
'''simple docstring'''







from __future__ import annotations

import time

__lowerCAmelCase         =    list[tuple[int, int]]

__lowerCAmelCase         =    [
    [0, 0, 0, 0, 0, 0, 0],
    [0, 1, 0, 0, 0, 0, 0],  # 0 are free path whereas 1's are obstacles
    [0, 0, 0, 0, 0, 0, 0],
    [0, 0, 1, 0, 0, 0, 0],
    [1, 0, 1, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 1, 0, 0],
]

__lowerCAmelCase         =    [[-1, 0], [0, -1], [1, 0], [0, 1]]  # up, left, down, right







class UpperCAmelCase__	:





  """simple docstring"""

  def __init__(		self      :     Any							,_a      :     int							,_a      :     int							,_a      :     int							,_a      :     int							,_a      :     Node | None       ):


     '''simple docstring'''






     _a				:	Union[str, Any]			=   pos_x
     _a				:	List[str]			=   pos_y
     _a				:	Optional[int]			=   (pos_y, pos_x)
     _a				:	Tuple			=   goal_x
     _a				:	Dict			=   goal_y
     _a				:	Tuple			=   parent







class UpperCAmelCase__	:





  """simple docstring"""

  def __init__(		self      :     Tuple							,_a      :     tuple[int, int]							,_a      :     tuple[int, int]       ):


     '''simple docstring'''






     _a				:	Dict			=   Node(start[1]							,start[0]							,goal[1]							,goal[0]							,_a       )
     _a				:	Any			=   Node(goal[1]							,goal[0]							,goal[1]							,goal[0]							,_a       )

     _a				:	Dict			=   [self.start]
     _a				:	str			=   False


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


     '''simple docstring'''






     while self.node_queue:
        _a				:	Optional[int]			=   self.node_queue.pop(0       )

        if current_node.pos == self.target.pos:
           _a				:	Dict			=   True
           return self.retrace_path(_a       )

        _a				:	Optional[Any]			=   self.get_successors(_a       )

        for node in successors:
           self.node_queue.append(_a       )

     if not self.reached:
        return [self.start.pos]
     return None


  def   __lowercase		(		self      :     List[Any]							,_a      :     Node       ):


     '''simple docstring'''






     _a				:	str			=   []
     for action in delta:
        _a				:	Any			=   parent.pos_x + action[1]
        _a				:	List[str]			=   parent.pos_y + action[0]
        if not (0 <= pos_x <= len(grid[0]       ) - 1 and 0 <= pos_y <= len(_a       ) - 1):
           continue

        if grid[pos_y][pos_x] != 0:
           continue

        successors.append(
            Node(_a							,_a							,self.target.pos_y							,self.target.pos_x							,_a       )       )
     return successors


  def   __lowercase		(		self      :     List[str]							,_a      :     Node | None       ):


     '''simple docstring'''






     _a				:	Dict			=   node
     _a				:	Optional[Any]			=   []
     while current_node is not None:
        path.append((current_node.pos_y, current_node.pos_x)       )
        _a				:	Union[str, Any]			=   current_node.parent
     path.reverse()
     return path







class UpperCAmelCase__	:





  """simple docstring"""

  def __init__(		self      :     int							,_a      :     str							,_a      :     Optional[int]       ):


     '''simple docstring'''






     _a				:	Any			=   BreadthFirstSearch(_a							,_a       )
     _a				:	int			=   BreadthFirstSearch(_a							,_a       )
     _a				:	Dict			=   False


  def   __lowercase		(		self      :     Tuple       ):


     '''simple docstring'''






     while self.fwd_bfs.node_queue or self.bwd_bfs.node_queue:
        _a				:	str			=   self.fwd_bfs.node_queue.pop(0       )
        _a				:	Optional[int]			=   self.bwd_bfs.node_queue.pop(0       )

        if current_bwd_node.pos == current_fwd_node.pos:
           _a				:	List[str]			=   True
           return self.retrace_bidirectional_path(
               _a							,_a       )

        _a				:	Tuple			=   current_bwd_node
        _a				:	Tuple			=   current_fwd_node

        _a				:	Tuple			=   {
            self.fwd_bfs: self.fwd_bfs.get_successors(_a       ),
            self.bwd_bfs: self.bwd_bfs.get_successors(_a       ),
        }

        for bfs in [self.fwd_bfs, self.bwd_bfs]:
           for node in successors[bfs]:
              bfs.node_queue.append(_a       )

     if not self.reached:
        return [self.fwd_bfs.start.pos]
     return None


  def   __lowercase		(		self      :     Dict							,_a      :     Node							,_a      :     Node       ):


