
import heapq
import sys

import numpy as np

_lowerCamelCase			:							str         =     tuple[int, int]




class   SCREAMING_SNAKE_CASE__	:





	'''simple docstring'''



	def __init__(    self      :       Optional[Any]      ):


				'''simple docstring'''


				_snake_case            =     []
				_snake_case            =     set()



	def 					A				(    self      :       List[Any]      ):


				'''simple docstring'''


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



	def 					A				(    self      :       Any      ):


				'''simple docstring'''


				return len(self.elements      ) == 0



	def 					A				(    self      :       List[Any]     ,    lowercase      :       Dict     ,    lowercase      :       List[str]      ):


				'''simple docstring'''


				if item not in self.set:
							heapq.heappush(self.elements     ,    (priority, item)      )
							self.set.add(a_      )
				else:
							# update
							# print("update", item)
							_snake_case            =     []
							(_snake_case)            =     heapq.heappop(self.elements      )
							while x != item:
										temp.append((pri, x)      )
										(_snake_case)            =     heapq.heappop(self.elements      )
							temp.append((priority, item)      )
							for pro, xxx in temp:
										heapq.heappush(self.elements     ,    (pro, xxx)      )



	def 					A				(    self      :       List[str]     ,    lowercase      :       Dict      ):


				'''simple docstring'''


				if item in self.set:
							self.set.remove(a_      )
							_snake_case            =     []
							(_snake_case)            =     heapq.heappop(self.elements      )
							while x != item:
										temp.append((pro, x)      )
										(_snake_case)            =     heapq.heappop(self.elements      )
							for prito, yyy in temp:
										heapq.heappush(self.elements     ,    (prito, yyy)      )



	def 					A				(    self      :       Dict      ):


				'''simple docstring'''


				return self.elements[0][1]





	def 					A				(    self      :       str      ):


				'''simple docstring'''


				(_snake_case)            =     heapq.heappop(self.elements      )
				self.set.remove(a_      )
				return (priority, item)






def 			a_						(			__lowercase  :      TPos			,   __lowercase  :      TPos					)					->    List[Any]:
			_snake_case            =     np.array(lowerCAmelCase__					)
			_snake_case            =     np.array(lowerCAmelCase__					)
			return np.linalg.norm(a - b					)






def 			a_						(			__lowercase  :      TPos			,   __lowercase  :      TPos					)					->    Any:
			return consistent_heuristic(lowerCAmelCase__			,   lowerCAmelCase__					) // t






def 			a_						(			__lowercase  :      TPos			,   __lowercase  :      TPos					)					->    List[Any]:
			return abs(p[0] - goal[0]					) + abs(p[1] - goal[1]					)






def 			a_						(			__lowercase  :      TPos			,   __lowercase  :      int			,   __lowercase  :      TPos			,   __lowercase  :      dict[TPos, float]					)					->    List[str]:
			_snake_case            =     g_function[start] + Wa * heuristics[i](lowerCAmelCase__			,   lowerCAmelCase__					)
			return ans






def 			a_						(			__lowercase  :      Tuple			,   __lowercase  :      Any			,   __lowercase  :      Tuple					)					->    Optional[Any]:
			_snake_case            =     np.chararray((n, n)					)
			for i in range(lowerCAmelCase__					):
						for j in range(lowerCAmelCase__					):
									_snake_case            =     "*"

			for i in range(lowerCAmelCase__					):
						for j in range(lowerCAmelCase__					):
									if (j, (n - 1) - i) in blocks:
												_snake_case            =     "#"

			_snake_case            =     "-"
			_snake_case            =     back_pointer[goal]
			while x != start:
						(_snake_case)            =     x
						# print(x)
						_snake_case            =     "-"
						_snake_case            =     back_pointer[x]
			_snake_case            =     "-"

			for i in range(lowerCAmelCase__					):
						for j in range(lowerCAmelCase__					):
									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(lowerCAmelCase__			,   end=' '					)
						_snake_case            =     back_pointer[x]
			print(lowerCAmelCase__					)
			sys.exit()






