




'''simple docstring'''



import string
from math import logaa







def       _UpperCamelCase			(_lowerCamelCase				:  str ,						_lowerCamelCase				:  str						)-> int:




     '''simple docstring'''





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







def       _UpperCamelCase			(_lowerCamelCase				:  str ,						_lowerCamelCase				:  str						)-> tuple[int, int]:




     '''simple docstring'''





     __snake_case            =	corpus.lower().translate(
         str.maketrans('''''' ,						'''''' ,						string.punctuation						)						)  # strip all punctuation and replace it with ''
     __snake_case            =	corpus_without_punctuation.split('''\n'''						)
     __snake_case            =	term.lower()
     return (len([doc for doc in docs if term in doc]						), len(_lowerCamelCase						))







def       _UpperCamelCase			(_lowerCamelCase				:  int ,						_lowerCamelCase				:  int ,						_lowerCamelCase				:  Optional[int]=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       _UpperCamelCase			(_lowerCamelCase				:  int ,						_lowerCamelCase				:  int						)-> float:




     '''simple docstring'''





     return round(tf * idf ,						3						)












'''simple docstring'''



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


UpperCAmelCase_							:	List[str]	     =		{
    '''configuration_gpt_bigcode''': ['''GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTBigCodeConfig'''],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCAmelCase_							:	int	     =		[
         '''GPT_BIGCODE_PRETRAINED_MODEL_ARCHIVE_LIST''',
         '''GPTBigCodeForSequenceClassification''',
         '''GPTBigCodeForTokenClassification''',
         '''GPTBigCodeForCausalLM''',
         '''GPTBigCodeModel''',
         '''GPTBigCodePreTrainedModel''',
     ]

if TYPE_CHECKING:
     from .configuration_gpt_bigcode import GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTBigCodeConfig

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_gpt_bigcode import (
              GPT_BIGCODE_PRETRAINED_MODEL_ARCHIVE_LIST,
              GPTBigCodeForCausalLM,
              GPTBigCodeForSequenceClassification,
              GPTBigCodeForTokenClassification,
              GPTBigCodeModel,
              GPTBigCodePreTrainedModel,
          )


else:
     import sys

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






