
"""simple docstring"""
from __future__ import annotations

import time
from math import sqrt

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

lowercase_       :						List[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_       :						str     					=	[[-1, 0], [0, -1], [1, 0], [0, 1]]  # up, left, down, right

lowercase_       :						List[str]     					=	tuple[int, int]
class 			UpperCamelCase      :
     def __init__(     self  ,     snake_case__  ,     snake_case__  ,     snake_case__  ,     snake_case__  ,     snake_case__  ,     snake_case__  ,     ):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = pos_x
       _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = pos_y
       _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = (pos_y, pos_x)
       _SCREAMING_SNAKE_CASE   :				Any							    = goal_x
       _SCREAMING_SNAKE_CASE   :				Any							    = goal_y
       _SCREAMING_SNAKE_CASE   :				str							    = g_cost
       _SCREAMING_SNAKE_CASE   :				Any							    = parent
       _SCREAMING_SNAKE_CASE   :				Optional[int]							    = self.calculate_heuristic()
       _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = self.g_cost + self.h_cost
     def 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				Dict							    = self.pos_x - self.goal_x
       _SCREAMING_SNAKE_CASE   :				Any							    = self.pos_y - self.goal_y
       if HEURISTIC == 1:
         return abs(snake_case__				) + abs(snake_case__				)
       else:
         return sqrt(dy**2 + dx**2				)




     def __lt__(     self  ,     snake_case__				):




       """simple docstring"""


       return self.f_cost < other.f_cost
class 			UpperCamelCase      :
     def __init__(     self  ,     snake_case__  ,     snake_case__				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				Optional[int]							    = Node(start[1]  ,     start[0]  ,     goal[1]  ,     goal[0]  ,     0  ,     snake_case__				)
       _SCREAMING_SNAKE_CASE   :				Any							    = Node(goal[1]  ,     goal[0]  ,     goal[1]  ,     goal[0]  ,     99999  ,     snake_case__				)

       _SCREAMING_SNAKE_CASE   :				int							    = [self.start]
       _SCREAMING_SNAKE_CASE   :				list[Node]							    = []

       _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = False
     def 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


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

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

         self.closed_nodes.append(snake_case__				)
         _SCREAMING_SNAKE_CASE   :				Tuple							    = self.get_successors(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(snake_case__				)
           else:
             # retrieve the best current path
             _SCREAMING_SNAKE_CASE   :				List[Any]							    = self.open_nodes.pop(self.open_nodes.index(snake_case__				)				)

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

       return [self.start.pos]
     def 				__SCREAMING_SNAKE_CASE							(     self  ,     snake_case__				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				int							    = []
       for action in delta:
         _SCREAMING_SNAKE_CASE   :				Dict							    = parent.pos_x + action[1]
         _SCREAMING_SNAKE_CASE   :				int							    = parent.pos_y + action[0]
         if not (0 <= pos_x <= len(grid[0]				) - 1 and 0 <= pos_y <= len(snake_case__				) - 1):
           continue

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

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




     def 				__SCREAMING_SNAKE_CASE							(     self  ,     snake_case__				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = node
       _SCREAMING_SNAKE_CASE   :				str							    = []
       while current_node is not None:
         path.append((current_node.pos_y, current_node.pos_x)				)
         _SCREAMING_SNAKE_CASE   :				Optional[int]							    = current_node.parent
       path.reverse()
       return path
class 			UpperCamelCase      :
     def __init__(     self  ,     snake_case__  ,     snake_case__				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				Optional[int]							    = AStar(snake_case__  ,     snake_case__				)
       _SCREAMING_SNAKE_CASE   :				Dict							    = AStar(snake_case__  ,     snake_case__				)
       _SCREAMING_SNAKE_CASE   :				List[Any]							    = False
     def 				__SCREAMING_SNAKE_CASE							(     self				):




       """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   :				str							    = self.fwd_astar.open_nodes.pop(0				)
         _SCREAMING_SNAKE_CASE   :				int							    = self.bwd_astar.open_nodes.pop(0				)

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

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

         _SCREAMING_SNAKE_CASE   :				List[str]							    = current_bwd_node
         _SCREAMING_SNAKE_CASE   :				Tuple							    = current_fwd_node

         _SCREAMING_SNAKE_CASE   :				List[Any]							    = {
             self.fwd_astar: self.fwd_astar.get_successors(snake_case__				),
             self.bwd_astar: self.bwd_astar.get_successors(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(snake_case__				)
             else:
               # retrieve the best current path
               _SCREAMING_SNAKE_CASE   :				Any							    = astar.open_nodes.pop(
                   astar.open_nodes.index(snake_case__				)				)

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

       return [self.fwd_astar.start.pos]




     def 				__SCREAMING_SNAKE_CASE							(     self  ,     snake_case__  ,     snake_case__				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				Optional[int]							    = self.fwd_astar.retrace_path(snake_case__				)
       _SCREAMING_SNAKE_CASE   :				List[Any]							    = self.bwd_astar.retrace_path(snake_case__				)
       bwd_path.pop()
       bwd_path.reverse()
       _SCREAMING_SNAKE_CASE   :				Dict							    = fwd_path + bwd_path
       return path


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

      lowercase_       :						Dict     					=	time.time()
      lowercase_       :						Optional[Any]     					=	AStar(init, goal)
      lowercase_       :						Any     					=	a_star.search()
      lowercase_       :						str     					=	time.time() - start_time
      print(F'AStar execution time = {end_time:f} seconds')

