code stringlengths 86 54.5k | code_codestyle int64 0 371 | style_context stringlengths 87 49.2k | style_context_codestyle int64 0 349 | label int64 0 1 |
|---|---|---|---|---|
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
a = {
'''configuration_efficientformer''': [
'''EFFICIENTFORMER_PRETRAINED_CONFIG_... | 354 |
"""simple docstring"""
import argparse
import os
import torch
from diffusers import (
CMStochasticIterativeScheduler,
ConsistencyModelPipeline,
UNetaDModel,
)
a = {
'''sample_size''': 32,
'''in_channels''': 3,
'''out_channels''': 3,
'''layers_per_block''': 2,
'''nu... | 271 | 0 |
"""simple docstring"""
import argparse
from transformers import BigBirdConfig, BigBirdForPreTraining, BigBirdForQuestionAnswering, load_tf_weights_in_big_bird
from transformers.utils import logging
logging.set_verbosity_info()
def _snake_case ( _snake_case : List[Any] , _snake_case ... | 355 |
"""simple docstring"""
import argparse
import os
import torch
from transformers import (
XLNetConfig,
XLNetForQuestionAnswering,
XLNetForSequenceClassification,
XLNetLMHeadModel,
load_tf_weights_in_xlnet,
)
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
a = ... | 271 | 0 |
"""simple docstring"""
import os
import re
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
a = logging.get_logger(__name__)
a = {
'''vocab_file''': '''vocab.txt''',
'''merg... | 356 |
"""simple docstring"""
from __future__ import annotations
import time
import numpy as np
a = [8, 5, 9, 7]
a = [
[2, 0, 1, 1],
[0, 1, 2, 1],
[4, 0, 0, 3],
[0, 2, 1, 0],
[1, 0, 3, 0],
]
a = [
[3, 2, 1, 4],
[0, 2, 5, 2],
[5, 1, 0, 5],
[1, ... | 271 | 0 |
"""simple docstring"""
from unittest import TestCase
from datasets import Sequence, Value
from datasets.arrow_dataset import Dataset
class lowercase_ ( __lowerCAmelCase ):
'''simple docstring'''
def lowerCAmelCase_ ( self : List[str] ):
return [
{"col_1":... | 357 |
"""simple docstring"""
import argparse
from collections import defaultdict
import yaml
a = '''docs/source/en/_toctree.yml'''
def _snake_case ( _snake_case : List[Any] ) -> Optional[Any]:
'''simple docstring'''
_A = defaultdict(_snake_c... | 271 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
a = {
'''configuration_owlvit''': [
'''OWLVIT_PRETRAIN... | 358 |
"""simple docstring"""
from dataclasses import dataclass
from typing import Dict, Optional, Tuple, Union
import torch
import torch.nn as nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .attention_processor import AttentionProcessor, At... | 271 | 0 |
"""simple docstring"""
def _snake_case ( _snake_case : List[str] ) -> bool:
'''simple docstring'''
if number < 0:
raise ValueError('number must not be negative' )
return number & (number - 1) == 0
if __name__ == "__main__":
import doctes... | 359 |
"""simple docstring"""
from typing import Any, Dict, List, Optional, Tuple, Union
import torch
from torch import nn
from torch.utils.data import DistributedSampler, RandomSampler
from transformers import PreTrainedModel, Trainer, logging
from transformers.integrations import is_fairscale_available
from transfor... | 271 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
a = {
'''configuration_convnext''': ['''CONVNEXT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ConvNextConfig''', '''ConvNe... | 360 |
"""simple docstring"""
import tempfile
import torch
from diffusers import IPNDMScheduler
from .test_schedulers import SchedulerCommonTest
class lowercase_ ( __lowerCAmelCase ):
'''simple docstring'''
UpperCAmelCase : Union[str, Any] = (IPNDMScheduler,)
UpperCAmelCase :... | 271 | 0 |
"""simple docstring"""
import os
import sys
from contextlib import contextmanager
# Windows only
if os.name == "nt":
import ctypes
import msvcrt # noqa
class lowercase_ ( ctypes.Structure ):
'''simple docstring'''
# _fields is a specific attr expected by... | 361 |
"""simple docstring"""
import logging
import os
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union
from filelock import FileLock
from transformers import PreTrainedTokenizer, is_tf_available, is_torch_available
a = logging.getLogger(__name__)
@da... | 271 | 0 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
a = logging.get_logger(__name__)
a = {
"google/bit-50": "https://huggingface.... | 362 |
"""simple docstring"""
import enum
import os
from hashlib import shaaaa
from typing import Optional
from .. import config
from .logging import get_logger
a = get_logger(__name__)
class lowercase_ ( enum.Enum ):
'''simple docstring'''
UpperCAmelCase : Optional[int] ... | 271 | 0 |
"""simple docstring"""
from typing import List, Union
import numpy as np
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils ... | 363 |
"""simple docstring"""
import os
from huggingface_hub.constants import HUGGINGFACE_HUB_CACHE, hf_cache_home
a = HUGGINGFACE_HUB_CACHE
a = '''config.json'''
a = '''diffusion_pytorch_model.bin'''
a = '''diffusion_flax_model.msgpack'''
a = '''mode... | 271 | 0 |
"""simple docstring"""
import dataclasses
import json
import sys
import types
from argparse import ArgumentDefaultsHelpFormatter, ArgumentParser, ArgumentTypeError
from copy import copy
from enum import Enum
from inspect import isclass
from pathlib import Path
from typing import Any, Callable, Dict, Iterable, Lis... | 364 |
"""simple docstring"""
import contextlib
import copy
import random
from typing import Any, Dict, Iterable, Optional, Union
import numpy as np
import torch
from .utils import deprecate, is_transformers_available
if is_transformers_available():
import transformers
def _snake_case ( _snake... | 271 | 0 |
"""simple docstring"""
def _snake_case ( _snake_case : int , _snake_case : int ) -> str:
'''simple docstring'''
if b == 0:
return 1
if (b % 2) == 0:
return actual_power(_a , int(b / 2 ) ) * actu... | 365 |
"""simple docstring"""
def _snake_case ( _snake_case : int ) -> list:
'''simple docstring'''
