






'''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


_A			:    Dict					  =logging.get_logger(__name__)

class _lowercase			(       _lowercase	):
							a          = ["""input_features""", """is_longer"""]




							def __init__(							self:       Dict    ,							UpperCamelCase__:       Optional[Any]=64    ,							UpperCamelCase__:       Dict=48_000    ,							UpperCamelCase__:       Any=480    ,							UpperCamelCase__:       str=10    ,							UpperCamelCase__:       Union[str, Any]=1_024    ,							UpperCamelCase__:       Optional[Any]=0.0    ,							UpperCamelCase__:       List[str]=False    ,							UpperCamelCase__:       float = 0    ,							UpperCamelCase__:       float = 14_000    ,							UpperCamelCase__:       int = None    ,							UpperCamelCase__:       str = "fusion"    ,							UpperCamelCase__:       str = "repeatpad"    ,							**UpperCamelCase__:       Any    ,							):
														super().__init__(
														    feature_size=UpperCamelCase__    ,							sampling_rate=UpperCamelCase__    ,							padding_value=UpperCamelCase__    ,							return_attention_mask=UpperCamelCase__    ,							**UpperCamelCase__    ,							)
														lowerCamelCase__      :			Union[str, Any] 						= top_db
														lowerCamelCase__      :			Tuple 						= truncation
														lowerCamelCase__      :			Any 						= padding
														lowerCamelCase__      :			str 						= fft_window_size
														lowerCamelCase__      :			Dict 						= (fft_window_size >> 1) + 1
														lowerCamelCase__      :			str 						= hop_length
														lowerCamelCase__      :			List[str] 						= max_length_s
														lowerCamelCase__      :			Any 						= max_length_s * sampling_rate
														lowerCamelCase__      :			Optional[int] 						= sampling_rate
														lowerCamelCase__      :			Tuple 						= frequency_min
														lowerCamelCase__      :			Tuple 						= frequency_max
														lowerCamelCase__      :			Tuple 						= 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"""    ,							)
														lowerCamelCase__      :			int 						= 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 	lowerCamelCase_       (							self:       Dict  ):
														lowerCamelCase__      :			Union[str, Any] 						= copy.deepcopy(self.__dict__  )
														lowerCamelCase__      :			Dict 						= 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:       Union[str, Any]    ,							UpperCamelCase__:       np.array    ,							UpperCamelCase__:       Optional[np.array] = None  ):
														lowerCamelCase__      :			List[str] 						= 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 	lowerCamelCase_       (							self:       Optional[Any]    ,							UpperCamelCase__:       Tuple    ,							UpperCamelCase__:       str    ,							UpperCamelCase__:       Dict  ):
														lowerCamelCase__      :			str 						= 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__      :			Dict 						= [0]
														if len(ranges[2]  ) == 0:
																					# if the audio is too short, we just use the first chunk
																					lowerCamelCase__      :			List[str] 						= [0]
														# randomly choose index for each part
														lowerCamelCase__      :			List[Any] 						= np.random.choice(ranges[0]  )
														lowerCamelCase__      :			List[str] 						= np.random.choice(ranges[1]  )
														lowerCamelCase__      :			Union[str, Any] 						= np.random.choice(ranges[2]  )

														lowerCamelCase__      :			List[Any] 						= mel[idx_front : idx_front + chunk_frames, :]
														lowerCamelCase__      :			Dict 						= mel[idx_middle : idx_middle + chunk_frames, :]
														lowerCamelCase__      :			List[str] 						= mel[idx_back : idx_back + chunk_frames, :]

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




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

														else:
																					lowerCamelCase__      :			Optional[int] 						= 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__      :			Union[str, Any] 						= int(max_length / len(UpperCamelCase__  )  )
																																			lowerCamelCase__      :			Dict 						= np.stack(np.tile(UpperCamelCase__    ,							n_repeat + 1  )  )[:max_length]
																												if padding == "repeatpad":
																																			lowerCamelCase__      :			Dict 						= int(max_length / len(UpperCamelCase__  )  )
																																			lowerCamelCase__      :			int 						= np.stack(np.tile(UpperCamelCase__    ,							UpperCamelCase__  )  )
																												lowerCamelCase__      :			str 						= np.pad(UpperCamelCase__    ,							(0, max_length - waveform.shape[0])    ,							mode="""constant"""    ,							constant_values=0  )

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

														return input_mel, longer




							def __call__(							self:       Dict    ,							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__:       Dict    ,							):
														lowerCamelCase__      :			List[Any] 						= truncation if truncation is not None else self.truncation
														lowerCamelCase__      :			Optional[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."""  )

														lowerCamelCase__      :			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}'''  )
														lowerCamelCase__      :			List[Any] 						= is_batched_numpy or (
														    isinstance(UpperCamelCase__    ,							(list, tuple)  ) and (isinstance(raw_speech[0]    ,							(np.ndarray, tuple, list)  ))
														)

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

														# always return batch
														if not is_batched:
																					lowerCamelCase__      :			int 						= [np.asarray(UpperCamelCase__  )]

