code: 
  '''simple docstring'''
  
  
  
  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
  
  			__A          =    True
  			__A          =    ""
  except Exception as e:
  			__A          =    False
  			__A          =    (
  			    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 			A							(       __UpperCAmelCase		,						__UpperCAmelCase				):
  			lowerCamelCase							:							str       		=  """mel_config.json"""
  
  
  
  			@register_to_config
  			def __init__(					self       ,     lowerCamelCase__ = 256       ,     lowerCamelCase__ = 256       ,     lowerCamelCase__ = 22_050       ,     lowerCamelCase__ = 2_048       ,     lowerCamelCase__ = 512       ,     lowerCamelCase__ = 80       ,     lowerCamelCase__ = 32       ,     )			->		str:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       hop_length
  									lowercase__							 =       sample_rate
  									lowercase__							 =       n_fft
  									lowercase__							 =       top_db
  									lowercase__							 =       n_iter
  									self.set_resolution(lowerCamelCase__       ,     lowerCamelCase__       )
  									lowercase__							 =       None
  
  									if not _librosa_can_be_imported:
  															raise ValueError(_import_error       )
  
  
  
  			def A__ (					self       ,     lowerCamelCase__       ,     lowerCamelCase__       )			->		Dict:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       x_res
  									lowercase__							 =       y_res
  									lowercase__							 =       self.y_res
  									lowercase__							 =       self.x_res * self.hop_length - 1
  
  
  
  			def A__ (					self       ,     lowerCamelCase__ = None       ,     lowerCamelCase__ = None       )			->		Optional[int]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									if audio_file is not None:
  															lowercase__ ,						lowercase__							 =       librosa.load(lowerCamelCase__       ,     mono=lowerCamelCase__       ,     sr=self.sr       )
  									else:
  															lowercase__							 =       raw_audio
  
  									# Pad with silence if necessary.
  									if len(self.audio       ) < self.x_res * self.hop_length:
  															lowercase__							 =       np.concatenate([self.audio, np.zeros((self.x_res * self.hop_length - len(self.audio       ),)       )]       )
  
  
  
  			def A__ (					self       )			->		int:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									return len(self.audio       ) // self.slice_size
  
  
  
  			def A__ (					self       ,     lowerCamelCase__ = 0       )			->		np.ndarray:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									return self.audio[self.slice_size * slice : self.slice_size * (slice + 1)]
  
  
  
  			def A__ (					self       )			->		int:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									return self.sr
  
  
  
  			def A__ (					self       ,     lowerCamelCase__       )			->		Image.Image:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       librosa.feature.melspectrogram(
  									    y=self.get_audio_slice(lowerCamelCase__       )       ,     sr=self.sr       ,     n_fft=self.n_fft       ,     hop_length=self.hop_length       ,     n_mels=self.n_mels       )
  									lowercase__							 =       librosa.power_to_db(lowerCamelCase__       ,     ref=np.max       ,     top_db=self.top_db       )
  									lowercase__							 =       (((log_S + self.top_db) * 255 / self.top_db).clip(0       ,     255       ) + 0.5).astype(np.uinta       )
  									lowercase__							 =       Image.fromarray(lowerCamelCase__       )
  									return image
  
  
  
  			def A__ (					self       ,     lowerCamelCase__       )			->		np.ndarray:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       np.frombuffer(image.tobytes()       ,     dtype="""uint8"""       ).reshape((image.height, image.width)       )
  									lowercase__							 =       bytedata.astype("""float"""       ) * self.top_db / 255 - self.top_db
  									lowercase__							 =       librosa.db_to_power(lowerCamelCase__       )
  									lowercase__							 =       librosa.feature.inverse.mel_to_audio(
  									    lowerCamelCase__       ,     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
  
  
  __A          =    {"configuration_detr": ["DETR_PRETRAINED_CONFIG_ARCHIVE_MAP", "DetrConfig", "DetrOnnxConfig"]}
  
  try:
  				if not is_vision_available():
  								raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  				pass
  else:
  				__A          =    ["DetrFeatureExtractor"]
  				__A          =    ["DetrImageProcessor"]
  
  try:
  				if not is_torch_available():
  								raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  				pass
  else:
  				__A          =    [
  				    "DETR_PRETRAINED_MODEL_ARCHIVE_LIST",
  				    "DetrForObjectDetection",
  				    "DetrForSegmentation",
  				    "DetrModel",
  				    "DetrPreTrainedModel",
  				]
  
  
  if TYPE_CHECKING:
  				from .configuration_detr import DETR_PRETRAINED_CONFIG_ARCHIVE_MAP, DetrConfig, DetrOnnxConfig
  
  				try:
  								if not is_vision_available():
  												raise OptionalDependencyNotAvailable()
  				except OptionalDependencyNotAvailable:
  								pass
  				else:
  								from .feature_extraction_detr import DetrFeatureExtractor
  								from .image_processing_detr import DetrImageProcessor
  
  				try:
  								if not is_torch_available():
  												raise OptionalDependencyNotAvailable()
  				except OptionalDependencyNotAvailable:
  								pass
  				else:
  								from .modeling_detr import (
  								    DETR_PRETRAINED_MODEL_ARCHIVE_LIST,
  								    DetrForObjectDetection,
  								    DetrForSegmentation,
  								    DetrModel,
  								    DetrPreTrainedModel,
  								)
  
  else:
  				import sys
  
  				__A          =    _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
  
  
  
  
  
  
  