

import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding

class 				a  (	UpperCAmelCase				):
						_lowercase    						=			["image_processor", "tokenizer"]
						_lowercase    						=			"CLIPImageProcessor"
						_lowercase    						=			("XLMRobertaTokenizer", "XLMRobertaTokenizerFast")


						def __init__(       self						,					A_=None						,					A_=None						,					**A_	):







										'''simple docstring'''


										_UpperCAmelCase       :		Optional[Any] 		=    None
										if "feature_extractor" in kwargs:
														warnings.warn(
														    "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
														    " instead."						,					A_						,					)
														_UpperCAmelCase       :		Optional[int] 		=    kwargs.pop("feature_extractor"	)

										_UpperCAmelCase       :		Optional[int] 		=    image_processor if image_processor is not None else feature_extractor
										if image_processor is None:
														raise ValueError("You need to specify an `image_processor`."	)
										if tokenizer is None:
														raise ValueError("You need to specify a `tokenizer`."	)

										super().__init__(A_						,					A_	)


						def __call__(       self						,					A_=None						,					A_=None						,					A_=None						,					**A_	):







										'''simple docstring'''


										if text is None and images is None:
														raise ValueError("You have to specify either text or images. Both cannot be none."	)

										if text is not None:
														_UpperCAmelCase       :		str 		=    self.tokenizer(A_						,					return_tensors=A_						,					**A_	)

										if images is not None:
														_UpperCAmelCase       :		List[Any] 		=    self.image_processor(A_						,					return_tensors=A_						,					**A_	)

										if text is not None and images is not None:
														_UpperCAmelCase       :		List[str] 		=    image_features.pixel_values
														return encoding
										elif text is not None:
														return encoding
										else:
														return BatchEncoding(data=dict(**A_	)						,					tensor_type=A_	)


						def        _UpperCAmelCase				(       self						,					*A_						,					**A_	):







										'''simple docstring'''


										return self.tokenizer.batch_decode(*A_						,					**A_	)


						def        _UpperCAmelCase				(       self						,					*A_						,					**A_	):







										'''simple docstring'''


										return self.tokenizer.decode(*A_						,					**A_	)





						@property
						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase       :		List[Any] 		=    self.tokenizer.model_input_names
										_UpperCAmelCase       :		int 		=    self.image_processor.model_input_names
										return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names	)	)





import fire

from utils import calculate_rouge, save_json

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


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


