

"""simple docstring"""




from __future__ import annotations

def 	A       (     snake_case__		,				snake_case__    ):




			'''simple docstring'''


			print(f"""Vertex\tShortest Distance from vertex {src}"""    )
			for i, d in enumerate(snake_case__    ):
						print(f"""{i}\t\t{d}"""    )

def 	A       (     snake_case__		,				snake_case__		,				snake_case__    ):




			'''simple docstring'''


			for j in range(snake_case__    ):
						SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__								=      (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 	A       (     snake_case__		,				snake_case__		,				snake_case__		,				snake_case__    ):




			'''simple docstring'''


			SCREAMING_SNAKE_CASE__								=      [float("""inf"""    )] * vertex_count
			SCREAMING_SNAKE_CASE__								=      0.0

			for _ in range(vertex_count - 1    ):
						for j in range(snake_case__    ):
									SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__								=      (graph[j][k] for k in ["""src""", """dst""", """weight"""])

									if distance[u] != float("""inf"""    ) and distance[u] + w < distance[v]:
												SCREAMING_SNAKE_CASE__								=      distance[u] + w

			SCREAMING_SNAKE_CASE__								=      check_negative_cycle(snake_case__		,				snake_case__		,				snake_case__    )
			if negative_cycle_exists:
						raise Exception("""Negative cycle found"""    )

			return distance


if __name__ == "__main__":
						import doctest

						doctest.testmod()

						A_							:			str											= int(input("Enter number of vertices: ").strip())
						A_							:			Dict											= int(input("Enter number of edges: ").strip())

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

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

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





"""simple docstring"""




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

import numpy as np
import PIL
import torch

from ...models import UNetaDModel
from ...schedulers import RePaintScheduler
from ...utils import PIL_INTERPOLATION, logging, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


A_							:			Tuple											= logging.get_logger(__name__)  # pylint: disable=invalid-name

def 	A       (     snake_case__    ):




			'''simple docstring'''


			warnings.warn(
			    """The preprocess method is deprecated and will be removed in a future version. Please"""
			    """ use VaeImageProcessor.preprocess instead"""		,				snake_case__		,				)
			if isinstance(snake_case__		,				torch.Tensor    ):
						return image
			elif isinstance(snake_case__		,				PIL.Image.Image    ):
						SCREAMING_SNAKE_CASE__								=      [image]

			if isinstance(image[0]		,				PIL.Image.Image    ):
						SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__								=      image[0].size
						SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__								=      (x - x % 8 for x in (w, h))  # resize to integer multiple of 8

						SCREAMING_SNAKE_CASE__								=      [np.array(i.resize((w, h)		,				resample=PIL_INTERPOLATION["""lanczos"""]    )    )[None, :] for i in image]
						SCREAMING_SNAKE_CASE__								=      np.concatenate(snake_case__		,				axis=0    )
						SCREAMING_SNAKE_CASE__								=      np.array(snake_case__    ).astype(np.floataa    ) / 2_55.0
						SCREAMING_SNAKE_CASE__								=      image.transpose(0		,				3		,				1		,				2    )
						SCREAMING_SNAKE_CASE__								=      2.0 * image - 1.0
						SCREAMING_SNAKE_CASE__								=      torch.from_numpy(snake_case__    )
			elif isinstance(image[0]		,				torch.Tensor    ):
						SCREAMING_SNAKE_CASE__								=      torch.cat(snake_case__		,				dim=0    )
			return image

def 	A       (     snake_case__    ):




			'''simple docstring'''


			if isinstance(snake_case__		,				torch.Tensor    ):
						return mask
			elif isinstance(snake_case__		,				PIL.Image.Image    ):
						SCREAMING_SNAKE_CASE__								=      [mask]

