




"""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		(		snake_case			:					Tuple      ,       snake_case			:					int=()      ,       snake_case			:					int=None      ,       snake_case			:					Optional[Any]="no"      ,       snake_case			:					List[Any]="29500"    ):
					_lowerCAmelCase:List[str]									=  False
					_lowerCAmelCase:List[Any]									=  False
					if any(key.startswith('''KAGGLE'''    ) for key in os.environ.keys()    ):
										_lowerCAmelCase:Optional[Any]									=  True
					elif "IPython" in sys.modules:
										_lowerCAmelCase:List[str]									=  '''google.colab''' in str(sys.modules['''IPython'''].get_ipython()    )

					try:
										_lowerCAmelCase: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'''      ,       snake_case    ) 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:
															_lowerCAmelCase:Any									=  8

										_lowerCAmelCase:str									=  PrepareForLaunch(snake_case      ,       distributed_type='''TPU'''    )
										print(F'Launching a training on {num_processes} TPU cores.'    )
										xmp.spawn(snake_case      ,       args=snake_case      ,       nprocs=snake_case      ,       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(*snake_case    )
					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=snake_case      ,       master_addr='''127.0.01'''      ,       master_port=snake_case      ,       mixed_precision=snake_case    ):
																				_lowerCAmelCase:Optional[int]									=  PrepareForLaunch(snake_case      ,       distributed_type='''MULTI_GPU'''    )
																				print(F'Launching training on {num_processes} GPUs.'    )
																				try:
																									start_processes(snake_case      ,       args=snake_case      ,       nprocs=snake_case      ,       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():
																				_lowerCAmelCase:Optional[int]									=  '''1'''
																				print('''Launching training on MPS.'''    )
															elif torch.cuda.is_available():
																				print('''Launching training on one GPU.'''    )
															else:
																				print('''Launching training on CPU.'''    )
															function(*snake_case    )





def 							UpperCAmelCase		(		snake_case			:					Dict      ,       snake_case			:					str=()      ,       snake_case			:					Dict=2    ):
					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=snake_case      ,       master_addr='''127.0.01'''      ,       master_port='''29500'''      ,       accelerate_mixed_precision='''no'''      ,       accelerate_debug_rdv_file=tmp_file.name      ,       accelerate_use_cpu='''yes'''      ,       ):
															_lowerCAmelCase:Union[str, Any]									=  PrepareForLaunch(snake_case      ,       debug=snake_case    )
															start_processes(snake_case      ,       args=snake_case      ,       nprocs=snake_case      ,       start_method='''fork'''    )












"""simple docstring"""






import inspect
import logging
import os
import random
import shutil
import tempfile
import unittest

import pytest
import torch
from torch import nn
from torch.utils.data import DataLoader, TensorDataset

from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_async, require_cuda
from accelerate.utils import ProjectConfiguration, set_seed


UpperCamelCase__						=			logging.getLogger(__name__)





def 							UpperCAmelCase		(		snake_case			:					List[Any]=2      ,       snake_case			:					int=3      ,       snake_case			:					Optional[int]=16      ,       snake_case			:					int = 10      ,       snake_case			:					int = 2    ):

					def get_dataset(snake_case			:					Any    ):
										_lowerCAmelCase:Union[str, Any]									=  torch.randn(batch_size * n_batches      ,       1    )
										return TensorDataset(snake_case      ,       a * x + b + 0.1 * torch.randn(batch_size * n_batches      ,       1    )    )

