

'''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		):






				"""simple docstring"""



				def   a__					(    self       )     ->							Dict:
											SCREAMING_SNAKE_CASE_				=				inspect.getfile(accelerate.test_utils       )
											SCREAMING_SNAKE_CASE_				=				os.path.sep.join(mod_file.split(os.path.sep       )[:-1] + ['scripts', 'test_script.py']       )
											SCREAMING_SNAKE_CASE_				=				os.path.sep.join(
											    mod_file.split(os.path.sep       )[:-1] + ['scripts', 'test_distributed_data_loop.py']       )
											SCREAMING_SNAKE_CASE_				=				os.path.sep.join(mod_file.split(os.path.sep       )[:-1] + ['scripts', 'test_ops.py']       )



				@require_multi_gpu
				def   a__					(    self       )     ->							Union[str, Any]:
											print(f"""Found {torch.cuda.device_count()} devices."""       )
											SCREAMING_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(_lowercase,     env=os.environ.copy()       )



				@require_multi_gpu
				def   a__					(    self       )     ->							str:
											print(f"""Found {torch.cuda.device_count()} devices."""       )
											SCREAMING_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(_lowercase,     env=os.environ.copy()       )



				@require_multi_gpu
				def   a__					(    self       )     ->							int:
											SCREAMING_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(_lowercase,     env=os.environ.copy()       )





				@require_multi_gpu
				def   a__					(    self       )     ->							Any:
											print(f"""Found {torch.cuda.device_count()} devices, using 2 devices only"""       )
											SCREAMING_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(_lowercase,     env=os.environ.copy()       )


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

							SCREAMING_SNAKE_CASE							:				List[str]     	=			""

							SCREAMING_SNAKE_CASE							:				Optional[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							:				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							:				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'''

def 							_UpperCamelCase				(   lowerCAmelCase__:					int		,lowerCAmelCase__:					int   )      -> str:
							if a < 0 or b < 0:
														raise ValueError('the value of both inputs must be positive'   )
							SCREAMING_SNAKE_CASE_				=				str(bin(lowerCAmelCase__   )   )[2:]  # remove the leading "0b"
							SCREAMING_SNAKE_CASE_				=				str(bin(lowerCAmelCase__   )   )[2:]
							SCREAMING_SNAKE_CASE_				=				max(len(lowerCAmelCase__   )		,len(lowerCAmelCase__   )   )
							return "0b" + "".join(
							    str(int('1' in (char_a, char_b)   )   )
							    for char_a, char_b in zip(a_binary.zfill(lowerCAmelCase__   )		,b_binary.zfill(lowerCAmelCase__   )   )   )


if __name__ == "__main__":
							import doctest

							doctest.testmod()





