

'''simple docstring'''



import numpy as np







class 		lowerCAmelCase__  :



 """simple docstring"""



 def __init__(						self   :			Any     )    ->					List[str]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = (0, 0)
      __SCREAMING_SNAKE_CASE      = None
      __SCREAMING_SNAKE_CASE      = 0
      __SCREAMING_SNAKE_CASE      = 0
      __SCREAMING_SNAKE_CASE      = 0
 def __eq__(						self   :			Tuple   ,				__SCREAMING_SNAKE_CASE   :			Optional[int]     )    ->					str:


      """simple docstring"""




      return self.position == cell.position


 def  UpperCAmelCase__						(						self   :			str     )    ->					str:


      """simple docstring"""




      print(self.position     )







class 		lowerCAmelCase__  :



 """simple docstring"""



 def __init__(						self   :			str   ,				__SCREAMING_SNAKE_CASE   :			Tuple=(5, 5)     )    ->					int:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = np.zeros(__SCREAMING_SNAKE_CASE     )
      __SCREAMING_SNAKE_CASE      = world_size[0]
      __SCREAMING_SNAKE_CASE      = world_size[1]
 def  UpperCAmelCase__						(						self   :			Union[str, Any]     )    ->					List[Any]:


      """simple docstring"""




      print(self.w     )


 def  UpperCAmelCase__						(						self   :			List[Any]   ,				__SCREAMING_SNAKE_CASE   :			List[Any]     )    ->					Dict:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = [
          (-1, -1),
          (-1, 0),
          (-1, 1),
          (0, -1),
          (0, 1),
          (1, -1),
          (1, 0),
          (1, 1),
      ]
      __SCREAMING_SNAKE_CASE      = cell.position[0]
      __SCREAMING_SNAKE_CASE      = cell.position[1]
      __SCREAMING_SNAKE_CASE      = []
      for n in neughbour_cord:
           __SCREAMING_SNAKE_CASE      = current_x + n[0]
           __SCREAMING_SNAKE_CASE      = current_y + n[1]
           if 0 <= x < self.world_x_limit and 0 <= y < self.world_y_limit:
                __SCREAMING_SNAKE_CASE      = Cell()
                __SCREAMING_SNAKE_CASE      = (x, y)
                __SCREAMING_SNAKE_CASE      = cell
                neighbours.append(__SCREAMING_SNAKE_CASE     )
      return neighbours







def   a__			(    a__ ,       a__ ,       a__		):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE      = []
     __SCREAMING_SNAKE_CASE      = []
     _open.append(a__		)

     while _open:
          __SCREAMING_SNAKE_CASE      = np.argmin([n.f for n in _open]		)
          __SCREAMING_SNAKE_CASE      = _open[min_f]
          _closed.append(_open.pop(a__		)		)
          if current == goal:
               break
          for n in world.get_neigbours(a__		):
               for c in _closed:
                    if c == n:
                         continue
               __SCREAMING_SNAKE_CASE      = current.g + 1
               __SCREAMING_SNAKE_CASE  ,     __SCREAMING_SNAKE_CASE      = n.position
               __SCREAMING_SNAKE_CASE  ,     __SCREAMING_SNAKE_CASE      = goal.position
               __SCREAMING_SNAKE_CASE      = (ya - ya) ** 2 + (xa - xa) ** 2
               __SCREAMING_SNAKE_CASE      = n.h + n.g

               for c in _open:
                    if c == n and c.f < n.f:
                         continue
               _open.append(a__		)
     __SCREAMING_SNAKE_CASE      = []
     while current.parent is not None:
          path.append(current.position		)
          __SCREAMING_SNAKE_CASE      = current.parent
     path.append(current.position		)
     return path[::-1]


if __name__ == "__main__":
  UpperCAmelCase					:			Optional[int]       =	Gridworld()
  # Start position and goal
  UpperCAmelCase					:			Tuple       =	Cell()
  UpperCAmelCase					:			Union[str, Any]       =	(0, 0)
  UpperCAmelCase					:			List[Any]       =	Cell()
  UpperCAmelCase					:			Tuple       =	(4, 4)
  print(f"""path from {start.position} to {goal.position}""")
  UpperCAmelCase					:			Tuple       =	astar(world, start, goal)
  #   Just for visual reasons.
  for i in s:
    UpperCAmelCase					:			str       =	1
  print(world.w)









'''simple docstring'''



import unittest

import numpy as np
import torch

from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()







class 		lowerCAmelCase__  ( unittest.TestCase   ):



 """simple docstring"""



 @property
 def  UpperCAmelCase__						(						self   :			int     )    ->					Optional[int]:


      """simple docstring"""




      torch.manual_seed(0     )
      __SCREAMING_SNAKE_CASE      = 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  UpperCAmelCase__						(						self   :			Optional[Any]     )    ->					Tuple:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = self.dummy_uncond_unet
      __SCREAMING_SNAKE_CASE      = KarrasVeScheduler()

      __SCREAMING_SNAKE_CASE      = KarrasVePipeline(unet=__SCREAMING_SNAKE_CASE   ,				scheduler=__SCREAMING_SNAKE_CASE     )
      pipe.to(__SCREAMING_SNAKE_CASE     )
      pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE     )

      __SCREAMING_SNAKE_CASE      = torch.manual_seed(0     )
      __SCREAMING_SNAKE_CASE      = pipe(num_inference_steps=2   ,				generator=__SCREAMING_SNAKE_CASE   ,				output_type="""numpy"""     ).images

      __SCREAMING_SNAKE_CASE      = torch.manual_seed(0     )
      __SCREAMING_SNAKE_CASE      = pipe(num_inference_steps=2   ,				generator=__SCREAMING_SNAKE_CASE   ,				output_type="""numpy"""   ,				return_dict=__SCREAMING_SNAKE_CASE     )[0]

      __SCREAMING_SNAKE_CASE      = image[0, -3:, -3:, -1]
      __SCREAMING_SNAKE_CASE      = image_from_tuple[0, -3:, -3:, -1]

      assert image.shape == (1, 32, 32, 3)
      __SCREAMING_SNAKE_CASE      = np.array([0.0, 1.0, 0.0, 0.0, 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 		lowerCAmelCase__  ( unittest.TestCase   ):



 """simple docstring"""



 def  UpperCAmelCase__						(						self   :			List[Any]     )    ->					Union[str, Any]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = """google/ncsnpp-celebahq-256"""
      __SCREAMING_SNAKE_CASE      = UNetaDModel.from_pretrained(__SCREAMING_SNAKE_CASE     )
      __SCREAMING_SNAKE_CASE      = KarrasVeScheduler()

      __SCREAMING_SNAKE_CASE      = KarrasVePipeline(unet=__SCREAMING_SNAKE_CASE   ,				scheduler=__SCREAMING_SNAKE_CASE     )
      pipe.to(__SCREAMING_SNAKE_CASE     )
      pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE     )

      __SCREAMING_SNAKE_CASE      = torch.manual_seed(0     )
      __SCREAMING_SNAKE_CASE      = pipe(num_inference_steps=20   ,				generator=__SCREAMING_SNAKE_CASE   ,				output_type="""numpy"""     ).images

      __SCREAMING_SNAKE_CASE      = image[0, -3:, -3:, -1]
      assert image.shape == (1, 256, 256, 3)
      __SCREAMING_SNAKE_CASE      = np.array([0.578, 0.5811, 0.5924, 0.5809, 0.587, 0.5886, 0.5861, 0.5802, 0.586]     )

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






