


import string
from math import logaa



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



def 							UpperCamelCase_( lowerCamelCase_				,					lowerCamelCase_					)       ->		tuple[int, int]:
    _lowercase			:				Dict 						=						corpus.lower().translate(
        str.maketrans(''				,					''				,					string.punctuation					)					)  # strip all punctuation and replace it with ''
    _lowercase			:				str 						=						corpus_without_punctuation.split('\n'					)
    _lowercase			:				Any 						=						term.lower()
    return (len([doc for doc in docs if term in doc]					), len(lowerCamelCase_					))



def 							UpperCamelCase_( lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_=False					)       ->		float:
    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_				,					lowerCamelCase_					)       ->		float:
    return round(tf * idf				,					3					)






from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE			: Optional[Any]				   =					{
    "configuration_conditional_detr": [
        "CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "ConditionalDetrConfig",
        "ConditionalDetrOnnxConfig",
    ]
}

try:
 if not is_vision_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE			: Union[str, Any]				   =					["ConditionalDetrFeatureExtractor"]
 SCREAMING_SNAKE_CASE			: Optional[Any]				   =					["ConditionalDetrImageProcessor"]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE			: Any				   =					[
     "CONDITIONAL_DETR_PRETRAINED_MODEL_ARCHIVE_LIST",
     "ConditionalDetrForObjectDetection",
     "ConditionalDetrForSegmentation",
     "ConditionalDetrModel",
     "ConditionalDetrPreTrainedModel",
 ]


if TYPE_CHECKING:
 from .configuration_conditional_detr import (
     CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP,
     ConditionalDetrConfig,
     ConditionalDetrOnnxConfig,
 )

 try:
  if not is_vision_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .feature_extraction_conditional_detr import ConditionalDetrFeatureExtractor
  from .image_processing_conditional_detr import ConditionalDetrImageProcessor

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_conditional_detr import (
      CONDITIONAL_DETR_PRETRAINED_MODEL_ARCHIVE_LIST,
      ConditionalDetrForObjectDetection,
      ConditionalDetrForSegmentation,
      ConditionalDetrModel,
      ConditionalDetrPreTrainedModel,
  )

else:
 import sys

 SCREAMING_SNAKE_CASE			: str				   =					_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


