

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


lowercase_             =     {
    'configuration_squeezebert': [
        'SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'SqueezeBertConfig',
        'SqueezeBertOnnxConfig',
    ],
    'tokenization_squeezebert': ['SqueezeBertTokenizer'],
}

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase_             =     ['SqueezeBertTokenizerFast']

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase_             =     [
      'SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
      'SqueezeBertForMaskedLM',
      'SqueezeBertForMultipleChoice',
      'SqueezeBertForQuestionAnswering',
      'SqueezeBertForSequenceClassification',
      'SqueezeBertForTokenClassification',
      'SqueezeBertModel',
      'SqueezeBertModule',
      'SqueezeBertPreTrainedModel',
  ]


if TYPE_CHECKING:
  from .configuration_squeezebert import (
      SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
      SqueezeBertConfig,
      SqueezeBertOnnxConfig,
  )
  from .tokenization_squeezebert import SqueezeBertTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_squeezebert_fast import SqueezeBertTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_squeezebert import (
        SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
        SqueezeBertForMaskedLM,
        SqueezeBertForMultipleChoice,
        SqueezeBertForQuestionAnswering,
        SqueezeBertForSequenceClassification,
        SqueezeBertForTokenClassification,
        SqueezeBertModel,
        SqueezeBertModule,
        SqueezeBertPreTrainedModel,
    )

else:
  import sys

  lowercase_             =     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





def   a   (    A__			:     str   ,     A__			:     int  )					->   list[str]:





      """simple docstring"""



      return [sentence[i : i + ngram_size] for i in range(len(A__  ) - ngram_size + 1  )]


if __name__ == "__main__":
  from doctest import testmod

  testmod()


