





'''simple docstring'''

from typing import List, Optional

import numpy as np

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





class lowerCamelCase						(_UpperCamelCase					):
  _lowercase  :	List[Any]       					=				"EncodecFeatureExtractor"
  _lowercase  :	Union[str, Any]       					=				("T5Tokenizer", "T5TokenizerFast")







  def __init__(		self     ,     lowercase__     ,     lowercase__		)		->						Union[str, Any]:






        """simple docstring"""





        super().__init__(__a     ,     __a		)
        _snake_case		:	str					     =     self.feature_extractor
        _snake_case		:	int					     =     False







  def 				UpperCAmelCase_				(		self     ,     lowercase__=None     ,     lowercase__=None     ,     lowercase__=True		)		->						Dict:






        """simple docstring"""





        return self.tokenizer.get_decoder_prompt_ids(task=__a     ,     language=__a     ,     no_timestamps=__a		)







  def __call__(		self     ,     *lowercase__     ,     **lowercase__		)		->						Any:






        """simple docstring"""





        if self._in_target_context_manager:
              return self.current_processor(*__a     ,     **__a		)

        _snake_case		:	Optional[int]					     =     kwargs.pop('''audio'''     ,     __a		)
        _snake_case		:	str					     =     kwargs.pop('''sampling_rate'''     ,     __a		)
        _snake_case		:	str					     =     kwargs.pop('''text'''     ,     __a		)
        if len(__a		) > 0:
              _snake_case		:	Dict					     =     args[0]
              _snake_case		:	Union[str, Any]					     =     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:
              _snake_case		:	List[str]					     =     self.tokenizer(__a     ,     **__a		)

        if audio is not None:
              _snake_case		:	Optional[Any]					     =     self.feature_extractor(__a     ,     *__a     ,     sampling_rate=__a     ,     **__a		)

        if audio is None:
              return inputs

        elif text is None:
              return audio_inputs

        else:
              _snake_case		:	Dict					     =     audio_inputs["input_values"]
              if "padding_mask" in audio_inputs:
                    _snake_case		:	Optional[Any]					     =     audio_inputs["padding_mask"]
              return inputs







  def 				UpperCAmelCase_				(		self     ,     *lowercase__     ,     **lowercase__		)		->						Optional[Any]:






        """simple docstring"""





        _snake_case		:	Dict					     =     kwargs.pop('''audio'''     ,     __a		)
        _snake_case		:	List[Any]					     =     kwargs.pop('''padding_mask'''     ,     __a		)

        if len(__a		) > 0:
              _snake_case		:	List[str]					     =     args[0]
              _snake_case		:	Optional[int]					     =     args[1:]

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







  def 				UpperCAmelCase_				(		self     ,     *lowercase__     ,     **lowercase__		)		->						Union[str, Any]:






        """simple docstring"""





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







  def 				UpperCAmelCase_				(		self     ,     lowercase__     ,     lowercase__ = None		)		->						List[np.ndarray]:






        """simple docstring"""





        _snake_case		:	str					     =     to_numpy(__a		)
        _snake_case		:	Optional[Any]					     =     audio_values.shape

        if padding_mask is None:
              return list(__a		)

        _snake_case		:	Optional[int]					     =     to_numpy(__a		)

        # 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)
        _snake_case		:	List[str]					     =     seq_len - padding_mask.shape[-1]
        _snake_case		:	List[str]					     =     1 - self.feature_extractor.padding_value
        _snake_case		:	Optional[Any]					     =     np.pad(__a     ,     ((0, 0), (0, difference))     ,     '''constant'''     ,     constant_values=__a		)

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

        return audio_values









'''simple docstring'''

from __future__ import annotations




def    _a    (							lowerCAmelCase_			,      lowerCAmelCase_ = None			,      lowerCAmelCase_ = None	):





      """simple docstring"""



      if start is None:
            _snake_case		:	Optional[Any]					     =     0

      if end is None:
            _snake_case		:	Any					     =     len(lowerCAmelCase_	) - 1

      if start >= end:
            return

      _snake_case		:	Optional[Any]					     =     (start + end) // 2

      slowsort(lowerCAmelCase_			,      lowerCAmelCase_			,      lowerCAmelCase_	)
      slowsort(lowerCAmelCase_			,      mid + 1			,      lowerCAmelCase_	)

      if sequence[end] < sequence[mid]:
            _snake_case     ,							_snake_case		:	int					     =     sequence[mid], sequence[end]

      slowsort(lowerCAmelCase_			,      lowerCAmelCase_			,      end - 1	)


if __name__ == "__main__":
   from doctest import testmod

   testmod()


