

from __future__ import annotations

import time
from math import sqrt

# 1 for manhattan, 0 for euclidean
__UpperCAmelCase    				=     0

__UpperCAmelCase    				=     [
    [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],
]

__UpperCAmelCase    				=     [[-1, 0], [0, -1], [1, 0], [0, 1]]  # up, left, down, right

__UpperCAmelCase    				=     tuple[int, int]





class A__    :



 """simple docstring"""







 def __init__(		self :	Tuple     ,						A_ :	int     ,						A_ :	int     ,						A_ :	int     ,						A_ :	int     ,						A_ :	int     ,						A_ :	Node | None     ,						):






      '''simple docstring'''




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

 def    __magic_name__       (		self :	List[str]       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Dict        =    self.pos_x - self.goal_x
      _lowerCAmelCase   :					int        =    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       ):






      '''simple docstring'''




      return self.f_cost < other.f_cost





class A__    :



 """simple docstring"""







 def __init__(		self :	Optional[Any]     ,						A_ :	TPosition     ,						A_ :	TPosition       ):






      '''simple docstring'''




      _lowerCAmelCase   :					str        =    Node(start[1]     ,						start[0]     ,						goal[1]     ,						goal[0]     ,						0     ,						A_       )
      _lowerCAmelCase   :					int        =    Node(goal[1]     ,						goal[0]     ,						goal[1]     ,						goal[0]     ,						9_9_9_9_9     ,						A_       )

      _lowerCAmelCase   :					str        =    [self.start]
      _lowerCAmelCase   :					list[Node]        =    []

      _lowerCAmelCase   :					Dict        =    False

 def    __magic_name__       (		self :	Optional[Any]       ):






      '''simple docstring'''




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

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

           self.closed_nodes.append(A_       )
           _lowerCAmelCase   :					str        =    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
                     _lowerCAmelCase   :					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    __magic_name__       (		self :	int     ,						A_ :	Node       ):






      '''simple docstring'''




      _lowerCAmelCase   :					List[str]        =    []
      for action in delta:
           _lowerCAmelCase   :					Union[str, Any]        =    parent.pos_x + action[1]
           _lowerCAmelCase   :					Optional[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    __magic_name__       (		self :	Tuple     ,						A_ :	Node | None       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Any        =    node
      _lowerCAmelCase   :					Dict        =    []
      while current_node is not None:
           path.append((current_node.pos_y, current_node.pos_x)       )
           _lowerCAmelCase   :					Tuple        =    current_node.parent
      path.reverse()
      return path





class A__    :



 """simple docstring"""







 def __init__(		self :	str     ,						A_ :	TPosition     ,						A_ :	TPosition       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Union[str, Any]        =    AStar(A_     ,						A_       )
      _lowerCAmelCase   :					Optional[Any]        =    AStar(A_     ,						A_       )
      _lowerCAmelCase   :					str        =    False

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






      '''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()
           _lowerCAmelCase   :					List[Any]        =    self.fwd_astar.open_nodes.pop(0       )
           _lowerCAmelCase   :					Optional[int]        =    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_       )

           _lowerCAmelCase   :					str        =    current_bwd_node
           _lowerCAmelCase   :					Dict        =    current_fwd_node

           _lowerCAmelCase   :					Any        =    {
               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
                          _lowerCAmelCase   :					List[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    __magic_name__       (		self :	Optional[int]     ,						A_ :	Node     ,						A_ :	Node       ):






      '''simple docstring'''




      _lowerCAmelCase   :					str        =    self.fwd_astar.retrace_path(A_       )
      _lowerCAmelCase   :					Tuple        =    self.bwd_astar.retrace_path(A_       )
      bwd_path.pop()
      bwd_path.reverse()
      _lowerCAmelCase   :					Optional[int]        =    fwd_path + bwd_path
      return path


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

  __UpperCAmelCase    				=     time.time()
  __UpperCAmelCase    				=     AStar(init, goal)
  __UpperCAmelCase    				=     a_star.search()
  __UpperCAmelCase    				=     time.time() - start_time
  print(f'''AStar execution time = {end_time:f} seconds''')

  __UpperCAmelCase    				=     time.time()
  __UpperCAmelCase    				=     BidirectionalAStar(init, goal)
  __UpperCAmelCase    				=     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_tensorflow_text_available, is_torch_available


__UpperCAmelCase    				=     {
    'configuration_ernie': ['ERNIE_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ErnieConfig', 'ErnieOnnxConfig'],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __UpperCAmelCase    				=     [
      'ERNIE_PRETRAINED_MODEL_ARCHIVE_LIST',
      'ErnieForCausalLM',
      'ErnieForMaskedLM',
      'ErnieForMultipleChoice',
      'ErnieForNextSentencePrediction',
      'ErnieForPreTraining',
      'ErnieForQuestionAnswering',
      'ErnieForSequenceClassification',
      'ErnieForTokenClassification',
      'ErnieModel',
      'ErniePreTrainedModel',
  ]

if TYPE_CHECKING:
  from .configuration_ernie import ERNIE_PRETRAINED_CONFIG_ARCHIVE_MAP, ErnieConfig, ErnieOnnxConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_ernie import (
        ERNIE_PRETRAINED_MODEL_ARCHIVE_LIST,
        ErnieForCausalLM,
        ErnieForMaskedLM,
        ErnieForMultipleChoice,
        ErnieForNextSentencePrediction,
        ErnieForPreTraining,
        ErnieForQuestionAnswering,
        ErnieForSequenceClassification,
        ErnieForTokenClassification,
        ErnieModel,
        ErniePreTrainedModel,
    )

else:
  import sys

  __UpperCAmelCase    				=     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)






