






from typing import List, Optional, Tuple, Union

import torch

from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class        __snake_case(					__UpperCamelCase      ):



 def __init__(	self   :   Dict ,		UpperCAmelCase__   :   Tuple ,		UpperCAmelCase__   :   int	):

    """simple docstring"""






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







 @torch.no_grad()
 def __call__(	self   :   Dict ,		UpperCAmelCase__   :   int = 1 ,		UpperCAmelCase__   :   Optional[Union[torch.Generator, List[torch.Generator]]] = None ,		UpperCAmelCase__   :   int = 1_000 ,		UpperCAmelCase__   :   Optional[str] = "pil" ,		UpperCAmelCase__   :   bool = True ,		):

    """simple docstring"""






    if isinstance(self.unet.config.sample_size ,		UpperCAmelCase__	):
       lowercase__						:	Optional[Any]         = (
           batch_size,
           self.unet.config.in_channels,
           self.unet.config.sample_size,
           self.unet.config.sample_size,
       )
    else:
       lowercase__						:	Dict         = (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__						:	Optional[int]         = randn_tensor(UpperCAmelCase__ ,		generator=UpperCAmelCase__	)
       lowercase__						:	Dict         = image.to(self.device	)
    else:
       lowercase__						:	str         = randn_tensor(UpperCAmelCase__ ,		generator=UpperCAmelCase__ ,		device=self.device	)

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

    for t in self.progress_bar(self.scheduler.timesteps	):
       # 1. predict noise model_output
       lowercase__						:	List[str]         = self.unet(UpperCAmelCase__ ,		UpperCAmelCase__	).sample

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

    lowercase__						:	Tuple         = (image / 2 + 0.5).clamp(0 ,		1	)
    lowercase__						:	List[str]         = image.cpu().permute(0 ,		2 ,		3 ,		1	).numpy()
    if output_type == "pil":
       lowercase__						:	List[str]         = self.numpy_to_pil(UpperCAmelCase__	)

    if not return_dict:
       return (image,)

    return ImagePipelineOutput(images=UpperCAmelCase__	)










from __future__ import annotations

from collections.abc import Iterator
from typing import Any
class        __snake_case:



 def __init__(	self   :   Union[str, Any] ,		UpperCAmelCase__   :   Any	):

    """simple docstring"""






    lowercase__						:	Any         = data
    lowercase__						:	Node | None         = None







 def __str__(	self   :   Dict	):

    """simple docstring"""






    return f'''{self.data}'''
class        __snake_case:



 def __init__(	self   :   Optional[Any]	):

    """simple docstring"""






    lowercase__						:	Node | None         = None
    lowercase__						:	Node | None         = None



 def __iter__(	self   :   Any	):

    """simple docstring"""






    lowercase__						:	Any         = self.front
    while node:
       yield node.data
       lowercase__						:	List[str]         = node.next



 def __len__(	self   :   Union[str, Any]	):

    """simple docstring"""






    return len(tuple(iter(self	)	)	)



 def __str__(	self   :   Optional[Any]	):

    """simple docstring"""






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



 def 						UpperCAmelCase    (	self   :   List[str]	):

    """simple docstring"""






    return len(self	) == 0



 def 						UpperCAmelCase    (	self   :   Optional[int] ,		UpperCAmelCase__   :   Any	):

    """simple docstring"""






    lowercase__						:	List[str]         = Node(UpperCAmelCase__	)
    if self.is_empty():
       lowercase__						:	List[Any]         = node
    else:
       assert isinstance(self.rear ,		UpperCAmelCase__	)
       lowercase__						:	Any         = node
       lowercase__						:	Union[str, Any]         = node



 def 						UpperCAmelCase    (	self   :   List[Any]	):

    """simple docstring"""






    if self.is_empty():
       raise IndexError("""dequeue from empty queue"""	)
    assert isinstance(self.front ,		UpperCAmelCase__	)
    lowercase__						:	Tuple         = self.front
    lowercase__						:	Any         = self.front.next
    if self.front is None:
       lowercase__						:	List[Any]         = None
    return node.data







 def 						UpperCAmelCase    (	self   :   Optional[Any]	):

    """simple docstring"""






    lowercase__						:	List[Any]         = None


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

 testmod()


