




"""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      =							logging.getLogger(__name__)





def 							lowercase					(snake_case__   : Tuple=2      ,       snake_case__   : str=3      ,       snake_case__   : Union[str, Any]=16      ,       snake_case__   : int = 10      ,       snake_case__   : int = 2 )     ->  Any:


				'''simple docstring'''






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

				lowerCAmelCase  =				get_dataset(snake_case__ )
				lowerCAmelCase  =				get_dataset(snake_case__ )
				lowerCAmelCase  =				DataLoader(snake_case__      ,       shuffle=snake_case__      ,       batch_size=snake_case__      ,       num_workers=4 )
				lowerCAmelCase  =				DataLoader(snake_case__      ,       shuffle=snake_case__      ,       batch_size=snake_case__      ,       num_workers=4 )
				return (train_dataloader, valid_dataloader)





def 							lowercase					(snake_case__   : Optional[Any]      ,       snake_case__   : Tuple      ,       snake_case__   : Tuple      ,       snake_case__   : Any      ,       snake_case__   : List[Any]      ,       snake_case__   : Union[str, Any]=None )     ->  List[str]:


				'''simple docstring'''






				lowerCAmelCase  =				[]
				for epoch in range(snake_case__ ):
								# Train quickly
								model.train()
								for batch in dataloader:
												lowerCAmelCase ,						lowerCAmelCase  =				batch
												lowerCAmelCase  =				model(snake_case__ )
												lowerCAmelCase  =				torch.nn.functional.mse_loss(snake_case__      ,       snake_case__ )
												accelerator.backward(snake_case__ )
												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					):


						def __init__(							self				:			Dict					):
										super().__init__()
										lowerCAmelCase  =				nn.Parameter(torch.randn(1					)					)
										lowerCAmelCase  =				nn.Parameter(torch.randn(1					)					)




						def   __lowercase (							self				:			Optional[Any]						,						lowerCAmelCase				:			List[Any]					):
										return x * self.a + self.b






class       SCREAMING_SNAKE_CASE__					(					unittest.TestCase					):


						def   __lowercase (							self				:			Union[str, Any]					):
										with tempfile.TemporaryDirectory() as tmpdir:
														set_seed(42					)
														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   __lowercase (							self				:			Optional[Any]					):
										with tempfile.TemporaryDirectory() as tmpdir:
														set_seed(42					)
														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(42					)
														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   __lowercase (							self				:			str					):
										with tempfile.TemporaryDirectory() as tmpdir:
														set_seed(42					)
														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(42					)
														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   __lowercase (							self				:			Union[str, Any]					):
										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   __lowercase (							self				:			str					):
										with tempfile.TemporaryDirectory() as tmpdir:
														set_seed(42					)
														lowerCAmelCase  =				DummyModel()
														lowerCAmelCase  =				torch.optim.Adam(params=model.parameters()						,						lr=1e-3					)
														lowerCAmelCase  =				torch.optim.lr_scheduler.StepLR(lowerCAmelCase						,						step_size=1						,						gamma=0.99					)
														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   __lowercase (							self				:			Optional[Any]					):
										with tempfile.TemporaryDirectory() as tmpdir:
														set_seed(42					)
														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(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   __lowercase (							self				:			Union[str, Any]					):
										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__":
		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=0.99)
		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()











"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


a      =							{
    'configuration_biogpt': ['BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'BioGptConfig'],
    'tokenization_biogpt': ['BioGptTokenizer'],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		a      =							[
		    'BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'BioGptForCausalLM',
		    'BioGptForTokenClassification',
		    'BioGptForSequenceClassification',
		    'BioGptModel',
		    'BioGptPreTrainedModel',
		]


if TYPE_CHECKING:
		from .configuration_biogpt import BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, BioGptConfig
		from .tokenization_biogpt import BioGptTokenizer

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_biogpt import (
				    BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    BioGptForCausalLM,
				    BioGptForSequenceClassification,
				    BioGptForTokenClassification,
				    BioGptModel,
				    BioGptPreTrainedModel,
				)


else:
		import sys

		a      =							_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





