code stringlengths 87 55.2k | code_codestyle int64 0 349 | style_context stringlengths 135 49.1k | style_context_codestyle int64 0 349 | label int64 0 1 |
|---|---|---|---|---|
'''simple docstring'''
from typing import List, Optional, Union
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
c... | 22 |
'''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 ... | 22 | 1 |
'''simple docstring'''
def UpperCAmelCase_ ( __lowercase : int ) -> list:
'''simple docstring'''
_UpperCAmelCase = int(__lowercase )
if n_element < 1:
_UpperCAmelCase = ValueError("a should be a positive number" ... | 22 |
'''simple docstring'''
from collections import OrderedDict
from typing import Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...feature_extraction_utils import FeatureExtractionMixin
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimens... | 22 | 1 |
'''simple docstring'''
import inspect
from typing import List, Optional, Tuple, Union
import numpy as np
import PIL
import torch
import torch.utils.checkpoint
from ...models import UNetaDModel, VQModel
from ...schedulers import (
DDIMScheduler,
DPMSolverMultistepScheduler,
EulerAncestralDiscrete... | 22 |
'''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
__SCREAMING_SNAKE_CASE :List[str] = (
'''This metric will... | 22 | 1 |
'''simple docstring'''
__SCREAMING_SNAKE_CASE :List[str] = '''
# Transformers installation
! pip install transformers datasets
# To install from source instead of the last release, comment the command above and uncomment the following one.
# ! pip install git+https://github.com/huggingface/transf... | 22 |
'''simple docstring'''
import argparse
from transformers import (
TapasConfig,
TapasForMaskedLM,
TapasForQuestionAnswering,
TapasForSequenceClassification,
TapasModel,
TapasTokenizer,
load_tf_weights_in_tapas,
)
from transformers.utils import logging
logging.set_verbosity_info()... | 22 | 1 |
'''simple docstring'''
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, requir... | 22 |
'''simple docstring'''
import os
from datetime import datetime as dt
from github import Github
__SCREAMING_SNAKE_CASE :str = [
'''good first issue''',
'''feature request''',
'''wip''',
]
def UpperCAmelCase_ ( ) -> Optional[Any]:
'''simple docstring'''
... | 22 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__SCREAMING_SNAKE_CASE :Any = {
'''configuration_r... | 22 |
'''simple docstring'''
import os
import pytest
import yaml
from datasets.features.features import Features, Value
from datasets.info import DatasetInfo, DatasetInfosDict
@pytest.mark.parametrize(
"files" , [
["full:README.md", "dataset_infos.json"],
["empty:README.md", "dataset_inf... | 22 | 1 |
'''simple docstring'''
import os
import pytest
import yaml
from datasets.features.features import Features, Value
from datasets.info import DatasetInfo, DatasetInfosDict
@pytest.mark.parametrize(
"files" , [
["full:README.md", "dataset_infos.json"],
["empty:README.md", "dataset_inf... | 22 |
'''simple docstring'''
def UpperCAmelCase_ ( __lowercase : str ) -> str:
'''simple docstring'''
return " ".join(
"".join(word[::-1] ) if len(__lowercase ) > 4 else word for word in sentence.split() )
if __name__ == "__main__":
im... | 22 | 1 |
'''simple docstring'''
from __future__ import annotations
def UpperCAmelCase_ ( __lowercase : list[int | float] , __lowercase : int , __lowercase : int ) -> int | float:
'''simple docstring'''
if len(__lowercase ) == 0:
... | 22 |
'''simple docstring'''
def UpperCAmelCase_ ( __lowercase : str ) -> list:
'''simple docstring'''
if n_term == "":
return []
_UpperCAmelCase = []
for temp in range(int(__lowercase ) ):
series.append(f'1/{te... | 22 | 1 |
'''simple docstring'''
import argparse
import json
import torch
from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel
def UpperCAmelCase_ ( __lowercase : Union[str, Any] , __lowercase : List[Any]=1 ) -> Union[str, Any]:
'''simple do... | 22 |
'''simple docstring'''
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 T... | 22 | 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 ... | 22 |
'''simple docstring'''
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available
if is_vision_available():
from PIL import Image
from transformers import AutoProcessor, BertTo... | 22 | 1 |
'''simple docstring'''
import multiprocessing
from typing import TYPE_CHECKING, Optional, Union
from .. import Dataset, Features, config
from ..formatting import query_table
from ..packaged_modules.sql.sql import Sql
from ..utils import logging
from .abc import AbstractDatasetInputStream
if TYPE_CHECKING:
... | 22 |
'''simple docstring'''
import inspect
from typing import List, Optional, Tuple, Union
import numpy as np
import PIL
import torch
import torch.utils.checkpoint
from ...models import UNetaDModel, VQModel
from ...schedulers import (
DDIMScheduler,
DPMSolverMultistepScheduler,
EulerAncestralDiscrete... | 22 | 1 |
'''simple docstring'''
from __future__ import annotations
def UpperCAmelCase_ ( __lowercase : str , __lowercase : list[str] | None = None ) -> list[list[str]]:
'''simple docstring'''
_UpperCAmelCase = word_bank or []
# create... | 22 |
'''simple docstring'''
import string
from math import logaa
def UpperCAmelCase_ ( __lowercase : str , __lowercase : str ) -> int:
'''simple docstring'''
_UpperCAmelCase = document.translate(
str.maketrans("" , "" ... | 22 | 1 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
__SCREAMING_SNAKE_CASE :Dict = {
'''albert-base-v1''': '''https://huggingface.co/albert-base-v1/resolve/main/config.json'... | 22 |
'''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 ..... | 22 | 1 |
'''simple docstring'''
import copy
import os
import tempfile
from unittest import TestCase
from unittest.mock import patch
import numpy as np
import pyarrow as pa
import pyarrow.parquet as pq
import pytest
from datasets.arrow_writer import ArrowWriter, OptimizedTypedSequence, ParquetWriter, TypedSequence
fr... | 22 |
'''simple docstring'''
