


from __future__ import annotations

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




def       __lowercase						(    _UpperCamelCase,     _UpperCamelCase,     _UpperCamelCase,     _UpperCamelCase,     _UpperCamelCase,     )     ->Dict:







      """simple docstring"""

      lowercase      : Optional[int]        =		[
          [0 for col in range(len(grid[0]  )  )] for row in range(len(_UpperCamelCase  )  )
      ]  # the reference grid
      lowercase      : List[str]        =		1
      lowercase      : str        =		[
          [0 for col in range(len(grid[0]  )  )] for row in range(len(_UpperCamelCase  )  )
      ]  # the action grid

      lowercase      : Union[str, Any]        =		init[0]
      lowercase      : Dict        =		init[1]
      lowercase      : List[Any]        =		0
      lowercase      : Optional[Any]        =		g + heuristic[x][y]  # cost from starting cell to destination cell
      lowercase      : str        =		[[f, g, x, y]]

      lowercase      : Optional[int]        =		False  # flag that is set when search is complete
      lowercase      : str        =		False  # flag set if we can't find expand

      while not found and not resign:
            if len(_UpperCamelCase  ) == 0:
                  raise ValueError('''Algorithm is unable to find solution'''  )
            else:  # to choose the least costliest action so as to move closer to the goal
                  cell.sort()
                  cell.reverse()
                  lowercase      : Tuple        =		cell.pop()
                  lowercase      : Optional[int]        =		next_cell[2]
                  lowercase      : Optional[int]        =		next_cell[3]
                  lowercase      : int        =		next_cell[1]

                  if x == goal[0] and y == goal[1]:
                        lowercase      : List[str]        =		True
                  else:
                        for i in range(len(_UpperCamelCase  )  ):  # to try out different valid actions
                              lowercase      : int        =		x + DIRECTIONS[i][0]
                              lowercase      : Tuple        =		y + DIRECTIONS[i][1]
                              if xa >= 0 and xa < len(_UpperCamelCase  ) and ya >= 0 and ya < len(grid[0]  ):
                                    if closed[xa][ya] == 0 and grid[xa][ya] == 0:
                                          lowercase      : str        =		g + cost
                                          lowercase      : Optional[int]        =		ga + heuristic[xa][ya]
                                          cell.append([fa, ga, xa, ya]  )
                                          lowercase      : List[str]        =		1
                                          lowercase      : str        =		i
      lowercase      : Any        =		[]
      lowercase      : Tuple        =		goal[0]
      lowercase      : Tuple        =		goal[1]
      invpath.append([x, y]  )  # we get the reverse path from here
      while x != init[0] or y != init[1]:
            lowercase      : Union[str, Any]        =		x - DIRECTIONS[action[x][y]][0]
            lowercase      : Union[str, Any]        =		y - DIRECTIONS[action[x][y]][1]
            lowercase      : Any        =		xa
            lowercase      : Any        =		ya
            invpath.append([x, y]  )

      lowercase      : List[Any]        =		[]
      for i in range(len(_UpperCamelCase  )  ):
            path.append(invpath[len(_UpperCamelCase  ) - 1 - i]  )
      return path, action


if __name__ == "__main__":
  __a 					=   [
      [0, 1, 0, 0, 0, 0],
      [0, 1, 0, 0, 0, 0],  # 0 are free path whereas 1's are obstacles
      [0, 1, 0, 0, 0, 0],
      [0, 1, 0, 0, 1, 0],
      [0, 0, 0, 0, 1, 0],
  ]

  __a 					=   [0, 0]
  # all coordinates are given in format [y,x]
  __a 					=   [len(grid) - 1, len(grid[0]) - 1]
  __a 					=   1

  # the cost map which pushes the path closer to the goal
  __a 					=   [[0 for row in range(len(grid[0]))] for col in range(len(grid))]
  for i in range(len(grid)):
    for j in range(len(grid[0])):
      __a 					=   abs(i - goal[0]) + abs(j - goal[1])
      if grid[i][j] == 1:
        # added extra penalty in the heuristic map
        __a 					=   99

  __a 					=   search(grid, init, goal, cost, heuristic)

  print('''ACTION MAP''')
  for i in range(len(action)):
    print(action[i])

  for i in range(len(path)):
    print(path[i])







'''simple docstring'''


import pytest

from datasets.splits import SplitDict, SplitInfo
from datasets.utils.py_utils import asdict
@pytest.mark.parametrize(
    '''split_dict'''					,		[
        SplitDict(),
        SplitDict({'''train''': SplitInfo(name='''train'''					,		num_bytes=1337					,		num_examples=42					,		dataset_name='''my_dataset''' )} ),
        SplitDict({'''train''': SplitInfo(name='''train'''					,		num_bytes=1337					,		num_examples=42 )} ),
        SplitDict({'''train''': SplitInfo()} ),
    ]					,		)
def 						snake_case_       (UpperCamelCase			:    SplitDict ):





							'''simple docstring'''


							_a      	=							split_dict._to_yaml_list()
							assert len(UpperCamelCase ) == len(UpperCamelCase )
							_a      	=							SplitDict._from_yaml_list(UpperCamelCase )
							for split_name, split_info in split_dict.items():
														# dataset_name field is deprecated, and is therefore not part of the YAML dump
														_a      	=							None
														# the split name of split_dict takes over the name of the split info object
														_a      	=							split_name
							assert split_dict == reloaded

@pytest.mark.parametrize(
    '''split_info'''					,		[SplitInfo(), SplitInfo(dataset_name=UpperCamelCase ), SplitInfo(dataset_name='''my_dataset''' )] )
def 						snake_case_       (UpperCamelCase			:    List[str] ):





							'''simple docstring'''


							_a      	=							asdict(SplitDict({'''train''': split_info} ) )
							assert "dataset_name" in split_dict_asdict["train"]
							assert split_dict_asdict["train"]["dataset_name"] == split_info.dataset_name

