




import warnings
from typing import Any, Dict, List, Optional, Union

import numpy as np

from ...audio_utils import mel_filter_bank, optimal_fft_length, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging


lowercase__							:	Tuple											=		logging.get_logger(__name__)
class 						UpperCAmelCase  (							UpperCAmelCase__     ):







						'''simple docstring'''





						lowerCAmelCase_							=  ['''input_values''', '''attention_mask''']






						def __init__(		self				:	List[Any]			,      __lowercase				:	int = 1			,      __lowercase				:	int = 1_60_00			,      __lowercase				:	float = 0.0			,      __lowercase				:	bool = False			,      __lowercase				:	int = 80			,      __lowercase				:	int = 16			,      __lowercase				:	int = 64			,      __lowercase				:	str = "hann_window"			,      __lowercase				:	float = 1.0			,      __lowercase				:	float = 80			,      __lowercase				:	float = 76_00			,      __lowercase				:	float = 1E-10			,      __lowercase				:	int = 2			,      __lowercase				:	bool = True			,      **__lowercase				:	Union[str, Any]			,      ):






							"""simple docstring"""






							super().__init__(feature_size=__lowercase			,      sampling_rate=__lowercase			,      padding_value=__lowercase			,      **__lowercase		)
							snake_case_						       =	do_normalize
							snake_case_						       =	return_attention_mask

							snake_case_						       =	num_mel_bins
							snake_case_						       =	hop_length
							snake_case_						       =	win_length
							snake_case_						       =	win_function
							snake_case_						       =	frame_signal_scale
							snake_case_						       =	fmin
							snake_case_						       =	fmax
							snake_case_						       =	mel_floor
							snake_case_						       =	reduction_factor

							snake_case_						       =	win_length * sampling_rate // 10_00
							snake_case_						       =	hop_length * sampling_rate // 10_00
							snake_case_						       =	optimal_fft_length(self.sample_size		)
							snake_case_						       =	(self.n_fft // 2) + 1

							snake_case_						       =	window_function(window_length=self.sample_size			,      name=self.win_function			,      periodic=__lowercase		)

							snake_case_						       =	mel_filter_bank(
							    num_frequency_bins=self.n_freqs			,      num_mel_filters=self.num_mel_bins			,      min_frequency=self.fmin			,      max_frequency=self.fmax			,      sampling_rate=self.sampling_rate			,      norm="slaney"			,      mel_scale="slaney"			,      )

							if frame_signal_scale != 1.0:
								warnings.warn(
								    "The argument `frame_signal_scale` is deprecated and will be removed in version 4.30.0 of Transformers"			,      __lowercase			,      )
							if reduction_factor != 2.0:
								warnings.warn(
								    "The argument `reduction_factor` is deprecated and will be removed in version 4.30.0 of Transformers"			,      __lowercase			,      )






						@staticmethod
						# Copied from transformers.models.wav2vec2.feature_extraction_wav2vec2.Wav2Vec2FeatureExtractor.zero_mean_unit_var_norm
						def 			snake_case__		(		__lowercase				:	List[np.ndarray]			,      __lowercase				:	List[np.ndarray]			,      __lowercase				:	float = 0.0		):






							"""simple docstring"""






							if attention_mask is not None:
								snake_case_						       =	np.array(__lowercase			,      np.intaa		)
								snake_case_						       =	[]

								for vector, length in zip(__lowercase			,      attention_mask.sum(-1		)		):
									snake_case_						       =	(vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1E-7		)
									if length < normed_slice.shape[0]:
										snake_case_						       =	padding_value

									normed_input_values.append(__lowercase		)
							else:
								snake_case_						       =	[(x - x.mean()) / np.sqrt(x.var() + 1E-7		) for x in input_values]

							return normed_input_values






						def 			snake_case__		(		self				:	str			,      __lowercase				:	np.ndarray			,      ):






							"""simple docstring"""






							snake_case_						       =	spectrogram(
							    __lowercase			,      window=self.window			,      frame_length=self.sample_size			,      hop_length=self.sample_stride			,      fft_length=self.n_fft			,      mel_filters=self.mel_filters			,      mel_floor=self.mel_floor			,      log_mel="log10"			,      )
							return log_mel_spec.T






						def __call__(		self				:	Optional[int]			,      __lowercase				:	Optional[Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]] = None			,      __lowercase				:	Optional[Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]] = None			,      __lowercase				:	Union[bool, str, PaddingStrategy] = False			,      __lowercase				:	Optional[int] = None			,      __lowercase				:	bool = False			,      __lowercase				:	Optional[int] = None			,      __lowercase				:	Optional[bool] = None			,      __lowercase				:	Optional[Union[str, TensorType]] = None			,      __lowercase				:	Optional[int] = None			,      **__lowercase				:	List[str]			,      ):






