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
  
   lowerCamelCase							:     int								=			True
   lowerCamelCase							:     Dict								=			''
  except Exception as e:
   lowerCamelCase							:     str								=			False
   lowerCamelCase							:     Optional[Any]								=			(
       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 						__lowerCAmelCase		(lowercase_						,  lowercase_  ):
  
  
  
  
  
        '''simple docstring'''
  
  
  
  
  
  
        lowerCAmelCase__      :  int						      =							"""mel_config.json"""
  
  
  
        @register_to_config
        def __init__(self					:					Dict     ,		UpperCamelCase					:					int = 256     ,		UpperCamelCase					:					int = 256     ,		UpperCamelCase					:					int = 22050     ,		UpperCamelCase					:					int = 2048     ,		UpperCamelCase					:					int = 512     ,		UpperCamelCase					:					int = 80     ,		UpperCamelCase					:					int = 32     ,		):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               lowercase__       =				hop_length
               lowercase__       =				sample_rate
               lowercase__       =				n_fft
               lowercase__       =				top_db
               lowercase__       =				n_iter
               self.set_resolution(UpperCamelCase     ,		UpperCamelCase				)
               lowercase__       =				None
  
               if not _librosa_can_be_imported:
                      raise ValueError(_import_error				)
  
  
  
        def    UpperCamelCase__	(self					:					List[Any]     ,		UpperCamelCase					:					int     ,		UpperCamelCase					:					int				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               lowercase__       =				x_res
               lowercase__       =				y_res
               lowercase__       =				self.y_res
               lowercase__       =				self.x_res * self.hop_length - 1
  
  
  
        def    UpperCamelCase__	(self					:					Optional[int]     ,		UpperCamelCase					:					str = None     ,		UpperCamelCase					:					np.ndarray = None				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               if audio_file is not None:
                      lowercase__    ,lowercase__       =				librosa.load(UpperCamelCase     ,		mono=UpperCamelCase     ,		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    UpperCamelCase__	(self					:					Optional[int]				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               return len(self.audio				) // self.slice_size
  
  
  
        def    UpperCamelCase__	(self					:					Union[str, Any]     ,		UpperCamelCase					:					int = 0				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               return self.audio[self.slice_size * slice : self.slice_size * (slice + 1)]
  
  
  
        def    UpperCamelCase__	(self					:					int				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               return self.sr
  
  
  
        def    UpperCamelCase__	(self					:					str     ,		UpperCamelCase					:					int				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               lowercase__       =				librosa.feature.melspectrogram(
                   y=self.get_audio_slice(UpperCamelCase				)     ,		sr=self.sr     ,		n_fft=self.n_fft     ,		hop_length=self.hop_length     ,		n_mels=self.n_mels				)
               lowercase__       =				librosa.power_to_db(UpperCamelCase     ,		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(UpperCamelCase				)
               return image
  
  
  
  
  
  
        def    UpperCamelCase__	(self					:					Optional[int]     ,		UpperCamelCase					:					Image.Image				):
  
               '''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(UpperCamelCase				)
               lowercase__       =				librosa.feature.inverse.mel_to_audio(
                   UpperCamelCase     ,		sr=self.sr     ,		n_fft=self.n_fft     ,		hop_length=self.hop_length     ,		n_iter=self.n_iter				)
               return audio
  
  
  
  
  
style_context: 
  
  
  '''simple docstring'''
  
  import torch
  
  from accelerate import Accelerator, DistributedType
  class 						__lowerCAmelCase		:
  
  
  
  
  
        '''simple docstring'''
  
  
  
  
  
  
        def __enter__(self					:					List[Any]				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               if self.enabled:
                      lowercase__       =				self.model.no_sync()
                      self.model_sync_obj.__enter__()
  
               return self
  
  
  
        def __exit__(self					:					List[str]     ,		UpperCamelCase					:					List[Any]     ,		UpperCamelCase					:					Optional[int]     ,		UpperCamelCase					:					Optional[int]				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               if self.enabled:
                      # Average all models on exit
                      self._sync_and_avg_model_params()
                      self.model_sync_obj.__exit__(UpperCamelCase     ,		UpperCamelCase     ,		UpperCamelCase				)
  
  
  
        def __init__(self					:					Dict     ,		UpperCamelCase					:					Accelerator     ,		UpperCamelCase					:					torch.nn.Module     ,		UpperCamelCase					:					int     ,		UpperCamelCase					:					bool = True				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               if accelerator.distributed_type not in [
                   DistributedType.NO,
                   DistributedType.MULTI_CPU,
                   DistributedType.MULTI_GPU,
               ]:
                      raise NotImplementedError('''LocalSGD is supported only for CPUs and GPUs (no DeepSpeed or MegatronLM)'''				)
               lowercase__       =				enabled and accelerator.distributed_type != DistributedType.NO
               lowercase__       =				0
               if self.enabled:
                      lowercase__       =				accelerator
                      lowercase__       =				model
                      lowercase__       =				local_sgd_steps
  
  
  
        def    UpperCamelCase__	(self					:					List[str]				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               self.num_steps += 1
               if not self.enabled:
                      return
  
               if self.num_steps % self.local_sgd_steps == 0:
                      self._sync_and_avg_model_params()
  
  
  
  
  
  
        def    UpperCamelCase__	(self					:					Any				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               self.accelerator.wait_for_everyone()
               with self.accelerator.autocast():
                      for param in self.model.parameters():
                             lowercase__       =				self.accelerator.reduce(param.data     ,		reduction='''mean'''				)
  
  
  
  
  