


import string
from math import logaa





def   _UpperCAmelCase       (     UpperCAmelCase      :			str						,	UpperCAmelCase      :			str      ):
      """simple docstring"""




      __lowerCamelCase     :      List[str]       						=						document.translate(
          str.maketrans(""""""						,	""""""						,	string.punctuation      )      ).replace("""\n"""						,	""""""      )
      __lowerCamelCase     :      Union[str, Any]       						=						document_without_punctuation.split(""" """      )  # word tokenization
      return len([word for word in tokenize_document if word.lower() == term.lower()]      )





def   _UpperCAmelCase       (     UpperCAmelCase      :			str						,	UpperCAmelCase      :			str      ):
      """simple docstring"""




      __lowerCamelCase     :      Any       						=						corpus.lower().translate(
          str.maketrans(""""""						,	""""""						,	string.punctuation      )      )  # strip all punctuation and replace it with ''
      __lowerCamelCase     :      Optional[int]       						=						corpus_without_punctuation.split("""\n"""      )
      __lowerCamelCase     :      Any       						=						term.lower()
      return (len([doc for doc in docs if term in doc]      ), len(UpperCAmelCase      ))





def   _UpperCAmelCase       (     UpperCAmelCase      :			int						,	UpperCAmelCase      :			int						,	UpperCAmelCase      :			Union[str, Any]=False      ):
      """simple docstring"""




      if smoothing:
            if n == 0:
                  raise ValueError("""log10(0) is undefined."""      )
            return round(1 + logaa(n / (1 + df)      )						,	3      )

      if df == 0:
            raise ZeroDivisionError("""df must be > 0"""      )
      elif n == 0:
            raise ValueError("""log10(0) is undefined."""      )
      return round(logaa(n / df      )						,	3      )





def   _UpperCAmelCase       (     UpperCAmelCase      :			int						,	UpperCAmelCase      :			int      ):
      """simple docstring"""




      return round(tf * idf						,	3      )







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__UpperCamelCase    :						Any     =							{
    'configuration_timesformer': ['TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TimesformerConfig'],
}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __UpperCamelCase    :						int     =							[
        'TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
        'TimesformerModel',
        'TimesformerForVideoClassification',
        'TimesformerPreTrainedModel',
    ]

if TYPE_CHECKING:
    from .configuration_timesformer import TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, TimesformerConfig

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_timesformer import (
            TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
            TimesformerForVideoClassification,
            TimesformerModel,
            TimesformerPreTrainedModel,
        )

else:
    import sys

    __UpperCamelCase    :						List[Any]     =							_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



