






from __future__ import annotations

__A					=	8.9_88E9  # units = N * m^s * C^-2







def       lowercase__	(					A_:						float  ,      A_:						float  ,      A_:						float  ,      A_:						float		)	->				dict[str, float]:







       """simple docstring"""






       __UpperCAmelCase     				=abs(chargea * chargea		)

       if (force, chargea, chargea, distance).count(0		) != 1:
              raise ValueError("""One and only one argument must be 0"""		)
       if distance < 0:
              raise ValueError("""Distance cannot be negative"""		)
       if force == 0:
              __UpperCAmelCase     				=COULOMBS_CONSTANT * charge_product / (distance**2)
              return {"force": force}
       elif chargea == 0:
              __UpperCAmelCase     				=abs(A_		) * (distance**2) / (COULOMBS_CONSTANT * chargea)
              return {"charge1": chargea}
       elif chargea == 0:
              __UpperCAmelCase     				=abs(A_		) * (distance**2) / (COULOMBS_CONSTANT * chargea)
              return {"charge2": chargea}
       elif distance == 0:
              __UpperCAmelCase     				=(COULOMBS_CONSTANT * charge_product / abs(A_		)) ** 0.5
              return {"distance": distance}
       raise ValueError("""Exactly one argument must be 0"""		)


if __name__ == "__main__":
   import doctest

   doctest.testmod()












import os
import sys

__a:     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,
)


__a:     Union[str, Any]   					=					[
    '''torch''',
    '''numpy''',
    '''tokenizers''',
    '''filelock''',
    '''requests''',
    '''tqdm''',
    '''regex''',
    '''sentencepiece''',
    '''sacremoses''',
    '''importlib_metadata''',
    '''huggingface_hub''',
]




@add_start_docstrings(AutoConfig.__doc__ )
def       _SCREAMING_SNAKE_CASE (							*__snake_case   ,      **__snake_case )				->						Union[str, Any]:
       return AutoConfig.from_pretrained(*__snake_case   ,      **__snake_case )




@add_start_docstrings(AutoTokenizer.__doc__ )
def       _SCREAMING_SNAKE_CASE (							*__snake_case   ,      **__snake_case )				->						Any:
       return AutoTokenizer.from_pretrained(*__snake_case   ,      **__snake_case )




@add_start_docstrings(AutoModel.__doc__ )
def       _SCREAMING_SNAKE_CASE (							*__snake_case   ,      **__snake_case )				->						Tuple:
       return AutoModel.from_pretrained(*__snake_case   ,      **__snake_case )




@add_start_docstrings(AutoModelForCausalLM.__doc__ )
def       _SCREAMING_SNAKE_CASE (							*__snake_case   ,      **__snake_case )				->						Tuple:
       return AutoModelForCausalLM.from_pretrained(*__snake_case   ,      **__snake_case )




@add_start_docstrings(AutoModelForMaskedLM.__doc__ )
def       _SCREAMING_SNAKE_CASE (							*__snake_case   ,      **__snake_case )				->						Optional[Any]:
       return AutoModelForMaskedLM.from_pretrained(*__snake_case   ,      **__snake_case )




@add_start_docstrings(AutoModelForSequenceClassification.__doc__ )
def       _SCREAMING_SNAKE_CASE (							*__snake_case   ,      **__snake_case )				->						List[str]:
       return AutoModelForSequenceClassification.from_pretrained(*__snake_case   ,      **__snake_case )





@add_start_docstrings(AutoModelForQuestionAnswering.__doc__ )
def       _SCREAMING_SNAKE_CASE (							*__snake_case   ,      **__snake_case )				->						List[Any]:
       return AutoModelForQuestionAnswering.from_pretrained(*__snake_case   ,      **__snake_case )