

import torch
from transformers import PreTrainedModel, XLMRobertaConfig, XLMRobertaModel






class      lowerCamelCase_      (	_lowercase	):
	_lowercase		:   Dict     					=				'''M-CLIP'''




	def __init__(     self						:     List[str]					,      __A						:     str=1024					,      __A						:     Optional[Any]=768					,      **__A						:     Any							):
		__A :			List[str]		    =			transformerDimSize
		__A :			int		    =			imageDimSize
		super().__init__(**__A							)






class      lowerCamelCase_      (	_lowercase	):
	_lowercase		:   List[str]     					=				MCLIPConfig




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




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








import fire

from utils import calculate_rouge, save_json
def 							__SCREAMING_SNAKE_CASE     (   a__    :   Any			,a__    :   Tuple			,a__    :   Any=None			,**a__    :   Dict       )					->				Optional[Any]:
	__A :			int		    =			[x.strip() for x in open(a__       ).readlines()]
	__A :			List[str]		    =			[x.strip() for x in open(a__       ).readlines()][: len(a__       )]
	__A :			List[Any]		    =			calculate_rouge(a__			,a__			,**a__       )
	if save_path is not None:
		save_json(a__			,a__			,indent=a__       )
	return metrics  # these print nicely


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





