




from __future__ import annotations

a__		     =     list[tuple[int, int]]

a__		     =     [
    [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],
]

a__		     =     ([-1, 0], [0, -1], [1, 0], [0, 1])  # up, left, down, right


class    SCREAMING_SNAKE_CASE_				:



	"""simple docstring"""


	def __init__(					self		:						List[str]					,     lowerCAmelCase		:						int					,     lowerCAmelCase		:						int					,     lowerCAmelCase		:						int					,     lowerCAmelCase		:						int					,     lowerCAmelCase		:						float					,     lowerCAmelCase		:						Node | None					,     )	->       List[str]:







						"""simple docstring"""







						__UpperCamelCase					:	str             =      pos_x
						__UpperCamelCase					:	Union[str, Any]             =      pos_y
						__UpperCamelCase					:	Tuple             =      (pos_y, pos_x)
						__UpperCamelCase					:	Dict             =      goal_x
						__UpperCamelCase					:	Optional[int]             =      goal_y
						__UpperCamelCase					:	List[Any]             =      g_cost
						__UpperCamelCase					:	Any             =      parent
						__UpperCamelCase					:	Union[str, Any]             =      self.calculate_heuristic()






	def 					lowerCamelCase__     (					self		:						Optional[int]	)	->       float:







						"""simple docstring"""







						__UpperCamelCase					:	Dict             =      abs(self.pos_x - self.goal_x	)
						__UpperCamelCase					:	str             =      abs(self.pos_y - self.goal_y	)
						return dx + dy






	def __lt__(					self		:						str					,     lowerCAmelCase		:						Optional[int]	)	->       bool:







						"""simple docstring"""







						return self.f_cost < other.f_cost






class    SCREAMING_SNAKE_CASE_				:



	"""simple docstring"""


	def __init__(					self		:						int					,     lowerCAmelCase		:						tuple[int, int]					,     lowerCAmelCase		:						tuple[int, int]	)	->       Tuple:







						"""simple docstring"""







						__UpperCamelCase					:	Tuple             =      Node(start[1]					,     start[0]					,     goal[1]					,     goal[0]					,     0					,     lowerCAmelCase	)
						__UpperCamelCase					:	Tuple             =      Node(goal[1]					,     goal[0]					,     goal[1]					,     goal[0]					,     99999					,     lowerCAmelCase	)

						__UpperCamelCase					:	List[str]             =      [self.start]
						__UpperCamelCase					:	list[Node]             =      []

						__UpperCamelCase					:	Optional[Any]             =      False






	def 					lowerCamelCase__     (					self		:						Union[str, Any]	)	->       Path | None:







						"""simple docstring"""







						while self.open_nodes:
											# Open Nodes are sorted using __lt__
											self.open_nodes.sort()
											__UpperCamelCase					:	List[Any]             =      self.open_nodes.pop(0	)

											if current_node.pos == self.target.pos:
																__UpperCamelCase					:	str             =      True
																return self.retrace_path(lowerCAmelCase	)

											self.closed_nodes.append(lowerCAmelCase	)
											__UpperCamelCase					:	Dict             =      self.get_successors(lowerCAmelCase	)

											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(lowerCAmelCase	)
																else:
																					# retrieve the best current path
																					__UpperCamelCase					:	Optional[int]             =      self.open_nodes.pop(self.open_nodes.index(lowerCAmelCase	)	)

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

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






	def 					lowerCamelCase__     (					self		:						Optional[Any]					,     lowerCAmelCase		:						Node	)	->       list[Node]:







						"""simple docstring"""







						__UpperCamelCase					:	List[Any]             =      []
						for action in delta:
											__UpperCamelCase					:	Tuple             =      parent.pos_x + action[1]
											__UpperCamelCase					:	str             =      parent.pos_y + action[0]

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

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

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






	def 					lowerCamelCase__     (					self		:						Optional[int]					,     lowerCAmelCase		:						Node | None	)	->       Path:







						"""simple docstring"""







						__UpperCamelCase					:	str             =      node
						__UpperCamelCase					:	Tuple             =      []
						while current_node is not None:
											path.append((current_node.pos_y, current_node.pos_x)	)
											__UpperCamelCase					:	Optional[Any]             =      current_node.parent
						path.reverse()
						return path


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

	print('''------''')

	a__		     =     GreedyBestFirst(init, goal)
	a__		     =     greedy_bf.search()
	if path:
		for pos_x, pos_y in path:
			a__		     =     2

		for elem in grid:
			print(elem)












# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import torch

from accelerate import PartialState
from accelerate.utils.operations import broadcast, gather, gather_object, pad_across_processes, reduce





def 				A__ (snake_case   :		List[Any]				)     ->						Optional[Any]:
					return (torch.arange(state.num_processes				) + 1.0 + (state.num_processes * state.process_index)).to(state.device				)





def 				A__ (snake_case   :		int				)     ->						Any:
					__UpperCamelCase					:	List[str]             =      create_tensor(snake_case				)
					__UpperCamelCase					:	List[Any]             =      gather(snake_case				)
					assert gathered_tensor.tolist() == list(range(1		,	state.num_processes**2 + 1				)				)





def 				A__ (snake_case   :		Optional[Any]				)     ->						Dict:
					__UpperCamelCase					:	Any             =      [state.process_index]
					__UpperCamelCase					:	int             =      gather_object(snake_case				)
					assert len(snake_case				) == state.num_processes, F'''{gathered_obj}, {len(snake_case				)} != {state.num_processes}'''
					assert gathered_obj == list(range(state.num_processes				)				), F'''{gathered_obj} != {list(range(state.num_processes				)				)}'''





def 				A__ (snake_case   :		Any				)     ->						Optional[Any]:
					__UpperCamelCase					:	Optional[Any]             =      create_tensor(snake_case				)
					__UpperCamelCase					:	Any             =      broadcast(snake_case				)
					assert broadcasted_tensor.shape == torch.Size([state.num_processes]				)
					assert broadcasted_tensor.tolist() == list(range(1		,	state.num_processes + 1				)				)





def 				A__ (snake_case   :		Tuple				)     ->						Dict:
					# We need to pad the tensor with one more element if we are the main process
					# to ensure that we can pad
					if state.is_main_process:
										__UpperCamelCase					:	List[Any]             =      torch.arange(state.num_processes + 1				).to(state.device				)
					else:
										__UpperCamelCase					:	List[str]             =      torch.arange(state.num_processes				).to(state.device				)
					__UpperCamelCase					:	List[str]             =      pad_across_processes(snake_case				)
					assert padded_tensor.shape == torch.Size([state.num_processes + 1]				)
					if not state.is_main_process:
										assert padded_tensor.tolist() == list(range(0		,	state.num_processes				)				) + [0]





def 				A__ (snake_case   :		Tuple				)     ->						Dict:
					# For now runs on only two processes
					if state.num_processes != 2:
										return
					__UpperCamelCase					:	Optional[int]             =      create_tensor(snake_case				)
					__UpperCamelCase					:	str             =      reduce(snake_case		,	"""sum"""				)
					__UpperCamelCase					:	Optional[int]             =      torch.tensor([4.0, 6]				).to(state.device				)
					assert torch.allclose(snake_case		,	snake_case				), F'''{reduced_tensor} != {truth_tensor}'''





def 				A__ (snake_case   :		Optional[Any]				)     ->						Optional[Any]:
					# For now runs on only two processes
					if state.num_processes != 2:
										return
					__UpperCamelCase					:	List[Any]             =      create_tensor(snake_case				)
					__UpperCamelCase					:	str             =      reduce(snake_case		,	"""mean"""				)
					__UpperCamelCase					:	Union[str, Any]             =      torch.tensor([2.0, 3]				).to(state.device				)
					assert torch.allclose(snake_case		,	snake_case				), F'''{reduced_tensor} != {truth_tensor}'''





def 				A__ (snake_case   :		Tuple				)     ->						int:
					# For xla_spawn (TPUs)
					main()





def 				A__ ()     ->						List[Any]:
					__UpperCamelCase					:	List[Any]             =      PartialState()
					state.print(F'''State: {state}'''				)
					state.print("""testing gather"""				)
					test_gather(snake_case				)
					state.print("""testing gather_object"""				)
					test_gather_object(snake_case				)
					state.print("""testing broadcast"""				)
					test_broadcast(snake_case				)
					state.print("""testing pad_across_processes"""				)
					test_pad_across_processes(snake_case				)
					state.print("""testing reduce_sum"""				)
					test_reduce_sum(snake_case				)
					state.print("""testing reduce_mean"""				)
					test_reduce_mean(snake_case				)


if __name__ == "__main__":
	main()






