
'''simple docstring'''





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   (				__magic_name__				,unittest.TestCase    ):
				_snake_case		    = '''hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline'''




				def 	__snake_case							(							self	:				str,    SCREAMING_SNAKE_CASE_	:				int=0      ):
										snake_case       :     str =    floats_tensor((1, 3, 1_28, 1_28),    rng=random.Random(SCREAMING_SNAKE_CASE_      )      )
										snake_case       :     int =    np.random.RandomState(SCREAMING_SNAKE_CASE_      )
										snake_case       :     str =    {
										    '''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 	__snake_case							(							self	:				Tuple      ):
										snake_case       :     List[str] =    OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint,    provider='''CPUExecutionProvider'''      )
										pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_      )

										snake_case       :     str =    self.get_dummy_inputs()
										snake_case       :     List[Any] =    pipe(**SCREAMING_SNAKE_CASE_      ).images
										snake_case       :     Dict =    image[0, -3:, -3:, -1].flatten()

										assert image.shape == (1, 1_28, 1_28, 3)
										snake_case       :     List[Any] =    np.array([0.69643, 0.58484, 0.50314, 0.58760, 0.55368, 0.59643, 0.51529, 0.41217, 0.49087]      )
										assert np.abs(image_slice - expected_slice      ).max() < 1e-1




				def 	__snake_case							(							self	:				Dict      ):
										snake_case       :     Optional[Any] =    OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint,    provider='''CPUExecutionProvider'''      )
										snake_case       :     List[Any] =    PNDMScheduler.from_config(pipe.scheduler.config,    skip_prk_steps=SCREAMING_SNAKE_CASE_      )
										pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_      )

										snake_case       :     Optional[int] =    self.get_dummy_inputs()
										snake_case       :     Union[str, Any] =    pipe(**SCREAMING_SNAKE_CASE_      ).images
										snake_case       :     Tuple =    image[0, -3:, -3:, -1]

										assert image.shape == (1, 1_28, 1_28, 3)
										snake_case       :     int =    np.array([0.61737, 0.54642, 0.53183, 0.54465, 0.52742, 0.60525, 0.49969, 0.40655, 0.48154]      )

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




				def 	__snake_case							(							self	:				Tuple      ):
										snake_case       :     Tuple =    OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint,    provider='''CPUExecutionProvider'''      )
										snake_case       :     Union[str, Any] =    LMSDiscreteScheduler.from_config(pipe.scheduler.config      )
										pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_      )

										# warmup pass to apply optimizations
										snake_case       :     Optional[Any] =    pipe(**self.get_dummy_inputs()      )

										snake_case       :     Dict =    self.get_dummy_inputs()
										snake_case       :     int =    pipe(**SCREAMING_SNAKE_CASE_      ).images
										snake_case       :     Any =    image[0, -3:, -3:, -1]

										assert image.shape == (1, 1_28, 1_28, 3)
										snake_case       :     Optional[Any] =    np.array([0.52761, 0.59977, 0.49033, 0.49619, 0.54282, 0.50311, 0.47600, 0.40918, 0.45203]      )

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




				def 	__snake_case							(							self	:				Any      ):
										snake_case       :     Optional[Any] =    OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint,    provider='''CPUExecutionProvider'''      )
										snake_case       :     Optional[Any] =    EulerDiscreteScheduler.from_config(pipe.scheduler.config      )
										pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_      )

										snake_case       :     Optional[Any] =    self.get_dummy_inputs()
										snake_case       :     Union[str, Any] =    pipe(**SCREAMING_SNAKE_CASE_      ).images
										snake_case       :     Any =    image[0, -3:, -3:, -1]

										assert image.shape == (1, 1_28, 1_28, 3)
										snake_case       :     Tuple =    np.array([0.52911, 0.60004, 0.49229, 0.49805, 0.54502, 0.50680, 0.47777, 0.41028, 0.45304]      )

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




				def 	__snake_case							(							self	:				Any      ):
										snake_case       :     Dict =    OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint,    provider='''CPUExecutionProvider'''      )
										snake_case       :     Any =    EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config      )
										pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_      )

										snake_case       :     Any =    self.get_dummy_inputs()
										snake_case       :     Any =    pipe(**SCREAMING_SNAKE_CASE_      ).images
										snake_case       :     Tuple =    image[0, -3:, -3:, -1]

										assert image.shape == (1, 1_28, 1_28, 3)
										snake_case       :     Dict =    np.array([0.52911, 0.60004, 0.49229, 0.49805, 0.54502, 0.50680, 0.47777, 0.41028, 0.45304]      )

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




				def 	__snake_case							(							self	:				Optional[int]      ):
										snake_case       :     Tuple =    OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint,    provider='''CPUExecutionProvider'''      )
										snake_case       :     Optional[int] =    DPMSolverMultistepScheduler.from_config(pipe.scheduler.config      )
										pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_      )

										snake_case       :     Optional[Any] =    self.get_dummy_inputs()
										snake_case       :     Optional[Any] =    pipe(**SCREAMING_SNAKE_CASE_      ).images
										snake_case       :     List[str] =    image[0, -3:, -3:, -1]

										assert image.shape == (1, 1_28, 1_28, 3)
										snake_case       :     Optional[int] =    np.array([0.65331, 0.58277, 0.48204, 0.56059, 0.53665, 0.56235, 0.50969, 0.40009, 0.46552]      )

