import math
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from diffusers.configuration_utils import ConfigMixin, register_to_config
from diffusers.schedulers.scheduling_utils import SchedulerMixin
from diffusers.utils import BaseOutput, deprecate





@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->DDIM
class       lowercase__		(     _lowerCamelCase):
						UpperCamelCase_  =	42
						UpperCamelCase_  =	None






def    A			(					_lowercase	,						_lowercase=0.999	,						_lowercase="cosine"	,						):
						if alpha_transform_type == "cosine":

												def alpha_bar_fn(_lowercase		):
																		return math.cos((t + 0.008) / 1.008 * math.pi / 2		) ** 2

						elif alpha_transform_type == "exp":

												def alpha_bar_fn(_lowercase		):
																		return math.exp(t * -12.0		)

						else:
												raise ValueError(f"""Unsupported alpha_tranform_type: {alpha_transform_type}"""		)

						SCREAMING_SNAKE_CASE							:					int           =							[]
						for i in range(SCREAMING_SNAKE_CASE_		):
												SCREAMING_SNAKE_CASE							:					str           =							i / num_diffusion_timesteps
												SCREAMING_SNAKE_CASE							:					List[Any]           =							(i + 1) / num_diffusion_timesteps
												betas.append(min(1 - alpha_bar_fn(SCREAMING_SNAKE_CASE_		) / alpha_bar_fn(SCREAMING_SNAKE_CASE_		)	,						SCREAMING_SNAKE_CASE_		)		)
						return torch.tensor(SCREAMING_SNAKE_CASE_	,						dtype=torch.floataa		)





class       lowercase__		(     _lowerCamelCase			,							_lowerCamelCase):
						UpperCamelCase_  =	1


						@register_to_config
						def __init__( self			:       Dict					,	UpperCamelCase__			:       str = 1000					,	UpperCamelCase__			:       List[Any] = 0.0001					,	UpperCamelCase__			:       Dict = 0.02					,	UpperCamelCase__			:       Union[str, Any] = "linear"					,	UpperCamelCase__			:       int = None					,	UpperCamelCase__			:       Optional[int] = True					,	UpperCamelCase__			:       List[Any] = True					,	UpperCamelCase__			:       Tuple = 0					,	UpperCamelCase__			:       List[Any] = "epsilon"					,	UpperCamelCase__			:       int = 1.0					,	**UpperCamelCase__			:       Tuple					,	):





												'''simple docstring'''





												if kwargs.get('''set_alpha_to_one'''					,	A__		) is not None:
																		SCREAMING_SNAKE_CASE							:					Any           =							(
																		    '''The `set_alpha_to_one` argument is deprecated. Please use `set_alpha_to_zero` instead.'''
																		)
																		deprecate('''set_alpha_to_one'''					,	'''1.0.0'''					,	A__					,	standard_warn=A__		)
																		SCREAMING_SNAKE_CASE							:					Any           =							kwargs['''set_alpha_to_one''']
												if trained_betas is not None:
																		SCREAMING_SNAKE_CASE							:					List[Any]           =							torch.tensor(A__					,	dtype=torch.floataa		)
												elif beta_schedule == "linear":
																		SCREAMING_SNAKE_CASE							:					Tuple           =							torch.linspace(A__					,	A__					,	A__					,	dtype=torch.floataa		)
												elif beta_schedule == "scaled_linear":
																		# this schedule is very specific to the latent diffusion model.
																		SCREAMING_SNAKE_CASE							:					str           =							(
																		    torch.linspace(beta_start**0.5					,	beta_end**0.5					,	A__					,	dtype=torch.floataa		) ** 2
																		)
												elif beta_schedule == "squaredcos_cap_v2":
																		# Glide cosine schedule
																		SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							betas_for_alpha_bar(A__		)
												else:
																		raise NotImplementedError(f"""{beta_schedule} does is not implemented for {self.__class__}"""		)

												SCREAMING_SNAKE_CASE							:					Dict           =							1.0 - self.betas
												SCREAMING_SNAKE_CASE							:					Dict           =							torch.cumprod(self.alphas					,	dim=0		)

