




import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available


if is_vision_available():
			from PIL import Image

			from transformers import (
			    AutoProcessor,
			    BertTokenizerFast,
			    BlipImageProcessor,
			    GPTaTokenizer,
			    InstructBlipProcessor,
			    PreTrainedTokenizerFast,
			)






@require_vision
class 			__magic_name__    (	unittest.TestCase):
				'''simple docstring'''







				def      _A				(				self: List[Any]		):
											SCREAMING_SNAKE_CASE_ 						=     tempfile.mkdtemp()

											SCREAMING_SNAKE_CASE_ 						=     BlipImageProcessor()
											SCREAMING_SNAKE_CASE_ 						=     GPTaTokenizer.from_pretrained('''hf-internal-testing/tiny-random-GPT2Model'''		)
											SCREAMING_SNAKE_CASE_ 						=     BertTokenizerFast.from_pretrained('''hf-internal-testing/tiny-random-bert'''		)

											SCREAMING_SNAKE_CASE_ 						=     InstructBlipProcessor(_lowercase		,       _lowercase		,       _lowercase		)

											processor.save_pretrained(self.tmpdirname		)


				def      _A				(				self: Any		,       **_lowerCamelCase: Tuple		):
											return AutoProcessor.from_pretrained(self.tmpdirname		,       **_lowercase		).tokenizer


				def      _A				(				self: int		,       **_lowerCamelCase: str		):
											return AutoProcessor.from_pretrained(self.tmpdirname		,       **_lowercase		).image_processor


				def      _A				(				self: Tuple		,       **_lowerCamelCase: Any		):
											return AutoProcessor.from_pretrained(self.tmpdirname		,       **_lowercase		).qformer_tokenizer


				def      _A				(				self: str		):
											shutil.rmtree(self.tmpdirname		)


				def      _A				(				self: Dict		):
											SCREAMING_SNAKE_CASE_ 						=     [np.random.randint(2_55		,       size=(3, 30, 4_00)		,       dtype=np.uinta		)]

											SCREAMING_SNAKE_CASE_ 						=     [Image.fromarray(np.moveaxis(_lowercase		,       0		,       -1		)		) for x in image_inputs]

											return image_inputs


				def      _A				(				self: Union[str, Any]		):
											SCREAMING_SNAKE_CASE_ 						=     InstructBlipProcessor(
											    tokenizer=self.get_tokenizer()		,       image_processor=self.get_image_processor()		,       qformer_tokenizer=self.get_qformer_tokenizer()		,       )
											processor.save_pretrained(self.tmpdirname		)

											SCREAMING_SNAKE_CASE_ 						=     self.get_tokenizer(bos_token='''(BOS)'''		,       eos_token='''(EOS)'''		)
											SCREAMING_SNAKE_CASE_ 						=     self.get_image_processor(do_normalize=_lowercase		,       padding_value=1.0		)

											SCREAMING_SNAKE_CASE_ 						=     InstructBlipProcessor.from_pretrained(
											    self.tmpdirname		,       bos_token='''(BOS)'''		,       eos_token='''(EOS)'''		,       do_normalize=_lowercase		,       padding_value=1.0		)

											self.assertEqual(processor.tokenizer.get_vocab()		,       tokenizer_add_kwargs.get_vocab()		)
											self.assertIsInstance(processor.tokenizer		,       _lowercase		)

											self.assertEqual(processor.image_processor.to_json_string()		,       image_processor_add_kwargs.to_json_string()		)
											self.assertIsInstance(processor.image_processor		,       _lowercase		)
											self.assertIsInstance(processor.qformer_tokenizer		,       _lowercase		)


				def      _A				(				self: str		):
											SCREAMING_SNAKE_CASE_ 						=     self.get_image_processor()
											SCREAMING_SNAKE_CASE_ 						=     self.get_tokenizer()
											SCREAMING_SNAKE_CASE_ 						=     self.get_qformer_tokenizer()

											SCREAMING_SNAKE_CASE_ 						=     InstructBlipProcessor(
											    tokenizer=_lowercase		,       image_processor=_lowercase		,       qformer_tokenizer=_lowercase		)

											SCREAMING_SNAKE_CASE_ 						=     self.prepare_image_inputs()

											SCREAMING_SNAKE_CASE_ 						=     image_processor(_lowercase		,       return_tensors='''np'''		)
											SCREAMING_SNAKE_CASE_ 						=     processor(images=_lowercase		,       return_tensors='''np'''		)

											for key in input_feat_extract.keys():
																		self.assertAlmostEqual(input_feat_extract[key].sum()		,       input_processor[key].sum()		,       delta=1E-2		)


				def      _A				(				self: List[Any]		):
											SCREAMING_SNAKE_CASE_ 						=     self.get_image_processor()
											SCREAMING_SNAKE_CASE_ 						=     self.get_tokenizer()
											SCREAMING_SNAKE_CASE_ 						=     self.get_qformer_tokenizer()

