
import enum
import inspect
from typing import Iterable, List, Optional, Tuple, Union






class 							SCREAMING_SNAKE_CASE__     (				enum.Enum     ):

							"""simple docstring"""






							_snake_case  		=	'timm'
							_snake_case  		=	'transformers'





def 						A_			(				snake_case				: Tuple			,   snake_case				: int			,   snake_case				: Optional[Any]			)   ->     int:



						'''simple docstring'''

						if stage_names is None:
												raise ValueError('''Stage_names must be set for transformers backbones'''			)

						if out_features is not None:
												if not isinstance(snake_case			,   (list,)			):
																		raise ValueError(f"out_features must be a list {type(snake_case			)}"			)
												if any(feat not in stage_names for feat in out_features			):
																		raise ValueError(f"out_features must be a subset of stage_names: {stage_names} got {out_features}"			)

						if out_indices is not None:
												if not isinstance(snake_case			,   (list, tuple)			):
																		raise ValueError(f"out_indices must be a list or tuple, got {type(snake_case			)}"			)
												if any(idx >= len(snake_case			) for idx in out_indices			):
																		raise ValueError('''out_indices must be valid indices for stage_names {stage_names}, got {out_indices}'''			)

						if out_features is not None and out_indices is not None:
												if len(snake_case			) != len(snake_case			):
																		raise ValueError('''out_features and out_indices should have the same length if both are set'''			)
												if out_features != [stage_names[idx] for idx in out_indices]:
																		raise ValueError('''out_features and out_indices should correspond to the same stages if both are set'''			)




def 						A_			(				snake_case				: Any			,   snake_case				: str			,   snake_case				: Optional[Any]			,   )   ->     List[Any]:



						'''simple docstring'''

						if out_indices is None and out_features is None:
												__UpperCamelCase  	=      [len(snake_case			) - 1]
												__UpperCamelCase  	=      [stage_names[-1]]
						elif out_indices is None and out_features is not None:
												__UpperCamelCase  	=      [stage_names.index(snake_case			) for layer in out_features]
						elif out_features is None and out_indices is not None:
												__UpperCamelCase  	=      [stage_names[idx] for idx in out_indices]
						return out_features, out_indices




def 						A_			(				snake_case				: str			,   snake_case				: str			,   snake_case				: Tuple			,   )   ->     Tuple[List[str], List[int]]:



						'''simple docstring'''

						verify_out_features_out_indices(out_features=snake_case			,   out_indices=snake_case			,   stage_names=snake_case			)
						__UpperCamelCase  	=      _align_output_features_output_indices(
						    out_features=snake_case			,   out_indices=snake_case			,   stage_names=snake_case			)
						# Verify that the aligned out_features and out_indices are valid
						verify_out_features_out_indices(out_features=snake_case			,   out_indices=snake_case			,   stage_names=snake_case			)
						return output_features, output_indices






class 							SCREAMING_SNAKE_CASE__     :

							"""simple docstring"""






							_snake_case  		=	None



							def      A__		(	self  ,						SCREAMING_SNAKE_CASE_      )-> None:





													'''simple docstring'''






													if getattr(self  ,						'''_backbone'''  ,						SCREAMING_SNAKE_CASE_      ) is None:
																			raise ValueError('''self._backbone must be set before calling _init_timm_backbone'''      )

													# These will diagree with the defaults for the transformers models e.g. for resnet50
													# the transformer model has out_features = ['stem', 'stage1', 'stage2', 'stage3', 'stage4']
													# the timm model has out_features = ['act', 'layer1', 'layer2', 'layer3', 'layer4']
													__UpperCamelCase  	=      [stage['''module'''] for stage in self._backbone.feature_info.info]
													__UpperCamelCase  	=      [stage['''num_chs'''] for stage in self._backbone.feature_info.info]
													__UpperCamelCase  	=      self._backbone.feature_info.out_indices
													__UpperCamelCase  	=      self._backbone.feature_info.module_name()

													# We verify the out indices and out features are valid
													verify_out_features_out_indices(
													    out_features=SCREAMING_SNAKE_CASE_  ,						out_indices=SCREAMING_SNAKE_CASE_  ,						stage_names=self.stage_names      )
													__UpperCamelCase  	=      out_features, out_indices



							def      A__		(	self  ,						SCREAMING_SNAKE_CASE_      )-> None:





													'''simple docstring'''