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




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




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




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




				def 	__snake_case							(							self	:				List[Any]      ):
										snake_case       :     str =    load_image(
										    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
										    '''/img2img/sketch-mountains-input.jpg'''      )
										snake_case       :     int =    init_image.resize((7_68, 5_12)      )
										# using the PNDM scheduler by default
										snake_case       :     str =    OnnxStableDiffusionImgaImgPipeline.from_pretrained(
										    '''CompVis/stable-diffusion-v1-4''',    revision='''onnx''',    safety_checker=SCREAMING_SNAKE_CASE_,    feature_extractor=SCREAMING_SNAKE_CASE_,    provider=self.gpu_provider,    sess_options=self.gpu_options,    )
										pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_      )

										snake_case       :     Dict =    '''A fantasy landscape, trending on artstation'''

										snake_case       :     Optional[Any] =    np.random.RandomState(0      )
										snake_case       :     Tuple =    pipe(
										    prompt=SCREAMING_SNAKE_CASE_,    image=SCREAMING_SNAKE_CASE_,    strength=0.75,    guidance_scale=7.5,    num_inference_steps=10,    generator=SCREAMING_SNAKE_CASE_,    output_type='''np''',    )
										snake_case       :     List[Any] =    output.images
										snake_case       :     Dict =    images[0, 2_55:2_58, 3_83:3_86, -1]

										assert images.shape == (1, 5_12, 7_68, 3)
										snake_case       :     str =    np.array([0.4909, 0.5059, 0.5372, 0.4623, 0.4876, 0.5049, 0.4820, 0.4956, 0.5019]      )
										# TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

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




				def 	__snake_case							(							self	:				int      ):
										snake_case       :     List[Any] =    load_image(
										    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
										    '''/img2img/sketch-mountains-input.jpg'''      )
										snake_case       :     Any =    init_image.resize((7_68, 5_12)      )
										snake_case       :     int =    LMSDiscreteScheduler.from_pretrained(
										    '''runwayml/stable-diffusion-v1-5''',    subfolder='''scheduler''',    revision='''onnx'''      )
										snake_case       :     Tuple =    OnnxStableDiffusionImgaImgPipeline.from_pretrained(
										    '''runwayml/stable-diffusion-v1-5''',    revision='''onnx''',    scheduler=SCREAMING_SNAKE_CASE_,    safety_checker=SCREAMING_SNAKE_CASE_,    feature_extractor=SCREAMING_SNAKE_CASE_,    provider=self.gpu_provider,    sess_options=self.gpu_options,    )
										pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_      )

										snake_case       :     Optional[Any] =    '''A fantasy landscape, trending on artstation'''

										snake_case       :     Tuple =    np.random.RandomState(0      )
										snake_case       :     Union[str, Any] =    pipe(
										    prompt=SCREAMING_SNAKE_CASE_,    image=SCREAMING_SNAKE_CASE_,    strength=0.75,    guidance_scale=7.5,    num_inference_steps=20,    generator=SCREAMING_SNAKE_CASE_,    output_type='''np''',    )
										snake_case       :     Any =    output.images
										snake_case       :     Dict =    images[0, 2_55:2_58, 3_83:3_86, -1]

										assert images.shape == (1, 5_12, 7_68, 3)
										snake_case       :     Tuple =    np.array([0.8043, 0.926, 0.9581, 0.8119, 0.8954, 0.913, 0.7209, 0.7463, 0.7431]      )
										# TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

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



'''simple docstring'''





import numpy as np

class 						a   :




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




				def __eq__(							self	:				str,    SCREAMING_SNAKE_CASE_	:				List[Any]      ):
										return self.position == cell.position




				def 	__snake_case							(							self	:				Optional[int]      ):
										print(self.position      )




class 						a   :




				def __init__(							self	:				Dict,    SCREAMING_SNAKE_CASE_	:				Union[str, Any]=(5, 5)      ):
										snake_case       :     int =    np.zeros(SCREAMING_SNAKE_CASE_      )
										snake_case       :     Optional[int] =    world_size[0]
										snake_case       :     Optional[Any] =    world_size[1]




				def 	__snake_case							(							self	:				Any      ):
										print(self.w      )




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




def      A (       A_							:					Tuple    ,							A_							:					Tuple    ,							A_							:					Union[str, Any]       ):
						snake_case       :     Union[str, Any] =    []
						snake_case       :     Union[str, Any] =    []
						_open.append(A_       )

						while _open:
												snake_case       :     List[Any] =    np.argmin([n.f for n in _open]       )
												snake_case       :     List[Any] =    _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
																		snake_case       :     Dict =    current.g + 1
																		snake_case,					snake_case       :     Optional[Any] =    n.position
																		snake_case,					snake_case       :     Union[str, Any] =    goal.position
																		snake_case       :     Tuple =    (ya - ya) ** 2 + (xa - xa) ** 2
																		snake_case       :     Union[str, Any] =    n.h + n.g

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


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