def 			a_						(			__lowercase  :      TPos					)					->    Dict:
			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 			a_						(			__lowercase  :      Union[str, Any]			,   __lowercase  :      List[str]			,   __lowercase  :      Tuple			,   __lowercase  :      Tuple			,   __lowercase  :      List[str]			,   __lowercase  :      List[Any]			,   __lowercase  :      Dict			,   __lowercase  :      List[str]			,   )					->    List[str]:
			for itera in range(lowerCAmelCase__					):
						open_list[itera].remove_element(lowerCAmelCase__					)
			# print("s", s)
			# print("j", j)
			(_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(lowerCAmelCase__					) and neighbours not in visited:
												# print("neighbour", neighbours)
												visited.add(lowerCAmelCase__					)
												_snake_case            =     -1
												_snake_case            =     float('inf'					)

									if valid(lowerCAmelCase__					) 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(lowerCAmelCase__			,   key(lowerCAmelCase__			,   0			,   lowerCAmelCase__			,   lowerCAmelCase__					)					)
															if neighbours not in close_list_inad:
																		for var in range(1			,   lowerCAmelCase__					):
																					if key(lowerCAmelCase__			,   lowerCAmelCase__			,   lowerCAmelCase__			,   lowerCAmelCase__					) <= Wa * key(
																					    lowerCAmelCase__			,   0			,   lowerCAmelCase__			,   lowerCAmelCase__					):
																								open_list[j].put(
																								    lowerCAmelCase__			,   key(lowerCAmelCase__			,   lowerCAmelCase__			,   lowerCAmelCase__			,   lowerCAmelCase__					)					)






def 			a_						(			)					->    List[str]:
			_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


_lowerCamelCase			:							Optional[Any]         =     {0: consistent_heuristic, 1: heuristic_a, 2: heuristic_a}

_lowerCamelCase			:							Union[str, Any]         =     [
    (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),
]
_lowerCamelCase			:							Optional[Any]         =     make_common_ground()


_lowerCamelCase			:							Optional[int]         =     blocks_blk
# hyper parameters
_lowerCamelCase			:							Dict         =     1
_lowerCamelCase			:							str         =     1
_lowerCamelCase			:							List[str]         =     20
_lowerCamelCase			:							Tuple         =     3  # one consistent and two other inconsistent

# start and end destination
_lowerCamelCase			:							Union[str, Any]         =     (0, 0)
_lowerCamelCase			:							Optional[Any]         =     (n - 1, n - 1)

_lowerCamelCase			:							List[Any]         =     1






def 			a_						(			__lowercase  :      TPos			,   __lowercase  :      TPos			,   __lowercase  :      int					)					->    Dict:
			_snake_case            =     {start: 0, goal: float('inf'					)}
			_snake_case            =     {start: -1, goal: -1}
			_snake_case            =     []
			_snake_case            =     set()

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

			_snake_case            =     []
			_snake_case            =     []
			while open_list[0].minkey() < float('inf'					):
						for i in range(1			,   lowerCAmelCase__					):
									# 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(lowerCAmelCase__			,   lowerCAmelCase__			,   lowerCAmelCase__					)
												else:
															_snake_case            =     open_list[i].top_show()
															visited.add(lowerCAmelCase__					)
															expand_state(
															    lowerCAmelCase__			,   lowerCAmelCase__			,   lowerCAmelCase__			,   lowerCAmelCase__			,   lowerCAmelCase__			,   lowerCAmelCase__			,   lowerCAmelCase__			,   lowerCAmelCase__			,   )
															close_list_inad.append(lowerCAmelCase__					)
									else:
												if g_function[goal] <= open_list[0].minkey():
															if g_function[goal] < float('inf'					):
																		do_something(lowerCAmelCase__			,   lowerCAmelCase__			,   lowerCAmelCase__					)
												else:
															_snake_case            =     open_list[0].top_show()
															visited.add(lowerCAmelCase__					)
															expand_state(
															    lowerCAmelCase__			,   0			,   lowerCAmelCase__			,   lowerCAmelCase__			,   lowerCAmelCase__			,   lowerCAmelCase__			,   lowerCAmelCase__			,   lowerCAmelCase__			,   )
															close_list_anchor.append(lowerCAmelCase__					)
			print('No path found to goal'					)
			print()
			for i in range(n - 1			,   -1			,   -1					):
						for j in range(lowerCAmelCase__					):
									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)