												# At every step in inverted ddim, we are looking into the next alphas_cumprod
												# For the final step, there is no next alphas_cumprod, and the index is out of bounds
												# `set_alpha_to_zero` decides whether we set this parameter simply to zero
												# in this case, self.step() just output the predicted noise
												# or whether we use the final alpha of the "non-previous" one.
												SCREAMING_SNAKE_CASE							:					List[str]           =							torch.tensor(0.0		) if set_alpha_to_zero else self.alphas_cumprod[-1]

												# standard deviation of the initial noise distribution
												SCREAMING_SNAKE_CASE							:					Any           =							1.0

												# setable values
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							None
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							torch.from_numpy(np.arange(0					,	A__		).copy().astype(np.intaa		)		)


						def  __A ( self			:       Union[str, Any]					,	UpperCamelCase__			:       Any					,	UpperCamelCase__			:       List[str] = None		):





												'''simple docstring'''





												return sample


						def  __A ( self			:       Any					,	UpperCamelCase__			:       Dict					,	UpperCamelCase__			:       List[Any] = None		):





												'''simple docstring'''





												if num_inference_steps > self.config.num_train_timesteps:
																		raise ValueError(
																		    f"""`num_inference_steps`: {num_inference_steps} cannot be larger than `self.config.train_timesteps`:"""
																		    f""" {self.config.num_train_timesteps} as the unet model trained with this scheduler can only handle"""
																		    f""" maximal {self.config.num_train_timesteps} timesteps."""		)

												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							num_inference_steps
												SCREAMING_SNAKE_CASE							:					List[str]           =							self.config.num_train_timesteps // self.num_inference_steps
												# creates integer timesteps by multiplying by ratio
												# casting to int to avoid issues when num_inference_step is power of 3
												SCREAMING_SNAKE_CASE							:					Dict           =							(np.arange(0					,	A__		) * step_ratio).round().copy().astype(np.intaa		)
												SCREAMING_SNAKE_CASE							:					List[Any]           =							torch.from_numpy(A__		).to(A__		)
												self.timesteps += self.config.steps_offset


						def  __A ( self			:       List[str]					,	UpperCamelCase__			:       Optional[int]					,	UpperCamelCase__			:       int					,	UpperCamelCase__			:       Tuple					,	UpperCamelCase__			:       Optional[Any] = 0.0					,	UpperCamelCase__			:       Union[str, Any] = False					,	UpperCamelCase__			:       Dict = None					,	UpperCamelCase__			:       List[Any] = True					,	):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Any           =							timestep + self.config.num_train_timesteps // self.num_inference_steps

												# 2. compute alphas, betas
												# change original implementation to exactly match noise levels for analogous forward process
												SCREAMING_SNAKE_CASE							:					List[Any]           =							self.alphas_cumprod[timestep]
												SCREAMING_SNAKE_CASE							:					Tuple           =							(
												    self.alphas_cumprod[prev_timestep]
												    if prev_timestep < self.config.num_train_timesteps
												    else self.final_alpha_cumprod
												)

												SCREAMING_SNAKE_CASE							:					str           =							1 - alpha_prod_t

												# 3. compute predicted original sample from predicted noise also called
												# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
												if self.config.prediction_type == "epsilon":
																		SCREAMING_SNAKE_CASE							:					Dict           =							(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
																		SCREAMING_SNAKE_CASE							:					Any           =							model_output
												elif self.config.prediction_type == "sample":
																		SCREAMING_SNAKE_CASE							:					List[str]           =							model_output
																		SCREAMING_SNAKE_CASE							:					Dict           =							(sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
												elif self.config.prediction_type == "v_prediction":
																		SCREAMING_SNAKE_CASE							:					Tuple           =							(alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
																		SCREAMING_SNAKE_CASE							:					Optional[Any]           =							(alpha_prod_t**0.5) * model_output + (beta_prod_t**0.5) * sample
												else:
																		raise ValueError(
																		    f"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or"""
																		    ''' `v_prediction`'''		)

												# 4. Clip or threshold "predicted x_0"
												if self.config.clip_sample:
																		SCREAMING_SNAKE_CASE							:					Any           =							pred_original_sample.clamp(
																		    -self.config.clip_sample_range					,	self.config.clip_sample_range		)

												# 5. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
												SCREAMING_SNAKE_CASE							:					List[str]           =							(1 - alpha_prod_t_prev) ** 0.5 * pred_epsilon

