


import inspect
import os
import unittest

import torch

import accelerate
from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_async, require_multi_gpu
from accelerate.utils import patch_environment

class   A__ (   unittest.TestCase     ):







    """simple docstring"""

    def 		a_      (	self   ):
         snake_case					=    inspect.getfile(accelerate.test_utils   )
         snake_case					=    os.path.sep.join(mod_file.split(os.path.sep   )[:-1] + ['''scripts''', '''test_script.py''']   )
         snake_case					=    os.path.sep.join(
             mod_file.split(os.path.sep   )[:-1] + ['''scripts''', '''test_distributed_data_loop.py''']   )
         snake_case					=    os.path.sep.join(mod_file.split(os.path.sep   )[:-1] + ['''scripts''', '''test_ops.py''']   )
    @require_multi_gpu
    def 		a_      (	self   ):
         print(F'''Found {torch.cuda.device_count()} devices.'''   )
         snake_case					=    ['''torchrun''', F'''--nproc_per_node={torch.cuda.device_count()}''', self.test_file_path]
         with patch_environment(omp_num_threads=1   ):
              execute_subprocess_async(__snake_case      , env=os.environ.copy()   )
    @require_multi_gpu
    def 		a_      (	self   ):
         print(F'''Found {torch.cuda.device_count()} devices.'''   )
         snake_case					=    ['''torchrun''', F'''--nproc_per_node={torch.cuda.device_count()}''', self.operation_file_path]
         print(F'''Command: {cmd}'''   )
         with patch_environment(omp_num_threads=1   ):
              execute_subprocess_async(__snake_case      , env=os.environ.copy()   )
    @require_multi_gpu
    def 		a_      (	self   ):
         snake_case					=    ['''torchrun''', F'''--nproc_per_node={torch.cuda.device_count()}''', inspect.getfile(self.__class__   )]
         with patch_environment(omp_num_threads=1   ):
              execute_subprocess_async(__snake_case      , env=os.environ.copy()   )




    @require_multi_gpu
    def 		a_      (	self   ):
         print(F'''Found {torch.cuda.device_count()} devices, using 2 devices only'''   )
         snake_case					=    ['''torchrun''', F'''--nproc_per_node={torch.cuda.device_count()}''', self.data_loop_file_path]
         with patch_environment(omp_num_threads=1      , cuda_visible_devices='''0,1'''   ):
              execute_subprocess_async(__snake_case      , env=os.environ.copy()   )


if __name__ == "__main__":
 _SCREAMING_SNAKE_CASE      :			Dict      				=   Accelerator()
 _SCREAMING_SNAKE_CASE      :			List[str]      				=   (accelerator.state.process_index + 2, 10)
 _SCREAMING_SNAKE_CASE      :			int      				=   torch.randint(0, 10, shape).to(accelerator.device)

 _SCREAMING_SNAKE_CASE      :			str      				=   ""

 _SCREAMING_SNAKE_CASE      :			int      				=   accelerator.pad_across_processes(tensor)
 if tensora.shape[0] != accelerator.state.num_processes + 1:
  error_msg += f"Found shape {tensora.shape} but should have {accelerator.state.num_processes + 1} at dim 0."
 if not torch.equal(tensora[: accelerator.state.process_index + 2], tensor):
  error_msg += "Tensors have different values."
 if not torch.all(tensora[accelerator.state.process_index + 2 :] == 0):
  error_msg += "Padding was not done with the right value (0)."

 _SCREAMING_SNAKE_CASE      :			Optional[int]      				=   accelerator.pad_across_processes(tensor, pad_first=True)
 if tensora.shape[0] != accelerator.state.num_processes + 1:
  error_msg += f"Found shape {tensora.shape} but should have {accelerator.state.num_processes + 1} at dim 0."
 _SCREAMING_SNAKE_CASE      :			str      				=   accelerator.state.num_processes - accelerator.state.process_index - 1
 if not torch.equal(tensora[index:], tensor):
  error_msg += "Tensors have different values."
 if not torch.all(tensora[:index] == 0):
  error_msg += "Padding was not done with the right value (0)."