"""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 					__UpperCAmelCase							(						_UpperCamelCase						,  unittest.TestCase  ):
				__lowerCamelCase				:	List[Any]				     =				"hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline"
				def       UpperCAmelCase      (		self    :			str	, a_    :			Optional[Any]=0    )	->	Union[str, Any]:



							'''simple docstring'''



							a__  :	Union[str, Any]		 =			np.random.RandomState(a_    )
							a__  :	List[Any]		 =			{
							    "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       UpperCAmelCase      (		self    :			Dict    )	->	Optional[Any]:



							'''simple docstring'''



							a__  :	Tuple		 =			OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint	, provider="CPUExecutionProvider"    )
							pipe.set_progress_bar_config(disable=a_    )

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

							assert image.shape == (1, 1_28, 1_28, 3)
							a__  :	List[str]		 =			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       UpperCAmelCase      (		self    :			Tuple    )	->	Union[str, Any]:



							'''simple docstring'''



							a__  :	Dict		 =			OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint	, provider="CPUExecutionProvider"    )
							a__  :	List[str]		 =			PNDMScheduler.from_config(pipe.scheduler.config	, skip_prk_steps=a_    )
							pipe.set_progress_bar_config(disable=a_    )

							a__  :	Optional[int]		 =			self.get_dummy_inputs()
							a__  :	List[Any]		 =			pipe(**a_    ).images
							a__  :	Union[str, Any]		 =			image[0, -3:, -3:, -1]

							assert image.shape == (1, 1_28, 1_28, 3)
							a__  :	List[str]		 =			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       UpperCAmelCase      (		self    :			Tuple    )	->	Tuple:



							'''simple docstring'''



							a__  :	Dict		 =			OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint	, provider="CPUExecutionProvider"    )
							a__  :	Tuple		 =			LMSDiscreteScheduler.from_config(pipe.scheduler.config    )
							pipe.set_progress_bar_config(disable=a_    )

							a__  :	List[Any]		 =			self.get_dummy_inputs()
							a__  :	Optional[Any]		 =			pipe(**a_    ).images
							a__  :	int		 =			image[0, -3:, -3:, -1]

							assert image.shape == (1, 1_28, 1_28, 3)
							a__  :	Dict		 =			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       UpperCAmelCase      (		self    :			Optional[Any]    )	->	Dict:



							'''simple docstring'''



							a__  :	Union[str, Any]		 =			OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint	, provider="CPUExecutionProvider"    )
							a__  :	Dict		 =			EulerDiscreteScheduler.from_config(pipe.scheduler.config    )
							pipe.set_progress_bar_config(disable=a_    )

							a__  :	Optional[int]		 =			self.get_dummy_inputs()
							a__  :	int		 =			pipe(**a_    ).images
							a__  :	Tuple		 =			image[0, -3:, -3:, -1]

							assert image.shape == (1, 1_28, 1_28, 3)
							a__  :	str		 =			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       UpperCAmelCase      (		self    :			Union[str, Any]    )	->	str:



							'''simple docstring'''



							a__  :	str		 =			OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint	, provider="CPUExecutionProvider"    )
							a__  :	Dict		 =			EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config    )
							pipe.set_progress_bar_config(disable=a_    )

							a__  :	Any		 =			self.get_dummy_inputs()
							a__  :	List[str]		 =			pipe(**a_    ).images
							a__  :	Union[str, Any]		 =			image[0, -3:, -3:, -1]

							assert image.shape == (1, 1_28, 1_28, 3)
							a__  :	Any		 =			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       UpperCAmelCase      (		self    :			Tuple    )	->	Any:



							'''simple docstring'''



							a__  :	str		 =			OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint	, provider="CPUExecutionProvider"    )
							a__  :	str		 =			DPMSolverMultistepScheduler.from_config(pipe.scheduler.config    )
							pipe.set_progress_bar_config(disable=a_    )

							a__  :	Tuple		 =			self.get_dummy_inputs()
							a__  :	List[str]		 =			pipe(**a_    ).images
							a__  :	Any		 =			image[0, -3:, -3:, -1]

							assert image.shape == (1, 1_28, 1_28, 3)
							a__  :	Any		 =			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       UpperCAmelCase      (		self    :			str    )	->	Tuple:



