
"""simple docstring"""






from __future__ import annotations

__SCREAMING_SNAKE_CASE							=     list[tuple[int, int]]

__SCREAMING_SNAKE_CASE							=     [
    [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],
]

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





class 				a__					:



     def __init__(							self :str      ,  _lowerCamelCase :int      ,  _lowerCamelCase :int      ,  _lowerCamelCase :int      ,  _lowerCamelCase :int      ,  _lowerCamelCase :float      ,  _lowerCamelCase :Node | None      ,  ):







      '''simple docstring'''



      UpperCamelCase_     :					Optional[Any]   	=pos_x
      UpperCamelCase_     :					Union[str, Any]   	=pos_y
      UpperCamelCase_     :					Optional[int]   	=(pos_y, pos_x)
      UpperCamelCase_     :					Dict   	=goal_x
      UpperCamelCase_     :					List[str]   	=goal_y
      UpperCamelCase_     :					Dict   	=g_cost
      UpperCamelCase_     :					Tuple   	=parent
      UpperCamelCase_     :					Dict   	=self.calculate_heuristic()



     def  lowerCamelCase_						(							self :Union[str, Any]   ):







      '''simple docstring'''



      UpperCamelCase_     :					Dict   	=abs(self.pos_x - self.goal_x   )
      UpperCamelCase_     :					List[str]   	=abs(self.pos_y - self.goal_y   )
      return dx + dy




     def __lt__(							self :List[Any]      ,  _lowerCamelCase :Optional[Any]   ):







      '''simple docstring'''



      return self.f_cost < other.f_cost





class 				a__					:



     def __init__(							self :Union[str, Any]      ,  _lowerCamelCase :tuple[int, int]      ,  _lowerCamelCase :tuple[int, int]   ):







      '''simple docstring'''



      UpperCamelCase_     :					Any   	=Node(start[1]      ,  start[0]      ,  goal[1]      ,  goal[0]      ,  0      ,  __lowerCamelCase   )
      UpperCamelCase_     :					Tuple   	=Node(goal[1]      ,  goal[0]      ,  goal[1]      ,  goal[0]      ,  99_999      ,  __lowerCamelCase   )

      UpperCamelCase_     :					Any   	=[self.start]
      UpperCamelCase_     :					Dict   	=[]

      UpperCamelCase_     :					str   	=False



     def  lowerCamelCase_						(							self :Any   ):







      '''simple docstring'''



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

       if current_node.pos == self.target.pos:
        UpperCamelCase_     :					Union[str, Any]   	=True
        return self.retrace_path(__lowerCamelCase   )

       self.closed_nodes.append(__lowerCamelCase   )
       UpperCamelCase_     :					Tuple   	=self.get_successors(__lowerCamelCase   )

       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(__lowerCamelCase   )
        else:
         # retrieve the best current path
         UpperCamelCase_     :					int   	=self.open_nodes.pop(self.open_nodes.index(__lowerCamelCase   )   )

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

      if not self.reached:
       return [self.start.pos]
      return None



     def  lowerCamelCase_						(							self :List[Any]      ,  _lowerCamelCase :Node   ):







      '''simple docstring'''



      UpperCamelCase_     :					Dict   	=[]
      for action in delta:
       UpperCamelCase_     :					int   	=parent.pos_x + action[1]
       UpperCamelCase_     :					Any   	=parent.pos_y + action[0]

       if not (0 <= pos_x <= len(grid[0]   ) - 1 and 0 <= pos_y <= len(__lowerCamelCase   ) - 1):
        continue

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

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




     def  lowerCamelCase_						(							self :List[Any]      ,  _lowerCamelCase :Node | None   ):







      '''simple docstring'''



      UpperCamelCase_     :					List[str]   	=node
      UpperCamelCase_     :					Union[str, Any]   	=[]
      while current_node is not None:
       path.append((current_node.pos_y, current_node.pos_x)   )
       UpperCamelCase_     :					Tuple   	=current_node.parent
      path.reverse()
      return path


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

 print('------')

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

  for elem in grid:
   print(elem)






from typing import TYPE_CHECKING

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


__magic_name__  :  Optional[int]					=       {
    """configuration_biogpt""": ["""BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """BioGptConfig"""],
    """tokenization_biogpt""": ["""BioGptTokenizer"""],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __magic_name__  :  int					=       [
          """BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """BioGptForCausalLM""",
          """BioGptForTokenClassification""",
          """BioGptForSequenceClassification""",
          """BioGptModel""",
          """BioGptPreTrainedModel""",
      ]


if TYPE_CHECKING:
      from .configuration_biogpt import BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, BioGptConfig
      from .tokenization_biogpt import BioGptTokenizer

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_biogpt import (
                BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
                BioGptForCausalLM,
                BioGptForSequenceClassification,
                BioGptForTokenClassification,
                BioGptModel,
                BioGptPreTrainedModel,
            )


else:
      import sys

      __magic_name__  :  int					=       _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

