



"""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


__SCREAMING_SNAKE_CASE  :						str    							=      logging.getLogger(__name__)



def        lowerCAmelCase_(   lowercase_     :							Dict=2						,   lowercase_     :							Optional[int]=3						,   lowercase_     :							List[Any]=16						,   lowercase_     :							int = 10						,   lowercase_     :							int = 2							)	->       Dict:

				def get_dataset(lowercase_     :							Tuple							):
								_lowerCamelCase        =				torch.randn(batch_size * n_batches						,   1							)
								return TensorDataset(lowercase_						,   a * x + b + 0.1 * torch.randn(batch_size * n_batches						,   1							)							)

				_lowerCamelCase        =				get_dataset(lowercase_							)
				_lowerCamelCase        =				get_dataset(lowercase_							)
				_lowerCamelCase        =				DataLoader(lowercase_						,   shuffle=lowercase_						,   batch_size=lowercase_						,   num_workers=4							)
				_lowerCamelCase        =				DataLoader(lowercase_						,   shuffle=lowercase_						,   batch_size=lowercase_						,   num_workers=4							)
				return (train_dataloader, valid_dataloader)



def        lowerCAmelCase_(   lowercase_     :							int						,   lowercase_     :							str						,   lowercase_     :							Any						,   lowercase_     :							str						,   lowercase_     :							Any						,   lowercase_     :							Tuple=None							)	->       Optional[Any]:
				_lowerCamelCase        =				[]
				for epoch in range(lowercase_							):
								# Train quickly
								model.train()
								for batch in dataloader:
												_lowerCamelCase  ,     _lowerCamelCase        =				batch
												_lowerCamelCase        =				model(lowercase_							)
												_lowerCamelCase        =				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   ):
									super().__init__()
									_lowerCamelCase        =				nn.Parameter(torch.randn(1   )   )
									_lowerCamelCase        =				nn.Parameter(torch.randn(1   )   )





					def 			snake_case__      (    self				,       lowerCamelCase__   ):
									return x * self.a + self.b


class 							lowerCamelCase_(   unittest.TestCase    ):





					'''simple docstring'''



					def 			snake_case__      (    self   ):
									with tempfile.TemporaryDirectory() as tmpdir:
													set_seed(4_2   )
													_lowerCamelCase        =				DummyModel()
													_lowerCamelCase        =				torch.optim.Adam(params=model.parameters()				,       lr=1e-3   )
													_lowerCamelCase  ,     _lowerCamelCase        =				dummy_dataloaders()
													_lowerCamelCase        =				ProjectConfiguration(total_limit=1				,       project_dir=lowerCamelCase__				,       automatic_checkpoint_naming=lowerCamelCase__   )
													# Train baseline
													_lowerCamelCase        =				Accelerator(project_config=lowerCamelCase__   )
													_lowerCamelCase  ,     _lowerCamelCase  ,     _lowerCamelCase  ,     _lowerCamelCase        =				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 			snake_case__      (    self   ):
									with tempfile.TemporaryDirectory() as tmpdir:
													set_seed(4_2   )
													_lowerCamelCase        =				DummyModel()
													_lowerCamelCase        =				torch.optim.Adam(params=model.parameters()				,       lr=1e-3   )
													_lowerCamelCase  ,     _lowerCamelCase        =				dummy_dataloaders()
													# Train baseline
													_lowerCamelCase        =				Accelerator()
													_lowerCamelCase  ,     _lowerCamelCase  ,     _lowerCamelCase  ,     _lowerCamelCase        =				accelerator.prepare(
													    lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__   )
													# Save initial
													_lowerCamelCase        =				os.path.join(lowerCamelCase__				,       '''initial'''   )
													accelerator.save_state(lowerCamelCase__   )
													((_lowerCamelCase)  ,     (_lowerCamelCase))        =				model.a.item(), model.b.item()
													_lowerCamelCase        =				optimizer.state_dict()
													_lowerCamelCase        =				train(3				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__   )
													((_lowerCamelCase)  ,     (_lowerCamelCase))        =				model.a.item(), model.b.item()
													_lowerCamelCase        =				optimizer.state_dict()

													# Train partially
													set_seed(4_2   )
													_lowerCamelCase        =				DummyModel()
													_lowerCamelCase        =				torch.optim.Adam(params=model.parameters()				,       lr=1e-3   )
													_lowerCamelCase  ,     _lowerCamelCase        =				dummy_dataloaders()
													_lowerCamelCase        =				Accelerator()
													_lowerCamelCase  ,     _lowerCamelCase  ,     _lowerCamelCase  ,     _lowerCamelCase        =				accelerator.prepare(
													    lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__   )
													accelerator.load_state(lowerCamelCase__   )
													((_lowerCamelCase)  ,     (_lowerCamelCase))        =				model.a.item(), model.b.item()
													_lowerCamelCase        =				optimizer.state_dict()
													self.assertEqual(lowerCamelCase__				,       lowerCamelCase__   )
													self.assertEqual(lowerCamelCase__				,       lowerCamelCase__   )
													self.assertEqual(lowerCamelCase__				,       lowerCamelCase__   )

