
import torch
from transformers import PreTrainedModel, XLMRobertaConfig, XLMRobertaModel


class   __lowerCAmelCase		(		UpperCamelCase__					):



      """simple docstring"""







      A__		:	Tuple         =			"""M-CLIP"""




      def __init__(					self						:				Tuple ,				_snake_case						:				Tuple=1024 ,				_snake_case						:				List[str]=768 ,				**_snake_case						:				str ):
          __lowercase						:	int					=				transformerDimSize
          __lowercase						:	Dict					=				imageDimSize
          super().__init__(**lowerCAmelCase__ )






class   __lowerCAmelCase		(		UpperCamelCase__					):



      """simple docstring"""







      A__		:	Optional[Any]         =			MCLIPConfig




      def __init__(					self						:				Tuple ,				_snake_case						:				Optional[int] ,				*_snake_case						:				List[Any] ,				**_snake_case						:				List[str] ):
          super().__init__(lowerCAmelCase__ ,				*lowerCAmelCase__ ,				**lowerCAmelCase__ )
          __lowercase						:	Tuple					=				XLMRobertaModel(lowerCAmelCase__ )
          __lowercase						:	List[str]					=				torch.nn.Linear(
              in_features=config.transformerDimensions ,				out_features=config.numDims )




      def 						snake_case_	(					self						:				Union[str, Any] ,				_snake_case						:				Tuple ,				_snake_case						:				Union[str, Any] ):
          __lowercase						:	Optional[Any]					=				self.transformer(input_ids=lowerCAmelCase__ ,				attention_mask=lowerCAmelCase__ )[0]
          __lowercase						:	int					=				(embs * attention_mask.unsqueeze(2 )).sum(dim=1 ) / attention_mask.sum(dim=1 )[:, None]
          return self.LinearTransformation(lowerCAmelCase__ ), embs









from math import pi
def      _A       (		SCREAMING_SNAKE_CASE     :   int       ,       SCREAMING_SNAKE_CASE     :   int						):





  """simple docstring"""



  return 2 * pi * radius * (angle / 360)


if __name__ == "__main__":
     print(arc_length(90, 10))






