

from typing import List, Optional, Tuple, Union

import torch

from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput



class     _a						(_snake_case				):


					'''simple docstring'''






					def __init__( self    ,       A__    ,       A__			):
						super().__init__()

						# make sure scheduler can always be converted to DDIM
						A__							:    int        =		DDIMScheduler.from_config(scheduler.config			)

						self.register_modules(unet=__snake_case    ,       scheduler=__snake_case			)






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

						# Sample gaussian noise to begin loop
						if isinstance(self.unet.config.sample_size    ,       __snake_case			):
							A__							:    Union[str, Any]        =		(
							    batch_size,
							    self.unet.config.in_channels,
							    self.unet.config.sample_size,
							    self.unet.config.sample_size,
							)
						else:
							A__							:    Union[str, Any]        =		(batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

						if isinstance(__snake_case    ,       __snake_case			) and len(__snake_case			) != batch_size:
							raise ValueError(
							    F"""You have passed a list of generators of length {len(__snake_case			)}, but requested an effective batch"""
							    F""" size of {batch_size}. Make sure the batch size matches the length of the generators."""			)

						A__							:    int        =		randn_tensor(__snake_case    ,       generator=__snake_case    ,       device=self.device    ,       dtype=self.unet.dtype			)

						# set step values
						self.scheduler.set_timesteps(__snake_case			)

						for t in self.progress_bar(self.scheduler.timesteps			):
							# 1. predict noise model_output
							A__							:    List[Any]        =		self.unet(__snake_case    ,       __snake_case			).sample

							# 2. predict previous mean of image x_t-1 and add variance depending on eta
							# eta corresponds to η in paper and should be between [0, 1]
							# do x_t -> x_t-1
							A__							:    Union[str, Any]        =		self.scheduler.step(
							    __snake_case    ,       __snake_case    ,       __snake_case    ,       eta=__snake_case    ,       use_clipped_model_output=__snake_case    ,       generator=__snake_case			).prev_sample

						A__							:    Any        =		(image / 2 + 0.5).clamp(0    ,       1			)
						A__							:    Optional[int]        =		image.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			)







'''simple docstring'''


from typing import List, Optional, Tuple, Union

import torch

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





class     lowercase__  (						_snake_case					):





							'''simple docstring'''


							A_							:      UNetaDModel
							A_							:      ScoreSdeVeScheduler






							def __init__(    self    ,					__snake_case    ,					__snake_case  ):
											super().__init__()
											self.register_modules(unet=__snake_case    ,					scheduler=__snake_case  )






							@torch.no_grad()
							def __call__(    self    ,					__snake_case = 1    ,					__snake_case = 2000    ,					__snake_case = None    ,					__snake_case = "pil"    ,					__snake_case = True    ,					**__snake_case    ,					):
											_SCREAMING_SNAKE_CASE :       Optional[int]     		=      self.unet.config.sample_size
											_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      (batch_size, 3, img_size, img_size)

											_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      self.unet

											_SCREAMING_SNAKE_CASE :       Optional[int]     		=      randn_tensor(__snake_case    ,					generator=__snake_case  ) * self.scheduler.init_noise_sigma
											_SCREAMING_SNAKE_CASE :       Any     		=      sample.to(self.device  )

											self.scheduler.set_timesteps(__snake_case  )
											self.scheduler.set_sigmas(__snake_case  )

											for i, t in enumerate(self.progress_bar(self.scheduler.timesteps  )  ):
															_SCREAMING_SNAKE_CASE :       List[Any]     		=      self.scheduler.sigmas[i] * torch.ones(shape[0]    ,					device=self.device  )

															# correction step
															for _ in range(self.scheduler.config.correct_steps  ):
																			_SCREAMING_SNAKE_CASE :       Any     		=      self.unet(__snake_case    ,					__snake_case  ).sample
																			_SCREAMING_SNAKE_CASE :       Any     		=      self.scheduler.step_correct(__snake_case    ,					__snake_case    ,					generator=__snake_case  ).prev_sample

															# prediction step
															_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      model(__snake_case    ,					__snake_case  ).sample
															_SCREAMING_SNAKE_CASE :       Tuple     		=      self.scheduler.step_pred(__snake_case    ,					__snake_case    ,					__snake_case    ,					generator=__snake_case  )

															_SCREAMING_SNAKE_CASE		,       _SCREAMING_SNAKE_CASE :       int     		=      output.prev_sample, output.prev_sample_mean

											_SCREAMING_SNAKE_CASE :       Tuple     		=      sample_mean.clamp(0    ,					1  )
											_SCREAMING_SNAKE_CASE :       str     		=      sample.cpu().permute(0    ,					2    ,					3    ,					1  ).numpy()
											if output_type == "pil":
															_SCREAMING_SNAKE_CASE :       Optional[int]     		=      self.numpy_to_pil(__snake_case  )

											if not return_dict:
															return (sample,)

											return ImagePipelineOutput(images=__snake_case  )