													_lowerCamelCase        =				train(2				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__   )
													# Save everything
													_lowerCamelCase        =				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__   )
													((_lowerCamelCase)  ,     (_lowerCamelCase))        =				model.a.item(), model.b.item()
													_lowerCamelCase        =				optimizer.state_dict()
													self.assertEqual(lowerCamelCase__				,       lowerCamelCase__   )
													self.assertEqual(lowerCamelCase__				,       lowerCamelCase__   )
													self.assertEqual(lowerCamelCase__				,       lowerCamelCase__   )
													self.assertEqual(lowerCamelCase__				,       lowerCamelCase__   )

					def 			snake_case__      (    self   ):
									with tempfile.TemporaryDirectory() as tmpdir:
													set_seed(4_2   )
													_lowerCamelCase        =				DummyModel()
													_lowerCamelCase        =				torch.optim.Adam(params=model.parameters()				,       lr=1e-3   )
													_lowerCamelCase  ,     _lowerCamelCase        =				dummy_dataloaders()
													_lowerCamelCase        =				ProjectConfiguration(automatic_checkpoint_naming=lowerCamelCase__   )

													# Train baseline
													_lowerCamelCase        =				Accelerator(project_dir=lowerCamelCase__				,       project_config=lowerCamelCase__   )
													_lowerCamelCase  ,     _lowerCamelCase  ,     _lowerCamelCase  ,     _lowerCamelCase        =				accelerator.prepare(
													    lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__   )
													# Save initial
													accelerator.save_state()
													((_lowerCamelCase)  ,     (_lowerCamelCase))        =				model.a.item(), model.b.item()
													_lowerCamelCase        =				optimizer.state_dict()
													_lowerCamelCase        =				train(3				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__   )
													((_lowerCamelCase)  ,     (_lowerCamelCase))        =				model.a.item(), model.b.item()
													_lowerCamelCase        =				optimizer.state_dict()

													# Train partially
													set_seed(4_2   )
													_lowerCamelCase        =				DummyModel()
													_lowerCamelCase        =				torch.optim.Adam(params=model.parameters()				,       lr=1e-3   )
													_lowerCamelCase  ,     _lowerCamelCase        =				dummy_dataloaders()
													_lowerCamelCase        =				ProjectConfiguration(iteration=1				,       automatic_checkpoint_naming=lowerCamelCase__   )
													_lowerCamelCase        =				Accelerator(project_dir=lowerCamelCase__				,       project_config=lowerCamelCase__   )
													_lowerCamelCase  ,     _lowerCamelCase  ,     _lowerCamelCase  ,     _lowerCamelCase        =				accelerator.prepare(
													    lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__   )
													accelerator.load_state(os.path.join(lowerCamelCase__				,       '''checkpoints'''				,       '''checkpoint_0'''   )   )
													((_lowerCamelCase)  ,     (_lowerCamelCase))        =				model.a.item(), model.b.item()
													_lowerCamelCase        =				optimizer.state_dict()
													self.assertEqual(lowerCamelCase__				,       lowerCamelCase__   )
													self.assertEqual(lowerCamelCase__				,       lowerCamelCase__   )
													self.assertEqual(lowerCamelCase__				,       lowerCamelCase__   )

													_lowerCamelCase        =				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__   )
													((_lowerCamelCase)  ,     (_lowerCamelCase))        =				model.a.item(), model.b.item()
													_lowerCamelCase        =				optimizer.state_dict()
													self.assertEqual(lowerCamelCase__				,       lowerCamelCase__   )
													self.assertEqual(lowerCamelCase__				,       lowerCamelCase__   )
													self.assertEqual(lowerCamelCase__				,       lowerCamelCase__   )
													self.assertEqual(lowerCamelCase__				,       lowerCamelCase__   )