							"""simple docstring"""






							if audio is None and audio_target is None:
								raise ValueError("You must provide either `audio` or `audio_target` values."		)

							if sampling_rate is not None:
								if sampling_rate != self.sampling_rate:
									raise ValueError(
									    f"The model corresponding to this feature extractor: {self} was trained using a sampling rate of"
									    f" {self.sampling_rate}. Please make sure that the provided audio input was sampled with"
									    f" {self.sampling_rate} and not {sampling_rate}."		)
							else:
								logger.warning(
								    "It is strongly recommended to pass the ``sampling_rate`` argument to this function. "
								    "Failing to do so can result in silent errors that might be hard to debug."		)

							if audio is not None:
								snake_case_						       =	self._process_audio(
								    __lowercase			,      __lowercase			,      __lowercase			,      __lowercase			,      __lowercase			,      __lowercase			,      __lowercase			,      __lowercase			,      **__lowercase			,      )
							else:
								snake_case_						       =	None

							if audio_target is not None:
								snake_case_						       =	self._process_audio(
								    __lowercase			,      __lowercase			,      __lowercase			,      __lowercase			,      __lowercase			,      __lowercase			,      __lowercase			,      __lowercase			,      **__lowercase			,      )

								if inputs is None:
									return inputs_target
								else:
									snake_case_						       =	inputs_target["input_values"]
									snake_case_						       =	inputs_target.get("attention_mask"		)
									if decoder_attention_mask is not None:
										snake_case_						       =	decoder_attention_mask

							return inputs






						def 			snake_case__		(		self				:	Dict			,      __lowercase				:	Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]			,      __lowercase				:	bool = False			,      __lowercase				:	Union[bool, str, PaddingStrategy] = False			,      __lowercase				:	Optional[int] = None			,      __lowercase				:	bool = False			,      __lowercase				:	Optional[int] = None			,      __lowercase				:	Optional[bool] = None			,      __lowercase				:	Optional[Union[str, TensorType]] = None			,      **__lowercase				:	Optional[Any]			,      ):






							"""simple docstring"""






							snake_case_						       =	isinstance(__lowercase			,      np.ndarray		) and len(speech.shape		) > 1
							if is_batched_numpy and len(speech.shape		) > 2:
								raise ValueError(f"Only mono-channel audio is supported for input to {self}"		)
							snake_case_						       =	is_batched_numpy or (
							    isinstance(__lowercase			,      (list, tuple)		) and (isinstance(speech[0]			,      (np.ndarray, tuple, list)		))
							)

							if is_batched:
								snake_case_						       =	[np.asarray(__lowercase			,      dtype=np.floataa		) for speech in speech]
							elif not is_batched and not isinstance(__lowercase			,      np.ndarray		):
								snake_case_						       =	np.asarray(__lowercase			,      dtype=np.floataa		)
							elif isinstance(__lowercase			,      np.ndarray		) and speech.dtype is np.dtype(np.floataa		):
								snake_case_						       =	speech.astype(np.floataa		)

							# always return batch
							if not is_batched:
								snake_case_						       =	[speech]

							# needed to make pad() work on spectrogram inputs
							snake_case_						       =	self.feature_size

							# convert into correct format for padding
							if is_target:
								snake_case_						       =	[self._extract_mel_features(__lowercase		) for waveform in speech]
								snake_case_						       =	BatchFeature({"input_values": features}		)
								snake_case_						       =	self.num_mel_bins
							else:
								snake_case_						       =	BatchFeature({"input_values": speech}		)

							snake_case_						       =	self.pad(
							    __lowercase			,      padding=__lowercase			,      max_length=__lowercase			,      truncation=__lowercase			,      pad_to_multiple_of=__lowercase			,      return_attention_mask=__lowercase			,      **__lowercase			,      )

							snake_case_						       =	feature_size_hack

							# convert input values to correct format
							snake_case_						       =	padded_inputs["input_values"]
							if not isinstance(input_values[0]			,      np.ndarray		):
								snake_case_						       =	[np.asarray(__lowercase			,      dtype=np.floataa		) for array in input_values]
							elif (
							    not isinstance(__lowercase			,      np.ndarray		)
							    and isinstance(input_values[0]			,      np.ndarray		)
							    and input_values[0].dtype is np.dtype(np.floataa		)
							):
								snake_case_						       =	[array.astype(np.floataa		) for array in input_values]
							elif isinstance(__lowercase			,      np.ndarray		) and input_values.dtype is np.dtype(np.floataa		):
								snake_case_						       =	input_values.astype(np.floataa		)

