



"""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


lowercase  							=					logging.getLogger(__name__)




def 	UpperCAmelCase     (		A	:						Dict=2     ,				A	:						int=3     ,				A	:						Optional[int]=16     ,				A	:						Union[str, Any] = 10     ,				A	:						Optional[Any] = 2       ):

		'''simple docstring'''


		def get_dataset(A	:						str       ):
				_UpperCAmelCase							 =		torch.randn(batch_size * n_batches     ,				1       )
				return TensorDataset(__lowerCAmelCase     ,				a * x + b + 0.1 * torch.randn(batch_size * n_batches     ,				1       )       )

		_UpperCAmelCase							 =		get_dataset(__lowerCAmelCase       )
		_UpperCAmelCase							 =		get_dataset(__lowerCAmelCase       )
		_UpperCAmelCase							 =		DataLoader(__lowerCAmelCase     ,				shuffle=__lowerCAmelCase     ,				batch_size=__lowerCAmelCase     ,				num_workers=4       )
		_UpperCAmelCase							 =		DataLoader(__lowerCAmelCase     ,				shuffle=__lowerCAmelCase     ,				batch_size=__lowerCAmelCase     ,				num_workers=4       )
		return (train_dataloader, valid_dataloader)




def 	UpperCAmelCase     (		A	:						Optional[int]     ,				A	:						str     ,				A	:						Any     ,				A	:						List[str]     ,				A	:						int     ,				A	:						List[str]=None       ):

		'''simple docstring'''


		_UpperCAmelCase							 =		[]
		for epoch in range(__lowerCAmelCase       ):
				# Train quickly
				model.train()
				for batch in dataloader:
						_UpperCAmelCase							 =		batch
						_UpperCAmelCase							 =		model(__lowerCAmelCase       )
						_UpperCAmelCase							 =		torch.nn.functional.mse_loss(__lowerCAmelCase     ,				__lowerCAmelCase       )
						accelerator.backward(__lowerCAmelCase       )
						optimizer.step()
						optimizer.zero_grad()
				rands.append(random.random()       )  # Introduce some randomness
				if scheduler is not None:
						scheduler.step()
		return rands




class 			lowercase__			(      nn.Module    ):
							'''simple docstring'''







							def __init__(  self						)			->							int:
									super().__init__()
									_UpperCAmelCase							 =		nn.Parameter(torch.randn(1						)						)
									_UpperCAmelCase							 =		nn.Parameter(torch.randn(1						)						)






							def        lowerCamelCase_   (  self	,       snake_case						)			->							Union[str, Any]:
									return x * self.a + self.b





class 			lowercase__			(      unittest.TestCase    ):
							'''simple docstring'''







							def        lowerCamelCase_   (  self						)			->							Any:







									with tempfile.TemporaryDirectory() as tmpdir:
											set_seed(42						)
											_UpperCAmelCase							 =		DummyModel()
											_UpperCAmelCase							 =		torch.optim.Adam(params=model.parameters()	,       lr=1E-3						)
											_UpperCAmelCase							 =		dummy_dataloaders()
											_UpperCAmelCase							 =		ProjectConfiguration(total_limit=1	,       project_dir=lowerCamelCase__	,       automatic_checkpoint_naming=lowerCamelCase__						)
											# Train baseline
											_UpperCAmelCase							 =		Accelerator(project_config=lowerCamelCase__						)
											_UpperCAmelCase							 =		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        lowerCamelCase_   (  self						)			->							Tuple:







									with tempfile.TemporaryDirectory() as tmpdir:
											set_seed(42						)
											_UpperCAmelCase							 =		DummyModel()
											_UpperCAmelCase							 =		torch.optim.Adam(params=model.parameters()	,       lr=1E-3						)
											_UpperCAmelCase							 =		dummy_dataloaders()
											# Train baseline
											_UpperCAmelCase							 =		Accelerator()
											_UpperCAmelCase							 =		accelerator.prepare(
											    lowerCamelCase__	,       lowerCamelCase__	,       lowerCamelCase__	,       lowerCamelCase__						)
											# Save initial
											_UpperCAmelCase							 =		os.path.join(lowerCamelCase__	,       'initial'						)
											accelerator.save_state(lowerCamelCase__						)
											(_UpperCAmelCase)							 =		model.a.item(), model.b.item()
											_UpperCAmelCase							 =		optimizer.state_dict()
											_UpperCAmelCase							 =		train(3	,       lowerCamelCase__	,       lowerCamelCase__	,       lowerCamelCase__	,       lowerCamelCase__						)
											(_UpperCAmelCase)							 =		model.a.item(), model.b.item()
											_UpperCAmelCase							 =		optimizer.state_dict()

