






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 					SCREAMING_SNAKE_CASE_  (       _a  ,						_a ):
       """simple docstring"""


       __lowerCAmelCase    :		int					=1





       @register_to_config
       def __init__(				self	:Union[str, Any],      snake_case	:int = 1000,      snake_case	:Optional[Union[np.ndarray, List[float]]] = None):



          """simple docstring"""

          self.set_timesteps(snake_case)

          # 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	:Optional[int],      snake_case	:int,      snake_case	: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(snake_case)

          _lowercase              =[]





       def 				UpperCamelCase__						(				self	:Dict,      snake_case	:torch.FloatTensor,      snake_case	:int,      snake_case	:torch.FloatTensor,      snake_case	: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(snake_case)

          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(snake_case,      snake_case,      snake_case,      snake_case)

          if not return_dict:
             return (prev_sample,)

          return SchedulerOutput(prev_sample=snake_case)





       def 				UpperCamelCase__						(				self	:List[Any],      snake_case	:torch.FloatTensor,      *snake_case	:int,      **snake_case	:Optional[Any]):



          """simple docstring"""

          return sample





       def 				UpperCamelCase__						(				self	:str,      snake_case	:Dict,      snake_case	:Union[str, Any],      snake_case	:Tuple,      snake_case	:int):



          """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(snake_case,      1e-8)
          _lowercase              =next_alpha * pred + ets * next_sigma

          return prev_sample





       def __len__(				self	:Optional[Any]):



          """simple docstring"""

          return self.config.num_train_timesteps










class 					SCREAMING_SNAKE_CASE_  :
       """simple docstring"""


       def __init__(				self	:List[Any]):



          """simple docstring"""

          _lowercase              ={}





       def 				UpperCamelCase__						(				self	:str):



          """simple docstring"""

          print(self.vertex)
          for i in self.vertex:
             print(snake_case,      ' -> ',      ' -> '.join([str(snake_case) for j in self.vertex[i]]))





       def 				UpperCamelCase__						(				self	:Any,      snake_case	:int,      snake_case	:int):



          """simple docstring"""

          if from_vertex in self.vertex:
             self.vertex[from_vertex].append(snake_case)
          else:
             # else make a new vertex
             _lowercase              =[to_vertex]





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



          """simple docstring"""

          _lowercase              =[False] * len(self.vertex)

          # call the recursive helper function
          for i in range(len(self.vertex)):
             if not visited[i]:
                self.dfs_recursive(snake_case,      snake_case)





       def 				UpperCamelCase__						(				self	:List[str],      snake_case	:int,      snake_case	:list):



          """simple docstring"""

          _lowercase              =True

          print(snake_case,      end=' ')

          # Recur for all the vertices that are adjacent to this node
          for i in self.vertex:
             if not visited[i]:
                self.dfs_recursive(snake_case,      snake_case)


if __name__ == "__main__":
    _SCREAMING_SNAKE_CASE   =  Graph()
    g.add_edge(0, 1)
    g.add_edge(0, 2)
    g.add_edge(1, 2)
    g.add_edge(2, 0)
    g.add_edge(2, 3)
    g.add_edge(3, 3)

    g.print_graph()
    print("DFS:")
    g.dfs()

    # OUTPUT:
    # 0  ->  1 -> 2
    # 1  ->  2
    # 2  ->  0 -> 3
    # 3  ->  3
    # DFS:
    #  0 1 2 3