							# convert attention_mask to correct format
							snake_case_						       =	padded_inputs.get("attention_mask"		)
							if attention_mask is not None:
								snake_case_						       =	[np.asarray(__lowercase			,      dtype=np.intaa		) for array in attention_mask]

							# zero-mean and unit-variance normalization
							if not is_target and self.do_normalize:
								snake_case_						       =	(
								    attention_mask
								    if self._get_padding_strategies(__lowercase			,      max_length=__lowercase		) is not PaddingStrategy.DO_NOT_PAD
								    else None
								)
								snake_case_						       =	self.zero_mean_unit_var_norm(
								    padded_inputs["input_values"]			,      attention_mask=__lowercase			,      padding_value=self.padding_value		)

							if return_tensors is not None:
								snake_case_						       =	padded_inputs.convert_to_tensors(__lowercase		)

							return padded_inputs






						def 			snake_case__		(		self				:	Tuple		):






							"""simple docstring"""






							snake_case_						       =	super().to_dict()

							# Don't serialize these as they are derived from the other properties.
							snake_case_						       =	["window", "mel_filters", "sample_size", "sample_stride", "n_fft", "n_freqs"]
							for name in names:
								if name in output:
									del output[name]

							return output






import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest
class 						UpperCAmelCase  (							UpperCAmelCase__     ):







						'''simple docstring'''





						lowerCAmelCase_							=  (DPMSolverSinglestepScheduler,)
						lowerCAmelCase_							=  (('''num_inference_steps''', 25),)






						def 			snake_case__		(		self				:	str			,      **__lowercase				:	Any		):






							"""simple docstring"""






							snake_case_						       =	{
							    "num_train_timesteps": 10_00,
							    "beta_start": 0.0001,
							    "beta_end": 0.02,
							    "beta_schedule": "linear",
							    "solver_order": 2,
							    "prediction_type": "epsilon",
							    "thresholding": False,
							    "sample_max_value": 1.0,
							    "algorithm_type": "dpmsolver++",
							    "solver_type": "midpoint",
							    "lambda_min_clipped": -float("inf"		),
							    "variance_type": None,
							}

							config.update(**__lowercase		)
							return config






						def 			snake_case__		(		self				:	str			,      __lowercase				:	int=0			,      **__lowercase				:	str		):






							"""simple docstring"""






							snake_case_						       =	dict(self.forward_default_kwargs		)
							snake_case_						       =	kwargs.pop("num_inference_steps"			,      __lowercase		)
							snake_case_						       =	self.dummy_sample
							snake_case_						       =	0.1 * sample
							snake_case_						       =	[residual + 0.2, residual + 0.15, residual + 0.10]

							for scheduler_class in self.scheduler_classes:
								snake_case_						       =	self.get_scheduler_config(**__lowercase		)
								snake_case_						       =	scheduler_class(**__lowercase		)
								scheduler.set_timesteps(__lowercase		)
								# copy over dummy past residuals
								snake_case_						       =	dummy_past_residuals[: scheduler.config.solver_order]

								with tempfile.TemporaryDirectory() as tmpdirname:
									scheduler.save_config(__lowercase		)
									snake_case_						       =	scheduler_class.from_pretrained(__lowercase		)
									new_scheduler.set_timesteps(__lowercase		)
									# copy over dummy past residuals
									snake_case_						       =	dummy_past_residuals[: new_scheduler.config.solver_order]

								snake_case_       , snake_case_						       =	sample, sample
								for t in range(__lowercase			,      time_step + scheduler.config.solver_order + 1		):
									snake_case_						       =	scheduler.step(__lowercase			,      __lowercase			,      __lowercase			,      **__lowercase		).prev_sample
									snake_case_						       =	new_scheduler.step(__lowercase			,      __lowercase			,      __lowercase			,      **__lowercase		).prev_sample

									assert torch.sum(torch.abs(output - new_output		)		) < 1E-5, "Scheduler outputs are not identical"






						def 			snake_case__		(		self				:	Union[str, Any]		):






							"""simple docstring"""






							pass






						def 			snake_case__		(		self				:	List[Any]			,      __lowercase				:	Optional[int]=0			,      **__lowercase				:	int		):






