






'''simple docstring'''




from __future__ import annotations

__lowerCamelCase   :					Dict   							=					[
    [-1, 0],  # left
    [0, -1],  # down
    [1, 0],  # right
    [0, 1],  # up
]

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



			"""simple docstring"""



			snake_case_				:					List[Any]   =       [
			    [0 for col in range(len(grid[0]						)						)] for row in range(len(__magic_name__						)						)
			]  # the reference grid
			snake_case_				:					Optional[Any]   =       1
			snake_case_				:					Union[str, Any]   =       [
			    [0 for col in range(len(grid[0]						)						)] for row in range(len(__magic_name__						)						)
			]  # the action grid

			snake_case_				:					List[Any]   =       init[0]
			snake_case_				:					List[Any]   =       init[1]
			snake_case_				:					Any   =       0
			snake_case_				:					Tuple   =       g + heuristic[x][y]  # cost from starting cell to destination cell
			snake_case_				:					Tuple   =       [[f, g, x, y]]

			snake_case_				:					List[Any]   =       False  # flag that is set when search is complete
			snake_case_				:					Tuple   =       False  # flag set if we can't find expand

			while not found and not resign:
						if len(__magic_name__						) == 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()
									snake_case_				:					Tuple   =       cell.pop()
									snake_case_				:					List[str]   =       next_cell[2]
									snake_case_				:					int   =       next_cell[3]
									snake_case_				:					Optional[int]   =       next_cell[1]

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

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


if __name__ == "__main__":
		__lowerCamelCase   :					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],
		]

		__lowerCamelCase   :					Any   							=					[0, 0]
		# all coordinates are given in format [y,x]
		__lowerCamelCase   :					Optional[Any]   							=					[len(grid) - 1, len(grid[0]) - 1]
		__lowerCamelCase   :					Any   							=					1

		# the cost map which pushes the path closer to the goal
		__lowerCamelCase   :					int   							=					[[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])):
						__lowerCamelCase   :					Union[str, Any]   							=					abs(i - goal[0]) + abs(j - goal[1])
						if grid[i][j] == 1:
								# added extra penalty in the heuristic map
								__lowerCamelCase   :					Dict   							=					99

		__lowerCamelCase							,      __lowerCamelCase   :					Dict   							=					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])












'''simple docstring'''




from unittest import TestCase

from datasets import Dataset
from minhash_deduplication import deduplicate_dataset, make_duplicate_clusters

def 	__UpperCAmelCase       ( )->    int:



			"""simple docstring"""



			snake_case_				:					Any   =       {
			    "repo_name": ["test_repo1", "test_repo2", "test_repo3"],
			    "path": ["test_1.py", "test_2.py", "unit_test.py"],
			    "content": ["a " * 20, "a " * 30, "b " * 7],
			}
			snake_case_				:					int   =       Dataset.from_dict(__magic_name__						)
			return dataset







class    A_	(a_							):
							"""simple docstring"""




							def 							_A	(		self			:List[str]				)				->  str:


										'''simple docstring'''




										snake_case_				:					Union[str, Any]   =       get_dataset()
										snake_case_				:					Optional[int]   =       make_duplicate_clusters(lowerCAmelCase__       ,							0.8_5				)
										self.assertEqual(len(duplicate_clusters[0]				)       ,							2				)






							def 							_A	(		self			:Union[str, Any]				)				->  List[str]:


										'''simple docstring'''




										snake_case_				:					Optional[int]   =       get_dataset()
										snake_case_,	snake_case_				:					List[Any]   =       deduplicate_dataset(lowerCAmelCase__				)
										self.assertEqual(len(lowerCAmelCase__				)       ,							2				)
										print(lowerCAmelCase__				)
										self.assertEqual(duplicate_clusters[0][0]["copies"]       ,							2				)
										self.assertEqual(duplicate_clusters[0][0]["is_extreme"]       ,							lowerCAmelCase__				)