					_lowerCAmelCase:Tuple									=  get_dataset(snake_case    )
					_lowerCAmelCase:List[Any]									=  get_dataset(snake_case    )
					_lowerCAmelCase:Dict									=  DataLoader(snake_case      ,       shuffle=snake_case      ,       batch_size=snake_case      ,       num_workers=4    )
					_lowerCAmelCase:Any									=  DataLoader(snake_case      ,       shuffle=snake_case      ,       batch_size=snake_case      ,       num_workers=4    )
					return (train_dataloader, valid_dataloader)





def 							UpperCAmelCase		(		snake_case			:					List[Any]      ,       snake_case			:					int      ,       snake_case			:					Dict      ,       snake_case			:					Dict      ,       snake_case			:					int      ,       snake_case			:					Tuple=None    ):
					_lowerCAmelCase:Optional[Any]									=  []
					for epoch in range(snake_case    ):
										# Train quickly
										model.train()
										for batch in dataloader:
															_lowerCAmelCase							,   _lowerCAmelCase:List[Any]									=  batch
															_lowerCAmelCase:int									=  model(snake_case    )
															_lowerCAmelCase:Optional[int]									=  torch.nn.functional.mse_loss(snake_case      ,       snake_case    )
															accelerator.backward(snake_case    )
															optimizer.step()
															optimizer.zero_grad()
										rands.append(random.random()    )  # Introduce some randomness
										if scheduler is not None:
															scheduler.step()
					return rands




class   a__			(       nn.Module  ):





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




											"""simple docstring"""







											super().__init__()
											_lowerCAmelCase:str									=  nn.Parameter(torch.randn(1))
											_lowerCAmelCase:Dict									=  nn.Parameter(torch.randn(1))







						def       __UpperCamelCase     (							self      :	Tuple     ,a__      :	List[Any])						->       Tuple:




											"""simple docstring"""







											return x * self.a + self.b




class   a__			(       unittest.TestCase  ):





						def       __UpperCamelCase     (							self      :	Any)						->       Optional[Any]:




											"""simple docstring"""







											with tempfile.TemporaryDirectory() as tmpdir:
																set_seed(42)
																_lowerCAmelCase:List[str]									=  DummyModel()
																_lowerCAmelCase:Any									=  torch.optim.Adam(params=model.parameters()     ,lr=1E-3)
																_lowerCAmelCase							,   _lowerCAmelCase:Optional[Any]									=  dummy_dataloaders()
																_lowerCAmelCase:List[str]									=  ProjectConfiguration(total_limit=1     ,project_dir=a__     ,automatic_checkpoint_naming=a__)
																# Train baseline
																_lowerCAmelCase:List[Any]									=  Accelerator(project_config=a__)
																_lowerCAmelCase							,   _lowerCAmelCase							,   _lowerCAmelCase							,   _lowerCAmelCase:int									=  accelerator.prepare(
																    a__     ,a__     ,a__     ,a__)
																# Save initial
																accelerator.save_state()

																# Save second state
																accelerator.save_state()
																self.assertEqual(len(os.listdir(accelerator.project_dir))     ,1)





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




											"""simple docstring"""







											with tempfile.TemporaryDirectory() as tmpdir:
																set_seed(42)
																_lowerCAmelCase:int									=  DummyModel()
																_lowerCAmelCase:Dict									=  torch.optim.Adam(params=model.parameters()     ,lr=1E-3)
																_lowerCAmelCase							,   _lowerCAmelCase:Dict									=  dummy_dataloaders()
																# Train baseline
																_lowerCAmelCase:List[str]									=  Accelerator()
																_lowerCAmelCase							,   _lowerCAmelCase							,   _lowerCAmelCase							,   _lowerCAmelCase:Optional[Any]									=  accelerator.prepare(
																    a__     ,a__     ,a__     ,a__)
																# Save initial
																_lowerCAmelCase:Union[str, Any]									=  os.path.join(a__     ,'''initial''')
																accelerator.save_state(a__)
																((_lowerCAmelCase)							,   (_lowerCAmelCase)):Any									=  model.a.item(), model.b.item()
																_lowerCAmelCase:Optional[int]									=  optimizer.state_dict()
																_lowerCAmelCase:Tuple									=  train(3     ,a__     ,a__     ,a__     ,a__)
																((_lowerCAmelCase)							,   (_lowerCAmelCase)):Optional[int]									=  model.a.item(), model.b.item()
																_lowerCAmelCase:Any									=  optimizer.state_dict()

