
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							:Union[str, Any]											=       True
					SCREAMING_SNAKE_CASE							:Optional[Any]											=       ''
except Exception as e:
					SCREAMING_SNAKE_CASE							:Optional[Any]											=       False
					SCREAMING_SNAKE_CASE							:int											=       (
					    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   (						__SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE						):




	'''simple docstring'''





	snake_case_      					=  "mel_config.json"






	@register_to_config
	def __init__( self      :				List[Any]							,A      :				int = 2_56							,A      :				int = 2_56							,A      :				int = 2_20_50							,A      :				int = 20_48							,A      :				int = 5_12							,A      :				int = 80							,A      :				int = 32							,):
					__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        UpperCamelCase_						( self      :				Union[str, Any]							,A      :				int							,A      :				int				):
					__A         =				x_res
					__A         =				y_res
					__A         =				self.y_res
					__A         =				self.x_res * self.hop_length - 1






	def        UpperCamelCase_						( self      :				str							,A      :				str = None							,A      :				np.ndarray = None				):
					if audio_file is not None:
									__A    ,							__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        UpperCamelCase_						( self      :				int				):
					return len(self.audio				) // self.slice_size






	def        UpperCamelCase_						( self      :				Optional[int]							,A      :				int = 0				):
					return self.audio[self.slice_size * slice : self.slice_size * (slice + 1)]






	def        UpperCamelCase_						( self      :				Union[str, Any]				):
					return self.sr






	def        UpperCamelCase_						( self      :				Optional[Any]							,A      :				int				):
					__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) * 2_55 / self.top_db).clip(0							,2_55				) + 0.5).astype(np.uinta				)
					__A         =				Image.fromarray(A				)
					return image







	def        UpperCamelCase_						( self      :				Union[str, Any]							,A      :				Image.Image				):
					__A         =				np.frombuffer(image.tobytes()							,dtype="uint8"				).reshape((image.height, image.width)				)
					__A         =				bytedata.astype("float"				) * self.top_db / 2_55 - 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


import torch

from transformers import PreTrainedModel

from .custom_configuration import CustomConfig, NoSuperInitConfig





class 							UpperCAmelCase   (						__SCREAMING_SNAKE_CASE						):




	'''simple docstring'''





	snake_case_      					=  CustomConfig






	def __init__( self      :				Optional[int]							,A      :				List[str]				):
					super().__init__(A				)
					__A         =				torch.nn.Linear(config.hidden_size							,config.hidden_size				)






	def        UpperCamelCase_						( self      :				Union[str, Any]							,A      :				Optional[int]				):
					return self.linear(A				)







	def        UpperCamelCase_						( self      :				Optional[int]							,A      :				int				):
					pass





class 							UpperCAmelCase   (						__SCREAMING_SNAKE_CASE						):




	'''simple docstring'''





	snake_case_      					=  NoSuperInitConfig






	def __init__( self      :				Optional[int]							,A      :				Union[str, Any]				):
					super().__init__(A				)
					__A         =				torch.nn.Linear(config.attribute							,config.attribute				)






	def        UpperCamelCase_						( self      :				str							,A      :				Any				):
					return self.linear(A				)







	def        UpperCamelCase_						( self      :				Tuple							,A      :				int				):
					pass
