




import fire

from utils import calculate_rouge, save_json

def    UpperCAmelCase_				( __UpperCAmelCase		:      Dict     ,     __UpperCAmelCase		:      List[Any]     ,     __UpperCAmelCase		:      Tuple=None     ,     **__UpperCAmelCase		:      List[str]				)   ->					Any:
      SCREAMING_SNAKE_CASE_      		=     [x.strip() for x in open(__UpperCAmelCase				).readlines()]
      SCREAMING_SNAKE_CASE_      		=     [x.strip() for x in open(__UpperCAmelCase				).readlines()][: len(__UpperCAmelCase				)]
      SCREAMING_SNAKE_CASE_      		=     calculate_rouge(__UpperCAmelCase     ,     __UpperCAmelCase     ,     **__UpperCAmelCase				)
      if save_path is not None:
            save_json(__UpperCAmelCase     ,     __UpperCAmelCase     ,     indent=__UpperCAmelCase				)
      return metrics  # these print nicely


if __name__ == "__main__":
   fire.Fire(calculate_rouge_path)





import torch
from transformers import PreTrainedModel, XLMRobertaConfig, XLMRobertaModel





class 			lowerCamelCase_				( _SCREAMING_SNAKE_CASE   ):


 '''simple docstring'''







 lowercase_							       =       "M-CLIP"



 def __init__(				self    :						Tuple							,							_lowerCAmelCase    :						List[str]=1_024							,							_lowerCAmelCase    :						str=768							,							**_lowerCAmelCase    :						Union[str, Any]		):
       SCREAMING_SNAKE_CASE_      		=     transformerDimSize
       SCREAMING_SNAKE_CASE_      		=     imageDimSize
       super().__init__(**_lowerCAmelCase		)





class 			lowerCamelCase_				( _SCREAMING_SNAKE_CASE   ):


 '''simple docstring'''







 lowercase_							       =       MCLIPConfig



 def __init__(				self    :						Dict							,							_lowerCAmelCase    :						Union[str, Any]							,							*_lowerCAmelCase    :						str							,							**_lowerCAmelCase    :						str		):
       super().__init__(_lowerCAmelCase							,							*_lowerCAmelCase							,							**_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_      		=     XLMRobertaModel(_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_      		=     torch.nn.Linear(
           in_features=config.transformerDimensions							,							out_features=config.numDims		)




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