'''simple docstring'''


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


snake_case__					:      Dict				      =  logging.get_logger(__name__)






class 					__SCREAMING_SNAKE_CASE						(	lowerCamelCase_	):







  '''simple docstring'''






  lowerCamelCase_		:Union[str, Any]						   =		['''input_features''', '''is_longer''']


  def __init__(			self				,		snake_case_=6_4				,		snake_case_=4_8_0_0_0				,		snake_case_=4_8_0				,		snake_case_=1_0				,		snake_case_=1_0_2_4				,		snake_case_=0.0				,		snake_case_=False				,		snake_case_ = 0				,		snake_case_ = 1_4_0_0_0				,		snake_case_ = None				,		snake_case_ = "fusion"				,		snake_case_ = "repeatpad"				,		**snake_case_				,		):




       '''simple docstring'''

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


  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       UpperCAmelCase_							:       List[Any]       =				copy.deepcopy(self.__dict__						)
       UpperCAmelCase_							:       List[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 _UpperCamelCase					(			self				,		snake_case_				,		snake_case_ = None						):




       '''simple docstring'''

       UpperCAmelCase_							:       int       =				spectrogram(
           snake_case_				,		window_function(self.fft_window_size				,		'hann'						)				,		frame_length=self.fft_window_size				,		hop_length=self.hop_length				,		power=2.0				,		mel_filters=snake_case_				,		log_mel='dB'				,		)
       return log_mel_spectrogram.T


  def _UpperCamelCase					(			self				,		snake_case_				,		snake_case_				,		snake_case_						):




       '''simple docstring'''

       UpperCAmelCase_							:       Dict       =				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
            UpperCAmelCase_							:       Tuple       =				[0]
       if len(ranges[2]						) == 0:
            # if the audio is too short, we just use the first chunk
            UpperCAmelCase_							:       Optional[int]       =				[0]
       # randomly choose index for each part
       UpperCAmelCase_							:       List[str]       =				np.random.choice(ranges[0]						)
       UpperCAmelCase_							:       Optional[int]       =				np.random.choice(ranges[1]						)
       UpperCAmelCase_							:       Tuple       =				np.random.choice(ranges[2]						)

       UpperCAmelCase_							:       str       =				mel[idx_front : idx_front + chunk_frames, :]
       UpperCAmelCase_							:       int       =				mel[idx_middle : idx_middle + chunk_frames, :]
       UpperCAmelCase_							:       Any       =				mel[idx_back : idx_back + chunk_frames, :]

       UpperCAmelCase_							:       int       =				torch.tensor(mel[None, None, :]						)
       UpperCAmelCase_							:       Dict       =				torch.nn.functional.interpolate(
           snake_case_				,		size=[chunk_frames, 6_4]				,		mode='bilinear'				,		align_corners=snake_case_						)
       UpperCAmelCase_							:       Dict       =				mel_shrink[0][0].numpy()
       UpperCAmelCase_							:       Union[str, Any]       =				np.stack([mel_shrink, mel_chunk_front, mel_chunk_middle, mel_chunk_back]				,		axis=0						)
       return mel_fusion


  def _UpperCamelCase					(			self				,		snake_case_				,		snake_case_				,		snake_case_				,		snake_case_						):




       '''simple docstring'''

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

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

            if truncation == "fusion":
                 UpperCAmelCase_							:       int       =				self._np_extract_fbank_features(snake_case_				,		self.mel_filters						)
                 UpperCAmelCase_							:       Tuple       =				np.stack([input_mel, input_mel, input_mel, input_mel]				,		axis=0						)
            else:
                 UpperCAmelCase_							:       str       =				self._np_extract_fbank_features(snake_case_				,		self.mel_filters_slaney						)[None, :]

       return input_mel, longer






  def __call__(			self				,		snake_case_				,		snake_case_ = None				,		snake_case_ = None				,		snake_case_ = None				,		snake_case_ = None				,		snake_case_ = None				,		**snake_case_				,		):




       '''simple docstring'''

       UpperCAmelCase_							:       List[Any]       =				truncation if truncation is not None else self.truncation
       UpperCAmelCase_							:       Union[str, Any]       =				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.'						)

       UpperCAmelCase_							:       int       =				isinstance(snake_case_				,		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}'''						)
       UpperCAmelCase_							:       List[str]       =				is_batched_numpy or (
           isinstance(snake_case_				,		(list, tuple)						) and (isinstance(raw_speech[0]				,		(np.ndarray, tuple, list)						))
       )

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

       # always return batch
       if not is_batched:
            UpperCAmelCase_							:       Union[str, Any]       =				[np.asarray(snake_case_						)]

       # convert to mel spectrogram, truncate and pad if needed.
       UpperCAmelCase_							:       Optional[Any]       =				[
           self._get_input_mel(snake_case_				,		max_length if max_length else self.nb_max_samples				,		snake_case_				,		snake_case_						)
           for waveform in raw_speech
       ]

       UpperCAmelCase_							:       Optional[Any]       =				[]
       UpperCAmelCase_							:       int       =				[]
       for mel, longer in padded_inputs:
            input_mel.append(snake_case_						)
            is_longer.append(snake_case_						)