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





"""simple docstring"""
import re
import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin
class 			UpperCamelCase      (							__SCREAMING_SNAKE_CASE  ):
     A__      =			["""image_processor""", """tokenizer"""]
     A__      =			"""AutoImageProcessor"""
     A__      =			"""AutoTokenizer"""
     def __init__(     self  ,     snake_case__=None  ,     snake_case__=None  ,     **snake_case__				):




       """simple docstring"""


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

       _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = 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__(snake_case__  ,     snake_case__				)
       _SCREAMING_SNAKE_CASE   :				Any							    = self.image_processor
       _SCREAMING_SNAKE_CASE   :				Any							    = False
     def __call__(     self  ,     *snake_case__  ,     **snake_case__				):




       """simple docstring"""


       if self._in_target_context_manager:
         return self.current_processor(*snake_case__  ,     **snake_case__				)

       _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = kwargs.pop("images"  ,     snake_case__				)
       _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = kwargs.pop("text"  ,     snake_case__				)
       if len(snake_case__				) > 0:
         _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = args[0]
         _SCREAMING_SNAKE_CASE   :				List[str]							    = args[1:]

       if images is None and text is None:
         raise ValueError("You need to specify either an `images` or `text` input to process."				)

       if images is not None:
         _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = self.image_processor(snake_case__  ,     *snake_case__  ,     **snake_case__				)
       if text is not None:
         _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = self.tokenizer(snake_case__  ,     **snake_case__				)

       if text is None:
         return inputs
       elif images is None:
         return encodings
       else:
         _SCREAMING_SNAKE_CASE   :				List[Any]							    = encodings["input_ids"]
         return inputs
     def 				__SCREAMING_SNAKE_CASE							(     self  ,     *snake_case__  ,     **snake_case__				):




       """simple docstring"""


       return self.tokenizer.batch_decode(*snake_case__  ,     **snake_case__				)
     def 				__SCREAMING_SNAKE_CASE							(     self  ,     *snake_case__  ,     **snake_case__				):




       """simple docstring"""


       return self.tokenizer.decode(*snake_case__  ,     **snake_case__				)
     @contextmanager
     def 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       warnings.warn(
           "`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your "
           "labels by using the argument `text` of the regular `__call__` method (either in the same call as "
           "your images inputs, or in a separate call."				)
       _SCREAMING_SNAKE_CASE   :				Dict							    = True
       _SCREAMING_SNAKE_CASE   :				str							    = self.tokenizer
       yield
       _SCREAMING_SNAKE_CASE   :				str							    = self.image_processor
       _SCREAMING_SNAKE_CASE   :				int							    = False
     def 				__SCREAMING_SNAKE_CASE							(     self  ,     snake_case__  ,     snake_case__=False  ,     snake_case__=None				):




       """simple docstring"""


       if added_vocab is None:
         _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = self.tokenizer.get_added_vocab()

       _SCREAMING_SNAKE_CASE   :				int							    = {}

       while tokens:
         _SCREAMING_SNAKE_CASE   :				List[Any]							    = re.search(r"<s_(.*?)>"  ,     snake_case__  ,     re.IGNORECASE				)
         if start_token is None:
           break
         _SCREAMING_SNAKE_CASE   :				List[Any]							    = start_token.group(1				)
         _SCREAMING_SNAKE_CASE   :				Any							    = re.search(rF'''</s_{key}>'''  ,     snake_case__  ,     re.IGNORECASE				)
         _SCREAMING_SNAKE_CASE   :				Tuple							    = start_token.group()
         if end_token is None:
           _SCREAMING_SNAKE_CASE   :				Optional[int]							    = tokens.replace(snake_case__  ,     ""				)
         else:
           _SCREAMING_SNAKE_CASE   :				Any							    = end_token.group()
           _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = re.escape(snake_case__				)
           _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = re.escape(snake_case__				)
           _SCREAMING_SNAKE_CASE   :				str							    = re.search(F'''{start_token_escaped}(.*?){end_token_escaped}'''  ,     snake_case__  ,     re.IGNORECASE				)
           if content is not None:
             _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = content.group(1				).strip()
             if r"<s_" in content and r"</s_" in content:  # non-leaf node
               _SCREAMING_SNAKE_CASE   :				Dict							    = self.tokenajson(snake_case__  ,     is_inner_value=snake_case__  ,     added_vocab=snake_case__				)
               if value:
                 if len(snake_case__				) == 1:
                   _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = value[0]
                 _SCREAMING_SNAKE_CASE   :				Dict							    = value
             else:  # leaf nodes
               _SCREAMING_SNAKE_CASE   :				Dict							    = []
               for leaf in content.split(r"<sep/>"				):
                 _SCREAMING_SNAKE_CASE   :				int							    = leaf.strip()
                 if leaf in added_vocab and leaf[0] == "<" and leaf[-2:] == "/>":
                   _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = leaf[1:-2]  # for categorical special tokens
                 output[key].append(snake_case__				)
               if len(output[key]				) == 1:
                 _SCREAMING_SNAKE_CASE   :				Dict							    = output[key][0]

           _SCREAMING_SNAKE_CASE   :				List[Any]							    = tokens[tokens.find(snake_case__				) + len(snake_case__				) :].strip()
           if tokens[:6] == r"<sep/>":  # non-leaf nodes
             return [output] + self.tokenajson(tokens[6:]  ,     is_inner_value=snake_case__  ,     added_vocab=snake_case__				)

       if len(snake_case__				):
         return [output] if is_inner_value else output
       else:
         return [] if is_inner_value else {"text_sequence": tokens}
     @property
     def 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


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




     @property
     def 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


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