																# Train partially
																set_seed(42)
																_lowerCAmelCase:str									=  DummyModel()
																_lowerCAmelCase:Union[str, Any]									=  torch.optim.Adam(params=model.parameters()     ,lr=1E-3)
																_lowerCAmelCase							,   _lowerCAmelCase:Any									=  dummy_dataloaders()
																_lowerCAmelCase:Union[str, Any]									=  Accelerator()
																_lowerCAmelCase							,   _lowerCAmelCase							,   _lowerCAmelCase							,   _lowerCAmelCase:Tuple									=  accelerator.prepare(
																    a__     ,a__     ,a__     ,a__)
																accelerator.load_state(a__)
																((_lowerCAmelCase)							,   (_lowerCAmelCase)):str									=  model.a.item(), model.b.item()
																_lowerCAmelCase:Any									=  optimizer.state_dict()
																self.assertEqual(a__     ,a__)
																self.assertEqual(a__     ,a__)
																self.assertEqual(a__     ,a__)

																_lowerCAmelCase:str									=  train(2     ,a__     ,a__     ,a__     ,a__)
																# Save everything
																_lowerCAmelCase:Optional[Any]									=  os.path.join(a__     ,'''checkpoint''')
																accelerator.save_state(a__)

																# Load everything back in and make sure all states work
																accelerator.load_state(a__)
																test_rands += train(1     ,a__     ,a__     ,a__     ,a__)
																((_lowerCAmelCase)							,   (_lowerCAmelCase)):List[Any]									=  model.a.item(), model.b.item()
																_lowerCAmelCase:List[Any]									=  optimizer.state_dict()
																self.assertEqual(a__     ,a__)
																self.assertEqual(a__     ,a__)
																self.assertEqual(a__     ,a__)
																self.assertEqual(a__     ,a__)





						def       __UpperCamelCase     (							self      :	Optional[Any])						->       Optional[Any]:




											"""simple docstring"""







											with tempfile.TemporaryDirectory() as tmpdir:
																set_seed(42)
																_lowerCAmelCase:List[str]									=  DummyModel()
																_lowerCAmelCase:int									=  torch.optim.Adam(params=model.parameters()     ,lr=1E-3)
																_lowerCAmelCase							,   _lowerCAmelCase:Tuple									=  dummy_dataloaders()
																_lowerCAmelCase:Any									=  ProjectConfiguration(automatic_checkpoint_naming=a__)

																# Train baseline
																_lowerCAmelCase:str									=  Accelerator(project_dir=a__     ,project_config=a__)
																_lowerCAmelCase							,   _lowerCAmelCase							,   _lowerCAmelCase							,   _lowerCAmelCase:List[Any]									=  accelerator.prepare(
																    a__     ,a__     ,a__     ,a__)
																# Save initial
																accelerator.save_state()
																((_lowerCAmelCase)							,   (_lowerCAmelCase)):List[str]									=  model.a.item(), model.b.item()
																_lowerCAmelCase:str									=  optimizer.state_dict()
																_lowerCAmelCase:Optional[int]									=  train(3     ,a__     ,a__     ,a__     ,a__)
																((_lowerCAmelCase)							,   (_lowerCAmelCase)):int									=  model.a.item(), model.b.item()
																_lowerCAmelCase:int									=  optimizer.state_dict()

																# Train partially
																set_seed(42)
																_lowerCAmelCase:Optional[int]									=  DummyModel()
																_lowerCAmelCase:Union[str, Any]									=  torch.optim.Adam(params=model.parameters()     ,lr=1E-3)
																_lowerCAmelCase							,   _lowerCAmelCase:Optional[Any]									=  dummy_dataloaders()
																_lowerCAmelCase:Union[str, Any]									=  ProjectConfiguration(iteration=1     ,automatic_checkpoint_naming=a__)
																_lowerCAmelCase:Dict									=  Accelerator(project_dir=a__     ,project_config=a__)
																_lowerCAmelCase							,   _lowerCAmelCase							,   _lowerCAmelCase							,   _lowerCAmelCase:Optional[int]									=  accelerator.prepare(
																    a__     ,a__     ,a__     ,a__)
																accelerator.load_state(os.path.join(a__     ,'''checkpoints'''     ,'''checkpoint_0'''))
																((_lowerCAmelCase)							,   (_lowerCAmelCase)):Dict									=  model.a.item(), model.b.item()
																_lowerCAmelCase:int									=  optimizer.state_dict()
																self.assertEqual(a__     ,a__)
																self.assertEqual(a__     ,a__)
																self.assertEqual(a__     ,a__)

