




'''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 			_snake_case		(     unittest.TestCase							):




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

										'''simple docstring'''




										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        lowerCAmelCase__    ( self							)    ->    Union[str, Any]:

										'''simple docstring'''




										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(a__    ,							env=os.environ.copy()							)




			@require_multi_gpu
			def        lowerCAmelCase__    ( self							)    ->    Optional[Any]:

										'''simple docstring'''




										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(a__    ,							env=os.environ.copy()							)




			@require_multi_gpu
			def        lowerCAmelCase__    ( self							)    ->    Any:

										'''simple docstring'''




										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(a__    ,							env=os.environ.copy()							)




			@require_multi_gpu
			def        lowerCAmelCase__    ( self							)    ->    Union[str, Any]:

										'''simple docstring'''




										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(a__    ,							env=os.environ.copy()							)


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

		_SCREAMING_SNAKE_CASE							:       Any						=			""

		_SCREAMING_SNAKE_CASE							:       List[str]						=			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[Any]						=			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							:       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'''





def 					UpperCamelCase_(     snake_case							:	int = 1_0_0_0					):

							'''simple docstring'''






							snake_case_      =				3
							snake_case_      =				0
							while a < n:
														if a % 3 == 0 or a % 5 == 0:
																					result += a
														elif a % 1_5 == 0:
																					result -= a
														a += 1
							return result


if __name__ == "__main__":
		print(F"{solution() = }")