_A = int(_snake_case )
if n_element < 1:
_A = ValueError('a should be a positive number' )
raise my_error
... | 271 | 0 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
a = logging.get_logger(__name__)
a = {
"""google/mo... | 366 |
"""simple docstring"""
from typing import List, Optional, Union
import torch
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
is_accelerate... | 271 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
a = {
'''configuration_nezha''': ['''NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''NezhaConfig'''],
}
try:
if not is_torch_available():
... | 367 |
"""simple docstring"""
from collections import deque
class lowercase_ :
'''simple docstring'''
def __init__( self : int , _UpperCAmelCase : str , _UpperCAmelCase : int , _UpperCAmelCase : int ):
_A = process_name # process name
_A = ... | 271 | 0 |
"""simple docstring"""
import random
import timeit
from functools import wraps
from typing import Callable, Optional
from ..configuration_utils import PretrainedConfig
from ..models.auto.modeling_tf_auto import TF_MODEL_MAPPING, TF_MODEL_WITH_LM_HEAD_MAPPING
from ..utils import is_pyanvml_available, is_tf_availa... | 368 |
"""simple docstring"""
from sklearn.metrics import fa_score
import datasets
a = '''
The F1 score is the harmonic mean of the precision and recall. It can be computed with the equation:
F1 = 2 * (precision * recall) / (precision + recall)
'''
a = '''
Args:
predictions (`list` of ... | 271 | 0 |
"""simple docstring"""
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
#... | 369 |
"""simple docstring"""
import logging
from dataclasses import dataclass, field
from pathlib import Path
from typing import Optional, Union
from .generation.configuration_utils import GenerationConfig
from .training_args import TrainingArguments
from .utils import add_start_docstrings
a = logging.g... | 271 | 0 |
"""simple docstring"""
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMInverseScheduler,
DDIMScheduler,
DPMSolverMultiste... | 370 |
"""simple docstring"""
import argparse
import json
from typing import List
from ltp import LTP
from transformers import BertTokenizer
def _snake_case ( _snake_case : Dict ) -> Any:
'''simple docstring'''
if (
(cp >= 0X4e00 and cp <= 0X9fff)
o... | 271 | 0 |
"""simple docstring"""
import logging
from pathlib import Path
import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only
from utils_rag import save_json
d... | 371 |
"""simple docstring"""
import secrets
from random import shuffle
from string import ascii_letters, ascii_lowercase, ascii_uppercase, digits, punctuation
def _snake_case ( _snake_case : int = 8 ) -> str:
'''simple docstring'''
_A = ascii_letters + digi... | 271 | 0 |
"""simple docstring"""
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized, parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAnalytics
... | 350 |
"""simple docstring"""
import argparse
import logging
import os
from datetime import datetime
import numpy as np
import torch
from torch import nn
from torch.utils.data import DataLoader, RandomSampler, TensorDataset
from tqdm import tqdm
from transformers import GPTaLMHeadModel
a = logging.getLo... | 271 | 0 |
"""simple docstring"""
import random
from .binary_exp_mod import bin_exp_mod
def _snake_case ( _snake_case : Any , _snake_case : str=10_00 ) -> Optional[Any]:
'''simple docstring'''
if n < 2:
return False
if n % 2 == 0:
re... | 351 |
"""simple docstring"""
def _snake_case ( _snake_case : str ) -> str:
'''simple docstring'''
_A = ''
for ch in key:
if ch == " " or ch not in key_no_dups and ch.isalpha():
key_no_dups += ch
return key_no_dups
d... | 271 | 0 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a = logging.get_logger(__name__)
a = {
'weiweishi/roc-bert-base-zh': 'https://huggingface.co/weiweishi/roc-bert-base-zh/resolve/main/config.json',
}
class lowercase_ ( __lo... | 352 |
"""simple docstring"""
import inspect
import os
import unittest
from pathlib import Path
import torch
import accelerate
from accelerate.test_utils import execute_subprocess_async
from accelerate.test_utils.testing import run_command
class lowercase_ ( unittest.TestCase ):
'''simple docstr... | 271 | 0 |
"""simple docstring"""
import sys
from pathlib import Path
a = Path(__file__).resolve().parents[3] / 'src'
sys.path.insert(1, str(git_repo_path))
import dataclasses # noqa
import io # noqa
import itertools # noqa
import json # noqa
import os # noqa
import unittest # noqa
from copy import dee... | 353 |
"""simple docstring"""
import random
import timeit
from functools import wraps
from typing import Callable, Optional
from ..configuration_utils import PretrainedConfig
from ..models.auto.modeling_tf_auto import TF_MODEL_MAPPING, TF_MODEL_WITH_LM_HEAD_MAPPING
from ..utils import is_pyanvml_available, is_tf_availa... | 271 | 0 |
"""simple docstring"""
from ..utils import is_flax_available, is_torch_available
if is_torch_available():
from .autoencoder_kl import AutoencoderKL
from .controlnet import ControlNetModel
from .dual_transformer_ad import DualTransformeraDModel
from .modeling_utils import ModelMixin
... | 354 |
"""simple docstring"""
import argparse
import os
import torch
from diffusers import (
CMStochasticIterativeScheduler,
ConsistencyModelPipeline,
UNetaDModel,
)
a = {
'''sample_size''': 32,
'''in_channels''': 3,
'''out_channels''': 3,
'''layers_per_block''': 2,
'''nu... | 271 | 0 |
"""simple docstring"""
import os
def _snake_case ( ) -> List[str]:
'''simple docstring'''
with open(os.path.dirname(_snake_case ) + '/grid.txt' ) as f:
_A = [] # noqa: E741
for _ in range(20 ):
l.append([in... | 355 |
"""simple docstring"""
import argparse
import os
import torch
from transformers import (
XLNetConfig,
XLNetForQuestionAnswering,
XLNetForSequenceClassification,
XLNetLMHeadModel,
load_tf_weights_in_xlnet,
)
