
from typing import List, Optional, Tuple, Union

import torch

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







class 							UpperCAmelCase	(   UpperCamelCase_					):

					def __init__(			self      :					Dict ,      lowerCAmelCase      :					int ,      lowerCAmelCase      :					List[Any]					):
											super().__init__()
											self.register_modules(unet=__SCREAMING_SNAKE_CASE ,      scheduler=__SCREAMING_SNAKE_CASE					)




					@torch.no_grad()
					def __call__(			self      :					Any ,      lowerCAmelCase      :					Union[str, Any] = 1 ,      lowerCAmelCase      :					Dict = None ,      lowerCAmelCase      :					List[Any] = 1000 ,      lowerCAmelCase      :					Optional[int] = "pil" ,      lowerCAmelCase      :					Tuple = True ,      ):
											# Sample gaussian noise to begin loop
											if isinstance(self.unet.config.sample_size ,      __SCREAMING_SNAKE_CASE					):
																	lowercase       :    Tuple											=  (
																	    batch_size,
																	    self.unet.config.in_channels,
																	    self.unet.config.sample_size,
																	    self.unet.config.sample_size,
																	)
											else:
																	lowercase       :    int											=  (batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

											if self.device.type == "mps":
																	# randn does not work reproducibly on mps
																	lowercase       :    Dict											=  randn_tensor(__SCREAMING_SNAKE_CASE ,      generator=__SCREAMING_SNAKE_CASE					)
																	lowercase       :    List[str]											=  image.to(self.device					)
											else:
																	lowercase       :    Any											=  randn_tensor(__SCREAMING_SNAKE_CASE ,      generator=__SCREAMING_SNAKE_CASE ,      device=self.device					)

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

											for t in self.progress_bar(self.scheduler.timesteps					):
																	# 1. predict noise model_output
																	lowercase       :    Any											=  self.unet(__SCREAMING_SNAKE_CASE ,      __SCREAMING_SNAKE_CASE					).sample

																	# 2. compute previous image: x_t -> x_t-1
																	lowercase       :    Tuple											=  self.scheduler.step(__SCREAMING_SNAKE_CASE ,      __SCREAMING_SNAKE_CASE ,      __SCREAMING_SNAKE_CASE ,      generator=__SCREAMING_SNAKE_CASE					).prev_sample

											lowercase       :    int											=  (image / 2 + 0.5).clamp(0 ,      1					)
											lowercase       :    List[Any]											=  image.cpu().permute(0 ,      2 ,      3 ,      1					).numpy()
											if output_type == "pil":
																	lowercase       :    Dict											=  self.numpy_to_pil(__SCREAMING_SNAKE_CASE					)

											if not return_dict:
																	return (image,)

											return ImagePipelineOutput(images=__SCREAMING_SNAKE_CASE					)







snake_case__  							=							"""
# Diffusers installation
! pip install diffusers transformers datasets accelerate
# To install from source instead of the last release, comment the command above and uncomment the following one.
# ! pip install git+https://github.com/huggingface/diffusers.git
"""

snake_case__  							=							[{"""type""": """code""", """content""": INSTALL_CONTENT}]