							'''simple docstring'''



							a__  :	Dict		 =			OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint	, provider="CPUExecutionProvider"    )
							pipe.set_progress_bar_config(disable=a_    )

							a__  :	Any		 =			self.get_dummy_inputs()
							a__  :	Any		 =			3 * [inputs["prompt"]]

							# forward
							a__  :	Union[str, Any]		 =			pipe(**a_    )
							a__  :	int		 =			output.images[0, -3:, -3:, -1]

							a__  :	Union[str, Any]		 =			self.get_dummy_inputs()
							a__  :	List[Any]		 =			3 * [inputs.pop("prompt"    )]

							a__  :	Optional[Any]		 =			pipe.tokenizer(
							    a_	, padding="max_length"	, max_length=pipe.tokenizer.model_max_length	, truncation=a_	, return_tensors="np"	, )
							a__  :	List[str]		 =			text_inputs["input_ids"]

							a__  :	int		 =			pipe.text_encoder(input_ids=text_inputs.astype(np.intaa    )    )[0]

							a__  :	List[Any]		 =			prompt_embeds

							# forward
							a__  :	List[Any]		 =			pipe(**a_    )
							a__  :	List[str]		 =			output.images[0, -3:, -3:, -1]

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






				def       UpperCAmelCase      (		self    :			Dict    )	->	Optional[int]:



							'''simple docstring'''



							a__  :	Tuple		 =			OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint	, provider="CPUExecutionProvider"    )
							pipe.set_progress_bar_config(disable=a_    )

							a__  :	Tuple		 =			self.get_dummy_inputs()
							a__  :	Dict		 =			3 * ["this is a negative prompt"]
							a__  :	Optional[Any]		 =			negative_prompt
							a__  :	Any		 =			3 * [inputs["prompt"]]

							# forward
							a__  :	str		 =			pipe(**a_    )
							a__  :	List[str]		 =			output.images[0, -3:, -3:, -1]

							a__  :	Union[str, Any]		 =			self.get_dummy_inputs()
							a__  :	Union[str, Any]		 =			3 * [inputs.pop("prompt"    )]

							a__  :	List[Any]		 =			[]
							for p in [prompt, negative_prompt]:
										a__  :	int		 =			pipe.tokenizer(
										    a_	, padding="max_length"	, max_length=pipe.tokenizer.model_max_length	, truncation=a_	, return_tensors="np"	, )
										a__  :	Any		 =			text_inputs["input_ids"]

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

							a__ ,	a__  :	Union[str, Any]		 =			embeds

							# forward
							a__  :	Dict		 =			pipe(**a_    )
							a__  :	Any		 =			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 					__UpperCAmelCase							(						unittest.TestCase  ):
				@property
				def       UpperCAmelCase      (		self    :			Optional[Any]    )	->	Optional[int]:



							'''simple docstring'''



							return (
							    "CUDAExecutionProvider",
							    {
							        "gpu_mem_limit": "15000000000",  # 15GB
							        "arena_extend_strategy": "kSameAsRequested",
							    },
							)
				@property
				def       UpperCAmelCase      (		self    :			Optional[int]    )	->	Tuple:



							'''simple docstring'''



							a__  :	List[str]		 =			ort.SessionOptions()
							a__  :	List[str]		 =			False
							return options
				def       UpperCAmelCase      (		self    :			Optional[int]    )	->	List[Any]:



							'''simple docstring'''



							a__  :	Dict		 =			OnnxStableDiffusionPipeline.from_pretrained(
							    "CompVis/stable-diffusion-v1-4"	, revision="onnx"	, safety_checker=a_	, feature_extractor=a_	, provider=self.gpu_provider	, sess_options=self.gpu_options	, )
							sd_pipe.set_progress_bar_config(disable=a_    )

							a__  :	Optional[int]		 =			"A painting of a squirrel eating a burger"
							np.random.seed(0    )
							a__  :	Dict		 =			sd_pipe([prompt]	, guidance_scale=6.0	, num_inference_steps=10	, output_type="np"    )
							a__  :	Any		 =			output.images

							a__  :	Optional[int]		 =			image[0, -3:, -3:, -1]

							assert image.shape == (1, 5_12, 5_12, 3)
							a__  :	str		 =			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       UpperCAmelCase      (		self    :			Union[str, Any]    )	->	Union[str, Any]:



							'''simple docstring'''



							a__  :	Any		 =			DDIMScheduler.from_pretrained(
							    "runwayml/stable-diffusion-v1-5"	, subfolder="scheduler"	, revision="onnx"    )
							a__  :	str		 =			OnnxStableDiffusionPipeline.from_pretrained(
							    "runwayml/stable-diffusion-v1-5"	, revision="onnx"	, scheduler=a_	, safety_checker=a_	, feature_extractor=a_	, provider=self.gpu_provider	, sess_options=self.gpu_options	, )
							sd_pipe.set_progress_bar_config(disable=a_    )

							a__  :	str		 =			"open neural network exchange"
							a__  :	Tuple		 =			np.random.RandomState(0    )
							a__  :	Dict		 =			sd_pipe([prompt]	, guidance_scale=7.5	, num_inference_steps=10	, generator=a_	, output_type="np"    )
							a__  :	Dict		 =			output.images
							a__  :	Union[str, Any]		 =			image[0, -3:, -3:, -1]

							assert image.shape == (1, 5_12, 5_12, 3)
							a__  :	Dict		 =			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       UpperCAmelCase      (		self    :			Optional[int]    )	->	Dict:



							'''simple docstring'''



							a__  :	List[Any]		 =			LMSDiscreteScheduler.from_pretrained(
							    "runwayml/stable-diffusion-v1-5"	, subfolder="scheduler"	, revision="onnx"    )
							a__  :	List[Any]		 =			OnnxStableDiffusionPipeline.from_pretrained(
							    "runwayml/stable-diffusion-v1-5"	, revision="onnx"	, scheduler=a_	, safety_checker=a_	, feature_extractor=a_	, provider=self.gpu_provider	, sess_options=self.gpu_options	, )
							sd_pipe.set_progress_bar_config(disable=a_    )

							a__  :	Any		 =			"open neural network exchange"
							a__  :	Optional[Any]		 =			np.random.RandomState(0    )
							a__  :	Optional[int]		 =			sd_pipe([prompt]	, guidance_scale=7.5	, num_inference_steps=10	, generator=a_	, output_type="np"    )
							a__  :	int		 =			output.images
							a__  :	Tuple		 =			image[0, -3:, -3:, -1]

							assert image.shape == (1, 5_12, 5_12, 3)
							a__  :	Dict		 =			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       UpperCAmelCase      (		self    :			int    )	->	Optional[Any]:



							'''simple docstring'''



							a__  :	List[str]		 =			0

							def test_callback_fn(a_    :			int	, a_    :			int	, a_    :			np.ndarray    ) -> None:
										a__  :	Optional[int]		 =			True
										nonlocal number_of_steps
										number_of_steps += 1
										if step == 0:
													assert latents.shape == (1, 4, 64, 64)
													a__  :	Any		 =			latents[0, -3:, -3:, -1]
													a__  :	Union[str, Any]		 =			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)
													a__  :	Union[str, Any]		 =			latents[0, -3:, -3:, -1]
													a__  :	Optional[int]		 =			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

							a__  :	Tuple		 =			False

							a__  :	Optional[int]		 =			OnnxStableDiffusionPipeline.from_pretrained(
							    "runwayml/stable-diffusion-v1-5"	, revision="onnx"	, safety_checker=a_	, feature_extractor=a_	, provider=self.gpu_provider	, sess_options=self.gpu_options	, )
							pipe.set_progress_bar_config(disable=a_    )

							a__  :	List[Any]		 =			"Andromeda galaxy in a bottle"

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






				def       UpperCAmelCase      (		self    :			Tuple    )	->	List[str]:



							'''simple docstring'''



							a__  :	Optional[Any]		 =			OnnxStableDiffusionPipeline.from_pretrained(
							    "runwayml/stable-diffusion-v1-5"	, revision="onnx"	, safety_checker=a_	, feature_extractor=a_	, provider=self.gpu_provider	, sess_options=self.gpu_options	, )
							assert isinstance(a_	, a_    )
							assert pipe.safety_checker is None

							a__  :	Tuple		 =			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(a_    )
										a__  :	Optional[int]		 =			OnnxStableDiffusionPipeline.from_pretrained(a_    )

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