def UpperCAmelCase_ ( __lowercase : int ) -> int:
'''simple docstring'''
if not isinstance(__lowercase , __lowercase ) or number < 0:
raise ValueError("Input must be a non-negative integer" )
_UpperCAmel... | 22 | 1 |
'''simple docstring'''
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
__SCREAMING_SNAKE_CASE :List[Any] = logging.get_logger(__name__)
__SCREAMING_SNAKE_CASE :Dict = {
'''ut/deta''': '''https://hu... | 22 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Callable
from typing import Any, Generic, TypeVar
__SCREAMING_SNAKE_CASE :Optional[int] = TypeVar('''T''')
class A_ ( Generic[T] ):
def __init__( self : List[Any] , snake_case_ ... | 22 | 1 |
'''simple docstring'''
from __future__ import annotations
def UpperCAmelCase_ ( __lowercase : list[float] , __lowercase : list[float] ) -> float:
'''simple docstring'''
_UpperCAmelCase = sorted(numsa + numsa )
_UpperCAm... | 22 |
'''simple docstring'''
import pytest
from datasets.utils.sharding import _distribute_shards, _number_of_shards_in_gen_kwargs, _split_gen_kwargs
@pytest.mark.parametrize(
"kwargs, expected" , [
({"num_shards": 0, "max_num_jobs": 1}, []),
({"num_shards": 10, "max_num_jobs": 1}, [range... | 22 | 1 |
'''simple docstring'''
import re
import jax.numpy as jnp
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey
from ..utils import logging
__SCREAMING_SNAKE_CASE :Dict = logging.get_logger(__name__)
def UpperCAmelCase_ ( __lowercase : ... | 22 |
'''simple docstring'''
import math
def UpperCAmelCase_ ( __lowercase : int ) -> bool:
'''simple docstring'''
return math.sqrt(__lowercase ) * math.sqrt(__lowercase ) == num
def UpperCAmelCase_ ( __lowercase : int ) -> ... | 22 | 1 |
'''simple docstring'''
import unittest
from typing import Tuple
import torch
from diffusers.utils import floats_tensor, randn_tensor, torch_all_close, torch_device
from diffusers.utils.testing_utils import require_torch
@require_torch
class A_ :
@property
def lowercase ( self :... | 22 |
'''simple docstring'''
import inspect
import tempfile
import unittest
from huggingface_hub import hf_hub_download
from transformers import is_torch_available
from transformers.testing_utils import is_flaky, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test... | 22 | 1 |
'''simple docstring'''
from .integrations import (
is_optuna_available,
is_ray_available,
is_sigopt_available,
is_wandb_available,
run_hp_search_optuna,
run_hp_search_ray,
run_hp_search_sigopt,
run_hp_search_wandb,
)
from .trainer_utils import (
HPSearchBackend,
default... | 22 |
'''simple docstring'''
from .integrations import (
is_optuna_available,
is_ray_available,
is_sigopt_available,
is_wandb_available,
run_hp_search_optuna,
run_hp_search_ray,
run_hp_search_sigopt,
run_hp_search_wandb,
)
from .trainer_utils import (
HPSearchBackend,
default... | 22 | 1 |
'''simple docstring'''
import unittest
from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
if is_torch_available():
import torch
from transformers import AutoModelForImageClassification
if is_vision_... | 22 |
'''simple docstring'''
__SCREAMING_SNAKE_CASE :List[str] = '''0.18.2'''
from .configuration_utils import ConfigMixin
from .utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_inflect_available,
is_invisible_watermark_available,
is_k_diffusion_available,
i... | 22 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import _LazyModule
__SCREAMING_SNAKE_CASE :Optional[int] = {'''tokenization_byt5''': ['''ByT5Tokenizer''']}
if TYPE_CHECKING:
from .tokenization_byta import ByTaTokenizer
else:
import sys
__SCREAMING_SNAKE_CASE ... | 22 |
'''simple docstring'''
import re
from filelock import FileLock
try:
import nltk
__SCREAMING_SNAKE_CASE :Optional[int] = True
except (ImportError, ModuleNotFoundError):
__SCREAMING_SNAKE_CASE :str = False
if NLTK_AVAILABLE:
with FileLock('''.lock''') as lock:
nltk.dow... | 22 | 1 |
'''simple docstring'''
from collections.abc import Iterator, MutableMapping
from dataclasses import dataclass
from typing import Generic, TypeVar
__SCREAMING_SNAKE_CASE :int = TypeVar('''KEY''')
__SCREAMING_SNAKE_CASE :List[Any] = TypeVar('''VAL''')
@dataclass(frozen=lowerCAm... | 22 |
'''simple docstring'''
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import DetrConfig, MaskFormerConfig, SwinConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_g... | 22 | 1 |
'''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
__SCREAMING_SNAKE_CASE :List[str] = (
'''This metric will... | 22 |
'''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 ... | 22 | 1 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__SCREAMING_SNAKE_CASE :Optional[Any] = logging.get_logger(__name... | 22 |
'''simple docstring'''
from collections import OrderedDict
from typing import Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...feature_extraction_utils import FeatureExtractionMixin
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimens... | 22 | 1 |
'''simple docstring'''
import argparse
import os
from pathlib import Path
import fairseq
import torch
from packaging import version
from torch import nn
from transformers import (
BartConfig,
BartForConditionalGeneration,
BartForSequenceClassification,
BartModel,
BartTokenizer,
)
from tr... | 22 |
'''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
__SCREAMING_SNAKE_CASE :List[str] = (
'''This metric will... | 22 | 1 |
'''simple docstring'''
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
__SCREAMING_SNAKE_CASE :List[Any] = 0b1_0_1_1_0_0_1... | 22 |
'''simple docstring'''
import argparse
from transformers import (
TapasConfig,
TapasForMaskedLM,
TapasForQuestionAnswering,
TapasForSequenceClassification,
TapasModel,
TapasTokenizer,
load_tf_weights_in_tapas,
)
from transformers.utils import logging
logging.set_verbosity_info()... | 22 | 1 |
'''simple docstring'''
def UpperCAmelCase_ ( __lowercase : int = 100 ) -> int:
'''simple docstring'''
_UpperCAmelCase = set()
_UpperCAmelCase = 0
_UpperCAmelCase = n + 1 # maximum limit
