





import random
import unittest

import numpy as np

from diffusers import (
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    OnnxStableDiffusionImgaImgPipeline,
    PNDMScheduler,
)
from diffusers.utils import floats_tensor
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   A							(						__lowercase		,   unittest.TestCase    ):
      _snake_case				  ='''hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline'''



      def 						lowerCAmelCase__     (						self:    Union[str, Any]		,	_lowerCAmelCase:    Dict=0							)    ->     Dict:
         '''simple docstring'''

         UpperCAmelCase_           =floats_tensor((1, 3, 128, 128)		,	rng=random.Random(_lowerCAmelCase							)							)
         UpperCAmelCase_           =np.random.RandomState(_lowerCAmelCase							)
         UpperCAmelCase_           ={
             "prompt": "A painting of a squirrel eating a burger",
             "image": image,
             "generator": generator,
             "num_inference_steps": 3,
             "strength": 0.75,
             "guidance_scale": 7.5,
             "output_type": "numpy",
         }
         return inputs



      def 						lowerCAmelCase__     (						self:    Any							)    ->     Any:
         '''simple docstring'''

         UpperCAmelCase_           =OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint		,	provider="CPUExecutionProvider"							)
         pipe.set_progress_bar_config(disable=_lowerCAmelCase							)

         UpperCAmelCase_           =self.get_dummy_inputs()
         UpperCAmelCase_           =pipe(**_lowerCAmelCase							).images
         UpperCAmelCase_           =image[0, -3:, -3:, -1].flatten()

         assert image.shape == (1, 128, 128, 3)
         UpperCAmelCase_           =np.array([0.6_96_43, 0.5_84_84, 0.5_03_14, 0.5_87_60, 0.5_53_68, 0.5_96_43, 0.5_15_29, 0.4_12_17, 0.4_90_87]							)
         assert np.abs(image_slice - expected_slice							).max() < 1e-1



      def 						lowerCAmelCase__     (						self:    Dict							)    ->     Tuple:
         '''simple docstring'''

         UpperCAmelCase_           =OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint		,	provider="CPUExecutionProvider"							)
         UpperCAmelCase_           =PNDMScheduler.from_config(pipe.scheduler.config		,	skip_prk_steps=_lowerCAmelCase							)
         pipe.set_progress_bar_config(disable=_lowerCAmelCase							)

         UpperCAmelCase_           =self.get_dummy_inputs()
         UpperCAmelCase_           =pipe(**_lowerCAmelCase							).images
         UpperCAmelCase_           =image[0, -3:, -3:, -1]

         assert image.shape == (1, 128, 128, 3)
         UpperCAmelCase_           =np.array([0.6_17_37, 0.5_46_42, 0.5_31_83, 0.5_44_65, 0.5_27_42, 0.6_05_25, 0.4_99_69, 0.4_06_55, 0.4_81_54]							)

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



      def 						lowerCAmelCase__     (						self:    int							)    ->     Dict:
         '''simple docstring'''

         UpperCAmelCase_           =OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint		,	provider="CPUExecutionProvider"							)
         UpperCAmelCase_           =LMSDiscreteScheduler.from_config(pipe.scheduler.config							)
         pipe.set_progress_bar_config(disable=_lowerCAmelCase							)

         # warmup pass to apply optimizations
         UpperCAmelCase_           =pipe(**self.get_dummy_inputs()							)

         UpperCAmelCase_           =self.get_dummy_inputs()
         UpperCAmelCase_           =pipe(**_lowerCAmelCase							).images
         UpperCAmelCase_           =image[0, -3:, -3:, -1]

         assert image.shape == (1, 128, 128, 3)
         UpperCAmelCase_           =np.array([0.5_27_61, 0.5_99_77, 0.4_90_33, 0.4_96_19, 0.5_42_82, 0.5_03_11, 0.4_76_00, 0.4_09_18, 0.4_52_03]							)

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



      def 						lowerCAmelCase__     (						self:    List[str]							)    ->     Optional[int]:
         '''simple docstring'''

         UpperCAmelCase_           =OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint		,	provider="CPUExecutionProvider"							)
         UpperCAmelCase_           =EulerDiscreteScheduler.from_config(pipe.scheduler.config							)
         pipe.set_progress_bar_config(disable=_lowerCAmelCase							)

         UpperCAmelCase_           =self.get_dummy_inputs()
         UpperCAmelCase_           =pipe(**_lowerCAmelCase							).images
         UpperCAmelCase_           =image[0, -3:, -3:, -1]

         assert image.shape == (1, 128, 128, 3)
         UpperCAmelCase_           =np.array([0.5_29_11, 0.6_00_04, 0.4_92_29, 0.4_98_05, 0.5_45_02, 0.5_06_80, 0.4_77_77, 0.4_10_28, 0.4_53_04]							)

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



      def 						lowerCAmelCase__     (						self:    str							)    ->     Optional[int]:
         '''simple docstring'''