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
a = ... | 271 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a = {
'configuration_pegasus_x': ['PEGASUS_X_PRETRAINED_CONFIG_ARCHIVE_MAP', 'PegasusXConfig'],
}
try:
if not is_torch_available():
... | 356 |
"""simple docstring"""
from __future__ import annotations
import time
import numpy as np
a = [8, 5, 9, 7]
a = [
[2, 0, 1, 1],
[0, 1, 2, 1],
[4, 0, 0, 3],
[0, 2, 1, 0],
[1, 0, 3, 0],
]
a = [
[3, 2, 1, 4],
[0, 2, 5, 2],
[5, 1, 0, 5],
[1, ... | 271 | 0 |
"""simple docstring"""
from ..utils import DummyObject, requires_backends
class lowercase_ ( metaclass=A__ ):
'''simple docstring'''
UpperCAmelCase : List[Any] = ['keras_nlp']
def __init__( self : str , *_UpperCAmelCase : Optional[Any] , **_UpperCAmelCase... | 357 |
"""simple docstring"""
import argparse
from collections import defaultdict
import yaml
a = '''docs/source/en/_toctree.yml'''
def _snake_case ( _snake_case : List[Any] ) -> Optional[Any]:
'''simple docstring'''
_A = defaultdict(_snake_c... | 271 | 0 |
import json
import os
from ...utils.constants import SAGEMAKER_PARALLEL_EC2_INSTANCES, TORCH_DYNAMO_MODES
from ...utils.dataclasses import ComputeEnvironment, SageMakerDistributedType
from ...utils.imports import is_botoa_available
from .config_args import SageMakerConfig
from .config_utils import (
DYNAMO_BA... | 358 |
"""simple docstring"""
from dataclasses import dataclass
from typing import Dict, Optional, Tuple, Union
import torch
import torch.nn as nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .attention_processor import AttentionProcessor, At... | 271 | 0 |
"""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,
RobertaT... | 359 |
"""simple docstring"""
from typing import Any, Dict, List, Optional, Tuple, Union
import torch
from torch import nn
from torch.utils.data import DistributedSampler, RandomSampler
from transformers import PreTrainedModel, Trainer, logging
from transformers.integrations import is_fairscale_available
from transfor... | 271 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a = {
"configuration_time_series_transformer": [
"TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
"TimeSeriesTransformerConfig",
],
}
try:
if... | 360 |
"""simple docstring"""
import tempfile
import torch
from diffusers import IPNDMScheduler
from .test_schedulers import SchedulerCommonTest
class lowercase_ ( __lowerCAmelCase ):
'''simple docstring'''
UpperCAmelCase : Union[str, Any] = (IPNDMScheduler,)
UpperCAmelCase :... | 271 | 0 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
a = logging.get_logger(__name__)
class lowercase_ ( __lowerCAmelCase , __lo... | 361 |
"""simple docstring"""
import logging
import os
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union
from filelock import FileLock
from transformers import PreTrainedTokenizer, is_tf_available, is_torch_available
a = logging.getLogger(__name__)
@da... | 271 | 0 |
"""simple docstring"""
def _snake_case ( _snake_case : List[str] ) -> list:
_A = [0] * len(__lowerCAmelCase )
for i in range(1 , len(__lowerCAmelCase ) ):
# use last results for better performance - dynamic programming
... | 362 |
"""simple docstring"""
import enum
import os
from hashlib import shaaaa
from typing import Optional
from .. import config
from .logging import get_logger
a = get_logger(__name__)
class lowercase_ ( enum.Enum ):
'''simple docstring'''
UpperCAmelCase : Optional[int] ... | 271 | 0 |
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import AutoTokenizer, PegasusConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configu... | 363 |
"""simple docstring"""
import os
from huggingface_hub.constants import HUGGINGFACE_HUB_CACHE, hf_cache_home
a = HUGGINGFACE_HUB_CACHE
a = '''config.json'''
a = '''diffusion_pytorch_model.bin'''
a = '''diffusion_flax_model.msgpack'''
a = '''mode... | 271 | 0 |
"""simple docstring"""
from __future__ import annotations
import typing
from collections import Counter
def _snake_case ( _snake_case : int ) -> typing.Counter[int]:
'''simple docstring'''
_A = Counter()
for base in range(1 , max_perimeter +... | 364 |
"""simple docstring"""
import contextlib
import copy
import random
from typing import Any, Dict, Iterable, Optional, Union
import numpy as np
import torch
from .utils import deprecate, is_transformers_available
if is_transformers_available():
import transformers
def _snake_case ( _snake... | 271 | 0 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_availabl... | 365 |
"""simple docstring"""
def _snake_case ( _snake_case : int ) -> list:
'''simple docstring'''
_A = int(_snake_case )
if n_element < 1:
_A = ValueError('a should be a positive number' )
raise my_error
... | 271 | 0 |
"""simple docstring"""
import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path
import torch_xla.distributed.xla_multiprocessing as xmp
def _snake_case ( ) -> str:
'''simple docstring'''
_A = ArgumentParser(
d... | 366 |
"""simple docstring"""
from typing import List, Optional, Union
import torch
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
is_accelerate... | 271 | 0 |
def _snake_case ( _snake_case : Dict , _snake_case : List[str] ) -> list[str]:
'''simple docstring'''
return [sentence[i : i + ngram_size] for i in range(len(SCREAMING_SNAKE_CASE__ ) - ngram_size + 1 )]
if __name__ == "__main__":
from docte... | 367 |
"""simple docstring"""
from collections import deque
class lowercase_ :
'''simple docstring'''
def __init__( self : int , _UpperCAmelCase : str , _UpperCAmelCase : int , _UpperCAmelCase : int ):
_A = process_name # process name
_A = ... | 271 | 0 |
"""simple docstring"""
import warnings
from diffusers import StableDiffusionInpaintPipeline as StableDiffusionInpaintPipeline # noqa F401
warnings.warn(
'''The `inpainting.py` script is outdated. Please use directly `from diffusers import'''
''' StableDiffusionInpaintPipeline` instead.'''
