


'''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  __magic_name__							(		unittest.TestCase    ):






       @property
       def       lowerCAmelCase		(      self)						->  Optional[Any]:




          '''simple docstring'''
          torch.manual_seed(0)
          _UpperCAmelCase       :       Tuple							   =UNetaDModel(
              block_out_channels=(3_2, 6_4)  ,							layers_per_block=2  ,							sample_size=3_2  ,							in_channels=3  ,							out_channels=3  ,							down_block_types=('DownBlock2D', 'AttnDownBlock2D')  ,							up_block_types=('AttnUpBlock2D', 'UpBlock2D')  ,							)
          return model






       def       lowerCAmelCase		(      self)						->  Optional[Any]:




          '''simple docstring'''
          _UpperCAmelCase       :       str							   =self.dummy_uncond_unet
          _UpperCAmelCase       :       Optional[int]							   =KarrasVeScheduler()

          _UpperCAmelCase       :       str							   =KarrasVePipeline(unet=__lowercase  ,							scheduler=__lowercase)
          pipe.to(__lowercase)
          pipe.set_progress_bar_config(disable=__lowercase)

          _UpperCAmelCase       :       List[Any]							   =torch.manual_seed(0)
          _UpperCAmelCase       :       List[Any]							   =pipe(num_inference_steps=2  ,							generator=__lowercase  ,							output_type='numpy').images

          _UpperCAmelCase       :       List[str]							   =torch.manual_seed(0)
          _UpperCAmelCase       :       Any							   =pipe(num_inference_steps=2  ,							generator=__lowercase  ,							output_type='numpy'  ,							return_dict=__lowercase)[0]

          _UpperCAmelCase       :       Dict							   =image[0, -3:, -3:, -1]
          _UpperCAmelCase       :       Optional[Any]							   =image_from_tuple[0, -3:, -3:, -1]

          assert image.shape == (1, 3_2, 3_2, 3)
          _UpperCAmelCase       :       Optional[int]							   =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  __magic_name__							(		unittest.TestCase    ):






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




          '''simple docstring'''
          _UpperCAmelCase       :       Any							   ='''google/ncsnpp-celebahq-256'''
          _UpperCAmelCase       :       List[str]							   =UNetaDModel.from_pretrained(__lowercase)
          _UpperCAmelCase       :       int							   =KarrasVeScheduler()

          _UpperCAmelCase       :       List[str]							   =KarrasVePipeline(unet=__lowercase  ,							scheduler=__lowercase)
          pipe.to(__lowercase)
          pipe.set_progress_bar_config(disable=__lowercase)

          _UpperCAmelCase       :       Dict							   =torch.manual_seed(0)
          _UpperCAmelCase       :       List[str]							   =pipe(num_inference_steps=2_0  ,							generator=__lowercase  ,							output_type='numpy').images

          _UpperCAmelCase       :       List[Any]							   =image[0, -3:, -3:, -1]
          assert image.shape == (1, 2_5_6, 2_5_6, 3)
          _UpperCAmelCase       :       Optional[Any]							   =np.array([0.5_78, 0.58_11, 0.59_24, 0.58_09, 0.5_87, 0.58_86, 0.58_61, 0.58_02, 0.5_86])

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



'''simple docstring'''






import numpy as np






class  __magic_name__							:






       def __init__(      self)						->  Optional[Any]:




          '''simple docstring'''
          _UpperCAmelCase       :       List[str]							   =(0, 0)
          _UpperCAmelCase       :       Any							   =None
          _UpperCAmelCase       :       List[str]							   =0
          _UpperCAmelCase       :       Dict							   =0
          _UpperCAmelCase       :       Dict							   =0






       def __eq__(      self  ,							snake_case)						->  Dict:




          '''simple docstring'''
          return self.position == cell.position






       def       lowerCAmelCase		(      self)						->  Optional[int]:




          '''simple docstring'''
          print(self.position)






class  __magic_name__							:






       def __init__(      self  ,							snake_case=(5, 5))						->  Optional[int]:




          '''simple docstring'''
          _UpperCAmelCase       :       List[str]							   =np.zeros(snake_case)
          _UpperCAmelCase       :       Optional[Any]							   =world_size[0]
          _UpperCAmelCase       :       Any							   =world_size[1]






       def       lowerCAmelCase		(      self)						->  Tuple:




          '''simple docstring'''
          print(self.w)






       def       lowerCAmelCase		(      self  ,							snake_case)						->  Any:




          '''simple docstring'''
          _UpperCAmelCase       :       str							   =[
              (-1, -1),
              (-1, 0),
              (-1, 1),
              (0, -1),
              (0, 1),
              (1, -1),
              (1, 0),
              (1, 1),
          ]
          _UpperCAmelCase       :       int							   =cell.position[0]
          _UpperCAmelCase       :       Optional[Any]							   =cell.position[1]
          _UpperCAmelCase       :       Dict							   =[]
          for n in neughbour_cord:
             _UpperCAmelCase       :       List[str]							   =current_x + n[0]
             _UpperCAmelCase       :       int							   =current_y + n[1]
             if 0 <= x < self.world_x_limit and 0 <= y < self.world_y_limit:
                _UpperCAmelCase       :       List[Any]							   =Cell()
                _UpperCAmelCase       :       Dict							   =(x, y)
                _UpperCAmelCase       :       Any							   =cell
                neighbours.append(snake_case)
          return neighbours






def lowerCamelCase__      (						__lowerCamelCase	:							List[str]	,   __lowerCamelCase	:							str	,   __lowerCamelCase	:							int							):







   '''simple docstring'''




   _UpperCAmelCase       :       List[Any]							   =[]
   _UpperCAmelCase       :       Union[str, Any]							   =[]
   _open.append(__lowerCamelCase							)

   while _open:
      _UpperCAmelCase       :       List[str]							   =np.argmin([n.f for n in _open]							)
      _UpperCAmelCase       :       Optional[int]							   =_open[min_f]
      _closed.append(_open.pop(__lowerCamelCase							)							)
      if current == goal:
         break
      for n in world.get_neigbours(__lowerCamelCase							):
         for c in _closed:
            if c == n:
               continue
         _UpperCAmelCase       :       Tuple							   =current.g + 1
         _UpperCAmelCase   ,	_UpperCAmelCase       :       Any							   =n.position
         _UpperCAmelCase   ,	_UpperCAmelCase       :       List[str]							   =goal.position
         _UpperCAmelCase       :       Optional[int]							   =(ya - ya) ** 2 + (xa - xa) ** 2
         _UpperCAmelCase       :       Optional[Any]							   =n.h + n.g

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


if __name__ == "__main__":
 lowercase							=Gridworld()
 # Start position and goal
 lowercase							=Cell()
 lowercase							=(0, 0)
 lowercase							=Cell()
 lowercase							=(4, 4)
 print(F"""path from {start.position} to {goal.position}""")
 lowercase							=astar(world, start, goal)
 #   Just for visual reasons.
 for i in s:
  lowercase							=1
 print(world.w)

