






"""simple docstring"""
import inspect
import logging
import os
import random
import shutil
import tempfile
import unittest

import pytest
import torch
from torch import nn
from torch.utils.data import DataLoader, TensorDataset

from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_async, require_cuda
from accelerate.utils import ProjectConfiguration, set_seed


_lowerCAmelCase         =							logging.getLogger(__name__)






def 			__UpperCamelCase	(		snake_case__=2   ,   snake_case__=3   ,   snake_case__=16   ,   snake_case__ = 10   ,   snake_case__ = 2					):

	def get_dataset(snake_case__					):
		A_		:						Any	    =   torch.randn(batch_size * n_batches   ,   1					)
		return TensorDataset(snake_case__   ,   a * x + b + 0.1 * torch.randn(batch_size * n_batches   ,   1					)					)

	A_		:						Dict	    =   get_dataset(snake_case__					)
	A_		:						str	    =   get_dataset(snake_case__					)
	A_		:						Optional[int]	    =   DataLoader(snake_case__   ,   shuffle=snake_case__   ,   batch_size=snake_case__   ,   num_workers=4					)
	A_		:						Union[str, Any]	    =   DataLoader(snake_case__   ,   shuffle=snake_case__   ,   batch_size=snake_case__   ,   num_workers=4					)
	return (train_dataloader, valid_dataloader)






def 			__UpperCamelCase	(		snake_case__   ,   snake_case__   ,   snake_case__   ,   snake_case__   ,   snake_case__   ,   snake_case__=None					):
	A_		:						Optional[Any]	    =   []
	for epoch in range(snake_case__					):
		# Train quickly
		model.train()
		for batch in dataloader:
			A_    ,     A_		:						int	    =   batch
			A_		:						Optional[int]	    =   model(snake_case__					)
			A_		:						Optional[Any]	    =   torch.nn.functional.mse_loss(snake_case__   ,   snake_case__					)
			accelerator.backward(snake_case__					)
			optimizer.step()
			optimizer.zero_grad()
		rands.append(random.random()					)  # Introduce some randomness
		if scheduler is not None:
			scheduler.step()
	return rands



class      SCREAMING_SNAKE_CASE  (				nn.Module						):





	"""simple docstring"""




	def __init__(self							):
		super().__init__()
		A_		:						Optional[Any]	    =   nn.Parameter(torch.randn(1							)							)
		A_		:						Optional[int]	    =   nn.Parameter(torch.randn(1							)							)





	def    lowerCamelCase(self     ,     lowerCAmelCase_							):
		return x * self.a + self.b






class      SCREAMING_SNAKE_CASE  (				unittest.TestCase						):





	"""simple docstring"""




	def    lowerCamelCase(self							):
		with tempfile.TemporaryDirectory() as tmpdir:
			set_seed(42							)
			A_		:						Tuple	    =   DummyModel()
			A_		:						Tuple	    =   torch.optim.Adam(params=model.parameters()     ,     lr=1e-3							)
			A_    ,     A_		:						Any	    =   dummy_dataloaders()
			A_		:						Tuple	    =   ProjectConfiguration(total_limit=1     ,     project_dir=lowerCAmelCase_     ,     automatic_checkpoint_naming=lowerCAmelCase_							)
			# Train baseline
			A_		:						List[Any]	    =   Accelerator(project_config=lowerCAmelCase_							)
			A_    ,     A_    ,     A_    ,     A_		:						Union[str, Any]	    =   accelerator.prepare(
			    lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_							)
			# Save initial
			accelerator.save_state()

			# Save second state
			accelerator.save_state()
			self.assertEqual(len(os.listdir(accelerator.project_dir							)							)     ,     1							)





	def    lowerCamelCase(self							):
		with tempfile.TemporaryDirectory() as tmpdir:
			set_seed(42							)
			A_		:						str	    =   DummyModel()
			A_		:						List[Any]	    =   torch.optim.Adam(params=model.parameters()     ,     lr=1e-3							)
			A_    ,     A_		:						Any	    =   dummy_dataloaders()
			# Train baseline
			A_		:						Dict	    =   Accelerator()
			A_    ,     A_    ,     A_    ,     A_		:						Dict	    =   accelerator.prepare(
			    lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_							)
			# Save initial
			A_		:						Optional[int]	    =   os.path.join(lowerCAmelCase_     ,     """initial"""							)
			accelerator.save_state(lowerCAmelCase_							)
			((A_)    ,     (A_))		:						int	    =   model.a.item(), model.b.item()
			A_		:						Optional[int]	    =   optimizer.state_dict()
			A_		:						Union[str, Any]	    =   train(3     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_							)
			((A_)    ,     (A_))		:						Dict	    =   model.a.item(), model.b.item()
			A_		:						Optional[Any]	    =   optimizer.state_dict()

