


from typing import List, Optional

import numpy as np

from ...processing_utils import ProcessorMixin
from ...utils import to_numpy





class  lowerCAmelCase__(    snake_case__						):






    '''simple docstring'''





    A_  :     Any			= 'EncodecFeatureExtractor'
    A_  :     Dict			= ('T5Tokenizer', 'T5TokenizerFast')





    def __init__(	self				:   Any       ,   __snake_case				:   Tuple       ,   __snake_case				:   Any	):





       '''simple docstring'''
       super().__init__(__snake_case       ,   __snake_case	)
       UpperCAmelCase_							:      Tuple						=   self.feature_extractor
       UpperCAmelCase_							:      int						=   False





    def 					_lowerCamelCase (	self				:   Union[str, Any]       ,   __snake_case				:   Optional[Any]=None       ,   __snake_case				:   Any=None       ,   __snake_case				:   Tuple=True	):





       '''simple docstring'''
       return self.tokenizer.get_decoder_prompt_ids(task=__snake_case       ,   language=__snake_case       ,   no_timestamps=__snake_case	)





    def __call__(	self				:   Optional[Any]       ,   *__snake_case				:   List[Any]       ,   **__snake_case				:   str	):





       '''simple docstring'''
       # For backward compatibility
       if self._in_target_context_manager:
          return self.current_processor(*__snake_case       ,   **__snake_case	)

       UpperCAmelCase_							:      int						=   kwargs.pop('''audio'''       ,   __snake_case	)
       UpperCAmelCase_							:      Dict						=   kwargs.pop('''sampling_rate'''       ,   __snake_case	)
       UpperCAmelCase_							:      str						=   kwargs.pop('''text'''       ,   __snake_case	)
       if len(__snake_case	) > 0:
          UpperCAmelCase_							:      Tuple						=   args[0]
          UpperCAmelCase_							:      List[str]						=   args[1:]

       if audio is None and text is None:
          raise ValueError('''You need to specify either an `audio` or `text` input to process.'''	)

       if text is not None:
          UpperCAmelCase_							:      Tuple						=   self.tokenizer(__snake_case       ,   **__snake_case	)

       if audio is not None:
          UpperCAmelCase_							:      Optional[int]						=   self.feature_extractor(__snake_case       ,   *__snake_case       ,   sampling_rate=__snake_case       ,   **__snake_case	)

       if audio is None:
          return inputs

       elif text is None:
          return audio_inputs

       else:
          UpperCAmelCase_							:      Optional[int]						=   audio_inputs['''input_values''']
          if "padding_mask" in audio_inputs:
             UpperCAmelCase_							:      Dict						=   audio_inputs['''padding_mask''']
          return inputs





    def 					_lowerCamelCase (	self				:   int       ,   *__snake_case				:   Tuple       ,   **__snake_case				:   Any	):





       '''simple docstring'''
       UpperCAmelCase_							:      Optional[Any]						=   kwargs.pop('''audio'''       ,   __snake_case	)
       UpperCAmelCase_							:      Optional[Any]						=   kwargs.pop('''padding_mask'''       ,   __snake_case	)

       if len(__snake_case	) > 0:
          UpperCAmelCase_							:      Optional[int]						=   args[0]
          UpperCAmelCase_							:      int						=   args[1:]

       if audio_values is not None:
          return self._decode_audio(__snake_case       ,   padding_mask=__snake_case	)
       else:
          return self.tokenizer.batch_decode(*__snake_case       ,   **__snake_case	)





    def 					_lowerCamelCase (	self				:   Optional[int]       ,   *__snake_case				:   str       ,   **__snake_case				:   str	):





       '''simple docstring'''
       return self.tokenizer.decode(*__snake_case       ,   **__snake_case	)





    def 					_lowerCamelCase (	self				:   Tuple       ,   __snake_case				:   int       ,   __snake_case				:   Optional = None	):





       '''simple docstring'''
       UpperCAmelCase_							:      Optional[Any]						=   to_numpy(__snake_case	)
       UpperCAmelCase_		,      UpperCAmelCase_		,      UpperCAmelCase_							:      Any						=   audio_values.shape

       if padding_mask is None:
          return list(__snake_case	)

       UpperCAmelCase_							:      Tuple						=   to_numpy(__snake_case	)

       # match the sequence length of the padding mask to the generated audio arrays by padding with the **non-padding**
       # token (so that the generated audio values are **not** treated as padded tokens)
       UpperCAmelCase_							:      Dict						=   seq_len - padding_mask.shape[-1]
       UpperCAmelCase_							:      Optional[Any]						=   1 - self.feature_extractor.padding_value
       UpperCAmelCase_							:      str						=   np.pad(__snake_case       ,   ((0, 0), (0, difference))       ,   '''constant'''       ,   constant_values=__snake_case	)

       UpperCAmelCase_							:      List[Any]						=   audio_values.tolist()
       for i in range(__snake_case	):
          UpperCAmelCase_							:      Union[str, Any]						=   np.asarray(audio_values[i]	)[
              padding_mask[i][None, :] != self.feature_extractor.padding_value
          ]
          UpperCAmelCase_							:      Any						=   sliced_audio.reshape(__snake_case       ,   -1	)

       return audio_values



import fire

from utils import calculate_rouge, save_json
def      snake_case_						( __lowercase			,       __lowercase			,       __lowercase=None			,       **__lowercase       ):
   UpperCAmelCase_							:      Tuple						=   [x.strip() for x in open(__lowercase       ).readlines()]
   UpperCAmelCase_							:      Dict						=   [x.strip() for x in open(__lowercase       ).readlines()][: len(__lowercase       )]
   UpperCAmelCase_							:      int						=   calculate_rouge(__lowercase			,       __lowercase			,       **__lowercase       )
   if save_path is not None:
      save_json(__lowercase			,       __lowercase			,       indent=__lowercase       )
   return metrics  # these print nicely


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