





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


snake_case_							=			logging.getLogger(__name__)


def 					A__	(       SCREAMING_SNAKE_CASE_=2							,       SCREAMING_SNAKE_CASE_=3							,       SCREAMING_SNAKE_CASE_=1_6							,       SCREAMING_SNAKE_CASE_ = 1_0							,       SCREAMING_SNAKE_CASE_ = 2		)   ->     Any:

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

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


def 					A__	(       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_=None		)   ->     List[Any]:
		lowerCamelCase   : int								=[]
		for epoch in range(SCREAMING_SNAKE_CASE_		):
				# Train quickly
				model.train()
				for batch in dataloader:
						lowerCamelCase   : Any								=batch
						lowerCamelCase   : Dict								=model(SCREAMING_SNAKE_CASE_		)
						lowerCamelCase   : Optional[Any]								=torch.nn.functional.mse_loss(SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_		)
						accelerator.backward(SCREAMING_SNAKE_CASE_		)
						optimizer.step()
						optimizer.zero_grad()
				rands.append(random.random()		)  # Introduce some randomness
				if scheduler is not None:
						scheduler.step()
		return rands
class        snake_case_		(			nn.Module):







		def __init__(		self   )				->   Union[str, Any]:
				super().__init__()
				lowerCamelCase   : List[Any]								=nn.Parameter(torch.randn(1   )   )
				lowerCamelCase   : Tuple								=nn.Parameter(torch.randn(1   )   )







		def 			__lowercase   (		self			,     __lowercase   )				->   Tuple:
				return x * self.a + self.b

class        snake_case_		(			unittest.TestCase):







		def 			__lowercase   (		self   )				->   Union[str, Any]:




				with tempfile.TemporaryDirectory() as tmpdir:
						set_seed(4_2   )
						lowerCamelCase   : List[Any]								=DummyModel()
						lowerCamelCase   : List[Any]								=torch.optim.Adam(params=model.parameters()			,     lr=1e-3   )
						lowerCamelCase   : Tuple								=dummy_dataloaders()
						lowerCamelCase   : Union[str, Any]								=ProjectConfiguration(total_limit=1			,     project_dir=__lowercase			,     automatic_checkpoint_naming=__lowercase   )
						# Train baseline
						lowerCamelCase   : Dict								=Accelerator(project_config=__lowercase   )
						lowerCamelCase   : Dict								=accelerator.prepare(
						    __lowercase			,     __lowercase			,     __lowercase			,     __lowercase   )
						# Save initial
						accelerator.save_state()

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







		def 			__lowercase   (		self   )				->   Union[str, Any]:




				with tempfile.TemporaryDirectory() as tmpdir:
						set_seed(4_2   )
						lowerCamelCase   : str								=DummyModel()
						lowerCamelCase   : List[str]								=torch.optim.Adam(params=model.parameters()			,     lr=1e-3   )
						lowerCamelCase   : str								=dummy_dataloaders()
						# Train baseline
						lowerCamelCase   : Dict								=Accelerator()
						lowerCamelCase   : Any								=accelerator.prepare(
						    __lowercase			,     __lowercase			,     __lowercase			,     __lowercase   )
						# Save initial
						lowerCamelCase   : Union[str, Any]								=os.path.join(__lowercase			,     '''initial'''   )
						accelerator.save_state(__lowercase   )
						(lowerCamelCase)   : Tuple								=model.a.item(), model.b.item()
						lowerCamelCase   : int								=optimizer.state_dict()
						lowerCamelCase   : Optional[Any]								=train(3			,     __lowercase			,     __lowercase			,     __lowercase			,     __lowercase   )
						(lowerCamelCase)   : int								=model.a.item(), model.b.item()
						lowerCamelCase   : str								=optimizer.state_dict()