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

														lowerCamelCase__      :			str 						= []
														lowerCamelCase__      :			int 						= []
														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
																					lowerCamelCase__      :			Optional[Any] 						= np.random.randint(0    ,							len(UpperCamelCase__  )  )
																					lowerCamelCase__      :			Optional[int] 						= True

														if isinstance(input_mel[0]    ,							UpperCamelCase__  ):
																					lowerCamelCase__      :			int 						= [np.asarray(UpperCamelCase__    ,							dtype=np.floataa  ) for feature in input_mel]

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

														lowerCamelCase__      :			Any 						= {"""input_features""": input_mel, """is_longer""": is_longer}
														lowerCamelCase__      :			Union[str, Any] 						= BatchFeature(UpperCamelCase__  )

														if return_tensors is not None:
																					lowerCamelCase__      :			str 						= input_features.convert_to_tensors(UpperCamelCase__  )

														return input_features









'''simple docstring'''







import unittest

import torch

from diffusers import DDIMScheduler, DDPMScheduler, UNetaDModel
from diffusers.training_utils import set_seed
from diffusers.utils.testing_utils import slow


_A			:    Union[str, Any]					  =False

class _lowercase			(       unittest.TestCase	):




							def 	lowerCamelCase_       (							self:       Dict    ,							UpperCamelCase__:       str=32  ):
														set_seed(0  )
														lowerCamelCase__      :			Optional[int] 						= UNetaDModel(sample_size=UpperCamelCase__    ,							in_channels=3    ,							out_channels=3  )
														lowerCamelCase__      :			List[Any] 						= torch.optim.SGD(model.parameters()    ,							lr=0.0_001  )
														return model, optimizer




							@slow
							def 	lowerCamelCase_       (							self:       List[str]  ):
														lowerCamelCase__      :			Optional[Any] 						= """cpu"""  # ensure full determinism without setting the CUBLAS_WORKSPACE_CONFIG env variable
														lowerCamelCase__      :			List[Any] 						= DDPMScheduler(
														    num_train_timesteps=1_000    ,							beta_start=0.0_001    ,							beta_end=0.02    ,							beta_schedule="""linear"""    ,							clip_sample=UpperCamelCase__    ,							)
														lowerCamelCase__      :			Any 						= DDIMScheduler(
														    num_train_timesteps=1_000    ,							beta_start=0.0_001    ,							beta_end=0.02    ,							beta_schedule="""linear"""    ,							clip_sample=UpperCamelCase__    ,							)

														assert ddpm_scheduler.config.num_train_timesteps == ddim_scheduler.config.num_train_timesteps

														# shared batches for DDPM and DDIM
														set_seed(0  )
														lowerCamelCase__      :			str 						= [torch.randn((4, 3, 32, 32)  ).clip(-1    ,							1  ).to(UpperCamelCase__  ) for _ in range(4  )]
														lowerCamelCase__      :			Tuple 						= [torch.randn((4, 3, 32, 32)  ).to(UpperCamelCase__  ) for _ in range(4  )]
														lowerCamelCase__      :			Tuple 						= [torch.randint(0    ,							1_000    ,							(4,)  ).long().to(UpperCamelCase__  ) for _ in range(4  )]

														# train with a DDPM scheduler
														lowerCamelCase__						,	lowerCamelCase__      :			Any 						= self.get_model_optimizer(resolution=32  )
														model.train().to(UpperCamelCase__  )
														for i in range(4  ):
																					optimizer.zero_grad()
																					lowerCamelCase__      :			str 						= ddpm_scheduler.add_noise(clean_images[i]    ,							noise[i]    ,							timesteps[i]  )
																					lowerCamelCase__      :			str 						= model(UpperCamelCase__    ,							timesteps[i]  ).sample
																					lowerCamelCase__      :			Tuple 						= torch.nn.functional.mse_loss(UpperCamelCase__    ,							noise[i]  )
																					loss.backward()
																					optimizer.step()
														del model, optimizer

														# recreate the model and optimizer, and retry with DDIM
														lowerCamelCase__						,	lowerCamelCase__      :			Optional[Any] 						= self.get_model_optimizer(resolution=32  )
														model.train().to(UpperCamelCase__  )
														for i in range(4  ):
																					optimizer.zero_grad()
																					lowerCamelCase__      :			Optional[Any] 						= ddim_scheduler.add_noise(clean_images[i]    ,							noise[i]    ,							timesteps[i]  )
																					lowerCamelCase__      :			Dict 						= model(UpperCamelCase__    ,							timesteps[i]  ).sample
																					lowerCamelCase__      :			Union[str, Any] 						= torch.nn.functional.mse_loss(UpperCamelCase__    ,							noise[i]  )
																					loss.backward()
																					optimizer.step()
														del model, optimizer

														self.assertTrue(torch.allclose(UpperCamelCase__    ,							UpperCamelCase__    ,							atol=1e-5  )  )
														self.assertTrue(torch.allclose(UpperCamelCase__    ,							UpperCamelCase__    ,							atol=1e-5  )  )