			# Train partially
			set_seed(42							)
			A_		:						int	    =   DummyModel()
			A_		:						Union[str, Any]	    =   torch.optim.Adam(params=model.parameters()     ,     lr=1e-3							)
			A_    ,     A_		:						Dict	    =   dummy_dataloaders()
			A_		:						Tuple	    =   Accelerator()
			A_    ,     A_    ,     A_    ,     A_		:						str	    =   accelerator.prepare(
			    lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_							)
			accelerator.load_state(lowerCAmelCase_							)
			((A_)    ,     (A_))		:						List[Any]	    =   model.a.item(), model.b.item()
			A_		:						Union[str, Any]	    =   optimizer.state_dict()
			self.assertEqual(lowerCAmelCase_     ,     lowerCAmelCase_							)
			self.assertEqual(lowerCAmelCase_     ,     lowerCAmelCase_							)
			self.assertEqual(lowerCAmelCase_     ,     lowerCAmelCase_							)

			A_		:						str	    =   train(2     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_							)
			# Save everything
			A_		:						Optional[int]	    =   os.path.join(lowerCAmelCase_     ,     """checkpoint"""							)
			accelerator.save_state(lowerCAmelCase_							)

			# Load everything back in and make sure all states work
			accelerator.load_state(lowerCAmelCase_							)
			test_rands += train(1     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_							)
			((A_)    ,     (A_))		:						List[Any]	    =   model.a.item(), model.b.item()
			A_		:						Optional[Any]	    =   optimizer.state_dict()
			self.assertEqual(lowerCAmelCase_     ,     lowerCAmelCase_							)
			self.assertEqual(lowerCAmelCase_     ,     lowerCAmelCase_							)
			self.assertEqual(lowerCAmelCase_     ,     lowerCAmelCase_							)
			self.assertEqual(lowerCAmelCase_     ,     lowerCAmelCase_							)





	def    lowerCamelCase(self							):
		with tempfile.TemporaryDirectory() as tmpdir:
			set_seed(42							)
			A_		:						Optional[int]	    =   DummyModel()
			A_		:						Optional[Any]	    =   torch.optim.Adam(params=model.parameters()     ,     lr=1e-3							)
			A_    ,     A_		:						Optional[Any]	    =   dummy_dataloaders()
			A_		:						Any	    =   ProjectConfiguration(automatic_checkpoint_naming=lowerCAmelCase_							)

			# Train baseline
			A_		:						Optional[Any]	    =   Accelerator(project_dir=lowerCAmelCase_     ,     project_config=lowerCAmelCase_							)
			A_    ,     A_    ,     A_    ,     A_		:						Any	    =   accelerator.prepare(
			    lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_							)
			# Save initial
			accelerator.save_state()
			((A_)    ,     (A_))		:						Any	    =   model.a.item(), model.b.item()
			A_		:						Dict	    =   optimizer.state_dict()
			A_		:						Optional[Any]	    =   train(3     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_							)
			((A_)    ,     (A_))		:						List[Any]	    =   model.a.item(), model.b.item()
			A_		:						Optional[int]	    =   optimizer.state_dict()

