



"""simple docstring"""
import math
class 				lowerCamelCase  :






					'''simple docstring'''





					def __init__(       self:							List[Any]						,     snake_case:							int=0						)   ->	int:  # a graph with Node 0,1,...,N-1
											snake_case_ :List[str]						=			n
											snake_case_ :int						=			[
											    [math.inf for j in range(0						,     snake_case						)] for i in range(0						,     snake_case						)
											]  # adjacency matrix for weight
											snake_case_ :str						=			[
											    [math.inf for j in range(0						,     snake_case						)] for i in range(0						,     snake_case						)
											]  # dp[i][j] stores minimum distance from i to j






					def    lowerCAmelCase_				(       self:							Optional[int]						,     snake_case:							str						,     snake_case:							Optional[Any]						,     snake_case:							str						)   ->	Tuple:
											snake_case_ :List[Any]						=			w






					def    lowerCAmelCase_				(       self:							List[str]						)   ->	str:
											for k in range(0						,     self.n						):
																	for i in range(0						,     self.n						):
																							for j in range(0						,     self.n						):
																													snake_case_ :Any						=			min(self.dp[i][j]						,     self.dp[i][k] + self.dp[k][j]						)






					def    lowerCAmelCase_				(       self:							int						,     snake_case:							List[Any]						,     snake_case:							Optional[Any]						)   ->	Union[str, Any]:
											return self.dp[u][v]


if __name__ == "__main__":
						__a      =				Graph(5)
						graph.add_edge(0, 2, 9)
						graph.add_edge(0, 4, 10)
						graph.add_edge(1, 3, 5)
						graph.add_edge(2, 3, 7)
						graph.add_edge(3, 0, 10)
						graph.add_edge(3, 1, 2)
						graph.add_edge(3, 2, 1)
						graph.add_edge(3, 4, 6)
						graph.add_edge(4, 1, 3)
						graph.add_edge(4, 2, 4)
						graph.add_edge(4, 3, 9)
						graph.floyd_warshall()
						graph.show_min(1, 4)
						graph.show_min(0, 3)






# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

import math
from typing import Union

import torch

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







class 						lowercase							(   UpperCamelCase__,UpperCamelCase__		):
     _a							 =						1

     @register_to_config
     def __init__(  self		,     _a=2000		,     _a=0.1		,     _a=20		,     _a=1e-3				)						->							List[Any]:
           _A							:     Dict			=						None
           _A							:     List[Any]			=						None
           _A							:     Dict			=						None

     def 					a__	(  self		,     _a		,     _a = None				)						->							Union[str, Any]:
           _A							:     Union[str, Any]			=						torch.linspace(1		,     self.config.sampling_eps		,     _a		,     device=_a				)

     def 					a__	(  self		,     _a		,     _a		,     _a		,     _a=None				)						->							Dict:
           if self.timesteps is None:
                 raise ValueError(
                     """`self.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"""				)

           # TODO(Patrick) better comments + non-PyTorch
           # postprocess model score
           _A							:     Any			=						(
               -0.25 * t**2 * (self.config.beta_max - self.config.beta_min) - 0.5 * t * self.config.beta_min
           )
           _A							:     List[Any]			=						torch.sqrt(1.0 - torch.exp(2.0 * log_mean_coeff				)				)
           _A							:     List[str]			=						std.flatten()
           while len(std.shape				) < len(score.shape				):
                 _A							:     List[Any]			=						std.unsqueeze(-1				)
           _A							:     int			=						-score / std

           # compute
           _A							:     Tuple			=						-1.0 / len(self.timesteps				)

           _A							:     str			=						self.config.beta_min + t * (self.config.beta_max - self.config.beta_min)
           _A							:     List[str]			=						beta_t.flatten()
           while len(beta_t.shape				) < len(x.shape				):
                 _A							:     Union[str, Any]			=						beta_t.unsqueeze(-1				)
           _A							:     Tuple			=						-0.5 * beta_t * x

           _A							:     Tuple			=						torch.sqrt(_a				)
           _A							:     Dict			=						drift - diffusion**2 * score
           _A							:     Dict			=						x + drift * dt

           # add noise
           _A							:     Any			=						randn_tensor(x.shape		,     layout=x.layout		,     generator=_a		,     device=x.device		,     dtype=x.dtype				)
           _A							:     str			=						x_mean + diffusion * math.sqrt(-dt				) * noise

           return x, x_mean



     def __len__(  self				)						->							Optional[Any]:
           return self.config.num_train_timesteps

