , lowercase__ , remaining_nums_sum - nums[index] , )
__lowerCAmelCase : List[str] =[3, 3_4, 4, 1_2, 5, 2]
__lowerCAmelCase : str =9
__lowerCAmelCase : List[str] =generate_sum_of_subsets_soln(nums, max_sum)
print(*result)
| 260 | 0 |
"""simple docstring"""
def snake_case ( lowerCAmelCase_ , lowerCAmelCase_ ) -> int:
return 1 if input_a == input_a else 0
def snake_case ( ) -> None:
assert xnor_gate(0 , 0 ) == 1
assert xnor_gate(0 , 1 ) == 0
assert xnor_gate(1 , 0 ) == 0
assert xnor_gate(1 , 1 ) == 1
if __name__ == "__main__":
print(xnor_gate(0, 0))
print(xnor_gate(0, 1))
print(xnor_gate(1, 0))
print(xnor_gate(1, 1))
| 103 |
"""simple docstring"""
from ..utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_scipy_available,
is_torch_available,
is_torchsde_available,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ..utils.dummy_pt_objects import * # noqa F403
else:
from .scheduling_consistency_models import CMStochasticIterativeScheduler
from .scheduling_ddim import DDIMScheduler
from .scheduling_ddim_inverse import DDIMInverseScheduler
from .scheduling_ddim_parallel import DDIMParallelScheduler
from .scheduling_ddpm import DDPMScheduler
from .scheduling_ddpm_parallel import DDPMParallelScheduler
from .scheduling_deis_multistep import DEISMultistepScheduler
from .scheduling_dpmsolver_multistep import DPMSolverMultistepScheduler
from .scheduling_dpmsolver_multistep_inverse import DPMSolverMultistepInverseScheduler
from .scheduling_dpmsolver_singlestep import DPMSolverSinglestepScheduler
from .scheduling_euler_ancestral_discrete import EulerAncestralDiscreteScheduler
from .scheduling_euler_discrete import EulerDiscreteScheduler
from .scheduling_heun_discrete import HeunDiscreteScheduler
from .scheduling_ipndm import IPNDMScheduler
from .scheduling_k_dpm_2_ancestral_discrete import KDPMaAncestralDiscreteScheduler
from .scheduling_k_dpm_2_discrete import KDPMaDiscreteScheduler
from .scheduling_karras_ve import KarrasVeScheduler
from .scheduling_pndm import PNDMScheduler
from .scheduling_repaint import RePaintScheduler
from .scheduling_sde_ve import ScoreSdeVeScheduler
from .scheduling_sde_vp import ScoreSdeVpScheduler
from .scheduling_unclip import UnCLIPScheduler
from .scheduling_unipc_multistep import UniPCMultistepScheduler
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin
from .scheduling_vq_diffusion import VQDiffusionScheduler
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ..utils.dummy_flax_objects import * # noqa F403
else:
from .scheduling_ddim_flax import FlaxDDIMScheduler
from .scheduling_ddpm_flax import FlaxDDPMScheduler
from .scheduling_dpmsolver_multistep_flax import FlaxDPMSolverMultistepScheduler
from .scheduling_karras_ve_flax import FlaxKarrasVeScheduler
from .scheduling_lms_discrete_flax import FlaxLMSDiscreteScheduler
from .scheduling_pndm_flax import FlaxPNDMScheduler
from .scheduling_sde_ve_flax import FlaxScoreSdeVeScheduler
from .scheduling_utils_flax import (
FlaxKarrasDiffusionSchedulers,
FlaxSchedulerMixin,
FlaxSchedulerOutput,
broadcast_to_shape_from_left,
)
try:
if not (is_torch_available() and is_scipy_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ..utils.dummy_torch_and_scipy_objects import * # noqa F403
else:
from .scheduling_lms_discrete import LMSDiscreteScheduler
try:
if not (is_torch_available() and is_torchsde_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ..utils.dummy_torch_and_torchsde_objects import * # noqa F403
else:
from .scheduling_dpmsolver_sde import DPMSolverSDEScheduler
| 633 | 0 |
_lowerCamelCase : List[str] = {
0: """0""",
1: """1""",
2: """2""",
3: """3""",
4: """4""",
5: """5""",
6: """6""",
7: """7""",
8: """8""",
9: """9""",
10: """a""",
11: """b""",
12: """c""",
13: """d""",
14: """e""",
15: """f""",
}
def __a ( __lowerCAmelCase ) -> str:
assert type(__lowerCAmelCase ) in (int, float) and decimal == int(__lowerCAmelCase )
SCREAMING_SNAKE_CASE : List[Any] = int(__lowerCAmelCase )
SCREAMING_SNAKE_CASE : Optional[int] = ''
SCREAMING_SNAKE_CASE : Union[str, Any] = False
if decimal < 0:
SCREAMING_SNAKE_CASE : List[str] = True
decimal *= -1
while decimal > 0:
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : Any = divmod(__lowerCAmelCase , 16 )
SCREAMING_SNAKE_CASE : Union[str, Any] = values[remainder] + hexadecimal
SCREAMING_SNAKE_CASE : Union[str, Any] = '0x' + hexadecimal
if negative:
SCREAMING_SNAKE_CASE : Union[str, Any] = '-' + hexadecimal
return hexadecimal
if __name__ == "__main__":
import doctest
doctest.testmod() | 308 |
import os
import time
import warnings
from dataclasses import dataclass, field
from enum import Enum
from typing import List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import logging
from ..processors.glue import glue_convert_examples_to_features, glue_output_modes, glue_processors
from ..processors.utils import InputFeatures
_lowerCamelCase : Dict = logging.get_logger(__name__)
@dataclass
class lowercase :
'''simple docstring'''
UpperCAmelCase : str = field(metadata={'help': 'The name of the task to train on: ' + ', '.join(glue_processors.keys())})
UpperCAmelCase : str = field(
metadata={'help': 'The input data dir. Should contain the .tsv files (or other data files) for the task.'})
UpperCAmelCase : int = field(
default=128 , metadata={
'help': (
'The maximum total input sequence length after tokenization. Sequences longer '
'than this will be truncated, sequences shorter will be padded.'
)
} , )
UpperCAmelCase : bool = field(
default=SCREAMING_SNAKE_CASE_ , metadata={'help': 'Overwrite the cached training and evaluation sets'})
def lowerCamelCase_ ( self : Union[str, Any] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Tuple = self.task_name.lower()
class lowercase ( SCREAMING_SNAKE_CASE_):
'''simple docstring'''
UpperCAmelCase : List[Any] = 'train'
UpperCAmelCase : Optional[Any] = 'dev'
UpperCAmelCase : Optional[int] = 'test'
class lowercase ( SCREAMING_SNAKE_CASE_):
'''simple docstring'''
UpperCAmelCase : GlueDataTrainingArguments
UpperCAmelCase : str
UpperCAmelCase : List[InputFeatures]
def __init__( self : Union[str, Any] , snake_case : GlueDataTrainingArguments , snake_case : PreTrainedTokenizerBase , snake_case : Optional[int] = None , snake_case : Union[str, Split] = Split.train , snake_case : Optional[str] = None , ):
'''simple docstring'''
warnings.warn(
'This dataset will be removed from the library soon, preprocessing should be handled with the 🤗 Datasets '
'library. You can have a look at this example script for pointers: '
'https://github.com/huggingface/transformers/blob/main/examples/pytorch/text-classification/run_glue.py' , snake_case , )
SCREAMING_SNAKE_CASE : Tuple = args
SCREAMING_SNAKE_CASE : int = glue_processors[args.task_name]()
SCREAMING_SNAKE_CASE : str = glue_output_modes[args.task_name]
if isinstance(snake_case , snake_case ):
try:
SCREAMING_SNAKE_CASE : Any = Split[mode]
except KeyError:
raise KeyError('mode is not a valid split name' )
# Load data features from cache or dataset file
SCREAMING_SNAKE_CASE : Optional[int] = os.path.join(
cache_dir if cache_dir is not None else args.data_dir , f'''cached_{mode.value}_{tokenizer.__class__.__name__}_{args.max_seq_length}_{args.task_name}''' , )
SCREAMING_SNAKE_CASE : Any = self.processor.get_labels()
if args.task_name in ["mnli", "mnli-mm"] and tokenizer.__class__.__name__ in (
"RobertaTokenizer",
"RobertaTokenizerFast",
"XLMRobertaTokenizer",
"BartTokenizer",
"BartTokenizerFast",
):
# HACK(label indices are swapped in RoBERTa pretrained model)
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : Any = label_list[2], label_list[1]
SCREAMING_SNAKE_CASE : List[Any] = label_list
# Make sure only the first process in distributed training processes the dataset,
# and the others will use the cache.
SCREAMING_SNAKE_CASE : Union[str, Any] = cached_features_file + '.lock'
with FileLock(snake_case ):
if os.path.exists(snake_case ) and not args.overwrite_cache:
SCREAMING_SNAKE_CASE : Optional[int] = time.time()
SCREAMING_SNAKE_CASE : int = torch.load(snake_case )
logger.info(
f'''Loading features from cached file {cached_features_file} [took %.3f s]''' , time.time() - start )
else:
logger.info(f'''Creating features from dataset file at {args.data_dir}''' )
if mode == Split.dev:
SCREAMING_SNAKE_CASE : str = self.processor.get_dev_examples(args.data_dir )
elif mode == Split.test:
SCREAMING_SNAKE_CASE : Dict = self.processor.get_test_examples(args.data_dir )
else:
SCREAMING_SNAKE_CASE : str = self.processor.get_train_examples(args.data_dir )
if limit_length is not None:
SCREAMING_SNAKE_CASE : Union[str, Any] = examples[:limit_length]
SCREAMING_SNAKE_CASE : Optional[Any] = glue_convert_examples_to_features(
snake_case , snake_case , max_length=args.max_seq_length , label_list=snake_case , output_mode=self.output_mode , )
SCREAMING_SNAKE_CASE : Tuple = time.time()
torch.save(self.features , snake_case )
# ^ This seems to take a lot of time so I want to investigate why and how we can improve.
logger.info(
f'''Saving features into cached file {cached_features_file} [took {time.time() - start:.3f} s]''' )
def __len__( self : int ):
'''simple docstring'''
return len(self.features )
def __getitem__( self : Dict , snake_case : Optional[int] ):
'''simple docstring'''
return self.features[i]
def lowerCamelCase_ ( self : Optional[Any] ):
'''simple docstring'''
return self.label_list | 308 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
UpperCamelCase__ : Optional[Any] = {
"""configuration_bloom""": ["""BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """BloomConfig""", """BloomOnnxConfig"""],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase__ : Tuple = ["""BloomTokenizerFast"""]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase__ : Dict = [
"""BLOOM_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""BloomForCausalLM""",
"""BloomModel""",
"""BloomPreTrainedModel""",
"""BloomForSequenceClassification""",
"""BloomForTokenClassification""",
"""BloomForQuestionAnswering""",
]
if TYPE_CHECKING:
from .configuration_bloom import BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP, BloomConfig, BloomOnnxConfig
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_bloom_fast import BloomTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_bloom import (
BLOOM_PRETRAINED_MODEL_ARCHIVE_LIST,
BloomForCausalLM,
BloomForQuestionAnswering,
BloomForSequenceClassification,
BloomForTokenClassification,
BloomModel,
BloomPreTrainedModel,
)
else:
import sys
UpperCamelCase__ : Dict = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 387 |
import gc
import unittest
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DDPMScheduler,
PriorTransformer,
StableUnCLIPPipeline,
UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.stable_unclip_image_normalizer import StableUnCLIPImageNormalizer
from diffusers.utils.testing_utils import enable_full_determinism, load_numpy, require_torch_gpu, slow, torch_device
from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import (
PipelineKarrasSchedulerTesterMixin,
PipelineLatentTesterMixin,
PipelineTesterMixin,
assert_mean_pixel_difference,
)
enable_full_determinism()
class lowerCamelCase_ ( a_ , a_ , a_ , unittest.TestCase ):
SCREAMING_SNAKE_CASE_ = StableUnCLIPPipeline
SCREAMING_SNAKE_CASE_ = TEXT_TO_IMAGE_PARAMS
SCREAMING_SNAKE_CASE_ = TEXT_TO_IMAGE_BATCH_PARAMS
SCREAMING_SNAKE_CASE_ = TEXT_TO_IMAGE_IMAGE_PARAMS
SCREAMING_SNAKE_CASE_ = TEXT_TO_IMAGE_IMAGE_PARAMS
# TODO(will) Expected attn_bias.stride(1) == 0 to be true, but got false
SCREAMING_SNAKE_CASE_ = False
def SCREAMING_SNAKE_CASE_ ( self : List[Any] ):
'''simple docstring'''
a = 32
a = embedder_hidden_size
# prior components
torch.manual_seed(0 )
a = CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''' )
torch.manual_seed(0 )
a = CLIPTextModelWithProjection(
CLIPTextConfig(
bos_token_id=0 ,eos_token_id=2 ,hidden_size=__lowerCamelCase ,projection_dim=__lowerCamelCase ,intermediate_size=37 ,layer_norm_eps=1e-05 ,num_attention_heads=4 ,num_hidden_layers=5 ,pad_token_id=1 ,vocab_size=10_00 ,) )
torch.manual_seed(0 )
a = PriorTransformer(
num_attention_heads=2 ,attention_head_dim=12 ,embedding_dim=__lowerCamelCase ,num_layers=1 ,)
torch.manual_seed(0 )
a = DDPMScheduler(
variance_type='''fixed_small_log''' ,prediction_type='''sample''' ,num_train_timesteps=10_00 ,clip_sample=__lowerCamelCase ,clip_sample_range=5.0 ,beta_schedule='''squaredcos_cap_v2''' ,)
# regular denoising components
torch.manual_seed(0 )
a = StableUnCLIPImageNormalizer(embedding_dim=__lowerCamelCase )
a = DDPMScheduler(beta_schedule='''squaredcos_cap_v2''' )
torch.manual_seed(0 )
a = CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''' )
torch.manual_seed(0 )
a = CLIPTextModel(
CLIPTextConfig(
bos_token_id=0 ,eos_token_id=2 ,hidden_size=__lowerCamelCase ,projection_dim=32 ,intermediate_size=37 ,layer_norm_eps=1e-05 ,num_attention_heads=4 ,num_hidden_layers=5 ,pad_token_id=1 ,vocab_size=10_00 ,) )
torch.manual_seed(0 )
a = UNetaDConditionModel(
sample_size=32 ,in_channels=4 ,out_channels=4 ,down_block_types=('''CrossAttnDownBlock2D''', '''DownBlock2D''') ,up_block_types=('''UpBlock2D''', '''CrossAttnUpBlock2D''') ,block_out_channels=(32, 64) ,attention_head_dim=(2, 4) ,class_embed_type='''projection''' ,projection_class_embeddings_input_dim=embedder_projection_dim * 2 ,cross_attention_dim=__lowerCamelCase ,layers_per_block=1 ,upcast_attention=__lowerCamelCase ,use_linear_projection=__lowerCamelCase ,)
torch.manual_seed(0 )
a = DDIMScheduler(
beta_schedule='''scaled_linear''' ,beta_start=0.00_085 ,beta_end=0.012 ,prediction_type='''v_prediction''' ,set_alpha_to_one=__lowerCamelCase ,steps_offset=1 ,)
torch.manual_seed(0 )
a = AutoencoderKL()
a = {
# prior components
'''prior_tokenizer''': prior_tokenizer,
'''prior_text_encoder''': prior_text_encoder,
'''prior''': prior,
'''prior_scheduler''': prior_scheduler,
# image noising components
'''image_normalizer''': image_normalizer,
'''image_noising_scheduler''': image_noising_scheduler,
# regular denoising components
'''tokenizer''': tokenizer,
'''text_encoder''': text_encoder,
'''unet''': unet,
'''scheduler''': scheduler,
'''vae''': vae,
}
return components
def SCREAMING_SNAKE_CASE_ ( self : Optional[Any] ,__lowerCamelCase : Dict ,__lowerCamelCase : int=0 ):
'''simple docstring'''
if str(__lowerCamelCase ).startswith('''mps''' ):
a = torch.manual_seed(__lowerCamelCase )
else:
a = torch.Generator(device=__lowerCamelCase ).manual_seed(__lowerCamelCase )
a = {
'''prompt''': '''A painting of a squirrel eating a burger''',
'''generator''': generator,
'''num_inference_steps''': 2,
'''prior_num_inference_steps''': 2,
'''output_type''': '''numpy''',
}
return inputs
def SCREAMING_SNAKE_CASE_ ( self : Any ):
'''simple docstring'''
a = torch_device == '''cpu'''
self._test_attention_slicing_forward_pass(test_max_difference=__lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self : Tuple ):
'''simple docstring'''
a = torch_device in ['''cpu''', '''mps''']
self._test_inference_batch_single_identical(test_max_difference=__lowerCamelCase )
@slow
@require_torch_gpu
class lowerCamelCase_ ( unittest.TestCase ):
def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] ):
'''simple docstring'''
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def SCREAMING_SNAKE_CASE_ ( self : Tuple ):
'''simple docstring'''
a = load_numpy(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_l_anime_turtle_fp16.npy''' )
a = StableUnCLIPPipeline.from_pretrained('''fusing/stable-unclip-2-1-l''' ,torch_dtype=torch.floataa )
pipe.to(__lowerCamelCase )
pipe.set_progress_bar_config(disable=__lowerCamelCase )
# stable unclip will oom when integration tests are run on a V100,
# so turn on memory savings
pipe.enable_attention_slicing()
pipe.enable_sequential_cpu_offload()
a = torch.Generator(device='''cpu''' ).manual_seed(0 )
a = pipe('''anime turle''' ,generator=__lowerCamelCase ,output_type='''np''' )
a = output.images[0]
assert image.shape == (7_68, 7_68, 3)
assert_mean_pixel_difference(__lowerCamelCase ,__lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self : Tuple ):
'''simple docstring'''
torch.cuda.empty_cache()
torch.cuda.reset_max_memory_allocated()
torch.cuda.reset_peak_memory_stats()
a = StableUnCLIPPipeline.from_pretrained('''fusing/stable-unclip-2-1-l''' ,torch_dtype=torch.floataa )
a = pipe.to(__lowerCamelCase )
pipe.set_progress_bar_config(disable=__lowerCamelCase )
pipe.enable_attention_slicing()
pipe.enable_sequential_cpu_offload()
a = pipe(
'''anime turtle''' ,prior_num_inference_steps=2 ,num_inference_steps=2 ,output_type='''np''' ,)
a = torch.cuda.max_memory_allocated()
# make sure that less than 7 GB is allocated
assert mem_bytes < 7 * 10**9
| 387 | 1 |
import json
import os
from collections import Counter
import torch
import torchvision
import torchvision.transforms as transforms
from PIL import Image
from torch import nn
from torch.utils.data import Dataset
__a = {1: (1, 1), 2: (2, 1), 3: (3, 1), 4: (2, 2), 5: (5, 1), 6: (3, 2), 7: (7, 1), 8: (4, 2), 9: (3, 3)}
class UpperCamelCase__( nn.Module ):
"""simple docstring"""
def __init__( self : Optional[int] , snake_case__ : List[str] ):
"""simple docstring"""
super().__init__()
A =torchvision.models.resnetaaa(pretrained=snake_case__ )
A =list(model.children() )[:-2]
A =nn.Sequential(*snake_case__ )
A =nn.AdaptiveAvgPoolad(POOLING_BREAKDOWN[args.num_image_embeds] )
def _a ( self : Union[str, Any] , snake_case__ : Optional[Any] ):
"""simple docstring"""
A =self.pool(self.model(snake_case__ ) )
A =torch.flatten(snake_case__ , start_dim=2 )
A =out.transpose(1 , 2 ).contiguous()
return out # BxNx2048
class UpperCamelCase__( lowerCAmelCase__ ):
"""simple docstring"""
def __init__( self : str , snake_case__ : Optional[int] , snake_case__ : List[str] , snake_case__ : Tuple , snake_case__ : Any , snake_case__ : Tuple ):
"""simple docstring"""
A =[json.loads(snake_case__ ) for l in open(snake_case__ )]
A =os.path.dirname(snake_case__ )
A =tokenizer
A =labels
A =len(snake_case__ )
A =max_seq_length
A =transforms
def __len__( self : Any ):
"""simple docstring"""
return len(self.data )
def __getitem__( self : Union[str, Any] , snake_case__ : str ):
"""simple docstring"""
A =torch.LongTensor(self.tokenizer.encode(self.data[index]["text"] , add_special_tokens=snake_case__ ) )
A , A , A =sentence[0], sentence[1:-1], sentence[-1]
A =sentence[: self.max_seq_length]
A =torch.zeros(self.n_classes )
A =1
A =Image.open(os.path.join(self.data_dir , self.data[index]["img"] ) ).convert("RGB" )
A =self.transforms(snake_case__ )
return {
"image_start_token": start_token,
"image_end_token": end_token,
"sentence": sentence,
"image": image,
"label": label,
}
def _a ( self : Dict ):
"""simple docstring"""
A =Counter()
for row in self.data:
label_freqs.update(row["label"] )
return label_freqs
def UpperCamelCase_ ( a_ ) ->List[str]:
A =[len(row["sentence"] ) for row in batch]
A , A =len(a_ ), max(a_ )
A =torch.zeros(a_ , a_ , dtype=torch.long )
A =torch.zeros(a_ , a_ , dtype=torch.long )
for i_batch, (input_row, length) in enumerate(zip(a_ , a_ ) ):
A =input_row["sentence"]
A =1
A =torch.stack([row["image"] for row in batch] )
A =torch.stack([row["label"] for row in batch] )
A =torch.stack([row["image_start_token"] for row in batch] )
A =torch.stack([row["image_end_token"] for row in batch] )
return text_tensor, mask_tensor, img_tensor, img_start_token, img_end_token, tgt_tensor
def UpperCamelCase_ ( ) ->Optional[int]:
return [
"Crime",
"Drama",
"Thriller",
"Action",
"Comedy",
"Romance",
"Documentary",
"Short",
"Mystery",
"History",
"Family",
"Adventure",
"Fantasy",
"Sci-Fi",
"Western",
"Horror",
"Sport",
"War",
"Music",
"Musical",
"Animation",
"Biography",
"Film-Noir",
]
def UpperCamelCase_ ( ) ->Optional[Any]:
return transforms.Compose(
[
transforms.Resize(256 ),
transforms.CenterCrop(224 ),
transforms.ToTensor(),
transforms.Normalize(
mean=[0.46777044, 0.44531429, 0.40661017] , std=[0.12221994, 0.12145835, 0.14380469] , ),
] )
| 689 |
import collections
import os
from typing import List, Optional, Tuple
from transformers.utils import is_jieba_available, requires_backends
if is_jieba_available():
import jieba
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
__a = logging.get_logger(__name__)
__a = {"""vocab_file""": """vocab.txt"""}
__a = {
"""vocab_file""": {
"""openbmb/cpm-ant-10b""": """https://huggingface.co/openbmb/cpm-ant-10b/blob/main/vocab.txt""",
},
}
__a = {
"""openbmb/cpm-ant-10b""": 1_0_2_4,
}
def UpperCamelCase_ ( a_ ) ->List[Any]:
A =collections.OrderedDict()
with open(a_ , "r" , encoding="utf-8" ) as reader:
A =reader.readlines()
for index, token in enumerate(a_ ):
A =token.rstrip("\n" )
A =index
return vocab
class UpperCamelCase__( lowerCAmelCase__ ):
"""simple docstring"""
def __init__( self : Tuple , snake_case__ : int , snake_case__ : int="<unk>" , snake_case__ : Optional[Any]=2_00 ):
"""simple docstring"""
A =vocab
A =unk_token
A =max_input_chars_per_word
def _a ( self : Optional[Any] , snake_case__ : Tuple ):
"""simple docstring"""
A =list(snake_case__ )
if len(snake_case__ ) > self.max_input_chars_per_word:
return [self.unk_token]
A =0
A =[]
while start < len(snake_case__ ):
A =len(snake_case__ )
A =None
while start < end:
A ="".join(chars[start:end] )
if substr in self.vocab:
A =substr
break
end -= 1
if cur_substr is None:
sub_tokens.append(self.unk_token )
start += 1
else:
sub_tokens.append(snake_case__ )
A =end
return sub_tokens
class UpperCamelCase__( lowerCAmelCase__ ):
"""simple docstring"""
_A = VOCAB_FILES_NAMES
_A = PRETRAINED_VOCAB_FILES_MAP
_A = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
_A = ["input_ids", "attention_mask"]
_A = False
def __init__( self : Dict , snake_case__ : Union[str, Any] , snake_case__ : Any="<d>" , snake_case__ : Optional[int]="</d>" , snake_case__ : Optional[int]="<s>" , snake_case__ : Union[str, Any]="</s>" , snake_case__ : List[str]="<pad>" , snake_case__ : Any="<unk>" , snake_case__ : List[str]="</n>" , snake_case__ : Any="</_>" , snake_case__ : List[str]="left" , **snake_case__ : Optional[int] , ):
"""simple docstring"""
requires_backends(self , ["jieba"] )
super().__init__(
bod_token=snake_case__ , eod_token=snake_case__ , bos_token=snake_case__ , eos_token=snake_case__ , pad_token=snake_case__ , unk_token=snake_case__ , line_token=snake_case__ , space_token=snake_case__ , padding_side=snake_case__ , **snake_case__ , )
A =bod_token
A =eod_token
A =load_vocab(snake_case__ )
A =self.encoder[space_token]
A =self.encoder[line_token]
del self.encoder[space_token]
del self.encoder[line_token]
A =collections.OrderedDict(sorted(self.encoder.items() , key=lambda snake_case__ : x[1] ) )
A ={v: k for k, v in self.encoder.items()}
A =WordpieceTokenizer(vocab=self.encoder , unk_token=self.unk_token )
@property
def _a ( self : Dict ):
"""simple docstring"""
return self.encoder[self.bod_token]
@property
def _a ( self : List[str] ):
"""simple docstring"""
return self.encoder[self.eod_token]
@property
def _a ( self : Any ):
"""simple docstring"""
return self.encoder["\n"]
@property
def _a ( self : List[str] ):
"""simple docstring"""
return len(self.encoder )
def _a ( self : Tuple ):
"""simple docstring"""
return dict(self.encoder , **self.added_tokens_encoder )
def _a ( self : Tuple , snake_case__ : int ):
"""simple docstring"""
A =[]
for x in jieba.cut(snake_case__ , cut_all=snake_case__ ):
output_tokens.extend(self.wordpiece_tokenizer.tokenize(snake_case__ ) )
return output_tokens
def _a ( self : List[Any] , snake_case__ : List[Any] , **snake_case__ : str ):
"""simple docstring"""
A =[i for i in token_ids if i >= 0]
A =[
x for x in token_ids if x != self.pad_token_id and x != self.eos_token_id and x != self.bos_token_id
]
return super()._decode(snake_case__ , **snake_case__ )
def _a ( self : List[Any] , snake_case__ : int ):
"""simple docstring"""
return token in self.encoder
def _a ( self : Optional[Any] , snake_case__ : List[str] ):
"""simple docstring"""
return "".join(snake_case__ )
def _a ( self : List[Any] , snake_case__ : Optional[Any] ):
"""simple docstring"""
return self.encoder.get(snake_case__ , self.encoder.get(self.unk_token ) )
def _a ( self : Dict , snake_case__ : Optional[int] ):
"""simple docstring"""
return self.decoder.get(snake_case__ , self.unk_token )
def _a ( self : Optional[int] , snake_case__ : str , snake_case__ : Optional[str] = None ):
"""simple docstring"""
if os.path.isdir(snake_case__ ):
A =os.path.join(
snake_case__ , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )
else:
A =(filename_prefix + "-" if filename_prefix else "") + save_directory
A =0
if " " in self.encoder:
A =self.encoder[" "]
del self.encoder[" "]
if "\n" in self.encoder:
A =self.encoder["\n"]
del self.encoder["\n"]
A =collections.OrderedDict(sorted(self.encoder.items() , key=lambda snake_case__ : x[1] ) )
with open(snake_case__ , "w" , encoding="utf-8" ) as writer:
for token, token_index in self.encoder.items():
if index != token_index:
logger.warning(
f'''Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive.'''
" Please check that the vocabulary is not corrupted!" )
A =token_index
writer.write(token + "\n" )
index += 1
return (vocab_file,)
def _a ( self : Any , snake_case__ : List[int] , snake_case__ : List[int] = None ):
"""simple docstring"""
if token_ids_a is None:
return [self.bos_token_id] + token_ids_a
return [self.bos_token_id] + token_ids_a + [self.bos_token_id] + token_ids_a
def _a ( self : Optional[int] , snake_case__ : List[int] , snake_case__ : Optional[List[int]] = None , snake_case__ : bool = False ):
"""simple docstring"""
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=snake_case__ , token_ids_a=snake_case__ , already_has_special_tokens=snake_case__ )
if token_ids_a is not None:
return [1] + ([0] * len(snake_case__ )) + [1] + ([0] * len(snake_case__ ))
return [1] + ([0] * len(snake_case__ ))
| 689 | 1 |
import warnings
from ...utils import logging
from .image_processing_clip import CLIPImageProcessor
UpperCamelCase = logging.get_logger(__name__)
class lowerCAmelCase_ ( __snake_case ):
def __init__( self , *_lowerCAmelCase , **_lowerCAmelCase ):
warnings.warn(
'The class CLIPFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'
' use CLIPImageProcessor instead.' , _lowerCAmelCase , )
super().__init__(*_lowerCAmelCase , **_lowerCAmelCase )
| 66 |
# Usage:
# ./gen-card-facebook-wmt19.py
import os
from pathlib import Path
def __snake_case ( __UpperCamelCase : str ,__UpperCamelCase : str ,__UpperCamelCase : List[str] ):
"""simple docstring"""
A_ = {
"en": "Machine learning is great, isn't it?",
"ru": "Машинное обучение - это здорово, не так ли?",
"de": "Maschinelles Lernen ist großartig, oder?",
}
# BLUE scores as follows:
# "pair": [fairseq, transformers]
A_ = {
"ru-en": ["[41.3](http://matrix.statmt.org/matrix/output/1907?run_id=6937)", "39.20"],
"en-ru": ["[36.4](http://matrix.statmt.org/matrix/output/1914?run_id=6724)", "33.47"],
"en-de": ["[43.1](http://matrix.statmt.org/matrix/output/1909?run_id=6862)", "42.83"],
"de-en": ["[42.3](http://matrix.statmt.org/matrix/output/1902?run_id=6750)", "41.35"],
}
A_ = f'''{src_lang}-{tgt_lang}'''
A_ = f'''
---
language:
- {src_lang}
- {tgt_lang}
thumbnail:
tags:
- translation
- wmt19
- facebook
license: apache-2.0
datasets:
- wmt19
metrics:
- bleu
---
# FSMT
## Model description
This is a ported version of [fairseq wmt19 transformer](https://github.com/pytorch/fairseq/blob/master/examples/wmt19/README.md) for {src_lang}-{tgt_lang}.
For more details, please see, [Facebook FAIR\'s WMT19 News Translation Task Submission](https://arxiv.org/abs/1907.06616).
The abbreviation FSMT stands for FairSeqMachineTranslation
All four models are available:
* [wmt19-en-ru](https://huggingface.co/facebook/wmt19-en-ru)
* [wmt19-ru-en](https://huggingface.co/facebook/wmt19-ru-en)
* [wmt19-en-de](https://huggingface.co/facebook/wmt19-en-de)
* [wmt19-de-en](https://huggingface.co/facebook/wmt19-de-en)
## Intended uses & limitations
#### How to use
```python
from transformers import FSMTForConditionalGeneration, FSMTTokenizer
mname = "facebook/wmt19-{src_lang}-{tgt_lang}"
tokenizer = FSMTTokenizer.from_pretrained(mname)
model = FSMTForConditionalGeneration.from_pretrained(mname)
input = "{texts[src_lang]}"
input_ids = tokenizer.encode(input, return_tensors="pt")
outputs = model.generate(input_ids)
decoded = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(decoded) # {texts[tgt_lang]}
```
#### Limitations and bias
- The original (and this ported model) doesn\'t seem to handle well inputs with repeated sub-phrases, [content gets truncated](https://discuss.huggingface.co/t/issues-with-translating-inputs-containing-repeated-phrases/981)
## Training data
Pretrained weights were left identical to the original model released by fairseq. For more details, please, see the [paper](https://arxiv.org/abs/1907.06616).
## Eval results
pair | fairseq | transformers
-------|---------|----------
{pair} | {scores[pair][0]} | {scores[pair][1]}
The score is slightly below the score reported by `fairseq`, since `transformers`` currently doesn\'t support:
- model ensemble, therefore the best performing checkpoint was ported (``model4.pt``).
- re-ranking
The score was calculated using this code:
```bash
git clone https://github.com/huggingface/transformers
cd transformers
export PAIR={pair}
export DATA_DIR=data/$PAIR
export SAVE_DIR=data/$PAIR
export BS=8
export NUM_BEAMS=15
mkdir -p $DATA_DIR
sacrebleu -t wmt19 -l $PAIR --echo src > $DATA_DIR/val.source
sacrebleu -t wmt19 -l $PAIR --echo ref > $DATA_DIR/val.target
echo $PAIR
PYTHONPATH="src:examples/seq2seq" python examples/seq2seq/run_eval.py facebook/wmt19-$PAIR $DATA_DIR/val.source $SAVE_DIR/test_translations.txt --reference_path $DATA_DIR/val.target --score_path $SAVE_DIR/test_bleu.json --bs $BS --task translation --num_beams $NUM_BEAMS
```
note: fairseq reports using a beam of 50, so you should get a slightly higher score if re-run with `--num_beams 50`.
## Data Sources
- [training, etc.](http://www.statmt.org/wmt19/)
- [test set](http://matrix.statmt.org/test_sets/newstest2019.tgz?1556572561)
### BibTeX entry and citation info
```bibtex
@inproceedings{{...,
year={{2020}},
title={{Facebook FAIR\'s WMT19 News Translation Task Submission}},
author={{Ng, Nathan and Yee, Kyra and Baevski, Alexei and Ott, Myle and Auli, Michael and Edunov, Sergey}},
booktitle={{Proc. of WMT}},
}}
```
## TODO
- port model ensemble (fairseq uses 4 model checkpoints)
'''
os.makedirs(__UpperCamelCase ,exist_ok=__UpperCamelCase )
A_ = os.path.join(__UpperCamelCase ,"README.md" )
print(f'''Generating {path}''' )
with open(__UpperCamelCase ,"w" ,encoding="utf-8" ) as f:
f.write(__UpperCamelCase )
# make sure we are under the root of the project
__a :Optional[Any] = Path(__file__).resolve().parent.parent.parent
__a :Optional[Any] = repo_dir / 'model_cards'
for model_name in ["wmt19-ru-en", "wmt19-en-ru", "wmt19-en-de", "wmt19-de-en"]:
__a , __a , __a :int = model_name.split('-')
__a :str = model_cards_dir / 'facebook' / model_name
write_model_card(model_card_dir, src_lang=src_lang, tgt_lang=tgt_lang) | 86 | 0 |
"""simple docstring"""
import collections.abc
from typing import Optional, Tuple, Union
import torch
import torch.utils.checkpoint
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...modeling_outputs import BaseModelOutputWithNoAttention, ImageClassifierOutputWithNoAttention
from ...modeling_utils import PreTrainedModel
from ...utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging
from .configuration_poolformer import PoolFormerConfig
a_ : Tuple = logging.get_logger(__name__)
# General docstring
a_ : List[str] = '''PoolFormerConfig'''
# Base docstring
a_ : Optional[Any] = '''sail/poolformer_s12'''
a_ : List[Any] = [1, 5_12, 7, 7]
# Image classification docstring
a_ : Any = '''sail/poolformer_s12'''
a_ : Optional[int] = '''tabby, tabby cat'''
a_ : Optional[Any] = [
'''sail/poolformer_s12''',
# See all PoolFormer models at https://huggingface.co/models?filter=poolformer
]
def UpperCAmelCase ( A__: Optional[Any] , A__: float = 0.0 , A__: bool = False ) -> Tuple:
if drop_prob == 0.0 or not training:
return input
__lowerCamelCase : Dict = 1 - drop_prob
__lowerCamelCase : List[Any] = (input.shape[0],) + (1,) * (input.ndim - 1) # work with diff dim tensors, not just 2D ConvNets
__lowerCamelCase : List[Any] = keep_prob + torch.rand(A__ , dtype=input.dtype , device=input.device )
random_tensor.floor_() # binarize
__lowerCamelCase : Any = input.div(A__ ) * random_tensor
return output
class __lowercase( nn.Module ):
'''simple docstring'''
def __init__( self , __a = None ):
super().__init__()
__lowerCamelCase : int = drop_prob
def snake_case_ ( self , __a ):
return drop_path(__a , self.drop_prob , self.training )
def snake_case_ ( self ):
return "p={}".format(self.drop_prob )
class __lowercase( nn.Module ):
'''simple docstring'''
def __init__( self , __a , __a , __a , __a , __a , __a=None ):
super().__init__()
__lowerCamelCase : int = patch_size if isinstance(__a , collections.abc.Iterable ) else (patch_size, patch_size)
__lowerCamelCase : int = stride if isinstance(__a , collections.abc.Iterable ) else (stride, stride)
__lowerCamelCase : Optional[int] = padding if isinstance(__a , collections.abc.Iterable ) else (padding, padding)
__lowerCamelCase : Optional[Any] = nn.Convad(__a , __a , kernel_size=__a , stride=__a , padding=__a )
__lowerCamelCase : List[str] = norm_layer(__a ) if norm_layer else nn.Identity()
def snake_case_ ( self , __a ):
__lowerCamelCase : List[Any] = self.projection(__a )
__lowerCamelCase : Dict = self.norm(__a )
return embeddings
class __lowercase( nn.GroupNorm ):
'''simple docstring'''
def __init__( self , __a , **__a ):
super().__init__(1 , __a , **__a )
class __lowercase( nn.Module ):
'''simple docstring'''
def __init__( self , __a ):
super().__init__()
__lowerCamelCase : str = nn.AvgPoolad(__a , stride=1 , padding=pool_size // 2 , count_include_pad=__a )
def snake_case_ ( self , __a ):
return self.pool(__a ) - hidden_states
class __lowercase( nn.Module ):
'''simple docstring'''
def __init__( self , __a , __a , __a , __a ):
super().__init__()
__lowerCamelCase : Any = nn.Convad(__a , __a , 1 )
__lowerCamelCase : Dict = nn.Convad(__a , __a , 1 )
__lowerCamelCase : List[Any] = PoolFormerDropPath(__a )
if isinstance(config.hidden_act , __a ):
__lowerCamelCase : List[str] = ACTaFN[config.hidden_act]
else:
__lowerCamelCase : str = config.hidden_act
def snake_case_ ( self , __a ):
__lowerCamelCase : int = self.conva(__a )
__lowerCamelCase : Dict = self.act_fn(__a )
__lowerCamelCase : List[str] = self.drop(__a )
__lowerCamelCase : int = self.conva(__a )
__lowerCamelCase : str = self.drop(__a )
return hidden_states
class __lowercase( nn.Module ):
'''simple docstring'''
def __init__( self , __a , __a , __a , __a , __a , __a ):
super().__init__()
__lowerCamelCase : Tuple = PoolFormerPooling(__a )
__lowerCamelCase : Union[str, Any] = PoolFormerOutput(__a , __a , __a , __a )
__lowerCamelCase : List[Any] = PoolFormerGroupNorm(__a )
__lowerCamelCase : List[Any] = PoolFormerGroupNorm(__a )
# Useful for training neural nets
__lowerCamelCase : Any = PoolFormerDropPath(__a ) if drop_path > 0.0 else nn.Identity()
__lowerCamelCase : Tuple = config.use_layer_scale
if config.use_layer_scale:
__lowerCamelCase : List[str] = nn.Parameter(
config.layer_scale_init_value * torch.ones((__a) ) , requires_grad=__a )
__lowerCamelCase : Optional[int] = nn.Parameter(
config.layer_scale_init_value * torch.ones((__a) ) , requires_grad=__a )
def snake_case_ ( self , __a ):
if self.use_layer_scale:
__lowerCamelCase : Union[str, Any] = self.pooling(self.before_norm(__a ) )
__lowerCamelCase : Any = self.layer_scale_a.unsqueeze(-1 ).unsqueeze(-1 ) * pooling_output
# First residual connection
__lowerCamelCase : Optional[Any] = hidden_states + self.drop_path(__a )
__lowerCamelCase : Tuple = ()
__lowerCamelCase : Optional[Any] = self.output(self.after_norm(__a ) )
__lowerCamelCase : List[Any] = self.layer_scale_a.unsqueeze(-1 ).unsqueeze(-1 ) * layer_output
# Second residual connection
__lowerCamelCase : List[Any] = hidden_states + self.drop_path(__a )
__lowerCamelCase : Optional[Any] = (output,) + outputs
return outputs
else:
__lowerCamelCase : Tuple = self.drop_path(self.pooling(self.before_norm(__a ) ) )
# First residual connection
__lowerCamelCase : List[str] = pooling_output + hidden_states
__lowerCamelCase : int = ()
# Second residual connection inside the PoolFormerOutput block
__lowerCamelCase : List[str] = self.drop_path(self.output(self.after_norm(__a ) ) )
__lowerCamelCase : str = hidden_states + layer_output
__lowerCamelCase : int = (output,) + outputs
return outputs
class __lowercase( nn.Module ):
'''simple docstring'''
def __init__( self , __a ):
super().__init__()
__lowerCamelCase : int = config
# stochastic depth decay rule
__lowerCamelCase : int = [x.item() for x in torch.linspace(0 , config.drop_path_rate , sum(config.depths ) )]
# patch embeddings
__lowerCamelCase : List[str] = []
for i in range(config.num_encoder_blocks ):
embeddings.append(
PoolFormerEmbeddings(
patch_size=config.patch_sizes[i] , stride=config.strides[i] , padding=config.padding[i] , num_channels=config.num_channels if i == 0 else config.hidden_sizes[i - 1] , hidden_size=config.hidden_sizes[i] , ) )
__lowerCamelCase : Optional[int] = nn.ModuleList(__a )
# Transformer blocks
__lowerCamelCase : Any = []
__lowerCamelCase : int = 0
for i in range(config.num_encoder_blocks ):
# each block consists of layers
__lowerCamelCase : Optional[int] = []
if i != 0:
cur += config.depths[i - 1]
for j in range(config.depths[i] ):
layers.append(
PoolFormerLayer(
__a , num_channels=config.hidden_sizes[i] , pool_size=config.pool_size , hidden_size=config.hidden_sizes[i] , intermediate_size=int(config.hidden_sizes[i] * config.mlp_ratio ) , drop_path=dpr[cur + j] , ) )
blocks.append(nn.ModuleList(__a ) )
__lowerCamelCase : str = nn.ModuleList(__a )
def snake_case_ ( self , __a , __a=False , __a=True ):
__lowerCamelCase : Union[str, Any] = () if output_hidden_states else None
__lowerCamelCase : int = pixel_values
for idx, layers in enumerate(zip(self.patch_embeddings , self.block ) ):
__lowerCamelCase , __lowerCamelCase : Any = layers
# Get patch embeddings from hidden_states
__lowerCamelCase : Any = embedding_layer(__a )
# Send the embeddings through the blocks
for _, blk in enumerate(__a ):
__lowerCamelCase : Optional[int] = blk(__a )
__lowerCamelCase : Tuple = layer_outputs[0]
if output_hidden_states:
__lowerCamelCase : Union[str, Any] = all_hidden_states + (hidden_states,)
if not return_dict:
return tuple(v for v in [hidden_states, all_hidden_states] if v is not None )
return BaseModelOutputWithNoAttention(last_hidden_state=__a , hidden_states=__a )
class __lowercase( lowercase__ ):
'''simple docstring'''
__a : Tuple = PoolFormerConfig
__a : Tuple = 'poolformer'
__a : Optional[int] = 'pixel_values'
__a : Optional[Any] = True
def snake_case_ ( self , __a ):
if isinstance(__a , (nn.Linear, nn.Convad) ):
module.weight.data.normal_(mean=0.0 , std=self.config.initializer_range )
if module.bias is not None:
module.bias.data.zero_()
elif isinstance(__a , nn.LayerNorm ):
module.bias.data.zero_()
module.weight.data.fill_(1.0 )
def snake_case_ ( self , __a , __a=False ):
if isinstance(__a , __a ):
__lowerCamelCase : Union[str, Any] = value
a_ : Union[str, Any] = R'''
This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use
it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.
Parameters:
config ([`PoolFormerConfig`]): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
'''
a_ : List[str] = R'''
Args:
pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See
[`PoolFormerImageProcessor.__call__`] for details.
'''
@add_start_docstrings(
'The bare PoolFormer Model transformer outputting raw hidden-states without any specific head on top.' , lowercase__ , )
class __lowercase( lowercase__ ):
'''simple docstring'''
def __init__( self , __a ):
super().__init__(__a )
__lowerCamelCase : Optional[Any] = config
__lowerCamelCase : Any = PoolFormerEncoder(__a )
# Initialize weights and apply final processing
self.post_init()
def snake_case_ ( self ):
return self.embeddings.patch_embeddings
@add_start_docstrings_to_model_forward(__a )
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC , output_type=__a , config_class=_CONFIG_FOR_DOC , modality='vision' , expected_output=_EXPECTED_OUTPUT_SHAPE , )
def snake_case_ ( self , __a = None , __a = None , __a = None , ):
__lowerCamelCase : Union[str, Any] = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
__lowerCamelCase : Dict = return_dict if return_dict is not None else self.config.use_return_dict
if pixel_values is None:
raise ValueError('You have to specify pixel_values' )
__lowerCamelCase : Any = self.encoder(
__a , output_hidden_states=__a , return_dict=__a , )
__lowerCamelCase : int = encoder_outputs[0]
if not return_dict:
return (sequence_output, None) + encoder_outputs[1:]
return BaseModelOutputWithNoAttention(
last_hidden_state=__a , hidden_states=encoder_outputs.hidden_states , )
class __lowercase( nn.Module ):
'''simple docstring'''
def __init__( self , __a ):
super().__init__()
__lowerCamelCase : Optional[Any] = nn.Linear(config.hidden_size , config.hidden_size )
def snake_case_ ( self , __a ):
__lowerCamelCase : List[Any] = self.dense(__a )
return output
@add_start_docstrings(
'\n PoolFormer Model transformer with an image classification head on top\n ' , lowercase__ , )
class __lowercase( lowercase__ ):
'''simple docstring'''
def __init__( self , __a ):
super().__init__(__a )
__lowerCamelCase : str = config.num_labels
__lowerCamelCase : Optional[Any] = PoolFormerModel(__a )
# Final norm
__lowerCamelCase : str = PoolFormerGroupNorm(config.hidden_sizes[-1] )
# Classifier head
__lowerCamelCase : Optional[Any] = (
nn.Linear(config.hidden_sizes[-1] , config.num_labels ) if config.num_labels > 0 else nn.Identity()
)
# Initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(__a )
@add_code_sample_docstrings(
checkpoint=_IMAGE_CLASS_CHECKPOINT , output_type=__a , config_class=_CONFIG_FOR_DOC , expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT , )
def snake_case_ ( self , __a = None , __a = None , __a = None , __a = None , ):
__lowerCamelCase : Any = return_dict if return_dict is not None else self.config.use_return_dict
__lowerCamelCase : Tuple = self.poolformer(
__a , output_hidden_states=__a , return_dict=__a , )
__lowerCamelCase : int = outputs[0]
__lowerCamelCase : Optional[int] = self.classifier(self.norm(__a ).mean([-2, -1] ) )
__lowerCamelCase : Union[str, Any] = None
if labels is not None:
if self.config.problem_type is None:
if self.num_labels == 1:
__lowerCamelCase : Any = 'regression'
elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
__lowerCamelCase : Any = 'single_label_classification'
else:
__lowerCamelCase : Optional[Any] = 'multi_label_classification'
if self.config.problem_type == "regression":
__lowerCamelCase : int = MSELoss()
if self.num_labels == 1:
__lowerCamelCase : Optional[Any] = loss_fct(logits.squeeze() , labels.squeeze() )
else:
__lowerCamelCase : Optional[Any] = loss_fct(__a , __a )
elif self.config.problem_type == "single_label_classification":
__lowerCamelCase : Tuple = CrossEntropyLoss()
__lowerCamelCase : int = loss_fct(logits.view(-1 , self.num_labels ) , labels.view(-1 ) )
elif self.config.problem_type == "multi_label_classification":
__lowerCamelCase : List[Any] = BCEWithLogitsLoss()
__lowerCamelCase : Optional[Any] = loss_fct(__a , __a )
if not return_dict:
__lowerCamelCase : Optional[Any] = (logits,) + outputs[2:]
return ((loss,) + output) if loss is not None else output
return ImageClassifierOutputWithNoAttention(loss=__a , logits=__a , hidden_states=outputs.hidden_states )
| 263 |
"""simple docstring"""
import unittest
from transformers import DebertaVaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
DebertaVaForMaskedLM,
DebertaVaForMultipleChoice,
DebertaVaForQuestionAnswering,
DebertaVaForSequenceClassification,
DebertaVaForTokenClassification,
DebertaVaModel,
)
from transformers.models.deberta_va.modeling_deberta_va import DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST
class __lowercase( lowercase__ ):
'''simple docstring'''
def __init__( self , __a , __a=13 , __a=7 , __a=True , __a=True , __a=True , __a=True , __a=99 , __a=32 , __a=5 , __a=4 , __a=37 , __a="gelu" , __a=0.1 , __a=0.1 , __a=512 , __a=16 , __a=2 , __a=0.02 , __a=False , __a=True , __a="None" , __a=3 , __a=4 , __a=None , ):
__lowerCamelCase : List[str] = parent
__lowerCamelCase : Dict = batch_size
__lowerCamelCase : str = seq_length
__lowerCamelCase : Optional[int] = is_training
__lowerCamelCase : Dict = use_input_mask
__lowerCamelCase : Dict = use_token_type_ids
__lowerCamelCase : Dict = use_labels
__lowerCamelCase : Optional[Any] = vocab_size
__lowerCamelCase : Any = hidden_size
__lowerCamelCase : List[Any] = num_hidden_layers
__lowerCamelCase : Tuple = num_attention_heads
__lowerCamelCase : Any = intermediate_size
__lowerCamelCase : Optional[Any] = hidden_act
__lowerCamelCase : Any = hidden_dropout_prob
__lowerCamelCase : Optional[Any] = attention_probs_dropout_prob
__lowerCamelCase : List[Any] = max_position_embeddings
__lowerCamelCase : Optional[Any] = type_vocab_size
__lowerCamelCase : Dict = type_sequence_label_size
__lowerCamelCase : Any = initializer_range
__lowerCamelCase : Union[str, Any] = num_labels
__lowerCamelCase : Tuple = num_choices
__lowerCamelCase : str = relative_attention
__lowerCamelCase : Optional[int] = position_biased_input
__lowerCamelCase : int = pos_att_type
__lowerCamelCase : str = scope
def snake_case_ ( self ):
__lowerCamelCase : Union[str, Any] = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
__lowerCamelCase : int = None
if self.use_input_mask:
__lowerCamelCase : int = ids_tensor([self.batch_size, self.seq_length] , vocab_size=2 )
__lowerCamelCase : Tuple = None
if self.use_token_type_ids:
__lowerCamelCase : Any = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
__lowerCamelCase : Optional[Any] = None
__lowerCamelCase : Optional[Any] = None
__lowerCamelCase : int = None
if self.use_labels:
__lowerCamelCase : Optional[int] = ids_tensor([self.batch_size] , self.type_sequence_label_size )
__lowerCamelCase : Optional[int] = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
__lowerCamelCase : List[Any] = ids_tensor([self.batch_size] , self.num_choices )
__lowerCamelCase : Optional[Any] = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def snake_case_ ( self ):
return DebertaVaConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , initializer_range=self.initializer_range , relative_attention=self.relative_attention , position_biased_input=self.position_biased_input , pos_att_type=self.pos_att_type , )
def snake_case_ ( self , __a ):
self.parent.assertListEqual(list(result.loss.size() ) , [] )
def snake_case_ ( self , __a , __a , __a , __a , __a , __a , __a ):
__lowerCamelCase : int = DebertaVaModel(config=__a )
model.to(__a )
model.eval()
__lowerCamelCase : str = model(__a , attention_mask=__a , token_type_ids=__a )[0]
__lowerCamelCase : str = model(__a , token_type_ids=__a )[0]
__lowerCamelCase : Optional[Any] = model(__a )[0]
self.parent.assertListEqual(list(sequence_output.size() ) , [self.batch_size, self.seq_length, self.hidden_size] )
def snake_case_ ( self , __a , __a , __a , __a , __a , __a , __a ):
__lowerCamelCase : List[str] = DebertaVaForMaskedLM(config=__a )
model.to(__a )
model.eval()
__lowerCamelCase : Union[str, Any] = model(__a , attention_mask=__a , token_type_ids=__a , labels=__a )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def snake_case_ ( self , __a , __a , __a , __a , __a , __a , __a ):
__lowerCamelCase : Optional[int] = self.num_labels
__lowerCamelCase : List[Any] = DebertaVaForSequenceClassification(__a )
model.to(__a )
model.eval()
__lowerCamelCase : Optional[Any] = model(__a , attention_mask=__a , token_type_ids=__a , labels=__a )
self.parent.assertListEqual(list(result.logits.size() ) , [self.batch_size, self.num_labels] )
self.check_loss_output(__a )
def snake_case_ ( self , __a , __a , __a , __a , __a , __a , __a ):
__lowerCamelCase : int = self.num_labels
__lowerCamelCase : Dict = DebertaVaForTokenClassification(config=__a )
model.to(__a )
model.eval()
__lowerCamelCase : int = model(__a , attention_mask=__a , token_type_ids=__a , labels=__a )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def snake_case_ ( self , __a , __a , __a , __a , __a , __a , __a ):
__lowerCamelCase : Optional[Any] = DebertaVaForQuestionAnswering(config=__a )
model.to(__a )
model.eval()
__lowerCamelCase : Any = model(
__a , attention_mask=__a , token_type_ids=__a , start_positions=__a , end_positions=__a , )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def snake_case_ ( self , __a , __a , __a , __a , __a , __a , __a ):
__lowerCamelCase : Any = DebertaVaForMultipleChoice(config=__a )
model.to(__a )
model.eval()
__lowerCamelCase : Union[str, Any] = input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
__lowerCamelCase : Optional[int] = token_type_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
__lowerCamelCase : int = input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
__lowerCamelCase : List[Any] = model(
__a , attention_mask=__a , token_type_ids=__a , labels=__a , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def snake_case_ ( self ):
__lowerCamelCase : Any = self.prepare_config_and_inputs()
(
(
__lowerCamelCase
) , (
__lowerCamelCase
) , (
__lowerCamelCase
) , (
__lowerCamelCase
) , (
__lowerCamelCase
) , (
__lowerCamelCase
) , (
__lowerCamelCase
) ,
) : List[str] = config_and_inputs
__lowerCamelCase : int = {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
return config, inputs_dict
@require_torch
class __lowercase( lowercase__ , lowercase__ , unittest.TestCase ):
'''simple docstring'''
__a : Dict = (
(
DebertaVaModel,
DebertaVaForMaskedLM,
DebertaVaForSequenceClassification,
DebertaVaForTokenClassification,
DebertaVaForQuestionAnswering,
DebertaVaForMultipleChoice,
)
if is_torch_available()
else ()
)
__a : Tuple = (
{
'feature-extraction': DebertaVaModel,
'fill-mask': DebertaVaForMaskedLM,
'question-answering': DebertaVaForQuestionAnswering,
'text-classification': DebertaVaForSequenceClassification,
'token-classification': DebertaVaForTokenClassification,
'zero-shot': DebertaVaForSequenceClassification,
}
if is_torch_available()
else {}
)
__a : str = True
__a : Dict = False
__a : Tuple = False
__a : Optional[Any] = False
__a : List[Any] = False
def snake_case_ ( self ):
__lowerCamelCase : List[str] = DebertaVaModelTester(self )
__lowerCamelCase : Tuple = ConfigTester(self , config_class=__a , hidden_size=37 )
def snake_case_ ( self ):
self.config_tester.run_common_tests()
def snake_case_ ( self ):
__lowerCamelCase : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_deberta_model(*__a )
def snake_case_ ( self ):
__lowerCamelCase : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_deberta_for_sequence_classification(*__a )
def snake_case_ ( self ):
__lowerCamelCase : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_deberta_for_masked_lm(*__a )
def snake_case_ ( self ):
__lowerCamelCase : str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_deberta_for_question_answering(*__a )
def snake_case_ ( self ):
__lowerCamelCase : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_deberta_for_token_classification(*__a )
def snake_case_ ( self ):
__lowerCamelCase : Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_deberta_for_multiple_choice(*__a )
@slow
def snake_case_ ( self ):
for model_name in DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__lowerCamelCase : Tuple = DebertaVaModel.from_pretrained(__a )
self.assertIsNotNone(__a )
@require_torch
@require_sentencepiece
@require_tokenizers
class __lowercase( unittest.TestCase ):
'''simple docstring'''
@unittest.skip(reason='Model not available yet' )
def snake_case_ ( self ):
pass
@slow
def snake_case_ ( self ):
__lowerCamelCase : Any = DebertaVaModel.from_pretrained('microsoft/deberta-v2-xlarge' )
__lowerCamelCase : Any = torch.tensor([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]] )
__lowerCamelCase : Any = torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]] )
with torch.no_grad():
__lowerCamelCase : Union[str, Any] = model(__a , attention_mask=__a )[0]
# compare the actual values for a slice.
__lowerCamelCase : str = torch.tensor(
[[[0.2_356, 0.1_948, 0.0_369], [-0.1_063, 0.3_586, -0.5_152], [-0.6_399, -0.0_259, -0.2_525]]] )
self.assertTrue(torch.allclose(output[:, 1:4, 1:4] , __a , atol=1E-4 ) , f'''{output[:, 1:4, 1:4]}''' )
| 263 | 1 |
'''simple docstring'''
import os
import tempfile
import unittest
from transformers.models.marian.convert_marian_tatoeba_to_pytorch import DEFAULT_REPO, TatoebaConverter
from transformers.testing_utils import slow
from transformers.utils import cached_property
@unittest.skipUnless(os.path.exists(SCREAMING_SNAKE_CASE__ ) , """Tatoeba directory does not exist.""" )
class UpperCAmelCase_ ( unittest.TestCase ):
"""simple docstring"""
@cached_property
def _lowercase ( self ):
snake_case_ = tempfile.mkdtemp()
return TatoebaConverter(save_dir=UpperCAmelCase_ )
@slow
def _lowercase ( self ):
self.resolver.convert_models(["heb-eng"] )
@slow
def _lowercase ( self ):
snake_case_ , snake_case_ = self.resolver.write_model_card("opus-mt-he-en" , dry_run=UpperCAmelCase_ )
assert mmeta["long_pair"] == "heb-eng"
| 508 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
lowercase__ = logging.get_logger(__name__)
lowercase__ = {
'''microsoft/focalnet-tiny''': '''https://huggingface.co/microsoft/focalnet-tiny/resolve/main/config.json''',
}
class UpperCAmelCase_ ( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
snake_case = """focalnet"""
def __init__( self , UpperCAmelCase_=2_24 , UpperCAmelCase_=4 , UpperCAmelCase_=3 , UpperCAmelCase_=96 , UpperCAmelCase_=False , UpperCAmelCase_=[1_92, 3_84, 7_68, 7_68] , UpperCAmelCase_=[2, 2, 6, 2] , UpperCAmelCase_=[2, 2, 2, 2] , UpperCAmelCase_=[3, 3, 3, 3] , UpperCAmelCase_="gelu" , UpperCAmelCase_=4.0 , UpperCAmelCase_=0.0 , UpperCAmelCase_=0.1 , UpperCAmelCase_=False , UpperCAmelCase_=1e-4 , UpperCAmelCase_=False , UpperCAmelCase_=False , UpperCAmelCase_=False , UpperCAmelCase_=0.02 , UpperCAmelCase_=1e-5 , UpperCAmelCase_=32 , UpperCAmelCase_=None , UpperCAmelCase_=None , **UpperCAmelCase_ , ):
super().__init__(**UpperCAmelCase_ )
snake_case_ = image_size
snake_case_ = patch_size
snake_case_ = num_channels
snake_case_ = embed_dim
snake_case_ = use_conv_embed
snake_case_ = hidden_sizes
snake_case_ = depths
snake_case_ = focal_levels
snake_case_ = focal_windows
snake_case_ = hidden_act
snake_case_ = mlp_ratio
snake_case_ = hidden_dropout_prob
snake_case_ = drop_path_rate
snake_case_ = use_layerscale
snake_case_ = layerscale_value
snake_case_ = use_post_layernorm
snake_case_ = use_post_layernorm_in_modulation
snake_case_ = normalize_modulator
snake_case_ = initializer_range
snake_case_ = layer_norm_eps
snake_case_ = encoder_stride
snake_case_ = ["stem"] + [f'''stage{idx}''' for idx in range(1 , len(self.depths ) + 1 )]
snake_case_ , snake_case_ = get_aligned_output_features_output_indices(
out_features=UpperCAmelCase_ , out_indices=UpperCAmelCase_ , stage_names=self.stage_names )
| 508 | 1 |
'''simple docstring'''
from scipy.stats import spearmanr
import datasets
lowercase : List[str] = '\nThe Spearman rank-order correlation coefficient is a measure of the\nrelationship between two datasets. Like other correlation coefficients,\nthis one varies between -1 and +1 with 0 implying no correlation.\nPositive correlations imply that as data in dataset x increases, so\ndoes data in dataset y. Negative correlations imply that as x increases,\ny decreases. Correlations of -1 or +1 imply an exact monotonic relationship.\n\nUnlike the Pearson correlation, the Spearman correlation does not\nassume that both datasets are normally distributed.\n\nThe p-value roughly indicates the probability of an uncorrelated system\nproducing datasets that have a Spearman correlation at least as extreme\nas the one computed from these datasets. The p-values are not entirely\nreliable but are probably reasonable for datasets larger than 500 or so.\n'
lowercase : Tuple = '\nArgs:\n predictions (`List[float]`): Predicted labels, as returned by a model.\n references (`List[float]`): Ground truth labels.\n return_pvalue (`bool`): If `True`, returns the p-value. If `False`, returns\n only the spearmanr score. Defaults to `False`.\nReturns:\n spearmanr (`float`): Spearman correlation coefficient.\n p-value (`float`): p-value. **Note**: is only returned if `return_pvalue=True` is input.\nExamples:\n Example 1:\n >>> spearmanr_metric = datasets.load_metric("spearmanr")\n >>> results = spearmanr_metric.compute(references=[1, 2, 3, 4, 5], predictions=[10, 9, 2.5, 6, 4])\n >>> print(results)\n {\'spearmanr\': -0.7}\n\n Example 2:\n >>> spearmanr_metric = datasets.load_metric("spearmanr")\n >>> results = spearmanr_metric.compute(references=[1, 2, 3, 4, 5],\n ... predictions=[10, 9, 2.5, 6, 4],\n ... return_pvalue=True)\n >>> print(results[\'spearmanr\'])\n -0.7\n >>> print(round(results[\'spearmanr_pvalue\'], 2))\n 0.19\n'
lowercase : int = r'\\n@book{kokoska2000crc,\n title={CRC standard probability and statistics tables and formulae},\n author={Kokoska, Stephen and Zwillinger, Daniel},\n year={2000},\n publisher={Crc Press}\n}\n@article{2020SciPy-NMeth,\n author = {Virtanen, Pauli and Gommers, Ralf and Oliphant, Travis E. and\n Haberland, Matt and Reddy, Tyler and Cournapeau, David and\n Burovski, Evgeni and Peterson, Pearu and Weckesser, Warren and\n Bright, Jonathan and {van der Walt}, St{\'e}fan J. and\n Brett, Matthew and Wilson, Joshua and Millman, K. Jarrod and\n Mayorov, Nikolay and Nelson, Andrew R. J. and Jones, Eric and\n Kern, Robert and Larson, Eric and Carey, C J and\n Polat, {\.I}lhan and Feng, Yu and Moore, Eric W. and\n {VanderPlas}, Jake and Laxalde, Denis and Perktold, Josef and\n Cimrman, Robert and Henriksen, Ian and Quintero, E. A. and\n Harris, Charles R. and Archibald, Anne M. and\n Ribeiro, Ant{\^o}nio H. and Pedregosa, Fabian and\n {van Mulbregt}, Paul and {SciPy 1.0 Contributors}},\n title = {{{SciPy} 1.0: Fundamental Algorithms for Scientific\n Computing in Python}},\n journal = {Nature Methods},\n year = {2020},\n volume = {17},\n pages = {261--272},\n adsurl = {https://rdcu.be/b08Wh},\n doi = {10.1038/s41592-019-0686-2},\n}\n'
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class _lowerCAmelCase ( datasets.Metric ):
"""simple docstring"""
def __A ( self : Optional[int] ) -> List[Any]:
"""simple docstring"""
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
"predictions": datasets.Value("float" ),
"references": datasets.Value("float" ),
} ) , reference_urls=["https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.spearmanr.html"] , )
def __A ( self : Union[str, Any] , SCREAMING_SNAKE_CASE : Union[str, Any] , SCREAMING_SNAKE_CASE : Union[str, Any] , SCREAMING_SNAKE_CASE : Optional[Any]=False ) -> Any:
"""simple docstring"""
lowerCAmelCase = spearmanr(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
if return_pvalue:
return {"spearmanr": results[0], "spearmanr_pvalue": results[1]}
else:
return {"spearmanr": results[0]}
| 159 |
'''simple docstring'''
import unittest
import numpy as np
from transformers import RobertaConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_flax_available():
from transformers.models.roberta.modeling_flax_roberta import (
FlaxRobertaForCausalLM,
FlaxRobertaForMaskedLM,
FlaxRobertaForMultipleChoice,
FlaxRobertaForQuestionAnswering,
FlaxRobertaForSequenceClassification,
FlaxRobertaForTokenClassification,
FlaxRobertaModel,
)
class _lowerCAmelCase ( unittest.TestCase ):
"""simple docstring"""
def __init__( self : Tuple , SCREAMING_SNAKE_CASE : List[Any] , SCREAMING_SNAKE_CASE : List[str]=1_3 , SCREAMING_SNAKE_CASE : Optional[Any]=7 , SCREAMING_SNAKE_CASE : Any=True , SCREAMING_SNAKE_CASE : List[Any]=True , SCREAMING_SNAKE_CASE : Union[str, Any]=True , SCREAMING_SNAKE_CASE : Optional[int]=True , SCREAMING_SNAKE_CASE : Optional[int]=9_9 , SCREAMING_SNAKE_CASE : Dict=3_2 , SCREAMING_SNAKE_CASE : Dict=5 , SCREAMING_SNAKE_CASE : Optional[int]=4 , SCREAMING_SNAKE_CASE : Optional[Any]=3_7 , SCREAMING_SNAKE_CASE : int="gelu" , SCREAMING_SNAKE_CASE : Any=0.1 , SCREAMING_SNAKE_CASE : int=0.1 , SCREAMING_SNAKE_CASE : Dict=5_1_2 , SCREAMING_SNAKE_CASE : Union[str, Any]=1_6 , SCREAMING_SNAKE_CASE : Dict=2 , SCREAMING_SNAKE_CASE : List[Any]=0.0_2 , SCREAMING_SNAKE_CASE : Optional[int]=4 , ) -> Optional[int]:
"""simple docstring"""
lowerCAmelCase = parent
lowerCAmelCase = batch_size
lowerCAmelCase = seq_length
lowerCAmelCase = is_training
lowerCAmelCase = use_attention_mask
lowerCAmelCase = use_token_type_ids
lowerCAmelCase = use_labels
lowerCAmelCase = vocab_size
lowerCAmelCase = hidden_size
lowerCAmelCase = num_hidden_layers
lowerCAmelCase = num_attention_heads
lowerCAmelCase = intermediate_size
lowerCAmelCase = hidden_act
lowerCAmelCase = hidden_dropout_prob
lowerCAmelCase = attention_probs_dropout_prob
lowerCAmelCase = max_position_embeddings
lowerCAmelCase = type_vocab_size
lowerCAmelCase = type_sequence_label_size
lowerCAmelCase = initializer_range
lowerCAmelCase = num_choices
def __A ( self : List[str] ) -> List[str]:
"""simple docstring"""
lowerCAmelCase = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
lowerCAmelCase = None
if self.use_attention_mask:
lowerCAmelCase = random_attention_mask([self.batch_size, self.seq_length] )
lowerCAmelCase = None
if self.use_token_type_ids:
lowerCAmelCase = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
lowerCAmelCase = RobertaConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=SCREAMING_SNAKE_CASE , initializer_range=self.initializer_range , )
return config, input_ids, token_type_ids, attention_mask
def __A ( self : Optional[int] ) -> Dict:
"""simple docstring"""
lowerCAmelCase = self.prepare_config_and_inputs()
lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase = config_and_inputs
lowerCAmelCase = {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": attention_mask}
return config, inputs_dict
def __A ( self : List[Any] ) -> Any:
"""simple docstring"""
lowerCAmelCase = self.prepare_config_and_inputs()
lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase = config_and_inputs
lowerCAmelCase = True
lowerCAmelCase = floats_tensor([self.batch_size, self.seq_length, self.hidden_size] )
lowerCAmelCase = ids_tensor([self.batch_size, self.seq_length] , vocab_size=2 )
return (
config,
input_ids,
token_type_ids,
encoder_hidden_states,
encoder_attention_mask,
)
@require_flax
class _lowerCAmelCase ( UpperCamelCase_ , unittest.TestCase ):
"""simple docstring"""
lowerCAmelCase = True
lowerCAmelCase = (
(
FlaxRobertaModel,
FlaxRobertaForCausalLM,
FlaxRobertaForMaskedLM,
FlaxRobertaForSequenceClassification,
FlaxRobertaForTokenClassification,
FlaxRobertaForMultipleChoice,
FlaxRobertaForQuestionAnswering,
)
if is_flax_available()
else ()
)
def __A ( self : Union[str, Any] ) -> Any:
"""simple docstring"""
lowerCAmelCase = FlaxRobertaModelTester(self )
@slow
def __A ( self : str ) -> Optional[Any]:
"""simple docstring"""
for model_class_name in self.all_model_classes:
lowerCAmelCase = model_class_name.from_pretrained("roberta-base" , from_pt=SCREAMING_SNAKE_CASE )
lowerCAmelCase = model(np.ones((1, 1) ) )
self.assertIsNotNone(SCREAMING_SNAKE_CASE )
| 159 | 1 |
'''simple docstring'''
import torch
def __lowerCamelCase ( ) -> Dict:
if torch.cuda.is_available():
_a : List[Any] = torch.cuda.device_count()
else:
_a : Optional[Any] = 0
print(f"""Successfully ran on {num_gpus} GPUs""" )
if __name__ == "__main__":
main()
| 358 |
'''simple docstring'''
def __lowerCamelCase ( lowerCAmelCase_ ) -> bool:
if not all(x.isalpha() for x in string ):
raise ValueError('String must only contain alphabetic characters.' )
_a : Tuple = sorted(string.lower() )
return len(lowerCAmelCase_ ) == len(set(lowerCAmelCase_ ) )
if __name__ == "__main__":
__lowerCAmelCase = input('''Enter a string ''').strip()
__lowerCAmelCase = is_isogram(input_str)
print(f"""{input_str} is {'an' if isogram else 'not an'} isogram.""")
| 358 | 1 |
'''simple docstring'''
import argparse
import json
import logging
import os
import sys
from unittest.mock import patch
from transformers.testing_utils import TestCasePlus, get_gpu_count, slow
__magic_name__ = [
os.path.join(os.path.dirname(__file__), dirname)
for dirname in [
"""text-classification""",
"""language-modeling""",
"""summarization""",
"""token-classification""",
"""question-answering""",
]
]
sys.path.extend(SRC_DIRS)
if SRC_DIRS is not None:
import run_clm_flax
import run_flax_glue
import run_flax_ner
import run_mlm_flax
import run_qa
import run_summarization_flax
import run_ta_mlm_flax
logging.basicConfig(level=logging.DEBUG)
__magic_name__ = logging.getLogger()
def snake_case_ ( ):
'''simple docstring'''
_snake_case = argparse.ArgumentParser()
parser.add_argument("-f" )
_snake_case = parser.parse_args()
return args.f
def snake_case_ ( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__="eval" ):
'''simple docstring'''
_snake_case = os.path.join(SCREAMING_SNAKE_CASE__ , f'''{split}_results.json''' )
if os.path.exists(SCREAMING_SNAKE_CASE__ ):
with open(SCREAMING_SNAKE_CASE__ , "r" ) as f:
return json.load(SCREAMING_SNAKE_CASE__ )
raise ValueError(f'''can\'t find {path}''' )
__magic_name__ = logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
class __SCREAMING_SNAKE_CASE ( __UpperCamelCase ):
'''simple docstring'''
def UpperCamelCase( self ):
_snake_case = self.get_auto_remove_tmp_dir()
_snake_case = F'''
run_glue.py
--model_name_or_path distilbert-base-uncased
--output_dir {tmp_dir}
--train_file ./tests/fixtures/tests_samples/MRPC/train.csv
--validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv
--per_device_train_batch_size=2
--per_device_eval_batch_size=1
--learning_rate=1e-4
--eval_steps=2
--warmup_steps=2
--seed=42
--max_seq_length=128
'''.split()
with patch.object(lowerCamelCase , "argv" , lowerCamelCase ):
run_flax_glue.main()
_snake_case = get_results(lowerCamelCase )
self.assertGreaterEqual(result["eval_accuracy"] , 0.75 )
@slow
def UpperCamelCase( self ):
_snake_case = self.get_auto_remove_tmp_dir()
_snake_case = F'''
run_clm_flax.py
--model_name_or_path distilgpt2
--train_file ./tests/fixtures/sample_text.txt
--validation_file ./tests/fixtures/sample_text.txt
--do_train
--do_eval
--block_size 128
--per_device_train_batch_size 4
--per_device_eval_batch_size 4
--num_train_epochs 2
--logging_steps 2 --eval_steps 2
--output_dir {tmp_dir}
--overwrite_output_dir
'''.split()
with patch.object(lowerCamelCase , "argv" , lowerCamelCase ):
run_clm_flax.main()
_snake_case = get_results(lowerCamelCase )
self.assertLess(result["eval_perplexity"] , 100 )
@slow
def UpperCamelCase( self ):
_snake_case = self.get_auto_remove_tmp_dir()
_snake_case = F'''
run_summarization.py
--model_name_or_path t5-small
--train_file tests/fixtures/tests_samples/xsum/sample.json
--validation_file tests/fixtures/tests_samples/xsum/sample.json
--test_file tests/fixtures/tests_samples/xsum/sample.json
--output_dir {tmp_dir}
--overwrite_output_dir
--num_train_epochs=3
--warmup_steps=8
--do_train
--do_eval
--do_predict
--learning_rate=2e-4
--per_device_train_batch_size=2
--per_device_eval_batch_size=1
--predict_with_generate
'''.split()
with patch.object(lowerCamelCase , "argv" , lowerCamelCase ):
run_summarization_flax.main()
_snake_case = get_results(lowerCamelCase , split="test" )
self.assertGreaterEqual(result["test_rouge1"] , 10 )
self.assertGreaterEqual(result["test_rouge2"] , 2 )
self.assertGreaterEqual(result["test_rougeL"] , 7 )
self.assertGreaterEqual(result["test_rougeLsum"] , 7 )
@slow
def UpperCamelCase( self ):
_snake_case = self.get_auto_remove_tmp_dir()
_snake_case = F'''
run_mlm.py
--model_name_or_path distilroberta-base
--train_file ./tests/fixtures/sample_text.txt
--validation_file ./tests/fixtures/sample_text.txt
--output_dir {tmp_dir}
--overwrite_output_dir
--max_seq_length 128
--per_device_train_batch_size 4
--per_device_eval_batch_size 4
--logging_steps 2 --eval_steps 2
--do_train
--do_eval
--num_train_epochs=1
'''.split()
with patch.object(lowerCamelCase , "argv" , lowerCamelCase ):
run_mlm_flax.main()
_snake_case = get_results(lowerCamelCase )
self.assertLess(result["eval_perplexity"] , 42 )
@slow
def UpperCamelCase( self ):
_snake_case = self.get_auto_remove_tmp_dir()
_snake_case = F'''
run_t5_mlm_flax.py
--model_name_or_path t5-small
--train_file ./tests/fixtures/sample_text.txt
--validation_file ./tests/fixtures/sample_text.txt
--do_train
--do_eval
--max_seq_length 128
--per_device_train_batch_size 4
--per_device_eval_batch_size 4
--num_train_epochs 2
--logging_steps 2 --eval_steps 2
--output_dir {tmp_dir}
--overwrite_output_dir
'''.split()
with patch.object(lowerCamelCase , "argv" , lowerCamelCase ):
run_ta_mlm_flax.main()
_snake_case = get_results(lowerCamelCase )
self.assertGreaterEqual(result["eval_accuracy"] , 0.42 )
@slow
def UpperCamelCase( self ):
# with so little data distributed training needs more epochs to get the score on par with 0/1 gpu
_snake_case = 7 if get_gpu_count() > 1 else 2
_snake_case = self.get_auto_remove_tmp_dir()
_snake_case = F'''
run_flax_ner.py
--model_name_or_path bert-base-uncased
--train_file tests/fixtures/tests_samples/conll/sample.json
--validation_file tests/fixtures/tests_samples/conll/sample.json
--output_dir {tmp_dir}
--overwrite_output_dir
--do_train
--do_eval
--warmup_steps=2
--learning_rate=2e-4
--logging_steps 2 --eval_steps 2
--per_device_train_batch_size=2
--per_device_eval_batch_size=2
--num_train_epochs={epochs}
--seed 7
'''.split()
with patch.object(lowerCamelCase , "argv" , lowerCamelCase ):
run_flax_ner.main()
_snake_case = get_results(lowerCamelCase )
self.assertGreaterEqual(result["eval_accuracy"] , 0.75 )
self.assertGreaterEqual(result["eval_f1"] , 0.3 )
@slow
def UpperCamelCase( self ):
_snake_case = self.get_auto_remove_tmp_dir()
_snake_case = F'''
run_qa.py
--model_name_or_path bert-base-uncased
--version_2_with_negative
--train_file tests/fixtures/tests_samples/SQUAD/sample.json
--validation_file tests/fixtures/tests_samples/SQUAD/sample.json
--output_dir {tmp_dir}
--overwrite_output_dir
--num_train_epochs=3
--warmup_steps=2
--do_train
--do_eval
--logging_steps 2 --eval_steps 2
--learning_rate=2e-4
--per_device_train_batch_size=2
--per_device_eval_batch_size=1
'''.split()
with patch.object(lowerCamelCase , "argv" , lowerCamelCase ):
run_qa.main()
_snake_case = get_results(lowerCamelCase )
self.assertGreaterEqual(result["eval_f1"] , 30 )
self.assertGreaterEqual(result["eval_exact"] , 30 )
| 708 |
'''simple docstring'''
import argparse
from pathlib import Path
import fairseq
import torch
from fairseq.models.xmod import XMODModel as FairseqXmodModel
from packaging import version
from transformers import XmodConfig, XmodForMaskedLM, XmodForSequenceClassification
from transformers.utils import logging
if version.parse(fairseq.__version__) < version.parse("""0.12.2"""):
raise Exception("""requires fairseq >= 0.12.2""")
if version.parse(fairseq.__version__) > version.parse("""2"""):
raise Exception("""requires fairseq < v2""")
logging.set_verbosity_info()
__magic_name__ : Union[str, Any] = logging.get_logger(__name__)
__magic_name__ : Tuple = """Hello, World!"""
__magic_name__ : Union[str, Any] = """en_XX"""
def snake_case_ ( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
_snake_case = Path("data_bin" )
_snake_case = FairseqXmodModel.from_pretrained(
model_name_or_path=str(Path(SCREAMING_SNAKE_CASE__ ).parent ) , checkpoint_file=Path(SCREAMING_SNAKE_CASE__ ).name , _name="xmod_base" , arch="xmod_base" , task="multilingual_masked_lm" , data_name_or_path=str(SCREAMING_SNAKE_CASE__ ) , bpe="sentencepiece" , sentencepiece_model=str(Path(SCREAMING_SNAKE_CASE__ ).parent / "sentencepiece.bpe.model" ) , src_dict=str(data_dir / "dict.txt" ) , )
xmod.eval() # disable dropout
print(SCREAMING_SNAKE_CASE__ )
_snake_case = xmod.model.encoder.sentence_encoder
_snake_case = XmodConfig(
vocab_size=xmod_sent_encoder.embed_tokens.num_embeddings , hidden_size=xmod.cfg.model.encoder_embed_dim , num_hidden_layers=xmod.cfg.model.encoder_layers , num_attention_heads=xmod.cfg.model.encoder_attention_heads , intermediate_size=xmod.cfg.model.encoder_ffn_embed_dim , max_position_embeddings=5_14 , type_vocab_size=1 , layer_norm_eps=1E-5 , pre_norm=xmod.cfg.model.encoder_normalize_before , adapter_reduction_factor=getattr(xmod.cfg.model , "bottleneck" , 2 ) , adapter_layer_norm=xmod.cfg.model.adapter_layer_norm , adapter_reuse_layer_norm=xmod.cfg.model.adapter_reuse_layer_norm , ln_before_adapter=xmod.cfg.model.ln_before_adapter , languages=xmod.cfg.model.languages , )
if classification_head:
_snake_case = xmod.model.classification_heads["mnli"].out_proj.weight.shape[0]
print("Our X-MOD config:" , SCREAMING_SNAKE_CASE__ )
_snake_case = XmodForSequenceClassification(SCREAMING_SNAKE_CASE__ ) if classification_head else XmodForMaskedLM(SCREAMING_SNAKE_CASE__ )
model.eval()
# Now let's copy all the weights.
# Embeddings
_snake_case = xmod_sent_encoder.embed_tokens.weight
_snake_case = xmod_sent_encoder.embed_positions.weight
_snake_case = torch.zeros_like(
model.roberta.embeddings.token_type_embeddings.weight ) # just zero them out b/c xmod doesn't use them.
_snake_case = xmod_sent_encoder.layernorm_embedding.weight
_snake_case = xmod_sent_encoder.layernorm_embedding.bias
for i in range(config.num_hidden_layers ):
# Encoder: start of layer
_snake_case = model.roberta.encoder.layer[i]
_snake_case = xmod_sent_encoder.layers[i]
# self attention
_snake_case = layer.attention.self
if not (
xmod_layer.self_attn.k_proj.weight.data.shape
== xmod_layer.self_attn.q_proj.weight.data.shape
== xmod_layer.self_attn.v_proj.weight.data.shape
== torch.Size((config.hidden_size, config.hidden_size) )
):
raise AssertionError("Dimensions of self-attention weights do not match." )
_snake_case = xmod_layer.self_attn.q_proj.weight
_snake_case = xmod_layer.self_attn.q_proj.bias
_snake_case = xmod_layer.self_attn.k_proj.weight
_snake_case = xmod_layer.self_attn.k_proj.bias
_snake_case = xmod_layer.self_attn.v_proj.weight
_snake_case = xmod_layer.self_attn.v_proj.bias
# self-attention output
_snake_case = layer.attention.output
if self_output.dense.weight.shape != xmod_layer.self_attn.out_proj.weight.shape:
raise AssertionError("Dimensions of self-attention output weights do not match." )
_snake_case = xmod_layer.self_attn.out_proj.weight
_snake_case = xmod_layer.self_attn.out_proj.bias
_snake_case = xmod_layer.self_attn_layer_norm.weight
_snake_case = xmod_layer.self_attn_layer_norm.bias
# intermediate
_snake_case = layer.intermediate
if intermediate.dense.weight.shape != xmod_layer.fca.weight.shape:
raise AssertionError("Dimensions of intermediate weights do not match." )
_snake_case = xmod_layer.fca.weight
_snake_case = xmod_layer.fca.bias
# output
_snake_case = layer.output
if bert_output.dense.weight.shape != xmod_layer.fca.weight.shape:
raise AssertionError("Dimensions of feed-forward weights do not match." )
_snake_case = xmod_layer.fca.weight
_snake_case = xmod_layer.fca.bias
_snake_case = xmod_layer.final_layer_norm.weight
_snake_case = xmod_layer.final_layer_norm.bias
if bert_output.adapter_layer_norm is not None:
_snake_case = xmod_layer.adapter_layer_norm.weight
_snake_case = xmod_layer.adapter_layer_norm.bias
if sorted(bert_output.adapter_modules.keys() ) != sorted(xmod_layer.adapter_modules.keys() ):
raise AssertionError("Lists of language adapters do not match." )
for lang_code, adapter in xmod_layer.adapter_modules.items():
_snake_case = bert_output.adapter_modules[lang_code]
_snake_case = xmod_layer.adapter_modules[lang_code]
_snake_case = from_adapter.fca.weight
_snake_case = from_adapter.fca.bias
_snake_case = from_adapter.fca.weight
_snake_case = from_adapter.fca.bias
# end of layer
if xmod_sent_encoder.layer_norm is not None:
_snake_case = xmod_sent_encoder.layer_norm.weight
_snake_case = xmod_sent_encoder.layer_norm.bias
if classification_head:
_snake_case = xmod.model.classification_heads["mnli"].dense.weight
_snake_case = xmod.model.classification_heads["mnli"].dense.bias
_snake_case = xmod.model.classification_heads["mnli"].out_proj.weight
_snake_case = xmod.model.classification_heads["mnli"].out_proj.bias
else:
# LM Head
_snake_case = xmod.model.encoder.lm_head.dense.weight
_snake_case = xmod.model.encoder.lm_head.dense.bias
_snake_case = xmod.model.encoder.lm_head.layer_norm.weight
_snake_case = xmod.model.encoder.lm_head.layer_norm.bias
_snake_case = xmod.model.encoder.lm_head.weight
_snake_case = xmod.model.encoder.lm_head.bias
# Let's check that we get the same results.
_snake_case = xmod.encode(SCREAMING_SNAKE_CASE__ ).unsqueeze(0 ) # batch of size 1
model.roberta.set_default_language(SCREAMING_SNAKE_CASE__ )
_snake_case = model(SCREAMING_SNAKE_CASE__ )[0]
if classification_head:
_snake_case = xmod.model.classification_heads["mnli"](xmod.extract_features(SCREAMING_SNAKE_CASE__ ) )
else:
_snake_case = xmod.model(SCREAMING_SNAKE_CASE__ , lang_id=[SAMPLE_LANGUAGE] )[0]
print(our_output.shape , their_output.shape )
_snake_case = torch.max(torch.abs(our_output - their_output ) ).item()
print(f'''max_absolute_diff = {max_absolute_diff}''' ) # ~ 1e-7
_snake_case = torch.allclose(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , atol=1E-3 )
print("Do both models output the same tensors?" , "🔥" if success else "💩" )
if not success:
raise Exception("Something went wRoNg" )
Path(SCREAMING_SNAKE_CASE__ ).mkdir(parents=SCREAMING_SNAKE_CASE__ , exist_ok=SCREAMING_SNAKE_CASE__ )
print(f'''Saving model to {pytorch_dump_folder_path}''' )
model.save_pretrained(SCREAMING_SNAKE_CASE__ )
if __name__ == "__main__":
__magic_name__ : str = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--xmod_checkpoint_path""", default=None, type=str, required=True, help="""Path the official PyTorch dump."""
)
parser.add_argument(
"""--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
)
parser.add_argument(
"""--classification_head""", action="""store_true""", help="""Whether to convert a final classification head."""
)
__magic_name__ : List[Any] = parser.parse_args()
convert_xmod_checkpoint_to_pytorch(
args.xmod_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
)
| 368 | 0 |
'''simple docstring'''
def lowerCamelCase ( _snake_case : Any ,_snake_case : Optional[Any] ):
'''simple docstring'''
lowercase__ = (boundary[1] - boundary[0]) / steps
lowercase__ = boundary[0]
lowercase__ = boundary[1]
lowercase__ = make_points(_snake_case ,_snake_case ,_snake_case )
lowercase__ = 0.0
y += (h / 2.0) * f(_snake_case )
for i in x_i:
# print(i)
y += h * f(_snake_case )
y += (h / 2.0) * f(_snake_case )
return y
def lowerCamelCase ( _snake_case : int ,_snake_case : Union[str, Any] ,_snake_case : List[Any] ):
'''simple docstring'''
lowercase__ = a + h
while x < (b - h):
yield x
lowercase__ = x + h
def lowerCamelCase ( _snake_case : str ): # enter your function here
'''simple docstring'''
lowercase__ = (x - 0) * (x - 0)
return y
def lowerCamelCase ( ):
'''simple docstring'''
lowercase__ = 0.0 # Lower bound of integration
lowercase__ = 1.0 # Upper bound of integration
lowercase__ = 10.0 # define number of steps or resolution
lowercase__ = [a, b] # define boundary of integration
lowercase__ = method_a(_snake_case ,_snake_case )
print(f'''y = {y}''' )
if __name__ == "__main__":
main()
| 267 |
'''simple docstring'''
import json
from typing import Iterator, List, Union
from tokenizers import AddedToken, Regex, Tokenizer, decoders, normalizers, pre_tokenizers, trainers
from tokenizers.implementations.base_tokenizer import BaseTokenizer
from tokenizers.models import Unigram
from tokenizers.processors import TemplateProcessing
class snake_case (UpperCamelCase ):
def __init__( self ,UpperCAmelCase_ = "▁" ,UpperCAmelCase_ = True ,UpperCAmelCase_ = "<unk>" ,UpperCAmelCase_ = "</s>" ,UpperCAmelCase_ = "<pad>" ,) -> Optional[int]:
lowercase__ = {
"pad": {"id": 0, "token": pad_token},
"eos": {"id": 1, "token": eos_token},
"unk": {"id": 2, "token": unk_token},
}
lowercase__ = [None] * len(self.special_tokens )
for token_dict in self.special_tokens.values():
lowercase__ = token_dict["token"]
lowercase__ = Tokenizer(Unigram() )
lowercase__ = normalizers.Sequence(
[
normalizers.Nmt(),
normalizers.NFKC(),
normalizers.Replace(Regex(" {2,}" ) ," " ),
normalizers.Lowercase(),
] )
lowercase__ = pre_tokenizers.Sequence(
[
pre_tokenizers.Metaspace(replacement=UpperCAmelCase_ ,add_prefix_space=UpperCAmelCase_ ),
pre_tokenizers.Digits(individual_digits=UpperCAmelCase_ ),
pre_tokenizers.Punctuation(),
] )
lowercase__ = decoders.Metaspace(replacement=UpperCAmelCase_ ,add_prefix_space=UpperCAmelCase_ )
lowercase__ = TemplateProcessing(
single=F'''$A {self.special_tokens['eos']['token']}''' ,special_tokens=[(self.special_tokens["eos"]["token"], self.special_tokens["eos"]["id"])] ,)
lowercase__ = {
"model": "SentencePieceUnigram",
"replacement": replacement,
"add_prefix_space": add_prefix_space,
}
super().__init__(UpperCAmelCase_ ,UpperCAmelCase_ )
def _a ( self ,UpperCAmelCase_ ,UpperCAmelCase_ = 8_000 ,UpperCAmelCase_ = True ,) -> List[str]:
lowercase__ = trainers.UnigramTrainer(
vocab_size=UpperCAmelCase_ ,special_tokens=self.special_tokens_list ,show_progress=UpperCAmelCase_ ,)
if isinstance(UpperCAmelCase_ ,UpperCAmelCase_ ):
lowercase__ = [files]
self._tokenizer.train(UpperCAmelCase_ ,trainer=UpperCAmelCase_ )
self.add_unk_id()
def _a ( self ,UpperCAmelCase_ ,UpperCAmelCase_ = 8_000 ,UpperCAmelCase_ = True ,) -> Union[str, Any]:
lowercase__ = trainers.UnigramTrainer(
vocab_size=UpperCAmelCase_ ,special_tokens=self.special_tokens_list ,show_progress=UpperCAmelCase_ ,)
self._tokenizer.train_from_iterator(UpperCAmelCase_ ,trainer=UpperCAmelCase_ )
self.add_unk_id()
def _a ( self ) -> str:
lowercase__ = json.loads(self._tokenizer.to_str() )
lowercase__ = self.special_tokens["unk"]["id"]
lowercase__ = Tokenizer.from_str(json.dumps(UpperCAmelCase_ ) )
| 267 | 1 |
import os
import pytest
from transformers.dynamic_module_utils import get_imports
_SCREAMING_SNAKE_CASE : Dict = '\nimport os\n'
_SCREAMING_SNAKE_CASE : Union[str, Any] = '\ndef foo():\n import os\n return False\n'
_SCREAMING_SNAKE_CASE : Dict = '\ndef foo():\n def bar():\n if True:\n import os\n return False\n return bar()\n'
_SCREAMING_SNAKE_CASE : Dict = '\nimport os\n\ntry:\n import bar\nexcept ImportError:\n raise ValueError()\n'
_SCREAMING_SNAKE_CASE : Any = '\nimport os\n\ndef foo():\n try:\n import bar\n except ImportError:\n raise ValueError()\n'
_SCREAMING_SNAKE_CASE : Optional[int] = '\nimport os\n\ntry:\n import bar\nexcept (ImportError, AttributeError):\n raise ValueError()\n'
_SCREAMING_SNAKE_CASE : List[Any] = '\nimport os\n\ntry:\n import bar\nexcept ImportError as e:\n raise ValueError()\n'
_SCREAMING_SNAKE_CASE : Any = '\nimport os\n\ntry:\n import bar\nexcept:\n raise ValueError()\n'
_SCREAMING_SNAKE_CASE : List[Any] = '\nimport os\n\ntry:\n import bar\n import baz\nexcept ImportError:\n raise ValueError()\n'
_SCREAMING_SNAKE_CASE : List[Any] = '\nimport os\n\ntry:\n import bar\n import baz\nexcept ImportError:\n x = 1\n raise ValueError()\n'
_SCREAMING_SNAKE_CASE : Any = [
TOP_LEVEL_IMPORT,
IMPORT_IN_FUNCTION,
DEEPLY_NESTED_IMPORT,
TOP_LEVEL_TRY_IMPORT,
GENERIC_EXCEPT_IMPORT,
MULTILINE_TRY_IMPORT,
MULTILINE_BOTH_IMPORT,
MULTIPLE_EXCEPTS_IMPORT,
EXCEPT_AS_IMPORT,
TRY_IMPORT_IN_FUNCTION,
]
@pytest.mark.parametrize("case" , __magic_name__ )
def __lowerCAmelCase ( __magic_name__ , __magic_name__ ):
_lowercase: int = os.path.join(__magic_name__ , "test_file.py" )
with open(__magic_name__ , "w" ) as _tmp_file:
_tmp_file.write(__magic_name__ )
_lowercase: Optional[int] = get_imports(__magic_name__ )
assert parsed_imports == ["os"]
| 206 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_SCREAMING_SNAKE_CASE : Optional[int] = logging.get_logger(__name__)
_SCREAMING_SNAKE_CASE : str = {
'google/canine-s': 'https://huggingface.co/google/canine-s/resolve/main/config.json',
# See all CANINE models at https://huggingface.co/models?filter=canine
}
class A ( lowerCamelCase_ ):
'''simple docstring'''
lowerCamelCase : Dict = """canine"""
def __init__( self : int , _UpperCamelCase : Tuple=768 , _UpperCamelCase : List[str]=12 , _UpperCamelCase : Any=12 , _UpperCamelCase : Union[str, Any]=3_072 , _UpperCamelCase : Any="gelu" , _UpperCamelCase : str=0.1 , _UpperCamelCase : Any=0.1 , _UpperCamelCase : Tuple=16_384 , _UpperCamelCase : Tuple=16 , _UpperCamelCase : List[str]=0.0_2 , _UpperCamelCase : List[str]=1e-12 , _UpperCamelCase : Optional[Any]=0 , _UpperCamelCase : Optional[Any]=0XE000 , _UpperCamelCase : Union[str, Any]=0XE001 , _UpperCamelCase : Optional[Any]=4 , _UpperCamelCase : Union[str, Any]=4 , _UpperCamelCase : List[Any]=8 , _UpperCamelCase : Union[str, Any]=16_384 , _UpperCamelCase : List[Any]=128 , **_UpperCamelCase : Optional[int] , ):
super().__init__(pad_token_id=_UpperCamelCase , bos_token_id=_UpperCamelCase , eos_token_id=_UpperCamelCase , **_UpperCamelCase)
_lowercase: Optional[int] = max_position_embeddings
_lowercase: Union[str, Any] = hidden_size
_lowercase: Tuple = num_hidden_layers
_lowercase: str = num_attention_heads
_lowercase: Any = intermediate_size
_lowercase: Dict = hidden_act
_lowercase: str = hidden_dropout_prob
_lowercase: List[Any] = attention_probs_dropout_prob
_lowercase: Union[str, Any] = initializer_range
_lowercase: str = type_vocab_size
_lowercase: Any = layer_norm_eps
# Character config:
_lowercase: Optional[int] = downsampling_rate
_lowercase: int = upsampling_kernel_size
_lowercase: Optional[int] = num_hash_functions
_lowercase: str = num_hash_buckets
_lowercase: List[str] = local_transformer_stride
| 206 | 1 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_camembert import CamembertTokenizer
else:
__snake_case : Union[str, Any] = None
__snake_case : Dict = logging.get_logger(__name__)
__snake_case : Tuple = {'''vocab_file''': '''sentencepiece.bpe.model''', '''tokenizer_file''': '''tokenizer.json'''}
__snake_case : Optional[int] = {
'''vocab_file''': {
'''camembert-base''': '''https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model''',
},
'''tokenizer_file''': {
'''camembert-base''': '''https://huggingface.co/camembert-base/resolve/main/tokenizer.json''',
},
}
__snake_case : Optional[int] = {
'''camembert-base''': 5_12,
}
__snake_case : Dict = '''▁'''
class lowercase_ ( _A ):
a_ = VOCAB_FILES_NAMES
a_ = PRETRAINED_VOCAB_FILES_MAP
a_ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
a_ = ["""input_ids""", """attention_mask"""]
a_ = CamembertTokenizer
def __init__( self , UpperCamelCase__=None , UpperCamelCase__=None , UpperCamelCase__="<s>" , UpperCamelCase__="</s>" , UpperCamelCase__="</s>" , UpperCamelCase__="<s>" , UpperCamelCase__="<unk>" , UpperCamelCase__="<pad>" , UpperCamelCase__="<mask>" , UpperCamelCase__=["<s>NOTUSED", "</s>NOTUSED"] , **UpperCamelCase__ , ) -> Union[str, Any]:
"""simple docstring"""
UpperCAmelCase_ = AddedToken(UpperCamelCase__ , lstrip=UpperCamelCase__ , rstrip=UpperCamelCase__ ) if isinstance(UpperCamelCase__ , UpperCamelCase__ ) else mask_token
super().__init__(
UpperCamelCase__ , tokenizer_file=UpperCamelCase__ , bos_token=UpperCamelCase__ , eos_token=UpperCamelCase__ , sep_token=UpperCamelCase__ , cls_token=UpperCamelCase__ , unk_token=UpperCamelCase__ , pad_token=UpperCamelCase__ , mask_token=UpperCamelCase__ , additional_special_tokens=UpperCamelCase__ , **UpperCamelCase__ , )
UpperCAmelCase_ = vocab_file
UpperCAmelCase_ = False if not self.vocab_file else True
def lowerCamelCase_ ( self , UpperCamelCase__ , UpperCamelCase__ = None ) -> List[int]:
"""simple docstring"""
if token_ids_a is None:
return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
UpperCAmelCase_ = [self.cls_token_id]
UpperCAmelCase_ = [self.sep_token_id]
return cls + token_ids_a + sep + sep + token_ids_a + sep
def lowerCamelCase_ ( self , UpperCamelCase__ , UpperCamelCase__ = None ) -> List[int]:
"""simple docstring"""
UpperCAmelCase_ = [self.sep_token_id]
UpperCAmelCase_ = [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]
def lowerCamelCase_ ( self , UpperCamelCase__ , UpperCamelCase__ = None ) -> Tuple[str]:
"""simple docstring"""
if not self.can_save_slow_tokenizer:
raise ValueError(
"Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
"tokenizer." )
if not os.path.isdir(UpperCamelCase__ ):
logger.error(F"""Vocabulary path ({save_directory}) should be a directory""" )
return
UpperCAmelCase_ = os.path.join(
UpperCamelCase__ , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(UpperCamelCase__ ):
copyfile(self.vocab_file , UpperCamelCase__ )
return (out_vocab_file,)
| 660 | '''simple docstring'''
import argparse
import os
import torch
from diffusers import (
CMStochasticIterativeScheduler,
ConsistencyModelPipeline,
UNetaDModel,
)
__snake_case : List[Any] = {
'''sample_size''': 32,
'''in_channels''': 3,
'''out_channels''': 3,
'''layers_per_block''': 2,
'''num_class_embeds''': 10_00,
'''block_out_channels''': [32, 64],
'''attention_head_dim''': 8,
'''down_block_types''': [
'''ResnetDownsampleBlock2D''',
'''AttnDownBlock2D''',
],
'''up_block_types''': [
'''AttnUpBlock2D''',
'''ResnetUpsampleBlock2D''',
],
'''resnet_time_scale_shift''': '''scale_shift''',
'''upsample_type''': '''resnet''',
'''downsample_type''': '''resnet''',
}
__snake_case : Optional[int] = {
'''sample_size''': 64,
'''in_channels''': 3,
'''out_channels''': 3,
'''layers_per_block''': 3,
'''num_class_embeds''': 10_00,
'''block_out_channels''': [1_92, 1_92 * 2, 1_92 * 3, 1_92 * 4],
'''attention_head_dim''': 64,
'''down_block_types''': [
'''ResnetDownsampleBlock2D''',
'''AttnDownBlock2D''',
'''AttnDownBlock2D''',
'''AttnDownBlock2D''',
],
'''up_block_types''': [
'''AttnUpBlock2D''',
'''AttnUpBlock2D''',
'''AttnUpBlock2D''',
'''ResnetUpsampleBlock2D''',
],
'''resnet_time_scale_shift''': '''scale_shift''',
'''upsample_type''': '''resnet''',
'''downsample_type''': '''resnet''',
}
__snake_case : int = {
'''sample_size''': 2_56,
'''in_channels''': 3,
'''out_channels''': 3,
'''layers_per_block''': 2,
'''num_class_embeds''': None,
'''block_out_channels''': [2_56, 2_56, 2_56 * 2, 2_56 * 2, 2_56 * 4, 2_56 * 4],
'''attention_head_dim''': 64,
'''down_block_types''': [
'''ResnetDownsampleBlock2D''',
'''ResnetDownsampleBlock2D''',
'''ResnetDownsampleBlock2D''',
'''AttnDownBlock2D''',
'''AttnDownBlock2D''',
'''AttnDownBlock2D''',
],
'''up_block_types''': [
'''AttnUpBlock2D''',
'''AttnUpBlock2D''',
'''AttnUpBlock2D''',
'''ResnetUpsampleBlock2D''',
'''ResnetUpsampleBlock2D''',
'''ResnetUpsampleBlock2D''',
],
'''resnet_time_scale_shift''': '''default''',
'''upsample_type''': '''resnet''',
'''downsample_type''': '''resnet''',
}
__snake_case : Dict = {
'''num_train_timesteps''': 40,
'''sigma_min''': 0.002,
'''sigma_max''': 80.0,
}
__snake_case : Tuple = {
'''num_train_timesteps''': 2_01,
'''sigma_min''': 0.002,
'''sigma_max''': 80.0,
}
__snake_case : str = {
'''num_train_timesteps''': 1_51,
'''sigma_min''': 0.002,
'''sigma_max''': 80.0,
}
def lowerCamelCase__ ( A_ ):
if isinstance(A_ , A_ ):
return v
if v.lower() in ("yes", "true", "t", "y", "1"):
return True
elif v.lower() in ("no", "false", "f", "n", "0"):
return False
else:
raise argparse.ArgumentTypeError("boolean value expected" )
def lowerCamelCase__ ( A_ , A_ , A_ , A_ , A_=False ):
UpperCAmelCase_ = checkpoint[F"""{old_prefix}.in_layers.0.weight"""]
UpperCAmelCase_ = checkpoint[F"""{old_prefix}.in_layers.0.bias"""]
UpperCAmelCase_ = checkpoint[F"""{old_prefix}.in_layers.2.weight"""]
UpperCAmelCase_ = checkpoint[F"""{old_prefix}.in_layers.2.bias"""]
UpperCAmelCase_ = checkpoint[F"""{old_prefix}.emb_layers.1.weight"""]
UpperCAmelCase_ = checkpoint[F"""{old_prefix}.emb_layers.1.bias"""]
UpperCAmelCase_ = checkpoint[F"""{old_prefix}.out_layers.0.weight"""]
UpperCAmelCase_ = checkpoint[F"""{old_prefix}.out_layers.0.bias"""]
UpperCAmelCase_ = checkpoint[F"""{old_prefix}.out_layers.3.weight"""]
UpperCAmelCase_ = checkpoint[F"""{old_prefix}.out_layers.3.bias"""]
if has_skip:
UpperCAmelCase_ = checkpoint[F"""{old_prefix}.skip_connection.weight"""]
UpperCAmelCase_ = checkpoint[F"""{old_prefix}.skip_connection.bias"""]
return new_checkpoint
def lowerCamelCase__ ( A_ , A_ , A_ , A_ , A_=None ):
UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ = checkpoint[F"""{old_prefix}.qkv.weight"""].chunk(3 , dim=0 )
UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ = checkpoint[F"""{old_prefix}.qkv.bias"""].chunk(3 , dim=0 )
UpperCAmelCase_ = checkpoint[F"""{old_prefix}.norm.weight"""]
UpperCAmelCase_ = checkpoint[F"""{old_prefix}.norm.bias"""]
UpperCAmelCase_ = weight_q.squeeze(-1 ).squeeze(-1 )
UpperCAmelCase_ = bias_q.squeeze(-1 ).squeeze(-1 )
UpperCAmelCase_ = weight_k.squeeze(-1 ).squeeze(-1 )
UpperCAmelCase_ = bias_k.squeeze(-1 ).squeeze(-1 )
UpperCAmelCase_ = weight_v.squeeze(-1 ).squeeze(-1 )
UpperCAmelCase_ = bias_v.squeeze(-1 ).squeeze(-1 )
UpperCAmelCase_ = (
checkpoint[F"""{old_prefix}.proj_out.weight"""].squeeze(-1 ).squeeze(-1 )
)
UpperCAmelCase_ = checkpoint[F"""{old_prefix}.proj_out.bias"""].squeeze(-1 ).squeeze(-1 )
return new_checkpoint
def lowerCamelCase__ ( A_ , A_ ):
UpperCAmelCase_ = torch.load(A_ , map_location="cpu" )
UpperCAmelCase_ = {}
UpperCAmelCase_ = checkpoint["time_embed.0.weight"]
UpperCAmelCase_ = checkpoint["time_embed.0.bias"]
UpperCAmelCase_ = checkpoint["time_embed.2.weight"]
UpperCAmelCase_ = checkpoint["time_embed.2.bias"]
if unet_config["num_class_embeds"] is not None:
UpperCAmelCase_ = checkpoint["label_emb.weight"]
UpperCAmelCase_ = checkpoint["input_blocks.0.0.weight"]
UpperCAmelCase_ = checkpoint["input_blocks.0.0.bias"]
UpperCAmelCase_ = unet_config["down_block_types"]
UpperCAmelCase_ = unet_config["layers_per_block"]
UpperCAmelCase_ = unet_config["attention_head_dim"]
UpperCAmelCase_ = unet_config["block_out_channels"]
UpperCAmelCase_ = 1
UpperCAmelCase_ = channels_list[0]
for i, layer_type in enumerate(A_ ):
UpperCAmelCase_ = channels_list[i]
UpperCAmelCase_ = current_channels != prev_channels
if layer_type == "ResnetDownsampleBlock2D":
for j in range(A_ ):
UpperCAmelCase_ = F"""down_blocks.{i}.resnets.{j}"""
UpperCAmelCase_ = F"""input_blocks.{current_layer}.0"""
UpperCAmelCase_ = True if j == 0 and downsample_block_has_skip else False
UpperCAmelCase_ = convert_resnet(A_ , A_ , A_ , A_ , has_skip=A_ )
current_layer += 1
elif layer_type == "AttnDownBlock2D":
for j in range(A_ ):
UpperCAmelCase_ = F"""down_blocks.{i}.resnets.{j}"""
UpperCAmelCase_ = F"""input_blocks.{current_layer}.0"""
UpperCAmelCase_ = True if j == 0 and downsample_block_has_skip else False
UpperCAmelCase_ = convert_resnet(A_ , A_ , A_ , A_ , has_skip=A_ )
UpperCAmelCase_ = F"""down_blocks.{i}.attentions.{j}"""
UpperCAmelCase_ = F"""input_blocks.{current_layer}.1"""
UpperCAmelCase_ = convert_attention(
A_ , A_ , A_ , A_ , A_ )
current_layer += 1
if i != len(A_ ) - 1:
UpperCAmelCase_ = F"""down_blocks.{i}.downsamplers.0"""
UpperCAmelCase_ = F"""input_blocks.{current_layer}.0"""
UpperCAmelCase_ = convert_resnet(A_ , A_ , A_ , A_ )
current_layer += 1
UpperCAmelCase_ = current_channels
# hardcoded the mid-block for now
UpperCAmelCase_ = "mid_block.resnets.0"
UpperCAmelCase_ = "middle_block.0"
UpperCAmelCase_ = convert_resnet(A_ , A_ , A_ , A_ )
UpperCAmelCase_ = "mid_block.attentions.0"
UpperCAmelCase_ = "middle_block.1"
UpperCAmelCase_ = convert_attention(A_ , A_ , A_ , A_ , A_ )
UpperCAmelCase_ = "mid_block.resnets.1"
UpperCAmelCase_ = "middle_block.2"
UpperCAmelCase_ = convert_resnet(A_ , A_ , A_ , A_ )
UpperCAmelCase_ = 0
UpperCAmelCase_ = unet_config["up_block_types"]
for i, layer_type in enumerate(A_ ):
if layer_type == "ResnetUpsampleBlock2D":
for j in range(layers_per_block + 1 ):
UpperCAmelCase_ = F"""up_blocks.{i}.resnets.{j}"""
UpperCAmelCase_ = F"""output_blocks.{current_layer}.0"""
UpperCAmelCase_ = convert_resnet(A_ , A_ , A_ , A_ , has_skip=A_ )
current_layer += 1
if i != len(A_ ) - 1:
UpperCAmelCase_ = F"""up_blocks.{i}.upsamplers.0"""
UpperCAmelCase_ = F"""output_blocks.{current_layer-1}.1"""
UpperCAmelCase_ = convert_resnet(A_ , A_ , A_ , A_ )
elif layer_type == "AttnUpBlock2D":
for j in range(layers_per_block + 1 ):
UpperCAmelCase_ = F"""up_blocks.{i}.resnets.{j}"""
UpperCAmelCase_ = F"""output_blocks.{current_layer}.0"""
UpperCAmelCase_ = convert_resnet(A_ , A_ , A_ , A_ , has_skip=A_ )
UpperCAmelCase_ = F"""up_blocks.{i}.attentions.{j}"""
UpperCAmelCase_ = F"""output_blocks.{current_layer}.1"""
UpperCAmelCase_ = convert_attention(
A_ , A_ , A_ , A_ , A_ )
current_layer += 1
if i != len(A_ ) - 1:
UpperCAmelCase_ = F"""up_blocks.{i}.upsamplers.0"""
UpperCAmelCase_ = F"""output_blocks.{current_layer-1}.2"""
UpperCAmelCase_ = convert_resnet(A_ , A_ , A_ , A_ )
UpperCAmelCase_ = checkpoint["out.0.weight"]
UpperCAmelCase_ = checkpoint["out.0.bias"]
UpperCAmelCase_ = checkpoint["out.2.weight"]
UpperCAmelCase_ = checkpoint["out.2.bias"]
return new_checkpoint
if __name__ == "__main__":
__snake_case : List[str] = argparse.ArgumentParser()
parser.add_argument('''--unet_path''', default=None, type=str, required=True, help='''Path to the unet.pt to convert.''')
parser.add_argument(
'''--dump_path''', default=None, type=str, required=True, help='''Path to output the converted UNet model.'''