			# Train partially
			set_seed(42							)
			A_		:						Union[str, Any]	    =   DummyModel()
			A_		:						int	    =   torch.optim.Adam(params=model.parameters()     ,     lr=1e-3							)
			A_    ,     A_		:						Optional[Any]	    =   dummy_dataloaders()
			A_		:						Tuple	    =   ProjectConfiguration(iteration=1     ,     automatic_checkpoint_naming=lowerCAmelCase_							)
			A_		:						Optional[Any]	    =   Accelerator(project_dir=lowerCAmelCase_     ,     project_config=lowerCAmelCase_							)
			A_    ,     A_    ,     A_    ,     A_		:						Tuple	    =   accelerator.prepare(
			    lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_							)
			accelerator.load_state(os.path.join(lowerCAmelCase_     ,     """checkpoints"""     ,     """checkpoint_0"""							)							)
			((A_)    ,     (A_))		:						Optional[int]	    =   model.a.item(), model.b.item()
			A_		:						List[Any]	    =   optimizer.state_dict()
			self.assertEqual(lowerCAmelCase_     ,     lowerCAmelCase_							)
			self.assertEqual(lowerCAmelCase_     ,     lowerCAmelCase_							)
			self.assertEqual(lowerCAmelCase_     ,     lowerCAmelCase_							)

			A_		:						str	    =   train(2     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_							)
			# Save everything
			accelerator.save_state()

			# Load everything back in and make sure all states work
			accelerator.load_state(os.path.join(lowerCAmelCase_     ,     """checkpoints"""     ,     """checkpoint_1"""							)							)
			test_rands += train(1     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_							)
			((A_)    ,     (A_))		:						Dict	    =   model.a.item(), model.b.item()
			A_		:						List[str]	    =   optimizer.state_dict()
			self.assertEqual(lowerCAmelCase_     ,     lowerCAmelCase_							)
			self.assertEqual(lowerCAmelCase_     ,     lowerCAmelCase_							)
			self.assertEqual(lowerCAmelCase_     ,     lowerCAmelCase_							)
			self.assertEqual(lowerCAmelCase_     ,     lowerCAmelCase_							)





	def    lowerCamelCase(self							):
		A_		:						str	    =   torch.tensor([1, 2, 3]							)
		A_		:						Tuple	    =   torch.tensor([2, 3, 4]							)
		A_		:						Dict	    =   DummyModel()
		A_		:						str	    =   torch.optim.Adam(net.parameters()							)
		A_		:						Tuple	    =   Accelerator()
		with self.assertRaises(lowerCAmelCase_							) as ve:
			accelerator.register_for_checkpointing(lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_							)
		A_		:						List[Any]	    =   str(ve.exception							)
		self.assertTrue("""Item at index 0""" in message							)
		self.assertTrue("""Item at index 1""" in message							)
		self.assertFalse("""Item at index 2""" in message							)
		self.assertFalse("""Item at index 3""" in message							)





	def    lowerCamelCase(self							):
		with tempfile.TemporaryDirectory() as tmpdir:
			set_seed(42							)
			A_		:						Dict	    =   DummyModel()
			A_		:						str	    =   torch.optim.Adam(params=model.parameters()     ,     lr=1e-3							)
			A_		:						Optional[Any]	    =   torch.optim.lr_scheduler.StepLR(lowerCAmelCase_     ,     step_size=1     ,     gamma=0.99							)
			A_    ,     A_		:						Any	    =   dummy_dataloaders()
			A_		:						Dict	    =   ProjectConfiguration(automatic_checkpoint_naming=lowerCAmelCase_							)
			# Train baseline
			A_		:						Dict	    =   Accelerator(project_dir=lowerCAmelCase_     ,     project_config=lowerCAmelCase_							)
			A_    ,     A_    ,     A_    ,     A_    ,     A_		:						Dict	    =   accelerator.prepare(
			    lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_							)
			# Save initial
			accelerator.save_state()
			A_		:						int	    =   scheduler.state_dict()
			train(3     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_							)
			self.assertNotEqual(lowerCAmelCase_     ,     scheduler.state_dict()							)

			# Load everything back in and make sure all states work
			accelerator.load_state(os.path.join(lowerCAmelCase_     ,     """checkpoints"""     ,     """checkpoint_0"""							)							)
			self.assertEqual(lowerCAmelCase_     ,     scheduler.state_dict()							)