         UpperCAmelCase_           =OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint		,	provider="CPUExecutionProvider"							)
         UpperCAmelCase_           =EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config							)
         pipe.set_progress_bar_config(disable=_lowerCAmelCase							)

         UpperCAmelCase_           =self.get_dummy_inputs()
         UpperCAmelCase_           =pipe(**_lowerCAmelCase							).images
         UpperCAmelCase_           =image[0, -3:, -3:, -1]

         assert image.shape == (1, 128, 128, 3)
         UpperCAmelCase_           =np.array([0.5_29_11, 0.6_00_04, 0.4_92_29, 0.4_98_05, 0.5_45_02, 0.5_06_80, 0.4_77_77, 0.4_10_28, 0.4_53_04]							)

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






      def 						lowerCAmelCase__     (						self:    Optional[int]							)    ->     str:
         '''simple docstring'''

         UpperCAmelCase_           =OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint		,	provider="CPUExecutionProvider"							)
         UpperCAmelCase_           =DPMSolverMultistepScheduler.from_config(pipe.scheduler.config							)
         pipe.set_progress_bar_config(disable=_lowerCAmelCase							)

         UpperCAmelCase_           =self.get_dummy_inputs()
         UpperCAmelCase_           =pipe(**_lowerCAmelCase							).images
         UpperCAmelCase_           =image[0, -3:, -3:, -1]

         assert image.shape == (1, 128, 128, 3)
         UpperCAmelCase_           =np.array([0.6_53_31, 0.5_82_77, 0.4_82_04, 0.5_60_59, 0.5_36_65, 0.5_62_35, 0.5_09_69, 0.4_00_09, 0.4_65_52]							)

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

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



      @property
      def 						lowerCAmelCase__     (						self:    List[str]							)    ->     Dict:
         '''simple docstring'''

         return (
             "CUDAExecutionProvider",
             {
                 "gpu_mem_limit": "15000000000",  # 15GB
                 "arena_extend_strategy": "kSameAsRequested",
             },
         )



      @property
      def 						lowerCAmelCase__     (						self:    List[str]							)    ->     Tuple:
         '''simple docstring'''

         UpperCAmelCase_           =ort.SessionOptions()
         UpperCAmelCase_           =False
         return options



      def 						lowerCAmelCase__     (						self:    Any							)    ->     List[Any]:
         '''simple docstring'''

         UpperCAmelCase_           =load_image(
             "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
             "/img2img/sketch-mountains-input.jpg"							)
         UpperCAmelCase_           =init_image.resize((768, 512)							)
         # using the PNDM scheduler by default
         UpperCAmelCase_           =OnnxStableDiffusionImgaImgPipeline.from_pretrained(
             "CompVis/stable-diffusion-v1-4"		,	revision="onnx"		,	safety_checker=_lowerCAmelCase		,	feature_extractor=_lowerCAmelCase		,	provider=self.gpu_provider		,	sess_options=self.gpu_options		,	)
         pipe.set_progress_bar_config(disable=_lowerCAmelCase							)

         UpperCAmelCase_           ="A fantasy landscape, trending on artstation"

         UpperCAmelCase_           =np.random.RandomState(0							)
         UpperCAmelCase_           =pipe(
             prompt=_lowerCAmelCase		,	image=_lowerCAmelCase		,	strength=0.75		,	guidance_scale=7.5		,	num_inference_steps=10		,	generator=_lowerCAmelCase		,	output_type="np"		,	)
         UpperCAmelCase_           =output.images
         UpperCAmelCase_           =images[0, 255:258, 383:386, -1]

         assert images.shape == (1, 512, 768, 3)
         UpperCAmelCase_           =np.array([0.49_09, 0.50_59, 0.53_72, 0.46_23, 0.48_76, 0.50_49, 0.48_20, 0.49_56, 0.50_19]							)
         # TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

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






      def 						lowerCAmelCase__     (						self:    str							)    ->     Union[str, Any]:
         '''simple docstring'''

         UpperCAmelCase_           =load_image(
             "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
             "/img2img/sketch-mountains-input.jpg"							)
         UpperCAmelCase_           =init_image.resize((768, 512)							)
         UpperCAmelCase_           =LMSDiscreteScheduler.from_pretrained(
             "runwayml/stable-diffusion-v1-5"		,	subfolder="scheduler"		,	revision="onnx"							)
         UpperCAmelCase_           =OnnxStableDiffusionImgaImgPipeline.from_pretrained(
             "runwayml/stable-diffusion-v1-5"		,	revision="onnx"		,	scheduler=_lowerCAmelCase		,	safety_checker=_lowerCAmelCase		,	feature_extractor=_lowerCAmelCase		,	provider=self.gpu_provider		,	sess_options=self.gpu_options		,	)
         pipe.set_progress_bar_config(disable=_lowerCAmelCase							)

         UpperCAmelCase_           ="A fantasy landscape, trending on artstation"

