





"""simple docstring"""




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

import numpy as np

from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import BatchFeature, SequenceFeatureExtractor
from ...utils import TensorType, logging


a						: List[Any]     	=			logging.get_logger(__name__)


class        __UpperCamelCase				(						a__						):
							lowerCamelCase   :					Any      					=["""audio_values""", """audio_mask"""]






							def __init__(   self      ,					lowerCAmelCase__=2048      ,					lowerCAmelCase__=1      ,					lowerCAmelCase__=[16, 16]      ,					lowerCAmelCase__=128      ,					lowerCAmelCase__=4_4100      ,					lowerCAmelCase__=86      ,					lowerCAmelCase__=2048      ,					lowerCAmelCase__=0.0      ,					**lowerCAmelCase__      ,					)	-> Any:
								super().__init__(
								    feature_size=lowerCAmelCase__      ,					sampling_rate=lowerCAmelCase__      ,					padding_value=lowerCAmelCase__      ,					**lowerCAmelCase__      ,					)

								a      :		Tuple	=				spectrogram_length
								a      :		Any	=				num_channels
								a      :		Union[str, Any]	=				patch_size
								a      :		Dict	=				feature_size // self.patch_size[1]
								a      :		Union[str, Any]	=				n_fft
								a      :		Optional[int]	=				sampling_rate // hop_length_to_sampling_rate
								a      :		Dict	=				sampling_rate
								a      :		List[Any]	=				padding_value
								a      :		Optional[int]	=				mel_filter_bank(
								    num_frequency_bins=1 + n_fft // 2      ,					num_mel_filters=lowerCAmelCase__      ,					min_frequency=0.0      ,					max_frequency=22_050.0      ,					sampling_rate=lowerCAmelCase__      ,					norm="slaney"      ,					mel_scale="slaney"      ,					).T






							def     __a   (   self      ,					lowerCAmelCase__      )	-> np.ndarray:
								a      :		List[Any]	=				spectrogram(
								    lowerCAmelCase__      ,					window_function(self.n_fft      ,					"hann"      )      ,					frame_length=self.n_fft      ,					hop_length=self.hop_length      ,					power=2.0      ,					mel_filters=self.mel_filters.T      ,					log_mel="dB"      ,					db_range=80.0      ,					)
								a      :		Dict	=				log_spec[:, :-1]
								a      :		str	=				log_spec - 20.0
								a      :		Optional[int]	=				np.clip(log_spec / 40.0      ,					-2.0      ,					0.0      ) + 1.0
								return log_spec






							def __call__(   self      ,					lowerCAmelCase__      ,					lowerCAmelCase__ = None      ,					lowerCAmelCase__ = True      ,					lowerCAmelCase__ = None      ,					lowerCAmelCase__ = False      ,					lowerCAmelCase__ = False      ,					**lowerCAmelCase__      ,					)	-> BatchFeature:

								if sampling_rate is not None:
									if sampling_rate != self.sampling_rate:
										raise ValueError(
										    "This feature extractor is set to support sampling rate"
										    f""" of {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled"""
										    f""" 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      :		List[Any]	=				isinstance(lowerCAmelCase__      ,					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      :		Any	=				is_batched_numpy or (
								    isinstance(lowerCAmelCase__      ,					(list, tuple)      ) and (isinstance(raw_speech[0]      ,					(np.ndarray, tuple, list)      ))
								)
								if is_batched:
									a      :		str	=				[np.asarray([speech]      ,					dtype=np.floataa      ).T for speech in raw_speech]
								elif not is_batched and not isinstance(lowerCAmelCase__      ,					np.ndarray      ):
									a      :		List[str]	=				np.asarray(lowerCAmelCase__      ,					dtype=np.floataa      )
								elif isinstance(lowerCAmelCase__      ,					np.ndarray      ) and raw_speech.dtype is np.dtype(np.floataa      ):
									a      :		int	=				raw_speech.astype(np.floataa      )
								# always return batch
								if not is_batched:
									a      :		List[str]	=				[np.asarray([raw_speech]      ).T]

								# Convert audio signals to log mel spectrograms, truncate by time axis
								a      :		Optional[int]	=				[
								    self._np_extract_fbank_features(waveform.squeeze()      ).T[: self.spectrogram_length] for waveform in raw_speech
								]
								if isinstance(audio_features[0]      ,					lowerCAmelCase__      ):
									a      :		List[str]	=				[np.asarray(lowerCAmelCase__      ,					dtype=np.floataa      ) for feature in audio_features]

								# Create audio attention mask
								a      :		Union[str, Any]	=				max(
								    [ceil(feature.shape[0] / self.patch_size[0]      ) * self.freq_len for feature in audio_features]      )  # The maximum number of audio patches in a batch
								if return_attention_mask:
									a      :		Any	=				[
									    (ceil(feature.shape[0] / self.patch_size[0]      ) * self.freq_len) * [1]
									    + (max_patch_len - ceil(feature.shape[0] / self.patch_size[0]      ) * self.freq_len) * [0]
									    for feature in audio_features
									]
									a      :		Tuple	=				np.array(lowerCAmelCase__      ).astype(np.floataa      )

								# convert into correct format for padding
								a      :		Optional[int]	=				max_patch_len // self.freq_len * self.patch_size[0]  # The maximum audio size in a batch
								a      :		Tuple	=				np.ones([len(lowerCAmelCase__      ), 1, max_time_len, self.feature_size]      ).astype(np.floataa      )
								a      :		Optional[Any]	=				padded_audio_features * self.padding_value
								for i in range(len(lowerCAmelCase__      )      ):
									a      :		Dict	=				audio_features[i]
									a      :		Optional[Any]	=				feature