	def    lowerCamelCase(self							):
		with tempfile.TemporaryDirectory() as tmpdir:
			set_seed(42							)
			A_		:						Tuple	    =   DummyModel()
			A_		:						Dict	    =   ProjectConfiguration(automatic_checkpoint_naming=lowerCAmelCase_     ,     total_limit=2							)
			# Train baseline
			A_		:						Optional[Any]	    =   Accelerator(project_dir=lowerCAmelCase_     ,     project_config=lowerCAmelCase_							)
			A_		:						Optional[int]	    =   accelerator.prepare(lowerCAmelCase_							)
			# Save 3 states:
			for _ in range(11							):
				accelerator.save_state()
			self.assertTrue(not os.path.exists(os.path.join(lowerCAmelCase_     ,     """checkpoints"""     ,     """checkpoint_0"""							)							)							)
			self.assertTrue(os.path.exists(os.path.join(lowerCAmelCase_     ,     """checkpoints"""     ,     """checkpoint_9"""							)							)							)
			self.assertTrue(os.path.exists(os.path.join(lowerCAmelCase_     ,     """checkpoints"""     ,     """checkpoint_10"""							)							)							)





	@require_cuda
	def    lowerCamelCase(self							):
		A_		:						Tuple	    =   ["""torchrun""", f"""--nproc_per_node={torch.cuda.device_count()}""", inspect.getfile(self.__class__							)]
		execute_subprocess_async(lowerCAmelCase_     ,     env=os.environ.copy()							)


if __name__ == "__main__":
							_lowerCAmelCase         =							"/tmp/accelerate/state_checkpointing"
							_lowerCAmelCase         =							DummyModel()
							_lowerCAmelCase         =							torch.optim.Adam(params=model.parameters(), lr=1e-3)
							_lowerCAmelCase         =							torch.optim.lr_scheduler.StepLR(optimizer, step_size=1, gamma=0.99)
							_lowerCAmelCase     ,    _lowerCAmelCase         =							dummy_dataloaders()
							_lowerCAmelCase         =							ProjectConfiguration(automatic_checkpoint_naming=True)
							# Train baseline
							_lowerCAmelCase         =							Accelerator(project_dir=savedir, project_config=project_config, mixed_precision="no")
							if accelerator.process_index == 0:
														if os.path.exists(savedir):
																					shutil.rmtree(savedir)
														os.makedirs(savedir)
							_lowerCAmelCase     ,    _lowerCAmelCase     ,    _lowerCAmelCase     ,    _lowerCAmelCase     ,    _lowerCAmelCase         =							accelerator.prepare(
							    model, optimizer, train_dataloader, valid_dataloader, scheduler
							)
							_lowerCAmelCase     ,    _lowerCAmelCase         =							accelerator.prepare(model, optimizer)
							train(3, model, train_dataloader, optimizer, accelerator, scheduler)
							# Check that the intial optimizer is loaded on the GPU
							for group in optimizer.param_groups:
														_lowerCAmelCase         =							group["params"][0].device
														break
							assert param_device.type == accelerator.device.type
							_lowerCAmelCase         =							model.cpu()
							accelerator.wait_for_everyone()
							accelerator.save_state()
							accelerator.wait_for_everyone()

							# Check CPU state
							accelerator.load_state(os.path.join(savedir, "checkpoints", "checkpoint_0"), map_location="cpu")
							for group in optimizer.param_groups:
														_lowerCAmelCase         =							group["params"][0].device
														break
							assert (
							    param_device.type == torch.device("cpu").type
							), F"Loaded optimizer states did not match, expected to be loaded on the CPU but got {param_device}"

							# Check device state
							model.to(accelerator.device)
							accelerator.load_state(os.path.join(savedir, "checkpoints", "checkpoint_0"), map_location="on_device")
							for group in optimizer.param_groups:
														_lowerCAmelCase         =							group["params"][0].device
														break
							assert (
							    param_device.type == accelerator.device.type
							), F"Loaded optimizer states did not match, expected to be loaded on {accelerator.device} but got {param_device}"

							# Check error
							with pytest.raises(TypeError, match="Unsupported optimizer map location passed"):
														accelerator.load_state(os.path.join(savedir, "checkpoints", "checkpoint_0"), map_location="invalid")
							accelerator.wait_for_everyone()
							if accelerator.process_index == 0:
														shutil.rmtree(savedir)
							accelerator.wait_for_everyone()









"""simple docstring"""
import heapq
import sys

import numpy as np

_lowerCAmelCase         =							tuple[int, int]



class      SCREAMING_SNAKE_CASE  :





	"""simple docstring"""




	def __init__(self							):
		A_		:						Any	    =   []
		A_		:						str	    =   set()





