




from __future__ import annotations

import time
from math import sqrt

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

lowercase						:      Tuple									= [
    [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						:      List[str]									= [[-1, 0], [0, -1], [1, 0], [0, 1]]  # up, left, down, right

lowercase						:      List[str]									= tuple[int, int]




class 				lowerCamelCase__							:




	'''simple docstring'''




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







	def      _lowerCamelCase       (      self	:Any		)	->				Union[str, Any]:
								__UpperCamelCase			:     Dict         =      self.pos_x - self.goal_x
								__UpperCamelCase			:     Optional[Any]         =      self.pos_y - self.goal_y
								if HEURISTIC == 1:
															return abs(_a		) + abs(_a		)
								else:
															return sqrt(dy**2 + dx**2		)







	def __lt__(      self	:List[Any]  ,     a	:Node		)	->				Any:
								return self.f_cost < other.f_cost




class 				lowerCamelCase__							:




	'''simple docstring'''




	def __init__(      self	:Optional[Any]  ,     a	:TPosition  ,     a	:TPosition		)	->				Union[str, Any]:
								__UpperCamelCase			:     str         =      Node(start[1]  ,     start[0]  ,     goal[1]  ,     goal[0]  ,     0  ,     _a		)
								__UpperCamelCase			:     List[str]         =      Node(goal[1]  ,     goal[0]  ,     goal[1]  ,     goal[0]  ,     9_9_9_9_9  ,     _a		)

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

								__UpperCamelCase			:     int         =      False







	def      _lowerCamelCase       (      self	:Dict		)	->				List[str]:




								while self.open_nodes:
															# Open Nodes are sorted using __lt__
															self.open_nodes.sort()
															__UpperCamelCase			:     Dict         =      self.open_nodes.pop(0		)

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

															self.closed_nodes.append(_a		)
															__UpperCamelCase			:     Union[str, Any]         =      self.get_successors(_a		)

															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(_a		)
																						else:
																													# retrieve the best current path
																													__UpperCamelCase			:     List[Any]         =      self.open_nodes.pop(self.open_nodes.index(_a		)		)

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

								return [self.start.pos]







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

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

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







	def      _lowerCamelCase       (      self	:List[Any]  ,     a	:Node | None		)	->				List[Any]:
								__UpperCamelCase			:     Dict         =      node
								__UpperCamelCase			:     Any         =      []
								while current_node is not None:
															path.append((current_node.pos_y, current_node.pos_x)		)
															__UpperCamelCase			:     Any         =      current_node.parent
								path.reverse()
								return path




class 				lowerCamelCase__							:




	'''simple docstring'''




	def __init__(      self	:Optional[Any]  ,     a	:TPosition  ,     a	:TPosition		)	->				List[str]:
								__UpperCamelCase			:     Optional[Any]         =      AStar(_a  ,     _a		)
								__UpperCamelCase			:     Dict         =      AStar(_a  ,     _a		)
								__UpperCamelCase			:     Dict         =      False







	def      _lowerCamelCase       (      self	:List[Any]		)	->				int:




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

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

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

															__UpperCamelCase			:     Any         =      current_bwd_node
															__UpperCamelCase			:     List[Any]         =      current_fwd_node

															__UpperCamelCase			:     int         =      {
															    self.fwd_astar: self.fwd_astar.get_successors(_a		),
															    self.bwd_astar: self.bwd_astar.get_successors(_a		),
															}

															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(_a		)
																													else:
																																				# retrieve the best current path
																																				__UpperCamelCase			:     str         =      astar.open_nodes.pop(
																																				    astar.open_nodes.index(_a		)		)

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

								return [self.fwd_astar.start.pos]







	def      _lowerCamelCase       (      self	:str  ,     a	:Node  ,     a	:Node		)	->				List[Any]:
								__UpperCamelCase			:     List[Any]         =      self.fwd_astar.retrace_path(_a		)
								__UpperCamelCase			:     Dict         =      self.bwd_astar.retrace_path(_a		)
								bwd_path.pop()
								bwd_path.reverse()
								__UpperCamelCase			:     Optional[Any]         =      fwd_path + bwd_path
								return path


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

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

		lowercase						:      Dict									= time.time()
		lowercase						:      str									= BidirectionalAStar(init, goal)
		lowercase						:      List[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_torch_available


lowercase						:      Optional[Any]									= {
    'configuration_blip_2': [
        'BLIP_2_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'Blip2Config',
        'Blip2QFormerConfig',
        'Blip2VisionConfig',
    ],
    'processing_blip_2': ['Blip2Processor'],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase						:      Tuple									= [
		    'BLIP_2_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'Blip2Model',
		    'Blip2QFormerModel',
		    'Blip2PreTrainedModel',
		    'Blip2ForConditionalGeneration',
		    'Blip2VisionModel',
		]

if TYPE_CHECKING:
		from .configuration_blip_a import (
		    BLIP_2_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    BlipaConfig,
		    BlipaQFormerConfig,
		    BlipaVisionConfig,
		)
		from .processing_blip_a import BlipaProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_blip_a import (
				    BLIP_2_PRETRAINED_MODEL_ARCHIVE_LIST,
				    BlipaForConditionalGeneration,
				    BlipaModel,
				    BlipaPreTrainedModel,
				    BlipaQFormerModel,
				    BlipaVisionModel,
				)

else:
		import sys

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