






from ...processing_utils import ProcessorMixin

class 		lowerCAmelCase     ( __SCREAMING_SNAKE_CASE			):

     '''simple docstring'''

     _A						:			Tuple									=      'SpeechT5FeatureExtractor'
     _A						:			int									=      'SpeechT5Tokenizer'



     def __init__(  self				:	Dict		,     __a				:	int		,     __a				:	Union[str, Any]							)			->  Tuple:


            """simple docstring"""

            super().__init__(_a		,     _a							)



     def __call__(  self				:	Optional[Any]		,     *__a				:	Any		,     **__a				:	Dict							)			->  int:


            """simple docstring"""

            __lowercase      :							List[str]				=     kwargs.pop("""audio"""		,     _a							)
            __lowercase      :							List[str]				=     kwargs.pop("""text"""		,     _a							)
            __lowercase      :							Dict				=     kwargs.pop("""text_target"""		,     _a							)
            __lowercase      :							int				=     kwargs.pop("""audio_target"""		,     _a							)
            __lowercase      :							List[str]				=     kwargs.pop("""sampling_rate"""		,     _a							)

            if audio is not None and text is not None:
                   raise ValueError(
                       """Cannot process both `audio` and `text` inputs. Did you mean `audio_target` or `text_target`?"""							)
            if audio_target is not None and text_target is not None:
                   raise ValueError(
                       """Cannot process both `audio_target` and `text_target` inputs. Did you mean `audio` or `text`?"""							)
            if audio is None and audio_target is None and text is None and text_target is None:
                   raise ValueError(
                       """You need to specify either an `audio`, `audio_target`, `text`, or `text_target` input to process."""							)

            if audio is not None:
                   __lowercase      :							Dict				=     self.feature_extractor(_a		,     *_a		,     sampling_rate=_a		,     **_a							)
            elif text is not None:
                   __lowercase      :							List[str]				=     self.tokenizer(_a		,     **_a							)
            else:
                   __lowercase      :							Tuple				=     None

            if audio_target is not None:
                   __lowercase      :							Optional[Any]				=     self.feature_extractor(audio_target=_a		,     *_a		,     sampling_rate=_a		,     **_a							)
                   __lowercase      :							Tuple				=     targets["""input_values"""]
            elif text_target is not None:
                   __lowercase      :							Dict				=     self.tokenizer(_a		,     **_a							)
                   __lowercase      :							Optional[Any]				=     targets["""input_ids"""]
            else:
                   __lowercase      :							Any				=     None

            if inputs is None:
                   return targets

            if targets is not None:
                   __lowercase      :							str				=     labels

                   __lowercase      :							Any				=     targets.get("""attention_mask"""							)
                   if decoder_attention_mask is not None:
                          __lowercase      :							Tuple				=     decoder_attention_mask

            return inputs



     def       lowerCAmelCase				(  self				:	Union[str, Any]		,     *__a				:	Optional[int]		,     **__a				:	Union[str, Any]							)			->  Optional[Any]:


            """simple docstring"""

            __lowercase      :							Optional[int]				=     kwargs.pop("""input_values"""		,     _a							)
            __lowercase      :							Dict				=     kwargs.pop("""input_ids"""		,     _a							)
            __lowercase      :							Dict				=     kwargs.pop("""labels"""		,     _a							)

            if input_values is not None and input_ids is not None:
                   raise ValueError("""Cannot process both `input_values` and `input_ids` inputs."""							)
            if input_values is None and input_ids is None and labels is None:
                   raise ValueError(
                       """You need to specify either an `input_values`, `input_ids`, or `labels` input to be padded."""							)

            if input_values is not None:
                   __lowercase      :							Any				=     self.feature_extractor.pad(_a		,     *_a		,     **_a							)
            elif input_ids is not None:
                   __lowercase      :							Optional[Any]				=     self.tokenizer.pad(_a		,     **_a							)
            else:
                   __lowercase      :							List[str]				=     None

            if labels is not None:
                   if "input_ids" in labels or (isinstance(_a		,     _a							) and "input_ids" in labels[0]):
                          __lowercase      :							List[str]				=     self.tokenizer.pad(_a		,     **_a							)
                          __lowercase      :							int				=     targets["""input_ids"""]
                   else:
                          __lowercase      :							Optional[Any]				=     self.feature_extractor.feature_size
                          __lowercase      :							Dict				=     self.feature_extractor.num_mel_bins
                          __lowercase      :							Union[str, Any]				=     self.feature_extractor.pad(_a		,     *_a		,     **_a							)
                          __lowercase      :							str				=     feature_size_hack
                          __lowercase      :							List[str]				=     targets["""input_values"""]
            else:
                   __lowercase      :							List[str]				=     None

            if inputs is None:
                   return targets

            if targets is not None:
                   __lowercase      :							List[Any]				=     labels

                   __lowercase      :							Tuple				=     targets.get("""attention_mask"""							)
                   if decoder_attention_mask is not None:
                          __lowercase      :							Optional[int]				=     decoder_attention_mask

            return inputs



     def       lowerCAmelCase				(  self				:	str		,     *__a				:	Dict		,     **__a				:	str							)			->  str:


            """simple docstring"""

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



     def       lowerCAmelCase				(  self				:	Tuple		,     *__a				:	Dict		,     **__a				:	List[str]							)			->  List[Any]:


            """simple docstring"""

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







import fire

from utils import calculate_rouge, save_json
def       snake_case_		(     lowerCAmelCase_       :						Any       ,       lowerCAmelCase_       :						str       ,       lowerCAmelCase_       :						str=None       ,       **lowerCAmelCase_       :						str      ):
							__lowercase      :							Tuple				=     [x.strip() for x in open(lowerCAmelCase_      ).readlines()]
							__lowercase      :							Dict				=     [x.strip() for x in open(lowerCAmelCase_      ).readlines()][: len(lowerCAmelCase_      )]
							__lowercase      :							Tuple				=     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)