





import numpy as np







class    __snake_case	:





       '''simple docstring'''







       def __init__(  self			:   List[str]		):
         __snake_case:						Union[str, Any]			  =						(0, 0)
         __snake_case:						str			  =						None
         __snake_case:						List[str]			  =						0
         __snake_case:						str			  =						0
         __snake_case:						Any			  =						0




       def __eq__(  self			:   int							,    A			:   Optional[Any]		):
         return self.position == cell.position






       def 							UpperCAmelCase__			(  self			:   str		):
         print(self.position		)







class    __snake_case	:





       '''simple docstring'''







       def __init__(  self			:   int							,    A			:   Optional[int]=(5, 5)		):
         __snake_case:						int			  =						np.zeros(A		)
         __snake_case:						str			  =						world_size[0]
         __snake_case:						List[Any]			  =						world_size[1]




       def 							UpperCAmelCase__			(  self			:   Optional[int]		):
         print(self.w		)






       def 							UpperCAmelCase__			(  self			:   Optional[int]							,    A			:   Optional[int]		):
         __snake_case:						Optional[Any]			  =						[
             (-1, -1),
             (-1, 0),
             (-1, 1),
             (0, -1),
             (0, 1),
             (1, -1),
             (1, 0),
             (1, 1),
         ]
         __snake_case:						Dict			  =						cell.position[0]
         __snake_case:						Tuple			  =						cell.position[1]
         __snake_case:						Optional[Any]			  =						[]
         for n in neughbour_cord:
           __snake_case:						Dict			  =						current_x + n[0]
           __snake_case:						Optional[int]			  =						current_y + n[1]
           if 0 <= x < self.world_x_limit and 0 <= y < self.world_y_limit:
             __snake_case:						str			  =						Cell()
             __snake_case:						List[Any]			  =						(x, y)
             __snake_case:						Dict			  =						cell
             neighbours.append(A		)
         return neighbours







def       A__							(   SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__)       ->   Optional[Any]:
  __snake_case:						Tuple			  =						[]
  __snake_case:						Optional[Any]			  =						[]
  _open.append(SCREAMING_SNAKE_CASE__)

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

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


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













import unittest

import numpy as np

from diffusers import LMSDiscreteScheduler, OnnxStableDiffusionInpaintPipeline
from diffusers.utils.testing_utils import (
    is_onnx_available,
    load_image,
    nightly,
    require_onnxruntime,
    require_torch_gpu,
)

from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin


if is_onnx_available():
      import onnxruntime as ort







class    __snake_case	(							__lowerCamelCase       ,      unittest.TestCase      ):





       '''simple docstring'''







       pass







@nightly
@require_onnxruntime
@require_torch_gpu
class    __snake_case	(							unittest.TestCase      ):





       '''simple docstring'''







       @property
       def 							UpperCAmelCase__			(  self			:   Dict		):
         return (
             "CUDAExecutionProvider",
             {
                 "gpu_mem_limit": "15000000000",  # 15GB
                 "arena_extend_strategy": "kSameAsRequested",
             },
         )




       @property
       def 							UpperCAmelCase__			(  self			:   Dict		):
         __snake_case:						Optional[int]			  =						ort.SessionOptions()
         __snake_case:						List[Any]			  =						False
         return options




       def 							UpperCAmelCase__			(  self			:   Optional[Any]		):
         __snake_case:						Optional[Any]			  =						load_image(
             """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
             """/in_paint/overture-creations-5sI6fQgYIuo.png"""		)
         __snake_case:						Any			  =						load_image(
             """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
             """/in_paint/overture-creations-5sI6fQgYIuo_mask.png"""		)
         __snake_case:						List[str]			  =						OnnxStableDiffusionInpaintPipeline.from_pretrained(
             """runwayml/stable-diffusion-inpainting"""							,    revision="""onnx"""							,    safety_checker=A							,    feature_extractor=A							,    provider=self.gpu_provider							,    sess_options=self.gpu_options							,    )
         pipe.set_progress_bar_config(disable=A		)

         __snake_case:						int			  =						"""A red cat sitting on a park bench"""

         __snake_case:						Any			  =						np.random.RandomState(0		)
         __snake_case:						Optional[Any]			  =						pipe(
             prompt=A							,    image=A							,    mask_image=A							,    guidance_scale=7.5							,    num_inference_steps=10							,    generator=A							,    output_type="""np"""							,    )
         __snake_case:						List[Any]			  =						output.images
         __snake_case:						str			  =						images[0, 255:258, 255:258, -1]

         assert images.shape == (1, 512, 512, 3)
         __snake_case:						Any			  =						np.array([0.2514, 0.3007, 0.3517, 0.1790, 0.2382, 0.3167, 0.1944, 0.2273, 0.2464]		)

         assert np.abs(image_slice.flatten() - expected_slice		).max() < 1E-3






       def 							UpperCAmelCase__			(  self			:   Optional[Any]		):
         __snake_case:						Optional[Any]			  =						load_image(
             """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
             """/in_paint/overture-creations-5sI6fQgYIuo.png"""		)
         __snake_case:						Optional[int]			  =						load_image(
             """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
             """/in_paint/overture-creations-5sI6fQgYIuo_mask.png"""		)
         __snake_case:						Optional[int]			  =						LMSDiscreteScheduler.from_pretrained(
             """runwayml/stable-diffusion-inpainting"""							,    subfolder="""scheduler"""							,    revision="""onnx"""		)
         __snake_case:						List[Any]			  =						OnnxStableDiffusionInpaintPipeline.from_pretrained(
             """runwayml/stable-diffusion-inpainting"""							,    revision="""onnx"""							,    scheduler=A							,    safety_checker=A							,    feature_extractor=A							,    provider=self.gpu_provider							,    sess_options=self.gpu_options							,    )
         pipe.set_progress_bar_config(disable=A		)

         __snake_case:						Optional[int]			  =						"""A red cat sitting on a park bench"""

         __snake_case:						Dict			  =						np.random.RandomState(0		)
         __snake_case:						Optional[Any]			  =						pipe(
             prompt=A							,    image=A							,    mask_image=A							,    guidance_scale=7.5							,    num_inference_steps=20							,    generator=A							,    output_type="""np"""							,    )
         __snake_case:						List[str]			  =						output.images
         __snake_case:						str			  =						images[0, 255:258, 255:258, -1]

         assert images.shape == (1, 512, 512, 3)
         __snake_case:						Union[str, Any]			  =						np.array([0.0086, 0.0077, 0.0083, 0.0093, 0.0107, 0.0139, 0.0094, 0.0097, 0.0125]		)

         assert np.abs(image_slice.flatten() - expected_slice		).max() < 1E-3






