

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									=  logging.getLogger(__name__)





def a(lowercase__=2      ,			lowercase__=3      ,			lowercase__=16      ,			lowercase__ = 10      ,			lowercase__ = 2						):





	'''simple docstring'''

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

	snake_case_   		=			get_dataset(lowercase__						)
	snake_case_   		=			get_dataset(lowercase__						)
	snake_case_   		=			DataLoader(lowercase__      ,			shuffle=lowercase__      ,			batch_size=lowercase__      ,			num_workers=4						)
	snake_case_   		=			DataLoader(lowercase__      ,			shuffle=lowercase__      ,			batch_size=lowercase__      ,			num_workers=4						)
	return (train_dataloader, valid_dataloader)





def a(lowercase__      ,			lowercase__      ,			lowercase__      ,			lowercase__      ,			lowercase__      ,			lowercase__=None						):





	'''simple docstring'''
	snake_case_   		=			[]
	for epoch in range(lowercase__						):
		# Train quickly
		model.train()
		for batch in dataloader:
			snake_case_		,							snake_case_   		=			batch
			snake_case_   		=			model(lowercase__						)
			snake_case_   		=			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 SCREAMING_SNAKE_CASE	(		nn.Module				):






			"""simple docstring"""




			def __init__(     self				):

				"""simple docstring"""


				super().__init__()
				snake_case_   		=			nn.Parameter(torch.randn(1				)				)
				snake_case_   		=			nn.Parameter(torch.randn(1				)				)







			def       __lowerCAmelCase			(     self			,					__UpperCamelCase				):

				"""simple docstring"""


				return x * self.a + self.b







class SCREAMING_SNAKE_CASE	(		unittest.TestCase				):






			"""simple docstring"""




			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


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

				"""simple docstring"""


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

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

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


			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


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

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

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

					snake_case_   		=			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				)
					((snake_case_)		,							(snake_case_))   		=			model.a.item(), model.b.item()
					snake_case_   		=			optimizer.state_dict()
					self.assertEqual(__UpperCamelCase			,					__UpperCamelCase				)
					self.assertEqual(__UpperCamelCase			,					__UpperCamelCase				)
					self.assertEqual(__UpperCamelCase			,					__UpperCamelCase				)
					self.assertEqual(__UpperCamelCase			,					__UpperCamelCase				)


			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				snake_case_   		=			torch.tensor([1, 2, 3]				)
				snake_case_   		=			torch.tensor([2, 3, 4]				)
				snake_case_   		=			DummyModel()
				snake_case_   		=			torch.optim.Adam(net.parameters()				)
				snake_case_   		=			Accelerator()
				with self.assertRaises(__UpperCamelCase				) as ve:
					accelerator.register_for_checkpointing(__UpperCamelCase			,					__UpperCamelCase			,					__UpperCamelCase			,					__UpperCamelCase				)
				snake_case_   		=			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				):

				"""simple docstring"""


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

				"""simple docstring"""


				with tempfile.TemporaryDirectory() as tmpdir:
					set_seed(42				)
					snake_case_   		=			DummyModel()
					snake_case_   		=			ProjectConfiguration(automatic_checkpoint_naming=__UpperCamelCase			,					total_limit=2				)
					# Train baseline
					snake_case_   		=			Accelerator(project_dir=__UpperCamelCase			,					project_config=__UpperCamelCase				)
					snake_case_   		=			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				):

				"""simple docstring"""


				snake_case_   		=			['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									=  '/tmp/accelerate/state_checkpointing'
		A									=  DummyModel()
		A									=  torch.optim.Adam(params=model.parameters(), lr=1e-3)
		A									=  torch.optim.lr_scheduler.StepLR(optimizer, step_size=1, gamma=0.9_9)
		A				,	A									=  dummy_dataloaders()
		A									=  ProjectConfiguration(automatic_checkpoint_naming=True)
		# Train baseline
		A									=  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									=  accelerator.prepare(
		    model, optimizer, train_dataloader, valid_dataloader, scheduler
		)
		A				,	A									=  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									=  group['params'][0].device
				break
		assert param_device.type == accelerator.device.type
		A									=  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									=  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									=  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()



A									=  [
    'Audio',
    'Array2D',
    'Array3D',
    'Array4D',
    'Array5D',
    'ClassLabel',
    'Features',
    'Sequence',
    'Value',
    'Image',
    'Translation',
    'TranslationVariableLanguages',
]
from .audio import Audio
from .features import ArrayaD, ArrayaD, ArrayaD, ArrayaD, ClassLabel, Features, Sequence, Value
from .image import Image
from .translation import Translation, TranslationVariableLanguages
