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   lowercase      (       unittest.TestCase       ):




							def 		__snake_case(  self				:   List[str] )   ->			List[str]:



												'''simple docstring'''

												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 		__snake_case(  self				:   Dict )   ->			str:



												'''simple docstring'''

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




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



												'''simple docstring'''

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




							@require_multi_gpu
							def 		__snake_case(  self				:   int )   ->			str:



												'''simple docstring'''

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





							@require_multi_gpu
							def 		__snake_case(  self				:   List[Any] )   ->			str:



												'''simple docstring'''

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


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

							_lowerCamelCase   :				List[str]     			=						''''''

							_lowerCamelCase   :				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)."

							_lowerCamelCase   :				str     			=						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   :				Optional[int]     			=						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)





def 		__lowerCamelCase							(UpperCAmelCase__       :							str ,	UpperCAmelCase__       :							str = " "					):
					SCREAMING_SNAKE_CASE           =    []

					SCREAMING_SNAKE_CASE           =    0
					for index, char in enumerate(UpperCAmelCase__					):
										if char == separator:
															split_words.append(string[last_index:index]					)
															SCREAMING_SNAKE_CASE           =    index + 1
										elif index + 1 == len(UpperCAmelCase__					):
															split_words.append(string[last_index : index + 1]					)
					return split_words


if __name__ == "__main__":
							from doctest import testmod

							testmod()