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

       if isinstance(input_mel[0]				,		snake_case_						):
            UpperCAmelCase_							:       List[Any]       =				[np.asarray(snake_case_				,		dtype=np.floataa						) for feature in input_mel]

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

       UpperCAmelCase_							:       Dict       =				{'input_features': input_mel, 'is_longer': is_longer}
       UpperCAmelCase_							:       Optional[int]       =				BatchFeature(snake_case_						)

       if return_tensors is not None:
            UpperCAmelCase_							:       List[str]       =				input_features.convert_to_tensors(snake_case_						)

       return input_features







'''simple docstring'''


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

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import SchedulerMixin, SchedulerOutput






class 					__SCREAMING_SNAKE_CASE						(	lowerCamelCase_						,     lowerCamelCase_	):







  '''simple docstring'''






  lowerCamelCase_		:Union[str, Any]						   =		1


  @register_to_config
  def __init__(			self				,		snake_case_ = 1_0_0_0				,		snake_case_ = None						):




       '''simple docstring'''

       self.set_timesteps(snake_case_						)

       # standard deviation of the initial noise distribution
       UpperCAmelCase_							:       Union[str, Any]       =				1.0

       # For now we only support F-PNDM, i.e. the runge-kutta method
       # For more information on the algorithm please take a look at the paper: https://arxiv.org/pdf/2202.09778.pdf
       # mainly at formula (9), (12), (13) and the Algorithm 2.
       UpperCAmelCase_							:       int       =				4

       # running values
       UpperCAmelCase_							:       str       =				[]


  def _UpperCamelCase					(			self				,		snake_case_				,		snake_case_ = None						):




       '''simple docstring'''

       UpperCAmelCase_							:       Union[str, Any]       =				num_inference_steps
       UpperCAmelCase_							:       int       =				torch.linspace(1				,		0				,		num_inference_steps + 1						)[:-1]
       UpperCAmelCase_							:       Tuple       =				torch.cat([steps, torch.tensor([0.0]						)]						)

       if self.config.trained_betas is not None:
            UpperCAmelCase_							:       Optional[int]       =				torch.tensor(self.config.trained_betas				,		dtype=torch.floataa						)
       else:
            UpperCAmelCase_							:       Tuple       =				torch.sin(steps * math.pi / 2						) ** 2

       UpperCAmelCase_							:       Dict       =				(1.0 - self.betas**2) ** 0.5

       UpperCAmelCase_							:       str       =				(torch.atana(self.betas				,		self.alphas						) / math.pi * 2)[:-1]
       UpperCAmelCase_							:       str       =				timesteps.to(snake_case_						)

       UpperCAmelCase_							:       Any       =				[]


  def _UpperCamelCase					(			self				,		snake_case_				,		snake_case_				,		snake_case_				,		snake_case_ = True				,		):




       '''simple docstring'''

       if self.num_inference_steps is None:
            raise ValueError(
                'Number of inference steps is \'None\', you need to run \'set_timesteps\' after creating the scheduler'						)

       UpperCAmelCase_							:       Any       =				(self.timesteps == timestep).nonzero().item()
       UpperCAmelCase_							:       Optional[Any]       =				timestep_index + 1

       UpperCAmelCase_							:       Dict       =				sample * self.betas[timestep_index] + model_output * self.alphas[timestep_index]
       self.ets.append(snake_case_						)

       if len(self.ets						) == 1:
            UpperCAmelCase_							:       Tuple       =				self.ets[-1]
       elif len(self.ets						) == 2:
            UpperCAmelCase_							:       Any       =				(3 * self.ets[-1] - self.ets[-2]) / 2
       elif len(self.ets						) == 3:
            UpperCAmelCase_							:       List[str]       =				(2_3 * self.ets[-1] - 1_6 * self.ets[-2] + 5 * self.ets[-3]) / 1_2
       else:
            UpperCAmelCase_							:       Union[str, Any]       =				(1 / 2_4) * (5_5 * self.ets[-1] - 5_9 * self.ets[-2] + 3_7 * self.ets[-3] - 9 * self.ets[-4])

       UpperCAmelCase_							:       Union[str, Any]       =				self._get_prev_sample(snake_case_				,		snake_case_				,		snake_case_				,		snake_case_						)

       if not return_dict:
            return (prev_sample,)

       return SchedulerOutput(prev_sample=snake_case_						)


  def _UpperCamelCase					(			self				,		snake_case_				,		*snake_case_				,		**snake_case_						):




       '''simple docstring'''

       return sample


  def _UpperCamelCase					(			self				,		snake_case_				,		snake_case_				,		snake_case_				,		snake_case_						):




       '''simple docstring'''

       UpperCAmelCase_							:       int       =				self.alphas[timestep_index]
       UpperCAmelCase_							:       Union[str, Any]       =				self.betas[timestep_index]

       UpperCAmelCase_							:       Any       =				self.alphas[prev_timestep_index]
       UpperCAmelCase_							:       Dict       =				self.betas[prev_timestep_index]

       UpperCAmelCase_							:       List[Any]       =				(sample - sigma * ets) / max(snake_case_				,		1E-8						)
       UpperCAmelCase_							:       Tuple       =				next_alpha * pred + ets * next_sigma

       return prev_sample






  def __len__(			self						):




       '''simple docstring'''

       return self.config.num_train_timesteps






