

from __future__ import annotations

import time

_SCREAMING_SNAKE_CASE					:  List[str]             =      list[tuple[int, int]]

_SCREAMING_SNAKE_CASE					:  str             =      [
    [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],
]

_SCREAMING_SNAKE_CASE					:  Any             =      [[-1, 0], [0, -1], [1, 0], [0, 1]]  # up, left, down, right






class 	UpperCAmelCase__		:






		"""simple docstring"""


		def __init__(							self						:		List[str]     ,       __lowerCamelCase						:		int     ,       __lowerCamelCase						:		int     ,       __lowerCamelCase						:		int     ,       __lowerCamelCase						:		int     ,       __lowerCamelCase						:		Node | None )		->							Tuple:
							SCREAMING_SNAKE_CASE__   =   pos_x
							SCREAMING_SNAKE_CASE__   =   pos_y
							SCREAMING_SNAKE_CASE__   =   (pos_y, pos_x)
							SCREAMING_SNAKE_CASE__   =   goal_x
							SCREAMING_SNAKE_CASE__   =   goal_y
							SCREAMING_SNAKE_CASE__   =   parent






class 	UpperCAmelCase__		:






		"""simple docstring"""


		def __init__(							self						:		Dict     ,       __lowerCamelCase						:		tuple[int, int]     ,       __lowerCamelCase						:		tuple[int, int] )		->							int:
							SCREAMING_SNAKE_CASE__   =   Node(start[1]     ,       start[0]     ,       goal[1]     ,       goal[0]     ,       __lowerCamelCase )
							SCREAMING_SNAKE_CASE__   =   Node(goal[1]     ,       goal[0]     ,       goal[1]     ,       goal[0]     ,       __lowerCamelCase )

							SCREAMING_SNAKE_CASE__   =   [self.start]
							SCREAMING_SNAKE_CASE__   =   False






		def 				lowercase_		(							self						:		Optional[int] )		->							Path | None:
							while self.node_queue:
												SCREAMING_SNAKE_CASE__   =   self.node_queue.pop(0 )

												if current_node.pos == self.target.pos:
																	SCREAMING_SNAKE_CASE__   =   True
																	return self.retrace_path(__lowerCamelCase )

												SCREAMING_SNAKE_CASE__   =   self.get_successors(__lowerCamelCase )

												for node in successors:
																	self.node_queue.append(__lowerCamelCase )

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






		def 				lowercase_		(							self						:		Dict     ,       __lowerCamelCase						:		Node )		->							list[Node]:
							SCREAMING_SNAKE_CASE__   =   []
							for action in delta:
												SCREAMING_SNAKE_CASE__   =   parent.pos_x + action[1]
												SCREAMING_SNAKE_CASE__   =   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     ,       __lowerCamelCase ) )
							return successors







		def 				lowercase_		(							self						:		Optional[Any]     ,       __lowerCamelCase						:		Node | None )		->							Path:
							SCREAMING_SNAKE_CASE__   =   node
							SCREAMING_SNAKE_CASE__   =   []
							while current_node is not None:
												path.append((current_node.pos_y, current_node.pos_x) )
												SCREAMING_SNAKE_CASE__   =   current_node.parent
							path.reverse()
							return path






class 	UpperCAmelCase__		:






		"""simple docstring"""


		def __init__(							self						:		Any     ,       __lowerCamelCase						:		Tuple     ,       __lowerCamelCase						:		Optional[int] )		->							List[Any]:
							SCREAMING_SNAKE_CASE__   =   BreadthFirstSearch(__lowerCamelCase     ,       __lowerCamelCase )
							SCREAMING_SNAKE_CASE__   =   BreadthFirstSearch(__lowerCamelCase     ,       __lowerCamelCase )
							SCREAMING_SNAKE_CASE__   =   False






		def 				lowercase_		(							self						:		Union[str, Any] )		->							Path | None:
							while self.fwd_bfs.node_queue or self.bwd_bfs.node_queue:
												SCREAMING_SNAKE_CASE__   =   self.fwd_bfs.node_queue.pop(0 )
												SCREAMING_SNAKE_CASE__   =   self.bwd_bfs.node_queue.pop(0 )

												if current_bwd_node.pos == current_fwd_node.pos:
																	SCREAMING_SNAKE_CASE__   =   True
																	return self.retrace_bidirectional_path(
																	    __lowerCamelCase     ,       __lowerCamelCase )

												SCREAMING_SNAKE_CASE__   =   current_bwd_node
												SCREAMING_SNAKE_CASE__   =   current_fwd_node

												SCREAMING_SNAKE_CASE__   =   {
												    self.fwd_bfs: self.fwd_bfs.get_successors(__lowerCamelCase ),
												    self.bwd_bfs: self.bwd_bfs.get_successors(__lowerCamelCase ),
												}

												for bfs in [self.fwd_bfs, self.bwd_bfs]:
																	for node in successors[bfs]:
																						bfs.node_queue.append(__lowerCamelCase )

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







		def 				lowercase_		(							self						:		Tuple     ,       __lowerCamelCase						:		Node     ,       __lowerCamelCase						:		Node )		->							Path:
							SCREAMING_SNAKE_CASE__   =   self.fwd_bfs.retrace_path(__lowerCamelCase )
							SCREAMING_SNAKE_CASE__   =   self.bwd_bfs.retrace_path(__lowerCamelCase )
							bwd_path.pop()
							bwd_path.reverse()
							SCREAMING_SNAKE_CASE__   =   fwd_path + bwd_path
							return path


if __name__ == "__main__":
							# all coordinates are given in format [y,x]
							import doctest

							doctest.testmod()
							_SCREAMING_SNAKE_CASE					:  Optional[int]             =      (0, 0)
							_SCREAMING_SNAKE_CASE					:  int             =      (len(grid) - 1, len(grid[0]) - 1)
							for elem in grid:
														print(elem)

							_SCREAMING_SNAKE_CASE					:  List[str]             =      time.time()
							_SCREAMING_SNAKE_CASE					:  Dict             =      BreadthFirstSearch(init, goal)
							_SCREAMING_SNAKE_CASE					:  List[str]             =      bfs.search()
							_SCREAMING_SNAKE_CASE					:  int             =      time.time() - start_bfs_time

							print('''Unidirectional BFS computation time : ''', bfs_time)

							_SCREAMING_SNAKE_CASE					:  Dict             =      time.time()
							_SCREAMING_SNAKE_CASE					:  Optional[Any]             =      BidirectionalBreadthFirstSearch(init, goal)
							_SCREAMING_SNAKE_CASE					:  int             =      bd_bfs.search()
							_SCREAMING_SNAKE_CASE					:  Optional[int]             =      time.time() - start_bd_bfs_time

							print('''Bidirectional BFS computation time : ''', bd_bfs_time)





from ...utils import is_torch_available, is_transformers_available


if is_transformers_available() and is_torch_available():
       from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline


