






"""simple docstring"""


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


__SCREAMING_SNAKE_CASE   =   logging.get_logger(__name__)




class 						__UpperCamelCase     (     __snake_case						):
					lowercase_    :				str      =  ["""input_values""", """attention_mask"""]
					def __init__(							self							:	int     , UpperCAmelCase							:	int = 1     , UpperCAmelCase							:	int = 1_6000     , UpperCAmelCase							:	float = 0.0     , UpperCAmelCase							:	bool = False     , UpperCAmelCase							:	int = 80     , UpperCAmelCase							:	int = 16     , UpperCAmelCase							:	int = 64     , UpperCAmelCase							:	str = "hann_window"     , UpperCAmelCase							:	float = 1.0     , UpperCAmelCase							:	float = 80     , UpperCAmelCase							:	float = 7600     , UpperCAmelCase							:	float = 1e-1_0     , UpperCAmelCase							:	int = 2     , UpperCAmelCase							:	bool = True     , **UpperCAmelCase							:	Any     , )	->       str:
											super().__init__(feature_size=UpperCAmelCase     , sampling_rate=UpperCAmelCase     , padding_value=UpperCAmelCase     , **UpperCAmelCase   )
											lowerCAmelCase   :int						=			do_normalize
											lowerCAmelCase   :Any						=			return_attention_mask

											lowerCAmelCase   :str						=			num_mel_bins
											lowerCAmelCase   :str						=			hop_length
											lowerCAmelCase   :List[Any]						=			win_length
											lowerCAmelCase   :Tuple						=			win_function
											lowerCAmelCase   :Any						=			frame_signal_scale
											lowerCAmelCase   :Any						=			fmin
											lowerCAmelCase   :Any						=			fmax
											lowerCAmelCase   :List[str]						=			mel_floor
											lowerCAmelCase   :str						=			reduction_factor

											lowerCAmelCase   :str						=			win_length * sampling_rate // 1000
											lowerCAmelCase   :Optional[int]						=			hop_length * sampling_rate // 1000
											lowerCAmelCase   :Tuple						=			optimal_fft_length(self.sample_size   )
											lowerCAmelCase   :int						=			(self.n_fft // 2) + 1

											lowerCAmelCase   :Tuple						=			window_function(window_length=self.sample_size     , name=self.win_function     , periodic=UpperCAmelCase   )

											lowerCAmelCase   :Optional[int]						=			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'     , UpperCAmelCase     , )
											if reduction_factor != 2.0:
																	warnings.warn(
																	    'The argument `reduction_factor` is deprecated and will be removed in version 4.30.0 of Transformers'     , UpperCAmelCase     , )
					@staticmethod
					# Copied from transformers.models.wav2vec2.feature_extraction_wav2vec2.Wav2Vec2FeatureExtractor.zero_mean_unit_var_norm
					def        UpperCAmelCase__ (							UpperCAmelCase							:	List[np.ndarray]     , UpperCAmelCase							:	List[np.ndarray]     , UpperCAmelCase							:	float = 0.0   )	->       List[np.ndarray]:
											if attention_mask is not None:
																	lowerCAmelCase   :List[str]						=			np.array(UpperCAmelCase     , np.intaa   )
																	lowerCAmelCase   :Dict						=			[]

																	for vector, length in zip(UpperCAmelCase     , attention_mask.sum(-1   )   ):
																							lowerCAmelCase   :Tuple						=			(vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1e-7   )
																							if length < normed_slice.shape[0]:
																													lowerCAmelCase   :List[str]						=			padding_value

																							normed_input_values.append(UpperCAmelCase   )
											else:
																	lowerCAmelCase   :Dict						=			[(x - x.mean()) / np.sqrt(x.var() + 1e-7   ) for x in input_values]

											return normed_input_values
					def        UpperCAmelCase__ (							self							:	Optional[Any]     , UpperCAmelCase							:	np.ndarray     , )	->       np.ndarray:
											lowerCAmelCase   :Tuple						=			spectrogram(
											    UpperCAmelCase     , 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							:	Tuple     , UpperCAmelCase							:	Optional[Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]] = None     , UpperCAmelCase							:	Optional[Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]] = None     , UpperCAmelCase							:	Union[bool, str, PaddingStrategy] = False     , UpperCAmelCase							:	Optional[int] = None     , UpperCAmelCase							:	bool = False     , UpperCAmelCase							:	Optional[int] = None     , UpperCAmelCase							:	Optional[bool] = None     , UpperCAmelCase							:	Optional[Union[str, TensorType]] = None     , UpperCAmelCase							:	Optional[int] = None     , **UpperCAmelCase							:	Union[str, Any]     , )	->       BatchFeature:
											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:
																	lowerCAmelCase   :Union[str, Any]						=			self._process_audio(
																	    UpperCAmelCase     , UpperCAmelCase     , UpperCAmelCase     , UpperCAmelCase     , UpperCAmelCase     , UpperCAmelCase     , UpperCAmelCase     , UpperCAmelCase     , **UpperCAmelCase     , )
											else:
																	lowerCAmelCase   :str						=			None

											if audio_target is not None:
																	lowerCAmelCase   :Union[str, Any]						=			self._process_audio(
																	    UpperCAmelCase     , UpperCAmelCase     , UpperCAmelCase     , UpperCAmelCase     , UpperCAmelCase     , UpperCAmelCase     , UpperCAmelCase     , UpperCAmelCase     , **UpperCAmelCase     , )

																	if inputs is None:
																							return inputs_target
																	else:
																							lowerCAmelCase   :int						=			inputs_target["input_values"]
																							lowerCAmelCase   :Optional[int]						=			inputs_target.get('attention_mask'   )
																							if decoder_attention_mask is not None:
																													lowerCAmelCase   :List[str]						=			decoder_attention_mask

											return inputs
					def        UpperCAmelCase__ (							self							:	Optional[Any]     , UpperCAmelCase							:	Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]     , UpperCAmelCase							:	bool = False     , UpperCAmelCase							:	Union[bool, str, PaddingStrategy] = False     , UpperCAmelCase							:	Optional[int] = None     , UpperCAmelCase							:	bool = False     , UpperCAmelCase							:	Optional[int] = None     , UpperCAmelCase							:	Optional[bool] = None     , UpperCAmelCase							:	Optional[Union[str, TensorType]] = None     , **UpperCAmelCase							:	int     , )	->       BatchFeature:
											lowerCAmelCase   :int						=			isinstance(UpperCAmelCase     , 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}"""   )
											lowerCAmelCase   :List[str]						=			is_batched_numpy or (
											    isinstance(UpperCAmelCase     , (list, tuple)   ) and (isinstance(speech[0]     , (np.ndarray, tuple, list)   ))
											)

											if is_batched:
																	lowerCAmelCase   :List[Any]						=			[np.asarray(UpperCAmelCase     , dtype=np.floataa   ) for speech in speech]
											elif not is_batched and not isinstance(UpperCAmelCase     , np.ndarray   ):
																	lowerCAmelCase   :Optional[Any]						=			np.asarray(UpperCAmelCase     , dtype=np.floataa   )
											elif isinstance(UpperCAmelCase     , np.ndarray   ) and speech.dtype is np.dtype(np.floataa   ):
																	lowerCAmelCase   :Optional[int]						=			speech.astype(np.floataa   )

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

											# needed to make pad() work on spectrogram inputs
											lowerCAmelCase   :Tuple						=			self.feature_size

											# convert into correct format for padding
											if is_target:
																	lowerCAmelCase   :List[str]						=			[self._extract_mel_features(UpperCAmelCase   ) for waveform in speech]
																	lowerCAmelCase   :int						=			BatchFeature({'input_values': features}   )
																	lowerCAmelCase   :str						=			self.num_mel_bins
											else:
																	lowerCAmelCase   :Optional[int]						=			BatchFeature({'input_values': speech}   )

											lowerCAmelCase   :List[Any]						=			self.pad(
											    UpperCAmelCase     , padding=UpperCAmelCase     , max_length=UpperCAmelCase     , truncation=UpperCAmelCase     , pad_to_multiple_of=UpperCAmelCase     , return_attention_mask=UpperCAmelCase     , **UpperCAmelCase     , )

											lowerCAmelCase   :Optional[Any]						=			feature_size_hack

											# convert input values to correct format
											lowerCAmelCase   :int						=			padded_inputs["input_values"]
											if not isinstance(input_values[0]     , np.ndarray   ):
																	lowerCAmelCase   :Any						=			[np.asarray(UpperCAmelCase     , dtype=np.floataa   ) for array in input_values]
											elif (
											    not isinstance(UpperCAmelCase     , np.ndarray   )
											    and isinstance(input_values[0]     , np.ndarray   )
											    and input_values[0].dtype is np.dtype(np.floataa   )
											):
																	lowerCAmelCase   :Union[str, Any]						=			[array.astype(np.floataa   ) for array in input_values]
											elif isinstance(UpperCAmelCase     , np.ndarray   ) and input_values.dtype is np.dtype(np.floataa   ):
																	lowerCAmelCase   :Tuple						=			input_values.astype(np.floataa   )

											# convert attention_mask to correct format
											lowerCAmelCase   :Union[str, Any]						=			padded_inputs.get('attention_mask'   )
											if attention_mask is not None:
																	lowerCAmelCase   :str						=			[np.asarray(UpperCAmelCase     , dtype=np.intaa   ) for array in attention_mask]

											# zero-mean and unit-variance normalization
											if not is_target and self.do_normalize:
																	lowerCAmelCase   :Dict						=			(
																	    attention_mask
																	    if self._get_padding_strategies(UpperCAmelCase     , max_length=UpperCAmelCase   ) is not PaddingStrategy.DO_NOT_PAD
																	    else None
																	)
																	lowerCAmelCase   :List[Any]						=			self.zero_mean_unit_var_norm(
																	    padded_inputs['input_values']     , attention_mask=UpperCAmelCase     , padding_value=self.padding_value   )

											if return_tensors is not None:
																	lowerCAmelCase   :Any						=			padded_inputs.convert_to_tensors(UpperCAmelCase   )

											return padded_inputs
					def        UpperCAmelCase__ (							self							:	Dict   )	->       Dict[str, Any]:
											lowerCAmelCase   :str						=			super().to_dict()

											# Don't serialize these as they are derived from the other properties.
											lowerCAmelCase   :Optional[Any]						=			["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   __lowercase						(						__snake_case       ):
	_A       =   (DEISMultistepScheduler,)
	_A       =   (("num_inference_steps", 25),)






	def       _a(self				: Optional[int]      ,	**snake_case				: str    ) ->			Optional[Any]:
							_lowercase       :	str           =   {
							    "num_train_timesteps": 1000,
							    "beta_start": 0.00_01,
							    "beta_end": 0.02,
							    "beta_schedule": "linear",
							    "solver_order": 2,
							}

							config.update(**snake_case    )
							return config






	def       _a(self				: Optional[int]      ,	snake_case				: Optional[int]=0      ,	**snake_case				: List[Any]    ) ->			Dict:
							_lowercase       :	Dict           =   dict(self.forward_default_kwargs    )
							_lowercase       :	Optional[Any]           =   kwargs.pop("num_inference_steps"      ,	snake_case    )
							_lowercase       :	Optional[int]           =   self.dummy_sample
							_lowercase       :	Tuple           =   0.1 * sample
							_lowercase       :	Optional[Any]           =   [residual + 0.2, residual + 0.15, residual + 0.10]

							for scheduler_class in self.scheduler_classes:
													_lowercase       :	Dict           =   self.get_scheduler_config(**snake_case    )
													_lowercase       :	int           =   scheduler_class(**snake_case    )
													scheduler.set_timesteps(snake_case    )
													# copy over dummy past residuals
													_lowercase       :	Any           =   dummy_past_residuals[: scheduler.config.solver_order]

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

													_lowercase      ,    _lowercase       :	Optional[Any]           =   sample, sample
													for t in range(snake_case      ,	time_step + scheduler.config.solver_order + 1    ):
																			_lowercase       :	List[Any]           =   scheduler.step(snake_case      ,	snake_case      ,	snake_case      ,	**snake_case    ).prev_sample
																			_lowercase       :	Union[str, Any]           =   new_scheduler.step(snake_case      ,	snake_case      ,	snake_case      ,	**snake_case    ).prev_sample

