





'''simple docstring'''







from ...processing_utils import ProcessorMixin
class 			_lowercase						(       lowerCAmelCase__					):
     _UpperCAmelCase										=					"SpeechT5FeatureExtractor"
     _UpperCAmelCase										=					"SpeechT5Tokenizer"







     def __init__( self    ,   A__    ,   A__    ) ->	Tuple:
      super().__init__(_lowerCamelCase    ,   _lowerCamelCase    )







     def __call__( self    ,   *A__    ,   **A__    ) ->	str:
      snake_case				=			kwargs.pop('''audio'''    ,   _lowerCamelCase    )
      snake_case				=			kwargs.pop('''text'''    ,   _lowerCamelCase    )
      snake_case				=			kwargs.pop('''text_target'''    ,   _lowerCamelCase    )
      snake_case				=			kwargs.pop('''audio_target'''    ,   _lowerCamelCase    )
      snake_case				=			kwargs.pop('''sampling_rate'''    ,   _lowerCamelCase    )

      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:
       snake_case				=			self.feature_extractor(_lowerCamelCase    ,   *_lowerCamelCase    ,   sampling_rate=_lowerCamelCase    ,   **_lowerCamelCase    )
      elif text is not None:
       snake_case				=			self.tokenizer(_lowerCamelCase    ,   **_lowerCamelCase    )
      else:
       snake_case				=			None

      if audio_target is not None:
       snake_case				=			self.feature_extractor(audio_target=_lowerCamelCase    ,   *_lowerCamelCase    ,   sampling_rate=_lowerCamelCase    ,   **_lowerCamelCase    )
       snake_case				=			targets['''input_values''']
      elif text_target is not None:
       snake_case				=			self.tokenizer(_lowerCamelCase    ,   **_lowerCamelCase    )
       snake_case				=			targets['''input_ids''']
      else:
       snake_case				=			None

      if inputs is None:
       return targets

      if targets is not None:
       snake_case				=			labels

       snake_case				=			targets.get('''attention_mask'''    )
       if decoder_attention_mask is not None:
        snake_case				=			decoder_attention_mask

      return inputs







     def     UpperCamelCase			( self    ,   *A__    ,   **A__    ) ->	List[Any]:
      snake_case				=			kwargs.pop('''input_values'''    ,   _lowerCamelCase    )
      snake_case				=			kwargs.pop('''input_ids'''    ,   _lowerCamelCase    )
      snake_case				=			kwargs.pop('''labels'''    ,   _lowerCamelCase    )

      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:
       snake_case				=			self.feature_extractor.pad(_lowerCamelCase    ,   *_lowerCamelCase    ,   **_lowerCamelCase    )
      elif input_ids is not None:
       snake_case				=			self.tokenizer.pad(_lowerCamelCase    ,   **_lowerCamelCase    )
      else:
       snake_case				=			None

      if labels is not None:
       if "input_ids" in labels or (isinstance(_lowerCamelCase    ,   _lowerCamelCase    ) and "input_ids" in labels[0]):
        snake_case				=			self.tokenizer.pad(_lowerCamelCase    ,   **_lowerCamelCase    )
        snake_case				=			targets['''input_ids''']
       else:
        snake_case				=			self.feature_extractor.feature_size
        snake_case				=			self.feature_extractor.num_mel_bins
        snake_case				=			self.feature_extractor.pad(_lowerCamelCase    ,   *_lowerCamelCase    ,   **_lowerCamelCase    )
        snake_case				=			feature_size_hack
        snake_case				=			targets['''input_values''']
      else:
       snake_case				=			None

      if inputs is None:
       return targets

      if targets is not None:
       snake_case				=			labels

       snake_case				=			targets.get('''attention_mask'''    )
       if decoder_attention_mask is not None:
        snake_case				=			decoder_attention_mask

      return inputs







     def     UpperCamelCase			( self    ,   *A__    ,   **A__    ) ->	int:
      return self.tokenizer.batch_decode(*_lowerCamelCase    ,   **_lowerCamelCase    )







     def     UpperCamelCase			( self    ,   *A__    ,   **A__    ) ->	Tuple:
      return self.tokenizer.decode(*_lowerCamelCase    ,   **_lowerCamelCase    )








'''simple docstring'''







from ...utils import is_note_seq_available, is_transformers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable


try:
   if not (is_transformers_available() and is_torch_available()):
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   from ...utils.dummy_torch_and_transformers_objects import *  # noqa F403
else:
   from .notes_encoder import SpectrogramNotesEncoder
   from .continous_encoder import SpectrogramContEncoder
   from .pipeline_spectrogram_diffusion import (
       SpectrogramContEncoder,
       SpectrogramDiffusionPipeline,
       TaFilmDecoder,
   )

try:
   if not (is_transformers_available() and is_torch_available() and is_note_seq_available()):
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   from ...utils.dummy_transformers_and_torch_and_note_seq_objects import *  # noqa F403
else:
   from .midi_utils import MidiProcessor