													__UpperCamelCase  	=      getattr(SCREAMING_SNAKE_CASE_  ,						'''stage_names'''      )
													__UpperCamelCase  	=      getattr(SCREAMING_SNAKE_CASE_  ,						'''out_features'''  ,						SCREAMING_SNAKE_CASE_      )
													__UpperCamelCase  	=      getattr(SCREAMING_SNAKE_CASE_  ,						'''out_indices'''  ,						SCREAMING_SNAKE_CASE_      )

													__UpperCamelCase  	=      stage_names
													__UpperCamelCase  	=      get_aligned_output_features_output_indices(
													    out_features=SCREAMING_SNAKE_CASE_  ,						out_indices=SCREAMING_SNAKE_CASE_  ,						stage_names=SCREAMING_SNAKE_CASE_      )
													# Number of channels for each stage. This is set in the transformer backbone model init
													__UpperCamelCase  	=      None



							def      A__		(	self  ,						SCREAMING_SNAKE_CASE_      )-> None:





													'''simple docstring'''






													__UpperCamelCase  	=      config

													__UpperCamelCase  	=      getattr(SCREAMING_SNAKE_CASE_  ,						'''use_timm_backbone'''  ,						SCREAMING_SNAKE_CASE_      )
													__UpperCamelCase  	=      BackboneType.TIMM if self.use_timm_backbone else BackboneType.TRANSFORMERS

													if self.backbone_type == BackboneType.TIMM:
																			self._init_timm_backbone(SCREAMING_SNAKE_CASE_      )
													elif self.backbone_type == BackboneType.TRANSFORMERS:
																			self._init_transformers_backbone(SCREAMING_SNAKE_CASE_      )
													else:
																			raise ValueError(F"backbone_type {self.backbone_type} not supported."      )



							@property
							def      A__		(	self      )-> str:





													'''simple docstring'''






													return self._out_features



							@out_features.setter
							def      A__		(	self  ,						SCREAMING_SNAKE_CASE_      )-> List[Any]:





													'''simple docstring'''






													__UpperCamelCase  	=      get_aligned_output_features_output_indices(
													    out_features=SCREAMING_SNAKE_CASE_  ,						out_indices=SCREAMING_SNAKE_CASE_  ,						stage_names=self.stage_names      )



							@property
							def      A__		(	self      )-> Tuple:





													'''simple docstring'''






													return self._out_indices



							@out_indices.setter
							def      A__		(	self  ,						SCREAMING_SNAKE_CASE_      )-> List[Any]:





													'''simple docstring'''






													__UpperCamelCase  	=      get_aligned_output_features_output_indices(
													    out_features=SCREAMING_SNAKE_CASE_  ,						out_indices=SCREAMING_SNAKE_CASE_  ,						stage_names=self.stage_names      )



							@property
							def      A__		(	self      )-> Tuple:





													'''simple docstring'''






													return {stage: self.num_features[i] for i, stage in enumerate(self.stage_names      )}



							@property
							def      A__		(	self      )-> Optional[Any]:





													'''simple docstring'''






													return [self.out_feature_channels[name] for name in self.out_features]



							def      A__		(	self  ,						*SCREAMING_SNAKE_CASE_  ,						**SCREAMING_SNAKE_CASE_      )-> Optional[int]:





													'''simple docstring'''






													__UpperCamelCase  	=      dict(inspect.signature(self.forward      ).parameters      )
													__UpperCamelCase  	=      {k: v for k, v in kwargs.items() if k in signature}
													return self(*SCREAMING_SNAKE_CASE_  ,						**SCREAMING_SNAKE_CASE_      )



							def      A__		(	self  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_ = None  ,						SCREAMING_SNAKE_CASE_ = None  ,						SCREAMING_SNAKE_CASE_ = None  ,						)-> Tuple:





													'''simple docstring'''






													raise NotImplementedError('''This method should be implemented by the derived class.'''      )



							def      A__		(	self      )-> Optional[int]:





													'''simple docstring'''






													__UpperCamelCase  	=      super().to_dict()
													__UpperCamelCase  	=      output.pop('''_out_features'''      )
													__UpperCamelCase  	=      output.pop('''_out_indices'''      )
													return output






class 							SCREAMING_SNAKE_CASE__     :

							"""simple docstring"""






							@property
							def      A__		(	self      )-> Dict:





