

'''simple docstring'''




from __future__ import annotations

import queue






class   _SCREAMING_SNAKE_CASE   :



 def __init__(   self		:   Tuple			,				a__		:   int      ):
      __magic_name__						       =    data
      __magic_name__						       =    None
      __magic_name__						       =    None




def 						UpperCamelCase	(			)				->     TreeNode:






     '''simple docstring'''
     print('''\n********Press N to stop entering at any point of time********\n'''			)
     __magic_name__						       =    input('''Enter the value of the root node: '''			).strip().lower()
     __magic_name__						       =    queue.Queue()
     __magic_name__						       =    TreeNode(int(a			)			)
     q.put(a			)
     while not q.empty():
          __magic_name__						       =    q.get()
          __magic_name__						       =    F'''Enter the left node of {node_found.data}: '''
          __magic_name__						       =    input(a			).strip().lower() or '''n'''
          if check == "n":
               return tree_node
          __magic_name__						       =    TreeNode(int(a			)			)
          __magic_name__						       =    left_node
          q.put(a			)
          __magic_name__						       =    F'''Enter the right node of {node_found.data}: '''
          __magic_name__						       =    input(a			).strip().lower() or '''n'''
          if check == "n":
               return tree_node
          __magic_name__						       =    TreeNode(int(a			)			)
          __magic_name__						       =    right_node
          q.put(a			)
     raise




def 						UpperCamelCase	(			a			)				->     None:






     '''simple docstring'''
     if not isinstance(a		,						a			) or not node:
          return
     print(node.data		,						end=''','''			)
     pre_order(node.left			)
     pre_order(node.right			)




def 						UpperCamelCase	(			a			)				->     None:






     '''simple docstring'''
     if not isinstance(a		,						a			) or not node:
          return
     in_order(node.left			)
     print(node.data		,						end=''','''			)
     in_order(node.right			)




def 						UpperCamelCase	(			a			)				->     None:






     '''simple docstring'''
     if not isinstance(a		,						a			) or not node:
          return
     post_order(node.left			)
     post_order(node.right			)
     print(node.data		,						end=''','''			)




def 						UpperCamelCase	(			a			)				->     None:






     '''simple docstring'''
     if not isinstance(a		,						a			) or not node:
          return
     __magic_name__						       =    queue.Queue()
     q.put(a			)
     while not q.empty():
          __magic_name__						       =    q.get()
          print(node_dequeued.data		,						end=''','''			)
          if node_dequeued.left:
               q.put(node_dequeued.left			)
          if node_dequeued.right:
               q.put(node_dequeued.right			)




def 						UpperCamelCase	(			a			)				->     None:






     '''simple docstring'''
     if not isinstance(a		,						a			) or not node:
          return
     __magic_name__						       =    queue.Queue()
     q.put(a			)
     while not q.empty():
          __magic_name__						       =    []
          while not q.empty():
               __magic_name__						       =    q.get()
               print(node_dequeued.data		,						end=''','''			)
               if node_dequeued.left:
                    list_.append(node_dequeued.left			)
               if node_dequeued.right:
                    list_.append(node_dequeued.right			)
          print()
          for node in list_:
               q.put(a			)




def 						UpperCamelCase	(			a			)				->     None:






     '''simple docstring'''
     if not isinstance(a		,						a			) or not node:
          return
     __magic_name__						       =    []
     __magic_name__						       =    node
     while n or stack:
          while n:  # start from root node, find its left child
               print(n.data		,						end=''','''			)
               stack.append(a			)
               __magic_name__						       =    n.left
          # end of while means current node doesn't have left child
          __magic_name__						       =    stack.pop()
          # start to traverse its right child
          __magic_name__						       =    n.right




def 						UpperCamelCase	(			a			)				->     None:






     '''simple docstring'''
     if not isinstance(a		,						a			) or not node:
          return
     __magic_name__						       =    []
     __magic_name__						       =    node
     while n or stack:
          while n:
               stack.append(a			)
               __magic_name__						       =    n.left
          __magic_name__						       =    stack.pop()
          print(n.data		,						end=''','''			)
          __magic_name__						       =    n.right




def 						UpperCamelCase	(			a			)				->     None:






     '''simple docstring'''
     if not isinstance(a		,						a			) or not node:
          return
     __magic_name__       ,  __magic_name__						       =    [], []
     __magic_name__						       =    node
     stacka.append(a			)
     while stacka:  # to find the reversed order of post order, store it in stack2
          __magic_name__						       =    stacka.pop()
          if n.left:
               stacka.append(n.left			)
          if n.right:
               stacka.append(n.right			)
          stacka.append(a			)
     while stacka:  # pop up from stack2 will be the post order
          print(stacka.pop().data		,						end=''','''			)




def 						UpperCamelCase	(			a = ""		,						a=50		,						a="*"			)				->     str:






     '''simple docstring'''
     if not s:
          return "\n" + width * char
     __magic_name__       ,  __magic_name__						       =    divmod(width - len(a			) - 2		,						2			)
     return F'''{left * char} {s} {(left + extra) * char}'''


if __name__ == "__main__":
      import doctest

      doctest.testmod()
      print(prompt("Binary Tree Traversals"))

      _lowerCAmelCase         =						build_tree()
      print(prompt("Pre Order Traversal"))
      pre_order(node)
      print(prompt() + "\n")

      print(prompt("In Order Traversal"))
      in_order(node)
      print(prompt() + "\n")

      print(prompt("Post Order Traversal"))
      post_order(node)
      print(prompt() + "\n")

      print(prompt("Level Order Traversal"))
      level_order(node)
      print(prompt() + "\n")

      print(prompt("Actual Level Order Traversal"))
      level_order_actual(node)
      print("*" * 50 + "\n")

      print(prompt("Pre Order Traversal - Iteration Version"))
      pre_order_iter(node)
      print(prompt() + "\n")

      print(prompt("In Order Traversal - Iteration Version"))
      in_order_iter(node)
      print(prompt() + "\n")

      print(prompt("Post Order Traversal - Iteration Version"))
      post_order_iter(node)
      print(prompt())









'''simple docstring'''




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     ):
 __SCREAMING_SNAKE_CASE					:Optional[Any]      	=					1



 @register_to_config
 def __init__(   self		:   Optional[int]			,				a__		:   int = 1000			,				a__		:   Optional[Union[np.ndarray, List[float]]] = None      ):
      # set `betas`, `alphas`, `timesteps`
      self.set_timesteps(a__      )

      # standard deviation of the initial noise distribution
      __magic_name__						       =    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.
      __magic_name__						       =    4

      # running values
      __magic_name__						       =    []



 def 			snake_case__  (   self		:   Dict			,				a__		:   int			,				a__		:   Union[str, torch.device] = None      ):
      __magic_name__						       =    num_inference_steps
      __magic_name__						       =    torch.linspace(1			,				0			,				num_inference_steps + 1      )[:-1]
      __magic_name__						       =    torch.cat([steps, torch.tensor([0.0]      )]      )

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

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

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

      __magic_name__						       =    []



 def 			snake_case__  (   self		:   Union[str, Any]			,				a__		:   torch.FloatTensor			,				a__		:   int			,				a__		:   torch.FloatTensor			,				a__		:   bool = True			,				):
      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'''      )

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

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

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

      __magic_name__						       =    self._get_prev_sample(a__			,				a__			,				a__			,				a__      )

      if not return_dict:
           return (prev_sample,)

      return SchedulerOutput(prev_sample=a__      )



 def 			snake_case__  (   self		:   Any			,				a__		:   torch.FloatTensor			,				*a__		:   Optional[int]			,				**a__		:   Union[str, Any]      ):
      return sample



 def 			snake_case__  (   self		:   str			,				a__		:   Optional[int]			,				a__		:   int			,				a__		:   List[Any]			,				a__		:   List[str]      ):
      __magic_name__						       =    self.alphas[timestep_index]
      __magic_name__						       =    self.betas[timestep_index]

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

      __magic_name__						       =    (sample - sigma * ets) / max(a__			,				1E-8      )
      __magic_name__						       =    next_alpha * pred + ets * next_sigma

      return prev_sample



 def __len__(   self		:   int      ):
      return self.config.num_train_timesteps






