


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



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__UpperCamelCase				:  str      					=							{
    'configuration_x_clip': [
        'XCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'XCLIPConfig',
        'XCLIPTextConfig',
        'XCLIPVisionConfig',
    ],
    'processing_x_clip': ['XCLIPProcessor'],
}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __UpperCamelCase				:  Any      					=							[
           'XCLIP_PRETRAINED_MODEL_ARCHIVE_LIST',
           'XCLIPModel',
           'XCLIPPreTrainedModel',
           'XCLIPTextModel',
           'XCLIPVisionModel',
       ]

if TYPE_CHECKING:
       from .configuration_x_clip import (
           XCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
           XCLIPConfig,
           XCLIPTextConfig,
           XCLIPVisionConfig,
       )
       from .processing_x_clip import XCLIPProcessor

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_x_clip import (
                  XCLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
                  XCLIPModel,
                  XCLIPPreTrainedModel,
                  XCLIPTextModel,
                  XCLIPVisionModel,
              )

else:
       import sys

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