for a in range(2... | 22 |
'''simple docstring'''
import os
from datetime import datetime as dt
from github import Github
__SCREAMING_SNAKE_CASE :str = [
'''good first issue''',
'''feature request''',
'''wip''',
]
def UpperCAmelCase_ ( ) -> Optional[Any]:
'''simple docstring'''
... | 22 | 1 |
'''simple docstring'''
import warnings
from contextlib import contextmanager
from ...processing_utils import ProcessorMixin
class A_ ( lowerCAmelCase_ ):
_lowerCamelCase : Tuple = """Speech2TextFeatureExtractor"""
_lowerCamelCase : Optional[Any] = """Speech2Tex... | 22 |
'''simple docstring'''
import os
import pytest
import yaml
from datasets.features.features import Features, Value
from datasets.info import DatasetInfo, DatasetInfosDict
@pytest.mark.parametrize(
"files" , [
["full:README.md", "dataset_infos.json"],
["empty:README.md", "dataset_inf... | 22 | 1 |
'''simple docstring'''
def UpperCAmelCase_ ( __lowercase : int ) -> int:
'''simple docstring'''
if not isinstance(__lowercase , __lowercase ):
raise ValueError("multiplicative_persistence() only accepts integral values" )
if nu... | 22 |
'''simple docstring'''
def UpperCAmelCase_ ( __lowercase : str ) -> str:
'''simple docstring'''
return " ".join(
"".join(word[::-1] ) if len(__lowercase ) > 4 else word for word in sentence.split() )
if __name__ == "__main__":
im... | 22 | 1 |
'''simple docstring'''
import os
from datetime import datetime as dt
from github import Github
__SCREAMING_SNAKE_CASE :str = [
'''good first issue''',
'''feature request''',
'''wip''',
]
def UpperCAmelCase_ ( ) -> Optional[Any]:
'''simple docstring'''
... | 22 |
'''simple docstring'''
def UpperCAmelCase_ ( __lowercase : str ) -> list:
'''simple docstring'''
if n_term == "":
return []
_UpperCAmelCase = []
for temp in range(int(__lowercase ) ):
series.append(f'1/{te... | 22 | 1 |
'''simple docstring'''
from transformers import DistilBertTokenizer, DistilBertTokenizerFast
from transformers.testing_utils import require_tokenizers, slow
from ..bert.test_tokenization_bert import BertTokenizationTest
@require_tokenizers
class A_ ( lowerCAmelCase_ ):
_lowerCamelCase : Optiona... | 22 |
'''simple docstring'''
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 T... | 22 | 1 |
'''simple docstring'''
from typing import Dict
import numpy as np
import torch
from . import residue_constants as rc
from .tensor_utils import tensor_tree_map, tree_map
def UpperCAmelCase_ ( __lowercase : Dict[str, torch.Tensor] ) -> Dict[str, torch.Tensor]:
'''simp... | 22 |
'''simple docstring'''
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available
if is_vision_available():
from PIL import Image
from transformers import AutoProcessor, BertTo... | 22 | 1 |
'''simple docstring'''
from __future__ import annotations
def UpperCAmelCase_ ( __lowercase : list[int] ) -> bool:
'''simple docstring'''
return len(set(__lowercase ) ) == len(__lowercase )
if __name__ == "__main__":
import doctest
doc... | 22 |
'''simple docstring'''
import inspect
from typing import List, Optional, Tuple, Union
import numpy as np
import PIL
import torch
import torch.utils.checkpoint
from ...models import UNetaDModel, VQModel
from ...schedulers import (
DDIMScheduler,
DPMSolverMultistepScheduler,
EulerAncestralDiscrete... | 22 | 1 |
'''simple docstring'''
from io import BytesIO
from typing import List, Union
import requests
from ..utils import add_end_docstrings, is_decord_available, is_torch_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_decord_available():
import numpy as np
from deco... | 22 |
'''simple docstring'''
import string
from math import logaa
def UpperCAmelCase_ ( __lowercase : str , __lowercase : str ) -> int:
'''simple docstring'''
_UpperCAmelCase = document.translate(
str.maketrans("" , "" ... | 22 | 1 |
'''simple docstring'''
import unittest
from transformers import (
MODEL_FOR_OBJECT_DETECTION_MAPPING,
AutoFeatureExtractor,
AutoModelForObjectDetection,
ObjectDetectionPipeline,
is_vision_available,
pipeline,
)
from transformers.testing_utils import (
is_pipeline_test,
nested_... | 22 |
'''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 ..... | 22 | 1 |
'''simple docstring'''
from collections.abc import Callable
def UpperCAmelCase_ ( __lowercase : Callable[[float], float] , __lowercase : float , __lowercase : float ) -> float:
'''simple docstring'''
_UpperCAmelCase ... | 22 |
'''simple docstring'''
def UpperCAmelCase_ ( __lowercase : int ) -> int:
'''simple docstring'''
if not isinstance(__lowercase , __lowercase ) or number < 0:
raise ValueError("Input must be a non-negative integer" )
_UpperCAmel... | 22 | 1 |
'''simple docstring'''
import copy
from typing import Any, Dict, List, Optional, Union
import numpy as np
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
fr... | 22 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Callable
from typing import Any, Generic, TypeVar
__SCREAMING_SNAKE_CASE :Optional[int] = TypeVar('''T''')
class A_ ( Generic[T] ):
def __init__( self : List[Any] , snake_case_ ... | 22 | 1 |
'''simple docstring'''
from __future__ import annotations
def UpperCAmelCase_ ( __lowercase : dict , __lowercase : str ) -> set[str]:
'''simple docstring'''
_UpperCAmelCase , _UpperCAmelCase = set(__lowercase ), [star... | 22 |
'''simple docstring'''
import pytest
from datasets.utils.sharding import _distribute_shards, _number_of_shards_in_gen_kwargs, _split_gen_kwargs
@pytest.mark.parametrize(
"kwargs, expected" , [
({"num_shards": 0, "max_num_jobs": 1}, []),
({"num_shards": 10, "max_num_jobs": 1}, [range... | 22 | 1 |
'''simple docstring'''
def UpperCAmelCase_ ( __lowercase : int ) -> bool:
'''simple docstring'''
if not isinstance(__lowercase , __lowercase ):
_UpperCAmelCase = f'Input value of [number={number}] must be an integer'
... | 22 |
'''simple docstring'''
import math
def UpperCAmelCase_ ( __lowercase : int ) -> bool:
'''simple docstring'''
return math.sqrt(__lowercase ) * math.sqrt(__lowercase ) == num
def UpperCAmelCase_ ( __lowercase : int ) -> ... | 22 | 1 |