							"""simple docstring"""






							snake_case_						       =	dict(self.forward_default_kwargs		)
							snake_case_						       =	kwargs.pop("num_inference_steps"			,      __lowercase		)
							snake_case_						       =	self.dummy_sample
							snake_case_						       =	0.1 * sample
							snake_case_						       =	[residual + 0.2, residual + 0.15, residual + 0.10]

							for scheduler_class in self.scheduler_classes:
								snake_case_						       =	self.get_scheduler_config()
								snake_case_						       =	scheduler_class(**__lowercase		)
								scheduler.set_timesteps(__lowercase		)

								# copy over dummy past residuals (must be after setting timesteps)
								snake_case_						       =	dummy_past_residuals[: scheduler.config.solver_order]

								with tempfile.TemporaryDirectory() as tmpdirname:
									scheduler.save_config(__lowercase		)
									snake_case_						       =	scheduler_class.from_pretrained(__lowercase		)
									# copy over dummy past residuals
									new_scheduler.set_timesteps(__lowercase		)

									# copy over dummy past residual (must be after setting timesteps)
									snake_case_						       =	dummy_past_residuals[: new_scheduler.config.solver_order]

								snake_case_						       =	scheduler.step(__lowercase			,      __lowercase			,      __lowercase			,      **__lowercase		).prev_sample
								snake_case_						       =	new_scheduler.step(__lowercase			,      __lowercase			,      __lowercase			,      **__lowercase		).prev_sample

								assert torch.sum(torch.abs(output - new_output		)		) < 1E-5, "Scheduler outputs are not identical"






						def 			snake_case__		(		self				:	List[str]			,      __lowercase				:	Any=None			,      **__lowercase				:	List[Any]		):






							"""simple docstring"""






							if scheduler is None:
								snake_case_						       =	self.scheduler_classes[0]
								snake_case_						       =	self.get_scheduler_config(**__lowercase		)
								snake_case_						       =	scheduler_class(**__lowercase		)

							snake_case_						       =	self.scheduler_classes[0]
							snake_case_						       =	self.get_scheduler_config(**__lowercase		)
							snake_case_						       =	scheduler_class(**__lowercase		)

							snake_case_						       =	10
							snake_case_						       =	self.dummy_model()
							snake_case_						       =	self.dummy_sample_deter
							scheduler.set_timesteps(__lowercase		)

							for i, t in enumerate(scheduler.timesteps		):
								snake_case_						       =	model(__lowercase			,      __lowercase		)
								snake_case_						       =	scheduler.step(__lowercase			,      __lowercase			,      __lowercase		).prev_sample

							return sample






						def 			snake_case__		(		self				:	Any		):






							"""simple docstring"""






							snake_case_						       =	DPMSolverSinglestepScheduler(**self.get_scheduler_config()		)
							snake_case_						       =	50
							snake_case_						       =	self.dummy_model()
							snake_case_						       =	self.dummy_sample_deter
							scheduler.set_timesteps(__lowercase		)

							# make sure that the first t is uneven
							for i, t in enumerate(scheduler.timesteps[3:]		):
								snake_case_						       =	model(__lowercase			,      __lowercase		)
								snake_case_						       =	scheduler.step(__lowercase			,      __lowercase			,      __lowercase		).prev_sample

							snake_case_						       =	torch.mean(torch.abs(__lowercase		)		)

							assert abs(result_mean.item() - 0.2574		) < 1E-3






						def 			snake_case__		(		self				:	Optional[int]		):






							"""simple docstring"""






							for timesteps in [25, 50, 1_00, 9_99, 10_00]:
								self.check_over_configs(num_train_timesteps=__lowercase		)






						def 			snake_case__		(		self				:	Dict		):






							"""simple docstring"""






							snake_case_						       =	DPMSolverSinglestepScheduler(**self.get_scheduler_config()		)
							snake_case_						       =	self.full_loop(scheduler=__lowercase		)
							snake_case_						       =	torch.mean(torch.abs(__lowercase		)		)

							assert abs(result_mean.item() - 0.2791		) < 1E-3

							snake_case_						       =	DEISMultistepScheduler.from_config(scheduler.config		)
							snake_case_						       =	DPMSolverMultistepScheduler.from_config(scheduler.config		)
							snake_case_						       =	UniPCMultistepScheduler.from_config(scheduler.config		)
							snake_case_						       =	DPMSolverSinglestepScheduler.from_config(scheduler.config		)

							snake_case_						       =	self.full_loop(scheduler=__lowercase		)
							snake_case_						       =	torch.mean(torch.abs(__lowercase		)		)

							assert abs(result_mean.item() - 0.2791		) < 1E-3






						def 			snake_case__		(		self				:	List[str]		):






