





"""simple docstring"""





import string
from math import logaa


def       lowerCamelCase_       (   UpperCAmelCase_  ,			UpperCAmelCase_  )						->int:





  """simple docstring"""


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


def       lowerCamelCase_       (   UpperCAmelCase_  ,			UpperCAmelCase_  )						->tuple[int, int]:





  """simple docstring"""


  __UpperCAmelCase    :						Tuple       =						corpus.lower().translate(
      str.maketrans(''''''  ,			''''''  ,			string.punctuation  )  )  # strip all punctuation and replace it with ''
  __UpperCAmelCase    :						Union[str, Any]       =						corpus_without_punctuation.split('''\n'''  )
  __UpperCAmelCase    :						Dict       =						term.lower()
  return (len([doc for doc in docs if term in doc]  ), len(UpperCAmelCase_  ))


def       lowerCamelCase_       (   UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_=False  )						->float:





  """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       lowerCamelCase_       (   UpperCAmelCase_  ,			UpperCAmelCase_  )						->float:





  """simple docstring"""


  return round(tf * idf  ,			3  )










"""simple docstring"""





from typing import TYPE_CHECKING

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


lowercase__				:Tuple				   =       {
    'configuration_xlm': ['XLM_PRETRAINED_CONFIG_ARCHIVE_MAP', 'XLMConfig', 'XLMOnnxConfig'],
    'tokenization_xlm': ['XLMTokenizer'],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase__				:Optional[Any]				   =       [
      'XLM_PRETRAINED_MODEL_ARCHIVE_LIST',
      'XLMForMultipleChoice',
      'XLMForQuestionAnswering',
      'XLMForQuestionAnsweringSimple',
      'XLMForSequenceClassification',
      'XLMForTokenClassification',
      'XLMModel',
      'XLMPreTrainedModel',
      'XLMWithLMHeadModel',
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase__				:List[Any]				   =       [
      'TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST',
      'TFXLMForMultipleChoice',
      'TFXLMForQuestionAnsweringSimple',
      'TFXLMForSequenceClassification',
      'TFXLMForTokenClassification',
      'TFXLMMainLayer',
      'TFXLMModel',
      'TFXLMPreTrainedModel',
      'TFXLMWithLMHeadModel',
  ]


if TYPE_CHECKING:
  from .configuration_xlm import XLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMConfig, XLMOnnxConfig
  from .tokenization_xlm import XLMTokenizer

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_xlm import (
        XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
        XLMForMultipleChoice,
        XLMForQuestionAnswering,
        XLMForQuestionAnsweringSimple,
        XLMForSequenceClassification,
        XLMForTokenClassification,
        XLMModel,
        XLMPreTrainedModel,
        XLMWithLMHeadModel,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_xlm import (
        TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFXLMForMultipleChoice,
        TFXLMForQuestionAnsweringSimple,
        TFXLMForSequenceClassification,
        TFXLMForTokenClassification,
        TFXLMMainLayer,
        TFXLMModel,
        TFXLMPreTrainedModel,
        TFXLMWithLMHeadModel,
    )

else:
  import sys

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