


from ..utils import DummyObject, requires_backends
class 	UpperCAmelCase_					(					metaclass=UpperCamelCase    ):

   '''simple docstring'''




   __A :      Any     		=		["note_seq"]






   def __init__(		self						,				*__A						,				**__A ):






     """simple docstring"""





     requires_backends(self						,				["note_seq"] )






   @classmethod
   def _snake_case    (		cls						,				*__A						,				**__A ):






     """simple docstring"""





     requires_backends(cls						,				["note_seq"] )






   @classmethod
   def _snake_case    (		cls						,				*__A						,				**__A ):






     """simple docstring"""





     requires_backends(cls						,				["note_seq"] )







import torch
from transformers import PreTrainedModel, XLMRobertaConfig, XLMRobertaModel
class 	UpperCAmelCase_					(					UpperCamelCase    ):

   '''simple docstring'''




   __A :      Optional[int]     		=		"M-CLIP"






   def __init__(		self						,				__A=1024						,				__A=768						,				**__A ):






     """simple docstring"""





     lowerCamelCase :					str    =				transformerDimSize
     lowerCamelCase :					Any    =				imageDimSize
     super().__init__(**__A )



class 	UpperCAmelCase_					(					UpperCamelCase    ):

   '''simple docstring'''




   __A :      Tuple     		=		MCLIPConfig






   def __init__(		self						,				__A						,				*__A						,				**__A ):






     """simple docstring"""





     super().__init__(__A						,				*__A						,				**__A )
     lowerCamelCase :					Tuple    =				XLMRobertaModel(__A )
     lowerCamelCase :					Optional[Any]    =				torch.nn.Linear(
         in_features=config.transformerDimensions						,				out_features=config.numDims )






   def _snake_case    (		self						,				__A						,				__A ):






     """simple docstring"""





     lowerCamelCase :					Any    =				self.transformer(input_ids=__A						,				attention_mask=__A )[0]
     lowerCamelCase :					int    =				(embs * attention_mask.unsqueeze(2 )).sum(dim=1 ) / attention_mask.sum(dim=1 )[:, None]
     return self.LinearTransformation(__A ), embs



