





from __future__ import annotations

import time

_UpperCAmelCase        =				list[tuple[int, int]]

_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



class 							_UpperCamelCase					:

       def __init__(		self: str					,   _SCREAMING_SNAKE_CASE: int					,   _SCREAMING_SNAKE_CASE: int					,   _SCREAMING_SNAKE_CASE: int					,   _SCREAMING_SNAKE_CASE: int					,   _SCREAMING_SNAKE_CASE: Node | None		)       -> List[str]:




              """simple docstring"""


              UpperCamelCase_          =  pos_x
              UpperCamelCase_          =  pos_y
              UpperCamelCase_          =  (pos_y, pos_x)
              UpperCamelCase_          =  goal_x
              UpperCamelCase_          =  goal_y
              UpperCamelCase_          =  parent



class 							_UpperCamelCase					:

       def __init__(		self: str					,   _SCREAMING_SNAKE_CASE: tuple[int, int]					,   _SCREAMING_SNAKE_CASE: tuple[int, int]		)       -> Optional[Any]:




              """simple docstring"""


              UpperCamelCase_          =  Node(start[1]					,   start[0]					,   goal[1]					,   goal[0]					,   _SCREAMING_SNAKE_CASE		)
              UpperCamelCase_          =  Node(goal[1]					,   goal[0]					,   goal[1]					,   goal[0]					,   _SCREAMING_SNAKE_CASE		)

              UpperCamelCase_          =  [self.start]
              UpperCamelCase_          =  False

       def lowercase (		self: List[str]		)       -> Path | None:




              """simple docstring"""


              while self.node_queue:
                     UpperCamelCase_          =  self.node_queue.pop(0		)

                     if current_node.pos == self.target.pos:
                            UpperCamelCase_          =  True
                            return self.retrace_path(_SCREAMING_SNAKE_CASE		)

                     UpperCamelCase_          =  self.get_successors(_SCREAMING_SNAKE_CASE		)

                     for node in successors:
                            self.node_queue.append(_SCREAMING_SNAKE_CASE		)

              if not self.reached:
                     return [self.start.pos]
              return None

       def lowercase (		self: List[str]					,   _SCREAMING_SNAKE_CASE: Node		)       -> list[Node]:




              """simple docstring"""


              UpperCamelCase_          =  []
              for action in delta:
                     UpperCamelCase_          =  parent.pos_x + action[1]
                     UpperCamelCase_          =  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					,   _SCREAMING_SNAKE_CASE		)		)
              return successors



       def lowercase (		self: Tuple					,   _SCREAMING_SNAKE_CASE: Node | None		)       -> Path:




              """simple docstring"""


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



class 							_UpperCamelCase					:

       def __init__(		self: Optional[int]					,   _SCREAMING_SNAKE_CASE: Dict					,   _SCREAMING_SNAKE_CASE: Optional[Any]		)       -> str:




              """simple docstring"""


              UpperCamelCase_          =  BreadthFirstSearch(_SCREAMING_SNAKE_CASE					,   _SCREAMING_SNAKE_CASE		)
              UpperCamelCase_          =  BreadthFirstSearch(_SCREAMING_SNAKE_CASE					,   _SCREAMING_SNAKE_CASE		)
              UpperCamelCase_          =  False

       def lowercase (		self: Dict		)       -> Path | None:




              """simple docstring"""


              while self.fwd_bfs.node_queue or self.bwd_bfs.node_queue:
                     UpperCamelCase_          =  self.fwd_bfs.node_queue.pop(0		)
                     UpperCamelCase_          =  self.bwd_bfs.node_queue.pop(0		)

                     if current_bwd_node.pos == current_fwd_node.pos:
                            UpperCamelCase_          =  True
                            return self.retrace_bidirectional_path(
                                _SCREAMING_SNAKE_CASE					,   _SCREAMING_SNAKE_CASE		)

                     UpperCamelCase_          =  current_bwd_node
                     UpperCamelCase_          =  current_fwd_node

                     UpperCamelCase_          =  {
                         self.fwd_bfs: self.fwd_bfs.get_successors(_SCREAMING_SNAKE_CASE		),
                         self.bwd_bfs: self.bwd_bfs.get_successors(_SCREAMING_SNAKE_CASE		),
                     }

                     for bfs in [self.fwd_bfs, self.bwd_bfs]:
                            for node in successors[bfs]:
                                   bfs.node_queue.append(_SCREAMING_SNAKE_CASE		)

              if not self.reached:
                     return [self.fwd_bfs.start.pos]
              return None



       def lowercase (		self: Tuple					,   _SCREAMING_SNAKE_CASE: Node					,   _SCREAMING_SNAKE_CASE: Node		)       -> Path:




              """simple docstring"""


              UpperCamelCase_          =  self.fwd_bfs.retrace_path(_SCREAMING_SNAKE_CASE		)
              UpperCamelCase_          =  self.bwd_bfs.retrace_path(_SCREAMING_SNAKE_CASE		)
              bwd_path.pop()
              bwd_path.reverse()
              UpperCamelCase_          =  fwd_path + bwd_path
              return path


if __name__ == "__main__":
     # all coordinates are given in format [y,x]
     import doctest

     doctest.testmod()
     _UpperCAmelCase        =				(0, 0)
     _UpperCAmelCase        =				(len(grid) - 1, len(grid[0]) - 1)
     for elem in grid:
          print(elem)

     _UpperCAmelCase        =				time.time()
     _UpperCAmelCase        =				BreadthFirstSearch(init, goal)
     _UpperCAmelCase        =				bfs.search()
     _UpperCAmelCase        =				time.time() - start_bfs_time

     print('Unidirectional BFS computation time : ', bfs_time)