											SCREAMING_SNAKE_CASE_ 						=     InstructBlipProcessor(
											    tokenizer=_lowercase		,       image_processor=_lowercase		,       qformer_tokenizer=_lowercase		)

											SCREAMING_SNAKE_CASE_ 						=     '''lower newer'''

											SCREAMING_SNAKE_CASE_ 						=     processor(text=_lowercase		)

											SCREAMING_SNAKE_CASE_ 						=     tokenizer(_lowercase		,       return_token_type_ids=_lowercase		)
											SCREAMING_SNAKE_CASE_ 						=     qformer_tokenizer(_lowercase		,       return_token_type_ids=_lowercase		)

											for key in encoded_tokens.keys():
																		self.assertListEqual(encoded_tokens[key]		,       encoded_processor[key]		)

											for key in encoded_tokens_qformer.keys():
																		self.assertListEqual(encoded_tokens_qformer[key]		,       encoded_processor['''qformer_''' + key]		)


				def      _A				(				self: Optional[Any]		):
											SCREAMING_SNAKE_CASE_ 						=     self.get_image_processor()
											SCREAMING_SNAKE_CASE_ 						=     self.get_tokenizer()
											SCREAMING_SNAKE_CASE_ 						=     self.get_qformer_tokenizer()

											SCREAMING_SNAKE_CASE_ 						=     InstructBlipProcessor(
											    tokenizer=_lowercase		,       image_processor=_lowercase		,       qformer_tokenizer=_lowercase		)

											SCREAMING_SNAKE_CASE_ 						=     '''lower newer'''
											SCREAMING_SNAKE_CASE_ 						=     self.prepare_image_inputs()

											SCREAMING_SNAKE_CASE_ 						=     processor(text=_lowercase		,       images=_lowercase		)

											self.assertListEqual(
											    list(inputs.keys()		)		,       ['''input_ids''', '''attention_mask''', '''qformer_input_ids''', '''qformer_attention_mask''', '''pixel_values''']		,       )

											# test if it raises when no input is passed
											with pytest.raises(_lowercase		):
																		processor()


				def      _A				(				self: Any		):
											SCREAMING_SNAKE_CASE_ 						=     self.get_image_processor()
											SCREAMING_SNAKE_CASE_ 						=     self.get_tokenizer()
											SCREAMING_SNAKE_CASE_ 						=     self.get_qformer_tokenizer()

											SCREAMING_SNAKE_CASE_ 						=     InstructBlipProcessor(
											    tokenizer=_lowercase		,       image_processor=_lowercase		,       qformer_tokenizer=_lowercase		)

											SCREAMING_SNAKE_CASE_ 						=     [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

											SCREAMING_SNAKE_CASE_ 						=     processor.batch_decode(_lowercase		)
											SCREAMING_SNAKE_CASE_ 						=     tokenizer.batch_decode(_lowercase		)

											self.assertListEqual(_lowercase		,       _lowercase		)





				def      _A				(				self: List[Any]		):
											SCREAMING_SNAKE_CASE_ 						=     self.get_image_processor()
											SCREAMING_SNAKE_CASE_ 						=     self.get_tokenizer()
											SCREAMING_SNAKE_CASE_ 						=     self.get_qformer_tokenizer()

											SCREAMING_SNAKE_CASE_ 						=     InstructBlipProcessor(
											    tokenizer=_lowercase		,       image_processor=_lowercase		,       qformer_tokenizer=_lowercase		)

											SCREAMING_SNAKE_CASE_ 						=     '''lower newer'''
											SCREAMING_SNAKE_CASE_ 						=     self.prepare_image_inputs()

											SCREAMING_SNAKE_CASE_ 						=     processor(text=_lowercase		,       images=_lowercase		)

											self.assertListEqual(
											    list(inputs.keys()		)		,       ['''input_ids''', '''attention_mask''', '''qformer_input_ids''', '''qformer_attention_mask''', '''pixel_values''']		,       )





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


SCREAMING_SNAKE_CASE		:Optional[int]						=     logging.getLogger(__name__)




def 			lowerCAmelCase(      SCREAMING_SNAKE_CASE_=2			,						SCREAMING_SNAKE_CASE_=3			,						SCREAMING_SNAKE_CASE_=1_6			,						SCREAMING_SNAKE_CASE_ = 1_0			,						SCREAMING_SNAKE_CASE_ = 2			)->						Optional[Any]:



