



import numpy as np



class    UpperCAmelCase__:

   '''simple docstring'''




   def __init__(    self	:      int)						->					int:




         """simple docstring"""

         lowercase__  =	(0, 0)
         lowercase__  =	None
         lowercase__  =	0
         lowercase__  =	0
         lowercase__  =	0


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




         """simple docstring"""

         return self.position == cell.position



   def       UpperCAmelCase		(    self	:      Dict)						->					List[Any]:




         """simple docstring"""

         print(self.position)



class    UpperCAmelCase__:

   '''simple docstring'''




   def __init__(    self	:      List[str]      ,					lowerCAmelCase	:      Tuple=(5, 5))						->					Optional[int]:




         """simple docstring"""

         lowercase__  =	np.zeros(lowerCAmelCase)
         lowercase__  =	world_size[0]
         lowercase__  =	world_size[1]


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




         """simple docstring"""

         print(self.w)



   def       UpperCAmelCase		(    self	:      str      ,					lowerCAmelCase	:      Any)						->					int:




         """simple docstring"""

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

def 	_lowerCAmelCase (    A__    ,	A__    ,	A__						):
      lowercase__  =	[]
      lowercase__  =	[]
      _open.append(A__						)

      while _open:
            lowercase__  =	np.argmin([n.f for n in _open]						)
            lowercase__  =	_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
                  lowercase__  =	current.g + 1
                  lowercase__,						lowercase__  =	n.position
                  lowercase__,						lowercase__  =	goal.position
                  lowercase__  =	(ya - ya) ** 2 + (xa - xa) ** 2
                  lowercase__  =	n.h + n.g

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


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





import random
import unittest

import torch

from diffusers import IFImgaImgSuperResolutionPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin



@skip_mps
class    UpperCAmelCase__(				lowerCamelCase  ,				lowerCamelCase  ,				unittest.TestCase						):

   '''simple docstring'''




   A     :	str												=     IFImgaImgSuperResolutionPipeline
   A     :	Dict												=     TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"width", "height"}
   A     :	Any												=     TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({"original_image"}						)
   A     :	str												=     PipelineTesterMixin.required_optional_params - {"latents"}


   def       UpperCAmelCase		(    self	:      Dict)						->					Dict:




         """simple docstring"""

         return self._get_superresolution_dummy_components()


   def       UpperCAmelCase		(    self	:      Optional[Any]      ,					lowerCAmelCase	:      Tuple      ,					lowerCAmelCase	:      Union[str, Any]=0)						->					str:




         """simple docstring"""

         if str(lowerCAmelCase).startswith('mps'):
               lowercase__  =	torch.manual_seed(lowerCAmelCase)
         else:
               lowercase__  =	torch.Generator(device=lowerCAmelCase).manual_seed(lowerCAmelCase)

         lowercase__  =	floats_tensor((1, 3, 32, 32)      ,					rng=random.Random(lowerCAmelCase)).to(lowerCAmelCase)
         lowercase__  =	floats_tensor((1, 3, 16, 16)      ,					rng=random.Random(lowerCAmelCase)).to(lowerCAmelCase)

         lowercase__  =	{
             'prompt': 'A painting of a squirrel eating a burger',
             'image': image,
             'original_image': original_image,
             'generator': generator,
             'num_inference_steps': 2,
             'output_type': 'numpy',
         }

         return inputs


   @unittest.skipIf(
       torch_device != 'cuda' or not is_xformers_available()      ,					reason='XFormers attention is only available with CUDA and `xformers` installed'      ,					)
   def       UpperCAmelCase		(    self	:      str)						->					Dict:




         """simple docstring"""

         self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3)


   def       UpperCAmelCase		(    self	:      List[Any])						->					Dict:




         """simple docstring"""

         self._test_save_load_optional_components()


   @unittest.skipIf(torch_device != 'cuda'      ,					reason='float16 requires CUDA')
   def       UpperCAmelCase		(    self	:      Optional[Any])						->					List[Any]:




         """simple docstring"""

         super().test_save_load_floataa(expected_max_diff=1E-1)


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




         """simple docstring"""

         self._test_attention_slicing_forward_pass(expected_max_diff=1E-2)


   def       UpperCAmelCase		(    self	:      Tuple)						->					Tuple:




         """simple docstring"""

         self._test_save_load_local()



   def       UpperCAmelCase		(    self	:      List[str])						->					Tuple:




         """simple docstring"""

         self._test_inference_batch_single_identical(
             expected_max_diff=1E-2      ,					)