'''simple docstring'''
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available
if is_vision_available():
from PIL import Image
from transformers import AutoProcessor, BertTo... | 22 |
'''simple docstring'''
import inspect
import tempfile
import unittest
from huggingface_hub import hf_hub_download
from transformers import is_torch_available
from transformers.testing_utils import is_flaky, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test... | 22 | 1 |
'''simple docstring'''
from __future__ import annotations
import collections
import tempfile
import unittest
import numpy as np
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import is_tf_available, is_vision_available
from ...test_modeling_tf_common import... | 22 |
'''simple docstring'''
from .integrations import (
is_optuna_available,
is_ray_available,
is_sigopt_available,
is_wandb_available,
run_hp_search_optuna,
run_hp_search_ray,
run_hp_search_sigopt,
run_hp_search_wandb,
)
from .trainer_utils import (
HPSearchBackend,
default... | 22 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__SCREAMING_SNAKE_CASE :Any = logging.get_logger(__name__)
__SCREAMING_SNAKE_CASE :int = {'''openai-gpt''': '''https://huggingface.co/openai-gpt/resolve/main/config.json'''}
... | 22 |
'''simple docstring'''
__SCREAMING_SNAKE_CASE :List[str] = '''0.18.2'''
from .configuration_utils import ConfigMixin
from .utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_inflect_available,
is_invisible_watermark_available,
is_k_diffusion_available,
i... | 22 | 1 |
'''simple docstring'''
__SCREAMING_SNAKE_CASE :Union[str, Any] = range(2, 20 + 1)
__SCREAMING_SNAKE_CASE :Optional[Any] = [10**k for k in range(ks[-1] + 1)]
__SCREAMING_SNAKE_CASE :dict[int, dict[int, list[list[int]]]] = {}
def UpperCAmelCase_ ( __lowercase ... | 22 |
'''simple docstring'''
import re
from filelock import FileLock
try:
import nltk
__SCREAMING_SNAKE_CASE :Optional[int] = True
except (ImportError, ModuleNotFoundError):
__SCREAMING_SNAKE_CASE :str = False
if NLTK_AVAILABLE:
with FileLock('''.lock''') as lock:
nltk.dow... | 22 | 1 |
'''simple docstring'''
import numpy as np
import torch
from torch.nn import CrossEntropyLoss
from transformers import AutoModelForCausalLM, AutoTokenizer
import datasets
from datasets import logging
__SCREAMING_SNAKE_CASE :int = '''\
'''
__SCREAMING_SNAKE_CASE :List[Any] = '''
P... | 22 |
'''simple docstring'''
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import DetrConfig, MaskFormerConfig, SwinConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_g... | 22 | 1 |
'''simple docstring'''
from __future__ import annotations
import time
from collections.abc import Sequence
from random import randint
from matplotlib import pyplot as plt
def UpperCAmelCase_ ( __lowercase : Sequence[float] , __lowercase : int , __lowercase ... | 22 |
'''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 ... | 22 | 1 |
'''simple docstring'''
import unittest
from transformers.testing_utils import require_bsa
from transformers.utils import is_bsa_available
from ...test_feature_extraction_common import FeatureExtractionSavingTestMixin
if is_bsa_available():
from transformers import MarkupLMFeatureExtractor
class ... | 22 |
'''simple docstring'''
from collections import OrderedDict
from typing import Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...feature_extraction_utils import FeatureExtractionMixin
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimens... | 22 | 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... | 22 |
'''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
__SCREAMING_SNAKE_CASE :List[str] = (
'''This metric will... | 22 | 1 |
'''simple docstring'''
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_herbert import HerbertTokenizer
__SCREAMING_SNAKE_CASE :Any = logging.get_logger(__name__)
__SCREAMING_SNAKE_CASE ... | 22 |
'''simple docstring'''
import argparse
from transformers import (
TapasConfig,
TapasForMaskedLM,
TapasForQuestionAnswering,
TapasForSequenceClassification,
TapasModel,
TapasTokenizer,
load_tf_weights_in_tapas,
)
from transformers.utils import logging
logging.set_verbosity_info()... | 22 | 1 |
'''simple docstring'''
def UpperCAmelCase_ ( ) -> list[list[int]]:
'''simple docstring'''
return [list(range(1000 - i , -1000 - i , -1 ) ) for i in range(1000 )]
__SCREAMING_SNAKE_CASE :str = generate_large_matrix()
__SCREAMING_SNAK... | 22 |
'''simple docstring'''
import os
from datetime import datetime as dt
from github import Github
__SCREAMING_SNAKE_CASE :str = [
'''good first issue''',
'''feature request''',
'''wip''',
]
def UpperCAmelCase_ ( ) -> Optional[Any]:
'''simple docstring'''
... | 22 | 1 |
'''simple docstring'''
import json
import logging
import os
import socket
import git
import numpy as np
import torch
logging.basicConfig(
format='''%(asctime)s - %(levelname)s - %(name)s - PID: %(process)d - %(message)s''',
datefmt='''%m/%d/%Y %H:%M:%S''',
level=logging.INFO,
)
__SCREAMING_SNA... | 22 |
'''simple docstring'''
import os
import pytest
import yaml
from datasets.features.features import Features, Value
from datasets.info import DatasetInfo, DatasetInfosDict
@pytest.mark.parametrize(
"files" , [
["full:README.md", "dataset_infos.json"],
["empty:README.md", "dataset_inf... | 22 | 1 |
'''simple docstring'''
import colorsys
from PIL import Image # type: ignore
def UpperCAmelCase_ ( __lowercase : float , __lowercase : float , __lowercase : int ) -> float:
'''simple docstring'''
_UpperCAmelCase = ... | 22 |
'''simple docstring'''
def UpperCAmelCase_ ( __lowercase : str ) -> str:
'''simple docstring'''
return " ".join(
"".join(word[::-1] ) if len(__lowercase ) > 4 else word for word in sentence.split() )
if __name__ == "__main__":
im... | 22 | 1 |
'''simple docstring'''
from itertools import permutations
def UpperCAmelCase_ ( __lowercase : tuple ) -> bool:
'''simple docstring'''
if num[3] % 2 != 0:
return False
if (num[2] + num[3] + num[4]) % 3 != 0:
return False
if num[5... | 22 |
'''simple docstring'''
def UpperCAmelCase_ ( __lowercase : str ) -> list:
'''simple docstring'''
if n_term == "":
return []
_UpperCAmelCase = []
for temp in range(int(__lowercase ) ):
series.append(f'1/{te... | 22 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__SCREAMING_SNAKE_CASE :str = logging.get_logger(__name__)
class A_ ( lowerCAmelCase_ ):
_lowerCamelCase : Tuple = """timm_backbone"""
def __init__( sel... | 22 |
'''simple docstring'''
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 T... | 22 | 1 |
'''simple docstring'''
import tempfile
import unittest
import numpy as np
from diffusers import (
DDIMScheduler,
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionPipeline,
PNDMScheduler,
)
from diffuser... | 22 |
'''simple docstring'''
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available
if is_vision_available():
from PIL import Image
from transformers import AutoProcessor, BertTo... | 22 | 1 |
'''simple docstring'''
import json
import os
from typing import Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
__SCREAMING_SNAKE_CASE :int = logging.get_logger(__name__)
__SCREAMING_SNAKE_CASE :Optional[Any] = {'''vocab_file''':... | 22 |
'''simple docstring'''
import inspect
from typing import List, Optional, Tuple, Union
import numpy as np
import PIL
import torch
import torch.utils.checkpoint
from ...models import UNetaDModel, VQModel
from ...schedulers import (
DDIMScheduler,
DPMSolverMultistepScheduler,
EulerAncestralDiscrete... | 22 | 1 |
'''simple docstring'''
import unittest
from transformers import SPIECE_UNDERLINE, ReformerTokenizer, ReformerTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property
from ...test_tokeniza... | 22 |
'''simple docstring'''
import string
from math import logaa
def UpperCAmelCase_ ( __lowercase : str , __lowercase : str ) -> int:
'''simple docstring'''
_UpperCAmelCase = document.translate(
str.maketrans("" , "" ... | 22 | 1 |
'''simple docstring'''
from math import factorial
def UpperCAmelCase_ ( __lowercase : int , __lowercase : int , __lowercase : float ) -> float:
'''simple docstring'''
if successes > trials:
raise ValueError("successe... | 22 |
'''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 ..... | 22 | 1 |
'''simple docstring'''
import flax.linen as nn
import jax
import jax.numpy as jnp
class A_ ( nn.Module ):
_lowerCamelCase : int
_lowerCamelCase : jnp.dtype = jnp.floataa
def lowercase ( self : int ):
_UpperCAmelCase = nn.Conv(
... | 22 |
'''simple docstring'''
def UpperCAmelCase_ ( __lowercase : int ) -> int:
'''simple docstring'''
if not isinstance(__lowercase , __lowercase ) or number < 0:
raise ValueError("Input must be a non-negative integer" )
_UpperCAmel... | 22 | 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
__SCREAMING_SNAKE_CASE :Union[str, Any] = [
os.path.join(os.path.dirname(__file__), dirname)
f... | 22 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Callable
from typing import Any, Generic, TypeVar
__SCREAMING_SNAKE_CASE :Optional[int] = TypeVar('''T''')
class A_ ( Generic[T] ):
def __init__( self : List[Any] , snake_case_ ... | 22 | 1 |
'''simple docstring'''
def UpperCAmelCase_ ( __lowercase : int , __lowercase : Optional[int] ) -> str:
'''simple docstring'''
_UpperCAmelCase = 0
_UpperCAmelCase = len(__lowercase ) - 1
while left <= ... | 22 |
'''simple docstring'''
import pytest
from datasets.utils.sharding import _distribute_shards, _number_of_shards_in_gen_kwargs, _split_gen_kwargs
@pytest.mark.parametrize(
"kwargs, expected" , [
({"num_shards": 0, "max_num_jobs": 1}, []),
({"num_shards": 10, "max_num_jobs": 1}, [range... | 22 | 1 |
'''simple docstring'''
def UpperCAmelCase_ ( __lowercase : int = 100_0000 ) -> int:
'''simple docstring'''
_UpperCAmelCase = [i - 1 for i in range(limit + 1 )]
for i in range(2 , limit + 1 ):
if phi[i] == i - 1:
... | 22 |
'''simple docstring'''
import math
def UpperCAmelCase_ ( __lowercase : int ) -> bool:
'''simple docstring'''
return math.sqrt(__lowercase ) * math.sqrt(__lowercase ) == num
def UpperCAmelCase_ ( __lowercase : int ) -> ... | 22 | 1 |
'''simple docstring'''
from __future__ import annotations
import math
from collections import Counter
from string import ascii_lowercase
def UpperCAmelCase_ ( __lowercase : str ) -> None:
'''simple docstring'''
_UpperCAmelCase , _UpperCAmelCase ... | 22 |
'''simple docstring'''
import inspect
import tempfile
import unittest
from huggingface_hub import hf_hub_download
from transformers import is_torch_available
from transformers.testing_utils import is_flaky, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test... | 22 | 1 |
'''simple docstring'''
import logging
from transformers import PretrainedConfig
__SCREAMING_SNAKE_CASE :Tuple = logging.getLogger(__name__)
__SCREAMING_SNAKE_CASE :str = {
'''bertabs-finetuned-cnndm''': '''https://huggingface.co/remi/bertabs-finetuned-cnndm-extractive-abstra... | 22 |
'''simple docstring'''
from .integrations import (
is_optuna_available,
is_ray_available,
is_sigopt_available,
is_wandb_available,
run_hp_search_optuna,
run_hp_search_ray,
run_hp_search_sigopt,
run_hp_search_wandb,
)
from .trainer_utils import (
HPSearchBackend,
default... | 22 | 1 |
'''simple docstring'''
from itertools import product
def UpperCAmelCase_ ( __lowercase : int , __lowercase : int ) -> list[int]:
'''simple docstring'''
_UpperCAmelCase = sides_number
_UpperCAmelCase = max_face_nu... | 22 |
'''simple docstring'''
__SCREAMING_SNAKE_CASE :List[str] = '''0.18.2'''
from .configuration_utils import ConfigMixin
from .utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_inflect_available,
is_invisible_watermark_available,
is_k_diffusion_available,
i... | 22 | 1 |
'''simple docstring'''
from __future__ import annotations
from collections import namedtuple
from dataclasses import dataclass
@dataclass
class A_ :
_lowerCamelCase : int
_lowerCamelCase : TreeNode | None = None
_lowerCamelCase : TreeNode | None = None
