






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 				A		(					lowercase__  :  str=2 ,		lowercase__  :  Dict=3 ,		lowercase__  :  Optional[int]=16 ,		lowercase__  :  Union[str, Any] = 10 ,		lowercase__  :  str = 2  )		-> List[Any]:


	def get_dataset(lowercase__  :  Optional[Any]  ):
		UpperCamelCase__ :List[str]						=      torch.randn(batch_size * n_batches ,		1  )
		return TensorDataset(lowercase__ ,		a * x + b + 0.1 * torch.randn(batch_size * n_batches ,		1  )  )

	UpperCamelCase__ :List[Any]						=      get_dataset(lowercase__  )
	UpperCamelCase__ :Tuple						=      get_dataset(lowercase__  )
	UpperCamelCase__ :int						=      DataLoader(lowercase__ ,		shuffle=lowercase__ ,		batch_size=lowercase__ ,		num_workers=4  )
	UpperCamelCase__ :List[str]						=      DataLoader(lowercase__ ,		shuffle=lowercase__ ,		batch_size=lowercase__ ,		num_workers=4  )
	return (train_dataloader, valid_dataloader)




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

class        lowerCAmelCase_				(      nn.Module	):







				"""simple docstring"""




				def __init__( self     :Optional[Any]     ):
					super().__init__()
					UpperCamelCase__ :Optional[int]						=      nn.Parameter(torch.randn(1     )     )
					UpperCamelCase__ :Tuple						=      nn.Parameter(torch.randn(1     )     )







				def 				__a   ( self     :Tuple  ,     lowerCamelCase__     :str     ):
					return x * self.a + self.b

class        lowerCAmelCase_				(      unittest.TestCase	):







				"""simple docstring"""




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

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







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

						# Train partially
						set_seed(42     )
						UpperCamelCase__ :Any						=      DummyModel()
						UpperCamelCase__ :int						=      torch.optim.Adam(params=model.parameters()  ,     lr=1e-3     )
						UpperCamelCase__				,		UpperCamelCase__ :List[Any]						=      dummy_dataloaders()
						UpperCamelCase__ :int						=      Accelerator()
						UpperCamelCase__				,		UpperCamelCase__				,		UpperCamelCase__				,		UpperCamelCase__ :Optional[int]						=      accelerator.prepare(
						    lowerCAmelCase__  ,     lowerCAmelCase__  ,     lowerCAmelCase__  ,     lowerCAmelCase__     )
						accelerator.load_state(lowerCAmelCase__     )
						((UpperCamelCase__)				,		(UpperCamelCase__)) :List[Any]						=      model.a.item(), model.b.item()
						UpperCamelCase__ :List[str]						=      optimizer.state_dict()
						self.assertEqual(lowerCAmelCase__  ,     lowerCAmelCase__     )
						self.assertEqual(lowerCAmelCase__  ,     lowerCAmelCase__     )
						self.assertEqual(lowerCAmelCase__  ,     lowerCAmelCase__     )

						UpperCamelCase__ :Dict						=      train(2  ,     lowerCAmelCase__  ,     lowerCAmelCase__  ,     lowerCAmelCase__  ,     lowerCAmelCase__     )
						# Save everything
						UpperCamelCase__ :Optional[Any]						=      os.path.join(lowerCAmelCase__  ,     """checkpoint"""     )
						accelerator.save_state(lowerCAmelCase__     )

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







				def 				__a   ( self     :Any     ):
					with tempfile.TemporaryDirectory() as tmpdir:
						set_seed(42     )
						UpperCamelCase__ :Optional[int]						=      DummyModel()
						UpperCamelCase__ :Any						=      torch.optim.Adam(params=model.parameters()  ,     lr=1e-3     )
						UpperCamelCase__				,		UpperCamelCase__ :Optional[Any]						=      dummy_dataloaders()
						UpperCamelCase__ :Dict						=      ProjectConfiguration(automatic_checkpoint_naming=lowerCAmelCase__     )

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

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

						UpperCamelCase__ :Tuple						=      train(2  ,     lowerCAmelCase__  ,     lowerCAmelCase__  ,     lowerCAmelCase__  ,     lowerCAmelCase__     )
						# Save everything
						accelerator.save_state()

