




"""simple docstring"""






from typing import Any

import numpy as np





def 							UpperCAmelCase		(		snake_case			:					np.ndarray    ):
					return np.array_equal(snake_case      ,       matrix.conjugate().T    )





def 							UpperCAmelCase		(		snake_case			:					np.ndarray      ,       snake_case			:					np.ndarray    ):
					_lowerCAmelCase:Optional[int]									=  v.conjugate().T
					_lowerCAmelCase:Union[str, Any]									=  v_star.dot(snake_case    )
					assert isinstance(snake_case      ,       np.ndarray    )
					return (v_star_dot.dot(snake_case    )) / (v_star.dot(snake_case    ))





def 							UpperCAmelCase		(		):
					_lowerCAmelCase:Optional[Any]									=  np.array([[2, 2 + 1j, 4], [2 - 1j, 3, 1j], [4, -1j, 1]]    )
					_lowerCAmelCase:Optional[Any]									=  np.array([[1], [2], [3]]    )
					assert is_hermitian(snake_case    ), F'{a} is not hermitian.'
					print(rayleigh_quotient(snake_case      ,       snake_case    )    )

					_lowerCAmelCase:Tuple									=  np.array([[1, 2, 4], [2, 3, -1], [4, -1, 1]]    )
					assert is_hermitian(snake_case    ), F'{a} is not hermitian.'
					assert rayleigh_quotient(snake_case      ,       snake_case    ) == float(3    )


if __name__ == "__main__":
				import doctest

				doctest.testmod()
				tests()












"""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()






