






# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

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

import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin, SchedulerOutput
@dataclass
class    _UpperCAmelCase      ( A__			):

				"""simple docstring"""




				lowercase__       							= 42
				lowercase__       							= 42



class    _UpperCAmelCase      ( A__	,A__			):

				"""simple docstring"""




				lowercase__       							= 1




				@register_to_config
				def __init__(  self		:					Union[str, Any],						lowerCamelCase		:					int = 2_000,						lowerCamelCase		:					float = 0.15,						lowerCamelCase		:					float = 0.01,						lowerCamelCase		:					float = 1348.0,						lowerCamelCase		:					float = 1E-5,						lowerCamelCase		:					int = 1,						):

								'''simple docstring'''
								# standard deviation of the initial noise distribution
								lowercase__     				=    sigma_max

								# setable values
								lowercase__     				=    None

								self.set_sigmas(lowerCamelCase,						lowerCamelCase,						lowerCamelCase,						lowerCamelCase						)




				def        lowercase__     (  self		:					List[str],						lowerCamelCase		:					torch.FloatTensor,						lowerCamelCase		:					Optional[int] = None						):

								'''simple docstring'''
								return sample




				def        lowercase__     (  self		:					Dict,						lowerCamelCase		:					int,						lowerCamelCase		:					float = None,						lowerCamelCase		:					Union[str, torch.device] = None						):

								'''simple docstring'''
								lowercase__     				=    sampling_eps if sampling_eps is not None else self.config.sampling_eps

								lowercase__     				=    torch.linspace(1,						lowerCamelCase,						lowerCamelCase,						device=lowerCamelCase						)




				def        lowercase__     (  self		:					str,						lowerCamelCase		:					int,						lowerCamelCase		:					float = None,						lowerCamelCase		:					float = None,						lowerCamelCase		:					float = None						):

								'''simple docstring'''
								lowercase__     				=    sigma_min if sigma_min is not None else self.config.sigma_min
								lowercase__     				=    sigma_max if sigma_max is not None else self.config.sigma_max
								lowercase__     				=    sampling_eps if sampling_eps is not None else self.config.sampling_eps
								if self.timesteps is None:
												self.set_timesteps(lowerCamelCase,						lowerCamelCase						)

								lowercase__     				=    sigma_min * (sigma_max / sigma_min) ** (self.timesteps / sampling_eps)
								lowercase__     				=    torch.exp(torch.linspace(math.log(lowerCamelCase						),						math.log(lowerCamelCase						),						lowerCamelCase						)						)
								lowercase__     				=    torch.tensor([sigma_min * (sigma_max / sigma_min) ** t for t in self.timesteps]						)




				def        lowercase__     (  self		:					Optional[int],						lowerCamelCase		:					str,						lowerCamelCase		:					str						):

								'''simple docstring'''
								return torch.where(
								    timesteps == 0,						torch.zeros_like(t.to(timesteps.device						)						),						self.discrete_sigmas[timesteps - 1].to(timesteps.device						),						)




				def        lowercase__     (  self		:					Tuple,						lowerCamelCase		:					torch.FloatTensor,						lowerCamelCase		:					int,						lowerCamelCase		:					torch.FloatTensor,						lowerCamelCase		:					Optional[torch.Generator] = None,						lowerCamelCase		:					bool = True,						):

								'''simple docstring'''
								if self.timesteps is None:
												raise ValueError(
												    '''`self.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler'''						)

								lowercase__     				=    timestep * torch.ones(
								    sample.shape[0],						device=sample.device						)  # torch.repeat_interleave(timestep, sample.shape[0])
								lowercase__     				=    (timestep * (len(self.timesteps						) - 1)).long()

								# mps requires indices to be in the same device, so we use cpu as is the default with cuda
								lowercase__     				=    timesteps.to(self.discrete_sigmas.device						)

								lowercase__     				=    self.discrete_sigmas[timesteps].to(sample.device						)
								lowercase__     				=    self.get_adjacent_sigma(lowerCamelCase,						lowerCamelCase						).to(sample.device						)
								lowercase__     				=    torch.zeros_like(lowerCamelCase						)
								lowercase__     				=    (sigma**2 - adjacent_sigma**2) ** 0.5