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





	def    lowerCamelCase(self							):
		return len(self.elements							) == 0





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





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





	def    lowerCamelCase(self							):
		return self.elements[0][1]





	def    lowerCamelCase(self							):
		((A_)    ,     (A_))		:						Optional[Any]	    =   heapq.heappop(self.elements							)
		self.set.remove(lowerCAmelCase_							)
		return (priority, item)






def 			__UpperCamelCase	(		snake_case__   ,   snake_case__					):
	# euclidean distance
	A_		:						Optional[Any]	    =   np.array(snake_case__					)
	A_		:						List[str]	    =   np.array(snake_case__					)
	return np.linalg.norm(a - b					)






def 			__UpperCamelCase	(		snake_case__   ,   snake_case__					):
	# integer division by time variable
	return consistent_heuristic(snake_case__   ,   snake_case__					) // t






def 			__UpperCamelCase	(		snake_case__   ,   snake_case__					):
	# manhattan distance
	return abs(p[0] - goal[0]					) + abs(p[1] - goal[1]					)






def 			__UpperCamelCase	(		snake_case__   ,   snake_case__   ,   snake_case__   ,   snake_case__					):
	A_		:						Optional[int]	    =   g_function[start] + Wa * heuristics[i](snake_case__   ,   snake_case__					)
	return ans






def 			__UpperCamelCase	(		snake_case__   ,   snake_case__   ,   snake_case__					):
	A_		:						Tuple	    =   np.chararray((n, n)					)
	for i in range(snake_case__					):
		for j in range(snake_case__					):
			A_		:						List[Any]	    =   """*"""

	for i in range(snake_case__					):
		for j in range(snake_case__					):
			if (j, (n - 1) - i) in blocks:
				A_		:						Optional[Any]	    =   """#"""

	A_		:						Optional[Any]	    =   """-"""
	A_		:						Optional[Any]	    =   back_pointer[goal]
	while x != start:
		((A_)    ,     (A_))		:						List[str]	    =   x
		# print(x)
		A_		:						Dict	    =   """-"""
		A_		:						int	    =   back_pointer[x]
	A_		:						Optional[int]	    =   """-"""

	for i in range(snake_case__					):
		for j in range(snake_case__					):
			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:-"""					)
	A_		:						List[str]	    =   back_pointer[goal]
	while x != start:
		print(snake_case__   ,   end=""" """					)
		A_		:						Optional[int]	    =   back_pointer[x]
	print(snake_case__					)
	sys.exit()






def 			__UpperCamelCase	(		snake_case__					):
	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 			__UpperCamelCase	(		snake_case__   ,   snake_case__   ,   snake_case__   ,   snake_case__   ,   snake_case__   ,   snake_case__   ,   snake_case__   ,   snake_case__   ,   ):
	for itera in range(snake_case__					):
		open_list[itera].remove_element(snake_case__					)
	# print("s", s)
	# print("j", j)
	((A_)    ,     (A_))		:						Any	    =   s
	A_		:						int	    =   (x - 1, y)
	A_		:						List[str]	    =   (x + 1, y)
	A_		:						Union[str, Any]	    =   (x, y + 1)
	A_		:						Any	    =   (x, y - 1)

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

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






def 			__UpperCamelCase	(		):
	A_		:						str	    =   []
	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         =							{0: consistent_heuristic, 1: heuristic_a, 2: heuristic_a}

_lowerCAmelCase         =							[
    (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         =							make_common_ground()


_lowerCAmelCase         =							blocks_blk
# hyper parameters
_lowerCAmelCase         =							1
_lowerCAmelCase         =							1
_lowerCAmelCase         =							20
_lowerCAmelCase         =							3  # one consistent and two other inconsistent

# start and end destination
_lowerCAmelCase         =							(0, 0)
_lowerCAmelCase         =							(n - 1, n - 1)

_lowerCAmelCase         =							1






def 			__UpperCamelCase	(		snake_case__   ,   snake_case__   ,   snake_case__					):
	A_		:						Tuple	    =   {start: 0, goal: float("""inf"""					)}
	A_		:						List[str]	    =   {start: -1, goal: -1}
	A_		:						str	    =   []
	A_		:						int	    =   set()

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

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

