



from __future__ import annotations

UpperCAmelCase      :							Tuple							       =[
    [-1, 0],  # left
    [0, -1],  # down
    [1, 0],  # right
    [0, 1],  # up
]



def       _lowerCAmelCase			(_lowerCAmelCase      ,		_lowerCAmelCase      ,		_lowerCAmelCase      ,		_lowerCAmelCase      ,		_lowerCAmelCase      ,		):
					UpperCamelCase_						     =      [
					    [0 for col in range(len(grid[0]))] for row in range(len(_lowerCamelCase))
					]  # the reference grid
					UpperCamelCase_						     =      1
					UpperCamelCase_						     =      [
					    [0 for col in range(len(grid[0]))] for row in range(len(_lowerCamelCase))
					]  # the action grid

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

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

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

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

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


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

	UpperCAmelCase      :							str							       =[0, 0]
	# all coordinates are given in format [y,x]
	UpperCAmelCase      :							int							       =[len(grid) - 1, len(grid[0]) - 1]
	UpperCAmelCase      :							List[str]							       =1

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

	UpperCAmelCase      :							str							       =search(grid, init, goal, cost, heuristic)

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

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


import unittest
from dataclasses import dataclass

import pytest

from accelerate.commands.config.config_args import SageMakerConfig
from accelerate.utils import ComputeEnvironment
from accelerate.utils.launch import _convert_nargs_to_dict




@dataclass
class  lowerCamelCase__							(				__lowercase):




	'''simple docstring'''




	_A								=     ComputeEnvironment.AMAZON_SAGEMAKER
	_A								=     True
	_A								=     'ml.p3.2xlarge'
	_A								=     'accelerate_sagemaker_execution_role'
	_A								=     'hf-sm'
	_A								=     'us-east-1'
	_A								=     1
	_A								=     'accelerate-sagemaker-1'
	_A								=     '1.6'
	_A								=     '4.4'
	_A								=     'train.py'
	_A								=     [
	    '--model_name_or_path',
	    'bert',
	    '--do_train',
	    'False',
	    '--epochs',
	    '3',
	    '--learning_rate',
	    '5e-5',
	    '--max_steps',
	    '50.5',
	]
	_A								=     [
	    '--model_name_or_path',
	    'bert',
	    '--do_train',
	    '--do_test',
	    'False',
	    '--do_predict',
	    '--epochs',
	    '3',
	    '--learning_rate',
	    '5e-5',
	    '--max_steps',
	    '50.5',
	]




class  lowerCamelCase__							(				unittest.TestCase):




	'''simple docstring'''




	def    _lowerCamelCase				(					self		:Any						)		-> Optional[int]:
								# If no defaults are changed, `to_kwargs` returns an empty dict.
								__UpperCamelCase	:   Optional[int]			   =					_convert_nargs_to_dict(MockLaunchConfig.success_training_script_args						)
								assert isinstance(converted_args["model_name_or_path"]     ,   a						)
								assert isinstance(converted_args["do_train"]     ,   a						)
								assert isinstance(converted_args["epochs"]     ,   a						)
								assert isinstance(converted_args["learning_rate"]     ,   a						)
								assert isinstance(converted_args["max_steps"]     ,   a						)

								with pytest.raises(a						):
															_convert_nargs_to_dict(MockLaunchConfig.fail_training_script_args						)