)
| 368 |
"""simple docstring"""
from sklearn.metrics import fa_score
import datasets
a = '''
The F1 score is the harmonic mean of the precision and recall. It can be computed with the equation:
F1 = 2 * (precision * recall) / (precision + recall)
'''
a = '''
Args:
predictions (`list` of ... | 271 | 0 |
"""simple docstring"""
import inspect
import unittest
from datasets import load_dataset
from packaging import version
from transformers import BeitConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_dev... | 369 |
"""simple docstring"""
import logging
from dataclasses import dataclass, field
from pathlib import Path
from typing import Optional, Union
from .generation.configuration_utils import GenerationConfig
from .training_args import TrainingArguments
from .utils import add_start_docstrings
a = logging.g... | 271 | 0 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
a = logging.get_logger(__name__)
a = {
'google/mobi... | 370 |
"""simple docstring"""
import argparse
import json
from typing import List
from ltp import LTP
from transformers import BertTokenizer
def _snake_case ( _snake_case : Dict ) -> Any:
'''simple docstring'''
if (
(cp >= 0X4e00 and cp <= 0X9fff)
o... | 271 | 0 |
"""simple docstring"""
import numpy as np
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
from tensorflow.keras.layers import LSTM, Dense
from tensorflow.keras.models import Sequential
if __name__ == "__main__":
a = pd.read_csv('''sample_data.csv''', header=No... | 371 |
"""simple docstring"""
import secrets
from random import shuffle
from string import ascii_letters, ascii_lowercase, ascii_uppercase, digits, punctuation
def _snake_case ( _snake_case : int = 8 ) -> str:
'''simple docstring'''
_A = ascii_letters + digi... | 271 | 0 |
"""simple docstring"""
import unittest
import torch
from diffusers import DDIMScheduler, DDPMScheduler, UNetaDModel
from diffusers.training_utils import set_seed
from diffusers.utils.testing_utils import slow
a = False
class lowercase_ ( unittest.TestCase ):
'''simple do... | 350 |
"""simple docstring"""
import argparse
import logging
import os
from datetime import datetime
import numpy as np
import torch
from torch import nn
from torch.utils.data import DataLoader, RandomSampler, TensorDataset
from tqdm import tqdm
from transformers import GPTaLMHeadModel
a = logging.getLo... | 271 | 0 |
"""simple docstring"""
import unittest
import numpy as np
from transformers.testing_utils import require_pytesseract, require_torch
from transformers.utils import is_pytesseract_available, is_torch_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if i... | 351 |
"""simple docstring"""
def _snake_case ( _snake_case : str ) -> str:
'''simple docstring'''
_A = ''
for ch in key:
if ch == " " or ch not in key_no_dups and ch.isalpha():
key_no_dups += ch
return key_no_dups
d... | 271 | 0 |
"""simple docstring"""
import argparse
import os
import transformers
from .convert_slow_tokenizer import SLOW_TO_FAST_CONVERTERS
from .utils import logging
logging.set_verbosity_info()
a = logging.get_logger(__name__)
a = {name: getattr(transformers, name + '''Fast''') for name in SLOW_TO... | 352 |
"""simple docstring"""
import inspect
import os
import unittest
from pathlib import Path
import torch
import accelerate
from accelerate.test_utils import execute_subprocess_async
from accelerate.test_utils.testing import run_command
class lowercase_ ( unittest.TestCase ):
'''simple docstr... | 271 | 0 |
"""simple docstring"""
from sklearn.metrics import fa_score
import datasets
a = """
The F1 score is the harmonic mean of the precision and recall. It can be computed with the equation:
F1 = 2 * (precision * recall) / (precision + recall)
"""
a = """
Args:
predictions (`list` of ... | 353 |
"""simple docstring"""
import random
import timeit
from functools import wraps
from typing import Callable, Optional
from ..configuration_utils import PretrainedConfig
from ..models.auto.modeling_tf_auto import TF_MODEL_MAPPING, TF_MODEL_WITH_LM_HEAD_MAPPING
from ..utils import is_pyanvml_available, is_tf_availa... | 271 | 0 |
"""simple docstring"""
from __future__ import annotations
import numpy as np
def _snake_case ( _snake_case : list[float] ) -> Dict:
'''simple docstring'''
return np.maximum(0 , _snake_case )
if __name__ == "__main__":
print(np.array(relu([-1, 0... | 354 |
"""simple docstring"""
import argparse
import os
import torch
from diffusers import (
CMStochasticIterativeScheduler,
ConsistencyModelPipeline,
UNetaDModel,
)
a = {
'''sample_size''': 32,
'''in_channels''': 3,
'''out_channels''': 3,
'''layers_per_block''': 2,
'''nu... | 271 | 0 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
a = logging.get_logger(__name__)
a = {'''vocab_file''': '''spie... | 355 |
"""simple docstring"""
import argparse
import os
import torch
from transformers import (
XLNetConfig,
XLNetForQuestionAnswering,
XLNetForSequenceClassification,
XLNetLMHeadModel,
load_tf_weights_in_xlnet,
)
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
a = ... | 271 | 0 |
"""simple docstring"""
# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny model through reduction of a normal pre-trained model, but keeping the
# full... | 356 |
"""simple docstring"""
from __future__ import annotations
import time
import numpy as np
a = [8, 5, 9, 7]
a = [
[2, 0, 1, 1],
[0, 1, 2, 1],
[4, 0, 0, 3],
[0, 2, 1, 0],
[1, 0, 3, 0],
]
a = [
[3, 2, 1, 4],
[0, 2, 5, 2],
[5, 1, 0, 5],
[1, ... | 271 | 0 |
"""simple docstring"""
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a = logging.get_logger(__name__)
a = {
"microsoft/unispeech-sat-base-100h-libri-ft": (
"https://huggingface.co/microsoft/unispeech-sat-... | 357 |
"""simple docstring"""
import argparse
from collections import defaultdict
import yaml
a = '''docs/source/en/_toctree.yml'''
def _snake_case ( _snake_case : List[Any] ) -> Optional[Any]:
'''simple docstring'''
_A = defaultdict(_snake_c... | 271 | 0 |
import unittest
import numpy as np
import torch
from diffusers import DDIMPipeline, DDIMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device
from ..pipeline_params import UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS, UNCONDITIONAL_IMAGE_G... | 358 |
"""simple docstring"""
from dataclasses import dataclass
from typing import Dict, Optional, Tuple, Union
import torch
import torch.nn as nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .attention_processor import AttentionProcessor, At... | 271 | 0 |
"""simple docstring"""
import inspect
