






"""simple docstring"""



import copy
from typing import Any, Dict, List, Optional, Union

import numpy as np

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_    =							logging.get_logger(__name__)






class 							__SCREAMING_SNAKE_CASE						(				UpperCamelCase		):
							snake_case_     =							['input_features']


							def __init__(			self      :						Tuple			,    snake_case      :						Dict=80			,    snake_case      :						Union[str, Any]=1_6000			,    snake_case      :						List[Any]=160			,    snake_case      :						int=30			,    snake_case      :						List[str]=400			,    snake_case      :						Any=0.0			,    snake_case      :						Optional[int]=False			,    **snake_case      :						str			,    ):
													'''simple docstring'''





													super().__init__(
													    feature_size=snake_case			,    sampling_rate=snake_case			,    padding_value=snake_case			,    return_attention_mask=snake_case			,    **snake_case			,    )
													A__						:  Optional[int]		  =  n_fft
													A__						:  Optional[int]		  =  hop_length
													A__						:  List[str]		  =  chunk_length
													A__						:  Optional[Any]		  =  chunk_length * sampling_rate
													A__						:  Dict		  =  self.n_samples // hop_length
													A__						:  Optional[int]		  =  sampling_rate
													A__						:  Any		  =  mel_filter_bank(
													    num_frequency_bins=1 + n_fft // 2			,    num_mel_filters=snake_case			,    min_frequency=0.0			,    max_frequency=8000.0			,    sampling_rate=snake_case			,    norm="""slaney"""			,    mel_scale="""slaney"""			,    )


							def 						_UpperCamelCase       (			self      :						List[Any]			,    snake_case      :						np.array							):
													'''simple docstring'''





													A__						:  Tuple		  =  spectrogram(
													    snake_case			,    window_function(self.n_fft			,    """hann"""							)			,    frame_length=self.n_fft			,    hop_length=self.hop_length			,    power=2.0			,    mel_filters=self.mel_filters			,    log_mel="""log10"""			,    )
													A__						:  Union[str, Any]		  =  log_spec[:, :-1]
													A__						:  Tuple		  =  np.maximum(snake_case			,    log_spec.max() - 8.0							)
													A__						:  Optional[Any]		  =  (log_spec + 4.0) / 4.0
													return log_spec


							@staticmethod
							# Copied from transformers.models.wav2vec2.feature_extraction_wav2vec2.Wav2Vec2FeatureExtractor.zero_mean_unit_var_norm
							def 						_UpperCamelCase       (			snake_case      :						List[np.ndarray]			,    snake_case      :						List[np.ndarray]			,    snake_case      :						float = 0.0							):
													'''simple docstring'''





													if attention_mask is not None:
																			A__						:  int		  =  np.array(snake_case			,    np.intaa							)
																			A__						:  Optional[Any]		  =  []

																			for vector, length in zip(snake_case			,    attention_mask.sum(-1							)							):
																									A__						:  int		  =  (vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1e-7							)
																									if length < normed_slice.shape[0]:
																															A__						:  Any		  =  padding_value

																									normed_input_values.append(snake_case							)
													else:
																			A__						:  Dict		  =  [(x - x.mean()) / np.sqrt(x.var() + 1e-7							) for x in input_values]

													return normed_input_values


							def __call__(			self      :						str			,    snake_case      :						Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]			,    snake_case      :						bool = True			,    snake_case      :						Optional[int] = None			,    snake_case      :						Optional[Union[str, TensorType]] = None			,    snake_case      :						Optional[bool] = None			,    snake_case      :						Optional[str] = "max_length"			,    snake_case      :						Optional[int] = None			,    snake_case      :						Optional[int] = None			,    snake_case      :						Optional[bool] = None			,    **snake_case      :						Optional[int]			,    ):
													'''simple docstring'''





													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."""							)

													A__						:  str		  =  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}'							)
													A__						:  Dict		  =  is_batched_numpy or (
													    isinstance(snake_case			,    (list, tuple)							) and (isinstance(raw_speech[0]			,    (np.ndarray, tuple, list)							))
													)

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

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

													A__						:  Optional[int]		  =  BatchFeature({"""input_features""": raw_speech}							)

													# convert into correct format for padding

													A__						:  Dict		  =  self.pad(
													    snake_case			,    padding=snake_case			,    max_length=max_length if max_length else self.n_samples			,    truncation=snake_case			,    pad_to_multiple_of=snake_case			,    return_attention_mask=return_attention_mask or do_normalize			,    )

													# zero-mean and unit-variance normalization
													if do_normalize:
																			A__						:  Optional[Any]		  =  self.zero_mean_unit_var_norm(
																			    padded_inputs["""input_features"""]			,    attention_mask=padded_inputs["""attention_mask"""]			,    padding_value=self.padding_value			,    )
																			A__						:  Any		  =  np.stack(padded_inputs["""input_features"""]			,    axis=0							)

