






"""simple docstring"""



from __future__ import annotations

_UpperCamelCase		:  Any						=		list[tuple[int, int]]

_UpperCamelCase		:  List[Any]						=		[
    [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		:  Optional[int]						=		([-1, 0], [0, -1], [1, 0], [0, 1])  # up, left, down, right

class        a     :







    def __init__(			self							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase							,		):
      lowercase							    =       pos_x
      lowercase							    =       pos_y
      lowercase							    =       (pos_y, pos_x)
      lowercase							    =       goal_x
      lowercase							    =       goal_y
      lowercase							    =       g_cost
      lowercase							    =       parent
      lowercase							    =       self.calculate_heuristic()







    def 			UpperCamelCase_      (			self       ):
      lowercase							    =       abs(self.pos_x - self.goal_x       )
      lowercase							    =       abs(self.pos_y - self.goal_y       )
      return dx + dy







    def __lt__(			self							,		_lowerCamelCase       ):
      return self.f_cost < other.f_cost


class        a     :







    def __init__(			self							,		_lowerCamelCase							,		_lowerCamelCase       ):
      lowercase							    =       Node(start[1]							,		start[0]							,		goal[1]							,		goal[0]							,		0							,		_lowerCamelCase       )
      lowercase							    =       Node(goal[1]							,		goal[0]							,		goal[1]							,		goal[0]							,		9_9_9_9_9							,		_lowerCamelCase       )

      lowercase							    =       [self.start]
      lowercase							    =       []

      lowercase							    =       False







    def 			UpperCamelCase_      (			self       ):
      while self.open_nodes:
        # Open Nodes are sorted using __lt__
        self.open_nodes.sort()
        lowercase							    =       self.open_nodes.pop(0       )

        if current_node.pos == self.target.pos:
          lowercase							    =       True
          return self.retrace_path(_lowerCamelCase       )

        self.closed_nodes.append(_lowerCamelCase       )
        lowercase							    =       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
            lowercase							    =       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 			UpperCamelCase_      (			self							,		_lowerCamelCase       ):
      lowercase							    =       []
      for action in delta:
        lowercase							    =       parent.pos_x + action[1]
        lowercase							    =       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 			UpperCamelCase_      (			self							,		_lowerCamelCase       ):
      lowercase							    =       node
      lowercase							    =       []
      while current_node is not None:
        path.append((current_node.pos_y, current_node.pos_x)       )
        lowercase							    =       current_node.parent
      path.reverse()
      return path


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

    print('------')

    _UpperCamelCase		:  Tuple						=		GreedyBestFirst(init, goal)
    _UpperCamelCase		:  Optional[Any]						=		greedy_bf.search()
    if path:
        for pos_x, pos_y in path:
            _UpperCamelCase		:  Optional[int]						=		2

        for elem in grid:
            print(elem)










"""simple docstring"""



import os
from argparse import ArgumentParser
from typing import List

import torch.utils.data

from datasets import Dataset, IterableDataset
from datasets.distributed import split_dataset_by_node


_UpperCamelCase		:  List[Any]						=		4
_UpperCamelCase		:  Optional[Any]						=		3

class        a     (   a_	):
    pass


def 						_SCREAMING_SNAKE_CASE (    __snake_case			:  List[str]    ):





  '''simple docstring'''





  for shard in shards:
    for i in range(__snake_case    ):
      yield {"i": i, "shard": shard}
def 						_SCREAMING_SNAKE_CASE (    ):





  '''simple docstring'''





  lowercase							    =       int(os.environ['RANK']    )
  lowercase							    =       int(os.environ['WORLD_SIZE']    )

  lowercase							    =       ArgumentParser()
  parser.add_argument('--streaming'		,					type=__snake_case    )
  parser.add_argument('--local_rank'		,					type=__snake_case    )
  parser.add_argument('--num_workers'		,					type=__snake_case		,					default=0    )
  lowercase							    =       parser.parse_args()
  lowercase							    =       args.streaming
  lowercase							    =       args.num_workers

  lowercase							    =       {'shards': [f'shard_{shard_idx}' for shard_idx in range(__snake_case    )]}
  lowercase							    =       IterableDataset.from_generator(__snake_case		,					gen_kwargs=__snake_case    )
  if not streaming:
    lowercase							    =       Dataset.from_list(list(__snake_case    )    )

  lowercase							    =       split_dataset_by_node(__snake_case		,					rank=__snake_case		,					world_size=__snake_case    )
  lowercase							    =       torch.utils.data.DataLoader(__snake_case		,					num_workers=__snake_case    )

  lowercase							    =       NUM_SHARDS * NUM_ITEMS_PER_SHARD
  lowercase							    =       full_size // world_size
  expected_local_size += int(rank < (full_size % world_size)    )

  lowercase							    =       sum(1 for _ in dataloader    )
  if local_size != expected_local_size:
    raise FailedTestError(f'local_size {local_size} != expected_local_size {expected_local_size}'    )


if __name__ == "__main__":
    main()


