



from __future__ import annotations

import time
from math import sqrt

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

a_      :      List[str]    =     [
    [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],
]

a_      :      str    =     [[-1, 0], [0, -1], [1, 0], [0, 1]]  # up, left, down, right

a_      :      List[Any]    =     tuple[int, int]
class    __UpperCamelCase			:

 """simple docstring"""



 def __init__(	self		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				) ->			None:
  a__												= pos_x
  a__												= pos_y
  a__												= (pos_y, pos_x)
  a__												= goal_x
  a__												= goal_y
  a__												= g_cost
  a__												= parent
  a__												= self.calculate_heuristic()
  a__												= self.g_cost + self.h_cost

 def 						_UpperCAmelCase       (	self      ) ->			float:
  a__												= self.pos_x - self.goal_x
  a__												= self.pos_y - self.goal_y
  if HEURISTIC == 1:
   return abs(SCREAMING_SNAKE_CASE      ) + abs(SCREAMING_SNAKE_CASE      )
  else:
   return sqrt(dy**2 + dx**2      )



 def __lt__(	self		,				SCREAMING_SNAKE_CASE      ) ->			bool:
  return self.f_cost < other.f_cost





class    __UpperCamelCase			:

 """simple docstring"""



 def __init__(	self		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE      ) ->			Optional[int]:
  a__												= Node(start[1]		,				start[0]		,				goal[1]		,				goal[0]		,				0		,				SCREAMING_SNAKE_CASE      )
  a__												= Node(goal[1]		,				goal[0]		,				goal[1]		,				goal[0]		,				9_9_9_9_9		,				SCREAMING_SNAKE_CASE      )

  a__												= [self.start]
  a__												= []

  a__												= False

 def 						_UpperCAmelCase       (	self      ) ->			list[TPosition]:
  while self.open_nodes:
   # Open Nodes are sorted using __lt__
   self.open_nodes.sort()
   a__												= self.open_nodes.pop(0      )

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

   self.closed_nodes.append(SCREAMING_SNAKE_CASE      )
   a__												= self.get_successors(SCREAMING_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(SCREAMING_SNAKE_CASE      )
    else:
     # retrieve the best current path
     a__												= self.open_nodes.pop(self.open_nodes.index(SCREAMING_SNAKE_CASE      )      )

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

  return [self.start.pos]

 def 						_UpperCAmelCase       (	self		,				SCREAMING_SNAKE_CASE      ) ->			list[Node]:
  a__												= []
  for action in delta:
   a__												= parent.pos_x + action[1]
   a__												= parent.pos_y + action[0]
   if not (0 <= pos_x <= len(grid[0]      ) - 1 and 0 <= pos_y <= len(SCREAMING_SNAKE_CASE      ) - 1):
    continue

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

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



 def 						_UpperCAmelCase       (	self		,				SCREAMING_SNAKE_CASE      ) ->			list[TPosition]:
  a__												= node
  a__												= []
  while current_node is not None:
   path.append((current_node.pos_y, current_node.pos_x)      )
   a__												= current_node.parent
  path.reverse()
  return path





class    __UpperCamelCase			:

 """simple docstring"""



 def __init__(	self		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE      ) ->			None:
  a__												= AStar(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE      )
  a__												= AStar(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE      )
  a__												= False

 def 						_UpperCAmelCase       (	self      ) ->			list[TPosition]:
  while self.fwd_astar.open_nodes or self.bwd_astar.open_nodes:
   self.fwd_astar.open_nodes.sort()
   self.bwd_astar.open_nodes.sort()
   a__												= self.fwd_astar.open_nodes.pop(0      )
   a__												= self.bwd_astar.open_nodes.pop(0      )

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

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

   a__												= current_bwd_node
   a__												= current_fwd_node

   a__												= {
       self.fwd_astar: self.fwd_astar.get_successors(SCREAMING_SNAKE_CASE      ),
       self.bwd_astar: self.bwd_astar.get_successors(SCREAMING_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(SCREAMING_SNAKE_CASE      )
     else:
      # retrieve the best current path
      a__												= astar.open_nodes.pop(
          astar.open_nodes.index(SCREAMING_SNAKE_CASE      )      )

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

  return [self.fwd_astar.start.pos]



 def 						_UpperCAmelCase       (	self		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE      ) ->			list[TPosition]:
  a__												= self.fwd_astar.retrace_path(SCREAMING_SNAKE_CASE      )
  a__												= self.bwd_astar.retrace_path(SCREAMING_SNAKE_CASE      )
  bwd_path.pop()
  bwd_path.reverse()
  a__												= fwd_path + bwd_path
  return path


