






"""simple docstring"""







from typing import List, Optional, Tuple, Union

import torch

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


class     __UpperCAmelCase(   SCREAMING_SNAKE_CASE__							):




						"""simple docstring"""




						def __init__(   self       ,  snake_case__       ,  snake_case__			):
												'''simple docstring'''
												super().__init__()
												self.register_modules(unet=snake_case__       ,  scheduler=snake_case__			)




						@torch.no_grad()
						def __call__(   self       ,  snake_case__ = 1       ,  snake_case__ = None       ,  snake_case__ = 1000       ,  snake_case__ = "pil"       ,  snake_case__ = True       ,  ):
												'''simple docstring'''
												# Sample gaussian noise to begin loop
												if isinstance(self.unet.config.sample_size       ,  snake_case__			):
																		lowercase__				:      Any=       (
																		    batch_size,
																		    self.unet.config.in_channels,
																		    self.unet.config.sample_size,
																		    self.unet.config.sample_size,
																		)
												else:
																		lowercase__				:      Any=       (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__				:      List[Any]=       randn_tensor(snake_case__       ,  generator=snake_case__			)
																		lowercase__				:      str=       image.to(self.device			)
												else:
																		lowercase__				:      int=       randn_tensor(snake_case__       ,  generator=snake_case__       ,  device=self.device			)

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

												for t in self.progress_bar(self.scheduler.timesteps			):
																		# 1. predict noise model_output
																		lowercase__				:      int=       self.unet(snake_case__       ,  snake_case__			).sample

																		# 2. compute previous image: x_t -> x_t-1
																		lowercase__				:      Dict=       self.scheduler.step(snake_case__       ,  snake_case__       ,  snake_case__       ,  generator=snake_case__			).prev_sample

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

												if not return_dict:
																		return (image,)

												return ImagePipelineOutput(images=snake_case__			)














"""simple docstring"""







import importlib.metadata
import subprocess
import sys





def 						lowercase__(A = False						)				->List[str]:




						"""simple docstring"""



						lowercase__				:      Any=       False
						if "IPython" in sys.modules:
												lowercase__				:      Tuple=       "google.colab" in str(sys.modules["IPython"].get_ipython()						)

						if in_colab:
												if upgrade:
																		lowercase__				:      Optional[int]=       ["pip", "install", "-U", "torch"]
																		subprocess.run(A					,		check=A						)
												# get the current version of torch
												lowercase__				:      Tuple=       importlib.metadata.version("torch"						)
												lowercase__				:      Dict=       torch_version[: torch_version.rindex("."						)]
												lowercase__				:      Dict=       f'''https://storage.googleapis.com/tpu-pytorch/wheels/colab/torch_xla-{torch_version_trunc}-cp37-cp37m-linux_x86_64.whl'''
												lowercase__				:      Union[str, Any]=       ["pip", "install", xla_wheel]
												subprocess.run(A					,		check=A						)
						else:
												raise RuntimeError("`install_xla` utility works only on google colab."						)






