


'''simple docstring'''






import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device


lowercase							=False






class  __magic_name__							(		unittest.TestCase    ):
       pass






@nightly
@require_torch_gpu
class  __magic_name__							(		unittest.TestCase    ):






       def       lowerCAmelCase		(      self)						->  Tuple:




          '''simple docstring'''
          # clean up the VRAM after each test
          super().tearDown()
          gc.collect()
          torch.cuda.empty_cache()






       def       lowerCAmelCase		(      self)						->  Dict:




          '''simple docstring'''
          _UpperCAmelCase       :       Union[str, Any]							   =VersatileDiffusionPipeline.from_pretrained('shi-labs/versatile-diffusion'  ,							torch_dtype=torch.floataa)
          pipe.to(snake_case)
          pipe.set_progress_bar_config(disable=snake_case)

          _UpperCAmelCase       :       Union[str, Any]							   =load_image(
              'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg')

          _UpperCAmelCase       :       Dict							   =torch.manual_seed(0)
          _UpperCAmelCase       :       str							   =pipe.dual_guided(
              prompt='first prompt'  ,							image=snake_case  ,							text_to_image_strength=0.75  ,							generator=snake_case  ,							guidance_scale=7.5  ,							num_inference_steps=2  ,							output_type='numpy'  ,							).images

          with tempfile.TemporaryDirectory() as tmpdirname:
             pipe.save_pretrained(snake_case)
             _UpperCAmelCase       :       Union[str, Any]							   =VersatileDiffusionPipeline.from_pretrained(snake_case  ,							torch_dtype=torch.floataa)
          pipe.to(snake_case)
          pipe.set_progress_bar_config(disable=snake_case)

          _UpperCAmelCase       :       List[Any]							   =generator.manual_seed(0)
          _UpperCAmelCase       :       Dict							   =pipe.dual_guided(
              prompt='first prompt'  ,							image=snake_case  ,							text_to_image_strength=0.75  ,							generator=snake_case  ,							guidance_scale=7.5  ,							num_inference_steps=2  ,							output_type='numpy'  ,							).images

          assert np.abs(image - new_image).sum() < 1E-5, "Models don't have the same forward pass"






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




          '''simple docstring'''
          _UpperCAmelCase       :       Union[str, Any]							   =VersatileDiffusionPipeline.from_pretrained('shi-labs/versatile-diffusion'  ,							torch_dtype=torch.floataa)
          pipe.to(snake_case)
          pipe.set_progress_bar_config(disable=snake_case)

          _UpperCAmelCase       :       List[str]							   ='cyberpunk 2077'
          _UpperCAmelCase       :       Dict							   =load_image(
              'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg')
          _UpperCAmelCase       :       Any							   =torch.manual_seed(0)
          _UpperCAmelCase       :       Optional[int]							   =pipe.dual_guided(
              prompt=snake_case  ,							image=snake_case  ,							text_to_image_strength=0.75  ,							generator=snake_case  ,							guidance_scale=7.5  ,							num_inference_steps=5_0  ,							output_type='numpy'  ,							).images

          _UpperCAmelCase       :       int							   =image[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

          assert image.shape == (1, 5_1_2, 5_1_2, 3)
          _UpperCAmelCase       :       List[str]							   =np.array([0.14_48, 0.16_19, 0.17_41, 0.10_86, 0.11_47, 0.11_28, 0.11_99, 0.11_65, 0.10_01])

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

          _UpperCAmelCase       :       Dict							   ='A painting of a squirrel eating a burger '
          _UpperCAmelCase       :       Union[str, Any]							   =torch.manual_seed(0)
          _UpperCAmelCase       :       str							   =pipe.text_to_image(
              prompt=snake_case  ,							generator=snake_case  ,							guidance_scale=7.5  ,							num_inference_steps=5_0  ,							output_type='numpy').images

          _UpperCAmelCase       :       Any							   =image[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

          assert image.shape == (1, 5_1_2, 5_1_2, 3)
          _UpperCAmelCase       :       str							   =np.array([0.33_67, 0.31_69, 0.26_56, 0.38_70, 0.47_90, 0.37_96, 0.40_09, 0.48_78, 0.47_78])

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

          _UpperCAmelCase       :       int							   =pipe.image_variation(snake_case  ,							generator=snake_case  ,							output_type='numpy').images

          _UpperCAmelCase       :       Optional[Any]							   =image[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

          assert image.shape == (1, 5_1_2, 5_1_2, 3)
          _UpperCAmelCase       :       List[str]							   =np.array([0.30_76, 0.31_23, 0.32_84, 0.37_82, 0.37_70, 0.38_94, 0.42_97, 0.43_31, 0.44_56])

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





'''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)