         UpperCAmelCase_           =np.random.RandomState(0							)
         UpperCAmelCase_           =pipe(
             prompt=_lowerCAmelCase		,	image=_lowerCAmelCase		,	strength=0.75		,	guidance_scale=7.5		,	num_inference_steps=20		,	generator=_lowerCAmelCase		,	output_type="np"		,	)
         UpperCAmelCase_           =output.images
         UpperCAmelCase_           =images[0, 255:258, 383:386, -1]

         assert images.shape == (1, 512, 768, 3)
         UpperCAmelCase_           =np.array([0.80_43, 0.9_26, 0.95_81, 0.81_19, 0.89_54, 0.9_13, 0.72_09, 0.74_63, 0.74_31]							)
         # TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

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






from __future__ import annotations

_A  =	[
    [-1, 0],  # left
    [0, -1],  # down
    [1, 0],  # right
    [0, 1],  # up
]






def   lowerCamelCase__							(							__lowerCAmelCase    :    list[list[int]]       ,				__lowerCAmelCase    :    list[int]       ,				__lowerCAmelCase    :    list[int]       ,				__lowerCAmelCase    :    int       ,				__lowerCAmelCase    :    list[list[int]]       ,				):




			"""simple docstring"""


			lowerCAmelCase_         =      [
			    [0 for col in range(len(grid[0]							)							)] for row in range(len(__lowerCAmelCase							)							)
			]  # the reference grid
			lowerCAmelCase_         =      1
			lowerCAmelCase_         =      [
			    [0 for col in range(len(grid[0]							)							)] for row in range(len(__lowerCAmelCase							)							)
			]  # the action grid

			lowerCAmelCase_         =      init[0]
			lowerCAmelCase_         =      init[1]
			lowerCAmelCase_         =      0
			lowerCAmelCase_         =      g + heuristic[x][y]  # cost from starting cell to destination cell
			lowerCAmelCase_         =      [[f, g, x, y]]

			lowerCAmelCase_         =      False  # flag that is set when search is complete
			lowerCAmelCase_         =      False  # flag set if we can't find expand

			while not found and not resign:
						if len(__lowerCAmelCase							) == 0:
									raise ValueError("Algorithm is unable to find solution"							)
						else:  # to choose the least costliest action so as to move closer to the goal
									cell.sort()
									cell.reverse()
									lowerCAmelCase_         =      cell.pop()
									lowerCAmelCase_         =      next_cell[2]
									lowerCAmelCase_         =      next_cell[3]
									lowerCAmelCase_         =      next_cell[1]

									if x == goal[0] and y == goal[1]:
												lowerCAmelCase_         =      True
									else:
												for i in range(len(__lowerCAmelCase							)							):  # to try out different valid actions
															lowerCAmelCase_         =      x + DIRECTIONS[i][0]
															lowerCAmelCase_         =      y + DIRECTIONS[i][1]
															if xa >= 0 and xa < len(__lowerCAmelCase							) and ya >= 0 and ya < len(grid[0]							):
																		if closed[xa][ya] == 0 and grid[xa][ya] == 0:
																					lowerCAmelCase_         =      g + cost
																					lowerCAmelCase_         =      ga + heuristic[xa][ya]
																					cell.append([fa, ga, xa, ya]							)
																					lowerCAmelCase_         =      1
																					lowerCAmelCase_         =      i
			lowerCAmelCase_         =      []
			lowerCAmelCase_         =      goal[0]
			lowerCAmelCase_         =      goal[1]
			invpath.append([x, y]							)  # we get the reverse path from here
			while x != init[0] or y != init[1]:
						lowerCAmelCase_         =      x - DIRECTIONS[action[x][y]][0]
						lowerCAmelCase_         =      y - DIRECTIONS[action[x][y]][1]
						lowerCAmelCase_         =      xa
						lowerCAmelCase_         =      ya
						invpath.append([x, y]							)

			lowerCAmelCase_         =      []
			for i in range(len(__lowerCAmelCase							)							):
						path.append(invpath[len(__lowerCAmelCase							) - 1 - i]							)
			return path, action


if __name__ == "__main__":
			_A  =	[
			    [0, 1, 0, 0, 0, 0],
			    [0, 1, 0, 0, 0, 0],  # 0 are free path whereas 1's are obstacles
			    [0, 1, 0, 0, 0, 0],
			    [0, 1, 0, 0, 1, 0],
			    [0, 0, 0, 0, 1, 0],
			]

			_A  =	[0, 0]
			# all coordinates are given in format [y,x]
			_A  =	[len(grid) - 1, len(grid[0]) - 1]
			_A  =	1

			# the cost map which pushes the path closer to the goal
			_A  =	[[0 for row in range(len(grid[0]))] for col in range(len(grid))]
			for i in range(len(grid)):
						for j in range(len(grid[0])):
									_A  =	abs(i - goal[0]) + abs(j - goal[1])
									if grid[i][j] == 1:
												# added extra penalty in the heuristic map
												_A  =	99

			_A, _A  =	search(grid, init, goal, cost, heuristic)

			print("ACTION MAP")
			for i in range(len(action)):
						print(action[i])

			for i in range(len(path)):
						print(path[i])