import unittest
import warnings
from math import ceil, floor
from transformers import LevitConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils impo... | 359 |
"""simple docstring"""
from typing import Any, Dict, List, Optional, Tuple, Union
import torch
from torch import nn
from torch.utils.data import DistributedSampler, RandomSampler
from transformers import PreTrainedModel, Trainer, logging
from transformers.integrations import is_fairscale_available
from transfor... | 271 | 0 |
import functools
def _snake_case ( _snake_case : Optional[int] , _snake_case : List[str] ) -> int:
'''simple docstring'''
_A = len(_snake_case )
_A = len(_snake_case )
@functools.cache
def min_dista... | 360 |
"""simple docstring"""
import tempfile
import torch
from diffusers import IPNDMScheduler
from .test_schedulers import SchedulerCommonTest
class lowercase_ ( __lowerCAmelCase ):
'''simple docstring'''
UpperCAmelCase : Union[str, Any] = (IPNDMScheduler,)
UpperCAmelCase :... | 271 | 0 |
"""simple docstring"""
from __future__ import annotations
import queue
class lowercase_ :
'''simple docstring'''
def __init__( self : str , _UpperCAmelCase : Union[str, Any] ):
_A = data
_A = None
_A = None
def _snake... | 361 |
"""simple docstring"""
import logging
import os
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union
from filelock import FileLock
from transformers import PreTrainedTokenizer, is_tf_available, is_torch_available
a = logging.getLogger(__name__)
@da... | 271 | 0 |
"""simple docstring"""
from typing import Dict, List, Optional, Type
from .. import config
from ..utils import logging
from .formatting import (
ArrowFormatter,
CustomFormatter,
Formatter,
PandasFormatter,
PythonFormatter,
TensorFormatter,
format_table,
query_table,
)
from .np_for... | 362 |
"""simple docstring"""
import enum
import os
from hashlib import shaaaa
from typing import Optional
from .. import config
from .logging import get_logger
a = get_logger(__name__)
class lowercase_ ( enum.Enum ):
'''simple docstring'''
UpperCAmelCase : Optional[int] ... | 271 | 0 |
"""simple docstring"""
import socket
def _snake_case ( ) -> List[str]:
'''simple docstring'''
_A = socket.socket(socket.AF_INET , socket.SOCK_STREAM )
_A = socket.gethostname()
_A = 1_23_12
sock.connect((host... | 363 |
"""simple docstring"""
import os
from huggingface_hub.constants import HUGGINGFACE_HUB_CACHE, hf_cache_home
a = HUGGINGFACE_HUB_CACHE
a = '''config.json'''
a = '''diffusion_pytorch_model.bin'''
a = '''diffusion_flax_model.msgpack'''
a = '''mode... | 271 | 0 |
"""simple docstring"""
import os
import numpy
import onnx
def _snake_case ( _snake_case : Tuple , _snake_case : Optional[int] ) -> Union[str, Any]:
'''simple docstring'''
_A = a.name
_A = b.name
_A = ''... | 364 |
"""simple docstring"""
import contextlib
import copy
import random
from typing import Any, Dict, Iterable, Optional, Union
import numpy as np
import torch
from .utils import deprecate, is_transformers_available
if is_transformers_available():
import transformers
def _snake_case ( _snake... | 271 | 0 |
"""simple docstring"""
a = {0: [2, 3], 1: [0], 2: [1], 3: [4], 4: []}
a = {0: [1, 2, 3], 1: [2], 2: [0], 3: [4], 4: [5], 5: [3]}
def _snake_case ( _snake_case : dict[int, list[int]] , _snake_case : int , _snake_case : list[bool] ... | 365 |
"""simple docstring"""
def _snake_case ( _snake_case : int ) -> list:
'''simple docstring'''
_A = int(_snake_case )
if n_element < 1:
_A = ValueError('a should be a positive number' )
raise my_error
... | 271 | 0 |
"""simple docstring"""
import os
import string
import sys
a = 1 << 8
a = {
"tab": ord('''\t'''),
"newline": ord('''\r'''),
"esc": 27,
"up": 65 + ARROW_KEY_FLAG,
"down": 66 + ARROW_KEY_FLAG,
"right": 67 + ARROW_KEY_FLAG,
"left": 68 + ARROW_KEY_FLAG,
"mod... | 366 |
"""simple docstring"""
from typing import List, Optional, Union
import torch
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
is_accelerate... | 271 | 0 |
import argparse
import tensorflow as tf
import torch
from transformers import BertConfig, BertForMaskedLM
from transformers.models.bert.modeling_bert import (
BertIntermediate,
BertLayer,
BertOutput,
BertPooler,
BertSelfAttention,
BertSelfOutput,
)
from transformers.utils import logging
... | 367 |
"""simple docstring"""
from collections import deque
class lowercase_ :
'''simple docstring'''
def __init__( self : int , _UpperCAmelCase : str , _UpperCAmelCase : int , _UpperCAmelCase : int ):
_A = process_name # process name
_A = ... | 271 | 0 |
"""simple docstring"""
import warnings
from ...utils import is_sklearn_available, requires_backends
if is_sklearn_available():
from scipy.stats import pearsonr, spearmanr
from sklearn.metrics import fa_score, matthews_corrcoef
a = (
'This metric will be removed from the library... | 368 |
"""simple docstring"""
from sklearn.metrics import fa_score
import datasets
a = '''
The F1 score is the harmonic mean of the precision and recall. It can be computed with the equation:
F1 = 2 * (precision * recall) / (precision + recall)
'''
a = '''
Args:
predictions (`list` of ... | 271 | 0 |
"""simple docstring"""
import qiskit
def _snake_case ( _snake_case : Any , _snake_case : List[Any] ) -> qiskit.result.counts.Counts:
'''simple docstring'''
_A = qiskit.Aer.get_backend('aer_simulator' )
# Create a Quantum Circuit... | 369 |
"""simple docstring"""
import logging
from dataclasses import dataclass, field
from pathlib import Path
from typing import Optional, Union
from .generation.configuration_utils import GenerationConfig
from .training_args import TrainingArguments
from .utils import add_start_docstrings
a = logging.g... | 271 | 0 |
"""simple docstring"""
def _snake_case ( _snake_case : str , _snake_case : Union[str, Any] , _snake_case : List[str]=False ) -> Any:
'''simple docstring'''
if isinstance(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) and isinstance(__SCR... | 370 |
"""simple docstring"""
import argparse
import json
from typing import List
from ltp import LTP
from transformers import BertTokenizer
def _snake_case ( _snake_case : Dict ) -> Any:
'''simple docstring'''
if (
(cp >= 0X4e00 and cp <= 0X9fff)
o... | 271 | 0 |
"""simple docstring"""
import json
import os
import unittest
from transformers import BatchEncoding, MvpTokenizer, MvpTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transforme... | 371 |
"""simple docstring"""
import secrets
from random import shuffle
from string import ascii_letters, ascii_lowercase, ascii_uppercase, digits, punctuation