																_lowerCAmelCase:Dict									=  train(2     ,a__     ,a__     ,a__     ,a__)
																# Save everything
																accelerator.save_state()

																# Load everything back in and make sure all states work
																accelerator.load_state(os.path.join(a__     ,'''checkpoints'''     ,'''checkpoint_1'''))
																test_rands += train(1     ,a__     ,a__     ,a__     ,a__)
																((_lowerCAmelCase)							,   (_lowerCAmelCase)):Dict									=  model.a.item(), model.b.item()
																_lowerCAmelCase:Union[str, Any]									=  optimizer.state_dict()
																self.assertEqual(a__     ,a__)
																self.assertEqual(a__     ,a__)
																self.assertEqual(a__     ,a__)
																self.assertEqual(a__     ,a__)





						def       __UpperCamelCase     (							self      :	Dict)						->       int:




											"""simple docstring"""







											_lowerCAmelCase:str									=  torch.tensor([1, 2, 3])
											_lowerCAmelCase:Tuple									=  torch.tensor([2, 3, 4])
											_lowerCAmelCase:Tuple									=  DummyModel()
											_lowerCAmelCase:Union[str, Any]									=  torch.optim.Adam(net.parameters())
											_lowerCAmelCase:Union[str, Any]									=  Accelerator()
											with self.assertRaises(a__) as ve:
																accelerator.register_for_checkpointing(a__     ,a__     ,a__     ,a__)
											_lowerCAmelCase:List[Any]									=  str(ve.exception)
											self.assertTrue('''Item at index 0''' in message)
											self.assertTrue('''Item at index 1''' in message)
											self.assertFalse('''Item at index 2''' in message)
											self.assertFalse('''Item at index 3''' in message)





						def       __UpperCamelCase     (							self      :	str)						->       Optional[Any]:




											"""simple docstring"""







											with tempfile.TemporaryDirectory() as tmpdir:
																set_seed(42)
																_lowerCAmelCase:int									=  DummyModel()
																_lowerCAmelCase:str									=  torch.optim.Adam(params=model.parameters()     ,lr=1E-3)
																_lowerCAmelCase:Union[str, Any]									=  torch.optim.lr_scheduler.StepLR(a__     ,step_size=1     ,gamma=0.99)
																_lowerCAmelCase							,   _lowerCAmelCase:List[str]									=  dummy_dataloaders()
																_lowerCAmelCase:Any									=  ProjectConfiguration(automatic_checkpoint_naming=a__)
																# Train baseline
																_lowerCAmelCase:Optional[int]									=  Accelerator(project_dir=a__     ,project_config=a__)
																_lowerCAmelCase							,   _lowerCAmelCase							,   _lowerCAmelCase							,   _lowerCAmelCase							,   _lowerCAmelCase:int									=  accelerator.prepare(
																    a__     ,a__     ,a__     ,a__     ,a__)
																# Save initial
																accelerator.save_state()
																_lowerCAmelCase:Dict									=  scheduler.state_dict()
																train(3     ,a__     ,a__     ,a__     ,a__     ,a__)
																self.assertNotEqual(a__     ,scheduler.state_dict())

																# Load everything back in and make sure all states work
																accelerator.load_state(os.path.join(a__     ,'''checkpoints'''     ,'''checkpoint_0'''))
																self.assertEqual(a__     ,scheduler.state_dict())





						def       __UpperCamelCase     (							self      :	Union[str, Any])						->       Tuple:




											"""simple docstring"""







											with tempfile.TemporaryDirectory() as tmpdir:
																set_seed(42)
																_lowerCAmelCase:str									=  DummyModel()
																_lowerCAmelCase:Optional[Any]									=  ProjectConfiguration(automatic_checkpoint_naming=a__     ,total_limit=2)
																# Train baseline
																_lowerCAmelCase:List[str]									=  Accelerator(project_dir=a__     ,project_config=a__)
																_lowerCAmelCase:List[str]									=  accelerator.prepare(a__)
																# Save 3 states:
																for _ in range(11):
																					accelerator.save_state()
																self.assertTrue(not os.path.exists(os.path.join(a__     ,'''checkpoints'''     ,'''checkpoint_0''')))
																self.assertTrue(os.path.exists(os.path.join(a__     ,'''checkpoints'''     ,'''checkpoint_9''')))
																self.assertTrue(os.path.exists(os.path.join(a__     ,'''checkpoints'''     ,'''checkpoint_10''')))







						@require_cuda
						def       __UpperCamelCase     (							self      :	int)						->       str:




											"""simple docstring"""







											_lowerCAmelCase:Union[str, Any]									=  ['''torchrun''', F'--nproc_per_node={torch.cuda.device_count()}', inspect.getfile(self.__class__)]
											execute_subprocess_async(a__     ,env=os.environ.copy())


if __name__ == "__main__":
				UpperCamelCase__						=			'''/tmp/accelerate/state_checkpointing'''
				UpperCamelCase__						=			DummyModel()
				UpperCamelCase__						=			torch.optim.Adam(params=model.parameters(), lr=1e-3)
				UpperCamelCase__						=			torch.optim.lr_scheduler.StepLR(optimizer, step_size=1, gamma=0.99)
				UpperCamelCase__						,							UpperCamelCase__						=			dummy_dataloaders()
				UpperCamelCase__						=			ProjectConfiguration(automatic_checkpoint_naming=True)
				# Train baseline
				UpperCamelCase__						=			Accelerator(project_dir=savedir, project_config=project_config, mixed_precision='''no''')
				if accelerator.process_index == 0:
								if os.path.exists(savedir):
												shutil.rmtree(savedir)
								os.makedirs(savedir)
				UpperCamelCase__						,							UpperCamelCase__						,							UpperCamelCase__						,							UpperCamelCase__						,							UpperCamelCase__						=			accelerator.prepare(
				    model, optimizer, train_dataloader, valid_dataloader, scheduler
				)
				UpperCamelCase__						,							UpperCamelCase__						=			accelerator.prepare(model, optimizer)
				train(3, model, train_dataloader, optimizer, accelerator, scheduler)
				# Check that the intial optimizer is loaded on the GPU
				for group in optimizer.param_groups:
								UpperCamelCase__						=			group['''params'''][0].device
								break
				assert param_device.type == accelerator.device.type
				UpperCamelCase__						=			model.cpu()
				accelerator.wait_for_everyone()
				accelerator.save_state()
				accelerator.wait_for_everyone()

				# Check CPU state
				accelerator.load_state(os.path.join(savedir, '''checkpoints''', '''checkpoint_0'''), map_location='''cpu''')
				for group in optimizer.param_groups:
								UpperCamelCase__						=			group['''params'''][0].device
								break
				assert (
				    param_device.type == torch.device('''cpu''').type
				), F"Loaded optimizer states did not match, expected to be loaded on the CPU but got {param_device}"

				# Check device state
				model.to(accelerator.device)
				accelerator.load_state(os.path.join(savedir, '''checkpoints''', '''checkpoint_0'''), map_location='''on_device''')
				for group in optimizer.param_groups:
								UpperCamelCase__						=			group['''params'''][0].device
								break
				assert (
				    param_device.type == accelerator.device.type
				), F"Loaded optimizer states did not match, expected to be loaded on {accelerator.device} but got {param_device}"

				# Check error
				with pytest.raises(TypeError, match='''Unsupported optimizer map location passed'''):
								accelerator.load_state(os.path.join(savedir, '''checkpoints''', '''checkpoint_0'''), map_location='''invalid''')
				accelerator.wait_for_everyone()
				if accelerator.process_index == 0:
								shutil.rmtree(savedir)
				accelerator.wait_for_everyone()