													'''simple docstring'''






													return self._out_features



							@out_features.setter
							def      A__		(	self  ,						SCREAMING_SNAKE_CASE_      )-> Union[str, Any]:





													'''simple docstring'''






													__UpperCamelCase  	=      get_aligned_output_features_output_indices(
													    out_features=SCREAMING_SNAKE_CASE_  ,						out_indices=SCREAMING_SNAKE_CASE_  ,						stage_names=self.stage_names      )



							@property
							def      A__		(	self      )-> Any:





													'''simple docstring'''






													return self._out_indices



							@out_indices.setter
							def      A__		(	self  ,						SCREAMING_SNAKE_CASE_      )-> int:





													'''simple docstring'''






													__UpperCamelCase  	=      get_aligned_output_features_output_indices(
													    out_features=SCREAMING_SNAKE_CASE_  ,						out_indices=SCREAMING_SNAKE_CASE_  ,						stage_names=self.stage_names      )



							def      A__		(	self      )-> Optional[Any]:





													'''simple docstring'''






													__UpperCamelCase  	=      super().to_dict()
													__UpperCamelCase  	=      output.pop('''_out_features'''      )
													__UpperCamelCase  	=      output.pop('''_out_indices'''      )
													return output












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						= True
						__lowerCamelCase						= ''
except Exception as e:
						__lowerCamelCase						= False
						__lowerCamelCase						= (
						    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_							(				UpperCamelCase ,       UpperCamelCase       ):
						lowercase									=  '''mel_config.json'''




						@register_to_config
						def __init__(    self		,	lowercase = 256		,	lowercase = 256		,	lowercase = 22_050		,	lowercase = 2_048		,	lowercase = 512		,	lowercase = 80		,	lowercase = 32		,	)			->						Optional[Any]:
													_a  :    Tuple			=						hop_length
													_a  :    Dict			=						sample_rate
													_a  :    Dict			=						n_fft
													_a  :    List[str]			=						top_db
													_a  :    Optional[int]			=						n_iter
													self.set_resolution(lowercase		,	lowercase       )
													_a  :    Optional[int]			=						None

													if not _librosa_can_be_imported:
																				raise ValueError(_import_error       )




						def      snake_case__(    self		,	lowercase		,	lowercase       )			->						Any:
													_a  :    Any			=						x_res
													_a  :    Optional[Any]			=						y_res
													_a  :    List[Any]			=						self.y_res
													_a  :    Tuple			=						self.x_res * self.hop_length - 1




						def      snake_case__(    self		,	lowercase = None		,	lowercase = None       )			->						Union[str, Any]:
													if audio_file is not None:
																				_a		,					_a  :    str			=						librosa.load(lowercase		,	mono=lowercase		,	sr=self.sr       )
													else:
																				_a  :    Any			=						raw_audio

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




						def      snake_case__(    self       )			->						int:
													return len(self.audio       ) // self.slice_size




						def      snake_case__(    self		,	lowercase = 0       )			->						np.ndarray:
													return self.audio[self.slice_size * slice : self.slice_size * (slice + 1)]




						def      snake_case__(    self       )			->						int:
													return self.sr




						def      snake_case__(    self		,	lowercase       )			->						Image.Image:
													_a  :    int			=						librosa.feature.melspectrogram(
													    y=self.get_audio_slice(lowercase       )		,	sr=self.sr		,	n_fft=self.n_fft		,	hop_length=self.hop_length		,	n_mels=self.n_mels       )
													_a  :    List[str]			=						librosa.power_to_db(lowercase		,	ref=np.max		,	top_db=self.top_db       )
													_a  :    List[str]			=						(((log_S + self.top_db) * 255 / self.top_db).clip(0		,	255       ) + 0.5).astype(np.uinta       )
													_a  :    Any			=						Image.fromarray(lowercase       )
													return image







						def      snake_case__(    self		,	lowercase       )			->						np.ndarray:
													_a  :    Any			=						np.frombuffer(image.tobytes()		,	dtype='''uint8'''       ).reshape((image.height, image.width)       )
													_a  :    Any			=						bytedata.astype('''float'''       ) * self.top_db / 255 - self.top_db
													_a  :    Dict			=						librosa.db_to_power(lowercase       )
													_a  :    List[str]			=						librosa.feature.inverse.mel_to_audio(
													    lowercase		,	sr=self.sr		,	n_fft=self.n_fft		,	hop_length=self.hop_length		,	n_iter=self.n_iter       )
													return audio