def _snake_case ( _snake_case : int = 8 ) -> str:
'''simple docstring'''
_A = ascii_letters + digi... | 271 | 0 |
"""simple docstring"""
import unittest
from transformers import (
MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
TextClassificationPipeline,
pipeline,
)
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_tf, require_torch, s... | 350 |
"""simple docstring"""
import argparse
import logging
import os
from datetime import datetime
import numpy as np
import torch
from torch import nn
from torch.utils.data import DataLoader, RandomSampler, TensorDataset
from tqdm import tqdm
from transformers import GPTaLMHeadModel
a = logging.getLo... | 271 | 0 |
"""simple docstring"""
import argparse
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt
if __name__ == "__main__":
a = argparse.ArgumentParser()
parser.add_argument(
'''--checkpoint_path''', default=None, type=str, req... | 351 |
"""simple docstring"""
def _snake_case ( _snake_case : str ) -> str:
'''simple docstring'''
_A = ''
for ch in key:
if ch == " " or ch not in key_no_dups and ch.isalpha():
key_no_dups += ch
return key_no_dups
d... | 271 | 0 |
"""simple docstring"""
from ... import PretrainedConfig
a = {
'''sijunhe/nezha-cn-base''': '''https://huggingface.co/sijunhe/nezha-cn-base/resolve/main/config.json''',
}
class lowercase_ ( __lowerCAmelCase ):
'''simple docstring'''
UpperCAmelCase : Union[str, Any] ... | 352 |
"""simple docstring"""
import inspect
import os
import unittest
from pathlib import Path
import torch
import accelerate
from accelerate.test_utils import execute_subprocess_async
from accelerate.test_utils.testing import run_command
class lowercase_ ( unittest.TestCase ):
'''simple docstr... | 271 | 0 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_mobilevit import MobileViTImageProcessor
a = logging.get_logger(__name__)
class lowercase_ ( __lowerCAmelCase ):
'''simple docstring'''
def __init__( self : Tuple , *_Upper... | 353 |
"""simple docstring"""
import random
import timeit
from functools import wraps
from typing import Callable, Optional
from ..configuration_utils import PretrainedConfig
from ..models.auto.modeling_tf_auto import TF_MODEL_MAPPING, TF_MODEL_WITH_LM_HEAD_MAPPING
from ..utils import is_pyanvml_available, is_tf_availa... | 271 | 0 |
"""simple docstring"""
from __future__ import annotations
import math
def _snake_case ( _snake_case : int ) -> bool:
'''simple docstring'''
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or num... | 354 |
"""simple docstring"""
import argparse
import os
import torch
from diffusers import (
CMStochasticIterativeScheduler,
ConsistencyModelPipeline,
UNetaDModel,
)
a = {
'''sample_size''': 32,
'''in_channels''': 3,
'''out_channels''': 3,
'''layers_per_block''': 2,
'''nu... | 271 | 0 |
"""simple docstring"""
import tempfile
import torch
from diffusers import IPNDMScheduler
from .test_schedulers import SchedulerCommonTest
class lowercase_ ( __lowerCAmelCase ):
'''simple docstring'''
UpperCAmelCase : Union[str, Any] = (IPNDMScheduler,)
UpperCAmelCase : ... | 355 |
"""simple docstring"""
import argparse
import os
import torch
from transformers import (
XLNetConfig,
XLNetForQuestionAnswering,
XLNetForSequenceClassification,
XLNetLMHeadModel,
load_tf_weights_in_xlnet,
)
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
a = ... | 271 | 0 |
"""simple docstring"""
import secrets
from random import shuffle
from string import ascii_letters, ascii_lowercase, ascii_uppercase, digits, punctuation
def _snake_case ( _snake_case : int = 8 ) -> str:
'''simple docstring'''
_A = ascii_letters + digi... | 356 |
"""simple docstring"""
from __future__ import annotations
import time
import numpy as np
a = [8, 5, 9, 7]
a = [
[2, 0, 1, 1],
[0, 1, 2, 1],
[4, 0, 0, 3],
[0, 2, 1, 0],
[1, 0, 3, 0],
]
a = [
[3, 2, 1, 4],
[0, 2, 5, 2],
[5, 1, 0, 5],
[1, ... | 271 | 0 |
"""simple docstring"""
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel
from diffusers import DDIMScheduler, LDMPipeline, UNetaDModel, VQModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_... | 357 |
"""simple docstring"""
import argparse
from collections import defaultdict
import yaml
a = '''docs/source/en/_toctree.yml'''
def _snake_case ( _snake_case : List[Any] ) -> Optional[Any]:
'''simple docstring'''
_A = defaultdict(_snake_c... | 271 | 0 |
import unittest
from transformers import (
MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
Pipeline,
ZeroShotClassificationPipeline,
pipeline,
)
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_tf, require_torch, slow
f... | 358 |
"""simple docstring"""
from dataclasses import dataclass
from typing import Dict, Optional, Tuple, Union
import torch
import torch.nn as nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .attention_processor import AttentionProcessor, At... | 271 | 0 |
"""simple docstring"""
def _snake_case ( _snake_case : int = 10_00 ) -> int:
'''simple docstring'''
return sum(2 * a * ((a - 1) // 2) for a in range(3 , n + 1 ) )
if __name__ == "__main__":
print(solution())
| 359 |
"""simple docstring"""
from typing import Any, Dict, List, Optional, Tuple, Union
import torch
from torch import nn
from torch.utils.data import DistributedSampler, RandomSampler
from transformers import PreTrainedModel, Trainer, logging
from transformers.integrations import is_fairscale_available
from transfor... | 271 | 0 |
from itertools import count
def _snake_case ( _snake_case : int = 50 ) -> int:
'''simple docstring'''
_A = [1] * min_block_length
for n in count(_snake_case ):
fill_count_functions.append(1 )
for block_length... | 360 |
"""simple docstring"""
import tempfile
import torch
from diffusers import IPNDMScheduler
from .test_schedulers import SchedulerCommonTest
class lowercase_ ( __lowerCAmelCase ):
'''simple docstring'''
UpperCAmelCase : Union[str, Any] = (IPNDMScheduler,)
UpperCAmelCase :... | 271 | 0 |
"""simple docstring"""
def _snake_case ( _snake_case : List[Any] ) -> List[str]:
'''simple docstring'''
_A = len(_snake_case )
_A = sum(_snake_case )
_A = [[False for x in range(s + 1 )] for y in range(... | 361 |
"""simple docstring"""
import logging
import os
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union
from filelock import FileLock
from transformers import PreTrainedTokenizer, is_tf_available, is_torch_available
a = logging.getLogger(__name__)
@da... | 271 | 0 |
"""simple docstring"""
from __future__ import annotations
from collections.abc import Sequence
