






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  _UpperCAmelCase  (  self			)      ->			Union[str, Any]:
     UpperCamelCase_						=  inspect.getfile(accelerate.test_utils			)
     UpperCamelCase_						=  os.path.sep.join(mod_file.split(os.path.sep			)[:-1] + ['scripts', 'test_script.py']			)
     UpperCamelCase_						=  os.path.sep.join(
         mod_file.split(os.path.sep			)[:-1] + ['scripts', 'test_distributed_data_loop.py']			)
     UpperCamelCase_						=  os.path.sep.join(mod_file.split(os.path.sep			)[:-1] + ['scripts', 'test_ops.py']			)




  @require_multi_gpu
  def  _UpperCAmelCase  (  self			)      ->			Dict:
     print(f"""Found {torch.cuda.device_count()} devices."""			)
     UpperCamelCase_						=  ['torchrun', f"""--nproc_per_node={torch.cuda.device_count()}""", self.test_file_path]
     with patch_environment(omp_num_threads=1			):
        execute_subprocess_async(_UpperCAmelCase		,		env=os.environ.copy()			)




  @require_multi_gpu
  def  _UpperCAmelCase  (  self			)      ->			Optional[Any]:
     print(f"""Found {torch.cuda.device_count()} devices."""			)
     UpperCamelCase_						=  ['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(_UpperCAmelCase		,		env=os.environ.copy()			)




  @require_multi_gpu
  def  _UpperCAmelCase  (  self			)      ->			Any:
     UpperCamelCase_						=  ['torchrun', f"""--nproc_per_node={torch.cuda.device_count()}""", inspect.getfile(self.__class__			)]
     with patch_environment(omp_num_threads=1			):
        execute_subprocess_async(_UpperCAmelCase		,		env=os.environ.copy()			)




  @require_multi_gpu
  def  _UpperCAmelCase  (  self			)      ->			List[str]:
     print(f"""Found {torch.cuda.device_count()} devices, using 2 devices only"""			)
     UpperCamelCase_						=  ['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(_UpperCAmelCase		,		env=os.environ.copy()			)


if __name__ == "__main__":
 snake_case__   :						Dict													=   Accelerator()
 snake_case__   :						str													=   (accelerator.state.process_index + 2, 1_0)
 snake_case__   :						int													=   torch.randint(0, 1_0, shape).to(accelerator.device)

 snake_case__   :						List[Any]													=   """"""

 snake_case__   :						Any													=   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)."

 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."
 snake_case__   :						Optional[Any]													=   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)









'''simple docstring'''






from __future__ import annotations

__UpperCAmelCase       							=				list[list[int]]

# assigning initial values to the grid
__UpperCAmelCase       							=				[
    [3, 0, 6, 5, 0, 8, 4, 0, 0],
    [5, 2, 0, 0, 0, 0, 0, 0, 0],
    [0, 8, 7, 0, 0, 0, 0, 3, 1],
    [0, 0, 3, 0, 1, 0, 0, 8, 0],
    [9, 0, 0, 8, 6, 3, 0, 0, 5],
    [0, 5, 0, 0, 9, 0, 6, 0, 0],
    [1, 3, 0, 0, 0, 0, 2, 5, 0],
    [0, 0, 0, 0, 0, 0, 0, 7, 4],
    [0, 0, 5, 2, 0, 6, 3, 0, 0],
]

# a grid with no solution
__UpperCAmelCase       							=				[
    [5, 0, 6, 5, 0, 8, 4, 0, 3],
    [5, 2, 0, 0, 0, 0, 0, 0, 2],
    [1, 8, 7, 0, 0, 0, 0, 3, 1],
    [0, 0, 3, 0, 1, 0, 0, 8, 0],
    [9, 0, 0, 8, 6, 3, 0, 0, 5],
    [0, 5, 0, 0, 9, 0, 6, 0, 0],
    [1, 3, 0, 0, 0, 0, 2, 5, 0],
    [0, 0, 0, 0, 0, 0, 0, 7, 4],
    [0, 0, 5, 2, 0, 6, 3, 0, 0],
]


def    _snake_case  (	A					,       A					,       A					,       A			)		->       bool:
							for i in range(9			):
														if grid[row][i] == n or grid[i][column] == n:
																					return False

							for i in range(3			):
														for j in range(3			):
																					if grid[(row - row % 3) + i][(column - column % 3) + j] == n:
																												return False

							return True


def    _snake_case  (	A			)		->       tuple[int, int] | None:
							for i in range(9			):
														for j in range(9			):
																					if grid[i][j] == 0:
																												return i, j
							return None


def    _snake_case  (	A			)		->       Matrix | None:
							if location := find_empty_location(A			):
														lowerCAmelCase__  ,   lowerCAmelCase__			  =      location
							else:
														# If the location is ``None``, then the grid is solved.
														return grid

							for digit in range(1					,       10			):
														if is_safe(A					,       A					,       A					,       A			):
																					lowerCAmelCase__			  =      digit

																					if sudoku(A			) is not None:
																												return grid

																					lowerCAmelCase__			  =      0

							return None


def    _snake_case  (	A			)		->       None:
							for row in grid:
														for cell in row:
																					print(A					,       end=''' '''			)
														print()


if __name__ == "__main__":
		# make a copy of grid so that you can compare with the unmodified grid
		for example_grid in (initial_grid, no_solution):
				print('''\nExample grid:\n''' + '''=''' * 20)
				print_solution(example_grid)
				print('''\nExample grid solution:''')
				__UpperCAmelCase       							=				sudoku(example_grid)
				if solution is not None:
						print_solution(solution)
				else:
						print('''Cannot find a solution.''')