


'''simple docstring'''




from typing import List, Optional

import numpy as np

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







class 					a			(				snake_case__  ):
 '''simple docstring'''




 __lowerCAmelCase						: Tuple							  =		"""EncodecFeatureExtractor"""
 __lowerCAmelCase						: List[str]							  =		("""T5Tokenizer""", """T5TokenizerFast""")




 def __init__(    self		,      lowerCamelCase_		,      lowerCamelCase_      )      ->    List[str]:
    super().__init__(lowerCamelCase_		,      lowerCamelCase_      )
    _a					:  Optional[int]						=  self.feature_extractor
    _a					:  Optional[int]						=  False




 def   __UpperCamelCase   (    self		,      lowerCamelCase_=None		,      lowerCamelCase_=None		,      lowerCamelCase_=True      )      ->    Optional[Any]:
    return self.tokenizer.get_decoder_prompt_ids(task=lowerCamelCase_		,      language=lowerCamelCase_		,      no_timestamps=lowerCamelCase_      )




 def __call__(    self		,      *lowerCamelCase_		,      **lowerCamelCase_      )      ->    int:
    # For backward compatibility
    if self._in_target_context_manager:
       return self.current_processor(*lowerCamelCase_		,      **lowerCamelCase_      )

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

    if audio is not None:
       _a					:  str						=  self.feature_extractor(lowerCamelCase_		,      *lowerCamelCase_		,      sampling_rate=lowerCamelCase_		,      **lowerCamelCase_      )

    if audio is None:
       return inputs

    elif text is None:
       return audio_inputs

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




 def   __UpperCamelCase   (    self		,      *lowerCamelCase_		,      **lowerCamelCase_      )      ->    List[Any]:
    _a					:  Union[str, Any]						=  kwargs.pop('audio'		,      lowerCamelCase_      )
    _a					:  str						=  kwargs.pop('padding_mask'		,      lowerCamelCase_      )

    if len(lowerCamelCase_      ) > 0:
       _a					:  str						=  args[0]
       _a					:  Dict						=  args[1:]

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




 def   __UpperCamelCase   (    self		,      *lowerCamelCase_		,      **lowerCamelCase_      )      ->    Any:
    return self.tokenizer.decode(*lowerCamelCase_		,      **lowerCamelCase_      )




 def   __UpperCamelCase   (    self		,      lowerCamelCase_		,      lowerCamelCase_ = None      )      ->    List[np.ndarray]:
    _a					:  Any						=  to_numpy(lowerCamelCase_      )
    _a		,						_a		,						_a					:  Union[str, Any]						=  audio_values.shape

    if padding_mask is None:
       return list(lowerCamelCase_      )

    _a					:  Tuple						=  to_numpy(lowerCamelCase_      )

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

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

    return audio_values






'''simple docstring'''




from ..utils import DummyObject, requires_backends







class 					a			(				metaclass=snake_case__  ):
 '''simple docstring'''




 __lowerCAmelCase						: Union[str, Any]							  =		["""note_seq"""]




 def __init__(    self		,      *lowerCamelCase_		,      **lowerCamelCase_      )      ->    int:
    requires_backends(self		,      ['note_seq']      )




 @classmethod
 def   __UpperCamelCase   (    cls		,      *lowerCamelCase_		,      **lowerCamelCase_      )      ->    Union[str, Any]:
    requires_backends(cls		,      ['note_seq']      )




 @classmethod
 def   __UpperCamelCase   (    cls		,      *lowerCamelCase_		,      **lowerCamelCase_      )      ->    List[str]:
    requires_backends(cls		,      ['note_seq']      )


