





'''simple docstring'''






import torch
from transformers import PreTrainedModel, XLMRobertaConfig, XLMRobertaModel
class 		lowerCamelCase  (      _A       ):
	snake_case_      =   "M-CLIP"



	def __init__(   self		,      a_=1_024		,      a_=768		,      **a_	):
			lowerCAmelCase     :    Optional[Any]						=   transformerDimSize
			lowerCAmelCase     :    Any						=   imageDimSize
			super().__init__(**a_	)






class 		lowerCamelCase  (      _A       ):
	snake_case_      =   MCLIPConfig



	def __init__(   self		,      a_		,      *a_		,      **a_	):
			super().__init__(a_		,      *a_		,      **a_	)
			lowerCAmelCase     :    Dict						=   XLMRobertaModel(a_	)
			lowerCAmelCase     :    Optional[Any]						=   torch.nn.Linear(
			    in_features=config.transformerDimensions		,      out_features=config.numDims	)





	def   _lowerCamelCase    (   self		,      a_		,      a_	):
			lowerCAmelCase     :    str						=   self.transformer(input_ids=a_		,      attention_mask=a_	)[0]
			lowerCAmelCase     :    Any						=   (embs * attention_mask.unsqueeze(2	)).sum(dim=1	) / attention_mask.sum(dim=1	)[:, None]
			return self.LinearTransformation(a_	), embs










'''simple docstring'''






from __future__ import annotations






def      __A						(							a_						:  float      ,a_						:  float      ,a_						:  float      ,):
		if (stress, tangential_force, area).count(0	) != 1:
				raise ValueError("You cannot supply more or less than 2 values"	)
		elif stress < 0:
				raise ValueError("Stress cannot be negative"	)
		elif tangential_force < 0:
				raise ValueError("Tangential Force cannot be negative"	)
		elif area < 0:
				raise ValueError("Area cannot be negative"	)
		elif stress == 0:
				return (
				    "stress",
				    tangential_force / area,
				)
		elif tangential_force == 0:
				return (
				    "tangential_force",
				    stress * area,
				)
		else:
				return (
				    "area",
				    tangential_force / stress,
				)


if __name__ == "__main__":
					import doctest

					doctest.testmod()



