





import os
import sys

UpperCamelCase__   : Optional[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__   : Union[str, Any]					  = [
    """torch""",
    """numpy""",
    """tokenizers""",
    """filelock""",
    """requests""",
    """tqdm""",
    """regex""",
    """sentencepiece""",
    """sacremoses""",
    """importlib_metadata""",
    """huggingface_hub""",
]




@add_start_docstrings(AutoConfig.__doc__   )
def    A_(			*A				,      **A   ):
	return AutoConfig.from_pretrained(*A				,      **A   )




@add_start_docstrings(AutoTokenizer.__doc__   )
def    A_(			*A				,      **A   ):
	return AutoTokenizer.from_pretrained(*A				,      **A   )




@add_start_docstrings(AutoModel.__doc__   )
def    A_(			*A				,      **A   ):
	return AutoModel.from_pretrained(*A				,      **A   )




@add_start_docstrings(AutoModelForCausalLM.__doc__   )
def    A_(			*A				,      **A   ):
	return AutoModelForCausalLM.from_pretrained(*A				,      **A   )




@add_start_docstrings(AutoModelForMaskedLM.__doc__   )
def    A_(			*A				,      **A   ):
	return AutoModelForMaskedLM.from_pretrained(*A				,      **A   )




@add_start_docstrings(AutoModelForSequenceClassification.__doc__   )
def    A_(			*A				,      **A   ):
	return AutoModelForSequenceClassification.from_pretrained(*A				,      **A   )




@add_start_docstrings(AutoModelForQuestionAnswering.__doc__   )
def    A_(			*A				,      **A   ):
	return AutoModelForQuestionAnswering.from_pretrained(*A				,      **A   )









from __future__ import annotations




def    A_(			A   ):
	if not nums:
		raise ValueError("""List is empty"""   )
	return sum(A   ) / len(A   )


if __name__ == "__main__":
				import doctest

				doctest.testmod()