     _UpperCAmelCase        =				time.time()
     _UpperCAmelCase        =				BidirectionalBreadthFirstSearch(init, goal)
     _UpperCAmelCase        =				bd_bfs.search()
     _UpperCAmelCase        =				time.time() - start_bd_bfs_time

     print('Bidirectional BFS computation time : ', bd_bfs_time)








import unittest

import torch
from torch import nn

from accelerate.test_utils import require_cuda
from accelerate.utils.memory import find_executable_batch_size, release_memory




def  lowerCAmelCase_ (							)     ->		Dict:
       raise RuntimeError("CUDA out of memory."  )



class 							_UpperCamelCase					(	nn.Module     ):

       def __init__(		self: int		)       -> List[Any]:




              """simple docstring"""


              super().__init__()
              UpperCamelCase_          =  nn.Linear(3					,   4		)
              UpperCamelCase_          =  nn.BatchNormad(4		)
              UpperCamelCase_          =  nn.Linear(4					,   5		)



       def lowercase (		self: Any					,   _SCREAMING_SNAKE_CASE: Optional[int]		)       -> Optional[Any]:




              """simple docstring"""


              return self.lineara(self.batchnorm(self.lineara(_SCREAMING_SNAKE_CASE		)		)		)



class 							_UpperCamelCase					(	unittest.TestCase     ):

       def lowercase (		self: List[Any]		)       -> Any:




              """simple docstring"""


              UpperCamelCase_          =  []

              @find_executable_batch_size(starting_batch_size=128		)
              def mock_training_loop_function(_SCREAMING_SNAKE_CASE: str		):
                     nonlocal batch_sizes
                     batch_sizes.append(_SCREAMING_SNAKE_CASE		)
                     if batch_size != 8:
                            raise_fake_out_of_memory()

              mock_training_loop_function()
              self.assertListEqual(_SCREAMING_SNAKE_CASE					,   [128, 64, 32, 16, 8]		)

       def lowercase (		self: Optional[int]		)       -> List[Any]:




              """simple docstring"""


              UpperCamelCase_          =  []

              @find_executable_batch_size(starting_batch_size=128		)
              def mock_training_loop_function(_SCREAMING_SNAKE_CASE: List[str]					,   _SCREAMING_SNAKE_CASE: Optional[Any]		):
                     nonlocal batch_sizes
                     batch_sizes.append(_SCREAMING_SNAKE_CASE		)
                     if batch_size != 8:
                            raise_fake_out_of_memory()
                     return batch_size, arga

              UpperCamelCase_       , UpperCamelCase_          =  mock_training_loop_function("hello"		)
              self.assertListEqual(_SCREAMING_SNAKE_CASE					,   [128, 64, 32, 16, 8]		)
              self.assertListEqual([bs, arga]					,   [8, "hello"]		)

       def lowercase (		self: Any		)       -> Optional[int]:




              """simple docstring"""


              @find_executable_batch_size(starting_batch_size=0		)
              def mock_training_loop_function(_SCREAMING_SNAKE_CASE: Union[str, Any]		):
                     pass

              with self.assertRaises(_SCREAMING_SNAKE_CASE		) as cm:
                     mock_training_loop_function()
                     self.assertIn("No executable batch size found, reached zero."					,   cm.exception.args[0]		)

       def lowercase (		self: Dict		)       -> Optional[int]:




              """simple docstring"""


              @find_executable_batch_size(starting_batch_size=16		)
              def mock_training_loop_function(_SCREAMING_SNAKE_CASE: Optional[int]		):
                     if batch_size > 0:
                            raise_fake_out_of_memory()
                     pass

              with self.assertRaises(_SCREAMING_SNAKE_CASE		) as cm:
                     mock_training_loop_function()
                     self.assertIn("No executable batch size found, reached zero."					,   cm.exception.args[0]		)

       def lowercase (		self: Optional[int]		)       -> Dict:




              """simple docstring"""


              @find_executable_batch_size(starting_batch_size=128		)
              def mock_training_loop_function(_SCREAMING_SNAKE_CASE: Any					,   _SCREAMING_SNAKE_CASE: int					,   _SCREAMING_SNAKE_CASE: Tuple		):
                     if batch_size != 8:
                            raise raise_fake_out_of_memory()

              with self.assertRaises(_SCREAMING_SNAKE_CASE		) as cm:
                     mock_training_loop_function(128					,   "hello"					,   "world"		)
                     self.assertIn("Batch size was passed into `f`"					,   cm.exception.args[0]		)
                     self.assertIn("`f(arg1='hello', arg2='world')"					,   cm.exception.args[0]		)

       def lowercase (		self: List[str]		)       -> Optional[Any]:




              """simple docstring"""


              @find_executable_batch_size(starting_batch_size=16		)
              def mock_training_loop_function(_SCREAMING_SNAKE_CASE: List[Any]		):
                     raise ValueError("Oops, we had an error!"		)

              with self.assertRaises(_SCREAMING_SNAKE_CASE		) as cm:
                     mock_training_loop_function()
                     self.assertIn("Oops, we had an error!"					,   cm.exception.args[0]		)



       @require_cuda
       def lowercase (		self: Optional[int]		)       -> Union[str, Any]:




              """simple docstring"""


              UpperCamelCase_          =  torch.cuda.memory_allocated()
              UpperCamelCase_          =  ModelForTest()
              model.cuda()
              self.assertGreater(torch.cuda.memory_allocated()					,   _SCREAMING_SNAKE_CASE		)
              UpperCamelCase_          =  release_memory(_SCREAMING_SNAKE_CASE		)
              self.assertEqual(torch.cuda.memory_allocated()					,   _SCREAMING_SNAKE_CASE		)