__SC... | 22 |
'''simple docstring'''
import re
from filelock import FileLock
try:
import nltk
__SCREAMING_SNAKE_CASE :Optional[int] = True
except (ImportError, ModuleNotFoundError):
__SCREAMING_SNAKE_CASE :str = False
if NLTK_AVAILABLE:
with FileLock('''.lock''') as lock:
nltk.dow... | 22 | 1 |
'''simple docstring'''
import argparse
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 acc... | 22 |
'''simple docstring'''
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import DetrConfig, MaskFormerConfig, SwinConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_g... | 22 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__SCREAMING_SNAKE_CASE :Optional[Any] = logging.get_logger(__name__)
__SCREAMING_SNAKE_CASE :Dict = {
'''microsoft/cvt-13''': '''https://huggingface.co/microsoft/cvt-13/reso... | 22 |
'''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 ... | 22 | 1 |
'''simple docstring'''
import argparse
import json
import os
import re
import shutil
import torch
from transformers import BioGptConfig, BioGptForCausalLM
from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES
from transformers.tokenization_utils_base import TOKENIZER_CONFIG_FILE
from ... | 22 |
'''simple docstring'''
from collections import OrderedDict
from typing import Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...feature_extraction_utils import FeatureExtractionMixin
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimens... | 22 | 1 |
'''simple docstring'''
import os
import unittest
from transformers import MobileBertTokenizer, MobileBertTokenizerFast
from transformers.models.bert.tokenization_bert import (
VOCAB_FILES_NAMES,
BasicTokenizer,
WordpieceTokenizer,
_is_control,
_is_punctuation,
_is_whitespace,
)
from t... | 22 |
'''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
__SCREAMING_SNAKE_CASE :List[str] = (
'''This metric will... | 22 | 1 |
'''simple docstring'''
import functools
from typing import Any
def UpperCAmelCase_ ( __lowercase : str , __lowercase : list[str] ) -> bool:
'''simple docstring'''
if not isinstance(__lowercase , __lowercase ) or len(__lowercase ... | 22 |
'''simple docstring'''
import argparse
from transformers import (
TapasConfig,
TapasForMaskedLM,
TapasForQuestionAnswering,
TapasForSequenceClassification,
TapasModel,
TapasTokenizer,
load_tf_weights_in_tapas,
)
from transformers.utils import logging
logging.set_verbosity_info()... | 22 | 1 |
'''simple docstring'''
import inspect
import re
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 utils/check_config_docstrings.py
__SCREAMING_SNAKE_CASE :Optional[Any] = ... | 22 |
'''simple docstring'''
import os
from datetime import datetime as dt
from github import Github
__SCREAMING_SNAKE_CASE :str = [
'''good first issue''',
'''feature request''',
'''wip''',
]
def UpperCAmelCase_ ( ) -> Optional[Any]:
'''simple docstring'''
... | 22 | 1 |
'''simple docstring'''
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
# Register SEW's fairseq modules
from sew_asapp import tasks # noqa: F401
from transformers import (
SEWConfig,
SEWForCTC,
SEWModel,
WavaVecaCTCTokenizer,
WavaVe... | 22 |
'''simple docstring'''
import os
import pytest
import yaml
from datasets.features.features import Features, Value
from datasets.info import DatasetInfo, DatasetInfosDict
@pytest.mark.parametrize(
"files" , [
["full:README.md", "dataset_infos.json"],
["empty:README.md", "dataset_inf... | 22 | 1 |
'''simple docstring'''
import sys
from .dependency_versions_table import deps
from .utils.versions import require_version, require_version_core
# define which module versions we always want to check at run time
# (usually the ones defined in `install_requires` in setup.py)
#
# order specific notes:
# - tqd... | 22 |
'''simple docstring'''
def UpperCAmelCase_ ( __lowercase : str ) -> str:
'''simple docstring'''
return " ".join(
"".join(word[::-1] ) if len(__lowercase ) > 4 else word for word in sentence.split() )
if __name__ == "__main__":
im... | 22 | 1 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import is_tf_available, is_torch_available
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, SMALL_MODEL_IDENTIFIER, is_pt_tf_cross_test, slow
if is_tf_available():
from transformers import (
... | 22 |
'''simple docstring'''
def UpperCAmelCase_ ( __lowercase : str ) -> list:
'''simple docstring'''
if n_term == "":
return []
_UpperCAmelCase = []
for temp in range(int(__lowercase ) ):
series.append(f'1/{te... | 22 | 1 |
'''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_c... | 22 |
'''simple docstring'''
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 T... | 22 | 1 |
'''simple docstring'''
import re
from filelock import FileLock
try:
import nltk
__SCREAMING_SNAKE_CASE :Optional[int] = True
except (ImportError, ModuleNotFoundError):
__SCREAMING_SNAKE_CASE :str = False
if NLTK_AVAILABLE:
with FileLock('''.lock''') as lock:
nltk.dow... | 22 |
'''simple docstring'''
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available
if is_vision_available():
from PIL import Image
from transformers import AutoProcessor, BertTo... | 22 | 1 |
'''simple docstring'''
def UpperCAmelCase_ ( __lowercase : list ) -> list:
'''simple docstring'''
if len(__lowercase ) <= 1:
return lst
_UpperCAmelCase = 1
while i < len(__lowercase ):
if lst[i - 1] <= l... | 22 |
'''simple docstring'''
import inspect
from typing import List, Optional, Tuple, Union
import numpy as np
import PIL
import torch
import torch.utils.checkpoint
from ...models import UNetaDModel, VQModel
from ...schedulers import (
DDIMScheduler,
DPMSolverMultistepScheduler,
EulerAncestralDiscrete... | 22 | 1 |
'''simple docstring'''
from abc import ABC, abstractmethod
from argparse import ArgumentParser
class A_ ( lowerCAmelCase_ ):
@staticmethod
@abstractmethod
def lowercase ( snake_case_ : ArgumentParser ):
raise NotImplementedError()
@abstractmethod
def lo... | 22 |
'''simple docstring'''
import string
from math import logaa
