





'''simple docstring'''






import os
import sys

A_		:  Dict		 =						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_		:  Optional[Any]		 =						[
    "torch",
    "numpy",
    "tokenizers",
    "filelock",
    "requests",
    "tqdm",
    "regex",
    "sentencepiece",
    "sacremoses",
    "importlib_metadata",
    "huggingface_hub",
]



@add_start_docstrings(AutoConfig.__doc__      )
def        UpperCamelCase__		(						*__magic_name__  :   str		,       **__magic_name__  :   Optional[int]      )				->			Union[str, Any]:
 '''simple docstring'''






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



@add_start_docstrings(AutoTokenizer.__doc__      )
def        UpperCamelCase__		(						*__magic_name__  :   int		,       **__magic_name__  :   int      )				->			str:
 '''simple docstring'''






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



@add_start_docstrings(AutoModel.__doc__      )
def        UpperCamelCase__		(						*__magic_name__  :   List[str]		,       **__magic_name__  :   int      )				->			Optional[Any]:
 '''simple docstring'''






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



@add_start_docstrings(AutoModelForCausalLM.__doc__      )
def        UpperCamelCase__		(						*__magic_name__  :   Union[str, Any]		,       **__magic_name__  :   Union[str, Any]      )				->			List[str]:
 '''simple docstring'''






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



@add_start_docstrings(AutoModelForMaskedLM.__doc__      )
def        UpperCamelCase__		(						*__magic_name__  :   List[str]		,       **__magic_name__  :   Dict      )				->			Tuple:
 '''simple docstring'''






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



@add_start_docstrings(AutoModelForSequenceClassification.__doc__      )
def        UpperCamelCase__		(						*__magic_name__  :   Dict		,       **__magic_name__  :   Tuple      )				->			Union[str, Any]:
 '''simple docstring'''






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





@add_start_docstrings(AutoModelForQuestionAnswering.__doc__      )
def        UpperCamelCase__		(						*__magic_name__  :   Dict		,       **__magic_name__  :   Tuple      )				->			int:
 '''simple docstring'''






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












"""simple docstring"""
from __future__ import annotations






def       __UpperCamelCase						(		snake_case__ , snake_case__       ):
	if len(snake_case__       ) == 0:
		return False
	A_		:    Union[str, Any]						     = len(snake_case__       ) // 2
	if a_list[midpoint] == item:
		return True
	if item < a_list[midpoint]:
		return binary_search(a_list[:midpoint] , snake_case__       )
	else:
		return binary_search(a_list[midpoint + 1 :] , snake_case__       )


if __name__ == "__main__":
							_lowerCAmelCase       =				input("Enter numbers separated by comma:\n").strip()
							_lowerCAmelCase       =				[int(item.strip()) for item in user_input.split(",")]
							_lowerCAmelCase       =				int(input("Enter the number to be found in the list:\n").strip())
							_lowerCAmelCase       =				"" if binary_search(sequence, target) else "not "
							print(F'{target} was {not_str}found in {sequence}')

