
import importlib.metadata
from typing import Union

from packaging.version import Version, parse

from .constants import STR_OPERATION_TO_FUNC


lowerCamelCase      =parse(importlib.metadata.version("torch"))


def 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__ ,		UpperCamelCase__ ,		UpperCamelCase__		):
      if operation not in STR_OPERATION_TO_FUNC.keys():
            raise ValueError(f'''`operation` must be one of {list(STR_OPERATION_TO_FUNC.keys()		)}, received {operation}'''		)
      UpperCamelCase__    :   Tuple					=				STR_OPERATION_TO_FUNC[operation]
      if isinstance(lowerCAmelCase__ ,		lowerCAmelCase__		):
            UpperCamelCase__    :   Union[str, Any]					=				parse(importlib.metadata.version(lowerCAmelCase__		)		)
      return operation(lowerCAmelCase__ ,		parse(lowerCAmelCase__		)		)


def 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__ ,		UpperCamelCase__		):
      return compare_versions(lowerCAmelCase__ ,		lowerCAmelCase__ ,		lowerCAmelCase__		)




import torch
from transformers import PreTrainedModel, XLMRobertaConfig, XLMRobertaModel




class 				_lowerCamelCase (      UpperCamelCase_ ):





     """simple docstring"""







     SCREAMING_SNAKE_CASE_		   =  '''M-CLIP'''






     def __init__(							self						,						__SCREAMING_SNAKE_CASE=1_0_2_4						,						__SCREAMING_SNAKE_CASE=7_6_8						,						**__SCREAMING_SNAKE_CASE  )  ->   List[Any]:




           """simple docstring"""







           UpperCamelCase__    :   Dict					=				transformerDimSize
           UpperCamelCase__    :   Any					=				imageDimSize
           super().__init__(**__SCREAMING_SNAKE_CASE  )




class 				_lowerCamelCase (      UpperCamelCase_ ):





     """simple docstring"""







     SCREAMING_SNAKE_CASE_		   =  MCLIPConfig






     def __init__(							self						,						__SCREAMING_SNAKE_CASE						,						*__SCREAMING_SNAKE_CASE						,						**__SCREAMING_SNAKE_CASE  )  ->   Optional[int]:




           """simple docstring"""







           super().__init__(__SCREAMING_SNAKE_CASE						,						*__SCREAMING_SNAKE_CASE						,						**__SCREAMING_SNAKE_CASE  )
           UpperCamelCase__    :   Union[str, Any]					=				XLMRobertaModel(__SCREAMING_SNAKE_CASE  )
           UpperCamelCase__    :   Tuple					=				torch.nn.Linear(
               in_features=config.transformerDimensions						,						out_features=config.numDims  )






     def    __SCREAMING_SNAKE_CASE			(							self						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE  )  ->   List[str]:




           """simple docstring"""







           UpperCamelCase__    :   Tuple					=				self.transformer(input_ids=__SCREAMING_SNAKE_CASE						,						attention_mask=__SCREAMING_SNAKE_CASE  )[0]
           UpperCamelCase__    :   Tuple					=				(embs * attention_mask.unsqueeze(2  )).sum(dim=1  ) / attention_mask.sum(dim=1  )[:, None]
           return self.LinearTransformation(__SCREAMING_SNAKE_CASE  ), embs


