



def  snake_case_					(     _SCREAMING_SNAKE_CASE ):
	if not isinstance(snake_case__	,						snake_case__ ):
		__lowercase									=      F"""Input value of [number={number}] must be an integer"""
		raise TypeError(snake_case__ )
	if number < 0:
		return False
	__lowercase									=      number * number
	while number > 0:
		if number % 1_0 != number_square % 1_0:
			return False
		number //= 1_0
		number_square //= 1_0
	return True


if __name__ == "__main__":
	import doctest

	doctest.testmod()







"""simple docstring"""






import os
import sys

UpperCamelCase__				:Union[str, 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,
)


UpperCamelCase__				:List[Any]			   =	[
    """torch""",
    """numpy""",
    """tokenizers""",
    """filelock""",
    """requests""",
    """tqdm""",
    """regex""",
    """sentencepiece""",
    """sacremoses""",
    """importlib_metadata""",
    """huggingface_hub""",
]





@add_start_docstrings(AutoConfig.__doc__		)
def 				A_       ( *snake_case__       ,					**snake_case__		)					->  int:
			return AutoConfig.from_pretrained(*snake_case__       ,					**snake_case__		)





@add_start_docstrings(AutoTokenizer.__doc__		)
def 				A_       ( *snake_case__       ,					**snake_case__		)					->  int:
			return AutoTokenizer.from_pretrained(*snake_case__       ,					**snake_case__		)





@add_start_docstrings(AutoModel.__doc__		)
def 				A_       ( *snake_case__       ,					**snake_case__		)					->  Dict:
			return AutoModel.from_pretrained(*snake_case__       ,					**snake_case__		)





@add_start_docstrings(AutoModelForCausalLM.__doc__		)
def 				A_       ( *snake_case__       ,					**snake_case__		)					->  Dict:
			return AutoModelForCausalLM.from_pretrained(*snake_case__       ,					**snake_case__		)





@add_start_docstrings(AutoModelForMaskedLM.__doc__		)
def 				A_       ( *snake_case__       ,					**snake_case__		)					->  Dict:
			return AutoModelForMaskedLM.from_pretrained(*snake_case__       ,					**snake_case__		)





@add_start_docstrings(AutoModelForSequenceClassification.__doc__		)
def 				A_       ( *snake_case__       ,					**snake_case__		)					->  Dict:
			return AutoModelForSequenceClassification.from_pretrained(*snake_case__       ,					**snake_case__		)





@add_start_docstrings(AutoModelForQuestionAnswering.__doc__		)
def 				A_       ( *snake_case__       ,					**snake_case__		)					->  Union[str, Any]:
			return AutoModelForQuestionAnswering.from_pretrained(*snake_case__       ,					**snake_case__		)