												# 6. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
												SCREAMING_SNAKE_CASE							:					Optional[int]           =							alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction

												if not return_dict:
																		return (prev_sample, pred_original_sample)
												return DDIMSchedulerOutput(prev_sample=A__					,	pred_original_sample=A__		)



						def __len__( self			:       List[str]		):





												'''simple docstring'''





												return self.config.num_train_timesteps






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

import numpy as np
import torch

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


__UpperCamelCase	: Union[str, Any]    		=  logging.get_logger(__name__)





class       lowercase__		(     UpperCamelCase_):
						UpperCamelCase_  =	["""input_features""", """is_longer"""]


						def __init__( self			:       Optional[Any]					,	UpperCamelCase__			:       Dict=64					,	UpperCamelCase__			:       Optional[Any]=4_8000					,	UpperCamelCase__			:       Tuple=480					,	UpperCamelCase__			:       Union[str, Any]=10					,	UpperCamelCase__			:       List[Any]=1024					,	UpperCamelCase__			:       Tuple=0.0					,	UpperCamelCase__			:       int=False					,	UpperCamelCase__			:       float = 0					,	UpperCamelCase__			:       float = 1_4000					,	UpperCamelCase__			:       int = None					,	UpperCamelCase__			:       str = "fusion"					,	UpperCamelCase__			:       str = "repeatpad"					,	**UpperCamelCase__			:       Union[str, Any]					,	):





												'''simple docstring'''





												super().__init__(
												    feature_size=UpperCamelCase__					,	sampling_rate=UpperCamelCase__					,	padding_value=UpperCamelCase__					,	return_attention_mask=UpperCamelCase__					,	**UpperCamelCase__					,	)
												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							top_db
												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							truncation
												SCREAMING_SNAKE_CASE							:					str           =							padding
												SCREAMING_SNAKE_CASE							:					List[Any]           =							fft_window_size
												SCREAMING_SNAKE_CASE							:					Tuple           =							(fft_window_size >> 1) + 1
												SCREAMING_SNAKE_CASE							:					List[str]           =							hop_length
												SCREAMING_SNAKE_CASE							:					List[Any]           =							max_length_s
												SCREAMING_SNAKE_CASE							:					Tuple           =							max_length_s * sampling_rate
												SCREAMING_SNAKE_CASE							:					List[Any]           =							sampling_rate
												SCREAMING_SNAKE_CASE							:					List[str]           =							frequency_min
												SCREAMING_SNAKE_CASE							:					Any           =							frequency_max
												SCREAMING_SNAKE_CASE							:					List[Any]           =							mel_filter_bank(
												    num_frequency_bins=self.nb_frequency_bins					,	num_mel_filters=UpperCamelCase__					,	min_frequency=UpperCamelCase__					,	max_frequency=UpperCamelCase__					,	sampling_rate=UpperCamelCase__					,	norm=UpperCamelCase__					,	mel_scale='''htk'''					,	)
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							mel_filter_bank(
												    num_frequency_bins=self.nb_frequency_bins					,	num_mel_filters=UpperCamelCase__					,	min_frequency=UpperCamelCase__					,	max_frequency=UpperCamelCase__					,	sampling_rate=UpperCamelCase__					,	norm='''slaney'''					,	mel_scale='''slaney'''					,	)


						def  __A ( self			:       Optional[int]		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Tuple           =							copy.deepcopy(self.__dict__		)
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							self.__class__.__name__
												if "mel_filters" in output:
																		del output["mel_filters"]
												if "mel_filters_slaney" in output:
																		del output["mel_filters_slaney"]
												return output


						def  __A ( self			:       Optional[int]					,	UpperCamelCase__			:       np.array					,	UpperCamelCase__			:       Optional[np.array] = None		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Tuple           =							spectrogram(
												    UpperCamelCase__					,	window_function(self.fft_window_size					,	'''hann'''		)					,	frame_length=self.fft_window_size					,	hop_length=self.hop_length					,	power=2.0					,	mel_filters=UpperCamelCase__					,	log_mel='''dB'''					,	)
												return log_mel_spectrogram.T


						def  __A ( self			:       Union[str, Any]					,	UpperCamelCase__			:       List[Any]					,	UpperCamelCase__			:       Dict					,	UpperCamelCase__			:       List[Any]		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					List[Any]           =							np.array_split(list(range(0					,	total_frames - chunk_frames + 1		)		)					,	3		)
												if len(ranges[1]		) == 0:
																		# if the audio is too short, we just use the first chunk
																		SCREAMING_SNAKE_CASE							:					int           =							[0]
												if len(ranges[2]		) == 0:
																		# if the audio is too short, we just use the first chunk
																		SCREAMING_SNAKE_CASE							:					Any           =							[0]
												# randomly choose index for each part
												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							np.random.choice(ranges[0]		)
												SCREAMING_SNAKE_CASE							:					List[Any]           =							np.random.choice(ranges[1]		)
												SCREAMING_SNAKE_CASE							:					int           =							np.random.choice(ranges[2]		)

