




import string
from math import logaa
def 		a_							( __lowerCAmelCase   , __lowerCAmelCase				):
  lowerCAmelCase__										=      document.translate(
      str.maketrans(''''''   , ''''''   , string.punctuation				)				).replace('''\n'''   , ''''''				)
  lowerCAmelCase__										=      document_without_punctuation.split(''' '''				)  # word tokenization
  return len([word for word in tokenize_document if word.lower() == term.lower()]				)
def 		a_							( __lowerCAmelCase   , __lowerCAmelCase				):
  lowerCAmelCase__										=      corpus.lower().translate(
      str.maketrans(''''''   , ''''''   , string.punctuation				)				)  # strip all punctuation and replace it with ''
  lowerCAmelCase__										=      corpus_without_punctuation.split('''\n'''				)
  lowerCAmelCase__										=      term.lower()
  return (len([doc for doc in docs if term in doc]				), len(__lowerCAmelCase				))
def 		a_							( __lowerCAmelCase   , __lowerCAmelCase   , __lowerCAmelCase=False				):
  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 		a_							( __lowerCAmelCase   , __lowerCAmelCase				):
  return round(tf * idf   , 3				)







from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__magic_name__  :  Optional[Any]					=       {
    """configuration_informer""": [
        """INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """InformerConfig""",
    ],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __magic_name__  :  int					=       [
          """INFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """InformerForPrediction""",
          """InformerModel""",
          """InformerPreTrainedModel""",
      ]


if TYPE_CHECKING:
      from .configuration_informer import INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, InformerConfig

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_informer import (
                INFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                InformerForPrediction,
                InformerModel,
                InformerPreTrainedModel,
            )

else:
      import sys

      __magic_name__  :  Any					=       _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