)
parser.add_argument('''--class_cond''', default=True, type=str, help='''Whether the model is class-conditional.''')
__snake_case : List[str] = parser.parse_args()
__snake_case : Any = strabool(args.class_cond)
__snake_case : List[str] = os.path.basename(args.unet_path)
print(F'''Checkpoint: {ckpt_name}''')
# Get U-Net config
if "imagenet64" in ckpt_name:
__snake_case : Optional[int] = IMAGENET_64_UNET_CONFIG
elif "256" in ckpt_name and (("bedroom" in ckpt_name) or ("cat" in ckpt_name)):
__snake_case : Union[str, Any] = LSUN_256_UNET_CONFIG
elif "test" in ckpt_name:
__snake_case : List[str] = TEST_UNET_CONFIG
else:
raise ValueError(F'''Checkpoint type {ckpt_name} is not currently supported.''')
if not args.class_cond:
__snake_case : Optional[Any] = None
__snake_case : Optional[int] = con_pt_to_diffuser(args.unet_path, unet_config)
__snake_case : str = UNetaDModel(**unet_config)
image_unet.load_state_dict(converted_unet_ckpt)
# Get scheduler config
if "cd" in ckpt_name or "test" in ckpt_name:
__snake_case : Tuple = CD_SCHEDULER_CONFIG
elif "ct" in ckpt_name and "imagenet64" in ckpt_name:
__snake_case : Optional[int] = CT_IMAGENET_64_SCHEDULER_CONFIG
elif "ct" in ckpt_name and "256" in ckpt_name and (("bedroom" in ckpt_name) or ("cat" in ckpt_name)):
__snake_case : Union[str, Any] = CT_LSUN_256_SCHEDULER_CONFIG
else:
raise ValueError(F'''Checkpoint type {ckpt_name} is not currently supported.''')
__snake_case : Optional[Any] = CMStochasticIterativeScheduler(**scheduler_config)
__snake_case : Dict = ConsistencyModelPipeline(unet=image_unet, scheduler=cm_scheduler)
consistency_model.save_pretrained(args.dump_path)
| 660 | 1 |
'''simple docstring'''
import argparse
import os.path as osp
import re
import torch
from safetensors.torch import load_file, save_file
# =================#
# UNet Conversion #
# =================#
lowercase = [
# (stable-diffusion, HF Diffusers)
('''time_embed.0.weight''', '''time_embedding.linear_1.weight'''),
('''time_embed.0.bias''', '''time_embedding.linear_1.bias'''),
('''time_embed.2.weight''', '''time_embedding.linear_2.weight'''),
('''time_embed.2.bias''', '''time_embedding.linear_2.bias'''),
('''input_blocks.0.0.weight''', '''conv_in.weight'''),
('''input_blocks.0.0.bias''', '''conv_in.bias'''),
('''out.0.weight''', '''conv_norm_out.weight'''),
('''out.0.bias''', '''conv_norm_out.bias'''),
('''out.2.weight''', '''conv_out.weight'''),
('''out.2.bias''', '''conv_out.bias'''),
]
lowercase = [
# (stable-diffusion, HF Diffusers)
('''in_layers.0''', '''norm1'''),
('''in_layers.2''', '''conv1'''),
('''out_layers.0''', '''norm2'''),
('''out_layers.3''', '''conv2'''),
('''emb_layers.1''', '''time_emb_proj'''),
('''skip_connection''', '''conv_shortcut'''),
]
lowercase = []
# hardcoded number of downblocks and resnets/attentions...
# would need smarter logic for other networks.
for i in range(4):
# loop over downblocks/upblocks
for j in range(2):
# loop over resnets/attentions for downblocks
lowercase = F"""down_blocks.{i}.resnets.{j}."""
lowercase = F"""input_blocks.{3*i + j + 1}.0."""
unet_conversion_map_layer.append((sd_down_res_prefix, hf_down_res_prefix))
if i < 3:
# no attention layers in down_blocks.3
lowercase = F"""down_blocks.{i}.attentions.{j}."""
lowercase = F"""input_blocks.{3*i + j + 1}.1."""
unet_conversion_map_layer.append((sd_down_atn_prefix, hf_down_atn_prefix))
for j in range(3):
# loop over resnets/attentions for upblocks
lowercase = F"""up_blocks.{i}.resnets.{j}."""
lowercase = F"""output_blocks.{3*i + j}.0."""
unet_conversion_map_layer.append((sd_up_res_prefix, hf_up_res_prefix))
if i > 0:
# no attention layers in up_blocks.0
lowercase = F"""up_blocks.{i}.attentions.{j}."""
lowercase = F"""output_blocks.{3*i + j}.1."""
unet_conversion_map_layer.append((sd_up_atn_prefix, hf_up_atn_prefix))
if i < 3:
# no downsample in down_blocks.3
lowercase = F"""down_blocks.{i}.downsamplers.0.conv."""
lowercase = F"""input_blocks.{3*(i+1)}.0.op."""
unet_conversion_map_layer.append((sd_downsample_prefix, hf_downsample_prefix))
# no upsample in up_blocks.3
lowercase = F"""up_blocks.{i}.upsamplers.0."""
lowercase = F"""output_blocks.{3*i + 2}.{1 if i == 0 else 2}."""
unet_conversion_map_layer.append((sd_upsample_prefix, hf_upsample_prefix))
lowercase = '''mid_block.attentions.0.'''
lowercase = '''middle_block.1.'''
unet_conversion_map_layer.append((sd_mid_atn_prefix, hf_mid_atn_prefix))
for j in range(2):
lowercase = F"""mid_block.resnets.{j}."""
lowercase = F"""middle_block.{2*j}."""
unet_conversion_map_layer.append((sd_mid_res_prefix, hf_mid_res_prefix))
def UpperCAmelCase_ ( lowercase__ ):
'''simple docstring'''
a_ ={k: k for k in unet_state_dict.keys()}
for sd_name, hf_name in unet_conversion_map:
a_ =sd_name
for k, v in mapping.items():
if "resnets" in k:
for sd_part, hf_part in unet_conversion_map_resnet:
a_ =v.replace(lowercase__ , lowercase__ )
a_ =v
for k, v in mapping.items():
for sd_part, hf_part in unet_conversion_map_layer:
a_ =v.replace(lowercase__ , lowercase__ )
a_ =v
a_ ={v: unet_state_dict[k] for k, v in mapping.items()}
return new_state_dict
# ================#
# VAE Conversion #
# ================#
lowercase = [
# (stable-diffusion, HF Diffusers)
('''nin_shortcut''', '''conv_shortcut'''),
('''norm_out''', '''conv_norm_out'''),
('''mid.attn_1.''', '''mid_block.attentions.0.'''),
]
for i in range(4):
# down_blocks have two resnets
for j in range(2):
lowercase = F"""encoder.down_blocks.{i}.resnets.{j}."""
lowercase = F"""encoder.down.{i}.block.{j}."""
vae_conversion_map.append((sd_down_prefix, hf_down_prefix))
if i < 3:
lowercase = F"""down_blocks.{i}.downsamplers.0."""
lowercase = F"""down.{i}.downsample."""
vae_conversion_map.append((sd_downsample_prefix, hf_downsample_prefix))
lowercase = F"""up_blocks.{i}.upsamplers.0."""
lowercase = F"""up.{3-i}.upsample."""
vae_conversion_map.append((sd_upsample_prefix, hf_upsample_prefix))
# up_blocks have three resnets
# also, up blocks in hf are numbered in reverse from sd
for j in range(3):
lowercase = F"""decoder.up_blocks.{i}.resnets.{j}."""
lowercase = F"""decoder.up.{3-i}.block.{j}."""
vae_conversion_map.append((sd_up_prefix, hf_up_prefix))
# this part accounts for mid blocks in both the encoder and the decoder
for i in range(2):
lowercase = F"""mid_block.resnets.{i}."""
lowercase = F"""mid.block_{i+1}."""
vae_conversion_map.append((sd_mid_res_prefix, hf_mid_res_prefix))
lowercase = [
# (stable-diffusion, HF Diffusers)
('''norm.''', '''group_norm.'''),
('''q.''', '''query.'''),
('''k.''', '''key.'''),
('''v.''', '''value.'''),
('''proj_out.''', '''proj_attn.'''),
]
def UpperCAmelCase_ ( lowercase__ ):
'''simple docstring'''
return w.reshape(*w.shape , 1 , 1 )
def UpperCAmelCase_ ( lowercase__ ):
'''simple docstring'''
a_ ={k: k for k in vae_state_dict.keys()}
for k, v in mapping.items():
for sd_part, hf_part in vae_conversion_map:
a_ =v.replace(lowercase__ , lowercase__ )
a_ =v
for k, v in mapping.items():
if "attentions" in k:
for sd_part, hf_part in vae_conversion_map_attn:
a_ =v.replace(lowercase__ , lowercase__ )
a_ =v
a_ ={v: vae_state_dict[k] for k, v in mapping.items()}
a_ =["q", "k", "v", "proj_out"]
for k, v in new_state_dict.items():
for weight_name in weights_to_convert:
if F"""mid.attn_1.{weight_name}.weight""" in k:
print(F"""Reshaping {k} for SD format""" )
a_ =reshape_weight_for_sd(lowercase__ )
return new_state_dict
# =========================#
# Text Encoder Conversion #
# =========================#
lowercase = [
# (stable-diffusion, HF Diffusers)
('''resblocks.''', '''text_model.encoder.layers.'''),
('''ln_1''', '''layer_norm1'''),
('''ln_2''', '''layer_norm2'''),
('''.c_fc.''', '''.fc1.'''),
('''.c_proj.''', '''.fc2.'''),
('''.attn''', '''.self_attn'''),
('''ln_final.''', '''transformer.text_model.final_layer_norm.'''),
('''token_embedding.weight''', '''transformer.text_model.embeddings.token_embedding.weight'''),
('''positional_embedding''', '''transformer.text_model.embeddings.position_embedding.weight'''),
]
lowercase = {re.escape(x[1]): x[0] for x in textenc_conversion_lst}
lowercase = re.compile('''|'''.join(protected.keys()))
# Ordering is from https://github.com/pytorch/pytorch/blob/master/test/cpp/api/modules.cpp
lowercase = {'''q''': 0, '''k''': 1, '''v''': 2}
def UpperCAmelCase_ ( lowercase__ ):
'''simple docstring'''
a_ ={}
a_ ={}
a_ ={}
for k, v in text_enc_dict.items():
if (
k.endswith(".self_attn.q_proj.weight" )
or k.endswith(".self_attn.k_proj.weight" )
or k.endswith(".self_attn.v_proj.weight" )
):
a_ =k[: -len(".q_proj.weight" )]
a_ =k[-len("q_proj.weight" )]
if k_pre not in capture_qkv_weight:
a_ =[None, None, None]
a_ =v
continue
if (
k.endswith(".self_attn.q_proj.bias" )
or k.endswith(".self_attn.k_proj.bias" )
or k.endswith(".self_attn.v_proj.bias" )
):
a_ =k[: -len(".q_proj.bias" )]
a_ =k[-len("q_proj.bias" )]
if k_pre not in capture_qkv_bias:
a_ =[None, None, None]
a_ =v
continue
a_ =textenc_pattern.sub(lambda lowercase__ : protected[re.escape(m.group(0 ) )] , lowercase__ )
a_ =v
for k_pre, tensors in capture_qkv_weight.items():
if None in tensors:
raise Exception("CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing" )
a_ =textenc_pattern.sub(lambda lowercase__ : protected[re.escape(m.group(0 ) )] , lowercase__ )
a_ =torch.cat(lowercase__ )
for k_pre, tensors in capture_qkv_bias.items():
if None in tensors:
raise Exception("CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing" )
a_ =textenc_pattern.sub(lambda lowercase__ : protected[re.escape(m.group(0 ) )] , lowercase__ )
a_ =torch.cat(lowercase__ )
return new_state_dict
def UpperCAmelCase_ ( lowercase__ ):
'''simple docstring'''
return text_enc_dict
if __name__ == "__main__":
lowercase = argparse.ArgumentParser()
parser.add_argument('''--model_path''', default=None, type=str, required=True, help='''Path to the model to convert.''')
parser.add_argument('''--checkpoint_path''', default=None, type=str, required=True, help='''Path to the output model.''')
parser.add_argument('''--half''', action='''store_true''', help='''Save weights in half precision.''')
parser.add_argument(
'''--use_safetensors''', action='''store_true''', help='''Save weights use safetensors, default is ckpt.'''
)
lowercase = parser.parse_args()
assert args.model_path is not None, "Must provide a model path!"
assert args.checkpoint_path is not None, "Must provide a checkpoint path!"
# Path for safetensors
lowercase = osp.join(args.model_path, '''unet''', '''diffusion_pytorch_model.safetensors''')
lowercase = osp.join(args.model_path, '''vae''', '''diffusion_pytorch_model.safetensors''')
lowercase = osp.join(args.model_path, '''text_encoder''', '''model.safetensors''')
# Load models from safetensors if it exists, if it doesn't pytorch
if osp.exists(unet_path):
lowercase = load_file(unet_path, device='''cpu''')
else:
lowercase = osp.join(args.model_path, '''unet''', '''diffusion_pytorch_model.bin''')
lowercase = torch.load(unet_path, map_location='''cpu''')
if osp.exists(vae_path):
lowercase = load_file(vae_path, device='''cpu''')
else:
lowercase = osp.join(args.model_path, '''vae''', '''diffusion_pytorch_model.bin''')
lowercase = torch.load(vae_path, map_location='''cpu''')
if osp.exists(text_enc_path):
lowercase = load_file(text_enc_path, device='''cpu''')
else:
lowercase = osp.join(args.model_path, '''text_encoder''', '''pytorch_model.bin''')
lowercase = torch.load(text_enc_path, map_location='''cpu''')
# Convert the UNet model
lowercase = convert_unet_state_dict(unet_state_dict)
lowercase = {'''model.diffusion_model.''' + k: v for k, v in unet_state_dict.items()}
# Convert the VAE model
lowercase = convert_vae_state_dict(vae_state_dict)
lowercase = {'''first_stage_model.''' + k: v for k, v in vae_state_dict.items()}
# Easiest way to identify v2.0 model seems to be that the text encoder (OpenCLIP) is deeper
lowercase = '''text_model.encoder.layers.22.layer_norm2.bias''' in text_enc_dict
if is_vaa_model:
# Need to add the tag 'transformer' in advance so we can knock it out from the final layer-norm
lowercase = {'''transformer.''' + k: v for k, v in text_enc_dict.items()}
lowercase = convert_text_enc_state_dict_vaa(text_enc_dict)
lowercase = {'''cond_stage_model.model.''' + k: v for k, v in text_enc_dict.items()}
else:
lowercase = convert_text_enc_state_dict(text_enc_dict)
lowercase = {'''cond_stage_model.transformer.''' + k: v for k, v in text_enc_dict.items()}
# Put together new checkpoint
lowercase = {**unet_state_dict, **vae_state_dict, **text_enc_dict}
if args.half:
lowercase = {k: v.half() for k, v in state_dict.items()}
if args.use_safetensors:
save_file(state_dict, args.checkpoint_path)
else:
lowercase = {'''state_dict''': state_dict}
torch.save(state_dict, args.checkpoint_path)
| 41 |
'''simple docstring'''
from __future__ import annotations
lowercase = []
def UpperCAmelCase_ ( lowercase__ , lowercase__ , lowercase__ ):
'''simple docstring'''
for i in range(len(lowercase__ ) ):
if board[row][i] == 1:
return False
for i in range(len(lowercase__ ) ):
if board[i][column] == 1:
return False
for i, j in zip(range(lowercase__ , -1 , -1 ) , range(lowercase__ , -1 , -1 ) ):
if board[i][j] == 1:
return False
for i, j in zip(range(lowercase__ , -1 , -1 ) , range(lowercase__ , len(lowercase__ ) ) ):
if board[i][j] == 1:
return False
return True
def UpperCAmelCase_ ( lowercase__ , lowercase__ ):
'''simple docstring'''
if row >= len(lowercase__ ):
solution.append(lowercase__ )
printboard(lowercase__ )
print()
return True
for i in range(len(lowercase__ ) ):
if is_safe(lowercase__ , lowercase__ , lowercase__ ):
a_ =1
solve(lowercase__ , row + 1 )
a_ =0
return False
def UpperCAmelCase_ ( lowercase__ ):
'''simple docstring'''
for i in range(len(lowercase__ ) ):
for j in range(len(lowercase__ ) ):
if board[i][j] == 1:
print("Q" , end=" " )
else:
print("." , end=" " )
print()
# n=int(input("The no. of queens"))
lowercase = 8
lowercase = [[0 for i in range(n)] for j in range(n)]
solve(board, 0)
print('''The total no. of solutions are :''', len(solution))
| 41 | 1 |
import torch
from ..models.auto import AutoModelForSequenceClassification, AutoTokenizer
from .base import PipelineTool
class UpperCamelCase( lowerCAmelCase_ ):
snake_case_ : List[str] = 'facebook/bart-large-mnli'
snake_case_ : List[Any] = (
'This is a tool that classifies an English text using provided labels. It takes two inputs: `text`, which '
'should be the text to classify, and `labels`, which should be the list of labels to use for classification. '
'It returns the most likely label in the list of provided `labels` for the input text.'
)
snake_case_ : str = 'text_classifier'
snake_case_ : Any = AutoTokenizer
snake_case_ : Dict = AutoModelForSequenceClassification
snake_case_ : List[Any] = ['text', ['text']]
snake_case_ : Optional[Any] = ['text']
def SCREAMING_SNAKE_CASE_ ( self : Dict ) -> Optional[int]:
'''simple docstring'''
super().setup()
__snake_case = self.model.config
__snake_case = -1
for idx, label in config.idalabel.items():
if label.lower().startswith("entail" ):
__snake_case = int(A__ )
if self.entailment_id == -1:
raise ValueError("Could not determine the entailment ID from the model config, please pass it at init." )
def SCREAMING_SNAKE_CASE_ ( self : int , SCREAMING_SNAKE_CASE : Optional[int] , SCREAMING_SNAKE_CASE : Any ) -> Union[str, Any]:
'''simple docstring'''
__snake_case = labels
return self.pre_processor(
[text] * len(A__ ) , [f'''This example is {label}''' for label in labels] , return_tensors="pt" , padding="max_length" , )
def SCREAMING_SNAKE_CASE_ ( self : str , SCREAMING_SNAKE_CASE : Any ) -> str:
'''simple docstring'''
__snake_case = outputs.logits
__snake_case = torch.argmax(logits[:, 2] ).item()
return self._labels[label_id]
| 371 |
'''simple docstring'''
from torch import nn
def __lowercase (_lowercase ) -> Union[str, Any]:
"""simple docstring"""
if act_fn in ["swish", "silu"]:
return nn.SiLU()
elif act_fn == "mish":
return nn.Mish()
elif act_fn == "gelu":
return nn.GELU()
else:
raise ValueError(f"Unsupported activation function: {act_fn}" )
| 150 | 0 |
from collections import OrderedDict
from typing import Any, List, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging
__a = logging.get_logger(__name__)
__a = {
'EleutherAI/gpt-j-6B': 'https://huggingface.co/EleutherAI/gpt-j-6B/resolve/main/config.json',
# See all GPT-J models at https://huggingface.co/models?filter=gpt_j
}
class lowercase__( UpperCAmelCase ):
"""simple docstring"""
a :Union[str, Any] = 'gptj'
a :str = {
'max_position_embeddings': 'n_positions',
'hidden_size': 'n_embd',
'num_attention_heads': 'n_head',
'num_hidden_layers': 'n_layer',
}
def __init__( self : Any , SCREAMING_SNAKE_CASE_ : str=5_0_4_0_0 , SCREAMING_SNAKE_CASE_ : Optional[int]=2_0_4_8 , SCREAMING_SNAKE_CASE_ : Optional[int]=4_0_9_6 , SCREAMING_SNAKE_CASE_ : List[Any]=2_8 , SCREAMING_SNAKE_CASE_ : Tuple=1_6 , SCREAMING_SNAKE_CASE_ : Any=6_4 , SCREAMING_SNAKE_CASE_ : List[Any]=None , SCREAMING_SNAKE_CASE_ : Optional[int]="gelu_new" , SCREAMING_SNAKE_CASE_ : Dict=0.0 , SCREAMING_SNAKE_CASE_ : int=0.0 , SCREAMING_SNAKE_CASE_ : Union[str, Any]=0.0 , SCREAMING_SNAKE_CASE_ : List[str]=1e-5 , SCREAMING_SNAKE_CASE_ : Tuple=0.02 , SCREAMING_SNAKE_CASE_ : Union[str, Any]=True , SCREAMING_SNAKE_CASE_ : Optional[int]=5_0_2_5_6 , SCREAMING_SNAKE_CASE_ : Union[str, Any]=5_0_2_5_6 , SCREAMING_SNAKE_CASE_ : Optional[Any]=False , **SCREAMING_SNAKE_CASE_ : Dict , ) -> Optional[Any]:
lowercase_ = vocab_size
lowercase_ = n_positions
lowercase_ = n_embd
lowercase_ = n_layer
lowercase_ = n_head
lowercase_ = n_inner
lowercase_ = rotary_dim
lowercase_ = activation_function
lowercase_ = resid_pdrop
lowercase_ = embd_pdrop
lowercase_ = attn_pdrop
lowercase_ = layer_norm_epsilon
lowercase_ = initializer_range
lowercase_ = use_cache
lowercase_ = bos_token_id
lowercase_ = eos_token_id
super().__init__(
bos_token_id=SCREAMING_SNAKE_CASE_ , eos_token_id=SCREAMING_SNAKE_CASE_ , tie_word_embeddings=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
class lowercase__( UpperCAmelCase ):
"""simple docstring"""
def __init__( self : List[str] , SCREAMING_SNAKE_CASE_ : PretrainedConfig , SCREAMING_SNAKE_CASE_ : str = "default" , SCREAMING_SNAKE_CASE_ : List[PatchingSpec] = None , SCREAMING_SNAKE_CASE_ : bool = False , ) -> Any:
super().__init__(SCREAMING_SNAKE_CASE_ , task=SCREAMING_SNAKE_CASE_ , patching_specs=SCREAMING_SNAKE_CASE_ , use_past=SCREAMING_SNAKE_CASE_ )
if not getattr(self._config , '''pad_token_id''' , SCREAMING_SNAKE_CASE_ ):
# TODO: how to do that better?
lowercase_ = 0
@property
def _lowercase ( self : Dict ) -> Mapping[str, Mapping[int, str]]:
lowercase_ = OrderedDict({'''input_ids''': {0: '''batch''', 1: '''sequence'''}} )
if self.use_past:
self.fill_with_past_key_values_(SCREAMING_SNAKE_CASE_ , direction='''inputs''' )
lowercase_ = {0: '''batch''', 1: '''past_sequence + sequence'''}
else:
lowercase_ = {0: '''batch''', 1: '''sequence'''}
return common_inputs
@property
def _lowercase ( self : List[Any] ) -> int:
return self._config.n_layer
@property
def _lowercase ( self : List[Any] ) -> int:
return self._config.n_head
def _lowercase ( self : Union[str, Any] , SCREAMING_SNAKE_CASE_ : PreTrainedTokenizer , SCREAMING_SNAKE_CASE_ : int = -1 , SCREAMING_SNAKE_CASE_ : int = -1 , SCREAMING_SNAKE_CASE_ : bool = False , SCREAMING_SNAKE_CASE_ : Optional[TensorType] = None , ) -> Mapping[str, Any]:
lowercase_ = super(SCREAMING_SNAKE_CASE_ , self ).generate_dummy_inputs(
SCREAMING_SNAKE_CASE_ , batch_size=SCREAMING_SNAKE_CASE_ , seq_length=SCREAMING_SNAKE_CASE_ , is_pair=SCREAMING_SNAKE_CASE_ , framework=SCREAMING_SNAKE_CASE_ )
# We need to order the input in the way they appears in the forward()
lowercase_ = OrderedDict({'''input_ids''': common_inputs['''input_ids''']} )
# Need to add the past_keys
if self.use_past:
if not is_torch_available():
raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.''' )
else:
import torch
lowercase_ , lowercase_ = common_inputs['''input_ids'''].shape
# Not using the same length for past_key_values
lowercase_ = seqlen + 2
lowercase_ = (
batch,
self.num_attention_heads,
past_key_values_length,
self._config.hidden_size // self.num_attention_heads,
)
lowercase_ = [
(torch.zeros(SCREAMING_SNAKE_CASE_ ), torch.zeros(SCREAMING_SNAKE_CASE_ )) for _ in range(self.num_layers )
]
lowercase_ = common_inputs['''attention_mask''']
if self.use_past:
lowercase_ = ordered_inputs['''attention_mask'''].dtype
lowercase_ = torch.cat(
[ordered_inputs['''attention_mask'''], torch.ones(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , dtype=SCREAMING_SNAKE_CASE_ )] , dim=1 )
return ordered_inputs
@property
def _lowercase ( self : Union[str, Any] ) -> int:
return 1_3
| 409 |
import unittest
from transformers import PegasusTokenizer, PegasusTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
__a = get_tests_dir('fixtures/test_sentencepiece_no_bos.model')
@require_sentencepiece
@require_tokenizers
class lowercase__( UpperCAmelCase , unittest.TestCase ):
"""simple docstring"""
a :str = PegasusTokenizer
a :Any = PegasusTokenizerFast
a :Optional[Any] = True
a :int = True
def _lowercase ( self : Any ) -> Dict:
super().setUp()
# We have a SentencePiece fixture for testing
lowercase_ = PegasusTokenizer(SCREAMING_SNAKE_CASE_ )
tokenizer.save_pretrained(self.tmpdirname )
@cached_property
def _lowercase ( self : List[str] ) -> Any:
return PegasusTokenizer.from_pretrained('''google/pegasus-large''' )
def _lowercase ( self : Any , **SCREAMING_SNAKE_CASE_ : Optional[int] ) -> PegasusTokenizer:
return PegasusTokenizer.from_pretrained(self.tmpdirname , **SCREAMING_SNAKE_CASE_ )
def _lowercase ( self : Dict , SCREAMING_SNAKE_CASE_ : Tuple ) -> Any:
return ("This is a test", "This is a test")
def _lowercase ( self : str ) -> Optional[Any]:
lowercase_ = '''</s>'''
lowercase_ = 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 _lowercase ( self : Any ) -> str:
lowercase_ = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , '''<pad>''' )
self.assertEqual(vocab_keys[1] , '''</s>''' )
self.assertEqual(vocab_keys[-1] , '''v''' )
self.assertEqual(len(SCREAMING_SNAKE_CASE_ ) , 1_1_0_3 )
def _lowercase ( self : Tuple ) -> List[str]:
self.assertEqual(self.get_tokenizer().vocab_size , 1_1_0_3 )
def _lowercase ( self : Optional[Any] ) -> Tuple:
lowercase_ = self.rust_tokenizer_class.from_pretrained(self.tmpdirname )
lowercase_ = self.tokenizer_class.from_pretrained(self.tmpdirname )
lowercase_ = (
'''Let\'s see which <unk> is the better <unk_token_11> one <mask_1> It seems like this <mask_2> was important'''
''' </s> <pad> <pad> <pad>'''
)
lowercase_ = rust_tokenizer([raw_input_str] , return_tensors=SCREAMING_SNAKE_CASE_ , add_special_tokens=SCREAMING_SNAKE_CASE_ ).input_ids[0]
lowercase_ = py_tokenizer([raw_input_str] , return_tensors=SCREAMING_SNAKE_CASE_ , add_special_tokens=SCREAMING_SNAKE_CASE_ ).input_ids[0]
self.assertListEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
def _lowercase ( self : int ) -> List[str]:
lowercase_ = self._large_tokenizer
# <mask_1> masks whole sentence while <mask_2> masks single word
lowercase_ = '''<mask_1> To ensure a <mask_2> flow of bank resolutions.'''
lowercase_ = [2, 4_1_3, 6_1_5, 1_1_4, 3, 1_9_7_1, 1_1_3, 1_6_7_9, 1_0_7_1_0, 1_0_7, 1]
lowercase_ = tokenizer([raw_input_str] , return_tensors=SCREAMING_SNAKE_CASE_ ).input_ids[0]
self.assertListEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
def _lowercase ( self : Tuple ) -> Union[str, Any]:
lowercase_ = self._large_tokenizer
# The tracebacks for the following asserts are **better** without messages or self.assertEqual
assert tokenizer.vocab_size == 9_6_1_0_3
assert tokenizer.pad_token_id == 0
assert tokenizer.eos_token_id == 1
assert tokenizer.offset == 1_0_3
assert tokenizer.unk_token_id == tokenizer.offset + 2 == 1_0_5
assert tokenizer.unk_token == "<unk>"
assert tokenizer.model_max_length == 1_0_2_4
lowercase_ = '''To ensure a smooth flow of bank resolutions.'''
lowercase_ = [4_1_3, 6_1_5, 1_1_4, 2_2_9_1, 1_9_7_1, 1_1_3, 1_6_7_9, 1_0_7_1_0, 1_0_7, 1]
lowercase_ = tokenizer([raw_input_str] , return_tensors=SCREAMING_SNAKE_CASE_ ).input_ids[0]
self.assertListEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
assert tokenizer.convert_ids_to_tokens([0, 1, 2, 3] ) == ["<pad>", "</s>", "<mask_1>", "<mask_2>"]
@require_torch
def _lowercase ( self : int ) -> Tuple:
lowercase_ = ['''This is going to be way too long.''' * 1_5_0, '''short example''']
lowercase_ = ['''not super long but more than 5 tokens''', '''tiny''']
lowercase_ = self._large_tokenizer(SCREAMING_SNAKE_CASE_ , padding=SCREAMING_SNAKE_CASE_ , truncation=SCREAMING_SNAKE_CASE_ , return_tensors='''pt''' )
lowercase_ = self._large_tokenizer(
text_target=SCREAMING_SNAKE_CASE_ , max_length=5 , padding=SCREAMING_SNAKE_CASE_ , truncation=SCREAMING_SNAKE_CASE_ , return_tensors='''pt''' )
assert batch.input_ids.shape == (2, 1_0_2_4)
assert batch.attention_mask.shape == (2, 1_0_2_4)
assert targets["input_ids"].shape == (2, 5)
assert len(SCREAMING_SNAKE_CASE_ ) == 2 # input_ids, attention_mask.
@slow
def _lowercase ( self : int ) -> Tuple:
# fmt: off
lowercase_ = {'''input_ids''': [[3_8_9_7_9, 1_4_3, 1_8_4_8_5, 6_0_6, 1_3_0, 2_6_6_6_9, 8_7_6_8_6, 1_2_1, 5_4_1_8_9, 1_1_2_9, 1_1_1, 2_6_6_6_9, 8_7_6_8_6, 1_2_1, 9_1_1_4, 1_4_7_8_7, 1_2_1, 1_3_2_4_9, 1_5_8, 5_9_2, 9_5_6, 1_2_1, 1_4_6_2_1, 3_1_5_7_6, 1_4_3, 6_2_6_1_3, 1_0_8, 9_6_8_8, 9_3_0, 4_3_4_3_0, 1_1_5_6_2, 6_2_6_1_3, 3_0_4, 1_0_8, 1_1_4_4_3, 8_9_7, 1_0_8, 9_3_1_4, 1_7_4_1_5, 6_3_3_9_9, 1_0_8, 1_1_4_4_3, 7_6_1_4, 1_8_3_1_6, 1_1_8, 4_2_8_4, 7_1_4_8, 1_2_4_3_0, 1_4_3, 1_4_0_0, 2_5_7_0_3, 1_5_8, 1_1_1, 4_2_8_4, 7_1_4_8, 1_1_7_7_2, 1_4_3, 2_1_2_9_7, 1_0_6_4, 1_5_8, 1_2_2, 2_0_4, 3_5_0_6, 1_7_5_4, 1_1_3_3, 1_4_7_8_7, 1_5_8_1, 1_1_5, 3_3_2_2_4, 4_4_8_2, 1_1_1, 1_3_5_5, 1_1_0, 2_9_1_7_3, 3_1_7, 5_0_8_3_3, 1_0_8, 2_0_1_4_7, 9_4_6_6_5, 1_1_1, 7_7_1_9_8, 1_0_7, 1], [1_1_0, 6_2_6_1_3, 1_1_7, 6_3_8, 1_1_2, 1_1_3_3, 1_2_1, 2_0_0_9_8, 1_3_5_5, 7_9_0_5_0, 1_3_8_7_2, 1_3_5, 1_5_9_6, 5_3_5_4_1, 1_3_5_2, 1_4_1, 1_3_0_3_9, 5_5_4_2, 1_2_4, 3_0_2, 5_1_8, 1_1_1, 2_6_8, 2_9_5_6, 1_1_5, 1_4_9, 4_4_2_7, 1_0_7, 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], [1_3_9, 1_2_3_5, 2_7_9_9, 1_8_2_8_9, 1_7_7_8_0, 2_0_4, 1_0_9, 9_4_7_4, 1_2_9_6, 1_0_7, 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]], '''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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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='''google/bigbird-pegasus-large-arxiv''' , revision='''ba85d0851d708441f91440d509690f1ab6353415''' , )
@require_sentencepiece
@require_tokenizers
class lowercase__( UpperCAmelCase , unittest.TestCase ):
"""simple docstring"""
a :Dict = PegasusTokenizer
a :List[Any] = PegasusTokenizerFast
a :Union[str, Any] = True
a :List[Any] = True
def _lowercase ( self : Optional[Any] ) -> Tuple:
super().setUp()
# We have a SentencePiece fixture for testing
lowercase_ = PegasusTokenizer(SCREAMING_SNAKE_CASE_ , offset=0 , mask_token_sent=SCREAMING_SNAKE_CASE_ , mask_token='''[MASK]''' )
tokenizer.save_pretrained(self.tmpdirname )
@cached_property
def _lowercase ( self : Optional[Any] ) -> Optional[Any]:
return PegasusTokenizer.from_pretrained('''google/bigbird-pegasus-large-arxiv''' )
def _lowercase ( self : Optional[int] , **SCREAMING_SNAKE_CASE_ : List[str] ) -> PegasusTokenizer:
return PegasusTokenizer.from_pretrained(self.tmpdirname , **SCREAMING_SNAKE_CASE_ )
def _lowercase ( self : List[Any] , SCREAMING_SNAKE_CASE_ : Tuple ) -> Any:
return ("This is a test", "This is a test")
def _lowercase ( self : List[Any] ) -> Union[str, Any]:
lowercase_ = self.rust_tokenizer_class.from_pretrained(self.tmpdirname )
lowercase_ = self.tokenizer_class.from_pretrained(self.tmpdirname )
lowercase_ = (
'''Let\'s see which <unk> is the better <unk_token> one [MASK] It seems like this [MASK] was important </s>'''
''' <pad> <pad> <pad>'''
)
lowercase_ = rust_tokenizer([raw_input_str] , return_tensors=SCREAMING_SNAKE_CASE_ , add_special_tokens=SCREAMING_SNAKE_CASE_ ).input_ids[0]
lowercase_ = py_tokenizer([raw_input_str] , return_tensors=SCREAMING_SNAKE_CASE_ , add_special_tokens=SCREAMING_SNAKE_CASE_ ).input_ids[0]
self.assertListEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
@require_torch
def _lowercase ( self : Optional[Any] ) -> int:
lowercase_ = ['''This is going to be way too long.''' * 1_0_0_0, '''short example''']
lowercase_ = ['''not super long but more than 5 tokens''', '''tiny''']
lowercase_ = self._large_tokenizer(SCREAMING_SNAKE_CASE_ , padding=SCREAMING_SNAKE_CASE_ , truncation=SCREAMING_SNAKE_CASE_ , return_tensors='''pt''' )
lowercase_ = self._large_tokenizer(
text_target=SCREAMING_SNAKE_CASE_ , max_length=5 , padding=SCREAMING_SNAKE_CASE_ , truncation=SCREAMING_SNAKE_CASE_ , return_tensors='''pt''' )
assert batch.input_ids.shape == (2, 4_0_9_6)
assert batch.attention_mask.shape == (2, 4_0_9_6)
assert targets["input_ids"].shape == (2, 5)
assert len(SCREAMING_SNAKE_CASE_ ) == 2 # input_ids, attention_mask.
def _lowercase ( self : Optional[int] ) -> Any:
lowercase_ = (
'''This is an example string that is used to test the original TF implementation against the HF'''
''' implementation'''
)
lowercase_ = self._large_tokenizer(SCREAMING_SNAKE_CASE_ ).input_ids
self.assertListEqual(
SCREAMING_SNAKE_CASE_ , [1_8_2, 1_1_7, 1_4_2, 5_8_7, 4_2_1_1, 1_2_0, 1_1_7, 2_6_3, 1_1_2, 8_0_4, 1_0_9, 8_5_6, 2_5_0_1_6, 3_1_3_7, 4_6_4, 1_0_9, 2_6_9_5_5, 3_1_3_7, 1] , )
| 409 | 1 |
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCAmelCase = logging.get_logger(__name__)
_lowerCAmelCase = {
"microsoft/unispeech-sat-base-100h-libri-ft": (
"https://huggingface.co/microsoft/unispeech-sat-base-100h-libri-ft/resolve/main/config.json"
),
# See all UniSpeechSat models at https://huggingface.co/models?filter=unispeech_sat
}
class lowerCAmelCase_ ( __lowercase ):
UpperCAmelCase = "unispeech-sat"
def __init__( self : List[Any] , _A : Dict=32 , _A : int=768 , _A : str=12 , _A : str=12 , _A : Any=3072 , _A : List[str]="gelu" , _A : Any=0.1 , _A : Union[str, Any]=0.1 , _A : Optional[int]=0.1 , _A : Optional[Any]=0.0 , _A : List[str]=0.0 , _A : Optional[Any]=0.1 , _A : str=0.1 , _A : List[str]=0.02 , _A : Optional[int]=1e-5 , _A : Dict="group" , _A : str="gelu" , _A : List[str]=(512, 512, 512, 512, 512, 512, 512) , _A : Any=(5, 2, 2, 2, 2, 2, 2) , _A : Dict=(10, 3, 3, 3, 3, 2, 2) , _A : Union[str, Any]=False , _A : str=128 , _A : Tuple=16 , _A : Optional[int]=False , _A : Dict=True , _A : Optional[Any]=0.05 , _A : Any=10 , _A : str=2 , _A : Dict=0.0 , _A : List[str]=10 , _A : Union[str, Any]=0 , _A : List[str]=320 , _A : List[Any]=2 , _A : Optional[Any]=0.1 , _A : Optional[Any]=100 , _A : List[str]=256 , _A : Any=256 , _A : List[Any]=0.1 , _A : Dict="mean" , _A : Dict=False , _A : List[str]=False , _A : List[Any]=256 , _A : Any=(512, 512, 512, 512, 1500) , _A : Any=(5, 3, 3, 1, 1) , _A : Dict=(1, 2, 3, 1, 1) , _A : str=512 , _A : Dict=0 , _A : List[str]=1 , _A : Tuple=2 , _A : Optional[Any]=504 , **_A : int , ):
super().__init__(**_A , pad_token_id=_A , bos_token_id=_A , eos_token_id=_A )
_UpperCamelCase = hidden_size
_UpperCamelCase = feat_extract_norm
_UpperCamelCase = feat_extract_activation
_UpperCamelCase = list(_A )
_UpperCamelCase = list(_A )
_UpperCamelCase = list(_A )
_UpperCamelCase = conv_bias
_UpperCamelCase = num_conv_pos_embeddings
_UpperCamelCase = num_conv_pos_embedding_groups
_UpperCamelCase = len(self.conv_dim )
_UpperCamelCase = num_hidden_layers
_UpperCamelCase = intermediate_size
_UpperCamelCase = hidden_act
_UpperCamelCase = num_attention_heads
_UpperCamelCase = hidden_dropout
_UpperCamelCase = attention_dropout
_UpperCamelCase = activation_dropout
_UpperCamelCase = feat_proj_dropout
_UpperCamelCase = final_dropout
_UpperCamelCase = layerdrop
_UpperCamelCase = layer_norm_eps
_UpperCamelCase = initializer_range
_UpperCamelCase = vocab_size
_UpperCamelCase = num_clusters
_UpperCamelCase = do_stable_layer_norm
_UpperCamelCase = 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
_UpperCamelCase = apply_spec_augment
_UpperCamelCase = mask_time_prob
_UpperCamelCase = mask_time_length
_UpperCamelCase = mask_time_min_masks
_UpperCamelCase = mask_feature_prob
_UpperCamelCase = mask_feature_length
_UpperCamelCase = mask_feature_min_masks
# parameters for pretraining with codevector quantized representations
_UpperCamelCase = num_codevectors_per_group
_UpperCamelCase = num_codevector_groups
_UpperCamelCase = contrastive_logits_temperature
_UpperCamelCase = feat_quantizer_dropout
_UpperCamelCase = num_negatives
_UpperCamelCase = codevector_dim
_UpperCamelCase = proj_codevector_dim
_UpperCamelCase = diversity_loss_weight
# ctc loss
_UpperCamelCase = ctc_loss_reduction
_UpperCamelCase = ctc_zero_infinity
# SequenceClassification-specific parameter. Feel free to ignore for other classes.
_UpperCamelCase = classifier_proj_size
# XVector-specific parameters. Feel free to ignore for other classes.
_UpperCamelCase = list(_A )
_UpperCamelCase = list(_A )
_UpperCamelCase = list(_A )
_UpperCamelCase = xvector_output_dim
@property
def UpperCamelCase_ ( self : List[str] ):
return functools.reduce(operator.mul , self.conv_stride , 1 )
| 10 |
UpperCAmelCase_ = {
"A": ".-", "B": "-...", "C": "-.-.", "D": "-..", "E": ".", "F": "..-.", "G": "--.",
"H": "....", "I": "..", "J": ".---", "K": "-.-", "L": ".-..", "M": "--", "N": "-.",
"O": "---", "P": ".--.", "Q": "--.-", "R": ".-.", "S": "...", "T": "-", "U": "..-",
"V": "...-", "W": ".--", "X": "-..-", "Y": "-.--", "Z": "--..", "1": ".----",
"2": "..---", "3": "...--", "4": "....-", "5": ".....", "6": "-....", "7": "--...",
"8": "---..", "9": "----.", "0": "-----", "&": ".-...", "@": ".--.-.",
":": "---...", ",": "--..--", ".": ".-.-.-", "'": ".----.", "\"": ".-..-.",
"?": "..--..", "/": "-..-.", "=": "-...-", "+": ".-.-.", "-": "-....-",
"(": "-.--.", ")": "-.--.-", "!": "-.-.--", " ": "/"
} # Exclamation mark is not in ITU-R recommendation
# fmt: on
UpperCAmelCase_ = {value: key for key, value in MORSE_CODE_DICT.items()}
def A__ ( SCREAMING_SNAKE_CASE_ : str ) -> str:
"""simple docstring"""
return " ".join(MORSE_CODE_DICT[char] for char in message.upper() )
def A__ ( SCREAMING_SNAKE_CASE_ : str ) -> str:
"""simple docstring"""
return "".join(REVERSE_DICT[char] for char in message.split() )
def A__ ( ) -> None:
"""simple docstring"""
_UpperCAmelCase = '''Morse code here!'''
print(SCREAMING_SNAKE_CASE_ )
_UpperCAmelCase = encrypt(SCREAMING_SNAKE_CASE_ )
print(SCREAMING_SNAKE_CASE_ )
_UpperCAmelCase = decrypt(SCREAMING_SNAKE_CASE_ )
print(SCREAMING_SNAKE_CASE_ )
if __name__ == "__main__":
main() | 32 | 0 |
import tempfile
import torch
from diffusers import PNDMScheduler
from .test_schedulers import SchedulerCommonTest
class __UpperCamelCase ( lowercase ):
SCREAMING_SNAKE_CASE__ = (PNDMScheduler,)
SCREAMING_SNAKE_CASE__ = (('num_inference_steps', 50),)
def __A ( self : Tuple , **lowerCAmelCase : int ):
'''simple docstring'''
UpperCAmelCase_ = {
"num_train_timesteps": 1_000,
"beta_start": 0.0_001,
"beta_end": 0.02,
"beta_schedule": "linear",
}
config.update(**lowerCAmelCase )
return config
def __A ( self : str , lowerCAmelCase : Optional[Any]=0 , **lowerCAmelCase : Any ):
'''simple docstring'''
UpperCAmelCase_ = dict(self.forward_default_kwargs )
UpperCAmelCase_ = kwargs.pop("num_inference_steps" , lowerCAmelCase )
UpperCAmelCase_ = self.dummy_sample
UpperCAmelCase_ = 0.1 * sample
UpperCAmelCase_ = [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
for scheduler_class in self.scheduler_classes:
UpperCAmelCase_ = self.get_scheduler_config(**lowerCAmelCase )
UpperCAmelCase_ = scheduler_class(**lowerCAmelCase )
scheduler.set_timesteps(lowerCAmelCase )
# copy over dummy past residuals
UpperCAmelCase_ = dummy_past_residuals[:]
with tempfile.TemporaryDirectory() as tmpdirname:
scheduler.save_config(lowerCAmelCase )
UpperCAmelCase_ = scheduler_class.from_pretrained(lowerCAmelCase )
new_scheduler.set_timesteps(lowerCAmelCase )
# copy over dummy past residuals
UpperCAmelCase_ = dummy_past_residuals[:]
UpperCAmelCase_ = scheduler.step_prk(lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , **lowerCAmelCase ).prev_sample
UpperCAmelCase_ = new_scheduler.step_prk(lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , **lowerCAmelCase ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1e-5, "Scheduler outputs are not identical"
UpperCAmelCase_ = scheduler.step_plms(lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , **lowerCAmelCase ).prev_sample
UpperCAmelCase_ = new_scheduler.step_plms(lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , **lowerCAmelCase ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1e-5, "Scheduler outputs are not identical"
def __A ( self : int ):
'''simple docstring'''
pass
def __A ( self : int , lowerCAmelCase : Any=0 , **lowerCAmelCase : Tuple ):
'''simple docstring'''
UpperCAmelCase_ = dict(self.forward_default_kwargs )
UpperCAmelCase_ = kwargs.pop("num_inference_steps" , lowerCAmelCase )
UpperCAmelCase_ = self.dummy_sample
UpperCAmelCase_ = 0.1 * sample
UpperCAmelCase_ = [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
for scheduler_class in self.scheduler_classes:
UpperCAmelCase_ = self.get_scheduler_config()
UpperCAmelCase_ = scheduler_class(**lowerCAmelCase )
scheduler.set_timesteps(lowerCAmelCase )
# copy over dummy past residuals (must be after setting timesteps)
UpperCAmelCase_ = dummy_past_residuals[:]
with tempfile.TemporaryDirectory() as tmpdirname:
scheduler.save_config(lowerCAmelCase )
UpperCAmelCase_ = scheduler_class.from_pretrained(lowerCAmelCase )
# copy over dummy past residuals
new_scheduler.set_timesteps(lowerCAmelCase )
# copy over dummy past residual (must be after setting timesteps)
UpperCAmelCase_ = dummy_past_residuals[:]
UpperCAmelCase_ = scheduler.step_prk(lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , **lowerCAmelCase ).prev_sample
UpperCAmelCase_ = new_scheduler.step_prk(lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , **lowerCAmelCase ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1e-5, "Scheduler outputs are not identical"
UpperCAmelCase_ = scheduler.step_plms(lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , **lowerCAmelCase ).prev_sample
UpperCAmelCase_ = new_scheduler.step_plms(lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , **lowerCAmelCase ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1e-5, "Scheduler outputs are not identical"
def __A ( self : Tuple , **lowerCAmelCase : List[str] ):
'''simple docstring'''
UpperCAmelCase_ = self.scheduler_classes[0]
UpperCAmelCase_ = self.get_scheduler_config(**lowerCAmelCase )
UpperCAmelCase_ = scheduler_class(**lowerCAmelCase )
UpperCAmelCase_ = 10
UpperCAmelCase_ = self.dummy_model()
UpperCAmelCase_ = self.dummy_sample_deter
scheduler.set_timesteps(lowerCAmelCase )
for i, t in enumerate(scheduler.prk_timesteps ):
UpperCAmelCase_ = model(lowerCAmelCase , lowerCAmelCase )
UpperCAmelCase_ = scheduler.step_prk(lowerCAmelCase , lowerCAmelCase , lowerCAmelCase ).prev_sample
for i, t in enumerate(scheduler.plms_timesteps ):
UpperCAmelCase_ = model(lowerCAmelCase , lowerCAmelCase )
UpperCAmelCase_ = scheduler.step_plms(lowerCAmelCase , lowerCAmelCase , lowerCAmelCase ).prev_sample
return sample
def __A ( self : str ):
'''simple docstring'''
UpperCAmelCase_ = dict(self.forward_default_kwargs )
UpperCAmelCase_ = kwargs.pop("num_inference_steps" , lowerCAmelCase )
for scheduler_class in self.scheduler_classes:
UpperCAmelCase_ = self.get_scheduler_config()
UpperCAmelCase_ = scheduler_class(**lowerCAmelCase )
UpperCAmelCase_ = self.dummy_sample
UpperCAmelCase_ = 0.1 * sample
if num_inference_steps is not None and hasattr(lowerCAmelCase , "set_timesteps" ):
scheduler.set_timesteps(lowerCAmelCase )
elif num_inference_steps is not None and not hasattr(lowerCAmelCase , "set_timesteps" ):
UpperCAmelCase_ = num_inference_steps
# copy over dummy past residuals (must be done after set_timesteps)
UpperCAmelCase_ = [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
UpperCAmelCase_ = dummy_past_residuals[:]
UpperCAmelCase_ = scheduler.step_prk(lowerCAmelCase , 0 , lowerCAmelCase , **lowerCAmelCase ).prev_sample
UpperCAmelCase_ = scheduler.step_prk(lowerCAmelCase , 1 , lowerCAmelCase , **lowerCAmelCase ).prev_sample
self.assertEqual(output_a.shape , sample.shape )
self.assertEqual(output_a.shape , output_a.shape )
UpperCAmelCase_ = scheduler.step_plms(lowerCAmelCase , 0 , lowerCAmelCase , **lowerCAmelCase ).prev_sample
UpperCAmelCase_ = scheduler.step_plms(lowerCAmelCase , 1 , lowerCAmelCase , **lowerCAmelCase ).prev_sample
self.assertEqual(output_a.shape , sample.shape )
self.assertEqual(output_a.shape , output_a.shape )
def __A ( self : Any ):
'''simple docstring'''
for timesteps in [100, 1_000]:
self.check_over_configs(num_train_timesteps=lowerCAmelCase )
def __A ( self : List[str] ):
'''simple docstring'''
for steps_offset in [0, 1]:
self.check_over_configs(steps_offset=lowerCAmelCase )
UpperCAmelCase_ = self.scheduler_classes[0]
UpperCAmelCase_ = self.get_scheduler_config(steps_offset=1 )
UpperCAmelCase_ = scheduler_class(**lowerCAmelCase )
scheduler.set_timesteps(10 )
assert torch.equal(
scheduler.timesteps , torch.LongTensor(
[901, 851, 851, 801, 801, 751, 751, 701, 701, 651, 651, 601, 601, 501, 401, 301, 201, 101, 1] ) , )
def __A ( self : Any ):
'''simple docstring'''
for beta_start, beta_end in zip([0.0_001, 0.001] , [0.002, 0.02] ):
self.check_over_configs(beta_start=lowerCAmelCase , beta_end=lowerCAmelCase )
def __A ( self : Dict ):
'''simple docstring'''
for schedule in ["linear", "squaredcos_cap_v2"]:
self.check_over_configs(beta_schedule=lowerCAmelCase )
def __A ( self : int ):
'''simple docstring'''
for prediction_type in ["epsilon", "v_prediction"]:
self.check_over_configs(prediction_type=lowerCAmelCase )
def __A ( self : Union[str, Any] ):
'''simple docstring'''
for t in [1, 5, 10]:
self.check_over_forward(time_step=lowerCAmelCase )
def __A ( self : Tuple ):
'''simple docstring'''
for t, num_inference_steps in zip([1, 5, 10] , [10, 50, 100] ):
self.check_over_forward(num_inference_steps=lowerCAmelCase )
def __A ( self : Dict ):
'''simple docstring'''
UpperCAmelCase_ = 27
for scheduler_class in self.scheduler_classes:
UpperCAmelCase_ = self.dummy_sample
UpperCAmelCase_ = 0.1 * sample
UpperCAmelCase_ = self.get_scheduler_config()
UpperCAmelCase_ = scheduler_class(**lowerCAmelCase )
scheduler.set_timesteps(lowerCAmelCase )
# before power of 3 fix, would error on first step, so we only need to do two
for i, t in enumerate(scheduler.prk_timesteps[:2] ):
UpperCAmelCase_ = scheduler.step_prk(lowerCAmelCase , lowerCAmelCase , lowerCAmelCase ).prev_sample
def __A ( self : str ):
'''simple docstring'''
with self.assertRaises(lowerCAmelCase ):
UpperCAmelCase_ = self.scheduler_classes[0]
UpperCAmelCase_ = self.get_scheduler_config()
UpperCAmelCase_ = scheduler_class(**lowerCAmelCase )
scheduler.step_plms(self.dummy_sample , 1 , self.dummy_sample ).prev_sample
def __A ( self : Dict ):
'''simple docstring'''
UpperCAmelCase_ = self.full_loop()
UpperCAmelCase_ = torch.sum(torch.abs(lowerCAmelCase ) )
UpperCAmelCase_ = torch.mean(torch.abs(lowerCAmelCase ) )
assert abs(result_sum.item() - 198.1_318 ) < 1e-2
assert abs(result_mean.item() - 0.2_580 ) < 1e-3
def __A ( self : List[str] ):
'''simple docstring'''
UpperCAmelCase_ = self.full_loop(prediction_type="v_prediction" )
UpperCAmelCase_ = torch.sum(torch.abs(lowerCAmelCase ) )
UpperCAmelCase_ = torch.mean(torch.abs(lowerCAmelCase ) )
assert abs(result_sum.item() - 67.3_986 ) < 1e-2
assert abs(result_mean.item() - 0.0_878 ) < 1e-3
def __A ( self : int ):
'''simple docstring'''
UpperCAmelCase_ = self.full_loop(set_alpha_to_one=lowerCAmelCase , beta_start=0.01 )
UpperCAmelCase_ = torch.sum(torch.abs(lowerCAmelCase ) )
UpperCAmelCase_ = torch.mean(torch.abs(lowerCAmelCase ) )
assert abs(result_sum.item() - 230.0_399 ) < 1e-2
assert abs(result_mean.item() - 0.2_995 ) < 1e-3
def __A ( self : int ):
'''simple docstring'''
UpperCAmelCase_ = self.full_loop(set_alpha_to_one=lowerCAmelCase , beta_start=0.01 )
UpperCAmelCase_ = torch.sum(torch.abs(lowerCAmelCase ) )
UpperCAmelCase_ = torch.mean(torch.abs(lowerCAmelCase ) )
assert abs(result_sum.item() - 186.9_482 ) < 1e-2
assert abs(result_mean.item() - 0.2_434 ) < 1e-3 | 268 |
import importlib
import torch
import yaml
from omegaconf import OmegaConf
from taming.models.vqgan import VQModel
def __lowerCAmelCase ( A , A=False ):
UpperCAmelCase_ = OmegaConf.load(A )
if display:
print(yaml.dump(OmegaConf.to_container(A ) ) )
return config
def __lowerCAmelCase ( A , A=None , A=None ):
if conf_path is None:
UpperCAmelCase_ = "./model_checkpoints/vqgan_only.yaml"
UpperCAmelCase_ = load_config(A , display=A )
UpperCAmelCase_ = VQModel(**config.model.params )
if ckpt_path is None:
UpperCAmelCase_ = "./model_checkpoints/vqgan_only.pt"
UpperCAmelCase_ = torch.load(A , map_location=A )
if ".ckpt" in ckpt_path:
UpperCAmelCase_ = sd["state_dict"]
model.load_state_dict(A , strict=A )
model.to(A )
del sd
return model
def __lowerCAmelCase ( A , A ):
UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ = model.encode(A )
print(F"VQGAN --- {model.__class__.__name__}: latent shape: {z.shape[2:]}" )
UpperCAmelCase_ = model.decode(A )
return xrec
def __lowerCAmelCase ( A , A=False ):
UpperCAmelCase_ , UpperCAmelCase_ = string.rsplit("." , 1 )
if reload:
UpperCAmelCase_ = importlib.import_module(A )
importlib.reload(A )
return getattr(importlib.import_module(A , package=A ) , cls )
def __lowerCAmelCase ( A ):
if "target" not in config:
raise KeyError("Expected key `target` to instantiate." )
return get_obj_from_str(config["target"] )(**config.get("params" , {} ) )
def __lowerCAmelCase ( A , A , A=True , A=True ):
UpperCAmelCase_ = instantiate_from_config(A )
if sd is not None:
model.load_state_dict(A )
if gpu:
model.cuda()
if eval_mode:
model.eval()
return {"model": model}
def __lowerCAmelCase ( A , A , A , A ):
# load the specified checkpoint
if ckpt:
UpperCAmelCase_ = torch.load(A , map_location="cpu" )
UpperCAmelCase_ = pl_sd["global_step"]
print(F"loaded model from global step {global_step}." )
else:
UpperCAmelCase_ = {"state_dict": None}
UpperCAmelCase_ = None
UpperCAmelCase_ = load_model_from_config(config.model , pl_sd["state_dict"] , gpu=A , eval_mode=A )["model"]
return model, global_step | 268 | 1 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from tokenizers import processors
from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_mbart import MBartTokenizer
else:
__a = None
__a = logging.get_logger(__name__)
__a = {'vocab_file': 'sentencepiece.bpe.model', 'tokenizer_file': 'tokenizer.json'}
__a = {
'vocab_file': {
'facebook/mbart-large-en-ro': (
'https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/sentencepiece.bpe.model'
),
'facebook/mbart-large-cc25': (
'https://huggingface.co/facebook/mbart-large-cc25/resolve/main/sentencepiece.bpe.model'
),
},
'tokenizer_file': {
'facebook/mbart-large-en-ro': 'https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/tokenizer.json',
'facebook/mbart-large-cc25': 'https://huggingface.co/facebook/mbart-large-cc25/resolve/main/tokenizer.json',
},
}
__a = {
'facebook/mbart-large-en-ro': 1_024,
'facebook/mbart-large-cc25': 1_024,
}
# fmt: off
__a = ['ar_AR', 'cs_CZ', 'de_DE', 'en_XX', 'es_XX', 'et_EE', 'fi_FI', 'fr_XX', 'gu_IN', 'hi_IN', 'it_IT', 'ja_XX', 'kk_KZ', 'ko_KR', 'lt_LT', 'lv_LV', 'my_MM', 'ne_NP', 'nl_XX', 'ro_RO', 'ru_RU', 'si_LK', 'tr_TR', 'vi_VN', 'zh_CN']
class A__ ( UpperCamelCase ):
"""simple docstring"""
UpperCamelCase_ : int = VOCAB_FILES_NAMES
UpperCamelCase_ : Any = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
UpperCamelCase_ : Union[str, Any] = PRETRAINED_VOCAB_FILES_MAP
UpperCamelCase_ : List[Any] = ['''input_ids''', '''attention_mask''']
UpperCamelCase_ : str = MBartTokenizer
UpperCamelCase_ : List[Any] = []
UpperCamelCase_ : int = []
def __init__( self : str , lowerCAmelCase__ : str=None , lowerCAmelCase__ : Optional[int]=None , lowerCAmelCase__ : int="<s>" , lowerCAmelCase__ : Optional[int]="</s>" , lowerCAmelCase__ : Union[str, Any]="</s>" , lowerCAmelCase__ : List[str]="<s>" , lowerCAmelCase__ : Union[str, Any]="<unk>" , lowerCAmelCase__ : Tuple="<pad>" , lowerCAmelCase__ : Dict="<mask>" , lowerCAmelCase__ : Optional[int]=None , lowerCAmelCase__ : List[Any]=None , lowerCAmelCase__ : str=None , **lowerCAmelCase__ : List[Any] , ) -> Dict:
"""simple docstring"""
_UpperCAmelCase : Dict = AddedToken(A_ , lstrip=A_ , rstrip=A_ ) if isinstance(A_ , A_ ) else mask_token
super().__init__(
vocab_file=A_ , tokenizer_file=A_ , bos_token=A_ , eos_token=A_ , sep_token=A_ , cls_token=A_ , unk_token=A_ , pad_token=A_ , mask_token=A_ , src_lang=A_ , tgt_lang=A_ , additional_special_tokens=A_ , **A_ , )
_UpperCAmelCase : Any = vocab_file
_UpperCAmelCase : Tuple = False if not self.vocab_file else True
_UpperCAmelCase : Any = FAIRSEQ_LANGUAGE_CODES.copy()
if additional_special_tokens is not None:
# Only add those special tokens if they are not already there.
_additional_special_tokens.extend(
[t for t in additional_special_tokens if t not in _additional_special_tokens] )
self.add_special_tokens({"additional_special_tokens": _additional_special_tokens} )
_UpperCAmelCase : Optional[Any] = {
lang_code: self.convert_tokens_to_ids(A_ ) for lang_code in FAIRSEQ_LANGUAGE_CODES
}
_UpperCAmelCase : Optional[Any] = src_lang if src_lang is not None else """en_XX"""
_UpperCAmelCase : List[str] = self.convert_tokens_to_ids(self._src_lang )
_UpperCAmelCase : Union[str, Any] = tgt_lang
self.set_src_lang_special_tokens(self._src_lang )
@property
def _lowerCAmelCase ( self : Optional[int] ) -> Optional[Any]:
"""simple docstring"""
return self._src_lang
@src_lang.setter
def _lowerCAmelCase ( self : str , lowerCAmelCase__ : Optional[Any] ) -> Optional[Any]:
"""simple docstring"""
_UpperCAmelCase : Optional[Any] = new_src_lang
self.set_src_lang_special_tokens(self._src_lang )
def _lowerCAmelCase ( self : List[str] , lowerCAmelCase__ : List[str] , lowerCAmelCase__ : Optional[Any] = None ) -> Union[str, Any]:
"""simple docstring"""
if token_ids_a is None:
return self.prefix_tokens + token_ids_a + self.suffix_tokens
# We don't expect to process pairs, but leave the pair logic for API consistency
return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens
def _lowerCAmelCase ( self : int , lowerCAmelCase__ : str , lowerCAmelCase__ : str = None ) -> Dict:
"""simple docstring"""
_UpperCAmelCase : List[Any] = [self.sep_token_id]
_UpperCAmelCase : List[Any] = [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]
def _lowerCAmelCase ( self : Union[str, Any] , lowerCAmelCase__ : str , lowerCAmelCase__ : List[Any] , lowerCAmelCase__ : Optional[Any] , lowerCAmelCase__ : Any , **lowerCAmelCase__ : Union[str, Any] ) -> Union[str, Any]:
"""simple docstring"""
if src_lang is None or tgt_lang is None:
raise ValueError("Translation requires a `src_lang` and a `tgt_lang` for this model" )
_UpperCAmelCase : Dict = src_lang
_UpperCAmelCase : Dict = self(A_ , add_special_tokens=A_ , return_tensors=A_ , **A_ )
_UpperCAmelCase : Optional[Any] = self.convert_tokens_to_ids(A_ )
_UpperCAmelCase : str = tgt_lang_id
return inputs
def _lowerCAmelCase ( self : List[str] , lowerCAmelCase__ : Any , lowerCAmelCase__ : Optional[Any] = "en_XX" , lowerCAmelCase__ : List[str] = None , lowerCAmelCase__ : Optional[int] = "ro_RO" , **lowerCAmelCase__ : Any , ) -> List[str]:
"""simple docstring"""
_UpperCAmelCase : int = src_lang
_UpperCAmelCase : int = tgt_lang
return super().prepare_seqaseq_batch(A_ , A_ , **A_ )
def _lowerCAmelCase ( self : Tuple ) -> str:
"""simple docstring"""
return self.set_src_lang_special_tokens(self.src_lang )
def _lowerCAmelCase ( self : Dict ) -> str:
"""simple docstring"""
return self.set_tgt_lang_special_tokens(self.tgt_lang )
def _lowerCAmelCase ( self : int , lowerCAmelCase__ : int ) -> int:
"""simple docstring"""
_UpperCAmelCase : int = self.convert_tokens_to_ids(A_ )
_UpperCAmelCase : Union[str, Any] = []
_UpperCAmelCase : Any = [self.eos_token_id, self.cur_lang_code]
_UpperCAmelCase : Union[str, Any] = self.convert_ids_to_tokens(self.prefix_tokens )
_UpperCAmelCase : Union[str, Any] = self.convert_ids_to_tokens(self.suffix_tokens )
_UpperCAmelCase : str = processors.TemplateProcessing(
single=prefix_tokens_str + ["$A"] + suffix_tokens_str , pair=prefix_tokens_str + ["$A", "$B"] + suffix_tokens_str , special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str , self.prefix_tokens + self.suffix_tokens ) ) , )
def _lowerCAmelCase ( self : Tuple , lowerCAmelCase__ : Any ) -> Any:
"""simple docstring"""
_UpperCAmelCase : Optional[int] = self.convert_tokens_to_ids(A_ )
_UpperCAmelCase : str = []
_UpperCAmelCase : Any = [self.eos_token_id, self.cur_lang_code]
_UpperCAmelCase : List[Any] = self.convert_ids_to_tokens(self.prefix_tokens )
_UpperCAmelCase : Union[str, Any] = self.convert_ids_to_tokens(self.suffix_tokens )
_UpperCAmelCase : int = processors.TemplateProcessing(
single=prefix_tokens_str + ["$A"] + suffix_tokens_str , pair=prefix_tokens_str + ["$A", "$B"] + suffix_tokens_str , special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str , self.prefix_tokens + self.suffix_tokens ) ) , )
def _lowerCAmelCase ( self : Union[str, Any] , lowerCAmelCase__ : Union[str, Any] , lowerCAmelCase__ : List[str] = None ) -> List[Any]:
"""simple docstring"""
if not self.can_save_slow_tokenizer:
raise ValueError(
"Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
"tokenizer." )
if not os.path.isdir(A_ ):
logger.error(F"""Vocabulary path ({save_directory}) should be a directory.""" )
return
_UpperCAmelCase : int = os.path.join(
A_ , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(A_ ):
copyfile(self.vocab_file , A_ )
return (out_vocab_file,) | 494 |
import unittest
from transformers import BertGenerationTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
SCREAMING_SNAKE_CASE__ : Optional[int] = '▁'
SCREAMING_SNAKE_CASE__ : Optional[Any] = get_tests_dir('fixtures/test_sentencepiece.model')
@require_sentencepiece
class _SCREAMING_SNAKE_CASE ( A , unittest.TestCase ):
__SCREAMING_SNAKE_CASE = BertGenerationTokenizer
__SCREAMING_SNAKE_CASE = False
__SCREAMING_SNAKE_CASE = True
def __snake_case( self ):
super().setUp()
_UpperCAmelCase : Optional[int] = BertGenerationTokenizer(A_ , keep_accents=A_ )
tokenizer.save_pretrained(self.tmpdirname )
def __snake_case( self ):
_UpperCAmelCase : Optional[int] = """<s>"""
_UpperCAmelCase : Any = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(A_ ) , A_ )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(A_ ) , A_ )
def __snake_case( self ):
_UpperCAmelCase : Optional[int] = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , """<unk>""" )
self.assertEqual(vocab_keys[1] , """<s>""" )
self.assertEqual(vocab_keys[-1] , """<pad>""" )
self.assertEqual(len(A_ ) , 10_02 )
def __snake_case( self ):
self.assertEqual(self.get_tokenizer().vocab_size , 10_00 )
def __snake_case( self ):
_UpperCAmelCase : Union[str, Any] = BertGenerationTokenizer(A_ , keep_accents=A_ )
_UpperCAmelCase : List[Any] = tokenizer.tokenize("""This is a test""" )
self.assertListEqual(A_ , ["""▁This""", """▁is""", """▁a""", """▁t""", """est"""] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(A_ ) , [2_85, 46, 10, 1_70, 3_82] , )
_UpperCAmelCase : int = tokenizer.tokenize("""I was born in 92000, and this is falsé.""" )
self.assertListEqual(
A_ , [
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""",
""".""",
] , )
_UpperCAmelCase : List[str] = tokenizer.convert_tokens_to_ids(A_ )
self.assertListEqual(
A_ , [8, 21, 84, 55, 24, 19, 7, 0, 6_02, 3_47, 3_47, 3_47, 3, 12, 66, 46, 72, 80, 6, 0, 4] , )
_UpperCAmelCase : Tuple = tokenizer.convert_ids_to_tokens(A_ )
self.assertListEqual(
A_ , [
SPIECE_UNDERLINE + """I""",
SPIECE_UNDERLINE + """was""",
SPIECE_UNDERLINE + """b""",
"""or""",
"""n""",
SPIECE_UNDERLINE + """in""",
SPIECE_UNDERLINE + """""",
"""<unk>""",
"""2""",
"""0""",
"""0""",
"""0""",
""",""",
SPIECE_UNDERLINE + """and""",
SPIECE_UNDERLINE + """this""",
SPIECE_UNDERLINE + """is""",
SPIECE_UNDERLINE + """f""",
"""al""",
"""s""",
"""<unk>""",
""".""",
] , )
@cached_property
def __snake_case( self ):
return BertGenerationTokenizer.from_pretrained("""google/bert_for_seq_generation_L-24_bbc_encoder""" )
@slow
def __snake_case( self ):
_UpperCAmelCase : Optional[Any] = """Hello World!"""
_UpperCAmelCase : str = [1_85_36, 22_60, 1_01]
self.assertListEqual(A_ , self.big_tokenizer.encode(A_ ) )
@slow
def __snake_case( self ):
_UpperCAmelCase : List[str] = (
"""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"""
)
_UpperCAmelCase : List[Any] = [
8_71,
4_19,
3_58,
9_46,
9_91,
25_21,
4_52,
3_58,
13_57,
3_87,
77_51,
35_36,
1_12,
9_85,
4_56,
1_26,
8_65,
9_38,
54_00,
57_34,
4_58,
13_68,
4_67,
7_86,
24_62,
52_46,
11_59,
6_33,
8_65,
45_19,
4_57,
5_82,
8_52,
25_57,
4_27,
9_16,
5_08,
4_05,
3_43_24,
4_97,
3_91,
4_08,
1_13_42,
12_44,
3_85,
1_00,
9_38,
9_85,
4_56,
5_74,
3_62,
1_25_97,
32_00,
31_29,
11_72,
]
self.assertListEqual(A_ , self.big_tokenizer.encode(A_ ) )
@require_torch
@slow
def __snake_case( self ):
import torch
from transformers import BertGenerationConfig, BertGenerationEncoder
# Build sequence
_UpperCAmelCase : Optional[Any] = list(self.big_tokenizer.get_vocab().keys() )[:10]
_UpperCAmelCase : Tuple = """ """.join(A_ )
_UpperCAmelCase : List[Any] = self.big_tokenizer.encode_plus(A_ , return_tensors="""pt""" , return_token_type_ids=A_ )
_UpperCAmelCase : Any = self.big_tokenizer.batch_encode_plus(
[sequence + """ """ + sequence] , return_tensors="""pt""" , return_token_type_ids=A_ )
_UpperCAmelCase : int = BertGenerationConfig()
_UpperCAmelCase : Dict = BertGenerationEncoder(A_ )
assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size
with torch.no_grad():
model(**A_ )
model(**A_ )
@slow
def __snake_case( self ):
# fmt: off
_UpperCAmelCase : Optional[int] = {"""input_ids""": [[3_92_86, 4_58, 3_63_35, 20_01, 4_56, 1_30_73, 1_32_66, 4_55, 1_13, 77_46, 17_41, 1_11_57, 3_91, 1_30_73, 1_32_66, 4_55, 1_13, 39_67, 3_54_12, 1_13, 49_36, 1_09, 38_70, 23_77, 1_13, 3_00_84, 4_57_20, 4_58, 1_34, 1_74_96, 1_12, 5_03, 1_16_72, 1_13, 1_18, 1_12, 56_65, 1_33_47, 3_86_87, 1_12, 14_96, 3_13_89, 1_12, 32_68, 4_72_64, 1_34, 9_62, 1_12, 1_63_77, 80_35, 2_31_30, 4_30, 1_21_69, 1_55_18, 2_85_92, 4_58, 1_46, 4_16_97, 1_09, 3_91, 1_21_69, 1_55_18, 1_66_89, 4_58, 1_46, 4_13_58, 1_09, 4_52, 7_26, 40_34, 1_11, 7_63, 3_54_12, 50_82, 3_88, 19_03, 1_11, 90_51, 3_91, 28_70, 4_89_18, 19_00, 11_23, 5_50, 9_98, 1_12, 95_86, 1_59_85, 4_55, 3_91, 4_10, 2_29_55, 3_76_36, 1_14], [4_48, 1_74_96, 4_19, 36_63, 3_85, 7_63, 1_13, 2_75_33, 28_70, 32_83, 1_30_43, 16_39, 2_47_13, 5_23, 6_56, 2_40_13, 1_85_50, 25_21, 5_17, 2_70_14, 2_12_44, 4_20, 12_12, 14_65, 3_91, 9_27, 48_33, 3_88, 5_78, 1_17_86, 1_14, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [4_84, 21_69, 76_87, 2_19_32, 1_81_46, 7_26, 3_63, 1_70_32, 33_91, 1_14, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], """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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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=A_ , model_name="""google/bert_for_seq_generation_L-24_bbc_encoder""" , revision="""c817d1fd1be2ffa69431227a1fe320544943d4db""" , )
| 643 | 0 |
"""simple docstring"""
import os
# Precomputes a list of the 100 first triangular numbers
UpperCAmelCase__ =[int(0.5 * n * (n + 1)) for n in range(1, 101)]
def lowerCAmelCase_ ( ):
"""simple docstring"""
__lowercase = os.path.dirname(os.path.realpath(SCREAMING_SNAKE_CASE_ ) )
__lowercase = os.path.join(SCREAMING_SNAKE_CASE_ , """words.txt""" )
__lowercase = """"""
with open(SCREAMING_SNAKE_CASE_ ) as f:
__lowercase = f.readline()
__lowercase = [word.strip("""\"""" ) for word in words.strip("""\r\n""" ).split(""",""" )]
__lowercase = [
word
for word in [sum(ord(SCREAMING_SNAKE_CASE_ ) - 64 for x in word ) for word in words]
if word in TRIANGULAR_NUMBERS
]
return len(SCREAMING_SNAKE_CASE_ )
if __name__ == "__main__":
print(solution())
| 700 |
"""simple docstring"""
import argparse
import logging
import pickle
from collections import Counter
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", datefmt="%m/%d/%Y %H:%M:%S", level=logging.INFO
)
UpperCAmelCase__ =logging.getLogger(__name__)
if __name__ == "__main__":
UpperCAmelCase__ =argparse.ArgumentParser(
description="Token Counts for smoothing the masking probabilities in MLM (cf XLM/word2vec)"
)
parser.add_argument(
"--data_file", type=str, default="data/dump.bert-base-uncased.pickle", help="The binarized dataset."
)
parser.add_argument(
"--token_counts_dump", type=str, default="data/token_counts.bert-base-uncased.pickle", help="The dump file."
)
parser.add_argument("--vocab_size", default=3_0522, type=int)
UpperCAmelCase__ =parser.parse_args()
logger.info(f"""Loading data from {args.data_file}""")
with open(args.data_file, "rb") as fp:
UpperCAmelCase__ =pickle.load(fp)
logger.info("Counting occurrences for MLM.")
UpperCAmelCase__ =Counter()
for tk_ids in data:
counter.update(tk_ids)
UpperCAmelCase__ =[0] * args.vocab_size
for k, v in counter.items():
UpperCAmelCase__ =v
logger.info(f"""Dump to {args.token_counts_dump}""")
with open(args.token_counts_dump, "wb") as handle:
pickle.dump(counts, handle, protocol=pickle.HIGHEST_PROTOCOL)
| 442 | 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 : Optional[int] = logging.get_logger(__name__)
__lowerCAmelCase : int = {
"xlm-roberta-base": "https://huggingface.co/xlm-roberta-base/resolve/main/config.json",
"xlm-roberta-large": "https://huggingface.co/xlm-roberta-large/resolve/main/config.json",
"xlm-roberta-large-finetuned-conll02-dutch": (
"https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/config.json"
),
"xlm-roberta-large-finetuned-conll02-spanish": (
"https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/config.json"
),
"xlm-roberta-large-finetuned-conll03-english": (
"https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/config.json"
),
"xlm-roberta-large-finetuned-conll03-german": (
"https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/config.json"
),
}
class A ( lowercase_ ):
a_ = '''xlm-roberta'''
def __init__( self : Dict , __a : List[str]=3_0_5_2_2 , __a : Any=7_6_8 , __a : str=1_2 , __a : Tuple=1_2 , __a : Optional[Any]=3_0_7_2 , __a : Tuple="gelu" , __a : Optional[Any]=0.1 , __a : Tuple=0.1 , __a : Optional[Any]=5_1_2 , __a : List[Any]=2 , __a : int=0.0_2 , __a : List[str]=1e-12 , __a : Union[str, Any]=1 , __a : Any=0 , __a : List[str]=2 , __a : int="absolute" , __a : Tuple=True , __a : List[Any]=None , **__a : str , ) -> Optional[Any]:
super().__init__(pad_token_id=__a , bos_token_id=__a , eos_token_id=__a , **__a )
__UpperCAmelCase = vocab_size
__UpperCAmelCase = hidden_size
__UpperCAmelCase = num_hidden_layers
__UpperCAmelCase = num_attention_heads
__UpperCAmelCase = hidden_act
__UpperCAmelCase = intermediate_size
__UpperCAmelCase = hidden_dropout_prob
__UpperCAmelCase = attention_probs_dropout_prob
__UpperCAmelCase = max_position_embeddings
__UpperCAmelCase = type_vocab_size
__UpperCAmelCase = initializer_range
__UpperCAmelCase = layer_norm_eps
__UpperCAmelCase = position_embedding_type
__UpperCAmelCase = use_cache
__UpperCAmelCase = classifier_dropout
class A ( lowercase_ ):
@property
def snake_case__ ( self : Optional[Any] ) -> Optional[int]:
if self.task == "multiple-choice":
__UpperCAmelCase = {0: 'batch', 1: 'choice', 2: 'sequence'}
else:
__UpperCAmelCase = {0: 'batch', 1: 'sequence'}
return OrderedDict(
[
('''input_ids''', dynamic_axis),
('''attention_mask''', dynamic_axis),
] )
| 262 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_glpn import GLPNImageProcessor
SCREAMING_SNAKE_CASE_ = logging.get_logger(__name__)
class SCREAMING_SNAKE_CASE ( lowercase_ ):
'''simple docstring'''
def __init__( self : Tuple , *snake_case : Any , **snake_case : Optional[int] ):
"""simple docstring"""
warnings.warn(
'The class GLPNFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'
' use GLPNImageProcessor instead.' , snake_case , )
super().__init__(*snake_case , **snake_case )
| 517 | 0 |
def UpperCAmelCase ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ) -> int:
'''simple docstring'''
if height >= 1:
move_tower(height - 1 , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
move_disk(UpperCamelCase__ , UpperCamelCase__ )
move_tower(height - 1 , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
def UpperCAmelCase ( UpperCamelCase__ , UpperCamelCase__ ) -> List[Any]:
'''simple docstring'''
print("""moving disk from""" , UpperCamelCase__ , """to""" , UpperCamelCase__ )
def UpperCAmelCase ( ) -> Optional[Any]:
'''simple docstring'''
__lowerCAmelCase = int(input("""Height of hanoi: """ ).strip() )
move_tower(UpperCamelCase__ , """A""" , """B""" , """C""" )
if __name__ == "__main__":
main()
| 701 |
import argparse
import os
import re
__A : List[Any] = "src/diffusers"
# Pattern that looks at the indentation in a line.
__A : Dict = re.compile(r"^(\s*)\S")
# Pattern that matches `"key":" and puts `key` in group 0.
__A : Optional[int] = re.compile(r"^\s*\"([^\"]+)\":")
# Pattern that matches `_import_structure["key"]` and puts `key` in group 0.
__A : Union[str, Any] = re.compile(r"^\s*_import_structure\[\"([^\"]+)\"\]")
# Pattern that matches `"key",` and puts `key` in group 0.
__A : List[Any] = re.compile(r"^\s*\"([^\"]+)\",\s*$")
# Pattern that matches any `[stuff]` and puts `stuff` in group 0.
__A : List[str] = re.compile(r"\[([^\]]+)\]")
def UpperCAmelCase ( UpperCamelCase__ ) -> Optional[int]:
'''simple docstring'''
__lowerCAmelCase = _re_indent.search(UpperCamelCase__ )
return "" if search is None else search.groups()[0]
def UpperCAmelCase ( UpperCamelCase__ , UpperCamelCase__="" , UpperCamelCase__=None , UpperCamelCase__=None ) -> List[str]:
'''simple docstring'''
__lowerCAmelCase = 0
__lowerCAmelCase = code.split("""\n""" )
if start_prompt is not None:
while not lines[index].startswith(UpperCamelCase__ ):
index += 1
__lowerCAmelCase = ["""\n""".join(lines[:index] )]
else:
__lowerCAmelCase = []
# We split into blocks until we get to the `end_prompt` (or the end of the block).
__lowerCAmelCase = [lines[index]]
index += 1
while index < len(UpperCamelCase__ ) and (end_prompt is None or not lines[index].startswith(UpperCamelCase__ )):
if len(lines[index] ) > 0 and get_indent(lines[index] ) == indent_level:
if len(UpperCamelCase__ ) > 0 and get_indent(current_block[-1] ).startswith(indent_level + """ """ ):
current_block.append(lines[index] )
blocks.append("""\n""".join(UpperCamelCase__ ) )
if index < len(UpperCamelCase__ ) - 1:
__lowerCAmelCase = [lines[index + 1]]
index += 1
else:
__lowerCAmelCase = []
else:
blocks.append("""\n""".join(UpperCamelCase__ ) )
__lowerCAmelCase = [lines[index]]
else:
current_block.append(lines[index] )
index += 1
# Adds current block if it's nonempty.
if len(UpperCamelCase__ ) > 0:
blocks.append("""\n""".join(UpperCamelCase__ ) )
# Add final block after end_prompt if provided.
if end_prompt is not None and index < len(UpperCamelCase__ ):
blocks.append("""\n""".join(lines[index:] ) )
return blocks
def UpperCAmelCase ( UpperCamelCase__ ) -> Dict:
'''simple docstring'''
def _inner(UpperCamelCase__ ):
return key(UpperCamelCase__ ).lower().replace("""_""" , """""" )
return _inner
def UpperCAmelCase ( UpperCamelCase__ , UpperCamelCase__=None ) -> Tuple:
'''simple docstring'''
def noop(UpperCamelCase__ ):
return x
if key is None:
__lowerCAmelCase = noop
# Constants are all uppercase, they go first.
__lowerCAmelCase = [obj for obj in objects if key(UpperCamelCase__ ).isupper()]
# Classes are not all uppercase but start with a capital, they go second.
__lowerCAmelCase = [obj for obj in objects if key(UpperCamelCase__ )[0].isupper() and not key(UpperCamelCase__ ).isupper()]
# Functions begin with a lowercase, they go last.
__lowerCAmelCase = [obj for obj in objects if not key(UpperCamelCase__ )[0].isupper()]
__lowerCAmelCase = ignore_underscore(UpperCamelCase__ )
return sorted(UpperCamelCase__ , key=UpperCamelCase__ ) + sorted(UpperCamelCase__ , key=UpperCamelCase__ ) + sorted(UpperCamelCase__ , key=UpperCamelCase__ )
def UpperCAmelCase ( UpperCamelCase__ ) -> Optional[int]:
'''simple docstring'''
def _replace(UpperCamelCase__ ):
__lowerCAmelCase = match.groups()[0]
if "," not in imports:
return F'''[{imports}]'''
__lowerCAmelCase = [part.strip().replace("""\"""" , """""" ) for part in imports.split(""",""" )]
# We will have a final empty element if the line finished with a comma.
if len(keys[-1] ) == 0:
__lowerCAmelCase = keys[:-1]
return "[" + ", ".join([F'''"{k}"''' for k in sort_objects(UpperCamelCase__ )] ) + "]"
__lowerCAmelCase = import_statement.split("""\n""" )
if len(UpperCamelCase__ ) > 3:
# Here we have to sort internal imports that are on several lines (one per name):
# key: [
# "object1",
# "object2",
# ...
# ]
# We may have to ignore one or two lines on each side.
__lowerCAmelCase = 2 if lines[1].strip() == """[""" else 1
__lowerCAmelCase = [(i, _re_strip_line.search(UpperCamelCase__ ).groups()[0]) for i, line in enumerate(lines[idx:-idx] )]
__lowerCAmelCase = sort_objects(UpperCamelCase__ , key=lambda UpperCamelCase__ : x[1] )
__lowerCAmelCase = [lines[x[0] + idx] for x in sorted_indices]
return "\n".join(lines[:idx] + sorted_lines + lines[-idx:] )
elif len(UpperCamelCase__ ) == 3:
# Here we have to sort internal imports that are on one separate line:
# key: [
# "object1", "object2", ...
# ]
if _re_bracket_content.search(lines[1] ) is not None:
__lowerCAmelCase = _re_bracket_content.sub(_replace , lines[1] )
else:
__lowerCAmelCase = [part.strip().replace("""\"""" , """""" ) for part in lines[1].split(""",""" )]
# We will have a final empty element if the line finished with a comma.
if len(keys[-1] ) == 0:
__lowerCAmelCase = keys[:-1]
__lowerCAmelCase = get_indent(lines[1] ) + """, """.join([F'''"{k}"''' for k in sort_objects(UpperCamelCase__ )] )
return "\n".join(UpperCamelCase__ )
else:
# Finally we have to deal with imports fitting on one line
__lowerCAmelCase = _re_bracket_content.sub(_replace , UpperCamelCase__ )
return import_statement
def UpperCAmelCase ( UpperCamelCase__ , UpperCamelCase__=True ) -> Optional[int]:
'''simple docstring'''
with open(UpperCamelCase__ , """r""" ) as f:
__lowerCAmelCase = f.read()
if "_import_structure" not in code:
return
# Blocks of indent level 0
__lowerCAmelCase = split_code_in_indented_blocks(
UpperCamelCase__ , start_prompt="""_import_structure = {""" , end_prompt="""if TYPE_CHECKING:""" )
# We ignore block 0 (everything until start_prompt) and the last block (everything after end_prompt).
for block_idx in range(1 , len(UpperCamelCase__ ) - 1 ):
# Check if the block contains some `_import_structure`s thingy to sort.
__lowerCAmelCase = main_blocks[block_idx]
__lowerCAmelCase = block.split("""\n""" )
# Get to the start of the imports.
__lowerCAmelCase = 0
while line_idx < len(UpperCamelCase__ ) and "_import_structure" not in block_lines[line_idx]:
# Skip dummy import blocks
if "import dummy" in block_lines[line_idx]:
__lowerCAmelCase = len(UpperCamelCase__ )
else:
line_idx += 1
if line_idx >= len(UpperCamelCase__ ):
continue
# Ignore beginning and last line: they don't contain anything.
__lowerCAmelCase = """\n""".join(block_lines[line_idx:-1] )
__lowerCAmelCase = get_indent(block_lines[1] )
# Slit the internal block into blocks of indent level 1.
__lowerCAmelCase = split_code_in_indented_blocks(UpperCamelCase__ , indent_level=UpperCamelCase__ )
# We have two categories of import key: list or _import_structure[key].append/extend
__lowerCAmelCase = _re_direct_key if """_import_structure""" in block_lines[0] else _re_indirect_key
# Grab the keys, but there is a trap: some lines are empty or just comments.
__lowerCAmelCase = [(pattern.search(UpperCamelCase__ ).groups()[0] if pattern.search(UpperCamelCase__ ) is not None else None) for b in internal_blocks]
# We only sort the lines with a key.
__lowerCAmelCase = [(i, key) for i, key in enumerate(UpperCamelCase__ ) if key is not None]
__lowerCAmelCase = [x[0] for x in sorted(UpperCamelCase__ , key=lambda UpperCamelCase__ : x[1] )]
# We reorder the blocks by leaving empty lines/comments as they were and reorder the rest.
__lowerCAmelCase = 0
__lowerCAmelCase = []
for i in range(len(UpperCamelCase__ ) ):
if keys[i] is None:
reordered_blocks.append(internal_blocks[i] )
else:
__lowerCAmelCase = sort_objects_in_import(internal_blocks[sorted_indices[count]] )
reordered_blocks.append(UpperCamelCase__ )
count += 1
# And we put our main block back together with its first and last line.
__lowerCAmelCase = """\n""".join(block_lines[:line_idx] + reordered_blocks + [block_lines[-1]] )
if code != "\n".join(UpperCamelCase__ ):
if check_only:
return True
else:
print(F'''Overwriting {file}.''' )
with open(UpperCamelCase__ , """w""" ) as f:
f.write("""\n""".join(UpperCamelCase__ ) )
def UpperCAmelCase ( UpperCamelCase__=True ) -> Tuple:
'''simple docstring'''
__lowerCAmelCase = []
for root, _, files in os.walk(UpperCamelCase__ ):
if "__init__.py" in files:
__lowerCAmelCase = sort_imports(os.path.join(UpperCamelCase__ , """__init__.py""" ) , check_only=UpperCamelCase__ )
if result:
__lowerCAmelCase = [os.path.join(UpperCamelCase__ , """__init__.py""" )]
if len(UpperCamelCase__ ) > 0:
raise ValueError(F'''Would overwrite {len(UpperCamelCase__ )} files, run `make style`.''' )
if __name__ == "__main__":
__A : Tuple = argparse.ArgumentParser()
parser.add_argument("--check_only", action="store_true", help="Whether to only check or fix style.")
__A : Any = parser.parse_args()
sort_imports_in_all_inits(check_only=args.check_only)
| 334 | 0 |
'''simple docstring'''
from __future__ import annotations
import time
from math import sqrt
# 1 for manhattan, 0 for euclidean
_UpperCamelCase : List[str] = 0
_UpperCamelCase : Any = [
[0, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0], # 0 are free path whereas 1's are obstacles
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0],
[1, 0, 1, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 1, 0, 0],
]
_UpperCamelCase : Tuple = [[-1, 0], [0, -1], [1, 0], [0, 1]] # up, left, down, right
_UpperCamelCase : str = tuple[int, int]
class _lowercase:
"""simple docstring"""
def __init__( self: List[str] ,a: int ,a: int ,a: int ,a: int ,a: int ,a: Node | None ,):
__UpperCAmelCase = pos_x
__UpperCAmelCase = pos_y
__UpperCAmelCase = (pos_y, pos_x)
__UpperCAmelCase = goal_x
__UpperCAmelCase = goal_y
__UpperCAmelCase = g_cost
__UpperCAmelCase = parent
__UpperCAmelCase = self.calculate_heuristic()
__UpperCAmelCase = self.g_cost + self.h_cost
def snake_case ( self: Tuple ):
__UpperCAmelCase = self.pos_x - self.goal_x
__UpperCAmelCase = self.pos_y - self.goal_y
if HEURISTIC == 1:
return abs(a ) + abs(a )
else:
return sqrt(dy**2 + dx**2 )
def __lt__( self: Any ,a: Node ):
return self.f_cost < other.f_cost
class _lowercase:
"""simple docstring"""
def __init__( self: Tuple ,a: TPosition ,a: TPosition ):
__UpperCAmelCase = Node(start[1] ,start[0] ,goal[1] ,goal[0] ,0 ,a )
__UpperCAmelCase = Node(goal[1] ,goal[0] ,goal[1] ,goal[0] ,99999 ,a )
__UpperCAmelCase = [self.start]
__UpperCAmelCase = []
__UpperCAmelCase = False
def snake_case ( self: List[Any] ):
while self.open_nodes:
# Open Nodes are sorted using __lt__
self.open_nodes.sort()
__UpperCAmelCase = self.open_nodes.pop(0 )
if current_node.pos == self.target.pos:
return self.retrace_path(a )
self.closed_nodes.append(a )
__UpperCAmelCase = self.get_successors(a )
for child_node in successors:
if child_node in self.closed_nodes:
continue
if child_node not in self.open_nodes:
self.open_nodes.append(a )
else:
# retrieve the best current path
__UpperCAmelCase = self.open_nodes.pop(self.open_nodes.index(a ) )
if child_node.g_cost < better_node.g_cost:
self.open_nodes.append(a )
else:
self.open_nodes.append(a )
return [self.start.pos]
def snake_case ( self: Optional[int] ,a: Node ):
__UpperCAmelCase = []
for action in delta:
__UpperCAmelCase = parent.pos_x + action[1]
__UpperCAmelCase = parent.pos_y + action[0]
if not (0 <= pos_x <= len(grid[0] ) - 1 and 0 <= pos_y <= len(a ) - 1):
continue
if grid[pos_y][pos_x] != 0:
continue
successors.append(
Node(
a ,a ,self.target.pos_y ,self.target.pos_x ,parent.g_cost + 1 ,a ,) )
return successors
def snake_case ( self: Optional[Any] ,a: Node | None ):
__UpperCAmelCase = node
__UpperCAmelCase = []
while current_node is not None:
path.append((current_node.pos_y, current_node.pos_x) )
__UpperCAmelCase = current_node.parent
path.reverse()
return path
class _lowercase:
"""simple docstring"""
def __init__( self: Optional[int] ,a: TPosition ,a: TPosition ):
__UpperCAmelCase = AStar(a ,a )
__UpperCAmelCase = AStar(a ,a )
__UpperCAmelCase = False
def snake_case ( self: Tuple ):
while self.fwd_astar.open_nodes or self.bwd_astar.open_nodes:
self.fwd_astar.open_nodes.sort()
self.bwd_astar.open_nodes.sort()
__UpperCAmelCase = self.fwd_astar.open_nodes.pop(0 )
__UpperCAmelCase = self.bwd_astar.open_nodes.pop(0 )
if current_bwd_node.pos == current_fwd_node.pos:
return self.retrace_bidirectional_path(
a ,a )
self.fwd_astar.closed_nodes.append(a )
self.bwd_astar.closed_nodes.append(a )
__UpperCAmelCase = current_bwd_node
__UpperCAmelCase = current_fwd_node
__UpperCAmelCase = {
self.fwd_astar: self.fwd_astar.get_successors(a ),
self.bwd_astar: self.bwd_astar.get_successors(a ),
}
for astar in [self.fwd_astar, self.bwd_astar]:
for child_node in successors[astar]:
if child_node in astar.closed_nodes:
continue
if child_node not in astar.open_nodes:
astar.open_nodes.append(a )
else:
# retrieve the best current path
__UpperCAmelCase = astar.open_nodes.pop(
astar.open_nodes.index(a ) )
if child_node.g_cost < better_node.g_cost:
astar.open_nodes.append(a )
else:
astar.open_nodes.append(a )
return [self.fwd_astar.start.pos]
def snake_case ( self: Optional[Any] ,a: Node ,a: Node ):
__UpperCAmelCase = self.fwd_astar.retrace_path(a )
__UpperCAmelCase = self.bwd_astar.retrace_path(a )
bwd_path.pop()
bwd_path.reverse()
__UpperCAmelCase = fwd_path + bwd_path
return path
if __name__ == "__main__":
# all coordinates are given in format [y,x]
_UpperCamelCase : Union[str, Any] = (0, 0)
_UpperCamelCase : Optional[int] = (len(grid) - 1, len(grid[0]) - 1)
for elem in grid:
print(elem)
_UpperCamelCase : Any = time.time()
_UpperCamelCase : Optional[Any] = AStar(init, goal)
_UpperCamelCase : Optional[Any] = a_star.search()
_UpperCamelCase : Dict = time.time() - start_time
print(f"AStar execution time = {end_time:f} seconds")
_UpperCamelCase : int = time.time()
_UpperCamelCase : List[str] = BidirectionalAStar(init, goal)
_UpperCamelCase : str = time.time() - bd_start_time
print(f"BidirectionalAStar execution time = {bd_end_time:f} seconds")
| 396 | '''simple docstring'''
from ..utils import DummyObject, requires_backends
class _lowercase( metaclass=_lowerCamelCase ):
"""simple docstring"""
__lowerCamelCase = ['''onnx''']
def __init__( self: Any ,*a: List[str] ,**a: str ):
requires_backends(self ,['onnx'] )
@classmethod
def snake_case ( cls: Optional[Any] ,*a: List[str] ,**a: Any ):
requires_backends(cls ,['onnx'] )
@classmethod
def snake_case ( cls: str ,*a: Dict ,**a: str ):
requires_backends(cls ,['onnx'] )
| 396 | 1 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm import create_model
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform
from transformers import BitConfig, BitForImageClassification, BitImageProcessor
from transformers.image_utils import PILImageResampling
from transformers.utils import logging
logging.set_verbosity_info()
__UpperCAmelCase = logging.get_logger(__name__)
def _lowerCamelCase ( A_ : Dict ) -> int:
'''simple docstring'''
UpperCamelCase__ : Any ="huggingface/label-files"
UpperCamelCase__ : int ="imagenet-1k-id2label.json"
UpperCamelCase__ : str =json.load(open(hf_hub_download(A_ , A_ , repo_type="dataset" ) , "r" ) )
UpperCamelCase__ : Dict ={int(A_ ): v for k, v in idalabel.items()}
UpperCamelCase__ : str ={v: k for k, v in idalabel.items()}
UpperCamelCase__ : List[str] ="std_conv" if "bit" in model_name else False
# note that when using BiT as backbone for ViT-hybrid checkpoints,
# one needs to additionally set config.layer_type = "bottleneck", config.stem_type = "same",
# config.conv_layer = "std_conv_same"
UpperCamelCase__ : Optional[Any] =BitConfig(
conv_layer=A_ , num_labels=1_0_0_0 , idalabel=A_ , labelaid=A_ , )
return config
def _lowerCamelCase ( A_ : str ) -> Dict:
'''simple docstring'''
if "stem.conv" in name:
UpperCamelCase__ : Optional[Any] =name.replace("stem.conv" , "bit.embedder.convolution" )
if "blocks" in name:
UpperCamelCase__ : List[str] =name.replace("blocks" , "layers" )
if "head.fc" in name:
UpperCamelCase__ : Optional[Any] =name.replace("head.fc" , "classifier.1" )
if name.startswith("norm" ):
UpperCamelCase__ : Any ="bit." + name
if "bit" not in name and "classifier" not in name:
UpperCamelCase__ : str ="bit.encoder." + name
return name
def _lowerCamelCase ( ) -> str:
'''simple docstring'''
UpperCamelCase__ : int ="http://images.cocodataset.org/val2017/000000039769.jpg"
UpperCamelCase__ : List[str] =Image.open(requests.get(A_ , stream=A_ ).raw )
return im
@torch.no_grad()
def _lowerCamelCase ( A_ : List[str] , A_ : str , A_ : Any=False ) -> Tuple:
'''simple docstring'''
UpperCamelCase__ : int =get_config(A_ )
# load original model from timm
UpperCamelCase__ : Any =create_model(A_ , pretrained=A_ )
timm_model.eval()
# load state_dict of original model
UpperCamelCase__ : Any =timm_model.state_dict()
for key in state_dict.copy().keys():
UpperCamelCase__ : Optional[Any] =state_dict.pop(A_ )
UpperCamelCase__ : Any =val.squeeze() if "head" in key else val
# load HuggingFace model
UpperCamelCase__ : List[Any] =BitForImageClassification(A_ )
model.eval()
model.load_state_dict(A_ )
# create image processor
UpperCamelCase__ : Optional[Any] =create_transform(**resolve_data_config({} , model=A_ ) )
UpperCamelCase__ : str =transform.transforms
UpperCamelCase__ : Any ={
"bilinear": PILImageResampling.BILINEAR,
"bicubic": PILImageResampling.BICUBIC,
"nearest": PILImageResampling.NEAREST,
}
UpperCamelCase__ : List[str] =BitImageProcessor(
do_resize=A_ , size={"shortest_edge": timm_transforms[0].size} , resample=pillow_resamplings[timm_transforms[0].interpolation.value] , do_center_crop=A_ , crop_size={"height": timm_transforms[1].size[0], "width": timm_transforms[1].size[1]} , do_normalize=A_ , image_mean=timm_transforms[-1].mean.tolist() , image_std=timm_transforms[-1].std.tolist() , )
UpperCamelCase__ : List[str] =prepare_img()
UpperCamelCase__ : Optional[Any] =transform(A_ ).unsqueeze(0 )
UpperCamelCase__ : Optional[Any] =processor(A_ , return_tensors="pt" ).pixel_values
# verify pixel values
assert torch.allclose(A_ , A_ )
# verify logits
with torch.no_grad():
UpperCamelCase__ : int =model(A_ )
UpperCamelCase__ : List[Any] =outputs.logits
print("Logits:" , logits[0, :3] )
print("Predicted class:" , model.config.idalabel[logits.argmax(-1 ).item()] )
UpperCamelCase__ : Dict =timm_model(A_ )
assert timm_logits.shape == outputs.logits.shape
assert torch.allclose(A_ , outputs.logits , atol=1E-3 )
print("Looks ok!" )
if pytorch_dump_folder_path is not None:
Path(A_ ).mkdir(exist_ok=A_ )
print(f'''Saving model {model_name} and processor to {pytorch_dump_folder_path}''' )
model.save_pretrained(A_ )
processor.save_pretrained(A_ )
if push_to_hub:
print(f'''Pushing model {model_name} and processor to the hub''' )
model.push_to_hub(f'''ybelkada/{model_name}''' )
processor.push_to_hub(f'''ybelkada/{model_name}''' )
if __name__ == "__main__":
__UpperCAmelCase = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--model_name""",
default="""resnetv2_50x1_bitm""",
type=str,
help="""Name of the BiT timm 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 to push the model to the hub.""",
)
__UpperCAmelCase = parser.parse_args()
convert_bit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
| 582 |
import json
import os
import unittest
from transformers import BatchEncoding, LEDTokenizer, LEDTokenizerFast
from transformers.models.led.tokenization_led import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class lowercase__( snake_case__ , unittest.TestCase ):
'''simple docstring'''
snake_case__ = LEDTokenizer
snake_case__ = LEDTokenizerFast
snake_case__ = True
def UpperCAmelCase ( self) -> List[Any]:
"""simple docstring"""
super().setUp()
UpperCamelCase__ : Any =[
"l",
"o",
"w",
"e",
"r",
"s",
"t",
"i",
"d",
"n",
"\u0120",
"\u0120l",
"\u0120n",
"\u0120lo",
"\u0120low",
"er",
"\u0120lowest",
"\u0120newer",
"\u0120wider",
"<unk>",
]
UpperCamelCase__ : int =dict(zip(__SCREAMING_SNAKE_CASE , range(len(__SCREAMING_SNAKE_CASE))))
UpperCamelCase__ : Optional[int] =["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
UpperCamelCase__ : Optional[int] ={"unk_token": "<unk>"}
UpperCamelCase__ : Optional[int] =os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["vocab_file"])
UpperCamelCase__ : Optional[int] =os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["merges_file"])
with open(self.vocab_file , "w" , encoding="utf-8") as fp:
fp.write(json.dumps(__SCREAMING_SNAKE_CASE) + "\n")
with open(self.merges_file , "w" , encoding="utf-8") as fp:
fp.write("\n".join(__SCREAMING_SNAKE_CASE))
def UpperCAmelCase ( self , **__SCREAMING_SNAKE_CASE) -> Optional[int]:
"""simple docstring"""
kwargs.update(self.special_tokens_map)
return self.tokenizer_class.from_pretrained(self.tmpdirname , **__SCREAMING_SNAKE_CASE)
def UpperCAmelCase ( self , **__SCREAMING_SNAKE_CASE) -> Optional[Any]:
"""simple docstring"""
kwargs.update(self.special_tokens_map)
return self.rust_tokenizer_class.from_pretrained(self.tmpdirname , **__SCREAMING_SNAKE_CASE)
def UpperCAmelCase ( self , __SCREAMING_SNAKE_CASE) -> Any:
"""simple docstring"""
return "lower newer", "lower newer"
@cached_property
def UpperCAmelCase ( self) -> Optional[Any]:
"""simple docstring"""
return LEDTokenizer.from_pretrained("allenai/led-base-16384")
@cached_property
def UpperCAmelCase ( self) -> Optional[Any]:
"""simple docstring"""
return LEDTokenizerFast.from_pretrained("allenai/led-base-16384")
@require_torch
def UpperCAmelCase ( self) -> Optional[int]:
"""simple docstring"""
UpperCamelCase__ : Optional[Any] =["A long paragraph for summarization.", "Another paragraph for summarization."]