												SCREAMING_SNAKE_CASE							:					Optional[int]           =							mel[idx_front : idx_front + chunk_frames, :]
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							mel[idx_middle : idx_middle + chunk_frames, :]
												SCREAMING_SNAKE_CASE							:					Tuple           =							mel[idx_back : idx_back + chunk_frames, :]

												SCREAMING_SNAKE_CASE							:					str           =							torch.tensor(mel[None, None, :]		)
												SCREAMING_SNAKE_CASE							:					Optional[int]           =							torch.nn.functional.interpolate(
												    UpperCamelCase__					,	size=[chunk_frames, 64]					,	mode='''bilinear'''					,	align_corners=UpperCamelCase__		)
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							mel_shrink[0][0].numpy()
												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							np.stack([mel_shrink, mel_chunk_front, mel_chunk_middle, mel_chunk_back]					,	axis=0		)
												return mel_fusion


						def  __A ( self			:       Dict					,	UpperCamelCase__			:       np.array					,	UpperCamelCase__			:       Union[str, Any]					,	UpperCamelCase__			:       Dict					,	UpperCamelCase__			:       List[str]		):





												'''simple docstring'''





												if waveform.shape[0] > max_length:
																		if truncation == "rand_trunc":
																								SCREAMING_SNAKE_CASE							:					Optional[Any]           =							True
																								# random crop to max_length (for compatibility) -> this should be handled by self.pad
																								SCREAMING_SNAKE_CASE							:					List[Any]           =							len(UpperCamelCase__		) - max_length
																								SCREAMING_SNAKE_CASE							:					Dict           =							np.random.randint(0					,	overflow + 1		)
																								SCREAMING_SNAKE_CASE							:					Optional[Any]           =							waveform[idx : idx + max_length]
																								SCREAMING_SNAKE_CASE							:					Any           =							self._np_extract_fbank_features(UpperCamelCase__					,	self.mel_filters_slaney		)[None, :]
																		elif truncation == "fusion":
																								SCREAMING_SNAKE_CASE							:					Any           =							self._np_extract_fbank_features(UpperCamelCase__					,	self.mel_filters		)
																								SCREAMING_SNAKE_CASE							:					List[str]           =							max_length // self.hop_length + 1  # the +1 related to how the spectrogram is computed
																								SCREAMING_SNAKE_CASE							:					List[Any]           =							mel.shape[0]
																								if chunk_frames == total_frames:
																														# there is a corner case where the audio length is larger than max_length but smaller than max_length+hop_length.
																														# In this case, we just use the whole audio.
																														SCREAMING_SNAKE_CASE							:					Optional[Any]           =							np.stack([mel, mel, mel, mel]					,	axis=0		)
																														SCREAMING_SNAKE_CASE							:					Tuple           =							False
																								else:
																														SCREAMING_SNAKE_CASE							:					str           =							self._random_mel_fusion(UpperCamelCase__					,	UpperCamelCase__					,	UpperCamelCase__		)
																														SCREAMING_SNAKE_CASE							:					Optional[int]           =							True
																		else:
																								raise NotImplementedError(f"""data_truncating {truncation} not implemented"""		)

												else:
																		SCREAMING_SNAKE_CASE							:					List[str]           =							False
																		# only use repeat as a new possible value for padding. you repeat the audio before applying the usual max_length padding
																		if waveform.shape[0] < max_length:
																								if padding == "repeat":
																														SCREAMING_SNAKE_CASE							:					Tuple           =							int(max_length / len(UpperCamelCase__		)		)
																														SCREAMING_SNAKE_CASE							:					Any           =							np.stack(np.tile(UpperCamelCase__					,	n_repeat + 1		)		)[:max_length]
																								if padding == "repeatpad":
																														SCREAMING_SNAKE_CASE							:					List[Any]           =							int(max_length / len(UpperCamelCase__		)		)
																														SCREAMING_SNAKE_CASE							:					Dict           =							np.stack(np.tile(UpperCamelCase__					,	UpperCamelCase__		)		)
																								SCREAMING_SNAKE_CASE							:					Dict           =							np.pad(UpperCamelCase__					,	(0, max_length - waveform.shape[0])					,	mode='''constant'''					,	constant_values=0		)