def UpperCAmelCase_ ( __lowercase : str , __lowercase : str ) -> int:
'''simple docstring'''
_UpperCAmelCase = document.translate(
str.maketrans("" , "" ... | 22 | 1 |
'''simple docstring'''
from __future__ import annotations
from statistics import mean
def UpperCAmelCase_ ( __lowercase : list[int] , __lowercase : list[int] , __lowercase : int ) -> list[int]:
'''simple docstring'''
_UpperCA... | 22 |
'''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 ..... | 22 | 1 |
'''simple docstring'''
import itertools
import json
import os
import unittest
from transformers import AddedToken, RobertaTokenizer, RobertaTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow
from ...tes... | 22 |
'''simple docstring'''
def UpperCAmelCase_ ( __lowercase : int ) -> int:
'''simple docstring'''
if not isinstance(__lowercase , __lowercase ) or number < 0:
raise ValueError("Input must be a non-negative integer" )
_UpperCAmel... | 22 | 1 |
'''simple docstring'''
# this script reports modified .py files under the desired list of top-level sub-dirs passed as a list of arguments, e.g.:
# python ./utils/get_modified_files.py utils src tests examples
#
# it uses git to find the forking point and which files were modified - i.e. files not under git... | 22 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Callable
from typing import Any, Generic, TypeVar
__SCREAMING_SNAKE_CASE :Optional[int] = TypeVar('''T''')
class A_ ( Generic[T] ):
def __init__( self : List[Any] , snake_case_ ... | 22 | 1 |
'''simple docstring'''
import torch
from transformers import AutoModel
class A_ ( torch.nn.Module ):
def __init__( self : Union[str, Any] , snake_case_ : Any="sayef/fsner-bert-base-uncased" ):
super(snake_case_ , self ).__init__()
_UpperCAmelCase ... | 22 |
'''simple docstring'''
import pytest
from datasets.utils.sharding import _distribute_shards, _number_of_shards_in_gen_kwargs, _split_gen_kwargs
@pytest.mark.parametrize(
"kwargs, expected" , [
({"num_shards": 0, "max_num_jobs": 1}, []),
({"num_shards": 10, "max_num_jobs": 1}, [range... | 22 | 1 |
'''simple docstring'''
import warnings
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class A_ ( lowerCAme... | 22 |
'''simple docstring'''
import math
def UpperCAmelCase_ ( __lowercase : int ) -> bool:
'''simple docstring'''
return math.sqrt(__lowercase ) * math.sqrt(__lowercase ) == num
def UpperCAmelCase_ ( __lowercase : int ) -> ... | 22 | 1 |
'''simple docstring'''
import unittest
from transformers import LiltConfig, 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_commo... | 22 |
'''simple docstring'''
import inspect
import tempfile
import unittest
from huggingface_hub import hf_hub_download
from transformers import is_torch_available
from transformers.testing_utils import is_flaky, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test... | 22 | 1 |
'''simple docstring'''
# Copyright 2022 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... | 22 |
'''simple docstring'''
from .integrations import (
is_optuna_available,
is_ray_available,
is_sigopt_available,
is_wandb_available,
run_hp_search_optuna,
run_hp_search_ray,
run_hp_search_sigopt,
run_hp_search_wandb,
)
from .trainer_utils import (
HPSearchBackend,
default... | 22 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tensorflow_text_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__SCREAMING_SNAKE_CASE :Union[str, Any... | 22 |
'''simple docstring'''
__SCREAMING_SNAKE_CASE :List[str] = '''0.18.2'''
from .configuration_utils import ConfigMixin
from .utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_inflect_available,
is_invisible_watermark_available,
is_k_diffusion_available,
i... | 22 | 1 |
'''simple docstring'''
import numpy as np
from cva import COLOR_BGR2GRAY, cvtColor, imread
from numpy import array, uinta
from PIL import Image
from digital_image_processing import change_contrast as cc
from digital_image_processing import convert_to_negative as cn
from digital_image_processing import sepia ... | 22 |
'''simple docstring'''
import re
from filelock import FileLock
try:
import nltk
__SCREAMING_SNAKE_CASE :Optional[int] = True
except (ImportError, ModuleNotFoundError):
__SCREAMING_SNAKE_CASE :str = False
if NLTK_AVAILABLE:
with FileLock('''.lock''') as lock:
nltk.dow... | 22 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__SCREAMING_SNAKE_CASE :str = {
'''configuration_roformer''... | 22 |
'''simple docstring'''
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import DetrConfig, MaskFormerConfig, SwinConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_g... | 22 | 1 |
'''simple docstring'''
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__SCREAMING_SNAKE_CASE :int = logging.get_logger(__name__)
__SCREAMING_SNAKE_CASE :List[Any] = {
'''microsoft/unispeech-large-1500h-cv''': ... | 22 |
'''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 ... | 22 | 1 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_layoutlmva import LayoutLMvaImageProcessor
__SCREAMING_SNAKE_CASE :List[Any] = logging.get_logger(__name__)
class A_ ( lowerCAmelCase_ ):
def __init__( self : str , *snake... | 22 |
'''simple docstring'''
from collections import OrderedDict
from typing import Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...feature_extraction_utils import FeatureExtractionMixin
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimens... | 22 | 1 |
'''simple docstring'''
from unittest import TestCase
from datasets import Dataset
from minhash_deduplication import deduplicate_dataset, make_duplicate_clusters
def UpperCAmelCase_ ( ) -> Any:
'''simple docstring'''
_UpperCAmelCase = {
"repo_name": ["tes... | 22 |
'''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
__SCREAMING_SNAKE_CASE :List[str] = (
'''This metric will... | 22 | 1 |
'''simple docstring'''
import datasets
__SCREAMING_SNAKE_CASE :List[Any] = '''\
@InProceedings{conneau2018xnli,
author = "Conneau, Alexis
and Rinott, Ruty
and Lample, Guillaume
and Williams, Adina
and Bowman, Samuel R.
