






"""simple docstring"""







from typing import 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 ....file_utils import PaddingStrategy, TensorType
from ....utils import logging


_UpperCamelCase					      =  logging.get_logger(__name__)







class  lowerCamelCase__ (	snake_case			):
    SCREAMING_SNAKE_CASE		=      ['''input_features''', '''attention_mask''']






    def __init__(			self     ,A=80     ,A=16_000     ,A=0.0     ,A=10     ,A=25     ,A="hamming_window"     ,A=32768.0     ,A=0.97     ,A=1.0     ,A=True     ,A=True     ,A=False     ,**A     ,):
          super().__init__(feature_size=A     ,sampling_rate=A     ,padding_value=A     ,**A )

          UpperCAmelCase          =	feature_size
          UpperCAmelCase          =	sampling_rate
          UpperCAmelCase          =	padding_value
          UpperCAmelCase          =	hop_length
          UpperCAmelCase          =	win_length
          UpperCAmelCase          =	frame_signal_scale
          UpperCAmelCase          =	preemphasis_coeff
          UpperCAmelCase          =	mel_floor
          UpperCAmelCase          =	normalize_means
          UpperCAmelCase          =	normalize_vars
          UpperCAmelCase          =	win_function
          UpperCAmelCase          =	return_attention_mask

          UpperCAmelCase          =	win_length * sampling_rate // 1_000
          UpperCAmelCase          =	hop_length * sampling_rate // 1_000

          UpperCAmelCase          =	optimal_fft_length(self.sample_size )
          UpperCAmelCase          =	(self.n_fft // 2) + 1






    def     _UpperCamelCase							(			self     ,A ):
          if self.win_function == "hamming_window":
                UpperCAmelCase          =	window_function(window_length=self.sample_size     ,name=self.win_function     ,periodic=A )
          else:
                UpperCAmelCase          =	window_function(window_length=self.sample_size     ,name=self.win_function )

          UpperCAmelCase          =	mel_filter_bank(
              num_frequency_bins=self.n_freqs     ,num_mel_filters=self.feature_size     ,min_frequency=0.0     ,max_frequency=self.sampling_rate / 2.0     ,sampling_rate=self.sampling_rate     ,)

          UpperCAmelCase          =	spectrogram(
              one_waveform * self.frame_signal_scale     ,window=A     ,frame_length=self.sample_size     ,hop_length=self.sample_stride     ,fft_length=self.n_fft     ,center=A     ,preemphasis=self.preemphasis_coeff     ,mel_filters=A     ,mel_floor=self.mel_floor     ,log_mel="""log"""     ,)
          return msfc_features.T






    def     _UpperCamelCase							(			self     ,A     ,A     ,A ):
          # make sure we normalize float32 arrays
          if self.normalize_means:
                UpperCAmelCase          =	x[:input_length].mean(axis=0 )
                UpperCAmelCase          =	np.subtract(A     ,A )
          if self.normalize_vars:
                UpperCAmelCase          =	x[:input_length].std(axis=0 )
                UpperCAmelCase          =	np.divide(A     ,A )

          if input_length < x.shape[0]:
                UpperCAmelCase          =	padding_value

          # make sure array is in float32
          UpperCAmelCase          =	x.astype(np.floataa )

          return x






    def     _UpperCamelCase							(			self     ,A     ,A = None ):
          UpperCAmelCase          =	attention_mask.sum(-1 ) if attention_mask is not None else [x.shape[0] for x in input_features]
          return [self._normalize_one(A     ,A     ,self.padding_value ) for x, n in zip(A     ,A )]






    def __call__(			self     ,A     ,A = False     ,A = None     ,A = False     ,A = None     ,A = None     ,A = None     ,A = None     ,**A     ,):

          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 `raw_speech` 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.""" )

          UpperCAmelCase          =	isinstance(A     ,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          =	is_batched_numpy or (
              isinstance(A     ,(list, tuple) ) and (isinstance(raw_speech[0]     ,(np.ndarray, tuple, list) ))
          )

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

          # always return batch
          if not is_batched:
                UpperCAmelCase          =	[raw_speech]

          # extract fbank features
          UpperCAmelCase          =	[self._extract_mfsc_features(A ) for one_waveform in raw_speech]

          # convert into correct format for padding
          UpperCAmelCase          =	BatchFeature({"""input_features""": features} )

          UpperCAmelCase          =	self.pad(
              A     ,padding=A     ,max_length=A     ,truncation=A     ,pad_to_multiple_of=A     ,return_attention_mask=A     ,**A     ,)
          # make sure list is in array format
          UpperCAmelCase          =	padded_inputs.get("""input_features""" )
          if isinstance(input_features[0]     ,A ):
                UpperCAmelCase          =	[np.asarray(A     ,dtype=np.floataa ) for feature in input_features]

          UpperCAmelCase          =	padded_inputs.get("""attention_mask""" )
          if attention_mask is not None:
                UpperCAmelCase          =	[np.asarray(A     ,dtype=np.intaa ) for array in attention_mask]

          if self.normalize_means or self.normalize_vars:
                UpperCAmelCase          =	(
                    np.array(A     ,dtype=np.intaa )
                    if self._get_padding_strategies(A     ,max_length=A ) is not PaddingStrategy.DO_NOT_PAD
                    and padding
                    else None
                )
                UpperCAmelCase          =	self.normalize(
                    padded_inputs["""input_features"""]     ,attention_mask=A )

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

          return padded_inputs











"""simple docstring"""







from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput







class  lowerCamelCase__ (	snake_case			):
    SCREAMING_SNAKE_CASE		=      42
    SCREAMING_SNAKE_CASE		=      42






    def __init__(			self     ,A     ,A ):
          super().__init__()
          self.register_modules(unet=A     ,scheduler=A )






    @torch.no_grad()
    def __call__(			self     ,A = 1     ,A = 2_000     ,A = None     ,A = "pil"     ,A = True     ,**A     ,):
          UpperCAmelCase          =	self.unet.config.sample_size
          UpperCAmelCase          =	(batch_size, 3, img_size, img_size)

          UpperCAmelCase          =	self.unet

          UpperCAmelCase          =	randn_tensor(A     ,generator=A ) * self.scheduler.init_noise_sigma
          UpperCAmelCase          =	sample.to(self.device )

          self.scheduler.set_timesteps(A )
          self.scheduler.set_sigmas(A )

          for i, t in enumerate(self.progress_bar(self.scheduler.timesteps ) ):
                UpperCAmelCase          =	self.scheduler.sigmas[i] * torch.ones(shape[0]     ,device=self.device )

                # correction step
                for _ in range(self.scheduler.config.correct_steps ):
                      UpperCAmelCase          =	self.unet(A     ,A ).sample
                      UpperCAmelCase          =	self.scheduler.step_correct(A     ,A     ,generator=A ).prev_sample

                # prediction step
                UpperCAmelCase          =	model(A     ,A ).sample
                UpperCAmelCase          =	self.scheduler.step_pred(A     ,A     ,A     ,generator=A )

                UpperCAmelCase      , UpperCAmelCase          =	output.prev_sample, output.prev_sample_mean

          UpperCAmelCase          =	sample_mean.clamp(0     ,1 )
          UpperCAmelCase          =	sample.cpu().permute(0     ,2     ,3     ,1 ).numpy()
          if output_type == "pil":
                UpperCAmelCase          =	self.numpy_to_pil(A )

          if not return_dict:
                return (sample,)

          return ImagePipelineOutput(images=A )



