




'''simple docstring'''

import tempfile
import unittest

import numpy as np

from diffusers import (
    DDIMScheduler,
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    OnnxStableDiffusionPipeline,
    PNDMScheduler,
)
from diffusers.utils.testing_utils import is_onnx_available, nightly, require_onnxruntime, require_torch_gpu

from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin


if is_onnx_available():
							import onnxruntime as ort






class 		a							(  _lowerCamelCase  ,	unittest.TestCase	):
				snake_case_           =						"hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline"


				def     A_					(						self       :   str			,							lowercase_       :   Dict=0   ):
									snake_case_		     =							np.random.RandomState(lowercase_   )
									snake_case_		     =							{
									    '''prompt''': '''A painting of a squirrel eating a burger''',
									    '''generator''': generator,
									    '''num_inference_steps''': 2,
									    '''guidance_scale''': 7.5,
									    '''output_type''': '''numpy''',
									}
									return inputs


				def     A_					(						self       :   Optional[Any]   ):
									snake_case_		     =							OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint			,							provider='''CPUExecutionProvider'''   )
									pipe.set_progress_bar_config(disable=lowercase_   )

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

									assert image.shape == (1, 128, 128, 3)
									snake_case_		     =							np.array([0.6_5072, 0.5_8492, 0.4_8219, 0.5_5521, 0.5_3180, 0.5_5939, 0.5_0697, 0.3_9800, 0.4_6455]   )

									assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1e-2


				def     A_					(						self       :   int   ):
									snake_case_		     =							OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint			,							provider='''CPUExecutionProvider'''   )
									snake_case_		     =							PNDMScheduler.from_config(pipe.scheduler.config			,							skip_prk_steps=lowercase_   )
									pipe.set_progress_bar_config(disable=lowercase_   )

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

									assert image.shape == (1, 128, 128, 3)
									snake_case_		     =							np.array([0.6_5863, 0.5_9425, 0.4_9326, 0.5_6313, 0.5_3875, 0.5_6627, 0.5_1065, 0.3_9777, 0.4_6330]   )

									assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1e-2


				def     A_					(						self       :   Optional[Any]   ):
									snake_case_		     =							OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint			,							provider='''CPUExecutionProvider'''   )
									snake_case_		     =							LMSDiscreteScheduler.from_config(pipe.scheduler.config   )
									pipe.set_progress_bar_config(disable=lowercase_   )

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

									assert image.shape == (1, 128, 128, 3)
									snake_case_		     =							np.array([0.5_3755, 0.6_0786, 0.4_7402, 0.4_9488, 0.5_1869, 0.4_9819, 0.4_7985, 0.3_8957, 0.4_4279]   )

									assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1e-2


				def     A_					(						self       :   List[Any]   ):
									snake_case_		     =							OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint			,							provider='''CPUExecutionProvider'''   )
									snake_case_		     =							EulerDiscreteScheduler.from_config(pipe.scheduler.config   )
									pipe.set_progress_bar_config(disable=lowercase_   )

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

									assert image.shape == (1, 128, 128, 3)
									snake_case_		     =							np.array([0.5_3755, 0.6_0786, 0.4_7402, 0.4_9488, 0.5_1869, 0.4_9819, 0.4_7985, 0.3_8957, 0.4_4279]   )

									assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1e-2


				def     A_					(						self       :   Optional[Any]   ):
									snake_case_		     =							OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint			,							provider='''CPUExecutionProvider'''   )
									snake_case_		     =							EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config   )
									pipe.set_progress_bar_config(disable=lowercase_   )

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

									assert image.shape == (1, 128, 128, 3)
									snake_case_		     =							np.array([0.5_3817, 0.6_0812, 0.4_7384, 0.4_9530, 0.5_1894, 0.4_9814, 0.4_7984, 0.3_8958, 0.4_4271]   )

									assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1e-2


				def     A_					(						self       :   Dict   ):
									snake_case_		     =							OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint			,							provider='''CPUExecutionProvider'''   )
									snake_case_		     =							DPMSolverMultistepScheduler.from_config(pipe.scheduler.config   )
									pipe.set_progress_bar_config(disable=lowercase_   )