UpperCamelCase__ : Optional[int] =[0, 2_50, 2_51, 1_78_18, 13, 3_91_86, 19_38, 4, 2]
for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
UpperCamelCase__ : Any =tokenizer(__SCREAMING_SNAKE_CASE , max_length=len(__SCREAMING_SNAKE_CASE) , padding=__SCREAMING_SNAKE_CASE , return_tensors="pt")
self.assertIsInstance(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE)
self.assertEqual((2, 9) , batch.input_ids.shape)
self.assertEqual((2, 9) , batch.attention_mask.shape)
UpperCamelCase__ : List[Any] =batch.input_ids.tolist()[0]
self.assertListEqual(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE)
@require_torch
def UpperCAmelCase ( self) -> Union[str, Any]:
"""simple docstring"""
UpperCamelCase__ : List[Any] =["A long paragraph for summarization.", "Another paragraph for summarization."]
for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
UpperCamelCase__ : int =tokenizer(__SCREAMING_SNAKE_CASE , padding=__SCREAMING_SNAKE_CASE , return_tensors="pt")
self.assertIn("input_ids" , __SCREAMING_SNAKE_CASE)
self.assertIn("attention_mask" , __SCREAMING_SNAKE_CASE)
self.assertNotIn("labels" , __SCREAMING_SNAKE_CASE)
self.assertNotIn("decoder_attention_mask" , __SCREAMING_SNAKE_CASE)
@require_torch
def UpperCAmelCase ( self) -> List[Any]:
"""simple docstring"""
UpperCamelCase__ : Optional[Any] =[
"Summary of the text.",
"Another summary.",
]
for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
UpperCamelCase__ : Tuple =tokenizer(text_target=__SCREAMING_SNAKE_CASE , max_length=32 , padding="max_length" , return_tensors="pt")
self.assertEqual(32 , targets["input_ids"].shape[1])
@require_torch
def UpperCAmelCase ( self) -> int:
"""simple docstring"""
for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
UpperCamelCase__ : Optional[int] =tokenizer(
["I am a small frog" * 10_24, "I am a small frog"] , padding=__SCREAMING_SNAKE_CASE , truncation=__SCREAMING_SNAKE_CASE , return_tensors="pt")
self.assertIsInstance(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE)
self.assertEqual(batch.input_ids.shape , (2, 51_22))
@require_torch
def UpperCAmelCase ( self) -> List[Any]:
"""simple docstring"""
UpperCamelCase__ : Union[str, Any] =["A long paragraph for summarization."]
UpperCamelCase__ : Any =[
"Summary of the text.",
]
for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
UpperCamelCase__ : str =tokenizer(__SCREAMING_SNAKE_CASE , return_tensors="pt")
UpperCamelCase__ : str =tokenizer(text_target=__SCREAMING_SNAKE_CASE , return_tensors="pt")
UpperCamelCase__ : int =inputs["input_ids"]
UpperCamelCase__ : Tuple =targets["input_ids"]
self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item())
self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item())
self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item())
self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item())
@require_torch
def UpperCAmelCase ( self) -> List[str]:
"""simple docstring"""
for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
UpperCamelCase__ : Any =["Summary of the text.", "Another summary."]
UpperCamelCase__ : List[str] =[[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, -1, -1]]
UpperCamelCase__ : str =tokenizer(__SCREAMING_SNAKE_CASE , padding=__SCREAMING_SNAKE_CASE)
UpperCamelCase__ : Optional[Any] =[[0] * len(__SCREAMING_SNAKE_CASE) for x in encoded_output["input_ids"]]
UpperCamelCase__ : Any =tokenizer.pad(__SCREAMING_SNAKE_CASE)
self.assertSequenceEqual(outputs["global_attention_mask"] , __SCREAMING_SNAKE_CASE)
def UpperCAmelCase ( self) -> Optional[int]:
"""simple docstring"""
pass
def UpperCAmelCase ( self) -> List[Any]:
"""simple docstring"""
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''):
UpperCamelCase__ : Dict =self.rust_tokenizer_class.from_pretrained(__SCREAMING_SNAKE_CASE , **__SCREAMING_SNAKE_CASE)
UpperCamelCase__ : Dict =self.tokenizer_class.from_pretrained(__SCREAMING_SNAKE_CASE , **__SCREAMING_SNAKE_CASE)
UpperCamelCase__ : List[str] ="A, <mask> AllenNLP sentence."
UpperCamelCase__ : List[Any] =tokenizer_r.encode_plus(__SCREAMING_SNAKE_CASE , add_special_tokens=__SCREAMING_SNAKE_CASE , return_token_type_ids=__SCREAMING_SNAKE_CASE)
UpperCamelCase__ : Dict =tokenizer_p.encode_plus(__SCREAMING_SNAKE_CASE , add_special_tokens=__SCREAMING_SNAKE_CASE , return_token_type_ids=__SCREAMING_SNAKE_CASE)
self.assertEqual(sum(tokens_r["token_type_ids"]) , sum(tokens_p["token_type_ids"]))
self.assertEqual(
sum(tokens_r["attention_mask"]) / len(tokens_r["attention_mask"]) , sum(tokens_p["attention_mask"]) / len(tokens_p["attention_mask"]) , )
UpperCamelCase__ : List[str] =tokenizer_r.convert_ids_to_tokens(tokens_r["input_ids"])
UpperCamelCase__ : str =tokenizer_p.convert_ids_to_tokens(tokens_p["input_ids"])
self.assertSequenceEqual(tokens_p["input_ids"] , [0, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2])
self.assertSequenceEqual(tokens_r["input_ids"] , [0, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2])
self.assertSequenceEqual(
__SCREAMING_SNAKE_CASE , ["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"])
self.assertSequenceEqual(
__SCREAMING_SNAKE_CASE , ["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"])
| 582 | 1 |
"""simple docstring"""
import logging
import os
import sys
from pathlib import Path
from unittest.mock import patch
from parameterized import parameterized
from run_eval import run_generate
from run_eval_search import run_search
from transformers.testing_utils import CaptureStdout, TestCasePlus, slow
from utils import ROUGE_KEYS
logging.basicConfig(level=logging.DEBUG)
_lowercase = logging.getLogger()
def lowerCAmelCase__ ( __magic_name__ , __magic_name__ ) ->Any:
__lowercase = "\n".join(__magic_name__ )
Path(__magic_name__ ).open("w" ).writelines(__magic_name__ )
_lowercase = '''patrickvonplaten/t5-tiny-random'''
_lowercase = '''sshleifer/bart-tiny-random'''
_lowercase = '''sshleifer/tiny-mbart'''
_lowercase = logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
logging.disable(logging.CRITICAL) # remove noisy download output from tracebacks
class __a ( __a ):
'''simple docstring'''
def SCREAMING_SNAKE_CASE ( self , _lowerCamelCase ) -> List[str]:
'''simple docstring'''
__lowercase = Path(self.get_auto_remove_tmp_dir() ) / "utest_input.source"
__lowercase = input_file_name.parent / "utest_output.txt"
assert not output_file_name.exists()
__lowercase = [" New York (CNN)When Liana Barrientos was 23 years old, she got married in Westchester County."]
_dump_articles(_lowerCamelCase , _lowerCamelCase )
__lowercase = str(Path(self.get_auto_remove_tmp_dir() ) / "scores.json" )
__lowercase = "translation_en_to_de" if model == T5_TINY else "summarization"
__lowercase = f'''
run_eval_search.py
{model}
{input_file_name}
{output_file_name}
--score_path {score_path}
--task {task}
--num_beams 2
--length_penalty 2.0
'''.split()
with patch.object(_lowerCamelCase , "argv" , _lowerCamelCase ):
run_generate()
assert Path(_lowerCamelCase ).exists()
# os.remove(Path(output_file_name))
def SCREAMING_SNAKE_CASE ( self ) -> Tuple:
'''simple docstring'''
self.run_eval_tester(_lowerCamelCase )
@parameterized.expand([BART_TINY, MBART_TINY] )
@slow
def SCREAMING_SNAKE_CASE ( self , _lowerCamelCase ) -> int:
'''simple docstring'''
self.run_eval_tester(_lowerCamelCase )
@parameterized.expand([T5_TINY, MBART_TINY] )
@slow
def SCREAMING_SNAKE_CASE ( self , _lowerCamelCase ) -> Dict:
'''simple docstring'''
__lowercase = Path(self.get_auto_remove_tmp_dir() ) / "utest_input.source"
__lowercase = input_file_name.parent / "utest_output.txt"
assert not output_file_name.exists()
__lowercase = {
"en": ["Machine learning is great, isn't it?", "I like to eat bananas", "Tomorrow is another great day!"],
"de": [
"Maschinelles Lernen ist großartig, oder?",
"Ich esse gerne Bananen",
"Morgen ist wieder ein toller Tag!",
],
}
__lowercase = Path(self.get_auto_remove_tmp_dir() )
__lowercase = str(tmp_dir / "scores.json" )
__lowercase = str(tmp_dir / "val.target" )
_dump_articles(_lowerCamelCase , text["en"] )
_dump_articles(_lowerCamelCase , text["de"] )
__lowercase = "translation_en_to_de" if model == T5_TINY else "summarization"
__lowercase = f'''
run_eval_search.py
{model}
{str(_lowerCamelCase )}
{str(_lowerCamelCase )}
--score_path {score_path}
--reference_path {reference_path}
--task {task}
'''.split()
testargs.extend(["--search", "num_beams=1:2 length_penalty=0.9:1.0"] )
with patch.object(_lowerCamelCase , "argv" , _lowerCamelCase ):
with CaptureStdout() as cs:
run_search()
__lowercase = [" num_beams | length_penalty", model, "Best score args"]
__lowercase = ["Info"]
if "translation" in task:
expected_strings.append("bleu" )
else:
expected_strings.extend(_lowerCamelCase )
for w in expected_strings:
assert w in cs.out
for w in un_expected_strings:
assert w not in cs.out
assert Path(_lowerCamelCase ).exists()
os.remove(Path(_lowerCamelCase ) )
| 118 |
"""simple docstring"""
def lowerCAmelCase__ ( __magic_name__ = 1_0 ) ->str:
if not isinstance(__magic_name__ , __magic_name__ ) or n < 0:
raise ValueError("Invalid input" )
__lowercase = 1_0**n
__lowercase = 2_8_4_3_3 * (pow(2 , 7_8_3_0_4_5_7 , __magic_name__ )) + 1
return str(number % modulus )
if __name__ == "__main__":
from doctest import testmod
testmod()
print(F"{solution(10) = }")
| 118 | 1 |
"""simple docstring"""
from copy import deepcopy
import torch
import torch.nn.functional as F
from torch.optim import AdamW
from torch.optim.lr_scheduler import LambdaLR
from torch.utils.data import DataLoader
from accelerate.accelerator import Accelerator
from accelerate.state import GradientState
from accelerate.test_utils import RegressionDataset, RegressionModel
from accelerate.utils import DistributedType, is_torch_version, set_seed
def A ( snake_case__ , snake_case__ , snake_case__ , snake_case__ ):
'''simple docstring'''
for param, grad_param in zip(model_a.parameters() , model_b.parameters() ):
if not param.requires_grad:
continue
if not did_step:
# Grads should not be in sync
assert (
torch.allclose(param.grad , grad_param.grad ) is False
), f"""Gradients in sync when they should not be at iteration {iteration}:\nmodel_a grad ({param.grad}) == model_b grad ({grad_param.grad})"""
else:
# Grads should be in sync
assert (
torch.allclose(param.grad , grad_param.grad ) is True
), f"""Gradients not in sync when they should be at iteration {iteration}:\nmodel_a grad ({param.grad}) != model_b grad ({grad_param.grad})"""
def A ( snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__=True ):
'''simple docstring'''
model.train()
SCREAMING_SNAKE_CASE__ = model(snake_case__ )
SCREAMING_SNAKE_CASE__ = F.mse_loss(snake_case__ , target.to(output.device ) )
if not do_backward:
loss /= accelerator.gradient_accumulation_steps
loss.backward()
else:
accelerator.backward(snake_case__ )
def A ( snake_case__ , snake_case__=False ):
'''simple docstring'''
set_seed(42 )
SCREAMING_SNAKE_CASE__ = RegressionModel()
SCREAMING_SNAKE_CASE__ = deepcopy(snake_case__ )
SCREAMING_SNAKE_CASE__ = RegressionDataset(length=80 )
SCREAMING_SNAKE_CASE__ = DataLoader(snake_case__ , batch_size=16 )
model.to(accelerator.device )
if sched:
SCREAMING_SNAKE_CASE__ = AdamW(params=model.parameters() , lr=1e-3 )
SCREAMING_SNAKE_CASE__ = AdamW(params=ddp_model.parameters() , lr=1e-3 )
SCREAMING_SNAKE_CASE__ = LambdaLR(snake_case__ , lr_lambda=lambda snake_case__ : epoch**0.65 )
SCREAMING_SNAKE_CASE__ = LambdaLR(snake_case__ , lr_lambda=lambda snake_case__ : epoch**0.65 )
# Make a copy of `model`
if sched:
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = accelerator.prepare(snake_case__ , snake_case__ , snake_case__ , snake_case__ )
else:
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = accelerator.prepare(snake_case__ , snake_case__ )
if sched:
return (model, opt, sched, dataloader, ddp_model, ddp_opt, ddp_sched)
return model, ddp_model, dataloader
def A ( snake_case__ ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = get_training_setup(snake_case__ )
# Use a single batch
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = next(iter(snake_case__ ) ).values()
for iteration in range(3 ):
# Gather the distributed inputs and targs for the base model
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = accelerator.gather((ddp_input, ddp_target) )
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = input.to(accelerator.device ), target.to(accelerator.device )
# Perform our initial ground truth step in non "DDP"
step_model(snake_case__ , snake_case__ , snake_case__ , snake_case__ )
# Do "gradient accumulation" (noop)
if iteration % 2 == 0:
# Accumulate grads locally
with accelerator.no_sync(snake_case__ ):
step_model(snake_case__ , snake_case__ , snake_case__ , snake_case__ )
else:
# Sync grads
step_model(snake_case__ , snake_case__ , snake_case__ , snake_case__ )
# Since `no_sync` is a noop, `ddp_model` and `model` grads should always be in sync
check_model_parameters(snake_case__ , snake_case__ , snake_case__ , snake_case__ )
for param, ddp_param in zip(model.parameters() , ddp_model.parameters() ):
if not param.requires_grad:
continue
assert torch.allclose(
param.grad , ddp_param.grad ), f"""Gradients not in sync when they should be:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})"""
# Shuffle ddp_input on each iteration
torch.manual_seed(13_37 + iteration )
SCREAMING_SNAKE_CASE__ = ddp_input[torch.randperm(len(snake_case__ ) )]
def A ( snake_case__ ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = get_training_setup(snake_case__ )
# Use a single batch
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = next(iter(snake_case__ ) ).values()
for iteration in range(3 ):
# Gather the distributed inputs and targs for the base model
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = accelerator.gather((ddp_input, ddp_target) )
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = input.to(accelerator.device ), target.to(accelerator.device )
# Perform our initial ground truth step in non "DDP"
step_model(snake_case__ , snake_case__ , snake_case__ , snake_case__ )
# Do "gradient accumulation" (noop)
if iteration % 2 == 0:
# Accumulate grads locally
with accelerator.no_sync(snake_case__ ):
step_model(snake_case__ , snake_case__ , snake_case__ , snake_case__ )
else:
# Sync grads
step_model(snake_case__ , snake_case__ , snake_case__ , snake_case__ )
# DDP model and model should only be in sync when not (iteration % 2 == 0)
for param, ddp_param in zip(model.parameters() , ddp_model.parameters() ):
if not param.requires_grad:
continue
if iteration % 2 == 0:
# Grads should not be in sync
assert (
torch.allclose(param.grad , ddp_param.grad ) is False
), f"""Gradients in sync when they should not be:\nModel grad ({param.grad}) == DDP grad ({ddp_param.grad})"""
else:
# Grads should be in sync
assert (
torch.allclose(param.grad , ddp_param.grad ) is True
), f"""Gradients not in sync when they should be:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})"""
# Shuffle ddp_input on each iteration
torch.manual_seed(13_37 + iteration )
SCREAMING_SNAKE_CASE__ = ddp_input[torch.randperm(len(snake_case__ ) )]
def A ( snake_case__=False , snake_case__=False ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ = Accelerator(
split_batches=snake_case__ , dispatch_batches=snake_case__ , gradient_accumulation_steps=2 )
# Test that context manager behaves properly
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = get_training_setup(snake_case__ )
for iteration, batch in enumerate(snake_case__ ):
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = batch.values()
# Gather the distributed inputs and targs for the base model
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = accelerator.gather((ddp_input, ddp_target) )
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = input.to(accelerator.device ), target.to(accelerator.device )
# Perform our initial ground truth step in non "DDP"
step_model(snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ )
# Do "gradient accumulation" (noop)
with accelerator.accumulate(snake_case__ ):
step_model(snake_case__ , snake_case__ , snake_case__ , snake_case__ )
# DDP model and model should only be in sync when not (iteration % 2 == 0)
for param, ddp_param in zip(model.parameters() , ddp_model.parameters() ):
if not param.requires_grad:
continue
if ((iteration + 1) % 2 == 0) or (iteration == len(snake_case__ ) - 1):
# Grads should be in sync
assert (
torch.allclose(param.grad , ddp_param.grad ) is True
), f"""Gradients not in sync when they should be at iteration {iteration}:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})"""
else:
# Grads should not be in sync
assert (
torch.allclose(param.grad , ddp_param.grad ) is False
), f"""Gradients in sync when they should not be at iteration {iteration}:\nModel grad ({param.grad}) == DDP grad ({ddp_param.grad})"""
# Shuffle ddp_input on each iteration
torch.manual_seed(13_37 + iteration )
SCREAMING_SNAKE_CASE__ = ddp_input[torch.randperm(len(snake_case__ ) )]
GradientState._reset_state()
def A ( snake_case__=False , snake_case__=False ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ = Accelerator(
split_batches=snake_case__ , dispatch_batches=snake_case__ , gradient_accumulation_steps=2 )
# Test that context manager behaves properly
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = get_training_setup(snake_case__ , snake_case__ )
for iteration, batch in enumerate(snake_case__ ):
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = batch.values()
# Gather the distributed inputs and targs for the base model
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = accelerator.gather((ddp_input, ddp_target) )
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = input.to(accelerator.device ), target.to(accelerator.device )
# Perform our initial ground truth step in non "DDP"
model.train()
ddp_model.train()
step_model(snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ )
opt.step()
if ((iteration + 1) % 2 == 0) or ((iteration + 1) == len(snake_case__ )):
if split_batches:
sched.step()
else:
for _ in range(accelerator.num_processes ):
sched.step()
opt.zero_grad()
# Perform gradient accumulation under wrapper
with accelerator.accumulate(snake_case__ ):
step_model(snake_case__ , snake_case__ , snake_case__ , snake_case__ )
ddp_opt.step()
ddp_sched.step()
ddp_opt.zero_grad()
# Learning rates should be the same
assert (
opt.param_groups[0]["lr"] == ddp_opt.param_groups[0]["lr"]
), f"""Learning rates found in each optimizer did not align\nopt: {opt.param_groups[0]["lr"]}\nDDP opt: {ddp_opt.param_groups[0]["lr"]}\n"""
SCREAMING_SNAKE_CASE__ = (((iteration + 1) % 2) == 0) or ((iteration + 1) == len(snake_case__ ))
if accelerator.num_processes > 1:
check_model_parameters(snake_case__ , snake_case__ , snake_case__ , snake_case__ )
# Shuffle ddp_input on each iteration
torch.manual_seed(13_37 + iteration )
GradientState._reset_state()
def A ( ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ = Accelerator()
SCREAMING_SNAKE_CASE__ = RegressionDataset(length=80 )
SCREAMING_SNAKE_CASE__ = DataLoader(snake_case__ , batch_size=16 )
SCREAMING_SNAKE_CASE__ = RegressionDataset(length=96 )
SCREAMING_SNAKE_CASE__ = DataLoader(snake_case__ , batch_size=16 )
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = accelerator.prepare(snake_case__ , snake_case__ )
assert accelerator.gradient_state.active_dataloader is None
for iteration, _ in enumerate(snake_case__ ):
assert id(accelerator.gradient_state.active_dataloader ) == id(snake_case__ )
if iteration < len(snake_case__ ) - 1:
assert not accelerator.gradient_state.end_of_dataloader
if iteration == 1:
for batch_num, _ in enumerate(snake_case__ ):
assert id(accelerator.gradient_state.active_dataloader ) == id(snake_case__ )
if batch_num < len(snake_case__ ) - 1:
assert not accelerator.gradient_state.end_of_dataloader
else:
assert accelerator.gradient_state.end_of_dataloader
else:
assert accelerator.gradient_state.end_of_dataloader
assert accelerator.gradient_state.active_dataloader is None
def A ( ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ = Accelerator()
SCREAMING_SNAKE_CASE__ = accelerator.state
if state.local_process_index == 0:
print("""**Test `accumulate` gradient accumulation with dataloader break**""" )
test_dataloader_break()
if state.distributed_type == DistributedType.NO:
if state.local_process_index == 0:
print("""**Test NOOP `no_sync` context manager**""" )
test_noop_sync(snake_case__ )
if state.distributed_type in (DistributedType.MULTI_GPU, DistributedType.MULTI_CPU):
if state.local_process_index == 0:
print("""**Test Distributed `no_sync` context manager**""" )
test_distributed_sync(snake_case__ )
if state.distributed_type == DistributedType.MULTI_GPU:
for split_batch in [True, False]:
for dispatch_batches in [True, False]:
if state.local_process_index == 0:
print(
"""**Test `accumulate` gradient accumulation, """ , f"""`split_batches={split_batch}` and `dispatch_batches={dispatch_batches}`**""" , )
test_gradient_accumulation(snake_case__ , snake_case__ )
# Currently will break on torch 2.0 +, need to investigate why
if is_torch_version("""<""" , """2.0""" ) or state.distributed_type == DistributedType.NO:
if state.local_process_index == 0:
print(
"""**Test `accumulate` gradient accumulation with optimizer and scheduler, """ , """`split_batches=False`, `dispatch_batches=False`**""" , )
test_gradient_accumulation_with_opt_and_scheduler()
if state.distributed_type == DistributedType.MULTI_GPU:
for split_batch in [True, False]:
for dispatch_batches in [True, False]:
if not split_batch and not dispatch_batches:
continue
if state.local_process_index == 0:
print(
"""**Test `accumulate` gradient accumulation with optimizer and scheduler, """ , f"""`split_batches={split_batch}` and `dispatch_batches={dispatch_batches}`**""" , )
test_gradient_accumulation_with_opt_and_scheduler(snake_case__ , snake_case__ )
def A ( snake_case__ ):
'''simple docstring'''
main()
if __name__ == "__main__":
main()
| 616 |
"""simple docstring"""
import pytest
import requests
from datasets.utils.file_utils import http_head
from .utils import OfflineSimulationMode, RequestWouldHangIndefinitelyError, offline
@pytest.mark.integration
def A ( ):
'''simple docstring'''
with offline(OfflineSimulationMode.CONNECTION_TIMES_OUT ):
with pytest.raises(snake_case__ ):
requests.request("""GET""" , """https://huggingface.co""" )
with pytest.raises(requests.exceptions.ConnectTimeout ):
requests.request("""GET""" , """https://huggingface.co""" , timeout=1.0 )
@pytest.mark.integration
def A ( ):
'''simple docstring'''
with offline(OfflineSimulationMode.CONNECTION_FAILS ):
with pytest.raises(requests.exceptions.ConnectionError ):
requests.request("""GET""" , """https://huggingface.co""" )
def A ( ):
'''simple docstring'''
with offline(OfflineSimulationMode.HF_DATASETS_OFFLINE_SET_TO_1 ):
with pytest.raises(snake_case__ ):
http_head("""https://huggingface.co""" )
| 616 | 1 |
import numpy as np
import torch
from imwatermark import WatermarkEncoder
# Copied from https://github.com/Stability-AI/generative-models/blob/613af104c6b85184091d42d374fef420eddb356d/scripts/demo/streamlit_helpers.py#L66
a = 0b1011_0011_1110_1100_1001_0000_0111_1011_1011_0001_1001_1110
# bin(x)[2:] gives bits of x as str, use int to convert them to 0/1
a = [int(bit) for bit in bin(WATERMARK_MESSAGE)[2:]]
class UpperCamelCase__ :
def __init__( self : Any ):
'''simple docstring'''
lowercase_ = WATERMARK_BITS
lowercase_ = WatermarkEncoder()
self.encoder.set_watermark("""bits""" , self.watermark )
def UpperCAmelCase__ ( self : Any , UpperCamelCase__ : torch.FloatTensor ):
'''simple docstring'''
if images.shape[-1] < 256:
return images
lowercase_ = (255 * (images / 2 + 0.5)).cpu().permute(0 , 2 , 3 , 1 ).float().numpy()
lowercase_ = [self.encoder.encode(UpperCamelCase__ , """dwtDct""" ) for image in images]
lowercase_ = torch.from_numpy(np.array(UpperCamelCase__ ) ).permute(0 , 3 , 1 , 2 )
lowercase_ = torch.clamp(2 * (images / 255 - 0.5) , min=-1.0 , max=1.0 )
return images
| 412 |
import re
def UpperCAmelCase_ ( UpperCAmelCase__ ):
lowercase_ = re.compile(r"""^(\+91[\-\s]?)?[0]?(91)?[789]\d{9}$""" )
if match := re.search(UpperCAmelCase__ , UpperCAmelCase__ ):
return match.string == phone
return False
if __name__ == "__main__":
print(indian_phone_validator('+918827897895'))
| 412 | 1 |
"""simple docstring"""
import string
def __UpperCAmelCase ( _snake_case : str ):
_lowercase = ""
for i in sequence:
_lowercase = ord(_snake_case )
if 6_5 <= extract <= 9_0:
output += chr(1_5_5 - extract )
elif 9_7 <= extract <= 1_2_2:
output += chr(2_1_9 - extract )
else:
output += i
return output
def __UpperCAmelCase ( _snake_case : str ):
_lowercase = string.ascii_letters
_lowercase = string.ascii_lowercase[::-1] + string.ascii_uppercase[::-1]
return "".join(
letters_reversed[letters.index(_snake_case )] if c in letters else c for c in sequence )
def __UpperCAmelCase ( ):
from timeit import timeit
print("Running performance benchmarks..." )
_lowercase = "from string import printable ; from __main__ import atbash, atbash_slow"
print(f"""> atbash_slow(): {timeit("atbash_slow(printable)", setup=_snake_case )} seconds""" )
print(f"""> atbash(): {timeit("atbash(printable)", setup=_snake_case )} seconds""" )
if __name__ == "__main__":
for example in ("ABCDEFGH", "123GGjj", "testStringtest", "with space"):
print(f'''{example} encrypted in atbash: {atbash(example)}''')
benchmark() | 227 | """simple docstring"""
import argparse
import datetime
def __UpperCAmelCase ( _snake_case : str ):
_lowercase = {
"0": "Sunday",
"1": "Monday",
"2": "Tuesday",
"3": "Wednesday",
"4": "Thursday",
"5": "Friday",
"6": "Saturday",
}
_lowercase = {0: 1, 1: 2, 2: 3, 3: 4, 4: 5, 5: 6, 6: 0}
# Validate
if not 0 < len(_snake_case ) < 1_1:
raise ValueError("Must be 10 characters long" )
# Get month
_lowercase = int(date_input[0] + date_input[1] )
# Validate
if not 0 < m < 1_3:
raise ValueError("Month must be between 1 - 12" )
_lowercase = date_input[2]
# Validate
if sep_a not in ["-", "/"]:
raise ValueError("Date separator must be '-' or '/'" )
# Get day
_lowercase = int(date_input[3] + date_input[4] )
# Validate
if not 0 < d < 3_2:
raise ValueError("Date must be between 1 - 31" )
# Get second separator
_lowercase = date_input[5]
# Validate
if sep_a not in ["-", "/"]:
raise ValueError("Date separator must be '-' or '/'" )
# Get year
_lowercase = int(date_input[6] + date_input[7] + date_input[8] + date_input[9] )
# Arbitrary year range
if not 4_5 < y < 8_5_0_0:
raise ValueError(
"Year out of range. There has to be some sort of limit...right?" )
# Get datetime obj for validation
_lowercase = datetime.date(int(_snake_case ), int(_snake_case ), int(_snake_case ) )
# Start math
if m <= 2:
_lowercase = y - 1
_lowercase = m + 1_2
# maths var
_lowercase = int(str(_snake_case )[:2] )
_lowercase = int(str(_snake_case )[2:] )
_lowercase = int(2.6 * m - 5.3_9 )
_lowercase = int(c / 4 )
_lowercase = int(k / 4 )
_lowercase = int(d + k )
_lowercase = int(t + u + v + x )
_lowercase = int(z - (2 * c) )
_lowercase = round(w % 7 )
# End math
# Validate math
if f != convert_datetime_days[dt_ck.weekday()]:
raise AssertionError("The date was evaluated incorrectly. Contact developer." )
# Response
_lowercase = f"""Your date {date_input}, is a {days[str(_snake_case )]}!"""
return response
if __name__ == "__main__":
import doctest
doctest.testmod()
__UpperCamelCase : Any = argparse.ArgumentParser(
description=(
"Find out what day of the week nearly any date is or was. Enter "
"date as a string in the mm-dd-yyyy or mm/dd/yyyy format"
)
)
parser.add_argument(
"date_input", type=str, help="Date as a string (mm-dd-yyyy or mm/dd/yyyy)"
)
__UpperCamelCase : str = parser.parse_args()
zeller(args.date_input) | 227 | 1 |
from collections.abc import Iterator, MutableMapping
from dataclasses import dataclass
from typing import Generic, TypeVar
lowerCamelCase__ : Optional[Any] = TypeVar("""KEY""")
lowerCamelCase__ : Any = TypeVar("""VAL""")
@dataclass(frozen=UpperCAmelCase_ , slots=UpperCAmelCase_ )
class _snake_case ( Generic[KEY, VAL] ):
__lowerCAmelCase : KEY
__lowerCAmelCase : VAL
class _snake_case ( _Item ):
def __init__( self):
'''simple docstring'''
super().__init__(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_)
def __bool__( self):
'''simple docstring'''
return False
lowerCamelCase__ : str = _DeletedItem()
class _snake_case ( MutableMapping[KEY, VAL] ):
def __init__( self , SCREAMING_SNAKE_CASE_ = 8 , SCREAMING_SNAKE_CASE_ = 0.7_5):
'''simple docstring'''
lowercase__ : int = initial_block_size
lowercase__ : list[_Item | None] = [None] * initial_block_size
assert 0.0 < capacity_factor < 1.0
lowercase__ : Union[str, Any] = capacity_factor
lowercase__ : Tuple = 0
def lowercase__ ( self , SCREAMING_SNAKE_CASE_):
'''simple docstring'''
return hash(SCREAMING_SNAKE_CASE_) % len(self._buckets)
def lowercase__ ( self , SCREAMING_SNAKE_CASE_):
'''simple docstring'''
return (ind + 1) % len(self._buckets)
def lowercase__ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_):
'''simple docstring'''
lowercase__ : Optional[int] = self._buckets[ind]
if not stored:
lowercase__ : Optional[Any] = _Item(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_)
self._len += 1
return True
elif stored.key == key:
lowercase__ : Optional[int] = _Item(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_)
return True
else:
return False
def lowercase__ ( self):
'''simple docstring'''
lowercase__ : List[Any] = len(self._buckets) * self._capacity_factor
return len(self) >= int(SCREAMING_SNAKE_CASE_)
def lowercase__ ( self):
'''simple docstring'''
if len(self._buckets) <= self._initial_block_size:
return False
lowercase__ : Optional[Any] = len(self._buckets) * self._capacity_factor / 2
return len(self) < limit
def lowercase__ ( self , SCREAMING_SNAKE_CASE_):
'''simple docstring'''
lowercase__ : str = self._buckets
lowercase__ : Optional[Any] = [None] * new_size
lowercase__ : Optional[Any] = 0
for item in old_buckets:
if item:
self._add_item(item.key , item.val)
def lowercase__ ( self):
'''simple docstring'''
self._resize(len(self._buckets) * 2)
def lowercase__ ( self):
'''simple docstring'''
self._resize(len(self._buckets) // 2)
def lowercase__ ( self , SCREAMING_SNAKE_CASE_):
'''simple docstring'''
lowercase__ : Dict = self._get_bucket_index(SCREAMING_SNAKE_CASE_)
for _ in range(len(self._buckets)):
yield ind
lowercase__ : List[Any] = self._get_next_ind(SCREAMING_SNAKE_CASE_)
def lowercase__ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_):
'''simple docstring'''
for ind in self._iterate_buckets(SCREAMING_SNAKE_CASE_):
if self._try_set(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_):
break
def __setitem__( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_):
'''simple docstring'''
if self._is_full():
self._size_up()
self._add_item(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_)
def __delitem__( self , SCREAMING_SNAKE_CASE_):
'''simple docstring'''
for ind in self._iterate_buckets(SCREAMING_SNAKE_CASE_):
lowercase__ : Any = self._buckets[ind]
if item is None:
raise KeyError(SCREAMING_SNAKE_CASE_)
if item is _deleted:
continue
if item.key == key:
lowercase__ : List[str] = _deleted
self._len -= 1
break
if self._is_sparse():
self._size_down()
def __getitem__( self , SCREAMING_SNAKE_CASE_):
'''simple docstring'''
for ind in self._iterate_buckets(SCREAMING_SNAKE_CASE_):
lowercase__ : Optional[Any] = self._buckets[ind]
if item is None:
break
if item is _deleted:
continue
if item.key == key:
return item.val
raise KeyError(SCREAMING_SNAKE_CASE_)
def __len__( self):
'''simple docstring'''
return self._len
def __iter__( self):
'''simple docstring'''
yield from (item.key for item in self._buckets if item)
def __repr__( self):
'''simple docstring'''
lowercase__ : Any = """ ,""".join(
f'{item.key}: {item.val}' for item in self._buckets if item)
return f'HashMap({val_string})'
| 12 | """simple docstring"""
import json
import os
from pathlib import Path
import pytest
from datasets.download.download_config import DownloadConfig
from datasets.download.download_manager import DownloadManager
from datasets.utils.file_utils import hash_url_to_filename
_A = 'http://www.mocksite.com/file1.txt'
_A = '"text": ["foo", "foo"]'
_A = '6d8ce9aa78a471c7477201efbeabd3bb01ac2e7d100a6dc024ba1608361f90a8'
class lowerCamelCase :
'''simple docstring'''
a = 2_0_0
a = {"Content-Length": "100"}
a = {}
def lowerCAmelCase_ ( self : Union[str, Any] , **_snake_case : str ) -> Any:
return [bytes(_snake_case , "utf-8" )]
def SCREAMING_SNAKE_CASE ( *__UpperCAmelCase , **__UpperCAmelCase ) -> Any:
return MockResponse()
@pytest.mark.parametrize("urls_type" , [str, list, dict] )
def SCREAMING_SNAKE_CASE ( __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ) -> Tuple:
import requests
monkeypatch.setattr(__UpperCAmelCase , "request" , __UpperCAmelCase )
SCREAMING_SNAKE_CASE__ = URL
if issubclass(__UpperCAmelCase , __UpperCAmelCase ):
SCREAMING_SNAKE_CASE__ = url
elif issubclass(__UpperCAmelCase , __UpperCAmelCase ):
SCREAMING_SNAKE_CASE__ = [url]
elif issubclass(__UpperCAmelCase , __UpperCAmelCase ):
SCREAMING_SNAKE_CASE__ = {"train": url}
SCREAMING_SNAKE_CASE__ = "dummy"
SCREAMING_SNAKE_CASE__ = "downloads"
SCREAMING_SNAKE_CASE__ = tmp_path
SCREAMING_SNAKE_CASE__ = DownloadConfig(
cache_dir=os.path.join(__UpperCAmelCase , __UpperCAmelCase ) , use_etag=__UpperCAmelCase , )
SCREAMING_SNAKE_CASE__ = DownloadManager(dataset_name=__UpperCAmelCase , download_config=__UpperCAmelCase )
SCREAMING_SNAKE_CASE__ = dl_manager.download(__UpperCAmelCase )
SCREAMING_SNAKE_CASE__ = urls
for downloaded_paths in [downloaded_paths]:
if isinstance(__UpperCAmelCase , __UpperCAmelCase ):
SCREAMING_SNAKE_CASE__ = [downloaded_paths]
SCREAMING_SNAKE_CASE__ = [urls]
elif isinstance(__UpperCAmelCase , __UpperCAmelCase ):
assert "train" in downloaded_paths.keys()
SCREAMING_SNAKE_CASE__ = downloaded_paths.values()
SCREAMING_SNAKE_CASE__ = urls.values()
assert downloaded_paths
for downloaded_path, input_url in zip(__UpperCAmelCase , __UpperCAmelCase ):
assert downloaded_path == dl_manager.downloaded_paths[input_url]
SCREAMING_SNAKE_CASE__ = Path(__UpperCAmelCase )
SCREAMING_SNAKE_CASE__ = downloaded_path.parts
assert parts[-1] == HASH
assert parts[-2] == cache_subdir
assert downloaded_path.exists()
SCREAMING_SNAKE_CASE__ = downloaded_path.read_text()
assert content == CONTENT
SCREAMING_SNAKE_CASE__ = downloaded_path.with_suffix(".json" )
assert metadata_downloaded_path.exists()
SCREAMING_SNAKE_CASE__ = json.loads(metadata_downloaded_path.read_text() )
assert metadata_content == {"url": URL, "etag": None}
@pytest.mark.parametrize("paths_type" , [str, list, dict] )
def SCREAMING_SNAKE_CASE ( __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ) -> Tuple:
SCREAMING_SNAKE_CASE__ = str(__UpperCAmelCase )
if issubclass(__UpperCAmelCase , __UpperCAmelCase ):
SCREAMING_SNAKE_CASE__ = filename
elif issubclass(__UpperCAmelCase , __UpperCAmelCase ):
SCREAMING_SNAKE_CASE__ = [filename]
elif issubclass(__UpperCAmelCase , __UpperCAmelCase ):
SCREAMING_SNAKE_CASE__ = {"train": filename}
SCREAMING_SNAKE_CASE__ = "dummy"
SCREAMING_SNAKE_CASE__ = xz_file.parent
SCREAMING_SNAKE_CASE__ = "extracted"
SCREAMING_SNAKE_CASE__ = DownloadConfig(
cache_dir=__UpperCAmelCase , use_etag=__UpperCAmelCase , )
SCREAMING_SNAKE_CASE__ = DownloadManager(dataset_name=__UpperCAmelCase , download_config=__UpperCAmelCase )
SCREAMING_SNAKE_CASE__ = dl_manager.extract(__UpperCAmelCase )
SCREAMING_SNAKE_CASE__ = paths
for extracted_paths in [extracted_paths]:
if isinstance(__UpperCAmelCase , __UpperCAmelCase ):
SCREAMING_SNAKE_CASE__ = [extracted_paths]
SCREAMING_SNAKE_CASE__ = [paths]
elif isinstance(__UpperCAmelCase , __UpperCAmelCase ):
assert "train" in extracted_paths.keys()
SCREAMING_SNAKE_CASE__ = extracted_paths.values()
SCREAMING_SNAKE_CASE__ = paths.values()
assert extracted_paths
for extracted_path, input_path in zip(__UpperCAmelCase , __UpperCAmelCase ):
assert extracted_path == dl_manager.extracted_paths[input_path]
SCREAMING_SNAKE_CASE__ = Path(__UpperCAmelCase )
SCREAMING_SNAKE_CASE__ = extracted_path.parts
assert parts[-1] == hash_url_to_filename(__UpperCAmelCase , etag=__UpperCAmelCase )
assert parts[-2] == extracted_subdir
assert extracted_path.exists()
SCREAMING_SNAKE_CASE__ = extracted_path.read_text()
SCREAMING_SNAKE_CASE__ = text_file.read_text()
assert extracted_file_content == expected_file_content
def SCREAMING_SNAKE_CASE ( __UpperCAmelCase , __UpperCAmelCase ) -> int:
assert path.endswith(".jsonl" )
for num_items, line in enumerate(__UpperCAmelCase , start=1 ):
SCREAMING_SNAKE_CASE__ = json.loads(line.decode("utf-8" ) )
assert item.keys() == {"col_1", "col_2", "col_3"}
assert num_items == 4
@pytest.mark.parametrize("archive_jsonl" , ["tar_jsonl_path", "zip_jsonl_path"] )
def SCREAMING_SNAKE_CASE ( __UpperCAmelCase , __UpperCAmelCase ) -> Union[str, Any]:
SCREAMING_SNAKE_CASE__ = request.getfixturevalue(__UpperCAmelCase )
SCREAMING_SNAKE_CASE__ = DownloadManager()
for num_jsonl, (path, file) in enumerate(dl_manager.iter_archive(__UpperCAmelCase ) , start=1 ):
_test_jsonl(__UpperCAmelCase , __UpperCAmelCase )
assert num_jsonl == 2
@pytest.mark.parametrize("archive_nested_jsonl" , ["tar_nested_jsonl_path", "zip_nested_jsonl_path"] )
def SCREAMING_SNAKE_CASE ( __UpperCAmelCase , __UpperCAmelCase ) -> Any:
SCREAMING_SNAKE_CASE__ = request.getfixturevalue(__UpperCAmelCase )
SCREAMING_SNAKE_CASE__ = DownloadManager()
for num_tar, (path, file) in enumerate(dl_manager.iter_archive(__UpperCAmelCase ) , start=1 ):
for num_jsonl, (subpath, subfile) in enumerate(dl_manager.iter_archive(__UpperCAmelCase ) , start=1 ):
_test_jsonl(__UpperCAmelCase , __UpperCAmelCase )
assert num_tar == 1
assert num_jsonl == 2
def SCREAMING_SNAKE_CASE ( __UpperCAmelCase ) -> Tuple:
SCREAMING_SNAKE_CASE__ = DownloadManager()
for num_file, file in enumerate(dl_manager.iter_files(__UpperCAmelCase ) , start=1 ):
assert os.path.basename(__UpperCAmelCase ) == ("test.txt" if num_file == 1 else "train.txt")
assert num_file == 2
| 159 | 0 |
def UpperCAmelCase__ ( _A ):
"""simple docstring"""
a_ = [int(lowercase_ ) for i in ip_va_address.split('''.''' ) if i.isdigit()]
return len(lowercase_ ) == 4 and all(0 <= int(lowercase_ ) <= 254 for octet in octets )
if __name__ == "__main__":
UpperCamelCase__ = input().strip()
UpperCamelCase__ = '''valid''' if is_ip_va_address_valid(ip) else '''invalid'''
print(F"""{ip} is a {valid_or_invalid} IP v4 address.""")
| 707 |
# Copyright 2022 The HuggingFace Team and The OpenBMB 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
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
UpperCamelCase__ = {
'''configuration_cpmant''': ['''CPMANT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''CpmAntConfig'''],
'''tokenization_cpmant''': ['''CpmAntTokenizer'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase__ = [
'''CPMANT_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''CpmAntForCausalLM''',
'''CpmAntModel''',
'''CpmAntPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_cpmant import CPMANT_PRETRAINED_CONFIG_ARCHIVE_MAP, CpmAntConfig
from .tokenization_cpmant import CpmAntTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_cpmant import (
CPMANT_PRETRAINED_MODEL_ARCHIVE_LIST,
CpmAntForCausalLM,
CpmAntModel,
CpmAntPreTrainedModel,
)
else:
import sys
UpperCamelCase__ = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 143 | 0 |
from ....utils import logging
SCREAMING_SNAKE_CASE__ = logging.get_logger(__name__)
class __lowerCAmelCase ( UpperCAmelCase_ ):
"""simple docstring"""
def __init__( self : List[Any] , _snake_case : str , _snake_case : List[Any]=None , _snake_case : Union[str, Any]=20_48 ):
"""simple docstring"""
A__ = config.__dict__
A__ = modal_hidden_size
if num_labels:
A__ = num_labels
| 9 |
from transformers import BertTokenizer, EncoderDecoderModel, SeqaSeqTrainer, SeqaSeqTrainingArguments
from transformers.testing_utils import TestCasePlus, require_torch, slow
from transformers.utils import is_datasets_available
if is_datasets_available():
import datasets
class __lowerCamelCase (_a ):
@slow
@require_torch
def snake_case_ ( self: Union[str, Any] ):
'''simple docstring'''
__UpperCamelCase = EncoderDecoderModel.from_encoder_decoder_pretrained('prajjwal1/bert-tiny','prajjwal1/bert-tiny' )
__UpperCamelCase = BertTokenizer.from_pretrained('bert-base-uncased' )
__UpperCamelCase = bertabert.config.encoder.vocab_size
__UpperCamelCase = tokenizer.sep_token_id
__UpperCamelCase = tokenizer.cls_token_id
__UpperCamelCase = 128
__UpperCamelCase = datasets.load_dataset('cnn_dailymail','3.0.0',split='train[:1%]' )
__UpperCamelCase = datasets.load_dataset('cnn_dailymail','3.0.0',split='validation[:1%]' )
__UpperCamelCase = train_dataset.select(range(32 ) )
__UpperCamelCase = val_dataset.select(range(16 ) )
__UpperCamelCase = 4
def _map_to_encoder_decoder_inputs(A_: Dict ):
# Tokenizer will automatically set [BOS] <text> [EOS]
__UpperCamelCase = tokenizer(batch['article'],padding='max_length',truncation=A_,max_length=512 )
__UpperCamelCase = tokenizer(batch['highlights'],padding='max_length',truncation=A_,max_length=128 )
__UpperCamelCase = inputs.input_ids
__UpperCamelCase = inputs.attention_mask
__UpperCamelCase = outputs.input_ids
__UpperCamelCase = outputs.input_ids.copy()
__UpperCamelCase = [
[-100 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch['labels']
]
__UpperCamelCase = outputs.attention_mask
assert all(len(A_ ) == 512 for x in inputs.input_ids )
assert all(len(A_ ) == 128 for x in outputs.input_ids )
return batch
def _compute_metrics(A_: str ):
__UpperCamelCase = pred.label_ids
__UpperCamelCase = pred.predictions
# all unnecessary tokens are removed
__UpperCamelCase = tokenizer.batch_decode(A_,skip_special_tokens=A_ )
__UpperCamelCase = tokenizer.batch_decode(A_,skip_special_tokens=A_ )
__UpperCamelCase = sum([int(pred_str[i] == label_str[i] ) for i in range(len(A_ ) )] ) / len(A_ )
return {"accuracy": accuracy}
# map train dataset
__UpperCamelCase = train_dataset.map(
_map_to_encoder_decoder_inputs,batched=A_,batch_size=A_,remove_columns=['article', 'highlights'],)
train_dataset.set_format(
type='torch',columns=['input_ids', 'attention_mask', 'decoder_input_ids', 'decoder_attention_mask', 'labels'],)
# same for validation dataset
__UpperCamelCase = val_dataset.map(
_map_to_encoder_decoder_inputs,batched=A_,batch_size=A_,remove_columns=['article', 'highlights'],)
val_dataset.set_format(
type='torch',columns=['input_ids', 'attention_mask', 'decoder_input_ids', 'decoder_attention_mask', 'labels'],)
__UpperCamelCase = self.get_auto_remove_tmp_dir()
__UpperCamelCase = SeqaSeqTrainingArguments(
output_dir=A_,per_device_train_batch_size=A_,per_device_eval_batch_size=A_,predict_with_generate=A_,evaluation_strategy='steps',do_train=A_,do_eval=A_,warmup_steps=0,eval_steps=2,logging_steps=2,)
# instantiate trainer
__UpperCamelCase = SeqaSeqTrainer(
model=A_,args=A_,compute_metrics=_compute_metrics,train_dataset=A_,eval_dataset=A_,tokenizer=A_,)
# start training
trainer.train()
| 1 | 0 |
import warnings
from ...utils import logging
from .image_processing_yolos import YolosImageProcessor
__A = logging.get_logger(__name__)
class _lowerCAmelCase ( a ):
"""simple docstring"""
def __init__( self , *__UpperCAmelCase , **__UpperCAmelCase ):
'''simple docstring'''
warnings.warn(
'The class YolosFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'
' use YolosImageProcessor instead.' , __UpperCAmelCase , )
super().__init__(*__UpperCAmelCase , **__UpperCAmelCase )
| 701 |
"""simple docstring"""
import os
import sys
from contextlib import contextmanager
# Windows only
if os.name == "nt":
import ctypes
import msvcrt # noqa
class _lowerCAmelCase ( ctypes.Structure ):
"""simple docstring"""
__magic_name__ :Union[str, Any] = [("""size""", ctypes.c_int), ("""visible""", ctypes.c_byte)]
def __A () ->Dict:
"""simple docstring"""
if os.name == "nt":
lowerCAmelCase__ :Optional[Any] = CursorInfo()
lowerCAmelCase__ :int = ctypes.windll.kernelaa.GetStdHandle(-11 )
ctypes.windll.kernelaa.GetConsoleCursorInfo(_SCREAMING_SNAKE_CASE , ctypes.byref(_SCREAMING_SNAKE_CASE ) )
lowerCAmelCase__ :Any = False
ctypes.windll.kernelaa.SetConsoleCursorInfo(_SCREAMING_SNAKE_CASE , ctypes.byref(_SCREAMING_SNAKE_CASE ) )
elif os.name == "posix":
sys.stdout.write('\033[?25l' )
sys.stdout.flush()
def __A () ->Any:
"""simple docstring"""
if os.name == "nt":
lowerCAmelCase__ :List[Any] = CursorInfo()
lowerCAmelCase__ :Optional[Any] = ctypes.windll.kernelaa.GetStdHandle(-11 )
ctypes.windll.kernelaa.GetConsoleCursorInfo(_SCREAMING_SNAKE_CASE , ctypes.byref(_SCREAMING_SNAKE_CASE ) )
lowerCAmelCase__ :Dict = True
ctypes.windll.kernelaa.SetConsoleCursorInfo(_SCREAMING_SNAKE_CASE , ctypes.byref(_SCREAMING_SNAKE_CASE ) )
elif os.name == "posix":
sys.stdout.write('\033[?25h' )
sys.stdout.flush()
@contextmanager
def __A () ->Any:
"""simple docstring"""
try:
hide_cursor()
yield
finally:
show_cursor()
| 560 | 0 |
import json
import os
import unittest
from transformers import AutoTokenizer, GPTaTokenizer, GPTaTokenizerFast
from transformers.models.gpta.tokenization_gpta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class lowerCAmelCase ( a_ , unittest.TestCase ):
'''simple docstring'''
_A : str = GPTaTokenizer
_A : Optional[Any] = GPTaTokenizerFast
_A : Optional[int] = True
_A : str = {'''add_prefix_space''': True}
_A : Union[str, Any] = False
def lowerCAmelCase ( self : str ) -> str:
"""simple docstring"""
super().setUp()
# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
__lowercase : Any = [
"l",
"o",
"w",
"e",
"r",
"s",
"t",
"i",
"d",
"n",
"\u0120",
"\u0120l",
"\u0120n",
"\u0120lo",
"\u0120low",
"er",
"\u0120lowest",
"\u0120newer",
"\u0120wider",
"<unk>",
"<|endoftext|>",
]
__lowercase : Any = dict(zip(_a , range(len(_a ) ) ) )
__lowercase : Dict = ["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
__lowercase : int = {"unk_token": "<unk>"}
__lowercase : Tuple = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["""vocab_file"""] )
__lowercase : Optional[Any] = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["""merges_file"""] )
with open(self.vocab_file , """w""" , encoding="""utf-8""" ) as fp:
fp.write(json.dumps(_a ) + """\n""" )
with open(self.merges_file , """w""" , encoding="""utf-8""" ) as fp:
fp.write("""\n""".join(_a ) )
def lowerCAmelCase ( self : str , **__a : str ) -> List[Any]:
"""simple docstring"""
kwargs.update(self.special_tokens_map )
return GPTaTokenizer.from_pretrained(self.tmpdirname , **_a )
def lowerCAmelCase ( self : Optional[int] , **__a : Dict ) -> Optional[int]:
"""simple docstring"""
kwargs.update(self.special_tokens_map )
return GPTaTokenizerFast.from_pretrained(self.tmpdirname , **_a )
def lowerCAmelCase ( self : Optional[Any] , __a : Tuple ) -> List[str]:
"""simple docstring"""
__lowercase : Tuple = "lower newer"
__lowercase : Dict = "lower newer"
return input_text, output_text
def lowerCAmelCase ( self : Optional[Any] ) -> str:
"""simple docstring"""
__lowercase : str = GPTaTokenizer(self.vocab_file , self.merges_file , **self.special_tokens_map )
__lowercase : Union[str, Any] = "lower newer"
__lowercase : Any = ["\u0120low", "er", "\u0120", "n", "e", "w", "er"]
__lowercase : int = tokenizer.tokenize(_a , add_prefix_space=_a )
self.assertListEqual(_a , _a )
__lowercase : int = tokens + [tokenizer.unk_token]
__lowercase : List[Any] = [14, 15, 10, 9, 3, 2, 15, 19]
self.assertListEqual(tokenizer.convert_tokens_to_ids(_a ) , _a )
def lowerCAmelCase ( self : Union[str, Any] ) -> Tuple:
"""simple docstring"""
if not self.test_rust_tokenizer:
return
__lowercase : Union[str, Any] = self.get_tokenizer()
__lowercase : int = self.get_rust_tokenizer(add_prefix_space=_a )
__lowercase : Union[str, Any] = "lower newer"
# Testing tokenization
__lowercase : Union[str, Any] = tokenizer.tokenize(_a , add_prefix_space=_a )
__lowercase : List[Any] = rust_tokenizer.tokenize(_a )
self.assertListEqual(_a , _a )
# Testing conversion to ids without special tokens
__lowercase : Dict = tokenizer.encode(_a , add_special_tokens=_a , add_prefix_space=_a )
__lowercase : Dict = rust_tokenizer.encode(_a , add_special_tokens=_a )
self.assertListEqual(_a , _a )
# Testing conversion to ids with special tokens
__lowercase : Any = self.get_rust_tokenizer(add_prefix_space=_a )
__lowercase : Any = tokenizer.encode(_a , add_prefix_space=_a )
__lowercase : Any = rust_tokenizer.encode(_a )
self.assertListEqual(_a , _a )
# Testing the unknown token
__lowercase : Union[str, Any] = tokens + [rust_tokenizer.unk_token]
__lowercase : str = [14, 15, 10, 9, 3, 2, 15, 19]
self.assertListEqual(rust_tokenizer.convert_tokens_to_ids(_a ) , _a )
def lowerCAmelCase ( self : int , *__a : Optional[int] , **__a : Optional[Any] ) -> Optional[int]:
"""simple docstring"""
pass
def lowerCAmelCase ( self : str , __a : Dict=15 ) -> List[str]:
"""simple docstring"""
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(F"{tokenizer.__class__.__name__} ({pretrained_name})" ):
__lowercase : int = self.rust_tokenizer_class.from_pretrained(_a , **_a )
# Simple input
__lowercase : int = "This is a simple input"
__lowercase : Optional[int] = ["This is a simple input 1", "This is a simple input 2"]
__lowercase : Union[str, Any] = ("This is a simple input", "This is a pair")
__lowercase : Any = [
("This is a simple input 1", "This is a simple input 2"),
("This is a simple pair 1", "This is a simple pair 2"),
]
# Simple input tests
self.assertRaises(_a , tokenizer_r.encode , _a , max_length=_a , padding="""max_length""" )
# Simple input
self.assertRaises(_a , tokenizer_r.encode_plus , _a , max_length=_a , padding="""max_length""" )
# Simple input
self.assertRaises(
_a , tokenizer_r.batch_encode_plus , _a , max_length=_a , padding="""max_length""" , )
# Pair input
self.assertRaises(_a , tokenizer_r.encode , _a , max_length=_a , padding="""max_length""" )
# Pair input
self.assertRaises(_a , tokenizer_r.encode_plus , _a , max_length=_a , padding="""max_length""" )
# Pair input
self.assertRaises(
_a , tokenizer_r.batch_encode_plus , _a , max_length=_a , padding="""max_length""" , )
def lowerCAmelCase ( self : Optional[Any] ) -> Optional[int]:
"""simple docstring"""
__lowercase : List[Any] = GPTaTokenizer.from_pretrained(self.tmpdirname , pad_token="""<pad>""" )
# Simple input
__lowercase : int = "This is a simple input"
__lowercase : Tuple = ["This is a simple input looooooooong", "This is a simple input"]
__lowercase : Any = ("This is a simple input", "This is a pair")
__lowercase : str = [
("This is a simple input loooooong", "This is a simple input"),
("This is a simple pair loooooong", "This is a simple pair"),
]
__lowercase : List[Any] = tokenizer.pad_token_id
__lowercase : List[Any] = tokenizer(_a , padding="""max_length""" , max_length=30 , return_tensors="""np""" )
__lowercase : List[str] = tokenizer(_a , padding=_a , truncate=_a , return_tensors="""np""" )
__lowercase : List[Any] = tokenizer(*_a , padding="""max_length""" , max_length=60 , return_tensors="""np""" )
__lowercase : Any = tokenizer(_a , padding=_a , truncate=_a , return_tensors="""np""" )
# s
# test single string max_length padding
self.assertEqual(out_s["""input_ids"""].shape[-1] , 30 )
self.assertTrue(pad_token_id in out_s["""input_ids"""] )
self.assertTrue(0 in out_s["""attention_mask"""] )
# s2
# test automatic padding
self.assertEqual(out_sa["""input_ids"""].shape[-1] , 33 )
# long slice doesn't have padding
self.assertFalse(pad_token_id in out_sa["""input_ids"""][0] )
self.assertFalse(0 in out_sa["""attention_mask"""][0] )
# short slice does have padding
self.assertTrue(pad_token_id in out_sa["""input_ids"""][1] )
self.assertTrue(0 in out_sa["""attention_mask"""][1] )
# p
# test single pair max_length padding
self.assertEqual(out_p["""input_ids"""].shape[-1] , 60 )
self.assertTrue(pad_token_id in out_p["""input_ids"""] )
self.assertTrue(0 in out_p["""attention_mask"""] )
# p2
# test automatic padding pair
self.assertEqual(out_pa["""input_ids"""].shape[-1] , 52 )
# long slice pair doesn't have padding
self.assertFalse(pad_token_id in out_pa["""input_ids"""][0] )
self.assertFalse(0 in out_pa["""attention_mask"""][0] )
# short slice pair does have padding
self.assertTrue(pad_token_id in out_pa["""input_ids"""][1] )
self.assertTrue(0 in out_pa["""attention_mask"""][1] )
def lowerCAmelCase ( self : Union[str, Any] ) -> List[str]:
"""simple docstring"""
__lowercase : List[Any] = "$$$"
__lowercase : List[str] = GPTaTokenizer.from_pretrained(self.tmpdirname , bos_token=_a , add_bos_token=_a )
__lowercase : List[Any] = "This is a simple input"
__lowercase : str = ["This is a simple input 1", "This is a simple input 2"]
__lowercase : Union[str, Any] = tokenizer.bos_token_id
__lowercase : List[str] = tokenizer(_a )
__lowercase : Any = tokenizer(_a )
self.assertEqual(out_s.input_ids[0] , _a )
self.assertTrue(all(o[0] == bos_token_id for o in out_sa.input_ids ) )
__lowercase : List[Any] = tokenizer.decode(out_s.input_ids )
__lowercase : Tuple = tokenizer.batch_decode(out_sa.input_ids )
self.assertEqual(decode_s.split()[0] , _a )
self.assertTrue(all(d.split()[0] == bos_token for d in decode_sa ) )
def lowerCAmelCase ( self : Dict ) -> List[Any]:
"""simple docstring"""
pass
def lowerCAmelCase ( self : Union[str, Any] ) -> Dict:
"""simple docstring"""
__lowercase : Dict = [self.get_tokenizer(do_lower_case=_a , add_bos_token=_a )]
for tokenizer in tokenizers:
with self.subTest(F"{tokenizer.__class__.__name__}" ):
__lowercase : Optional[Any] = "Encode this."
__lowercase : Union[str, Any] = "This one too please."
__lowercase : Dict = tokenizer.encode(_a , add_special_tokens=_a )
encoded_sequence += tokenizer.encode(_a , add_special_tokens=_a )
__lowercase : Optional[Any] = tokenizer.encode_plus(
_a , _a , add_special_tokens=_a , return_special_tokens_mask=_a , )
__lowercase : Tuple = encoded_sequence_dict["input_ids"]
__lowercase : int = encoded_sequence_dict["special_tokens_mask"]
self.assertEqual(len(_a ) , len(_a ) )
__lowercase : List[str] = [
(x if not special_tokens_mask[i] else None) for i, x in enumerate(_a )
]
__lowercase : List[str] = [x for x in filtered_sequence if x is not None]
self.assertEqual(_a , _a )
@require_tokenizers
class lowerCAmelCase ( unittest.TestCase ):
'''simple docstring'''
def lowerCAmelCase ( self : Tuple ) -> List[str]:
"""simple docstring"""
__lowercase : Tuple = AutoTokenizer.from_pretrained("""facebook/opt-350m""" , from_slow=_a )
__lowercase : Any = "A photo of a cat"
__lowercase : List[str] = tokenizer.encode(
_a , )
self.assertEqual(_a , [2, 250, 1345, 9, 10, 4758] )
tokenizer.save_pretrained("""test_opt""" )
__lowercase : Union[str, Any] = AutoTokenizer.from_pretrained("""./test_opt""" )
__lowercase : Tuple = tokenizer.encode(
_a , )
self.assertEqual(_a , [2, 250, 1345, 9, 10, 4758] )
def lowerCAmelCase ( self : str ) -> Dict:
"""simple docstring"""
__lowercase : Union[str, Any] = AutoTokenizer.from_pretrained("""facebook/opt-350m""" , use_slow=_a )
__lowercase : Union[str, Any] = "A photo of a cat"
__lowercase : Union[str, Any] = tokenizer.encode(
_a , )
# Same as above
self.assertEqual(_a , [2, 250, 1345, 9, 10, 4758] )
@unittest.skip("""This test is failing because of a bug in the fast tokenizer""" )
def lowerCAmelCase ( self : List[str] ) -> int:
"""simple docstring"""
__lowercase : int = AutoTokenizer.from_pretrained("""facebook/opt-350m""" , from_slow=_a )
__lowercase : List[Any] = "bos"
__lowercase : Optional[int] = tokenizer.get_vocab()["bos"]
__lowercase : List[str] = "A photo of a cat"
__lowercase : Dict = tokenizer.encode(
_a , )
# We changed the bos token
self.assertEqual(_a , [31957, 250, 1345, 9, 10, 4758] )
tokenizer.save_pretrained("""./tok""" )
__lowercase : Tuple = AutoTokenizer.from_pretrained("""./tok""" )
self.assertTrue(tokenizer.is_fast )
__lowercase : str = tokenizer.encode(
_a , )
self.assertEqual(_a , [31957, 250, 1345, 9, 10, 4758] ) | 149 |
"""simple docstring"""
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import YolosConfig, YolosForObjectDetection, YolosImageProcessor
from transformers.utils import logging
logging.set_verbosity_info()
_A : Optional[int] = logging.get_logger(__name__)
def __magic_name__ ( __snake_case : str ) -> YolosConfig:
lowercase : int = YolosConfig()
# size of the architecture
if "yolos_ti" in yolos_name:
lowercase : Union[str, Any] = 192
lowercase : Tuple = 768
lowercase : Optional[Any] = 12
lowercase : List[str] = 3
lowercase : int = [800, 1333]
lowercase : Union[str, Any] = False
elif yolos_name == "yolos_s_dWr":
lowercase : int = 330
lowercase : List[str] = 14
lowercase : Dict = 6
lowercase : Any = 1320
elif "yolos_s" in yolos_name:
lowercase : str = 384
lowercase : int = 1536
lowercase : Any = 12
lowercase : int = 6
elif "yolos_b" in yolos_name:
lowercase : str = [800, 1344]
lowercase : Tuple = 91
lowercase : Tuple = "huggingface/label-files"
lowercase : Any = "coco-detection-id2label.json"
lowercase : str = json.load(open(hf_hub_download(__snake_case , __snake_case , repo_type="dataset" ) , "r" ) )
lowercase : List[str] = {int(__snake_case ): v for k, v in idalabel.items()}
lowercase : Union[str, Any] = idalabel
lowercase : Union[str, Any] = {v: k for k, v in idalabel.items()}
return config
def __magic_name__ ( __snake_case : dict , __snake_case : YolosConfig , __snake_case : bool = False ) -> int:
for i in range(config.num_hidden_layers ):
# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
lowercase : Dict = state_dict.pop(f"""blocks.{i}.attn.qkv.weight""" )
lowercase : int = state_dict.pop(f"""blocks.{i}.attn.qkv.bias""" )
# next, add query, keys and values (in that order) to the state dict
lowercase : Union[str, Any] = in_proj_weight[: config.hidden_size, :]
lowercase : int = in_proj_bias[: config.hidden_size]
lowercase : str = in_proj_weight[
config.hidden_size : config.hidden_size * 2, :
]
lowercase : List[Any] = in_proj_bias[
config.hidden_size : config.hidden_size * 2
]
lowercase : Dict = in_proj_weight[-config.hidden_size :, :]
lowercase : List[Any] = in_proj_bias[-config.hidden_size :]
def __magic_name__ ( __snake_case : str ) -> str:
if "backbone" in name:
lowercase : Union[str, Any] = name.replace("backbone" , "vit" )
if "cls_token" in name:
lowercase : Union[str, Any] = name.replace("cls_token" , "embeddings.cls_token" )
if "det_token" in name:
lowercase : Dict = name.replace("det_token" , "embeddings.detection_tokens" )
if "mid_pos_embed" in name:
lowercase : Optional[Any] = name.replace("mid_pos_embed" , "encoder.mid_position_embeddings" )
if "pos_embed" in name:
lowercase : Any = name.replace("pos_embed" , "embeddings.position_embeddings" )
if "patch_embed.proj" in name:
lowercase : Optional[int] = name.replace("patch_embed.proj" , "embeddings.patch_embeddings.projection" )
if "blocks" in name:
lowercase : Union[str, Any] = name.replace("blocks" , "encoder.layer" )
if "attn.proj" in name:
lowercase : Optional[int] = name.replace("attn.proj" , "attention.output.dense" )
if "attn" in name:
lowercase : Dict = name.replace("attn" , "attention.self" )
if "norm1" in name:
lowercase : List[str] = name.replace("norm1" , "layernorm_before" )
if "norm2" in name:
lowercase : Union[str, Any] = name.replace("norm2" , "layernorm_after" )
if "mlp.fc1" in name:
lowercase : Union[str, Any] = name.replace("mlp.fc1" , "intermediate.dense" )
if "mlp.fc2" in name:
lowercase : List[str] = name.replace("mlp.fc2" , "output.dense" )
if "class_embed" in name:
lowercase : List[Any] = name.replace("class_embed" , "class_labels_classifier" )
if "bbox_embed" in name:
lowercase : Optional[Any] = name.replace("bbox_embed" , "bbox_predictor" )
if "vit.norm" in name:
lowercase : Optional[int] = name.replace("vit.norm" , "vit.layernorm" )
return name
def __magic_name__ ( __snake_case : dict , __snake_case : YolosForObjectDetection ) -> dict:
for key in orig_state_dict.copy().keys():
lowercase : List[Any] = orig_state_dict.pop(__snake_case )
if "qkv" in key:
lowercase : Union[str, Any] = key.split("." )
lowercase : List[Any] = int(key_split[2] )
lowercase : Union[str, Any] = model.vit.encoder.layer[layer_num].attention.attention.all_head_size
if "weight" in key:
lowercase : int = val[:dim, :]
lowercase : List[Any] = val[
dim : dim * 2, :
]
lowercase : Any = val[-dim:, :]
else:
lowercase : Dict = val[:dim]
lowercase : Dict = val[dim : dim * 2]
lowercase : str = val[-dim:]
else:
lowercase : List[str] = val
return orig_state_dict
def __magic_name__ ( ) -> torch.Tensor:
lowercase : Optional[int] = "http://images.cocodataset.org/val2017/000000039769.jpg"
lowercase : Tuple = Image.open(requests.get(__snake_case , stream=__snake_case ).raw )
return im
@torch.no_grad()
def __magic_name__ ( __snake_case : str , __snake_case : str , __snake_case : str , __snake_case : bool = False ) -> Dict:
lowercase : Optional[int] = get_yolos_config(__snake_case )
# load original state_dict
lowercase : str = torch.load(__snake_case , map_location="cpu" )["model"]
# load 🤗 model
lowercase : str = YolosForObjectDetection(__snake_case )
model.eval()
lowercase : Dict = convert_state_dict(__snake_case , __snake_case )
model.load_state_dict(__snake_case )
# Check outputs on an image, prepared by YolosImageProcessor
lowercase : Any = 800 if yolos_name != "yolos_ti" else 512
lowercase : List[Any] = YolosImageProcessor(format="coco_detection" , size=__snake_case )
lowercase : Any = image_processor(images=prepare_img() , return_tensors="pt" )
lowercase : Any = model(**__snake_case )
lowercase , lowercase : Tuple = outputs.logits, outputs.pred_boxes
lowercase , lowercase : int = None, None
if yolos_name == "yolos_ti":
lowercase : int = torch.tensor(
[[-39.50_22, -11.98_20, -17.68_88], [-29.95_74, -9.97_69, -17.76_91], [-42.32_81, -20.72_00, -30.62_94]] )
lowercase : List[Any] = torch.tensor(
[[0.40_21, 0.08_36, 0.79_79], [0.01_84, 0.26_09, 0.03_64], [0.17_81, 0.20_04, 0.20_95]] )
elif yolos_name == "yolos_s_200_pre":
lowercase : List[Any] = torch.tensor(
[[-24.02_48, -10.30_24, -14.82_90], [-42.03_92, -16.82_00, -27.43_34], [-27.27_43, -11.81_54, -18.71_48]] )
lowercase : List[Any] = torch.tensor(
[[0.25_59, 0.54_55, 0.47_06], [0.29_89, 0.72_79, 0.18_75], [0.77_32, 0.40_17, 0.44_62]] )
elif yolos_name == "yolos_s_300_pre":
lowercase : Optional[int] = torch.tensor(
[[-36.22_20, -14.43_85, -23.54_57], [-35.69_70, -14.75_83, -21.39_35], [-31.59_39, -13.60_42, -16.80_49]] )
lowercase : Tuple = torch.tensor(
[[0.76_14, 0.23_16, 0.47_28], [0.71_68, 0.44_95, 0.38_55], [0.49_96, 0.14_66, 0.99_96]] )
elif yolos_name == "yolos_s_dWr":
lowercase : Tuple = torch.tensor(
[[-42.86_68, -24.10_49, -41.16_90], [-34.74_56, -14.12_74, -24.91_94], [-33.78_98, -12.19_46, -25.64_95]] )
lowercase : List[str] = torch.tensor(
[[0.55_87, 0.27_73, 0.06_05], [0.50_04, 0.30_14, 0.99_94], [0.49_99, 0.15_48, 0.99_94]] )
elif yolos_name == "yolos_base":
lowercase : Optional[Any] = torch.tensor(
[[-40.60_64, -24.30_84, -32.64_47], [-55.19_90, -30.77_19, -35.58_77], [-51.43_11, -33.35_07, -35.64_62]] )
lowercase : List[str] = torch.tensor(
[[0.55_55, 0.27_94, 0.06_55], [0.90_49, 0.26_64, 0.18_94], [0.91_83, 0.19_84, 0.16_35]] )
else:
raise ValueError(f"""Unknown yolos_name: {yolos_name}""" )
assert torch.allclose(logits[0, :3, :3] , __snake_case , atol=1E-4 )
assert torch.allclose(pred_boxes[0, :3, :3] , __snake_case , atol=1E-4 )
Path(__snake_case ).mkdir(exist_ok=__snake_case )
print(f"""Saving model {yolos_name} to {pytorch_dump_folder_path}""" )
model.save_pretrained(__snake_case )
print(f"""Saving image processor to {pytorch_dump_folder_path}""" )
image_processor.save_pretrained(__snake_case )
if push_to_hub:
lowercase : Optional[int] = {
"yolos_ti": "yolos-tiny",
"yolos_s_200_pre": "yolos-small",
"yolos_s_300_pre": "yolos-small-300",
"yolos_s_dWr": "yolos-small-dwr",
"yolos_base": "yolos-base",
}
print("Pushing to the hub..." )
lowercase : Optional[Any] = model_mapping[yolos_name]
image_processor.push_to_hub(__snake_case , organization="hustvl" )
model.push_to_hub(__snake_case , organization="hustvl" )
if __name__ == "__main__":
_A : List[Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--yolos_name""",
default="""yolos_s_200_pre""",
type=str,
help=(
"""Name of the YOLOS model you'd like to convert. Should be one of 'yolos_ti', 'yolos_s_200_pre',"""
""" 'yolos_s_300_pre', 'yolos_s_dWr', 'yolos_base'."""
),
)
parser.add_argument(
"""--checkpoint_path""", default=None, type=str, help="""Path to the original state dict (.pth file)."""
)
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."""
)
_A : Any = parser.parse_args()
convert_yolos_checkpoint(args.yolos_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)
| 361 | 0 |
"""simple docstring"""
import os
import shutil
import tempfile
import unittest
import numpy as np
from transformers import AutoTokenizer, BarkProcessor
from transformers.testing_utils import require_torch, slow
@require_torch
class lowerCamelCase_ ( unittest.TestCase ):
"""simple docstring"""
def lowerCAmelCase__ ( self ):
SCREAMING_SNAKE_CASE__ = "ylacombe/bark-small"
SCREAMING_SNAKE_CASE__ = tempfile.mkdtemp()
SCREAMING_SNAKE_CASE__ = "en_speaker_1"
SCREAMING_SNAKE_CASE__ = "This is a test string"
SCREAMING_SNAKE_CASE__ = "speaker_embeddings_path.json"
SCREAMING_SNAKE_CASE__ = "speaker_embeddings"
def lowerCAmelCase__ ( self , **UpperCAmelCase__ ):
return AutoTokenizer.from_pretrained(self.checkpoint , **UpperCAmelCase__ )
def lowerCAmelCase__ ( self ):
shutil.rmtree(self.tmpdirname )
def lowerCAmelCase__ ( self ):
SCREAMING_SNAKE_CASE__ = self.get_tokenizer()
SCREAMING_SNAKE_CASE__ = BarkProcessor(tokenizer=UpperCAmelCase__ )
processor.save_pretrained(self.tmpdirname )
SCREAMING_SNAKE_CASE__ = BarkProcessor.from_pretrained(self.tmpdirname )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer.get_vocab() )
@slow
def lowerCAmelCase__ ( self ):
SCREAMING_SNAKE_CASE__ = BarkProcessor.from_pretrained(
pretrained_processor_name_or_path=self.checkpoint , speaker_embeddings_dict_path=self.speaker_embeddings_dict_path , )
processor.save_pretrained(
self.tmpdirname , speaker_embeddings_dict_path=self.speaker_embeddings_dict_path , speaker_embeddings_directory=self.speaker_embeddings_directory , )
SCREAMING_SNAKE_CASE__ = self.get_tokenizer(bos_token="(BOS)" , eos_token="(EOS)" )
SCREAMING_SNAKE_CASE__ = BarkProcessor.from_pretrained(
self.tmpdirname , self.speaker_embeddings_dict_path , bos_token="(BOS)" , eos_token="(EOS)" , )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() )
def lowerCAmelCase__ ( self ):
SCREAMING_SNAKE_CASE__ = BarkProcessor.from_pretrained(
pretrained_processor_name_or_path=self.checkpoint , speaker_embeddings_dict_path=self.speaker_embeddings_dict_path , )
SCREAMING_SNAKE_CASE__ = 35
SCREAMING_SNAKE_CASE__ = 2
SCREAMING_SNAKE_CASE__ = 8
SCREAMING_SNAKE_CASE__ = {
"semantic_prompt": np.ones(UpperCAmelCase__ ),
"coarse_prompt": np.ones((nb_codebooks_coarse, seq_len) ),
"fine_prompt": np.ones((nb_codebooks_total, seq_len) ),
}
# test providing already loaded voice_preset
SCREAMING_SNAKE_CASE__ = processor(text=self.input_string , voice_preset=UpperCAmelCase__ )
SCREAMING_SNAKE_CASE__ = inputs["history_prompt"]
for key in voice_preset:
self.assertListEqual(voice_preset[key].tolist() , processed_voice_preset.get(UpperCAmelCase__ , np.array([] ) ).tolist() )
# test loading voice preset from npz file
SCREAMING_SNAKE_CASE__ = os.path.join(self.tmpdirname , "file.npz" )
np.savez(UpperCAmelCase__ , **UpperCAmelCase__ )
SCREAMING_SNAKE_CASE__ = processor(text=self.input_string , voice_preset=UpperCAmelCase__ )
SCREAMING_SNAKE_CASE__ = inputs["history_prompt"]
for key in voice_preset:
self.assertListEqual(voice_preset[key].tolist() , processed_voice_preset.get(UpperCAmelCase__ , np.array([] ) ).tolist() )
# test loading voice preset from the hub
SCREAMING_SNAKE_CASE__ = processor(text=self.input_string , voice_preset=self.voice_preset )
def lowerCAmelCase__ ( self ):
SCREAMING_SNAKE_CASE__ = self.get_tokenizer()
SCREAMING_SNAKE_CASE__ = BarkProcessor(tokenizer=UpperCAmelCase__ )
SCREAMING_SNAKE_CASE__ = processor(text=self.input_string )
SCREAMING_SNAKE_CASE__ = tokenizer(
self.input_string , padding="max_length" , max_length=256 , add_special_tokens=UpperCAmelCase__ , return_attention_mask=UpperCAmelCase__ , return_token_type_ids=UpperCAmelCase__ , )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key].squeeze().tolist() )
| 711 |
"""simple docstring"""
import argparse
import shlex
import runhouse as rh
if __name__ == "__main__":
# Refer to https://runhouse-docs.readthedocs-hosted.com/en/latest/api/python/cluster.html#hardware-setup for cloud access
# setup instructions, if using on-demand hardware
# If user passes --user <user> --host <host> --key_path <key_path> <example> <args>, fill them in as BYO cluster
# If user passes --instance <instance> --provider <provider> <example> <args>, fill them in as on-demand cluster
# Throw an error if user passes both BYO and on-demand cluster args
# Otherwise, use default values
_lowerCamelCase = argparse.ArgumentParser()
parser.add_argument('--user', type=str, default='ubuntu')
parser.add_argument('--host', type=str, default='localhost')
parser.add_argument('--key_path', type=str, default=None)
parser.add_argument('--instance', type=str, default='V100:1')
parser.add_argument('--provider', type=str, default='cheapest')
parser.add_argument('--use_spot', type=bool, default=False)
parser.add_argument('--example', type=str, default='pytorch/text-generation/run_generation.py')
_lowerCamelCase , _lowerCamelCase = parser.parse_known_args()
if args.host != "localhost":
if args.instance != "V100:1" or args.provider != "cheapest":
raise ValueError('Cannot specify both BYO and on-demand cluster args')
_lowerCamelCase = rh.cluster(
name='rh-cluster', ips=[args.host], ssh_creds={'ssh_user': args.user, 'ssh_private_key': args.key_path}
)
else:
_lowerCamelCase = rh.cluster(
name='rh-cluster', instance_type=args.instance, provider=args.provider, use_spot=args.use_spot
)
_lowerCamelCase = args.example.rsplit('/', 1)[0]
# Set up remote environment
cluster.install_packages(['pip:./']) # Installs transformers from local source
# Note transformers is copied into the home directory on the remote machine, so we can install from there
cluster.run([f"""pip install -r transformers/examples/{example_dir}/requirements.txt"""])
cluster.run(['pip install torch --upgrade --extra-index-url https://download.pytorch.org/whl/cu117'])
# Run example. You can bypass the CLI wrapper and paste your own code here.
cluster.run([f"""python transformers/examples/{args.example} {' '.join(shlex.quote(arg) for arg in unknown)}"""])
# Alternatively, we can just import and run a training function (especially if there's no wrapper CLI):
# from my_script... import train
# reqs = ['pip:./', 'torch', 'datasets', 'accelerate', 'evaluate', 'tqdm', 'scipy', 'scikit-learn', 'tensorboard']
# launch_train_gpu = rh.function(fn=train,
# system=gpu,
# reqs=reqs,
# name='train_bert_glue')
#
# We can pass in arguments just like we would to a function:
# launch_train_gpu(num_epochs = 3, lr = 2e-5, seed = 42, batch_size = 16
# stream_logs=True)
| 112 | 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 AddedToken, PreTrainedTokenizer
from ...utils import logging
__lowercase : List[str] = logging.get_logger(__name__)
__lowercase : Dict = "▁"
__lowercase : Union[str, Any] = {"vocab_file": "sentencepiece.bpe.model"}
__lowercase : Optional[int] = {
"vocab_file": {
"xlm-roberta-base": "https://huggingface.co/xlm-roberta-base/resolve/main/sentencepiece.bpe.model",
"xlm-roberta-large": "https://huggingface.co/xlm-roberta-large/resolve/main/sentencepiece.bpe.model",
"xlm-roberta-large-finetuned-conll02-dutch": (
"https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/sentencepiece.bpe.model"
),
"xlm-roberta-large-finetuned-conll02-spanish": (
"https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/sentencepiece.bpe.model"
),
"xlm-roberta-large-finetuned-conll03-english": (
"https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/sentencepiece.bpe.model"
),
"xlm-roberta-large-finetuned-conll03-german": (
"https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/sentencepiece.bpe.model"
),
}
}
__lowercase : Any = {
"xlm-roberta-base": 512,
"xlm-roberta-large": 512,
"xlm-roberta-large-finetuned-conll02-dutch": 512,
"xlm-roberta-large-finetuned-conll02-spanish": 512,
"xlm-roberta-large-finetuned-conll03-english": 512,
"xlm-roberta-large-finetuned-conll03-german": 512,
}
class _A ( _UpperCAmelCase ):
"""simple docstring"""
UpperCamelCase_ : List[str] = VOCAB_FILES_NAMES
UpperCamelCase_ : Union[str, Any] = PRETRAINED_VOCAB_FILES_MAP
UpperCamelCase_ : List[Any] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
UpperCamelCase_ : Any = ['''input_ids''', '''attention_mask''']
def __init__( self : str , A_ : Any , A_ : Union[str, Any]="<s>" , A_ : Any="</s>" , A_ : Any="</s>" , A_ : Union[str, Any]="<s>" , A_ : str="<unk>" , A_ : Union[str, Any]="<pad>" , A_ : str="<mask>" , A_ : Optional[Dict[str, Any]] = None , **A_ : Any , ) -> None:
# Mask token behave like a normal word, i.e. include the space before it
__snake_case = AddedToken(A_ , lstrip=A_ , rstrip=A_ ) if isinstance(A_ , A_ ) else mask_token
__snake_case = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
bos_token=A_ , eos_token=A_ , unk_token=A_ , sep_token=A_ , cls_token=A_ , pad_token=A_ , mask_token=A_ , sp_model_kwargs=self.sp_model_kwargs , **A_ , )
__snake_case = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(str(A_ ) )
__snake_case = vocab_file
# Original fairseq vocab and spm vocab must be "aligned":
# Vocab | 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9
# -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
# fairseq | '<s>' | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's' | '▁de' | '-'
# spm | '<unk>' | '<s>' | '</s>' | ',' | '.' | '▁' | 's' | '▁de' | '-' | '▁a'
# Mimic fairseq token-to-id alignment for the first 4 token
__snake_case = {'''<s>''': 0, '''<pad>''': 1, '''</s>''': 2, '''<unk>''': 3}
# The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
__snake_case = 1
__snake_case = len(self.sp_model ) + self.fairseq_offset
__snake_case = {v: k for k, v in self.fairseq_tokens_to_ids.items()}
def __getstate__( self : Union[str, Any] ) -> Union[str, Any]:
__snake_case = self.__dict__.copy()
__snake_case = None
__snake_case = self.sp_model.serialized_model_proto()
return state
def __setstate__( self : List[str] , A_ : Tuple ) -> str:
__snake_case = d
# for backward compatibility
if not hasattr(self , '''sp_model_kwargs''' ):
__snake_case = {}
__snake_case = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.LoadFromSerializedProto(self.sp_model_proto )
def lowercase ( self : int , A_ : List[int] , A_ : Optional[List[int]] = None ) -> List[int]:
if token_ids_a is None:
return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
__snake_case = [self.cls_token_id]
__snake_case = [self.sep_token_id]
return cls + token_ids_a + sep + sep + token_ids_a + sep
def lowercase ( self : List[Any] , A_ : List[int] , A_ : Optional[List[int]] = None , A_ : bool = False ) -> List[int]:
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=A_ , token_ids_a=A_ , already_has_special_tokens=A_ )
if token_ids_a is None:
return [1] + ([0] * len(A_ )) + [1]
return [1] + ([0] * len(A_ )) + [1, 1] + ([0] * len(A_ )) + [1]
def lowercase ( self : List[str] , A_ : List[int] , A_ : Optional[List[int]] = None ) -> List[int]:
__snake_case = [self.sep_token_id]
__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 lowercase ( self : Any ) -> str:
return len(self.sp_model ) + self.fairseq_offset + 1 # Add the <mask> token
def lowercase ( self : Optional[Any] ) -> Union[str, Any]:
__snake_case = {self.convert_ids_to_tokens(A_ ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def lowercase ( self : Union[str, Any] , A_ : str ) -> List[str]:
return self.sp_model.encode(A_ , out_type=A_ )
def lowercase ( self : Optional[Any] , A_ : List[str] ) -> Union[str, Any]:
if token in self.fairseq_tokens_to_ids:
return self.fairseq_tokens_to_ids[token]
__snake_case = self.sp_model.PieceToId(A_ )
# Need to return unknown token if the SP model returned 0
return spm_id + self.fairseq_offset if spm_id else self.unk_token_id
def lowercase ( self : str , A_ : Optional[int] ) -> str:
if index in self.fairseq_ids_to_tokens:
return self.fairseq_ids_to_tokens[index]
return self.sp_model.IdToPiece(index - self.fairseq_offset )
def lowercase ( self : str , A_ : Dict ) -> Any:
__snake_case = ''''''.join(A_ ).replace(A_ , ''' ''' ).strip()
return out_string
def lowercase ( self : Any , A_ : str , A_ : Optional[str] = None ) -> Tuple[str]:
if not os.path.isdir(A_ ):
logger.error(f"Vocabulary path ({save_directory}) should be a directory" )
return
__snake_case = os.path.join(
A_ , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(A_ ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file , A_ )
elif not os.path.isfile(self.vocab_file ):
with open(A_ , '''wb''' ) as fi:
__snake_case = self.sp_model.serialized_model_proto()
fi.write(A_ )
return (out_vocab_file,) | 564 | """simple docstring"""
from typing import Dict, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
from ...image_utils import (
IMAGENET_STANDARD_MEAN,
IMAGENET_STANDARD_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_torch_available, is_torch_tensor, logging
if is_torch_available():
import torch
__lowercase : Tuple = logging.get_logger(__name__)
class _A ( _UpperCAmelCase ):
"""simple docstring"""
UpperCamelCase_ : List[str] = ['''pixel_values''']
def __init__( self : Optional[int] , A_ : bool = True , A_ : Optional[Dict[str, int]] = None , A_ : PILImageResampling = PILImageResampling.BILINEAR , A_ : bool = True , A_ : Dict[str, int] = None , A_ : bool = True , A_ : Union[int, float] = 1 / 255 , A_ : bool = True , A_ : Optional[Union[float, List[float]]] = None , A_ : Optional[Union[float, List[float]]] = None , **A_ : str , ) -> None:
super().__init__(**A_ )
__snake_case = size if size is not None else {'''shortest_edge''': 256}
__snake_case = get_size_dict(A_ , default_to_square=A_ )
__snake_case = crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
__snake_case = get_size_dict(A_ , param_name='''crop_size''' )
__snake_case = do_resize
__snake_case = size
__snake_case = resample
__snake_case = do_center_crop
__snake_case = crop_size
__snake_case = do_rescale
__snake_case = rescale_factor
__snake_case = do_normalize
__snake_case = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
__snake_case = image_std if image_std is not None else IMAGENET_STANDARD_STD
def lowercase ( self : List[str] , A_ : np.ndarray , A_ : Dict[str, int] , A_ : PILImageResampling = PILImageResampling.BICUBIC , A_ : Optional[Union[str, ChannelDimension]] = None , **A_ : List[Any] , ) -> np.ndarray:
__snake_case = get_size_dict(A_ , default_to_square=A_ )
if "shortest_edge" not in size:
raise ValueError(f"The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}" )
__snake_case = get_resize_output_image_size(A_ , size=size['''shortest_edge'''] , default_to_square=A_ )
return resize(A_ , size=A_ , resample=A_ , data_format=A_ , **A_ )
def lowercase ( self : Tuple , A_ : np.ndarray , A_ : Dict[str, int] , A_ : Optional[Union[str, ChannelDimension]] = None , **A_ : Union[str, Any] , ) -> np.ndarray:
__snake_case = get_size_dict(A_ )
if "height" not in size or "width" not in size:
raise ValueError(f"The `size` parameter must contain the keys `height` and `width`. Got {size.keys()}" )
return center_crop(A_ , size=(size['''height'''], size['''width''']) , data_format=A_ , **A_ )
def lowercase ( self : Optional[int] , A_ : np.ndarray , A_ : float , A_ : Optional[Union[str, ChannelDimension]] = None , **A_ : int ) -> np.ndarray:
return rescale(A_ , scale=A_ , data_format=A_ , **A_ )
def lowercase ( self : Tuple , A_ : np.ndarray , A_ : Union[float, List[float]] , A_ : Union[float, List[float]] , A_ : Optional[Union[str, ChannelDimension]] = None , **A_ : Tuple , ) -> np.ndarray:
return normalize(A_ , mean=A_ , std=A_ , data_format=A_ , **A_ )
def lowercase ( self : List[Any] , A_ : ImageInput , A_ : Optional[bool] = None , A_ : Dict[str, int] = None , A_ : PILImageResampling = None , A_ : bool = None , A_ : Dict[str, int] = None , A_ : Optional[bool] = None , A_ : Optional[float] = None , A_ : Optional[bool] = None , A_ : Optional[Union[float, List[float]]] = None , A_ : Optional[Union[float, List[float]]] = None , A_ : Optional[Union[str, TensorType]] = None , A_ : Union[str, ChannelDimension] = ChannelDimension.FIRST , **A_ : Dict , ) -> Optional[Any]:
__snake_case = do_resize if do_resize is not None else self.do_resize
__snake_case = size if size is not None else self.size
__snake_case = get_size_dict(A_ , default_to_square=A_ )
__snake_case = resample if resample is not None else self.resample
__snake_case = do_center_crop if do_center_crop is not None else self.do_center_crop
__snake_case = crop_size if crop_size is not None else self.crop_size
__snake_case = get_size_dict(A_ , param_name='''crop_size''' )
__snake_case = do_rescale if do_rescale is not None else self.do_rescale
__snake_case = rescale_factor if rescale_factor is not None else self.rescale_factor
__snake_case = do_normalize if do_normalize is not None else self.do_normalize
__snake_case = image_mean if image_mean is not None else self.image_mean
__snake_case = image_std if image_std is not None else self.image_std
__snake_case = make_list_of_images(A_ )
if not valid_images(A_ ):
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:
raise ValueError('''Size must be specified if do_resize is True.''' )
if do_center_crop and crop_size is None:
raise ValueError('''Crop size must be specified if do_center_crop 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.
__snake_case = [to_numpy_array(A_ ) for image in images]
if do_resize:
__snake_case = [self.resize(image=A_ , size=A_ , resample=A_ ) for image in images]
if do_center_crop:
__snake_case = [self.center_crop(image=A_ , size=A_ ) for image in images]
if do_rescale:
__snake_case = [self.rescale(image=A_ , scale=A_ ) for image in images]
if do_normalize:
__snake_case = [self.normalize(image=A_ , mean=A_ , std=A_ ) for image in images]
__snake_case = [to_channel_dimension_format(A_ , A_ ) for image in images]
__snake_case = {'''pixel_values''': images}
return BatchFeature(data=A_ , tensor_type=A_ )
def lowercase ( self : List[str] , A_ : Optional[Any] , A_ : List[Tuple] = None ) -> List[Any]:
__snake_case = outputs.logits
# Resize logits and compute semantic segmentation maps
if target_sizes is not None:
if len(A_ ) != len(A_ ):
raise ValueError(
'''Make sure that you pass in as many target sizes as the batch dimension of the logits''' )
if is_torch_tensor(A_ ):
__snake_case = target_sizes.numpy()
__snake_case = []
for idx in range(len(A_ ) ):
__snake_case = torch.nn.functional.interpolate(
logits[idx].unsqueeze(dim=0 ) , size=target_sizes[idx] , mode='''bilinear''' , align_corners=A_ )
__snake_case = resized_logits[0].argmax(dim=0 )
semantic_segmentation.append(A_ )
else:
__snake_case = logits.argmax(dim=1 )
__snake_case = [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0] )]
return semantic_segmentation | 564 | 1 |
snake_case : Tuple = [
(1_0_0_0, 'M'),
(9_0_0, 'CM'),
(5_0_0, 'D'),
(4_0_0, 'CD'),
(1_0_0, 'C'),
(9_0, 'XC'),
(5_0, 'L'),
(4_0, 'XL'),
(1_0, 'X'),
(9, 'IX'),
(5, 'V'),
(4, 'IV'),
(1, 'I'),
]
def snake_case__ ( __lowercase ) -> int:
"""simple docstring"""
A__ : Optional[int] = {"I": 1, "V": 5, "X": 1_0, "L": 5_0, "C": 1_0_0, "D": 5_0_0, "M": 1_0_0_0}
A__ : Any = 0
A__ : List[Any] = 0
while place < len(__lowercase ):
if (place + 1 < len(__lowercase )) and (vals[roman[place]] < vals[roman[place + 1]]):
total += vals[roman[place + 1]] - vals[roman[place]]
place += 2
else:
total += vals[roman[place]]
place += 1
return total
def snake_case__ ( __lowercase ) -> str:
"""simple docstring"""
A__ : List[str] = []
for arabic, roman in ROMAN:
((A__) , (A__)) : List[Any] = divmod(__lowercase , __lowercase )
result.append(roman * factor )
if number == 0:
break
return "".join(__lowercase )
if __name__ == "__main__":
import doctest
doctest.testmod() | 182 |
import json
import os
import shutil
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import AutoConfig, BertConfig, GPTaConfig
from transformers.configuration_utils import PretrainedConfig
from transformers.testing_utils import TOKEN, USER, is_staging_test
sys.path.append(str(Path(__file__).parent.parent / 'utils'))
from test_module.custom_configuration import CustomConfig # noqa E402
snake_case : List[str] = {
'return_dict': False,
'output_hidden_states': True,
'output_attentions': True,
'torchscript': True,
'torch_dtype': 'float16',
'use_bfloat16': True,
'tf_legacy_loss': True,
'pruned_heads': {'a': 1},
'tie_word_embeddings': False,
'is_decoder': True,
'cross_attention_hidden_size': 1_2_8,
'add_cross_attention': True,
'tie_encoder_decoder': True,
'max_length': 5_0,
'min_length': 3,
'do_sample': True,
'early_stopping': True,
'num_beams': 3,
'num_beam_groups': 3,
'diversity_penalty': 0.5,
'temperature': 2.0,
'top_k': 1_0,
'top_p': 0.7,
'typical_p': 0.2,
'repetition_penalty': 0.8,
'length_penalty': 0.8,
'no_repeat_ngram_size': 5,
'encoder_no_repeat_ngram_size': 5,
'bad_words_ids': [1, 2, 3],
'num_return_sequences': 3,
'chunk_size_feed_forward': 5,
'output_scores': True,
'return_dict_in_generate': True,
'forced_bos_token_id': 2,
'forced_eos_token_id': 3,
'remove_invalid_values': True,
'architectures': ['BertModel'],
'finetuning_task': 'translation',
'id2label': {0: 'label'},
'label2id': {'label': '0'},
'tokenizer_class': 'BertTokenizerFast',
'prefix': 'prefix',
'bos_token_id': 6,
'pad_token_id': 7,
'eos_token_id': 8,
'sep_token_id': 9,
'decoder_start_token_id': 1_0,
'exponential_decay_length_penalty': (5, 1.01),
'suppress_tokens': [0, 1],
'begin_suppress_tokens': 2,
'task_specific_params': {'translation': 'some_params'},
'problem_type': 'regression',
}
@is_staging_test
class lowerCAmelCase__ ( unittest.TestCase ):
@classmethod
def _lowercase ( cls : str):
A__ : int = TOKEN
HfFolder.save_token(_A)
@classmethod
def _lowercase ( cls : Optional[Any]):
try:
delete_repo(token=cls._token , repo_id="test-config")
except HTTPError:
pass
try:
delete_repo(token=cls._token , repo_id="valid_org/test-config-org")
except HTTPError:
pass
try:
delete_repo(token=cls._token , repo_id="test-dynamic-config")
except HTTPError:
pass
def _lowercase ( self : Dict):
A__ : List[str] = BertConfig(
vocab_size=99 , hidden_size=32 , num_hidden_layers=5 , num_attention_heads=4 , intermediate_size=37)
config.push_to_hub("test-config" , use_auth_token=self._token)
A__ : Dict = BertConfig.from_pretrained(F'{USER}/test-config')
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(_A , getattr(_A , _A))
# Reset repo
delete_repo(token=self._token , repo_id="test-config")
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(_A , repo_id="test-config" , push_to_hub=_A , use_auth_token=self._token)
A__ : Tuple = BertConfig.from_pretrained(F'{USER}/test-config')
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(_A , getattr(_A , _A))
def _lowercase ( self : List[Any]):
A__ : List[Any] = BertConfig(
vocab_size=99 , hidden_size=32 , num_hidden_layers=5 , num_attention_heads=4 , intermediate_size=37)
config.push_to_hub("valid_org/test-config-org" , use_auth_token=self._token)
A__ : Union[str, Any] = BertConfig.from_pretrained("valid_org/test-config-org")
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(_A , getattr(_A , _A))
# Reset repo
delete_repo(token=self._token , repo_id="valid_org/test-config-org")
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(
_A , repo_id="valid_org/test-config-org" , push_to_hub=_A , use_auth_token=self._token)
A__ : List[str] = BertConfig.from_pretrained("valid_org/test-config-org")
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(_A , getattr(_A , _A))
def _lowercase ( self : Optional[int]):
CustomConfig.register_for_auto_class()
A__ : Optional[Any] = CustomConfig(attribute=42)
config.push_to_hub("test-dynamic-config" , use_auth_token=self._token)
# This has added the proper auto_map field to the config
self.assertDictEqual(config.auto_map , {"AutoConfig": "custom_configuration.CustomConfig"})
A__ : Optional[Any] = AutoConfig.from_pretrained(F'{USER}/test-dynamic-config' , trust_remote_code=_A)
# Can't make an isinstance check because the new_config is from the FakeConfig class of a dynamic module
self.assertEqual(new_config.__class__.__name__ , "CustomConfig")
self.assertEqual(new_config.attribute , 42)
class lowerCAmelCase__ ( unittest.TestCase ):
def _lowercase ( self : Optional[int]):
A__ : List[str] = GPTaConfig()
# attempt to modify each of int/float/bool/str config records and verify they were updated
A__ : Optional[int] = c.n_embd + 1 # int
A__ : Tuple = c.resid_pdrop + 1.0 # float
A__ : Any = not c.scale_attn_weights # bool
A__ : List[Any] = c.summary_type + "foo" # str
c.update_from_string(
F'n_embd={n_embd},resid_pdrop={resid_pdrop},scale_attn_weights={scale_attn_weights},summary_type={summary_type}')
self.assertEqual(_A , c.n_embd , "mismatch for key: n_embd")
self.assertEqual(_A , c.resid_pdrop , "mismatch for key: resid_pdrop")
self.assertEqual(_A , c.scale_attn_weights , "mismatch for key: scale_attn_weights")
self.assertEqual(_A , c.summary_type , "mismatch for key: summary_type")
def _lowercase ( self : Optional[int]):
A__ : Any = PretrainedConfig()
A__ : Optional[Any] = [key for key in base_config.__dict__ if key not in config_common_kwargs]
# If this part of the test fails, you have arguments to addin config_common_kwargs above.
self.assertListEqual(
_A , ["is_encoder_decoder", "_name_or_path", "_commit_hash", "transformers_version"])
A__ : Optional[Any] = [key for key, value in config_common_kwargs.items() if value == getattr(_A , _A)]
if len(_A) > 0:
raise ValueError(
"The following keys are set with the default values in"
" `test_configuration_common.config_common_kwargs` pick another value for them:"
F' {", ".join(_A)}.')
def _lowercase ( self : List[str]):
with self.assertRaises(_A):
# config is in subfolder, the following should not work without specifying the subfolder
A__ : Any = BertConfig.from_pretrained("hf-internal-testing/tiny-random-bert-subfolder")
A__ : str = BertConfig.from_pretrained("hf-internal-testing/tiny-random-bert-subfolder" , subfolder="bert")
self.assertIsNotNone(_A)
def _lowercase ( self : List[str]):
# A mock response for an HTTP head request to emulate server down
A__ : Tuple = mock.Mock()
A__ : Dict = 500
A__ : Union[str, Any] = {}
A__ : List[Any] = HTTPError
A__ : Optional[int] = {}
# Download this model to make sure it's in the cache.
A__ : Any = BertConfig.from_pretrained("hf-internal-testing/tiny-random-bert")
# Under the mock environment we get a 500 error when trying to reach the model.
with mock.patch("requests.Session.request" , return_value=_A) as mock_head:
A__ : List[str] = BertConfig.from_pretrained("hf-internal-testing/tiny-random-bert")
# This check we did call the fake head request
mock_head.assert_called()
def _lowercase ( self : Optional[int]):
# This test is for deprecated behavior and can be removed in v5
A__ : List[str] = BertConfig.from_pretrained(
"https://huggingface.co/hf-internal-testing/tiny-random-bert/resolve/main/config.json")
def _lowercase ( self : Union[str, Any]):
A__ : Union[str, Any] = AutoConfig.from_pretrained("bert-base-cased")
A__ : str = ["config.4.0.0.json"]
with tempfile.TemporaryDirectory() as tmp_dir:
configuration.save_pretrained(_A)
A__ : Dict = 2
json.dump(configuration.to_dict() , open(os.path.join(_A , "config.4.0.0.json") , "w"))
# This should pick the new configuration file as the version of Transformers is > 4.0.0
A__ : Any = AutoConfig.from_pretrained(_A)
self.assertEqual(new_configuration.hidden_size , 2)
# Will need to be adjusted if we reach v42 and this test is still here.
# Should pick the old configuration file as the version of Transformers is < 4.42.0
A__ : str = ["config.42.0.0.json"]
A__ : Optional[int] = 768
configuration.save_pretrained(_A)
shutil.move(os.path.join(_A , "config.4.0.0.json") , os.path.join(_A , "config.42.0.0.json"))
A__ : Optional[int] = AutoConfig.from_pretrained(_A)
self.assertEqual(new_configuration.hidden_size , 768)
def _lowercase ( self : Optional[int]):
# This repo has two configuration files, one for v4.0.0 and above with a different hidden size.
A__ : List[str] = "hf-internal-testing/test-two-configs"
import transformers as new_transformers
A__ : int = "v4.0.0"
A__ , A__ : str = new_transformers.models.auto.AutoConfig.from_pretrained(
_A , return_unused_kwargs=_A)
self.assertEqual(new_configuration.hidden_size , 2)
# This checks `_configuration_file` ia not kept in the kwargs by mistake.
self.assertDictEqual(_A , {})
# Testing an older version by monkey-patching the version in the module it's used.
import transformers as old_transformers
A__ : List[Any] = "v3.0.0"
A__ : Optional[Any] = old_transformers.models.auto.AutoConfig.from_pretrained(_A)
self.assertEqual(old_configuration.hidden_size , 768) | 182 | 1 |
_snake_case = '\n# Transformers installation\n! pip install transformers datasets\n# To install from source instead of the last release, comment the command above and uncomment the following one.\n# ! pip install git+https://github.com/huggingface/transformers.git\n'
_snake_case = [{'type': 'code', 'content': INSTALL_CONTENT}]
_snake_case = {
'{processor_class}': 'FakeProcessorClass',
'{model_class}': 'FakeModelClass',
'{object_class}': 'FakeObjectClass',
}
| 383 |
from manim import *
class lowerCAmelCase_ ( _lowercase ):
"""simple docstring"""
def __lowercase( self ) -> Optional[Any]:
__UpperCamelCase = Rectangle(height=0.5 , width=0.5 )
__UpperCamelCase = Rectangle(height=0.4_6 , width=0.4_6 ).set_stroke(width=0 )
__UpperCamelCase = [mem.copy() for i in range(6 )]
__UpperCamelCase = [mem.copy() for i in range(6 )]
__UpperCamelCase = VGroup(*_SCREAMING_SNAKE_CASE ).arrange(_SCREAMING_SNAKE_CASE , buff=0 )
__UpperCamelCase = VGroup(*_SCREAMING_SNAKE_CASE ).arrange(_SCREAMING_SNAKE_CASE , buff=0 )
__UpperCamelCase = VGroup(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ).arrange(_SCREAMING_SNAKE_CASE , buff=0 )
__UpperCamelCase = Text('CPU' , font_size=24 )
__UpperCamelCase = Group(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ).arrange(_SCREAMING_SNAKE_CASE , buff=0.5 , aligned_edge=_SCREAMING_SNAKE_CASE )
cpu.move_to([-2.5, -0.5, 0] )
self.add(_SCREAMING_SNAKE_CASE )
__UpperCamelCase = [mem.copy() for i in range(1 )]
__UpperCamelCase = VGroup(*_SCREAMING_SNAKE_CASE ).arrange(_SCREAMING_SNAKE_CASE , buff=0 )
__UpperCamelCase = Text('GPU' , font_size=24 )
__UpperCamelCase = Group(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ).arrange(_SCREAMING_SNAKE_CASE , buff=0.5 , aligned_edge=_SCREAMING_SNAKE_CASE )
gpu.align_to(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
gpu.set_x(gpu.get_x() - 1 )
self.add(_SCREAMING_SNAKE_CASE )
__UpperCamelCase = [mem.copy() for i in range(6 )]
__UpperCamelCase = VGroup(*_SCREAMING_SNAKE_CASE ).arrange(_SCREAMING_SNAKE_CASE , buff=0 )
__UpperCamelCase = Text('Model' , font_size=24 )
__UpperCamelCase = Group(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ).arrange(_SCREAMING_SNAKE_CASE , buff=0.5 , aligned_edge=_SCREAMING_SNAKE_CASE )
model.move_to([3, -1.0, 0] )
self.play(
Create(_SCREAMING_SNAKE_CASE , run_time=1 ) , Create(_SCREAMING_SNAKE_CASE , run_time=1 ) , Create(_SCREAMING_SNAKE_CASE , run_time=1 ) , )
__UpperCamelCase = MarkupText(
f"""First, an empty model skeleton is loaded\ninto <span fgcolor='{YELLOW}'>memory</span> without using much RAM.""" , font_size=24 , )
__UpperCamelCase = Square(side_length=2.2 )
key.move_to([-5, 2, 0] )
__UpperCamelCase = MarkupText(
f"""<b>Key:</b>\n\n<span fgcolor='{YELLOW}'>●</span> Empty Model""" , font_size=18 , )
key_text.move_to([-5, 2.4, 0] )
step_a.move_to([2, 2, 0] )
self.play(Write(_SCREAMING_SNAKE_CASE , run_time=2.5 ) , Write(_SCREAMING_SNAKE_CASE ) , Write(_SCREAMING_SNAKE_CASE ) )
self.add(_SCREAMING_SNAKE_CASE )
__UpperCamelCase = []
__UpperCamelCase = []
__UpperCamelCase = []
for i, rect in enumerate(_SCREAMING_SNAKE_CASE ):
__UpperCamelCase = Rectangle(height=0.4_6 , width=0.4_6 ).set_stroke(width=0.0 ).set_fill(_SCREAMING_SNAKE_CASE , opacity=0.7 )
cpu_target.move_to(_SCREAMING_SNAKE_CASE )
cpu_target.generate_target()
__UpperCamelCase = 0.4_6 / 4
__UpperCamelCase = 0.4_6 / 3
if i == 0:
cpu_target.target.next_to(cpu_left_col_base[0].get_corner(DOWN + LEFT ) , buff=0.0_2 , direction=_SCREAMING_SNAKE_CASE )
cpu_target.target.set_x(cpu_target.target.get_x() + 0.1 )
elif i == 3:
cpu_target.target.next_to(cpu_targs[0].target , direction=_SCREAMING_SNAKE_CASE , buff=0.0 )
else:
cpu_target.target.next_to(cpu_targs[i - 1].target , direction=_SCREAMING_SNAKE_CASE , buff=0.0 )
cpu_targs.append(_SCREAMING_SNAKE_CASE )
first_animations.append(rect.animate(run_time=0.5 ).set_stroke(_SCREAMING_SNAKE_CASE ) )
second_animations.append(MoveToTarget(_SCREAMING_SNAKE_CASE , run_time=1.5 ) )
self.play(*_SCREAMING_SNAKE_CASE )
self.play(*_SCREAMING_SNAKE_CASE )
self.wait()
| 383 | 1 |
"""simple docstring"""
import argparse
import io
import requests
import torch
from omegaconf import OmegaConf
from diffusers import AutoencoderKL
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import (
assign_to_checkpoint,
conv_attn_to_linear,
create_vae_diffusers_config,
renew_vae_attention_paths,
renew_vae_resnet_paths,
)
def _snake_case ( snake_case__ : Optional[int] , snake_case__ : List[Any] ):
A = checkpoint
A = {}
A = vae_state_dict['encoder.conv_in.weight']
A = vae_state_dict['encoder.conv_in.bias']
A = vae_state_dict['encoder.conv_out.weight']
A = vae_state_dict['encoder.conv_out.bias']
A = vae_state_dict['encoder.norm_out.weight']
A = vae_state_dict['encoder.norm_out.bias']
A = vae_state_dict['decoder.conv_in.weight']
A = vae_state_dict['decoder.conv_in.bias']
A = vae_state_dict['decoder.conv_out.weight']
A = vae_state_dict['decoder.conv_out.bias']
A = vae_state_dict['decoder.norm_out.weight']
A = vae_state_dict['decoder.norm_out.bias']
A = vae_state_dict['quant_conv.weight']
A = vae_state_dict['quant_conv.bias']
A = vae_state_dict['post_quant_conv.weight']
A = vae_state_dict['post_quant_conv.bias']
# Retrieves the keys for the encoder down blocks only
A = len({'.'.join(layer.split('.' )[:3] ) for layer in vae_state_dict if 'encoder.down' in layer} )
A = {
layer_id: [key for key in vae_state_dict if F'down.{layer_id}' in key] for layer_id in range(snake_case__ )
}
# Retrieves the keys for the decoder up blocks only
A = len({'.'.join(layer.split('.' )[:3] ) for layer in vae_state_dict if 'decoder.up' in layer} )
A = {
layer_id: [key for key in vae_state_dict if F'up.{layer_id}' in key] for layer_id in range(snake_case__ )
}
for i in range(snake_case__ ):
A = [key for key in down_blocks[i] if F'down.{i}' in key and F'down.{i}.downsample' not in key]
if F'encoder.down.{i}.downsample.conv.weight' in vae_state_dict:
A = vae_state_dict.pop(
F'encoder.down.{i}.downsample.conv.weight' )
A = vae_state_dict.pop(
F'encoder.down.{i}.downsample.conv.bias' )
A = renew_vae_resnet_paths(snake_case__ )
A = {'old': F'down.{i}.block', 'new': F'down_blocks.{i}.resnets'}
assign_to_checkpoint(snake_case__ , snake_case__ , snake_case__ , additional_replacements=[meta_path] , config=snake_case__ )
A = [key for key in vae_state_dict if 'encoder.mid.block' in key]
A = 2
for i in range(1 , num_mid_res_blocks + 1 ):
A = [key for key in mid_resnets if F'encoder.mid.block_{i}' in key]
A = renew_vae_resnet_paths(snake_case__ )
A = {'old': F'mid.block_{i}', 'new': F'mid_block.resnets.{i - 1}'}
assign_to_checkpoint(snake_case__ , snake_case__ , snake_case__ , additional_replacements=[meta_path] , config=snake_case__ )
A = [key for key in vae_state_dict if 'encoder.mid.attn' in key]
A = renew_vae_attention_paths(snake_case__ )
A = {'old': 'mid.attn_1', 'new': 'mid_block.attentions.0'}
assign_to_checkpoint(snake_case__ , snake_case__ , snake_case__ , additional_replacements=[meta_path] , config=snake_case__ )
conv_attn_to_linear(snake_case__ )
for i in range(snake_case__ ):
A = num_up_blocks - 1 - i
A = [
key for key in up_blocks[block_id] if F'up.{block_id}' in key and F'up.{block_id}.upsample' not in key
]
if F'decoder.up.{block_id}.upsample.conv.weight' in vae_state_dict:
A = vae_state_dict[
F'decoder.up.{block_id}.upsample.conv.weight'
]
A = vae_state_dict[
F'decoder.up.{block_id}.upsample.conv.bias'
]
A = renew_vae_resnet_paths(snake_case__ )
A = {'old': F'up.{block_id}.block', 'new': F'up_blocks.{i}.resnets'}
assign_to_checkpoint(snake_case__ , snake_case__ , snake_case__ , additional_replacements=[meta_path] , config=snake_case__ )
A = [key for key in vae_state_dict if 'decoder.mid.block' in key]
A = 2
for i in range(1 , num_mid_res_blocks + 1 ):
A = [key for key in mid_resnets if F'decoder.mid.block_{i}' in key]
A = renew_vae_resnet_paths(snake_case__ )
A = {'old': F'mid.block_{i}', 'new': F'mid_block.resnets.{i - 1}'}
assign_to_checkpoint(snake_case__ , snake_case__ , snake_case__ , additional_replacements=[meta_path] , config=snake_case__ )
A = [key for key in vae_state_dict if 'decoder.mid.attn' in key]
A = renew_vae_attention_paths(snake_case__ )
A = {'old': 'mid.attn_1', 'new': 'mid_block.attentions.0'}
assign_to_checkpoint(snake_case__ , snake_case__ , snake_case__ , additional_replacements=[meta_path] , config=snake_case__ )
conv_attn_to_linear(snake_case__ )
return new_checkpoint
def _snake_case ( snake_case__ : str , snake_case__ : str , ):
# Only support V1
A = requests.get(
' https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml' )
A = io.BytesIO(r.content )
A = OmegaConf.load(snake_case__ )
A = 512
A = 'cuda' if torch.cuda.is_available() else 'cpu'
if checkpoint_path.endswith('safetensors' ):
from safetensors import safe_open
A = {}
with safe_open(snake_case__ , framework='pt' , device='cpu' ) as f:
for key in f.keys():
A = f.get_tensor(snake_case__ )
else:
A = torch.load(snake_case__ , map_location=snake_case__ )['state_dict']
# Convert the VAE model.
A = create_vae_diffusers_config(snake_case__ , image_size=snake_case__ )
A = custom_convert_ldm_vae_checkpoint(snake_case__ , snake_case__ )
A = AutoencoderKL(**snake_case__ )
vae.load_state_dict(snake_case__ )
vae.save_pretrained(snake_case__ )
if __name__ == "__main__":
_lowercase = argparse.ArgumentParser()
parser.add_argument('''--vae_pt_path''', default=None, type=str, required=True, help='''Path to the VAE.pt to convert.''')
parser.add_argument('''--dump_path''', default=None, type=str, required=True, help='''Path to the VAE.pt to convert.''')