								# equation 6 in the paper: the model_output modeled by the network is grad_x log pt(x)
								# also equation 47 shows the analog from SDE models to ancestral sampling methods
								lowercase__     				=    diffusion.flatten()
								while len(diffusion.shape						) < len(sample.shape						):
												lowercase__     				=    diffusion.unsqueeze(-1						)
								lowercase__     				=    drift - diffusion**2 * model_output

								#  equation 6: sample noise for the diffusion term of
								lowercase__     				=    randn_tensor(
								    sample.shape,						layout=sample.layout,						generator=lowerCamelCase,						device=sample.device,						dtype=sample.dtype						)
								lowercase__     				=    sample - drift  # subtract because `dt` is a small negative timestep
								# TODO is the variable diffusion the correct scaling term for the noise?
								lowercase__     				=    prev_sample_mean + diffusion * noise  # add impact of diffusion field g

								if not return_dict:
												return (prev_sample, prev_sample_mean)

								return SdeVeOutput(prev_sample=lowerCamelCase,						prev_sample_mean=lowerCamelCase						)




				def        lowercase__     (  self		:					int,						lowerCamelCase		:					torch.FloatTensor,						lowerCamelCase		:					torch.FloatTensor,						lowerCamelCase		:					Optional[torch.Generator] = None,						lowerCamelCase		:					bool = True,						):

								'''simple docstring'''
								if self.timesteps is None:
												raise ValueError(
												    '''`self.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler'''						)

								# For small batch sizes, the paper "suggest replacing norm(z) with sqrt(d), where d is the dim. of z"
								# sample noise for correction
								lowercase__     				=    randn_tensor(sample.shape,						layout=sample.layout,						generator=lowerCamelCase						).to(sample.device						)

								# compute step size from the model_output, the noise, and the snr
								lowercase__     				=    torch.norm(model_output.reshape(model_output.shape[0],						-1						),						dim=-1						).mean()
								lowercase__     				=    torch.norm(noise.reshape(noise.shape[0],						-1						),						dim=-1						).mean()
								lowercase__     				=    (self.config.snr * noise_norm / grad_norm) ** 2 * 2
								lowercase__     				=    step_size * torch.ones(sample.shape[0]						).to(sample.device						)
								# self.repeat_scalar(step_size, sample.shape[0])

								# compute corrected sample: model_output term and noise term
								lowercase__     				=    step_size.flatten()
								while len(step_size.shape						) < len(sample.shape						):
												lowercase__     				=    step_size.unsqueeze(-1						)
								lowercase__     				=    sample + step_size * model_output
								lowercase__     				=    prev_sample_mean + ((step_size * 2) ** 0.5) * noise

								if not return_dict:
												return (prev_sample,)

								return SchedulerOutput(prev_sample=lowerCamelCase						)




				def        lowercase__     (  self		:					List[str],						lowerCamelCase		:					torch.FloatTensor,						lowerCamelCase		:					torch.FloatTensor,						lowerCamelCase		:					torch.FloatTensor,						):

								'''simple docstring'''
								# Make sure sigmas and timesteps have the same device and dtype as original_samples
								lowercase__     				=    timesteps.to(original_samples.device						)
								lowercase__     				=    self.discrete_sigmas.to(original_samples.device						)[timesteps]
								lowercase__     				=    (
								    noise * sigmas[:, None, None, None]
								    if noise is not None
								    else torch.randn_like(lowerCamelCase						) * sigmas[:, None, None, None]
								)
								lowercase__     				=    noise + original_samples
								return noisy_samples




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

								'''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


A__			:    Tuple         =				logging.get_logger(__name__)
class    _UpperCAmelCase      ( A__			):

