





'''simple docstring'''


import inspect
from typing import List, Optional, Tuple, Union

import numpy as np
import PIL
import torch
import torch.utils.checkpoint

from ...models import UNetaDModel, VQModel
from ...schedulers import (
    DDIMScheduler,
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
)
from ...utils import PIL_INTERPOLATION, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
def 							UpperCAmelCase_  (						__lowerCamelCase : Tuple			):
   lowercase_      ,							lowercase_      :List[str]         =			image.size
   lowercase_      ,							lowercase_      :Any         =			(x - x % 32 for x in (w, h))  # resize to integer multiple of 32
   lowercase_      :Any         =			image.resize((w, h)     ,resample=PIL_INTERPOLATION["lanczos"]			)
   lowercase_      :Optional[Any]         =			np.array(__lowerCamelCase			).astype(np.floataa			) / 255.0
   lowercase_      :Any         =			image[None].transpose(0     ,3     ,1     ,2			)
   lowercase_      :str         =			torch.from_numpy(__lowerCamelCase			)
   return 2.0 * image - 1.0


class 				a_       (    _lowerCAmelCase							):





     def __init__(			self		: Union[str, Any]     ,				lowercase		: VQModel     ,				lowercase		: UNetaDModel     ,				lowercase		: Union[
             DDIMScheduler,
             PNDMScheduler,
             LMSDiscreteScheduler,
             EulerDiscreteScheduler,
             EulerAncestralDiscreteScheduler,
             DPMSolverMultistepScheduler,
         ]     ,				):





        """simple docstring"""




        super().__init__()
        self.register_modules(vqvae=lowercase     ,				unet=lowercase     ,				scheduler=lowercase						)







     @torch.no_grad()
     def __call__(			self		: Any     ,				lowercase		: Union[torch.Tensor, PIL.Image.Image] = None     ,				lowercase		: Optional[int] = 1     ,				lowercase		: Optional[int] = 100     ,				lowercase		: Optional[float] = 0.0     ,				lowercase		: Optional[Union[torch.Generator, List[torch.Generator]]] = None     ,				lowercase		: Optional[str] = "pil"     ,				lowercase		: bool = True     ,				):





        """simple docstring"""




        if isinstance(lowercase     ,				PIL.Image.Image						):
           lowercase_      :Union[str, Any]         =			1
        elif isinstance(lowercase     ,				torch.Tensor						):
           lowercase_      :List[str]         =			image.shape[0]
        else:
           raise ValueError(F'`image` has to be of type `PIL.Image.Image` or `torch.Tensor` but is {type(lowercase						)}'						)

        if isinstance(lowercase     ,				PIL.Image.Image						):
           lowercase_      :str         =			preprocess(lowercase						)

        lowercase_      ,							lowercase_      :Optional[int]         =			image.shape[-2:]

        # in_channels should be 6: 3 for latents, 3 for low resolution image
        lowercase_      :Optional[int]         =			(batch_size, self.unet.config.in_channels // 2, height, width)
        lowercase_      :Optional[int]         =			next(self.unet.parameters()						).dtype

        lowercase_      :Dict         =			randn_tensor(lowercase     ,				generator=lowercase     ,				device=self.device     ,				dtype=lowercase						)

        lowercase_      :List[Any]         =			image.to(device=self.device     ,				dtype=lowercase						)

        # set timesteps and move to the correct device
        self.scheduler.set_timesteps(lowercase     ,				device=self.device						)
        lowercase_      :Optional[int]         =			self.scheduler.timesteps

        # scale the initial noise by the standard deviation required by the scheduler
        lowercase_      :Tuple         =			latents * self.scheduler.init_noise_sigma

        # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature.
        # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
        # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
        # and should be between [0, 1]
        lowercase_      :Tuple         =			"eta" in set(inspect.signature(self.scheduler.step						).parameters.keys()						)
        lowercase_      :List[str]         =			{}
        if accepts_eta:
           lowercase_      :Dict         =			eta

        for t in self.progress_bar(lowercase						):
           # concat latents and low resolution image in the channel dimension.
           lowercase_      :int         =			torch.cat([latents, image]     ,				dim=1						)
           lowercase_      :Any         =			self.scheduler.scale_model_input(lowercase     ,				lowercase						)
           # predict the noise residual
           lowercase_      :List[Any]         =			self.unet(lowercase     ,				lowercase						).sample
           # compute the previous noisy sample x_t -> x_t-1
           lowercase_      :Tuple         =			self.scheduler.step(lowercase     ,				lowercase     ,				lowercase     ,				**lowercase						).prev_sample

        # decode the image latents with the VQVAE
        lowercase_      :Tuple         =			self.vqvae.decode(lowercase						).sample
        lowercase_      :int         =			torch.clamp(lowercase     ,				-1.0     ,				1.0						)
        lowercase_      :Any         =			image / 2 + 0.5
        lowercase_      :Tuple         =			image.cpu().permute(0     ,				2     ,				3     ,				1						).numpy()

        if output_type == "pil":
           lowercase_      :List[str]         =			self.numpy_to_pil(lowercase						)

        if not return_dict:
           return (image,)

        return ImagePipelineOutput(images=lowercase						)









'''simple docstring'''


from __future__ import annotations

lowerCAmelCase :					Any 							={
    '''A''': ['''B''', '''C''', '''E'''],
    '''B''': ['''A''', '''D''', '''E'''],
    '''C''': ['''A''', '''F''', '''G'''],
    '''D''': ['''B'''],
    '''E''': ['''A''', '''B''', '''D'''],
    '''F''': ['''C'''],
    '''G''': ['''C'''],
}
class 				a_       :





     def __init__(			self		: List[Any]     ,				lowercase		: dict[str, list[str]]     ,				lowercase		: str						):





        """simple docstring"""




        lowercase_      :List[str]         =			graph
        # mapping node to its parent in resulting breadth first tree
        lowercase_      :dict[str, str | None]         =			{}
        lowercase_      :Dict         =			source_vertex





     def 							lowercase__				(			self		: Tuple						):





        """simple docstring"""




        lowercase_      :Union[str, Any]         =			{self.source_vertex}
        lowercase_      :Tuple         =			None
        lowercase_      :Dict         =			[self.source_vertex]  # first in first out queue

        while queue:
           lowercase_      :int         =			queue.pop(0						)
           for adjacent_vertex in self.graph[vertex]:
              if adjacent_vertex not in visited:
                 visited.add(lowercase						)
                 lowercase_      :Optional[Any]         =			vertex
                 queue.append(lowercase						)







     def 							lowercase__				(			self		: Tuple     ,				lowercase		: str						):





        """simple docstring"""




        if target_vertex == self.source_vertex:
           return self.source_vertex

        lowercase_      :List[str]         =			self.parent.get(lowercase						)
        if target_vertex_parent is None:
           lowercase_      :Union[str, Any]         =			(
               F'No path from vertex: {self.source_vertex} to vertex: {target_vertex}'
           )
           raise ValueError(lowercase						)

        return self.shortest_path(lowercase						) + F'->{target_vertex}'


if __name__ == "__main__":
 lowerCAmelCase :					Dict 							=Graph(graph, '''G''')
 g.breath_first_search()
 print(g.shortest_path('''D'''))
 print(g.shortest_path('''G'''))
 print(g.shortest_path('''Foo'''))


