





'''simple docstring'''


from __future__ import annotations

lowerCAmelCase					:     str												=[
    [-1, 0],  # left
    [0, -1],  # down
    [1, 0],  # right
    [0, 1],  # up
]
def 							UpperCAmelCase_  (						__lowerCamelCase					:					list[list[int]]	,__lowerCamelCase					:					list[int]	,__lowerCamelCase					:					list[int]	,__lowerCamelCase					:					int	,__lowerCamelCase					:					list[list[int]]	,):
   lowercase_			:str    			= [
       [0 for col in range(len(grid[0] ) )] for row in range(len(__lowerCamelCase ) )
   ]  # the reference grid
   lowercase_			:Tuple    			= 1
   lowercase_			:List[Any]    			= [
       [0 for col in range(len(grid[0] ) )] for row in range(len(__lowerCamelCase ) )
   ]  # the action grid

   lowercase_			:str    			= init[0]
   lowercase_			:Tuple    			= init[1]
   lowercase_			:int    			= 0
   lowercase_			:Any    			= g + heuristic[x][y]  # cost from starting cell to destination cell
   lowercase_			:Dict    			= [[f, g, x, y]]

   lowercase_			:Dict    			= False  # flag that is set when search is complete
   lowercase_			:Any    			= False  # flag set if we can't find expand

   while not found and not resign:
      if len(__lowerCamelCase ) == 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_			:Optional[int]    			= cell.pop()
         lowercase_			:List[str]    			= next_cell[2]
         lowercase_			:Tuple    			= next_cell[3]
         lowercase_			:Any    			= next_cell[1]

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

   lowercase_			:Union[str, Any]    			= []
   for i in range(len(__lowerCamelCase ) ):
      path.append(invpath[len(__lowerCamelCase ) - 1 - i] )
   return path, action


if __name__ == "__main__":
 lowerCAmelCase					:     Dict												=[
     [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],
 ]

 lowerCAmelCase					:     int												=[0, 0]
 # all coordinates are given in format [y,x]
 lowerCAmelCase					:     List[str]												=[len(grid) - 1, len(grid[0]) - 1]
 lowerCAmelCase					:     Optional[Any]												=1

 # the cost map which pushes the path closer to the goal
 lowerCAmelCase					:     Any												=[[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])):
   lowerCAmelCase					:     List[Any]												=abs(i - goal[0]) + abs(j - goal[1])
   if grid[i][j] == 1:
    # added extra penalty in the heuristic map
    lowerCAmelCase					:     int												=99

 lowerCAmelCase							,		lowerCAmelCase					:     Tuple												=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"""



from typing import List

import numpy as np







def   __lowerCAmelCase						(						lowercase  :  dict )	->						int:


    """simple docstring"""





    snake_case    :      Union[str, Any]      					=    {key: len(lowercase ) for key, value in gen_kwargs.items() if isinstance(lowercase						,							lowercase )}
    if len(set(lists_lengths.values() ) ) > 1:
        raise RuntimeError(
            (
                "Sharding is ambiguous for this dataset: "
                + "we found several data sources lists of different lengths, and we don't know over which list we should parallelize:\n"
                + "\n".join(F'\t- key {key} has length {length}' for key, length in lists_lengths.items() )
                + "\nTo fix this, check the 'gen_kwargs' and make sure to use lists only for data sources, "
                + "and use tuples otherwise. In the end there should only be one single list, or several lists with the same length."
            ) )
    snake_case    :      int      					=    max(lists_lengths.values()						,							default=0 )
    return max(1						,							lowercase )







def   __lowerCAmelCase						(						lowercase  :  int						,							lowercase  :  int )	->						List[range]:


    """simple docstring"""





    snake_case    :      Union[str, Any]      					=    []
    for group_idx in range(lowercase ):
        snake_case    :      Union[str, Any]      					=    num_shards // max_num_jobs + (group_idx < (num_shards % max_num_jobs))
        if num_shards_to_add == 0:
            break
        snake_case    :      int      					=    shards_indices_per_group[-1].stop if shards_indices_per_group else 0
        snake_case    :      Dict      					=    range(lowercase						,							start + num_shards_to_add )
        shards_indices_per_group.append(lowercase )
    return shards_indices_per_group







def   __lowerCAmelCase						(						lowercase  :  dict						,							lowercase  :  int )	->						List[dict]:


    """simple docstring"""





    snake_case    :      int      					=    _number_of_shards_in_gen_kwargs(lowercase )
    if num_shards == 1:
        return [dict(lowercase )]
    else:
        snake_case    :      Optional[int]      					=    _distribute_shards(num_shards=lowercase						,							max_num_jobs=lowercase )
        return [
            {
                key: [value[shard_idx] for shard_idx in shard_indices_per_group[group_idx]]
                if isinstance(lowercase						,							lowercase )
                else value
                for key, value in gen_kwargs.items()
            }
            for group_idx in range(len(lowercase ) )
        ]







def   __lowerCAmelCase						(						lowercase  :  List[dict] )	->						dict:


    """simple docstring"""





    return {
        key: [value for gen_kwargs in gen_kwargs_list for value in gen_kwargs[key]]
        if isinstance(gen_kwargs_list[0][key]						,							lowercase )
        else gen_kwargs_list[0][key]
        for key in gen_kwargs_list[0]
    }







def   __lowerCAmelCase						(						lowercase  :  np.random.Generator						,							lowercase  :  dict )	->						dict:


    """simple docstring"""





    snake_case    :      Tuple      					=    {len(lowercase ) for value in gen_kwargs.values() if isinstance(lowercase						,							lowercase )}
    snake_case    :      str      					=    {}
    for size in list_sizes:
        snake_case    :      Optional[int]      					=    list(range(lowercase ) )
        rng.shuffle(indices_per_size[size] )
    # Now let's copy the gen_kwargs and shuffle the lists based on their sizes
    snake_case    :      Dict      					=    dict(lowercase )
    for key, value in shuffled_kwargs.items():
        if isinstance(lowercase						,							lowercase ):
            snake_case    :      Dict      					=    [value[i] for i in indices_per_size[len(lowercase )]]
    return shuffled_kwargs





