





from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput




class 			__UpperCamelCase    (							A__    ):
     __A	:  UNetaDModel
     __A	:  ScoreSdeVeScheduler





     def __init__(     self				,							_UpperCamelCase				,							_UpperCamelCase					):
        super().__init__()
        self.register_modules(unet=_UpperCamelCase				,							scheduler=_UpperCamelCase					)





     @torch.no_grad()
     def __call__(     self				,							_UpperCamelCase = 1				,							_UpperCamelCase = 2000				,							_UpperCamelCase = None				,							_UpperCamelCase = "pil"				,							_UpperCamelCase = True				,							**_UpperCamelCase				,							):
        _UpperCAmelCase           =   self.unet.config.sample_size
        _UpperCAmelCase           =   (batch_size, 3, img_size, img_size)

        _UpperCAmelCase           =   self.unet

        _UpperCAmelCase           =   randn_tensor(_UpperCamelCase				,							generator=_UpperCamelCase					) * self.scheduler.init_noise_sigma
        _UpperCAmelCase           =   sample.to(self.device					)

        self.scheduler.set_timesteps(_UpperCamelCase					)
        self.scheduler.set_sigmas(_UpperCamelCase					)

        for i, t in enumerate(self.progress_bar(self.scheduler.timesteps					)					):
           _UpperCAmelCase           =   self.scheduler.sigmas[i] * torch.ones(shape[0]				,							device=self.device					)

           # correction step
           for _ in range(self.scheduler.config.correct_steps					):
              _UpperCAmelCase           =   self.unet(_UpperCamelCase				,							_UpperCamelCase					).sample
              _UpperCAmelCase           =   self.scheduler.step_correct(_UpperCamelCase				,							_UpperCamelCase				,							generator=_UpperCamelCase					).prev_sample

           # prediction step
           _UpperCAmelCase           =   model(_UpperCamelCase				,							_UpperCamelCase					).sample
           _UpperCAmelCase           =   self.scheduler.step_pred(_UpperCamelCase				,							_UpperCamelCase				,							_UpperCamelCase				,							generator=_UpperCamelCase					)

           _UpperCAmelCase       ,      _UpperCAmelCase           =   output.prev_sample, output.prev_sample_mean

        _UpperCAmelCase           =   sample_mean.clamp(0				,							1					)
        _UpperCAmelCase           =   sample.cpu().permute(0				,							2				,							3				,							1					).numpy()
        if output_type == "pil":
           _UpperCAmelCase           =   self.numpy_to_pil(_UpperCamelCase					)

        if not return_dict:
           return (sample,)

        return ImagePipelineOutput(images=_UpperCamelCase					)






from __future__ import annotations


def       SCREAMING_SNAKE_CASE__  (       _lowercase     : list[float]   ,    _lowercase     : Tuple			)   ->							int:




		'''simple docstring'''
		print(f"""Vertex\tShortest Distance from vertex {src}"""			)
		for i, d in enumerate(_lowercase			):
				print(f"""{i}\t\t{d}"""			)


def       SCREAMING_SNAKE_CASE__  (       _lowercase     : list[dict[str, int]]   ,    _lowercase     : list[float]   ,    _lowercase     : int			)   ->							Any:




		'''simple docstring'''
		for j in range(_lowercase			):
				lowercase__   ,				lowercase__   ,				lowercase__					:      Dict      =		(graph[j][k] for k in ['src', 'dst', 'weight'])
				if distance[u] != float('inf'			) and distance[u] + w < distance[v]:
						return True
		return False


def       SCREAMING_SNAKE_CASE__  (       _lowercase     : list[dict[str, int]]   ,    _lowercase     : int   ,    _lowercase     : int   ,    _lowercase     : int			)   ->							list[float]:




		'''simple docstring'''
		lowercase__					:      Dict      =		[float('inf'			)] * vertex_count
		lowercase__					:      Dict      =		0.0

		for _ in range(vertex_count - 1			):
				for j in range(_lowercase			):
						lowercase__   ,				lowercase__   ,				lowercase__					:      int      =		(graph[j][k] for k in ['src', 'dst', 'weight'])

						if distance[u] != float('inf'			) and distance[u] + w < distance[v]:
								lowercase__					:      str      =		distance[u] + w

		lowercase__					:      str      =		check_negative_cycle(_lowercase   ,    _lowercase   ,    _lowercase			)
		if negative_cycle_exists:
				raise Exception('Negative cycle found'			)

		return distance


if __name__ == "__main__":
			import doctest

			doctest.testmod()

			__UpperCamelCase:	Optional[int] = int(input("""Enter number of vertices: """).strip())
			__UpperCamelCase:	Union[str, Any] = int(input("""Enter number of edges: """).strip())

			__UpperCamelCase:	list[dict[str, int]] = [{} for _ in range(E)]

			for i in range(E):
						print("""Edge """, i + 1)
						__UpperCamelCase,	__UpperCamelCase,	__UpperCamelCase:	List[str] = (
						    int(x)
						    for x in input("""Enter source, destination, weight: """).strip().split(""" """)
						)
						__UpperCamelCase:	List[Any] = {"""src""": src, """dst""": dest, """weight""": weight}

			__UpperCamelCase:	Optional[int] = int(input("""\nEnter shortest path source:""").strip())
			__UpperCamelCase:	Dict = bellman_ford(graph, V, E, source)
			print_distance(shortest_distance, 0)