			if isinstance(mask[0]		,				PIL.Image.Image    ):
						SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__								=      mask[0].size
						SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__								=      (x - x % 32 for x in (w, h))  # resize to integer multiple of 32
						SCREAMING_SNAKE_CASE__								=      [np.array(m.convert("""L"""    ).resize((w, h)		,				resample=PIL_INTERPOLATION["""nearest"""]    )    )[None, :] for m in mask]
						SCREAMING_SNAKE_CASE__								=      np.concatenate(snake_case__		,				axis=0    )
						SCREAMING_SNAKE_CASE__								=      mask.astype(np.floataa    ) / 2_55.0
						SCREAMING_SNAKE_CASE__								=      0
						SCREAMING_SNAKE_CASE__								=      1
						SCREAMING_SNAKE_CASE__								=      torch.from_numpy(snake_case__    )
			elif isinstance(mask[0]		,				torch.Tensor    ):
						SCREAMING_SNAKE_CASE__								=      torch.cat(snake_case__		,				dim=0    )
			return mask





class    lowerCamelCase						(A__			):
			lowerCamelCase__   :		UNetaDModel
			lowerCamelCase__   :		RePaintScheduler

			def __init__(					self				:						int    ,				__UpperCAmelCase				:						List[str]    ,				__UpperCAmelCase				:						Dict       )   ->    Tuple:
						super().__init__()
						self.register_modules(unet=__UpperCAmelCase    ,				scheduler=__UpperCAmelCase       )





			@torch.no_grad()
			def __call__(					self				:						List[str]    ,				__UpperCAmelCase				:						Union[torch.Tensor, PIL.Image.Image]    ,				__UpperCAmelCase				:						Union[torch.Tensor, PIL.Image.Image]    ,				__UpperCAmelCase				:						int = 2_5_0    ,				__UpperCAmelCase				:						float = 0.0    ,				__UpperCAmelCase				:						int = 1_0    ,				__UpperCAmelCase				:						int = 1_0    ,				__UpperCAmelCase				:						Optional[Union[torch.Generator, List[torch.Generator]]] = None    ,				__UpperCAmelCase				:						Optional[str] = "pil"    ,				__UpperCAmelCase				:						bool = True    ,				)   ->    Union[ImagePipelineOutput, Tuple]:
						SCREAMING_SNAKE_CASE__								=      image

						SCREAMING_SNAKE_CASE__								=      _preprocess_image(__UpperCAmelCase       )
						SCREAMING_SNAKE_CASE__								=      original_image.to(device=self.device    ,				dtype=self.unet.dtype       )
						SCREAMING_SNAKE_CASE__								=      _preprocess_mask(__UpperCAmelCase       )
						SCREAMING_SNAKE_CASE__								=      mask_image.to(device=self.device    ,				dtype=self.unet.dtype       )

						SCREAMING_SNAKE_CASE__								=      original_image.shape[0]

						# sample gaussian noise to begin the loop
						if isinstance(__UpperCAmelCase    ,				__UpperCAmelCase       ) and len(__UpperCAmelCase       ) != batch_size:
									raise ValueError(
									    F"""You have passed a list of generators of length {len(__UpperCAmelCase       )}, but requested an effective batch"""
									    F""" size of {batch_size}. Make sure the batch size matches the length of the generators."""       )

						SCREAMING_SNAKE_CASE__								=      original_image.shape
						SCREAMING_SNAKE_CASE__								=      randn_tensor(__UpperCAmelCase    ,				generator=__UpperCAmelCase    ,				device=self.device    ,				dtype=self.unet.dtype       )

						# set step values
						self.scheduler.set_timesteps(__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				self.device       )
						SCREAMING_SNAKE_CASE__								=      eta

						SCREAMING_SNAKE_CASE__								=      self.scheduler.timesteps[0] + 1
						SCREAMING_SNAKE_CASE__								=      generator[0] if isinstance(__UpperCAmelCase    ,				__UpperCAmelCase       ) else generator
						for i, t in enumerate(self.progress_bar(self.scheduler.timesteps       )       ):
									if t < t_last:
												# predict the noise residual
												SCREAMING_SNAKE_CASE__								=      self.unet(__UpperCAmelCase    ,				__UpperCAmelCase       ).sample
												# compute previous image: x_t -> x_t-1
												SCREAMING_SNAKE_CASE__								=      self.scheduler.step(__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase       ).prev_sample

									else:
												# compute the reverse: x_t-1 -> x_t
												SCREAMING_SNAKE_CASE__								=      self.scheduler.undo_step(__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase       )
									SCREAMING_SNAKE_CASE__								=      t

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

						if not return_dict:
									return (image,)

						return ImagePipelineOutput(images=__UpperCAmelCase       )


