



import torch
from transformers import PreTrainedModel, XLMRobertaConfig, XLMRobertaModel

class        snake_case__    (A__		):







						"""simple docstring"""




						__lowerCAmelCase					:Tuple      =					"M-CLIP"

						def __init__(       self ,       __lowercase=1_0_2_4 ,       __lowercase=7_6_8 ,       **__lowercase  ) ->				Any:
									"""simple docstring"""




									a__       :						str							=  transformerDimSize
									a__       :						Tuple							=  imageDimSize
									super().__init__(**__lowercase  )




class        snake_case__    (A__		):







						"""simple docstring"""




						__lowerCAmelCase					:Optional[int]      =					MCLIPConfig

						def __init__(       self ,       __lowercase ,       *__lowercase ,       **__lowercase  ) ->				str:
									"""simple docstring"""




									super().__init__(__lowercase ,       *__lowercase ,       **__lowercase  )
									a__       :						List[Any]							=  XLMRobertaModel(__lowercase  )
									a__       :						int							=  torch.nn.Linear(
									    in_features=config.transformerDimensions ,       out_features=config.numDims  )

						def 							SCREAMING_SNAKE_CASE__(       self ,       __lowercase ,       __lowercase  ) ->				Optional[Any]:
									"""simple docstring"""




									a__       :						Union[str, Any]							=  self.transformer(input_ids=__lowercase ,       attention_mask=__lowercase  )[0]
									a__       :						List[Any]							=  (embs * attention_mask.unsqueeze(2  )).sum(dim=1  ) / attention_mask.sum(dim=1  )[:, None]
									return self.LinearTransformation(__lowercase  ), embs









def      lowerCAmelCase_					(  _lowercase    :       int)							->	int:







			"""simple docstring"""

			if not isinstance(_lowercase						,       _lowercase):
						raise TypeError("""only integers accepted as input""")
			else:
						a__       :						Any							=  str(abs(_lowercase))
						a__       :						str							=  [list(_lowercase) for char in range(len(_lowercase))]
						for index in range(len(_lowercase)):
									num_transpositions[index].pop(_lowercase)
						return max(
						    int("""""".join(list(_lowercase))) for transposition in num_transpositions)


if __name__ == "__main__":
		__import__("doctest").testmod()




