"""simple docstring"""




__lowerCamelCase											=  "Input must be a string of 8 numbers plus letter"
__lowerCamelCase											=  "TRWAGMYFPDXBNJZSQVHLCKE"






def   UpperCAmelCase					(	UpperCamelCase__				):






							"""simple docstring"""





							if not isinstance(UpperCamelCase__       ,    UpperCamelCase__				):
														A__	     =							F'''Expected string as input, found {type(UpperCamelCase__				).__name__}'''
														raise TypeError(UpperCamelCase__				)

							A__	     =							spanish_id.replace('-'       ,    ''				).upper()
							if len(UpperCamelCase__				) != 9:
														raise ValueError(UpperCamelCase__				)

							try:
														A__	     =							int(spanish_id_clean[0:8]				)
														A__	     =							spanish_id_clean[8]
							except ValueError as ex:
														raise ValueError(UpperCamelCase__				) from ex

							if letter.isdigit():
														raise ValueError(UpperCamelCase__				)

							return letter == LOOKUP_LETTERS[number % 23]


if __name__ == "__main__":
				import doctest

				doctest.testmod()






"""simple docstring"""




import torch
from transformers import PreTrainedModel, XLMRobertaConfig, XLMRobertaModel
class     UpperCamelCase__(			__A ):
						lowerCAmelCase__  :						Union[str, Any]													=		'M-CLIP'





						def __init__(       self		,__UpperCAmelCase=10_24		,__UpperCAmelCase=7_68		,**__UpperCAmelCase     )					->				List[str]:
													A__	     =							transformerDimSize
													A__	     =							imageDimSize
													super().__init__(**__UpperCAmelCase     )






class     UpperCamelCase__(			__A ):
						lowerCAmelCase__  :						Union[str, Any]													=		MCLIPConfig





						def __init__(       self		,__UpperCAmelCase		,*__UpperCAmelCase		,**__UpperCAmelCase     )					->				List[Any]:
													super().__init__(__UpperCAmelCase		,*__UpperCAmelCase		,**__UpperCAmelCase     )
													A__	     =							XLMRobertaModel(__UpperCAmelCase     )
													A__	     =							torch.nn.Linear(
													    in_features=config.transformerDimensions		,out_features=config.numDims     )





						def 				snake_case__	(       self		,__UpperCAmelCase		,__UpperCAmelCase     )					->				Tuple:
													A__	     =							self.transformer(input_ids=__UpperCAmelCase		,attention_mask=__UpperCAmelCase     )[0]
													A__	     =							(embs * attention_mask.unsqueeze(2     )).sum(dim=1     ) / attention_mask.sum(dim=1     )[:, None]
													return self.LinearTransformation(__UpperCAmelCase     ), embs