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

									assert image.shape == (1, 128, 128, 3)
									snake_case_		     =							np.array([0.5_3895, 0.6_0808, 0.4_7933, 0.4_9608, 0.5_1886, 0.4_9950, 0.4_8053, 0.3_8957, 0.4_4200]   )

									assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1e-2


				def     A_					(						self       :   List[Any]   ):
									snake_case_		     =							OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint			,							provider='''CPUExecutionProvider'''   )
									pipe.set_progress_bar_config(disable=lowercase_   )

									snake_case_		     =							self.get_dummy_inputs()
									snake_case_		     =							3 * [inputs['''prompt''']]

									# forward
									snake_case_		     =							pipe(**lowercase_   )
									snake_case_		     =							output.images[0, -3:, -3:, -1]

									snake_case_		     =							self.get_dummy_inputs()
									snake_case_		     =							3 * [inputs.pop('''prompt'''   )]

									snake_case_		     =							pipe.tokenizer(
									    lowercase_			,							padding='''max_length'''			,							max_length=pipe.tokenizer.model_max_length			,							truncation=lowercase_			,							return_tensors='''np'''			,							)
									snake_case_		     =							text_inputs['''input_ids''']

									snake_case_		     =							pipe.text_encoder(input_ids=text_inputs.astype(np.intaa   )   )[0]

									snake_case_		     =							prompt_embeds

									# forward
									snake_case_		     =							pipe(**lowercase_   )
									snake_case_		     =							output.images[0, -3:, -3:, -1]

									assert np.abs(image_slice_a.flatten() - image_slice_a.flatten()   ).max() < 1e-4


				def     A_					(						self       :   Optional[int]   ):
									snake_case_		     =							OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint			,							provider='''CPUExecutionProvider'''   )
									pipe.set_progress_bar_config(disable=lowercase_   )

									snake_case_		     =							self.get_dummy_inputs()
									snake_case_		     =							3 * ['''this is a negative prompt''']
									snake_case_		     =							negative_prompt
									snake_case_		     =							3 * [inputs['''prompt''']]

									# forward
									snake_case_		     =							pipe(**lowercase_   )
									snake_case_		     =							output.images[0, -3:, -3:, -1]

									snake_case_		     =							self.get_dummy_inputs()
									snake_case_		     =							3 * [inputs.pop('''prompt'''   )]

									snake_case_		     =							[]
									for p in [prompt, negative_prompt]:
														snake_case_		     =							pipe.tokenizer(
														    lowercase_			,							padding='''max_length'''			,							max_length=pipe.tokenizer.model_max_length			,							truncation=lowercase_			,							return_tensors='''np'''			,							)
														snake_case_		     =							text_inputs['''input_ids''']

														embeds.append(pipe.text_encoder(input_ids=text_inputs.astype(np.intaa   )   )[0]   )

									snake_case_     ,snake_case_		     =							embeds

									# forward
									snake_case_		     =							pipe(**lowercase_   )
									snake_case_		     =							output.images[0, -3:, -3:, -1]

									assert np.abs(image_slice_a.flatten() - image_slice_a.flatten()   ).max() < 1e-4







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


				@property
				def     A_					(						self       :   List[Any]   ):
									return (
									    "CUDAExecutionProvider",
									    {
									        "gpu_mem_limit": "15000000000",  # 15GB
									        "arena_extend_strategy": "kSameAsRequested",
									    },
									)


				@property
				def     A_					(						self       :   Tuple   ):
									snake_case_		     =							ort.SessionOptions()
									snake_case_		     =							False
									return options


				def     A_					(						self       :   Dict   ):
									# using the PNDM scheduler by default
									snake_case_		     =							OnnxStableDiffusionPipeline.from_pretrained(
									    '''CompVis/stable-diffusion-v1-4'''			,							revision='''onnx'''			,							safety_checker=lowercase_			,							feature_extractor=lowercase_			,							provider=self.gpu_provider			,							sess_options=self.gpu_options			,							)
									sd_pipe.set_progress_bar_config(disable=lowercase_   )

									snake_case_		     =							'''A painting of a squirrel eating a burger'''
									np.random.seed(0   )
									snake_case_		     =							sd_pipe([prompt]			,							guidance_scale=6.0			,							num_inference_steps=10			,							output_type='''np'''   )
									snake_case_		     =							output.images

									snake_case_		     =							image[0, -3:, -3:, -1]

									assert image.shape == (1, 512, 512, 3)
									snake_case_		     =							np.array([0.0452, 0.0390, 0.0087, 0.0350, 0.0617, 0.0364, 0.0544, 0.0523, 0.0720]   )