_lowercase = parser.parse_args()
vae_pt_to_vae_diffuser(args.vae_pt_path, args.dump_path) | 22 |
"""simple docstring"""
from argparse import ArgumentParser
from . import BaseTransformersCLICommand
def _snake_case ( snake_case__ : Optional[int] ):
return DownloadCommand(args.model , args.cache_dir , args.force , args.trust_remote_code )
class lowerCAmelCase_ ( _lowercase ):
'''simple docstring'''
@staticmethod
def _SCREAMING_SNAKE_CASE ( A_ : ArgumentParser ) -> Any:
A = parser.add_parser('download' )
download_parser.add_argument(
'--cache-dir' ,type=A_ ,default=A_ ,help='Path to location to store the models' )
download_parser.add_argument(
'--force' ,action='store_true' ,help='Force the model to be download even if already in cache-dir' )
download_parser.add_argument(
'--trust-remote-code' ,action='store_true' ,help='Whether or not to allow for custom models defined on the Hub in their own modeling files. Use only if you\'ve reviewed the code as it will execute on your local machine' ,)
download_parser.add_argument('model' ,type=A_ ,help='Name of the model to download' )
download_parser.set_defaults(func=A_ )
def __init__( self : Dict ,A_ : str ,A_ : str ,A_ : bool ,A_ : bool ) -> Union[str, Any]:
A = model
A = cache
A = force
A = trust_remote_code
def _SCREAMING_SNAKE_CASE ( self : Dict ) -> Optional[int]:
from ..models.auto import AutoModel, AutoTokenizer
AutoModel.from_pretrained(
self._model ,cache_dir=self._cache ,force_download=self._force ,trust_remote_code=self._trust_remote_code )
AutoTokenizer.from_pretrained(
self._model ,cache_dir=self._cache ,force_download=self._force ,trust_remote_code=self._trust_remote_code ) | 22 | 1 |
"""simple docstring"""
import numpy as np
# Importing the Keras libraries and packages
import tensorflow as tf
from tensorflow.keras import layers, models
if __name__ == "__main__":
# Initialising the CNN
# (Sequential- Building the model layer by layer)
__UpperCAmelCase = models.Sequential()
# Step 1 - Convolution
# Here 64,64 is the length & breadth of dataset images and 3 is for the RGB channel
# (3,3) is the kernel size (filter matrix)
classifier.add(
layers.ConvaD(32, (3, 3), input_shape=(64, 64, 3), activation='relu')
)
# Step 2 - Pooling
classifier.add(layers.MaxPoolingaD(pool_size=(2, 2)))
# Adding a second convolutional layer
classifier.add(layers.ConvaD(32, (3, 3), activation='relu'))
classifier.add(layers.MaxPoolingaD(pool_size=(2, 2)))
# Step 3 - Flattening
classifier.add(layers.Flatten())
# Step 4 - Full connection
classifier.add(layers.Dense(units=128, activation='relu'))
classifier.add(layers.Dense(units=1, activation='sigmoid'))
# Compiling the CNN
classifier.compile(
optimizer='adam', loss='binary_crossentropy', metrics=['accuracy']
)
# Part 2 - Fitting the CNN to the images
# Load Trained model weights
# from keras.models import load_model
# regressor=load_model('cnn.h5')
__UpperCAmelCase = tf.keras.preprocessing.image.ImageDataGenerator(
rescale=1.0 / 255, shear_range=0.2, zoom_range=0.2, horizontal_flip=True
)
__UpperCAmelCase = tf.keras.preprocessing.image.ImageDataGenerator(rescale=1.0 / 255)
__UpperCAmelCase = train_datagen.flow_from_directory(
'dataset/training_set', target_size=(64, 64), batch_size=32, class_mode='binary'
)
__UpperCAmelCase = test_datagen.flow_from_directory(
'dataset/test_set', target_size=(64, 64), batch_size=32, class_mode='binary'
)
classifier.fit_generator(
training_set, steps_per_epoch=5, epochs=30, validation_data=test_set
)
classifier.save('cnn.h5')
# Part 3 - Making new predictions
__UpperCAmelCase = tf.keras.preprocessing.image.load_img(
'dataset/single_prediction/image.png', target_size=(64, 64)
)
__UpperCAmelCase = tf.keras.preprocessing.image.img_to_array(test_image)
__UpperCAmelCase = np.expand_dims(test_image, axis=0)
__UpperCAmelCase = classifier.predict(test_image)
# training_set.class_indices
if result[0][0] == 0:
__UpperCAmelCase = 'Normal'
if result[0][0] == 1:
__UpperCAmelCase = 'Abnormality detected'
| 65 |
"""simple docstring"""
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile
from transformers import BatchEncoding, MarianTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import is_sentencepiece_available, is_tf_available, is_torch_available
if is_sentencepiece_available():
from transformers.models.marian.tokenization_marian import VOCAB_FILES_NAMES, save_json
from ...test_tokenization_common import TokenizerTesterMixin
__UpperCAmelCase = get_tests_dir('fixtures/test_sentencepiece.model')
__UpperCAmelCase = {'target_lang': 'fi', 'source_lang': 'en'}
__UpperCAmelCase = '>>zh<<'
__UpperCAmelCase = 'Helsinki-NLP/'
if is_torch_available():
__UpperCAmelCase = 'pt'
elif is_tf_available():
__UpperCAmelCase = 'tf'
else:
__UpperCAmelCase = 'jax'
@require_sentencepiece
class __lowercase ( __lowerCamelCase , unittest.TestCase ):
snake_case_ = MarianTokenizer
snake_case_ = False
snake_case_ = True
def __lowercase ( self : Optional[int] ):
'''simple docstring'''
super().setUp()
UpperCAmelCase__ : Optional[Any] = ["""</s>""", """<unk>""", """▁This""", """▁is""", """▁a""", """▁t""", """est""", """\u0120""", """<pad>"""]
UpperCAmelCase__ : int = dict(zip(A ,range(len(A ) ) ) )
UpperCAmelCase__ : Optional[int] = Path(self.tmpdirname )
save_json(A ,save_dir / VOCAB_FILES_NAMES["""vocab"""] )
save_json(A ,save_dir / VOCAB_FILES_NAMES["""tokenizer_config_file"""] )
if not (save_dir / VOCAB_FILES_NAMES["source_spm"]).exists():
copyfile(A ,save_dir / VOCAB_FILES_NAMES["""source_spm"""] )
copyfile(A ,save_dir / VOCAB_FILES_NAMES["""target_spm"""] )
UpperCAmelCase__ : Dict = MarianTokenizer.from_pretrained(self.tmpdirname )
tokenizer.save_pretrained(self.tmpdirname )
def __lowercase ( self : List[Any] ,**A : List[Any] ):
'''simple docstring'''
return MarianTokenizer.from_pretrained(self.tmpdirname ,**A )
def __lowercase ( self : Union[str, Any] ,A : Tuple ):
'''simple docstring'''
return (
"This is a test",
"This is a test",
)
def __lowercase ( self : List[Any] ):
'''simple docstring'''
UpperCAmelCase__ : Optional[Any] = """</s>"""
UpperCAmelCase__ : int = 0
self.assertEqual(self.get_tokenizer()._convert_token_to_id(A ) ,A )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(A ) ,A )
def __lowercase ( self : Union[str, Any] ):
'''simple docstring'''
UpperCAmelCase__ : Dict = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] ,"""</s>""" )
self.assertEqual(vocab_keys[1] ,"""<unk>""" )
self.assertEqual(vocab_keys[-1] ,"""<pad>""" )
self.assertEqual(len(A ) ,9 )
def __lowercase ( self : Dict ):
'''simple docstring'''
self.assertEqual(self.get_tokenizer().vocab_size ,9 )
def __lowercase ( self : List[Any] ):
'''simple docstring'''
UpperCAmelCase__ : Optional[int] = MarianTokenizer.from_pretrained(f"{ORG_NAME}opus-mt-en-de" )
UpperCAmelCase__ : List[str] = en_de_tokenizer(["""I am a small frog"""] ,return_tensors=A )
self.assertIsInstance(A ,A )
UpperCAmelCase__ : str = [38, 121, 14, 697, 38_848, 0]
self.assertListEqual(A ,batch.input_ids[0] )
UpperCAmelCase__ : Optional[Any] = tempfile.mkdtemp()
en_de_tokenizer.save_pretrained(A )
UpperCAmelCase__ : Tuple = [x.name for x in Path(A ).glob("""*""" )]
self.assertIn("""source.spm""" ,A )
MarianTokenizer.from_pretrained(A )
def __lowercase ( self : Union[str, Any] ):
'''simple docstring'''
UpperCAmelCase__ : Optional[int] = self.get_tokenizer()
UpperCAmelCase__ : Any = tok(
["""I am a small frog""" * 1_000, """I am a small frog"""] ,padding=A ,truncation=A ,return_tensors=A )
self.assertIsInstance(A ,A )
self.assertEqual(batch.input_ids.shape ,(2, 512) )
def __lowercase ( self : Optional[Any] ):
'''simple docstring'''
UpperCAmelCase__ : int = self.get_tokenizer()
UpperCAmelCase__ : Tuple = tok(["""I am a tiny frog""", """I am a small frog"""] ,padding=A ,return_tensors=A )
self.assertIsInstance(A ,A )
self.assertEqual(batch_smaller.input_ids.shape ,(2, 10) )
@slow
def __lowercase ( self : Dict ):
'''simple docstring'''
# fmt: off
UpperCAmelCase__ : Optional[int] = {"""input_ids""": [[43_495, 462, 20, 42_164, 1_369, 52, 464, 132, 1_703, 492, 13, 7_491, 38_999, 6, 8, 464, 132, 1_703, 492, 13, 4_669, 37_867, 13, 7_525, 27, 1_593, 988, 13, 33_972, 7_029, 6, 20, 8_251, 383, 2, 270, 5_866, 3_788, 2, 2_353, 8_251, 12_338, 2, 13_958, 387, 2, 3_629, 6_953, 188, 2_900, 2, 13_958, 8_011, 11_501, 23, 8_460, 4_073, 34_009, 20, 435, 11_439, 27, 8, 8_460, 4_073, 6_004, 20, 9_988, 375, 27, 33, 266, 1_945, 1_076, 1_350, 37_867, 3_288, 5, 577, 1_076, 4_374, 8, 5_082, 5, 26_453, 257, 556, 403, 2, 242, 132, 383, 316, 492, 8, 10_767, 6, 316, 304, 4_239, 3, 0], [148, 15_722, 19, 1_839, 12, 1_350, 13, 22_327, 5_082, 5_418, 47_567, 35_938, 59, 318, 19_552, 108, 2_183, 54, 14_976, 4_835, 32, 547, 1_114, 8, 315, 2_417, 5, 92, 19_088, 3, 0, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100], [36, 6_395, 12_570, 39_147, 11_597, 6, 266, 4, 45_405, 7_296, 3, 0, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100]], """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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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=A ,model_name="""Helsinki-NLP/opus-mt-en-de""" ,revision="""1a8c2263da11e68e50938f97e10cd57820bd504c""" ,decode_kwargs={"""use_source_tokenizer""": True} ,)
def __lowercase ( self : List[Any] ):
'''simple docstring'''
UpperCAmelCase__ : List[str] = MarianTokenizer.from_pretrained("""hf-internal-testing/test-marian-two-vocabs""" )
UpperCAmelCase__ : Any = """Tämä on testi"""
UpperCAmelCase__ : int = """This is a test"""
UpperCAmelCase__ : List[str] = [76, 7, 2_047, 2]
UpperCAmelCase__ : Optional[Any] = [69, 12, 11, 940, 2]
UpperCAmelCase__ : List[str] = tokenizer(A ).input_ids
self.assertListEqual(A ,A )
UpperCAmelCase__ : Optional[int] = tokenizer(text_target=A ).input_ids
self.assertListEqual(A ,A )
UpperCAmelCase__ : int = tokenizer.decode(A ,skip_special_tokens=A )
self.assertEqual(A ,A )
| 65 | 1 |
'''simple docstring'''
from tempfile import TemporaryDirectory
from unittest import TestCase
from unittest.mock import MagicMock, patch
from transformers import AutoModel, TFAutoModel
from transformers.onnx import FeaturesManager
from transformers.testing_utils import SMALL_MODEL_IDENTIFIER, require_tf, require_torch
@require_torch
@require_tf
class __lowercase ( _lowerCamelCase ):
"""simple docstring"""
def lowerCAmelCase ( self ):
__UpperCamelCase : Tuple = SMALL_MODEL_IDENTIFIER
__UpperCamelCase : List[str] = 'pt'
__UpperCamelCase : List[str] = 'tf'
def lowerCAmelCase ( self , _lowerCamelCase ):
__UpperCamelCase : Union[str, Any] = AutoModel.from_pretrained(self.test_model )
model_pt.save_pretrained(_lowerCamelCase )
def lowerCAmelCase ( self , _lowerCamelCase ):
__UpperCamelCase : Optional[int] = TFAutoModel.from_pretrained(self.test_model , from_pt=_lowerCamelCase )
model_tf.save_pretrained(_lowerCamelCase )
def lowerCAmelCase ( self ):
__UpperCamelCase : int = 'mock_framework'
# Framework provided - return whatever the user provides
__UpperCamelCase : Tuple = FeaturesManager.determine_framework(self.test_model , _lowerCamelCase )
self.assertEqual(_lowerCamelCase , _lowerCamelCase )
# Local checkpoint and framework provided - return provided framework
# PyTorch checkpoint
with TemporaryDirectory() as local_pt_ckpt:
self._setup_pt_ckpt(_lowerCamelCase )
__UpperCamelCase : int = FeaturesManager.determine_framework(_lowerCamelCase , _lowerCamelCase )
self.assertEqual(_lowerCamelCase , _lowerCamelCase )
# TensorFlow checkpoint
with TemporaryDirectory() as local_tf_ckpt:
self._setup_tf_ckpt(_lowerCamelCase )
__UpperCamelCase : Tuple = FeaturesManager.determine_framework(_lowerCamelCase , _lowerCamelCase )
self.assertEqual(_lowerCamelCase , _lowerCamelCase )
def lowerCAmelCase ( self ):
# PyTorch checkpoint
with TemporaryDirectory() as local_pt_ckpt:
self._setup_pt_ckpt(_lowerCamelCase )
__UpperCamelCase : Any = FeaturesManager.determine_framework(_lowerCamelCase )
self.assertEqual(_lowerCamelCase , self.framework_pt )
# TensorFlow checkpoint
with TemporaryDirectory() as local_tf_ckpt:
self._setup_tf_ckpt(_lowerCamelCase )
__UpperCamelCase : int = FeaturesManager.determine_framework(_lowerCamelCase )
self.assertEqual(_lowerCamelCase , self.framework_tf )
# Invalid local checkpoint
with TemporaryDirectory() as local_invalid_ckpt:
with self.assertRaises(_lowerCamelCase ):
__UpperCamelCase : Union[str, Any] = FeaturesManager.determine_framework(_lowerCamelCase )
def lowerCAmelCase ( self ):
__UpperCamelCase : Any = MagicMock(return_value=_lowerCamelCase )
with patch('transformers.onnx.features.is_tf_available' , _lowerCamelCase ):
__UpperCamelCase : Optional[int] = FeaturesManager.determine_framework(self.test_model )
self.assertEqual(_lowerCamelCase , self.framework_pt )
# PyTorch not in environment -> use TensorFlow
__UpperCamelCase : List[Any] = MagicMock(return_value=_lowerCamelCase )
with patch('transformers.onnx.features.is_torch_available' , _lowerCamelCase ):
__UpperCamelCase : List[str] = FeaturesManager.determine_framework(self.test_model )
self.assertEqual(_lowerCamelCase , self.framework_tf )
# Both in environment -> use PyTorch
__UpperCamelCase : Any = MagicMock(return_value=_lowerCamelCase )
__UpperCamelCase : Dict = MagicMock(return_value=_lowerCamelCase )
with patch('transformers.onnx.features.is_tf_available' , _lowerCamelCase ), patch(
'transformers.onnx.features.is_torch_available' , _lowerCamelCase ):
__UpperCamelCase : Union[str, Any] = FeaturesManager.determine_framework(self.test_model )
self.assertEqual(_lowerCamelCase , self.framework_pt )
# Both not in environment -> raise error
__UpperCamelCase : Tuple = MagicMock(return_value=_lowerCamelCase )
__UpperCamelCase : str = MagicMock(return_value=_lowerCamelCase )
with patch('transformers.onnx.features.is_tf_available' , _lowerCamelCase ), patch(
'transformers.onnx.features.is_torch_available' , _lowerCamelCase ):
with self.assertRaises(_lowerCamelCase ):
__UpperCamelCase : Any = FeaturesManager.determine_framework(self.test_model )
| 287 | '''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a= {'''configuration_vit_msn''': ['''VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ViTMSNConfig''']}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a= [
'''VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''ViTMSNModel''',
'''ViTMSNForImageClassification''',
'''ViTMSNPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_vit_msn import VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTMSNConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_vit_msn import (
VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST,
ViTMSNForImageClassification,
ViTMSNModel,
ViTMSNPreTrainedModel,
)
else:
import sys
a= _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 287 | 1 |
'''simple docstring'''
import os
import pytest
from transformers.dynamic_module_utils import get_imports
lowerCAmelCase: str = '\nimport os\n'
lowerCAmelCase: int = '\ndef foo():\n import os\n return False\n'
lowerCAmelCase: Dict = '\ndef foo():\n def bar():\n if True:\n import os\n return False\n return bar()\n'
lowerCAmelCase: List[Any] = '\nimport os\n\ntry:\n import bar\nexcept ImportError:\n raise ValueError()\n'
lowerCAmelCase: int = '\nimport os\n\ndef foo():\n try:\n import bar\n except ImportError:\n raise ValueError()\n'
lowerCAmelCase: Optional[int] = '\nimport os\n\ntry:\n import bar\nexcept (ImportError, AttributeError):\n raise ValueError()\n'
lowerCAmelCase: Union[str, Any] = '\nimport os\n\ntry:\n import bar\nexcept ImportError as e:\n raise ValueError()\n'
lowerCAmelCase: Union[str, Any] = '\nimport os\n\ntry:\n import bar\nexcept:\n raise ValueError()\n'
lowerCAmelCase: Optional[Any] = '\nimport os\n\ntry:\n import bar\n import baz\nexcept ImportError:\n raise ValueError()\n'
lowerCAmelCase: str = '\nimport os\n\ntry:\n import bar\n import baz\nexcept ImportError:\n x = 1\n raise ValueError()\n'
lowerCAmelCase: List[str] = [
TOP_LEVEL_IMPORT,
IMPORT_IN_FUNCTION,
DEEPLY_NESTED_IMPORT,
TOP_LEVEL_TRY_IMPORT,
GENERIC_EXCEPT_IMPORT,
MULTILINE_TRY_IMPORT,
MULTILINE_BOTH_IMPORT,
MULTIPLE_EXCEPTS_IMPORT,
EXCEPT_AS_IMPORT,
TRY_IMPORT_IN_FUNCTION,
]
@pytest.mark.parametrize('case' , _A )
def lowerCamelCase__ ( _A , _A ):
a : List[str] = os.path.join(_A , 'test_file.py' )
with open(_A , 'w' ) as _tmp_file:
_tmp_file.write(_A )
a : Optional[int] = get_imports(_A )
assert parsed_imports == ["os"] | 526 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
lowerCAmelCase: Dict = {'configuration_vit_mae': ['VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ViTMAEConfig']}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase: Optional[int] = [
'VIT_MAE_PRETRAINED_MODEL_ARCHIVE_LIST',
'ViTMAEForPreTraining',
'ViTMAELayer',
'ViTMAEModel',
'ViTMAEPreTrainedModel',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase: List[Any] = [
'TFViTMAEForPreTraining',
'TFViTMAEModel',
'TFViTMAEPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_vit_mae import VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTMAEConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_vit_mae import (
VIT_MAE_PRETRAINED_MODEL_ARCHIVE_LIST,
ViTMAEForPreTraining,
ViTMAELayer,
ViTMAEModel,
ViTMAEPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_vit_mae import TFViTMAEForPreTraining, TFViTMAEModel, TFViTMAEPreTrainedModel
else:
import sys
lowerCAmelCase: Tuple = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__) | 526 | 1 |
'''simple docstring'''
import argparse
import json
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, DistributedType
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler
lowercase__ = 16
lowercase__ = 32
def UpperCamelCase( UpperCAmelCase_ , UpperCAmelCase_ = 16 , UpperCAmelCase_ = "bert-base-cased" ):
UpperCAmelCase : Any = AutoTokenizer.from_pretrained(UpperCAmelCase_ )
UpperCAmelCase : Tuple = load_dataset('glue' , 'mrpc' )
def tokenize_function(UpperCAmelCase_ ):
# max_length=None => use the model max length (it's actually the default)
UpperCAmelCase : Optional[int] = tokenizer(examples['sentence1'] , examples['sentence2'] , truncation=UpperCAmelCase_ , max_length=UpperCAmelCase_ )
return outputs
# Apply the method we just defined to all the examples in all the splits of the dataset
UpperCAmelCase : Optional[Any] = datasets.map(
UpperCAmelCase_ , batched=UpperCAmelCase_ , remove_columns=['idx', 'sentence1', 'sentence2'] , load_from_cache_file=UpperCAmelCase_ )
# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
# transformers library
UpperCAmelCase : Optional[int] = tokenized_datasets.rename_column('label' , 'labels' )
def collate_fn(UpperCAmelCase_ ):
# On TPU it's best to pad everything to the same length or training will be very slow.
if accelerator.distributed_type == DistributedType.TPU:
return tokenizer.pad(UpperCAmelCase_ , padding='max_length' , max_length=1_28 , return_tensors='pt' )
return tokenizer.pad(UpperCAmelCase_ , padding='longest' , return_tensors='pt' )
# Instantiate dataloaders.
UpperCAmelCase : Any = DataLoader(
tokenized_datasets['train'] , shuffle=UpperCAmelCase_ , collate_fn=UpperCAmelCase_ , batch_size=UpperCAmelCase_ )
UpperCAmelCase : Union[str, Any] = DataLoader(
tokenized_datasets['validation'] , shuffle=UpperCAmelCase_ , collate_fn=UpperCAmelCase_ , batch_size=UpperCAmelCase_ )
return train_dataloader, eval_dataloader
def UpperCamelCase( UpperCAmelCase_ , UpperCAmelCase_ ):
# Initialize accelerator
UpperCAmelCase : List[str] = Accelerator()
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
UpperCAmelCase : Any = config['lr']
UpperCAmelCase : Tuple = int(config['num_epochs'] )
UpperCAmelCase : List[Any] = int(config['seed'] )
UpperCAmelCase : Tuple = int(config['batch_size'] )
UpperCAmelCase : str = args.model_name_or_path
set_seed(UpperCAmelCase_ )
UpperCAmelCase : Optional[Any] = get_dataloaders(UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ )
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
UpperCAmelCase : Optional[Any] = AutoModelForSequenceClassification.from_pretrained(UpperCAmelCase_ , return_dict=UpperCAmelCase_ )
# Instantiate optimizer
UpperCAmelCase : Optional[int] = (
AdamW
if accelerator.state.deepspeed_plugin is None
or 'optimizer' not in accelerator.state.deepspeed_plugin.deepspeed_config
else DummyOptim
)
UpperCAmelCase : List[str] = optimizer_cls(params=model.parameters() , lr=UpperCAmelCase_ )
if accelerator.state.deepspeed_plugin is not None:
UpperCAmelCase : int = accelerator.state.deepspeed_plugin.deepspeed_config[
'gradient_accumulation_steps'
]
else:
UpperCAmelCase : str = 1
UpperCAmelCase : List[str] = (len(UpperCAmelCase_ ) * num_epochs) // gradient_accumulation_steps
# Instantiate scheduler
if (
accelerator.state.deepspeed_plugin is None
or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
):
UpperCAmelCase : Optional[int] = get_linear_schedule_with_warmup(
optimizer=UpperCAmelCase_ , num_warmup_steps=0 , num_training_steps=UpperCAmelCase_ , )
else:
UpperCAmelCase : List[Any] = DummyScheduler(UpperCAmelCase_ , total_num_steps=UpperCAmelCase_ , warmup_num_steps=0 )
# Prepare everything
# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
# prepare method.
UpperCAmelCase : Tuple = accelerator.prepare(
UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ )
# We need to keep track of how many total steps we have iterated over
UpperCAmelCase : Optional[Any] = 0
# We also need to keep track of the stating epoch so files are named properly
UpperCAmelCase : Optional[int] = 0
# Now we train the model
UpperCAmelCase : Union[str, Any] = evaluate.load('glue' , 'mrpc' )
UpperCAmelCase : str = 0
UpperCAmelCase : int = {}
for epoch in range(UpperCAmelCase_ , UpperCAmelCase_ ):
model.train()
for step, batch in enumerate(UpperCAmelCase_ ):
UpperCAmelCase : Optional[Any] = model(**UpperCAmelCase_ )
UpperCAmelCase : List[Any] = outputs.loss
UpperCAmelCase : int = loss / gradient_accumulation_steps
accelerator.backward(UpperCAmelCase_ )
if step % gradient_accumulation_steps == 0:
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
overall_step += 1
model.eval()
UpperCAmelCase : str = 0
for step, batch in enumerate(UpperCAmelCase_ ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
with torch.no_grad():
UpperCAmelCase : Any = model(**UpperCAmelCase_ )
UpperCAmelCase : Optional[Any] = outputs.logits.argmax(dim=-1 )
# It is slightly faster to call this once, than multiple times
UpperCAmelCase : Dict = accelerator.gather(
(predictions, batch['labels']) ) # If we are in a multiprocess environment, the last batch has duplicates
if accelerator.use_distributed:
if step == len(UpperCAmelCase_ ) - 1:
UpperCAmelCase : List[str] = predictions[: len(eval_dataloader.dataset ) - samples_seen]
UpperCAmelCase : int = references[: len(eval_dataloader.dataset ) - samples_seen]
else:
samples_seen += references.shape[0]
metric.add_batch(
predictions=UpperCAmelCase_ , references=UpperCAmelCase_ , )
UpperCAmelCase : str = metric.compute()
# Use accelerator.print to print only on the main process.
accelerator.print(F"""epoch {epoch}:""" , UpperCAmelCase_ )
UpperCAmelCase : Any = eval_metric['accuracy']
if best_performance < eval_metric["accuracy"]:
UpperCAmelCase : Optional[int] = eval_metric['accuracy']
if args.performance_lower_bound is not None:
assert (
args.performance_lower_bound <= best_performance
), F"""Best performance metric {best_performance} is lower than the lower bound {args.performance_lower_bound}"""
accelerator.wait_for_everyone()
if accelerator.is_main_process:
with open(os.path.join(args.output_dir , 'all_results.json' ) , 'w' ) as f:
json.dump(UpperCAmelCase_ , UpperCAmelCase_ )
def UpperCamelCase( ):
UpperCAmelCase : Union[str, Any] = argparse.ArgumentParser(description='Simple example of training script tracking peak GPU memory usage.' )
parser.add_argument(
'--model_name_or_path' , type=UpperCAmelCase_ , default='bert-base-cased' , help='Path to pretrained model or model identifier from huggingface.co/models.' , required=UpperCAmelCase_ , )
parser.add_argument(
'--output_dir' , type=UpperCAmelCase_ , default='.' , help='Optional save directory where all checkpoint folders will be stored. Default is the current working directory.' , )
parser.add_argument(
'--performance_lower_bound' , type=UpperCAmelCase_ , default=UpperCAmelCase_ , help='Optional lower bound for the performance metric. If set, the training will throw error when the performance metric drops below this value.' , )
parser.add_argument(
'--num_epochs' , type=UpperCAmelCase_ , default=3 , help='Number of train epochs.' , )
UpperCAmelCase : str = parser.parse_args()
UpperCAmelCase : str = {'lr': 2E-5, 'num_epochs': args.num_epochs, 'seed': 42, 'batch_size': 16}
training_function(UpperCAmelCase_ , UpperCAmelCase_ )
if __name__ == "__main__":
main()
| 702 |
'''simple docstring'''
from __future__ import annotations
import sys
from collections import deque
from typing import Generic, TypeVar
lowercase__ = TypeVar("T")
class A_ ( Generic[T] ):
'''simple docstring'''
UpperCAmelCase_ : deque[T] # Cache store of keys
UpperCAmelCase_ : set[T] # References of the keys in cache
UpperCAmelCase_ : int = 10 # Maximum capacity of cache
def __init__( self : List[Any] , lowercase_ : int ) -> None:
UpperCAmelCase : Any = deque()
UpperCAmelCase : Dict = set()
if not n:
UpperCAmelCase : Optional[int] = sys.maxsize
elif n < 0:
raise ValueError('n should be an integer greater than 0.' )
else:
UpperCAmelCase : str = n
def UpperCAmelCase_ ( self : List[str] , lowercase_ : T ) -> None:
if x not in self.key_reference:
if len(self.dq_store ) == LRUCache._MAX_CAPACITY:
UpperCAmelCase : Optional[Any] = self.dq_store.pop()
self.key_reference.remove(lowercase_ )
else:
self.dq_store.remove(lowercase_ )
self.dq_store.appendleft(lowercase_ )
self.key_reference.add(lowercase_ )
def UpperCAmelCase_ ( self : Dict ) -> None:
for k in self.dq_store:
print(lowercase_ )
def __repr__( self : Union[str, Any] ) -> str:
return f"""LRUCache({self._MAX_CAPACITY}) => {list(self.dq_store )}"""
if __name__ == "__main__":
import doctest
doctest.testmod()
lowercase__ = LRUCache(4)
lru_cache.refer("A")
lru_cache.refer(2)
lru_cache.refer(3)
lru_cache.refer("A")
lru_cache.refer(4)
lru_cache.refer(5)
lru_cache.display()
print(lru_cache)
assert str(lru_cache) == "LRUCache(4) => [5, 4, 'A', 3]"
| 695 | 0 |
"""simple docstring"""
import unittest
from transformers import XLMConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
XLMForMultipleChoice,
XLMForQuestionAnswering,
XLMForQuestionAnsweringSimple,
XLMForSequenceClassification,
XLMForTokenClassification,
XLMModel,
XLMWithLMHeadModel,
)
from transformers.models.xlm.modeling_xlm import XLM_PRETRAINED_MODEL_ARCHIVE_LIST
class lowerCamelCase :
'''simple docstring'''
def __init__( self : Optional[Any] , _snake_case : Optional[int] , _snake_case : Tuple=13 , _snake_case : Tuple=7 , _snake_case : Any=True , _snake_case : Optional[int]=True , _snake_case : Tuple=True , _snake_case : List[str]=True , _snake_case : Optional[int]=True , _snake_case : Any=False , _snake_case : Optional[Any]=False , _snake_case : List[Any]=False , _snake_case : Union[str, Any]=2 , _snake_case : Optional[Any]=99 , _snake_case : Union[str, Any]=0 , _snake_case : Union[str, Any]=32 , _snake_case : Union[str, Any]=5 , _snake_case : List[str]=4 , _snake_case : Any=0.1 , _snake_case : List[Any]=0.1 , _snake_case : Any=512 , _snake_case : int=2 , _snake_case : Tuple=0.02 , _snake_case : Dict=2 , _snake_case : int=4 , _snake_case : List[str]="last" , _snake_case : Optional[Any]=True , _snake_case : Optional[int]=None , _snake_case : List[str]=0 , ) -> Dict:
SCREAMING_SNAKE_CASE__ = parent
SCREAMING_SNAKE_CASE__ = batch_size
SCREAMING_SNAKE_CASE__ = seq_length
SCREAMING_SNAKE_CASE__ = is_training
SCREAMING_SNAKE_CASE__ = use_input_lengths
SCREAMING_SNAKE_CASE__ = use_token_type_ids
SCREAMING_SNAKE_CASE__ = use_labels
SCREAMING_SNAKE_CASE__ = gelu_activation
SCREAMING_SNAKE_CASE__ = sinusoidal_embeddings
SCREAMING_SNAKE_CASE__ = causal
SCREAMING_SNAKE_CASE__ = asm
SCREAMING_SNAKE_CASE__ = n_langs
SCREAMING_SNAKE_CASE__ = vocab_size
SCREAMING_SNAKE_CASE__ = n_special
SCREAMING_SNAKE_CASE__ = hidden_size
SCREAMING_SNAKE_CASE__ = num_hidden_layers
SCREAMING_SNAKE_CASE__ = num_attention_heads
SCREAMING_SNAKE_CASE__ = hidden_dropout_prob
SCREAMING_SNAKE_CASE__ = attention_probs_dropout_prob
SCREAMING_SNAKE_CASE__ = max_position_embeddings
SCREAMING_SNAKE_CASE__ = type_sequence_label_size
SCREAMING_SNAKE_CASE__ = initializer_range
SCREAMING_SNAKE_CASE__ = num_labels
SCREAMING_SNAKE_CASE__ = num_choices
SCREAMING_SNAKE_CASE__ = summary_type
SCREAMING_SNAKE_CASE__ = use_proj
SCREAMING_SNAKE_CASE__ = scope
SCREAMING_SNAKE_CASE__ = bos_token_id
def lowerCAmelCase_ ( self : Any ) -> Optional[int]:
SCREAMING_SNAKE_CASE__ = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
SCREAMING_SNAKE_CASE__ = random_attention_mask([self.batch_size, self.seq_length] )
SCREAMING_SNAKE_CASE__ = None
if self.use_input_lengths:
SCREAMING_SNAKE_CASE__ = (
ids_tensor([self.batch_size] , vocab_size=2 ) + self.seq_length - 2
) # small variation of seq_length
SCREAMING_SNAKE_CASE__ = None
if self.use_token_type_ids:
SCREAMING_SNAKE_CASE__ = ids_tensor([self.batch_size, self.seq_length] , self.n_langs )
SCREAMING_SNAKE_CASE__ = None
SCREAMING_SNAKE_CASE__ = None
SCREAMING_SNAKE_CASE__ = None
if self.use_labels:
SCREAMING_SNAKE_CASE__ = ids_tensor([self.batch_size] , self.type_sequence_label_size )
SCREAMING_SNAKE_CASE__ = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
SCREAMING_SNAKE_CASE__ = ids_tensor([self.batch_size] , 2 ).float()
SCREAMING_SNAKE_CASE__ = ids_tensor([self.batch_size] , self.num_choices )
SCREAMING_SNAKE_CASE__ = self.get_config()
return (
config,
input_ids,
token_type_ids,
input_lengths,
sequence_labels,
token_labels,
is_impossible_labels,
choice_labels,
input_mask,
)
def lowerCAmelCase_ ( self : Dict ) -> List[Any]:
return XLMConfig(
vocab_size=self.vocab_size , n_special=self.n_special , emb_dim=self.hidden_size , n_layers=self.num_hidden_layers , n_heads=self.num_attention_heads , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , gelu_activation=self.gelu_activation , sinusoidal_embeddings=self.sinusoidal_embeddings , asm=self.asm , causal=self.causal , n_langs=self.n_langs , max_position_embeddings=self.max_position_embeddings , initializer_range=self.initializer_range , summary_type=self.summary_type , use_proj=self.use_proj , num_labels=self.num_labels , bos_token_id=self.bos_token_id , )
def lowerCAmelCase_ ( self : List[Any] , _snake_case : List[Any] , _snake_case : Optional[Any] , _snake_case : Dict , _snake_case : Union[str, Any] , _snake_case : Optional[Any] , _snake_case : Dict , _snake_case : Union[str, Any] , _snake_case : List[Any] , _snake_case : Any , ) -> Optional[int]:
SCREAMING_SNAKE_CASE__ = XLMModel(config=_snake_case )
model.to(_snake_case )
model.eval()
SCREAMING_SNAKE_CASE__ = model(_snake_case , lengths=_snake_case , langs=_snake_case )
SCREAMING_SNAKE_CASE__ = model(_snake_case , langs=_snake_case )
SCREAMING_SNAKE_CASE__ = model(_snake_case )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def lowerCAmelCase_ ( self : Optional[Any] , _snake_case : Tuple , _snake_case : Optional[int] , _snake_case : Dict , _snake_case : List[Any] , _snake_case : Union[str, Any] , _snake_case : Union[str, Any] , _snake_case : str , _snake_case : str , _snake_case : Optional[Any] , ) -> Any:
SCREAMING_SNAKE_CASE__ = XLMWithLMHeadModel(_snake_case )
model.to(_snake_case )
model.eval()
SCREAMING_SNAKE_CASE__ = model(_snake_case , token_type_ids=_snake_case , labels=_snake_case )
self.parent.assertEqual(result.loss.shape , () )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def lowerCAmelCase_ ( self : Dict , _snake_case : Dict , _snake_case : str , _snake_case : List[Any] , _snake_case : List[Any] , _snake_case : str , _snake_case : str , _snake_case : List[str] , _snake_case : int , _snake_case : List[str] , ) -> Tuple:
SCREAMING_SNAKE_CASE__ = XLMForQuestionAnsweringSimple(_snake_case )
model.to(_snake_case )
model.eval()
SCREAMING_SNAKE_CASE__ = model(_snake_case )
SCREAMING_SNAKE_CASE__ = model(_snake_case , start_positions=_snake_case , end_positions=_snake_case )
SCREAMING_SNAKE_CASE__ = outputs
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def lowerCAmelCase_ ( self : Tuple , _snake_case : Any , _snake_case : Union[str, Any] , _snake_case : Any , _snake_case : int , _snake_case : int , _snake_case : str , _snake_case : Optional[Any] , _snake_case : str , _snake_case : Tuple , ) -> Tuple:
SCREAMING_SNAKE_CASE__ = XLMForQuestionAnswering(_snake_case )
model.to(_snake_case )
model.eval()
SCREAMING_SNAKE_CASE__ = model(_snake_case )
SCREAMING_SNAKE_CASE__ = model(
_snake_case , start_positions=_snake_case , end_positions=_snake_case , cls_index=_snake_case , is_impossible=_snake_case , p_mask=_snake_case , )
SCREAMING_SNAKE_CASE__ = model(
_snake_case , start_positions=_snake_case , end_positions=_snake_case , cls_index=_snake_case , is_impossible=_snake_case , )
((SCREAMING_SNAKE_CASE__) , ) = result_with_labels.to_tuple()
SCREAMING_SNAKE_CASE__ = model(_snake_case , start_positions=_snake_case , end_positions=_snake_case )
((SCREAMING_SNAKE_CASE__) , ) = result_with_labels.to_tuple()
self.parent.assertEqual(result_with_labels.loss.shape , () )
self.parent.assertEqual(result.start_top_log_probs.shape , (self.batch_size, model.config.start_n_top) )
self.parent.assertEqual(result.start_top_index.shape , (self.batch_size, model.config.start_n_top) )
self.parent.assertEqual(
result.end_top_log_probs.shape , (self.batch_size, model.config.start_n_top * model.config.end_n_top) )
self.parent.assertEqual(
result.end_top_index.shape , (self.batch_size, model.config.start_n_top * model.config.end_n_top) )
self.parent.assertEqual(result.cls_logits.shape , (self.batch_size,) )
def lowerCAmelCase_ ( self : List[str] , _snake_case : Any , _snake_case : Dict , _snake_case : Optional[Any] , _snake_case : List[Any] , _snake_case : Optional[Any] , _snake_case : str , _snake_case : Optional[int] , _snake_case : Optional[int] , _snake_case : Dict , ) -> Any:
SCREAMING_SNAKE_CASE__ = XLMForSequenceClassification(_snake_case )
model.to(_snake_case )
model.eval()
SCREAMING_SNAKE_CASE__ = model(_snake_case )
SCREAMING_SNAKE_CASE__ = model(_snake_case , labels=_snake_case )
self.parent.assertEqual(result.loss.shape , () )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
def lowerCAmelCase_ ( self : Any , _snake_case : Optional[Any] , _snake_case : List[str] , _snake_case : Any , _snake_case : str , _snake_case : str , _snake_case : Union[str, Any] , _snake_case : Optional[Any] , _snake_case : Tuple , _snake_case : int , ) -> Union[str, Any]:
SCREAMING_SNAKE_CASE__ = self.num_labels
SCREAMING_SNAKE_CASE__ = XLMForTokenClassification(_snake_case )
model.to(_snake_case )
model.eval()
SCREAMING_SNAKE_CASE__ = model(_snake_case , attention_mask=_snake_case , labels=_snake_case )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def lowerCAmelCase_ ( self : Dict , _snake_case : Any , _snake_case : List[Any] , _snake_case : Optional[Any] , _snake_case : Any , _snake_case : List[Any] , _snake_case : Optional[int] , _snake_case : Optional[Any] , _snake_case : Optional[int] , _snake_case : Union[str, Any] , ) -> Tuple:
SCREAMING_SNAKE_CASE__ = self.num_choices
SCREAMING_SNAKE_CASE__ = XLMForMultipleChoice(config=_snake_case )
model.to(_snake_case )
model.eval()
SCREAMING_SNAKE_CASE__ = input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
SCREAMING_SNAKE_CASE__ = token_type_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
SCREAMING_SNAKE_CASE__ = input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
SCREAMING_SNAKE_CASE__ = model(
_snake_case , attention_mask=_snake_case , token_type_ids=_snake_case , labels=_snake_case , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def lowerCAmelCase_ ( self : Optional[Any] ) -> str:
SCREAMING_SNAKE_CASE__ = self.prepare_config_and_inputs()
(
(
SCREAMING_SNAKE_CASE__
) , (
SCREAMING_SNAKE_CASE__
) , (
SCREAMING_SNAKE_CASE__
) , (
SCREAMING_SNAKE_CASE__
) , (
SCREAMING_SNAKE_CASE__
) , (
SCREAMING_SNAKE_CASE__
) , (
SCREAMING_SNAKE_CASE__
) , (
SCREAMING_SNAKE_CASE__
) , (
SCREAMING_SNAKE_CASE__
) ,
) = config_and_inputs
SCREAMING_SNAKE_CASE__ = {"input_ids": input_ids, "token_type_ids": token_type_ids, "lengths": input_lengths}
return config, inputs_dict
@require_torch
class lowerCamelCase (_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , unittest.TestCase ):
'''simple docstring'''
a = (
(
XLMModel,
XLMWithLMHeadModel,
XLMForQuestionAnswering,
XLMForSequenceClassification,
XLMForQuestionAnsweringSimple,
XLMForTokenClassification,
XLMForMultipleChoice,
)
if is_torch_available()
else ()
)
a = (
(XLMWithLMHeadModel,) if is_torch_available() else ()
) # TODO (PVP): Check other models whether language generation is also applicable
a = (
{
"feature-extraction": XLMModel,
"fill-mask": XLMWithLMHeadModel,
"question-answering": XLMForQuestionAnsweringSimple,
"text-classification": XLMForSequenceClassification,
"text-generation": XLMWithLMHeadModel,
"token-classification": XLMForTokenClassification,
"zero-shot": XLMForSequenceClassification,
}
if is_torch_available()
else {}
)
def lowerCAmelCase_ ( self : Tuple , _snake_case : Union[str, Any] , _snake_case : str , _snake_case : Any , _snake_case : Tuple , _snake_case : str ) -> Dict:
if (
pipeline_test_casse_name == "QAPipelineTests"
and tokenizer_name is not None
and not tokenizer_name.endswith("Fast" )
):
# `QAPipelineTests` fails for a few models when the slower tokenizer are used.
# (The slower tokenizers were never used for pipeline tests before the pipeline testing rework)
# TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer
return True
return False
def lowerCAmelCase_ ( self : Dict , _snake_case : Any , _snake_case : str , _snake_case : Optional[Any]=False ) -> Optional[Any]:
SCREAMING_SNAKE_CASE__ = super()._prepare_for_class(_snake_case , _snake_case , return_labels=_snake_case )
if return_labels:
if model_class.__name__ == "XLMForQuestionAnswering":
SCREAMING_SNAKE_CASE__ = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=_snake_case )
SCREAMING_SNAKE_CASE__ = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=_snake_case )
return inputs_dict
def lowerCAmelCase_ ( self : int ) -> int:
SCREAMING_SNAKE_CASE__ = XLMModelTester(self )
SCREAMING_SNAKE_CASE__ = ConfigTester(self , config_class=_snake_case , emb_dim=37 )
def lowerCAmelCase_ ( self : Optional[int] ) -> List[Any]:
self.config_tester.run_common_tests()
def lowerCAmelCase_ ( self : List[Any] ) -> str:
SCREAMING_SNAKE_CASE__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_model(*_snake_case )
def lowerCAmelCase_ ( self : Union[str, Any] ) -> Dict:
SCREAMING_SNAKE_CASE__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_lm_head(*_snake_case )
def lowerCAmelCase_ ( self : Optional[Any] ) -> Optional[Any]:
SCREAMING_SNAKE_CASE__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_simple_qa(*_snake_case )
def lowerCAmelCase_ ( self : int ) -> Optional[Any]:
SCREAMING_SNAKE_CASE__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_qa(*_snake_case )
def lowerCAmelCase_ ( self : Any ) -> Dict:
SCREAMING_SNAKE_CASE__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_sequence_classif(*_snake_case )
def lowerCAmelCase_ ( self : Dict ) -> Tuple:
SCREAMING_SNAKE_CASE__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_token_classif(*_snake_case )
def lowerCAmelCase_ ( self : str ) -> List[Any]:
SCREAMING_SNAKE_CASE__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_for_multiple_choice(*_snake_case )
def lowerCAmelCase_ ( self : str , _snake_case : Optional[int] , _snake_case : Dict , _snake_case : Optional[int] , _snake_case : Union[str, Any] , _snake_case : Tuple , _snake_case : Any=False , _snake_case : Tuple=1 ) -> Optional[Any]:
self.assertIsInstance(_snake_case , _snake_case )
self.assertListEqual(
[isinstance(_snake_case , _snake_case ) for iter_attentions in attentions] , [True] * len(_snake_case ) )
self.assertEqual(len(_snake_case ) , (max_length - min_length) * num_beam_groups )
for idx, iter_attentions in enumerate(_snake_case ):
# adds PAD dummy token
SCREAMING_SNAKE_CASE__ = min_length + idx + 1
SCREAMING_SNAKE_CASE__ = min_length + idx + 1
SCREAMING_SNAKE_CASE__ = (
batch_size * num_beam_groups,
config.num_attention_heads,
tgt_len,
src_len,
)
# check attn size
self.assertListEqual(
[layer_attention.shape for layer_attention in iter_attentions] , [expected_shape] * len(_snake_case ) )
def lowerCAmelCase_ ( self : Any , _snake_case : Any , _snake_case : Dict , _snake_case : Tuple , _snake_case : List[Any] , _snake_case : str , _snake_case : Any=False , _snake_case : Tuple=1 ) -> Dict:
self.assertIsInstance(_snake_case , _snake_case )
self.assertListEqual(
[isinstance(_snake_case , _snake_case ) for iter_hidden_states in hidden_states] , [True] * len(_snake_case ) , )
self.assertEqual(len(_snake_case ) , (max_length - min_length) * num_beam_groups )
for idx, iter_hidden_states in enumerate(_snake_case ):
# adds PAD dummy token
SCREAMING_SNAKE_CASE__ = min_length + idx + 1
SCREAMING_SNAKE_CASE__ = (batch_size * num_beam_groups, seq_len, config.hidden_size)
# check hidden size
self.assertListEqual(
[layer_hidden_states.shape for layer_hidden_states in iter_hidden_states] , [expected_shape] * len(_snake_case ) , )
pass
@slow
def lowerCAmelCase_ ( self : List[str] ) -> Any:
for model_name in XLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
SCREAMING_SNAKE_CASE__ = XLMModel.from_pretrained(_snake_case )
self.assertIsNotNone(_snake_case )
@require_torch
class lowerCamelCase (unittest.TestCase ):
'''simple docstring'''
@slow
def lowerCAmelCase_ ( self : Any ) -> List[str]:
SCREAMING_SNAKE_CASE__ = XLMWithLMHeadModel.from_pretrained("xlm-mlm-en-2048" )
model.to(_snake_case )
SCREAMING_SNAKE_CASE__ = torch.tensor([[14, 447]] , dtype=torch.long , device=_snake_case ) # the president
SCREAMING_SNAKE_CASE__ = [
14,
447,
14,
447,
14,
447,
14,
447,
14,
447,
14,
447,
14,
447,
14,
447,
14,
447,
14,
447,
] # the president the president the president the president the president the president the president the president the president the president
# TODO(PVP): this and other input_ids I tried for generation give pretty bad results. Not sure why. Model might just not be made for auto-regressive inference
SCREAMING_SNAKE_CASE__ = model.generate(_snake_case , do_sample=_snake_case )
self.assertListEqual(output_ids[0].cpu().numpy().tolist() , _snake_case )
| 159 | """simple docstring"""
from decimal import Decimal, getcontext
from math import ceil, factorial
def SCREAMING_SNAKE_CASE ( __UpperCAmelCase ) -> str:
if not isinstance(__UpperCAmelCase , __UpperCAmelCase ):
raise TypeError("Undefined for non-integers" )
elif precision < 1:
raise ValueError("Undefined for non-natural numbers" )
SCREAMING_SNAKE_CASE__ = precision
SCREAMING_SNAKE_CASE__ = ceil(precision / 14 )
SCREAMING_SNAKE_CASE__ = 426_880 * Decimal(10_005 ).sqrt()
SCREAMING_SNAKE_CASE__ = 1
SCREAMING_SNAKE_CASE__ = 13_591_409
SCREAMING_SNAKE_CASE__ = Decimal(__UpperCAmelCase )
for k in range(1 , __UpperCAmelCase ):
SCREAMING_SNAKE_CASE__ = factorial(6 * k ) // (factorial(3 * k ) * factorial(__UpperCAmelCase ) ** 3)
linear_term += 545_140_134
exponential_term *= -262_537_412_640_768_000
partial_sum += Decimal(multinomial_term * linear_term ) / exponential_term
return str(constant_term / partial_sum )[:-1]
if __name__ == "__main__":
_A = 5_0
print(F'The first {n} digits of pi is: {pi(n)}')
| 159 | 1 |
'''simple docstring'''
lowercase__ : List[Any] = '''Input must be a string of 8 numbers plus letter'''
lowercase__ : Optional[Any] = '''TRWAGMYFPDXBNJZSQVHLCKE'''
def _lowerCAmelCase ( __snake_case : str ) -> bool:
if not isinstance(__snake_case , __snake_case ):
__A : str = f'Expected string as input, found {type(__snake_case ).__name__}'
raise TypeError(__snake_case )
__A : Any = spanish_id.replace('-' , '' ).upper()
if len(__snake_case ) != 9:
raise ValueError(__snake_case )
try:
__A : Optional[Any] = int(spanish_id_clean[0:8] )
__A : int = spanish_id_clean[8]
except ValueError as ex:
raise ValueError(__snake_case ) from ex
if letter.isdigit():
raise ValueError(__snake_case )
return letter == LOOKUP_LETTERS[number % 23]
if __name__ == "__main__":
import doctest
doctest.testmod() | 709 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_tf_available,
is_torch_available,
)
lowercase__ : str = {
'''configuration_speech_to_text''': ['''SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''Speech2TextConfig'''],
'''processing_speech_to_text''': ['''Speech2TextProcessor'''],
}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase__ : Dict = ['''Speech2TextTokenizer''']
try:
if not is_speech_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase__ : Optional[int] = ['''Speech2TextFeatureExtractor''']
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase__ : List[str] = [
'''TF_SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''TFSpeech2TextForConditionalGeneration''',
'''TFSpeech2TextModel''',
'''TFSpeech2TextPreTrainedModel''',
]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase__ : str = [
'''SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''Speech2TextForConditionalGeneration''',
'''Speech2TextModel''',
'''Speech2TextPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_speech_to_text import SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP, SpeechaTextConfig
from .processing_speech_to_text import SpeechaTextProcessor
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_speech_to_text import SpeechaTextTokenizer
try:
if not is_speech_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_speech_to_text import SpeechaTextFeatureExtractor
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_speech_to_text import (
TF_SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFSpeechaTextForConditionalGeneration,
TFSpeechaTextModel,
TFSpeechaTextPreTrainedModel,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_speech_to_text import (
SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
SpeechaTextForConditionalGeneration,
SpeechaTextModel,
SpeechaTextPreTrainedModel,
)
else:
import sys
lowercase__ : str = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__) | 338 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_torch_available,
)
a_ : int = {
'configuration_speecht5': [
'SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP',
'SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP',
'SpeechT5Config',
'SpeechT5HifiGanConfig',
],
'feature_extraction_speecht5': ['SpeechT5FeatureExtractor'],
'processing_speecht5': ['SpeechT5Processor'],
}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ : Any = ['SpeechT5Tokenizer']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ : Any = [
'SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST',
'SpeechT5ForSpeechToText',
'SpeechT5ForSpeechToSpeech',
'SpeechT5ForTextToSpeech',
'SpeechT5Model',
'SpeechT5PreTrainedModel',
'SpeechT5HifiGan',
]
if TYPE_CHECKING:
from .configuration_speechta import (
SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP,
SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP,
SpeechTaConfig,
SpeechTaHifiGanConfig,
)
from .feature_extraction_speechta import SpeechTaFeatureExtractor
from .processing_speechta import SpeechTaProcessor
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_speechta import SpeechTaTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_speechta import (
SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST,
SpeechTaForSpeechToSpeech,
SpeechTaForSpeechToText,
SpeechTaForTextToSpeech,
SpeechTaHifiGan,
SpeechTaModel,
SpeechTaPreTrainedModel,
)
else:
import sys
a_ : Union[str, Any] = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__) | 623 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, DDIMScheduler, DDPMScheduler, StableDiffusionUpscalePipeline, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
enable_full_determinism()
class lowerCamelCase__ ( unittest.TestCase):
"""simple docstring"""
def _a (self ):
'''simple docstring'''
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
@property
def _a (self ):
'''simple docstring'''
lowerCamelCase = 1
lowerCamelCase = 3
lowerCamelCase = (32, 32)
lowerCamelCase = floats_tensor((batch_size, num_channels) + sizes , rng=random.Random(0 ) ).to(__a )
return image
@property
def _a (self ):
'''simple docstring'''
torch.manual_seed(0 )
lowerCamelCase = UNetaDConditionModel(
block_out_channels=(32, 32, 64) , layers_per_block=2 , sample_size=32 , in_channels=7 , out_channels=4 , down_block_types=("DownBlock2D", "CrossAttnDownBlock2D", "CrossAttnDownBlock2D") , up_block_types=("CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "UpBlock2D") , cross_attention_dim=32 , attention_head_dim=8 , use_linear_projection=__a , only_cross_attention=(True, True, False) , num_class_embeds=1_00 , )
return model
@property
def _a (self ):
'''simple docstring'''
torch.manual_seed(0 )
lowerCamelCase = AutoencoderKL(
block_out_channels=[32, 32, 64] , in_channels=3 , out_channels=3 , down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D", "DownEncoderBlock2D"] , up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D"] , latent_channels=4 , )
return model
@property
def _a (self ):
'''simple docstring'''
torch.manual_seed(0 )
lowerCamelCase = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=32 , intermediate_size=37 , layer_norm_eps=1E-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=10_00 , hidden_act="gelu" , projection_dim=5_12 , )
return CLIPTextModel(__a )
def _a (self ):
'''simple docstring'''
lowerCamelCase = "cpu" # ensure determinism for the device-dependent torch.Generator
lowerCamelCase = self.dummy_cond_unet_upscale
lowerCamelCase = DDPMScheduler()
lowerCamelCase = DDIMScheduler(prediction_type="v_prediction" )
lowerCamelCase = self.dummy_vae
lowerCamelCase = self.dummy_text_encoder
lowerCamelCase = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip" )
lowerCamelCase = self.dummy_image.cpu().permute(0 , 2 , 3 , 1 )[0]
lowerCamelCase = Image.fromarray(np.uinta(__a ) ).convert("RGB" ).resize((64, 64) )
# make sure here that pndm scheduler skips prk
lowerCamelCase = StableDiffusionUpscalePipeline(
unet=__a , low_res_scheduler=__a , scheduler=__a , vae=__a , text_encoder=__a , tokenizer=__a , max_noise_level=3_50 , )
lowerCamelCase = sd_pipe.to(__a )
sd_pipe.set_progress_bar_config(disable=__a )
lowerCamelCase = "A painting of a squirrel eating a burger"
lowerCamelCase = torch.Generator(device=__a ).manual_seed(0 )
lowerCamelCase = sd_pipe(
[prompt] , image=__a , generator=__a , guidance_scale=6.0 , noise_level=20 , num_inference_steps=2 , output_type="np" , )
lowerCamelCase = output.images
lowerCamelCase = torch.Generator(device=__a ).manual_seed(0 )
lowerCamelCase = sd_pipe(
[prompt] , image=__a , generator=__a , guidance_scale=6.0 , noise_level=20 , num_inference_steps=2 , output_type="np" , return_dict=__a , )[0]
lowerCamelCase = image[0, -3:, -3:, -1]
lowerCamelCase = image_from_tuple[0, -3:, -3:, -1]
lowerCamelCase = low_res_image.size[0] * 4
assert image.shape == (1, expected_height_width, expected_height_width, 3)
lowerCamelCase = np.array([0.3113, 0.3910, 0.4272, 0.4859, 0.5061, 0.4652, 0.5362, 0.5715, 0.5661] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1E-2
def _a (self ):
'''simple docstring'''
lowerCamelCase = "cpu" # ensure determinism for the device-dependent torch.Generator
lowerCamelCase = self.dummy_cond_unet_upscale
lowerCamelCase = DDPMScheduler()
lowerCamelCase = DDIMScheduler(prediction_type="v_prediction" )
lowerCamelCase = self.dummy_vae
lowerCamelCase = self.dummy_text_encoder
lowerCamelCase = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip" )
lowerCamelCase = self.dummy_image.cpu().permute(0 , 2 , 3 , 1 )[0]
lowerCamelCase = Image.fromarray(np.uinta(__a ) ).convert("RGB" ).resize((64, 64) )
# make sure here that pndm scheduler skips prk
lowerCamelCase = StableDiffusionUpscalePipeline(
unet=__a , low_res_scheduler=__a , scheduler=__a , vae=__a , text_encoder=__a , tokenizer=__a , max_noise_level=3_50 , )
lowerCamelCase = sd_pipe.to(__a )
sd_pipe.set_progress_bar_config(disable=__a )
lowerCamelCase = "A painting of a squirrel eating a burger"
lowerCamelCase = sd_pipe(
2 * [prompt] , image=2 * [low_res_image] , guidance_scale=6.0 , noise_level=20 , num_inference_steps=2 , output_type="np" , )
lowerCamelCase = output.images
assert image.shape[0] == 2
lowerCamelCase = torch.Generator(device=__a ).manual_seed(0 )
lowerCamelCase = sd_pipe(
[prompt] , image=__a , generator=__a , num_images_per_prompt=2 , guidance_scale=6.0 , noise_level=20 , num_inference_steps=2 , output_type="np" , )
lowerCamelCase = output.images
assert image.shape[0] == 2
@unittest.skipIf(torch_device != "cuda" , "This test requires a GPU" )
def _a (self ):
'''simple docstring'''
lowerCamelCase = self.dummy_cond_unet_upscale
lowerCamelCase = DDPMScheduler()
lowerCamelCase = DDIMScheduler(prediction_type="v_prediction" )
lowerCamelCase = self.dummy_vae
lowerCamelCase = self.dummy_text_encoder
lowerCamelCase = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip" )
lowerCamelCase = self.dummy_image.cpu().permute(0 , 2 , 3 , 1 )[0]
lowerCamelCase = Image.fromarray(np.uinta(__a ) ).convert("RGB" ).resize((64, 64) )
# put models in fp16, except vae as it overflows in fp16
lowerCamelCase = unet.half()
lowerCamelCase = text_encoder.half()
# make sure here that pndm scheduler skips prk
lowerCamelCase = StableDiffusionUpscalePipeline(
unet=__a , low_res_scheduler=__a , scheduler=__a , vae=__a , text_encoder=__a , tokenizer=__a , max_noise_level=3_50 , )
lowerCamelCase = sd_pipe.to(__a )
sd_pipe.set_progress_bar_config(disable=__a )
lowerCamelCase = "A painting of a squirrel eating a burger"
lowerCamelCase = torch.manual_seed(0 )
lowerCamelCase = sd_pipe(
[prompt] , image=__a , generator=__a , num_inference_steps=2 , output_type="np" , ).images
lowerCamelCase = low_res_image.size[0] * 4
assert image.shape == (1, expected_height_width, expected_height_width, 3)
@slow
@require_torch_gpu
class lowerCamelCase__ ( unittest.TestCase):
"""simple docstring"""
def _a (self ):
'''simple docstring'''
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def _a (self ):
'''simple docstring'''
lowerCamelCase = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/sd2-upscale/low_res_cat.png" )
lowerCamelCase = load_numpy(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale"
"/upsampled_cat.npy" )
lowerCamelCase = "stabilityai/stable-diffusion-x4-upscaler"
lowerCamelCase = StableDiffusionUpscalePipeline.from_pretrained(__a )
pipe.to(__a )
pipe.set_progress_bar_config(disable=__a )
pipe.enable_attention_slicing()
lowerCamelCase = "a cat sitting on a park bench"
lowerCamelCase = torch.manual_seed(0 )
lowerCamelCase = pipe(
prompt=__a , image=__a , generator=__a , output_type="np" , )
lowerCamelCase = output.images[0]
assert image.shape == (5_12, 5_12, 3)
assert np.abs(expected_image - image ).max() < 1E-3
def _a (self ):
'''simple docstring'''
lowerCamelCase = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/sd2-upscale/low_res_cat.png" )
lowerCamelCase = load_numpy(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale"
"/upsampled_cat_fp16.npy" )
lowerCamelCase = "stabilityai/stable-diffusion-x4-upscaler"
lowerCamelCase = StableDiffusionUpscalePipeline.from_pretrained(
__a , torch_dtype=torch.floataa , )
pipe.to(__a )
pipe.set_progress_bar_config(disable=__a )
pipe.enable_attention_slicing()
lowerCamelCase = "a cat sitting on a park bench"
lowerCamelCase = torch.manual_seed(0 )
lowerCamelCase = pipe(
prompt=__a , image=__a , generator=__a , output_type="np" , )
lowerCamelCase = output.images[0]
assert image.shape == (5_12, 5_12, 3)
assert np.abs(expected_image - image ).max() < 5E-1
def _a (self ):
'''simple docstring'''
torch.cuda.empty_cache()
torch.cuda.reset_max_memory_allocated()
torch.cuda.reset_peak_memory_stats()
lowerCamelCase = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/sd2-upscale/low_res_cat.png" )
lowerCamelCase = "stabilityai/stable-diffusion-x4-upscaler"
lowerCamelCase = StableDiffusionUpscalePipeline.from_pretrained(
__a , torch_dtype=torch.floataa , )
pipe.to(__a )
pipe.set_progress_bar_config(disable=__a )
pipe.enable_attention_slicing(1 )
pipe.enable_sequential_cpu_offload()
lowerCamelCase = "a cat sitting on a park bench"
lowerCamelCase = torch.manual_seed(0 )
lowerCamelCase = pipe(
prompt=__a , image=__a , generator=__a , num_inference_steps=5 , output_type="np" , )
lowerCamelCase = torch.cuda.max_memory_allocated()
# make sure that less than 2.9 GB is allocated
assert mem_bytes < 2.9 * 10**9 | 623 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
A__ : Dict = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A__ : str = ['MLukeTokenizer']
if TYPE_CHECKING:
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_mluke import MLukeTokenizer
else:
import sys
A__ : Tuple = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 701 |
"""simple docstring"""
import comet # From: unbabel-comet
import torch
import datasets
A__ : int = datasets.logging.get_logger(__name__)
A__ : Optional[Any] = '\\n@inproceedings{rei-EtAl:2020:WMT,\n author = {Rei, Ricardo and Stewart, Craig and Farinha, Ana C and Lavie, Alon},\n title = {Unbabel\'s Participation in the WMT20 Metrics Shared Task},\n booktitle = {Proceedings of the Fifth Conference on Machine Translation},\n month = {November},\n year = {2020},\n address = {Online},\n publisher = {Association for Computational Linguistics},\n pages = {909--918},\n}\n@inproceedings{rei-etal-2020-comet,\n title = "{COMET}: A Neural Framework for {MT} Evaluation",\n author = "Rei, Ricardo and\n Stewart, Craig and\n Farinha, Ana C and\n Lavie, Alon",\n booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",\n month = nov,\n year = "2020",\n address = "Online",\n publisher = "Association for Computational Linguistics",\n url = "https://www.aclweb.org/anthology/2020.emnlp-main.213",\n pages = "2685--2702",\n}\n'
A__ : Union[str, Any] = '\\nCrosslingual Optimized Metric for Evaluation of Translation (COMET) is an open-source framework used to train Machine Translation metrics that achieve high levels of correlation with different types of human judgments (HTER, DA\'s or MQM).\nWith the release of the framework the authors also released fully trained models that were used to compete in the WMT20 Metrics Shared Task achieving SOTA in that years competition.\n\nSee the [README.md] file at https://unbabel.github.io/COMET/html/models.html for more information.\n'
A__ : int = '\nCOMET score.\n\nArgs:\n\n`sources` (list of str): Source sentences\n`predictions` (list of str): candidate translations\n`references` (list of str): reference translations\n`cuda` (bool): If set to True, runs COMET using GPU\n`show_progress` (bool): Shows progress\n`model`: COMET model to be used. Will default to `wmt-large-da-estimator-1719` if None.\n\nReturns:\n `samples`: List of dictionaries with `src`, `mt`, `ref` and `score`.\n `scores`: List of scores.\n\nExamples:\n\n >>> comet_metric = datasets.load_metric(\'comet\')\n >>> # comet_metric = load_metric(\'comet\', \'wmt20-comet-da\') # you can also choose which model to use\n >>> source = ["Dem Feuer konnte Einhalt geboten werden", "Schulen und Kindergärten wurden eröffnet."]\n >>> hypothesis = ["The fire could be stopped", "Schools and kindergartens were open"]\n >>> reference = ["They were able to control the fire.", "Schools and kindergartens opened"]\n >>> results = comet_metric.compute(predictions=hypothesis, references=reference, sources=source)\n >>> print([round(v, 2) for v in results["scores"]])\n [0.19, 0.92]\n'
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class __magic_name__ ( datasets.Metric ):
def lowercase_ ( self ) -> Tuple:
"""simple docstring"""
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , homepage='''https://unbabel.github.io/COMET/html/index.html''' , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
'''sources''': datasets.Value('''string''' , id='''sequence''' ),
'''predictions''': datasets.Value('''string''' , id='''sequence''' ),
'''references''': datasets.Value('''string''' , id='''sequence''' ),
} ) , codebase_urls=['''https://github.com/Unbabel/COMET'''] , reference_urls=[
'''https://github.com/Unbabel/COMET''',
'''https://www.aclweb.org/anthology/2020.emnlp-main.213/''',
'''http://www.statmt.org/wmt20/pdf/2020.wmt-1.101.pdf6''',
] , )
def lowercase_ ( self , A_ ) -> Dict:
"""simple docstring"""
if self.config_name == "default":
_lowercase: str = comet.load_from_checkpoint(comet.download_model('''wmt20-comet-da''' ) )
else:
_lowercase: Optional[Any] = comet.load_from_checkpoint(comet.download_model(self.config_name ) )
def lowercase_ ( self , A_ , A_ , A_ , A_=None , A_=False ) -> List[str]:
"""simple docstring"""
if gpus is None:
_lowercase: Union[str, Any] = 1 if torch.cuda.is_available() else 0
_lowercase: Any = {'''src''': sources, '''mt''': predictions, '''ref''': references}
_lowercase: int = [dict(zip(A_ , A_ ) ) for t in zip(*data.values() )]
_lowercase , _lowercase: Tuple = self.scorer.predict(A_ , gpus=A_ , progress_bar=A_ )
return {"mean_score": mean_score, "scores": scores}
| 272 | 0 |
'''simple docstring'''
def SCREAMING_SNAKE_CASE ( lowercase_ : int = 1000 ):
return sum(2 * a * ((a - 1) // 2) for a in range(3 , n + 1 ) )
if __name__ == "__main__":
print(solution())
| 588 |
'''simple docstring'''
import operator
def SCREAMING_SNAKE_CASE ( lowercase_ : list , lowercase_ : bool = False , lowercase_ : list | None = None ):
lowercase = operator.lt if reverse else operator.gt
lowercase = solution or []
if not arr:
return solution
lowercase = [arr.pop(0 )]
for i, item in enumerate(lowercase_ ):
if _operator(lowercase_ , sublist[-1] ):
sublist.append(lowercase_ )
arr.pop(lowercase_ )
# merging sublist into solution list
if not solution:
solution.extend(lowercase_ )
else:
while sublist:
lowercase = sublist.pop(0 )
for i, xx in enumerate(lowercase_ ):
if not _operator(lowercase_ , lowercase_ ):
solution.insert(lowercase_ , lowercase_ )
break
else:
solution.append(lowercase_ )
strand_sort(lowercase_ , lowercase_ , lowercase_ )
return solution
if __name__ == "__main__":
assert strand_sort([4, 3, 5, 1, 2]) == [1, 2, 3, 4, 5]
assert strand_sort([4, 3, 5, 1, 2], reverse=True) == [5, 4, 3, 2, 1]
| 588 | 1 |
'''simple docstring'''
def UpperCamelCase( UpperCAmelCase_ ):
if isinstance(__UpperCamelCase , __UpperCamelCase ):
raise TypeError('\'float\' object cannot be interpreted as an integer' )
if isinstance(__UpperCamelCase , __UpperCamelCase ):
raise TypeError('\'str\' object cannot be interpreted as an integer' )
if num == 0:
return "0b0"
UpperCAmelCase : List[str] = False
if num < 0:
UpperCAmelCase : Union[str, Any] = True
UpperCAmelCase : Any = -num
UpperCAmelCase : Dict = []
while num > 0:
binary.insert(0 , num % 2 )
num >>= 1
if negative:
return "-0b" + "".join(str(__UpperCamelCase ) for e in binary )
return "0b" + "".join(str(__UpperCamelCase ) for e in binary )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 708 |
'''simple docstring'''
import logging
import os
from dataclasses import dataclass
from typing import List, Optional, Union
import tqdm
from filelock import FileLock
from transformers import (
BartTokenizer,
BartTokenizerFast,
DataProcessor,
PreTrainedTokenizer,
RobertaTokenizer,
RobertaTokenizerFast,
XLMRobertaTokenizer,
is_tf_available,
is_torch_available,
)
lowercase__ = logging.getLogger(__name__)
@dataclass(frozen=_snake_case )
class A_ :
'''simple docstring'''
UpperCAmelCase_ : str
UpperCAmelCase_ : str
UpperCAmelCase_ : Optional[str] = None
UpperCAmelCase_ : Optional[str] = None
UpperCAmelCase_ : Optional[str] = None
@dataclass(frozen=_snake_case )
class A_ :
'''simple docstring'''
UpperCAmelCase_ : List[int]
UpperCAmelCase_ : Optional[List[int]] = None
UpperCAmelCase_ : Optional[List[int]] = None
UpperCAmelCase_ : Optional[Union[int, float]] = None
UpperCAmelCase_ : Optional[int] = None
if is_torch_available():
import torch
from torch.utils.data import Dataset
class A_ ( _snake_case ):
'''simple docstring'''
UpperCAmelCase_ : List[InputFeatures]
def __init__( self : List[str] , lowercase_ : str , lowercase_ : PreTrainedTokenizer , lowercase_ : str , lowercase_ : Optional[int] = None , lowercase_ : List[str]=False , lowercase_ : bool = False , ) -> Optional[Any]:
UpperCAmelCase : Dict = hans_processors[task]()
UpperCAmelCase : List[Any] = os.path.join(
lowercase_ , 'cached_{}_{}_{}_{}'.format(
'dev' if evaluate else 'train' , tokenizer.__class__.__name__ , str(lowercase_ ) , lowercase_ , ) , )
UpperCAmelCase : Optional[int] = processor.get_labels()
if tokenizer.__class__ in (
RobertaTokenizer,
RobertaTokenizerFast,
XLMRobertaTokenizer,
BartTokenizer,
BartTokenizerFast,
):
# HACK(label indices are swapped in RoBERTa pretrained model)
UpperCAmelCase , UpperCAmelCase : Tuple = label_list[2], label_list[1]
UpperCAmelCase : Optional[int] = label_list
# Make sure only the first process in distributed training processes the dataset,
# and the others will use the cache.
UpperCAmelCase : int = cached_features_file + '.lock'
with FileLock(lowercase_ ):
if os.path.exists(lowercase_ ) and not overwrite_cache:
logger.info(f"""Loading features from cached file {cached_features_file}""" )
UpperCAmelCase : Tuple = torch.load(lowercase_ )
else:
logger.info(f"""Creating features from dataset file at {data_dir}""" )
UpperCAmelCase : int = (
processor.get_dev_examples(lowercase_ ) if evaluate else processor.get_train_examples(lowercase_ )
)
logger.info('Training examples: %s' , len(lowercase_ ) )
UpperCAmelCase : Dict = hans_convert_examples_to_features(lowercase_ , lowercase_ , lowercase_ , lowercase_ )
logger.info('Saving features into cached file %s' , lowercase_ )
torch.save(self.features , lowercase_ )
def __len__( self : Union[str, Any] ) -> str:
return len(self.features )
def __getitem__( self : Dict , lowercase_ : Dict ) -> InputFeatures:
return self.features[i]
def UpperCAmelCase_ ( self : Tuple ) -> Optional[Any]:
return self.label_list
if is_tf_available():
import tensorflow as tf
class A_ :
'''simple docstring'''
UpperCAmelCase_ : List[InputFeatures]
def __init__( self : Tuple , lowercase_ : str , lowercase_ : PreTrainedTokenizer , lowercase_ : str , lowercase_ : Optional[int] = 128 , lowercase_ : int=False , lowercase_ : bool = False , ) -> Union[str, Any]:
UpperCAmelCase : int = hans_processors[task]()
UpperCAmelCase : Optional[int] = processor.get_labels()
if tokenizer.__class__ in (
RobertaTokenizer,
RobertaTokenizerFast,
XLMRobertaTokenizer,
BartTokenizer,
BartTokenizerFast,
):
# HACK(label indices are swapped in RoBERTa pretrained model)
UpperCAmelCase , UpperCAmelCase : Tuple = label_list[2], label_list[1]
UpperCAmelCase : Any = label_list
UpperCAmelCase : str = processor.get_dev_examples(lowercase_ ) if evaluate else processor.get_train_examples(lowercase_ )
UpperCAmelCase : int = hans_convert_examples_to_features(lowercase_ , lowercase_ , lowercase_ , lowercase_ )
def gen():
for ex_index, ex in tqdm.tqdm(enumerate(self.features ) , desc='convert examples to features' ):
if ex_index % 10_000 == 0:
logger.info('Writing example %d of %d' % (ex_index, len(lowercase_ )) )
yield (
{
"example_id": 0,
"input_ids": ex.input_ids,
"attention_mask": ex.attention_mask,
"token_type_ids": ex.token_type_ids,
},
ex.label,
)
UpperCAmelCase : Optional[Any] = tf.data.Dataset.from_generator(
lowercase_ , (
{
'example_id': tf.intaa,
'input_ids': tf.intaa,
'attention_mask': tf.intaa,
'token_type_ids': tf.intaa,
},
tf.intaa,
) , (
{
'example_id': tf.TensorShape([] ),
'input_ids': tf.TensorShape([None, None] ),
'attention_mask': tf.TensorShape([None, None] ),
'token_type_ids': tf.TensorShape([None, None] ),
},
tf.TensorShape([] ),
) , )
def UpperCAmelCase_ ( self : Union[str, Any] ) -> Dict:
return self.dataset
def __len__( self : Tuple ) -> Optional[Any]:
return len(self.features )
def __getitem__( self : List[Any] , lowercase_ : Union[str, Any] ) -> InputFeatures:
return self.features[i]
def UpperCAmelCase_ ( self : Tuple ) -> Union[str, Any]:
return self.label_list
class A_ ( _snake_case ):
'''simple docstring'''
def UpperCAmelCase_ ( self : int , lowercase_ : Optional[int] ) -> Any:
return self._create_examples(self._read_tsv(os.path.join(lowercase_ , 'heuristics_train_set.txt' ) ) , 'train' )
def UpperCAmelCase_ ( self : Optional[int] , lowercase_ : Dict ) -> List[str]:
return self._create_examples(self._read_tsv(os.path.join(lowercase_ , 'heuristics_evaluation_set.txt' ) ) , 'dev' )
def UpperCAmelCase_ ( self : str ) -> Optional[int]:
return ["contradiction", "entailment", "neutral"]
def UpperCAmelCase_ ( self : Optional[int] , lowercase_ : Tuple , lowercase_ : str ) -> Dict:
UpperCAmelCase : Union[str, Any] = []
for i, line in enumerate(lowercase_ ):
if i == 0:
continue
UpperCAmelCase : Tuple = '%s-%s' % (set_type, line[0])
UpperCAmelCase : Tuple = line[5]
UpperCAmelCase : Dict = line[6]
UpperCAmelCase : Optional[Any] = line[7][2:] if line[7].startswith('ex' ) else line[7]
UpperCAmelCase : Optional[Any] = line[0]
examples.append(InputExample(guid=lowercase_ , text_a=lowercase_ , text_b=lowercase_ , label=lowercase_ , pairID=lowercase_ ) )
return examples
def UpperCamelCase( UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , ):
UpperCAmelCase : List[str] = {label: i for i, label in enumerate(UpperCAmelCase_ )}
UpperCAmelCase : Optional[Any] = []
for ex_index, example in tqdm.tqdm(enumerate(UpperCAmelCase_ ) , desc='convert examples to features' ):
if ex_index % 1_00_00 == 0:
logger.info('Writing example %d' % (ex_index) )
UpperCAmelCase : int = tokenizer(
example.text_a , example.text_b , add_special_tokens=UpperCAmelCase_ , max_length=UpperCAmelCase_ , padding='max_length' , truncation=UpperCAmelCase_ , return_overflowing_tokens=UpperCAmelCase_ , )
UpperCAmelCase : List[str] = label_map[example.label] if example.label in label_map else 0
UpperCAmelCase : Any = int(example.pairID )
features.append(InputFeatures(**UpperCAmelCase_ , label=UpperCAmelCase_ , pairID=UpperCAmelCase_ ) )
for i, example in enumerate(examples[:5] ):
logger.info('*** Example ***' )
logger.info(F"""guid: {example}""" )
logger.info(F"""features: {features[i]}""" )
return features
lowercase__ = {
"hans": 3,
}
lowercase__ = {
"hans": HansProcessor,
}
| 695 | 0 |
'''simple docstring'''
from sklearn.metrics import fa_score, matthews_corrcoef
import datasets
from .record_evaluation import evaluate as evaluate_record
__lowerCAmelCase = '\\n@article{wang2019superglue,\n title={SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems},\n author={Wang, Alex and Pruksachatkun, Yada and Nangia, Nikita and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel R},\n journal={arXiv preprint arXiv:1905.00537},\n year={2019}\n}\n'
__lowerCAmelCase = '\\nSuperGLUE (https://super.gluebenchmark.com/) is a new benchmark styled after\nGLUE with a new set of more difficult language understanding tasks, improved\nresources, and a new public leaderboard.\n'
__lowerCAmelCase = '\nCompute SuperGLUE evaluation metric associated to each SuperGLUE dataset.\nArgs:\n predictions: list of predictions to score. Depending on the SuperGlUE subset:\n - for \'record\': list of question-answer dictionaries with the following keys:\n - \'idx\': index of the question as specified by the dataset\n - \'prediction_text\': the predicted answer text\n - for \'multirc\': list of question-answer dictionaries with the following keys:\n - \'idx\': index of the question-answer pair as specified by the dataset\n - \'prediction\': the predicted answer label\n - otherwise: list of predicted labels\n references: list of reference labels. Depending on the SuperGLUE subset:\n - for \'record\': list of question-answers dictionaries with the following keys:\n - \'idx\': index of the question as specified by the dataset\n - \'answers\': list of possible answers\n - otherwise: list of reference labels\nReturns: depending on the SuperGLUE subset:\n - for \'record\':\n - \'exact_match\': Exact match between answer and gold answer\n - \'f1\': F1 score\n - for \'multirc\':\n - \'exact_match\': Exact match between answer and gold answer\n - \'f1_m\': Per-question macro-F1 score\n - \'f1_a\': Average F1 score over all answers\n - for \'axb\':\n \'matthews_correlation\': Matthew Correlation\n - for \'cb\':\n - \'accuracy\': Accuracy\n - \'f1\': F1 score\n - for all others:\n - \'accuracy\': Accuracy\nExamples:\n\n >>> super_glue_metric = datasets.load_metric(\'super_glue\', \'copa\') # any of ["copa", "rte", "wic", "wsc", "wsc.fixed", "boolq", "axg"]\n >>> predictions = [0, 1]\n >>> references = [0, 1]\n >>> results = super_glue_metric.compute(predictions=predictions, references=references)\n >>> print(results)\n {\'accuracy\': 1.0}\n\n >>> super_glue_metric = datasets.load_metric(\'super_glue\', \'cb\')\n >>> predictions = [0, 1]\n >>> references = [0, 1]\n >>> results = super_glue_metric.compute(predictions=predictions, references=references)\n >>> print(results)\n {\'accuracy\': 1.0, \'f1\': 1.0}\n\n >>> super_glue_metric = datasets.load_metric(\'super_glue\', \'record\')\n >>> predictions = [{\'idx\': {\'passage\': 0, \'query\': 0}, \'prediction_text\': \'answer\'}]\n >>> references = [{\'idx\': {\'passage\': 0, \'query\': 0}, \'answers\': [\'answer\', \'another_answer\']}]\n >>> results = super_glue_metric.compute(predictions=predictions, references=references)\n >>> print(results)\n {\'exact_match\': 1.0, \'f1\': 1.0}\n\n >>> super_glue_metric = datasets.load_metric(\'super_glue\', \'multirc\')\n >>> predictions = [{\'idx\': {\'answer\': 0, \'paragraph\': 0, \'question\': 0}, \'prediction\': 0}, {\'idx\': {\'answer\': 1, \'paragraph\': 2, \'question\': 3}, \'prediction\': 1}]\n >>> references = [0, 1]\n >>> results = super_glue_metric.compute(predictions=predictions, references=references)\n >>> print(results)\n {\'exact_match\': 1.0, \'f1_m\': 1.0, \'f1_a\': 1.0}\n\n >>> super_glue_metric = datasets.load_metric(\'super_glue\', \'axb\')\n >>> references = [0, 1]\n >>> predictions = [0, 1]\n >>> results = super_glue_metric.compute(predictions=predictions, references=references)\n >>> print(results)\n {\'matthews_correlation\': 1.0}\n'
def __SCREAMING_SNAKE_CASE ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
return float((preds == labels).mean() )
def __SCREAMING_SNAKE_CASE ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE="binary" ):
_snake_case = simple_accuracy(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
_snake_case = float(fa_score(y_true=_SCREAMING_SNAKE_CASE , y_pred=_SCREAMING_SNAKE_CASE , average=_SCREAMING_SNAKE_CASE ) )
return {
"accuracy": acc,
"f1": fa,
}
def __SCREAMING_SNAKE_CASE ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
_snake_case = {}
for id_pred, label in zip(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
_snake_case = f"""{id_pred["idx"]["paragraph"]}-{id_pred["idx"]["question"]}"""
_snake_case = id_pred["""prediction"""]
if question_id in question_map:
question_map[question_id].append((pred, label) )
else:
_snake_case = [(pred, label)]
_snake_case, _snake_case = [], []
for question, preds_labels in question_map.items():
_snake_case, _snake_case = zip(*_SCREAMING_SNAKE_CASE )
_snake_case = fa_score(y_true=_SCREAMING_SNAKE_CASE , y_pred=_SCREAMING_SNAKE_CASE , average="""macro""" )
fas.append(_SCREAMING_SNAKE_CASE )
_snake_case = int(sum(pred == label for pred, label in preds_labels ) == len(_SCREAMING_SNAKE_CASE ) )
ems.append(_SCREAMING_SNAKE_CASE )
_snake_case = float(sum(_SCREAMING_SNAKE_CASE ) / len(_SCREAMING_SNAKE_CASE ) )
_snake_case = sum(_SCREAMING_SNAKE_CASE ) / len(_SCREAMING_SNAKE_CASE )
_snake_case = float(fa_score(y_true=_SCREAMING_SNAKE_CASE , y_pred=[id_pred["""prediction"""] for id_pred in ids_preds] ) )
return {"exact_match": em, "f1_m": fa_m, "f1_a": fa_a}
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class _lowerCAmelCase ( datasets.Metric ):
'''simple docstring'''
def lowercase (self ) -> int:
if self.config_name not in [
"boolq",
"cb",
"copa",
"multirc",
"record",
"rte",
"wic",
"wsc",
"wsc.fixed",
"axb",
"axg",
]:
raise KeyError(
"""You should supply a configuration name selected in """
"""[\"boolq\", \"cb\", \"copa\", \"multirc\", \"record\", \"rte\", \"wic\", \"wsc\", \"wsc.fixed\", \"axb\", \"axg\",]""" )
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(self._get_feature_types() ) , codebase_urls=[] , reference_urls=[] , format="""numpy""" if not self.config_name == """record""" and not self.config_name == """multirc""" else None , )
def lowercase (self ) -> Dict:
if self.config_name == "record":
return {
"predictions": {
"idx": {
"passage": datasets.Value("""int64""" ),
"query": datasets.Value("""int64""" ),
},
"prediction_text": datasets.Value("""string""" ),
},
"references": {
"idx": {
"passage": datasets.Value("""int64""" ),
"query": datasets.Value("""int64""" ),
},
"answers": datasets.Sequence(datasets.Value("""string""" ) ),
},
}
elif self.config_name == "multirc":
return {
"predictions": {
"idx": {
"answer": datasets.Value("""int64""" ),
"paragraph": datasets.Value("""int64""" ),
"question": datasets.Value("""int64""" ),
},
"prediction": datasets.Value("""int64""" ),
},
"references": datasets.Value("""int64""" ),
}
else:
return {
"predictions": datasets.Value("""int64""" ),
"references": datasets.Value("""int64""" ),
}
def lowercase (self , UpperCAmelCase , UpperCAmelCase ) -> Optional[int]:
if self.config_name == "axb":
return {"matthews_correlation": matthews_corrcoef(UpperCAmelCase , UpperCAmelCase )}
elif self.config_name == "cb":
return acc_and_fa(UpperCAmelCase , UpperCAmelCase , fa_avg="""macro""" )
elif self.config_name == "record":
_snake_case = [
{
"""qas""": [
{"""id""": ref["""idx"""]["""query"""], """answers""": [{"""text""": ans} for ans in ref["""answers"""]]}
for ref in references
]
}
]
_snake_case = {pred["""idx"""]["""query"""]: pred["""prediction_text"""] for pred in predictions}
return evaluate_record(UpperCAmelCase , UpperCAmelCase )[0]
elif self.config_name == "multirc":
return evaluate_multirc(UpperCAmelCase , UpperCAmelCase )
elif self.config_name in ["copa", "rte", "wic", "wsc", "wsc.fixed", "boolq", "axg"]:
return {"accuracy": simple_accuracy(UpperCAmelCase , UpperCAmelCase )}
else:
raise KeyError(
"""You should supply a configuration name selected in """
"""[\"boolq\", \"cb\", \"copa\", \"multirc\", \"record\", \"rte\", \"wic\", \"wsc\", \"wsc.fixed\", \"axb\", \"axg\",]""" ) | 585 |
'''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 __SCREAMING_SNAKE_CASE ( _SCREAMING_SNAKE_CASE ):
_snake_case = []
for i in range(len(_SCREAMING_SNAKE_CASE ) ):
_snake_case = []
for j in range(len(cells[i] ) ):
# Get the number of live neighbours
_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(_SCREAMING_SNAKE_CASE ) - 1 and j > 0:
neighbour_count += cells[i + 1][j - 1]
if i < len(_SCREAMING_SNAKE_CASE ) - 1:
neighbour_count += cells[i + 1][j]
if i < len(_SCREAMING_SNAKE_CASE ) - 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.
_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(_SCREAMING_SNAKE_CASE )
return next_generation
def __SCREAMING_SNAKE_CASE ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
_snake_case = []
for _ in range(_SCREAMING_SNAKE_CASE ):
# Create output image
_snake_case = Image.new("""RGB""" , (len(cells[0] ), len(_SCREAMING_SNAKE_CASE )) )
_snake_case = img.load()
# Save cells to image
for x in range(len(_SCREAMING_SNAKE_CASE ) ):
for y in range(len(cells[0] ) ):
_snake_case = 255 - cells[y][x] * 255
_snake_case = (colour, colour, colour)
# Save image
images.append(_SCREAMING_SNAKE_CASE )
_snake_case = new_generation(_SCREAMING_SNAKE_CASE )
return images
if __name__ == "__main__":
__lowerCAmelCase = generate_images(GLIDER, 16)
images[0].save('out.gif', save_all=True, append_images=images[1:]) | 585 | 1 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
UpperCamelCase__ = logging.get_logger(__name__)
UpperCamelCase__ = {
'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 ( __a ):
UpperCamelCase : int = """roformer"""
def __init__( self , UpperCamelCase_=50_000 , UpperCamelCase_=None , UpperCamelCase_=768 , UpperCamelCase_=12 , UpperCamelCase_=12 , UpperCamelCase_=3_072 , UpperCamelCase_="gelu" , UpperCamelCase_=0.1 , UpperCamelCase_=0.1 , UpperCamelCase_=1_536 , UpperCamelCase_=2 , UpperCamelCase_=0.02 , UpperCamelCase_=1E-12 , UpperCamelCase_=0 , UpperCamelCase_=False , UpperCamelCase_=True , **UpperCamelCase_ , ):
super().__init__(pad_token_id=a_ , **a_ )
UpperCAmelCase__ : int = vocab_size
UpperCAmelCase__ : int = hidden_size if embedding_size is None else embedding_size
UpperCAmelCase__ : Dict = hidden_size
UpperCAmelCase__ : Optional[int] = num_hidden_layers
UpperCAmelCase__ : Any = num_attention_heads
UpperCAmelCase__ : Dict = hidden_act
UpperCAmelCase__ : Optional[int] = intermediate_size
UpperCAmelCase__ : List[Any] = hidden_dropout_prob
UpperCAmelCase__ : Union[str, Any] = attention_probs_dropout_prob
UpperCAmelCase__ : Any = max_position_embeddings
UpperCAmelCase__ : Tuple = type_vocab_size
UpperCAmelCase__ : List[Any] = initializer_range
UpperCAmelCase__ : List[Any] = layer_norm_eps
UpperCAmelCase__ : Optional[Any] = rotary_value
UpperCAmelCase__ : List[str] = use_cache
class a ( __a ):
@property
def __snake_case ( self ):
if self.task == "multiple-choice":
UpperCAmelCase__ : str = {0: """batch""", 1: """choice""", 2: """sequence"""}
else:
UpperCAmelCase__ : List[str] = {0: """batch""", 1: """sequence"""}
UpperCAmelCase__ : List[Any] = {0: """batch""", 1: """sequence"""}
return OrderedDict(
[
('input_ids', dynamic_axis),
('attention_mask', dynamic_axis),
('token_type_ids', dynamic_axis),
] )
| 706 |
"""simple docstring"""
from typing import Optional
import numpy as np
import torch
from torch import nn
from transformers import GPTaConfig, GPTaLMHeadModel
from transformers.modeling_utils import ModuleUtilsMixin
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class a ( lowercase , lowercase , lowercase ):
UpperCamelCase : Any = [r"""h\.\d+\.attn\.bias""", r"""h\.\d+\.attn\.masked_bias"""]
@register_to_config
def __init__( self , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ = None , UpperCamelCase_ = 50_257 , UpperCamelCase_ = 1_024 , UpperCamelCase_ = 768 , UpperCamelCase_ = 12 , UpperCamelCase_ = 12 , UpperCamelCase_ = None , UpperCamelCase_ = "gelu_new" , UpperCamelCase_ = 0.1 , UpperCamelCase_ = 0.1 , UpperCamelCase_ = 0.1 , UpperCamelCase_ = 1E-5 , UpperCamelCase_ = 0.02 , UpperCamelCase_ = True , UpperCamelCase_ = True , UpperCamelCase_ = False , UpperCamelCase_ = False , ):
super().__init__()
UpperCAmelCase__ : List[Any] = prefix_length
if prefix_inner_dim != n_embd and prefix_hidden_dim is None:
raise ValueError(
F'''`prefix_hidden_dim` cannot be `None` when `prefix_inner_dim`: {prefix_hidden_dim} and'''
F''' `n_embd`: {n_embd} are not equal.''' )
UpperCAmelCase__ : Dict = prefix_inner_dim
UpperCAmelCase__ : List[Any] = prefix_hidden_dim
UpperCAmelCase__ : List[Any] = (
nn.Linear(self.prefix_inner_dim , self.prefix_hidden_dim )
if self.prefix_hidden_dim is not None
else nn.Identity()
)
UpperCAmelCase__ : Any = (
nn.Linear(self.prefix_hidden_dim , UpperCamelCase_ ) if self.prefix_hidden_dim is not None else nn.Identity()
)
UpperCAmelCase__ : Union[str, Any] = GPTaConfig(
vocab_size=UpperCamelCase_ , n_positions=UpperCamelCase_ , n_embd=UpperCamelCase_ , n_layer=UpperCamelCase_ , n_head=UpperCamelCase_ , n_inner=UpperCamelCase_ , activation_function=UpperCamelCase_ , resid_pdrop=UpperCamelCase_ , embd_pdrop=UpperCamelCase_ , attn_pdrop=UpperCamelCase_ , layer_norm_epsilon=UpperCamelCase_ , initializer_range=UpperCamelCase_ , scale_attn_weights=UpperCamelCase_ , use_cache=UpperCamelCase_ , scale_attn_by_inverse_layer_idx=UpperCamelCase_ , reorder_and_upcast_attn=UpperCamelCase_ , )
UpperCAmelCase__ : List[str] = GPTaLMHeadModel(UpperCamelCase_ )
def __snake_case ( self , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ = None , UpperCamelCase_ = None , ):
UpperCAmelCase__ : Optional[Any] = self.transformer.transformer.wte(UpperCamelCase_ )
UpperCAmelCase__ : str = self.encode_prefix(UpperCamelCase_ )
UpperCAmelCase__ : Tuple = self.decode_prefix(UpperCamelCase_ )
UpperCAmelCase__ : List[str] = torch.cat((prefix_embeds, embedding_text) , dim=1 )
if labels is not None:
UpperCAmelCase__ : List[Any] = self.get_dummy_token(input_ids.shape[0] , input_ids.device )
UpperCAmelCase__ : List[Any] = torch.cat((dummy_token, input_ids) , dim=1 )
UpperCAmelCase__ : List[str] = self.transformer(inputs_embeds=UpperCamelCase_ , labels=UpperCamelCase_ , attention_mask=UpperCamelCase_ )
if self.prefix_hidden_dim is not None:
return out, hidden
else:
return out
def __snake_case ( self , UpperCamelCase_ , UpperCamelCase_ ):
return torch.zeros(UpperCamelCase_ , self.prefix_length , dtype=torch.intaa , device=UpperCamelCase_ )
def __snake_case ( self , UpperCamelCase_ ):
return self.encode_prefix(UpperCamelCase_ )
@torch.no_grad()
def __snake_case ( self , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ ):
UpperCAmelCase__ : List[str] = torch.split(UpperCamelCase_ , 1 , dim=0 )
UpperCAmelCase__ : Any = []
UpperCAmelCase__ : Optional[Any] = []
for feature in features:
UpperCAmelCase__ : List[str] = self.decode_prefix(feature.to(UpperCamelCase_ ) ) # back to the clip feature
# Only support beam search for now
UpperCAmelCase__ , UpperCAmelCase__ : int = self.generate_beam(
input_embeds=UpperCamelCase_ , device=UpperCamelCase_ , eos_token_id=UpperCamelCase_ )
generated_tokens.append(output_tokens[0] )
generated_seq_lengths.append(seq_lengths[0] )
UpperCAmelCase__ : Optional[int] = torch.stack(UpperCamelCase_ )
UpperCAmelCase__ : Any = torch.stack(UpperCamelCase_ )
return generated_tokens, generated_seq_lengths
@torch.no_grad()
def __snake_case ( self , UpperCamelCase_=None , UpperCamelCase_=None , UpperCamelCase_=None , UpperCamelCase_ = 5 , UpperCamelCase_ = 67 , UpperCamelCase_ = 1.0 , UpperCamelCase_ = None , ):
UpperCAmelCase__ : Optional[int] = eos_token_id
UpperCAmelCase__ : List[Any] = None
UpperCAmelCase__ : Union[str, Any] = None
UpperCAmelCase__ : List[str] = torch.ones(UpperCamelCase_ , device=UpperCamelCase_ , dtype=torch.int )
UpperCAmelCase__ : str = torch.zeros(UpperCamelCase_ , device=UpperCamelCase_ , dtype=torch.bool )
if input_embeds is not None:
UpperCAmelCase__ : List[Any] = input_embeds
else:
UpperCAmelCase__ : Union[str, Any] = self.transformer.transformer.wte(UpperCamelCase_ )
for i in range(UpperCamelCase_ ):
UpperCAmelCase__ : Tuple = self.transformer(inputs_embeds=UpperCamelCase_ )
UpperCAmelCase__ : Dict = outputs.logits
UpperCAmelCase__ : List[Any] = logits[:, -1, :] / (temperature if temperature > 0 else 1.0)
UpperCAmelCase__ : int = logits.softmax(-1 ).log()
if scores is None:
UpperCAmelCase__ , UpperCAmelCase__ : Optional[int] = logits.topk(UpperCamelCase_ , -1 )
UpperCAmelCase__ : List[Any] = generated.expand(UpperCamelCase_ , *generated.shape[1:] )
UpperCAmelCase__ , UpperCAmelCase__ : List[str] = next_tokens.permute(1 , 0 ), scores.squeeze(0 )
if tokens is None:
UpperCAmelCase__ : Dict = next_tokens
else:
UpperCAmelCase__ : Optional[int] = tokens.expand(UpperCamelCase_ , *tokens.shape[1:] )
UpperCAmelCase__ : List[Any] = torch.cat((tokens, next_tokens) , dim=1 )
else:
UpperCAmelCase__ : str = -float(np.inf )
UpperCAmelCase__ : Any = 0
UpperCAmelCase__ : int = scores[:, None] + logits
seq_lengths[~is_stopped] += 1
UpperCAmelCase__ : Optional[int] = scores_sum / seq_lengths[:, None]
UpperCAmelCase__ , UpperCAmelCase__ : Union[str, Any] = scores_sum_average.view(-1 ).topk(UpperCamelCase_ , -1 )
UpperCAmelCase__ : List[Any] = next_tokens // scores_sum.shape[1]
UpperCAmelCase__ : str = seq_lengths[next_tokens_source]
UpperCAmelCase__ : str = next_tokens % scores_sum.shape[1]
UpperCAmelCase__ : Optional[Any] = next_tokens.unsqueeze(1 )
UpperCAmelCase__ : List[str] = tokens[next_tokens_source]
UpperCAmelCase__ : List[str] = torch.cat((tokens, next_tokens) , dim=1 )
UpperCAmelCase__ : Any = generated[next_tokens_source]
UpperCAmelCase__ : Tuple = scores_sum_average * seq_lengths
UpperCAmelCase__ : Tuple = is_stopped[next_tokens_source]
UpperCAmelCase__ : List[Any] = self.transformer.transformer.wte(next_tokens.squeeze() ).view(generated.shape[0] , 1 , -1 )
UpperCAmelCase__ : Optional[int] = torch.cat((generated, next_token_embed) , dim=1 )
UpperCAmelCase__ : List[Any] = is_stopped + next_tokens.eq(UpperCamelCase_ ).squeeze()
if is_stopped.all():
break
UpperCAmelCase__ : Dict = scores / seq_lengths
UpperCAmelCase__ : Optional[Any] = scores.argsort(descending=UpperCamelCase_ )
# tokens tensors are already padded to max_seq_length
UpperCAmelCase__ : Dict = [tokens[i] for i in order]
UpperCAmelCase__ : Optional[Any] = torch.stack(UpperCamelCase_ , dim=0 )
UpperCAmelCase__ : List[str] = torch.tensor([seq_lengths[i] for i in order] , dtype=seq_lengths.dtype )
return output_texts, seq_lengths
| 254 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
_lowerCAmelCase : Optional[Any] = {'''configuration_speech_encoder_decoder''': ['''SpeechEncoderDecoderConfig''']}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowerCAmelCase : Optional[Any] = ['''SpeechEncoderDecoderModel''']
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowerCAmelCase : Tuple = ['''FlaxSpeechEncoderDecoderModel''']
if TYPE_CHECKING:
from .configuration_speech_encoder_decoder import SpeechEncoderDecoderConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_speech_encoder_decoder import SpeechEncoderDecoderModel
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_speech_encoder_decoder import FlaxSpeechEncoderDecoderModel
else:
import sys
_lowerCAmelCase : Optional[Any] = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 454 |
"""simple docstring"""
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging
__UpperCamelCase : List[Any] = logging.get_logger(__name__)
__UpperCamelCase : Any = {
'''t5-small''': '''https://huggingface.co/t5-small/resolve/main/config.json''',
'''t5-base''': '''https://huggingface.co/t5-base/resolve/main/config.json''',
'''t5-large''': '''https://huggingface.co/t5-large/resolve/main/config.json''',
'''t5-3b''': '''https://huggingface.co/t5-3b/resolve/main/config.json''',
'''t5-11b''': '''https://huggingface.co/t5-11b/resolve/main/config.json''',
}
class SCREAMING_SNAKE_CASE ( a_ ):
"""simple docstring"""
lowercase__ = "t5"
lowercase__ = ["past_key_values"]
lowercase__ = {"hidden_size": "d_model", "num_attention_heads": "num_heads", "num_hidden_layers": "num_layers"}
def __init__( self : Tuple ,lowercase_ : Tuple=3_2_1_2_8 ,lowercase_ : Union[str, Any]=5_1_2 ,lowercase_ : Dict=6_4 ,lowercase_ : Any=2_0_4_8 ,lowercase_ : Dict=6 ,lowercase_ : Dict=None ,lowercase_ : Dict=8 ,lowercase_ : Optional[int]=3_2 ,lowercase_ : List[Any]=1_2_8 ,lowercase_ : Any=0.1 ,lowercase_ : Optional[Any]=1E-6 ,lowercase_ : Any=1.0 ,lowercase_ : Dict="relu" ,lowercase_ : Tuple=True ,lowercase_ : List[Any]=True ,lowercase_ : Tuple=0 ,lowercase_ : Optional[Any]=1 ,**lowercase_ : Any ,):
lowerCAmelCase__ : Tuple = vocab_size
lowerCAmelCase__ : List[str] = d_model
lowerCAmelCase__ : Union[str, Any] = d_kv
lowerCAmelCase__ : List[Any] = d_ff
lowerCAmelCase__ : int = num_layers
lowerCAmelCase__ : Union[str, Any] = (
num_decoder_layers if num_decoder_layers is not None else self.num_layers
) # default = symmetry
lowerCAmelCase__ : Any = num_heads
lowerCAmelCase__ : Optional[int] = relative_attention_num_buckets
lowerCAmelCase__ : Union[str, Any] = relative_attention_max_distance
lowerCAmelCase__ : List[Any] = dropout_rate
lowerCAmelCase__ : str = layer_norm_epsilon
lowerCAmelCase__ : int = initializer_factor
lowerCAmelCase__ : Dict = feed_forward_proj
lowerCAmelCase__ : int = use_cache
lowerCAmelCase__ : int = self.feed_forward_proj.split('''-''' )
lowerCAmelCase__ : str = act_info[-1]
lowerCAmelCase__ : Tuple = act_info[0] == '''gated'''
if len(lowercase_ ) > 1 and act_info[0] != "gated" or len(lowercase_ ) > 2:
raise ValueError(
F'`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer.'