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






	def       _a(self				: str    ) ->			List[str]:
							pass






	def       _a(self				: Optional[Any]      ,	snake_case				: List[Any]=0      ,	**snake_case				: str    ) ->			Union[str, Any]:
							_lowercase       :	List[str]           =   dict(self.forward_default_kwargs    )
							_lowercase       :	Optional[int]           =   kwargs.pop("num_inference_steps"      ,	snake_case    )
							_lowercase       :	Dict           =   self.dummy_sample
							_lowercase       :	Union[str, Any]           =   0.1 * sample
							_lowercase       :	List[str]           =   [residual + 0.2, residual + 0.15, residual + 0.10]

							for scheduler_class in self.scheduler_classes:
													_lowercase       :	List[Any]           =   self.get_scheduler_config()
													_lowercase       :	int           =   scheduler_class(**snake_case    )
													scheduler.set_timesteps(snake_case    )

													# copy over dummy past residuals (must be after setting timesteps)
													_lowercase       :	Union[str, Any]           =   dummy_past_residuals[: scheduler.config.solver_order]

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

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

													_lowercase       :	Any           =   scheduler.step(snake_case      ,	snake_case      ,	snake_case      ,	**snake_case    ).prev_sample
													_lowercase       :	Dict           =   new_scheduler.step(snake_case      ,	snake_case      ,	snake_case      ,	**snake_case    ).prev_sample

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






	def       _a(self				: Dict      ,	snake_case				: str=None      ,	**snake_case				: Dict    ) ->			Optional[Any]:
							if scheduler is None:
													_lowercase       :	Optional[Any]           =   self.scheduler_classes[0]
													_lowercase       :	Dict           =   self.get_scheduler_config(**snake_case    )
													_lowercase       :	Optional[int]           =   scheduler_class(**snake_case    )

							_lowercase       :	Tuple           =   self.scheduler_classes[0]
							_lowercase       :	str           =   self.get_scheduler_config(**snake_case    )
							_lowercase       :	Tuple           =   scheduler_class(**snake_case    )

							_lowercase       :	Optional[int]           =   10
							_lowercase       :	List[str]           =   self.dummy_model()
							_lowercase       :	List[Any]           =   self.dummy_sample_deter
							scheduler.set_timesteps(snake_case    )

							for i, t in enumerate(scheduler.timesteps    ):
													_lowercase       :	str           =   model(snake_case      ,	snake_case    )
													_lowercase       :	int           =   scheduler.step(snake_case      ,	snake_case      ,	snake_case    ).prev_sample

							return sample






	def       _a(self				: Any    ) ->			Tuple:
							_lowercase       :	Union[str, Any]           =   dict(self.forward_default_kwargs    )

							_lowercase       :	Optional[int]           =   kwargs.pop("num_inference_steps"      ,	snake_case    )

							for scheduler_class in self.scheduler_classes:
													_lowercase       :	List[Any]           =   self.get_scheduler_config()
													_lowercase       :	Optional[Any]           =   scheduler_class(**snake_case    )

													_lowercase       :	Tuple           =   self.dummy_sample
													_lowercase       :	List[Any]           =   0.1 * sample

													if num_inference_steps is not None and hasattr(snake_case      ,	"set_timesteps"    ):
																			scheduler.set_timesteps(snake_case    )
													elif num_inference_steps is not None and not hasattr(snake_case      ,	"set_timesteps"    ):
																			_lowercase       :	str           =   num_inference_steps

													# copy over dummy past residuals (must be done after set_timesteps)
													_lowercase       :	int           =   [residual + 0.2, residual + 0.15, residual + 0.10]
													_lowercase       :	Dict           =   dummy_past_residuals[: scheduler.config.solver_order]

													_lowercase       :	Tuple           =   scheduler.timesteps[5]
													_lowercase       :	Dict           =   scheduler.timesteps[6]

													_lowercase       :	Tuple           =   scheduler.step(snake_case      ,	snake_case      ,	snake_case      ,	**snake_case    ).prev_sample
													_lowercase       :	Union[str, Any]           =   scheduler.step(snake_case      ,	snake_case      ,	snake_case      ,	**snake_case    ).prev_sample

													self.assertEqual(output_a.shape      ,	sample.shape    )
													self.assertEqual(output_a.shape      ,	output_a.shape    )






	def       _a(self				: Optional[int]    ) ->			List[Any]:
							# make sure that iterating over schedulers with same config names gives same results
							# for defaults
							_lowercase       :	Any           =   DEISMultistepScheduler(**self.get_scheduler_config()    )
							_lowercase       :	List[Any]           =   self.full_loop(scheduler=snake_case    )
							_lowercase       :	Tuple           =   torch.mean(torch.abs(snake_case    )    )

							assert abs(result_mean.item() - 0.2_39_16    ) < 1e-3

							_lowercase       :	str           =   DPMSolverSinglestepScheduler.from_config(scheduler.config    )
							_lowercase       :	Optional[int]           =   DPMSolverMultistepScheduler.from_config(scheduler.config    )
							_lowercase       :	List[Any]           =   UniPCMultistepScheduler.from_config(scheduler.config    )
							_lowercase       :	int           =   DEISMultistepScheduler.from_config(scheduler.config    )

