


from __future__ import annotations

import time
from math import sqrt

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

snake_case_						:			Dict													=							[
    [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],
]

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

snake_case_						:			Optional[Any]													=							tuple[int, int]

class 						A__							:




						def __init__(       self					:      str				,    _a					:      int				,    _a					:      int				,    _a					:      int				,    _a					:      int				,    _a					:      int				,    _a					:      Node | None				,    )							->     None:


										"""simple docstring"""





										_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			  =g_cost
										_SCREAMING_SNAKE_CASE			  =parent
										_SCREAMING_SNAKE_CASE			  =self.calculate_heuristic()
										_SCREAMING_SNAKE_CASE			  =self.g_cost + self.h_cost




						def 							__UpperCamelCase				(       self					:      List[str]  )							->     float:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =self.pos_x - self.goal_x
										_SCREAMING_SNAKE_CASE			  =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[str]				,    _a					:      Node  )							->     bool:


										"""simple docstring"""





										return self.f_cost < other.f_cost

class 						A__							:




						def __init__(       self					:      int				,    _a					:      TPosition				,    _a					:      TPosition  )							->     List[Any]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =Node(start[1]				,    start[0]				,    goal[1]				,    goal[0]				,    0				,    _a  )
										_SCREAMING_SNAKE_CASE			  =Node(goal[1]				,    goal[0]				,    goal[1]				,    goal[0]				,    9_9999				,    _a  )

										_SCREAMING_SNAKE_CASE			  =[self.start]
										_SCREAMING_SNAKE_CASE			  =[]

										_SCREAMING_SNAKE_CASE			  =False




						def 							__UpperCamelCase				(       self					:      Dict  )							->     list[TPosition]:


										"""simple docstring"""





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

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

														self.closed_nodes.append(_a  )
														_SCREAMING_SNAKE_CASE			  =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
																						_SCREAMING_SNAKE_CASE			  =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 							__UpperCamelCase				(       self					:      Tuple				,    _a					:      Node  )							->     list[Node]:


										"""simple docstring"""





										_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(_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 							__UpperCamelCase				(       self					:      List[Any]				,    _a					:      Node | None  )							->     list[TPosition]:


										"""simple docstring"""





										_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 						A__							:




						def __init__(       self					:      Any				,    _a					:      TPosition				,    _a					:      TPosition  )							->     None:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =AStar(_a				,    _a  )
										_SCREAMING_SNAKE_CASE			  =AStar(_a				,    _a  )
										_SCREAMING_SNAKE_CASE			  =False




						def 							__UpperCamelCase				(       self					:      Any  )							->     list[TPosition]:


										"""simple docstring"""





										while self.fwd_astar.open_nodes or self.bwd_astar.open_nodes:
														self.fwd_astar.open_nodes.sort()
														self.bwd_astar.open_nodes.sort()
														_SCREAMING_SNAKE_CASE			  =self.fwd_astar.open_nodes.pop(0  )
														_SCREAMING_SNAKE_CASE			  =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  )

														_SCREAMING_SNAKE_CASE			  =current_bwd_node
														_SCREAMING_SNAKE_CASE			  =current_fwd_node

														_SCREAMING_SNAKE_CASE			  ={
														    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
																										_SCREAMING_SNAKE_CASE			  =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 							__UpperCamelCase				(       self					:      int				,    _a					:      Node				,    _a					:      Node  )							->     list[TPosition]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =self.fwd_astar.retrace_path(_a  )
										_SCREAMING_SNAKE_CASE			  =self.bwd_astar.retrace_path(_a  )
										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]
							snake_case_						:			Dict													=							(0, 0)
							snake_case_						:			List[str]													=							(len(grid) - 1, len(grid[0]) - 1)
							for elem in grid:
														print(elem)

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

							snake_case_						:			Tuple													=							time.time()
							snake_case_						:			Any													=							BidirectionalAStar(init, goal)
							snake_case_						:			int													=							time.time() - bd_start_time
							print(f"""BidirectionalAStar execution time = {bd_end_time:f} seconds""")



import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding

class 						A__							(		UpperCamelCase__  ):
						UpperCAmelCase             =						["image_processor", "tokenizer"]
						UpperCAmelCase             =						"ViTImageProcessor"
						UpperCAmelCase             =						("CLIPTokenizer", "CLIPTokenizerFast")




						def __init__(       self					:      int				,    _a					:      int=None				,    _a					:      Optional[int]=None				,    **_a					:      str  )							->     List[str]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =None
										if "feature_extractor" in kwargs:
														warnings.warn(
														    '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
														    ''' instead.'''				,    _a				,    )
														_SCREAMING_SNAKE_CASE			  =kwargs.pop('''feature_extractor'''  )

										_SCREAMING_SNAKE_CASE			  =image_processor if image_processor is not None else feature_extractor
										if image_processor is None:
														raise ValueError('''You need to specify an `image_processor`.'''  )
										if tokenizer is None:
														raise ValueError('''You need to specify a `tokenizer`.'''  )

										super().__init__(_a				,    _a  )




						def __call__(       self					:      List[str]				,    _a					:      Tuple=None				,    _a					:      Optional[Any]=None				,    _a					:      List[Any]=None				,    _a					:      List[Any]=None				,    **_a					:      List[Any]  )							->     Optional[Any]:


										"""simple docstring"""





										if text is None and visual_prompt is None and images is None:
														raise ValueError('''You have to specify either text, visual prompt or images.'''  )

										if text is not None and visual_prompt is not None:
														raise ValueError('''You have to specify exactly one type of prompt. Either text or visual prompt.'''  )

										if text is not None:
														_SCREAMING_SNAKE_CASE			  =self.tokenizer(_a				,    return_tensors=_a				,    **_a  )

										if visual_prompt is not None:
														_SCREAMING_SNAKE_CASE			  =self.image_processor(_a				,    return_tensors=_a				,    **_a  )

										if images is not None:
														_SCREAMING_SNAKE_CASE			  =self.image_processor(_a				,    return_tensors=_a				,    **_a  )

										if visual_prompt is not None and images is not None:
														_SCREAMING_SNAKE_CASE			  ={
														    '''pixel_values''': image_features.pixel_values,
														    '''conditional_pixel_values''': prompt_features.pixel_values,
														}
														return encoding
										elif text is not None and images is not None:
														_SCREAMING_SNAKE_CASE			  =image_features.pixel_values
														return encoding
										elif text is not None:
														return encoding
										elif visual_prompt is not None:
														_SCREAMING_SNAKE_CASE			  ={
														    '''conditional_pixel_values''': prompt_features.pixel_values,
														}
														return encoding
										else:
														return BatchEncoding(data=dict(**_a  )				,    tensor_type=_a  )




						def 							__UpperCamelCase				(       self					:      Any				,    *_a					:      str				,    **_a					:      List[Any]  )							->     Tuple:


										"""simple docstring"""





										return self.tokenizer.batch_decode(*_a				,    **_a  )




						def 							__UpperCamelCase				(       self					:      Dict				,    *_a					:      str				,    **_a					:      List[Any]  )							->     List[Any]:


										"""simple docstring"""





										return self.tokenizer.decode(*_a				,    **_a  )




						@property
						def 							__UpperCamelCase				(       self					:      Optional[int]  )							->     int:


										"""simple docstring"""





										warnings.warn(
										    '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''				,    _a				,    )
										return self.image_processor_class




						@property
						def 							__UpperCamelCase				(       self					:      Optional[Any]  )							->     Union[str, Any]:


										"""simple docstring"""





										warnings.warn(
										    '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''				,    _a				,    )
										return self.image_processor