... | 22 |
'''simple docstring'''
import argparse
from transformers import (
TapasConfig,
TapasForMaskedLM,
TapasForQuestionAnswering,
TapasForSequenceClassification,
TapasModel,
TapasTokenizer,
load_tf_weights_in_tapas,
)
from transformers.utils import logging
logging.set_verbosity_info()... | 22 | 1 |
'''simple docstring'''
__SCREAMING_SNAKE_CASE :int = {0: [2, 3], 1: [0], 2: [1], 3: [4], 4: []}
__SCREAMING_SNAKE_CASE :Any = {0: [1, 2, 3], 1: [2], 2: [0], 3: [4], 4: [5], 5: [3]}
def UpperCAmelCase_ ( __lowercase : dict[int, list[int]] , __lowercase ... | 22 |
'''simple docstring'''
import os
from datetime import datetime as dt
from github import Github
__SCREAMING_SNAKE_CASE :str = [
'''good first issue''',
'''feature request''',
'''wip''',
]
def UpperCAmelCase_ ( ) -> Optional[Any]:
'''simple docstring'''
... | 22 | 1 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_beit import BeitImageProcessor
__SCREAMING_SNAKE_CASE :Dict = logging.get_logger(__name__)
class A_ ( lowerCAmelCase_ ):
def __init__( self : Dict , *snake_case_ : ... | 22 |
'''simple docstring'''
import os
import pytest
import yaml
from datasets.features.features import Features, Value
from datasets.info import DatasetInfo, DatasetInfosDict
@pytest.mark.parametrize(
"files" , [
["full:README.md", "dataset_infos.json"],
["empty:README.md", "dataset_inf... | 22 | 1 |
'''simple docstring'''
def UpperCAmelCase_ ( __lowercase : Union[str, Any] ) -> List[Any]:
'''simple docstring'''
_UpperCAmelCase = len(__lowercase )
_UpperCAmelCase = sum(__lowercase )
_UpperCAmelCase ... | 22 |
'''simple docstring'''
def UpperCAmelCase_ ( __lowercase : str ) -> str:
'''simple docstring'''
return " ".join(
"".join(word[::-1] ) if len(__lowercase ) > 4 else word for word in sentence.split() )
if __name__ == "__main__":
im... | 22 | 1 |
'''simple docstring'''
import json
import os
from datetime import date
from pathlib import Path
from tabulate import DataRow, TableFormat, tabulate
__SCREAMING_SNAKE_CASE :Optional[int] = TableFormat(
lineabove=None,
linebelowheader=None,
linebetweenrows=None,
linebelow=None,
... | 22 |
'''simple docstring'''
def UpperCAmelCase_ ( __lowercase : str ) -> list:
'''simple docstring'''
if n_term == "":
return []
_UpperCAmelCase = []
for temp in range(int(__lowercase ) ):
series.append(f'1/{te... | 22 | 1 |
'''simple docstring'''
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional
from packaging import version
if TYPE_CHECKING:
from ... import PreTrainedTokenizer, TensorType
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWith... | 22 |
'''simple docstring'''
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 T... | 22 | 1 |
'''simple docstring'''
import os
import tempfile
import unittest
from pathlib import Path
from transformers import AutoConfig, is_torch_available
from transformers.testing_utils import require_torch, torch_device
if is_torch_available():
from transformers import PyTorchBenchmark, PyTorchBenchmarkArgument... | 22 |
'''simple docstring'''
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available
if is_vision_available():
from PIL import Image
from transformers import AutoProcessor, BertTo... | 22 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
__SCREAMING_SNAKE_CASE :List[Any] = {
... | 22 |
'''simple docstring'''
import inspect
from typing import List, Optional, Tuple, Union
import numpy as np
import PIL
import torch
import torch.utils.checkpoint
from ...models import UNetaDModel, VQModel
from ...schedulers import (
DDIMScheduler,
DPMSolverMultistepScheduler,
EulerAncestralDiscrete... | 22 | 1 |
'''simple docstring'''
import pytest
from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.text import TextDatasetReader
from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases
def UpperCAmelCase_ ( __lowercase : int , ... | 22 |
'''simple docstring'''
import string
from math import logaa
def UpperCAmelCase_ ( __lowercase : str , __lowercase : str ) -> int:
'''simple docstring'''
_UpperCAmelCase = document.translate(
str.maketrans("" , "" ... | 22 | 1 |
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