							_lowercase       :	Union[str, Any]           =   self.full_loop(scheduler=snake_case    )
							_lowercase       :	Dict           =   torch.mean(torch.abs(snake_case    )    )

							assert abs(result_mean.item() - 0.2_39_16    ) < 1e-3






	def       _a(self				: Union[str, Any]    ) ->			Optional[int]:
							for timesteps in [25, 50, 100, 999, 1000]:
													self.check_over_configs(num_train_timesteps=snake_case    )






	def       _a(self				: Optional[Any]    ) ->			List[str]:
							self.check_over_configs(thresholding=snake_case    )
							for order in [1, 2, 3]:
													for solver_type in ["logrho"]:
																			for threshold in [0.5, 1.0, 2.0]:
																									for prediction_type in ["epsilon", "sample"]:
																															self.check_over_configs(
																															    thresholding=snake_case      ,	prediction_type=snake_case      ,	sample_max_value=snake_case      ,	algorithm_type="deis"      ,	solver_order=snake_case      ,	solver_type=snake_case      ,	)






	def       _a(self				: int    ) ->			Tuple:
							for prediction_type in ["epsilon", "v_prediction"]:
													self.check_over_configs(prediction_type=snake_case    )






	def       _a(self				: Tuple    ) ->			List[Any]:
							for algorithm_type in ["deis"]:
													for solver_type in ["logrho"]:
																			for order in [1, 2, 3]:
																									for prediction_type in ["epsilon", "sample"]:
																															self.check_over_configs(
																															    solver_order=snake_case      ,	solver_type=snake_case      ,	prediction_type=snake_case      ,	algorithm_type=snake_case      ,	)
																															_lowercase       :	Any           =   self.full_loop(
																															    solver_order=snake_case      ,	solver_type=snake_case      ,	prediction_type=snake_case      ,	algorithm_type=snake_case      ,	)
																															assert not torch.isnan(snake_case    ).any(), "Samples have nan numbers"






	def       _a(self				: int    ) ->			List[str]:
							self.check_over_configs(lower_order_final=snake_case    )
							self.check_over_configs(lower_order_final=snake_case    )






	def       _a(self				: Any    ) ->			List[Any]:
							for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1000]:
													self.check_over_forward(num_inference_steps=snake_case      ,	time_step=0    )






	def       _a(self				: Tuple    ) ->			Optional[Any]:
							_lowercase       :	Optional[Any]           =   self.full_loop()
							_lowercase       :	Tuple           =   torch.mean(torch.abs(snake_case    )    )

							assert abs(result_mean.item() - 0.2_39_16    ) < 1e-3






	def       _a(self				: Optional[int]    ) ->			Dict:
							_lowercase       :	int           =   self.full_loop(prediction_type="v_prediction"    )
							_lowercase       :	str           =   torch.mean(torch.abs(snake_case    )    )

							assert abs(result_mean.item() - 0.0_91    ) < 1e-3






	def       _a(self				: int    ) ->			Optional[int]:
							_lowercase       :	int           =   self.scheduler_classes[0]
							_lowercase       :	Tuple           =   self.get_scheduler_config(thresholding=snake_case      ,	dynamic_thresholding_ratio=0    )
							_lowercase       :	int           =   scheduler_class(**snake_case    )

							_lowercase       :	Optional[int]           =   10
							_lowercase       :	Union[str, Any]           =   self.dummy_model()
							_lowercase       :	Dict           =   self.dummy_sample_deter.half()
							scheduler.set_timesteps(snake_case    )

							for i, t in enumerate(scheduler.timesteps    ):
													_lowercase       :	Tuple           =   model(snake_case      ,	snake_case    )
													_lowercase       :	List[str]           =   scheduler.step(snake_case      ,	snake_case      ,	snake_case    ).prev_sample

							assert sample.dtype == torch.floataa