'''Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. '''
'''\'gated-gelu\' or \'relu\'''' )
# for backwards compatibility
if feed_forward_proj == "gated-gelu":
lowerCAmelCase__ : Tuple = '''gelu_new'''
super().__init__(
pad_token_id=lowercase_ ,eos_token_id=lowercase_ ,is_encoder_decoder=lowercase_ ,**lowercase_ ,)
class SCREAMING_SNAKE_CASE ( a_ ):
"""simple docstring"""
@property
def __lowerCAmelCase ( self : List[Any] ):
lowerCAmelCase__ : int = {
'''input_ids''': {0: '''batch''', 1: '''encoder_sequence'''},
'''attention_mask''': {0: '''batch''', 1: '''encoder_sequence'''},
}
if self.use_past:
lowerCAmelCase__ : Union[str, Any] = '''past_encoder_sequence + sequence'''
lowerCAmelCase__ : List[Any] = {0: '''batch'''}
lowerCAmelCase__ : Union[str, Any] = {0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
else:
lowerCAmelCase__ : Union[str, Any] = {0: '''batch''', 1: '''decoder_sequence'''}
lowerCAmelCase__ : Any = {0: '''batch''', 1: '''decoder_sequence'''}
if self.use_past:
self.fill_with_past_key_values_(lowercase_ ,direction='''inputs''' )
return common_inputs
@property
def __lowerCAmelCase ( self : Optional[int] ):
return 1_3
| 450 | 0 |
"""simple docstring"""
from collections import OrderedDict
from typing import Any, List, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging
UpperCAmelCase__ =logging.get_logger(__name__)
UpperCAmelCase__ ={
"EleutherAI/gpt-j-6B": "https://huggingface.co/EleutherAI/gpt-j-6B/resolve/main/config.json",
# See all GPT-J models at https://huggingface.co/models?filter=gpt_j
}
class lowerCamelCase__ ( _a ):
a : Dict = """gptj"""
a : Any = {
"""max_position_embeddings""": """n_positions""",
"""hidden_size""": """n_embd""",
"""num_attention_heads""": """n_head""",
"""num_hidden_layers""": """n_layer""",
}
def __init__( self : List[Any] , A_ : Tuple=5_0_4_0_0 , A_ : str=2_0_4_8 , A_ : str=4_0_9_6 , A_ : Dict=2_8 , A_ : Union[str, Any]=1_6 , A_ : List[str]=6_4 , A_ : Optional[int]=None , A_ : Optional[Any]="gelu_new" , A_ : Optional[int]=0.0 , A_ : str=0.0 , A_ : str=0.0 , A_ : Tuple=1e-5 , A_ : List[str]=0.02 , A_ : Any=True , A_ : int=5_0_2_5_6 , A_ : Optional[Any]=5_0_2_5_6 , A_ : Optional[int]=False , **A_ : List[Any] , ):
'''simple docstring'''
__lowercase = vocab_size
__lowercase = n_positions
__lowercase = n_embd
__lowercase = n_layer
__lowercase = n_head
__lowercase = n_inner
__lowercase = rotary_dim
__lowercase = activation_function
__lowercase = resid_pdrop
__lowercase = embd_pdrop
__lowercase = attn_pdrop
__lowercase = layer_norm_epsilon
__lowercase = initializer_range
__lowercase = use_cache
__lowercase = bos_token_id
__lowercase = eos_token_id
super().__init__(
bos_token_id=A_ , eos_token_id=A_ , tie_word_embeddings=A_ , **A_ )
class lowerCamelCase__ ( _a ):
def __init__( self : Union[str, Any] , A_ : PretrainedConfig , A_ : str = "default" , A_ : List[PatchingSpec] = None , A_ : bool = False , ):
'''simple docstring'''
super().__init__(A_ , task=A_ , patching_specs=A_ , use_past=A_ )
if not getattr(self._config , """pad_token_id""" , A_ ):
# TODO: how to do that better?
__lowercase = 0
@property
def SCREAMING_SNAKE_CASE_ ( self : Optional[int] ):
'''simple docstring'''
__lowercase = OrderedDict({"""input_ids""": {0: """batch""", 1: """sequence"""}} )
if self.use_past:
self.fill_with_past_key_values_(A_ , direction="""inputs""" )
__lowercase = {0: """batch""", 1: """past_sequence + sequence"""}
else:
__lowercase = {0: """batch""", 1: """sequence"""}
return common_inputs
@property
def SCREAMING_SNAKE_CASE_ ( self : Dict ):
'''simple docstring'''
return self._config.n_layer
@property
def SCREAMING_SNAKE_CASE_ ( self : Dict ):
'''simple docstring'''
return self._config.n_head
def SCREAMING_SNAKE_CASE_ ( self : int , A_ : PreTrainedTokenizer , A_ : int = -1 , A_ : int = -1 , A_ : bool = False , A_ : Optional[TensorType] = None , ):
'''simple docstring'''
__lowercase = super(A_ , self ).generate_dummy_inputs(
A_ , batch_size=A_ , seq_length=A_ , is_pair=A_ , framework=A_ )
# We need to order the input in the way they appears in the forward()
__lowercase = OrderedDict({"""input_ids""": common_inputs["""input_ids"""]} )
# Need to add the past_keys
if self.use_past:
if not is_torch_available():
raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed.""" )
else:
import torch
__lowercase , __lowercase = common_inputs["""input_ids"""].shape
# Not using the same length for past_key_values
__lowercase = seqlen + 2
__lowercase = (
batch,
self.num_attention_heads,
past_key_values_length,
self._config.hidden_size // self.num_attention_heads,
)
__lowercase = [
(torch.zeros(A_ ), torch.zeros(A_ )) for _ in range(self.num_layers )
]
__lowercase = common_inputs["""attention_mask"""]
if self.use_past:
__lowercase = ordered_inputs["""attention_mask"""].dtype
__lowercase = torch.cat(
[ordered_inputs["""attention_mask"""], torch.ones(A_ , A_ , dtype=A_ )] , dim=1 )
return ordered_inputs
@property
def SCREAMING_SNAKE_CASE_ ( self : Optional[int] ):
'''simple docstring'''
return 1_3
| 703 |
"""simple docstring"""
import os
import numpy
import onnx
def lowerCAmelCase_ ( UpperCamelCase__ : Optional[int] , UpperCamelCase__ : List[str] ):
"""simple docstring"""
__lowercase = a.name
__lowercase = b.name
__lowercase = """"""
__lowercase = """"""
__lowercase = a == b
__lowercase = name_a
__lowercase = name_b
return res
def lowerCAmelCase_ ( UpperCamelCase__ : Union[str, Any] , UpperCamelCase__ : str , UpperCamelCase__ : Dict ):
"""simple docstring"""
for i, input_name in enumerate(node_proto.input ):
if input_name == name:
node_proto.input.insert(UpperCamelCase__ , UpperCamelCase__ )
node_proto.input.pop(i + 1 )
if node_proto.op_type == "If":
_graph_replace_input_with(node_proto.attribute[0].g , UpperCamelCase__ , UpperCamelCase__ )
_graph_replace_input_with(node_proto.attribute[1].g , UpperCamelCase__ , UpperCamelCase__ )
if node_proto.op_type == "Loop":
_graph_replace_input_with(node_proto.attribute[0].g , UpperCamelCase__ , UpperCamelCase__ )
def lowerCAmelCase_ ( UpperCamelCase__ : Optional[Any] , UpperCamelCase__ : Optional[int] , UpperCamelCase__ : str ):
"""simple docstring"""
for n in graph_proto.node:
_node_replace_input_with(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
def lowerCAmelCase_ ( UpperCamelCase__ : Tuple , UpperCamelCase__ : str , UpperCamelCase__ : Any ):
"""simple docstring"""
__lowercase = list(model.graph.initializer )
__lowercase = list(model_without_ext.graph.initializer )
for i, ref_i in ind_to_replace:
assert inits_with_data[i].name == inits[i].name
assert inits_with_data[ref_i].name == inits[ref_i].name
assert i > ref_i
__lowercase = inits[i].name
__lowercase = inits[ref_i].name
model_without_ext.graph.initializer.remove(inits[i] )
# for n in model.graph.node:
_graph_replace_input_with(model_without_ext.graph , UpperCamelCase__ , UpperCamelCase__ )
def lowerCAmelCase_ ( UpperCamelCase__ : List[Any] ):
"""simple docstring"""
__lowercase = os.path.dirname(UpperCamelCase__ )
__lowercase = os.path.basename(UpperCamelCase__ )
__lowercase = onnx.load(os.path.join(UpperCamelCase__ , UpperCamelCase__ ) )
__lowercase = list(model.graph.initializer )
__lowercase = set()
__lowercase = {}
__lowercase = []
__lowercase = 0
for i in range(len(UpperCamelCase__ ) ):
if i in dup_set:
continue
for j in range(i + 1 , len(UpperCamelCase__ ) ):
if j in dup_set:
continue
if _is_equal_tensor_proto(inits[i] , inits[j] ):
dup_set.add(UpperCamelCase__ )
dup_set.add(UpperCamelCase__ )
__lowercase = inits[j].data_type
__lowercase = numpy.prod(inits[j].dims )
if dtype == 1:
mem_size *= 4
elif dtype == 6:
mem_size *= 4
elif dtype == 7 or dtype == 11:
mem_size *= 8
else:
print("""unexpected data type: """ , UpperCamelCase__ )
total_reduced_size += mem_size
__lowercase = inits[i].name
__lowercase = inits[j].name
if name_i in dup_map:
dup_map[name_i].append(UpperCamelCase__ )
else:
__lowercase = [name_j]
ind_to_replace.append((j, i) )
print("""total reduced size: """ , total_reduced_size / 1024 / 1024 / 1024 , """GB""" )
__lowercase = sorted(UpperCamelCase__ )
_remove_dup_initializers_from_model(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
__lowercase = """optimized_""" + model_file_name
__lowercase = os.path.join(UpperCamelCase__ , UpperCamelCase__ )
onnx.save(UpperCamelCase__ , UpperCamelCase__ )
return new_model
| 442 | 0 |
import argparse
import json
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
ConditionalDetrConfig,
ConditionalDetrForObjectDetection,
ConditionalDetrForSegmentation,
ConditionalDetrImageProcessor,
)
from transformers.utils import logging
logging.set_verbosity_info()
A_ : List[Any] = logging.get_logger(__name__)
# here we list all keys to be renamed (original name on the left, our name on the right)
A_ : Dict = []
for i in range(6):
# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
rename_keys.append(
(F'''transformer.encoder.layers.{i}.self_attn.out_proj.weight''', F'''encoder.layers.{i}.self_attn.out_proj.weight''')
)
rename_keys.append(
(F'''transformer.encoder.layers.{i}.self_attn.out_proj.bias''', F'''encoder.layers.{i}.self_attn.out_proj.bias''')
)
rename_keys.append((F'''transformer.encoder.layers.{i}.linear1.weight''', F'''encoder.layers.{i}.fc1.weight'''))
rename_keys.append((F'''transformer.encoder.layers.{i}.linear1.bias''', F'''encoder.layers.{i}.fc1.bias'''))
rename_keys.append((F'''transformer.encoder.layers.{i}.linear2.weight''', F'''encoder.layers.{i}.fc2.weight'''))
rename_keys.append((F'''transformer.encoder.layers.{i}.linear2.bias''', F'''encoder.layers.{i}.fc2.bias'''))
rename_keys.append(
(F'''transformer.encoder.layers.{i}.norm1.weight''', F'''encoder.layers.{i}.self_attn_layer_norm.weight''')
)
rename_keys.append((F'''transformer.encoder.layers.{i}.norm1.bias''', F'''encoder.layers.{i}.self_attn_layer_norm.bias'''))
rename_keys.append((F'''transformer.encoder.layers.{i}.norm2.weight''', F'''encoder.layers.{i}.final_layer_norm.weight'''))
rename_keys.append((F'''transformer.encoder.layers.{i}.norm2.bias''', F'''encoder.layers.{i}.final_layer_norm.bias'''))
# decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms
rename_keys.append(
(F'''transformer.decoder.layers.{i}.self_attn.out_proj.weight''', F'''decoder.layers.{i}.self_attn.out_proj.weight''')
)
rename_keys.append(
(F'''transformer.decoder.layers.{i}.self_attn.out_proj.bias''', F'''decoder.layers.{i}.self_attn.out_proj.bias''')
)
rename_keys.append(
(
F'''transformer.decoder.layers.{i}.cross_attn.out_proj.weight''',
F'''decoder.layers.{i}.encoder_attn.out_proj.weight''',
)
)
rename_keys.append(
(
F'''transformer.decoder.layers.{i}.cross_attn.out_proj.bias''',
F'''decoder.layers.{i}.encoder_attn.out_proj.bias''',
)
)
rename_keys.append((F'''transformer.decoder.layers.{i}.linear1.weight''', F'''decoder.layers.{i}.fc1.weight'''))
rename_keys.append((F'''transformer.decoder.layers.{i}.linear1.bias''', F'''decoder.layers.{i}.fc1.bias'''))
rename_keys.append((F'''transformer.decoder.layers.{i}.linear2.weight''', F'''decoder.layers.{i}.fc2.weight'''))
rename_keys.append((F'''transformer.decoder.layers.{i}.linear2.bias''', F'''decoder.layers.{i}.fc2.bias'''))
rename_keys.append(
(F'''transformer.decoder.layers.{i}.norm1.weight''', F'''decoder.layers.{i}.self_attn_layer_norm.weight''')
)
rename_keys.append((F'''transformer.decoder.layers.{i}.norm1.bias''', F'''decoder.layers.{i}.self_attn_layer_norm.bias'''))
rename_keys.append(
(F'''transformer.decoder.layers.{i}.norm2.weight''', F'''decoder.layers.{i}.encoder_attn_layer_norm.weight''')
)
rename_keys.append(
(F'''transformer.decoder.layers.{i}.norm2.bias''', F'''decoder.layers.{i}.encoder_attn_layer_norm.bias''')
)
rename_keys.append((F'''transformer.decoder.layers.{i}.norm3.weight''', F'''decoder.layers.{i}.final_layer_norm.weight'''))
rename_keys.append((F'''transformer.decoder.layers.{i}.norm3.bias''', F'''decoder.layers.{i}.final_layer_norm.bias'''))
# q, k, v projections in self/cross-attention in decoder for conditional DETR
rename_keys.append(
(F'''transformer.decoder.layers.{i}.sa_qcontent_proj.weight''', F'''decoder.layers.{i}.sa_qcontent_proj.weight''')
)
rename_keys.append(
(F'''transformer.decoder.layers.{i}.sa_kcontent_proj.weight''', F'''decoder.layers.{i}.sa_kcontent_proj.weight''')
)
rename_keys.append(
(F'''transformer.decoder.layers.{i}.sa_qpos_proj.weight''', F'''decoder.layers.{i}.sa_qpos_proj.weight''')
)
rename_keys.append(
(F'''transformer.decoder.layers.{i}.sa_kpos_proj.weight''', F'''decoder.layers.{i}.sa_kpos_proj.weight''')
)
rename_keys.append((F'''transformer.decoder.layers.{i}.sa_v_proj.weight''', F'''decoder.layers.{i}.sa_v_proj.weight'''))
rename_keys.append(
(F'''transformer.decoder.layers.{i}.ca_qcontent_proj.weight''', F'''decoder.layers.{i}.ca_qcontent_proj.weight''')
)
# rename_keys.append((f"transformer.decoder.layers.{i}.ca_qpos_proj.weight", f"decoder.layers.{i}.ca_qpos_proj.weight"))
rename_keys.append(
(F'''transformer.decoder.layers.{i}.ca_kcontent_proj.weight''', F'''decoder.layers.{i}.ca_kcontent_proj.weight''')
)
rename_keys.append(
(F'''transformer.decoder.layers.{i}.ca_kpos_proj.weight''', F'''decoder.layers.{i}.ca_kpos_proj.weight''')
)
rename_keys.append((F'''transformer.decoder.layers.{i}.ca_v_proj.weight''', F'''decoder.layers.{i}.ca_v_proj.weight'''))
rename_keys.append(
(F'''transformer.decoder.layers.{i}.ca_qpos_sine_proj.weight''', F'''decoder.layers.{i}.ca_qpos_sine_proj.weight''')
)
rename_keys.append(
(F'''transformer.decoder.layers.{i}.sa_qcontent_proj.bias''', F'''decoder.layers.{i}.sa_qcontent_proj.bias''')
)
rename_keys.append(
(F'''transformer.decoder.layers.{i}.sa_kcontent_proj.bias''', F'''decoder.layers.{i}.sa_kcontent_proj.bias''')
)
rename_keys.append((F'''transformer.decoder.layers.{i}.sa_qpos_proj.bias''', F'''decoder.layers.{i}.sa_qpos_proj.bias'''))
rename_keys.append((F'''transformer.decoder.layers.{i}.sa_kpos_proj.bias''', F'''decoder.layers.{i}.sa_kpos_proj.bias'''))
rename_keys.append((F'''transformer.decoder.layers.{i}.sa_v_proj.bias''', F'''decoder.layers.{i}.sa_v_proj.bias'''))
rename_keys.append(
(F'''transformer.decoder.layers.{i}.ca_qcontent_proj.bias''', F'''decoder.layers.{i}.ca_qcontent_proj.bias''')
)
# rename_keys.append((f"transformer.decoder.layers.{i}.ca_qpos_proj.bias", f"decoder.layers.{i}.ca_qpos_proj.bias"))
rename_keys.append(
(F'''transformer.decoder.layers.{i}.ca_kcontent_proj.bias''', F'''decoder.layers.{i}.ca_kcontent_proj.bias''')
)
rename_keys.append((F'''transformer.decoder.layers.{i}.ca_kpos_proj.bias''', F'''decoder.layers.{i}.ca_kpos_proj.bias'''))
rename_keys.append((F'''transformer.decoder.layers.{i}.ca_v_proj.bias''', F'''decoder.layers.{i}.ca_v_proj.bias'''))
rename_keys.append(
(F'''transformer.decoder.layers.{i}.ca_qpos_sine_proj.bias''', F'''decoder.layers.{i}.ca_qpos_sine_proj.bias''')
)
# convolutional projection + query embeddings + layernorm of decoder + class and bounding box heads
# for conditional DETR, also convert reference point head and query scale MLP
rename_keys.extend(
[
('input_proj.weight', 'input_projection.weight'),
('input_proj.bias', 'input_projection.bias'),
('query_embed.weight', 'query_position_embeddings.weight'),
('transformer.decoder.norm.weight', 'decoder.layernorm.weight'),
('transformer.decoder.norm.bias', 'decoder.layernorm.bias'),
('class_embed.weight', 'class_labels_classifier.weight'),
('class_embed.bias', 'class_labels_classifier.bias'),
('bbox_embed.layers.0.weight', 'bbox_predictor.layers.0.weight'),
('bbox_embed.layers.0.bias', 'bbox_predictor.layers.0.bias'),
('bbox_embed.layers.1.weight', 'bbox_predictor.layers.1.weight'),
('bbox_embed.layers.1.bias', 'bbox_predictor.layers.1.bias'),
('bbox_embed.layers.2.weight', 'bbox_predictor.layers.2.weight'),
('bbox_embed.layers.2.bias', 'bbox_predictor.layers.2.bias'),
('transformer.decoder.ref_point_head.layers.0.weight', 'decoder.ref_point_head.layers.0.weight'),
('transformer.decoder.ref_point_head.layers.0.bias', 'decoder.ref_point_head.layers.0.bias'),
('transformer.decoder.ref_point_head.layers.1.weight', 'decoder.ref_point_head.layers.1.weight'),
('transformer.decoder.ref_point_head.layers.1.bias', 'decoder.ref_point_head.layers.1.bias'),
('transformer.decoder.query_scale.layers.0.weight', 'decoder.query_scale.layers.0.weight'),
('transformer.decoder.query_scale.layers.0.bias', 'decoder.query_scale.layers.0.bias'),
('transformer.decoder.query_scale.layers.1.weight', 'decoder.query_scale.layers.1.weight'),
('transformer.decoder.query_scale.layers.1.bias', 'decoder.query_scale.layers.1.bias'),
('transformer.decoder.layers.0.ca_qpos_proj.weight', 'decoder.layers.0.ca_qpos_proj.weight'),
('transformer.decoder.layers.0.ca_qpos_proj.bias', 'decoder.layers.0.ca_qpos_proj.bias'),
]
)
def snake_case (UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ ) -> Optional[Any]:
UpperCamelCase_: str = state_dict.pop(UpperCAmelCase__ )
UpperCamelCase_: Union[str, Any] = val
def snake_case (UpperCAmelCase__ ) -> int:
UpperCamelCase_: Optional[Any] = OrderedDict()
for key, value in state_dict.items():
if "backbone.0.body" in key:
UpperCamelCase_: int = key.replace('backbone.0.body' , 'backbone.conv_encoder.model' )
UpperCamelCase_: str = value
else:
UpperCamelCase_: Optional[int] = value
return new_state_dict
def snake_case (UpperCAmelCase__ , UpperCAmelCase__=False ) -> Union[str, Any]:
UpperCamelCase_: List[Any] = ''
if is_panoptic:
UpperCamelCase_: Union[str, Any] = 'conditional_detr.'
# first: transformer encoder
for i in range(6 ):
# read in weights + bias of input projection layer (in PyTorch's MultiHeadAttention, this is a single matrix + bias)
UpperCamelCase_: Tuple = state_dict.pop(F'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight''' )
UpperCamelCase_: Optional[int] = state_dict.pop(F'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_bias''' )
# next, add query, keys and values (in that order) to the state dict
UpperCamelCase_: str = in_proj_weight[:2_5_6, :]
UpperCamelCase_: int = in_proj_bias[:2_5_6]
UpperCamelCase_: Optional[Any] = in_proj_weight[2_5_6:5_1_2, :]
UpperCamelCase_: List[str] = in_proj_bias[2_5_6:5_1_2]
UpperCamelCase_: Dict = in_proj_weight[-2_5_6:, :]
UpperCamelCase_: List[str] = in_proj_bias[-2_5_6:]
def snake_case () -> int:
UpperCamelCase_: Optional[int] = 'http://images.cocodataset.org/val2017/000000039769.jpg'
UpperCamelCase_: List[Any] = Image.open(requests.get(UpperCAmelCase__ , stream=UpperCAmelCase__ ).raw )
return im
@torch.no_grad()
def snake_case (UpperCAmelCase__ , UpperCAmelCase__ ) -> List[str]:
UpperCamelCase_: List[Any] = ConditionalDetrConfig()
# set backbone and dilation attributes
if "resnet101" in model_name:
UpperCamelCase_: Dict = 'resnet101'
if "dc5" in model_name:
UpperCamelCase_: Union[str, Any] = True
UpperCamelCase_: Optional[int] = 'panoptic' in model_name
if is_panoptic:
UpperCamelCase_: Optional[Any] = 2_5_0
else:
UpperCamelCase_: Optional[Any] = 9_1
UpperCamelCase_: Any = 'huggingface/label-files'
UpperCamelCase_: List[str] = 'coco-detection-id2label.json'
UpperCamelCase_: Any = json.load(open(hf_hub_download(UpperCAmelCase__ , UpperCAmelCase__ , repo_type='dataset' ) , 'r' ) )
UpperCamelCase_: str = {int(UpperCAmelCase__ ): v for k, v in idalabel.items()}
UpperCamelCase_: List[str] = idalabel
UpperCamelCase_: List[Any] = {v: k for k, v in idalabel.items()}
# load image processor
UpperCamelCase_: List[Any] = 'coco_panoptic' if is_panoptic else 'coco_detection'
UpperCamelCase_: List[Any] = ConditionalDetrImageProcessor(format=UpperCAmelCase__ )
# prepare image
UpperCamelCase_: Optional[int] = prepare_img()
UpperCamelCase_: Union[str, Any] = image_processor(images=UpperCAmelCase__ , return_tensors='pt' )
UpperCamelCase_: Optional[int] = encoding['pixel_values']
logger.info(F'''Converting model {model_name}...''' )
# load original model from torch hub
UpperCamelCase_: Tuple = torch.hub.load('DeppMeng/ConditionalDETR' , UpperCAmelCase__ , pretrained=UpperCAmelCase__ ).eval()
UpperCamelCase_: Union[str, Any] = conditional_detr.state_dict()
# rename keys
for src, dest in rename_keys:
if is_panoptic:
UpperCamelCase_: List[str] = 'conditional_detr.' + src
rename_key(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ )
UpperCamelCase_: Union[str, Any] = rename_backbone_keys(UpperCAmelCase__ )
# query, key and value matrices need special treatment
read_in_q_k_v(UpperCAmelCase__ , is_panoptic=UpperCAmelCase__ )
# important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
UpperCamelCase_: Optional[Any] = 'conditional_detr.model.' if is_panoptic else 'model.'
for key in state_dict.copy().keys():
if is_panoptic:
if (
key.startswith('conditional_detr' )
and not key.startswith('class_labels_classifier' )
and not key.startswith('bbox_predictor' )
):
UpperCamelCase_: int = state_dict.pop(UpperCAmelCase__ )
UpperCamelCase_: Union[str, Any] = val
elif "class_labels_classifier" in key or "bbox_predictor" in key:
UpperCamelCase_: int = state_dict.pop(UpperCAmelCase__ )
UpperCamelCase_: Dict = val
elif key.startswith('bbox_attention' ) or key.startswith('mask_head' ):
continue
else:
UpperCamelCase_: List[str] = state_dict.pop(UpperCAmelCase__ )
UpperCamelCase_: Union[str, Any] = val
else:
if not key.startswith('class_labels_classifier' ) and not key.startswith('bbox_predictor' ):
UpperCamelCase_: Optional[int] = state_dict.pop(UpperCAmelCase__ )
UpperCamelCase_: List[str] = val
# finally, create HuggingFace model and load state dict
UpperCamelCase_: List[Any] = ConditionalDetrForSegmentation(UpperCAmelCase__ ) if is_panoptic else ConditionalDetrForObjectDetection(UpperCAmelCase__ )
model.load_state_dict(UpperCAmelCase__ )
model.eval()
model.push_to_hub(repo_id=UpperCAmelCase__ , organization='DepuMeng' , commit_message='Add model' )
# verify our conversion
UpperCamelCase_: Union[str, Any] = conditional_detr(UpperCAmelCase__ )
UpperCamelCase_: Optional[int] = model(UpperCAmelCase__ )
assert torch.allclose(outputs.logits , original_outputs['pred_logits'] , atol=1E-4 )
assert torch.allclose(outputs.pred_boxes , original_outputs['pred_boxes'] , atol=1E-4 )
if is_panoptic:
assert torch.allclose(outputs.pred_masks , original_outputs['pred_masks'] , atol=1E-4 )
# Save model and image processor
logger.info(F'''Saving PyTorch model and image processor to {pytorch_dump_folder_path}...''' )
Path(UpperCAmelCase__ ).mkdir(exist_ok=UpperCAmelCase__ )
model.save_pretrained(UpperCAmelCase__ )
image_processor.save_pretrained(UpperCAmelCase__ )
if __name__ == "__main__":
A_ : Dict = argparse.ArgumentParser()
parser.add_argument(
'--model_name',
default='conditional_detr_resnet50',
type=str,
help='Name of the CONDITIONAL_DETR model you\'d like to convert.',
)
parser.add_argument(
'--pytorch_dump_folder_path', default=None, type=str, help='Path to the folder to output PyTorch model.'
)
A_ : Dict = parser.parse_args()
convert_conditional_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path) | 57 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A_ : str = logging.get_logger(__name__)
A_ : Union[str, Any] = {
's-JoL/Open-Llama-V1': 'https://huggingface.co/s-JoL/Open-Llama-V1/blob/main/config.json',
}
class _lowerCAmelCase( UpperCAmelCase_ ):
"""simple docstring"""
a : Tuple ='''open-llama'''
def __init__( self , _lowerCamelCase=1_0_0_0_0_0 , _lowerCamelCase=4_0_9_6 , _lowerCamelCase=1_1_0_0_8 , _lowerCamelCase=3_2 , _lowerCamelCase=3_2 , _lowerCamelCase="silu" , _lowerCamelCase=2_0_4_8 , _lowerCamelCase=0.0_2 , _lowerCamelCase=1e-6 , _lowerCamelCase=True , _lowerCamelCase=0 , _lowerCamelCase=1 , _lowerCamelCase=2 , _lowerCamelCase=False , _lowerCamelCase=True , _lowerCamelCase=0.1 , _lowerCamelCase=0.1 , _lowerCamelCase=True , _lowerCamelCase=True , _lowerCamelCase=None , **_lowerCamelCase , ):
UpperCamelCase_: int = vocab_size
UpperCamelCase_: List[Any] = max_position_embeddings
UpperCamelCase_: Dict = hidden_size
UpperCamelCase_: Dict = intermediate_size
UpperCamelCase_: Union[str, Any] = num_hidden_layers
UpperCamelCase_: Dict = num_attention_heads
UpperCamelCase_: Union[str, Any] = hidden_act
UpperCamelCase_: Union[str, Any] = initializer_range
UpperCamelCase_: List[Any] = rms_norm_eps
UpperCamelCase_: Union[str, Any] = use_cache
UpperCamelCase_: Dict = kwargs.pop(
'use_memorry_efficient_attention' , _lowerCamelCase )
UpperCamelCase_: Union[str, Any] = hidden_dropout_prob
UpperCamelCase_: Any = attention_dropout_prob
UpperCamelCase_: int = use_stable_embedding
UpperCamelCase_: Tuple = shared_input_output_embedding
UpperCamelCase_: str = rope_scaling
self._rope_scaling_validation()
super().__init__(
pad_token_id=_lowerCamelCase , bos_token_id=_lowerCamelCase , eos_token_id=_lowerCamelCase , tie_word_embeddings=_lowerCamelCase , **_lowerCamelCase , )
def _a ( self ):
if self.rope_scaling is None:
return
if not isinstance(self.rope_scaling , _lowerCamelCase ) or len(self.rope_scaling ) != 2:
raise ValueError(
'`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, '
f'''got {self.rope_scaling}''' )
UpperCamelCase_: str = self.rope_scaling.get('type' , _lowerCamelCase )
UpperCamelCase_: int = self.rope_scaling.get('factor' , _lowerCamelCase )
if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
raise ValueError(
f'''`rope_scaling`\'s name field must be one of [\'linear\', \'dynamic\'], got {rope_scaling_type}''' )
if rope_scaling_factor is None or not isinstance(_lowerCamelCase , _lowerCamelCase ) or rope_scaling_factor <= 1.0:
raise ValueError(f'''`rope_scaling`\'s factor field must be an float > 1, got {rope_scaling_factor}''' ) | 57 | 1 |
"""simple docstring"""
from copy import deepcopy
import torch
import torch.nn.functional as F
from torch.optim import AdamW
from torch.optim.lr_scheduler import LambdaLR
from torch.utils.data import DataLoader
from accelerate.accelerator import Accelerator
from accelerate.state import GradientState
from accelerate.test_utils import RegressionDataset, RegressionModel
from accelerate.utils import DistributedType, is_torch_version, set_seed
def __magic_name__ ( lowercase , lowercase , lowercase , lowercase ):
for param, grad_param in zip(model_a.parameters() , model_b.parameters() ):
if not param.requires_grad:
continue
if not did_step:
# Grads should not be in sync
assert (
torch.allclose(param.grad , grad_param.grad ) is False
), f'''Gradients in sync when they should not be at iteration {iteration}:\nmodel_a grad ({param.grad}) == model_b grad ({grad_param.grad})'''
else:
# Grads should be in sync
assert (
torch.allclose(param.grad , grad_param.grad ) is True
), f'''Gradients not in sync when they should be at iteration {iteration}:\nmodel_a grad ({param.grad}) != model_b grad ({grad_param.grad})'''
def __magic_name__ ( lowercase , lowercase , lowercase , lowercase , lowercase=True ):
model.train()
SCREAMING_SNAKE_CASE_: Any =model(lowercase )
SCREAMING_SNAKE_CASE_: int =F.mse_loss(lowercase , target.to(output.device ) )
if not do_backward:
loss /= accelerator.gradient_accumulation_steps
loss.backward()
else:
accelerator.backward(lowercase )
def __magic_name__ ( lowercase , lowercase=False ):
set_seed(42 )
SCREAMING_SNAKE_CASE_: int =RegressionModel()
SCREAMING_SNAKE_CASE_: List[str] =deepcopy(lowercase )
SCREAMING_SNAKE_CASE_: List[str] =RegressionDataset(length=80 )
SCREAMING_SNAKE_CASE_: Optional[int] =DataLoader(lowercase , batch_size=16 )
model.to(accelerator.device )
if sched:
SCREAMING_SNAKE_CASE_: Dict =AdamW(params=model.parameters() , lr=1e-3 )
SCREAMING_SNAKE_CASE_: Dict =AdamW(params=ddp_model.parameters() , lr=1e-3 )
SCREAMING_SNAKE_CASE_: int =LambdaLR(lowercase , lr_lambda=lambda lowercase : epoch**0.65 )
SCREAMING_SNAKE_CASE_: Optional[int] =LambdaLR(lowercase , lr_lambda=lambda lowercase : epoch**0.65 )
# Make a copy of `model`
if sched:
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_: Optional[int] =accelerator.prepare(lowercase , lowercase , lowercase , lowercase )
else:
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_: Dict =accelerator.prepare(lowercase , lowercase )
if sched:
return (model, opt, sched, dataloader, ddp_model, ddp_opt, ddp_sched)
return model, ddp_model, dataloader
def __magic_name__ ( lowercase ):
# Test when on a single CPU or GPU that the context manager does nothing
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_: Optional[Any] =get_training_setup(lowercase )
# Use a single batch
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_: Dict =next(iter(lowercase ) ).values()
for iteration in range(3 ):
# Gather the distributed inputs and targs for the base model
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_: Optional[int] =accelerator.gather((ddp_input, ddp_target) )
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_: Dict =input.to(accelerator.device ), target.to(accelerator.device )
# Perform our initial ground truth step in non "DDP"
step_model(lowercase , lowercase , lowercase , lowercase )
# Do "gradient accumulation" (noop)
if iteration % 2 == 0:
# Accumulate grads locally
with accelerator.no_sync(lowercase ):
step_model(lowercase , lowercase , lowercase , lowercase )
else:
# Sync grads
step_model(lowercase , lowercase , lowercase , lowercase )
# Since `no_sync` is a noop, `ddp_model` and `model` grads should always be in sync
check_model_parameters(lowercase , lowercase , lowercase , lowercase )
for param, ddp_param in zip(model.parameters() , ddp_model.parameters() ):
if not param.requires_grad:
continue
assert torch.allclose(
param.grad , ddp_param.grad ), f'''Gradients not in sync when they should be:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})'''
# Shuffle ddp_input on each iteration
torch.manual_seed(1337 + iteration )
SCREAMING_SNAKE_CASE_: Optional[Any] =ddp_input[torch.randperm(len(lowercase ) )]
def __magic_name__ ( lowercase ):
# Test on distributed setup that context manager behaves properly
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_: Union[str, Any] =get_training_setup(lowercase )
# Use a single batch
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_: Union[str, Any] =next(iter(lowercase ) ).values()
for iteration in range(3 ):
# Gather the distributed inputs and targs for the base model
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_: Tuple =accelerator.gather((ddp_input, ddp_target) )
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_: List[Any] =input.to(accelerator.device ), target.to(accelerator.device )
# Perform our initial ground truth step in non "DDP"
step_model(lowercase , lowercase , lowercase , lowercase )
# Do "gradient accumulation" (noop)
if iteration % 2 == 0:
# Accumulate grads locally
with accelerator.no_sync(lowercase ):
step_model(lowercase , lowercase , lowercase , lowercase )
else:
# Sync grads
step_model(lowercase , lowercase , lowercase , lowercase )
# DDP model and model should only be in sync when not (iteration % 2 == 0)
for param, ddp_param in zip(model.parameters() , ddp_model.parameters() ):
if not param.requires_grad:
continue
if iteration % 2 == 0:
# Grads should not be in sync
assert (
torch.allclose(param.grad , ddp_param.grad ) is False
), f'''Gradients in sync when they should not be:\nModel grad ({param.grad}) == DDP grad ({ddp_param.grad})'''
else:
# Grads should be in sync
assert (
torch.allclose(param.grad , ddp_param.grad ) is True
), f'''Gradients not in sync when they should be:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})'''
# Shuffle ddp_input on each iteration
torch.manual_seed(1337 + iteration )
SCREAMING_SNAKE_CASE_: Optional[Any] =ddp_input[torch.randperm(len(lowercase ) )]
def __magic_name__ ( lowercase=False , lowercase=False ):
SCREAMING_SNAKE_CASE_: Optional[Any] =Accelerator(
split_batches=lowercase , dispatch_batches=lowercase , gradient_accumulation_steps=2 )
# Test that context manager behaves properly
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_: str =get_training_setup(lowercase )
for iteration, batch in enumerate(lowercase ):
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_: List[Any] =batch.values()
# Gather the distributed inputs and targs for the base model
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_: Optional[Any] =accelerator.gather((ddp_input, ddp_target) )
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_: Optional[Any] =input.to(accelerator.device ), target.to(accelerator.device )
# Perform our initial ground truth step in non "DDP"
step_model(lowercase , lowercase , lowercase , lowercase , lowercase )
# Do "gradient accumulation" (noop)
with accelerator.accumulate(lowercase ):
step_model(lowercase , lowercase , lowercase , lowercase )
# DDP model and model should only be in sync when not (iteration % 2 == 0)
for param, ddp_param in zip(model.parameters() , ddp_model.parameters() ):
if not param.requires_grad:
continue
if ((iteration + 1) % 2 == 0) or (iteration == len(lowercase ) - 1):
# Grads should be in sync
assert (
torch.allclose(param.grad , ddp_param.grad ) is True
), f'''Gradients not in sync when they should be at iteration {iteration}:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})'''
else:
# Grads should not be in sync
assert (
torch.allclose(param.grad , ddp_param.grad ) is False
), f'''Gradients in sync when they should not be at iteration {iteration}:\nModel grad ({param.grad}) == DDP grad ({ddp_param.grad})'''
# Shuffle ddp_input on each iteration
torch.manual_seed(1337 + iteration )
SCREAMING_SNAKE_CASE_: int =ddp_input[torch.randperm(len(lowercase ) )]
GradientState._reset_state()
def __magic_name__ ( lowercase=False , lowercase=False ):
SCREAMING_SNAKE_CASE_: Dict =Accelerator(
split_batches=lowercase , dispatch_batches=lowercase , gradient_accumulation_steps=2 )
# Test that context manager behaves properly
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_: str =get_training_setup(lowercase , lowercase )
for iteration, batch in enumerate(lowercase ):
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_: Optional[Any] =batch.values()
# Gather the distributed inputs and targs for the base model
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_: str =accelerator.gather((ddp_input, ddp_target) )
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_: Dict =input.to(accelerator.device ), target.to(accelerator.device )
# Perform our initial ground truth step in non "DDP"
model.train()
ddp_model.train()
step_model(lowercase , lowercase , lowercase , lowercase , lowercase )
opt.step()
if ((iteration + 1) % 2 == 0) or ((iteration + 1) == len(lowercase )):
if split_batches:
sched.step()
else:
for _ in range(accelerator.num_processes ):
sched.step()
opt.zero_grad()
# Perform gradient accumulation under wrapper
with accelerator.accumulate(lowercase ):
step_model(lowercase , lowercase , lowercase , lowercase )
ddp_opt.step()
ddp_sched.step()
ddp_opt.zero_grad()
# Learning rates should be the same
assert (
opt.param_groups[0]["lr"] == ddp_opt.param_groups[0]["lr"]
), f'''Learning rates found in each optimizer did not align\nopt: {opt.param_groups[0]["lr"]}\nDDP opt: {ddp_opt.param_groups[0]["lr"]}\n'''
SCREAMING_SNAKE_CASE_: str =(((iteration + 1) % 2) == 0) or ((iteration + 1) == len(lowercase ))
if accelerator.num_processes > 1:
check_model_parameters(lowercase , lowercase , lowercase , lowercase )
# Shuffle ddp_input on each iteration
torch.manual_seed(1337 + iteration )
GradientState._reset_state()
def __magic_name__ ( ):
SCREAMING_SNAKE_CASE_: List[Any] =Accelerator()
SCREAMING_SNAKE_CASE_: Tuple =RegressionDataset(length=80 )
SCREAMING_SNAKE_CASE_: Optional[int] =DataLoader(lowercase , batch_size=16 )
SCREAMING_SNAKE_CASE_: str =RegressionDataset(length=96 )
SCREAMING_SNAKE_CASE_: Optional[Any] =DataLoader(lowercase , batch_size=16 )
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_: Optional[Any] =accelerator.prepare(lowercase , lowercase )
assert accelerator.gradient_state.active_dataloader is None
for iteration, _ in enumerate(lowercase ):
assert id(accelerator.gradient_state.active_dataloader ) == id(lowercase )
if iteration < len(lowercase ) - 1:
assert not accelerator.gradient_state.end_of_dataloader
if iteration == 1:
for batch_num, _ in enumerate(lowercase ):
assert id(accelerator.gradient_state.active_dataloader ) == id(lowercase )
if batch_num < len(lowercase ) - 1:
assert not accelerator.gradient_state.end_of_dataloader
else:
assert accelerator.gradient_state.end_of_dataloader
else:
assert accelerator.gradient_state.end_of_dataloader
assert accelerator.gradient_state.active_dataloader is None
def __magic_name__ ( ):
SCREAMING_SNAKE_CASE_: Optional[Any] =Accelerator()
SCREAMING_SNAKE_CASE_: Any =accelerator.state
if state.local_process_index == 0:
print("""**Test `accumulate` gradient accumulation with dataloader break**""" )
test_dataloader_break()
if state.distributed_type == DistributedType.NO:
if state.local_process_index == 0:
print("""**Test NOOP `no_sync` context manager**""" )
test_noop_sync(lowercase )
if state.distributed_type in (DistributedType.MULTI_GPU, DistributedType.MULTI_CPU):
if state.local_process_index == 0:
print("""**Test Distributed `no_sync` context manager**""" )
test_distributed_sync(lowercase )
if state.distributed_type == DistributedType.MULTI_GPU:
for split_batch in [True, False]:
for dispatch_batches in [True, False]:
if state.local_process_index == 0:
print(
"""**Test `accumulate` gradient accumulation, """ , f'''`split_batches={split_batch}` and `dispatch_batches={dispatch_batches}`**''' , )
test_gradient_accumulation(lowercase , lowercase )
# Currently will break on torch 2.0 +, need to investigate why
if is_torch_version("""<""" , """2.0""" ) or state.distributed_type == DistributedType.NO:
if state.local_process_index == 0:
print(
"""**Test `accumulate` gradient accumulation with optimizer and scheduler, """ , """`split_batches=False`, `dispatch_batches=False`**""" , )
test_gradient_accumulation_with_opt_and_scheduler()
if state.distributed_type == DistributedType.MULTI_GPU:
for split_batch in [True, False]:
for dispatch_batches in [True, False]:
if not split_batch and not dispatch_batches:
continue
if state.local_process_index == 0:
print(
"""**Test `accumulate` gradient accumulation with optimizer and scheduler, """ , f'''`split_batches={split_batch}` and `dispatch_batches={dispatch_batches}`**''' , )
test_gradient_accumulation_with_opt_and_scheduler(lowercase , lowercase )
def __magic_name__ ( lowercase ):
# For xla_spawn (TPUs)
main()
if __name__ == "__main__":
main()
| 36 |
"""simple docstring"""
from __future__ import annotations
def __magic_name__ ( lowercase , lowercase ):
SCREAMING_SNAKE_CASE_: List[Any] =sorted(numsa + numsa )
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_: Tuple =divmod(len(lowercase ) , 2 )
if mod == 1:
return all_numbers[div]
else:
return (all_numbers[div] + all_numbers[div - 1]) / 2
if __name__ == "__main__":
import doctest
doctest.testmod()
_UpperCAmelCase = [float(x) for x in input("""Enter the elements of first array: """).split()]
_UpperCAmelCase = [float(x) for x in input("""Enter the elements of second array: """).split()]
print(f"""The median of two arrays is: {median_of_two_arrays(array_a, array_a)}""")
| 36 | 1 |
'''simple docstring'''
import collections
from typing import List, Optional, Union
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, add_end_docstrings, add_start_docstrings, logging
from ..bert.tokenization_bert_fast import BertTokenizerFast
from .tokenization_dpr import DPRContextEncoderTokenizer, DPRQuestionEncoderTokenizer, DPRReaderTokenizer
__lowerCAmelCase = logging.get_logger(__name__)
__lowerCAmelCase = {"""vocab_file""": """vocab.txt""", """tokenizer_file""": """tokenizer.json"""}
__lowerCAmelCase = {
"""vocab_file""": {
"""facebook/dpr-ctx_encoder-single-nq-base""": (
"""https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/vocab.txt"""
),
"""facebook/dpr-ctx_encoder-multiset-base""": (
"""https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/vocab.txt"""
),
},
"""tokenizer_file""": {
"""facebook/dpr-ctx_encoder-single-nq-base""": (
"""https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/tokenizer.json"""
),
"""facebook/dpr-ctx_encoder-multiset-base""": (
"""https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/tokenizer.json"""
),
},
}
__lowerCAmelCase = {
"""vocab_file""": {
"""facebook/dpr-question_encoder-single-nq-base""": (
"""https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/vocab.txt"""
),
"""facebook/dpr-question_encoder-multiset-base""": (
"""https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/vocab.txt"""
),
},
"""tokenizer_file""": {
"""facebook/dpr-question_encoder-single-nq-base""": (
"""https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/tokenizer.json"""
),
"""facebook/dpr-question_encoder-multiset-base""": (
"""https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/tokenizer.json"""
),
},
}
__lowerCAmelCase = {
"""vocab_file""": {
"""facebook/dpr-reader-single-nq-base""": (
"""https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/vocab.txt"""
),
"""facebook/dpr-reader-multiset-base""": (
"""https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/vocab.txt"""
),
},
"""tokenizer_file""": {
"""facebook/dpr-reader-single-nq-base""": (
"""https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/tokenizer.json"""
),
"""facebook/dpr-reader-multiset-base""": (
"""https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/tokenizer.json"""
),
},
}
__lowerCAmelCase = {
"""facebook/dpr-ctx_encoder-single-nq-base""": 5_1_2,
"""facebook/dpr-ctx_encoder-multiset-base""": 5_1_2,
}
__lowerCAmelCase = {
"""facebook/dpr-question_encoder-single-nq-base""": 5_1_2,
"""facebook/dpr-question_encoder-multiset-base""": 5_1_2,
}
__lowerCAmelCase = {
"""facebook/dpr-reader-single-nq-base""": 5_1_2,
"""facebook/dpr-reader-multiset-base""": 5_1_2,
}
__lowerCAmelCase = {
"""facebook/dpr-ctx_encoder-single-nq-base""": {"""do_lower_case""": True},
"""facebook/dpr-ctx_encoder-multiset-base""": {"""do_lower_case""": True},
}
__lowerCAmelCase = {
"""facebook/dpr-question_encoder-single-nq-base""": {"""do_lower_case""": True},
"""facebook/dpr-question_encoder-multiset-base""": {"""do_lower_case""": True},
}
__lowerCAmelCase = {
"""facebook/dpr-reader-single-nq-base""": {"""do_lower_case""": True},
"""facebook/dpr-reader-multiset-base""": {"""do_lower_case""": True},
}
class UpperCAmelCase__ ( lowerCAmelCase__ ):
"""simple docstring"""
__UpperCAmelCase : Dict = VOCAB_FILES_NAMES
__UpperCAmelCase : int = CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
__UpperCAmelCase : List[str] = CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__UpperCAmelCase : Any = CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION
__UpperCAmelCase : List[Any] = DPRContextEncoderTokenizer
class UpperCAmelCase__ ( lowerCAmelCase__ ):
"""simple docstring"""
__UpperCAmelCase : List[Any] = VOCAB_FILES_NAMES
__UpperCAmelCase : Dict = QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
__UpperCAmelCase : Optional[int] = QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__UpperCAmelCase : Optional[int] = QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION
__UpperCAmelCase : int = DPRQuestionEncoderTokenizer
__lowerCAmelCase = collections.namedtuple(
"""DPRSpanPrediction""", ["""span_score""", """relevance_score""", """doc_id""", """start_index""", """end_index""", """text"""]
)
__lowerCAmelCase = collections.namedtuple("""DPRReaderOutput""", ["""start_logits""", """end_logits""", """relevance_logits"""])
__lowerCAmelCase = r"""
Return a dictionary with the token ids of the input strings and other information to give to `.decode_best_spans`.
It converts the strings of a question and different passages (title and text) in a sequence of IDs (integers),
using the tokenizer and vocabulary. The resulting `input_ids` is a matrix of size `(n_passages, sequence_length)`
with the format:
[CLS] <question token ids> [SEP] <titles ids> [SEP] <texts ids>
Args:
questions (`str` or `List[str]`):
The questions to be encoded. You can specify one question for many passages. In this case, the question
will be duplicated like `[questions] * n_passages`. Otherwise you have to specify as many questions as in
`titles` or `texts`.
titles (`str` or `List[str]`):
The passages titles to be encoded. This can be a string or a list of strings if there are several passages.
texts (`str` or `List[str]`):
The passages texts to be encoded. This can be a string or a list of strings if there are several passages.
padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `False`):
Activates and controls padding. Accepts the following values:
- `True` or `'longest'`: Pad to the longest sequence in the batch (or no padding if only a single sequence
if provided).
- `'max_length'`: Pad to a maximum length specified with the argument `max_length` or to the maximum
acceptable input length for the model if that argument is not provided.
- `False` or `'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of different
lengths).
truncation (`bool`, `str` or [`~tokenization_utils_base.TruncationStrategy`], *optional*, defaults to `False`):
Activates and controls truncation. Accepts the following values:
- `True` or `'longest_first'`: Truncate to a maximum length specified with the argument `max_length` or to
the maximum acceptable input length for the model if that argument is not provided. This will truncate
token by token, removing a token from the longest sequence in the pair if a pair of sequences (or a batch
of pairs) is provided.
- `'only_first'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum
acceptable input length for the model if that argument is not provided. This will only truncate the first
sequence of a pair if a pair of sequences (or a batch of pairs) is provided.
- `'only_second'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum
acceptable input length for the model if that argument is not provided. This will only truncate the
second sequence of a pair if a pair of sequences (or a batch of pairs) is provided.
- `False` or `'do_not_truncate'` (default): No truncation (i.e., can output batch with sequence lengths
greater than the model maximum admissible input size).
max_length (`int`, *optional*):
Controls the maximum length to use by one of the truncation/padding parameters.
If left unset or set to `None`, this will use the predefined model maximum length if a maximum length
is required by one of the truncation/padding parameters. If the model has no specific maximum input
length (like XLNet) truncation/padding to a maximum length will be deactivated.
return_tensors (`str` or [`~utils.TensorType`], *optional*):
If set, will return tensors instead of list of python integers. Acceptable values are:
- `'tf'`: Return TensorFlow `tf.constant` objects.
- `'pt'`: Return PyTorch `torch.Tensor` objects.
- `'np'`: Return Numpy `np.ndarray` objects.
return_attention_mask (`bool`, *optional*):
Whether or not to return the attention mask. If not set, will return the attention mask according to the
specific tokenizer's default, defined by the `return_outputs` attribute.
[What are attention masks?](../glossary#attention-mask)
Return:
`Dict[str, List[List[int]]]`: A dictionary with the following keys:
- `input_ids`: List of token ids to be fed to a model.
- `attention_mask`: List of indices specifying which tokens should be attended to by the model.
"""
@add_start_docstrings(lowerCAmelCase__ )
class UpperCAmelCase__ :
"""simple docstring"""
def __call__( self : List[str] ,_a : Tuple ,_a : Optional[str] = None ,_a : Optional[str] = None ,_a : Union[bool, str] = False ,_a : Union[bool, str] = False ,_a : Optional[int] = None ,_a : Optional[Union[str, TensorType]] = None ,_a : Optional[bool] = None ,**_a : Dict ,):
'''simple docstring'''
if titles is None and texts is None:
return super().__call__(
_A ,padding=_A ,truncation=_A ,max_length=_A ,return_tensors=_A ,return_attention_mask=_A ,**_A ,)
elif titles is None or texts is None:
_a : Tuple = titles if texts is None else texts
return super().__call__(
_A ,_A ,padding=_A ,truncation=_A ,max_length=_A ,return_tensors=_A ,return_attention_mask=_A ,**_A ,)
_a : List[Any] = titles if not isinstance(_A ,_A ) else [titles]
_a : Any = texts if not isinstance(_A ,_A ) else [texts]
_a : List[Any] = len(_A )
_a : Optional[Any] = questions if not isinstance(_A ,_A ) else [questions] * n_passages
assert len(_A ) == len(
_A ), F"""There should be as many titles than texts but got {len(_A )} titles and {len(_A )} texts."""
_a : Tuple = super().__call__(_A ,_A ,padding=_A ,truncation=_A )['''input_ids''']
_a : Any = super().__call__(_A ,add_special_tokens=_A ,padding=_A ,truncation=_A )['''input_ids''']
_a : str = {
'''input_ids''': [
(encoded_question_and_title + encoded_text)[:max_length]
if max_length is not None and truncation
else encoded_question_and_title + encoded_text
for encoded_question_and_title, encoded_text in zip(_A ,_A )
]
}
if return_attention_mask is not False:
_a : Union[str, Any] = []
for input_ids in encoded_inputs["input_ids"]:
attention_mask.append([int(input_id != self.pad_token_id ) for input_id in input_ids] )
_a : str = attention_mask
return self.pad(_A ,padding=_A ,max_length=_A ,return_tensors=_A )
def __lowercase ( self : int ,_a : BatchEncoding ,_a : DPRReaderOutput ,_a : int = 16 ,_a : int = 64 ,_a : int = 4 ,):
'''simple docstring'''
_a : Union[str, Any] = reader_input['''input_ids''']
_a : List[Any] = reader_output[:3]
_a : int = len(_A )
_a : str = sorted(range(_A ) ,reverse=_A ,key=relevance_logits.__getitem__ )
_a : List[DPRReaderOutput] = []
for doc_id in sorted_docs:
_a : Optional[int] = list(input_ids[doc_id] )
# assuming question & title information is at the beginning of the sequence
_a : Optional[int] = sequence_ids.index(self.sep_token_id ,2 ) + 1 # second sep id
if sequence_ids[-1] == self.pad_token_id:
_a : int = sequence_ids.index(self.pad_token_id )
else:
_a : Union[str, Any] = len(_A )
_a : Tuple = self._get_best_spans(
start_logits=start_logits[doc_id][passage_offset:sequence_len] ,end_logits=end_logits[doc_id][passage_offset:sequence_len] ,max_answer_length=_A ,top_spans=_A ,)
for start_index, end_index in best_spans:
start_index += passage_offset
end_index += passage_offset
nbest_spans_predictions.append(
DPRSpanPrediction(
span_score=start_logits[doc_id][start_index] + end_logits[doc_id][end_index] ,relevance_score=relevance_logits[doc_id] ,doc_id=_A ,start_index=_A ,end_index=_A ,text=self.decode(sequence_ids[start_index : end_index + 1] ) ,) )
if len(_A ) >= num_spans:
break
return nbest_spans_predictions[:num_spans]
def __lowercase ( self : List[str] ,_a : List[int] ,_a : List[int] ,_a : int ,_a : int ,):
'''simple docstring'''
_a : Dict = []
for start_index, start_score in enumerate(_A ):
for answer_length, end_score in enumerate(end_logits[start_index : start_index + max_answer_length] ):
scores.append(((start_index, start_index + answer_length), start_score + end_score) )
_a : Optional[Any] = sorted(_A ,key=lambda _a : x[1] ,reverse=_A )
_a : int = []
for (start_index, end_index), score in scores:
assert start_index <= end_index, F"""Wrong span indices: [{start_index}:{end_index}]"""
_a : Dict = end_index - start_index + 1
assert length <= max_answer_length, F"""Span is too long: {length} > {max_answer_length}"""
if any(
start_index <= prev_start_index <= prev_end_index <= end_index
or prev_start_index <= start_index <= end_index <= prev_end_index
for (prev_start_index, prev_end_index) in chosen_span_intervals ):
continue
chosen_span_intervals.append((start_index, end_index) )
if len(_A ) == top_spans:
break
return chosen_span_intervals
@add_end_docstrings(lowerCAmelCase__ )
class UpperCAmelCase__ ( lowerCAmelCase__ , lowerCAmelCase__ ):
"""simple docstring"""
__UpperCAmelCase : Optional[int] = VOCAB_FILES_NAMES
__UpperCAmelCase : Optional[int] = READER_PRETRAINED_VOCAB_FILES_MAP
__UpperCAmelCase : Optional[Any] = READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__UpperCAmelCase : Any = READER_PRETRAINED_INIT_CONFIGURATION
__UpperCAmelCase : Dict = ['''input_ids''', '''attention_mask''']
__UpperCAmelCase : Tuple = DPRReaderTokenizer
| 229 |
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
import numpy as np
import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor
import transformers
from transformers import (
CONFIG_MAPPING,
IMAGE_PROCESSOR_MAPPING,
MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING,
AutoConfig,
AutoImageProcessor,
AutoModelForMaskedImageModeling,
HfArgumentParser,
Trainer,
TrainingArguments,
)
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
lowercase_ = 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-pretraining/requirements.txt""")
lowercase_ = list(MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING.keys())
lowercase_ = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
@dataclass
class __UpperCamelCase :
"""simple docstring"""
lowerCAmelCase_ = field(
default='''cifar10''' , metadata={'''help''': '''Name of a dataset from the datasets package'''} )
lowerCAmelCase_ = field(
default=lowerCAmelCase__ , metadata={'''help''': '''The configuration name of the dataset to use (via the datasets library).'''} )
lowerCAmelCase_ = field(
default=lowerCAmelCase__ , metadata={'''help''': '''The column name of the images in the files. If not set, will try to use \'image\' or \'img\'.'''} , )
lowerCAmelCase_ = field(default=lowerCAmelCase__ , metadata={'''help''': '''A folder containing the training data.'''} )
lowerCAmelCase_ = field(default=lowerCAmelCase__ , 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=32 , metadata={'''help''': '''The size of the square patches to use for masking.'''} )
lowerCAmelCase_ = field(
default=0.6 , metadata={'''help''': '''Percentage of patches to mask.'''} , )
lowerCAmelCase_ = field(
default=lowerCAmelCase__ , metadata={
'''help''': (
'''For debugging purposes or quicker training, truncate the number of training examples to this '''
'''value if set.'''
)
} , )
lowerCAmelCase_ = field(
default=lowerCAmelCase__ , metadata={
'''help''': (
'''For debugging purposes or quicker training, truncate the number of evaluation examples to this '''
'''value if set.'''
)
} , )
def UpperCAmelCase__ ( self : Tuple ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : int = {}
if self.train_dir is not None:
__SCREAMING_SNAKE_CASE : Dict = self.train_dir
if self.validation_dir is not None:
__SCREAMING_SNAKE_CASE : Any = self.validation_dir
__SCREAMING_SNAKE_CASE : List[Any] = data_files if data_files else None
@dataclass
class __UpperCamelCase :
"""simple docstring"""
lowerCAmelCase_ = field(
default=lowerCAmelCase__ , metadata={
'''help''': (
'''The model checkpoint for weights initialization. Can be a local path to a pytorch_model.bin or a '''
'''checkpoint identifier on the hub. '''
'''Don\'t set if you want to train a model from scratch.'''
)
} , )
lowerCAmelCase_ = field(
default=lowerCAmelCase__ , metadata={'''help''': '''If training from scratch, pass a model type from the list: ''' + ''', '''.join(lowerCAmelCase__ )} , )
lowerCAmelCase_ = field(
default=lowerCAmelCase__ , metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''} )
lowerCAmelCase_ = field(
default=lowerCAmelCase__ , metadata={
'''help''': (
'''Override some existing default config settings when a model is trained from scratch. Example: '''
'''n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index'''
)
} , )
lowerCAmelCase_ = field(
default=lowerCAmelCase__ , metadata={'''help''': '''Where do you want to store (cache) the pretrained models/datasets downloaded from the hub'''} , )
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=lowerCAmelCase__ , metadata={'''help''': '''Name or path of preprocessor config.'''} )
lowerCAmelCase_ = field(
default=lowerCAmelCase__ , metadata={
'''help''': (
'''Will use the token generated when running `huggingface-cli login` (necessary to use this script '''
'''with private models).'''
)
} , )
lowerCAmelCase_ = field(
default=lowerCAmelCase__ , metadata={
'''help''': (
'''The size (resolution) of each image. If not specified, will use `image_size` of the configuration.'''
)
} , )
lowerCAmelCase_ = field(
default=lowerCAmelCase__ , metadata={
'''help''': (
'''The size (resolution) of each patch. If not specified, will use `patch_size` of the configuration.'''
)
} , )
lowerCAmelCase_ = field(
default=lowerCAmelCase__ , metadata={'''help''': '''Stride to use for the encoder.'''} , )
class __UpperCamelCase :
"""simple docstring"""
def __init__( self : Tuple , _A : Optional[int]=192 , _A : List[Any]=32 , _A : Optional[int]=4 , _A : str=0.6 ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : List[str] = input_size
__SCREAMING_SNAKE_CASE : List[str] = mask_patch_size
__SCREAMING_SNAKE_CASE : Dict = model_patch_size
__SCREAMING_SNAKE_CASE : int = mask_ratio
if self.input_size % self.mask_patch_size != 0:
raise ValueError('''Input size must be divisible by mask patch size''' )
if self.mask_patch_size % self.model_patch_size != 0:
raise ValueError('''Mask patch size must be divisible by model patch size''' )
__SCREAMING_SNAKE_CASE : Any = self.input_size // self.mask_patch_size
__SCREAMING_SNAKE_CASE : Optional[Any] = self.mask_patch_size // self.model_patch_size
__SCREAMING_SNAKE_CASE : int = self.rand_size**2
__SCREAMING_SNAKE_CASE : Optional[int] = int(np.ceil(self.token_count * self.mask_ratio ) )
def __call__( self : List[str] ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : List[str] = np.random.permutation(self.token_count )[: self.mask_count]
__SCREAMING_SNAKE_CASE : Union[str, Any] = np.zeros(self.token_count , dtype=_A )
__SCREAMING_SNAKE_CASE : Optional[int] = 1
__SCREAMING_SNAKE_CASE : List[str] = mask.reshape((self.rand_size, self.rand_size) )
__SCREAMING_SNAKE_CASE : List[Any] = mask.repeat(self.scale , axis=0 ).repeat(self.scale , axis=1 )
return torch.tensor(mask.flatten() )
def a__ ( snake_case ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : Union[str, Any] = torch.stack([example['''pixel_values'''] for example in examples] )
__SCREAMING_SNAKE_CASE : Any = torch.stack([example['''mask'''] for example in examples] )
return {"pixel_values": pixel_values, "bool_masked_pos": mask}
def a__ ( ):
"""simple docstring"""
# 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 : List[Any] = 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 : Dict = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
else:
__SCREAMING_SNAKE_CASE, __SCREAMING_SNAKE_CASE, __SCREAMING_SNAKE_CASE : Union[str, Any] = 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_mim''' , snake_case , snake_case )
# 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 : Tuple = training_args.get_process_log_level()
logger.setLevel(snake_case )
transformers.utils.logging.set_verbosity(snake_case )
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 : Tuple = None
if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
__SCREAMING_SNAKE_CASE : Optional[int] = 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.''' )
# Initialize our dataset.
__SCREAMING_SNAKE_CASE : Tuple = load_dataset(
data_args.dataset_name , data_args.dataset_config_name , data_files=data_args.data_files , cache_dir=model_args.cache_dir , use_auth_token=True if model_args.use_auth_token else None , )
# If we don't have a validation split, split off a percentage of train as validation.
__SCREAMING_SNAKE_CASE : Any = None if '''validation''' in ds.keys() else data_args.train_val_split
if isinstance(data_args.train_val_split , snake_case ) and data_args.train_val_split > 0.0:
__SCREAMING_SNAKE_CASE : List[str] = ds['''train'''].train_test_split(data_args.train_val_split )
__SCREAMING_SNAKE_CASE : int = split['''train''']
__SCREAMING_SNAKE_CASE : Dict = split['''test''']
# Create config
# Distributed training:
# The .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
__SCREAMING_SNAKE_CASE : List[Any] = {
'''cache_dir''': model_args.cache_dir,
'''revision''': model_args.model_revision,
'''use_auth_token''': True if model_args.use_auth_token else None,
}
if model_args.config_name_or_path:
__SCREAMING_SNAKE_CASE : str = AutoConfig.from_pretrained(model_args.config_name_or_path , **snake_case )
elif model_args.model_name_or_path:
__SCREAMING_SNAKE_CASE : Optional[Any] = AutoConfig.from_pretrained(model_args.model_name_or_path , **snake_case )
else:
__SCREAMING_SNAKE_CASE : List[Any] = CONFIG_MAPPING[model_args.model_type]()
logger.warning('''You are instantiating a new config instance from scratch.''' )
if model_args.config_overrides is not None:
logger.info(F'''Overriding config: {model_args.config_overrides}''' )
config.update_from_string(model_args.config_overrides )
logger.info(F'''New config: {config}''' )
# make sure the decoder_type is "simmim" (only relevant for BEiT)
if hasattr(snake_case , '''decoder_type''' ):
__SCREAMING_SNAKE_CASE : Any = '''simmim'''
# adapt config
__SCREAMING_SNAKE_CASE : str = model_args.image_size if model_args.image_size is not None else config.image_size
__SCREAMING_SNAKE_CASE : int = model_args.patch_size if model_args.patch_size is not None else config.patch_size
__SCREAMING_SNAKE_CASE : str = (
model_args.encoder_stride if model_args.encoder_stride is not None else config.encoder_stride
)
config.update(
{
'''image_size''': model_args.image_size,
'''patch_size''': model_args.patch_size,
'''encoder_stride''': model_args.encoder_stride,
} )
# create image processor
if model_args.image_processor_name:
__SCREAMING_SNAKE_CASE : int = AutoImageProcessor.from_pretrained(model_args.image_processor_name , **snake_case )
elif model_args.model_name_or_path:
__SCREAMING_SNAKE_CASE : List[Any] = AutoImageProcessor.from_pretrained(model_args.model_name_or_path , **snake_case )
else:
__SCREAMING_SNAKE_CASE : List[Any] = {
conf.model_type: image_processor_class for conf, image_processor_class in IMAGE_PROCESSOR_MAPPING.items()
}
__SCREAMING_SNAKE_CASE : str = IMAGE_PROCESSOR_TYPES[model_args.model_type]()
# create model
if model_args.model_name_or_path:
__SCREAMING_SNAKE_CASE : int = AutoModelForMaskedImageModeling.from_pretrained(
model_args.model_name_or_path , from_tf=bool('''.ckpt''' in model_args.model_name_or_path ) , config=snake_case , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
else:
logger.info('''Training new model from scratch''' )
__SCREAMING_SNAKE_CASE : List[Any] = AutoModelForMaskedImageModeling.from_config(snake_case )
if training_args.do_train:
__SCREAMING_SNAKE_CASE : Any = ds['''train'''].column_names
else:
__SCREAMING_SNAKE_CASE : int = ds['''validation'''].column_names
if data_args.image_column_name is not None:
__SCREAMING_SNAKE_CASE : List[Any] = data_args.image_column_name
elif "image" in column_names:
__SCREAMING_SNAKE_CASE : str = '''image'''
elif "img" in column_names:
__SCREAMING_SNAKE_CASE : List[str] = '''img'''
else:
__SCREAMING_SNAKE_CASE : Tuple = column_names[0]
# transformations as done in original SimMIM paper
# source: https://github.com/microsoft/SimMIM/blob/main/data/data_simmim.py
__SCREAMING_SNAKE_CASE : Any = Compose(
[
Lambda(lambda snake_case : img.convert('''RGB''' ) if img.mode != "RGB" else img ),
RandomResizedCrop(model_args.image_size , scale=(0.67, 1.0) , ratio=(3.0 / 4.0, 4.0 / 3.0) ),
RandomHorizontalFlip(),
ToTensor(),
Normalize(mean=image_processor.image_mean , std=image_processor.image_std ),
] )
# create mask generator
__SCREAMING_SNAKE_CASE : str = MaskGenerator(
input_size=model_args.image_size , mask_patch_size=data_args.mask_patch_size , model_patch_size=model_args.patch_size , mask_ratio=data_args.mask_ratio , )
def preprocess_images(snake_case ):
__SCREAMING_SNAKE_CASE : str = [transforms(snake_case ) for image in examples[image_column_name]]
__SCREAMING_SNAKE_CASE : str = [mask_generator() for i in range(len(examples[image_column_name] ) )]
return examples
if training_args.do_train:
if "train" not in ds:
raise ValueError('''--do_train requires a train dataset''' )
if data_args.max_train_samples is not None:
__SCREAMING_SNAKE_CASE : Dict = ds['''train'''].shuffle(seed=training_args.seed ).select(range(data_args.max_train_samples ) )
# Set the training transforms
ds["train"].set_transform(snake_case )
if training_args.do_eval:
if "validation" not in ds:
raise ValueError('''--do_eval requires a validation dataset''' )
if data_args.max_eval_samples is not None:
__SCREAMING_SNAKE_CASE : Union[str, Any] = (
ds['''validation'''].shuffle(seed=training_args.seed ).select(range(data_args.max_eval_samples ) )
)
# Set the validation transforms
ds["validation"].set_transform(snake_case )
# Initialize our trainer
__SCREAMING_SNAKE_CASE : List[str] = Trainer(
model=snake_case , args=snake_case , train_dataset=ds['''train'''] if training_args.do_train else None , eval_dataset=ds['''validation'''] if training_args.do_eval else None , tokenizer=snake_case , data_collator=snake_case , )
# Training
if training_args.do_train:
__SCREAMING_SNAKE_CASE : Union[str, Any] = None
if training_args.resume_from_checkpoint is not None:
__SCREAMING_SNAKE_CASE : Tuple = training_args.resume_from_checkpoint
elif last_checkpoint is not None:
__SCREAMING_SNAKE_CASE : int = last_checkpoint
__SCREAMING_SNAKE_CASE : Tuple = trainer.train(resume_from_checkpoint=snake_case )
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 : Union[str, Any] = trainer.evaluate()
trainer.log_metrics('''eval''' , snake_case )
trainer.save_metrics('''eval''' , snake_case )
# Write model card and (optionally) push to hub
__SCREAMING_SNAKE_CASE : Optional[Any] = {
'''finetuned_from''': model_args.model_name_or_path,
'''tasks''': '''masked-image-modeling''',
'''dataset''': data_args.dataset_name,
'''tags''': ['''masked-image-modeling'''],
}
if training_args.push_to_hub:
trainer.push_to_hub(**snake_case )
else:
trainer.create_model_card(**snake_case )
if __name__ == "__main__":
main()
| 74 | 0 |
import re
from pathlib import Path
from unittest import TestCase
import pytest
@pytest.mark.integration
class __A ( UpperCamelCase__ ):
def A__ ( self :Tuple , __snake_case :str ):
'''simple docstring'''
with open(__snake_case , encoding="""utf-8""" ) as input_file:
__magic_name__ : str =re.compile(r"""(?!.*\b(?:encoding|rb|w|wb|w+|wb+|ab|ab+)\b)(?<=\s)(open)\((.*)\)""" )
__magic_name__ : str =input_file.read()
__magic_name__ : List[Any] =regexp.search(__snake_case )
return match
def A__ ( self :List[Any] , __snake_case :str ):
'''simple docstring'''
with open(__snake_case , encoding="""utf-8""" ) as input_file:
__magic_name__ : Union[str, Any] =re.compile(r"""#[^\r\n]*print\(|\"[^\r\n]*print\(|\"\"\".*?print\(.*?\"\"\"|(print\()""" , re.DOTALL )
__magic_name__ : Optional[int] =input_file.read()
# use `re.finditer` to handle the case where the ignored groups would be matched first by `re.search`
__magic_name__ : Optional[Any] =regexp.finditer(__snake_case )
__magic_name__ : Tuple =[match for match in matches if match is not None and match.group(1 ) is not None]
return matches[0] if matches else None
def A__ ( self :List[Any] ):
'''simple docstring'''
__magic_name__ : Tuple =Path("""./datasets""" )
__magic_name__ : List[str] =list(dataset_paths.absolute().glob("""**/*.py""" ) )
for dataset in dataset_files:
if self._no_encoding_on_file_open(str(__snake_case ) ):
raise AssertionError(f"open(...) must use utf-8 encoding in {dataset}" )
def A__ ( self :Optional[Any] ):
'''simple docstring'''
__magic_name__ : Dict =Path("""./datasets""" )
__magic_name__ : str =list(dataset_paths.absolute().glob("""**/*.py""" ) )
for dataset in dataset_files:
if self._no_print_statements(str(__snake_case ) ):
raise AssertionError(f"print statement found in {dataset}. Use datasets.logger/logging instead." )
| 718 |
def lowerCAmelCase_ ( lowerCamelCase ):
__magic_name__ : Optional[int] =[]
__magic_name__ : int =[]
__magic_name__ : str ={
"""^""": 3,
"""*""": 2,
"""/""": 2,
"""%""": 2,
"""+""": 1,
"""-""": 1,
} # Priority of each operator
__magic_name__ : Dict =len(lowerCamelCase ) if (len(lowerCamelCase ) > 7) else 7
# Print table header for output
print(
"""Symbol""".center(8 ) , """Stack""".center(lowerCamelCase ) , """Postfix""".center(lowerCamelCase ) , sep=""" | """ , )
print("""-""" * (print_width * 3 + 7) )
for x in infix:
if x.isalpha() or x.isdigit():
post_fix.append(lowerCamelCase ) # if x is Alphabet / Digit, add it to Postfix
elif x == "(":
stack.append(lowerCamelCase ) # if x is "(" push to Stack
elif x == ")": # if x is ")" pop stack until "(" is encountered
while stack[-1] != "(":
post_fix.append(stack.pop() ) # Pop stack & add the content to Postfix
stack.pop()
else:
if len(lowerCamelCase ) == 0:
stack.append(lowerCamelCase ) # If stack is empty, push x to stack
else: # while priority of x is not > priority of element in the stack
while len(lowerCamelCase ) > 0 and priority[x] <= priority[stack[-1]]:
post_fix.append(stack.pop() ) # pop stack & add to Postfix
stack.append(lowerCamelCase ) # push x to stack
print(
x.center(8 ) , ("""""".join(lowerCamelCase )).ljust(lowerCamelCase ) , ("""""".join(lowerCamelCase )).ljust(lowerCamelCase ) , sep=""" | """ , ) # Output in tabular format
while len(lowerCamelCase ) > 0: # while stack is not empty
post_fix.append(stack.pop() ) # pop stack & add to Postfix
print(
""" """.center(8 ) , ("""""".join(lowerCamelCase )).ljust(lowerCamelCase ) , ("""""".join(lowerCamelCase )).ljust(lowerCamelCase ) , sep=""" | """ , ) # Output in tabular format
return "".join(lowerCamelCase ) # return Postfix as str
def lowerCAmelCase_ ( lowerCamelCase ):
__magic_name__ : Union[str, Any] =list(infix[::-1] ) # reverse the infix equation
for i in range(len(lowerCamelCase ) ):
if infix[i] == "(":
__magic_name__ : str =""")""" # change "(" to ")"
elif infix[i] == ")":
__magic_name__ : int ="""(""" # change ")" to "("
return (infix_2_postfix("""""".join(lowerCamelCase ) ))[
::-1
] # call infix_2_postfix on Infix, return reverse of Postfix
if __name__ == "__main__":
UpperCAmelCase_ : Tuple = input("\nEnter an Infix Equation = ") # Input an Infix equation
UpperCAmelCase_ : Dict = "".join(Infix.split()) # Remove spaces from the input
print("\n\t", Infix, "(Infix) -> ", infix_2_prefix(Infix), "(Prefix)")
| 367 | 0 |
from __future__ import annotations
class lowerCamelCase__ :
'''simple docstring'''
def __init__( self :Dict , a :str , a :str ) -> Union[str, Any]:
__UpperCamelCase , __UpperCamelCase : Optional[int] = text, pattern
__UpperCamelCase , __UpperCamelCase : Tuple = len(a ), len(a )
def _lowerCamelCase ( self :Any , a :str ) -> int:
for i in range(self.patLen - 1 , -1 , -1 ):
if char == self.pattern[i]:
return i
return -1
def _lowerCamelCase ( self :str , a :int ) -> int:
for i in range(self.patLen - 1 , -1 , -1 ):
if self.pattern[i] != self.text[current_pos + i]:
return current_pos + i
return -1
def _lowerCamelCase ( self :Union[str, Any] ) -> list[int]:
# searches pattern in text and returns index positions
__UpperCamelCase : Any = []
for i in range(self.textLen - self.patLen + 1 ):
__UpperCamelCase : List[Any] = self.mismatch_in_text(a )
if mismatch_index == -1:
positions.append(a )
else:
__UpperCamelCase : Any = self.match_in_pattern(self.text[mismatch_index] )
__UpperCamelCase : Dict = (
mismatch_index - match_index
) # shifting index lgtm [py/multiple-definition]
return positions
lowercase : Any = 'ABAABA'
lowercase : str = 'AB'
lowercase : str = BoyerMooreSearch(text, pattern)
lowercase : Union[str, Any] = bms.bad_character_heuristic()
if len(positions) == 0:
print('No match found')
else:
print('Pattern found in following positions: ')
print(positions) | 557 |
import warnings
from ...utils import logging
from .image_processing_perceiver import PerceiverImageProcessor
lowercase : List[str] = logging.get_logger(__name__)
class lowerCamelCase__ ( __lowercase):
'''simple docstring'''
def __init__( self :Dict , *a :Tuple , **a :List[Any] ) -> None:
warnings.warn(
"The class PerceiverFeatureExtractor is deprecated and will be removed in version 5 of Transformers."
" Please use PerceiverImageProcessor instead." , a , )
super().__init__(*a , **a ) | 557 | 1 |
from __future__ import annotations
import random
# Maximum size of the population. Bigger could be faster but is more memory expensive.
_a : str = 2_00
# 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.
_a : Optional[int] = 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.
_a : str = 0.4
# Just a seed to improve randomness required by the algorithm.
random.seed(random.randint(0, 10_00))
def UpperCamelCase__ ( _A: str , _A: str ):
'''simple docstring'''
__lowerCamelCase = len([g for position, g in enumerate(_A ) if g == main_target[position]] )
return (item, float(_A ))
def UpperCamelCase__ ( _A: str , _A: str ):
'''simple docstring'''
__lowerCamelCase = random.randint(0 , len(_A ) - 1 )
__lowerCamelCase = parent_a[:random_slice] + parent_a[random_slice:]
__lowerCamelCase = parent_a[:random_slice] + parent_a[random_slice:]
return (child_a, child_a)
def UpperCamelCase__ ( _A: str , _A: list[str] ):
'''simple docstring'''
__lowerCamelCase = list(_A )
if random.uniform(0 , 1 ) < MUTATION_PROBABILITY:
__lowerCamelCase = random.choice(_A )
return "".join(_A )
def UpperCamelCase__ ( _A: tuple[str, float] , _A: list[tuple[str, float]] , _A: list[str] , ):
'''simple docstring'''
__lowerCamelCase = []
# Generate more children proportionally to the fitness score.
__lowerCamelCase = int(parent_a[1] * 100 ) + 1
__lowerCamelCase = 10 if child_n >= 10 else child_n
for _ in range(_A ):
__lowerCamelCase = population_score[random.randint(0 , _A )][0]
__lowerCamelCase , __lowerCamelCase = crossover(parent_a[0] , _A )
# Append new string to the population list.
pop.append(mutate(_A , _A ) )
pop.append(mutate(_A , _A ) )
return pop
def UpperCamelCase__ ( _A: str , _A: list[str] , _A: bool = True ):
'''simple docstring'''
if N_POPULATION < N_SELECTED:
__lowerCamelCase = f'''{N_POPULATION} must be bigger than {N_SELECTED}'''
raise ValueError(_A )
# Verify that the target contains no genes besides the ones inside genes variable.
__lowerCamelCase = sorted({c for c in target if c not in genes} )
if not_in_genes_list:
__lowerCamelCase = f'''{not_in_genes_list} is not in genes list, evolution cannot converge'''
raise ValueError(_A )
# Generate random starting population.
__lowerCamelCase = []
for _ in range(_A ):
population.append("""""".join([random.choice(_A ) for i in range(len(_A ) )] ) )
# Just some logs to know what the algorithms is doing.
__lowerCamelCase , __lowerCamelCase = 0, 0
# This loop will end when we find a perfect match for our target.
while True:
generation += 1
total_population += len(_A )
# 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.
__lowerCamelCase = [evaluate(_A , _A ) for item in population]
# Check if there is a matching evolution.
__lowerCamelCase = sorted(_A , key=lambda _A : x[1] , reverse=_A )
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.
__lowerCamelCase = population[: int(N_POPULATION / 3 )]
population.clear()
population.extend(_A )
# Normalize population score to be between 0 and 1.
__lowerCamelCase = [
(item, score / len(_A )) for item, score in population_score
]
# This is selection
for i in range(_A ):
population.extend(select(population_score[int(_A )] , _A , _A ) )
# 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(_A ) > N_POPULATION:
break
if __name__ == "__main__":
_a : Tuple = (
'This is a genetic algorithm to evaluate, combine, evolve, and mutate a string!'
)
_a : List[Any] = list(
' ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklm'
'nopqrstuvwxyz.,;!?+-*#@^\'èéòà€ù=)(&%$£/\\'
)
_a ,_a ,_a : Union[str, Any] = basic(target_str, genes_list)
print(
F"""\nGeneration: {generation}\nTotal Population: {population}\nTarget: {target}"""
)
| 571 |
def UpperCamelCase__ ( _A: Tuple , _A: List[str] , _A: Tuple , _A: Dict , _A: int , _A: List[str] ):
'''simple docstring'''
if index == r:
for j in range(_A ):
print(data[j] , end=""" """ )
print(""" """ )
return
# When no more elements are there to put in data[]
if i >= n:
return
# current is included, put next at next location
__lowerCamelCase = arr[i]
combination_util(_A , _A , _A , index + 1 , _A , i + 1 )
# current is excluded, replace it with
# next (Note that i+1 is passed, but
# index is not changed)
combination_util(_A , _A , _A , _A , _A , i + 1 )
# The main function that prints all combinations
# of size r in arr[] of size n. This function
# mainly uses combinationUtil()
def UpperCamelCase__ ( _A: Union[str, Any] , _A: List[str] , _A: str ):
'''simple docstring'''
__lowerCamelCase = [0] * r
# Print all combination using temporary array 'data[]'
combination_util(_A , _A , _A , 0 , _A , 0 )
if __name__ == "__main__":
# Driver code to check the function above
_a : Union[str, Any] = [10, 20, 30, 40, 50]
print_combination(arr, len(arr), 3)
# This code is contributed by Ambuj sahu
| 571 | 1 |
from __future__ import annotations
import copy
import inspect
import unittest
import numpy as np
from transformers import is_tf_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_tf, slow
from transformers.utils import cached_property
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import (
TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST,
TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
LayoutLMvaConfig,
TFLayoutLMvaForQuestionAnswering,
TFLayoutLMvaForSequenceClassification,
TFLayoutLMvaForTokenClassification,
TFLayoutLMvaModel,
)
if is_vision_available():
from PIL import Image
from transformers import LayoutLMvaImageProcessor
class __lowercase :
"""simple docstring"""
def __init__( self , __UpperCAmelCase , __UpperCAmelCase=2 , __UpperCAmelCase=3 , __UpperCAmelCase=4 , __UpperCAmelCase=2 , __UpperCAmelCase=7 , __UpperCAmelCase=True , __UpperCAmelCase=True , __UpperCAmelCase=True , __UpperCAmelCase=True , __UpperCAmelCase=99 , __UpperCAmelCase=36 , __UpperCAmelCase=2 , __UpperCAmelCase=4 , __UpperCAmelCase=37 , __UpperCAmelCase="gelu" , __UpperCAmelCase=0.1 , __UpperCAmelCase=0.1 , __UpperCAmelCase=5_12 , __UpperCAmelCase=16 , __UpperCAmelCase=2 , __UpperCAmelCase=0.0_2 , __UpperCAmelCase=6 , __UpperCAmelCase=6 , __UpperCAmelCase=3 , __UpperCAmelCase=4 , __UpperCAmelCase=None , __UpperCAmelCase=10_00 , ) -> Optional[int]:
A : int = parent
A : int = batch_size
A : Optional[Any] = num_channels
A : int = image_size
A : Any = patch_size
A : Any = is_training
A : int = use_input_mask
A : Union[str, Any] = use_token_type_ids
A : Optional[Any] = use_labels
A : str = vocab_size
A : Any = hidden_size
A : str = num_hidden_layers
A : Union[str, Any] = num_attention_heads
A : Tuple = intermediate_size
A : Optional[Any] = hidden_act
A : Union[str, Any] = hidden_dropout_prob
A : Tuple = attention_probs_dropout_prob
A : Optional[Any] = max_position_embeddings
A : List[str] = type_vocab_size
A : Tuple = type_sequence_label_size
A : Optional[Any] = initializer_range
A : Tuple = coordinate_size
A : Tuple = shape_size
A : List[str] = num_labels
A : Optional[Any] = num_choices
A : Optional[int] = scope
A : List[str] = range_bbox
# LayoutLMv3's sequence length equals the number of text tokens + number of patches + 1 (we add 1 for the CLS token)
A : Optional[Any] = text_seq_length
A : int = (image_size // patch_size) ** 2 + 1
A : Any = self.text_seq_length + self.image_seq_length
def snake_case ( self ) -> Dict:
A : Optional[int] = ids_tensor([self.batch_size, self.text_seq_length] , self.vocab_size )
A : Union[str, Any] = ids_tensor([self.batch_size, self.text_seq_length, 4] , self.range_bbox )
A : Tuple = bbox.numpy()
# Ensure that bbox is legal
for i in range(bbox.shape[0] ):
for j in range(bbox.shape[1] ):
if bbox[i, j, 3] < bbox[i, j, 1]:
A : Any = bbox[i, j, 3]
A : Tuple = bbox[i, j, 1]
A : int = tmp_coordinate
if bbox[i, j, 2] < bbox[i, j, 0]:
A : str = bbox[i, j, 2]
A : int = bbox[i, j, 0]
A : Union[str, Any] = tmp_coordinate
A : Tuple = tf.constant(__UpperCAmelCase )
A : Tuple = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
A : Union[str, Any] = None
if self.use_input_mask:
A : Dict = random_attention_mask([self.batch_size, self.text_seq_length] )
A : List[Any] = None
if self.use_token_type_ids:
A : List[Any] = ids_tensor([self.batch_size, self.text_seq_length] , self.type_vocab_size )
A : Any = None
A : str = None
if self.use_labels:
A : List[Any] = ids_tensor([self.batch_size] , self.type_sequence_label_size )
A : List[str] = ids_tensor([self.batch_size, self.text_seq_length] , self.num_labels )
A : Dict = LayoutLMvaConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , initializer_range=self.initializer_range , coordinate_size=self.coordinate_size , shape_size=self.shape_size , input_size=self.image_size , patch_size=self.patch_size , )
return config, input_ids, bbox, pixel_values, token_type_ids, input_mask, sequence_labels, token_labels
def snake_case ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ) -> Union[str, Any]:
A : int = TFLayoutLMvaModel(config=__UpperCAmelCase )
# text + image
A : Dict = model(__UpperCAmelCase , pixel_values=__UpperCAmelCase , training=__UpperCAmelCase )
A : List[Any] = model(
__UpperCAmelCase , bbox=__UpperCAmelCase , pixel_values=__UpperCAmelCase , attention_mask=__UpperCAmelCase , token_type_ids=__UpperCAmelCase , training=__UpperCAmelCase , )
A : Any = model(__UpperCAmelCase , bbox=__UpperCAmelCase , pixel_values=__UpperCAmelCase , training=__UpperCAmelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
# text only
A : Union[str, Any] = model(__UpperCAmelCase , training=__UpperCAmelCase )
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.text_seq_length, self.hidden_size) )
# image only
A : Tuple = model({'''pixel_values''': pixel_values} , training=__UpperCAmelCase )
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.image_seq_length, self.hidden_size) )
def snake_case ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ) -> Union[str, Any]:
A : str = self.num_labels
A : Any = TFLayoutLMvaForSequenceClassification(config=__UpperCAmelCase )
A : str = model(
__UpperCAmelCase , bbox=__UpperCAmelCase , pixel_values=__UpperCAmelCase , attention_mask=__UpperCAmelCase , token_type_ids=__UpperCAmelCase , labels=__UpperCAmelCase , training=__UpperCAmelCase , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def snake_case ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ) -> Tuple:
A : Optional[int] = self.num_labels
A : int = TFLayoutLMvaForTokenClassification(config=__UpperCAmelCase )
A : Optional[Any] = model(
__UpperCAmelCase , bbox=__UpperCAmelCase , pixel_values=__UpperCAmelCase , attention_mask=__UpperCAmelCase , token_type_ids=__UpperCAmelCase , labels=__UpperCAmelCase , training=__UpperCAmelCase , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.text_seq_length, self.num_labels) )
def snake_case ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ) -> Optional[Any]:
A : Tuple = 2
A : List[Any] = TFLayoutLMvaForQuestionAnswering(config=__UpperCAmelCase )
A : str = model(
__UpperCAmelCase , bbox=__UpperCAmelCase , pixel_values=__UpperCAmelCase , attention_mask=__UpperCAmelCase , token_type_ids=__UpperCAmelCase , start_positions=__UpperCAmelCase , end_positions=__UpperCAmelCase , training=__UpperCAmelCase , )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def snake_case ( self ) -> Optional[Any]:
A : Tuple = self.prepare_config_and_inputs()
((A) , (A) , (A) , (A) , (A) , (A) , (A) , (A)) : int = config_and_inputs
A : Union[str, Any] = {
'''input_ids''': input_ids,
'''bbox''': bbox,
'''pixel_values''': pixel_values,
'''token_type_ids''': token_type_ids,
'''attention_mask''': input_mask,
}
return config, inputs_dict
@require_tf
class __lowercase ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , unittest.TestCase ):
"""simple docstring"""
UpperCAmelCase_ : Optional[int] = (
(
TFLayoutLMvaModel,
TFLayoutLMvaForQuestionAnswering,
TFLayoutLMvaForSequenceClassification,
TFLayoutLMvaForTokenClassification,
)
if is_tf_available()
else ()
)
UpperCAmelCase_ : List[str] = (
{'''document-question-answering''': TFLayoutLMvaForQuestionAnswering, '''feature-extraction''': TFLayoutLMvaModel}
if is_tf_available()
else {}
)
UpperCAmelCase_ : Dict = False
UpperCAmelCase_ : int = False
UpperCAmelCase_ : str = False
def snake_case ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ) -> Union[str, Any]:
return True
def snake_case ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase=False ) -> dict:
A : Dict = copy.deepcopy(__UpperCAmelCase )
if model_class in get_values(__UpperCAmelCase ):
A : Any = {
k: tf.tile(tf.expand_dims(__UpperCAmelCase , 1 ) , (1, self.model_tester.num_choices) + (1,) * (v.ndim - 1) )
if isinstance(__UpperCAmelCase , tf.Tensor ) and v.ndim > 0
else v
for k, v in inputs_dict.items()
}
if return_labels:
if model_class in get_values(__UpperCAmelCase ):
A : Optional[Any] = tf.ones(self.model_tester.batch_size , dtype=tf.intaa )
elif model_class in get_values(__UpperCAmelCase ):
A : Optional[Any] = tf.zeros(self.model_tester.batch_size , dtype=tf.intaa )
A : Optional[Any] = tf.zeros(self.model_tester.batch_size , dtype=tf.intaa )
elif model_class in get_values(__UpperCAmelCase ):
A : List[str] = tf.zeros(self.model_tester.batch_size , dtype=tf.intaa )
elif model_class in get_values(__UpperCAmelCase ):
A : Optional[int] = tf.zeros(
(self.model_tester.batch_size, self.model_tester.text_seq_length) , dtype=tf.intaa )
return inputs_dict
def snake_case ( self ) -> Optional[int]:
A : str = TFLayoutLMvaModelTester(self )
A : Optional[int] = ConfigTester(self , config_class=__UpperCAmelCase , hidden_size=37 )
def snake_case ( self ) -> Any:
self.config_tester.run_common_tests()
def snake_case ( self ) -> Union[str, Any]:
A , A : int = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
A : int = model_class(__UpperCAmelCase )
if getattr(__UpperCAmelCase , '''hf_compute_loss''' , __UpperCAmelCase ):
# The number of elements in the loss should be the same as the number of elements in the label
A : Optional[Any] = self._prepare_for_class(inputs_dict.copy() , __UpperCAmelCase , return_labels=__UpperCAmelCase )
A : int = prepared_for_class[
sorted(prepared_for_class.keys() - inputs_dict.keys() , reverse=__UpperCAmelCase )[0]
]
A : Optional[int] = added_label.shape.as_list()[:1]
# Test that model correctly compute the loss with kwargs
A : Optional[Any] = self._prepare_for_class(inputs_dict.copy() , __UpperCAmelCase , return_labels=__UpperCAmelCase )
A : Optional[int] = prepared_for_class.pop('''input_ids''' )
A : Any = model(__UpperCAmelCase , **__UpperCAmelCase )[0]
self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1] )
# Test that model correctly compute the loss when we mask some positions
A : Dict = self._prepare_for_class(inputs_dict.copy() , __UpperCAmelCase , return_labels=__UpperCAmelCase )
A : Optional[Any] = prepared_for_class.pop('''input_ids''' )
if "labels" in prepared_for_class:
A : Optional[int] = prepared_for_class['''labels'''].numpy()
if len(labels.shape ) > 1 and labels.shape[1] != 1:
A : List[Any] = -1_00
A : Any = tf.convert_to_tensor(__UpperCAmelCase )
A : Any = model(__UpperCAmelCase , **__UpperCAmelCase )[0]
self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1] )
self.assertTrue(not np.any(np.isnan(loss.numpy() ) ) )
# Test that model correctly compute the loss with a dict
A : Any = self._prepare_for_class(inputs_dict.copy() , __UpperCAmelCase , return_labels=__UpperCAmelCase )
A : List[str] = model(__UpperCAmelCase )[0]
self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1] )
# Test that model correctly compute the loss with a tuple
A : str = self._prepare_for_class(inputs_dict.copy() , __UpperCAmelCase , return_labels=__UpperCAmelCase )
# Get keys that were added with the _prepare_for_class function
A : Optional[Any] = prepared_for_class.keys() - inputs_dict.keys()
A : List[str] = inspect.signature(model.call ).parameters
A : List[Any] = list(signature.keys() )
# Create a dictionary holding the location of the tensors in the tuple
A : Dict = {0: '''input_ids'''}
for label_key in label_keys:
A : Optional[int] = signature_names.index(__UpperCAmelCase )
A : int = label_key
A : Optional[int] = sorted(tuple_index_mapping.items() )
# Initialize a list with their default values, update the values and convert to a tuple
A : Tuple = []
for name in signature_names:
if name != "kwargs":
list_input.append(signature[name].default )
for index, value in sorted_tuple_index_mapping:
A : int = prepared_for_class[value]
A : Union[str, Any] = tuple(__UpperCAmelCase )
# Send to model
A : Any = model(tuple_input[:-1] )[0]
self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1] )
def snake_case ( self ) -> Optional[int]:
(
(
A
) , (
A
) , (
A
) , (
A
) , (
A
) , (
A
) , (
A
) , (
A
) ,
) : Union[str, Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase )
def snake_case ( self ) -> List[Any]:
(
(
A
) , (
A
) , (
A
) , (
A
) , (
A
) , (
A
) , (
A
) , (
A
) ,
) : List[Any] = self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
A : Dict = type
self.model_tester.create_and_check_model(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase )
def snake_case ( self ) -> Any:
(
(
A
) , (
A
) , (
A
) , (
A
) , (
A
) , (
A
) , (
A
) , (
A
) ,
) : Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(
__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase )
def snake_case ( self ) -> Any:
(
(
A
) , (
A
) , (
A
) , (
A
) , (
A
) , (
A
) , (
A
) , (
A
) ,
) : Union[str, Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(
__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase )
def snake_case ( self ) -> Optional[int]:
(
(
A
) , (
A
) , (
A
) , (
A
) , (
A
) , (
A
) , (
A
) , (
A
) ,
) : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(
__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase )
@slow
def snake_case ( self ) -> Optional[Any]:
for model_name in TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
A : Tuple = TFLayoutLMvaModel.from_pretrained(__UpperCAmelCase )
self.assertIsNotNone(__UpperCAmelCase )
def snake_case__ ( ):
A : Dict = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
return image
@require_tf
class __lowercase ( unittest.TestCase ):
"""simple docstring"""
@cached_property
def snake_case ( self ) -> Tuple:
return LayoutLMvaImageProcessor(apply_ocr=__UpperCAmelCase ) if is_vision_available() else None
@slow
def snake_case ( self ) -> Tuple:
A : Dict = TFLayoutLMvaModel.from_pretrained('''microsoft/layoutlmv3-base''' )
A : List[str] = self.default_image_processor
A : str = prepare_img()
A : List[str] = image_processor(images=__UpperCAmelCase , return_tensors='''tf''' ).pixel_values
A : Any = tf.constant([[1, 2]] )
A : int = tf.expand_dims(tf.constant([[1, 2, 3, 4], [5, 6, 7, 8]] ) , axis=0 )
# forward pass
A : Tuple = model(input_ids=__UpperCAmelCase , bbox=__UpperCAmelCase , pixel_values=__UpperCAmelCase , training=__UpperCAmelCase )
# verify the logits
A : List[str] = (1, 1_99, 7_68)
self.assertEqual(outputs.last_hidden_state.shape , __UpperCAmelCase )
A : str = tf.constant(
[[-0.0_5_2_9, 0.3_6_1_8, 0.1_6_3_2], [-0.1_5_8_7, -0.1_6_6_7, -0.0_4_0_0], [-0.1_5_5_7, -0.1_6_7_1, -0.0_5_0_5]] )
self.assertTrue(np.allclose(outputs.last_hidden_state[0, :3, :3] , __UpperCAmelCase , atol=1E-4 ) )
| 542 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
EulerAncestralDiscreteScheduler,
LMSDiscreteScheduler,
PNDMScheduler,
StableDiffusionInstructPixaPixPipeline,
UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.utils import floats_tensor, load_image, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..pipeline_params import (
IMAGE_TO_IMAGE_IMAGE_PARAMS,
TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin
enable_full_determinism()
class __lowercase ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , unittest.TestCase ):
"""simple docstring"""
UpperCAmelCase_ : Optional[int] = StableDiffusionInstructPixaPixPipeline
UpperCAmelCase_ : str = TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'''height''', '''width''', '''cross_attention_kwargs'''}
UpperCAmelCase_ : List[Any] = TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
UpperCAmelCase_ : Optional[int] = IMAGE_TO_IMAGE_IMAGE_PARAMS
UpperCAmelCase_ : Any = IMAGE_TO_IMAGE_IMAGE_PARAMS
def snake_case ( self ) -> List[Any]:
torch.manual_seed(0 )
A : Tuple = UNetaDConditionModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=8 , out_channels=4 , down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''') , up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''') , cross_attention_dim=32 , )
A : Any = PNDMScheduler(skip_prk_steps=__UpperCAmelCase )
torch.manual_seed(0 )
A : Dict = 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 )
A : str = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=32 , intermediate_size=37 , layer_norm_eps=1E-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=10_00 , )
A : Dict = CLIPTextModel(__UpperCAmelCase )
A : Dict = CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''' )
A : int = {
'''unet''': unet,
'''scheduler''': scheduler,
'''vae''': vae,
'''text_encoder''': text_encoder,
'''tokenizer''': tokenizer,
'''safety_checker''': None,
'''feature_extractor''': None,
}
return components
def snake_case ( self , __UpperCAmelCase , __UpperCAmelCase=0 ) -> Dict:
A : Any = floats_tensor((1, 3, 32, 32) , rng=random.Random(__UpperCAmelCase ) ).to(__UpperCAmelCase )
A : Dict = image.cpu().permute(0 , 2 , 3 , 1 )[0]
A : Optional[Any] = Image.fromarray(np.uinta(__UpperCAmelCase ) ).convert('''RGB''' )
if str(__UpperCAmelCase ).startswith('''mps''' ):
A : str = torch.manual_seed(__UpperCAmelCase )
else:
A : Optional[Any] = torch.Generator(device=__UpperCAmelCase ).manual_seed(__UpperCAmelCase )
A : List[Any] = {
'''prompt''': '''A painting of a squirrel eating a burger''',
'''image''': image,
'''generator''': generator,
'''num_inference_steps''': 2,
'''guidance_scale''': 6.0,
'''image_guidance_scale''': 1,
'''output_type''': '''numpy''',
}
return inputs
def snake_case ( self ) -> Union[str, Any]:
A : Optional[Any] = '''cpu''' # ensure determinism for the device-dependent torch.Generator
A : Dict = self.get_dummy_components()
A : str = StableDiffusionInstructPixaPixPipeline(**__UpperCAmelCase )
A : Any = sd_pipe.to(__UpperCAmelCase )
sd_pipe.set_progress_bar_config(disable=__UpperCAmelCase )
A : Optional[Any] = self.get_dummy_inputs(__UpperCAmelCase )
A : int = sd_pipe(**__UpperCAmelCase ).images
A : List[str] = image[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
A : Optional[Any] = np.array([0.7_5_2_6, 0.3_7_5_0, 0.4_5_4_7, 0.6_1_1_7, 0.5_8_6_6, 0.5_0_1_6, 0.4_3_2_7, 0.5_6_4_2, 0.4_8_1_5] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-3
def snake_case ( self ) -> int:
A : Any = '''cpu''' # ensure determinism for the device-dependent torch.Generator
A : Optional[Any] = self.get_dummy_components()
A : Any = StableDiffusionInstructPixaPixPipeline(**__UpperCAmelCase )
A : str = sd_pipe.to(__UpperCAmelCase )
sd_pipe.set_progress_bar_config(disable=__UpperCAmelCase )
A : str = self.get_dummy_inputs(__UpperCAmelCase )
A : Optional[int] = '''french fries'''
A : Any = sd_pipe(**__UpperCAmelCase , negative_prompt=__UpperCAmelCase )
A : Optional[int] = output.images
A : str = image[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
A : Any = np.array([0.7_5_1_1, 0.3_6_4_2, 0.4_5_5_3, 0.6_2_3_6, 0.5_7_9_7, 0.5_0_1_3, 0.4_3_4_3, 0.5_6_1_1, 0.4_8_3_1] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-3
def snake_case ( self ) -> Tuple:
A : List[Any] = '''cpu''' # ensure determinism for the device-dependent torch.Generator
A : List[Any] = self.get_dummy_components()
A : Optional[int] = StableDiffusionInstructPixaPixPipeline(**__UpperCAmelCase )
A : Tuple = sd_pipe.to(__UpperCAmelCase )
sd_pipe.set_progress_bar_config(disable=__UpperCAmelCase )
A : str = self.get_dummy_inputs(__UpperCAmelCase )
A : int = [inputs['''prompt''']] * 2
A : List[str] = np.array(inputs['''image'''] ).astype(np.floataa ) / 2_5_5.0
A : Any = torch.from_numpy(__UpperCAmelCase ).unsqueeze(0 ).to(__UpperCAmelCase )
A : Union[str, Any] = image / 2 + 0.5
A : str = image.permute(0 , 3 , 1 , 2 )
A : List[str] = image.repeat(2 , 1 , 1 , 1 )
A : Union[str, Any] = sd_pipe(**__UpperCAmelCase ).images
A : int = image[-1, -3:, -3:, -1]
assert image.shape == (2, 32, 32, 3)
A : Any = np.array([0.5_8_1_2, 0.5_7_4_8, 0.5_2_2_2, 0.5_9_0_8, 0.5_6_9_5, 0.7_1_7_4, 0.6_8_0_4, 0.5_5_2_3, 0.5_5_7_9] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-3
def snake_case ( self ) -> Dict:
A : int = '''cpu''' # ensure determinism for the device-dependent torch.Generator
A : List[Any] = self.get_dummy_components()
A : Union[str, Any] = EulerAncestralDiscreteScheduler(
beta_start=0.0_0_0_8_5 , beta_end=0.0_1_2 , beta_schedule='''scaled_linear''' )
A : int = StableDiffusionInstructPixaPixPipeline(**__UpperCAmelCase )
A : Tuple = sd_pipe.to(__UpperCAmelCase )
sd_pipe.set_progress_bar_config(disable=__UpperCAmelCase )
A : Tuple = self.get_dummy_inputs(__UpperCAmelCase )
A : Optional[Any] = sd_pipe(**__UpperCAmelCase ).images
A : Dict = image[0, -3:, -3:, -1]
A : Optional[int] = [round(__UpperCAmelCase , 4 ) for x in image_slice.flatten().tolist()]
print(''','''.join([str(__UpperCAmelCase ) for x in slice] ) )
assert image.shape == (1, 32, 32, 3)
A : Dict = np.array([0.7_4_1_7, 0.3_8_4_2, 0.4_7_3_2, 0.5_7_7_6, 0.5_8_9_1, 0.5_1_3_9, 0.4_0_5_2, 0.5_6_7_3, 0.4_9_8_6] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-3
def snake_case ( self ) -> Union[str, Any]:
super().test_inference_batch_single_identical(expected_max_diff=3E-3 )
def snake_case ( self ) -> Union[str, Any]:
A : int = self.get_dummy_components()
A : int = StableDiffusionInstructPixaPixPipeline(**__UpperCAmelCase )
A : Any = VaeImageProcessor(do_resize=__UpperCAmelCase , do_normalize=__UpperCAmelCase )
A : Optional[Any] = pipe.to(__UpperCAmelCase )
pipe.set_progress_bar_config(disable=__UpperCAmelCase )
A : Union[str, Any] = pipe(**self.get_dummy_inputs_by_type(__UpperCAmelCase , input_image_type='''pt''' ) )[0]
A : Optional[Any] = components['''vae''']
A : Dict = self.get_dummy_inputs_by_type(__UpperCAmelCase , input_image_type='''pt''' )
for image_param in self.image_latents_params:
if image_param in inputs.keys():
A : Optional[Any] = vae.encode(inputs[image_param] ).latent_dist.mode()
A : List[str] = pipe(**__UpperCAmelCase )[0]
A : Any = np.abs(out - out_latents_inputs ).max()
self.assertLess(__UpperCAmelCase , 1E-4 , '''passing latents as image input generate different result from passing image''' )
@slow
@require_torch_gpu
class __lowercase ( unittest.TestCase ):
"""simple docstring"""
def snake_case ( self ) -> str:
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def snake_case ( self , __UpperCAmelCase=0 ) -> Tuple:
A : List[str] = torch.manual_seed(__UpperCAmelCase )
A : List[str] = load_image(
'''https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_pix2pix/example.jpg''' )
A : Dict = {
'''prompt''': '''turn him into a cyborg''',
'''image''': image,
'''generator''': generator,
'''num_inference_steps''': 3,
'''guidance_scale''': 7.5,
'''image_guidance_scale''': 1.0,
'''output_type''': '''numpy''',
}
return inputs
def snake_case ( self ) -> Optional[int]:
A : Union[str, Any] = StableDiffusionInstructPixaPixPipeline.from_pretrained(
'''timbrooks/instruct-pix2pix''' , safety_checker=__UpperCAmelCase )
pipe.to(__UpperCAmelCase )
pipe.set_progress_bar_config(disable=__UpperCAmelCase )
pipe.enable_attention_slicing()
A : Optional[int] = self.get_inputs()
A : Any = pipe(**__UpperCAmelCase ).images
A : Union[str, Any] = image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 5_12, 5_12, 3)
A : Dict = np.array([0.5_9_0_2, 0.6_0_1_5, 0.6_0_2_7, 0.5_9_8_3, 0.6_0_9_2, 0.6_0_6_1, 0.5_7_6_5, 0.5_7_8_5, 0.5_5_5_5] )
assert np.abs(expected_slice - image_slice ).max() < 1E-3
def snake_case ( self ) -> Dict:
A : str = StableDiffusionInstructPixaPixPipeline.from_pretrained(
'''timbrooks/instruct-pix2pix''' , safety_checker=__UpperCAmelCase )
A : List[str] = LMSDiscreteScheduler.from_config(pipe.scheduler.config )
pipe.to(__UpperCAmelCase )
pipe.set_progress_bar_config(disable=__UpperCAmelCase )
pipe.enable_attention_slicing()
A : Union[str, Any] = self.get_inputs()
A : Union[str, Any] = pipe(**__UpperCAmelCase ).images
A : str = image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 5_12, 5_12, 3)
A : Dict = np.array([0.6_5_7_8, 0.6_8_1_7, 0.6_9_7_2, 0.6_7_6_1, 0.6_8_5_6, 0.6_9_1_6, 0.6_4_2_8, 0.6_5_1_6, 0.6_3_0_1] )
assert np.abs(expected_slice - image_slice ).max() < 1E-3
def snake_case ( self ) -> List[Any]:
A : List[Any] = StableDiffusionInstructPixaPixPipeline.from_pretrained(
'''timbrooks/instruct-pix2pix''' , safety_checker=__UpperCAmelCase )
A : List[str] = DDIMScheduler.from_config(pipe.scheduler.config )
pipe.to(__UpperCAmelCase )
pipe.set_progress_bar_config(disable=__UpperCAmelCase )
pipe.enable_attention_slicing()
A : str = self.get_inputs()
A : List[Any] = pipe(**__UpperCAmelCase ).images
A : str = image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 5_12, 5_12, 3)
A : Any = np.array([0.3_8_2_8, 0.3_8_3_4, 0.3_8_1_8, 0.3_7_9_2, 0.3_8_6_5, 0.3_7_5_2, 0.3_7_9_2, 0.3_8_4_7, 0.3_7_5_3] )
assert np.abs(expected_slice - image_slice ).max() < 1E-3
def snake_case ( self ) -> Optional[Any]:
A : Optional[Any] = 0
def callback_fn(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ) -> None:
A : List[Any] = True
nonlocal number_of_steps
number_of_steps += 1
if step == 1:
A : Optional[Any] = latents.detach().cpu().numpy()
assert latents.shape == (1, 4, 64, 64)
A : Union[str, Any] = latents[0, -3:, -3:, -1]
A : List[str] = np.array([-0.2_4_6_3, -0.4_6_4_4, -0.9_7_5_6, 1.5_1_7_6, 1.4_4_1_4, 0.7_8_6_6, 0.9_8_9_7, 0.8_5_2_1, 0.7_9_8_3] )
assert np.abs(latents_slice.flatten() - expected_slice ).max() < 5E-2
elif step == 2:
A : int = latents.detach().cpu().numpy()
assert latents.shape == (1, 4, 64, 64)
A : Optional[int] = latents[0, -3:, -3:, -1]
A : str = np.array([-0.2_6_4_4, -0.4_6_2_6, -0.9_6_5_3, 1.5_1_7_6, 1.4_5_5_1, 0.7_6_8_6, 0.9_8_0_5, 0.8_4_5_2, 0.8_1_1_5] )
assert np.abs(latents_slice.flatten() - expected_slice ).max() < 5E-2
A : Optional[Any] = False
A : str = StableDiffusionInstructPixaPixPipeline.from_pretrained(
'''timbrooks/instruct-pix2pix''' , safety_checker=__UpperCAmelCase , torch_dtype=torch.floataa )
A : List[str] = pipe.to(__UpperCAmelCase )
pipe.set_progress_bar_config(disable=__UpperCAmelCase )
pipe.enable_attention_slicing()
A : List[Any] = self.get_inputs()
pipe(**__UpperCAmelCase , callback=__UpperCAmelCase , callback_steps=1 )
assert callback_fn.has_been_called
assert number_of_steps == 3
def snake_case ( self ) -> Tuple:
torch.cuda.empty_cache()
torch.cuda.reset_max_memory_allocated()
torch.cuda.reset_peak_memory_stats()
A : Optional[Any] = StableDiffusionInstructPixaPixPipeline.from_pretrained(
'''timbrooks/instruct-pix2pix''' , safety_checker=__UpperCAmelCase , torch_dtype=torch.floataa )
A : Dict = pipe.to(__UpperCAmelCase )
pipe.set_progress_bar_config(disable=__UpperCAmelCase )
pipe.enable_attention_slicing(1 )
pipe.enable_sequential_cpu_offload()
A : Optional[Any] = self.get_inputs()
A : str = pipe(**__UpperCAmelCase )
A : Dict = torch.cuda.max_memory_allocated()
# make sure that less than 2.2 GB is allocated
assert mem_bytes < 2.2 * 10**9
def snake_case ( self ) -> int:
A : Union[str, Any] = self.get_inputs()
# resize to resolution that is divisible by 8 but not 16 or 32
A : Union[str, Any] = inputs['''image'''].resize((5_04, 5_04) )
A : str = '''timbrooks/instruct-pix2pix'''
A : Tuple = StableDiffusionInstructPixaPixPipeline.from_pretrained(
__UpperCAmelCase , safety_checker=__UpperCAmelCase , )
pipe.to(__UpperCAmelCase )
pipe.set_progress_bar_config(disable=__UpperCAmelCase )
pipe.enable_attention_slicing()
A : Tuple = pipe(**__UpperCAmelCase )
A : List[str] = output.images[0]
A : List[Any] = image[2_55:2_58, 3_83:3_86, -1]
assert image.shape == (5_04, 5_04, 3)
A : Any = np.array([0.2_7_2_6, 0.2_5_2_9, 0.2_6_6_4, 0.2_6_5_5, 0.2_6_4_1, 0.2_6_4_2, 0.2_5_9_1, 0.2_6_4_9, 0.2_5_9_0] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 5E-3
| 542 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase__ : List[Any] = logging.get_logger(__name__)
UpperCamelCase__ : Optional[int] = {
'google/switch-base-8': 'https://huggingface.co/google/switch-base-8/blob/main/config.json',
}
class _UpperCamelCase ( lowerCamelCase__ ):
'''simple docstring'''
_A : Dict = 'switch_transformers'
_A : Optional[int] = ['past_key_values']
_A : Tuple = {'hidden_size': 'd_model', 'num_attention_heads': 'num_heads', 'num_hidden_layers': 'num_layers'}
def __init__( self : Any , lowerCAmelCase__ : Optional[Any]=3_2_1_2_8 , lowerCAmelCase__ : List[str]=7_6_8 , lowerCAmelCase__ : List[Any]=6_4 , lowerCAmelCase__ : List[str]=2_0_4_8 , lowerCAmelCase__ : Any=6_4 , lowerCAmelCase__ : Optional[Any]=1_2 , lowerCAmelCase__ : List[str]=3 , lowerCAmelCase__ : Any=1_2 , lowerCAmelCase__ : Tuple=3 , lowerCAmelCase__ : str=1_2 , lowerCAmelCase__ : Optional[Any]=8 , lowerCAmelCase__ : Dict=False , lowerCAmelCase__ : Dict=0.01 , lowerCAmelCase__ : str="float32" , lowerCAmelCase__ : Tuple=False , lowerCAmelCase__ : int=3_2 , lowerCAmelCase__ : Tuple=1_2_8 , lowerCAmelCase__ : int=0.1 , lowerCAmelCase__ : Optional[int]=1E-6 , lowerCAmelCase__ : Optional[Any]=0.0_01 , lowerCAmelCase__ : int=0.0_01 , lowerCAmelCase__ : Dict=1.0 , lowerCAmelCase__ : Any="relu" , lowerCAmelCase__ : Optional[Any]=True , lowerCAmelCase__ : Optional[Any]=False , lowerCAmelCase__ : List[Any]=True , lowerCAmelCase__ : Union[str, Any]=0 , lowerCAmelCase__ : List[Any]=1 , **lowerCAmelCase__ : Dict , ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : int = vocab_size