								# return as BatchFeature
								if return_attention_mask:
									a      :		Union[str, Any]	=				{"audio_values": padded_audio_features, "audio_mask": audio_mask}
								else:
									a      :		Any	=				{"audio_values": padded_audio_features}

								a      :		Tuple	=				BatchFeature(data=lowerCAmelCase__      ,					tensor_type=lowerCAmelCase__      )
								return encoded_inputs









"""simple docstring"""




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

import numpy as np
import PIL
import torch
import torch.utils.checkpoint

from ...models import UNetaDModel, VQModel
from ...schedulers import (
    DDIMScheduler,
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
)
from ...utils import PIL_INTERPOLATION, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput






def       _SCREAMING_SNAKE_CASE		(	_lowercase						:    Union[str, Any]					) ->str:






	'''simple docstring'''






	a,       a      :		str	=				image.size
	a,       a      :		Tuple	=				(x - x % 32 for x in (w, h))  # resize to integer multiple of 32
	a      :		Union[str, Any]	=				image.resize((w, h)    ,       resample=PIL_INTERPOLATION["lanczos"]					)
	a      :		int	=				np.array(_lowercase					).astype(np.floataa					) / 255.0
	a      :		List[str]	=				image[None].transpose(0    ,       3    ,       1    ,       2					)
	a      :		Dict	=				torch.from_numpy(_lowercase					)
	return 2.0 * image - 1.0







class        __UpperCamelCase				(						a__						):






							def __init__(   self      ,					lowerCAmelCase__      ,					lowerCAmelCase__      ,					lowerCAmelCase__      ,					)	-> str:
								super().__init__()
								self.register_modules(vqvae=lowerCAmelCase__      ,					unet=lowerCAmelCase__      ,					scheduler=lowerCAmelCase__      )






							@torch.no_grad()
							def __call__(   self      ,					lowerCAmelCase__ = None      ,					lowerCAmelCase__ = 1      ,					lowerCAmelCase__ = 100      ,					lowerCAmelCase__ = 0.0      ,					lowerCAmelCase__ = None      ,					lowerCAmelCase__ = "pil"      ,					lowerCAmelCase__ = True      ,					)	-> Union[Tuple, ImagePipelineOutput]:
								if isinstance(lowerCAmelCase__      ,					PIL.Image.Image      ):
									a      :		int	=				1
								elif isinstance(lowerCAmelCase__      ,					torch.Tensor      ):
									a      :		str	=				image.shape[0]
								else:
									raise ValueError(f"""`image` has to be of type `PIL.Image.Image` or `torch.Tensor` but is {type(lowerCAmelCase__      )}"""      )

								if isinstance(lowerCAmelCase__      ,					PIL.Image.Image      ):
									a      :		Tuple	=				preprocess(lowerCAmelCase__      )

								a,       a      :		Optional[Any]	=				image.shape[-2:]

								# in_channels should be 6: 3 for latents, 3 for low resolution image
								a      :		Tuple	=				(batch_size, self.unet.config.in_channels // 2, height, width)
								a      :		List[str]	=				next(self.unet.parameters()      ).dtype

								a      :		Any	=				randn_tensor(lowerCAmelCase__      ,					generator=lowerCAmelCase__      ,					device=self.device      ,					dtype=lowerCAmelCase__      )

								a      :		Union[str, Any]	=				image.to(device=self.device      ,					dtype=lowerCAmelCase__      )

								# set timesteps and move to the correct device
								self.scheduler.set_timesteps(lowerCAmelCase__      ,					device=self.device      )
								a      :		int	=				self.scheduler.timesteps

								# scale the initial noise by the standard deviation required by the scheduler
								a      :		str	=				latents * self.scheduler.init_noise_sigma

								# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature.
								# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
								# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
								# and should be between [0, 1]
								a      :		str	=				"eta" in set(inspect.signature(self.scheduler.step      ).parameters.keys()      )
								a      :		List[Any]	=				{}
								if accepts_eta:
									a      :		Any	=				eta

								for t in self.progress_bar(lowerCAmelCase__      ):
									# concat latents and low resolution image in the channel dimension.
									a      :		str	=				torch.cat([latents, image]      ,					dim=1      )
									a      :		int	=				self.scheduler.scale_model_input(lowerCAmelCase__      ,					lowerCAmelCase__      )
									# predict the noise residual
									a      :		Union[str, Any]	=				self.unet(lowerCAmelCase__      ,					lowerCAmelCase__      ).sample
									# compute the previous noisy sample x_t -> x_t-1
									a      :		Tuple	=				self.scheduler.step(lowerCAmelCase__      ,					lowerCAmelCase__      ,					lowerCAmelCase__      ,					**lowerCAmelCase__      ).prev_sample

								# decode the image latents with the VQVAE
								a      :		List[Any]	=				self.vqvae.decode(lowerCAmelCase__      ).sample
								a      :		int	=				torch.clamp(lowerCAmelCase__      ,					-1.0      ,					1.0      )
								a      :		Optional[int]	=				image / 2 + 0.5
								a      :		Optional[int]	=				image.cpu().permute(0      ,					2      ,					3      ,					1      ).numpy()

								if output_type == "pil":
									a      :		Union[str, Any]	=				self.numpy_to_pil(lowerCAmelCase__      )

								if not return_dict:
									return (image,)

								return ImagePipelineOutput(images=lowerCAmelCase__      )