from typing import Literal
def _snake_case ( _snake_case : str , _snake_case : str ) -> str | Literal[False]:
_A = list(_snake_case )
_A = ... | 362 |
"""simple docstring"""
import enum
import os
from hashlib import shaaaa
from typing import Optional
from .. import config
from .logging import get_logger
a = get_logger(__name__)
class lowercase_ ( enum.Enum ):
'''simple docstring'''
UpperCAmelCase : Optional[int] ... | 271 | 0 |
"""simple docstring"""
from __future__ import annotations
def _snake_case ( _snake_case : list[list[int]] ) -> int:
'''simple docstring'''
for i in range(1 , len(matrix[0] ) ):
matrix[0][i] += matrix[0][i - 1]
# preprocessing the... | 363 |
"""simple docstring"""
import os
from huggingface_hub.constants import HUGGINGFACE_HUB_CACHE, hf_cache_home
a = HUGGINGFACE_HUB_CACHE
a = '''config.json'''
a = '''diffusion_pytorch_model.bin'''
a = '''diffusion_flax_model.msgpack'''
a = '''mode... | 271 | 0 |
"""simple docstring"""
import logging
import os
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union
from filelock import FileLock
from transformers import PreTrainedTokenizer, is_tf_available, is_torch_available
a = logging.getLogger(__name__)
@da... | 364 |
"""simple docstring"""
import contextlib
import copy
import random
from typing import Any, Dict, Iterable, Optional, Union
import numpy as np
import torch
from .utils import deprecate, is_transformers_available
if is_transformers_available():
import transformers
def _snake_case ( _snake... | 271 | 0 |
"""simple docstring"""
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import ... | 365 |
"""simple docstring"""
def _snake_case ( _snake_case : int ) -> list:
'''simple docstring'''
_A = int(_snake_case )
if n_element < 1:
_A = ValueError('a should be a positive number' )
raise my_error
... | 271 | 0 |
"""simple docstring"""
a = [
'''VerificationMode''',
'''Version''',
'''disable_progress_bar''',
'''enable_progress_bar''',
'''is_progress_bar_enabled''',
'''experimental''',
]
from .info_utils import VerificationMode
from .logging import disable_progress_bar, enable_progress_... | 366 |
"""simple docstring"""
from typing import List, Optional, Union
import torch
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
is_accelerate... | 271 | 0 |
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
... | 367 |
"""simple docstring"""
from collections import deque
class lowercase_ :
'''simple docstring'''
def __init__( self : int , _UpperCAmelCase : str , _UpperCAmelCase : int , _UpperCAmelCase : int ):
_A = process_name # process name
_A = ... | 271 | 0 |
"""simple docstring"""
import argparse
import pickle
import numpy as np
import torch
from torch import nn
from transformers import ReformerConfig, ReformerModelWithLMHead
from transformers.utils import logging
logging.set_verbosity_info()
def UpperCAmelCase__ ( _snake_case : Optional[Any... | 368 |
"""simple docstring"""
from sklearn.metrics import fa_score
import datasets
a = '''
The F1 score is the harmonic mean of the precision and recall. It can be computed with the equation:
F1 = 2 * (precision * recall) / (precision + recall)
'''
a = '''
Args:
predictions (`list` of ... | 271 | 0 |
"""simple docstring"""
import gc
import unittest
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DDPMScheduler,
PriorTransformer,
StableUnCLIPPipeline,
UNetaDConditionMod... | 369 |
"""simple docstring"""
import logging
from dataclasses import dataclass, field
from pathlib import Path
from typing import Optional, Union
from .generation.configuration_utils import GenerationConfig
from .training_args import TrainingArguments
from .utils import add_start_docstrings
a = logging.g... | 271 | 0 |
"""simple docstring"""
from __future__ import annotations
a = 10
def _snake_case ( _snake_case : list[int] ) -> list[int]:
'''simple docstring'''
_A = 1
_A = max(_snake_case )
while placement <= max_digit:
... | 370 |
"""simple docstring"""
import argparse
import json
from typing import List
from ltp import LTP
from transformers import BertTokenizer
def _snake_case ( _snake_case : Dict ) -> Any:
'''simple docstring'''
if (
(cp >= 0X4e00 and cp <= 0X9fff)
o... | 271 | 0 |
"""simple docstring"""
import inspect
import os
import re
from transformers.configuration_utils import PretrainedConfig
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python ut... | 371 |
"""simple docstring"""
import secrets
from random import shuffle
from string import ascii_letters, ascii_lowercase, ascii_uppercase, digits, punctuation
def _snake_case ( _snake_case : int = 8 ) -> str:
'''simple docstring'''
_A = ascii_letters + digi... | 271 | 0 |
"""simple docstring"""
import argparse
from collections import defaultdict
import yaml
a = '''docs/source/en/_toctree.yml'''
def _snake_case ( _snake_case : List[Any] ) -> Optional[Any]:
'''simple docstring'''
_A = defaultdict(_snak... | 350 |
"""simple docstring"""
import argparse
import logging
import os
from datetime import datetime
import numpy as np
import torch
from torch import nn
from torch.utils.data import DataLoader, RandomSampler, TensorDataset
from tqdm import tqdm
from transformers import GPTaLMHeadModel
a = logging.getLo... | 271 | 0 |
"""simple docstring"""
import copy
import tempfile
import unittest
from huggingface_hub import HfFolder, delete_repo
from parameterized import parameterized
from requests.exceptions import HTTPError
from transformers import AutoConfig, GenerationConfig
from transformers.testing_utils import TOKEN, USER, is_stag... | 351 |
"""simple docstring"""
def _snake_case ( _snake_case : str ) -> str:
'''simple docstring'''
_A = ''
for ch in key:
if ch == " " or ch not in key_no_dups and ch.isalpha():
key_no_dups += ch
return key_no_dups
d... | 271 | 0 |
"""simple docstring"""
import json
import os
import unittest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_tokenizers
from ...test_tokenization_common import Tokenize... | 352 |
"""simple docstring"""
import inspect
import os
import unittest
from pathlib import Path
import torch
import accelerate
from accelerate.test_utils import execute_subprocess_async
from accelerate.test_utils.testing import run_command
class lowercase_ ( unittest.TestCase ):
'''simple docstr... | 271 | 0 |
"""simple docstring"""
import copy
from typing import TYPE_CHECKING, Any, Mapping, Optional, OrderedDict
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig
if TYPE_CHECKI... | 353 |
"""simple docstring"""
import random
import timeit
from functools import wraps