__SCREAMING_SNAKE_CASE : Any = d_model
__SCREAMING_SNAKE_CASE : Optional[Any] = d_kv
__SCREAMING_SNAKE_CASE : List[str] = d_ff
__SCREAMING_SNAKE_CASE : Optional[int] = num_sparse_encoder_layers
__SCREAMING_SNAKE_CASE : Dict = num_layers
__SCREAMING_SNAKE_CASE : List[str] = (
num_decoder_layers if num_decoder_layers is not None else self.num_layers
) # default = symmetry
__SCREAMING_SNAKE_CASE : Optional[Any] = num_sparse_decoder_layers
# This tells us, each how many encoder layer we'll have to set a sparse layer.
if self.num_sparse_encoder_layers > 0:
__SCREAMING_SNAKE_CASE : Dict = self.num_layers // self.num_sparse_encoder_layers
else:
__SCREAMING_SNAKE_CASE : List[str] = self.num_layers # HACK: this will create 0 sparse layers
# This tells us, each how many encoder layer we'll have to set a sparse layer.
if self.num_sparse_decoder_layers > 0:
__SCREAMING_SNAKE_CASE : Any = self.num_decoder_layers // self.num_sparse_decoder_layers
else:
__SCREAMING_SNAKE_CASE : List[str] = self.num_decoder_layers # HACK: this will create 0 sparse layers
__SCREAMING_SNAKE_CASE : Tuple = num_heads
__SCREAMING_SNAKE_CASE : int = num_experts
__SCREAMING_SNAKE_CASE : Optional[int] = expert_capacity
__SCREAMING_SNAKE_CASE : Optional[int] = router_bias
__SCREAMING_SNAKE_CASE : Union[str, Any] = router_jitter_noise
if router_dtype not in ["float32", "float16", "bfloat16"]:
raise ValueError(F"`router_dtype` must be one of 'float32', 'float16' or 'bfloat16', got {router_dtype}" )
__SCREAMING_SNAKE_CASE : int = router_dtype
__SCREAMING_SNAKE_CASE : Tuple = router_ignore_padding_tokens
__SCREAMING_SNAKE_CASE : List[str] = relative_attention_num_buckets
__SCREAMING_SNAKE_CASE : Tuple = relative_attention_max_distance
__SCREAMING_SNAKE_CASE : List[str] = dropout_rate
__SCREAMING_SNAKE_CASE : List[Any] = layer_norm_epsilon
__SCREAMING_SNAKE_CASE : str = initializer_factor
__SCREAMING_SNAKE_CASE : Dict = feed_forward_proj
__SCREAMING_SNAKE_CASE : str = use_cache
__SCREAMING_SNAKE_CASE : List[str] = add_router_probs
__SCREAMING_SNAKE_CASE : Dict = router_z_loss_coef
__SCREAMING_SNAKE_CASE : Union[str, Any] = router_aux_loss_coef
__SCREAMING_SNAKE_CASE : Tuple = self.feed_forward_proj.split("""-""" )
__SCREAMING_SNAKE_CASE : int = act_info[-1]
__SCREAMING_SNAKE_CASE : Union[str, Any] = act_info[0] == '''gated'''
if len(UpperCAmelCase__ ) > 1 and act_info[0] != "gated" or len(UpperCAmelCase__ ) > 2:
raise ValueError(
F"`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer."
"""Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. """
"""\'gated-gelu\' or \'relu\'""" )
# for backwards compatibility
if feed_forward_proj == "gated-gelu":
__SCREAMING_SNAKE_CASE : Tuple = '''gelu_new'''
super().__init__(
pad_token_id=UpperCAmelCase__ , eos_token_id=UpperCAmelCase__ , is_encoder_decoder=UpperCAmelCase__ , **UpperCAmelCase__ , ) | 718 |
'''simple docstring'''
import copy
from typing import Dict, Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
from ..detr import DetrConfig
from ..swin import SwinConfig
UpperCamelCase__ : Optional[int] = {
'''facebook/maskformer-swin-base-ade''': (
'''https://huggingface.co/facebook/maskformer-swin-base-ade/blob/main/config.json'''
)
# See all MaskFormer models at https://huggingface.co/models?filter=maskformer
}
UpperCamelCase__ : Union[str, Any] = logging.get_logger(__name__)
class _UpperCamelCase ( lowerCamelCase__ ):
'''simple docstring'''
_A : Optional[Any] = '''maskformer'''
_A : Optional[int] = {'''hidden_size''': '''mask_feature_size'''}
_A : int = ['''resnet''', '''swin''']
_A : Any = ['''detr''']
def __init__( self : Any , lowerCAmelCase__ : int = 2_5_6 , lowerCAmelCase__ : int = 2_5_6 , lowerCAmelCase__ : float = 0.1 , lowerCAmelCase__ : bool = False , lowerCAmelCase__ : Optional[Dict] = None , lowerCAmelCase__ : Optional[Dict] = None , lowerCAmelCase__ : float = 0.02 , lowerCAmelCase__ : float = 1.0 , lowerCAmelCase__ : float = 1.0 , lowerCAmelCase__ : float = 1.0 , lowerCAmelCase__ : float = 20.0 , lowerCAmelCase__ : Optional[bool] = None , **lowerCAmelCase__ : Any , ):
"""simple docstring"""
if backbone_config is None:
# fall back to https://huggingface.co/microsoft/swin-base-patch4-window12-384-in22k
__SCREAMING_SNAKE_CASE : Any = SwinConfig(
image_size=3_8_4 , in_channels=3 , patch_size=4 , embed_dim=1_2_8 , depths=[2, 2, 1_8, 2] , num_heads=[4, 8, 1_6, 3_2] , window_size=1_2 , drop_path_rate=0.3 , out_features=["""stage1""", """stage2""", """stage3""", """stage4"""] , )
if isinstance(lowerCAmelCase__ , lowerCAmelCase__ ):
__SCREAMING_SNAKE_CASE : Dict = backbone_config.pop("""model_type""" )
__SCREAMING_SNAKE_CASE : List[str] = CONFIG_MAPPING[backbone_model_type]
__SCREAMING_SNAKE_CASE : Union[str, Any] = config_class.from_dict(lowerCAmelCase__ )
# verify that the backbone is supported
if backbone_config.model_type not in self.backbones_supported:
logger.warning_once(
F"Backbone {backbone_config.model_type} is not a supported model and may not be compatible with MaskFormer. "
F"Supported model types: {','.join(self.backbones_supported )}" )
if decoder_config is None:
# fall back to https://huggingface.co/facebook/detr-resnet-50
__SCREAMING_SNAKE_CASE : List[str] = DetrConfig()
else:
# verify that the decoder is supported
__SCREAMING_SNAKE_CASE : List[Any] = (
decoder_config.pop("""model_type""" ) if isinstance(lowerCAmelCase__ , lowerCAmelCase__ ) else decoder_config.model_type
)
if decoder_type not in self.decoders_supported:
raise ValueError(
F"Transformer Decoder {decoder_type} not supported, please use one of"
F" {','.join(self.decoders_supported )}" )
if isinstance(lowerCAmelCase__ , lowerCAmelCase__ ):
__SCREAMING_SNAKE_CASE : int = CONFIG_MAPPING[decoder_type]
__SCREAMING_SNAKE_CASE : Dict = config_class.from_dict(lowerCAmelCase__ )
__SCREAMING_SNAKE_CASE : Any = backbone_config
__SCREAMING_SNAKE_CASE : Optional[int] = decoder_config
# main feature dimension for the model
__SCREAMING_SNAKE_CASE : List[str] = fpn_feature_size
__SCREAMING_SNAKE_CASE : int = mask_feature_size
# initializer
__SCREAMING_SNAKE_CASE : str = init_std
__SCREAMING_SNAKE_CASE : Optional[Any] = init_xavier_std
# Hungarian matcher && loss
__SCREAMING_SNAKE_CASE : Tuple = cross_entropy_weight
__SCREAMING_SNAKE_CASE : Dict = dice_weight
__SCREAMING_SNAKE_CASE : Union[str, Any] = mask_weight
__SCREAMING_SNAKE_CASE : Optional[Any] = use_auxiliary_loss
__SCREAMING_SNAKE_CASE : Union[str, Any] = no_object_weight
__SCREAMING_SNAKE_CASE : Dict = output_auxiliary_logits
__SCREAMING_SNAKE_CASE : Any = self.decoder_config.encoder_attention_heads
__SCREAMING_SNAKE_CASE : int = self.decoder_config.num_hidden_layers
super().__init__(**lowerCAmelCase__ )
@classmethod
def UpperCamelCase__ ( cls : List[Any] , lowerCAmelCase__ : PretrainedConfig , lowerCAmelCase__ : PretrainedConfig , **lowerCAmelCase__ : Optional[int] ):
"""simple docstring"""
return cls(
backbone_config=lowerCAmelCase__ , decoder_config=lowerCAmelCase__ , **lowerCAmelCase__ , )
def UpperCamelCase__ ( self : Union[str, Any] ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : str = copy.deepcopy(self.__dict__ )
__SCREAMING_SNAKE_CASE : Dict = self.backbone_config.to_dict()
__SCREAMING_SNAKE_CASE : str = self.decoder_config.to_dict()
__SCREAMING_SNAKE_CASE : List[str] = self.__class__.model_type
return output | 178 | 0 |
from __future__ import annotations
class A_ :
"""simple docstring"""
def __init__( self : Optional[int] ,__A : Dict ,__A : List[str] ) -> Optional[Any]:
_lowercase , _lowercase = text, pattern
_lowercase , _lowercase = len(a__ ), len(a__ )
def __UpperCAmelCase ( self : int ,__A : int ) -> List[Any]:
for i in range(self.patLen - 1 ,-1 ,-1 ):
if char == self.pattern[i]:
return i
return -1
def __UpperCAmelCase ( self : List[Any] ,__A : Optional[int] ) -> Optional[int]:
for i in range(self.patLen - 1 ,-1 ,-1 ):
if self.pattern[i] != self.text[current_pos + i]:
return current_pos + i
return -1
def __UpperCAmelCase ( self : Tuple ) -> int:
# searches pattern in text and returns index positions
_lowercase = []
for i in range(self.textLen - self.patLen + 1 ):
_lowercase = self.mismatch_in_text(a__ )
if mismatch_index == -1:
positions.append(a__ )
else:
_lowercase = self.match_in_pattern(self.text[mismatch_index] )
_lowercase = (
mismatch_index - match_index
) # shifting index lgtm [py/multiple-definition]
return positions
snake_case = """ABAABA"""
snake_case = """AB"""
snake_case = BoyerMooreSearch(text, pattern)
snake_case = bms.bad_character_heuristic()
if len(positions) == 0:
print("""No match found""")
else:
print("""Pattern found in following positions: """)
print(positions) | 67 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowercase = logging.get_logger(__name__)
class _UpperCAmelCase ( A__ ):
UpperCamelCase__ = '''timm_backbone'''
def __init__( self , a__=None , a__=3 , a__=True , a__=True , a__=None , **a__ , ):
super().__init__(**a__)
A__ = backbone
A__ = num_channels
A__ = features_only
A__ = use_pretrained_backbone
A__ = True
A__ = out_indices if out_indices is not None else (-1,)
| 632 | 0 |
"""simple docstring"""
import argparse
from pathlib import Path
import torch
from packaging import version
from torch.onnx import export
from diffusers import AutoencoderKL
lowerCamelCase = version.parse(version.parse(torch.__version__).base_version) < version.parse("""1.11""")
def a__ ( lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__=False , ):
output_path.parent.mkdir(parents=lowerCAmelCase__ , exist_ok=lowerCAmelCase__ )
# 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(
lowerCAmelCase__ , lowerCAmelCase__ , f=output_path.as_posix() , input_names=lowerCAmelCase__ , output_names=lowerCAmelCase__ , dynamic_axes=lowerCAmelCase__ , do_constant_folding=lowerCAmelCase__ , use_external_data_format=lowerCAmelCase__ , enable_onnx_checker=lowerCAmelCase__ , opset_version=lowerCAmelCase__ , )
else:
export(
lowerCAmelCase__ , lowerCAmelCase__ , f=output_path.as_posix() , input_names=lowerCAmelCase__ , output_names=lowerCAmelCase__ , dynamic_axes=lowerCAmelCase__ , do_constant_folding=lowerCAmelCase__ , opset_version=lowerCAmelCase__ , )
@torch.no_grad()
def a__ ( lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ = False ):
UpperCAmelCase_ = torch.floataa if fpaa else torch.floataa
if fpaa and torch.cuda.is_available():
UpperCAmelCase_ = "cuda"
elif fpaa and not torch.cuda.is_available():
raise ValueError("`float16` model export is only supported on GPUs with CUDA" )
else:
UpperCAmelCase_ = "cpu"
UpperCAmelCase_ = Path(lowerCAmelCase__ )
# VAE DECODER
UpperCAmelCase_ = AutoencoderKL.from_pretrained(model_path + "/vae" )
UpperCAmelCase_ = vae_decoder.config.latent_channels
# forward only through the decoder part
UpperCAmelCase_ = vae_decoder.decode
onnx_export(
lowerCAmelCase__ , model_args=(
torch.randn(1 , lowerCAmelCase__ , 25 , 25 ).to(device=lowerCAmelCase__ , dtype=lowerCAmelCase__ ),
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=lowerCAmelCase__ , )
del vae_decoder
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()
print(args.output_path)
convert_models(args.model_path, args.output_path, args.opset, args.fpaa)
print("""SD: Done: ONNX""")
| 14 |
"""simple docstring"""
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
from ...image_utils import (
IMAGENET_STANDARD_MEAN,
IMAGENET_STANDARD_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_vision_available, logging
if is_vision_available():
import PIL
lowerCamelCase = logging.get_logger(__name__)
class lowercase__ ( SCREAMING_SNAKE_CASE ):
'''simple docstring'''
UpperCamelCase = ['''pixel_values''']
def __init__( self : str , _UpperCAmelCase : bool = True , _UpperCAmelCase : Dict[str, int] = None , _UpperCAmelCase : float = None , _UpperCAmelCase : PILImageResampling = PILImageResampling.BILINEAR , _UpperCAmelCase : bool = True , _UpperCAmelCase : Union[int, float] = 1 / 255 , _UpperCAmelCase : bool = True , _UpperCAmelCase : Optional[Union[float, List[float]]] = None , _UpperCAmelCase : Optional[Union[float, List[float]]] = None , **_UpperCAmelCase : Optional[int] , ) -> None:
'''simple docstring'''
super().__init__(**_UpperCAmelCase )
UpperCAmelCase_ = size if size is not None else {"shortest_edge": 384}
UpperCAmelCase_ = get_size_dict(_UpperCAmelCase , default_to_square=_UpperCAmelCase )
UpperCAmelCase_ = do_resize
UpperCAmelCase_ = size
# Default value set here for backwards compatibility where the value in config is None
UpperCAmelCase_ = crop_pct if crop_pct is not None else 224 / 256
UpperCAmelCase_ = resample
UpperCAmelCase_ = do_rescale
UpperCAmelCase_ = rescale_factor
UpperCAmelCase_ = do_normalize
UpperCAmelCase_ = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
UpperCAmelCase_ = image_std if image_std is not None else IMAGENET_STANDARD_STD
def lowercase__ ( self : Union[str, Any] , _UpperCAmelCase : np.ndarray , _UpperCAmelCase : Dict[str, int] , _UpperCAmelCase : float , _UpperCAmelCase : PILImageResampling = PILImageResampling.BICUBIC , _UpperCAmelCase : Optional[Union[str, ChannelDimension]] = None , **_UpperCAmelCase : Optional[int] , ) -> np.ndarray:
'''simple docstring'''
UpperCAmelCase_ = get_size_dict(_UpperCAmelCase , default_to_square=_UpperCAmelCase )
if "shortest_edge" not in size:
raise ValueError(F"""Size dictionary must contain 'shortest_edge' key. Got {size.keys()}""" )
UpperCAmelCase_ = size["shortest_edge"]
if shortest_edge < 384:
# maintain same ratio, resizing shortest edge to shortest_edge/crop_pct
UpperCAmelCase_ = int(shortest_edge / crop_pct )
UpperCAmelCase_ = get_resize_output_image_size(_UpperCAmelCase , size=_UpperCAmelCase , default_to_square=_UpperCAmelCase )
UpperCAmelCase_ = resize(image=_UpperCAmelCase , size=_UpperCAmelCase , resample=_UpperCAmelCase , data_format=_UpperCAmelCase , **_UpperCAmelCase )
# then crop to (shortest_edge, shortest_edge)
return center_crop(image=_UpperCAmelCase , size=(shortest_edge, shortest_edge) , data_format=_UpperCAmelCase , **_UpperCAmelCase )
else:
# warping (no cropping) when evaluated at 384 or larger
return resize(
_UpperCAmelCase , size=(shortest_edge, shortest_edge) , resample=_UpperCAmelCase , data_format=_UpperCAmelCase , **_UpperCAmelCase )
def lowercase__ ( self : Tuple , _UpperCAmelCase : np.ndarray , _UpperCAmelCase : Union[int, float] , _UpperCAmelCase : Optional[Union[str, ChannelDimension]] = None , **_UpperCAmelCase : List[Any] , ) -> Any:
'''simple docstring'''
return rescale(_UpperCAmelCase , scale=_UpperCAmelCase , data_format=_UpperCAmelCase , **_UpperCAmelCase )
def lowercase__ ( self : Dict , _UpperCAmelCase : np.ndarray , _UpperCAmelCase : Union[float, List[float]] , _UpperCAmelCase : Union[float, List[float]] , _UpperCAmelCase : Optional[Union[str, ChannelDimension]] = None , **_UpperCAmelCase : List[str] , ) -> np.ndarray:
'''simple docstring'''
return normalize(_UpperCAmelCase , mean=_UpperCAmelCase , std=_UpperCAmelCase , data_format=_UpperCAmelCase , **_UpperCAmelCase )
def lowercase__ ( self : Union[str, Any] , _UpperCAmelCase : ImageInput , _UpperCAmelCase : bool = None , _UpperCAmelCase : Dict[str, int] = None , _UpperCAmelCase : float = None , _UpperCAmelCase : PILImageResampling = None , _UpperCAmelCase : bool = None , _UpperCAmelCase : float = None , _UpperCAmelCase : bool = None , _UpperCAmelCase : Optional[Union[float, List[float]]] = None , _UpperCAmelCase : Optional[Union[float, List[float]]] = None , _UpperCAmelCase : Optional[Union[str, TensorType]] = None , _UpperCAmelCase : ChannelDimension = ChannelDimension.FIRST , **_UpperCAmelCase : Optional[int] , ) -> PIL.Image.Image:
'''simple docstring'''
UpperCAmelCase_ = do_resize if do_resize is not None else self.do_resize
UpperCAmelCase_ = crop_pct if crop_pct is not None else self.crop_pct
UpperCAmelCase_ = resample if resample is not None else self.resample
UpperCAmelCase_ = do_rescale if do_rescale is not None else self.do_rescale
UpperCAmelCase_ = rescale_factor if rescale_factor is not None else self.rescale_factor
UpperCAmelCase_ = do_normalize if do_normalize is not None else self.do_normalize
UpperCAmelCase_ = image_mean if image_mean is not None else self.image_mean
UpperCAmelCase_ = image_std if image_std is not None else self.image_std
UpperCAmelCase_ = size if size is not None else self.size
UpperCAmelCase_ = get_size_dict(_UpperCAmelCase , default_to_square=_UpperCAmelCase )
UpperCAmelCase_ = make_list_of_images(_UpperCAmelCase )
if not valid_images(_UpperCAmelCase ):
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_resize and size["shortest_edge"] < 384 and crop_pct is None:
raise ValueError("crop_pct must be specified if size < 384." )
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.
UpperCAmelCase_ = [to_numpy_array(_UpperCAmelCase ) for image in images]
if do_resize:
UpperCAmelCase_ = [self.resize(image=_UpperCAmelCase , size=_UpperCAmelCase , crop_pct=_UpperCAmelCase , resample=_UpperCAmelCase ) for image in images]
if do_rescale:
UpperCAmelCase_ = [self.rescale(image=_UpperCAmelCase , scale=_UpperCAmelCase ) for image in images]
if do_normalize:
UpperCAmelCase_ = [self.normalize(image=_UpperCAmelCase , mean=_UpperCAmelCase , std=_UpperCAmelCase ) for image in images]
UpperCAmelCase_ = [to_channel_dimension_format(_UpperCAmelCase , _UpperCAmelCase ) for image in images]
UpperCAmelCase_ = {"pixel_values": images}
return BatchFeature(data=_UpperCAmelCase , tensor_type=_UpperCAmelCase )
| 14 | 1 |
from collections import OrderedDict
from ...utils import logging
from .auto_factory import _BaseAutoModelClass, _LazyAutoMapping, auto_class_update
from .configuration_auto import CONFIG_MAPPING_NAMES
UpperCAmelCase_ = logging.get_logger(__name__)
UpperCAmelCase_ = OrderedDict(
[
# Base model mapping
("albert", "FlaxAlbertModel"),
("bart", "FlaxBartModel"),
("beit", "FlaxBeitModel"),
("bert", "FlaxBertModel"),
("big_bird", "FlaxBigBirdModel"),
("blenderbot", "FlaxBlenderbotModel"),
("blenderbot-small", "FlaxBlenderbotSmallModel"),
("clip", "FlaxCLIPModel"),
("distilbert", "FlaxDistilBertModel"),
("electra", "FlaxElectraModel"),
("gpt-sw3", "FlaxGPT2Model"),
("gpt2", "FlaxGPT2Model"),
("gpt_neo", "FlaxGPTNeoModel"),
("gptj", "FlaxGPTJModel"),
("longt5", "FlaxLongT5Model"),
("marian", "FlaxMarianModel"),
("mbart", "FlaxMBartModel"),
("mt5", "FlaxMT5Model"),
("opt", "FlaxOPTModel"),
("pegasus", "FlaxPegasusModel"),
("regnet", "FlaxRegNetModel"),
("resnet", "FlaxResNetModel"),
("roberta", "FlaxRobertaModel"),
("roberta-prelayernorm", "FlaxRobertaPreLayerNormModel"),
("roformer", "FlaxRoFormerModel"),
("t5", "FlaxT5Model"),
("vision-text-dual-encoder", "FlaxVisionTextDualEncoderModel"),
("vit", "FlaxViTModel"),
("wav2vec2", "FlaxWav2Vec2Model"),
("whisper", "FlaxWhisperModel"),
("xglm", "FlaxXGLMModel"),
("xlm-roberta", "FlaxXLMRobertaModel"),
]
)
UpperCAmelCase_ = OrderedDict(
[
# Model for pre-training mapping
("albert", "FlaxAlbertForPreTraining"),
("bart", "FlaxBartForConditionalGeneration"),
("bert", "FlaxBertForPreTraining"),
("big_bird", "FlaxBigBirdForPreTraining"),
("electra", "FlaxElectraForPreTraining"),
("longt5", "FlaxLongT5ForConditionalGeneration"),
("mbart", "FlaxMBartForConditionalGeneration"),
("mt5", "FlaxMT5ForConditionalGeneration"),
("roberta", "FlaxRobertaForMaskedLM"),
("roberta-prelayernorm", "FlaxRobertaPreLayerNormForMaskedLM"),
("roformer", "FlaxRoFormerForMaskedLM"),
("t5", "FlaxT5ForConditionalGeneration"),
("wav2vec2", "FlaxWav2Vec2ForPreTraining"),
("whisper", "FlaxWhisperForConditionalGeneration"),
("xlm-roberta", "FlaxXLMRobertaForMaskedLM"),
]
)
UpperCAmelCase_ = OrderedDict(
[
# Model for Masked LM mapping
("albert", "FlaxAlbertForMaskedLM"),
("bart", "FlaxBartForConditionalGeneration"),
("bert", "FlaxBertForMaskedLM"),
("big_bird", "FlaxBigBirdForMaskedLM"),
("distilbert", "FlaxDistilBertForMaskedLM"),
("electra", "FlaxElectraForMaskedLM"),
("mbart", "FlaxMBartForConditionalGeneration"),
("roberta", "FlaxRobertaForMaskedLM"),
("roberta-prelayernorm", "FlaxRobertaPreLayerNormForMaskedLM"),
("roformer", "FlaxRoFormerForMaskedLM"),
("xlm-roberta", "FlaxXLMRobertaForMaskedLM"),
]
)
UpperCAmelCase_ = OrderedDict(
[
# Model for Seq2Seq Causal LM mapping
("bart", "FlaxBartForConditionalGeneration"),
("blenderbot", "FlaxBlenderbotForConditionalGeneration"),
("blenderbot-small", "FlaxBlenderbotSmallForConditionalGeneration"),
("encoder-decoder", "FlaxEncoderDecoderModel"),
("longt5", "FlaxLongT5ForConditionalGeneration"),
("marian", "FlaxMarianMTModel"),
("mbart", "FlaxMBartForConditionalGeneration"),
("mt5", "FlaxMT5ForConditionalGeneration"),
("pegasus", "FlaxPegasusForConditionalGeneration"),
("t5", "FlaxT5ForConditionalGeneration"),
]
)
UpperCAmelCase_ = OrderedDict(
[
# Model for Image-classsification
("beit", "FlaxBeitForImageClassification"),
("regnet", "FlaxRegNetForImageClassification"),
("resnet", "FlaxResNetForImageClassification"),
("vit", "FlaxViTForImageClassification"),
]
)
UpperCAmelCase_ = OrderedDict(
[
("vision-encoder-decoder", "FlaxVisionEncoderDecoderModel"),
]
)
UpperCAmelCase_ = OrderedDict(
[
# Model for Causal LM mapping
("bart", "FlaxBartForCausalLM"),
("bert", "FlaxBertForCausalLM"),
("big_bird", "FlaxBigBirdForCausalLM"),
("electra", "FlaxElectraForCausalLM"),
("gpt-sw3", "FlaxGPT2LMHeadModel"),
("gpt2", "FlaxGPT2LMHeadModel"),
("gpt_neo", "FlaxGPTNeoForCausalLM"),
("gptj", "FlaxGPTJForCausalLM"),
("opt", "FlaxOPTForCausalLM"),
("roberta", "FlaxRobertaForCausalLM"),
("roberta-prelayernorm", "FlaxRobertaPreLayerNormForCausalLM"),
("xglm", "FlaxXGLMForCausalLM"),
("xlm-roberta", "FlaxXLMRobertaForCausalLM"),
]
)
UpperCAmelCase_ = OrderedDict(
[
# Model for Sequence Classification mapping
("albert", "FlaxAlbertForSequenceClassification"),
("bart", "FlaxBartForSequenceClassification"),
("bert", "FlaxBertForSequenceClassification"),
("big_bird", "FlaxBigBirdForSequenceClassification"),
("distilbert", "FlaxDistilBertForSequenceClassification"),
("electra", "FlaxElectraForSequenceClassification"),
("mbart", "FlaxMBartForSequenceClassification"),
("roberta", "FlaxRobertaForSequenceClassification"),
("roberta-prelayernorm", "FlaxRobertaPreLayerNormForSequenceClassification"),
("roformer", "FlaxRoFormerForSequenceClassification"),
("xlm-roberta", "FlaxXLMRobertaForSequenceClassification"),
]
)
UpperCAmelCase_ = OrderedDict(
[
# Model for Question Answering mapping
("albert", "FlaxAlbertForQuestionAnswering"),
("bart", "FlaxBartForQuestionAnswering"),
("bert", "FlaxBertForQuestionAnswering"),
("big_bird", "FlaxBigBirdForQuestionAnswering"),
("distilbert", "FlaxDistilBertForQuestionAnswering"),
("electra", "FlaxElectraForQuestionAnswering"),
("mbart", "FlaxMBartForQuestionAnswering"),
("roberta", "FlaxRobertaForQuestionAnswering"),
("roberta-prelayernorm", "FlaxRobertaPreLayerNormForQuestionAnswering"),
("roformer", "FlaxRoFormerForQuestionAnswering"),
("xlm-roberta", "FlaxXLMRobertaForQuestionAnswering"),
]
)
UpperCAmelCase_ = OrderedDict(
[
# Model for Token Classification mapping
("albert", "FlaxAlbertForTokenClassification"),
("bert", "FlaxBertForTokenClassification"),
("big_bird", "FlaxBigBirdForTokenClassification"),
("distilbert", "FlaxDistilBertForTokenClassification"),
("electra", "FlaxElectraForTokenClassification"),
("roberta", "FlaxRobertaForTokenClassification"),
("roberta-prelayernorm", "FlaxRobertaPreLayerNormForTokenClassification"),
("roformer", "FlaxRoFormerForTokenClassification"),
("xlm-roberta", "FlaxXLMRobertaForTokenClassification"),
]
)
UpperCAmelCase_ = OrderedDict(
[
# Model for Multiple Choice mapping
("albert", "FlaxAlbertForMultipleChoice"),
("bert", "FlaxBertForMultipleChoice"),
("big_bird", "FlaxBigBirdForMultipleChoice"),
("distilbert", "FlaxDistilBertForMultipleChoice"),
("electra", "FlaxElectraForMultipleChoice"),
("roberta", "FlaxRobertaForMultipleChoice"),
("roberta-prelayernorm", "FlaxRobertaPreLayerNormForMultipleChoice"),
("roformer", "FlaxRoFormerForMultipleChoice"),
("xlm-roberta", "FlaxXLMRobertaForMultipleChoice"),
]
)
UpperCAmelCase_ = OrderedDict(
[
("bert", "FlaxBertForNextSentencePrediction"),
]
)
UpperCAmelCase_ = OrderedDict(
[
("speech-encoder-decoder", "FlaxSpeechEncoderDecoderModel"),
("whisper", "FlaxWhisperForConditionalGeneration"),
]
)
UpperCAmelCase_ = OrderedDict(
[
("whisper", "FlaxWhisperForAudioClassification"),
]
)
UpperCAmelCase_ = _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_MAPPING_NAMES)
UpperCAmelCase_ = _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_PRETRAINING_MAPPING_NAMES)
UpperCAmelCase_ = _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_MASKED_LM_MAPPING_NAMES)
UpperCAmelCase_ = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES
)
UpperCAmelCase_ = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES
)
UpperCAmelCase_ = _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING_NAMES)
UpperCAmelCase_ = _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_CAUSAL_LM_MAPPING_NAMES)
UpperCAmelCase_ = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES
)
UpperCAmelCase_ = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES
)
UpperCAmelCase_ = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES
)
UpperCAmelCase_ = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES
)
UpperCAmelCase_ = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING_NAMES
)
UpperCAmelCase_ = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES
)
UpperCAmelCase_ = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES
)
class __UpperCamelCase ( _BaseAutoModelClass ):
__A : List[str] = FLAX_MODEL_MAPPING
UpperCAmelCase_ = auto_class_update(FlaxAutoModel)
class __UpperCamelCase ( _BaseAutoModelClass ):
__A : Optional[Any] = FLAX_MODEL_FOR_PRETRAINING_MAPPING
UpperCAmelCase_ = auto_class_update(FlaxAutoModelForPreTraining, head_doc="pretraining")
class __UpperCamelCase ( _BaseAutoModelClass ):
__A : List[Any] = FLAX_MODEL_FOR_CAUSAL_LM_MAPPING
UpperCAmelCase_ = auto_class_update(FlaxAutoModelForCausalLM, head_doc="causal language modeling")
class __UpperCamelCase ( _BaseAutoModelClass ):
__A : List[str] = FLAX_MODEL_FOR_MASKED_LM_MAPPING
UpperCAmelCase_ = auto_class_update(FlaxAutoModelForMaskedLM, head_doc="masked language modeling")
class __UpperCamelCase ( _BaseAutoModelClass ):
__A : Dict = FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
UpperCAmelCase_ = auto_class_update(
FlaxAutoModelForSeqaSeqLM, head_doc="sequence-to-sequence language modeling", checkpoint_for_example="t5-base"
)
class __UpperCamelCase ( _BaseAutoModelClass ):
__A : List[str] = FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
UpperCAmelCase_ = auto_class_update(
FlaxAutoModelForSequenceClassification, head_doc="sequence classification"
)
class __UpperCamelCase ( _BaseAutoModelClass ):
__A : Dict = FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING
UpperCAmelCase_ = auto_class_update(FlaxAutoModelForQuestionAnswering, head_doc="question answering")
class __UpperCamelCase ( _BaseAutoModelClass ):
__A : Union[str, Any] = FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING
UpperCAmelCase_ = auto_class_update(
FlaxAutoModelForTokenClassification, head_doc="token classification"
)
class __UpperCamelCase ( _BaseAutoModelClass ):
__A : Union[str, Any] = FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING
UpperCAmelCase_ = auto_class_update(FlaxAutoModelForMultipleChoice, head_doc="multiple choice")
class __UpperCamelCase ( _BaseAutoModelClass ):
__A : Any = FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING
UpperCAmelCase_ = auto_class_update(
FlaxAutoModelForNextSentencePrediction, head_doc="next sentence prediction"
)
class __UpperCamelCase ( _BaseAutoModelClass ):
__A : Dict = FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
UpperCAmelCase_ = auto_class_update(
FlaxAutoModelForImageClassification, head_doc="image classification"
)
class __UpperCamelCase ( _BaseAutoModelClass ):
__A : Optional[int] = FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING
UpperCAmelCase_ = auto_class_update(FlaxAutoModelForVisionaSeq, head_doc="vision-to-text modeling")
class __UpperCamelCase ( _BaseAutoModelClass ):
__A : str = FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING
UpperCAmelCase_ = auto_class_update(
FlaxAutoModelForSpeechSeqaSeq, head_doc="sequence-to-sequence speech-to-text modeling"
) | 32 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
__a = {"""configuration_reformer""": ["""REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ReformerConfig"""]}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__a = ["""ReformerTokenizer"""]
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__a = ["""ReformerTokenizerFast"""]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__a = [
"""REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""ReformerAttention""",
"""ReformerForMaskedLM""",
"""ReformerForQuestionAnswering""",
"""ReformerForSequenceClassification""",
"""ReformerLayer""",
"""ReformerModel""",
"""ReformerModelWithLMHead""",
"""ReformerPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_reformer import REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, ReformerConfig
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_reformer import ReformerTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_reformer_fast import ReformerTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_reformer import (
REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
ReformerAttention,
ReformerForMaskedLM,
ReformerForQuestionAnswering,
ReformerForSequenceClassification,
ReformerLayer,
ReformerModel,
ReformerModelWithLMHead,
ReformerPreTrainedModel,
)
else:
import sys
__a = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 377 | 0 |
import unittest
from .lib import (
Matrix,
Vector,
axpy,
square_zero_matrix,
unit_basis_vector,
zero_vector,
)
class lowerCamelCase_ ( unittest.TestCase ):
def __magic_name__ ( self ):
a_ = Vector([1, 2, 3] )
self.assertEqual(x.component(0 ) , 1 )
self.assertEqual(x.component(2 ) , 3 )
a_ = Vector()
def __magic_name__ ( self ):
a_ = Vector([0, 0, 0, 0, 0, 1] )
self.assertEqual(str(_SCREAMING_SNAKE_CASE ) , """(0,0,0,0,0,1)""" )
def __magic_name__ ( self ):
a_ = Vector([1, 2, 3, 4] )
self.assertEqual(len(_SCREAMING_SNAKE_CASE ) , 4 )
def __magic_name__ ( self ):
a_ = Vector([1, 2] )
a_ = Vector([1, 2, 3, 4, 5] )
a_ = Vector([0, 0, 0, 0, 0, 0, 0, 0, 0, 0] )
a_ = Vector([1, -1, 1, -1, 2, -3, 4, -5] )
self.assertAlmostEqual(x.euclidean_length() , 2.2_3_6 , 3 )
self.assertAlmostEqual(y.euclidean_length() , 7.4_1_6 , 3 )
self.assertEqual(z.euclidean_length() , 0 )
self.assertAlmostEqual(w.euclidean_length() , 7.6_1_6 , 3 )
def __magic_name__ ( self ):
a_ = Vector([1, 2, 3] )
a_ = Vector([1, 1, 1] )
self.assertEqual((x + y).component(0 ) , 2 )
self.assertEqual((x + y).component(1 ) , 3 )
self.assertEqual((x + y).component(2 ) , 4 )
def __magic_name__ ( self ):
a_ = Vector([1, 2, 3] )
a_ = Vector([1, 1, 1] )
self.assertEqual((x - y).component(0 ) , 0 )
self.assertEqual((x - y).component(1 ) , 1 )
self.assertEqual((x - y).component(2 ) , 2 )
def __magic_name__ ( self ):
a_ = Vector([1, 2, 3] )
a_ = Vector([2, -1, 4] ) # for test of dot product
a_ = Vector([1, -2, -1] )
self.assertEqual(str(x * 3.0 ) , """(3.0,6.0,9.0)""" )
self.assertEqual((a * b) , 0 )
def __magic_name__ ( self ):
self.assertEqual(str(zero_vector(10 ) ).count("""0""" ) , 10 )
def __magic_name__ ( self ):
self.assertEqual(str(unit_basis_vector(3 , 1 ) ) , """(0,1,0)""" )
def __magic_name__ ( self ):
a_ = Vector([1, 2, 3] )
a_ = Vector([1, 0, 1] )
self.assertEqual(str(axpy(2 , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ) , """(3,4,7)""" )
def __magic_name__ ( self ):
a_ = Vector([1, 0, 0, 0, 0, 0] )
a_ = x.copy()
self.assertEqual(str(_SCREAMING_SNAKE_CASE ) , str(_SCREAMING_SNAKE_CASE ) )
def __magic_name__ ( self ):
a_ = Vector([1, 0, 0] )
x.change_component(0 , 0 )
x.change_component(1 , 1 )
self.assertEqual(str(_SCREAMING_SNAKE_CASE ) , """(0,1,0)""" )
def __magic_name__ ( self ):
a_ = Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]] , 3 , 3 )
self.assertEqual("""|1,2,3|\n|2,4,5|\n|6,7,8|\n""" , str(_SCREAMING_SNAKE_CASE ) )
def __magic_name__ ( self ):
a_ = Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]] , 3 , 3 )
a_ = [[-3, -14, -10], [-5, -10, -5], [-2, -1, 0]]
for x in range(a.height() ):
for y in range(a.width() ):
self.assertEqual(minors[x][y] , a.minor(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) )
def __magic_name__ ( self ):
a_ = Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]] , 3 , 3 )
a_ = [[-3, 14, -10], [5, -10, 5], [-2, 1, 0]]
for x in range(a.height() ):
for y in range(a.width() ):
self.assertEqual(cofactors[x][y] , a.cofactor(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) )
def __magic_name__ ( self ):
a_ = Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]] , 3 , 3 )
self.assertEqual(-5 , a.determinant() )
def __magic_name__ ( self ):
a_ = Matrix([[1, 2, 3], [4, 5, 6], [7, 8, 9]] , 3 , 3 )
a_ = Vector([1, 2, 3] )
self.assertEqual("""(14,32,50)""" , str(a * x ) )
self.assertEqual("""|2,4,6|\n|8,10,12|\n|14,16,18|\n""" , str(a * 2 ) )
def __magic_name__ ( self ):
a_ = Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]] , 3 , 3 )
a.change_component(0 , 2 , 5 )
self.assertEqual("""|1,2,5|\n|2,4,5|\n|6,7,8|\n""" , str(_SCREAMING_SNAKE_CASE ) )
def __magic_name__ ( self ):
a_ = Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]] , 3 , 3 )
self.assertEqual(7 , a.component(2 , 1 ) , 0.0_1 )
def __magic_name__ ( self ):
a_ = Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]] , 3 , 3 )
a_ = Matrix([[1, 2, 7], [2, 4, 5], [6, 7, 10]] , 3 , 3 )
self.assertEqual("""|2,4,10|\n|4,8,10|\n|12,14,18|\n""" , str(a + b ) )
def __magic_name__ ( self ):
a_ = Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]] , 3 , 3 )
a_ = Matrix([[1, 2, 7], [2, 4, 5], [6, 7, 10]] , 3 , 3 )
self.assertEqual("""|0,0,-4|\n|0,0,0|\n|0,0,-2|\n""" , str(a - b ) )
def __magic_name__ ( self ):
self.assertEqual(
"""|0,0,0,0,0|\n|0,0,0,0,0|\n|0,0,0,0,0|\n|0,0,0,0,0|\n|0,0,0,0,0|\n""" , str(square_zero_matrix(5 ) ) , )
if __name__ == "__main__":
unittest.main() | 403 |
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
_A = logging.get_logger(__name__)
def __SCREAMING_SNAKE_CASE ( ) -> Tuple:
"""simple docstring"""
a_ = os.getenv("""SM_HP_MP_PARAMETERS""" , """{}""" )
try:
# Parse it and check the field "partitions" is included, it is required for model parallel.
a_ = 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.
a_ = os.getenv("""SM_FRAMEWORK_PARAMS""" , """{}""" )
try:
# Parse it and check the field "sagemaker_distributed_dataparallel_enabled".
a_ = 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 lowerCamelCase_ ( _SCREAMING_SNAKE_CASE ):
_lowerCamelCase : str = field(
default="""""" , metadata={"""help""": """Used by the SageMaker launcher to send mp-specific args. Ignored in SageMakerTrainer"""} , )
def __magic_name__ ( self ):
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 __magic_name__ ( self ):
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:
a_ = torch.device("""cpu""" )
a_ = 0
elif is_sagemaker_model_parallel_available():
a_ = smp.local_rank()
a_ = torch.device("""cuda""" , _SCREAMING_SNAKE_CASE )
a_ = 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 )
a_ = int(os.getenv("""SMDATAPARALLEL_LOCAL_RANK""" ) )
a_ = torch.device("""cuda""" , self.local_rank )
a_ = 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
a_ = 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.
a_ = 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 )
a_ = torch.device("""cuda""" , self.local_rank )
a_ = 1
if device.type == "cuda":
torch.cuda.set_device(_SCREAMING_SNAKE_CASE )
return device
@property
def __magic_name__ ( self ):
if is_sagemaker_model_parallel_available():
return smp.dp_size()
return super().world_size
@property
def __magic_name__ ( self ):
return not is_sagemaker_model_parallel_available()
@property
def __magic_name__ ( self ):
return False | 403 | 1 |
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
SCREAMING_SNAKE_CASE :Optional[int] = """"""
SCREAMING_SNAKE_CASE :List[str] = """"""
SCREAMING_SNAKE_CASE :Optional[int] = """"""
SCREAMING_SNAKE_CASE :Dict = 1 # (0 is vertical, 1 is horizontal)
def lowerCAmelCase( )-> None:
"""simple docstring"""
UpperCamelCase_ , UpperCamelCase_ = get_dataset(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
print("Processing..." )
UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ = update_image_and_anno(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
for index, image in enumerate(SCREAMING_SNAKE_CASE_ ):
# Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
UpperCamelCase_ = random_chars(3_2 )
UpperCamelCase_ = paths[index].split(os.sep )[-1].rsplit("." , 1 )[0]
UpperCamelCase_ = f"{OUTPUT_DIR}/{file_name}_FLIP_{letter_code}"
cva.imwrite(f"/{file_root}.jpg" , SCREAMING_SNAKE_CASE_ , [cva.IMWRITE_JPEG_QUALITY, 8_5] )
print(f"Success {index+1}/{len(SCREAMING_SNAKE_CASE_ )} with {file_name}" )
UpperCamelCase_ = []
for anno in new_annos[index]:
UpperCamelCase_ = f"{anno[0]} {anno[1]} {anno[2]} {anno[3]} {anno[4]}"
annos_list.append(SCREAMING_SNAKE_CASE_ )
with open(f"/{file_root}.txt" , "w" ) as outfile:
outfile.write("\n".join(line for line in annos_list ) )
def lowerCAmelCase( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )-> tuple[list, list]:
"""simple docstring"""
UpperCamelCase_ = []
UpperCamelCase_ = []
for label_file in glob.glob(os.path.join(SCREAMING_SNAKE_CASE_ , "*.txt" ) ):
UpperCamelCase_ = label_file.split(os.sep )[-1].rsplit("." , 1 )[0]
with open(SCREAMING_SNAKE_CASE_ ) as in_file:
UpperCamelCase_ = in_file.readlines()
UpperCamelCase_ = os.path.join(SCREAMING_SNAKE_CASE_ , f"{label_name}.jpg" )
UpperCamelCase_ = []
for obj_list in obj_lists:
UpperCamelCase_ = obj_list.rstrip("\n" ).split(" " )
boxes.append(
[
int(obj[0] ),
float(obj[1] ),
float(obj[2] ),
float(obj[3] ),
float(obj[4] ),
] )
if not boxes:
continue
img_paths.append(SCREAMING_SNAKE_CASE_ )
labels.append(SCREAMING_SNAKE_CASE_ )
return img_paths, labels
def lowerCAmelCase( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = 1 )-> tuple[list, list, list]:
"""simple docstring"""
UpperCamelCase_ = []
UpperCamelCase_ = []
UpperCamelCase_ = []
for idx in range(len(SCREAMING_SNAKE_CASE_ ) ):
UpperCamelCase_ = []
UpperCamelCase_ = img_list[idx]
path_list.append(SCREAMING_SNAKE_CASE_ )
UpperCamelCase_ = anno_list[idx]
UpperCamelCase_ = cva.imread(SCREAMING_SNAKE_CASE_ )
if flip_type == 1:
UpperCamelCase_ = cva.flip(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
for bbox in img_annos:
UpperCamelCase_ = 1 - bbox[1]
new_annos.append([bbox[0], x_center_new, bbox[2], bbox[3], bbox[4]] )
elif flip_type == 0:
UpperCamelCase_ = cva.flip(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
for bbox in img_annos:
UpperCamelCase_ = 1 - bbox[2]
new_annos.append([bbox[0], bbox[1], y_center_new, bbox[3], bbox[4]] )
new_annos_lists.append(SCREAMING_SNAKE_CASE_ )
new_imgs_list.append(SCREAMING_SNAKE_CASE_ )
return new_imgs_list, new_annos_lists, path_list
def lowerCAmelCase( SCREAMING_SNAKE_CASE_ = 3_2 )-> str:
"""simple docstring"""
assert number_char > 1, "The number of character should greater than 1"
UpperCamelCase_ = ascii_lowercase + digits
return "".join(random.choice(SCREAMING_SNAKE_CASE_ ) for _ in range(SCREAMING_SNAKE_CASE_ ) )
if __name__ == "__main__":
main()
print("""DONE ✅""")
| 628 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_torch_available,
)
SCREAMING_SNAKE_CASE :Union[str, Any] = {
"""configuration_speecht5""": [
"""SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP""",
"""SpeechT5Config""",
"""SpeechT5HifiGanConfig""",
],
"""feature_extraction_speecht5""": ["""SpeechT5FeatureExtractor"""],
"""processing_speecht5""": ["""SpeechT5Processor"""],
}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE :Union[str, Any] = ["""SpeechT5Tokenizer"""]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE :Any = [
"""SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""SpeechT5ForSpeechToText""",
"""SpeechT5ForSpeechToSpeech""",
"""SpeechT5ForTextToSpeech""",
"""SpeechT5Model""",
"""SpeechT5PreTrainedModel""",
"""SpeechT5HifiGan""",
]
if TYPE_CHECKING:
from .configuration_speechta import (
SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP,
SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP,
SpeechTaConfig,
SpeechTaHifiGanConfig,
)
from .feature_extraction_speechta import SpeechTaFeatureExtractor
from .processing_speechta import SpeechTaProcessor
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_speechta import SpeechTaTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_speechta import (
SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST,
SpeechTaForSpeechToSpeech,
SpeechTaForSpeechToText,
SpeechTaForTextToSpeech,
SpeechTaHifiGan,
SpeechTaModel,
SpeechTaPreTrainedModel,
)
else:
import sys
SCREAMING_SNAKE_CASE :List[str] = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 628 | 1 |
import unittest
import numpy as np
from transformers.file_utils import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import DPTImageProcessor
class A_ ( unittest.TestCase ):
def __init__( self : Optional[Any] , __SCREAMING_SNAKE_CASE : int , __SCREAMING_SNAKE_CASE : Optional[int]=7 , __SCREAMING_SNAKE_CASE : List[str]=3 , __SCREAMING_SNAKE_CASE : Union[str, Any]=18 , __SCREAMING_SNAKE_CASE : Tuple=30 , __SCREAMING_SNAKE_CASE : List[Any]=4_00 , __SCREAMING_SNAKE_CASE : Any=True , __SCREAMING_SNAKE_CASE : List[Any]=None , __SCREAMING_SNAKE_CASE : Any=True , __SCREAMING_SNAKE_CASE : Tuple=[0.5, 0.5, 0.5] , __SCREAMING_SNAKE_CASE : Optional[Any]=[0.5, 0.5, 0.5] , ):
__a = size if size is not None else {"height": 18, "width": 18}
__a = parent
__a = batch_size
__a = num_channels
__a = image_size
__a = min_resolution
__a = max_resolution
__a = do_resize
__a = size
__a = do_normalize
__a = image_mean
__a = image_std
def _UpperCAmelCase ( self : int ):
return {
"image_mean": self.image_mean,
"image_std": self.image_std,
"do_normalize": self.do_normalize,
"do_resize": self.do_resize,
"size": self.size,
}
@require_torch
@require_vision
class A_ ( a_ , unittest.TestCase ):
_SCREAMING_SNAKE_CASE = DPTImageProcessor if is_vision_available() else None
def _UpperCAmelCase ( self : Dict ):
__a = DPTImageProcessingTester(self )
@property
def _UpperCAmelCase ( self : Optional[int] ):
return self.image_processor_tester.prepare_image_processor_dict()
def _UpperCAmelCase ( self : Any ):
__a = 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" ) )
def _UpperCAmelCase ( self : Dict ):
__a = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {"height": 18, "width": 18} )
__a = self.image_processing_class.from_dict(self.image_processor_dict , size=42 )
self.assertEqual(image_processor.size , {"height": 42, "width": 42} )
def _UpperCAmelCase ( self : str ):
# Initialize image_processing
__a = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
__a = prepare_image_inputs(self.image_processor_tester , equal_resolution=__SCREAMING_SNAKE_CASE )
for image in image_inputs:
self.assertIsInstance(__SCREAMING_SNAKE_CASE , Image.Image )
# Test not batched input
__a = image_processing(image_inputs[0] , return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.size["height"],
self.image_processor_tester.size["width"],
) , )
# Test batched
__a = image_processing(__SCREAMING_SNAKE_CASE , return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.size["height"],
self.image_processor_tester.size["width"],
) , )
def _UpperCAmelCase ( self : List[Any] ):
# Initialize image_processing
__a = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
__a = prepare_image_inputs(self.image_processor_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
__a = image_processing(image_inputs[0] , return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.size["height"],
self.image_processor_tester.size["width"],
) , )
# Test batched
__a = image_processing(__SCREAMING_SNAKE_CASE , return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.size["height"],
self.image_processor_tester.size["width"],
) , )
def _UpperCAmelCase ( self : List[Any] ):
# Initialize image_processing
__a = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
__a = prepare_image_inputs(self.image_processor_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
__a = image_processing(image_inputs[0] , return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.size["height"],
self.image_processor_tester.size["width"],
) , )
# Test batched
__a = image_processing(__SCREAMING_SNAKE_CASE , return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.size["height"],
self.image_processor_tester.size["width"],
) , )
| 525 | from ... import PretrainedConfig
SCREAMING_SNAKE_CASE : Any = {
"""sijunhe/nezha-cn-base""": """https://huggingface.co/sijunhe/nezha-cn-base/resolve/main/config.json""",
}
class A_ ( a_ ):
_SCREAMING_SNAKE_CASE = NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP
_SCREAMING_SNAKE_CASE = """nezha"""
def __init__( self : Any , __SCREAMING_SNAKE_CASE : List[str]=2_11_28 , __SCREAMING_SNAKE_CASE : Dict=7_68 , __SCREAMING_SNAKE_CASE : List[Any]=12 , __SCREAMING_SNAKE_CASE : str=12 , __SCREAMING_SNAKE_CASE : Optional[Any]=30_72 , __SCREAMING_SNAKE_CASE : Any="gelu" , __SCREAMING_SNAKE_CASE : int=0.1 , __SCREAMING_SNAKE_CASE : List[str]=0.1 , __SCREAMING_SNAKE_CASE : int=5_12 , __SCREAMING_SNAKE_CASE : Union[str, Any]=64 , __SCREAMING_SNAKE_CASE : List[Any]=2 , __SCREAMING_SNAKE_CASE : int=0.02 , __SCREAMING_SNAKE_CASE : Optional[Any]=1E-12 , __SCREAMING_SNAKE_CASE : int=0.1 , __SCREAMING_SNAKE_CASE : Any=0 , __SCREAMING_SNAKE_CASE : Any=2 , __SCREAMING_SNAKE_CASE : Optional[Any]=3 , __SCREAMING_SNAKE_CASE : List[Any]=True , **__SCREAMING_SNAKE_CASE : List[str] , ):
super().__init__(pad_token_id=__SCREAMING_SNAKE_CASE , bos_token_id=__SCREAMING_SNAKE_CASE , eos_token_id=__SCREAMING_SNAKE_CASE , **__SCREAMING_SNAKE_CASE )
__a = vocab_size
__a = hidden_size
__a = num_hidden_layers
__a = num_attention_heads
__a = hidden_act
__a = intermediate_size
__a = hidden_dropout_prob
__a = attention_probs_dropout_prob
__a = max_position_embeddings
__a = max_relative_position
__a = type_vocab_size
__a = initializer_range
__a = layer_norm_eps
__a = classifier_dropout
__a = use_cache
| 525 | 1 |
from __future__ import annotations
def UpperCAmelCase_ ( __lowerCAmelCase , __lowerCAmelCase ) -> tuple[int, int]:
if b == 0:
return (1, 0)
((__lowercase) , (__lowercase)) : Any = extended_euclid(__lowerCAmelCase , a % b )
__lowercase : Dict = a // b
return (y, x - k * y)
def UpperCAmelCase_ ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> int:
((__lowercase) , (__lowercase)) : List[str] = extended_euclid(__lowerCAmelCase , __lowerCAmelCase )
__lowercase : List[str] = na * na
__lowercase : Union[str, Any] = ra * x * na + ra * y * na
return (n % m + m) % m
def UpperCAmelCase_ ( __lowerCAmelCase , __lowerCAmelCase ) -> int:
((__lowercase) , (__lowercase)) : Optional[int] = extended_euclid(__lowerCAmelCase , __lowerCAmelCase )
if b < 0:
__lowercase : Any = (b % n + n) % n
return b
def UpperCAmelCase_ ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> int:
__lowercase , __lowercase : Optional[int] = invert_modulo(__lowerCAmelCase , __lowerCAmelCase ), invert_modulo(__lowerCAmelCase , __lowerCAmelCase )
__lowercase : Any = na * na
__lowercase : List[str] = ra * x * na + ra * y * na
return (n % m + m) % m
if __name__ == "__main__":
from doctest import testmod
testmod(name="chinese_remainder_theorem", verbose=True)
testmod(name="chinese_remainder_theorem2", verbose=True)
testmod(name="invert_modulo", verbose=True)
testmod(name="extended_euclid", verbose=True)
| 509 |
import io
import json
import unittest
from parameterized import parameterized
from transformers import FSMTForConditionalGeneration, FSMTTokenizer
from transformers.testing_utils import get_tests_dir, require_torch, slow, torch_device
from utils import calculate_bleu
__lowerCAmelCase : Any = get_tests_dir() + "/test_data/fsmt/fsmt_val_data.json"
with io.open(filename, "r", encoding="utf-8") as f:
__lowerCAmelCase : List[Any] = json.load(f)
@require_torch
class __lowerCAmelCase ( unittest.TestCase ):
"""simple docstring"""
def snake_case_ ( self : List[str] , _snake_case : List[Any] ):
return FSMTTokenizer.from_pretrained(_snake_case )
def snake_case_ ( self : Any , _snake_case : List[str] ):
__lowercase : str = FSMTForConditionalGeneration.from_pretrained(_snake_case ).to(_snake_case )
if torch_device == "cuda":
model.half()
return model
@parameterized.expand(
[
['''en-ru''', 26.0],
['''ru-en''', 22.0],
['''en-de''', 22.0],
['''de-en''', 29.0],
] )
@slow
def snake_case_ ( self : Tuple , _snake_case : int , _snake_case : Union[str, Any] ):
# note: this test is not testing the best performance since it only evals a small batch
# but it should be enough to detect a regression in the output quality
__lowercase : Tuple = F'facebook/wmt19-{pair}'
__lowercase : Tuple = self.get_tokenizer(_snake_case )
__lowercase : Dict = self.get_model(_snake_case )
__lowercase : Dict = bleu_data[pair]['''src''']
__lowercase : Any = bleu_data[pair]['''tgt''']
__lowercase : Any = tokenizer(_snake_case , return_tensors='''pt''' , truncation=_snake_case , padding='''longest''' ).to(_snake_case )
__lowercase : Optional[int] = model.generate(
input_ids=batch.input_ids , num_beams=8 , )
__lowercase : Any = tokenizer.batch_decode(
_snake_case , skip_special_tokens=_snake_case , clean_up_tokenization_spaces=_snake_case )
__lowercase : Tuple = calculate_bleu(_snake_case , _snake_case )
print(_snake_case )
self.assertGreaterEqual(scores['''bleu'''] , _snake_case )
| 509 | 1 |
"""simple docstring"""
import unittest
from transformers import BarthezTokenizer, BarthezTokenizerFast, BatchEncoding
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
@require_sentencepiece
@slow # see https://github.com/huggingface/transformers/issues/11457
class lowerCAmelCase__ ( UpperCAmelCase_ , unittest.TestCase ):
lowercase__ : Any = BarthezTokenizer
lowercase__ : int = BarthezTokenizerFast
lowercase__ : int = True
lowercase__ : List[str] = True
def lowercase_ ( self ):
'''simple docstring'''
super().setUp()
A__ = BarthezTokenizerFast.from_pretrained("moussaKam/mbarthez" )
tokenizer.save_pretrained(self.tmpdirname )
tokenizer.save_pretrained(self.tmpdirname , legacy_format=UpperCamelCase__ )
A__ = tokenizer
def lowercase_ ( self ):
'''simple docstring'''
A__ = "<pad>"
A__ = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(UpperCamelCase__ ) , UpperCamelCase__ )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(UpperCamelCase__ ) , UpperCamelCase__ )
def lowercase_ ( self ):
'''simple docstring'''
A__ = 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(UpperCamelCase__ ) , 10_11_22 )
def lowercase_ ( self ):
'''simple docstring'''
self.assertEqual(self.get_tokenizer().vocab_size , 10_11_22 )
@require_torch
def lowercase_ ( self ):
'''simple docstring'''
A__ = ["A long paragraph for summarization.", "Another paragraph for summarization."]
A__ = [0, 57, 30_18, 7_03_07, 91, 2]
A__ = self.tokenizer(
UpperCamelCase__ , max_length=len(UpperCamelCase__ ) , padding=UpperCamelCase__ , truncation=UpperCamelCase__ , return_tensors="pt" )
self.assertIsInstance(UpperCamelCase__ , UpperCamelCase__ )
self.assertEqual((2, 6) , batch.input_ids.shape )
self.assertEqual((2, 6) , batch.attention_mask.shape )
A__ = batch.input_ids.tolist()[0]
self.assertListEqual(UpperCamelCase__ , UpperCamelCase__ )
def lowercase_ ( self ):
'''simple docstring'''
if not self.test_rust_tokenizer:
return
A__ = self.get_tokenizer()
A__ = self.get_rust_tokenizer()
A__ = "I was born in 92000, and this is falsé."
A__ = tokenizer.tokenize(UpperCamelCase__ )
A__ = rust_tokenizer.tokenize(UpperCamelCase__ )
self.assertListEqual(UpperCamelCase__ , UpperCamelCase__ )
A__ = tokenizer.encode(UpperCamelCase__ , add_special_tokens=UpperCamelCase__ )
A__ = rust_tokenizer.encode(UpperCamelCase__ , add_special_tokens=UpperCamelCase__ )
self.assertListEqual(UpperCamelCase__ , UpperCamelCase__ )
A__ = self.get_rust_tokenizer()
A__ = tokenizer.encode(UpperCamelCase__ )
A__ = rust_tokenizer.encode(UpperCamelCase__ )
self.assertListEqual(UpperCamelCase__ , UpperCamelCase__ )
@slow
def lowercase_ ( self ):
'''simple docstring'''
A__ = {"input_ids": [[0, 4_90, 1_43_28, 45_07, 3_54, 47, 4_36_69, 95, 25, 7_81_17, 2_02_15, 1_97_79, 1_90, 22, 4_00, 4, 3_53_43, 8_03_10, 6_03, 86, 2_49_37, 1_05, 3_34_38, 9_47_62, 1_96, 3_96_42, 7, 15, 1_59_33, 1_73, 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], [0, 1_05_34, 87, 25, 66, 33_58, 1_96, 5_52_89, 8, 8_29_61, 81, 22_04, 7_52_03, 7, 15, 7_63, 1_29_56, 2_16, 1_78, 1_43_28, 95_95, 13_77, 6_96_93, 7, 4_48, 7_10_21, 1_96, 1_81_06, 14_37, 1_39_74, 1_08, 90_83, 4, 4_93_15, 7, 39, 86, 13_26, 27_93, 4_63_33, 4, 4_48, 1_96, 7_45_88, 7, 4_93_15, 7, 39, 21, 8_22, 3_84_70, 74, 21, 6_67_23, 6_24_80, 8, 2_20_50, 5, 2]], "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, 0, 0, 0, 0, 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, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]} # noqa: E501
# fmt: on
# moussaKam/mbarthez is a french model. So we also use french texts.
A__ = [
"Le transformeur est un modèle d'apprentissage profond introduit en 2017, "
"utilisé principalement dans le domaine du traitement automatique des langues (TAL).",
"À l'instar des réseaux de neurones récurrents (RNN), les transformeurs sont conçus "
"pour gérer des données séquentielles, telles que le langage naturel, pour des tâches "
"telles que la traduction et la synthèse de texte.",
]
self.tokenizer_integration_test_util(
expected_encoding=UpperCamelCase__ , model_name="moussaKam/mbarthez" , revision="c2e4ecbca5e3cd2c37fe1ac285ca4fbdf1366fb6" , sequences=UpperCamelCase__ , ) | 261 |
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
if is_tf_available():
import numpy as np
import tensorflow as tf
from transformers import TFXLMRobertaModel
@require_tf
@require_sentencepiece
@require_tokenizers
class lowerCAmelCase__ ( unittest.TestCase ):
@slow
def lowercase_ ( self ):
'''simple docstring'''
A__ = TFXLMRobertaModel.from_pretrained("jplu/tf-xlm-roberta-base" )
A__ = {
"input_ids": tf.convert_to_tensor([[0, 26_46, 1_02_69, 83, 9_99_42, 2]] , dtype=tf.intaa ), # "My dog is cute"
"attention_mask": tf.convert_to_tensor([[1, 1, 1, 1, 1, 1]] , dtype=tf.intaa ),
}
A__ = model(UpperCamelCase__ )["last_hidden_state"]
A__ = tf.TensorShape((1, 6, 7_68) )
self.assertEqual(output.shape , UpperCamelCase__ )
# compare the actual values for a slice.
A__ = tf.convert_to_tensor(
[
[
[0.068_1762, 0.1089_4451, 0.0677_2504],
[-0.0642_3668, 0.0236_6615, 0.0432_9344],
[-0.0605_7295, 0.0997_4135, -0.0007_0584],
]
] , dtype=tf.floataa , )
self.assertTrue(np.allclose(output[:, :3, :3].numpy() , expected_slice.numpy() , atol=1e-4 ) ) | 261 | 1 |
"""simple docstring"""
import inspect
from typing import Optional, Union
import numpy as np
import PIL
import torch
from torch.nn import functional as F
from torchvision import transforms
from transformers import CLIPFeatureExtractor, CLIPModel, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DiffusionPipeline,
DPMSolverMultistepScheduler,
LMSDiscreteScheduler,
PNDMScheduler,
UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput
from diffusers.utils import (
PIL_INTERPOLATION,
randn_tensor,
)
def snake_case ( A__ ,A__ ,A__ ):
if isinstance(A__ ,torch.Tensor ):
return image
elif isinstance(A__ ,PIL.Image.Image ):
UpperCAmelCase_ : int = [image]
if isinstance(image[0] ,PIL.Image.Image ):
UpperCAmelCase_ : List[Any] = [np.array(i.resize((w, h) ,resample=PIL_INTERPOLATION["lanczos"] ) )[None, :] for i in image]
UpperCAmelCase_ : Dict = np.concatenate(A__ ,axis=0 )
UpperCAmelCase_ : List[Any] = np.array(A__ ).astype(np.floataa ) / 255.0
UpperCAmelCase_ : List[str] = image.transpose(0 ,3 ,1 ,2 )
UpperCAmelCase_ : Any = 2.0 * image - 1.0
UpperCAmelCase_ : Optional[Any] = torch.from_numpy(A__ )
elif isinstance(image[0] ,torch.Tensor ):
UpperCAmelCase_ : Tuple = torch.cat(A__ ,dim=0 )
return image
def snake_case ( A__ ,A__ ,A__ ,A__=0.9995 ):
if not isinstance(A__ ,np.ndarray ):
UpperCAmelCase_ : Any = True
UpperCAmelCase_ : Optional[Any] = va.device
UpperCAmelCase_ : Any = va.cpu().numpy()
UpperCAmelCase_ : Union[str, Any] = va.cpu().numpy()
UpperCAmelCase_ : List[str] = np.sum(va * va / (np.linalg.norm(A__ ) * np.linalg.norm(A__ )) )
if np.abs(A__ ) > DOT_THRESHOLD:
UpperCAmelCase_ : str = (1 - t) * va + t * va
else:
UpperCAmelCase_ : Union[str, Any] = np.arccos(A__ )
UpperCAmelCase_ : Optional[Any] = np.sin(A__ )
UpperCAmelCase_ : Optional[int] = theta_a * t
UpperCAmelCase_ : Any = np.sin(A__ )
UpperCAmelCase_ : int = np.sin(theta_a - theta_t ) / sin_theta_a
UpperCAmelCase_ : int = sin_theta_t / sin_theta_a
UpperCAmelCase_ : int = sa * va + sa * va
if inputs_are_torch:
UpperCAmelCase_ : List[Any] = torch.from_numpy(A__ ).to(A__ )
return va
def snake_case ( A__ ,A__ ):
UpperCAmelCase_ : Dict = F.normalize(A__ ,dim=-1 )
UpperCAmelCase_ : str = F.normalize(A__ ,dim=-1 )
return (x - y).norm(dim=-1 ).div(2 ).arcsin().pow(2 ).mul(2 )
def snake_case ( A__ ,A__ ):
for param in model.parameters():
UpperCAmelCase_ : List[str] = value
class UpperCamelCase_ (__A ):
def __init__( self : Optional[int] , lowerCAmelCase_ : AutoencoderKL , lowerCAmelCase_ : CLIPTextModel , lowerCAmelCase_ : CLIPModel , lowerCAmelCase_ : CLIPTokenizer , lowerCAmelCase_ : UNetaDConditionModel , lowerCAmelCase_ : Union[PNDMScheduler, LMSDiscreteScheduler, DDIMScheduler, DPMSolverMultistepScheduler] , lowerCAmelCase_ : CLIPFeatureExtractor , lowerCAmelCase_ : Optional[int]=None , lowerCAmelCase_ : List[str]=None , lowerCAmelCase_ : Tuple=None , ) -> Any:
super().__init__()
self.register_modules(
vae=lowerCAmelCase_ , text_encoder=lowerCAmelCase_ , clip_model=lowerCAmelCase_ , tokenizer=lowerCAmelCase_ , unet=lowerCAmelCase_ , scheduler=lowerCAmelCase_ , feature_extractor=lowerCAmelCase_ , coca_model=lowerCAmelCase_ , coca_tokenizer=lowerCAmelCase_ , coca_transform=lowerCAmelCase_ , )
UpperCAmelCase_ : str = (
feature_extractor.size
if isinstance(feature_extractor.size , lowerCAmelCase_ )
else feature_extractor.size["shortest_edge"]
)
UpperCAmelCase_ : str = transforms.Normalize(mean=feature_extractor.image_mean , std=feature_extractor.image_std )
set_requires_grad(self.text_encoder , lowerCAmelCase_ )
set_requires_grad(self.clip_model , lowerCAmelCase_ )
def _SCREAMING_SNAKE_CASE ( self : str , lowerCAmelCase_ : Optional[Union[str, int]] = "auto" ) -> Union[str, Any]:
if slice_size == "auto":
# half the attention head size is usually a good trade-off between
# speed and memory
UpperCAmelCase_ : List[Any] = self.unet.config.attention_head_dim // 2
self.unet.set_attention_slice(lowerCAmelCase_ )
def _SCREAMING_SNAKE_CASE ( self : Union[str, Any] ) -> int:
self.enable_attention_slicing(lowerCAmelCase_ )
def _SCREAMING_SNAKE_CASE ( self : Optional[int] ) -> Optional[int]:
set_requires_grad(self.vae , lowerCAmelCase_ )
def _SCREAMING_SNAKE_CASE ( self : str ) -> int:
set_requires_grad(self.vae , lowerCAmelCase_ )
def _SCREAMING_SNAKE_CASE ( self : Tuple ) -> Optional[Any]:
set_requires_grad(self.unet , lowerCAmelCase_ )
def _SCREAMING_SNAKE_CASE ( self : Optional[Any] ) -> Union[str, Any]:
set_requires_grad(self.unet , lowerCAmelCase_ )
def _SCREAMING_SNAKE_CASE ( self : List[Any] , lowerCAmelCase_ : List[Any] , lowerCAmelCase_ : Tuple , lowerCAmelCase_ : Tuple ) -> List[str]:
# get the original timestep using init_timestep
UpperCAmelCase_ : Optional[Any] = min(int(num_inference_steps * strength ) , lowerCAmelCase_ )
UpperCAmelCase_ : Union[str, Any] = max(num_inference_steps - init_timestep , 0 )
UpperCAmelCase_ : Union[str, Any] = self.scheduler.timesteps[t_start:]
return timesteps, num_inference_steps - t_start
def _SCREAMING_SNAKE_CASE ( self : Dict , lowerCAmelCase_ : List[Any] , lowerCAmelCase_ : List[Any] , lowerCAmelCase_ : Optional[int] , lowerCAmelCase_ : Dict , lowerCAmelCase_ : List[Any] , lowerCAmelCase_ : Optional[int]=None ) -> Tuple:
if not isinstance(lowerCAmelCase_ , torch.Tensor ):
raise ValueError(f"""`image` has to be of type `torch.Tensor` but is {type(lowerCAmelCase_ )}""" )
UpperCAmelCase_ : Optional[Any] = image.to(device=lowerCAmelCase_ , dtype=lowerCAmelCase_ )
if isinstance(lowerCAmelCase_ , lowerCAmelCase_ ):
UpperCAmelCase_ : Dict = [
self.vae.encode(image[i : i + 1] ).latent_dist.sample(generator[i] ) for i in range(lowerCAmelCase_ )
]
UpperCAmelCase_ : str = torch.cat(lowerCAmelCase_ , dim=0 )
else:
UpperCAmelCase_ : Optional[int] = self.vae.encode(lowerCAmelCase_ ).latent_dist.sample(lowerCAmelCase_ )
# Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
UpperCAmelCase_ : Any = 0.1_8_2_1_5 * init_latents
UpperCAmelCase_ : List[str] = init_latents.repeat_interleave(lowerCAmelCase_ , dim=0 )
UpperCAmelCase_ : Optional[Any] = randn_tensor(init_latents.shape , generator=lowerCAmelCase_ , device=lowerCAmelCase_ , dtype=lowerCAmelCase_ )
# get latents
UpperCAmelCase_ : Dict = self.scheduler.add_noise(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ )
UpperCAmelCase_ : Union[str, Any] = init_latents
return latents
def _SCREAMING_SNAKE_CASE ( self : List[Any] , lowerCAmelCase_ : List[str] ) -> int:
UpperCAmelCase_ : Any = self.coca_transform(lowerCAmelCase_ ).unsqueeze(0 )
with torch.no_grad(), torch.cuda.amp.autocast():
UpperCAmelCase_ : int = self.coca_model.generate(transformed_image.to(device=self.device , dtype=self.coca_model.dtype ) )
UpperCAmelCase_ : List[Any] = self.coca_tokenizer.decode(generated[0].cpu().numpy() )
return generated.split("<end_of_text>" )[0].replace("<start_of_text>" , "" ).rstrip(" .," )
def _SCREAMING_SNAKE_CASE ( self : Optional[Any] , lowerCAmelCase_ : List[str] , lowerCAmelCase_ : Optional[Any] ) -> List[Any]:
UpperCAmelCase_ : Optional[Any] = self.feature_extractor.preprocess(lowerCAmelCase_ )
UpperCAmelCase_ : Optional[int] = torch.from_numpy(clip_image_input["pixel_values"][0] ).unsqueeze(0 ).to(self.device ).half()
UpperCAmelCase_ : List[Any] = self.clip_model.get_image_features(lowerCAmelCase_ )
UpperCAmelCase_ : Union[str, Any] = image_embeddings_clip / image_embeddings_clip.norm(p=2 , dim=-1 , keepdim=lowerCAmelCase_ )
UpperCAmelCase_ : str = image_embeddings_clip.repeat_interleave(lowerCAmelCase_ , dim=0 )
return image_embeddings_clip
@torch.enable_grad()
def _SCREAMING_SNAKE_CASE ( self : List[str] , lowerCAmelCase_ : Tuple , lowerCAmelCase_ : Tuple , lowerCAmelCase_ : Dict , lowerCAmelCase_ : str , lowerCAmelCase_ : str , lowerCAmelCase_ : Any , lowerCAmelCase_ : str , ) -> List[str]:
UpperCAmelCase_ : str = latents.detach().requires_grad_()
UpperCAmelCase_ : List[Any] = self.scheduler.scale_model_input(lowerCAmelCase_ , lowerCAmelCase_ )
# predict the noise residual
UpperCAmelCase_ : int = self.unet(lowerCAmelCase_ , lowerCAmelCase_ , encoder_hidden_states=lowerCAmelCase_ ).sample
if isinstance(self.scheduler , (PNDMScheduler, DDIMScheduler, DPMSolverMultistepScheduler) ):
UpperCAmelCase_ : str = self.scheduler.alphas_cumprod[timestep]
UpperCAmelCase_ : int = 1 - alpha_prod_t
# compute predicted original sample from predicted noise also called
# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
UpperCAmelCase_ : List[Any] = (latents - beta_prod_t ** 0.5 * noise_pred) / alpha_prod_t ** 0.5
UpperCAmelCase_ : Union[str, Any] = torch.sqrt(lowerCAmelCase_ )
UpperCAmelCase_ : Optional[Any] = pred_original_sample * (fac) + latents * (1 - fac)
elif isinstance(self.scheduler , lowerCAmelCase_ ):
UpperCAmelCase_ : Any = self.scheduler.sigmas[index]
UpperCAmelCase_ : Any = latents - sigma * noise_pred
else:
raise ValueError(f"""scheduler type {type(self.scheduler )} not supported""" )
# Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
UpperCAmelCase_ : List[Any] = 1 / 0.1_8_2_1_5 * sample
UpperCAmelCase_ : Dict = self.vae.decode(lowerCAmelCase_ ).sample
UpperCAmelCase_ : str = (image / 2 + 0.5).clamp(0 , 1 )
UpperCAmelCase_ : Any = transforms.Resize(self.feature_extractor_size )(lowerCAmelCase_ )
UpperCAmelCase_ : Any = self.normalize(lowerCAmelCase_ ).to(latents.dtype )
UpperCAmelCase_ : Union[str, Any] = self.clip_model.get_image_features(lowerCAmelCase_ )
UpperCAmelCase_ : Union[str, Any] = image_embeddings_clip / image_embeddings_clip.norm(p=2 , dim=-1 , keepdim=lowerCAmelCase_ )
UpperCAmelCase_ : Dict = spherical_dist_loss(lowerCAmelCase_ , lowerCAmelCase_ ).mean() * clip_guidance_scale
UpperCAmelCase_ : str = -torch.autograd.grad(lowerCAmelCase_ , lowerCAmelCase_ )[0]
if isinstance(self.scheduler , lowerCAmelCase_ ):
UpperCAmelCase_ : str = latents.detach() + grads * (sigma**2)
UpperCAmelCase_ : Optional[Any] = noise_pred_original
else:
UpperCAmelCase_ : Optional[int] = noise_pred_original - torch.sqrt(lowerCAmelCase_ ) * grads
return noise_pred, latents
@torch.no_grad()
def __call__( self : Union[str, Any] , lowerCAmelCase_ : Union[torch.FloatTensor, PIL.Image.Image] , lowerCAmelCase_ : Union[torch.FloatTensor, PIL.Image.Image] , lowerCAmelCase_ : Optional[str] = None , lowerCAmelCase_ : Optional[str] = None , lowerCAmelCase_ : Optional[int] = 512 , lowerCAmelCase_ : Optional[int] = 512 , lowerCAmelCase_ : float = 0.6 , lowerCAmelCase_ : Optional[int] = 50 , lowerCAmelCase_ : Optional[float] = 7.5 , lowerCAmelCase_ : Optional[int] = 1 , lowerCAmelCase_ : float = 0.0 , lowerCAmelCase_ : Optional[float] = 100 , lowerCAmelCase_ : Optional[torch.Generator] = None , lowerCAmelCase_ : Optional[str] = "pil" , lowerCAmelCase_ : bool = True , lowerCAmelCase_ : float = 0.8 , lowerCAmelCase_ : float = 0.1 , lowerCAmelCase_ : float = 0.1 , ) -> int:
if isinstance(lowerCAmelCase_ , lowerCAmelCase_ ) and len(lowerCAmelCase_ ) != batch_size:
raise ValueError(f"""You have passed {batch_size} batch_size, but only {len(lowerCAmelCase_ )} generators.""" )
if height % 8 != 0 or width % 8 != 0:
raise ValueError(f"""`height` and `width` have to be divisible by 8 but are {height} and {width}.""" )
if isinstance(lowerCAmelCase_ , torch.Generator ) and batch_size > 1:
UpperCAmelCase_ : Optional[Any] = [generator] + [None] * (batch_size - 1)
UpperCAmelCase_ : Optional[int] = [
("model", self.coca_model is None),
("tokenizer", self.coca_tokenizer is None),
("transform", self.coca_transform is None),
]
UpperCAmelCase_ : Dict = [x[0] for x in coca_is_none if x[1]]
UpperCAmelCase_ : str = ", ".join(lowerCAmelCase_ )
# generate prompts with coca model if prompt is None
if content_prompt is None:
if len(lowerCAmelCase_ ):
raise ValueError(
f"""Content prompt is None and CoCa [{coca_is_none_str}] is None."""
f"""Set prompt or pass Coca [{coca_is_none_str}] to DiffusionPipeline.""" )
UpperCAmelCase_ : List[str] = self.get_image_description(lowerCAmelCase_ )
if style_prompt is None:
if len(lowerCAmelCase_ ):
raise ValueError(
f"""Style prompt is None and CoCa [{coca_is_none_str}] is None."""