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

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

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










from typing import List, Optional, TypeVar

from .arrow_dataset import Dataset, _concatenate_map_style_datasets, _interleave_map_style_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .info import DatasetInfo
from .iterable_dataset import IterableDataset, _concatenate_iterable_datasets, _interleave_iterable_datasets
from .splits import NamedSplit
from .utils import logging
from .utils.py_utils import Literal


a_      :      Any    =     logging.get_logger(__name__)


a_      :      str    =     TypeVar('DatasetType', Dataset, IterableDataset)






def     __a					(		__UpperCAmelCase							,  __UpperCAmelCase = None							,  __UpperCAmelCase = None							,  __UpperCAmelCase = None							,  __UpperCAmelCase = None							,  __UpperCAmelCase = "first_exhausted"							,  ):
 from .arrow_dataset import Dataset
 from .iterable_dataset import IterableDataset

 if not datasets:
  raise ValueError('''Unable to interleave an empty list of datasets.'''       )
 for i, dataset in enumerate(__UpperCAmelCase       ):
  if not isinstance(__UpperCAmelCase							,  (Dataset, IterableDataset)       ):
   if isinstance(__UpperCAmelCase							,  (DatasetDict, IterableDatasetDict)       ):
    if not dataset:
     raise ValueError(
         f"Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} "
         '''is an empty dataset dictionary.'''       )
    raise ValueError(
        f"Dataset at position {i} has at least one split: {list(__UpperCAmelCase       )}\n"
        f"Please pick one to interleave with the other datasets, for example: dataset['{next(iter(__UpperCAmelCase       )       )}']"       )
   raise ValueError(
       f"Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(__UpperCAmelCase       ).__name__}."       )
  if i == 0:
   a__							,			a__												= (
       (Dataset, IterableDataset) if isinstance(__UpperCAmelCase							,  __UpperCAmelCase       ) else (IterableDataset, Dataset)
   )
  elif not isinstance(__UpperCAmelCase							,  __UpperCAmelCase       ):
   raise ValueError(
       f"Unable to interleave a {dataset_type.__name__} (at position 0) with a {other_type.__name__} (at position {i}). Expected a list of Dataset objects or a list of IterableDataset objects."       )
 if stopping_strategy not in ["first_exhausted", "all_exhausted"]:
  raise ValueError(f"{stopping_strategy} is not supported. Please enter a valid stopping_strategy."       )
 if dataset_type is Dataset:
  return _interleave_map_style_datasets(
      __UpperCAmelCase							,  __UpperCAmelCase							,  __UpperCAmelCase							,  info=__UpperCAmelCase							,  split=__UpperCAmelCase							,  stopping_strategy=__UpperCAmelCase       )
 else:
  return _interleave_iterable_datasets(
      __UpperCAmelCase							,  __UpperCAmelCase							,  __UpperCAmelCase							,  info=__UpperCAmelCase							,  split=__UpperCAmelCase							,  stopping_strategy=__UpperCAmelCase       )






def     __a					(		__UpperCAmelCase							,  __UpperCAmelCase = None							,  __UpperCAmelCase = None							,  __UpperCAmelCase = 0							,  ):

 if not dsets:
  raise ValueError('''Unable to concatenate an empty list of datasets.'''       )
 for i, dataset in enumerate(__UpperCAmelCase       ):
  if not isinstance(__UpperCAmelCase							,  (Dataset, IterableDataset)       ):
   if isinstance(__UpperCAmelCase							,  (DatasetDict, IterableDatasetDict)       ):
    if not dataset:
     raise ValueError(
         f"Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} "
         '''is an empty dataset dictionary.'''       )
    raise ValueError(
        f"Dataset at position {i} has at least one split: {list(__UpperCAmelCase       )}\n"
        f"Please pick one to interleave with the other datasets, for example: dataset['{next(iter(__UpperCAmelCase       )       )}']"       )
   raise ValueError(
       f"Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(__UpperCAmelCase       ).__name__}."       )
  if i == 0:
   a__							,			a__												= (
       (Dataset, IterableDataset) if isinstance(__UpperCAmelCase							,  __UpperCAmelCase       ) else (IterableDataset, Dataset)
   )
  elif not isinstance(__UpperCAmelCase							,  __UpperCAmelCase       ):
   raise ValueError(
       f"Unable to interleave a {dataset_type.__name__} (at position 0) with a {other_type.__name__} (at position {i}). Expected a list of Dataset objects or a list of IterableDataset objects."       )
 if dataset_type is Dataset:
  return _concatenate_map_style_datasets(__UpperCAmelCase							,  info=__UpperCAmelCase							,  split=__UpperCAmelCase							,  axis=__UpperCAmelCase       )
 else:
  return _concatenate_iterable_datasets(__UpperCAmelCase							,  info=__UpperCAmelCase							,  split=__UpperCAmelCase							,  axis=__UpperCAmelCase       )