						# Train partially
						set_seed(4_2   )
						lowerCamelCase   : Tuple								=DummyModel()
						lowerCamelCase   : Tuple								=torch.optim.Adam(params=model.parameters()			,     lr=1e-3   )
						lowerCamelCase   : Any								=dummy_dataloaders()
						lowerCamelCase   : List[Any]								=Accelerator()
						lowerCamelCase   : List[Any]								=accelerator.prepare(
						    __lowercase			,     __lowercase			,     __lowercase			,     __lowercase   )
						accelerator.load_state(__lowercase   )
						(lowerCamelCase)   : int								=model.a.item(), model.b.item()
						lowerCamelCase   : Union[str, Any]								=optimizer.state_dict()
						self.assertEqual(__lowercase			,     __lowercase   )
						self.assertEqual(__lowercase			,     __lowercase   )
						self.assertEqual(__lowercase			,     __lowercase   )

						lowerCamelCase   : Tuple								=train(2			,     __lowercase			,     __lowercase			,     __lowercase			,     __lowercase   )
						# Save everything
						lowerCamelCase   : Any								=os.path.join(__lowercase			,     '''checkpoint'''   )
						accelerator.save_state(__lowercase   )

						# Load everything back in and make sure all states work
						accelerator.load_state(__lowercase   )
						test_rands += train(1			,     __lowercase			,     __lowercase			,     __lowercase			,     __lowercase   )
						(lowerCamelCase)   : str								=model.a.item(), model.b.item()
						lowerCamelCase   : List[str]								=optimizer.state_dict()
						self.assertEqual(__lowercase			,     __lowercase   )
						self.assertEqual(__lowercase			,     __lowercase   )
						self.assertEqual(__lowercase			,     __lowercase   )
						self.assertEqual(__lowercase			,     __lowercase   )







		def 			__lowercase   (		self   )				->   Optional[Any]:




				with tempfile.TemporaryDirectory() as tmpdir:
						set_seed(4_2   )
						lowerCamelCase   : Optional[int]								=DummyModel()
						lowerCamelCase   : Any								=torch.optim.Adam(params=model.parameters()			,     lr=1e-3   )
						lowerCamelCase   : str								=dummy_dataloaders()
						lowerCamelCase   : Optional[Any]								=ProjectConfiguration(automatic_checkpoint_naming=__lowercase   )

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

						# Train partially
						set_seed(4_2   )
						lowerCamelCase   : Union[str, Any]								=DummyModel()
						lowerCamelCase   : Any								=torch.optim.Adam(params=model.parameters()			,     lr=1e-3   )
						lowerCamelCase   : Dict								=dummy_dataloaders()
						lowerCamelCase   : List[Any]								=ProjectConfiguration(iteration=1			,     automatic_checkpoint_naming=__lowercase   )
						lowerCamelCase   : Dict								=Accelerator(project_dir=__lowercase			,     project_config=__lowercase   )
						lowerCamelCase   : Dict								=accelerator.prepare(
						    __lowercase			,     __lowercase			,     __lowercase			,     __lowercase   )
						accelerator.load_state(os.path.join(__lowercase			,     '''checkpoints'''			,     '''checkpoint_0'''   )   )
						(lowerCamelCase)   : Union[str, Any]								=model.a.item(), model.b.item()
						lowerCamelCase   : Union[str, Any]								=optimizer.state_dict()
						self.assertEqual(__lowercase			,     __lowercase   )
						self.assertEqual(__lowercase			,     __lowercase   )
						self.assertEqual(__lowercase			,     __lowercase   )

						lowerCamelCase   : Optional[int]								=train(2			,     __lowercase			,     __lowercase			,     __lowercase			,     __lowercase   )
						# Save everything
						accelerator.save_state()

						# Load everything back in and make sure all states work
						accelerator.load_state(os.path.join(__lowercase			,     '''checkpoints'''			,     '''checkpoint_1'''   )   )
						test_rands += train(1			,     __lowercase			,     __lowercase			,     __lowercase			,     __lowercase   )
						(lowerCamelCase)   : Optional[Any]								=model.a.item(), model.b.item()
						lowerCamelCase   : int								=optimizer.state_dict()
						self.assertEqual(__lowercase			,     __lowercase   )
						self.assertEqual(__lowercase			,     __lowercase   )
						self.assertEqual(__lowercase			,     __lowercase   )
						self.assertEqual(__lowercase			,     __lowercase   )







