





import numpy as np  # noqa: E402

from ...configuration_utils import ConfigMixin, register_to_config
from ...schedulers.scheduling_utils import SchedulerMixin


try:
      import librosa  # noqa: E402

      __lowercase				    =      True
      __lowercase				    =      """"""
except Exception as e:
      __lowercase				    =      False
      __lowercase				    =      (
          f"""Cannot import librosa because {e}. Make sure to correctly install librosa to be able to install it."""
      )


from PIL import Image  # noqa: E402

class 	__snake_case					(      _a							,					_a ):






    '''simple docstring'''
    _lowerCAmelCase  =						"""mel_config.json"""





    @register_to_config
    def __init__( self    :		str      ,    snake_case    :		int = 256      ,    snake_case    :		int = 256      ,    snake_case    :		int = 22_050      ,    snake_case    :		int = 2_048      ,    snake_case    :		int = 512      ,    snake_case    :		int = 80      ,    snake_case    :		int = 32      ,    ):

      """simple docstring"""





      __lowerCAmelCase								=					hop_length
      __lowerCAmelCase								=					sample_rate
      __lowerCAmelCase								=					n_fft
      __lowerCAmelCase								=					top_db
      __lowerCAmelCase								=					n_iter
      self.set_resolution(snake_case      ,    snake_case				)
      __lowerCAmelCase								=					None

      if not _librosa_can_be_imported:
        raise ValueError(_import_error				)





    def 						A		( self    :		int      ,    snake_case    :		int      ,    snake_case    :		int				):

      """simple docstring"""





      __lowerCAmelCase								=					x_res
      __lowerCAmelCase								=					y_res
      __lowerCAmelCase								=					self.y_res
      __lowerCAmelCase								=					self.x_res * self.hop_length - 1





    def 						A		( self    :		List[str]      ,    snake_case    :		str = None      ,    snake_case    :		np.ndarray = None				):

      """simple docstring"""





      if audio_file is not None:
        __lowerCAmelCase						,    __lowerCAmelCase								=					librosa.load(snake_case      ,    mono=snake_case      ,    sr=self.sr				)
      else:
        __lowerCAmelCase								=					raw_audio

      # Pad with silence if necessary.
      if len(self.audio				) < self.x_res * self.hop_length:
        __lowerCAmelCase								=					np.concatenate([self.audio, np.zeros((self.x_res * self.hop_length - len(self.audio				),)				)]				)





    def 						A		( self    :		str				):

      """simple docstring"""





      return len(self.audio				) // self.slice_size





    def 						A		( self    :		Tuple      ,    snake_case    :		int = 0				):

      """simple docstring"""





      return self.audio[self.slice_size * slice : self.slice_size * (slice + 1)]





    def 						A		( self    :		Any				):

      """simple docstring"""





      return self.sr





    def 						A		( self    :		Any      ,    snake_case    :		int				):

      """simple docstring"""





      __lowerCAmelCase								=					librosa.feature.melspectrogram(
          y=self.get_audio_slice(snake_case				)      ,    sr=self.sr      ,    n_fft=self.n_fft      ,    hop_length=self.hop_length      ,    n_mels=self.n_mels				)
      __lowerCAmelCase								=					librosa.power_to_db(snake_case      ,    ref=np.max      ,    top_db=self.top_db				)
      __lowerCAmelCase								=					(((log_S + self.top_db) * 255 / self.top_db).clip(0      ,    255				) + 0.5).astype(np.uinta				)
      __lowerCAmelCase								=					Image.fromarray(snake_case				)
      return image





    def 						A		( self    :		Union[str, Any]      ,    snake_case    :		Image.Image				):

      """simple docstring"""





      __lowerCAmelCase								=					np.frombuffer(image.tobytes()      ,    dtype='''uint8'''				).reshape((image.height, image.width)				)
      __lowerCAmelCase								=					bytedata.astype('''float'''				) * self.top_db / 255 - self.top_db
      __lowerCAmelCase								=					librosa.db_to_power(snake_case				)
      __lowerCAmelCase								=					librosa.feature.inverse.mel_to_audio(
          snake_case      ,    sr=self.sr      ,    n_fft=self.n_fft      ,    hop_length=self.hop_length      ,    n_iter=self.n_iter				)
      return audio







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__lowercase				    =      {
    """configuration_bart""": ["""BART_PRETRAINED_CONFIG_ARCHIVE_MAP""", """BartConfig""", """BartOnnxConfig"""],
    """tokenization_bart""": ["""BartTokenizer"""],
}

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __lowercase				    =      ["""BartTokenizerFast"""]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __lowercase				    =      [
          """BART_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """BartForCausalLM""",
          """BartForConditionalGeneration""",
          """BartForQuestionAnswering""",
          """BartForSequenceClassification""",
          """BartModel""",
          """BartPretrainedModel""",
          """PretrainedBartModel""",
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __lowercase				    =      [
          """TFBartForConditionalGeneration""",
          """TFBartForSequenceClassification""",
          """TFBartModel""",
          """TFBartPretrainedModel""",
      ]

try:
      if not is_flax_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __lowercase				    =      [
          """FlaxBartDecoderPreTrainedModel""",
          """FlaxBartForCausalLM""",
          """FlaxBartForConditionalGeneration""",
          """FlaxBartForQuestionAnswering""",
          """FlaxBartForSequenceClassification""",
          """FlaxBartModel""",
          """FlaxBartPreTrainedModel""",
      ]

if TYPE_CHECKING:
      from .configuration_bart import BART_PRETRAINED_CONFIG_ARCHIVE_MAP, BartConfig, BartOnnxConfig
      from .tokenization_bart import BartTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_bart_fast import BartTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_bart import (
                BART_PRETRAINED_MODEL_ARCHIVE_LIST,
                BartForCausalLM,
                BartForConditionalGeneration,
                BartForQuestionAnswering,
                BartForSequenceClassification,
                BartModel,
                BartPretrainedModel,
                PretrainedBartModel,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_bart import (
                TFBartForConditionalGeneration,
                TFBartForSequenceClassification,
                TFBartModel,
                TFBartPretrainedModel,
            )

      try:
            if not is_flax_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_flax_bart import (
                FlaxBartDecoderPreTrainedModel,
                FlaxBartForCausalLM,
                FlaxBartForConditionalGeneration,
                FlaxBartForQuestionAnswering,
                FlaxBartForSequenceClassification,
                FlaxBartModel,
                FlaxBartPreTrainedModel,
            )

else:
      import sys

      __lowercase				    =      _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
