code: 
  
  
  
  
  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
  
        SCREAMING_SNAKE_CASE       = True
        SCREAMING_SNAKE_CASE       = ''''''
  except Exception as e:
        SCREAMING_SNAKE_CASE       = False
        SCREAMING_SNAKE_CASE       = (
            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 			UpperCAmelCase_						(		lowercase__,     lowercase__							):
      lowercase__		      =   """mel_config.json"""
  
  
  
  
      @register_to_config
      def __init__(					self		: Union[str, Any]			,				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			,				)   ->							Any:
  
  
  
  
       '''simple docstring'''
  
       A__ =       hop_length
       A__ =       sample_rate
       A__ =       n_fft
       A__ =       top_db
       A__ =       n_iter
       self.set_resolution(_A			,				_A   )
       A__ =       None
  
       if not _librosa_can_be_imported:
        raise ValueError(_import_error   )
  
  
  
  
      def 							__magic_name__							(					self		: Optional[Any]			,				snake_case_		: int			,				snake_case_		: int   )   ->							Tuple:
  
  
  
  
       '''simple docstring'''
  
       A__ =       x_res
       A__ =       y_res
       A__ =       self.y_res
       A__ =       self.x_res * self.hop_length - 1
  
  
  
  
      def 							__magic_name__							(					self		: List[str]			,				snake_case_		: str = None			,				snake_case_		: np.ndarray = None   )   ->							Optional[int]:
  
  
  
  
       '''simple docstring'''
  
       if audio_file is not None:
        A__ =       librosa.load(_A			,				mono=_A			,				sr=self.sr   )
       else:
        A__ =       raw_audio
  
       # Pad with silence if necessary.
       if len(self.audio   ) < self.x_res * self.hop_length:
        A__ =       np.concatenate([self.audio, np.zeros((self.x_res * self.hop_length - len(self.audio   ),)   )]   )
  
  
  
  
      def 							__magic_name__							(					self		: List[Any]   )   ->							int:
  
  
  
  
       '''simple docstring'''
  
       return len(self.audio   ) // self.slice_size
  
  
  
  
      def 							__magic_name__							(					self		: Optional[Any]			,				snake_case_		: int = 0   )   ->							np.ndarray:
  
  
  
  
       '''simple docstring'''
  
       return self.audio[self.slice_size * slice : self.slice_size * (slice + 1)]
  
  
  
  
      def 							__magic_name__							(					self		: int   )   ->							int:
  
  
  
  
       '''simple docstring'''
  
       return self.sr
  
  
  
  
      def 							__magic_name__							(					self		: Dict			,				snake_case_		: int   )   ->							Image.Image:
  
  
  
  
       '''simple docstring'''
  
       A__ =       librosa.feature.melspectrogram(
           y=self.get_audio_slice(_A   )			,				sr=self.sr			,				n_fft=self.n_fft			,				hop_length=self.hop_length			,				n_mels=self.n_mels   )
       A__ =       librosa.power_to_db(_A			,				ref=np.max			,				top_db=self.top_db   )
       A__ =       (((log_S + self.top_db) * 255 / self.top_db).clip(0			,				255   ) + 0.5).astype(np.uinta   )
       A__ =       Image.fromarray(_A   )
       return image
  
  
  
  
      def 							__magic_name__							(					self		: List[Any]			,				snake_case_		: Image.Image   )   ->							np.ndarray:
  
  
  
  
       '''simple docstring'''
  
       A__ =       np.frombuffer(image.tobytes()			,				dtype="uint8"   ).reshape((image.height, image.width)   )
       A__ =       bytedata.astype("float"   ) * self.top_db / 255 - self.top_db
       A__ =       librosa.db_to_power(_A   )
       A__ =       librosa.feature.inverse.mel_to_audio(
           _A			,				sr=self.sr			,				n_fft=self.n_fft			,				hop_length=self.hop_length			,				n_iter=self.n_iter   )
       return audio
  
  
  
  
  
  
style_context: 
  
  
  
  
  """simple docstring"""
  
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
  
  
  SCREAMING_SNAKE_CASE       = {
      "configuration_deta": ["DETA_PRETRAINED_CONFIG_ARCHIVE_MAP", "DetaConfig"],
  }
  
  try:
        if not is_vision_available():
              raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
        pass
  else:
        SCREAMING_SNAKE_CASE       = ["DetaImageProcessor"]
  
  try:
        if not is_torch_available():
              raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
        pass
  else:
        SCREAMING_SNAKE_CASE       = [
            "DETA_PRETRAINED_MODEL_ARCHIVE_LIST",
            "DetaForObjectDetection",
            "DetaModel",
            "DetaPreTrainedModel",
        ]
  
  
  if TYPE_CHECKING:
        from .configuration_deta import DETA_PRETRAINED_CONFIG_ARCHIVE_MAP, DetaConfig
  
        try:
              if not is_vision_available():
                    raise OptionalDependencyNotAvailable()
        except OptionalDependencyNotAvailable:
              pass
        else:
              from .image_processing_deta import DetaImageProcessor
  
        try:
              if not is_torch_available():
                    raise OptionalDependencyNotAvailable()
        except OptionalDependencyNotAvailable:
              pass
        else:
              from .modeling_deta import (
                  DETA_PRETRAINED_MODEL_ARCHIVE_LIST,
                  DetaForObjectDetection,
                  DetaModel,
                  DetaPreTrainedModel,
              )
  
  else:
        import sys
  
        SCREAMING_SNAKE_CASE       = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
  
  
  
  
  
  