											# Train partially
											set_seed(42						)
											_UpperCAmelCase							 =		DummyModel()
											_UpperCAmelCase							 =		torch.optim.Adam(params=model.parameters()	,       lr=1E-3						)
											_UpperCAmelCase							 =		dummy_dataloaders()
											_UpperCAmelCase							 =		Accelerator()
											_UpperCAmelCase							 =		accelerator.prepare(
											    lowerCamelCase__	,       lowerCamelCase__	,       lowerCamelCase__	,       lowerCamelCase__						)
											accelerator.load_state(lowerCamelCase__						)
											(_UpperCAmelCase)							 =		model.a.item(), model.b.item()
											_UpperCAmelCase							 =		optimizer.state_dict()
											self.assertEqual(lowerCamelCase__	,       lowerCamelCase__						)
											self.assertEqual(lowerCamelCase__	,       lowerCamelCase__						)
											self.assertEqual(lowerCamelCase__	,       lowerCamelCase__						)

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






							def        lowerCamelCase_   (  self						)			->							Any:







									with tempfile.TemporaryDirectory() as tmpdir:
											set_seed(42						)
											_UpperCAmelCase							 =		DummyModel()
											_UpperCAmelCase							 =		torch.optim.Adam(params=model.parameters()	,       lr=1E-3						)
											_UpperCAmelCase							 =		dummy_dataloaders()
											_UpperCAmelCase							 =		ProjectConfiguration(automatic_checkpoint_naming=lowerCamelCase__						)

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

											# Train partially
											set_seed(42						)
											_UpperCAmelCase							 =		DummyModel()
											_UpperCAmelCase							 =		torch.optim.Adam(params=model.parameters()	,       lr=1E-3						)
											_UpperCAmelCase							 =		dummy_dataloaders()
											_UpperCAmelCase							 =		ProjectConfiguration(iteration=1	,       automatic_checkpoint_naming=lowerCamelCase__						)
											_UpperCAmelCase							 =		Accelerator(project_dir=lowerCamelCase__	,       project_config=lowerCamelCase__						)
											_UpperCAmelCase							 =		accelerator.prepare(
											    lowerCamelCase__	,       lowerCamelCase__	,       lowerCamelCase__	,       lowerCamelCase__						)
											accelerator.load_state(os.path.join(lowerCamelCase__	,       'checkpoints'	,       'checkpoint_0'						)						)
											(_UpperCAmelCase)							 =		model.a.item(), model.b.item()
											_UpperCAmelCase							 =		optimizer.state_dict()
											self.assertEqual(lowerCamelCase__	,       lowerCamelCase__						)
											self.assertEqual(lowerCamelCase__	,       lowerCamelCase__						)
											self.assertEqual(lowerCamelCase__	,       lowerCamelCase__						)

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






							def        lowerCamelCase_   (  self						)			->							str:
									_UpperCAmelCase							 =		torch.tensor([1, 2, 3]						)
									_UpperCAmelCase							 =		torch.tensor([2, 3, 4]						)
									_UpperCAmelCase							 =		DummyModel()
									_UpperCAmelCase							 =		torch.optim.Adam(net.parameters()						)
									_UpperCAmelCase							 =		Accelerator()
									with self.assertRaises(lowerCamelCase__						) as ve:
											accelerator.register_for_checkpointing(lowerCamelCase__	,       lowerCamelCase__	,       lowerCamelCase__	,       lowerCamelCase__						)
									_UpperCAmelCase							 =		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        lowerCamelCase_   (  self						)			->							Optional[int]:







									with tempfile.TemporaryDirectory() as tmpdir:
											set_seed(42						)
											_UpperCAmelCase							 =		DummyModel()
											_UpperCAmelCase							 =		torch.optim.Adam(params=model.parameters()	,       lr=1E-3						)
											_UpperCAmelCase							 =		torch.optim.lr_scheduler.StepLR(lowerCamelCase__	,       step_size=1	,       gamma=0.99						)
											_UpperCAmelCase							 =		dummy_dataloaders()
											_UpperCAmelCase							 =		ProjectConfiguration(automatic_checkpoint_naming=lowerCamelCase__						)
											# Train baseline
											_UpperCAmelCase							 =		Accelerator(project_dir=lowerCamelCase__	,       project_config=lowerCamelCase__						)
											_UpperCAmelCase							 =		accelerator.prepare(
											    lowerCamelCase__	,       lowerCamelCase__	,       lowerCamelCase__	,       lowerCamelCase__	,       lowerCamelCase__						)
											# Save initial
											accelerator.save_state()
											_UpperCAmelCase							 =		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        lowerCamelCase_   (  self						)			->							Optional[Any]:







									with tempfile.TemporaryDirectory() as tmpdir:
											set_seed(42						)
											_UpperCAmelCase							 =		DummyModel()
											_UpperCAmelCase							 =		ProjectConfiguration(automatic_checkpoint_naming=lowerCamelCase__	,       total_limit=2						)
											# Train baseline
											_UpperCAmelCase							 =		Accelerator(project_dir=lowerCamelCase__	,       project_config=lowerCamelCase__						)
											_UpperCAmelCase							 =		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        lowerCamelCase_   (  self						)			->							Union[str, Any]:
									_UpperCAmelCase							 =		["torchrun", f'--nproc_per_node={torch.cuda.device_count()}', inspect.getfile(self.__class__						)]
									execute_subprocess_async(lowerCamelCase__	,       env=os.environ.copy()						)


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





import os

lowercase  							=					{'''I''': 1, '''V''': 5, '''X''': 10, '''L''': 50, '''C''': 1_00, '''D''': 5_00, '''M''': 10_00}




def 	UpperCAmelCase     (		A	:						str       ):

		'''simple docstring'''


		_UpperCAmelCase							 =		0

		_UpperCAmelCase							 =		0
		while index < len(A       ) - 1:
				_UpperCAmelCase							 =		SYMBOLS[numerals[index]]
				_UpperCAmelCase							 =		SYMBOLS[numerals[index + 1]]
				if current_value < next_value:
						total_value -= current_value
				else:
						total_value += current_value
				index += 1
		total_value += SYMBOLS[numerals[index]]

		return total_value




def 	UpperCAmelCase     (		A	:						int       ):

		'''simple docstring'''


		_UpperCAmelCase							 =		''

		_UpperCAmelCase							 =		num // 1000
		numerals += m_count * "M"
		num %= 1000

		_UpperCAmelCase							 =		num // 100
		if c_count == 9:
				numerals += "CM"
				c_count -= 9
		elif c_count == 4:
				numerals += "CD"
				c_count -= 4
		if c_count >= 5:
				numerals += "D"
				c_count -= 5
		numerals += c_count * "C"
		num %= 100

		_UpperCAmelCase							 =		num // 10
		if x_count == 9:
				numerals += "XC"
				x_count -= 9
		elif x_count == 4:
				numerals += "XL"
				x_count -= 4
		if x_count >= 5:
				numerals += "L"
				x_count -= 5
		numerals += x_count * "X"
		num %= 10

		if num == 9:
				numerals += "IX"
				num -= 9
		elif num == 4:
				numerals += "IV"
				num -= 4
		if num >= 5:
				numerals += "V"
				num -= 5
		numerals += num * "I"

		return numerals




def 	UpperCAmelCase     (		A	:						str = "/p089_roman.txt"       ):

		'''simple docstring'''


		_UpperCAmelCase							 =		0

		with open(os.path.dirname(A       ) + roman_numerals_filename       ) as filea:
				_UpperCAmelCase							 =		filea.readlines()

		for line in lines:
				_UpperCAmelCase							 =		line.strip()
				_UpperCAmelCase							 =		parse_roman_numerals(A       )
				_UpperCAmelCase							 =		generate_roman_numerals(A       )
				savings += len(A       ) - len(A       )

		return savings


if __name__ == "__main__":
					print(F'''{solution() = }''')