from typing import Callable, Optional
from ..configuration_utils import PretrainedConfig
from ..models.auto.modeling_tf_auto import TF_MODEL_MAPPING, TF_MODEL_WITH_LM_HEAD_MAPPING
from ..utils import is_pyanvml_available, is_tf_availa... | 271 | 0 |
"""simple docstring"""
import os
from huggingface_hub.constants import HUGGINGFACE_HUB_CACHE, hf_cache_home
a = HUGGINGFACE_HUB_CACHE
a = '''config.json'''
a = '''diffusion_pytorch_model.bin'''
a = '''diffusion_flax_model.msgpack'''
a = '''mode... | 354 |
"""simple docstring"""
import argparse
import os
import torch
from diffusers import (
CMStochasticIterativeScheduler,
ConsistencyModelPipeline,
UNetaDModel,
)
a = {
'''sample_size''': 32,
'''in_channels''': 3,
'''out_channels''': 3,
'''layers_per_block''': 2,
'''nu... | 271 | 0 |
"""simple docstring"""
from pathlib import PurePosixPath
from typing import Optional
import fsspec
from fsspec import AbstractFileSystem
from huggingface_hub.hf_api import DatasetInfo
from ..utils.file_utils import get_authentication_headers_for_url
from ..utils.hub import hf_hub_url
class lowercase_ ( ... | 355 |
"""simple docstring"""
import argparse
import os
import torch
from transformers import (
XLNetConfig,
XLNetForQuestionAnswering,
XLNetForSequenceClassification,
XLNetLMHeadModel,
load_tf_weights_in_xlnet,
)
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
a = ... | 271 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a = {
'''configuration_clap''': [
'''CLAP_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''ClapAudioConfig''',
'''ClapConfig''',
'''C... | 356 |
"""simple docstring"""
from __future__ import annotations
import time
import numpy as np
a = [8, 5, 9, 7]
a = [
[2, 0, 1, 1],
[0, 1, 2, 1],
[4, 0, 0, 3],
[0, 2, 1, 0],
[1, 0, 3, 0],
]
a = [
[3, 2, 1, 4],
[0, 2, 5, 2],
[5, 1, 0, 5],
[1, ... | 271 | 0 |
"""simple docstring"""
import os
import string
import sys
a = 1 << 8
a = {
'''tab''': ord('''\t'''),
'''newline''': ord('''\r'''),
'''esc''': 27,
'''up''': 65 + ARROW_KEY_FLAG,
'''down''': 66 + ARROW_KEY_FLAG,
'''right''': 67 + ARROW_KEY_FLAG,
'''left''': 6... | 357 |
"""simple docstring"""
import argparse
from collections import defaultdict
import yaml
a = '''docs/source/en/_toctree.yml'''
def _snake_case ( _snake_case : List[Any] ) -> Optional[Any]:
'''simple docstring'''
_A = defaultdict(_snake_c... | 271 | 0 |
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import MaskaFormerConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transfo... | 358 |
"""simple docstring"""
from dataclasses import dataclass
from typing import Dict, Optional, Tuple, Union
import torch
import torch.nn as nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .attention_processor import AttentionProcessor, At... | 271 | 0 |
"""simple docstring"""
import argparse
from typing import List
import evaluate
import numpy as np
import torch
from datasets import DatasetDict, load_dataset
# New Code #
# We'll be using StratifiedKFold for this example
from sklearn.model_selection import StratifiedKFold
from torch.optim import AdamW
from torc... | 359 |
"""simple docstring"""
from typing import Any, Dict, List, Optional, Tuple, Union
import torch
from torch import nn
from torch.utils.data import DistributedSampler, RandomSampler
from transformers import PreTrainedModel, Trainer, logging
from transformers.integrations import is_fairscale_available
from transfor... | 271 | 0 |
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin
if is_torch_available():
import torch
if is_vision_a... | 360 |
"""simple docstring"""
import tempfile
import torch
from diffusers import IPNDMScheduler
from .test_schedulers import SchedulerCommonTest
class lowercase_ ( __lowerCAmelCase ):
'''simple docstring'''
UpperCAmelCase : Union[str, Any] = (IPNDMScheduler,)
UpperCAmelCase :... | 271 | 0 |
"""simple docstring"""
def _snake_case ( _snake_case : str ) -> str:
'''simple docstring'''
_A = ''
for ch in key:
if ch == " " or ch not in key_no_dups and ch.isalpha():
key_no_dups += ch
return key_no_dups
def... | 361 |
"""simple docstring"""
import logging
import os
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union
from filelock import FileLock
from transformers import PreTrainedTokenizer, is_tf_available, is_torch_available
a = logging.getLogger(__name__)
@da... | 271 | 0 |
"""simple docstring"""
import logging
import math
import os
from dataclasses import dataclass, field
from glob import glob
from typing import Optional
from torch.utils.data import ConcatDataset
import transformers
from transformers import (
CONFIG_MAPPING,
MODEL_WITH_LM_HEAD_MAPPING,
AutoConfig,
... | 362 |
"""simple docstring"""
import enum
import os
from hashlib import shaaaa
from typing import Optional
from .. import config
from .logging import get_logger
a = get_logger(__name__)
class lowercase_ ( enum.Enum ):
'''simple docstring'''
UpperCAmelCase : Optional[int] ... | 271 | 0 |
"""simple docstring"""
import re
from filelock import FileLock
try:
import nltk
a = True
except (ImportError, ModuleNotFoundError):
a = False
if NLTK_AVAILABLE:
with FileLock('''.lock''') as lock:
nltk.download('''punkt''', quiet=True)
... | 363 |
"""simple docstring"""
import os
from huggingface_hub.constants import HUGGINGFACE_HUB_CACHE, hf_cache_home
a = HUGGINGFACE_HUB_CACHE
a = '''config.json'''
a = '''diffusion_pytorch_model.bin'''
a = '''diffusion_flax_model.msgpack'''
a = '''mode... | 271 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
a = {
'''configuration_rag''': ['''RagConfig'''],
'''retrieval_rag''': ['''RagRetriever'''],
'''tokenization_rag''': ['''RagToke... | 364 |
"""simple docstring"""
import contextlib
import copy
import random
from typing import Any, Dict, Iterable, Optional, Union
import numpy as np
import torch
from .utils import deprecate, is_transformers_available
if is_transformers_available():
import transformers
def _snake_case ( _snake... | 271 | 0 |
"""simple docstring"""
import os
from argparse import ArgumentParser, Namespace
from ..data import SingleSentenceClassificationProcessor as Processor
from ..pipelines import TextClassificationPipeline
from ..utils import is_tf_available, is_torch_available, logging
from . import BaseTransformersCLIC... | 365 |
"""simple docstring"""
def _snake_case ( _snake_case : int ) -> list:
'''simple docstring'''
_A = int(_snake_case )
if n_element < 1:
_A = ValueError('a should be a positive number' )
raise my_error
... | 271 | 0 |
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