


from typing import List, Optional

import numpy as np

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







class        snake_case_   (lowerCamelCase_      ):
					UpperCAmelCase__					:    str          =			'''EncodecFeatureExtractor'''
					UpperCAmelCase__					:    str          =			('''T5Tokenizer''', '''T5TokenizerFast''')
					def __init__(      self   :str			,__snake_case   :Optional[Any]			,__snake_case   :Dict      )					->    Optional[int]:
												super().__init__(__snake_case			,__snake_case      )
												a__             =     self.feature_extractor
												a__             =     False
					def  lowerCamelCase__(      self   :Tuple			,__snake_case   :str=None			,__snake_case   :int=None			,__snake_case   :Dict=True      )					->    List[Any]:
												return self.tokenizer.get_decoder_prompt_ids(task=__snake_case			,language=__snake_case			,no_timestamps=__snake_case      )
					def __call__(      self   :Dict			,*__snake_case   :List[Any]			,**__snake_case   :Any      )					->    List[Any]:
												# For backward compatibility
												if self._in_target_context_manager:
																			return self.current_processor(*__snake_case			,**__snake_case      )

												a__             =     kwargs.pop('audio'			,__snake_case      )
												a__             =     kwargs.pop('sampling_rate'			,__snake_case      )
												a__             =     kwargs.pop('text'			,__snake_case      )
												if len(__snake_case      ) > 0:
																			a__             =     args[0]
																			a__             =     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:
																			a__             =     self.tokenizer(__snake_case			,**__snake_case      )

												if audio is not None:
																			a__             =     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:
																			a__             =     audio_inputs['input_values']
																			if "padding_mask" in audio_inputs:
																										a__             =     audio_inputs['padding_mask']
																			return inputs
					def  lowerCamelCase__(      self   :List[Any]			,*__snake_case   :Any			,**__snake_case   :List[Any]      )					->    str:
												a__             =     kwargs.pop('audio'			,__snake_case      )
												a__             =     kwargs.pop('padding_mask'			,__snake_case      )

												if len(__snake_case      ) > 0:
																			a__             =     args[0]
																			a__             =     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   :List[str]			,*__snake_case   :str			,**__snake_case   :Optional[int]      )					->    Any:
												return self.tokenizer.decode(*__snake_case			,**__snake_case      )



					def  lowerCamelCase__(      self   :int			,__snake_case   :Tuple			,__snake_case   :Optional = None      )					->    List[np.ndarray]:
												a__             =     to_numpy(__snake_case      )
												a__      ,					a__      ,					a__             =     audio_values.shape

												if padding_mask is None:
																			return list(__snake_case      )

												a__             =     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)
												a__             =     seq_len - padding_mask.shape[-1]
												a__             =     1 - self.feature_extractor.padding_value
												a__             =     np.pad(__snake_case			,((0, 0), (0, difference))			,'constant'			,constant_values=__snake_case      )

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

												return audio_values








from abc import ABC, abstractmethod
from argparse import ArgumentParser







class        snake_case_   (lowerCamelCase_      ):
					@staticmethod
					@abstractmethod
					def  lowerCamelCase__(      __snake_case   :ArgumentParser      )					->    Dict:
												raise NotImplementedError()



					@abstractmethod
					def  lowerCamelCase__(      self   :Union[str, Any]      )					->    Dict:
												raise NotImplementedError()