		def 			__lowercase   (		self   )				->   Optional[int]:
				lowerCamelCase   : Optional[int]								=torch.tensor([1, 2, 3]   )
				lowerCamelCase   : Tuple								=torch.tensor([2, 3, 4]   )
				lowerCamelCase   : int								=DummyModel()
				lowerCamelCase   : Union[str, Any]								=torch.optim.Adam(net.parameters()   )
				lowerCamelCase   : Optional[Any]								=Accelerator()
				with self.assertRaises(__lowercase   ) as ve:
						accelerator.register_for_checkpointing(__lowercase			,     __lowercase			,     __lowercase			,     __lowercase   )
				lowerCamelCase   : Dict								=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   )				->   int:




				with tempfile.TemporaryDirectory() as tmpdir:
						set_seed(4_2   )
						lowerCamelCase   : List[str]								=DummyModel()
						lowerCamelCase   : Optional[Any]								=torch.optim.Adam(params=model.parameters()			,     lr=1e-3   )
						lowerCamelCase   : int								=torch.optim.lr_scheduler.StepLR(__lowercase			,     step_size=1			,     gamma=0.9_9   )
						lowerCamelCase   : Optional[int]								=dummy_dataloaders()
						lowerCamelCase   : Tuple								=ProjectConfiguration(automatic_checkpoint_naming=__lowercase   )
						# Train baseline
						lowerCamelCase   : Dict								=Accelerator(project_dir=__lowercase			,     project_config=__lowercase   )
						lowerCamelCase   : int								=accelerator.prepare(
						    __lowercase			,     __lowercase			,     __lowercase			,     __lowercase			,     __lowercase   )
						# Save initial
						accelerator.save_state()
						lowerCamelCase   : List[Any]								=scheduler.state_dict()
						train(3			,     __lowercase			,     __lowercase			,     __lowercase			,     __lowercase			,     __lowercase   )
						self.assertNotEqual(__lowercase			,     scheduler.state_dict()   )

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







		def 			__lowercase   (		self   )				->   Optional[Any]:




				with tempfile.TemporaryDirectory() as tmpdir:
						set_seed(4_2   )
						lowerCamelCase   : Any								=DummyModel()
						lowerCamelCase   : Optional[int]								=ProjectConfiguration(automatic_checkpoint_naming=__lowercase			,     total_limit=2   )
						# Train baseline
						lowerCamelCase   : Union[str, Any]								=Accelerator(project_dir=__lowercase			,     project_config=__lowercase   )
						lowerCamelCase   : Union[str, Any]								=accelerator.prepare(__lowercase   )
						# Save 3 states:
						for _ in range(1_1   ):
								accelerator.save_state()
						self.assertTrue(not os.path.exists(os.path.join(__lowercase			,     '''checkpoints'''			,     '''checkpoint_0'''   )   )   )
						self.assertTrue(os.path.exists(os.path.join(__lowercase			,     '''checkpoints'''			,     '''checkpoint_9'''   )   )   )
						self.assertTrue(os.path.exists(os.path.join(__lowercase			,     '''checkpoints'''			,     '''checkpoint_10'''   )   )   )







		@require_cuda
		def 			__lowercase   (		self   )				->   Union[str, Any]:
				lowerCamelCase   : List[Any]								=["""torchrun""", F"--nproc_per_node={torch.cuda.device_count()}", inspect.getfile(self.__class__   )]
				execute_subprocess_async(__lowercase			,     env=os.environ.copy()   )


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








from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


snake_case_							=			{
    '''configuration_instructblip''': [
        '''INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''InstructBlipConfig''',
        '''InstructBlipQFormerConfig''',
        '''InstructBlipVisionConfig''',
    ],
    '''processing_instructblip''': ['''InstructBlipProcessor'''],
}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				snake_case_							=			[
				    '''INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST''',
				    '''InstructBlipQFormerModel''',
				    '''InstructBlipPreTrainedModel''',
				    '''InstructBlipForConditionalGeneration''',
				    '''InstructBlipVisionModel''',
				]

if TYPE_CHECKING:
				from .configuration_instructblip import (
				    INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
				    InstructBlipConfig,
				    InstructBlipQFormerConfig,
				    InstructBlipVisionConfig,
				)
				from .processing_instructblip import InstructBlipProcessor

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_instructblip import (
								    INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
								    InstructBlipForConditionalGeneration,
								    InstructBlipPreTrainedModel,
								    InstructBlipQFormerModel,
								    InstructBlipVisionModel,
								)

else:
				import sys

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

