
'''simple docstring'''






from __future__ import annotations

__A :  Dict    							=				list[tuple[int, int]]

__A :  List[str]    							=				[
    [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],
]

__A :  List[Any]    							=				([-1, 0], [0, -1], [1, 0], [0, 1])  # up, left, down, right






class        __snake_case	:







    """simple docstring"""



    def __init__(       self   :  List[Any]   ,						lowerCamelCase   :  int   ,						lowerCamelCase   :  int   ,						lowerCamelCase   :  int   ,						lowerCamelCase   :  int   ,						lowerCamelCase   :  float   ,						lowerCamelCase   :  Node | None   ,						)       ->  Any:
      lowerCAmelCase_      :				Optional[int]      =       pos_x
      lowerCAmelCase_      :				str      =       pos_y
      lowerCAmelCase_      :				List[Any]      =       (pos_y, pos_x)
      lowerCAmelCase_      :				Optional[int]      =       goal_x
      lowerCAmelCase_      :				Dict      =       goal_y
      lowerCAmelCase_      :				Dict      =       g_cost
      lowerCAmelCase_      :				List[str]      =       parent
      lowerCAmelCase_      :				List[Any]      =       self.calculate_heuristic()

    def __lowercase       (       self   :  int		)       ->  float:
      lowerCAmelCase_      :				List[Any]      =       abs(self.pos_x - self.goal_x		)
      lowerCAmelCase_      :				Any      =       abs(self.pos_y - self.goal_y		)
      return dx + dy





    def __lt__(       self   :  Dict   ,						lowerCamelCase   :  int		)       ->  bool:
      return self.f_cost < other.f_cost







class        __snake_case	:







    """simple docstring"""



    def __init__(       self   :  Union[str, Any]   ,						lowerCamelCase   :  tuple[int, int]   ,						lowerCamelCase   :  tuple[int, int]		)       ->  int:
      lowerCAmelCase_      :				List[str]      =       Node(start[1]   ,						start[0]   ,						goal[1]   ,						goal[0]   ,						0   ,						lowerCamelCase		)
      lowerCAmelCase_      :				Tuple      =       Node(goal[1]   ,						goal[0]   ,						goal[1]   ,						goal[0]   ,						9_99_99   ,						lowerCamelCase		)

      lowerCAmelCase_      :				str      =       [self.start]
      lowerCAmelCase_      :				list[Node]      =       []

      lowerCAmelCase_      :				Any      =       False

    def __lowercase       (       self   :  List[Any]		)       ->  Path | None:
      while self.open_nodes:
        # Open Nodes are sorted using __lt__
        self.open_nodes.sort()
        lowerCAmelCase_      :				List[Any]      =       self.open_nodes.pop(0		)

        if current_node.pos == self.target.pos:
          lowerCAmelCase_      :				Optional[Any]      =       True
          return self.retrace_path(lowerCamelCase		)

        self.closed_nodes.append(lowerCamelCase		)
        lowerCAmelCase_      :				int      =       self.get_successors(lowerCamelCase		)

        for child_node in successors:
          if child_node in self.closed_nodes:
            continue

          if child_node not in self.open_nodes:
            self.open_nodes.append(lowerCamelCase		)
          else:
            # retrieve the best current path
            lowerCAmelCase_      :				Union[str, Any]      =       self.open_nodes.pop(self.open_nodes.index(lowerCamelCase		)		)

            if child_node.g_cost < better_node.g_cost:
              self.open_nodes.append(lowerCamelCase		)
            else:
              self.open_nodes.append(lowerCamelCase		)

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

    def __lowercase       (       self   :  Any   ,						lowerCamelCase   :  Node		)       ->  list[Node]:
      lowerCAmelCase_      :				List[str]      =       []
      for action in delta:
        lowerCAmelCase_      :				Dict      =       parent.pos_x + action[1]
        lowerCAmelCase_      :				Optional[Any]      =       parent.pos_y + action[0]

        if not (0 <= pos_x <= len(grid[0]		) - 1 and 0 <= pos_y <= len(lowerCamelCase		) - 1):
          continue