							"""simple docstring"""






							self.check_over_configs(thresholding=__lowercase		)
							for order in [1, 2, 3]:
								for solver_type in ["midpoint", "heun"]:
									for threshold in [0.5, 1.0, 2.0]:
										for prediction_type in ["epsilon", "sample"]:
											self.check_over_configs(
											    thresholding=__lowercase			,      prediction_type=__lowercase			,      sample_max_value=__lowercase			,      algorithm_type="dpmsolver++"			,      solver_order=__lowercase			,      solver_type=__lowercase			,      )






						def 			snake_case__		(		self				:	Union[str, Any]		):






							"""simple docstring"""






							for prediction_type in ["epsilon", "v_prediction"]:
								self.check_over_configs(prediction_type=__lowercase		)






						def 			snake_case__		(		self				:	Optional[Any]		):






							"""simple docstring"""






							for algorithm_type in ["dpmsolver", "dpmsolver++"]:
								for solver_type in ["midpoint", "heun"]:
									for order in [1, 2, 3]:
										for prediction_type in ["epsilon", "sample"]:
											self.check_over_configs(
											    solver_order=__lowercase			,      solver_type=__lowercase			,      prediction_type=__lowercase			,      algorithm_type=__lowercase			,      )
											snake_case_						       =	self.full_loop(
											    solver_order=__lowercase			,      solver_type=__lowercase			,      prediction_type=__lowercase			,      algorithm_type=__lowercase			,      )
											assert not torch.isnan(__lowercase		).any(), "Samples have nan numbers"






						def 			snake_case__		(		self				:	List[Any]		):






							"""simple docstring"""






							self.check_over_configs(lower_order_final=__lowercase		)
							self.check_over_configs(lower_order_final=__lowercase		)






						def 			snake_case__		(		self				:	Union[str, Any]		):






							"""simple docstring"""






							self.check_over_configs(lambda_min_clipped=-float("inf"		)		)
							self.check_over_configs(lambda_min_clipped=-5.1		)






						def 			snake_case__		(		self				:	List[Any]		):






							"""simple docstring"""






							self.check_over_configs(variance_type=__lowercase		)
							self.check_over_configs(variance_type="learned_range"		)






						def 			snake_case__		(		self				:	List[Any]		):






							"""simple docstring"""






							for num_inference_steps in [1, 2, 3, 5, 10, 50, 1_00, 9_99, 10_00]:
								self.check_over_forward(num_inference_steps=__lowercase			,      time_step=0		)






						def 			snake_case__		(		self				:	int		):






							"""simple docstring"""






							snake_case_						       =	self.full_loop()
							snake_case_						       =	torch.mean(torch.abs(__lowercase		)		)

							assert abs(result_mean.item() - 0.2791		) < 1E-3






						def 			snake_case__		(		self				:	Any		):






							"""simple docstring"""






							snake_case_						       =	self.full_loop(use_karras_sigmas=__lowercase		)
							snake_case_						       =	torch.mean(torch.abs(__lowercase		)		)

							assert abs(result_mean.item() - 0.2248		) < 1E-3






						def 			snake_case__		(		self				:	str		):






							"""simple docstring"""






							snake_case_						       =	self.full_loop(prediction_type="v_prediction"		)
							snake_case_						       =	torch.mean(torch.abs(__lowercase		)		)

							assert abs(result_mean.item() - 0.1453		) < 1E-3






						def 			snake_case__		(		self				:	int		):






							"""simple docstring"""






							snake_case_						       =	self.full_loop(prediction_type="v_prediction"			,      use_karras_sigmas=__lowercase		)
							snake_case_						       =	torch.mean(torch.abs(__lowercase		)		)

							assert abs(result_mean.item() - 0.0649		) < 1E-3






						def 			snake_case__		(		self				:	Optional[Any]		):






							"""simple docstring"""






							snake_case_						       =	self.scheduler_classes[0]
							snake_case_						       =	self.get_scheduler_config(thresholding=__lowercase			,      dynamic_thresholding_ratio=0		)
							snake_case_						       =	scheduler_class(**__lowercase		)

							snake_case_						       =	10
							snake_case_						       =	self.dummy_model()
							snake_case_						       =	self.dummy_sample_deter.half()
							scheduler.set_timesteps(__lowercase		)

							for i, t in enumerate(scheduler.timesteps		):
								snake_case_						       =	model(__lowercase			,      __lowercase		)
								snake_case_						       =	scheduler.step(__lowercase			,      __lowercase			,      __lowercase		).prev_sample

							assert sample.dtype == torch.floataa
