



'''simple docstring'''

from __future__ import annotations

__lowerCAmelCase									=   list[tuple[int, int]]

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

class 	__magic_name__						:







							def __init__(							self					:       List[Any]	,_UpperCAmelCase					:       int	,_UpperCAmelCase					:       int	,_UpperCAmelCase					:       int	,_UpperCAmelCase					:       int	,_UpperCAmelCase					:       float	,_UpperCAmelCase					:       Node | None	,):
									_a     :      Optional[int]        =							pos_x
									_a     :      int        =							pos_y
									_a     :      str        =							(pos_y, pos_x)
									_a     :      Optional[Any]        =							goal_x
									_a     :      List[Any]        =							goal_y
									_a     :      Optional[int]        =							g_cost
									_a     :      str        =							parent
									_a     :      List[str]        =							self.calculate_heuristic()







							def 							__lowercase			(							self					:       str		):
									_a     :      Optional[int]        =							abs(self.pos_x - self.goal_x		)
									_a     :      Optional[Any]        =							abs(self.pos_y - self.goal_y		)
									return dx + dy







							def __lt__(							self					:       Optional[int]	,_UpperCAmelCase					:       Dict		):
									return self.f_cost < other.f_cost

class 	__magic_name__						:







							def __init__(							self					:       List[Any]	,_UpperCAmelCase					:       tuple[int, int]	,_UpperCAmelCase					:       tuple[int, int]		):
									_a     :      Union[str, Any]        =							Node(start[1]	,start[0]	,goal[1]	,goal[0]	,0	,_UpperCAmelCase		)
									_a     :      Tuple        =							Node(goal[1]	,goal[0]	,goal[1]	,goal[0]	,99999	,_UpperCAmelCase		)

									_a     :      Dict        =							[self.start]
									_a     :      list[Node]        =							[]

									_a     :      Optional[int]        =							False







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

											if current_node.pos == self.target.pos:
													_a     :      List[str]        =							True
													return self.retrace_path(_UpperCAmelCase		)

											self.closed_nodes.append(_UpperCAmelCase		)
											_a     :      Optional[Any]        =							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     :      Optional[Any]        =							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 							__lowercase			(							self					:       Union[str, Any]	,_UpperCAmelCase					:       Node		):
									_a     :      Any        =							[]
									for action in delta:
											_a     :      int        =							parent.pos_x + action[1]
											_a     :      Optional[int]        =							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 							__lowercase			(							self					:       Optional[int]	,_UpperCAmelCase					:       Node | None		):
									_a     :      List[Any]        =							node
									_a     :      List[Any]        =							[]
									while current_node is not None:
											path.append((current_node.pos_y, current_node.pos_x)		)
											_a     :      List[str]        =							current_node.parent
									path.reverse()
									return path


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

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

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

														for elem in grid:
																					print(elem)









'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__lowerCAmelCase									=   {
    '''configuration_lilt''': ['''LILT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''LiltConfig'''],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__lowerCAmelCase									=   [
							    '''LILT_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''LiltForQuestionAnswering''',
							    '''LiltForSequenceClassification''',
							    '''LiltForTokenClassification''',
							    '''LiltModel''',
							    '''LiltPreTrainedModel''',
							]

if TYPE_CHECKING:
							from .configuration_lilt import LILT_PRETRAINED_CONFIG_ARCHIVE_MAP, LiltConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_lilt import (
														    LILT_PRETRAINED_MODEL_ARCHIVE_LIST,
														    LiltForQuestionAnswering,
														    LiltForSequenceClassification,
														    LiltForTokenClassification,
														    LiltModel,
														    LiltPreTrainedModel,
														)

else:
							import sys

							__lowerCAmelCase									=   _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




