






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

import torch

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





class       snake_case_       (    _lowerCamelCase       ):



	"""simple docstring"""

	def __init__(      self  ,					__a  ,					__a  ,					__a						):


					"""simple docstring"""

					super().__init__()
					self.register_modules(vqvae=__a  ,					unet=__a  ,					scheduler=__a						)






	@torch.no_grad()
	def __call__(      self  ,					__a = 1  ,					__a = None  ,					__a = 0.0  ,					__a = 50  ,					__a = "pil"  ,					__a = True  ,					**__a  ,					):


					"""simple docstring"""

					A__							 =       randn_tensor(
					    (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)  ,					generator=__a  ,					)
					A__							 =       latents.to(self.device						)

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

					self.scheduler.set_timesteps(__a						)

					# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
					A__							 =       'eta' in set(inspect.signature(self.scheduler.step						).parameters.keys()						)

					A__							 =       {}
					if accepts_eta:
									A__							 =       eta

					for t in self.progress_bar(self.scheduler.timesteps						):
									A__							 =       self.scheduler.scale_model_input(__a  ,					__a						)
									# predict the noise residual
									A__							 =       self.unet(__a  ,					__a						).sample
									# compute the previous noisy sample x_t -> x_t-1
									A__							 =       self.scheduler.step(__a  ,					__a  ,					__a  ,					**__a						).prev_sample

					# decode the image latents with the VAE
					A__							 =       self.vqvae.decode(__a						).sample

					A__							 =       (image / 2 + 0.5).clamp(0  ,					1						)
					A__							 =       image.cpu().permute(0  ,					2  ,					3  ,					1						).numpy()
					if output_type == "pil":
									A__							 =       self.numpy_to_pil(__a						)

					if not return_dict:
									return (image,)

					return ImagePipelineOutput(images=__a						)










"""simple docstring"""






from ...processing_utils import ProcessorMixin





class       snake_case_       (    _lowerCamelCase       ):



	"""simple docstring"""

	SCREAMING_SNAKE_CASE_:							Union[str, Any]   			=					["""image_processor""", """feature_extractor"""]
	SCREAMING_SNAKE_CASE_:							Optional[int]   			=					"""TvltImageProcessor"""
	SCREAMING_SNAKE_CASE_:							Optional[int]   			=					"""TvltFeatureExtractor"""


	def __init__(      self  ,					__a  ,					__a						):


					"""simple docstring"""

					super().__init__(image_processor=__a  ,					feature_extractor=__a						)

					A__							 =       image_processor
					A__							 =       feature_extractor


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


					"""simple docstring"""

					if images is None and audio is None:
									raise ValueError('You need to specify either an `images` or `audio` input to process.'						)

					A__							 =       None
					if images is not None:
									A__							 =       self.image_processor(__a  ,					mask_pixel=__a  ,					*__a  ,					**__a						)
					if images_mixed is not None:
									A__							 =       self.image_processor(__a  ,					is_mixed=__a  ,					*__a  ,					**__a						)
					if audio is not None:
									A__							 =       self.feature_extractor(
									    __a  ,					*__a  ,					sampling_rate=__a  ,					mask_audio=__a  ,					**__a						)

					A__							 =       {}
					if audio is not None:
									output_dict.update(__a						)
					if images is not None:
									output_dict.update(__a						)
					if images_mixed_dict is not None:
									output_dict.update(__a						)
					return output_dict






	@property
	def       _UpperCAmelCase						(      self						):


					"""simple docstring"""

					A__							 =       self.image_processor.model_input_names
					A__							 =       self.feature_extractor.model_input_names
					return list(dict.fromkeys(image_processor_input_names + feature_extractor_input_names						)						)