 # Raise error at the end to make sure we don't stop at the first failure.
 if len(error_msg) > 0:
  raise ValueError(error_msg)








from __future__ import annotations

import time

_SCREAMING_SNAKE_CASE      :			List[Any]      				=   list[tuple[int, int]]

_SCREAMING_SNAKE_CASE      :			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],
]

_SCREAMING_SNAKE_CASE      :			Any      				=   [[-1, 0], [0, -1], [1, 0], [0, 1]]  # up, left, down, right

class   A__ :







    """simple docstring"""

    def __init__(	self      , __snake_case      , __snake_case      , __snake_case      , __snake_case      , __snake_case   ):
         snake_case					=    pos_x
         snake_case					=    pos_y
         snake_case					=    (pos_y, pos_x)
         snake_case					=    goal_x
         snake_case					=    goal_y
         snake_case					=    parent






class   A__ :







    """simple docstring"""

    def __init__(	self      , __snake_case      , __snake_case   ):
         snake_case					=    Node(start[1]      , start[0]      , goal[1]      , goal[0]      , __snake_case   )
         snake_case					=    Node(goal[1]      , goal[0]      , goal[1]      , goal[0]      , __snake_case   )

         snake_case					=    [self.start]
         snake_case					=    False
    def 		a_      (	self   ):
         while self.node_queue:
              snake_case					=    self.node_queue.pop(0   )

              if current_node.pos == self.target.pos:
                   snake_case					=    True
                   return self.retrace_path(__snake_case   )

              snake_case					=    self.get_successors(__snake_case   )

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

         if not self.reached:
              return [self.start.pos]
         return None
    def 		a_      (	self      , __snake_case   ):
         snake_case					=    []
         for action in delta:
              snake_case					=    parent.pos_x + action[1]
              snake_case					=    parent.pos_y + action[0]
              if not (0 <= pos_x <= len(grid[0]   ) - 1 and 0 <= pos_y <= len(__snake_case   ) - 1):
                   continue

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

              successors.append(
                  Node(__snake_case      , __snake_case      , self.target.pos_y      , self.target.pos_x      , __snake_case   )   )
         return successors




    def 		a_      (	self      , __snake_case   ):
         snake_case					=    node
         snake_case					=    []
         while current_node is not None:
              path.append((current_node.pos_y, current_node.pos_x)   )
              snake_case					=    current_node.parent
         path.reverse()
         return path






class   A__ :







    """simple docstring"""

    def __init__(	self      , __snake_case      , __snake_case   ):
         snake_case					=    BreadthFirstSearch(__snake_case      , __snake_case   )
         snake_case					=    BreadthFirstSearch(__snake_case      , __snake_case   )
         snake_case					=    False
    def 		a_      (	self   ):
         while self.fwd_bfs.node_queue or self.bwd_bfs.node_queue:
              snake_case					=    self.fwd_bfs.node_queue.pop(0   )
              snake_case					=    self.bwd_bfs.node_queue.pop(0   )

              if current_bwd_node.pos == current_fwd_node.pos:
                   snake_case					=    True
                   return self.retrace_bidirectional_path(
                       __snake_case      , __snake_case   )

              snake_case					=    current_bwd_node
              snake_case					=    current_fwd_node

              snake_case					=    {
                  self.fwd_bfs: self.fwd_bfs.get_successors(__snake_case   ),
                  self.bwd_bfs: self.bwd_bfs.get_successors(__snake_case   ),
              }

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

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




    def 		a_      (	self      , __snake_case      , __snake_case   ):
         snake_case					=    self.fwd_bfs.retrace_path(__snake_case   )
         snake_case					=    self.bwd_bfs.retrace_path(__snake_case   )
         bwd_path.pop()
         bwd_path.reverse()
         snake_case					=    fwd_path + bwd_path
         return path


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

 doctest.testmod()
 _SCREAMING_SNAKE_CASE      :			Optional[Any]      				=   (0, 0)
 _SCREAMING_SNAKE_CASE      :			List[Any]      				=   (len(grid) - 1, len(grid[0]) - 1)
 for elem in grid:
  print(elem)

 _SCREAMING_SNAKE_CASE      :			List[Any]      				=   time.time()
 _SCREAMING_SNAKE_CASE      :			List[Any]      				=   BreadthFirstSearch(init, goal)
 _SCREAMING_SNAKE_CASE      :			List[str]      				=   bfs.search()
 _SCREAMING_SNAKE_CASE      :			int      				=   time.time() - start_bfs_time

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

 _SCREAMING_SNAKE_CASE      :			Any      				=   time.time()
 _SCREAMING_SNAKE_CASE      :			Union[str, Any]      				=   BidirectionalBreadthFirstSearch(init, goal)
 _SCREAMING_SNAKE_CASE      :			Union[str, Any]      				=   bd_bfs.search()
 _SCREAMING_SNAKE_CASE      :			Tuple      				=   time.time() - start_bd_bfs_time

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