f""" Set prompt or pass Coca [{coca_is_none_str}] to DiffusionPipeline.""" )
UpperCAmelCase_ : List[str] = self.get_image_description(lowerCAmelCase_ )
# get prompt text embeddings for content and style
UpperCAmelCase_ : str = self.tokenizer(
lowerCAmelCase_ , padding="max_length" , max_length=self.tokenizer.model_max_length , truncation=lowerCAmelCase_ , return_tensors="pt" , )
UpperCAmelCase_ : Optional[int] = self.text_encoder(content_text_input.input_ids.to(self.device ) )[0]
UpperCAmelCase_ : int = self.tokenizer(
lowerCAmelCase_ , padding="max_length" , max_length=self.tokenizer.model_max_length , truncation=lowerCAmelCase_ , return_tensors="pt" , )
UpperCAmelCase_ : List[Any] = self.text_encoder(style_text_input.input_ids.to(self.device ) )[0]
UpperCAmelCase_ : str = slerp(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ )
# duplicate text embeddings for each generation per prompt
UpperCAmelCase_ : Dict = text_embeddings.repeat_interleave(lowerCAmelCase_ , dim=0 )
# set timesteps
UpperCAmelCase_ : List[str] = "offset" in set(inspect.signature(self.scheduler.set_timesteps ).parameters.keys() )
UpperCAmelCase_ : List[str] = {}
if accepts_offset:
UpperCAmelCase_ : Optional[Any] = 1
self.scheduler.set_timesteps(lowerCAmelCase_ , **lowerCAmelCase_ )
# Some schedulers like PNDM have timesteps as arrays
# It's more optimized to move all timesteps to correct device beforehand
self.scheduler.timesteps.to(self.device )
UpperCAmelCase_ , UpperCAmelCase_ : str = self.get_timesteps(lowerCAmelCase_ , lowerCAmelCase_ , self.device )
UpperCAmelCase_ : List[str] = timesteps[:1].repeat(lowerCAmelCase_ )
# Preprocess image
UpperCAmelCase_ : Optional[Any] = preprocess(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ )
UpperCAmelCase_ : Any = self.prepare_latents(
lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , text_embeddings.dtype , self.device , lowerCAmelCase_ )
UpperCAmelCase_ : Any = preprocess(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ )
UpperCAmelCase_ : Dict = self.prepare_latents(
lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , text_embeddings.dtype , self.device , lowerCAmelCase_ )
UpperCAmelCase_ : str = slerp(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ )
if clip_guidance_scale > 0:
UpperCAmelCase_ : int = self.get_clip_image_embeddings(lowerCAmelCase_ , lowerCAmelCase_ )
UpperCAmelCase_ : Any = self.get_clip_image_embeddings(lowerCAmelCase_ , lowerCAmelCase_ )
UpperCAmelCase_ : Union[str, Any] = slerp(
lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ )
# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
# corresponds to doing no classifier free guidance.
UpperCAmelCase_ : List[Any] = guidance_scale > 1.0
# get unconditional embeddings for classifier free guidance
if do_classifier_free_guidance:
UpperCAmelCase_ : List[str] = content_text_input.input_ids.shape[-1]
UpperCAmelCase_ : Tuple = self.tokenizer([""] , padding="max_length" , max_length=lowerCAmelCase_ , return_tensors="pt" )
UpperCAmelCase_ : List[str] = self.text_encoder(uncond_input.input_ids.to(self.device ) )[0]
# duplicate unconditional embeddings for each generation per prompt
UpperCAmelCase_ : Optional[int] = uncond_embeddings.repeat_interleave(lowerCAmelCase_ , dim=0 )
# 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
UpperCAmelCase_ : Optional[int] = torch.cat([uncond_embeddings, text_embeddings] )
# get the initial random noise unless the user supplied it
# Unlike in other pipelines, latents need to be generated in the target device
# for 1-to-1 results reproducibility with the CompVis implementation.
# However this currently doesn't work in `mps`.
UpperCAmelCase_ : List[Any] = (batch_size, self.unet.config.in_channels, height // 8, width // 8)
UpperCAmelCase_ : Union[str, Any] = text_embeddings.dtype
if latents is None:
if self.device.type == "mps":
# randn does not work reproducibly on mps
UpperCAmelCase_ : Tuple = torch.randn(lowerCAmelCase_ , generator=lowerCAmelCase_ , device="cpu" , dtype=lowerCAmelCase_ ).to(
self.device )
else:
UpperCAmelCase_ : Optional[Any] = torch.randn(lowerCAmelCase_ , generator=lowerCAmelCase_ , device=self.device , dtype=lowerCAmelCase_ )
else:
if latents.shape != latents_shape:
raise ValueError(f"""Unexpected latents shape, got {latents.shape}, expected {latents_shape}""" )
UpperCAmelCase_ : Any = latents.to(self.device )
# scale the initial noise by the standard deviation required by the scheduler
UpperCAmelCase_ : List[Any] = latents * self.scheduler.init_noise_sigma
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
# and should be between [0, 1]
UpperCAmelCase_ : str = "eta" in set(inspect.signature(self.scheduler.step ).parameters.keys() )
UpperCAmelCase_ : Optional[int] = {}
if accepts_eta:
UpperCAmelCase_ : Any = eta
# check if the scheduler accepts generator
UpperCAmelCase_ : Optional[int] = "generator" in set(inspect.signature(self.scheduler.step ).parameters.keys() )
if accepts_generator:
UpperCAmelCase_ : List[Any] = generator
with self.progress_bar(total=lowerCAmelCase_ ):
for i, t in enumerate(lowerCAmelCase_ ):
# expand the latents if we are doing classifier free guidance
UpperCAmelCase_ : str = torch.cat([latents] * 2 ) if do_classifier_free_guidance else latents
UpperCAmelCase_ : int = self.scheduler.scale_model_input(lowerCAmelCase_ , lowerCAmelCase_ )
# predict the noise residual
UpperCAmelCase_ : List[Any] = self.unet(lowerCAmelCase_ , lowerCAmelCase_ , encoder_hidden_states=lowerCAmelCase_ ).sample
# perform classifier free guidance
if do_classifier_free_guidance:
UpperCAmelCase_ , UpperCAmelCase_ : Tuple = noise_pred.chunk(2 )
UpperCAmelCase_ : Dict = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
# perform clip guidance
if clip_guidance_scale > 0:
UpperCAmelCase_ : Union[str, Any] = (
text_embeddings.chunk(2 )[1] if do_classifier_free_guidance else text_embeddings
)
UpperCAmelCase_ , UpperCAmelCase_ : Tuple = self.cond_fn(
lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , )
# compute the previous noisy sample x_t -> x_t-1
UpperCAmelCase_ : int = self.scheduler.step(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , **lowerCAmelCase_ ).prev_sample
# Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
UpperCAmelCase_ : Tuple = 1 / 0.1_8_2_1_5 * latents
UpperCAmelCase_ : Any = self.vae.decode(lowerCAmelCase_ ).sample
UpperCAmelCase_ : List[Any] = (image / 2 + 0.5).clamp(0 , 1 )
UpperCAmelCase_ : Union[str, Any] = image.cpu().permute(0 , 2 , 3 , 1 ).numpy()
if output_type == "pil":
UpperCAmelCase_ : List[str] = self.numpy_to_pil(lowerCAmelCase_ )
if not return_dict:
return (image, None)
return StableDiffusionPipelineOutput(images=lowerCAmelCase_ , nsfw_content_detected=lowerCAmelCase_ )
| 95 |
def _lowerCamelCase ( SCREAMING_SNAKE_CASE ):
'''simple docstring'''
if len(SCREAMING_SNAKE_CASE ) < 2:
return collection
def circle_sort_util(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) -> bool:
A_ = False
if low == high:
return swapped
A_ = low
A_ = high
while left < right:
if collection[left] > collection[right]:
A_ ,A_ = (
collection[right],
collection[left],
)
A_ = True
left += 1
right -= 1
if left == right and collection[left] > collection[right + 1]:
A_ ,A_ = (
collection[right + 1],
collection[left],
)
A_ = True
A_ = low + int((high - low) / 2 )
A_ = circle_sort_util(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
A_ = circle_sort_util(SCREAMING_SNAKE_CASE , mid + 1 , SCREAMING_SNAKE_CASE )
return swapped or left_swap or right_swap
A_ = True
while is_not_sorted is True:
A_ = circle_sort_util(SCREAMING_SNAKE_CASE , 0 , len(SCREAMING_SNAKE_CASE ) - 1 )
return collection
if __name__ == "__main__":
__lowercase = input("""Enter numbers separated by a comma:\n""").strip()
__lowercase = [int(item) for item in user_input.split(""",""")]
print(circle_sort(unsorted))
| 203 | 0 |
"""simple docstring"""
from __future__ import annotations
from collections.abc import Callable
UpperCamelCase_ : Tuple = list[list[float | int]]
def __lowercase ( a : Matrix , a : Matrix ) -> Matrix:
__snake_case : int =len(a )
__snake_case : Matrix =[[0 for _ in range(size + 1 )] for _ in range(a )]
__snake_case : int
__snake_case : int
__snake_case : int
__snake_case : int
__snake_case : int
__snake_case : float
for row in range(a ):
for col in range(a ):
__snake_case : Optional[Any] =matrix[row][col]
__snake_case : List[str] =vector[row][0]
__snake_case : Optional[int] =0
__snake_case : Union[str, Any] =0
while row < size and col < size:
# pivoting
__snake_case : List[str] =max((abs(augmented[rowa][col] ), rowa) for rowa in range(a , a ) )[
1
]
if augmented[pivot_row][col] == 0:
col += 1
continue
else:
__snake_case : Optional[Any] =augmented[pivot_row], augmented[row]
for rowa in range(row + 1 , a ):
__snake_case : List[Any] =augmented[rowa][col] / augmented[row][col]
__snake_case : List[str] =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 , a ):
for row in range(a ):
__snake_case : List[Any] =augmented[row][col] / augmented[col][col]
for cola in range(a , 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(a )
]
def __lowercase ( a : list[int] ) -> Callable[[int], int]:
__snake_case : int =len(a )
__snake_case : Matrix =[[0 for _ in range(a )] for _ in range(a )]
__snake_case : Matrix =[[0] for _ in range(a )]
__snake_case : Matrix
__snake_case : int
__snake_case : int
__snake_case : int
for x_val, y_val in enumerate(a ):
for col in range(a ):
__snake_case : List[Any] =(x_val + 1) ** (size - col - 1)
__snake_case : Optional[int] =y_val
__snake_case : Dict =solve(a , a )
def interpolated_func(a : int ) -> int:
return sum(
round(coeffs[x_val][0] ) * (var ** (size - x_val - 1))
for x_val in range(a ) )
return interpolated_func
def __lowercase ( a : int ) -> int:
return (
1
- variable
+ variable**2
- variable**3
+ variable**4
- variable**5
+ variable**6
- variable**7
+ variable**8
- variable**9
+ variable**10
)
def __lowercase ( a : Callable[[int], int] = question_function , a : int = 10 ) -> int:
__snake_case : list[int] =[func(a ) for x_val in range(1 , order + 1 )]
__snake_case : list[Callable[[int], int]] =[
interpolate(data_points[:max_coeff] ) for max_coeff in range(1 , order + 1 )
]
__snake_case : int =0
__snake_case : Callable[[int], int]
__snake_case : int
for poly in polynomials:
__snake_case : Optional[Any] =1
while func(a ) == poly(a ):
x_val += 1
ret += poly(a )
return ret
if __name__ == "__main__":
print(F'''{solution() = }''')
| 713 |
"""simple docstring"""
import tempfile
import unittest
import numpy as np
from diffusers import (
DDIMScheduler,
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionPipeline,
PNDMScheduler,
)
from diffusers.utils.testing_utils import is_onnx_available, nightly, require_onnxruntime, require_torch_gpu
from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin
if is_onnx_available():
import onnxruntime as ort
class _lowercase ( lowerCAmelCase , unittest.TestCase ):
_a : Tuple = '''hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline'''
def _UpperCamelCase ( self : Dict , a : Optional[Any]=0 ):
"""simple docstring"""
__snake_case : List[str] =np.random.RandomState(a )
__snake_case : Union[str, Any] ={
'''prompt''': '''A painting of a squirrel eating a burger''',
'''generator''': generator,
'''num_inference_steps''': 2,
'''guidance_scale''': 7.5,
'''output_type''': '''numpy''',
}
return inputs
def _UpperCamelCase ( self : str ):
"""simple docstring"""
__snake_case : Dict =OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint , provider='''CPUExecutionProvider''' )
pipe.set_progress_bar_config(disable=a )
__snake_case : Tuple =self.get_dummy_inputs()
__snake_case : List[str] =pipe(**a ).images
__snake_case : int =image[0, -3:, -3:, -1]
assert image.shape == (1, 1_2_8, 1_2_8, 3)
__snake_case : str =np.array([0.6_5_0_7_2, 0.5_8_4_9_2, 0.4_8_2_1_9, 0.5_5_5_2_1, 0.5_3_1_8_0, 0.5_5_9_3_9, 0.5_0_6_9_7, 0.3_9_8_0_0, 0.4_6_4_5_5] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
def _UpperCamelCase ( self : Union[str, Any] ):
"""simple docstring"""
__snake_case : Dict =OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint , provider='''CPUExecutionProvider''' )
__snake_case : Dict =PNDMScheduler.from_config(pipe.scheduler.config , skip_prk_steps=a )
pipe.set_progress_bar_config(disable=a )
__snake_case : List[Any] =self.get_dummy_inputs()
__snake_case : Dict =pipe(**a ).images
__snake_case : Union[str, Any] =image[0, -3:, -3:, -1]
assert image.shape == (1, 1_2_8, 1_2_8, 3)
__snake_case : Optional[Any] =np.array([0.6_5_8_6_3, 0.5_9_4_2_5, 0.4_9_3_2_6, 0.5_6_3_1_3, 0.5_3_8_7_5, 0.5_6_6_2_7, 0.5_1_0_6_5, 0.3_9_7_7_7, 0.4_6_3_3_0] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
def _UpperCamelCase ( self : Optional[int] ):
"""simple docstring"""
__snake_case : Any =OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint , provider='''CPUExecutionProvider''' )
__snake_case : Any =LMSDiscreteScheduler.from_config(pipe.scheduler.config )
pipe.set_progress_bar_config(disable=a )
__snake_case : int =self.get_dummy_inputs()
__snake_case : List[str] =pipe(**a ).images
__snake_case : str =image[0, -3:, -3:, -1]
assert image.shape == (1, 1_2_8, 1_2_8, 3)
__snake_case : int =np.array([0.5_3_7_5_5, 0.6_0_7_8_6, 0.4_7_4_0_2, 0.4_9_4_8_8, 0.5_1_8_6_9, 0.4_9_8_1_9, 0.4_7_9_8_5, 0.3_8_9_5_7, 0.4_4_2_7_9] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
def _UpperCamelCase ( self : List[str] ):
"""simple docstring"""
__snake_case : Optional[int] =OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint , provider='''CPUExecutionProvider''' )
__snake_case : Optional[Any] =EulerDiscreteScheduler.from_config(pipe.scheduler.config )
pipe.set_progress_bar_config(disable=a )
__snake_case : str =self.get_dummy_inputs()
__snake_case : int =pipe(**a ).images
__snake_case : str =image[0, -3:, -3:, -1]
assert image.shape == (1, 1_2_8, 1_2_8, 3)
__snake_case : Union[str, Any] =np.array([0.5_3_7_5_5, 0.6_0_7_8_6, 0.4_7_4_0_2, 0.4_9_4_8_8, 0.5_1_8_6_9, 0.4_9_8_1_9, 0.4_7_9_8_5, 0.3_8_9_5_7, 0.4_4_2_7_9] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
def _UpperCamelCase ( self : List[Any] ):
"""simple docstring"""
__snake_case : Any =OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint , provider='''CPUExecutionProvider''' )
__snake_case : List[str] =EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config )
pipe.set_progress_bar_config(disable=a )
__snake_case : List[str] =self.get_dummy_inputs()
__snake_case : Dict =pipe(**a ).images
__snake_case : List[Any] =image[0, -3:, -3:, -1]
assert image.shape == (1, 1_2_8, 1_2_8, 3)
__snake_case : Tuple =np.array([0.5_3_8_1_7, 0.6_0_8_1_2, 0.4_7_3_8_4, 0.4_9_5_3_0, 0.5_1_8_9_4, 0.4_9_8_1_4, 0.4_7_9_8_4, 0.3_8_9_5_8, 0.4_4_2_7_1] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
def _UpperCamelCase ( self : Dict ):
"""simple docstring"""
__snake_case : Tuple =OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint , provider='''CPUExecutionProvider''' )
__snake_case : Any =DPMSolverMultistepScheduler.from_config(pipe.scheduler.config )
pipe.set_progress_bar_config(disable=a )
__snake_case : Tuple =self.get_dummy_inputs()
__snake_case : Tuple =pipe(**a ).images
__snake_case : Optional[Any] =image[0, -3:, -3:, -1]
assert image.shape == (1, 1_2_8, 1_2_8, 3)
__snake_case : Dict =np.array([0.5_3_8_9_5, 0.6_0_8_0_8, 0.4_7_9_3_3, 0.4_9_6_0_8, 0.5_1_8_8_6, 0.4_9_9_5_0, 0.4_8_0_5_3, 0.3_8_9_5_7, 0.4_4_2_0_0] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
def _UpperCamelCase ( self : int ):
"""simple docstring"""
__snake_case : Dict =OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint , provider='''CPUExecutionProvider''' )
pipe.set_progress_bar_config(disable=a )
__snake_case : Optional[int] =self.get_dummy_inputs()
__snake_case : Any =3 * [inputs['''prompt''']]
# forward
__snake_case : Any =pipe(**a )
__snake_case : str =output.images[0, -3:, -3:, -1]
__snake_case : Tuple =self.get_dummy_inputs()
__snake_case : Any =3 * [inputs.pop('''prompt''' )]
__snake_case : Optional[Any] =pipe.tokenizer(
a , padding='''max_length''' , max_length=pipe.tokenizer.model_max_length , truncation=a , return_tensors='''np''' , )
__snake_case : List[Any] =text_inputs['''input_ids''']
__snake_case : str =pipe.text_encoder(input_ids=text_inputs.astype(np.intaa ) )[0]
__snake_case : Optional[Any] =prompt_embeds
# forward
__snake_case : Dict =pipe(**a )
__snake_case : Any =output.images[0, -3:, -3:, -1]
assert np.abs(image_slice_a.flatten() - image_slice_a.flatten() ).max() < 1e-4
def _UpperCamelCase ( self : Any ):
"""simple docstring"""
__snake_case : Dict =OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint , provider='''CPUExecutionProvider''' )
pipe.set_progress_bar_config(disable=a )
__snake_case : List[Any] =self.get_dummy_inputs()
__snake_case : Optional[Any] =3 * ['''this is a negative prompt''']
__snake_case : List[str] =negative_prompt
__snake_case : str =3 * [inputs['''prompt''']]
# forward
__snake_case : int =pipe(**a )
__snake_case : Union[str, Any] =output.images[0, -3:, -3:, -1]
__snake_case : Tuple =self.get_dummy_inputs()
__snake_case : Union[str, Any] =3 * [inputs.pop('''prompt''' )]
__snake_case : Optional[int] =[]
for p in [prompt, negative_prompt]:
__snake_case : Optional[int] =pipe.tokenizer(
a , padding='''max_length''' , max_length=pipe.tokenizer.model_max_length , truncation=a , return_tensors='''np''' , )
__snake_case : Optional[Any] =text_inputs['''input_ids''']
embeds.append(pipe.text_encoder(input_ids=text_inputs.astype(np.intaa ) )[0] )
__snake_case , __snake_case : Optional[Any] =embeds
# forward
__snake_case : Any =pipe(**a )
__snake_case : Union[str, Any] =output.images[0, -3:, -3:, -1]
assert np.abs(image_slice_a.flatten() - image_slice_a.flatten() ).max() < 1e-4
@nightly
@require_onnxruntime
@require_torch_gpu
class _lowercase ( unittest.TestCase ):
@property
def _UpperCamelCase ( self : List[Any] ):
"""simple docstring"""
return (
"CUDAExecutionProvider",
{
"gpu_mem_limit": "15000000000", # 15GB
"arena_extend_strategy": "kSameAsRequested",
},
)
@property
def _UpperCamelCase ( self : Union[str, Any] ):
"""simple docstring"""
__snake_case : List[str] =ort.SessionOptions()
__snake_case : Optional[int] =False
return options
def _UpperCamelCase ( self : Any ):
"""simple docstring"""
__snake_case : List[Any] =OnnxStableDiffusionPipeline.from_pretrained(
'''CompVis/stable-diffusion-v1-4''' , revision='''onnx''' , safety_checker=a , feature_extractor=a , provider=self.gpu_provider , sess_options=self.gpu_options , )
sd_pipe.set_progress_bar_config(disable=a )
__snake_case : List[str] ='''A painting of a squirrel eating a burger'''
np.random.seed(0 )
__snake_case : Tuple =sd_pipe([prompt] , guidance_scale=6.0 , num_inference_steps=1_0 , output_type='''np''' )
__snake_case : Union[str, Any] =output.images
__snake_case : Optional[Any] =image[0, -3:, -3:, -1]
assert image.shape == (1, 5_1_2, 5_1_2, 3)
__snake_case : Any =np.array([0.0_4_5_2, 0.0_3_9_0, 0.0_0_8_7, 0.0_3_5_0, 0.0_6_1_7, 0.0_3_6_4, 0.0_5_4_4, 0.0_5_2_3, 0.0_7_2_0] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3
def _UpperCamelCase ( self : Optional[Any] ):
"""simple docstring"""
__snake_case : List[str] =DDIMScheduler.from_pretrained(
'''runwayml/stable-diffusion-v1-5''' , subfolder='''scheduler''' , revision='''onnx''' )
__snake_case : List[Any] =OnnxStableDiffusionPipeline.from_pretrained(
'''runwayml/stable-diffusion-v1-5''' , revision='''onnx''' , scheduler=a , safety_checker=a , feature_extractor=a , provider=self.gpu_provider , sess_options=self.gpu_options , )
sd_pipe.set_progress_bar_config(disable=a )
__snake_case : Optional[Any] ='''open neural network exchange'''
__snake_case : Optional[int] =np.random.RandomState(0 )
__snake_case : int =sd_pipe([prompt] , guidance_scale=7.5 , num_inference_steps=1_0 , generator=a , output_type='''np''' )
__snake_case : Union[str, Any] =output.images
__snake_case : str =image[0, -3:, -3:, -1]
assert image.shape == (1, 5_1_2, 5_1_2, 3)
__snake_case : Union[str, Any] =np.array([0.2_8_6_7, 0.1_9_7_4, 0.1_4_8_1, 0.7_2_9_4, 0.7_2_5_1, 0.6_6_6_7, 0.4_1_9_4, 0.5_6_4_2, 0.6_4_8_6] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3
def _UpperCamelCase ( self : Tuple ):
"""simple docstring"""
__snake_case : List[str] =LMSDiscreteScheduler.from_pretrained(
'''runwayml/stable-diffusion-v1-5''' , subfolder='''scheduler''' , revision='''onnx''' )
__snake_case : int =OnnxStableDiffusionPipeline.from_pretrained(
'''runwayml/stable-diffusion-v1-5''' , revision='''onnx''' , scheduler=a , safety_checker=a , feature_extractor=a , provider=self.gpu_provider , sess_options=self.gpu_options , )
sd_pipe.set_progress_bar_config(disable=a )
__snake_case : Optional[int] ='''open neural network exchange'''
__snake_case : Optional[Any] =np.random.RandomState(0 )
__snake_case : Any =sd_pipe([prompt] , guidance_scale=7.5 , num_inference_steps=1_0 , generator=a , output_type='''np''' )
__snake_case : Optional[int] =output.images
__snake_case : Any =image[0, -3:, -3:, -1]
assert image.shape == (1, 5_1_2, 5_1_2, 3)
__snake_case : Optional[int] =np.array([0.2_3_0_6, 0.1_9_5_9, 0.1_5_9_3, 0.6_5_4_9, 0.6_3_9_4, 0.5_4_0_8, 0.5_0_6_5, 0.6_0_1_0, 0.6_1_6_1] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3
def _UpperCamelCase ( self : Union[str, Any] ):
"""simple docstring"""
__snake_case : Union[str, Any] =0
def test_callback_fn(a : int , a : int , a : np.ndarray ) -> None:
__snake_case : Dict =True
nonlocal number_of_steps
number_of_steps += 1
if step == 0:
assert latents.shape == (1, 4, 6_4, 6_4)
__snake_case : Union[str, Any] =latents[0, -3:, -3:, -1]
__snake_case : str =np.array(
[-0.6_7_7_2, -0.3_8_3_5, -1.2_4_5_6, 0.1_9_0_5, -1.0_9_7_4, 0.6_9_6_7, -1.9_3_5_3, 0.0_1_7_8, 1.0_1_6_7] )
assert np.abs(latents_slice.flatten() - expected_slice ).max() < 1e-3
elif step == 5:
assert latents.shape == (1, 4, 6_4, 6_4)
__snake_case : List[Any] =latents[0, -3:, -3:, -1]
__snake_case : List[Any] =np.array(
[-0.3_3_5_1, 0.2_2_4_1, -0.1_8_3_7, -0.2_3_2_5, -0.6_5_7_7, 0.3_3_9_3, -0.0_2_4_1, 0.5_8_9_9, 1.3_8_7_5] )
assert np.abs(latents_slice.flatten() - expected_slice ).max() < 1e-3
__snake_case : str =False
__snake_case : int =OnnxStableDiffusionPipeline.from_pretrained(
'''runwayml/stable-diffusion-v1-5''' , revision='''onnx''' , safety_checker=a , feature_extractor=a , provider=self.gpu_provider , sess_options=self.gpu_options , )
pipe.set_progress_bar_config(disable=a )
__snake_case : List[Any] ='''Andromeda galaxy in a bottle'''
__snake_case : Optional[int] =np.random.RandomState(0 )
pipe(
prompt=a , num_inference_steps=5 , guidance_scale=7.5 , generator=a , callback=a , callback_steps=1 , )
assert test_callback_fn.has_been_called
assert number_of_steps == 6
def _UpperCamelCase ( self : List[Any] ):
"""simple docstring"""
__snake_case : Optional[Any] =OnnxStableDiffusionPipeline.from_pretrained(
'''runwayml/stable-diffusion-v1-5''' , revision='''onnx''' , safety_checker=a , feature_extractor=a , provider=self.gpu_provider , sess_options=self.gpu_options , )
assert isinstance(a , a )
assert pipe.safety_checker is None
__snake_case : int =pipe('''example prompt''' , num_inference_steps=2 ).images[0]
assert image is not None
# check that there's no error when saving a pipeline with one of the models being None
with tempfile.TemporaryDirectory() as tmpdirname:
pipe.save_pretrained(a )
__snake_case : List[Any] =OnnxStableDiffusionPipeline.from_pretrained(a )
# sanity check that the pipeline still works
assert pipe.safety_checker is None
__snake_case : Any =pipe('''example prompt''' , num_inference_steps=2 ).images[0]
assert image is not None
| 497 | 0 |
'''simple docstring'''
def A (__lowerCamelCase :int = 100 ):
_lowerCAmelCase = 0
_lowerCAmelCase = 0
for i in range(1 , n + 1 ):
sum_of_squares += i**2
sum_of_ints += i
return sum_of_ints**2 - sum_of_squares
if __name__ == "__main__":
print(F"""{solution() = }""")
| 5 |
from PIL import Image
def __magic_name__ ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) -> Image:
def brightness(SCREAMING_SNAKE_CASE ) -> float:
return 128 + level + (c - 128)
if not -255.0 <= level <= 255.0:
raise ValueError('level must be between -255.0 (black) and 255.0 (white)' )
return img.point(SCREAMING_SNAKE_CASE )
if __name__ == "__main__":
# Load image
with Image.open("image_data/lena.jpg") as img:
# Change brightness to 100
UpperCamelCase = change_brightness(img, 100)
brigt_img.save("image_data/lena_brightness.png", format="png")
| 66 | 0 |
"""simple docstring"""
def _lowerCamelCase( a ):
__a = [0] * len(a )
for i in range(1 , len(a ) ):
# use last results for better performance - dynamic programming
__a = prefix_result[i - 1]
while j > 0 and input_string[i] != input_string[j]:
__a = prefix_result[j - 1]
if input_string[i] == input_string[j]:
j += 1
__a = j
return prefix_result
def _lowerCamelCase( a ):
return max(prefix_function(a ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 67 | """simple docstring"""
import argparse
import OmegaConf
import torch
from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel
def _lowerCamelCase( a , a , a ):
__a = OmegaConf.load(a )
__a = torch.load(a , map_location="cpu" )["model"]
__a = list(state_dict.keys() )
# extract state_dict for VQVAE
__a = {}
__a = "first_stage_model."
for key in keys:
if key.startswith(a ):
__a = state_dict[key]
# extract state_dict for UNetLDM
__a = {}
__a = "model.diffusion_model."
for key in keys:
if key.startswith(a ):
__a = state_dict[key]
__a = config.model.params.first_stage_config.params
__a = config.model.params.unet_config.params
__a = VQModel(**a ).eval()
vqvae.load_state_dict(a )
__a = UNetLDMModel(**a ).eval()
unet.load_state_dict(a )
__a = DDIMScheduler(
timesteps=config.model.params.timesteps , beta_schedule="scaled_linear" , beta_start=config.model.params.linear_start , beta_end=config.model.params.linear_end , clip_sample=a , )
__a = LDMPipeline(a , a , a )
pipeline.save_pretrained(a )
if __name__ == "__main__":
SCREAMING_SNAKE_CASE__:List[Any] = argparse.ArgumentParser()
parser.add_argument("""--checkpoint_path""", type=str, required=True)
parser.add_argument("""--config_path""", type=str, required=True)
parser.add_argument("""--output_path""", type=str, required=True)
SCREAMING_SNAKE_CASE__:Union[str, Any] = parser.parse_args()
convert_ldm_original(args.checkpoint_path, args.config_path, args.output_path)
| 67 | 1 |
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE ):
if not isinstance(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
lowercase = F'''Input value of [number={number}] must be an integer'''
raise TypeError(__SCREAMING_SNAKE_CASE )
if number < 1:
lowercase = F'''Input value of [number={number}] must be > 0'''
raise ValueError(__SCREAMING_SNAKE_CASE )
lowercase = 1
for i in range(1 , __SCREAMING_SNAKE_CASE ):
current_number *= 4 * i - 2
current_number //= i + 1
return current_number
if __name__ == "__main__":
import doctest
doctest.testmod()
| 84 |
"""simple docstring"""
import unittest
from transformers import DebertaVaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
DebertaVaForMaskedLM,
DebertaVaForMultipleChoice,
DebertaVaForQuestionAnswering,
DebertaVaForSequenceClassification,
DebertaVaForTokenClassification,
DebertaVaModel,
)
from transformers.models.deberta_va.modeling_deberta_va import DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST
class __lowercase( lowercase__ ):
'''simple docstring'''
def __init__( self , __a , __a=13 , __a=7 , __a=True , __a=True , __a=True , __a=True , __a=99 , __a=32 , __a=5 , __a=4 , __a=37 , __a="gelu" , __a=0.1 , __a=0.1 , __a=512 , __a=16 , __a=2 , __a=0.02 , __a=False , __a=True , __a="None" , __a=3 , __a=4 , __a=None , ):
__lowerCamelCase : List[str] = parent
__lowerCamelCase : Optional[int] = batch_size
__lowerCamelCase : Union[str, Any] = seq_length
__lowerCamelCase : Optional[int] = is_training
__lowerCamelCase : List[str] = use_input_mask
__lowerCamelCase : Dict = use_token_type_ids
__lowerCamelCase : Dict = use_labels
__lowerCamelCase : Union[str, Any] = vocab_size
__lowerCamelCase : List[Any] = hidden_size
__lowerCamelCase : int = num_hidden_layers
__lowerCamelCase : int = num_attention_heads
__lowerCamelCase : int = intermediate_size
__lowerCamelCase : Dict = hidden_act
__lowerCamelCase : Optional[int] = hidden_dropout_prob
__lowerCamelCase : Any = attention_probs_dropout_prob
__lowerCamelCase : List[str] = max_position_embeddings
__lowerCamelCase : Tuple = type_vocab_size
__lowerCamelCase : List[Any] = type_sequence_label_size
__lowerCamelCase : Optional[Any] = initializer_range
__lowerCamelCase : Any = num_labels
__lowerCamelCase : Union[str, Any] = num_choices
__lowerCamelCase : int = relative_attention
__lowerCamelCase : Tuple = position_biased_input
__lowerCamelCase : int = pos_att_type
__lowerCamelCase : Dict = scope
def snake_case_ ( self ):
__lowerCamelCase : Tuple = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
__lowerCamelCase : Dict = None
if self.use_input_mask:
__lowerCamelCase : List[Any] = ids_tensor([self.batch_size, self.seq_length] , vocab_size=2 )
__lowerCamelCase : Dict = None
if self.use_token_type_ids:
__lowerCamelCase : Optional[Any] = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
__lowerCamelCase : Any = None
__lowerCamelCase : Optional[int] = None
__lowerCamelCase : Union[str, Any] = None
if self.use_labels:
__lowerCamelCase : Dict = ids_tensor([self.batch_size] , self.type_sequence_label_size )
__lowerCamelCase : Tuple = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
__lowerCamelCase : Optional[Any] = ids_tensor([self.batch_size] , self.num_choices )
__lowerCamelCase : List[str] = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def snake_case_ ( self ):
return DebertaVaConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , initializer_range=self.initializer_range , relative_attention=self.relative_attention , position_biased_input=self.position_biased_input , pos_att_type=self.pos_att_type , )
def snake_case_ ( self , __a ):
self.parent.assertListEqual(list(result.loss.size() ) , [] )
def snake_case_ ( self , __a , __a , __a , __a , __a , __a , __a ):
__lowerCamelCase : Optional[int] = DebertaVaModel(config=__a )
model.to(__a )
model.eval()
__lowerCamelCase : int = model(__a , attention_mask=__a , token_type_ids=__a )[0]
__lowerCamelCase : Optional[int] = model(__a , token_type_ids=__a )[0]
__lowerCamelCase : Optional[Any] = model(__a )[0]
self.parent.assertListEqual(list(sequence_output.size() ) , [self.batch_size, self.seq_length, self.hidden_size] )
def snake_case_ ( self , __a , __a , __a , __a , __a , __a , __a ):
__lowerCamelCase : List[str] = DebertaVaForMaskedLM(config=__a )
model.to(__a )
model.eval()
__lowerCamelCase : List[str] = model(__a , attention_mask=__a , token_type_ids=__a , labels=__a )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def snake_case_ ( self , __a , __a , __a , __a , __a , __a , __a ):
__lowerCamelCase : str = self.num_labels
__lowerCamelCase : Tuple = DebertaVaForSequenceClassification(__a )
model.to(__a )
model.eval()
__lowerCamelCase : Optional[int] = model(__a , attention_mask=__a , token_type_ids=__a , labels=__a )
self.parent.assertListEqual(list(result.logits.size() ) , [self.batch_size, self.num_labels] )
self.check_loss_output(__a )
def snake_case_ ( self , __a , __a , __a , __a , __a , __a , __a ):
__lowerCamelCase : Optional[Any] = self.num_labels
__lowerCamelCase : List[str] = DebertaVaForTokenClassification(config=__a )
model.to(__a )
model.eval()
__lowerCamelCase : List[Any] = model(__a , attention_mask=__a , token_type_ids=__a , labels=__a )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def snake_case_ ( self , __a , __a , __a , __a , __a , __a , __a ):
__lowerCamelCase : Optional[int] = DebertaVaForQuestionAnswering(config=__a )
model.to(__a )
model.eval()
__lowerCamelCase : List[str] = model(
__a , attention_mask=__a , token_type_ids=__a , start_positions=__a , end_positions=__a , )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def snake_case_ ( self , __a , __a , __a , __a , __a , __a , __a ):
__lowerCamelCase : Optional[Any] = DebertaVaForMultipleChoice(config=__a )
model.to(__a )
model.eval()
__lowerCamelCase : str = input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
__lowerCamelCase : Dict = token_type_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
__lowerCamelCase : int = input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
__lowerCamelCase : str = model(
__a , attention_mask=__a , token_type_ids=__a , labels=__a , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def snake_case_ ( self ):
__lowerCamelCase : Tuple = self.prepare_config_and_inputs()
(
(
__lowerCamelCase
) , (
__lowerCamelCase
) , (
__lowerCamelCase
) , (
__lowerCamelCase
) , (
__lowerCamelCase
) , (
__lowerCamelCase
) , (
__lowerCamelCase
) ,
) : int = config_and_inputs
__lowerCamelCase : Tuple = {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
return config, inputs_dict
@require_torch
class __lowercase( lowercase__ , lowercase__ , unittest.TestCase ):
'''simple docstring'''
__a : str = (
(
DebertaVaModel,
DebertaVaForMaskedLM,
DebertaVaForSequenceClassification,
DebertaVaForTokenClassification,
DebertaVaForQuestionAnswering,
DebertaVaForMultipleChoice,
)
if is_torch_available()
else ()
)
__a : Optional[int] = (
{
'feature-extraction': DebertaVaModel,
'fill-mask': DebertaVaForMaskedLM,
'question-answering': DebertaVaForQuestionAnswering,
'text-classification': DebertaVaForSequenceClassification,
'token-classification': DebertaVaForTokenClassification,
'zero-shot': DebertaVaForSequenceClassification,
}
if is_torch_available()
else {}
)
__a : Tuple = True
__a : List[Any] = False
__a : Any = False
__a : Tuple = False
__a : Tuple = False
def snake_case_ ( self ):
__lowerCamelCase : Optional[int] = DebertaVaModelTester(self )
__lowerCamelCase : Union[str, Any] = ConfigTester(self , config_class=__a , hidden_size=37 )
def snake_case_ ( self ):
self.config_tester.run_common_tests()
def snake_case_ ( self ):
__lowerCamelCase : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_deberta_model(*__a )
def snake_case_ ( self ):
__lowerCamelCase : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_deberta_for_sequence_classification(*__a )
def snake_case_ ( self ):
__lowerCamelCase : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_deberta_for_masked_lm(*__a )
def snake_case_ ( self ):
__lowerCamelCase : str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_deberta_for_question_answering(*__a )
def snake_case_ ( self ):
__lowerCamelCase : List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_deberta_for_token_classification(*__a )
def snake_case_ ( self ):
__lowerCamelCase : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_deberta_for_multiple_choice(*__a )
@slow
def snake_case_ ( self ):
for model_name in DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__lowerCamelCase : List[str] = DebertaVaModel.from_pretrained(__a )
self.assertIsNotNone(__a )
@require_torch
@require_sentencepiece
@require_tokenizers
class __lowercase( unittest.TestCase ):
'''simple docstring'''
@unittest.skip(reason='Model not available yet' )
def snake_case_ ( self ):
pass
@slow
def snake_case_ ( self ):
__lowerCamelCase : Optional[int] = DebertaVaModel.from_pretrained('microsoft/deberta-v2-xlarge' )
__lowerCamelCase : Dict = torch.tensor([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]] )
__lowerCamelCase : Optional[int] = torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]] )
with torch.no_grad():
__lowerCamelCase : Union[str, Any] = model(__a , attention_mask=__a )[0]
# compare the actual values for a slice.
__lowerCamelCase : Optional[int] = torch.tensor(
[[[0.2_356, 0.1_948, 0.0_369], [-0.1_063, 0.3_586, -0.5_152], [-0.6_399, -0.0_259, -0.2_525]]] )
self.assertTrue(torch.allclose(output[:, 1:4, 1:4] , __a , atol=1E-4 ) , f'''{output[:, 1:4, 1:4]}''' )
| 594 | 0 |
import json
from typing import Dict, List, Optional, Tuple, Union
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding, EncodedInput
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
from .tokenization_led import LEDTokenizer
A : List[str] = logging.get_logger(__name__)
A : Optional[Any] = {"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}
A : Union[str, Any] = {
"vocab_file": {
"allenai/led-base-16384": "https://huggingface.co/allenai/led-base-16384/resolve/main/vocab.json",
},
"merges_file": {
"allenai/led-base-16384": "https://huggingface.co/allenai/led-base-16384/resolve/main/merges.txt",
},
"tokenizer_file": {
"allenai/led-base-16384": "https://huggingface.co/allenai/led-base-16384/resolve/main/tokenizer.json",
},
}
A : Optional[int] = {
"allenai/led-base-16384": 16384,
}
class _lowercase ( lowercase__):
"""simple docstring"""
A__ = VOCAB_FILES_NAMES
A__ = PRETRAINED_VOCAB_FILES_MAP
A__ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
A__ = LEDTokenizer
A__ = ["input_ids", "attention_mask"]
def __init__( self : List[Any] , __lowerCamelCase : str=None , __lowerCamelCase : Optional[Any]=None , __lowerCamelCase : List[str]=None , __lowerCamelCase : Optional[int]="replace" , __lowerCamelCase : List[str]="<s>" , __lowerCamelCase : Any="</s>" , __lowerCamelCase : Optional[int]="</s>" , __lowerCamelCase : Dict="<s>" , __lowerCamelCase : str="<unk>" , __lowerCamelCase : List[str]="<pad>" , __lowerCamelCase : Dict="<mask>" , __lowerCamelCase : Optional[Any]=False , __lowerCamelCase : int=True , **__lowerCamelCase : List[Any] , ):
'''simple docstring'''
super().__init__(
__lowerCamelCase , __lowerCamelCase , tokenizer_file=__lowerCamelCase , errors=__lowerCamelCase , bos_token=__lowerCamelCase , eos_token=__lowerCamelCase , sep_token=__lowerCamelCase , cls_token=__lowerCamelCase , unk_token=__lowerCamelCase , pad_token=__lowerCamelCase , mask_token=__lowerCamelCase , add_prefix_space=__lowerCamelCase , trim_offsets=__lowerCamelCase , **__lowerCamelCase , )
lowerCamelCase__ : Optional[int] = json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__() )
if pre_tok_state.get("add_prefix_space" , __lowerCamelCase ) != add_prefix_space:
lowerCamelCase__ : List[str] = getattr(__lowerCamelCase , pre_tok_state.pop("type" ) )
lowerCamelCase__ : Tuple = add_prefix_space
lowerCamelCase__ : str = pre_tok_class(**__lowerCamelCase )
lowerCamelCase__ : List[str] = add_prefix_space
# the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
lowerCamelCase__ : Dict = "post_processor"
lowerCamelCase__ : List[Any] = getattr(self.backend_tokenizer , __lowerCamelCase , __lowerCamelCase )
if tokenizer_component_instance:
lowerCamelCase__ : str = 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:
lowerCamelCase__ : List[str] = tuple(state["sep"] )
if "cls" in state:
lowerCamelCase__ : List[str] = tuple(state["cls"] )
lowerCamelCase__ : str = False
if state.get("add_prefix_space" , __lowerCamelCase ) != add_prefix_space:
lowerCamelCase__ : Any = add_prefix_space
lowerCamelCase__ : Dict = True
if state.get("trim_offsets" , __lowerCamelCase ) != trim_offsets:
lowerCamelCase__ : int = trim_offsets
lowerCamelCase__ : Any = True
if changes_to_apply:
lowerCamelCase__ : Union[str, Any] = getattr(__lowerCamelCase , state.pop("type" ) )
lowerCamelCase__ : Union[str, Any] = component_class(**__lowerCamelCase )
setattr(self.backend_tokenizer , __lowerCamelCase , __lowerCamelCase )
@property
# Copied from transformers.models.bart.tokenization_bart_fast.BartTokenizerFast.mask_token with BART->LED
def lowerCAmelCase ( self : List[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 lowerCAmelCase ( self : Optional[Any] , __lowerCamelCase : List[str] ):
'''simple docstring'''
lowerCamelCase__ : int = AddedToken(__lowerCamelCase , lstrip=__lowerCamelCase , rstrip=__lowerCamelCase ) if isinstance(__lowerCamelCase , __lowerCamelCase ) else value
lowerCamelCase__ : Optional[Any] = value
def lowerCAmelCase ( self : Tuple , *__lowerCamelCase : Optional[Any] , **__lowerCamelCase : Dict ):
'''simple docstring'''
lowerCamelCase__ : Optional[Any] = kwargs.get("is_split_into_words" , __lowerCamelCase )
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(*__lowerCamelCase , **__lowerCamelCase )
def lowerCAmelCase ( self : Optional[Any] , *__lowerCamelCase : Dict , **__lowerCamelCase : Any ):
'''simple docstring'''
lowerCamelCase__ : List[Any] = kwargs.get("is_split_into_words" , __lowerCamelCase )
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(*__lowerCamelCase , **__lowerCamelCase )
def lowerCAmelCase ( self : Optional[int] , __lowerCamelCase : str , __lowerCamelCase : Optional[str] = None ):
'''simple docstring'''
lowerCamelCase__ : Dict = self._tokenizer.model.save(__lowerCamelCase , name=__lowerCamelCase )
return tuple(__lowerCamelCase )
def lowerCAmelCase ( self : int , __lowerCamelCase : Union[str, Any] , __lowerCamelCase : Dict=None ):
'''simple docstring'''
lowerCamelCase__ : Union[str, Any] = [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 lowerCAmelCase ( self : str , __lowerCamelCase : List[int] , __lowerCamelCase : Optional[List[int]] = None ):
'''simple docstring'''
lowerCamelCase__ : Optional[Any] = [self.sep_token_id]
lowerCamelCase__ : Optional[Any] = [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]
def lowerCAmelCase ( self : List[Any] , __lowerCamelCase : Union[Dict[str, EncodedInput], BatchEncoding] , __lowerCamelCase : Optional[int] = None , __lowerCamelCase : PaddingStrategy = PaddingStrategy.DO_NOT_PAD , __lowerCamelCase : Optional[int] = None , __lowerCamelCase : Optional[bool] = None , ):
'''simple docstring'''
lowerCamelCase__ : Optional[Any] = super()._pad(
encoded_inputs=__lowerCamelCase , max_length=__lowerCamelCase , padding_strategy=__lowerCamelCase , pad_to_multiple_of=__lowerCamelCase , return_attention_mask=__lowerCamelCase , )
# Load from model defaults
if return_attention_mask is None:
lowerCamelCase__ : Optional[Any] = "attention_mask" in self.model_input_names
if return_attention_mask and "global_attention_mask" in encoded_inputs:
lowerCamelCase__ : Any = encoded_inputs[self.model_input_names[0]]
# `global_attention_mask` need to have the same length as other (sequential) inputs.
lowerCamelCase__ : int = len(encoded_inputs["global_attention_mask"] ) != len(__lowerCamelCase )
if needs_to_be_padded:
lowerCamelCase__ : Any = len(__lowerCamelCase ) - len(encoded_inputs["global_attention_mask"] )
if self.padding_side == "right":
# Use `-1` since `0` in `global_attention_mask` means `local attention` instead of `not to attend`
lowerCamelCase__ : Optional[Any] = (
encoded_inputs["global_attention_mask"] + [-1] * difference
)
elif self.padding_side == "left":
lowerCamelCase__ : Dict = [-1] * difference + encoded_inputs[
"global_attention_mask"
]
else:
raise ValueError("Invalid padding strategy:" + str(self.padding_side ) )
return encoded_inputs
| 5 |
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...file_utils import TensorType, is_torch_available
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import logging
A : List[Any] = logging.get_logger(__name__)
A : Any = {
"facebook/blenderbot_small-90M": "https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/config.json",
# See all BlenderbotSmall models at https://huggingface.co/models?filter=blenderbot_small
}
class _lowercase ( lowercase__):
"""simple docstring"""
A__ = "blenderbot-small"
A__ = ["past_key_values"]
A__ = {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}
def __init__( self : Dict , __lowerCamelCase : List[str]=50265 , __lowerCamelCase : str=512 , __lowerCamelCase : Tuple=8 , __lowerCamelCase : str=2048 , __lowerCamelCase : str=16 , __lowerCamelCase : List[Any]=8 , __lowerCamelCase : Any=2048 , __lowerCamelCase : List[str]=16 , __lowerCamelCase : Dict=0.0 , __lowerCamelCase : List[Any]=0.0 , __lowerCamelCase : Optional[int]=True , __lowerCamelCase : Union[str, Any]=True , __lowerCamelCase : Tuple="gelu" , __lowerCamelCase : Tuple=512 , __lowerCamelCase : Dict=0.1 , __lowerCamelCase : int=0.0 , __lowerCamelCase : Union[str, Any]=0.0 , __lowerCamelCase : Any=0.0_2 , __lowerCamelCase : str=1 , __lowerCamelCase : Dict=False , __lowerCamelCase : int=0 , __lowerCamelCase : Optional[Any]=1 , __lowerCamelCase : str=2 , __lowerCamelCase : Any=2 , **__lowerCamelCase : int , ):
'''simple docstring'''
lowerCamelCase__ : str = vocab_size
lowerCamelCase__ : Union[str, Any] = max_position_embeddings
lowerCamelCase__ : Union[str, Any] = d_model
lowerCamelCase__ : Optional[int] = encoder_ffn_dim
lowerCamelCase__ : Dict = encoder_layers
lowerCamelCase__ : Any = encoder_attention_heads
lowerCamelCase__ : Union[str, Any] = decoder_ffn_dim
lowerCamelCase__ : str = decoder_layers
lowerCamelCase__ : Optional[Any] = decoder_attention_heads
lowerCamelCase__ : List[str] = dropout
lowerCamelCase__ : List[Any] = attention_dropout
lowerCamelCase__ : Dict = activation_dropout
lowerCamelCase__ : Optional[Any] = activation_function
lowerCamelCase__ : Dict = init_std
lowerCamelCase__ : List[str] = encoder_layerdrop
lowerCamelCase__ : Dict = decoder_layerdrop
lowerCamelCase__ : int = use_cache
lowerCamelCase__ : List[Any] = encoder_layers
lowerCamelCase__ : Tuple = scale_embedding # scale factor will be sqrt(d_model) if True
super().__init__(
pad_token_id=__lowerCamelCase , bos_token_id=__lowerCamelCase , eos_token_id=__lowerCamelCase , is_encoder_decoder=__lowerCamelCase , decoder_start_token_id=__lowerCamelCase , forced_eos_token_id=__lowerCamelCase , **__lowerCamelCase , )
class _lowercase ( lowercase__):
"""simple docstring"""
@property
def lowerCAmelCase ( self : List[str] ):
'''simple docstring'''
if self.task in ["default", "seq2seq-lm"]:
lowerCamelCase__ : int = OrderedDict(
[
("input_ids", {0: "batch", 1: "encoder_sequence"}),
("attention_mask", {0: "batch", 1: "encoder_sequence"}),
] )
if self.use_past:
lowerCamelCase__ : Union[str, Any] = {0: "batch"}
lowerCamelCase__ : int = {0: "batch", 1: "past_decoder_sequence + sequence"}
else:
lowerCamelCase__ : Tuple = {0: "batch", 1: "decoder_sequence"}
lowerCamelCase__ : str = {0: "batch", 1: "decoder_sequence"}
if self.use_past:
self.fill_with_past_key_values_(__lowerCamelCase , direction="inputs" )
elif self.task == "causal-lm":
# TODO: figure this case out.
lowerCamelCase__ : Tuple = OrderedDict(
[
("input_ids", {0: "batch", 1: "encoder_sequence"}),
("attention_mask", {0: "batch", 1: "encoder_sequence"}),
] )
if self.use_past:
lowerCamelCase__ , lowerCamelCase__ : Tuple = self.num_layers
for i in range(__lowerCamelCase ):
lowerCamelCase__ : Union[str, Any] = {0: "batch", 2: "past_sequence + sequence"}
lowerCamelCase__ : Optional[int] = {0: "batch", 2: "past_sequence + sequence"}
else:
lowerCamelCase__ : Any = OrderedDict(
[
("input_ids", {0: "batch", 1: "encoder_sequence"}),
("attention_mask", {0: "batch", 1: "encoder_sequence"}),
("decoder_input_ids", {0: "batch", 1: "decoder_sequence"}),
("decoder_attention_mask", {0: "batch", 1: "decoder_sequence"}),
] )
return common_inputs
@property
def lowerCAmelCase ( self : Optional[int] ):
'''simple docstring'''
if self.task in ["default", "seq2seq-lm"]:
lowerCamelCase__ : Union[str, Any] = super().outputs
else:
lowerCamelCase__ : int = super(__lowerCamelCase , self ).outputs
if self.use_past:
lowerCamelCase__ , lowerCamelCase__ : Tuple = self.num_layers
for i in range(__lowerCamelCase ):
lowerCamelCase__ : Tuple = {0: "batch", 2: "past_sequence + sequence"}
lowerCamelCase__ : Any = {0: "batch", 2: "past_sequence + sequence"}
return common_outputs
def lowerCAmelCase ( self : int , __lowerCamelCase : PreTrainedTokenizer , __lowerCamelCase : int = -1 , __lowerCamelCase : int = -1 , __lowerCamelCase : bool = False , __lowerCamelCase : Optional[TensorType] = None , ):
'''simple docstring'''
lowerCamelCase__ : Union[str, Any] = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
__lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase )
# Generate decoder inputs
lowerCamelCase__ : List[str] = seq_length if not self.use_past else 1
lowerCamelCase__ : List[Any] = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
__lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase )
lowerCamelCase__ : Optional[Any] = {f"decoder_{name}": tensor for name, tensor in decoder_inputs.items()}
lowerCamelCase__ : Optional[Any] = dict(**__lowerCamelCase , **__lowerCamelCase )
if self.use_past:
if not is_torch_available():
raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed." )
else:
import torch
lowerCamelCase__ , lowerCamelCase__ : Tuple = common_inputs["input_ids"].shape
lowerCamelCase__ : int = common_inputs["decoder_input_ids"].shape[1]
lowerCamelCase__ , lowerCamelCase__ : Optional[Any] = self.num_attention_heads
lowerCamelCase__ : str = (
batch,
num_encoder_attention_heads,
encoder_seq_length,
self._config.hidden_size // num_encoder_attention_heads,
)
lowerCamelCase__ : Optional[int] = decoder_seq_length + 3
lowerCamelCase__ : Dict = (
batch,
num_decoder_attention_heads,
decoder_past_length,
self._config.hidden_size // num_decoder_attention_heads,
)
lowerCamelCase__ : List[Any] = torch.cat(
[common_inputs["decoder_attention_mask"], torch.ones(__lowerCamelCase , __lowerCamelCase )] , dim=1 )
lowerCamelCase__ : Optional[Any] = []
# If the number of encoder and decoder layers are present in the model configuration, both are considered
lowerCamelCase__ , lowerCamelCase__ : str = self.num_layers
lowerCamelCase__ : Union[str, Any] = min(__lowerCamelCase , __lowerCamelCase )
lowerCamelCase__ : Union[str, Any] = max(__lowerCamelCase , __lowerCamelCase ) - min_num_layers
lowerCamelCase__ : str = "encoder" if num_encoder_layers > num_decoder_layers else "decoder"
for _ in range(__lowerCamelCase ):
common_inputs["past_key_values"].append(
(
torch.zeros(__lowerCamelCase ),
torch.zeros(__lowerCamelCase ),
torch.zeros(__lowerCamelCase ),
torch.zeros(__lowerCamelCase ),
) )
# TODO: test this.
lowerCamelCase__ : Optional[int] = encoder_shape if remaining_side_name == "encoder" else decoder_shape
for _ in range(__lowerCamelCase , __lowerCamelCase ):
common_inputs["past_key_values"].append((torch.zeros(__lowerCamelCase ), torch.zeros(__lowerCamelCase )) )
return common_inputs
def lowerCAmelCase ( self : Tuple , __lowerCamelCase : PreTrainedTokenizer , __lowerCamelCase : int = -1 , __lowerCamelCase : int = -1 , __lowerCamelCase : bool = False , __lowerCamelCase : Optional[TensorType] = None , ):
'''simple docstring'''
lowerCamelCase__ : str = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
__lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase )
if self.use_past:
if not is_torch_available():
raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed." )
else:
import torch
lowerCamelCase__ , lowerCamelCase__ : int = common_inputs["input_ids"].shape
# Not using the same length for past_key_values
lowerCamelCase__ : str = seqlen + 2
lowerCamelCase__ , lowerCamelCase__ : Optional[int] = self.num_layers
lowerCamelCase__ , lowerCamelCase__ : int = self.num_attention_heads
lowerCamelCase__ : Tuple = (
batch,
num_encoder_attention_heads,
past_key_values_length,
self._config.hidden_size // num_encoder_attention_heads,
)
lowerCamelCase__ : Union[str, Any] = common_inputs["attention_mask"].dtype
lowerCamelCase__ : List[str] = torch.cat(
[common_inputs["attention_mask"], torch.ones(__lowerCamelCase , __lowerCamelCase , dtype=__lowerCamelCase )] , dim=1 )
lowerCamelCase__ : Tuple = [
(torch.zeros(__lowerCamelCase ), torch.zeros(__lowerCamelCase )) for _ in range(__lowerCamelCase )
]
return common_inputs
def lowerCAmelCase ( self : Union[str, Any] , __lowerCamelCase : PreTrainedTokenizer , __lowerCamelCase : int = -1 , __lowerCamelCase : int = -1 , __lowerCamelCase : bool = False , __lowerCamelCase : Optional[TensorType] = None , ):
'''simple docstring'''
lowerCamelCase__ : str = compute_effective_axis_dimension(
__lowerCamelCase , fixed_dimension=OnnxConfig.default_fixed_batch , num_token_to_add=0 )
# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
lowerCamelCase__ : List[str] = tokenizer.num_special_tokens_to_add(__lowerCamelCase )
lowerCamelCase__ : Dict = compute_effective_axis_dimension(
__lowerCamelCase , fixed_dimension=OnnxConfig.default_fixed_sequence , num_token_to_add=__lowerCamelCase )
# Generate dummy inputs according to compute batch and sequence
lowerCamelCase__ : Optional[int] = [" ".join([tokenizer.unk_token] ) * seq_length] * batch_size
lowerCamelCase__ : Optional[Any] = dict(tokenizer(__lowerCamelCase , return_tensors=__lowerCamelCase ) )
return common_inputs
def lowerCAmelCase ( self : Any , __lowerCamelCase : PreTrainedTokenizer , __lowerCamelCase : int = -1 , __lowerCamelCase : int = -1 , __lowerCamelCase : bool = False , __lowerCamelCase : Optional[TensorType] = None , ):
'''simple docstring'''
if self.task in ["default", "seq2seq-lm"]:
lowerCamelCase__ : Optional[int] = self._generate_dummy_inputs_for_default_and_seqaseq_lm(
__lowerCamelCase , batch_size=__lowerCamelCase , seq_length=__lowerCamelCase , is_pair=__lowerCamelCase , framework=__lowerCamelCase )
elif self.task == "causal-lm":
lowerCamelCase__ : Any = self._generate_dummy_inputs_for_causal_lm(
__lowerCamelCase , batch_size=__lowerCamelCase , seq_length=__lowerCamelCase , is_pair=__lowerCamelCase , framework=__lowerCamelCase )
else:
lowerCamelCase__ : Any = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
__lowerCamelCase , batch_size=__lowerCamelCase , seq_length=__lowerCamelCase , is_pair=__lowerCamelCase , framework=__lowerCamelCase )
return common_inputs
def lowerCAmelCase ( self : Any , __lowerCamelCase : Union[str, Any] , __lowerCamelCase : int , __lowerCamelCase : Optional[Any] , __lowerCamelCase : str ):
'''simple docstring'''
if self.task in ["default", "seq2seq-lm"]:
lowerCamelCase__ : Dict = super()._flatten_past_key_values_(__lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase )
else:
lowerCamelCase__ : int = super(__lowerCamelCase , self )._flatten_past_key_values_(
__lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase )
| 5 | 1 |
"""simple docstring"""
def __snake_case ( _lowercase ):
"""simple docstring"""
UpperCamelCase = int(_lowercase )
if n_element < 1:
UpperCamelCase = ValueError('''a should be a positive number''' )
raise my_error
UpperCamelCase = [1]
UpperCamelCase , UpperCamelCase , UpperCamelCase = (0, 0, 0)
UpperCamelCase = 1
while index < n_element:
while hamming_list[i] * 2 <= hamming_list[-1]:
i += 1
while hamming_list[j] * 3 <= hamming_list[-1]:
j += 1
while hamming_list[k] * 5 <= hamming_list[-1]:
k += 1
hamming_list.append(
min(hamming_list[i] * 2 ,hamming_list[j] * 3 ,hamming_list[k] * 5 ) )
index += 1
return hamming_list
if __name__ == "__main__":
SCREAMING_SNAKE_CASE_ = input('Enter the last number (nth term) of the Hamming Number Series: ')
print('Formula of Hamming Number Series => 2^i * 3^j * 5^k')
SCREAMING_SNAKE_CASE_ = hamming(int(n))
print('-----------------------------------------------------')
print(f'The list with nth numbers is: {hamming_numbers}')
print('-----------------------------------------------------') | 34 |
from __future__ import annotations
from numpy import array, cos, cross, floataa, radians, sin
from numpy.typing import NDArray
def lowerCamelCase__ (_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = False):
if radian_mode:
return [magnitude * cos(_UpperCAmelCase), magnitude * sin(_UpperCAmelCase)]
return [magnitude * cos(radians(_UpperCAmelCase)), magnitude * sin(radians(_UpperCAmelCase))]
def lowerCamelCase__ (_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = 10**-1):
SCREAMING_SNAKE_CASE = cross(_UpperCAmelCase , _UpperCAmelCase)
SCREAMING_SNAKE_CASE = sum(_UpperCAmelCase)
return abs(_UpperCAmelCase) < eps
if __name__ == "__main__":
# Test to check if it works
a_ : int = array(
[
polar_force(718.4, 1_80 - 30),
polar_force(879.54, 45),
polar_force(1_00, -90),
]
)
a_ : NDArray[floataa] = array([[0, 0], [0, 0], [0, 0]])
assert in_static_equilibrium(forces, location)
# Problem 1 in image_data/2D_problems.jpg
a_ : Dict = array(
[
polar_force(30 * 9.81, 15),
polar_force(2_15, 1_80 - 45),
polar_force(2_64, 90 - 30),
]
)
a_ : Any = array([[0, 0], [0, 0], [0, 0]])
assert in_static_equilibrium(forces, location)
# Problem in image_data/2D_problems_1.jpg
a_ : int = array([[0, -20_00], [0, -12_00], [0, 1_56_00], [0, -1_24_00]])
a_ : Optional[Any] = array([[0, 0], [6, 0], [10, 0], [12, 0]])
assert in_static_equilibrium(forces, location)
import doctest
doctest.testmod()
| 73 | 0 |
'''simple docstring'''
from __future__ import annotations
def __a ( __lowerCamelCase : list[float] ) -> float:
'''simple docstring'''
lowercase_ = 0.0_0
lowercase_ = 0
for resistor in resistors:
if resistor <= 0:
lowercase_ = f'Resistor at index {index} has a negative or zero value!'
raise ValueError(__lowerCamelCase )
first_sum += 1 / float(__lowerCamelCase )
index += 1
return 1 / first_sum
def __a ( __lowerCamelCase : list[float] ) -> float:
'''simple docstring'''
lowercase_ = 0.0_0
lowercase_ = 0
for resistor in resistors:
sum_r += resistor
if resistor < 0:
lowercase_ = f'Resistor at index {index} has a negative value!'
raise ValueError(__lowerCamelCase )
index += 1
return sum_r
if __name__ == "__main__":
import doctest
doctest.testmod()
| 710 | '''simple docstring'''
import json
from typing import TYPE_CHECKING, List, Optional, Tuple
from tokenizers import pre_tokenizers
from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_gpta import GPTaTokenizer
if TYPE_CHECKING:
from transformers.pipelines.conversational import Conversation
lowerCAmelCase_ : Optional[Any] = logging.get_logger(__name__)
lowerCAmelCase_ : Any = {"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}
lowerCAmelCase_ : Tuple = {
"vocab_file": {
"gpt2": "https://huggingface.co/gpt2/resolve/main/vocab.json",
"gpt2-medium": "https://huggingface.co/gpt2-medium/resolve/main/vocab.json",
"gpt2-large": "https://huggingface.co/gpt2-large/resolve/main/vocab.json",
"gpt2-xl": "https://huggingface.co/gpt2-xl/resolve/main/vocab.json",
"distilgpt2": "https://huggingface.co/distilgpt2/resolve/main/vocab.json",
},
"merges_file": {
"gpt2": "https://huggingface.co/gpt2/resolve/main/merges.txt",
"gpt2-medium": "https://huggingface.co/gpt2-medium/resolve/main/merges.txt",
"gpt2-large": "https://huggingface.co/gpt2-large/resolve/main/merges.txt",
"gpt2-xl": "https://huggingface.co/gpt2-xl/resolve/main/merges.txt",
"distilgpt2": "https://huggingface.co/distilgpt2/resolve/main/merges.txt",
},
"tokenizer_file": {
"gpt2": "https://huggingface.co/gpt2/resolve/main/tokenizer.json",
"gpt2-medium": "https://huggingface.co/gpt2-medium/resolve/main/tokenizer.json",
"gpt2-large": "https://huggingface.co/gpt2-large/resolve/main/tokenizer.json",
"gpt2-xl": "https://huggingface.co/gpt2-xl/resolve/main/tokenizer.json",
"distilgpt2": "https://huggingface.co/distilgpt2/resolve/main/tokenizer.json",
},
}
lowerCAmelCase_ : Dict = {
"gpt2": 1_024,
"gpt2-medium": 1_024,
"gpt2-large": 1_024,
"gpt2-xl": 1_024,
"distilgpt2": 1_024,
}
class lowercase ( __lowerCamelCase ):
lowerCamelCase_ =VOCAB_FILES_NAMES
lowerCamelCase_ =PRETRAINED_VOCAB_FILES_MAP
lowerCamelCase_ =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowerCamelCase_ =['input_ids', 'attention_mask']
lowerCamelCase_ =GPTaTokenizer
def __init__( self : Dict , __lowerCAmelCase : List[Any]=None , __lowerCAmelCase : Optional[Any]=None , __lowerCAmelCase : Optional[int]=None , __lowerCAmelCase : Any="<|endoftext|>" , __lowerCAmelCase : Union[str, Any]="<|endoftext|>" , __lowerCAmelCase : List[Any]="<|endoftext|>" , __lowerCAmelCase : Optional[Any]=False , **__lowerCAmelCase : Dict , ) -> int:
super().__init__(
__lowerCAmelCase , __lowerCAmelCase , tokenizer_file=__lowerCAmelCase , unk_token=__lowerCAmelCase , bos_token=__lowerCAmelCase , eos_token=__lowerCAmelCase , add_prefix_space=__lowerCAmelCase , **__lowerCAmelCase , )
lowercase_ = kwargs.pop("add_bos_token" , __lowerCAmelCase)
lowercase_ = json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__())
if pre_tok_state.get("add_prefix_space" , __lowerCAmelCase) != add_prefix_space:
lowercase_ = getattr(__lowerCAmelCase , pre_tok_state.pop("type"))
lowercase_ = add_prefix_space
lowercase_ = pre_tok_class(**__lowerCAmelCase)
lowercase_ = add_prefix_space
def __UpperCAmelCase ( self : List[str] , *__lowerCAmelCase : List[str] , **__lowerCAmelCase : str) -> BatchEncoding:
lowercase_ = kwargs.get("is_split_into_words" , __lowerCAmelCase)
assert self.add_prefix_space or not is_split_into_words, (
F'You need to instantiate {self.__class__.__name__} with add_prefix_space=True '
"to use it with pretokenized inputs."
)
return super()._batch_encode_plus(*__lowerCAmelCase , **__lowerCAmelCase)
def __UpperCAmelCase ( self : List[Any] , *__lowerCAmelCase : Union[str, Any] , **__lowerCAmelCase : Union[str, Any]) -> BatchEncoding:
lowercase_ = kwargs.get("is_split_into_words" , __lowerCAmelCase)
assert self.add_prefix_space or not is_split_into_words, (
F'You need to instantiate {self.__class__.__name__} with add_prefix_space=True '
"to use it with pretokenized inputs."
)
return super()._encode_plus(*__lowerCAmelCase , **__lowerCAmelCase)
def __UpperCAmelCase ( self : Optional[int] , __lowerCAmelCase : str , __lowerCAmelCase : Optional[str] = None) -> Tuple[str]:
lowercase_ = self._tokenizer.model.save(__lowerCAmelCase , name=__lowerCAmelCase)
return tuple(__lowerCAmelCase)
def __UpperCAmelCase ( self : Tuple , __lowerCAmelCase : "Conversation") -> List[int]:
lowercase_ = []
for is_user, text in conversation.iter_texts():
input_ids.extend(self.encode(__lowerCAmelCase , add_special_tokens=__lowerCAmelCase) + [self.eos_token_id])
if len(__lowerCAmelCase) > self.model_max_length:
lowercase_ = input_ids[-self.model_max_length :]
return input_ids
| 461 | 0 |
'''simple docstring'''
import json
import os
import unittest
from transformers import BatchEncoding, LEDTokenizer, LEDTokenizerFast
from transformers.models.led.tokenization_led import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class UpperCAmelCase__ ( A , unittest.TestCase ):
lowerCAmelCase_ = LEDTokenizer
lowerCAmelCase_ = LEDTokenizerFast
lowerCAmelCase_ = True
def lowerCamelCase_ ( self : Dict ):
super().setUp()
_lowerCamelCase : Tuple = [
"l",
"o",
"w",
"e",
"r",
"s",
"t",
"i",
"d",
"n",
"\u0120",
"\u0120l",
"\u0120n",
"\u0120lo",
"\u0120low",
"er",
"\u0120lowest",
"\u0120newer",
"\u0120wider",
"<unk>",
]
_lowerCamelCase : Union[str, Any] = dict(zip(__A,range(len(__A ) ) ) )
_lowerCamelCase : List[str] = ["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
_lowerCamelCase : Dict = {"unk_token": "<unk>"}
_lowerCamelCase : Any = os.path.join(self.tmpdirname,VOCAB_FILES_NAMES["vocab_file"] )
_lowerCamelCase : int = os.path.join(self.tmpdirname,VOCAB_FILES_NAMES["merges_file"] )
with open(self.vocab_file,"w",encoding="utf-8" ) as fp:
fp.write(json.dumps(__A ) + "\n" )
with open(self.merges_file,"w",encoding="utf-8" ) as fp:
fp.write("\n".join(__A ) )
def lowerCamelCase_ ( self : str,**__A : str ):
kwargs.update(self.special_tokens_map )
return self.tokenizer_class.from_pretrained(self.tmpdirname,**__A )
def lowerCamelCase_ ( self : Dict,**__A : Dict ):
kwargs.update(self.special_tokens_map )
return self.rust_tokenizer_class.from_pretrained(self.tmpdirname,**__A )
def lowerCamelCase_ ( self : Any,__A : str ):
return "lower newer", "lower newer"
@cached_property
def lowerCamelCase_ ( self : Optional[int] ):
return LEDTokenizer.from_pretrained("allenai/led-base-16384" )
@cached_property
def lowerCamelCase_ ( self : Optional[int] ):
return LEDTokenizerFast.from_pretrained("allenai/led-base-16384" )
@require_torch
def lowerCamelCase_ ( self : List[Any] ):
_lowerCamelCase : Optional[Any] = ["A long paragraph for summarization.", "Another paragraph for summarization."]
_lowerCamelCase : List[str] = [0, 2_5_0, 2_5_1, 1_7_8_1_8, 1_3, 3_9_1_8_6, 1_9_3_8, 4, 2]
for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
_lowerCamelCase : Any = tokenizer(__A,max_length=len(__A ),padding=__A,return_tensors="pt" )
self.assertIsInstance(__A,__A )
self.assertEqual((2, 9),batch.input_ids.shape )
self.assertEqual((2, 9),batch.attention_mask.shape )
_lowerCamelCase : Dict = batch.input_ids.tolist()[0]
self.assertListEqual(__A,__A )
@require_torch
def lowerCamelCase_ ( self : Any ):
_lowerCamelCase : Dict = ["A long paragraph for summarization.", "Another paragraph for summarization."]
for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
_lowerCamelCase : Optional[int] = tokenizer(__A,padding=__A,return_tensors="pt" )
self.assertIn("input_ids",__A )
self.assertIn("attention_mask",__A )
self.assertNotIn("labels",__A )
self.assertNotIn("decoder_attention_mask",__A )
@require_torch
def lowerCamelCase_ ( self : str ):
_lowerCamelCase : Dict = [
"Summary of the text.",
"Another summary.",
]
for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
_lowerCamelCase : Optional[int] = tokenizer(text_target=__A,max_length=3_2,padding="max_length",return_tensors="pt" )
self.assertEqual(3_2,targets["input_ids"].shape[1] )
@require_torch
def lowerCamelCase_ ( self : Union[str, Any] ):
for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
_lowerCamelCase : Tuple = tokenizer(
["I am a small frog" * 1_0_2_4, "I am a small frog"],padding=__A,truncation=__A,return_tensors="pt" )
self.assertIsInstance(__A,__A )
self.assertEqual(batch.input_ids.shape,(2, 5_1_2_2) )
@require_torch
def lowerCamelCase_ ( self : Union[str, Any] ):
_lowerCamelCase : Optional[Any] = ["A long paragraph for summarization."]
_lowerCamelCase : Any = [
"Summary of the text.",
]
for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
_lowerCamelCase : int = tokenizer(__A,return_tensors="pt" )
_lowerCamelCase : str = tokenizer(text_target=__A,return_tensors="pt" )
_lowerCamelCase : Union[str, Any] = inputs["input_ids"]
_lowerCamelCase : List[Any] = targets["input_ids"]
self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item() )
self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item() )
self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item() )
self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item() )
@require_torch
def lowerCamelCase_ ( self : Union[str, Any] ):
for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
_lowerCamelCase : Union[str, Any] = ["Summary of the text.", "Another summary."]
_lowerCamelCase : Union[str, Any] = [[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, -1, -1]]
_lowerCamelCase : Optional[Any] = tokenizer(__A,padding=__A )
_lowerCamelCase : Optional[int] = [[0] * len(__A ) for x in encoded_output["input_ids"]]
_lowerCamelCase : Any = tokenizer.pad(__A )
self.assertSequenceEqual(outputs["global_attention_mask"],__A )
def lowerCamelCase_ ( self : Any ):
pass
def lowerCamelCase_ ( self : str ):
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f'{tokenizer.__class__.__name__} ({pretrained_name})' ):
_lowerCamelCase : Optional[Any] = self.rust_tokenizer_class.from_pretrained(__A,**__A )
_lowerCamelCase : Dict = self.tokenizer_class.from_pretrained(__A,**__A )
_lowerCamelCase : Union[str, Any] = "A, <mask> AllenNLP sentence."
_lowerCamelCase : Dict = tokenizer_r.encode_plus(__A,add_special_tokens=__A,return_token_type_ids=__A )
_lowerCamelCase : int = tokenizer_p.encode_plus(__A,add_special_tokens=__A,return_token_type_ids=__A )
self.assertEqual(sum(tokens_r["token_type_ids"] ),sum(tokens_p["token_type_ids"] ) )
self.assertEqual(
sum(tokens_r["attention_mask"] ) / len(tokens_r["attention_mask"] ),sum(tokens_p["attention_mask"] ) / len(tokens_p["attention_mask"] ),)
_lowerCamelCase : Optional[Any] = tokenizer_r.convert_ids_to_tokens(tokens_r["input_ids"] )
_lowerCamelCase : Dict = tokenizer_p.convert_ids_to_tokens(tokens_p["input_ids"] )
self.assertSequenceEqual(tokens_p["input_ids"],[0, 2_5_0, 6, 5_0_2_6_4, 3_8_2_3, 4_8_7, 2_1_9_9_2, 3_6_4_5, 4, 2] )
self.assertSequenceEqual(tokens_r["input_ids"],[0, 2_5_0, 6, 5_0_2_6_4, 3_8_2_3, 4_8_7, 2_1_9_9_2, 3_6_4_5, 4, 2] )
self.assertSequenceEqual(
__A,["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"] )
self.assertSequenceEqual(
__A,["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"] ) | 44 |
'''simple docstring'''
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
from seqaseq_trainer import SeqaSeqTrainer
from seqaseq_training_args import SeqaSeqTrainingArguments
import transformers
from transformers import (
AutoConfig,
AutoModelForSeqaSeqLM,
AutoTokenizer,
HfArgumentParser,
MBartTokenizer,
MBartTokenizerFast,
set_seed,
)
from transformers.trainer_utils import EvaluationStrategy, is_main_process
from transformers.training_args import ParallelMode
from utils import (
SeqaSeqDataCollator,
SeqaSeqDataset,
assert_all_frozen,
build_compute_metrics_fn,
check_output_dir,
freeze_embeds,
freeze_params,
lmap,
save_json,
use_task_specific_params,
write_txt_file,
)
UpperCAmelCase_ : Any = logging.getLogger(__name__)
@dataclass
class UpperCAmelCase__ :
lowerCAmelCase_ = field(
metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'} )
lowerCAmelCase_ = field(
default=A , metadata={'help': 'Pretrained config name or path if not the same as model_name'} )
lowerCAmelCase_ = field(
default=A , metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'} )
lowerCAmelCase_ = field(
default=A , metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'} , )
lowerCAmelCase_ = field(default=A , metadata={'help': 'Whether tp freeze the encoder.'} )
lowerCAmelCase_ = field(default=A , metadata={'help': 'Whether to freeze the embeddings.'} )
@dataclass
class UpperCAmelCase__ :
lowerCAmelCase_ = field(
metadata={'help': 'The input data dir. Should contain the .tsv files (or other data files) for the task.'} )
lowerCAmelCase_ = field(
default='summarization' , metadata={'help': 'Task name, summarization (or summarization_{dataset} for pegasus) or translation'} , )
lowerCAmelCase_ = field(
default=1024 , 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=128 , metadata={
'help': (
'The maximum total sequence length for target text after tokenization. Sequences longer '
'than this will be truncated, sequences shorter will be padded.'
)
} , )
lowerCAmelCase_ = field(
default=142 , metadata={
'help': (
'The maximum total sequence length for validation target text after tokenization. Sequences longer '
'than this will be truncated, sequences shorter will be padded. '
'This argument is also used to override the ``max_length`` param of ``model.generate``, which is used '
'during ``evaluate`` and ``predict``.'
)
} , )
lowerCAmelCase_ = field(
default=142 , metadata={
'help': (
'The maximum total sequence length for test target text after tokenization. Sequences longer '
'than this will be truncated, sequences shorter will be padded.'
)
} , )
lowerCAmelCase_ = field(default=-1 , metadata={'help': '# training examples. -1 means use all.'} )
lowerCAmelCase_ = field(default=-1 , metadata={'help': '# validation examples. -1 means use all.'} )
lowerCAmelCase_ = field(default=-1 , metadata={'help': '# test examples. -1 means use all.'} )
lowerCAmelCase_ = field(default=A , metadata={'help': 'Source language id for translation.'} )
lowerCAmelCase_ = field(default=A , metadata={'help': 'Target language id for translation.'} )
lowerCAmelCase_ = field(default=A , metadata={'help': '# num_beams to use for evaluation.'} )
lowerCAmelCase_ = field(
default=A , metadata={'help': 'If only pad tokens should be ignored. This assumes that `config.pad_token_id` is defined.'} , )
def A_ ( _lowerCAmelCase : Optional[int] , _lowerCAmelCase : List[str] , _lowerCAmelCase : Any ):
"""simple docstring"""
logger.info(F'***** {split} metrics *****' )
for key in sorted(metrics.keys() ):
logger.info(F' {key} = {metrics[key]}' )
save_json(_lowerCAmelCase , os.path.join(_lowerCAmelCase , F'{split}_results.json' ) )
def A_ ( ):
"""simple docstring"""
_lowerCamelCase : str = HfArgumentParser((ModelArguments, DataTrainingArguments, SeqaSeqTrainingArguments) )
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.
_lowerCamelCase , _lowerCamelCase , _lowerCamelCase : int = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
else:
_lowerCamelCase , _lowerCamelCase , _lowerCamelCase : Optional[Any] = parser.parse_args_into_dataclasses()
check_output_dir(_lowerCAmelCase )
# 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.parallel_mode == ParallelMode.DISTRIBUTED ) , training_args.fpaa , )
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
# 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()
logger.info("Training/evaluation parameters %s" , _lowerCAmelCase )
# Set seed
set_seed(training_args.seed )
# Load pretrained model and tokenizer
#
# Distributed training:
# The .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
_lowerCamelCase : Optional[int] = AutoConfig.from_pretrained(
model_args.config_name if model_args.config_name else model_args.model_name_or_path , cache_dir=model_args.cache_dir , )
_lowerCamelCase : Tuple = ("encoder_layerdrop", "decoder_layerdrop", "dropout", "attention_dropout")
for p in extra_model_params:
if getattr(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ):
assert hasattr(_lowerCAmelCase , _lowerCAmelCase ), F'({config.__class__.__name__}) doesn\'t have a `{p}` attribute'
setattr(_lowerCAmelCase , _lowerCAmelCase , getattr(_lowerCAmelCase , _lowerCAmelCase ) )
_lowerCamelCase : List[Any] = 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 , )
_lowerCamelCase : int = AutoModelForSeqaSeqLM.from_pretrained(
model_args.model_name_or_path , from_tf=".ckpt" in model_args.model_name_or_path , config=_lowerCAmelCase , cache_dir=model_args.cache_dir , )
# use task specific params
use_task_specific_params(_lowerCAmelCase , data_args.task )
# set num_beams for evaluation
if data_args.eval_beams is None:
_lowerCamelCase : List[Any] = model.config.num_beams
# set decoder_start_token_id for MBart
if model.config.decoder_start_token_id is None and isinstance(_lowerCAmelCase , (MBartTokenizer, MBartTokenizerFast) ):
assert (
data_args.tgt_lang is not None and data_args.src_lang is not None
), "mBart requires --tgt_lang and --src_lang"
if isinstance(_lowerCAmelCase , _lowerCAmelCase ):
_lowerCamelCase : Any = tokenizer.lang_code_to_id[data_args.tgt_lang]
else:
_lowerCamelCase : int = tokenizer.convert_tokens_to_ids(data_args.tgt_lang )
if model_args.freeze_embeds:
freeze_embeds(_lowerCAmelCase )
if model_args.freeze_encoder:
freeze_params(model.get_encoder() )
assert_all_frozen(model.get_encoder() )
_lowerCamelCase : int = SeqaSeqDataset
# Get datasets
_lowerCamelCase : Tuple = (
dataset_class(
_lowerCAmelCase , type_path="train" , data_dir=data_args.data_dir , n_obs=data_args.n_train , max_target_length=data_args.max_target_length , max_source_length=data_args.max_source_length , prefix=model.config.prefix or "" , )
if training_args.do_train
else None
)
_lowerCamelCase : List[Any] = (
dataset_class(
_lowerCAmelCase , type_path="val" , data_dir=data_args.data_dir , n_obs=data_args.n_val , max_target_length=data_args.val_max_target_length , max_source_length=data_args.max_source_length , prefix=model.config.prefix or "" , )
if training_args.do_eval or training_args.evaluation_strategy != EvaluationStrategy.NO
else None
)
_lowerCamelCase : Optional[int] = (
dataset_class(
_lowerCAmelCase , type_path="test" , data_dir=data_args.data_dir , n_obs=data_args.n_test , max_target_length=data_args.test_max_target_length , max_source_length=data_args.max_source_length , prefix=model.config.prefix or "" , )
if training_args.do_predict
else None
)
# Initialize our Trainer
_lowerCamelCase : int = (
build_compute_metrics_fn(data_args.task , _lowerCAmelCase ) if training_args.predict_with_generate else None
)
_lowerCamelCase : List[Any] = SeqaSeqTrainer(
model=_lowerCAmelCase , args=_lowerCAmelCase , data_args=_lowerCAmelCase , train_dataset=_lowerCAmelCase , eval_dataset=_lowerCAmelCase , data_collator=SeqaSeqDataCollator(
_lowerCAmelCase , _lowerCAmelCase , model.config.decoder_start_token_id , training_args.tpu_num_cores ) , compute_metrics=_lowerCAmelCase , tokenizer=_lowerCAmelCase , )
_lowerCamelCase : Optional[Any] = {}
# Training
if training_args.do_train:
logger.info("*** Train ***" )
_lowerCamelCase : Optional[Any] = trainer.train(
model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path ) else None )
_lowerCamelCase : int = train_result.metrics
_lowerCamelCase : Optional[int] = data_args.n_train
trainer.save_model() # this also saves the tokenizer
if trainer.is_world_process_zero():
handle_metrics("train" , _lowerCAmelCase , training_args.output_dir )
all_metrics.update(_lowerCAmelCase )
# Need to save the state, since Trainer.save_model saves only the tokenizer with the model
trainer.state.save_to_json(os.path.join(training_args.output_dir , "trainer_state.json" ) )
# For convenience, we also re-save the tokenizer to the same directory,
# so that you can share your model easily on huggingface.co/models =)
tokenizer.save_pretrained(training_args.output_dir )
# Evaluation
if training_args.do_eval:
logger.info("*** Evaluate ***" )
_lowerCamelCase : Optional[Any] = trainer.evaluate(metric_key_prefix="val" )
_lowerCamelCase : Dict = data_args.n_val
_lowerCamelCase : List[Any] = round(metrics["val_loss"] , 4 )
if trainer.is_world_process_zero():
handle_metrics("val" , _lowerCAmelCase , training_args.output_dir )
all_metrics.update(_lowerCAmelCase )
if training_args.do_predict:
logger.info("*** Predict ***" )
_lowerCamelCase : Any = trainer.predict(test_dataset=_lowerCAmelCase , metric_key_prefix="test" )
_lowerCamelCase : Dict = test_output.metrics
_lowerCamelCase : Optional[int] = data_args.n_test
if trainer.is_world_process_zero():
_lowerCamelCase : int = round(metrics["test_loss"] , 4 )
handle_metrics("test" , _lowerCAmelCase , training_args.output_dir )
all_metrics.update(_lowerCAmelCase )
if training_args.predict_with_generate:
_lowerCamelCase : List[str] = tokenizer.batch_decode(
test_output.predictions , skip_special_tokens=_lowerCAmelCase , clean_up_tokenization_spaces=_lowerCAmelCase )
_lowerCamelCase : Any = lmap(str.strip , _lowerCAmelCase )
write_txt_file(_lowerCAmelCase , os.path.join(training_args.output_dir , "test_generations.txt" ) )
if trainer.is_world_process_zero():
save_json(_lowerCAmelCase , os.path.join(training_args.output_dir , "all_results.json" ) )
return all_metrics
def A_ ( _lowerCAmelCase : int ):
"""simple docstring"""
main()
if __name__ == "__main__":
main() | 44 | 1 |
"""simple docstring"""
import unittest
import numpy as np
import torch
from diffusers import PNDMPipeline, PNDMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class __lowerCamelCase ( unittest.TestCase ):
@property
def SCREAMING_SNAKE_CASE__ ( self ) -> str:
torch.manual_seed(0 )
UpperCamelCase__ = 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 SCREAMING_SNAKE_CASE__ ( self ) -> str:
UpperCamelCase__ = self.dummy_uncond_unet
UpperCamelCase__ = PNDMScheduler()
UpperCamelCase__ = PNDMPipeline(unet=snake_case_ , scheduler=snake_case_ )
pndm.to(snake_case_ )
pndm.set_progress_bar_config(disable=snake_case_ )
UpperCamelCase__ = torch.manual_seed(0 )
UpperCamelCase__ = pndm(generator=snake_case_ , num_inference_steps=20 , output_type='numpy' ).images
UpperCamelCase__ = torch.manual_seed(0 )
UpperCamelCase__ = pndm(generator=snake_case_ , num_inference_steps=20 , output_type='numpy' , return_dict=snake_case_ )[0]
UpperCamelCase__ = image[0, -3:, -3:, -1]
UpperCamelCase__ = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
UpperCamelCase__ = np.array([1.0, 1.0, 0.0, 1.0, 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 __lowerCamelCase ( unittest.TestCase ):
def SCREAMING_SNAKE_CASE__ ( self ) -> Optional[Any]:
UpperCamelCase__ = 'google/ddpm-cifar10-32'
UpperCamelCase__ = UNetaDModel.from_pretrained(snake_case_ )
UpperCamelCase__ = PNDMScheduler()
UpperCamelCase__ = PNDMPipeline(unet=snake_case_ , scheduler=snake_case_ )
pndm.to(snake_case_ )
pndm.set_progress_bar_config(disable=snake_case_ )
UpperCamelCase__ = torch.manual_seed(0 )
UpperCamelCase__ = pndm(generator=snake_case_ , output_type='numpy' ).images
UpperCamelCase__ = image[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
UpperCamelCase__ = np.array([0.1_564, 0.14_645, 0.1_406, 0.14_715, 0.12_425, 0.14_045, 0.13_115, 0.12_175, 0.125] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
| 710 |
"""simple docstring"""
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import logging
if TYPE_CHECKING:
from ...processing_utils import ProcessorMixin
from ...utils import TensorType
A__ : Any= logging.get_logger(__name__)
A__ : str= {
"""microsoft/layoutlmv3-base""": """https://huggingface.co/microsoft/layoutlmv3-base/resolve/main/config.json""",
}
class __lowerCamelCase ( _a ):
a : List[str] ="""layoutlmv3"""
def __init__( self , snake_case_=5_0265 , snake_case_=768 , snake_case_=12 , snake_case_=12 , snake_case_=3072 , snake_case_="gelu" , snake_case_=0.1 , snake_case_=0.1 , snake_case_=512 , snake_case_=2 , snake_case_=0.02 , snake_case_=1E-5 , snake_case_=1 , snake_case_=0 , snake_case_=2 , snake_case_=1024 , snake_case_=128 , snake_case_=128 , snake_case_=True , snake_case_=32 , snake_case_=128 , snake_case_=64 , snake_case_=256 , snake_case_=True , snake_case_=True , snake_case_=True , snake_case_=224 , snake_case_=3 , snake_case_=16 , snake_case_=None , **snake_case_ , ) -> Union[str, Any]:
super().__init__(
vocab_size=snake_case_ , hidden_size=snake_case_ , num_hidden_layers=snake_case_ , num_attention_heads=snake_case_ , intermediate_size=snake_case_ , hidden_act=snake_case_ , hidden_dropout_prob=snake_case_ , attention_probs_dropout_prob=snake_case_ , max_position_embeddings=snake_case_ , type_vocab_size=snake_case_ , initializer_range=snake_case_ , layer_norm_eps=snake_case_ , pad_token_id=snake_case_ , bos_token_id=snake_case_ , eos_token_id=snake_case_ , **snake_case_ , )
UpperCamelCase__ = max_ad_position_embeddings
UpperCamelCase__ = coordinate_size
UpperCamelCase__ = shape_size
UpperCamelCase__ = has_relative_attention_bias
UpperCamelCase__ = rel_pos_bins
UpperCamelCase__ = max_rel_pos
UpperCamelCase__ = has_spatial_attention_bias
UpperCamelCase__ = rel_ad_pos_bins
UpperCamelCase__ = max_rel_ad_pos
UpperCamelCase__ = text_embed
UpperCamelCase__ = visual_embed
UpperCamelCase__ = input_size
UpperCamelCase__ = num_channels
UpperCamelCase__ = patch_size
UpperCamelCase__ = classifier_dropout
class __lowerCamelCase ( _a ):
a : Tuple =version.parse("""1.12""" )
@property
def SCREAMING_SNAKE_CASE__ ( self ) -> Mapping[str, Mapping[int, str]]:
# The order of inputs is different for question answering and sequence classification
if self.task in ["question-answering", "sequence-classification"]:
return OrderedDict(
[
('input_ids', {0: 'batch', 1: 'sequence'}),
('attention_mask', {0: 'batch', 1: 'sequence'}),
('bbox', {0: 'batch', 1: 'sequence'}),
('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
] )
else:
return OrderedDict(
[
('input_ids', {0: 'batch', 1: 'sequence'}),
('bbox', {0: 'batch', 1: 'sequence'}),
('attention_mask', {0: 'batch', 1: 'sequence'}),
('pixel_values', {0: 'batch', 1: 'num_channels'}),
] )
@property
def SCREAMING_SNAKE_CASE__ ( self ) -> float:
return 1E-5
@property
def SCREAMING_SNAKE_CASE__ ( self ) -> int:
return 12
def SCREAMING_SNAKE_CASE__ ( self , snake_case_ , snake_case_ = -1 , snake_case_ = -1 , snake_case_ = False , snake_case_ = None , snake_case_ = 3 , snake_case_ = 40 , snake_case_ = 40 , ) -> Mapping[str, Any]:
setattr(processor.image_processor , 'apply_ocr' , snake_case_ )
# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
UpperCamelCase__ = compute_effective_axis_dimension(
snake_case_ , fixed_dimension=OnnxConfig.default_fixed_batch , num_token_to_add=0 )
# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
UpperCamelCase__ = processor.tokenizer.num_special_tokens_to_add(snake_case_ )
UpperCamelCase__ = compute_effective_axis_dimension(
snake_case_ , fixed_dimension=OnnxConfig.default_fixed_sequence , num_token_to_add=snake_case_ )
# Generate dummy inputs according to compute batch and sequence
UpperCamelCase__ = [[' '.join([processor.tokenizer.unk_token] ) * seq_length]] * batch_size
# Generate dummy bounding boxes
UpperCamelCase__ = [[[48, 84, 73, 128]]] * batch_size
# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
# batch_size = compute_effective_axis_dimension(batch_size, fixed_dimension=OnnxConfig.default_fixed_batch)
UpperCamelCase__ = self._generate_dummy_images(snake_case_ , snake_case_ , snake_case_ , snake_case_ )
UpperCamelCase__ = dict(
processor(
snake_case_ , text=snake_case_ , boxes=snake_case_ , return_tensors=snake_case_ , ) )
return inputs
| 20 | 0 |
"""simple docstring"""
from __future__ import annotations
from math import pi
# Define the Reduced Planck Constant ℏ (H bar), speed of light C, value of
# Pi and the function
a_ : Any = 1.0_5457_1817e-34 # unit of ℏ : J * s
a_ : List[Any] = 3e8 # unit of c : m * s^-1
def UpperCAmelCase ( A__: float , A__: float , A__: float ) -> dict[str, float]:
if (force, area, distance).count(0 ) != 1:
raise ValueError('One and only one argument must be 0' )
if force < 0:
raise ValueError('Magnitude of force can not be negative' )
if distance < 0:
raise ValueError('Distance can not be negative' )
if area < 0:
raise ValueError('Area can not be negative' )
if force == 0:
__lowerCamelCase : Optional[int] = (REDUCED_PLANCK_CONSTANT * SPEED_OF_LIGHT * pi**2 * area) / (
240 * (distance) ** 4
)
return {"force": force}
elif area == 0:
__lowerCamelCase : Any = (240 * force * (distance) ** 4) / (
REDUCED_PLANCK_CONSTANT * SPEED_OF_LIGHT * pi**2
)
return {"area": area}
elif distance == 0:
__lowerCamelCase : List[Any] = (
(REDUCED_PLANCK_CONSTANT * SPEED_OF_LIGHT * pi**2 * area) / (240 * force)
) ** (1 / 4)
return {"distance": distance}
raise ValueError('One and only one argument must be 0' )
# Run doctest
if __name__ == "__main__":
import doctest
doctest.testmod()
| 594 |
"""simple docstring"""
from __future__ import annotations
import unittest
import numpy as np
from transformers import OPTConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, slow
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 GPTaTokenizer, TFOPTForCausalLM, TFOPTModel
def UpperCAmelCase ( A__: int , A__: str , A__: List[Any]=None , A__: Dict=None ) -> List[str]:
if attention_mask is None:
__lowerCamelCase : int = tf.cast(tf.math.not_equal(A__ , config.pad_token_id ) , tf.inta )
return {"input_ids": input_ids, "attention_mask": attention_mask}
@require_tf
class __lowercase:
'''simple docstring'''
__a : Any = OPTConfig
__a : Union[str, Any] = {}
__a : Any = 'gelu'
def __init__( self , __a , __a=13 , __a=7 , __a=True , __a=False , __a=99 , __a=16 , __a=2 , __a=4 , __a=4 , __a="gelu" , __a=0.1 , __a=0.1 , __a=20 , __a=2 , __a=1 , __a=0 , __a=16 , __a=16 , ):
__lowerCamelCase : Dict = parent
__lowerCamelCase : List[str] = batch_size
__lowerCamelCase : Tuple = seq_length
__lowerCamelCase : int = is_training
__lowerCamelCase : Optional[int] = use_labels
__lowerCamelCase : Optional[int] = vocab_size
__lowerCamelCase : Any = hidden_size
__lowerCamelCase : Tuple = num_hidden_layers
__lowerCamelCase : Optional[int] = num_attention_heads
__lowerCamelCase : Any = intermediate_size
__lowerCamelCase : Union[str, Any] = hidden_act
__lowerCamelCase : int = hidden_dropout_prob
__lowerCamelCase : List[Any] = attention_probs_dropout_prob
__lowerCamelCase : Dict = max_position_embeddings
__lowerCamelCase : str = eos_token_id
__lowerCamelCase : int = pad_token_id
__lowerCamelCase : Union[str, Any] = bos_token_id
__lowerCamelCase : Dict = embed_dim
__lowerCamelCase : Tuple = word_embed_proj_dim
__lowerCamelCase : Any = False
def snake_case_ ( self ):
__lowerCamelCase : str = ids_tensor([self.batch_size, self.seq_length - 1] , self.vocab_size )
__lowerCamelCase : int = tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size ) , 1 )
__lowerCamelCase : Tuple = tf.concat([input_ids, eos_tensor] , axis=1 )
__lowerCamelCase : Any = self.config_cls(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , 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 , embed_dim=self.embed_dim , word_embed_proj_dim=self.word_embed_proj_dim , is_encoder_decoder=__a , **self.config_updates , )
__lowerCamelCase : int = prepare_opt_inputs_dict(__a , __a )
return config, inputs_dict
def snake_case_ ( self , __a , __a ):
__lowerCamelCase : Optional[int] = TFOPTModel(config=__a )
__lowerCamelCase : Dict = inputs_dict['input_ids']
__lowerCamelCase : List[Any] = input_ids[:1, :]
__lowerCamelCase : Optional[int] = inputs_dict['attention_mask'][:1, :]
__lowerCamelCase : Any = 1
# first forward pass
__lowerCamelCase : int = model(__a , attention_mask=__a , use_cache=__a )
__lowerCamelCase , __lowerCamelCase : Tuple = outputs.to_tuple()
# create hypothetical next token and extent to next_input_ids
__lowerCamelCase : Dict = ids_tensor((self.batch_size, 3) , config.vocab_size )
__lowerCamelCase : Union[str, Any] = tf.cast(ids_tensor((self.batch_size, 3) , 2 ) , tf.inta )
# append to next input_ids and
__lowerCamelCase : Union[str, Any] = tf.concat([input_ids, next_tokens] , axis=-1 )
__lowerCamelCase : Dict = tf.concat([attention_mask, next_attn_mask] , axis=-1 )
__lowerCamelCase : Dict = model(__a , attention_mask=__a )[0]
__lowerCamelCase : str = model(__a , attention_mask=__a , past_key_values=__a )[0]
self.parent.assertEqual(next_tokens.shape[1] , output_from_past.shape[1] )
# select random slice
__lowerCamelCase : Dict = int(ids_tensor((1,) , output_from_past.shape[-1] ) )
__lowerCamelCase : Dict = output_from_no_past[:, -3:, random_slice_idx]
__lowerCamelCase : Any = output_from_past[:, :, random_slice_idx]
# test that outputs are equal for slice
tf.debugging.assert_near(__a , __a , rtol=1E-3 )
@require_tf
class __lowercase( lowercase__ , lowercase__ , unittest.TestCase ):
'''simple docstring'''
__a : List[Any] = (TFOPTModel, TFOPTForCausalLM) if is_tf_available() else ()
__a : List[Any] = (TFOPTForCausalLM,) if is_tf_available() else ()
__a : List[str] = (
{'feature-extraction': TFOPTModel, 'text-generation': TFOPTForCausalLM} if is_tf_available() else {}
)
__a : List[Any] = False
__a : Dict = False
__a : Dict = False
__a : int = 10
def snake_case_ ( self ):
__lowerCamelCase : Optional[int] = TFOPTModelTester(self )
__lowerCamelCase : List[Any] = ConfigTester(self , config_class=__a )
def snake_case_ ( self ):
self.config_tester.run_common_tests()
def snake_case_ ( self ):
__lowerCamelCase : List[str] = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.check_decoder_model_past_large_inputs(*__a )
def snake_case_ ( self ):
__lowerCamelCase , __lowerCamelCase : Tuple = self.model_tester.prepare_config_and_inputs_for_common()
def _get_word_embedding_weight(__a , __a ):
if hasattr(__a , 'weight' ):
return embedding_layer.weight
else:
# Here we build the word embeddings weights if not exists.
# And then we retry to get the attribute once built.
model.build()
if hasattr(__a , 'weight' ):
return embedding_layer.weight
else:
return None
for model_class in self.all_model_classes:
for size in [config.vocab_size - 10, config.vocab_size + 10]:
# build the embeddings
__lowerCamelCase : Any = model_class(config=__a )
__lowerCamelCase : Union[str, Any] = _get_word_embedding_weight(__a , model.get_input_embeddings() )
__lowerCamelCase : Any = _get_word_embedding_weight(__a , model.get_output_embeddings() )
# reshape the embeddings
model.resize_token_embeddings(__a )
__lowerCamelCase : int = _get_word_embedding_weight(__a , model.get_input_embeddings() )
__lowerCamelCase : Tuple = _get_word_embedding_weight(__a , model.get_output_embeddings() )
# check that the resized embeddings size matches the desired size.
__lowerCamelCase : Tuple = size if size is not None else config.vocab_size
self.assertEqual(new_input_embeddings.shape[0] , __a )
# check that weights remain the same after resizing
__lowerCamelCase : List[Any] = True
for pa, pa in zip(old_input_embeddings.value() , new_input_embeddings.value() ):
if tf.math.reduce_sum(tf.math.abs(pa - pa ) ) > 0:
__lowerCamelCase : str = False
self.assertTrue(__a )
if old_output_embeddings is not None and new_output_embeddings is not None:
self.assertEqual(new_output_embeddings.shape[0] , __a )
__lowerCamelCase : Dict = True
for pa, pa in zip(old_output_embeddings.value() , new_output_embeddings.value() ):
if tf.math.reduce_sum(tf.math.abs(pa - pa ) ) > 0:
__lowerCamelCase : Tuple = False
self.assertTrue(__a )
def UpperCAmelCase ( A__: Tuple ) -> Dict:
return tf.constant(A__ , dtype=tf.intaa )
@require_tf
class __lowercase( unittest.TestCase ):
'''simple docstring'''
__a : str = 99
def snake_case_ ( self ):
__lowerCamelCase : str = tf.ones((4, 1) , dtype=tf.intaa ) * 2
__lowerCamelCase : int = tf.concat([ids_tensor((4, 6) , self.vocab_size - 3 ) + 3, eos_column_vector] , axis=1 )
__lowerCamelCase : int = input_ids.shape[0]
__lowerCamelCase : Optional[int] = OPTConfig(
vocab_size=self.vocab_size , hidden_size=24 , num_hidden_layers=2 , num_attention_heads=2 , 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
@require_sentencepiece
@require_tf
class __lowercase( unittest.TestCase ):
'''simple docstring'''
@slow
def snake_case_ ( self ):
__lowerCamelCase : str = TFOPTModel.from_pretrained('facebook/opt-350m' )
__lowerCamelCase : Dict = _long_tensor([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]] )
__lowerCamelCase : List[Any] = tf.not_equal(__a , model.config.pad_token_id )
with tf.GradientTape():
__lowerCamelCase : Dict = model(input_ids=__a , attention_mask=__a ).last_hidden_state
__lowerCamelCase : List[Any] = (1, 11, 512)
self.assertEqual(output.shape , __a )
__lowerCamelCase : str = tf.constant(
[[-0.2_873, -1.9_218, -0.3_033], [-1.2_710, -0.1_338, -0.1_902], [0.4_095, 0.1_214, -1.3_121]] )
self.assertTrue(np.allclose(output[:, :3, :3] , __a , atol=4E-3 ) )
__lowerCamelCase : int = tf.function(__a , jit_compile=__a )
__lowerCamelCase : Optional[int] = xla_generate(__a , __a )[0]
self.assertTrue(np.allclose(output[:, :3, :3] , __a , atol=4E-2 ) )
@require_tf
@slow
class __lowercase( unittest.TestCase ):
'''simple docstring'''
def snake_case_ ( self ):
super().setUp()
__lowerCamelCase : str = 'facebook/opt-350m'
def snake_case_ ( self ):
__lowerCamelCase : int = TFOPTForCausalLM.from_pretrained(self.path_model )
__lowerCamelCase : List[Any] = GPTaTokenizer.from_pretrained(self.path_model )
__lowerCamelCase : Optional[int] = [
'Today is a beautiful day and I want to',
'In the city of',
'Paris is the capital of France and',
'Computers and mobile phones have taken',
]
# verify that prompt without BOS token is identical to Metaseq -> add_special_tokens=False
__lowerCamelCase : str = tokenizer(__a , return_tensors='tf' , padding=__a , add_special_tokens=__a )
__lowerCamelCase : Tuple = tf.math.reduce_mean(model(inputs.input_ids , attention_mask=inputs.attention_mask )[0] , axis=-1 )
__lowerCamelCase : List[str] = tf.constant(
[
[1.3_851, -13.8_923, -10.5_229, -10.7_533, -0.2_309, -10.2_384, -0.5_365, -9.0_947, -5.1_670],
[-4.7_073, -10.6_276, -3.9_415, -21.5_242, -0.2_822, -0.2_822, -0.2_822, -0.2_822, -0.2_822],
[0.6_247, -3.4_229, -8.9_179, -1.4_297, -14.1_650, 1.4_146, -9.0_218, -0.2_703, -0.2_703],
[6.4_783, -1.9_913, -10.7_926, -2.3_336, 1.5_092, -0.9_974, -6.8_213, 1.3_477, 1.3_477],
] )
self.assertTrue(np.allclose(__a , __a , atol=1E-4 ) )
__lowerCamelCase : Union[str, Any] = tf.function(__a , jit_compile=__a )
__lowerCamelCase : List[Any] = tf.math.reduce_mean(xla_generate(inputs.input_ids , attention_mask=inputs.attention_mask )[0] , axis=-1 )
self.assertTrue(np.allclose(__a , __a , atol=1E-4 ) )
@require_tf
@slow
class __lowercase( unittest.TestCase ):
'''simple docstring'''
@property
def snake_case_ ( self ):
return [
"Today is a beautiful day and I want",
"In the city of",
"Paris is the capital of France and",
"Computers and mobile phones have taken",
]
def snake_case_ ( self ):
__lowerCamelCase : List[str] = 'facebook/opt-125m'
__lowerCamelCase : Dict = [
'Today is a beautiful day and I want to',
'In the city of New York, the city',
'Paris is the capital of France and the capital',
'Computers and mobile phones have taken over the',
]
__lowerCamelCase : Tuple = []
__lowerCamelCase : str = GPTaTokenizer.from_pretrained(__a )
__lowerCamelCase : Dict = TFOPTForCausalLM.from_pretrained(__a )
for prompt in self.prompts:
__lowerCamelCase : Dict = tokenizer(__a , return_tensors='tf' ).input_ids
__lowerCamelCase : int = model.generate(__a , max_length=10 )
__lowerCamelCase : List[Any] = tokenizer.batch_decode(__a , skip_special_tokens=__a )
predicted_outputs += generated_string
self.assertListEqual(__a , __a )
def snake_case_ ( self ):
__lowerCamelCase : int = 'facebook/opt-350m'
__lowerCamelCase : List[Any] = GPTaTokenizer.from_pretrained(__a )
__lowerCamelCase : str = TFOPTForCausalLM.from_pretrained(__a )
__lowerCamelCase : Optional[int] = 'left'
# use different length sentences to test batching
__lowerCamelCase : List[Any] = [
'Hello, my dog is a little',
'Today, I',
]
__lowerCamelCase : Optional[int] = tokenizer(__a , return_tensors='tf' , padding=__a )
__lowerCamelCase : Tuple = inputs['input_ids']
__lowerCamelCase : Optional[Any] = model.generate(input_ids=__a , attention_mask=inputs['attention_mask'] )
__lowerCamelCase : Any = tokenizer(sentences[0] , return_tensors='tf' ).input_ids
__lowerCamelCase : Optional[Any] = model.generate(input_ids=__a )
__lowerCamelCase : List[Any] = inputs_non_padded.shape[-1] - tf.math.reduce_sum(
tf.cast(inputs['attention_mask'][-1] , tf.intaa ) )
__lowerCamelCase : Union[str, Any] = tokenizer(sentences[1] , return_tensors='tf' ).input_ids
__lowerCamelCase : Any = model.generate(input_ids=__a , max_length=model.config.max_length - num_paddings )
__lowerCamelCase : Optional[int] = tokenizer.batch_decode(__a , skip_special_tokens=__a )
__lowerCamelCase : str = tokenizer.decode(output_non_padded[0] , skip_special_tokens=__a )
__lowerCamelCase : Union[str, Any] = tokenizer.decode(output_padded[0] , skip_special_tokens=__a )
__lowerCamelCase : Any = [
'Hello, my dog is a little bit of a dork.\nI\'m a little bit',
'Today, I was in the middle of a conversation with a friend about the',
]
self.assertListEqual(__a , __a )
self.assertListEqual(__a , [non_padded_sentence, padded_sentence] )
def snake_case_ ( self ):
__lowerCamelCase : Any = 'facebook/opt-350m'
__lowerCamelCase : str = [
'Today is a beautiful day and I want to',
'In the city of San Francisco, the city',
'Paris is the capital of France and the capital',
'Computers and mobile phones have taken over the',
]
__lowerCamelCase : int = []
__lowerCamelCase : Tuple = GPTaTokenizer.from_pretrained(__a )
__lowerCamelCase : List[Any] = TFOPTForCausalLM.from_pretrained(__a )
for prompt in self.prompts:
__lowerCamelCase : Optional[Any] = tokenizer(__a , return_tensors='tf' ).input_ids
__lowerCamelCase : List[Any] = model.generate(__a , max_length=10 )
__lowerCamelCase : Union[str, Any] = tokenizer.batch_decode(__a , skip_special_tokens=__a )
predicted_outputs += generated_string
self.assertListEqual(__a , __a )
| 594 | 1 |
import numpy as np
import torch
import tqdm
from ...models.unet_ad import UNetaDModel
from ...pipelines import DiffusionPipeline
from ...utils import randn_tensor
from ...utils.dummy_pt_objects import DDPMScheduler
class _A ( UpperCAmelCase_ ):
def __init__( self : Optional[Any] , lowerCamelCase__ : UNetaDModel , lowerCamelCase__ : UNetaDModel , lowerCamelCase__ : DDPMScheduler , lowerCamelCase__ : Any , ):
"""simple docstring"""
super().__init__()
__UpperCamelCase : Tuple = value_function
__UpperCamelCase : List[str] = unet
__UpperCamelCase : int = scheduler
__UpperCamelCase : Optional[Any] = env
__UpperCamelCase : Union[str, Any] = env.get_dataset()
__UpperCamelCase : List[str] = {}
for key in self.data.keys():
try:
__UpperCamelCase : Union[str, Any] = self.data[key].mean()
except: # noqa: E722
pass
__UpperCamelCase : Dict = {}
for key in self.data.keys():
try:
__UpperCamelCase : List[Any] = self.data[key].std()
except: # noqa: E722
pass
__UpperCamelCase : Any = env.observation_space.shape[0]
__UpperCamelCase : Tuple = env.action_space.shape[0]
def a ( self : Optional[Any] , lowerCamelCase__ : Any , lowerCamelCase__ : int ):
"""simple docstring"""
return (x_in - self.means[key]) / self.stds[key]
def a ( self : Optional[int] , lowerCamelCase__ : str , lowerCamelCase__ : List[str] ):
"""simple docstring"""
return x_in * self.stds[key] + self.means[key]
def a ( self : List[str] , lowerCamelCase__ : Tuple ):
"""simple docstring"""
if type(lowerCamelCase__ ) is dict:
return {k: self.to_torch(lowerCamelCase__ ) for k, v in x_in.items()}
elif torch.is_tensor(lowerCamelCase__ ):
return x_in.to(self.unet.device )
return torch.tensor(lowerCamelCase__ , device=self.unet.device )
def a ( self : Optional[Any] , lowerCamelCase__ : Optional[int] , lowerCamelCase__ : Dict , lowerCamelCase__ : Optional[Any] ):
"""simple docstring"""
for key, val in cond.items():
__UpperCamelCase : int = val.clone()
return x_in
def a ( self : Tuple , lowerCamelCase__ : Optional[Any] , lowerCamelCase__ : Any , lowerCamelCase__ : Optional[Any] , lowerCamelCase__ : Optional[int] ):
"""simple docstring"""
__UpperCamelCase : int = x.shape[0]
__UpperCamelCase : List[Any] = None
for i in tqdm.tqdm(self.scheduler.timesteps ):
# create batch of timesteps to pass into model
__UpperCamelCase : Dict = torch.full((batch_size,) , lowerCamelCase__ , device=self.unet.device , dtype=torch.long )
for _ in range(lowerCamelCase__ ):
with torch.enable_grad():
x.requires_grad_()
# permute to match dimension for pre-trained models
__UpperCamelCase : List[str] = self.value_function(x.permute(0 , 2 , 1 ) , lowerCamelCase__ ).sample
__UpperCamelCase : int = torch.autograd.grad([y.sum()] , [x] )[0]
__UpperCamelCase : Any = self.scheduler._get_variance(lowerCamelCase__ )
__UpperCamelCase : Union[str, Any] = torch.exp(0.5 * posterior_variance )
__UpperCamelCase : Tuple = model_std * grad
__UpperCamelCase : Any = 0
__UpperCamelCase : List[Any] = x.detach()
__UpperCamelCase : List[Any] = x + scale * grad
__UpperCamelCase : Optional[Any] = self.reset_xa(lowerCamelCase__ , lowerCamelCase__ , self.action_dim )
__UpperCamelCase : str = self.unet(x.permute(0 , 2 , 1 ) , lowerCamelCase__ ).sample.permute(0 , 2 , 1 )
# TODO: verify deprecation of this kwarg
__UpperCamelCase : List[str] = self.scheduler.step(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , predict_epsilon=lowerCamelCase__ )["""prev_sample"""]
# apply conditions to the trajectory (set the initial state)
__UpperCamelCase : int = self.reset_xa(lowerCamelCase__ , lowerCamelCase__ , self.action_dim )
__UpperCamelCase : Tuple = self.to_torch(lowerCamelCase__ )
return x, y
def __call__( self : Any , lowerCamelCase__ : Tuple , lowerCamelCase__ : int=64 , lowerCamelCase__ : Tuple=32 , lowerCamelCase__ : List[Any]=2 , lowerCamelCase__ : List[str]=0.1 ):
"""simple docstring"""
__UpperCamelCase : int = self.normalize(lowerCamelCase__ , """observations""" )
__UpperCamelCase : Dict = obs[None].repeat(lowerCamelCase__ , axis=0 )
__UpperCamelCase : Any = {0: self.to_torch(lowerCamelCase__ )}
__UpperCamelCase : str = (batch_size, planning_horizon, self.state_dim + self.action_dim)
# generate initial noise and apply our conditions (to make the trajectories start at current state)
__UpperCamelCase : Optional[Any] = randn_tensor(lowerCamelCase__ , device=self.unet.device )
__UpperCamelCase : Any = self.reset_xa(lowerCamelCase__ , lowerCamelCase__ , self.action_dim )
__UpperCamelCase : List[Any] = self.to_torch(lowerCamelCase__ )
# run the diffusion process
__UpperCamelCase , __UpperCamelCase : Any = self.run_diffusion(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ )
# sort output trajectories by value
__UpperCamelCase : str = y.argsort(0 , descending=lowerCamelCase__ ).squeeze()
__UpperCamelCase : List[str] = x[sorted_idx]
__UpperCamelCase : int = sorted_values[:, :, : self.action_dim]
__UpperCamelCase : List[Any] = actions.detach().cpu().numpy()
__UpperCamelCase : Dict = self.de_normalize(lowerCamelCase__ , key="""actions""" )
# select the action with the highest value
if y is not None:
__UpperCamelCase : List[str] = 0
else:
# if we didn't run value guiding, select a random action
__UpperCamelCase : str = np.random.randint(0 , lowerCamelCase__ )
__UpperCamelCase : Any = denorm_actions[selected_index, 0]
return denorm_actions
| 701 |
from .imports import is_rich_available
if is_rich_available():
from rich.traceback import install
install(show_locals=False)
else:
raise ModuleNotFoundError('To use the rich extension, install rich with `pip install rich`')
| 515 | 0 |
import timeit
import numpy as np
import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features.features import _ArrayXD
def A__ ( snake_case_ : Tuple ):
def wrapper(*snake_case_ : Optional[Any] , **snake_case_ : Optional[int] ):
SCREAMING_SNAKE_CASE__: Optional[int]= timeit.default_timer()
SCREAMING_SNAKE_CASE__: str= func(*snake_case_ , **snake_case_ )
SCREAMING_SNAKE_CASE__: str= timeit.default_timer() - starttime
return delta
SCREAMING_SNAKE_CASE__: List[Any]= func.__name__
return wrapper
def A__ ( snake_case_ : dict , snake_case_ : Any=100 , snake_case_ : str=None ):
SCREAMING_SNAKE_CASE__: Tuple= []
SCREAMING_SNAKE_CASE__: Any= seq_shapes or {}
for i in range(snake_case_ ):
SCREAMING_SNAKE_CASE__: Any= {}
for col_id, (k, v) in enumerate(features.items() ):
if isinstance(snake_case_ , _ArrayXD ):
SCREAMING_SNAKE_CASE__: Union[str, Any]= np.random.rand(*v.shape ).astype(v.dtype )
elif isinstance(snake_case_ , datasets.Value ):
if v.dtype == "string":
SCREAMING_SNAKE_CASE__: Optional[int]= '''The small grey turtle was surprisingly fast when challenged.'''
else:
SCREAMING_SNAKE_CASE__: Union[str, Any]= np.random.randint(10 , size=1 ).astype(v.dtype ).item()
elif isinstance(snake_case_ , datasets.Sequence ):
while isinstance(snake_case_ , datasets.Sequence ):
SCREAMING_SNAKE_CASE__: Dict= v.feature
SCREAMING_SNAKE_CASE__: Dict= seq_shapes[k]
SCREAMING_SNAKE_CASE__: Dict= np.random.rand(*snake_case_ ).astype(v.dtype )
SCREAMING_SNAKE_CASE__: Dict= data
dummy_data.append((i, example) )
return dummy_data
def A__ ( snake_case_ : int , snake_case_ : Optional[Any] , snake_case_ : Optional[Any]=100 , snake_case_ : Optional[Any]=None ):
SCREAMING_SNAKE_CASE__: Union[str, Any]= generate_examples(snake_case_ , num_examples=snake_case_ , seq_shapes=snake_case_ )
with ArrowWriter(features=snake_case_ , path=snake_case_ ) as writer:
for key, record in dummy_data:
SCREAMING_SNAKE_CASE__: Tuple= features.encode_example(snake_case_ )
writer.write(snake_case_ )
SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__: Tuple= writer.finalize()
if not num_final_examples == num_examples:
raise ValueError(
F'Error writing the dataset, wrote {num_final_examples} examples but should have written {num_examples}.' )
SCREAMING_SNAKE_CASE__: Union[str, Any]= datasets.Dataset.from_file(filename=snake_case_ , info=datasets.DatasetInfo(features=snake_case_ ) )
return dataset
| 64 |
"""simple docstring"""
from scipy.stats import spearmanr
import datasets
A_ = '''
The Spearman rank-order correlation coefficient is a measure of the
relationship between two datasets. Like other correlation coefficients,
this one varies between -1 and +1 with 0 implying no correlation.
Positive correlations imply that as data in dataset x increases, so
does data in dataset y. Negative correlations imply that as x increases,
y decreases. Correlations of -1 or +1 imply an exact monotonic relationship.
Unlike the Pearson correlation, the Spearman correlation does not
assume that both datasets are normally distributed.
The p-value roughly indicates the probability of an uncorrelated system
producing datasets that have a Spearman correlation at least as extreme
as the one computed from these datasets. The p-values are not entirely
reliable but are probably reasonable for datasets larger than 500 or so.
'''
A_ = '''
Args:
predictions (`List[float]`): Predicted labels, as returned by a model.
references (`List[float]`): Ground truth labels.
return_pvalue (`bool`): If `True`, returns the p-value. If `False`, returns
only the spearmanr score. Defaults to `False`.
Returns:
spearmanr (`float`): Spearman correlation coefficient.
p-value (`float`): p-value. **Note**: is only returned if `return_pvalue=True` is input.
