






'''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


_A     :      List[Any]   					=logging.getLogger(__name__)





def 			SCREAMING_SNAKE_CASE_	(UpperCamelCase=2    ,      UpperCamelCase=3    ,      UpperCamelCase=16    ,      UpperCamelCase = 10    ,      UpperCamelCase = 2				)				->	Tuple:

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

							lowerCamelCase__ :     Optional[int]	  =	get_dataset(UpperCamelCase				)
							lowerCamelCase__ :     Optional[int]	  =	get_dataset(UpperCamelCase				)
							lowerCamelCase__ :     Tuple	  =	DataLoader(UpperCamelCase    ,      shuffle=UpperCamelCase    ,      batch_size=UpperCamelCase    ,      num_workers=4				)
							lowerCamelCase__ :     Tuple	  =	DataLoader(UpperCamelCase    ,      shuffle=UpperCamelCase    ,      batch_size=UpperCamelCase    ,      num_workers=4				)
							return (train_dataloader, valid_dataloader)





def 			SCREAMING_SNAKE_CASE_	(UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase=None				)				->	Tuple:
							lowerCamelCase__ :     List[str]	  =	[]
							for epoch in range(UpperCamelCase				):
														# Train quickly
														model.train()
														for batch in dataloader:
																					lowerCamelCase__  ,		lowerCamelCase__ :     Dict	  =	batch
																					lowerCamelCase__ :     Optional[int]	  =	model(UpperCamelCase				)
																					lowerCamelCase__ :     Optional[Any]	  =	torch.nn.functional.mse_loss(UpperCamelCase    ,      UpperCamelCase				)
																					accelerator.backward(UpperCamelCase				)
																					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		):




							def __init__(							self:    List[str]					):
														super().__init__()
														lowerCamelCase__ :     List[Any]	  =	nn.Parameter(torch.randn(1					)					)
														lowerCamelCase__ :     int	  =	nn.Parameter(torch.randn(1					)					)




							def 		lowerCamelCase_    (							self:    Tuple      ,							UpperCamelCase__:    Optional[int]					):
														return x * self.a + self.b



class _lowercase     (    unittest.TestCase		):




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

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




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

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

																					lowerCamelCase__ :     Optional[int]	  =	train(2      ,							UpperCamelCase__      ,							UpperCamelCase__      ,							UpperCamelCase__      ,							UpperCamelCase__					)
																					# Save everything
																					lowerCamelCase__ :     str	  =	os.path.join(UpperCamelCase__      ,							"""checkpoint"""					)
																					accelerator.save_state(UpperCamelCase__					)

																					# Load everything back in and make sure all states work
																					accelerator.load_state(UpperCamelCase__					)
																					test_rands += train(1      ,							UpperCamelCase__      ,							UpperCamelCase__      ,							UpperCamelCase__      ,							UpperCamelCase__					)
																					((lowerCamelCase__)  ,		(lowerCamelCase__)) :     Optional[int]	  =	model.a.item(), model.b.item()
																					lowerCamelCase__ :     Tuple	  =	optimizer.state_dict()
																					self.assertEqual(UpperCamelCase__      ,							UpperCamelCase__					)
																					self.assertEqual(UpperCamelCase__      ,							UpperCamelCase__					)
																					self.assertEqual(UpperCamelCase__      ,							UpperCamelCase__					)
																					self.assertEqual(UpperCamelCase__      ,							UpperCamelCase__					)




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

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

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

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

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




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

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




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




							@require_cuda
							def 		lowerCamelCase_    (							self:    List[Any]					):
														lowerCamelCase__ :     Optional[Any]	  =	["""torchrun""", F'''--nproc_per_node={torch.cuda.device_count()}''', inspect.getfile(self.__class__					)]
														execute_subprocess_async(UpperCamelCase__      ,							env=os.environ.copy()					)


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







def 			SCREAMING_SNAKE_CASE_	(UpperCamelCase				)				->	list:

							# bit count represents no. of bits in the gray code
							if bit_count < 0:
														raise ValueError("""The given input must be positive"""				)

							# get the generated string sequence
							lowerCamelCase__ :     List[str]	  =	gray_code_sequence_string(UpperCamelCase				)
							#
							# convert them to integers
							for i in range(len(UpperCamelCase				)				):
														lowerCamelCase__ :     int	  =	int(sequence[i]    ,      2				)

							return sequence





def 			SCREAMING_SNAKE_CASE_	(UpperCamelCase				)				->	list:

							# The approach is a recursive one
							# Base case achieved when either n = 0 or n=1
							if bit_count == 0:
														return ["0"]

							if bit_count == 1:
														return ["0", "1"]

							lowerCamelCase__ :     Optional[Any]	  =	1 << bit_count  # defines the length of the sequence
							# 1<< n is equivalent to 2^n

							# recursive answer will generate answer for n-1 bits
							lowerCamelCase__ :     Any	  =	gray_code_sequence_string(bit_count - 1				)

							lowerCamelCase__ :     Union[str, Any]	  =	[]

							# append 0 to first half of the smaller sequence generated
							for i in range(seq_len // 2				):
														lowerCamelCase__ :     Optional[int]	  =	"""0""" + smaller_sequence[i]
														sequence.append(UpperCamelCase				)

							# append 1 to second half ... start from the end of the list
							for i in reversed(range(seq_len // 2				)				):
														lowerCamelCase__ :     int	  =	"""1""" + smaller_sequence[i]
														sequence.append(UpperCamelCase				)

							return sequence


if __name__ == "__main__":
			import doctest

			doctest.testmod()

