
"""simple docstring"""






import functools





def __A						(   a_ :							str		,		a_ :							str     )->							int:




		'''simple docstring'''






		SCREAMING_SNAKE_CASE       :     Union[str, Any]				= len(a_     )
		SCREAMING_SNAKE_CASE       :     Tuple				= len(a_     )

		@functools.cache
		def min_distance(a_ :							int		,		a_ :							int     ) -> int:
				# if first word index is overflow - delete all from the second word
				if indexa >= len_worda:
						return len_worda - indexa
				# if second word index is overflow - delete all from the first word
				if indexa >= len_worda:
						return len_worda - indexa
				SCREAMING_SNAKE_CASE       :     Tuple				= int(worda[indexa] != worda[indexa]     )  # current letters not identical
				return min(
				    1 + min_distance(indexa + 1		,		a_     )		,		1 + min_distance(a_		,		indexa + 1     )		,		diff + min_distance(indexa + 1		,		indexa + 1     )		,		)

		return min_distance(0		,		0     )


if __name__ == "__main__":
					import doctest

					doctest.testmod()







"""simple docstring"""






import os
import sys

lowerCamelCase__ :       List[Any]						     =			os.path.join(os.path.dirname(__file__), "src")
sys.path.append(SRC_DIR)


from transformers import (
    AutoConfig,
    AutoModel,
    AutoModelForCausalLM,
    AutoModelForMaskedLM,
    AutoModelForQuestionAnswering,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    add_start_docstrings,
)


lowerCamelCase__ :       str						     =			[
    "torch",
    "numpy",
    "tokenizers",
    "filelock",
    "requests",
    "tqdm",
    "regex",
    "sentencepiece",
    "sacremoses",
    "importlib_metadata",
    "huggingface_hub",
]





@add_start_docstrings(AutoConfig.__doc__     )
def __A						(   *a_ :							Any		,		**a_ :							Union[str, Any]     )->							Dict:




		'''simple docstring'''






		return AutoConfig.from_pretrained(*a_		,		**a_     )





@add_start_docstrings(AutoTokenizer.__doc__     )
def __A						(   *a_ :							str		,		**a_ :							Union[str, Any]     )->							Union[str, Any]:




		'''simple docstring'''






		return AutoTokenizer.from_pretrained(*a_		,		**a_     )





@add_start_docstrings(AutoModel.__doc__     )
def __A						(   *a_ :							List[str]		,		**a_ :							int     )->							Dict:




		'''simple docstring'''






		return AutoModel.from_pretrained(*a_		,		**a_     )





@add_start_docstrings(AutoModelForCausalLM.__doc__     )
def __A						(   *a_ :							Any		,		**a_ :							Tuple     )->							Dict:




		'''simple docstring'''






		return AutoModelForCausalLM.from_pretrained(*a_		,		**a_     )





@add_start_docstrings(AutoModelForMaskedLM.__doc__     )
def __A						(   *a_ :							Dict		,		**a_ :							Optional[Any]     )->							Optional[int]:




		'''simple docstring'''






		return AutoModelForMaskedLM.from_pretrained(*a_		,		**a_     )





@add_start_docstrings(AutoModelForSequenceClassification.__doc__     )
def __A						(   *a_ :							Optional[int]		,		**a_ :							str     )->							Optional[int]:




		'''simple docstring'''






		return AutoModelForSequenceClassification.from_pretrained(*a_		,		**a_     )





@add_start_docstrings(AutoModelForQuestionAnswering.__doc__     )
def __A						(   *a_ :							List[str]		,		**a_ :							int     )->							List[Any]:




		'''simple docstring'''






		return AutoModelForQuestionAnswering.from_pretrained(*a_		,		**a_     )