Examples:
Example 1:
>>> spearmanr_metric = datasets.load_metric("spearmanr")
>>> results = spearmanr_metric.compute(references=[1, 2, 3, 4, 5], predictions=[10, 9, 2.5, 6, 4])
>>> print(results)
{\'spearmanr\': -0.7}
Example 2:
>>> spearmanr_metric = datasets.load_metric("spearmanr")
>>> results = spearmanr_metric.compute(references=[1, 2, 3, 4, 5],
... predictions=[10, 9, 2.5, 6, 4],
... return_pvalue=True)
>>> print(results[\'spearmanr\'])
-0.7
>>> print(round(results[\'spearmanr_pvalue\'], 2))
0.19
'''
A_ = r'''\
@book{kokoska2000crc,
title={CRC standard probability and statistics tables and formulae},
author={Kokoska, Stephen and Zwillinger, Daniel},
year={2000},
publisher={Crc Press}
}
@article{2020SciPy-NMeth,
author = {Virtanen, Pauli and Gommers, Ralf and Oliphant, Travis E. and
Haberland, Matt and Reddy, Tyler and Cournapeau, David and
Burovski, Evgeni and Peterson, Pearu and Weckesser, Warren and
Bright, Jonathan and {van der Walt}, St{\'e}fan J. and
Brett, Matthew and Wilson, Joshua and Millman, K. Jarrod and
Mayorov, Nikolay and Nelson, Andrew R. J. and Jones, Eric and
Kern, Robert and Larson, Eric and Carey, C J and
Polat, {\.I}lhan and Feng, Yu and Moore, Eric W. and
{VanderPlas}, Jake and Laxalde, Denis and Perktold, Josef and
Cimrman, Robert and Henriksen, Ian and Quintero, E. A. and
Harris, Charles R. and Archibald, Anne M. and
Ribeiro, Ant{\^o}nio H. and Pedregosa, Fabian and
{van Mulbregt}, Paul and {SciPy 1.0 Contributors}},
title = {{{SciPy} 1.0: Fundamental Algorithms for Scientific
Computing in Python}},
journal = {Nature Methods},
year = {2020},
volume = {17},
pages = {261--272},
adsurl = {https://rdcu.be/b08Wh},
doi = {10.1038/s41592-019-0686-2},
}
'''
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class lowercase( datasets.Metric ):
'''simple docstring'''
def UpperCamelCase_ ( self: Dict ):
'''simple docstring'''
return datasets.MetricInfo(
description=_DESCRIPTION, citation=_CITATION, inputs_description=_KWARGS_DESCRIPTION, features=datasets.Features(
{
"""predictions""": datasets.Value("""float""" ),
"""references""": datasets.Value("""float""" ),
} ), reference_urls=["""https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.spearmanr.html"""], )
def UpperCamelCase_ ( self: Any, a_: Union[str, Any], a_: Union[str, Any], a_: str=False ):
'''simple docstring'''
_snake_case : Optional[Any] = spearmanr(a_, a_ )
if return_pvalue:
return {"spearmanr": results[0], "spearmanr_pvalue": results[1]}
else:
return {"spearmanr": results[0]}
| 609 | 0 |
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import AutoFeatureExtractor, WavaVecaFeatureExtractor
from transformers.testing_utils import TOKEN, USER, get_tests_dir, is_staging_test
sys.path.append(str(Path(__file__).parent.parent / 'utils'))
from test_module.custom_feature_extraction import CustomFeatureExtractor # noqa E402
A = get_tests_dir('fixtures')
class lowerCAmelCase__ ( unittest.TestCase ):
'''simple docstring'''
def _snake_case ( self : Union[str, Any] ) -> Dict:
_lowerCamelCase = mock.Mock()
_lowerCamelCase = 5_0_0
_lowerCamelCase = {}
_lowerCamelCase = HTTPError
_lowerCamelCase = {}
# Download this model to make sure it's in the cache.
_lowerCamelCase = WavaVecaFeatureExtractor.from_pretrained('hf-internal-testing/tiny-random-wav2vec2' )
# Under the mock environment we get a 500 error when trying to reach the model.
with mock.patch('requests.Session.request' , return_value=a_ ) as mock_head:
_lowerCamelCase = WavaVecaFeatureExtractor.from_pretrained('hf-internal-testing/tiny-random-wav2vec2' )
# This check we did call the fake head request
mock_head.assert_called()
def _snake_case ( self : Dict ) -> List[Any]:
_lowerCamelCase = WavaVecaFeatureExtractor.from_pretrained(
'https://huggingface.co/hf-internal-testing/tiny-random-wav2vec2/resolve/main/preprocessor_config.json' )
@is_staging_test
class lowerCAmelCase__ ( unittest.TestCase ):
'''simple docstring'''
@classmethod
def _snake_case ( cls : Union[str, Any] ) -> Union[str, Any]:
_lowerCamelCase = TOKEN
HfFolder.save_token(a_ )
@classmethod
def _snake_case ( cls : Tuple ) -> Optional[int]:
try:
delete_repo(token=cls._token , repo_id='test-feature-extractor' )
except HTTPError:
pass
try:
delete_repo(token=cls._token , repo_id='valid_org/test-feature-extractor-org' )
except HTTPError:
pass
try:
delete_repo(token=cls._token , repo_id='test-dynamic-feature-extractor' )
except HTTPError:
pass
def _snake_case ( self : List[str] ) -> Tuple:
_lowerCamelCase = WavaVecaFeatureExtractor.from_pretrained(a_ )
feature_extractor.push_to_hub('test-feature-extractor' , use_auth_token=self._token )
_lowerCamelCase = WavaVecaFeatureExtractor.from_pretrained(f"""{USER}/test-feature-extractor""" )
for k, v in feature_extractor.__dict__.items():
self.assertEqual(a_ , getattr(a_ , a_ ) )
# Reset repo
delete_repo(token=self._token , repo_id='test-feature-extractor' )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
feature_extractor.save_pretrained(
a_ , repo_id='test-feature-extractor' , push_to_hub=a_ , use_auth_token=self._token )
_lowerCamelCase = WavaVecaFeatureExtractor.from_pretrained(f"""{USER}/test-feature-extractor""" )
for k, v in feature_extractor.__dict__.items():
self.assertEqual(a_ , getattr(a_ , a_ ) )
def _snake_case ( self : str ) -> int:
_lowerCamelCase = WavaVecaFeatureExtractor.from_pretrained(a_ )
feature_extractor.push_to_hub('valid_org/test-feature-extractor' , use_auth_token=self._token )
_lowerCamelCase = WavaVecaFeatureExtractor.from_pretrained('valid_org/test-feature-extractor' )
for k, v in feature_extractor.__dict__.items():
self.assertEqual(a_ , getattr(a_ , a_ ) )
# Reset repo
delete_repo(token=self._token , repo_id='valid_org/test-feature-extractor' )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
feature_extractor.save_pretrained(
a_ , repo_id='valid_org/test-feature-extractor-org' , push_to_hub=a_ , use_auth_token=self._token )
_lowerCamelCase = WavaVecaFeatureExtractor.from_pretrained('valid_org/test-feature-extractor-org' )
for k, v in feature_extractor.__dict__.items():
self.assertEqual(a_ , getattr(a_ , a_ ) )
def _snake_case ( self : Dict ) -> List[Any]:
CustomFeatureExtractor.register_for_auto_class()
_lowerCamelCase = CustomFeatureExtractor.from_pretrained(a_ )
feature_extractor.push_to_hub('test-dynamic-feature-extractor' , use_auth_token=self._token )
# This has added the proper auto_map field to the config
self.assertDictEqual(
feature_extractor.auto_map , {'AutoFeatureExtractor': 'custom_feature_extraction.CustomFeatureExtractor'} , )
_lowerCamelCase = AutoFeatureExtractor.from_pretrained(
f"""{USER}/test-dynamic-feature-extractor""" , trust_remote_code=a_ )
# Can't make an isinstance check because the new_feature_extractor is from the CustomFeatureExtractor class of a dynamic module
self.assertEqual(new_feature_extractor.__class__.__name__ , 'CustomFeatureExtractor' ) | 706 | from typing import List, Optional, Tuple, Union
import torch
from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
lowerCAmelCase_ = 42
lowerCAmelCase_ = 42
def __init__( self : int , snake_case__ : UNetaDModel , snake_case__ : ScoreSdeVeScheduler ) -> Dict:
super().__init__()
self.register_modules(unet=snake_case__ , scheduler=snake_case__ )
@torch.no_grad()
def __call__( self : Any , snake_case__ : int = 1 , snake_case__ : int = 2_0_0_0 , snake_case__ : Optional[Union[torch.Generator, List[torch.Generator]]] = None , snake_case__ : Optional[str] = "pil" , snake_case__ : bool = True , **snake_case__ : Tuple , ) -> Union[ImagePipelineOutput, Tuple]:
_lowerCamelCase = self.unet.config.sample_size
_lowerCamelCase = (batch_size, 3, img_size, img_size)
_lowerCamelCase = self.unet
_lowerCamelCase = randn_tensor(snake_case__ , generator=snake_case__ ) * self.scheduler.init_noise_sigma
_lowerCamelCase = sample.to(self.device )
self.scheduler.set_timesteps(snake_case__ )
self.scheduler.set_sigmas(snake_case__ )
for i, t in enumerate(self.progress_bar(self.scheduler.timesteps ) ):
_lowerCamelCase = self.scheduler.sigmas[i] * torch.ones(shape[0] , device=self.device )
# correction step
for _ in range(self.scheduler.config.correct_steps ):
_lowerCamelCase = self.unet(snake_case__ , snake_case__ ).sample
_lowerCamelCase = self.scheduler.step_correct(snake_case__ , snake_case__ , generator=snake_case__ ).prev_sample
# prediction step
_lowerCamelCase = model(snake_case__ , snake_case__ ).sample
_lowerCamelCase = self.scheduler.step_pred(snake_case__ , snake_case__ , snake_case__ , generator=snake_case__ )
_lowerCamelCase , _lowerCamelCase = output.prev_sample, output.prev_sample_mean
_lowerCamelCase = sample_mean.clamp(0 , 1 )
_lowerCamelCase = sample.cpu().permute(0 , 2 , 3 , 1 ).numpy()
if output_type == "pil":
_lowerCamelCase = self.numpy_to_pil(snake_case__ )
if not return_dict:
return (sample,)
return ImagePipelineOutput(images=snake_case__ ) | 234 | 0 |
'''simple docstring'''
from cva import destroyAllWindows, imread, imshow, waitKey
def _SCREAMING_SNAKE_CASE (A ) -> Any:
"""simple docstring"""
lowercase__ ,lowercase__ = img.shape[0], img.shape[1]
# converting each pixel's color to its negative
for i in range(A ):
for j in range(A ):
lowercase__ = [255, 255, 255] - img[i][j]
return img
if __name__ == "__main__":
# read original image
lowerCamelCase : Tuple = imread('image_data/lena.jpg', 1)
# convert to its negative
lowerCamelCase : Any = convert_to_negative(img)
# show result image
imshow('negative of original image', img)
waitKey(0)
destroyAllWindows()
| 460 |
'''simple docstring'''
import unittest
from transformers import RoFormerTokenizer, RoFormerTokenizerFast
from transformers.testing_utils import require_rjieba, require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_rjieba
@require_tokenizers
class __lowerCAmelCase (lowercase_ , unittest.TestCase ):
'''simple docstring'''
lowerCAmelCase__ : str = RoFormerTokenizer
lowerCAmelCase__ : Dict = RoFormerTokenizerFast
lowerCAmelCase__ : List[str] = True
lowerCAmelCase__ : List[str] = True
def UpperCamelCase__ (self : Dict ):
'''simple docstring'''
super().setUp()
def UpperCamelCase__ (self : List[str] , **UpperCamelCase : Dict ):
'''simple docstring'''
return self.tokenizer_class.from_pretrained('''junnyu/roformer_chinese_base''' , **UpperCamelCase )
def UpperCamelCase__ (self : List[str] , **UpperCamelCase : int ):
'''simple docstring'''
return self.rust_tokenizer_class.from_pretrained('''junnyu/roformer_chinese_base''' , **UpperCamelCase )
def UpperCamelCase__ (self : List[str] ):
'''simple docstring'''
lowercase__ = '''永和服装饰品有限公司,今天天气非常好'''
lowercase__ = '''永和 服装 饰品 有限公司 , 今 天 天 气 非常 好'''
return input_text, output_text
def UpperCamelCase__ (self : int ):
'''simple docstring'''
lowercase__ = self.get_tokenizer()
lowercase__ ,lowercase__ = self.get_chinese_input_output_texts()
lowercase__ = tokenizer.tokenize(UpperCamelCase )
self.assertListEqual(UpperCamelCase , output_text.split() )
lowercase__ = tokens + [tokenizer.unk_token]
lowercase__ = [22943, 21332, 34431, 45904, 117, 306, 1231, 1231, 2653, 33994, 1266, 100]
self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCamelCase ) , UpperCamelCase )
def UpperCamelCase__ (self : Union[str, Any] ):
'''simple docstring'''
lowercase__ = self.get_rust_tokenizer()
lowercase__ ,lowercase__ = self.get_chinese_input_output_texts()
lowercase__ = tokenizer.tokenize(UpperCamelCase )
self.assertListEqual(UpperCamelCase , output_text.split() )
lowercase__ = tokens + [tokenizer.unk_token]
lowercase__ = [22943, 21332, 34431, 45904, 117, 306, 1231, 1231, 2653, 33994, 1266, 100]
self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCamelCase ) , UpperCamelCase )
def UpperCamelCase__ (self : str ):
'''simple docstring'''
pass
def UpperCamelCase__ (self : Any ):
'''simple docstring'''
pass
def UpperCamelCase__ (self : Union[str, Any] ):
'''simple docstring'''
pass
| 460 | 1 |
'''simple docstring'''
import random
import unittest
import torch
from diffusers import IFImgaImgSuperResolutionPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device
from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin
@skip_mps
class UpperCamelCase__ ( a , a , unittest.TestCase ):
'''simple docstring'''
_snake_case = IFImgaImgSuperResolutionPipeline
_snake_case = TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'''width''', '''height'''}
_snake_case = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({'''original_image'''} )
_snake_case = PipelineTesterMixin.required_optional_params - {'''latents'''}
def snake_case ( self ) -> Union[str, Any]:
return self._get_superresolution_dummy_components()
def snake_case ( self , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE=0 ) -> Tuple:
if str(SCREAMING_SNAKE_CASE ).startswith('mps' ):
__lowerCAmelCase : Tuple = torch.manual_seed(SCREAMING_SNAKE_CASE )
else:
__lowerCAmelCase : List[str] = torch.Generator(device=SCREAMING_SNAKE_CASE ).manual_seed(SCREAMING_SNAKE_CASE )
__lowerCAmelCase : Any = floats_tensor((1, 3, 32, 32) , rng=random.Random(SCREAMING_SNAKE_CASE ) ).to(SCREAMING_SNAKE_CASE )
__lowerCAmelCase : Tuple = floats_tensor((1, 3, 16, 16) , rng=random.Random(SCREAMING_SNAKE_CASE ) ).to(SCREAMING_SNAKE_CASE )
__lowerCAmelCase : Dict = {
'prompt': 'A painting of a squirrel eating a burger',
'image': image,
'original_image': original_image,
'generator': generator,
'num_inference_steps': 2,
'output_type': 'numpy',
}
return inputs
@unittest.skipIf(
torch_device != 'cuda' or not is_xformers_available() , reason='XFormers attention is only available with CUDA and `xformers` installed' , )
def snake_case ( self ) -> Any:
self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3 )
def snake_case ( self ) -> Tuple:
self._test_save_load_optional_components()
@unittest.skipIf(torch_device != 'cuda' , reason='float16 requires CUDA' )
def snake_case ( self ) -> List[str]:
# Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
super().test_save_load_floataa(expected_max_diff=1e-1 )
def snake_case ( self ) -> Optional[int]:
self._test_attention_slicing_forward_pass(expected_max_diff=1e-2 )
def snake_case ( self ) -> Optional[int]:
self._test_save_load_local()
def snake_case ( self ) -> Any:
self._test_inference_batch_single_identical(
expected_max_diff=1e-2 , )
| 123 |
'''simple docstring'''
def A ( _UpperCAmelCase : int = 5_0 ) -> int:
'''simple docstring'''
__lowerCAmelCase : Any = [1] * (length + 1)
for row_length in range(3 ,length + 1 ):
for block_length in range(3 ,row_length + 1 ):
for block_start in range(row_length - block_length ):
ways_number[row_length] += ways_number[
row_length - block_start - block_length - 1
]
ways_number[row_length] += 1
return ways_number[length]
if __name__ == "__main__":
print(F'''{solution() = }''')
| 123 | 1 |
from collections import Counter
from timeit import timeit
def a__ ( A__ = "", ):
return sum(c % 2 for c in Counter(input_str.replace(' ', '' ).lower() ).values() ) < 2
def a__ ( A__ = "" ):
if len(A__ ) == 0:
return True
SCREAMING_SNAKE_CASE_ : Optional[int] = input_str.replace(' ', '' ).lower()
# character_freq_dict: Stores the frequency of every character in the input string
SCREAMING_SNAKE_CASE_ : dict[str, int] = {}
for character in lower_case_input_str:
SCREAMING_SNAKE_CASE_ : Optional[Any] = character_freq_dict.get(A__, 0 ) + 1
SCREAMING_SNAKE_CASE_ : str = 0
for character_count in character_freq_dict.values():
if character_count % 2:
odd_char += 1
if odd_char > 1:
return False
return True
def a__ ( A__ = "" ):
print('\nFor string = ', A__, ':' )
print(
'> can_string_be_rearranged_as_palindrome_counter()', '\tans =', can_string_be_rearranged_as_palindrome_counter(A__ ), '\ttime =', timeit(
'z.can_string_be_rearranged_as_palindrome_counter(z.check_str)', setup='import __main__ as z', ), 'seconds', )
print(
'> can_string_be_rearranged_as_palindrome()', '\tans =', can_string_be_rearranged_as_palindrome(A__ ), '\ttime =', timeit(
'z.can_string_be_rearranged_as_palindrome(z.check_str)', setup='import __main__ as z', ), 'seconds', )
if __name__ == "__main__":
lowerCAmelCase__ : int =input(
'Enter string to determine if it can be rearranged as a palindrome or not: '
).strip()
benchmark(check_str)
lowerCAmelCase__ : List[Any] =can_string_be_rearranged_as_palindrome_counter(check_str)
print(F"""{check_str} can {"" if status else "not "}be rearranged as a palindrome""")
| 101 |
from __future__ import annotations
lowerCAmelCase__ : Union[str, Any] =[
[-1, 0], # left
[0, -1], # down
[1, 0], # right
[0, 1], # up
]
def a__ ( A__, A__, A__, A__, A__, ):
SCREAMING_SNAKE_CASE_ : List[Any] = [
[0 for col in range(len(grid[0] ) )] for row in range(len(A__ ) )
] # the reference grid
SCREAMING_SNAKE_CASE_ : List[Any] = 1
SCREAMING_SNAKE_CASE_ : Union[str, Any] = [
[0 for col in range(len(grid[0] ) )] for row in range(len(A__ ) )
] # the action grid
SCREAMING_SNAKE_CASE_ : Optional[int] = init[0]
SCREAMING_SNAKE_CASE_ : List[str] = init[1]
SCREAMING_SNAKE_CASE_ : Dict = 0
SCREAMING_SNAKE_CASE_ : List[Any] = g + heuristic[x][y] # cost from starting cell to destination cell
SCREAMING_SNAKE_CASE_ : List[str] = [[f, g, x, y]]
SCREAMING_SNAKE_CASE_ : Dict = False # flag that is set when search is complete
SCREAMING_SNAKE_CASE_ : Tuple = False # flag set if we can't find expand
while not found and not resign:
if len(A__ ) == 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_ : Any = cell.pop()
SCREAMING_SNAKE_CASE_ : Any = next_cell[2]
SCREAMING_SNAKE_CASE_ : str = next_cell[3]
SCREAMING_SNAKE_CASE_ : Tuple = next_cell[1]
if x == goal[0] and y == goal[1]:
SCREAMING_SNAKE_CASE_ : Dict = True
else:
for i in range(len(A__ ) ): # to try out different valid actions
SCREAMING_SNAKE_CASE_ : Optional[int] = x + DIRECTIONS[i][0]
SCREAMING_SNAKE_CASE_ : List[Any] = y + DIRECTIONS[i][1]
if xa >= 0 and xa < len(A__ ) and ya >= 0 and ya < len(grid[0] ):
if closed[xa][ya] == 0 and grid[xa][ya] == 0:
SCREAMING_SNAKE_CASE_ : List[Any] = g + cost
SCREAMING_SNAKE_CASE_ : List[Any] = ga + heuristic[xa][ya]
cell.append([fa, ga, xa, ya] )
SCREAMING_SNAKE_CASE_ : int = 1
SCREAMING_SNAKE_CASE_ : int = i
SCREAMING_SNAKE_CASE_ : Union[str, Any] = []
SCREAMING_SNAKE_CASE_ : List[str] = goal[0]
SCREAMING_SNAKE_CASE_ : Optional[Any] = goal[1]
invpath.append([x, y] ) # we get the reverse path from here
while x != init[0] or y != init[1]:
SCREAMING_SNAKE_CASE_ : Any = x - DIRECTIONS[action[x][y]][0]
SCREAMING_SNAKE_CASE_ : int = y - DIRECTIONS[action[x][y]][1]
SCREAMING_SNAKE_CASE_ : int = xa
SCREAMING_SNAKE_CASE_ : List[Any] = ya
invpath.append([x, y] )
SCREAMING_SNAKE_CASE_ : List[Any] = []
for i in range(len(A__ ) ):
path.append(invpath[len(A__ ) - 1 - i] )
return path, action
if __name__ == "__main__":
lowerCAmelCase__ : Union[str, Any] =[
[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__ : Any =[0, 0]
# all coordinates are given in format [y,x]
lowerCAmelCase__ : List[str] =[len(grid) - 1, len(grid[0]) - 1]
lowerCAmelCase__ : str =1
# the cost map which pushes the path closer to the goal
lowerCAmelCase__ : Union[str, Any] =[[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__ : List[str] =abs(i - goal[0]) + abs(j - goal[1])
if grid[i][j] == 1:
# added extra penalty in the heuristic map
lowerCAmelCase__ : List[str] =99
lowerCAmelCase__ ,lowerCAmelCase__ : Any =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])
| 101 | 1 |
import heapq as hq
import math
from collections.abc import Iterator
class SCREAMING_SNAKE_CASE_ :
"""simple docstring"""
def __init__( self , A ) -> Optional[int]:
'''simple docstring'''
__magic_name__ = str(id_ )
__magic_name__ = None
__magic_name__ = None
__magic_name__ = []
__magic_name__ = {} # {vertex:distance}
def __lt__( self , A ) -> Optional[Any]:
'''simple docstring'''
return self.key < other.key
def __repr__( self ) -> int:
'''simple docstring'''
return self.id
def __A ( self , A ) -> Union[str, Any]:
'''simple docstring'''
self.neighbors.append(A )
def __A ( self , A , A ) -> Optional[int]:
'''simple docstring'''
__magic_name__ = weight
def _SCREAMING_SNAKE_CASE ( snake_case_ : List[str] , snake_case_ : List[Any] , snake_case_ : int , snake_case_ : Optional[int] ):
# add the neighbors:
graph[a - 1].add_neighbor(graph[b - 1] )
graph[b - 1].add_neighbor(graph[a - 1] )
# add the edges:
graph[a - 1].add_edge(graph[b - 1] , snake_case_ )
graph[b - 1].add_edge(graph[a - 1] , snake_case_ )
def _SCREAMING_SNAKE_CASE ( snake_case_ : list , snake_case_ : Vertex ):
__magic_name__ = []
for u in graph:
__magic_name__ = math.inf
__magic_name__ = None
__magic_name__ = 0
__magic_name__ = graph[:]
while q:
__magic_name__ = min(snake_case_ )
q.remove(snake_case_ )
for v in u.neighbors:
if (v in q) and (u.edges[v.id] < v.key):
__magic_name__ = u
__magic_name__ = u.edges[v.id]
for i in range(1 , len(snake_case_ ) ):
a.append((int(graph[i].id ) + 1, int(graph[i].pi.id ) + 1) )
return a
def _SCREAMING_SNAKE_CASE ( snake_case_ : list , snake_case_ : Vertex ):
for u in graph:
__magic_name__ = math.inf
__magic_name__ = None
__magic_name__ = 0
__magic_name__ = list(snake_case_ )
hq.heapify(snake_case_ )
while h:
__magic_name__ = hq.heappop(snake_case_ )
for v in u.neighbors:
if (v in h) and (u.edges[v.id] < v.key):
__magic_name__ = u
__magic_name__ = u.edges[v.id]
hq.heapify(snake_case_ )
for i in range(1 , len(snake_case_ ) ):
yield (int(graph[i].id ) + 1, int(graph[i].pi.id ) + 1)
def _SCREAMING_SNAKE_CASE ( ):
pass
if __name__ == "__main__":
import doctest
doctest.testmod() | 714 |
def _SCREAMING_SNAKE_CASE ( snake_case_ : str ):
return " ".join(
''''''.join(word[::-1] ) if len(snake_case_ ) > 4 else word for word in sentence.split() )
if __name__ == "__main__":
import doctest
doctest.testmod()
print(reverse_long_words('Hey wollef sroirraw')) | 678 | 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 AddedToken, BatchEncoding, PreTrainedTokenizer
from ...utils import logging
snake_case : Optional[int] = logging.get_logger(__name__)
snake_case : str = '▁'
snake_case : Optional[int] = {'vocab_file': 'sentencepiece.bpe.model'}
snake_case : str = {
'vocab_file': {
'facebook/mbart-large-en-ro': (
'https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/sentencepiece.bpe.model'
),
'facebook/mbart-large-cc25': (
'https://huggingface.co/facebook/mbart-large-cc25/resolve/main/sentencepiece.bpe.model'
),
}
}
snake_case : Tuple = {
'facebook/mbart-large-en-ro': 1_024,
'facebook/mbart-large-cc25': 1_024,
}
# fmt: off
snake_case : List[str] = ['ar_AR', 'cs_CZ', 'de_DE', 'en_XX', 'es_XX', 'et_EE', 'fi_FI', 'fr_XX', 'gu_IN', 'hi_IN', 'it_IT', 'ja_XX', 'kk_KZ', 'ko_KR', 'lt_LT', 'lv_LV', 'my_MM', 'ne_NP', 'nl_XX', 'ro_RO', 'ru_RU', 'si_LK', 'tr_TR', 'vi_VN', 'zh_CN']
class lowerCamelCase__( snake_case_ ):
UpperCamelCase : List[str] = VOCAB_FILES_NAMES
UpperCamelCase : Any = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
UpperCamelCase : int = PRETRAINED_VOCAB_FILES_MAP
UpperCamelCase : Dict = ["input_ids", "attention_mask"]
UpperCamelCase : List[int] = []
UpperCamelCase : List[int] = []
def __init__( self , __UpperCAmelCase , __UpperCAmelCase="<s>" , __UpperCAmelCase="</s>" , __UpperCAmelCase="</s>" , __UpperCAmelCase="<s>" , __UpperCAmelCase="<unk>" , __UpperCAmelCase="<pad>" , __UpperCAmelCase="<mask>" , __UpperCAmelCase=None , __UpperCAmelCase=None , __UpperCAmelCase=None , __UpperCAmelCase = None , __UpperCAmelCase=None , **__UpperCAmelCase , ):
"""simple docstring"""
__lowercase = AddedToken(__UpperCAmelCase , lstrip=__UpperCAmelCase , rstrip=__UpperCAmelCase ) if isinstance(__UpperCAmelCase , __UpperCAmelCase ) else mask_token
__lowercase = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
bos_token=__UpperCAmelCase , eos_token=__UpperCAmelCase , unk_token=__UpperCAmelCase , sep_token=__UpperCAmelCase , cls_token=__UpperCAmelCase , pad_token=__UpperCAmelCase , mask_token=__UpperCAmelCase , tokenizer_file=__UpperCAmelCase , src_lang=__UpperCAmelCase , tgt_lang=__UpperCAmelCase , additional_special_tokens=__UpperCAmelCase , sp_model_kwargs=self.sp_model_kwargs , **__UpperCAmelCase , )
__lowercase = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(str(__UpperCAmelCase ) )
__lowercase = vocab_file
# Original fairseq vocab and spm vocab must be "aligned":
# Vocab | 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9
# -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
# fairseq | '<s>' | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's' | '▁de' | '-'
# spm | '<unk>' | '<s>' | '</s>' | ',' | '.' | '▁' | 's' | '▁de' | '-' | '▁a'
# Mimic fairseq token-to-id alignment for the first 4 token
__lowercase = {"""<s>""": 0, """<pad>""": 1, """</s>""": 2, """<unk>""": 3}
# The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
__lowercase = 1
__lowercase = len(self.sp_model )
__lowercase = {
code: self.sp_model_size + i + self.fairseq_offset for i, code in enumerate(__UpperCAmelCase )
}
__lowercase = {v: k for k, v in self.lang_code_to_id.items()}
__lowercase = len(self.sp_model ) + len(self.lang_code_to_id ) + self.fairseq_offset
self.fairseq_tokens_to_ids.update(self.lang_code_to_id )
__lowercase = {v: k for k, v in self.fairseq_tokens_to_ids.items()}
__lowercase = list(self.lang_code_to_id.keys() )
if additional_special_tokens is not None:
# Only add those special tokens if they are not already there.
self._additional_special_tokens.extend(
[t for t in additional_special_tokens if t not in self._additional_special_tokens] )
__lowercase = src_lang if src_lang is not None else """en_XX"""
__lowercase = self.lang_code_to_id[self._src_lang]
__lowercase = tgt_lang
self.set_src_lang_special_tokens(self._src_lang )
def __getstate__( self ):
"""simple docstring"""
__lowercase = self.__dict__.copy()
__lowercase = None
__lowercase = self.sp_model.serialized_model_proto()
return state
def __setstate__( self , __UpperCAmelCase ):
"""simple docstring"""
__lowercase = d
# for backward compatibility
if not hasattr(self , """sp_model_kwargs""" ):
__lowercase = {}
__lowercase = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.LoadFromSerializedProto(self.sp_model_proto )
@property
def __magic_name__ ( self ):
"""simple docstring"""
return len(self.sp_model ) + len(self.lang_code_to_id ) + self.fairseq_offset + 1 # Plus 1 for the mask token
@property
def __magic_name__ ( self ):
"""simple docstring"""
return self._src_lang
@src_lang.setter
def __magic_name__ ( self , __UpperCAmelCase ):
"""simple docstring"""
__lowercase = new_src_lang
self.set_src_lang_special_tokens(self._src_lang )
def __magic_name__ ( self , __UpperCAmelCase , __UpperCAmelCase = None , __UpperCAmelCase = False ):
"""simple docstring"""
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=__UpperCAmelCase , token_ids_a=__UpperCAmelCase , already_has_special_tokens=__UpperCAmelCase )
__lowercase = [1] * len(self.prefix_tokens )
__lowercase = [1] * len(self.suffix_tokens )
if token_ids_a is None:
return prefix_ones + ([0] * len(__UpperCAmelCase )) + suffix_ones
return prefix_ones + ([0] * len(__UpperCAmelCase )) + ([0] * len(__UpperCAmelCase )) + suffix_ones
def __magic_name__ ( self , __UpperCAmelCase , __UpperCAmelCase = None ):
"""simple docstring"""
if token_ids_a is None:
return self.prefix_tokens + token_ids_a + self.suffix_tokens
# We don't expect to process pairs, but leave the pair logic for API consistency
return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens
def __magic_name__ ( self , __UpperCAmelCase , __UpperCAmelCase = None ):
"""simple docstring"""
__lowercase = [self.sep_token_id]
__lowercase = [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]
def __magic_name__ ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , **__UpperCAmelCase ):
"""simple docstring"""
if src_lang is None or tgt_lang is None:
raise ValueError("""Translation requires a `src_lang` and a `tgt_lang` for this model""" )
__lowercase = src_lang
__lowercase = self(__UpperCAmelCase , add_special_tokens=__UpperCAmelCase , return_tensors=__UpperCAmelCase , **__UpperCAmelCase )
__lowercase = self.convert_tokens_to_ids(__UpperCAmelCase )
__lowercase = tgt_lang_id
return inputs
def __magic_name__ ( self ):
"""simple docstring"""
__lowercase = {self.convert_ids_to_tokens(__UpperCAmelCase ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def __magic_name__ ( self , __UpperCAmelCase ):
"""simple docstring"""
return self.sp_model.encode(__UpperCAmelCase , out_type=__UpperCAmelCase )
def __magic_name__ ( self , __UpperCAmelCase ):
"""simple docstring"""
if token in self.fairseq_tokens_to_ids:
return self.fairseq_tokens_to_ids[token]
__lowercase = self.sp_model.PieceToId(__UpperCAmelCase )
# Need to return unknown token if the SP model returned 0
return spm_id + self.fairseq_offset if spm_id else self.unk_token_id
def __magic_name__ ( self , __UpperCAmelCase ):
"""simple docstring"""
if index in self.fairseq_ids_to_tokens:
return self.fairseq_ids_to_tokens[index]
return self.sp_model.IdToPiece(index - self.fairseq_offset )
def __magic_name__ ( self , __UpperCAmelCase ):
"""simple docstring"""
__lowercase = """""".join(__UpperCAmelCase ).replace(__UpperCAmelCase , """ """ ).strip()
return out_string
def __magic_name__ ( self , __UpperCAmelCase , __UpperCAmelCase = None ):
"""simple docstring"""
if not os.path.isdir(__UpperCAmelCase ):
logger.error(F'''Vocabulary path ({save_directory}) should be a directory''' )
return
__lowercase = os.path.join(
__UpperCAmelCase , (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(__UpperCAmelCase ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file , __UpperCAmelCase )
elif not os.path.isfile(self.vocab_file ):
with open(__UpperCAmelCase , """wb""" ) as fi:
__lowercase = self.sp_model.serialized_model_proto()
fi.write(__UpperCAmelCase )
return (out_vocab_file,)
def __magic_name__ ( self , __UpperCAmelCase , __UpperCAmelCase = "en_XX" , __UpperCAmelCase = None , __UpperCAmelCase = "ro_RO" , **__UpperCAmelCase , ):
"""simple docstring"""
__lowercase = src_lang
__lowercase = tgt_lang
return super().prepare_seqaseq_batch(__UpperCAmelCase , __UpperCAmelCase , **__UpperCAmelCase )
def __magic_name__ ( self ):
"""simple docstring"""
return self.set_src_lang_special_tokens(self.src_lang )
def __magic_name__ ( self ):
"""simple docstring"""
return self.set_tgt_lang_special_tokens(self.tgt_lang )
def __magic_name__ ( self , __UpperCAmelCase ):
"""simple docstring"""
__lowercase = self.lang_code_to_id[src_lang]
__lowercase = []
__lowercase = [self.eos_token_id, self.cur_lang_code]
def __magic_name__ ( self , __UpperCAmelCase ):
"""simple docstring"""
__lowercase = self.lang_code_to_id[lang]
__lowercase = []
__lowercase = [self.eos_token_id, self.cur_lang_code]
| 566 |
'''simple docstring'''
import argparse
import json
import os
import numpy as np
import PIL
import requests
import tensorflow.keras.applications.efficientnet as efficientnet
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from tensorflow.keras.preprocessing import image
from transformers import (
EfficientNetConfig,
EfficientNetForImageClassification,
EfficientNetImageProcessor,
)
from transformers.utils import logging
logging.set_verbosity_info()
snake_case : Union[str, Any] = logging.get_logger(__name__)
snake_case : Any = {
'b0': efficientnet.EfficientNetBa,
'b1': efficientnet.EfficientNetBa,
'b2': efficientnet.EfficientNetBa,
'b3': efficientnet.EfficientNetBa,
'b4': efficientnet.EfficientNetBa,
'b5': efficientnet.EfficientNetBa,
'b6': efficientnet.EfficientNetBa,
'b7': efficientnet.EfficientNetBa,
}
snake_case : Union[str, Any] = {
'b0': {
'hidden_dim': 1_280,
'width_coef': 1.0,
'depth_coef': 1.0,
'image_size': 224,
'dropout_rate': 0.2,
'dw_padding': [],
},
'b1': {
'hidden_dim': 1_280,
'width_coef': 1.0,
'depth_coef': 1.1,
'image_size': 240,
'dropout_rate': 0.2,
'dw_padding': [16],
},
'b2': {
'hidden_dim': 1_408,
'width_coef': 1.1,
'depth_coef': 1.2,
'image_size': 260,
'dropout_rate': 0.3,
'dw_padding': [5, 8, 16],
},
'b3': {
'hidden_dim': 1_536,
'width_coef': 1.2,
'depth_coef': 1.4,
'image_size': 300,
'dropout_rate': 0.3,
'dw_padding': [5, 18],
},
'b4': {
'hidden_dim': 1_792,
'width_coef': 1.4,
'depth_coef': 1.8,
'image_size': 380,
'dropout_rate': 0.4,
'dw_padding': [6],
},
'b5': {
'hidden_dim': 2_048,
'width_coef': 1.6,
'depth_coef': 2.2,
'image_size': 456,
'dropout_rate': 0.4,
'dw_padding': [13, 27],
},
'b6': {
'hidden_dim': 2_304,
'width_coef': 1.8,
'depth_coef': 2.6,
'image_size': 528,
'dropout_rate': 0.5,
'dw_padding': [31],
},
'b7': {
'hidden_dim': 2_560,
'width_coef': 2.0,
'depth_coef': 3.1,
'image_size': 600,
'dropout_rate': 0.5,
'dw_padding': [18],
},
}
def lowercase__ ( __UpperCamelCase : str ):
'''simple docstring'''
__lowercase = EfficientNetConfig()
__lowercase = CONFIG_MAP[model_name]["""hidden_dim"""]
__lowercase = CONFIG_MAP[model_name]["""width_coef"""]
__lowercase = CONFIG_MAP[model_name]["""depth_coef"""]
__lowercase = CONFIG_MAP[model_name]["""image_size"""]
__lowercase = CONFIG_MAP[model_name]["""dropout_rate"""]
__lowercase = CONFIG_MAP[model_name]["""dw_padding"""]
__lowercase = """huggingface/label-files"""
__lowercase = """imagenet-1k-id2label.json"""
__lowercase = 1000
__lowercase = json.load(open(hf_hub_download(__UpperCamelCase , __UpperCamelCase , repo_type="""dataset""" ) , """r""" ) )
__lowercase = {int(__UpperCamelCase ): v for k, v in idalabel.items()}
__lowercase = idalabel
__lowercase = {v: k for k, v in idalabel.items()}
return config
def lowercase__ ( ):
'''simple docstring'''
__lowercase = """http://images.cocodataset.org/val2017/000000039769.jpg"""
__lowercase = Image.open(requests.get(__UpperCamelCase , stream=__UpperCamelCase ).raw )
return im
def lowercase__ ( __UpperCamelCase : Union[str, Any] ):
'''simple docstring'''
__lowercase = CONFIG_MAP[model_name]["""image_size"""]
__lowercase = EfficientNetImageProcessor(
size={"""height""": size, """width""": size} , image_mean=[0.485, 0.456, 0.406] , image_std=[0.4785_3944, 0.473_2864, 0.4743_4163] , do_center_crop=__UpperCamelCase , )
return preprocessor
def lowercase__ ( __UpperCamelCase : str ):
'''simple docstring'''
__lowercase = [v.split("""_""" )[0].split("""block""" )[1] for v in original_param_names if v.startswith("""block""" )]
__lowercase = sorted(set(__UpperCamelCase ) )
__lowercase = len(__UpperCamelCase )
__lowercase = {b: str(__UpperCamelCase ) for b, i in zip(__UpperCamelCase , range(__UpperCamelCase ) )}
__lowercase = []
rename_keys.append(("""stem_conv/kernel:0""", """embeddings.convolution.weight""") )
rename_keys.append(("""stem_bn/gamma:0""", """embeddings.batchnorm.weight""") )
rename_keys.append(("""stem_bn/beta:0""", """embeddings.batchnorm.bias""") )
rename_keys.append(("""stem_bn/moving_mean:0""", """embeddings.batchnorm.running_mean""") )
rename_keys.append(("""stem_bn/moving_variance:0""", """embeddings.batchnorm.running_var""") )
for b in block_names:
__lowercase = block_name_mapping[b]
rename_keys.append((F'''block{b}_expand_conv/kernel:0''', F'''encoder.blocks.{hf_b}.expansion.expand_conv.weight''') )
rename_keys.append((F'''block{b}_expand_bn/gamma:0''', F'''encoder.blocks.{hf_b}.expansion.expand_bn.weight''') )
rename_keys.append((F'''block{b}_expand_bn/beta:0''', F'''encoder.blocks.{hf_b}.expansion.expand_bn.bias''') )
rename_keys.append(
(F'''block{b}_expand_bn/moving_mean:0''', F'''encoder.blocks.{hf_b}.expansion.expand_bn.running_mean''') )
rename_keys.append(
(F'''block{b}_expand_bn/moving_variance:0''', F'''encoder.blocks.{hf_b}.expansion.expand_bn.running_var''') )
rename_keys.append(
(F'''block{b}_dwconv/depthwise_kernel:0''', F'''encoder.blocks.{hf_b}.depthwise_conv.depthwise_conv.weight''') )
rename_keys.append((F'''block{b}_bn/gamma:0''', F'''encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.weight''') )
rename_keys.append((F'''block{b}_bn/beta:0''', F'''encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.bias''') )
rename_keys.append(
(F'''block{b}_bn/moving_mean:0''', F'''encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.running_mean''') )
rename_keys.append(
(F'''block{b}_bn/moving_variance:0''', F'''encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.running_var''') )
rename_keys.append((F'''block{b}_se_reduce/kernel:0''', F'''encoder.blocks.{hf_b}.squeeze_excite.reduce.weight''') )
rename_keys.append((F'''block{b}_se_reduce/bias:0''', F'''encoder.blocks.{hf_b}.squeeze_excite.reduce.bias''') )
rename_keys.append((F'''block{b}_se_expand/kernel:0''', F'''encoder.blocks.{hf_b}.squeeze_excite.expand.weight''') )
rename_keys.append((F'''block{b}_se_expand/bias:0''', F'''encoder.blocks.{hf_b}.squeeze_excite.expand.bias''') )
rename_keys.append(
(F'''block{b}_project_conv/kernel:0''', F'''encoder.blocks.{hf_b}.projection.project_conv.weight''') )
rename_keys.append((F'''block{b}_project_bn/gamma:0''', F'''encoder.blocks.{hf_b}.projection.project_bn.weight''') )
rename_keys.append((F'''block{b}_project_bn/beta:0''', F'''encoder.blocks.{hf_b}.projection.project_bn.bias''') )
rename_keys.append(
(F'''block{b}_project_bn/moving_mean:0''', F'''encoder.blocks.{hf_b}.projection.project_bn.running_mean''') )
rename_keys.append(
(F'''block{b}_project_bn/moving_variance:0''', F'''encoder.blocks.{hf_b}.projection.project_bn.running_var''') )
rename_keys.append(("""top_conv/kernel:0""", """encoder.top_conv.weight""") )
rename_keys.append(("""top_bn/gamma:0""", """encoder.top_bn.weight""") )
rename_keys.append(("""top_bn/beta:0""", """encoder.top_bn.bias""") )
rename_keys.append(("""top_bn/moving_mean:0""", """encoder.top_bn.running_mean""") )
rename_keys.append(("""top_bn/moving_variance:0""", """encoder.top_bn.running_var""") )
__lowercase = {}
for item in rename_keys:
if item[0] in original_param_names:
__lowercase = """efficientnet.""" + item[1]
__lowercase = """classifier.weight"""
__lowercase = """classifier.bias"""
return key_mapping
def lowercase__ ( __UpperCamelCase : Tuple , __UpperCamelCase : Any , __UpperCamelCase : Optional[Any] ):
'''simple docstring'''
for key, value in tf_params.items():
if "normalization" in key:
continue
__lowercase = key_mapping[key]
if "_conv" in key and "kernel" in key:
__lowercase = torch.from_numpy(__UpperCamelCase ).permute(3 , 2 , 0 , 1 )
elif "depthwise_kernel" in key:
__lowercase = torch.from_numpy(__UpperCamelCase ).permute(2 , 3 , 0 , 1 )
elif "kernel" in key:
__lowercase = torch.from_numpy(np.transpose(__UpperCamelCase ) )
else:
__lowercase = torch.from_numpy(__UpperCamelCase )
# Replace HF parameters with original TF model parameters
assert hf_params[hf_key].shape == new_hf_value.shape
hf_params[hf_key].copy_(__UpperCamelCase )
@torch.no_grad()
def lowercase__ ( __UpperCamelCase : Optional[int] , __UpperCamelCase : List[Any] , __UpperCamelCase : List[str] , __UpperCamelCase : str ):
'''simple docstring'''
__lowercase = model_classes[model_name](
include_top=__UpperCamelCase , weights="""imagenet""" , input_tensor=__UpperCamelCase , input_shape=__UpperCamelCase , pooling=__UpperCamelCase , classes=1000 , classifier_activation="""softmax""" , )
__lowercase = original_model.trainable_variables
__lowercase = original_model.non_trainable_variables
__lowercase = {param.name: param.numpy() for param in tf_params}
for param in tf_non_train_params:
__lowercase = param.numpy()
__lowercase = list(tf_params.keys() )
# Load HuggingFace model
__lowercase = get_efficientnet_config(__UpperCamelCase )
__lowercase = EfficientNetForImageClassification(__UpperCamelCase ).eval()
__lowercase = hf_model.state_dict()
# Create src-to-dst parameter name mapping dictionary
print("""Converting parameters...""" )
__lowercase = rename_keys(__UpperCamelCase )
replace_params(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
# Initialize preprocessor and preprocess input image
__lowercase = convert_image_processor(__UpperCamelCase )
__lowercase = preprocessor(images=prepare_img() , return_tensors="""pt""" )
# HF model inference
hf_model.eval()
with torch.no_grad():
__lowercase = hf_model(**__UpperCamelCase )
__lowercase = outputs.logits.detach().numpy()
# Original model inference
__lowercase = False
__lowercase = CONFIG_MAP[model_name]["""image_size"""]
__lowercase = prepare_img().resize((image_size, image_size) , resample=PIL.Image.NEAREST )
__lowercase = image.img_to_array(__UpperCamelCase )
__lowercase = np.expand_dims(__UpperCamelCase , axis=0 )
__lowercase = original_model.predict(__UpperCamelCase )
# Check whether original and HF model outputs match -> np.allclose
assert np.allclose(__UpperCamelCase , __UpperCamelCase , atol=1E-3 ), "The predicted logits are not the same."
print("""Model outputs match!""" )
if save_model:
# Create folder to save model
if not os.path.isdir(__UpperCamelCase ):
os.mkdir(__UpperCamelCase )
# Save converted model and image processor
hf_model.save_pretrained(__UpperCamelCase )
preprocessor.save_pretrained(__UpperCamelCase )
if push_to_hub:
# Push model and image processor to hub
print(F'''Pushing converted {model_name} to the hub...''' )
__lowercase = F'''efficientnet-{model_name}'''
preprocessor.push_to_hub(__UpperCamelCase )
hf_model.push_to_hub(__UpperCamelCase )
if __name__ == "__main__":
snake_case : Optional[int] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--model_name',
default='b0',
type=str,
help='Version name of the EfficientNet model you want to convert, select from [b0, b1, b2, b3, b4, b5, b6, b7].',
)
parser.add_argument(
'--pytorch_dump_folder_path',
default='hf_model',
type=str,
help='Path to the output PyTorch model directory.',
)
parser.add_argument('--save_model', action='store_true', help='Save model to local')
parser.add_argument('--push_to_hub', action='store_true', help='Push model and image processor to the hub')
snake_case : Tuple = parser.parse_args()
convert_efficientnet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub)
| 566 | 1 |
import collections
import json
import os
import re
from typing import TYPE_CHECKING, List, Optional, Tuple
import numpy as np
from ...tokenization_utils_fast import PreTrainedTokenizer
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.conversational import Conversation
_lowerCAmelCase = logging.get_logger(__name__)
_lowerCAmelCase = {"""vocab_file""": """vocab.txt""", """emoji_file""": """emoji.json"""}
_lowerCAmelCase = {
"""vocab_file""": {
"""abeja/gpt-neox-japanese-2.7b""": """https://huggingface.co/abeja/gpt-neox-japanese-2.7b/resolve/main/vocab.txt""",
},
"""emoji_file""": {
"""abeja/gpt-neox-japanese-2.7b""": """https://huggingface.co/abeja/gpt-neox-japanese-2.7b/resolve/main/emoji.json""",
},
}
_lowerCAmelCase = {
"""abeja/gpt-neox-japanese-2.7b""": 2_0_4_8,
}
def lowerCamelCase__ ( _lowerCamelCase , _lowerCamelCase ):
'''simple docstring'''
with open(_lowerCamelCase , 'r' , encoding='utf-8' ) as f:
_lowerCAmelCase : Tuple = json.loads(f.read() )
_lowerCAmelCase : Tuple = collections.OrderedDict()
_lowerCAmelCase : List[str] = collections.OrderedDict()
_lowerCAmelCase : Union[str, Any] = collections.OrderedDict()
with open(_lowerCamelCase , 'r' , encoding='utf-8' ) as f:
_lowerCAmelCase : Tuple = f.readlines()
_lowerCAmelCase : int = [[t.rstrip('\n' )] if (t == ',' or ',' not in t) else t.rstrip('\n' ).split(',' ) for t in token]
for idx, b in enumerate(_lowerCamelCase ):
_lowerCAmelCase : Optional[int] = b
_lowerCAmelCase : Dict = idx
for wd in b:
_lowerCAmelCase : Dict = idx
return vocab, raw_vocab, ids_to_tokens, emoji
class __UpperCamelCase ( a__ ):
_UpperCAmelCase = VOCAB_FILES_NAMES
_UpperCAmelCase = PRETRAINED_VOCAB_FILES_MAP
_UpperCAmelCase = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
_UpperCAmelCase = ["input_ids", "attention_mask"]
def __init__( self ,_A ,_A ,_A="<|endoftext|>" ,_A="<|endoftext|>" ,_A="<|startoftext|>" ,_A="<|endoftext|>" ,_A=False ,**_A ,):
'''simple docstring'''
super().__init__(
unk_token=_A ,pad_token=_A ,bos_token=_A ,eos_token=_A ,do_clean_text=_A ,**_A ,)
if not os.path.isfile(_A ):
raise ValueError(
F"""Can't find a vocabulary file at path '{vocab_file}'. To load the vocabulary from a Google pretrained"""
' model use `tokenizer = GPTNeoXJapaneseokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`' )
if not os.path.isfile(_A ):
raise ValueError(
F"""Can't find a emoji file at path '{emoji_file}'. To load the emoji information from a Google"""
' pretrained model use `tokenizer = GPTNeoXJapaneseokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`' )
_lowerCAmelCase : Tuple = do_clean_text
_lowerCAmelCase : str = load_vocab_and_emoji(_A ,_A )
_lowerCAmelCase : List[Any] = SubWordJapaneseTokenizer(
vocab=self.vocab ,ids_to_tokens=self.ids_to_tokens ,emoji=self.emoji )
@property
def __lowerCamelCase ( self ):
'''simple docstring'''
return len(self.raw_vocab )
def __lowerCamelCase ( self ):
'''simple docstring'''
return dict(self.raw_vocab ,**self.added_tokens_encoder )
def __lowerCamelCase ( self ,_A ):
'''simple docstring'''
return self.subword_tokenizer.tokenize(_A ,clean=self.do_clean_text )
def __lowerCamelCase ( self ,_A ):
'''simple docstring'''
return self.vocab.get(_A ,self.vocab.get(self.unk_token ) )
def __lowerCamelCase ( self ,_A ):
'''simple docstring'''
return self.subword_tokenizer.convert_id_to_token(_A )
def __lowerCamelCase ( self ,_A ):
'''simple docstring'''
_lowerCAmelCase : List[str] = ''.join(_A ).strip()
return out_string
def __lowerCamelCase ( self ,_A ):
'''simple docstring'''
_lowerCAmelCase : int = []
for is_user, text in conversation.iter_texts():
input_ids.extend(self.encode(_A ,add_special_tokens=_A ) + [self.eos_token_id] )
if len(_A ) > self.model_max_length:
_lowerCAmelCase : Optional[int] = input_ids[-self.model_max_length :]
return input_ids
def __lowerCamelCase ( self ,_A ,_A = None ):
'''simple docstring'''
_lowerCAmelCase : Tuple = 0
if os.path.isdir(_A ):
_lowerCAmelCase : int = os.path.join(
_A ,(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'] )
_lowerCAmelCase : Optional[Any] = os.path.join(
_A ,(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['emoji_file'] )
else:
_lowerCAmelCase : Dict = (
(filename_prefix + '-' if filename_prefix else '') + save_directory + VOCAB_FILES_NAMES['vocab_file']
)
_lowerCAmelCase : List[Any] = (
(filename_prefix + '-' if filename_prefix else '') + save_directory + VOCAB_FILES_NAMES['emoji_file']
)
with open(_A ,'w' ,encoding='utf-8' ) as writer:
for token_index, token in self.ids_to_tokens.items():
if index != token_index:
logger.warning(
F"""Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive."""
' Please check that the vocabulary is not corrupted!' )
_lowerCAmelCase : Optional[int] = token_index
writer.write(','.join(_A ) + '\n' )
index += 1
with open(_A ,'w' ,encoding='utf-8' ) as writer:
json.dump(self.emoji ,_A )
return vocab_file, emoji_file
class __UpperCamelCase ( a__ ):
def __init__( self ,_A ,_A ,_A ):
'''simple docstring'''
_lowerCAmelCase : Any = vocab # same as swe
_lowerCAmelCase : int = ids_to_tokens # same as bpe
_lowerCAmelCase : List[Any] = emoji
_lowerCAmelCase : Tuple = np.max([len(_A ) for w in self.vocab.keys()] )
_lowerCAmelCase : Dict = re.compile(r'(https?|ftp)(:\/\/[-_\.!~*\'()a-zA-Z0-9;\/?:\@&=\+$,%#]+)' )
_lowerCAmelCase : int = re.compile(r'[A-Za-z0-9\._+]*@[\-_0-9A-Za-z]+(\.[A-Za-z]+)*' )
_lowerCAmelCase : Optional[int] = re.compile(r'[\(]{0,1}[0-9]{2,4}[\)\-\(]{0,1}[0-9]{2,4}[\)\-]{0,1}[0-9]{3,4}' )
_lowerCAmelCase : Optional[int] = re.compile(
r'([12]\d{3}[/\-年])*(0?[1-9]|1[0-2])[/\-月]((0?[1-9]|[12][0-9]|3[01])日?)*(\d{1,2}|:|\d{1,2}時|\d{1,2}分|\(日\)|\(月\)|\(火\)|\(水\)|\(木\)|\(金\)|\(土\)|㈰|㈪|㈫|㈬|㈭|㈮|㈯)*' )
_lowerCAmelCase : Optional[int] = re.compile(
r'(明治|大正|昭和|平成|令和|㍾|㍽|㍼|㍻|\u32ff)\d{1,2}年(0?[1-9]|1[0-2])月(0?[1-9]|[12][0-9]|3[01])日(\d{1,2}|:|\d{1,2}時|\d{1,2}分|\(日\)|\(月\)|\(火\)|\(水\)|\(木\)|\(金\)|\(土\)|㈰|㈪|㈫|㈬|㈭|㈮|㈯)*' )
_lowerCAmelCase : Dict = re.compile(
r'((0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*億)*((0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*万)*((0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*千)*(0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*(千円|万円|千万円|円|千ドル|万ドル|千万ドル|ドル|千ユーロ|万ユーロ|千万ユーロ|ユーロ)+(\(税込\)|\(税抜\)|\+tax)*' )
_lowerCAmelCase : str = '─━│┃┄┅┆┇┈┉┊┋┌┍┎┏┐┑┒┓└┕┖┗┘┙┚┛├┝┞┟┠┡┢┣┤┥┦┧┨┩┪┫┬┭┮┯┰┱┲┳┴┵┶┷┸┹┺┻┼┽┾┿╀╁╂╃╄╅╆╇╈╉╊╋╌╍╎╏═║╒╓╔╕╖╗╘╙╚╛╜╝╞╟╠╡╢╣╤╥╦╧╨╩╪╫╬╭╮╯╰╱╲╳╴╵╶╷╸╹╺╻╼╽╾╿'
_lowerCAmelCase : List[str] = '▀▁▂▃▄▅▆▇█▉▊▋▌▍▎▏▐░▒▓▔▕▖▗▘▙▚▛▜▝▞▟'
_lowerCAmelCase : Union[str, Any] = str.maketrans({k: '<BLOCK>' for k in keisen + blocks} )
def __len__( self ):
'''simple docstring'''
return len(self.ids_to_tokens )
def __lowerCamelCase ( self ,_A ):
'''simple docstring'''
_lowerCAmelCase : Optional[Any] = self.content_repattera.sub('<URL>' ,_A )
_lowerCAmelCase : int = self.content_repattera.sub('<EMAIL>' ,_A )
_lowerCAmelCase : List[Any] = self.content_repattera.sub('<TEL>' ,_A )
_lowerCAmelCase : str = self.content_repattera.sub('<DATE>' ,_A )
_lowerCAmelCase : Union[str, Any] = self.content_repattera.sub('<DATE>' ,_A )
_lowerCAmelCase : Any = self.content_repattera.sub('<PRICE>' ,_A )
_lowerCAmelCase : Union[str, Any] = content.translate(self.content_transa )
while "<BLOCK><BLOCK>" in content:
_lowerCAmelCase : int = content.replace('<BLOCK><BLOCK>' ,'<BLOCK>' )
return content
def __lowerCamelCase ( self ,_A ,_A=False ):
'''simple docstring'''
_lowerCAmelCase : Tuple = text.replace(' ' ,'<SP>' )
_lowerCAmelCase : Optional[Any] = text.replace(' ' ,'<SP>' )
_lowerCAmelCase : List[str] = text.replace('\r\n' ,'<BR>' )
_lowerCAmelCase : Dict = text.replace('\n' ,'<BR>' )
_lowerCAmelCase : int = text.replace('\r' ,'<BR>' )
_lowerCAmelCase : int = text.replace('\t' ,'<TAB>' )
_lowerCAmelCase : Optional[Any] = text.replace('—' ,'ー' )
_lowerCAmelCase : List[str] = text.replace('−' ,'ー' )
for k, v in self.emoji["emoji"].items():
if k in text:
_lowerCAmelCase : List[Any] = text.replace(_A ,_A )
if clean:
_lowerCAmelCase : Any = self.clean_text(_A )
def check_simbol(_A ):
_lowerCAmelCase : Tuple = x.encode()
if len(_A ) == 1 and len(_A ) == 2:
_lowerCAmelCase : Union[str, Any] = (int(e[0] ) << 8) + int(e[1] )
if (
(c >= 0xC2A1 and c <= 0xC2BF)
or (c >= 0xC780 and c <= 0xC783)
or (c >= 0xCAB9 and c <= 0xCBBF)
or (c >= 0xCC80 and c <= 0xCDA2)
):
return True
return False
def checkuae(_A ):
_lowerCAmelCase : Optional[Any] = x.encode()
if len(_A ) == 1 and len(_A ) == 3:
_lowerCAmelCase : Dict = (int(e[0] ) << 16) + (int(e[1] ) << 8) + int(e[2] )
if c >= 0xE2_8080 and c <= 0xE2_B07F:
return True
return False
_lowerCAmelCase : Tuple = 0
_lowerCAmelCase : int = []
while pos < len(_A ):
_lowerCAmelCase : Tuple = min(len(_A ) ,pos + self.maxlen + 1 ) if text[pos] == '<' else pos + 3
_lowerCAmelCase : int = [] # (token_id, token, pos)
for e in range(_A ,_A ,-1 ):
_lowerCAmelCase : Optional[int] = text[pos:e]
if wd in self.vocab:
if wd[0] == "<" and len(_A ) > 2:
_lowerCAmelCase : Optional[int] = [(self.vocab[wd], wd, e)]
break
else:
candidates.append((self.vocab[wd], wd, e) )
if len(_A ) > 0:
# the smallest token_id is adopted
_lowerCAmelCase : Union[str, Any] = sorted(_A ,key=lambda _A : x[0] )[0]
result.append(_A )
_lowerCAmelCase : List[str] = e
else:
_lowerCAmelCase : Dict = pos + 1
_lowerCAmelCase : Tuple = text[pos:end]
if check_simbol(_A ):
result.append('<KIGOU>' )
elif checkuae(_A ):
result.append('<U2000U2BFF>' )
else:
for i in wd.encode('utf-8' ):
result.append('<|byte%d|>' % i )
_lowerCAmelCase : str = end
return result
def __lowerCamelCase ( self ,_A ,_A="\n" ):
'''simple docstring'''
_lowerCAmelCase : Optional[Any] = []
_lowerCAmelCase : Optional[int] = []
_lowerCAmelCase : List[str] = self.ids_to_tokens[index][0]
if word[:6] == "<|byte" and word[-2:] == "|>":
byte_tokens.append(int(word[6:-2] ) )
else:
if len(_A ) > 0:
words.append(bytearray(_A ).decode('utf-8' ,errors='replace' ) )
_lowerCAmelCase : int = []
if word[:7] == "<|emoji" and word[-2:] == "|>":
words.append(self.emoji['emoji_inv'][word] )
elif word == "<SP>":
words.append(' ' )
elif word == "<BR>":
words.append(_A )
elif word == "<TAB>":
words.append('\t' )
elif word == "<BLOCK>":
words.append('▀' )
elif word == "<KIGOU>":
words.append('ǀ' )
elif word == "<U2000U2BFF>":
words.append('‖' )
else:
words.append(_A )
if len(_A ) > 0:
words.append(bytearray(_A ).decode('utf-8' ,errors='replace' ) )
_lowerCAmelCase : Tuple = ''.join(_A )
return text
| 710 |
"""simple docstring"""
from __future__ import annotations
def lowerCamelCase__ ( _lowerCamelCase ):
'''simple docstring'''
create_state_space_tree(_lowerCamelCase , [] , 0 , [0 for i in range(len(_lowerCamelCase ) )] )
def lowerCamelCase__ ( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , ):
'''simple docstring'''
if index == len(_lowerCamelCase ):
print(_lowerCamelCase )
return
for i in range(len(_lowerCamelCase ) ):
if not index_used[i]:
current_sequence.append(sequence[i] )
_lowerCAmelCase : List[str] = True
create_state_space_tree(_lowerCamelCase , _lowerCamelCase , index + 1 , _lowerCamelCase )
current_sequence.pop()
_lowerCAmelCase : int = False
_lowerCAmelCase = [3, 1, 2, 4]
generate_all_permutations(sequence)
_lowerCAmelCase = ["A", "B", "C"]
generate_all_permutations(sequence_a)
| 16 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
a :Dict = {
"configuration_convnext": ["CONVNEXT_PRETRAINED_CONFIG_ARCHIVE_MAP", "ConvNextConfig", "ConvNextOnnxConfig"]
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a :Tuple = ["ConvNextFeatureExtractor"]
a :Any = ["ConvNextImageProcessor"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a :Tuple = [
"CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST",
"ConvNextForImageClassification",
"ConvNextModel",
"ConvNextPreTrainedModel",
"ConvNextBackbone",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a :Optional[int] = [
"TFConvNextForImageClassification",
"TFConvNextModel",
"TFConvNextPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_convnext import CONVNEXT_PRETRAINED_CONFIG_ARCHIVE_MAP, ConvNextConfig, ConvNextOnnxConfig
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_convnext import ConvNextFeatureExtractor
from .image_processing_convnext import ConvNextImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_convnext import (
CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
ConvNextBackbone,
ConvNextForImageClassification,
ConvNextModel,
ConvNextPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_convnext import TFConvNextForImageClassification, TFConvNextModel, TFConvNextPreTrainedModel
else:
import sys
a :Union[str, Any] = _LazyModule(__name__, globals()["__file__"], _import_structure)
| 680 |
def UpperCamelCase ( _A : int )-> int:
"""simple docstring"""
if not isinstance(_A , _A ):
raise ValueError("multiplicative_persistence() only accepts integral values" )
if num < 0:
raise ValueError("multiplicative_persistence() does not accept negative values" )
A__ = 0
A__ = str(_A )
while len(_A ) != 1:
A__ = [int(_A ) for i in num_string]
A__ = 1
for i in range(0 , len(_A ) ):
total *= numbers[i]
A__ = str(_A )
steps += 1
return steps
def UpperCamelCase ( _A : int )-> int:
"""simple docstring"""
if not isinstance(_A , _A ):
raise ValueError("additive_persistence() only accepts integral values" )
if num < 0:
raise ValueError("additive_persistence() does not accept negative values" )
A__ = 0
A__ = str(_A )
while len(_A ) != 1:
A__ = [int(_A ) for i in num_string]
A__ = 0
for i in range(0 , len(_A ) ):
total += numbers[i]
A__ = str(_A )
steps += 1
return steps
if __name__ == "__main__":
import doctest
doctest.testmod()
| 491 | 0 |
import re
from filelock import FileLock
try:
import nltk
UpperCamelCase_ : str = True
except (ImportError, ModuleNotFoundError):
UpperCamelCase_ : Tuple = False
if NLTK_AVAILABLE:
with FileLock('''.lock''') as lock:
nltk.download('''punkt''', quiet=True)
def lowerCamelCase_ ( _a : str ):
'''simple docstring'''
re.sub("""<n>""" , """""" , a_ ) # remove pegasus newline char
assert NLTK_AVAILABLE, "nltk must be installed to separate newlines between sentences. (pip install nltk)"
return "\n".join(nltk.sent_tokenize(a_ ) )
| 713 |
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
UpperCamelCase_ = logging.get_logger(__name__)
UpperCamelCase_ = {'''vocab_file''': '''spm_char.model'''}
UpperCamelCase_ = {
'''vocab_file''': {
'''microsoft/speecht5_asr''': '''https://huggingface.co/microsoft/speecht5_asr/resolve/main/spm_char.model''',
'''microsoft/speecht5_tts''': '''https://huggingface.co/microsoft/speecht5_tts/resolve/main/spm_char.model''',
'''microsoft/speecht5_vc''': '''https://huggingface.co/microsoft/speecht5_vc/resolve/main/spm_char.model''',
}
}
UpperCamelCase_ = {
'''microsoft/speecht5_asr''': 1024,
'''microsoft/speecht5_tts''': 1024,
'''microsoft/speecht5_vc''': 1024,
}
class _snake_case ( __snake_case ):
'''simple docstring'''
A__ : Tuple = VOCAB_FILES_NAMES
A__ : List[Any] = PRETRAINED_VOCAB_FILES_MAP
A__ : int = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
A__ : Union[str, Any] = ["input_ids", "attention_mask"]
def __init__( self: List[str] ,lowerCamelCase_: List[str] ,lowerCamelCase_: Dict="<s>" ,lowerCamelCase_: str="</s>" ,lowerCamelCase_: Tuple="<unk>" ,lowerCamelCase_: str="<pad>" ,lowerCamelCase_: Optional[Dict[str, Any]] = None ,**lowerCamelCase_: List[str] ,) -> None:
UpperCAmelCase_ : Union[str, Any] = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
bos_token=lowerCamelCase_ ,eos_token=lowerCamelCase_ ,unk_token=lowerCamelCase_ ,pad_token=lowerCamelCase_ ,sp_model_kwargs=self.sp_model_kwargs ,**lowerCamelCase_ ,)
UpperCAmelCase_ : List[Any] = vocab_file
UpperCAmelCase_ : Tuple = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(lowerCamelCase_ )
@property
def A__ ( self: List[str] ) -> List[Any]:
return self.sp_model.get_piece_size()
def A__ ( self: List[Any] ) -> Dict:
UpperCAmelCase_ : List[Any] = {self.convert_ids_to_tokens(lowerCamelCase_ ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def __getstate__( self: Union[str, Any] ) -> int:
UpperCAmelCase_ : Dict = self.__dict__.copy()
UpperCAmelCase_ : Union[str, Any] = None
return state
def __setstate__( self: List[str] ,lowerCamelCase_: Any ) -> Any:
UpperCAmelCase_ : str = d
# for backward compatibility
if not hasattr(self ,"""sp_model_kwargs""" ):
UpperCAmelCase_ : Tuple = {}
UpperCAmelCase_ : int = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.vocab_file )
def A__ ( self: List[str] ,lowerCamelCase_: str ) -> List[str]:
return self.sp_model.encode(lowerCamelCase_ ,out_type=lowerCamelCase_ )
def A__ ( self: str ,lowerCamelCase_: List[str] ) -> Optional[Any]:
return self.sp_model.piece_to_id(lowerCamelCase_ )
def A__ ( self: Tuple ,lowerCamelCase_: int ) -> Optional[int]:
UpperCAmelCase_ : int = self.sp_model.IdToPiece(lowerCamelCase_ )
return token
def A__ ( self: Any ,lowerCamelCase_: Union[str, Any] ) -> int:
UpperCAmelCase_ : Tuple = []
UpperCAmelCase_ : Any = """"""
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(lowerCamelCase_ ) + token
UpperCAmelCase_ : Any = []
else:
current_sub_tokens.append(lowerCamelCase_ )
out_string += self.sp_model.decode(lowerCamelCase_ )
return out_string.strip()
def A__ ( self: List[Any] ,lowerCamelCase_: str ,lowerCamelCase_: Dict=None ) -> List[int]:
if token_ids_a is None:
return token_ids_a + [self.eos_token_id]
# We don't expect to process pairs, but leave the pair logic for API consistency
return token_ids_a + token_ids_a + [self.eos_token_id]
def A__ ( self: int ,lowerCamelCase_: List[int] ,lowerCamelCase_: Optional[List[int]] = None ,lowerCamelCase_: bool = False ) -> List[int]:
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=lowerCamelCase_ ,token_ids_a=lowerCamelCase_ ,already_has_special_tokens=lowerCamelCase_ )
UpperCAmelCase_ : Optional[Any] = [1]
if token_ids_a is None:
return ([0] * len(lowerCamelCase_ )) + suffix_ones
return ([0] * len(lowerCamelCase_ )) + ([0] * len(lowerCamelCase_ )) + suffix_ones
def A__ ( self: Tuple ,lowerCamelCase_: str ,lowerCamelCase_: Optional[str] = None ) -> Tuple[str]:
if not os.path.isdir(lowerCamelCase_ ):
logger.error(F'''Vocabulary path ({save_directory}) should be a directory''' )
return
UpperCAmelCase_ : Optional[Any] = os.path.join(
lowerCamelCase_ ,(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(lowerCamelCase_ ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file ,lowerCamelCase_ )
elif not os.path.isfile(self.vocab_file ):
with open(lowerCamelCase_ ,"""wb""" ) as fi:
UpperCAmelCase_ : List[Any] = self.sp_model.serialized_model_proto()
fi.write(lowerCamelCase_ )
return (out_vocab_file,)
| 322 | 0 |
from scipy.stats import spearmanr
import datasets
__lowercase : str ="""
The Spearman rank-order correlation coefficient is a measure of the
relationship between two datasets. Like other correlation coefficients,
this one varies between -1 and +1 with 0 implying no correlation.
Positive correlations imply that as data in dataset x increases, so
does data in dataset y. Negative correlations imply that as x increases,
y decreases. Correlations of -1 or +1 imply an exact monotonic relationship.
Unlike the Pearson correlation, the Spearman correlation does not
assume that both datasets are normally distributed.
The p-value roughly indicates the probability of an uncorrelated system
producing datasets that have a Spearman correlation at least as extreme
as the one computed from these datasets. The p-values are not entirely
reliable but are probably reasonable for datasets larger than 500 or so.
"""
__lowercase : Optional[Any] ="""
Args:
predictions (`List[float]`): Predicted labels, as returned by a model.
references (`List[float]`): Ground truth labels.
return_pvalue (`bool`): If `True`, returns the p-value. If `False`, returns
only the spearmanr score. Defaults to `False`.
Returns:
spearmanr (`float`): Spearman correlation coefficient.
p-value (`float`): p-value. **Note**: is only returned if `return_pvalue=True` is input.
Examples:
Example 1:
>>> spearmanr_metric = datasets.load_metric(\"spearmanr\")
>>> results = spearmanr_metric.compute(references=[1, 2, 3, 4, 5], predictions=[10, 9, 2.5, 6, 4])
>>> print(results)
{'spearmanr': -0.7}
Example 2:
>>> spearmanr_metric = datasets.load_metric(\"spearmanr\")
>>> results = spearmanr_metric.compute(references=[1, 2, 3, 4, 5],
... predictions=[10, 9, 2.5, 6, 4],
... return_pvalue=True)
>>> print(results['spearmanr'])
-0.7
>>> print(round(results['spearmanr_pvalue'], 2))
0.19
"""
__lowercase : Dict =R"""\
@book{kokoska2000crc,
title={CRC standard probability and statistics tables and formulae},
author={Kokoska, Stephen and Zwillinger, Daniel},
year={2000},
publisher={Crc Press}
}
@article{2020SciPy-NMeth,
author = {Virtanen, Pauli and Gommers, Ralf and Oliphant, Travis E. and
Haberland, Matt and Reddy, Tyler and Cournapeau, David and
Burovski, Evgeni and Peterson, Pearu and Weckesser, Warren and
Bright, Jonathan and {van der Walt}, St{\'e}fan J. and
Brett, Matthew and Wilson, Joshua and Millman, K. Jarrod and
Mayorov, Nikolay and Nelson, Andrew R. J. and Jones, Eric and
Kern, Robert and Larson, Eric and Carey, C J and
Polat, {\.I}lhan and Feng, Yu and Moore, Eric W. and
{VanderPlas}, Jake and Laxalde, Denis and Perktold, Josef and
Cimrman, Robert and Henriksen, Ian and Quintero, E. A. and
Harris, Charles R. and Archibald, Anne M. and
Ribeiro, Ant{\^o}nio H. and Pedregosa, Fabian and
{van Mulbregt}, Paul and {SciPy 1.0 Contributors}},
title = {{{SciPy} 1.0: Fundamental Algorithms for Scientific
Computing in Python}},
journal = {Nature Methods},
year = {2020},
volume = {17},
pages = {261--272},
adsurl = {https://rdcu.be/b08Wh},
doi = {10.1038/s41592-019-0686-2},
}
"""
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class A ( datasets.Metric ):
def lowerCAmelCase__ ( self: Union[str, Any] ) -> Optional[Any]:
'''simple docstring'''
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
"predictions": datasets.Value("float" ),
"references": datasets.Value("float" ),
} ) , reference_urls=["https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.spearmanr.html"] , )
def lowerCAmelCase__ ( self: Union[str, Any] , _lowerCAmelCase: Optional[Any] , _lowerCAmelCase: Any , _lowerCAmelCase: int=False ) -> str:
'''simple docstring'''
UpperCAmelCase_ =spearmanr(_lowerCAmelCase , _lowerCAmelCase )
if return_pvalue:
return {"spearmanr": results[0], "spearmanr_pvalue": results[1]}
else:
return {"spearmanr": results[0]}
| 54 |
import logging
from transformers import PretrainedConfig
_a = logging.getLogger(__name__)
_a = {
"bertabs-finetuned-cnndm": "https://huggingface.co/remi/bertabs-finetuned-cnndm-extractive-abstractive-summarization/resolve/main/config.json",
}
class __A ( lowerCAmelCase ):
'''simple docstring'''
lowerCAmelCase_ = """bertabs"""
def __init__( self , __lowerCAmelCase=3_0_5_2_2 , __lowerCAmelCase=5_1_2 , __lowerCAmelCase=6 , __lowerCAmelCase=5_1_2 , __lowerCAmelCase=8 , __lowerCAmelCase=5_1_2 , __lowerCAmelCase=0.2 , __lowerCAmelCase=6 , __lowerCAmelCase=7_6_8 , __lowerCAmelCase=8 , __lowerCAmelCase=2_0_4_8 , __lowerCAmelCase=0.2 , **__lowerCAmelCase , ):
'''simple docstring'''
super().__init__(**__lowerCAmelCase )
lowerCamelCase__ = vocab_size
lowerCamelCase__ = max_pos
lowerCamelCase__ = enc_layers
lowerCamelCase__ = enc_hidden_size
lowerCamelCase__ = enc_heads
lowerCamelCase__ = enc_ff_size
lowerCamelCase__ = enc_dropout
lowerCamelCase__ = dec_layers
lowerCamelCase__ = dec_hidden_size
lowerCamelCase__ = dec_heads
lowerCamelCase__ = dec_ff_size
lowerCamelCase__ = dec_dropout
| 481 | 0 |
'''simple docstring'''
import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import List
import timm
import torch
import torch.nn as nn
from huggingface_hub import hf_hub_download
from torch import Tensor
from transformers import AutoImageProcessor, ResNetConfig, ResNetForImageClassification
from transformers.utils import logging
logging.set_verbosity_info()
_snake_case : Dict = logging.get_logger()
@dataclass
class lowerCAmelCase :
a : nn.Module
a : List[nn.Module] = field(default_factory=__UpperCAmelCase )
a : list = field(default_factory=__UpperCAmelCase )
def lowercase ( self , UpperCamelCase , UpperCamelCase , UpperCamelCase ):
_SCREAMING_SNAKE_CASE = len(list(m.modules() ) ) == 1 or isinstance(UpperCamelCase , nn.Convad ) or isinstance(UpperCamelCase , nn.BatchNormad )
if has_not_submodules:
self.traced.append(UpperCamelCase )
def __call__( self , UpperCamelCase ):
for m in self.module.modules():
self.handles.append(m.register_forward_hook(self._forward_hook ) )
self.module(UpperCamelCase )
[x.remove() for x in self.handles]
return self
@property
def lowercase ( self ):
# check the len of the state_dict keys to see if we have learnable params
return list(filter(lambda UpperCamelCase : len(list(x.state_dict().keys() ) ) > 0 , self.traced ) )
@dataclass
class lowerCAmelCase :
a : nn.Module
a : nn.Module
a : int = 0
a : List = field(default_factory=__UpperCAmelCase )
a : List = field(default_factory=__UpperCAmelCase )
def __call__( self , UpperCamelCase ):
_SCREAMING_SNAKE_CASE = Tracker(self.dest )(UpperCamelCase ).parametrized
_SCREAMING_SNAKE_CASE = Tracker(self.src )(UpperCamelCase ).parametrized
_SCREAMING_SNAKE_CASE = list(filter(lambda UpperCamelCase : type(UpperCamelCase ) not in self.src_skip , UpperCamelCase ) )
_SCREAMING_SNAKE_CASE = list(filter(lambda UpperCamelCase : type(UpperCamelCase ) not in self.dest_skip , UpperCamelCase ) )
if len(UpperCamelCase ) != len(UpperCamelCase ):
raise Exception(
F'Numbers of operations are different. Source module has {len(UpperCamelCase )} operations while'
F' destination module has {len(UpperCamelCase )}.' )
for dest_m, src_m in zip(UpperCamelCase , UpperCamelCase ):
dest_m.load_state_dict(src_m.state_dict() )
if self.verbose == 1:
print(F'Transfered from={src_m} to={dest_m}' )
def _a ( _SCREAMING_SNAKE_CASE : str , _SCREAMING_SNAKE_CASE : ResNetConfig , _SCREAMING_SNAKE_CASE : Path , _SCREAMING_SNAKE_CASE : bool = True ):
print(F'Converting {name}...' )
with torch.no_grad():
_SCREAMING_SNAKE_CASE = timm.create_model(_SCREAMING_SNAKE_CASE , pretrained=_SCREAMING_SNAKE_CASE ).eval()
_SCREAMING_SNAKE_CASE = ResNetForImageClassification(_SCREAMING_SNAKE_CASE ).eval()
_SCREAMING_SNAKE_CASE = ModuleTransfer(src=_SCREAMING_SNAKE_CASE , dest=_SCREAMING_SNAKE_CASE )
_SCREAMING_SNAKE_CASE = torch.randn((1, 3, 224, 224) )
module_transfer(_SCREAMING_SNAKE_CASE )
assert torch.allclose(from_model(_SCREAMING_SNAKE_CASE ) , our_model(_SCREAMING_SNAKE_CASE ).logits ), "The model logits don't match the original one."
_SCREAMING_SNAKE_CASE = F'resnet{"-".join(name.split("resnet" ) )}'
print(_SCREAMING_SNAKE_CASE )
if push_to_hub:
our_model.push_to_hub(
repo_path_or_name=save_directory / checkpoint_name , commit_message="Add model" , use_temp_dir=_SCREAMING_SNAKE_CASE , )
# we can use the convnext one
_SCREAMING_SNAKE_CASE = AutoImageProcessor.from_pretrained("facebook/convnext-base-224-22k-1k" )
image_processor.push_to_hub(
repo_path_or_name=save_directory / checkpoint_name , commit_message="Add image processor" , use_temp_dir=_SCREAMING_SNAKE_CASE , )
print(F'Pushed {checkpoint_name}' )
def _a ( _SCREAMING_SNAKE_CASE : Path , _SCREAMING_SNAKE_CASE : str = None , _SCREAMING_SNAKE_CASE : bool = True ):
_SCREAMING_SNAKE_CASE = "imagenet-1k-id2label.json"
_SCREAMING_SNAKE_CASE = 1000
_SCREAMING_SNAKE_CASE = (1, num_labels)
_SCREAMING_SNAKE_CASE = "huggingface/label-files"
_SCREAMING_SNAKE_CASE = num_labels
_SCREAMING_SNAKE_CASE = json.load(open(hf_hub_download(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , repo_type="dataset" ) , "r" ) )
_SCREAMING_SNAKE_CASE = {int(_SCREAMING_SNAKE_CASE ): v for k, v in idalabel.items()}
_SCREAMING_SNAKE_CASE = idalabel
_SCREAMING_SNAKE_CASE = {v: k for k, v in idalabel.items()}
_SCREAMING_SNAKE_CASE = partial(_SCREAMING_SNAKE_CASE , num_labels=_SCREAMING_SNAKE_CASE , idalabel=_SCREAMING_SNAKE_CASE , labelaid=_SCREAMING_SNAKE_CASE )
_SCREAMING_SNAKE_CASE = {
"resnet18": ImageNetPreTrainedConfig(
depths=[2, 2, 2, 2] , hidden_sizes=[64, 128, 256, 512] , layer_type="basic" ),
"resnet26": ImageNetPreTrainedConfig(
depths=[2, 2, 2, 2] , hidden_sizes=[256, 512, 1024, 2048] , layer_type="bottleneck" ),
"resnet34": ImageNetPreTrainedConfig(
depths=[3, 4, 6, 3] , hidden_sizes=[64, 128, 256, 512] , layer_type="basic" ),
"resnet50": ImageNetPreTrainedConfig(
depths=[3, 4, 6, 3] , hidden_sizes=[256, 512, 1024, 2048] , layer_type="bottleneck" ),
"resnet101": ImageNetPreTrainedConfig(
depths=[3, 4, 23, 3] , hidden_sizes=[256, 512, 1024, 2048] , layer_type="bottleneck" ),
"resnet152": ImageNetPreTrainedConfig(
depths=[3, 8, 36, 3] , hidden_sizes=[256, 512, 1024, 2048] , layer_type="bottleneck" ),
}
if model_name:
convert_weight_and_push(_SCREAMING_SNAKE_CASE , names_to_config[model_name] , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
else:
for model_name, config in names_to_config.items():
convert_weight_and_push(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
return config, expected_shape
if __name__ == "__main__":
_snake_case : Optional[int] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--model_name""",
default=None,
type=str,
help=(
"""The name of the model you wish to convert, it must be one of the supported resnet* architecture,"""
""" currently: resnet18,26,34,50,101,152. If `None`, all of them will the converted."""
),
)
parser.add_argument(
"""--pytorch_dump_folder_path""",
default=None,
type=Path,
required=True,
help="""Path to the output PyTorch model directory.""",
)
parser.add_argument(
"""--push_to_hub""",
default=True,
type=bool,
required=False,
help="""If True, push model and image processor to the hub.""",
)
_snake_case : Optional[int] = parser.parse_args()
_snake_case : Path = args.pytorch_dump_folder_path
pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub) | 493 |
'''simple docstring'''
import os
import random
import sys
from . import cryptomath_module as cryptoMath # noqa: N812
from . import rabin_miller as rabinMiller # noqa: N812
def _a ( ):
print("Making key files..." )
make_key_files("rsa" , 1024 )
print("Key files generation successful." )
def _a ( _SCREAMING_SNAKE_CASE : int ):
print("Generating prime p..." )
_SCREAMING_SNAKE_CASE = rabinMiller.generate_large_prime(_SCREAMING_SNAKE_CASE )
print("Generating prime q..." )
_SCREAMING_SNAKE_CASE = rabinMiller.generate_large_prime(_SCREAMING_SNAKE_CASE )
_SCREAMING_SNAKE_CASE = p * q
print("Generating e that is relatively prime to (p - 1) * (q - 1)..." )
while True:
_SCREAMING_SNAKE_CASE = random.randrange(2 ** (key_size - 1) , 2 ** (key_size) )
if cryptoMath.gcd(_SCREAMING_SNAKE_CASE , (p - 1) * (q - 1) ) == 1:
break
print("Calculating d that is mod inverse of e..." )
_SCREAMING_SNAKE_CASE = cryptoMath.find_mod_inverse(_SCREAMING_SNAKE_CASE , (p - 1) * (q - 1) )
_SCREAMING_SNAKE_CASE = (n, e)
_SCREAMING_SNAKE_CASE = (n, d)
return (public_key, private_key)
def _a ( _SCREAMING_SNAKE_CASE : str , _SCREAMING_SNAKE_CASE : int ):
if os.path.exists(F'{name}_pubkey.txt' ) or os.path.exists(F'{name}_privkey.txt' ):
print("\nWARNING:" )
print(
F'"{name}_pubkey.txt" or "{name}_privkey.txt" already exists. \n'
"Use a different name or delete these files and re-run this program." )
sys.exit()
_SCREAMING_SNAKE_CASE, _SCREAMING_SNAKE_CASE = generate_key(_SCREAMING_SNAKE_CASE )
print(F'\nWriting public key to file {name}_pubkey.txt...' )
with open(F'{name}_pubkey.txt' , "w" ) as out_file:
out_file.write(F'{key_size},{public_key[0]},{public_key[1]}' )
print(F'Writing private key to file {name}_privkey.txt...' )
with open(F'{name}_privkey.txt' , "w" ) as out_file:
out_file.write(F'{key_size},{private_key[0]},{private_key[1]}' )
if __name__ == "__main__":
main() | 493 | 1 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, PNDMScheduler, StableDiffusionInpaintPipeline, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow
from ..pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin
enable_full_determinism()
class lowerCamelCase_ ( __a , __a , __a , unittest.TestCase ):
lowerCAmelCase__ = StableDiffusionInpaintPipeline
lowerCAmelCase__ = TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
lowerCAmelCase__ = TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
lowerCAmelCase__ = frozenset(
[] ) # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
lowerCAmelCase__ = frozenset([] )
def lowercase_ ( self : Optional[int] ):
'''simple docstring'''
torch.manual_seed(0 )
UpperCAmelCase__ : List[str] = UNetaDConditionModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=9 , out_channels=4 , down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''') , up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''') , cross_attention_dim=32 , attention_head_dim=(2, 4) , use_linear_projection=_A , )
UpperCAmelCase__ : int = PNDMScheduler(skip_prk_steps=_A )
torch.manual_seed(0 )
UpperCAmelCase__ : str = 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 , sample_size=128 , )
torch.manual_seed(0 )
UpperCAmelCase__ : Optional[Any] = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=32 , intermediate_size=37 , layer_norm_eps=1e-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1_000 , hidden_act='''gelu''' , projection_dim=512 , )
UpperCAmelCase__ : Union[str, Any] = CLIPTextModel(_A )
UpperCAmelCase__ : List[Any] = CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''' )
UpperCAmelCase__ : str = {
'''unet''': unet,
'''scheduler''': scheduler,
'''vae''': vae,
'''text_encoder''': text_encoder,
'''tokenizer''': tokenizer,
'''safety_checker''': None,
'''feature_extractor''': None,
}
return components
def lowercase_ ( self : str , _A : Dict , _A : Any=0 ):
'''simple docstring'''
UpperCAmelCase__ : int = floats_tensor((1, 3, 32, 32) , rng=random.Random(_A ) ).to(_A )
UpperCAmelCase__ : Dict = image.cpu().permute(0 , 2 , 3 , 1 )[0]
UpperCAmelCase__ : Optional[int] = Image.fromarray(np.uinta(_A ) ).convert('''RGB''' ).resize((64, 64) )
UpperCAmelCase__ : int = Image.fromarray(np.uinta(image + 4 ) ).convert('''RGB''' ).resize((64, 64) )
if str(_A ).startswith('''mps''' ):
UpperCAmelCase__ : List[Any] = torch.manual_seed(_A )
else:
UpperCAmelCase__ : str = torch.Generator(device=_A ).manual_seed(_A )
UpperCAmelCase__ : Optional[int] = {
'''prompt''': '''A painting of a squirrel eating a burger''',
'''image''': init_image,
'''mask_image''': mask_image,
'''generator''': generator,
'''num_inference_steps''': 2,
'''guidance_scale''': 6.0,
'''output_type''': '''numpy''',
}
return inputs
def lowercase_ ( self : Union[str, Any] ):
'''simple docstring'''
UpperCAmelCase__ : Dict = '''cpu''' # ensure determinism for the device-dependent torch.Generator
UpperCAmelCase__ : Tuple = self.get_dummy_components()
UpperCAmelCase__ : str = StableDiffusionInpaintPipeline(**_A )
UpperCAmelCase__ : List[str] = sd_pipe.to(_A )
sd_pipe.set_progress_bar_config(disable=_A )
UpperCAmelCase__ : Dict = self.get_dummy_inputs(_A )
UpperCAmelCase__ : Any = sd_pipe(**_A ).images
UpperCAmelCase__ : Dict = image[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
UpperCAmelCase__ : int = np.array([0.4_7_2_7, 0.5_7_3_5, 0.3_9_4_1, 0.5_4_4_6, 0.5_9_2_6, 0.4_3_9_4, 0.5_0_6_2, 0.4_6_5_4, 0.4_4_7_6] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
def lowercase_ ( self : Tuple ):
'''simple docstring'''
super().test_inference_batch_single_identical(expected_max_diff=3e-3 )
@slow
@require_torch_gpu
class lowerCamelCase_ ( unittest.TestCase ):
def lowercase_ ( self : List[Any] ):
'''simple docstring'''
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def lowercase_ ( self : Optional[int] ):
'''simple docstring'''
UpperCAmelCase__ : Dict = load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
'''/sd2-inpaint/init_image.png''' )
UpperCAmelCase__ : Any = load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png''' )
UpperCAmelCase__ : List[Any] = load_numpy(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint'''
'''/yellow_cat_sitting_on_a_park_bench.npy''' )
UpperCAmelCase__ : Dict = '''stabilityai/stable-diffusion-2-inpainting'''
UpperCAmelCase__ : Optional[int] = StableDiffusionInpaintPipeline.from_pretrained(_A , safety_checker=_A )
pipe.to(_A )
pipe.set_progress_bar_config(disable=_A )
pipe.enable_attention_slicing()
UpperCAmelCase__ : Tuple = '''Face of a yellow cat, high resolution, sitting on a park bench'''
UpperCAmelCase__ : str = torch.manual_seed(0 )
UpperCAmelCase__ : str = pipe(
prompt=_A , image=_A , mask_image=_A , generator=_A , output_type='''np''' , )
UpperCAmelCase__ : int = output.images[0]
assert image.shape == (512, 512, 3)
assert np.abs(expected_image - image ).max() < 9e-3
def lowercase_ ( self : Any ):
'''simple docstring'''
UpperCAmelCase__ : List[str] = load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
'''/sd2-inpaint/init_image.png''' )
UpperCAmelCase__ : Any = load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png''' )
UpperCAmelCase__ : Union[str, Any] = load_numpy(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint'''
'''/yellow_cat_sitting_on_a_park_bench_fp16.npy''' )
UpperCAmelCase__ : Tuple = '''stabilityai/stable-diffusion-2-inpainting'''
UpperCAmelCase__ : Any = StableDiffusionInpaintPipeline.from_pretrained(
_A , torch_dtype=torch.floataa , safety_checker=_A , )
pipe.to(_A )
pipe.set_progress_bar_config(disable=_A )
pipe.enable_attention_slicing()
UpperCAmelCase__ : Tuple = '''Face of a yellow cat, high resolution, sitting on a park bench'''
UpperCAmelCase__ : Union[str, Any] = torch.manual_seed(0 )
UpperCAmelCase__ : Optional[Any] = pipe(
prompt=_A , image=_A , mask_image=_A , generator=_A , output_type='''np''' , )
UpperCAmelCase__ : Tuple = output.images[0]
assert image.shape == (512, 512, 3)
assert np.abs(expected_image - image ).max() < 5e-1
def lowercase_ ( self : Any ):
'''simple docstring'''
torch.cuda.empty_cache()
torch.cuda.reset_max_memory_allocated()
torch.cuda.reset_peak_memory_stats()
UpperCAmelCase__ : Union[str, Any] = load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
'''/sd2-inpaint/init_image.png''' )
UpperCAmelCase__ : Dict = load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png''' )
UpperCAmelCase__ : Optional[Any] = '''stabilityai/stable-diffusion-2-inpainting'''
UpperCAmelCase__ : str = PNDMScheduler.from_pretrained(_A , subfolder='''scheduler''' )
UpperCAmelCase__ : Optional[Any] = StableDiffusionInpaintPipeline.from_pretrained(
_A , safety_checker=_A , scheduler=_A , torch_dtype=torch.floataa , )
pipe.to(_A )
pipe.set_progress_bar_config(disable=_A )
pipe.enable_attention_slicing(1 )
pipe.enable_sequential_cpu_offload()
UpperCAmelCase__ : Optional[int] = '''Face of a yellow cat, high resolution, sitting on a park bench'''
UpperCAmelCase__ : Tuple = torch.manual_seed(0 )
UpperCAmelCase__ : Any = pipe(
prompt=_A , image=_A , mask_image=_A , generator=_A , num_inference_steps=2 , output_type='''np''' , )
UpperCAmelCase__ : int = torch.cuda.max_memory_allocated()
# make sure that less than 2.65 GB is allocated
assert mem_bytes < 2.6_5 * 10**9
| 75 |
'''simple docstring'''
import asyncio
import os
import shutil
import subprocess
import sys
import tempfile
import unittest
from distutils.util import strtobool
from functools import partial
from pathlib import Path
from typing import List, Union
from unittest import mock
import torch
from ..state import AcceleratorState, PartialState
from ..utils import (
gather,
is_bnb_available,
is_comet_ml_available,
is_datasets_available,
is_deepspeed_available,
is_mps_available,
is_safetensors_available,
is_tensorboard_available,
is_torch_version,
is_tpu_available,
is_transformers_available,
is_wandb_available,
is_xpu_available,
)
def lowerCAmelCase_ ( a : Optional[int] , a : Tuple=False ):
try:
a__ = os.environ[key]
except KeyError:
# KEY isn't set, default to `default`.
a__ = default
else:
# KEY is set, convert it to True or False.
try:
a__ = strtobool(a )
except ValueError:
# More values are supported, but let's keep the message simple.
raise ValueError(f'''If set, {key} must be yes or no.''' )
return _value
__A : Optional[int] = parse_flag_from_env('RUN_SLOW', default=False)
def lowerCAmelCase_ ( a : List[str] ):
return unittest.skip('Test was skipped' )(a )
def lowerCAmelCase_ ( a : Union[str, Any] ):
return unittest.skipUnless(_run_slow_tests , 'test is slow' )(a )
def lowerCAmelCase_ ( a : Dict ):
return unittest.skipUnless(not torch.cuda.is_available() , 'test requires only a CPU' )(a )
def lowerCAmelCase_ ( a : str ):
return unittest.skipUnless(torch.cuda.is_available() , 'test requires a GPU' )(a )
def lowerCAmelCase_ ( a : Union[str, Any] ):
return unittest.skipUnless(is_xpu_available() , 'test requires a XPU' )(a )
def lowerCAmelCase_ ( a : Union[str, Any] ):
return unittest.skipUnless(is_mps_available() , 'test requires a `mps` backend support in `torch`' )(a )
def lowerCAmelCase_ ( a : Optional[int] ):
return unittest.skipUnless(
is_transformers_available() and is_datasets_available() , 'test requires the Hugging Face suite' )(a )
def lowerCAmelCase_ ( a : Optional[Any] ):
return unittest.skipUnless(is_bnb_available() , 'test requires the bitsandbytes library' )(a )
def lowerCAmelCase_ ( a : List[str] ):
return unittest.skipUnless(is_tpu_available() , 'test requires TPU' )(a )
def lowerCAmelCase_ ( a : Tuple ):
return unittest.skipUnless(torch.cuda.device_count() == 1 , 'test requires a GPU' )(a )
def lowerCAmelCase_ ( a : Dict ):
return unittest.skipUnless(torch.xpu.device_count() == 1 , 'test requires a XPU' )(a )
def lowerCAmelCase_ ( a : List[str] ):
return unittest.skipUnless(torch.cuda.device_count() > 1 , 'test requires multiple GPUs' )(a )
def lowerCAmelCase_ ( a : Dict ):
return unittest.skipUnless(torch.xpu.device_count() > 1 , 'test requires multiple XPUs' )(a )
def lowerCAmelCase_ ( a : Tuple ):
return unittest.skipUnless(is_safetensors_available() , 'test requires safetensors' )(a )
def lowerCAmelCase_ ( a : int ):
return unittest.skipUnless(is_deepspeed_available() , 'test requires DeepSpeed' )(a )
def lowerCAmelCase_ ( a : Union[str, Any] ):
return unittest.skipUnless(is_torch_version('>=' , '1.12.0' ) , 'test requires torch version >= 1.12.0' )(a )
def lowerCAmelCase_ ( a : int=None , a : Dict=None ):
if test_case is None:
return partial(a , version=a )
return unittest.skipUnless(is_torch_version('>=' , a ) , f'''test requires torch version >= {version}''' )(a )
def lowerCAmelCase_ ( a : Any ):
return unittest.skipUnless(is_tensorboard_available() , 'test requires Tensorboard' )(a )
def lowerCAmelCase_ ( a : str ):
return unittest.skipUnless(is_wandb_available() , 'test requires wandb' )(a )
def lowerCAmelCase_ ( a : int ):
return unittest.skipUnless(is_comet_ml_available() , 'test requires comet_ml' )(a )
__A : Optional[Any] = (
any([is_wandb_available(), is_tensorboard_available()]) and not is_comet_ml_available()
)
def lowerCAmelCase_ ( a : int ):
return unittest.skipUnless(
_atleast_one_tracker_available , 'test requires at least one tracker to be available and for `comet_ml` to not be installed' , )(a )
class _UpperCamelCase ( unittest.TestCase ):
'''simple docstring'''
SCREAMING_SNAKE_CASE:int = True
@classmethod
def lowercase__ ( cls ):
"""simple docstring"""
a__ = tempfile.mkdtemp()
@classmethod
def lowercase__ ( cls ):
"""simple docstring"""
if os.path.exists(cls.tmpdir ):
shutil.rmtree(cls.tmpdir )
def lowercase__ ( self ):
"""simple docstring"""
if self.clear_on_setup:
for path in Path(self.tmpdir ).glob('**/*' ):
if path.is_file():
path.unlink()
elif path.is_dir():
shutil.rmtree(_a )
class _UpperCamelCase ( unittest.TestCase ):
'''simple docstring'''
def lowercase__ ( self ):
"""simple docstring"""
super().tearDown()
# Reset the state of the AcceleratorState singleton.
AcceleratorState._reset_state()
PartialState._reset_state()
class _UpperCamelCase ( unittest.TestCase ):
'''simple docstring'''
def lowercase__ ( self , _a ):
"""simple docstring"""
a__ = mocks if isinstance(_a , (tuple, list) ) else [mocks]
for m in self.mocks:
m.start()
self.addCleanup(m.stop )
def lowerCAmelCase_ ( a : List[str] ):
a__ = AcceleratorState()
a__ = tensor[None].clone().to(state.device )
a__ = gather(a ).cpu()
a__ = tensor[0].cpu()
for i in range(tensors.shape[0] ):
if not torch.equal(tensors[i] , a ):
return False
return True
class _UpperCamelCase :
'''simple docstring'''
def __init__( self , _a , _a , _a ):
"""simple docstring"""
a__ = returncode
a__ = stdout
a__ = stderr
async def lowerCAmelCase_ ( a : Any , a : int ):
while True:
a__ = await stream.readline()
if line:
callback(a )
else:
break
async def lowerCAmelCase_ ( a : int , a : Tuple=None , a : Optional[Any]=None , a : Tuple=None , a : str=False , a : Dict=False ):
if echo:
print('\nRunning: ' , ' '.join(a ) )
a__ = await asyncio.create_subprocess_exec(
cmd[0] , *cmd[1:] , stdin=a , stdout=asyncio.subprocess.PIPE , stderr=asyncio.subprocess.PIPE , env=a , )
# note: there is a warning for a possible deadlock when using `wait` with huge amounts of data in the pipe
# https://docs.python.org/3/library/asyncio-subprocess.html#asyncio.asyncio.subprocess.Process.wait
#
# If it starts hanging, will need to switch to the following code. The problem is that no data
# will be seen until it's done and if it hangs for example there will be no debug info.
# out, err = await p.communicate()
# return _RunOutput(p.returncode, out, err)
a__ = []
a__ = []
def tee(a : str , a : Optional[Any] , a : Any , a : Optional[int]="" ):
a__ = line.decode('utf-8' ).rstrip()
sink.append(a )
if not quiet:
print(a , a , file=a )
# XXX: the timeout doesn't seem to make any difference here
await asyncio.wait(
[
asyncio.create_task(_read_stream(p.stdout , lambda a : tee(a , a , sys.stdout , label='stdout:' ) ) ),
asyncio.create_task(_read_stream(p.stderr , lambda a : tee(a , a , sys.stderr , label='stderr:' ) ) ),
] , timeout=a , )
return _RunOutput(await p.wait() , a , a )
def lowerCAmelCase_ ( a : Union[str, Any] , a : str=None , a : Dict=None , a : List[Any]=180 , a : Optional[Any]=False , a : int=True ):
a__ = asyncio.get_event_loop()
a__ = loop.run_until_complete(
_stream_subprocess(a , env=a , stdin=a , timeout=a , quiet=a , echo=a ) )
a__ = ' '.join(a )
if result.returncode > 0:
a__ = '\n'.join(result.stderr )
raise RuntimeError(
f'''\'{cmd_str}\' failed with returncode {result.returncode}\n\n'''
f'''The combined stderr from workers follows:\n{stderr}''' )
return result
class _UpperCamelCase ( _A ):
'''simple docstring'''
pass
def lowerCAmelCase_ ( a : List[str] , a : Dict=False ):
try:
a__ = subprocess.check_output(a , stderr=subprocess.STDOUT )
if return_stdout:
if hasattr(a , 'decode' ):
a__ = output.decode('utf-8' )
return output
except subprocess.CalledProcessError as e:
raise SubprocessCallException(
f'''Command `{' '.join(a )}` failed with the following error:\n\n{e.output.decode()}''' ) from e
| 394 | 0 |
from collections import OrderedDict
from typing import List, Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowerCAmelCase__: Dict = logging.get_logger(__name__)
lowerCAmelCase__: Dict = {
"google/efficientnet-b7": "https://huggingface.co/google/efficientnet-b7/resolve/main/config.json",
}
class snake_case_ ( _lowercase ):
__lowerCamelCase : List[str] = '''efficientnet'''
def __init__( self , __lowerCAmelCase = 3 , __lowerCAmelCase = 600 , __lowerCAmelCase = 2.0 , __lowerCAmelCase = 3.1 , __lowerCAmelCase = 8 , __lowerCAmelCase = [3, 3, 5, 3, 5, 5, 3] , __lowerCAmelCase = [32, 16, 24, 40, 80, 112, 192] , __lowerCAmelCase = [16, 24, 40, 80, 112, 192, 320] , __lowerCAmelCase = [] , __lowerCAmelCase = [1, 2, 2, 2, 1, 2, 1] , __lowerCAmelCase = [1, 2, 2, 3, 3, 4, 1] , __lowerCAmelCase = [1, 6, 6, 6, 6, 6, 6] , __lowerCAmelCase = 0.25 , __lowerCAmelCase = "swish" , __lowerCAmelCase = 2_560 , __lowerCAmelCase = "mean" , __lowerCAmelCase = 0.02 , __lowerCAmelCase = 0.0_01 , __lowerCAmelCase = 0.99 , __lowerCAmelCase = 0.5 , __lowerCAmelCase = 0.2 , **__lowerCAmelCase , ):
super().__init__(**A_ )
SCREAMING_SNAKE_CASE_ : Any = num_channels
SCREAMING_SNAKE_CASE_ : int = image_size
SCREAMING_SNAKE_CASE_ : Optional[int] = width_coefficient
SCREAMING_SNAKE_CASE_ : Optional[int] = depth_coefficient
SCREAMING_SNAKE_CASE_ : Optional[int] = depth_divisor
SCREAMING_SNAKE_CASE_ : Tuple = kernel_sizes
SCREAMING_SNAKE_CASE_ : Union[str, Any] = in_channels
SCREAMING_SNAKE_CASE_ : Union[str, Any] = out_channels
SCREAMING_SNAKE_CASE_ : List[Any] = depthwise_padding
SCREAMING_SNAKE_CASE_ : Optional[int] = strides
SCREAMING_SNAKE_CASE_ : Optional[int] = num_block_repeats
SCREAMING_SNAKE_CASE_ : Any = expand_ratios
SCREAMING_SNAKE_CASE_ : List[str] = squeeze_expansion_ratio
SCREAMING_SNAKE_CASE_ : Union[str, Any] = hidden_act
SCREAMING_SNAKE_CASE_ : List[Any] = hidden_dim
SCREAMING_SNAKE_CASE_ : Union[str, Any] = pooling_type
SCREAMING_SNAKE_CASE_ : int = initializer_range
SCREAMING_SNAKE_CASE_ : int = batch_norm_eps
SCREAMING_SNAKE_CASE_ : Tuple = batch_norm_momentum
SCREAMING_SNAKE_CASE_ : List[str] = dropout_rate
SCREAMING_SNAKE_CASE_ : Optional[int] = drop_connect_rate
SCREAMING_SNAKE_CASE_ : Union[str, Any] = sum(A_ ) * 4
class snake_case_ ( _lowercase ):
__lowerCamelCase : Optional[int] = version.parse('1.11' )
@property
def __A ( self ):
return OrderedDict(
[
('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
] )
@property
def __A ( self ):
return 1e-5
| 700 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
lowerCAmelCase__: Dict = {
"configuration_gpt_neo": ["GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTNeoConfig", "GPTNeoOnnxConfig"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase__: List[str] = [
"GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST",
"GPTNeoForCausalLM",
"GPTNeoForQuestionAnswering",
"GPTNeoForSequenceClassification",
"GPTNeoForTokenClassification",
"GPTNeoModel",
"GPTNeoPreTrainedModel",
"load_tf_weights_in_gpt_neo",
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase__: Union[str, Any] = [
"FlaxGPTNeoForCausalLM",
"FlaxGPTNeoModel",
"FlaxGPTNeoPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_gpt_neo import GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoConfig, GPTNeoOnnxConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_gpt_neo import (
GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST,
GPTNeoForCausalLM,
GPTNeoForQuestionAnswering,
GPTNeoForSequenceClassification,
GPTNeoForTokenClassification,
GPTNeoModel,
GPTNeoPreTrainedModel,
load_tf_weights_in_gpt_neo,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_gpt_neo import FlaxGPTNeoForCausalLM, FlaxGPTNeoModel, FlaxGPTNeoPreTrainedModel
else:
import sys
lowerCAmelCase__: Optional[Any] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 311 | 0 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.