

from __future__ import annotations

def 		UpperCamelCase      (lowercase_:				List[Any]					,   lowercase_:				Any      )  ->   str:






	if len(lowercase_      ) <= 1 or n <= 1:
		return

	insert_next(lowercase_					,   n - 1      )
	rec_insertion_sort(lowercase_					,   n - 1      )

def 		UpperCamelCase      (lowercase_:				Union[str, Any]					,   lowercase_:				Any      )  ->   List[Any]:






	if index >= len(lowercase_      ) or collection[index - 1] <= collection[index]:
		return

	# Swaps adjacent elements since they are not in ascending order
	A__							:					Dict       =	(
	    collection[index],
	    collection[index - 1],
	)

	insert_next(lowercase_					,   index + 1      )


if __name__ == "__main__":
	A_ :				Union[str, Any]						 =    input('Enter integers separated by spaces: ')
	A_ :				list[int]						 =    [int(num) for num in numbers.split()]
	rec_insertion_sort(number_list, len(number_list))
	print(number_list)






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


A_ :				Any						 =    logging.getLogger(__name__)

def 		UpperCamelCase      (lowercase_:				Optional[Any]=2					,   lowercase_:				Union[str, Any]=3					,   lowercase_:				int=16					,   lowercase_:				int = 10					,   lowercase_:				int = 2      )  ->   int:

	def get_dataset(lowercase_:				Optional[int]      ):
		A__							:					Optional[Any]       =	torch.randn(batch_size * n_batches					,   1      )
		return TensorDataset(lowercase_					,   a * x + b + 0.1 * torch.randn(batch_size * n_batches					,   1      )      )

	A__							:					Dict       =	get_dataset(lowercase_      )
	A__							:					Any       =	get_dataset(lowercase_      )
	A__							:					Dict       =	DataLoader(lowercase_					,   shuffle=lowercase_					,   batch_size=lowercase_					,   num_workers=4      )
	A__							:					Optional[Any]       =	DataLoader(lowercase_					,   shuffle=lowercase_					,   batch_size=lowercase_					,   num_workers=4      )
	return (train_dataloader, valid_dataloader)

def 		UpperCamelCase      (lowercase_:				Optional[Any]					,   lowercase_:				List[str]					,   lowercase_:				int					,   lowercase_:				int					,   lowercase_:				List[str]					,   lowercase_:				Dict=None      )  ->   List[Any]:
	A__							:					List[Any]       =	[]
	for epoch in range(lowercase_      ):
		# Train quickly
		model.train()
		for batch in dataloader:
			A__     ,     A__							:					Any       =	batch
			A__							:					Any       =	model(lowercase_      )
			A__							:					Any       =	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 					_a		(nn.Module       ):


					'''simple docstring'''






					def __init__(				self    ):
						super().__init__()
						A__							:					str       =	nn.Parameter(torch.randn(1    )    )
						A__							:					Any       =	nn.Parameter(torch.randn(1    )    )






					def 						__A    (				self					, A__    ):
						return x * self.a + self.b



class 					_a		(unittest.TestCase       ):


					'''simple docstring'''






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

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

							A__							:					List[str]       =	train(2					, A__					, A__					, A__					, A__    )
							# Save everything
							A__							:					Optional[int]       =	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__    )
							((A__)     ,     (A__))							:					Union[str, Any]       =	model.a.item(), model.b.item()
							A__							:					Optional[int]       =	optimizer.state_dict()
							self.assertEqual(A__					, A__    )
							self.assertEqual(A__					, A__    )
							self.assertEqual(A__					, A__    )
							self.assertEqual(A__					, A__    )






					def 						__A    (				self    ):
						with tempfile.TemporaryDirectory() as tmpdir:
							set_seed(42    )
							A__							:					int       =	DummyModel()
							A__							:					Optional[int]       =	torch.optim.Adam(params=model.parameters()					, lr=1e-3    )
							A__     ,     A__							:					List[str]       =	dummy_dataloaders()
							A__							:					str       =	ProjectConfiguration(automatic_checkpoint_naming=A__    )

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

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

							A__							:					str       =	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__    )
							((A__)     ,     (A__))							:					Optional[int]       =	model.a.item(), model.b.item()
							A__							:					List[Any]       =	optimizer.state_dict()
							self.assertEqual(A__					, A__    )
							self.assertEqual(A__					, A__    )
							self.assertEqual(A__					, A__    )
							self.assertEqual(A__					, A__    )






					def 						__A    (				self    ):
						A__							:					Union[str, Any]       =	torch.tensor([1, 2, 3]    )
						A__							:					int       =	torch.tensor([2, 3, 4]    )
						A__							:					List[Any]       =	DummyModel()
						A__							:					List[Any]       =	torch.optim.Adam(net.parameters()    )
						A__							:					Tuple       =	Accelerator()
						with self.assertRaises(A__    ) as ve:
							accelerator.register_for_checkpointing(A__					, A__					, A__					, A__    )
						A__							:					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    ):
						with tempfile.TemporaryDirectory() as tmpdir:
							set_seed(42    )
							A__							:					Any       =	DummyModel()
							A__							:					Union[str, Any]       =	torch.optim.Adam(params=model.parameters()					, lr=1e-3    )
							A__							:					Dict       =	torch.optim.lr_scheduler.StepLR(A__					, step_size=1					, gamma=0.9_9    )
							A__     ,     A__							:					List[Any]       =	dummy_dataloaders()
							A__							:					Tuple       =	ProjectConfiguration(automatic_checkpoint_naming=A__    )
							# Train baseline
							A__							:					Optional[Any]       =	Accelerator(project_dir=A__					, project_config=A__    )
							A__     ,     A__     ,     A__     ,     A__     ,     A__							:					Union[str, Any]       =	accelerator.prepare(
							    A__					, A__					, A__					, A__					, A__    )
							# Save initial
							accelerator.save_state()
							A__							:					Tuple       =	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 						__A    (				self    ):
						with tempfile.TemporaryDirectory() as tmpdir:
							set_seed(42    )
							A__							:					Optional[Any]       =	DummyModel()
							A__							:					int       =	ProjectConfiguration(automatic_checkpoint_naming=A__					, total_limit=2    )
							# Train baseline
							A__							:					List[str]       =	Accelerator(project_dir=A__					, project_config=A__    )
							A__							:					Union[str, Any]       =	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 						__A    (				self    ):
						A__							:					Dict       =	["""torchrun""", F"""--nproc_per_node={torch.cuda.device_count()}""", inspect.getfile(self.__class__    )]
						execute_subprocess_async(A__					, env=os.environ.copy()    )


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



