





'''simple docstring'''


import os
import sys

_SCREAMING_SNAKE_CASE									=				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,
)


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


@add_start_docstrings(AutoConfig.__doc__     )
def 						__a(*SCREAMING_SNAKE_CASE_	:       List[Any]		,							**SCREAMING_SNAKE_CASE_	:       str     ):



    '''simple docstring'''





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


@add_start_docstrings(AutoTokenizer.__doc__     )
def 						__a(*SCREAMING_SNAKE_CASE_	:       int		,							**SCREAMING_SNAKE_CASE_	:       List[str]     ):



    '''simple docstring'''





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


@add_start_docstrings(AutoModel.__doc__     )
def 						__a(*SCREAMING_SNAKE_CASE_	:       Tuple		,							**SCREAMING_SNAKE_CASE_	:       int     ):



    '''simple docstring'''





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


@add_start_docstrings(AutoModelForCausalLM.__doc__     )
def 						__a(*SCREAMING_SNAKE_CASE_	:       str		,							**SCREAMING_SNAKE_CASE_	:       Dict     ):



    '''simple docstring'''





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


@add_start_docstrings(AutoModelForMaskedLM.__doc__     )
def 						__a(*SCREAMING_SNAKE_CASE_	:       List[Any]		,							**SCREAMING_SNAKE_CASE_	:       str     ):



    '''simple docstring'''





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


@add_start_docstrings(AutoModelForSequenceClassification.__doc__     )
def 						__a(*SCREAMING_SNAKE_CASE_	:       Union[str, Any]		,							**SCREAMING_SNAKE_CASE_	:       Tuple     ):



    '''simple docstring'''





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





@add_start_docstrings(AutoModelForQuestionAnswering.__doc__     )
def 						__a(*SCREAMING_SNAKE_CASE_	:       Tuple		,							**SCREAMING_SNAKE_CASE_	:       Optional[int]     ):



    '''simple docstring'''





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








def      lowerCamelCase_						(			UpperCamelCase__							: str )	->			bool:





				"""simple docstring"""


				__lowerCamelCase									=       0
				for ch in input_str:
								__lowerCamelCase									=       ord(UpperCamelCase__ )
								__lowerCamelCase									=       pow(2					,	UpperCamelCase__ )

								# If we already turned on bit for current character's unicode
								if bitmap >> ch_unicode & 1 == 1:
												return False
								bitmap |= ch_bit_index_on
				return True


if __name__ == "__main__":
					import doctest

					doctest.testmod()