						# Load everything back in and make sure all states work
						accelerator.load_state(os.path.join(lowerCAmelCase__  ,     """checkpoints"""  ,     """checkpoint_1"""     )     )
						test_rands += train(1  ,     lowerCAmelCase__  ,     lowerCAmelCase__  ,     lowerCAmelCase__  ,     lowerCAmelCase__     )
						((UpperCamelCase__)				,		(UpperCamelCase__)) :Optional[int]						=      model.a.item(), model.b.item()
						UpperCamelCase__ :List[str]						=      optimizer.state_dict()
						self.assertEqual(lowerCAmelCase__  ,     lowerCAmelCase__     )
						self.assertEqual(lowerCAmelCase__  ,     lowerCAmelCase__     )
						self.assertEqual(lowerCAmelCase__  ,     lowerCAmelCase__     )
						self.assertEqual(lowerCAmelCase__  ,     lowerCAmelCase__     )







				def 				__a   ( self     :str     ):
					UpperCamelCase__ :List[Any]						=      torch.tensor([1, 2, 3]     )
					UpperCamelCase__ :Any						=      torch.tensor([2, 3, 4]     )
					UpperCamelCase__ :Optional[Any]						=      DummyModel()
					UpperCamelCase__ :Tuple						=      torch.optim.Adam(net.parameters()     )
					UpperCamelCase__ :Union[str, Any]						=      Accelerator()
					with self.assertRaises(lowerCAmelCase__     ) as ve:
						accelerator.register_for_checkpointing(lowerCAmelCase__  ,     lowerCAmelCase__  ,     lowerCAmelCase__  ,     lowerCAmelCase__     )
					UpperCamelCase__ :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 				__a   ( self     :Optional[Any]     ):
					with tempfile.TemporaryDirectory() as tmpdir:
						set_seed(42     )
						UpperCamelCase__ :Union[str, Any]						=      DummyModel()
						UpperCamelCase__ :Union[str, Any]						=      torch.optim.Adam(params=model.parameters()  ,     lr=1e-3     )
						UpperCamelCase__ :Any						=      torch.optim.lr_scheduler.StepLR(lowerCAmelCase__  ,     step_size=1  ,     gamma=0.99     )
						UpperCamelCase__				,		UpperCamelCase__ :Any						=      dummy_dataloaders()
						UpperCamelCase__ :Tuple						=      ProjectConfiguration(automatic_checkpoint_naming=lowerCAmelCase__     )
						# Train baseline
						UpperCamelCase__ :Optional[int]						=      Accelerator(project_dir=lowerCAmelCase__  ,     project_config=lowerCAmelCase__     )
						UpperCamelCase__				,		UpperCamelCase__				,		UpperCamelCase__				,		UpperCamelCase__				,		UpperCamelCase__ :int						=      accelerator.prepare(
						    lowerCAmelCase__  ,     lowerCAmelCase__  ,     lowerCAmelCase__  ,     lowerCAmelCase__  ,     lowerCAmelCase__     )
						# Save initial
						accelerator.save_state()
						UpperCamelCase__ :List[Any]						=      scheduler.state_dict()
						train(3  ,     lowerCAmelCase__  ,     lowerCAmelCase__  ,     lowerCAmelCase__  ,     lowerCAmelCase__  ,     lowerCAmelCase__     )
						self.assertNotEqual(lowerCAmelCase__  ,     scheduler.state_dict()     )

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







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







				@require_cuda
				def 				__a   ( self     :List[str]     ):
					UpperCamelCase__ :Union[str, Any]						=      ["""torchrun""", f"""--nproc_per_node={torch.cuda.device_count()}""", inspect.getfile(self.__class__     )]
					execute_subprocess_async(lowerCAmelCase__  ,     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        =				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        =				accelerator.prepare(
							    model, optimizer, train_dataloader, valid_dataloader, scheduler
							)
							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()

import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path

import torch_xla.distributed.xla_multiprocessing as xmp

def  __snake_case							(	):







				'''simple docstring'''


				lowercase         =      ArgumentParser(
				    description=(
				        "PyTorch TPU distributed training launch helper utility that will spawn up multiple distributed processes"
				    )						)

				# Optional arguments for the launch helper
				parser.add_argument("--num_cores"		,    type=__magic_name__		,    default=1		,    help="Number of TPU cores to use (1 or 8)."						)

				# positional
				parser.add_argument(
				    "training_script"		,    type=__magic_name__		,    help=(
				        "The full path to the single TPU training "
				        "program/script to be launched in parallel, "
				        "followed by all the arguments for the "
				        "training script"
				    )		,    )

				# rest from the training program
				parser.add_argument("training_script_args"		,    nargs=__magic_name__						)

				return parser.parse_args()

def  __snake_case							(	):







				'''simple docstring'''


				lowercase         =      parse_args()

				# Import training_script as a module.
				lowercase         =      Path(args.training_script						)
				sys.path.append(str(script_fpath.parent.resolve()						)						)
				lowercase         =      script_fpath.stem
				lowercase         =      importlib.import_module(__magic_name__						)

				# Patch sys.argv
				lowercase         =      [args.training_script] + args.training_script_args + ["--tpu_num_cores", str(args.num_cores						)]

				xmp.spawn(mod._mp_fn		,    args=()		,    nprocs=args.num_cores						)


if __name__ == "__main__":
						main()