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


				def     A_					(						self       :   str   ):
									snake_case_		     =							DDIMScheduler.from_pretrained(
									    '''runwayml/stable-diffusion-v1-5'''			,							subfolder='''scheduler'''			,							revision='''onnx'''   )
									snake_case_		     =							OnnxStableDiffusionPipeline.from_pretrained(
									    '''runwayml/stable-diffusion-v1-5'''			,							revision='''onnx'''			,							scheduler=lowercase_			,							safety_checker=lowercase_			,							feature_extractor=lowercase_			,							provider=self.gpu_provider			,							sess_options=self.gpu_options			,							)
									sd_pipe.set_progress_bar_config(disable=lowercase_   )

									snake_case_		     =							'''open neural network exchange'''
									snake_case_		     =							np.random.RandomState(0   )
									snake_case_		     =							sd_pipe([prompt]			,							guidance_scale=7.5			,							num_inference_steps=10			,							generator=lowercase_			,							output_type='''np'''   )
									snake_case_		     =							output.images
									snake_case_		     =							image[0, -3:, -3:, -1]

									assert image.shape == (1, 512, 512, 3)
									snake_case_		     =							np.array([0.2867, 0.1974, 0.1481, 0.7294, 0.7251, 0.6667, 0.4194, 0.5642, 0.6486]   )

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


				def     A_					(						self       :   Dict   ):
									snake_case_		     =							LMSDiscreteScheduler.from_pretrained(
									    '''runwayml/stable-diffusion-v1-5'''			,							subfolder='''scheduler'''			,							revision='''onnx'''   )
									snake_case_		     =							OnnxStableDiffusionPipeline.from_pretrained(
									    '''runwayml/stable-diffusion-v1-5'''			,							revision='''onnx'''			,							scheduler=lowercase_			,							safety_checker=lowercase_			,							feature_extractor=lowercase_			,							provider=self.gpu_provider			,							sess_options=self.gpu_options			,							)
									sd_pipe.set_progress_bar_config(disable=lowercase_   )

									snake_case_		     =							'''open neural network exchange'''
									snake_case_		     =							np.random.RandomState(0   )
									snake_case_		     =							sd_pipe([prompt]			,							guidance_scale=7.5			,							num_inference_steps=10			,							generator=lowercase_			,							output_type='''np'''   )
									snake_case_		     =							output.images
									snake_case_		     =							image[0, -3:, -3:, -1]

									assert image.shape == (1, 512, 512, 3)
									snake_case_		     =							np.array([0.2306, 0.1959, 0.1593, 0.6549, 0.6394, 0.5408, 0.5065, 0.6010, 0.6161]   )

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


				def     A_					(						self       :   Optional[Any]   ):
									snake_case_		     =							0

									def test_callback_fn(lowercase_       :   int			,							lowercase_       :   int			,							lowercase_       :   np.ndarray   ) -> None:
														snake_case_		     =							True
														nonlocal number_of_steps
														number_of_steps += 1
														if step == 0:
																			assert latents.shape == (1, 4, 64, 64)
																			snake_case_		     =							latents[0, -3:, -3:, -1]
																			snake_case_		     =							np.array(
																			    [-0.6772, -0.3835, -1.2456, 0.1905, -1.0974, 0.6967, -1.9353, 0.0178, 1.0167]   )

																			assert np.abs(latents_slice.flatten() - expected_slice   ).max() < 1e-3
														elif step == 5:
																			assert latents.shape == (1, 4, 64, 64)
																			snake_case_		     =							latents[0, -3:, -3:, -1]
																			snake_case_		     =							np.array(
																			    [-0.3351, 0.2241, -0.1837, -0.2325, -0.6577, 0.3393, -0.0241, 0.5899, 1.3875]   )

																			assert np.abs(latents_slice.flatten() - expected_slice   ).max() < 1e-3

									snake_case_		     =							False

									snake_case_		     =							OnnxStableDiffusionPipeline.from_pretrained(
									    '''runwayml/stable-diffusion-v1-5'''			,							revision='''onnx'''			,							safety_checker=lowercase_			,							feature_extractor=lowercase_			,							provider=self.gpu_provider			,							sess_options=self.gpu_options			,							)
									pipe.set_progress_bar_config(disable=lowercase_   )

									snake_case_		     =							'''Andromeda galaxy in a bottle'''

									snake_case_		     =							np.random.RandomState(0   )
									pipe(
									    prompt=lowercase_			,							num_inference_steps=5			,							guidance_scale=7.5			,							generator=lowercase_			,							callback=lowercase_			,							callback_steps=1			,							)
									assert test_callback_fn.has_been_called
									assert number_of_steps == 6


				def     A_					(						self       :   Union[str, Any]   ):
									snake_case_		     =							OnnxStableDiffusionPipeline.from_pretrained(
									    '''runwayml/stable-diffusion-v1-5'''			,							revision='''onnx'''			,							safety_checker=lowercase_			,							feature_extractor=lowercase_			,							provider=self.gpu_provider			,							sess_options=self.gpu_options			,							)
									assert isinstance(lowercase_			,							lowercase_   )
									assert pipe.safety_checker is None

									snake_case_		     =							pipe('''example prompt'''			,							num_inference_steps=2   ).images[0]
									assert image is not None

									# check that there's no error when saving a pipeline with one of the models being None
									with tempfile.TemporaryDirectory() as tmpdirname:
														pipe.save_pretrained(lowercase_   )
														snake_case_		     =							OnnxStableDiffusionPipeline.from_pretrained(lowercase_   )

									# sanity check that the pipeline still works
									assert pipe.safety_checker is None
									snake_case_		     =							pipe('''example prompt'''			,							num_inference_steps=2   ).images[0]
									assert image is not None







'''simple docstring'''

import heapq
import sys

import numpy as np

a    :							Dict										=    tuple[int, int]






class 		a							:


				def __init__(						self       :   Dict   ):
									snake_case_		     =							[]
									snake_case_		     =							set()


				def     A_					(						self       :   int   ):
									if not self.empty():
														return self.elements[0][0]
									else:
														return float('''inf'''   )


				def     A_					(						self       :   List[str]   ):
									return len(self.elements   ) == 0


				def     A_					(						self       :   Union[str, Any]			,							lowercase_       :   str			,							lowercase_       :   List[str]   ):
									if item not in self.set:
														heapq.heappush(self.elements			,							(priority, item)   )
														self.set.add(lowercase_   )
									else:
														# update
														# print("update", item)
														snake_case_		     =							[]
														((snake_case_)     ,(snake_case_))		     =							heapq.heappop(self.elements   )
														while x != item:
																			temp.append((pri, x)   )
																			((snake_case_)     ,(snake_case_))		     =							heapq.heappop(self.elements   )
														temp.append((priority, item)   )
														for pro, xxx in temp:
																			heapq.heappush(self.elements			,							(pro, xxx)   )


				def     A_					(						self       :   Union[str, Any]			,							lowercase_       :   List[Any]   ):
									if item in self.set:
														self.set.remove(lowercase_   )
														snake_case_		     =							[]
														((snake_case_)     ,(snake_case_))		     =							heapq.heappop(self.elements   )
														while x != item:
																			temp.append((pro, x)   )
																			((snake_case_)     ,(snake_case_))		     =							heapq.heappop(self.elements   )
														for prito, yyy in temp:
																			heapq.heappush(self.elements			,							(prito, yyy)   )


				def     A_					(						self       :   Optional[int]   ):
									return self.elements[0][1]


				def     A_					(						self       :   Tuple   ):
									((snake_case_)     ,(snake_case_))		     =							heapq.heappop(self.elements   )
									self.set.remove(lowercase_   )
									return (priority, item)







def 						__magic_name__       (      __UpperCAmelCase, __UpperCAmelCase      )						->   Any:

					'''simple docstring'''

					snake_case_		     =							np.array(__UpperCAmelCase      )
					snake_case_		     =							np.array(__UpperCAmelCase      )
					return np.linalg.norm(a - b      )


def 						__magic_name__       (      __UpperCAmelCase, __UpperCAmelCase      )						->   int:

					'''simple docstring'''

					return consistent_heuristic(__UpperCAmelCase, __UpperCAmelCase      ) // t


def 						__magic_name__       (      __UpperCAmelCase, __UpperCAmelCase      )						->   List[str]:

					'''simple docstring'''

					return abs(p[0] - goal[0]      ) + abs(p[1] - goal[1]      )


def 						__magic_name__       (      __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase      )						->   Optional[int]:

					'''simple docstring'''

					snake_case_		     =							g_function[start] + Wa * heuristics[i](__UpperCAmelCase, __UpperCAmelCase      )
					return ans


def 						__magic_name__       (      __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase      )						->   Optional[Any]:

					'''simple docstring'''

					snake_case_		     =							np.chararray((n, n)      )
					for i in range(__UpperCAmelCase      ):
										for j in range(__UpperCAmelCase      ):
															snake_case_		     =							'''*'''

					for i in range(__UpperCAmelCase      ):
										for j in range(__UpperCAmelCase      ):
															if (j, (n - 1) - i) in blocks:
																				snake_case_		     =							'''#'''

					snake_case_		     =							'''-'''
					snake_case_		     =							back_pointer[goal]
					while x != start:
										((snake_case_)     ,(snake_case_))		     =							x
										# print(x)
										snake_case_		     =							'''-'''
										snake_case_		     =							back_pointer[x]
					snake_case_		     =							'''-'''

					for i in range(__UpperCAmelCase      ):
										for j in range(__UpperCAmelCase      ):
															if (i, j) == (0, n - 1):
																				print(grid[i][j], end=''' '''      )
																				print('''<-- End position''', end=''' '''      )
															else:
																				print(grid[i][j], end=''' '''      )
										print()
					print('''^'''      )
					print('''Start position'''      )
					print()
					print('''# is an obstacle'''      )
					print('''- is the path taken by algorithm'''      )
					print('''PATH TAKEN BY THE ALGORITHM IS:-'''      )
					snake_case_		     =							back_pointer[goal]
					while x != start:
										print(__UpperCAmelCase, end=''' '''      )
										snake_case_		     =							back_pointer[x]
					print(__UpperCAmelCase      )
					sys.exit()


def 						__magic_name__       (      __UpperCAmelCase      )						->   int:

					'''simple docstring'''

					if p[0] < 0 or p[0] > n - 1:
										return False
					if p[1] < 0 or p[1] > n - 1:
										return False
					return True


def 						__magic_name__       (      __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, )						->   str:

					'''simple docstring'''

					for itera in range(__UpperCAmelCase      ):
										open_list[itera].remove_element(__UpperCAmelCase      )
					# print("s", s)
					# print("j", j)
					((snake_case_)     ,(snake_case_))		     =							s
					snake_case_		     =							(x - 1, y)
					snake_case_		     =							(x + 1, y)
					snake_case_		     =							(x, y + 1)
					snake_case_		     =							(x, y - 1)

					for neighbours in [left, right, up, down]:
										if neighbours not in blocks:
															if valid(__UpperCAmelCase      ) and neighbours not in visited:
																				# print("neighbour", neighbours)
																				visited.add(__UpperCAmelCase      )
																				snake_case_		     =							-1
																				snake_case_		     =							float('''inf'''      )

															if valid(__UpperCAmelCase      ) and g_function[neighbours] > g_function[s] + 1:
																				snake_case_		     =							g_function[s] + 1
																				snake_case_		     =							s
																				if neighbours not in close_list_anchor:
																									open_list[0].put(__UpperCAmelCase, key(__UpperCAmelCase, 0, __UpperCAmelCase, __UpperCAmelCase      )      )
																									if neighbours not in close_list_inad:
																														for var in range(1, __UpperCAmelCase      ):
																																			if key(__UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase      ) <= Wa * key(
																																			    __UpperCAmelCase, 0, __UpperCAmelCase, __UpperCAmelCase      ):
																																								open_list[j].put(
																																								    __UpperCAmelCase, key(__UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase      )      )


def 						__magic_name__       (      )						->   str:

					'''simple docstring'''

					snake_case_		     =							[]
					for x in range(1, 5      ):
										for y in range(1, 6      ):
															some_list.append((x, y)      )

					for x in range(15, 20      ):
										some_list.append((x, 17)      )

					for x in range(10, 19      ):
										for y in range(1, 15      ):
															some_list.append((x, y)      )

    # L block
					for x in range(1, 4      ):
										for y in range(12, 19      ):
															some_list.append((x, y)      )
					for x in range(3, 13      ):
										for y in range(16, 19      ):
															some_list.append((x, y)      )
					return some_list


a    :							Tuple										=    {0: consistent_heuristic, 1: heuristic_a, 2: heuristic_a}

a    :							Dict										=    [
    (0, 1),
    (1, 1),
    (2, 1),
    (3, 1),
    (4, 1),
    (5, 1),
    (6, 1),
    (7, 1),
    (8, 1),
    (9, 1),
    (10, 1),
    (11, 1),
    (12, 1),
    (13, 1),
    (14, 1),
    (15, 1),
    (16, 1),
    (17, 1),
    (18, 1),
    (19, 1),
]
a    :							List[str]										=    make_common_ground()


a    :							Optional[int]										=    blocks_blk
# hyper parameters
a    :							Union[str, Any]										=    1
a    :							str										=    1
a    :							int										=    20
a    :							Optional[int]										=    3  # one consistent and two other inconsistent

# start and end destination
a    :							List[str]										=    (0, 0)
a    :							str										=    (n - 1, n - 1)

a    :							str										=    1


def 						__magic_name__       (      __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase      )						->   List[str]:

					'''simple docstring'''

					snake_case_		     =							{start: 0, goal: float('''inf'''      )}
					snake_case_		     =							{start: -1, goal: -1}
					snake_case_		     =							[]
					snake_case_		     =							set()

					for i in range(__UpperCAmelCase      ):
										open_list.append(PriorityQueue()      )
										open_list[i].put(__UpperCAmelCase, key(__UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase      )      )

					snake_case_		     =							[]
					snake_case_		     =							[]
					while open_list[0].minkey() < float('''inf'''      ):
										for i in range(1, __UpperCAmelCase      ):
															# print(open_list[0].minkey(), open_list[i].minkey())
															if open_list[i].minkey() <= Wa * open_list[0].minkey():
																				global t
																				t += 1
																				if g_function[goal] <= open_list[i].minkey():
																									if g_function[goal] < float('''inf'''      ):
																														do_something(__UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase      )
																				else:
																									snake_case_     ,snake_case_		     =							open_list[i].top_show()
																									visited.add(__UpperCAmelCase      )
																									expand_state(
																									    __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, )
																									close_list_inad.append(__UpperCAmelCase      )
															else:
																				if g_function[goal] <= open_list[0].minkey():
																									if g_function[goal] < float('''inf'''      ):
																														do_something(__UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase      )
																				else:
																									snake_case_		     =							open_list[0].top_show()
																									visited.add(__UpperCAmelCase      )
																									expand_state(
																									    __UpperCAmelCase, 0, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, )
																									close_list_anchor.append(__UpperCAmelCase      )
					print('''No path found to goal'''      )
					print()
					for i in range(n - 1, -1, -1      ):
										for j in range(__UpperCAmelCase      ):
															if (j, i) in blocks:
																				print('''#''', end=''' '''      )
															elif (j, i) in back_pointer:
																				if (j, i) == (n - 1, n - 1):
																									print('''*''', end=''' '''      )
																				else:
																									print('''-''', end=''' '''      )
															else:
																				print('''*''', end=''' '''      )
															if (j, i) == (n - 1, n - 1):
																				print('''<-- End position''', end=''' '''      )
										print()
					print('''^'''      )
					print('''Start position'''      )
					print()
					print('''# is an obstacle'''      )
					print('''- is the path taken by algorithm'''      )


if __name__ == "__main__":
							multi_a_star(start, goal, n_heuristic)

