
"""simple docstring"""




from typing import List, Optional, Tuple, Union

import PIL
import torch
from torchvision import transforms

from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from diffusers.schedulers import DDIMScheduler
from diffusers.utils import randn_tensor


a		:       str     						=       transforms.Compose(
    [
        transforms.Resize((2_56, 2_56)),
        transforms.ToTensor(),
        transforms.Normalize([0.5], [0.5]),
    ]
)



def     __magic_name__		(					UpperCamelCase     :		Optional[int]     )     ->						Any:
    if isinstance(UpperCamelCase							,		torch.Tensor     ):
        return image
    elif isinstance(UpperCamelCase							,		PIL.Image.Image     ):
        a__		=			[image]

    a__		=			[trans(img.convert('RGB'     )     ) for img in image]
    a__		=			torch.stack(UpperCamelCase     )
    return image







class     lowercase(_lowercase       ):



     def __init__(							self     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE   )		->     int:







         """simple docstring"""



         super().__init__()

         # make sure scheduler can always be converted to DDIM
         a__		=			DDIMScheduler.from_config(scheduler.config   )

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



     def 			lowercase__  (							self     ,				__SCREAMING_SNAKE_CASE   )		->     Union[str, Any]:







         """simple docstring"""



         if strength < 0 or strength > 1:
             raise ValueError(f'The value of strength should in [0.0, 1.0] but is {strength}'   )



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







         """simple docstring"""



         a__		=			min(int(num_inference_steps * strength   )     ,				__SCREAMING_SNAKE_CASE   )

         a__		=			max(num_inference_steps - init_timestep     ,				0   )
         a__		=			self.scheduler.timesteps[t_start:]

         return timesteps, num_inference_steps - t_start



     def 			lowercase__  (							self     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE=None   )		->     int:







         """simple docstring"""



         if not isinstance(__SCREAMING_SNAKE_CASE     ,				(torch.Tensor, PIL.Image.Image, list)   ):
             raise ValueError(
                 f'`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(__SCREAMING_SNAKE_CASE   )}'   )

         a__		=			image.to(device=__SCREAMING_SNAKE_CASE     ,				dtype=__SCREAMING_SNAKE_CASE   )

         if isinstance(__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE   ) and len(__SCREAMING_SNAKE_CASE   ) != batch_size:
             raise ValueError(
                 f'You have passed a list of generators of length {len(__SCREAMING_SNAKE_CASE   )}, but requested an effective batch'
                 f' size of {batch_size}. Make sure the batch size matches the length of the generators.'   )

         a__		=			init_latents.shape
         a__		=			randn_tensor(__SCREAMING_SNAKE_CASE     ,				generator=__SCREAMING_SNAKE_CASE     ,				device=__SCREAMING_SNAKE_CASE     ,				dtype=__SCREAMING_SNAKE_CASE   )

         # get latents
         print('add noise to latents at timestep'     ,				__SCREAMING_SNAKE_CASE   )
         a__		=			self.scheduler.add_noise(__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE   )
         a__		=			init_latents

         return latents




     @torch.no_grad()
     def __call__(							self     ,				__SCREAMING_SNAKE_CASE = None     ,				__SCREAMING_SNAKE_CASE = 0.8     ,				__SCREAMING_SNAKE_CASE = 1     ,				__SCREAMING_SNAKE_CASE = None     ,				__SCREAMING_SNAKE_CASE = 0.0     ,				__SCREAMING_SNAKE_CASE = 5_0     ,				__SCREAMING_SNAKE_CASE = None     ,				__SCREAMING_SNAKE_CASE = "pil"     ,				__SCREAMING_SNAKE_CASE = True     ,				)		->     Union[ImagePipelineOutput, Tuple]:







         """simple docstring"""



         self.check_inputs(__SCREAMING_SNAKE_CASE   )

         # 2. Preprocess image
         a__		=			preprocess(__SCREAMING_SNAKE_CASE   )

         # 3. set timesteps
         self.scheduler.set_timesteps(__SCREAMING_SNAKE_CASE     ,				device=self.device   )
         a__	,     a__		=			self.get_timesteps(__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE     ,				self.device   )
         a__		=			timesteps[:1].repeat(__SCREAMING_SNAKE_CASE   )

         # 4. Prepare latent variables
         a__		=			self.prepare_latents(__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE     ,				self.unet.dtype     ,				self.device     ,				__SCREAMING_SNAKE_CASE   )
         a__		=			latents

         # 5. Denoising loop
         for t in self.progress_bar(__SCREAMING_SNAKE_CASE   ):
             # 1. predict noise model_output
             a__		=			self.unet(__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE   ).sample

             # 2. predict previous mean of image x_t-1 and add variance depending on eta
             # eta corresponds to η in paper and should be between [0, 1]
             # do x_t -> x_t-1
             a__		=			self.scheduler.step(
                 __SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE     ,				eta=__SCREAMING_SNAKE_CASE     ,				use_clipped_model_output=__SCREAMING_SNAKE_CASE     ,				generator=__SCREAMING_SNAKE_CASE     ,				).prev_sample

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

         if not return_dict:
             return (image, latent_timestep.item())

         return ImagePipelineOutput(images=__SCREAMING_SNAKE_CASE   )




"""simple docstring"""




from __future__ import annotations

a		:       str     						=       {
    'A': ['B', 'C', 'E'],
    'B': ['A', 'D', 'E'],
    'C': ['A', 'F', 'G'],
    'D': ['B'],
    'E': ['A', 'B', 'D'],
    'F': ['C'],
    'G': ['C'],
}







class     lowercase:



     def __init__(							self     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE   )		->     None:







         """simple docstring"""



         a__		=			graph
         # mapping node to its parent in resulting breadth first tree
         a__		=			{}
         a__		=			source_vertex



     def 			lowercase__  (							self   )		->     None:







         """simple docstring"""



         a__		=			{self.source_vertex}
         a__		=			None
         a__		=			[self.source_vertex]  # first in first out queue

         while queue:
             a__		=			queue.pop(0   )
             for adjacent_vertex in self.graph[vertex]:
                 if adjacent_vertex not in visited:
                     visited.add(__SCREAMING_SNAKE_CASE   )
                     a__		=			vertex
                     queue.append(__SCREAMING_SNAKE_CASE   )




     def 			lowercase__  (							self     ,				__SCREAMING_SNAKE_CASE   )		->     str:







         """simple docstring"""



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

         a__		=			self.parent.get(__SCREAMING_SNAKE_CASE   )
         if target_vertex_parent is None:
             a__		=			(
                 f'No path from vertex: {self.source_vertex} to vertex: {target_vertex}'
             )
             raise ValueError(__SCREAMING_SNAKE_CASE   )

         return self.shortest_path(__SCREAMING_SNAKE_CASE   ) + f'->{target_vertex}'


if __name__ == "__main__":
    a		:       Optional[Any]     						=       Graph(graph, 'G')
    g.breath_first_search()
    print(g.shortest_path('D'))
    print(g.shortest_path('G'))
    print(g.shortest_path('Foo'))