     '''simple docstring'''






     _a				:	List[Any]			=   self.fwd_bfs.retrace_path(_a       )
     _a				:	List[str]			=   self.bwd_bfs.retrace_path(_a       )
     bwd_path.pop()
     bwd_path.reverse()
     _a				:	List[str]			=   fwd_path + bwd_path
     return path


if __name__ == "__main__":
  # all coordinates are given in format [y,x]
  import doctest

  doctest.testmod()
  __lowerCAmelCase         =    (0, 0)
  __lowerCAmelCase         =    (len(grid) - 1, len(grid[0]) - 1)
  for elem in grid:
    print(elem)

  __lowerCAmelCase         =    time.time()
  __lowerCAmelCase         =    BreadthFirstSearch(init, goal)
  __lowerCAmelCase         =    bfs.search()
  __lowerCAmelCase         =    time.time() - start_bfs_time

  print("""Unidirectional BFS computation time : """, bfs_time)

  __lowerCAmelCase         =    time.time()
  __lowerCAmelCase         =    BidirectionalBreadthFirstSearch(init, goal)
  __lowerCAmelCase         =    bd_bfs.search()
  __lowerCAmelCase         =    time.time() - start_bd_bfs_time

  print("""Bidirectional BFS computation time : """, bd_bfs_time)





'''simple docstring'''







import unittest

import numpy as np
import torch

from diffusers import PNDMPipeline, PNDMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()







class UpperCAmelCase__	( unittest.TestCase	):





  """simple docstring"""

  @property
  def   __lowercase		(		self      :     List[str]       ):


     '''simple docstring'''






     torch.manual_seed(0       )
     _a				:	int			=   UNetaDModel(
         block_out_channels=(32, 64)							,layers_per_block=2							,sample_size=32							,in_channels=3							,out_channels=3							,down_block_types=('DownBlock2D', 'AttnDownBlock2D')							,up_block_types=('AttnUpBlock2D', 'UpBlock2D')							,)
     return model


  def   __lowercase		(		self      :     Optional[Any]       ):


     '''simple docstring'''






     _a				:	str			=   self.dummy_uncond_unet
     _a				:	int			=   PNDMScheduler()

     _a				:	str			=   PNDMPipeline(unet=_a							,scheduler=_a       )
     pndm.to(_a       )
     pndm.set_progress_bar_config(disable=_a       )

     _a				:	Optional[int]			=   torch.manual_seed(0       )
     _a				:	Optional[Any]			=   pndm(generator=_a							,num_inference_steps=20							,output_type='numpy'       ).images

     _a				:	List[str]			=   torch.manual_seed(0       )
     _a				:	Any			=   pndm(generator=_a							,num_inference_steps=20							,output_type='numpy'							,return_dict=_a       )[0]

     _a				:	List[Any]			=   image[0, -3:, -3:, -1]
     _a				:	Any			=   image_from_tuple[0, -3:, -3:, -1]

     assert image.shape == (1, 32, 32, 3)
     _a				:	List[Any]			=   np.array([1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0]       )

     assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1E-2
     assert np.abs(image_from_tuple_slice.flatten() - expected_slice       ).max() < 1E-2







@slow
@require_torch
class UpperCAmelCase__	( unittest.TestCase	):





  """simple docstring"""

  def   __lowercase		(		self      :     Tuple       ):


     '''simple docstring'''






     _a				:	List[str]			=   'google/ddpm-cifar10-32'

     _a				:	str			=   UNetaDModel.from_pretrained(_a       )
     _a				:	Union[str, Any]			=   PNDMScheduler()

     _a				:	Tuple			=   PNDMPipeline(unet=_a							,scheduler=_a       )
     pndm.to(_a       )
     pndm.set_progress_bar_config(disable=_a       )
     _a				:	str			=   torch.manual_seed(0       )
     _a				:	Optional[Any]			=   pndm(generator=_a							,output_type='numpy'       ).images

     _a				:	Dict			=   image[0, -3:, -3:, -1]

     assert image.shape == (1, 32, 32, 3)
     _a				:	Tuple			=   np.array([0.1564, 0.1_4645, 0.1406, 0.1_4715, 0.1_2425, 0.1_4045, 0.1_3115, 0.1_2175, 0.125]       )

     assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1E-2



