






from __future__ import annotations

import time
from math import sqrt

# 1 for manhattan, 0 for euclidean
lowercase  :							Tuple			  =    0

lowercase  :							Optional[Any]			  =    [
    [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],
]

lowercase  :							Optional[int]			  =    [[-1, 0], [0, -1], [1, 0], [0, 1]]  # up, left, down, right

lowercase  :							Union[str, Any]			  =    tuple[int, int]




class 		UpperCAmelCase_						:


						'''simple docstring'''




						def __init__(	self		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE		,				) ->				None:
								snake_case_				:							List[Any]						 =       pos_x
								snake_case_				:							Tuple						 =       pos_y
								snake_case_				:							Any						 =       (pos_y, pos_x)
								snake_case_				:							Union[str, Any]						 =       goal_x
								snake_case_				:							Any						 =       goal_y
								snake_case_				:							Tuple						 =       g_cost
								snake_case_				:							Optional[int]						 =       parent
								snake_case_				:							str						 =       self.calculate_heuristic()
								snake_case_				:							Optional[Any]						 =       self.g_cost + self.h_cost






						def       _lowerCAmelCase       (	self				) ->				float:
								snake_case_				:							List[Any]						 =       self.pos_x - self.goal_x
								snake_case_				:							Dict						 =       self.pos_y - self.goal_y
								if HEURISTIC == 1:
										return abs(_SCREAMING_SNAKE_CASE				) + abs(_SCREAMING_SNAKE_CASE				)
								else:
										return sqrt(dy**2 + dx**2				)






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




class 		UpperCAmelCase_						:


						'''simple docstring'''




						def __init__(	self		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE				) ->				List[Any]:
								snake_case_				:							List[Any]						 =       Node(start[1]		,				start[0]		,				goal[1]		,				goal[0]		,				0		,				_SCREAMING_SNAKE_CASE				)
								snake_case_				:							str						 =       Node(goal[1]		,				goal[0]		,				goal[1]		,				goal[0]		,				9_9999		,				_SCREAMING_SNAKE_CASE				)

								snake_case_				:							Tuple						 =       [self.start]
								snake_case_				:							list[Node]						 =       []

								snake_case_				:							Optional[int]						 =       False






						def       _lowerCAmelCase       (	self				) ->				list[TPosition]:

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

										if current_node.pos == self.target.pos:
												return self.retrace_path(_SCREAMING_SNAKE_CASE				)

										self.closed_nodes.append(_SCREAMING_SNAKE_CASE				)
										snake_case_				:							Optional[Any]						 =       self.get_successors(_SCREAMING_SNAKE_CASE				)

										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(_SCREAMING_SNAKE_CASE				)
												else:
														# retrieve the best current path
														snake_case_				:							List[str]						 =       self.open_nodes.pop(self.open_nodes.index(_SCREAMING_SNAKE_CASE				)				)

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

								return [self.start.pos]






						def       _lowerCAmelCase       (	self		,				_SCREAMING_SNAKE_CASE				) ->				list[Node]:
								snake_case_				:							Optional[Any]						 =       []
								for action in delta:
										snake_case_				:							Optional[int]						 =       parent.pos_x + action[1]
										snake_case_				:							List[str]						 =       parent.pos_y + action[0]
										if not (0 <= pos_x <= len(grid[0]				) - 1 and 0 <= pos_y <= len(_SCREAMING_SNAKE_CASE				) - 1):
												continue

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

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






						def       _lowerCAmelCase       (	self		,				_SCREAMING_SNAKE_CASE				) ->				list[TPosition]:
								snake_case_				:							Union[str, Any]						 =       node
								snake_case_				:							Union[str, Any]						 =       []
								while current_node is not None:
										path.append((current_node.pos_y, current_node.pos_x)				)
										snake_case_				:							Tuple						 =       current_node.parent
								path.reverse()
								return path




class 		UpperCAmelCase_						:


						'''simple docstring'''




						def __init__(	self		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE				) ->				None:
								snake_case_				:							List[Any]						 =       AStar(_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE				)
								snake_case_				:							Optional[Any]						 =       AStar(_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE				)
								snake_case_				:							List[str]						 =       False






						def       _lowerCAmelCase       (	self				) ->				list[TPosition]:

								while self.fwd_astar.open_nodes or self.bwd_astar.open_nodes:
										self.fwd_astar.open_nodes.sort()
										self.bwd_astar.open_nodes.sort()
										snake_case_				:							Tuple						 =       self.fwd_astar.open_nodes.pop(0				)
										snake_case_				:							Union[str, Any]						 =       self.bwd_astar.open_nodes.pop(0				)

										if current_bwd_node.pos == current_fwd_node.pos:
												return self.retrace_bidirectional_path(
												    _SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE				)

										self.fwd_astar.closed_nodes.append(_SCREAMING_SNAKE_CASE				)
										self.bwd_astar.closed_nodes.append(_SCREAMING_SNAKE_CASE				)

										snake_case_				:							Optional[int]						 =       current_bwd_node
										snake_case_				:							Optional[Any]						 =       current_fwd_node

										snake_case_				:							Optional[Any]						 =       {
										    self.fwd_astar: self.fwd_astar.get_successors(_SCREAMING_SNAKE_CASE				),
										    self.bwd_astar: self.bwd_astar.get_successors(_SCREAMING_SNAKE_CASE				),
										}

										for astar in [self.fwd_astar, self.bwd_astar]:
												for child_node in successors[astar]:
														if child_node in astar.closed_nodes:
																continue

														if child_node not in astar.open_nodes:
																astar.open_nodes.append(_SCREAMING_SNAKE_CASE				)
														else:
																# retrieve the best current path
																snake_case_				:							Optional[Any]						 =       astar.open_nodes.pop(
																    astar.open_nodes.index(_SCREAMING_SNAKE_CASE				)				)

																if child_node.g_cost < better_node.g_cost:
																		astar.open_nodes.append(_SCREAMING_SNAKE_CASE				)
																else:
																		astar.open_nodes.append(_SCREAMING_SNAKE_CASE				)

								return [self.fwd_astar.start.pos]






						def       _lowerCAmelCase       (	self		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE				) ->				list[TPosition]:
								snake_case_				:							Any						 =       self.fwd_astar.retrace_path(_SCREAMING_SNAKE_CASE				)
								snake_case_				:							Tuple						 =       self.bwd_astar.retrace_path(_SCREAMING_SNAKE_CASE				)
								bwd_path.pop()
								bwd_path.reverse()
								snake_case_				:							Optional[int]						 =       fwd_path + bwd_path
								return path


if __name__ == "__main__":
							# all coordinates are given in format [y,x]
							lowercase  :							str			  =    (0, 0)
							lowercase  :							str			  =    (len(grid) - 1, len(grid[0]) - 1)
							for elem in grid:
														print(elem)

							lowercase  :							int			  =    time.time()
							lowercase  :							Dict			  =    AStar(init, goal)
							lowercase  :							List[Any]			  =    a_star.search()
							lowercase  :							Union[str, Any]			  =    time.time() - start_time
							print(F"""AStar execution time = {end_time:f} seconds""")

							lowercase  :							int			  =    time.time()
							lowercase  :							str			  =    BidirectionalAStar(init, goal)
							lowercase  :							Optional[Any]			  =    time.time() - bd_start_time
							print(F"""BidirectionalAStar execution time = {bd_end_time:f} seconds""")








from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_speech_available, is_torch_available


lowercase  :							int			  =    {
    '''configuration_audio_spectrogram_transformer''': [
        '''AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''ASTConfig''',
    ]
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowercase  :							Optional[Any]			  =    [
							    '''AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''ASTForAudioClassification''',
							    '''ASTModel''',
							    '''ASTPreTrainedModel''',
							]

try:
							if not is_speech_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowercase  :							Optional[int]			  =    ['''ASTFeatureExtractor''']

if TYPE_CHECKING:
							from .configuration_audio_spectrogram_transformer import (
							    AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
							    ASTConfig,
							)

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_audio_spectrogram_transformer import (
														    AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
														    ASTForAudioClassification,
														    ASTModel,
														    ASTPreTrainedModel,
														)

							try:
														if not is_speech_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .feature_extraction_audio_spectrogram_transformer import ASTFeatureExtractor


else:
							import sys

							lowercase  :							Optional[int]			  =    _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