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

        successors.append(
            Node(
                lowerCamelCase   ,						lowerCamelCase   ,						self.target.pos_y   ,						self.target.pos_x   ,						parent.g_cost + 1   ,						lowerCamelCase   ,						)		)
      return successors





    def __lowercase       (       self   :  Optional[Any]   ,						lowerCamelCase   :  Node | None		)       ->  Path:
      lowerCAmelCase_      :				Tuple      =       node
      lowerCAmelCase_      :				Tuple      =       []
      while current_node is not None:
        path.append((current_node.pos_y, current_node.pos_x)		)
        lowerCAmelCase_      :				Dict      =       current_node.parent
      path.reverse()
      return path


if __name__ == "__main__":
  __A :  Any    							=				(0, 0)
  __A :  List[Any]    							=				(len(grid) - 1, len(grid[0]) - 1)
  for elem in grid:
    print(elem)

  print("------")

  __A :  int    							=				GreedyBestFirst(init, goal)
  __A :  Dict    							=				greedy_bf.search()
  if path:
    for pos_x, pos_y in path:
      __A :  Tuple    							=				2

    for elem in grid:
      print(elem)







'''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        __snake_case	(       unittest.TestCase):







    """simple docstring"""



    @property
    def __lowercase       (       self   :  str		)       ->  List[Any]:
      torch.manual_seed(0		)
      lowerCAmelCase_      :				Optional[Any]      =       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   :  Tuple		)       ->  Optional[Any]:
      lowerCAmelCase_      :				Optional[int]      =       self.dummy_uncond_unet
      lowerCAmelCase_      :				Tuple      =       PNDMScheduler()

      lowerCAmelCase_      :				List[Any]      =       PNDMPipeline(unet=lowerCamelCase   ,						scheduler=lowerCamelCase		)
      pndm.to(lowerCamelCase		)
      pndm.set_progress_bar_config(disable=lowerCamelCase		)

      lowerCAmelCase_      :				Dict      =       torch.manual_seed(0		)
      lowerCAmelCase_      :				List[Any]      =       pndm(generator=lowerCamelCase   ,						num_inference_steps=20   ,						output_type="""numpy"""		).images

      lowerCAmelCase_      :				str      =       torch.manual_seed(0		)
      lowerCAmelCase_      :				int      =       pndm(generator=lowerCamelCase   ,						num_inference_steps=20   ,						output_type="""numpy"""   ,						return_dict=lowerCamelCase		)[0]

      lowerCAmelCase_      :				Optional[int]      =       image[0, -3:, -3:, -1]
      lowerCAmelCase_      :				Optional[Any]      =       image_from_tuple[0, -3:, -3:, -1]

      assert image.shape == (1, 32, 32, 3)
      lowerCAmelCase_      :				int      =       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        __snake_case	(       unittest.TestCase):







    """simple docstring"""



    def __lowercase       (       self   :  str		)       ->  Tuple:
      lowerCAmelCase_      :				str      =       """google/ddpm-cifar10-32"""

      lowerCAmelCase_      :				Dict      =       UNetaDModel.from_pretrained(lowerCamelCase		)
      lowerCAmelCase_      :				Dict      =       PNDMScheduler()

      lowerCAmelCase_      :				Union[str, Any]      =       PNDMPipeline(unet=lowerCamelCase   ,						scheduler=lowerCamelCase		)
      pndm.to(lowerCamelCase		)
      pndm.set_progress_bar_config(disable=lowerCamelCase		)
      lowerCAmelCase_      :				Any      =       torch.manual_seed(0		)
      lowerCAmelCase_      :				Union[str, Any]      =       pndm(generator=lowerCamelCase   ,						output_type="""numpy"""		).images

      lowerCAmelCase_      :				Optional[int]      =       image[0, -3:, -3:, -1]

      assert image.shape == (1, 32, 32, 3)
      lowerCAmelCase_      :				List[Any]      =       np.array([0.1_564, 0.14_645, 0.1_406, 0.14_715, 0.12_425, 0.14_045, 0.13_115, 0.12_175, 0.125]		)

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