				"""simple docstring"""




				lowercase__       							= ["""input_features""", """is_longer"""]




				def __init__(  self		:					Optional[int],						lowerCamelCase		:					int=64,						lowerCamelCase		:					Union[str, Any]=48_000,						lowerCamelCase		:					str=480,						lowerCamelCase		:					Tuple=10,						lowerCamelCase		:					List[Any]=1_024,						lowerCamelCase		:					Optional[int]=0.0,						lowerCamelCase		:					Optional[Any]=False,						lowerCamelCase		:					float = 0,						lowerCamelCase		:					float = 14_000,						lowerCamelCase		:					int = None,						lowerCamelCase		:					str = "fusion",						lowerCamelCase		:					str = "repeatpad",						**lowerCamelCase		:					Dict,						):

								'''simple docstring'''
								super().__init__(
								    feature_size=lowerCamelCase,						sampling_rate=lowerCamelCase,						padding_value=lowerCamelCase,						return_attention_mask=lowerCamelCase,						**lowerCamelCase,						)
								lowercase__     				=    top_db
								lowercase__     				=    truncation
								lowercase__     				=    padding
								lowercase__     				=    fft_window_size
								lowercase__     				=    (fft_window_size >> 1) + 1
								lowercase__     				=    hop_length
								lowercase__     				=    max_length_s
								lowercase__     				=    max_length_s * sampling_rate
								lowercase__     				=    sampling_rate
								lowercase__     				=    frequency_min
								lowercase__     				=    frequency_max
								lowercase__     				=    mel_filter_bank(
								    num_frequency_bins=self.nb_frequency_bins,						num_mel_filters=lowerCamelCase,						min_frequency=lowerCamelCase,						max_frequency=lowerCamelCase,						sampling_rate=lowerCamelCase,						norm=lowerCamelCase,						mel_scale='''htk''',						)
								lowercase__     				=    mel_filter_bank(
								    num_frequency_bins=self.nb_frequency_bins,						num_mel_filters=lowerCamelCase,						min_frequency=lowerCamelCase,						max_frequency=lowerCamelCase,						sampling_rate=lowerCamelCase,						norm='''slaney''',						mel_scale='''slaney''',						)




				def        lowercase__     (  self		:					Tuple						):

								'''simple docstring'''
								lowercase__     				=    copy.deepcopy(self.__dict__						)
								lowercase__     				=    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        lowercase__     (  self		:					Optional[int],						lowerCamelCase		:					np.array,						lowerCamelCase		:					Optional[np.array] = None						):

								'''simple docstring'''
								lowercase__     				=    spectrogram(
								    lowerCamelCase,						window_function(self.fft_window_size,						'''hann'''						),						frame_length=self.fft_window_size,						hop_length=self.hop_length,						power=2.0,						mel_filters=lowerCamelCase,						log_mel='''dB''',						)
								return log_mel_spectrogram.T




				def        lowercase__     (  self		:					int,						lowerCamelCase		:					str,						lowerCamelCase		:					List[str],						lowerCamelCase		:					Optional[Any]						):

								'''simple docstring'''
								lowercase__     				=    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
												lowercase__     				=    [0]
								if len(ranges[2]						) == 0:
												# if the audio is too short, we just use the first chunk
												lowercase__     				=    [0]
								# randomly choose index for each part
								lowercase__     				=    np.random.choice(ranges[0]						)
								lowercase__     				=    np.random.choice(ranges[1]						)
								lowercase__     				=    np.random.choice(ranges[2]						)

								lowercase__     				=    mel[idx_front : idx_front + chunk_frames, :]
								lowercase__     				=    mel[idx_middle : idx_middle + chunk_frames, :]
								lowercase__     				=    mel[idx_back : idx_back + chunk_frames, :]

								lowercase__     				=    torch.tensor(mel[None, None, :]						)
								lowercase__     				=    torch.nn.functional.interpolate(
								    lowerCamelCase,						size=[chunk_frames, 64],						mode='''bilinear''',						align_corners=lowerCamelCase						)
								lowercase__     				=    mel_shrink[0][0].numpy()
								lowercase__     				=    np.stack([mel_shrink, mel_chunk_front, mel_chunk_middle, mel_chunk_back],						axis=0						)
								return mel_fusion




				def        lowercase__     (  self		:					List[str],						lowerCamelCase		:					np.array,						lowerCamelCase		:					int,						lowerCamelCase		:					Dict,						lowerCamelCase		:					Union[str, Any]						):

