



'''simple docstring'''

import string
from math import logaa







def a_     (    __UpperCAmelCase				,							__UpperCAmelCase					)				->					Union[str, Any]:





			"""simple docstring"""







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







def a_     (    __UpperCAmelCase				,							__UpperCAmelCase					)				->					Union[str, Any]:





			"""simple docstring"""







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







def a_     (    __UpperCAmelCase				,							__UpperCAmelCase				,							__UpperCAmelCase=False					)				->					str:





			"""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 a_     (    __UpperCAmelCase				,							__UpperCAmelCase					)				->					Optional[int]:





			"""simple docstring"""







			return round(tf * idf				,							3					)








"""simple docstring"""




from typing import TYPE_CHECKING

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


__magic_name__						: Any    =				{'configuration_glpn': ['GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GLPNConfig']}

try:
					if not is_vision_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__magic_name__						: Dict    =				['GLPNFeatureExtractor']
					__magic_name__						: Union[str, Any]    =				['GLPNImageProcessor']

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__magic_name__						: int    =				[
					    'GLPN_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'GLPNForDepthEstimation',
					    'GLPNLayer',
					    'GLPNModel',
					    'GLPNPreTrainedModel',
					]


if TYPE_CHECKING:
					from .configuration_glpn import GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP, GLPNConfig

					try:
										if not is_vision_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .feature_extraction_glpn import GLPNFeatureExtractor
										from .image_processing_glpn import GLPNImageProcessor

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_glpn import (
										    GLPN_PRETRAINED_MODEL_ARCHIVE_LIST,
										    GLPNForDepthEstimation,
										    GLPNLayer,
										    GLPNModel,
										    GLPNPreTrainedModel,
										)


else:
					import sys

					__magic_name__						: Optional[int]    =				_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