					def 			snake_case__      (    self   ):
									_lowerCamelCase        =				torch.tensor([1, 2, 3]   )
									_lowerCamelCase        =				torch.tensor([2, 3, 4]   )
									_lowerCamelCase        =				DummyModel()
									_lowerCamelCase        =				torch.optim.Adam(net.parameters()   )
									_lowerCamelCase        =				Accelerator()
									with self.assertRaises(lowerCamelCase__   ) as ve:
													accelerator.register_for_checkpointing(lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__   )
									_lowerCamelCase        =				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 			snake_case__      (    self   ):
									with tempfile.TemporaryDirectory() as tmpdir:
													set_seed(4_2   )
													_lowerCamelCase        =				DummyModel()
													_lowerCamelCase        =				torch.optim.Adam(params=model.parameters()				,       lr=1e-3   )
													_lowerCamelCase        =				torch.optim.lr_scheduler.StepLR(lowerCamelCase__				,       step_size=1				,       gamma=0.9_9   )
													_lowerCamelCase  ,     _lowerCamelCase        =				dummy_dataloaders()
													_lowerCamelCase        =				ProjectConfiguration(automatic_checkpoint_naming=lowerCamelCase__   )
													# Train baseline
													_lowerCamelCase        =				Accelerator(project_dir=lowerCamelCase__				,       project_config=lowerCamelCase__   )
													_lowerCamelCase  ,     _lowerCamelCase  ,     _lowerCamelCase  ,     _lowerCamelCase  ,     _lowerCamelCase        =				accelerator.prepare(
													    lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__   )
													# Save initial
													accelerator.save_state()
													_lowerCamelCase        =				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 			snake_case__      (    self   ):
									with tempfile.TemporaryDirectory() as tmpdir:
													set_seed(4_2   )
													_lowerCamelCase        =				DummyModel()
													_lowerCamelCase        =				ProjectConfiguration(automatic_checkpoint_naming=lowerCamelCase__				,       total_limit=2   )
													# Train baseline
													_lowerCamelCase        =				Accelerator(project_dir=lowerCamelCase__				,       project_config=lowerCamelCase__   )
													_lowerCamelCase        =				accelerator.prepare(lowerCamelCase__   )
													# Save 3 states:
													for _ in range(1_1   ):
																	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 			snake_case__      (    self   ):
									_lowerCamelCase        =				['''torchrun''', F"""--nproc_per_node={torch.cuda.device_count()}""", inspect.getfile(self.__class__   )]
									execute_subprocess_async(lowerCamelCase__				,       env=os.environ.copy()   )


if __name__ == "__main__":
			__SCREAMING_SNAKE_CASE  :						Optional[int]    							=      '''/tmp/accelerate/state_checkpointing'''
			__SCREAMING_SNAKE_CASE  :						str    							=      DummyModel()
			__SCREAMING_SNAKE_CASE  :						int    							=      torch.optim.Adam(params=model.parameters(), lr=1e-3)
			__SCREAMING_SNAKE_CASE  :						int    							=      torch.optim.lr_scheduler.StepLR(optimizer, step_size=1, gamma=0.99)
			__SCREAMING_SNAKE_CASE							,					__SCREAMING_SNAKE_CASE  :						List[Any]    							=      dummy_dataloaders()
			__SCREAMING_SNAKE_CASE  :						int    							=      ProjectConfiguration(automatic_checkpoint_naming=True)
			# Train baseline
			__SCREAMING_SNAKE_CASE  :						str    							=      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)
			__SCREAMING_SNAKE_CASE							,					__SCREAMING_SNAKE_CASE							,					__SCREAMING_SNAKE_CASE							,					__SCREAMING_SNAKE_CASE							,					__SCREAMING_SNAKE_CASE  :						int    							=      accelerator.prepare(
			    model, optimizer, train_dataloader, valid_dataloader, scheduler
			)
			__SCREAMING_SNAKE_CASE							,					__SCREAMING_SNAKE_CASE  :						Optional[Any]    							=      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:
						__SCREAMING_SNAKE_CASE  :						Optional[Any]    							=      group['''params'''][0].device
						break
			assert param_device.type == accelerator.device.type
			__SCREAMING_SNAKE_CASE  :						Optional[Any]    							=      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:
						__SCREAMING_SNAKE_CASE  :						Tuple    							=      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:
						__SCREAMING_SNAKE_CASE  :						Optional[int]    							=      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()





"""simple docstring"""
from __future__ import annotations

from math import pow, sqrt



def        lowerCAmelCase_(   lowercase_     :							float						,   lowercase_     :							float						,   lowercase_     :							float							)	->       dict[str, float]:
				if (resistance, reactance, impedance).count(0							) != 1:
								raise ValueError('''One and only one argument must be 0'''							)
				if resistance == 0:
								return {"resistance": sqrt(pow(lowercase_						,   2							) - pow(lowercase_						,   2							)							)}
				elif reactance == 0:
								return {"reactance": sqrt(pow(lowercase_						,   2							) - pow(lowercase_						,   2							)							)}
				elif impedance == 0:
								return {"impedance": sqrt(pow(lowercase_						,   2							) + pow(lowercase_						,   2							)							)}
				else:
								raise ValueError('''Exactly one argument must be 0'''							)


if __name__ == "__main__":
			import doctest

			doctest.testmod()