								'''simple docstring'''
								if waveform.shape[0] > max_length:
												if truncation == "rand_trunc":
																lowercase__     				=    True
																# random crop to max_length (for compatibility) -> this should be handled by self.pad
																lowercase__     				=    len(lowerCamelCase						) - max_length
																lowercase__     				=    np.random.randint(0,						overflow + 1						)
																lowercase__     				=    waveform[idx : idx + max_length]
																lowercase__     				=    self._np_extract_fbank_features(lowerCamelCase,						self.mel_filters_slaney						)[None, :]
												elif truncation == "fusion":
																lowercase__     				=    self._np_extract_fbank_features(lowerCamelCase,						self.mel_filters						)
																lowercase__     				=    max_length // self.hop_length + 1  # the +1 related to how the spectrogram is computed
																lowercase__     				=    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.
																				lowercase__     				=    np.stack([mel, mel, mel, mel],						axis=0						)
																				lowercase__     				=    False
																else:
																				lowercase__     				=    self._random_mel_fusion(lowerCamelCase,						lowerCamelCase,						lowerCamelCase						)
																				lowercase__     				=    True
												else:
																raise NotImplementedError(F"""data_truncating {truncation} not implemented"""						)

								else:
												lowercase__     				=    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":
																				lowercase__     				=    int(max_length / len(lowerCamelCase						)						)
																				lowercase__     				=    np.stack(np.tile(lowerCamelCase,						n_repeat + 1						)						)[:max_length]
																if padding == "repeatpad":
																				lowercase__     				=    int(max_length / len(lowerCamelCase						)						)
																				lowercase__     				=    np.stack(np.tile(lowerCamelCase,						lowerCamelCase						)						)
																lowercase__     				=    np.pad(lowerCamelCase,						(0, max_length - waveform.shape[0]),						mode='''constant''',						constant_values=0						)

												if truncation == "fusion":
																lowercase__     				=    self._np_extract_fbank_features(lowerCamelCase,						self.mel_filters						)
																lowercase__     				=    np.stack([input_mel, input_mel, input_mel, input_mel],						axis=0						)
												else:
																lowercase__     				=    self._np_extract_fbank_features(lowerCamelCase,						self.mel_filters_slaney						)[None, :]

								return input_mel, longer




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

								'''simple docstring'''
								lowercase__     				=    truncation if truncation is not None else self.truncation
								lowercase__     				=    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.'''						)

								lowercase__     				=    isinstance(lowerCamelCase,						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}"""						)
								lowercase__     				=    is_batched_numpy or (
								    isinstance(lowerCamelCase,						(list, tuple)						) and (isinstance(raw_speech[0],						(np.ndarray, tuple, list)						))
								)

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

								# always return batch
								if not is_batched:
												lowercase__     				=    [np.asarray(lowerCamelCase						)]

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

								lowercase__     				=    []
								lowercase__     				=    []
								for mel, longer in padded_inputs:
												input_mel.append(lowerCamelCase						)
												is_longer.append(lowerCamelCase						)

								if truncation == "fusion" and sum(lowerCamelCase						) == 0:
												# if no audio is longer than 10s, then randomly select one audio to be longer
												lowercase__     				=    np.random.randint(0,						len(lowerCamelCase						)						)
												lowercase__     				=    True

								if isinstance(input_mel[0],						lowerCamelCase						):
												lowercase__     				=    [np.asarray(lowerCamelCase,						dtype=np.floataa						) for feature in input_mel]

								# is_longer is a list of bool
								lowercase__     				=    [[longer] for longer in is_longer]

								lowercase__     				=    {'''input_features''': input_mel, '''is_longer''': is_longer}
								lowercase__     				=    BatchFeature(lowerCamelCase						)

								if return_tensors is not None:
												lowercase__     				=    input_features.convert_to_tensors(lowerCamelCase						)

								return input_features




