


'''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    A_	(unittest.TestCase							):
							"""simple docstring"""




							def 							_A	(		self			:List[Any]				)				->  Optional[int]:


										'''simple docstring'''




										snake_case_				:					str   =       inspect.getfile(accelerate.test_utils				)
										snake_case_				:					Tuple   =       os.path.sep.join(mod_file.split(os.path.sep				)[:-1] + ["scripts", "test_script.py"]				)
										snake_case_				:					Optional[int]   =       os.path.sep.join(
										    mod_file.split(os.path.sep				)[:-1] + ["scripts", "test_distributed_data_loop.py"]				)
										snake_case_				:					Dict   =       os.path.sep.join(mod_file.split(os.path.sep				)[:-1] + ["scripts", "test_ops.py"]				)

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


										'''simple docstring'''




										print(F'''Found {torch.cuda.device_count()} devices.'''				)
										snake_case_				:					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 							_A	(		self			:Optional[int]				)				->  Tuple:


										'''simple docstring'''




										print(F'''Found {torch.cuda.device_count()} devices.'''				)
										snake_case_				:					Dict   =       ["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 							_A	(		self			:Tuple				)				->  Dict:


										'''simple docstring'''




										snake_case_				:					str   =       ["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 							_A	(		self			:str				)				->  str:


										'''simple docstring'''




										print(F'''Found {torch.cuda.device_count()} devices, using 2 devices only'''				)
										snake_case_				:					Union[str, 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   :					Optional[int]   							=					Accelerator()
		__lowerCamelCase   :					Any   							=					(accelerator.state.process_index + 2, 10)
		__lowerCamelCase   :					List[Any]   							=					torch.randint(0, 10, shape).to(accelerator.device)

		__lowerCamelCase   :					Optional[Any]   							=					''''''

		__lowerCamelCase   :					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)."

		__lowerCamelCase   :					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."
		__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)












'''simple docstring'''




import os
import sys
import tempfile

import torch

from .state import AcceleratorState
from .utils import PrecisionType, PrepareForLaunch, is_mps_available, patch_environment

def 	__UpperCAmelCase       ( __magic_name__     ,__magic_name__=()     ,__magic_name__=None     ,__magic_name__="no"     ,__magic_name__="29500"						)->    Optional[int]:



			"""simple docstring"""



			snake_case_				:					str   =       False
			snake_case_				:					int   =       False
			if any(key.startswith("KAGGLE"						) for key in os.environ.keys()						):
						snake_case_				:					Any   =       True
			elif "IPython" in sys.modules:
						snake_case_				:					Union[str, Any]   =       "google.colab" in str(sys.modules["IPython"].get_ipython()						)

			try:
						snake_case_				:					Any   =       PrecisionType(mixed_precision.lower()						)
			except ValueError:
						raise ValueError(
						    F'''Unknown mixed_precision mode: {args.mixed_precision.lower()}. Choose between {PrecisionType.list()}.'''						)

			if (in_colab or in_kaggle) and (os.environ.get("TPU_NAME"     ,__magic_name__						) is not None):
						# TPU launch
						import torch_xla.distributed.xla_multiprocessing as xmp

						if len(AcceleratorState._shared_state						) > 0:
									raise ValueError(
									    "To train on TPU in Colab or Kaggle Kernel, the `Accelerator` should only be initialized inside "
									    "your training function. Restart your notebook and make sure no cells initializes an "
									    "`Accelerator`."						)
						if num_processes is None:
									snake_case_				:					Tuple   =       8

						snake_case_				:					Optional[int]   =       PrepareForLaunch(__magic_name__     ,distributed_type="TPU"						)
						print(F'''Launching a training on {num_processes} TPU cores.'''						)
						xmp.spawn(__magic_name__     ,args=__magic_name__     ,nprocs=__magic_name__     ,start_method="fork"						)
			elif in_colab:
						# No need for a distributed launch otherwise as it's either CPU or one GPU.
						if torch.cuda.is_available():
									print("Launching training on one GPU."						)
						else:
									print("Launching training on one CPU."						)
						function(*__magic_name__						)
			else:
						if num_processes is None:
									raise ValueError(
									    "You have to specify the number of GPUs you would like to use, add `num_processes=...` to your call."						)

						if num_processes > 1:
									# Multi-GPU launch
									from torch.multiprocessing import start_processes
									from torch.multiprocessing.spawn import ProcessRaisedException

									if len(AcceleratorState._shared_state						) > 0:
												raise ValueError(
												    "To launch a multi-GPU training from your notebook, the `Accelerator` should only be initialized "
												    "inside your training function. Restart your notebook and make sure no cells initializes an "
												    "`Accelerator`."						)

									if torch.cuda.is_initialized():
												raise ValueError(
												    "To launch a multi-GPU training from your notebook, you need to avoid running any instruction "
												    "using `torch.cuda` in any cell. Restart your notebook and make sure no cells use any CUDA "
												    "function."						)

									# torch.distributed will expect a few environment variable to be here. We set the ones common to each
									# process here (the other ones will be set be the launcher).
									with patch_environment(
									    world_size=__magic_name__     ,master_addr="127.0.01"     ,master_port=__magic_name__     ,mixed_precision=__magic_name__						):
												snake_case_				:					Optional[int]   =       PrepareForLaunch(__magic_name__     ,distributed_type="MULTI_GPU"						)
												print(F'''Launching training on {num_processes} GPUs.'''						)
												try:
															start_processes(__magic_name__     ,args=__magic_name__     ,nprocs=__magic_name__     ,start_method="fork"						)
												except ProcessRaisedException as e:
															if "Cannot re-initialize CUDA in forked subprocess" in e.args[0]:
																		raise RuntimeError(
																		    "CUDA has been initialized before the `notebook_launcher` could create a forked subprocess. "
																		    "This likely stems from an outside import causing issues once the `notebook_launcher()` is called. "
																		    "Please review your imports and test them when running the `notebook_launcher()` to identify "
																		    "which one is problematic."						) from e

						else:
									# No need for a distributed launch otherwise as it's either CPU, GPU or MPS.
									if is_mps_available():
												snake_case_				:					Any   =       "1"
												print("Launching training on MPS."						)
									elif torch.cuda.is_available():
												print("Launching training on one GPU."						)
									else:
												print("Launching training on CPU."						)
									function(*__magic_name__						)






def 	__UpperCAmelCase       ( __magic_name__     ,__magic_name__=()     ,__magic_name__=2						)->    Dict:



			"""simple docstring"""



			from torch.multiprocessing import start_processes

			with tempfile.NamedTemporaryFile() as tmp_file:
						# torch.distributed will expect a few environment variable to be here. We set the ones common to each
						# process here (the other ones will be set be the launcher).
						with patch_environment(
						    world_size=__magic_name__     ,master_addr="127.0.01"     ,master_port="29500"     ,accelerate_mixed_precision="no"     ,accelerate_debug_rdv_file=tmp_file.name     ,accelerate_use_cpu="yes"     ,):
									snake_case_				:					Any   =       PrepareForLaunch(__magic_name__     ,debug=__magic_name__						)
									start_processes(__magic_name__     ,args=__magic_name__     ,nprocs=__magic_name__     ,start_method="fork"						)