																		if truncation == "fusion":
																								SCREAMING_SNAKE_CASE							:					List[Any]           =							self._np_extract_fbank_features(UpperCamelCase__					,	self.mel_filters		)
																								SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							np.stack([input_mel, input_mel, input_mel, input_mel]					,	axis=0		)
																		else:
																								SCREAMING_SNAKE_CASE							:					List[str]           =							self._np_extract_fbank_features(UpperCamelCase__					,	self.mel_filters_slaney		)[None, :]

												return input_mel, longer



						def __call__( self			:       Union[str, Any]					,	UpperCamelCase__			:       Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]					,	UpperCamelCase__			:       str = None					,	UpperCamelCase__			:       Optional[str] = None					,	UpperCamelCase__			:       Optional[int] = None					,	UpperCamelCase__			:       Optional[int] = None					,	UpperCamelCase__			:       Optional[Union[str, TensorType]] = None					,	**UpperCamelCase__			:       Any					,	):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					str           =							truncation if truncation is not None else self.truncation
												SCREAMING_SNAKE_CASE							:					List[str]           =							padding if padding else self.padding

												if sampling_rate is not None:
																		if sampling_rate != self.sampling_rate:
																								raise ValueError(
																								    f"""The model corresponding to this feature extractor: {self.__class__.__name__} was trained using a"""
																								    f""" sampling rate of {self.sampling_rate}. Please make sure that the provided `raw_speech` input"""
																								    f""" was sampled with {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.'''		)

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

												if is_batched:
																		SCREAMING_SNAKE_CASE							:					Any           =							[np.asarray(UpperCamelCase__					,	dtype=np.floataa		) for speech in raw_speech]
												elif not is_batched and not isinstance(UpperCamelCase__					,	np.ndarray		):
																		SCREAMING_SNAKE_CASE							:					List[Any]           =							np.asarray(UpperCamelCase__					,	dtype=np.floataa		)
												elif isinstance(UpperCamelCase__					,	np.ndarray		) and raw_speech.dtype is np.dtype(np.floataa		):
																		SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							raw_speech.astype(np.floataa		)

												# always return batch
												if not is_batched:
																		SCREAMING_SNAKE_CASE							:					List[str]           =							[np.asarray(UpperCamelCase__		)]

												# convert to mel spectrogram, truncate and pad if needed.
												SCREAMING_SNAKE_CASE							:					int           =							[
												    self._get_input_mel(UpperCamelCase__					,	max_length if max_length else self.nb_max_samples					,	UpperCamelCase__					,	UpperCamelCase__		)
												    for waveform in raw_speech
												]

												SCREAMING_SNAKE_CASE							:					str           =							[]
												SCREAMING_SNAKE_CASE							:					List[str]           =							[]
												for mel, longer in padded_inputs:
																		input_mel.append(UpperCamelCase__		)
																		is_longer.append(UpperCamelCase__		)

												if truncation == "fusion" and sum(UpperCamelCase__		) == 0:
																		# if no audio is longer than 10s, then randomly select one audio to be longer
																		SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							np.random.randint(0					,	len(UpperCamelCase__		)		)
																		SCREAMING_SNAKE_CASE							:					Optional[Any]           =							True

												if isinstance(input_mel[0]					,	UpperCamelCase__		):
																		SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							[np.asarray(UpperCamelCase__					,	dtype=np.floataa		) for feature in input_mel]

												# is_longer is a list of bool
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							[[longer] for longer in is_longer]

												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							{'''input_features''': input_mel, '''is_longer''': is_longer}
												SCREAMING_SNAKE_CASE							:					int           =							BatchFeature(UpperCamelCase__		)

												if return_tensors is not None:
																		SCREAMING_SNAKE_CASE							:					int           =							input_features.convert_to_tensors(UpperCamelCase__		)

												return input_features