													# make sure list is in array format
													A__						:  Dict		  =  padded_inputs.get("""input_features"""							).transpose(2			,    0			,    1							)

													A__						:  str		  =  [self._np_extract_fbank_features(snake_case							) for waveform in input_features[0]]

													if isinstance(input_features[0]			,    snake_case							):
																			A__						:  int		  =  [np.asarray(snake_case			,    dtype=np.floataa							) for feature in input_features]
													else:
																			A__						:  Optional[int]		  =  input_features

													if return_attention_mask:
																			# rescale from sample (48000) to feature (3000)
																			A__						:  Tuple		  =  padded_inputs["""attention_mask"""][:, :: self.hop_length]

													if return_tensors is not None:
																			A__						:  List[Any]		  =  padded_inputs.convert_to_tensors(snake_case							)

													return padded_inputs


							def 						_UpperCamelCase       (			self      :						int							):
													'''simple docstring'''





													A__						:  Union[str, Any]		  =  copy.deepcopy(self.__dict__							)
													A__						:  Tuple		  =  self.__class__.__name__
													if "mel_filters" in output:
																			del output["mel_filters"]
													return output







"""simple docstring"""



from typing import List, Optional, Tuple, Union

import torch

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






class 							__SCREAMING_SNAKE_CASE						(				UpperCamelCase		):
							snake_case_     =							42
							snake_case_     =							42


							def __init__(			self      :						Union[str, Any]			,    snake_case      :						UNetaDModel			,    snake_case      :						KarrasVeScheduler							):
													'''simple docstring'''





													super().__init__()
													self.register_modules(unet=snake_case			,    scheduler=snake_case							)


							@torch.no_grad()
							def __call__(			self      :						Any			,    snake_case      :						int = 1			,    snake_case      :						int = 50			,    snake_case      :						Optional[Union[torch.Generator, List[torch.Generator]]] = None			,    snake_case      :						Optional[str] = "pil"			,    snake_case      :						bool = True			,    **snake_case      :						Dict			,    ):
													'''simple docstring'''





													A__						:  Optional[int]		  =  self.unet.config.sample_size
													A__						:  Dict		  =  (batch_size, 3, img_size, img_size)

													A__						:  Tuple		  =  self.unet

													# sample x_0 ~ N(0, sigma_0^2 * I)
													A__						:  Union[str, Any]		  =  randn_tensor(snake_case			,    generator=snake_case			,    device=self.device							) * self.scheduler.init_noise_sigma

													self.scheduler.set_timesteps(snake_case							)

													for t in self.progress_bar(self.scheduler.timesteps							):
																			# here sigma_t == t_i from the paper
																			A__						:  Optional[Any]		  =  self.scheduler.schedule[t]
																			A__						:  Union[str, Any]		  =  self.scheduler.schedule[t - 1] if t > 0 else 0

																			# 1. Select temporarily increased noise level sigma_hat
																			# 2. Add new noise to move from sample_i to sample_hat
																			A__  ,  A__						:  Dict		  =  self.scheduler.add_noise_to_input(snake_case			,    snake_case			,    generator=snake_case							)

																			# 3. Predict the noise residual given the noise magnitude `sigma_hat`
																			# The model inputs and output are adjusted by following eq. (213) in [1].
																			A__						:  int		  =  (sigma_hat / 2) * model((sample_hat + 1) / 2			,    sigma_hat / 2							).sample

																			# 4. Evaluate dx/dt at sigma_hat
																			# 5. Take Euler step from sigma to sigma_prev
																			A__						:  Tuple		  =  self.scheduler.step(snake_case			,    snake_case			,    snake_case			,    snake_case							)

																			if sigma_prev != 0:
																									# 6. Apply 2nd order correction
																									# The model inputs and output are adjusted by following eq. (213) in [1].
																									A__						:  Dict		  =  (sigma_prev / 2) * model((step_output.prev_sample + 1) / 2			,    sigma_prev / 2							).sample
																									A__						:  Tuple		  =  self.scheduler.step_correct(
																									    snake_case			,    snake_case			,    snake_case			,    snake_case			,    step_output.prev_sample			,    step_output["""derivative"""]			,    )
																			A__						:  Tuple		  =  step_output.prev_sample

													A__						:  Dict		  =  (sample / 2 + 0.5).clamp(0			,    1							)
													A__						:  Dict		  =  sample.cpu().permute(0			,    2			,    3			,    1							).numpy()
													if output_type == "pil":
																			A__						:  Optional[Any]		  =  self.numpy_to_pil(snake_case							)

													if not return_dict:
																			return (image,)

													return ImagePipelineOutput(images=snake_case							)






