




"""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


lowerCAmelCase							:    Any  							=							logging.get_logger(__name__)






class 					__magic_name__							( UpperCAmelCase__       ):






				'''simple docstring'''
				__UpperCamelCase    						=		["input_features", "is_longer"]





				def __init__( self	,				_a=64	,				_a=48_000	,				_a=480	,				_a=10	,				_a=1_024	,				_a=0.0	,				_a=False	,				_a = 0	,				_a = 14_000	,				_a = None	,				_a = "fusion"	,				_a = "repeatpad"	,				**_a	,				):





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





				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							lowerCamelCase       =      copy.deepcopy(self.__dict__				)
							lowerCamelCase       =      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      _lowerCAmelCase       ( self	,				_a	,				_a = None				):





							"""simple docstring"""
							lowerCamelCase       =      spectrogram(
							    _a	,				window_function(self.fft_window_size	,				"""hann"""				)	,				frame_length=self.fft_window_size	,				hop_length=self.hop_length	,				power=2.0	,				mel_filters=_a	,				log_mel="""dB"""	,				)
							return log_mel_spectrogram.T





				def      _lowerCAmelCase       ( self	,				_a	,				_a	,				_a				):





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

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

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





				def      _lowerCAmelCase       ( self	,				_a	,				_a	,				_a	,				_a				):





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

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

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

							return input_mel, longer





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





							"""simple docstring"""
							lowerCamelCase       =      truncation if truncation is not None else self.truncation
							lowerCamelCase       =      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."""				)

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

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

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

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

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

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

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

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

							lowerCamelCase       =      {"""input_features""": input_mel, """is_longer""": is_longer}
							lowerCamelCase       =      BatchFeature(_a				)

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

							return input_features









"""simple docstring"""




import unittest

import numpy as np
import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()






class 					__magic_name__							( unittest.TestCase       ):






				'''simple docstring'''





				@property
				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							torch.manual_seed(0				)
							lowerCamelCase       =      UNetaDModel(
							    block_out_channels=(32, 64)	,				layers_per_block=2	,				sample_size=32	,				in_channels=3	,				out_channels=3	,				down_block_types=("""DownBlock2D""", """AttnDownBlock2D""")	,				up_block_types=("""AttnUpBlock2D""", """UpBlock2D""")	,				)
							return model





				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							lowerCamelCase       =      self.dummy_uncond_unet
							lowerCamelCase       =      ScoreSdeVeScheduler()

							lowerCamelCase       =      ScoreSdeVePipeline(unet=_a	,				scheduler=_a				)
							sde_ve.to(_a				)
							sde_ve.set_progress_bar_config(disable=_a				)

							lowerCamelCase       =      torch.manual_seed(0				)
							lowerCamelCase       =      sde_ve(num_inference_steps=2	,				output_type="""numpy"""	,				generator=_a				).images

							lowerCamelCase       =      torch.manual_seed(0				)
							lowerCamelCase       =      sde_ve(num_inference_steps=2	,				output_type="""numpy"""	,				generator=_a	,				return_dict=_a				)[
							    0
							]

							lowerCamelCase       =      image[0, -3:, -3:, -1]
							lowerCamelCase       =      image_from_tuple[0, -3:, -3:, -1]

							assert image.shape == (1, 32, 32, 3)
							lowerCamelCase       =      np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]				)

							assert np.abs(image_slice.flatten() - expected_slice				).max() < 1e-2
							assert np.abs(image_from_tuple_slice.flatten() - expected_slice				).max() < 1e-2






@slow
@require_torch
class 					__magic_name__							( unittest.TestCase       ):






				'''simple docstring'''





				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							lowerCamelCase       =      """google/ncsnpp-church-256"""
							lowerCamelCase       =      UNetaDModel.from_pretrained(_a				)

							lowerCamelCase       =      ScoreSdeVeScheduler.from_pretrained(_a				)

							lowerCamelCase       =      ScoreSdeVePipeline(unet=_a	,				scheduler=_a				)
							sde_ve.to(_a				)
							sde_ve.set_progress_bar_config(disable=_a				)

							lowerCamelCase       =      torch.manual_seed(0				)
							lowerCamelCase       =      sde_ve(num_inference_steps=10	,				output_type="""numpy"""	,				generator=_a				).images

							lowerCamelCase       =      image[0, -3:, -3:, -1]

							assert image.shape == (1, 256, 256, 3)

							lowerCamelCase       =      np.array([0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0]				)

							assert np.abs(image_slice.flatten() - expected_slice				).max() < 1e-2



