



"""simple docstring"""

import os
import sys

a_       =       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,
)


a_       =       [
    '''torch''',
    '''numpy''',
    '''tokenizers''',
    '''filelock''',
    '''requests''',
    '''tqdm''',
    '''regex''',
    '''sentencepiece''',
    '''sacremoses''',
    '''importlib_metadata''',
    '''huggingface_hub''',
]







@add_start_docstrings(AutoConfig.__doc__      )
def 				SCREAMING_SNAKE_CASE__   (		*SCREAMING_SNAKE_CASE__			:	str					,     **SCREAMING_SNAKE_CASE__			:	Optional[Any]      ):



					"""simple docstring"""





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







@add_start_docstrings(AutoTokenizer.__doc__      )
def 				SCREAMING_SNAKE_CASE__   (		*SCREAMING_SNAKE_CASE__			:	str					,     **SCREAMING_SNAKE_CASE__			:	Optional[int]      ):



					"""simple docstring"""





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







@add_start_docstrings(AutoModel.__doc__      )
def 				SCREAMING_SNAKE_CASE__   (		*SCREAMING_SNAKE_CASE__			:	Union[str, Any]					,     **SCREAMING_SNAKE_CASE__			:	List[Any]      ):



					"""simple docstring"""





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







@add_start_docstrings(AutoModelForCausalLM.__doc__      )
def 				SCREAMING_SNAKE_CASE__   (		*SCREAMING_SNAKE_CASE__			:	List[Any]					,     **SCREAMING_SNAKE_CASE__			:	str      ):



					"""simple docstring"""





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







@add_start_docstrings(AutoModelForMaskedLM.__doc__      )
def 				SCREAMING_SNAKE_CASE__   (		*SCREAMING_SNAKE_CASE__			:	Optional[int]					,     **SCREAMING_SNAKE_CASE__			:	List[str]      ):



					"""simple docstring"""





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







@add_start_docstrings(AutoModelForSequenceClassification.__doc__      )
def 				SCREAMING_SNAKE_CASE__   (		*SCREAMING_SNAKE_CASE__			:	Any					,     **SCREAMING_SNAKE_CASE__			:	Optional[Any]      ):



					"""simple docstring"""





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







@add_start_docstrings(AutoModelForQuestionAnswering.__doc__      )
def 				SCREAMING_SNAKE_CASE__   (		*SCREAMING_SNAKE_CASE__			:	str					,     **SCREAMING_SNAKE_CASE__			:	Any      ):



					"""simple docstring"""





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








'''simple docstring'''






def    _snake_case  (	A					,       A			)		->       int:
							return x if y == 0 else greatest_common_divisor(A					,       x % y			)


def    _snake_case  (	A					,       A			)		->       int:
							return (x * y) // greatest_common_divisor(A					,       A			)


def    _snake_case  (	A = 20			)		->       int:
							lowerCAmelCase__			  =      1
							for i in range(1					,       n + 1			):
														lowerCAmelCase__			  =      lcm(A					,       A			)
							return g


if __name__ == "__main__":
		print(f"""{solution() = }""")