





import math





class  lowerCAmelCase							:







  def __init__(      self  :Optional[int]   ,							_lowercase  :Optional[int]=0 ):  # a graph with Node 0,1,...,N-1






     '''simple docstring'''





     lowercase__			       =  n
     lowercase__			       =  [
         [math.inf for j in range(0   ,							_lowercase )] for i in range(0   ,							_lowercase )
     ]  # adjacency matrix for weight
     lowercase__			       =  [
         [math.inf for j in range(0   ,							_lowercase )] for i in range(0   ,							_lowercase )
     ]  # dp[i][j] stores minimum distance from i to j







  def   UpperCAmelCase					(      self  :str   ,							_lowercase  :Union[str, Any]   ,							_lowercase  :Optional[Any]   ,							_lowercase  :Union[str, Any] ):






     '''simple docstring'''





     lowercase__			       =  w







  def   UpperCAmelCase					(      self  :Tuple ):






     '''simple docstring'''





     for k in range(0   ,							self.n ):
        for i in range(0   ,							self.n ):
           for j in range(0   ,							self.n ):
              lowercase__			       =  min(self.dp[i][j]   ,							self.dp[i][k] + self.dp[k][j] )







  def   UpperCAmelCase					(      self  :Dict   ,							_lowercase  :Any   ,							_lowercase  :Tuple ):






     '''simple docstring'''





     return self.dp[u][v]


if __name__ == "__main__":
  _snake_case           =			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)













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

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import SchedulerMixin, SchedulerOutput





class  lowerCAmelCase							(       lowercase_							,							lowercase_ ):
  __lowerCamelCase		=      1







  @register_to_config
  def __init__(      self  :Dict   ,							_lowercase  :int = 10_00   ,							_lowercase  :Optional[Union[np.ndarray, List[float]]] = None ):






     '''simple docstring'''





     self.set_timesteps(_lowercase )

     # standard deviation of the initial noise distribution
     lowercase__			       =  1.0

     # For now we only support F-PNDM, i.e. the runge-kutta method
     # For more information on the algorithm please take a look at the paper: https://arxiv.org/pdf/2202.09778.pdf
     # mainly at formula (9), (12), (13) and the Algorithm 2.
     lowercase__			       =  4

     # running values
     lowercase__			       =  []







  def   UpperCAmelCase					(      self  :str   ,							_lowercase  :int   ,							_lowercase  :Union[str, torch.device] = None ):






     '''simple docstring'''





     lowercase__			       =  num_inference_steps
     lowercase__			       =  torch.linspace(1   ,							0   ,							num_inference_steps + 1 )[:-1]
     lowercase__			       =  torch.cat([steps, torch.tensor([0.0] )] )

     if self.config.trained_betas is not None:
        lowercase__			       =  torch.tensor(self.config.trained_betas   ,							dtype=torch.floataa )
     else:
        lowercase__			       =  torch.sin(steps * math.pi / 2 ) ** 2

     lowercase__			       =  (1.0 - self.betas**2) ** 0.5

     lowercase__			       =  (torch.atana(self.betas   ,							self.alphas ) / math.pi * 2)[:-1]
     lowercase__			       =  timesteps.to(_lowercase )

     lowercase__			       =  []







  def   UpperCAmelCase					(      self  :Optional[int]   ,							_lowercase  :torch.FloatTensor   ,							_lowercase  :int   ,							_lowercase  :torch.FloatTensor   ,							_lowercase  :bool = True   ,							):






     '''simple docstring'''





     if self.num_inference_steps is None:
        raise ValueError(
            "Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler" )

     lowercase__			       =  (self.timesteps == timestep).nonzero().item()
     lowercase__			       =  timestep_index + 1

     lowercase__			       =  sample * self.betas[timestep_index] + model_output * self.alphas[timestep_index]
     self.ets.append(_lowercase )

     if len(self.ets ) == 1:
        lowercase__			       =  self.ets[-1]
     elif len(self.ets ) == 2:
        lowercase__			       =  (3 * self.ets[-1] - self.ets[-2]) / 2
     elif len(self.ets ) == 3:
        lowercase__			       =  (23 * self.ets[-1] - 16 * self.ets[-2] + 5 * self.ets[-3]) / 12
     else:
        lowercase__			       =  (1 / 24) * (55 * self.ets[-1] - 59 * self.ets[-2] + 37 * self.ets[-3] - 9 * self.ets[-4])

     lowercase__			       =  self._get_prev_sample(_lowercase   ,							_lowercase   ,							_lowercase   ,							_lowercase )

     if not return_dict:
        return (prev_sample,)

     return SchedulerOutput(prev_sample=_lowercase )







  def   UpperCAmelCase					(      self  :Any   ,							_lowercase  :torch.FloatTensor   ,							*_lowercase  :int   ,							**_lowercase  :int ):






     '''simple docstring'''





     return sample







  def   UpperCAmelCase					(      self  :str   ,							_lowercase  :Tuple   ,							_lowercase  :int   ,							_lowercase  :Optional[Any]   ,							_lowercase  :List[str] ):






     '''simple docstring'''





     lowercase__			       =  self.alphas[timestep_index]
     lowercase__			       =  self.betas[timestep_index]

     lowercase__			       =  self.alphas[prev_timestep_index]
     lowercase__			       =  self.betas[prev_timestep_index]

     lowercase__			       =  (sample - sigma * ets) / max(_lowercase   ,							1e-8 )
     lowercase__			       =  next_alpha * pred + ets * next_sigma

     return prev_sample







  def __len__(      self  :Union[str, Any] ):






     '''simple docstring'''





     return self.config.num_train_timesteps






