





from dataclasses import dataclass
from typing import Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin


@dataclass
class       __lowerCAmelCase					(       _UpperCamelCase      ):


	'''simple docstring'''





	_A													=				42
	_A													=				42
	_A													=				None


class       __lowerCAmelCase					(       _UpperCamelCase   ,    _UpperCamelCase      ):


	'''simple docstring'''





	_A													=				2

	@register_to_config
	def __init__(						self:						Union[str, Any],					lowerCamelCase_:						float = 0.0_2,					lowerCamelCase_:						float = 100,					lowerCamelCase_:						float = 1.0_0_7,					lowerCamelCase_:						float = 80,					lowerCamelCase_:						float = 0.0_5,					lowerCamelCase_:						float = 50,					):
			# standard deviation of the initial noise distribution
			lowercase__					:      Optional[int]      =		sigma_max

			# setable values
			lowercase__					:      int      =		None
			lowercase__					:      np.IntTensor      =		None
			lowercase__					:      torch.FloatTensor      =		None  # sigma(t_i)

	def    snake_case__(						self:						List[Any],					lowerCamelCase_:						torch.FloatTensor,					lowerCamelCase_:						Optional[int] = None			):
			return sample

	def    snake_case__(						self:						List[Any],					lowerCamelCase_:						int,					lowerCamelCase_:						Union[str, torch.device] = None			):
			lowercase__					:      List[Any]      =		num_inference_steps
			lowercase__					:      Optional[int]      =		np.arange(0,					self.num_inference_steps			)[::-1].copy()
			lowercase__					:      Tuple      =		torch.from_numpy(lowerCamelCase_			).to(lowerCamelCase_			)
			lowercase__					:      Any      =		[
			    (
			        self.config.sigma_max**2
			        * (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
			    )
			    for i in self.timesteps
			]
			lowercase__					:      Union[str, Any]      =		torch.tensor(lowerCamelCase_,					dtype=torch.floataa,					device=lowerCamelCase_			)

	def    snake_case__(						self:						List[Any],					lowerCamelCase_:						torch.FloatTensor,					lowerCamelCase_:						float,					lowerCamelCase_:						Optional[torch.Generator] = None			):
			if self.config.s_min <= sigma <= self.config.s_max:
					lowercase__					:      Optional[Any]      =		min(self.config.s_churn / self.num_inference_steps,					2**0.5 - 1			)
			else:
					lowercase__					:      Union[str, Any]      =		0

			# sample eps ~ N(0, S_noise^2 * I)
			lowercase__					:      int      =		self.config.s_noise * randn_tensor(sample.shape,					generator=lowerCamelCase_			).to(sample.device			)
			lowercase__					:      List[Any]      =		sigma + gamma * sigma
			lowercase__					:      Optional[int]      =		sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

			return sample_hat, sigma_hat

	def    snake_case__(						self:						Union[str, Any],					lowerCamelCase_:						torch.FloatTensor,					lowerCamelCase_:						float,					lowerCamelCase_:						float,					lowerCamelCase_:						torch.FloatTensor,					lowerCamelCase_:						bool = True,					):
			lowercase__					:      List[Any]      =		sample_hat + sigma_hat * model_output
			lowercase__					:      int      =		(sample_hat - pred_original_sample) / sigma_hat
			lowercase__					:      Optional[int]      =		sample_hat + (sigma_prev - sigma_hat) * derivative

			if not return_dict:
					return (sample_prev, derivative)

			return KarrasVeOutput(
			    prev_sample=lowerCamelCase_,					derivative=lowerCamelCase_,					pred_original_sample=lowerCamelCase_			)

	def    snake_case__(						self:						List[str],					lowerCamelCase_:						torch.FloatTensor,					lowerCamelCase_:						float,					lowerCamelCase_:						float,					lowerCamelCase_:						torch.FloatTensor,					lowerCamelCase_:						torch.FloatTensor,					lowerCamelCase_:						torch.FloatTensor,					lowerCamelCase_:						bool = True,					):
			lowercase__					:      Optional[int]      =		sample_prev + sigma_prev * model_output
			lowercase__					:      Dict      =		(sample_prev - pred_original_sample) / sigma_prev
			lowercase__					:      Optional[Any]      =		sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)

			if not return_dict:
					return (sample_prev, derivative)

			return KarrasVeOutput(
			    prev_sample=lowerCamelCase_,					derivative=lowerCamelCase_,					pred_original_sample=lowerCamelCase_			)





	def    snake_case__(						self:						Optional[int],					lowerCamelCase_:						List[Any],					lowerCamelCase_:						Tuple,					lowerCamelCase_:						Tuple			):
			raise NotImplementedError()









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)


