



"""simple docstring"""




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      UpperCAmelCase_  (    unittest.TestCase						):



						def 						snake_case_ (					self					:     Tuple						):
											_UpperCAmelCase      :     Optional[Any]        =   inspect.getfile(accelerate.test_utils						)
											_UpperCAmelCase      :     Tuple        =   os.path.sep.join(mod_file.split(os.path.sep						)[:-1] + ["scripts", "test_script.py"]						)
											_UpperCAmelCase      :     List[Any]        =   os.path.sep.join(
											    mod_file.split(os.path.sep						)[:-1] + ["scripts", "test_distributed_data_loop.py"]						)
											_UpperCAmelCase      :     Union[str, Any]        =   os.path.sep.join(mod_file.split(os.path.sep						)[:-1] + ["scripts", "test_ops.py"]						)



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



						@require_multi_gpu
						def 						snake_case_ (					self					:     str						):
											print(f'Found {torch.cuda.device_count()} devices.'						)
											_UpperCAmelCase      :     Any        =   ["""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(A					,     env=os.environ.copy()						)



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



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


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

	_lowerCAmelCase    : Union[str, Any]							=							""

	_lowerCAmelCase    : Dict							=							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)."

	_lowerCAmelCase    : Tuple							=							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."
	_lowerCAmelCase    : Dict							=							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

from typing import Any

def 						__snake_case						(				SCREAMING_SNAKE_CASE__     :						list[Any]    )  ->       None:





     '''simple docstring'''

     create_state_space_tree(SCREAMING_SNAKE_CASE__						,  []						,  0    )

def 						__snake_case						(				SCREAMING_SNAKE_CASE__     :						list[Any]						,  SCREAMING_SNAKE_CASE__     :						list[Any]						,  SCREAMING_SNAKE_CASE__     :						int    )  ->       None:





     '''simple docstring'''

     if index == len(SCREAMING_SNAKE_CASE__    ):
          print(SCREAMING_SNAKE_CASE__    )
          return

     create_state_space_tree(SCREAMING_SNAKE_CASE__						,  SCREAMING_SNAKE_CASE__						,  index + 1    )
     current_subsequence.append(sequence[index]    )
     create_state_space_tree(SCREAMING_SNAKE_CASE__						,  SCREAMING_SNAKE_CASE__						,  index + 1    )
     current_subsequence.pop()


if __name__ == "__main__":
 _lowerCAmelCase    : list[Any]							=							[3, 1, 2, 4]
 generate_all_subsequences(seq)

 seq.clear()
 seq.extend(["A", "B", "C"])
 generate_all_subsequences(seq)
