





"""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

						UpperCamelCase_           =True
						UpperCamelCase_           =""""""
except Exception as e:
						UpperCamelCase_           =False
						UpperCamelCase_           =(
						    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						(      _lowerCAmelCase	,				_lowerCAmelCase				):
				UpperCamelCase								=    '''mel_config.json'''






				@register_to_config
				def __init__( self		:    Union[str, Any], lowerCAmelCase__		:    int = 2_5_6, lowerCAmelCase__		:    int = 2_5_6, lowerCAmelCase__		:    int = 2_2_0_5_0, lowerCAmelCase__		:    int = 2_0_4_8, lowerCAmelCase__		:    int = 5_1_2, lowerCAmelCase__		:    int = 8_0, lowerCAmelCase__		:    int = 3_2, )	->      Tuple:




										'''simple docstring'''



										_UpperCamelCase			:					List[Any]     =    hop_length
										_UpperCamelCase			:					str     =    sample_rate
										_UpperCamelCase			:					Dict     =    n_fft
										_UpperCamelCase			:					Union[str, Any]     =    top_db
										_UpperCamelCase			:					List[str]     =    n_iter
										self.set_resolution(lowerCAmelCase__, lowerCAmelCase__							)
										_UpperCamelCase			:					str     =    None

										if not _librosa_can_be_imported:
																raise ValueError(_import_error							)






				def 				snake_case	( self		:    List[Any], lowerCAmelCase__		:    int, lowerCAmelCase__		:    int							)	->      List[Any]:




										'''simple docstring'''



										_UpperCamelCase			:					Union[str, Any]     =    x_res
										_UpperCamelCase			:					str     =    y_res
										_UpperCamelCase			:					int     =    self.y_res
										_UpperCamelCase			:					Optional[int]     =    self.x_res * self.hop_length - 1






				def 				snake_case	( self		:    List[str], lowerCAmelCase__		:    str = None, lowerCAmelCase__		:    np.ndarray = None							)	->      Union[str, Any]:




										'''simple docstring'''



										if audio_file is not None:
																_UpperCamelCase   ,							_UpperCamelCase			:					Dict     =    librosa.load(lowerCAmelCase__, mono=lowerCAmelCase__, sr=self.sr							)
										else:
																_UpperCamelCase			:					Dict     =    raw_audio

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






				def 				snake_case	( self		:    Union[str, Any]							)	->      int:




										'''simple docstring'''



										return len(self.audio							) // self.slice_size






				def 				snake_case	( self		:    Dict, lowerCAmelCase__		:    int = 0							)	->      np.ndarray:




										'''simple docstring'''



										return self.audio[self.slice_size * slice : self.slice_size * (slice + 1)]






				def 				snake_case	( self		:    Union[str, Any]							)	->      int:




										'''simple docstring'''



										return self.sr






				def 				snake_case	( self		:    Tuple, lowerCAmelCase__		:    int							)	->      Image.Image:




										'''simple docstring'''



										_UpperCamelCase			:					Optional[Any]     =    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							)
										_UpperCamelCase			:					Tuple     =    librosa.power_to_db(lowerCAmelCase__, ref=np.max, top_db=self.top_db							)
										_UpperCamelCase			:					Tuple     =    (((log_S + self.top_db) * 2_5_5 / self.top_db).clip(0, 2_5_5							) + 0.5).astype(np.uinta							)
										_UpperCamelCase			:					Tuple     =    Image.fromarray(lowerCAmelCase__							)
										return image






				def 				snake_case	( self		:    List[Any], lowerCAmelCase__		:    Image.Image							)	->      np.ndarray:




										'''simple docstring'''



										_UpperCamelCase			:					Optional[int]     =    np.frombuffer(image.tobytes(), dtype='''uint8'''							).reshape((image.height, image.width)							)
										_UpperCamelCase			:					Optional[int]     =    bytedata.astype('''float'''							) * self.top_db / 2_5_5 - self.top_db
										_UpperCamelCase			:					Any     =    librosa.db_to_power(lowerCAmelCase__							)
										_UpperCamelCase			:					str     =    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










"""simple docstring"""




import torch
from torch import autograd




class  _a						(      autograd.Function				):






				@staticmethod
				def 				snake_case	( lowerCAmelCase__		:    str, lowerCAmelCase__		:    torch.tensor, lowerCAmelCase__		:    float, lowerCAmelCase__		:    bool							)	->      List[Any]:




										'''simple docstring'''



										_UpperCamelCase			:					Optional[Any]     =    inputs.numel()
										_UpperCamelCase			:					int     =    int(0.005 * nb_elems							) + 1
										if sigmoid:
																_UpperCamelCase			:					Dict     =    (torch.sigmoid(lowerCAmelCase__							) > threshold).type(inputs.type()							)
										else:
																_UpperCamelCase			:					Optional[int]     =    (inputs > threshold).type(inputs.type()							)
										if mask.sum() < nb_min:
																# We limit the pruning so that at least 0.5% (half a percent) of the weights are remaining
																_UpperCamelCase			:					int     =    inputs.flatten().kthvalue(max(nb_elems - nb_min, 1							)							).values
																_UpperCamelCase			:					List[str]     =    (inputs > k_threshold).type(inputs.type()							)
										return mask






				@staticmethod
				def 				snake_case	( lowerCAmelCase__		:    str, lowerCAmelCase__		:    Optional[int]							)	->      Union[str, Any]:




										'''simple docstring'''



										return gradOutput, None, None




class  _a						(      autograd.Function				):






				@staticmethod
				def 				snake_case	( lowerCAmelCase__		:    Any, lowerCAmelCase__		:    torch.tensor, lowerCAmelCase__		:    float							)	->      Dict:




										'''simple docstring'''



										_UpperCamelCase			:					str     =    inputs.clone()
										_UpperCamelCase   ,							_UpperCamelCase			:					Union[str, Any]     =    inputs.flatten().sort(descending=lowerCAmelCase__							)
										_UpperCamelCase			:					Optional[Any]     =    int(threshold * inputs.numel()							)

										# flat_out and mask access the same memory.
										_UpperCamelCase			:					Tuple     =    mask.flatten()
										_UpperCamelCase			:					int     =    0
										_UpperCamelCase			:					Tuple     =    1
										return mask






				@staticmethod
				def 				snake_case	( lowerCAmelCase__		:    Any, lowerCAmelCase__		:    Union[str, Any]							)	->      int:




										'''simple docstring'''



										return gradOutput, None




class  _a						(      _lowerCAmelCase				):






				@staticmethod
				def 				snake_case	( lowerCAmelCase__		:    torch.tensor, lowerCAmelCase__		:    float							)	->      Dict:




										'''simple docstring'''



										_UpperCamelCase			:					List[Any]     =    inputs.clone()
										_UpperCamelCase   ,							_UpperCamelCase			:					Optional[int]     =    inputs.abs().flatten().sort(descending=lowerCAmelCase__							)
										_UpperCamelCase			:					List[Any]     =    int(threshold * inputs.numel()							)

										# flat_out and mask access the same memory.
										_UpperCamelCase			:					List[str]     =    mask.flatten()
										_UpperCamelCase			:					List[Any]     =    0
										_UpperCamelCase			:					str     =    1
										return mask



