


"""simple docstring"""



from __future__ import annotations

from math import pow, sqrt







def   __lowerCAmelCase						(						lowercase  :  Tuple						,							lowercase  :  int						,							lowercase  :  int )	->						dict[str, float]:


    """simple docstring"""





    if (resistance, reactance, impedance).count(0 ) != 1:
        raise ValueError("One and only one argument must be 0" )
    if resistance == 0:
        return {"resistance": sqrt(pow(_UpperCAmelCase						,							2 ) - pow(_UpperCAmelCase						,							2 ) )}
    elif reactance == 0:
        return {"reactance": sqrt(pow(_UpperCAmelCase						,							2 ) - pow(_UpperCAmelCase						,							2 ) )}
    elif impedance == 0:
        return {"impedance": sqrt(pow(_UpperCAmelCase						,							2 ) + pow(_UpperCAmelCase						,							2 ) )}
    else:
        raise ValueError("Exactly one argument must be 0" )


if __name__ == "__main__":
  import doctest

  doctest.testmod()












import torch
from transformers import PreTrainedModel, XLMRobertaConfig, XLMRobertaModel

class    lowerCAmelCase							(					__UpperCamelCase			):
 UpperCAmelCase__													=		"""M-CLIP"""
 def __init__(      self  :      Optional[Any]			,     UpperCAmelCase  :      Union[str, Any]=1024			,     UpperCAmelCase  :      Tuple=768			,     **UpperCAmelCase  :      Optional[int]	)			->	Dict:
     lowerCamelCase__    :      Optional[int]					 =		transformerDimSize
     lowerCamelCase__    :      Optional[Any]					 =		imageDimSize
     super().__init__(**UpperCAmelCase	)


class    lowerCAmelCase							(					__UpperCamelCase			):
 UpperCAmelCase__													=		MCLIPConfig
 def __init__(      self  :      List[Any]			,     UpperCAmelCase  :      Dict			,     *UpperCAmelCase  :      Any			,     **UpperCAmelCase  :      Dict	)			->	Dict:
     super().__init__(UpperCAmelCase			,     *UpperCAmelCase			,     **UpperCAmelCase	)
     lowerCamelCase__    :      Tuple					 =		XLMRobertaModel(UpperCAmelCase	)
     lowerCamelCase__    :      Union[str, Any]					 =		torch.nn.Linear(
         in_features=config.transformerDimensions			,     out_features=config.numDims	)







 def      A_	(      self  :      Optional[int]			,     UpperCAmelCase  :      Optional[Any]			,     UpperCAmelCase  :      Dict	)			->	Tuple:
     lowerCamelCase__    :      Any					 =		self.transformer(input_ids=UpperCAmelCase			,     attention_mask=UpperCAmelCase	)[0]
     lowerCamelCase__    :      int					 =		(embs * attention_mask.unsqueeze(2	)).sum(dim=1	) / attention_mask.sum(dim=1	)[:, None]
     return self.LinearTransformation(UpperCAmelCase	), embs





