



"""simple docstring"""
import re
from pathlib import Path
from unittest import TestCase

import pytest



@pytest.mark.integration
class       SCREAMING_SNAKE_CASE__  (					UpperCAmelCase__ ):

   def 	SCREAMING_SNAKE_CASE     (   self      ,     _SCREAMING_SNAKE_CASE						)  ->      Optional[Any]:





    '''simple docstring'''




    with open(_SCREAMING_SNAKE_CASE      ,     encoding="""utf-8"""						) as input_file:
     UpperCAmelCase	:					List[str]	=	re.compile(r"""(?!.*\b(?:encoding|rb|w|wb|w+|wb+|ab|ab+)\b)(?<=\s)(open)\((.*)\)"""						)
     UpperCAmelCase	:					str	=	input_file.read()
     UpperCAmelCase	:					int	=	regexp.search(_SCREAMING_SNAKE_CASE						)

    return match

   def 	SCREAMING_SNAKE_CASE     (   self      ,     _SCREAMING_SNAKE_CASE						)  ->      Any:





    '''simple docstring'''




    with open(_SCREAMING_SNAKE_CASE      ,     encoding="""utf-8"""						) as input_file:
     UpperCAmelCase	:					Union[str, Any]	=	re.compile(r"""#[^\r\n]*print\(|\"[^\r\n]*print\(|\"\"\".*?print\(.*?\"\"\"|(print\()"""      ,     re.DOTALL						)
     UpperCAmelCase	:					Optional[Any]	=	input_file.read()
     # use `re.finditer` to handle the case where the ignored groups would be matched first by `re.search`
     UpperCAmelCase	:					int	=	regexp.finditer(_SCREAMING_SNAKE_CASE						)

    UpperCAmelCase	:					Optional[int]	=	[match for match in matches if match is not None and match.group(1						) is not None]
    return matches[0] if matches else None

   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      int:





    '''simple docstring'''




    UpperCAmelCase	:					Dict	=	Path("""./datasets"""						)
    UpperCAmelCase	:					Optional[int]	=	list(dataset_paths.absolute().glob("""**/*.py"""						)						)

    for dataset in dataset_files:
     if self._no_encoding_on_file_open(str(_SCREAMING_SNAKE_CASE						)						):
      raise AssertionError(F"open(...) must use utf-8 encoding in {dataset}"						)




   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      List[Any]:





    '''simple docstring'''




    UpperCAmelCase	:					Any	=	Path("""./datasets"""						)
    UpperCAmelCase	:					Optional[Any]	=	list(dataset_paths.absolute().glob("""**/*.py"""						)						)

    for dataset in dataset_files:
     if self._no_print_statements(str(_SCREAMING_SNAKE_CASE						)						):
      raise AssertionError(F"print statement found in {dataset}. Use datasets.logger/logging instead."						)





"""simple docstring"""
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


A:   str								=		{
    "configuration_distilbert": [
        "DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "DistilBertConfig",
        "DistilBertOnnxConfig",
    ],
    "tokenization_distilbert": ["DistilBertTokenizer"],
}

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A:   Dict								=		["DistilBertTokenizerFast"]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A:   List[Any]								=		[
          "DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
          "DistilBertForMaskedLM",
          "DistilBertForMultipleChoice",
          "DistilBertForQuestionAnswering",
          "DistilBertForSequenceClassification",
          "DistilBertForTokenClassification",
          "DistilBertModel",
          "DistilBertPreTrainedModel",
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A:   Dict								=		[
          "TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
          "TFDistilBertForMaskedLM",
          "TFDistilBertForMultipleChoice",
          "TFDistilBertForQuestionAnswering",
          "TFDistilBertForSequenceClassification",
          "TFDistilBertForTokenClassification",
          "TFDistilBertMainLayer",
          "TFDistilBertModel",
          "TFDistilBertPreTrainedModel",
      ]

try:
      if not is_flax_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A:   int								=		[
          "FlaxDistilBertForMaskedLM",
          "FlaxDistilBertForMultipleChoice",
          "FlaxDistilBertForQuestionAnswering",
          "FlaxDistilBertForSequenceClassification",
          "FlaxDistilBertForTokenClassification",
          "FlaxDistilBertModel",
          "FlaxDistilBertPreTrainedModel",
      ]


if TYPE_CHECKING:
      from .configuration_distilbert import (
          DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
          DistilBertConfig,
          DistilBertOnnxConfig,
      )
      from .tokenization_distilbert import DistilBertTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_distilbert_fast import DistilBertTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_distilbert import (
                DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                DistilBertForMaskedLM,
                DistilBertForMultipleChoice,
                DistilBertForQuestionAnswering,
                DistilBertForSequenceClassification,
                DistilBertForTokenClassification,
                DistilBertModel,
                DistilBertPreTrainedModel,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_distilbert import (
                TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFDistilBertForMaskedLM,
                TFDistilBertForMultipleChoice,
                TFDistilBertForQuestionAnswering,
                TFDistilBertForSequenceClassification,
                TFDistilBertForTokenClassification,
                TFDistilBertMainLayer,
                TFDistilBertModel,
                TFDistilBertPreTrainedModel,
            )

      try:
            if not is_flax_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_flax_distilbert import (
                FlaxDistilBertForMaskedLM,
                FlaxDistilBertForMultipleChoice,
                FlaxDistilBertForQuestionAnswering,
                FlaxDistilBertForSequenceClassification,
                FlaxDistilBertForTokenClassification,
                FlaxDistilBertModel,
                FlaxDistilBertPreTrainedModel,
            )

else:
      import sys

      A:   Optional[int]								=		_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
