from __future__ import annotations

lowerCamelCase_    :		Optional[Any]							=	list[tuple[int, int]]

lowerCamelCase_    :		int							=	[
    [0, 0, 0, 0, 0, 0, 0],
    [0, 1, 0, 0, 0, 0, 0],  # 0 are free path whereas 1's are obstacles
    [0, 0, 0, 0, 0, 0, 0],
    [0, 0, 1, 0, 0, 0, 0],
    [1, 0, 1, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 1, 0, 0],
]

lowerCamelCase_    :		str							=	([-1, 0], [0, -1], [1, 0], [0, 1])  # up, left, down, right


class 						a__							:



			def __init__(						self				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase				,						)			->   Optional[Any]:
								__a		 =       pos_x
								__a		 =       pos_y
								__a		 =       (pos_y, pos_x)
								__a		 =       goal_x
								__a		 =       goal_y
								__a		 =       g_cost
								__a		 =       parent
								__a		 =       self.calculate_heuristic()



			def __SCREAMING_SNAKE_CASE							(						self   )			->   float:
								__a		 =       abs(self.pos_x - self.goal_x   )
								__a		 =       abs(self.pos_y - self.goal_y   )
								return dx + dy







			def __lt__(						self				,						UpperCAmelCase   )			->   bool:
								return self.f_cost < other.f_cost






class 						a__							:



			def __init__(						self				,						UpperCAmelCase				,						UpperCAmelCase   )			->   Dict:
								__a		 =       Node(start[1]				,						start[0]				,						goal[1]				,						goal[0]				,						0				,						UpperCAmelCase   )
								__a		 =       Node(goal[1]				,						goal[0]				,						goal[1]				,						goal[0]				,						9_9_9_9_9				,						UpperCAmelCase   )

								__a		 =       [self.start]
								__a		 =       []

								__a		 =       False



			def __SCREAMING_SNAKE_CASE							(						self   )			->   Path | None:
								while self.open_nodes:
													# Open Nodes are sorted using __lt__
													self.open_nodes.sort()
													__a		 =       self.open_nodes.pop(0   )

													if current_node.pos == self.target.pos:
																		__a		 =       True
																		return self.retrace_path(UpperCAmelCase   )

													self.closed_nodes.append(UpperCAmelCase   )
													__a		 =       self.get_successors(UpperCAmelCase   )

													for child_node in successors:
																		if child_node in self.closed_nodes:
																							continue

																		if child_node not in self.open_nodes:
																							self.open_nodes.append(UpperCAmelCase   )
																		else:
																							# retrieve the best current path
																							__a		 =       self.open_nodes.pop(self.open_nodes.index(UpperCAmelCase   )   )

																							if child_node.g_cost < better_node.g_cost:
																												self.open_nodes.append(UpperCAmelCase   )
																							else:
																												self.open_nodes.append(UpperCAmelCase   )

								if not self.reached:
													return [self.start.pos]
								return None



			def __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase   )			->   list[Node]:
								__a		 =       []
								for action in delta:
													__a		 =       parent.pos_x + action[1]
													__a		 =       parent.pos_y + action[0]

													if not (0 <= pos_x <= len(grid[0]   ) - 1 and 0 <= pos_y <= len(UpperCAmelCase   ) - 1):
																		continue

													if grid[pos_y][pos_x] != 0:
																		continue

													successors.append(
													    Node(
													        UpperCAmelCase				,						UpperCAmelCase				,						self.target.pos_y				,						self.target.pos_x				,						parent.g_cost + 1				,						UpperCAmelCase				,						)   )
								return successors







			def __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase   )			->   Path:
								__a		 =       node
								__a		 =       []
								while current_node is not None:
													path.append((current_node.pos_y, current_node.pos_x)   )
													__a		 =       current_node.parent
								path.reverse()
								return path


if __name__ == "__main__":
	lowerCamelCase_    :		Any							=	(0, 0)
	lowerCamelCase_    :		Tuple							=	(len(grid) - 1, len(grid[0]) - 1)
	for elem in grid:
		print(elem)

	print("""------""")

	lowerCamelCase_    :		Optional[int]							=	GreedyBestFirst(init, goal)
	lowerCamelCase_    :		List[Any]							=	greedy_bf.search()
	if path:
		for pos_x, pos_y in path:
			lowerCamelCase_    :		str							=	2

		for elem in grid:
			print(elem)





from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCamelCase_    :		Tuple							=	logging.get_logger(__name__)

lowerCamelCase_    :		List[Any]							=	{
    """google/fnet-base""": """https://huggingface.co/google/fnet-base/resolve/main/config.json""",
    """google/fnet-large""": """https://huggingface.co/google/fnet-large/resolve/main/config.json"""
    # See all FNet models at https://huggingface.co/models?filter=fnet
}


class 						a__							(       __snake_case		):
			A__    :       Dict        =      'fnet'



			def __init__(						self				,						UpperCAmelCase=3_2_0_0_0				,						UpperCAmelCase=7_6_8				,						UpperCAmelCase=1_2				,						UpperCAmelCase=3_0_7_2				,						UpperCAmelCase="gelu_new"				,						UpperCAmelCase=0.1				,						UpperCAmelCase=5_1_2				,						UpperCAmelCase=4				,						UpperCAmelCase=0.02				,						UpperCAmelCase=1e-12				,						UpperCAmelCase=False				,						UpperCAmelCase=5_1_2				,						UpperCAmelCase=3				,						UpperCAmelCase=1				,						UpperCAmelCase=2				,						**UpperCAmelCase				,						)			->   Dict:
								super().__init__(pad_token_id=UpperCAmelCase				,						bos_token_id=UpperCAmelCase				,						eos_token_id=UpperCAmelCase				,						**UpperCAmelCase   )

								__a		 =       vocab_size
								__a		 =       max_position_embeddings
								__a		 =       hidden_size
								__a		 =       num_hidden_layers
								__a		 =       intermediate_size
								__a		 =       hidden_act
								__a		 =       hidden_dropout_prob
								__a		 =       initializer_range
								__a		 =       type_vocab_size
								__a		 =       layer_norm_eps
								__a		 =       use_tpu_fourier_optimizations
								__a		 =       tpu_short_seq_length




