




'''simple docstring'''



from typing import List, Optional, Tuple, Union

import torch

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





class       lowerCAmelCase				(	__lowerCAmelCase):


       def __init__(				self      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE					) ->					List[str]:



            '''simple docstring'''




            super().__init__()
            self.register_modules(unet=__SCREAMING_SNAKE_CASE      ,					scheduler=__SCREAMING_SNAKE_CASE					)




       @torch.no_grad()
       def __call__(				self      ,					__SCREAMING_SNAKE_CASE = 1      ,					__SCREAMING_SNAKE_CASE = None      ,					__SCREAMING_SNAKE_CASE = 1000      ,					__SCREAMING_SNAKE_CASE = "pil"      ,					__SCREAMING_SNAKE_CASE = True      ,					) ->					Union[ImagePipelineOutput, Tuple]:



            '''simple docstring'''




            if isinstance(self.unet.config.sample_size      ,					__SCREAMING_SNAKE_CASE					):
                 __snake_case         =					(
                     batch_size,
                     self.unet.config.in_channels,
                     self.unet.config.sample_size,
                     self.unet.config.sample_size,
                 )
            else:
                 __snake_case         =					(batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

            if self.device.type == "mps":
                 # randn does not work reproducibly on mps
                 __snake_case         =					randn_tensor(__SCREAMING_SNAKE_CASE      ,					generator=__SCREAMING_SNAKE_CASE					)
                 __snake_case         =					image.to(self.device					)
            else:
                 __snake_case         =					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
                 __snake_case         =					self.unet(__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE					).sample

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

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

            if not return_dict:
                 return (image,)

            return ImagePipelineOutput(images=__SCREAMING_SNAKE_CASE					)












'''simple docstring'''



from __future__ import annotations

from collections.abc import Iterator
from typing import Any





class       lowerCAmelCase				:


       def __init__(				self      ,					__SCREAMING_SNAKE_CASE					) ->					None:



            '''simple docstring'''




            __snake_case         =					data
            __snake_case         =					None




       def __str__(				self					) ->					str:



            '''simple docstring'''




            return F'''{self.data}'''





class       lowerCAmelCase				:


       def __init__(				self					) ->					None:



            '''simple docstring'''




            __snake_case         =					None
            __snake_case         =					None


       def __iter__(				self					) ->					Iterator[Any]:



            '''simple docstring'''




            __snake_case         =					self.front
            while node:
                 yield node.data
                 __snake_case         =					node.next


       def __len__(				self					) ->					int:



            '''simple docstring'''




            return len(tuple(iter(self					)					)					)


       def __str__(				self					) ->					str:



            '''simple docstring'''




            return " <- ".join(str(__SCREAMING_SNAKE_CASE					) for item in self					)


       def  lowerCAmelCase (				self					) ->					bool:



            '''simple docstring'''




            return len(self					) == 0


       def  lowerCAmelCase (				self      ,					__SCREAMING_SNAKE_CASE					) ->					None:



            '''simple docstring'''




            __snake_case         =					Node(__SCREAMING_SNAKE_CASE					)
            if self.is_empty():
                 __snake_case         =					__snake_case         =					node
            else:
                 assert isinstance(self.rear      ,					__SCREAMING_SNAKE_CASE					)
                 __snake_case         =					node
                 __snake_case         =					node


       def  lowerCAmelCase (				self					) ->					Any:



            '''simple docstring'''




            if self.is_empty():
                 raise IndexError('''dequeue from empty queue'''					)
            assert isinstance(self.front      ,					__SCREAMING_SNAKE_CASE					)
            __snake_case         =					self.front
            __snake_case         =					self.front.next
            if self.front is None:
                 __snake_case         =					None
            return node.data




       def  lowerCAmelCase (				self					) ->					None:



            '''simple docstring'''




            __snake_case         =					__snake_case         =					None


if __name__ == "__main__":
     from doctest import testmod

     testmod()