    """simple docstring"""






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

    UpperCamelCase_			  =						get_dataset(SCREAMING_SNAKE_CASE_			)
    UpperCamelCase_			  =						get_dataset(SCREAMING_SNAKE_CASE_			)
    UpperCamelCase_			  =						DataLoader(SCREAMING_SNAKE_CASE_			,						shuffle=SCREAMING_SNAKE_CASE_			,						batch_size=SCREAMING_SNAKE_CASE_			,						num_workers=4			)
    UpperCamelCase_			  =						DataLoader(SCREAMING_SNAKE_CASE_			,						shuffle=SCREAMING_SNAKE_CASE_			,						batch_size=SCREAMING_SNAKE_CASE_			,						num_workers=4			)
    return (train_dataloader, valid_dataloader)




def 			lowerCAmelCase(      SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_=None			)->						Any:



    """simple docstring"""






    UpperCamelCase_			  =						[]
    for epoch in range(SCREAMING_SNAKE_CASE_			):
        # Train quickly
        model.train()
        for batch in dataloader:
            UpperCamelCase_  ,       UpperCamelCase_			  =						batch
            UpperCamelCase_			  =						model(SCREAMING_SNAKE_CASE_			)
            UpperCamelCase_			  =						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 						__magic_name__					(							nn.Module				):






     def __init__(				self	)->     List[Any]:
         super().__init__()
         UpperCamelCase_			  =						nn.Parameter(torch.randn(1	)	)
         UpperCamelCase_			  =						nn.Parameter(torch.randn(1	)	)






     def 	UpperCAmelCase_				(				self		,						_lowercase	)->     str:
         return x * self.a + self.b






class 						__magic_name__					(							unittest.TestCase				):






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

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

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






     def 	UpperCAmelCase_				(				self	)->     Union[str, Any]:
         with tempfile.TemporaryDirectory() as tmpdir:
             set_seed(42	)
             UpperCamelCase_			  =						DummyModel()
             UpperCamelCase_			  =						torch.optim.Adam(params=model.parameters()		,						lr=1e-3	)
             UpperCamelCase_  ,       UpperCamelCase_			  =						dummy_dataloaders()
             UpperCamelCase_			  =						ProjectConfiguration(automatic_checkpoint_naming=_lowercase	)

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

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

             UpperCamelCase_			  =						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	)
             ((UpperCamelCase_)  ,       (UpperCamelCase_))			  =						model.a.item(), model.b.item()
             UpperCamelCase_			  =						optimizer.state_dict()
             self.assertEqual(_lowercase		,						_lowercase	)
             self.assertEqual(_lowercase		,						_lowercase	)
             self.assertEqual(_lowercase		,						_lowercase	)
             self.assertEqual(_lowercase		,						_lowercase	)






     def 	UpperCAmelCase_				(				self	)->     Any:
         UpperCamelCase_			  =						torch.tensor([1, 2, 3]	)
         UpperCamelCase_			  =						torch.tensor([2, 3, 4]	)
         UpperCamelCase_			  =						DummyModel()
         UpperCamelCase_			  =						torch.optim.Adam(net.parameters()	)
         UpperCamelCase_			  =						Accelerator()
         with self.assertRaises(_lowercase	) as ve:
             accelerator.register_for_checkpointing(_lowercase		,						_lowercase		,						_lowercase		,						_lowercase	)
         UpperCamelCase_			  =						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 	UpperCAmelCase_				(				self	)->     Optional[Any]:
         with tempfile.TemporaryDirectory() as tmpdir:
             set_seed(42	)
             UpperCamelCase_			  =						DummyModel()
             UpperCamelCase_			  =						torch.optim.Adam(params=model.parameters()		,						lr=1e-3	)
             UpperCamelCase_			  =						torch.optim.lr_scheduler.StepLR(_lowercase		,						step_size=1		,						gamma=0.99	)
             UpperCamelCase_  ,       UpperCamelCase_			  =						dummy_dataloaders()
             UpperCamelCase_			  =						ProjectConfiguration(automatic_checkpoint_naming=_lowercase	)
             # Train baseline
             UpperCamelCase_			  =						Accelerator(project_dir=_lowercase		,						project_config=_lowercase	)
             UpperCamelCase_  ,       UpperCamelCase_  ,       UpperCamelCase_  ,       UpperCamelCase_  ,       UpperCamelCase_			  =						accelerator.prepare(
                 _lowercase		,						_lowercase		,						_lowercase		,						_lowercase		,						_lowercase	)
             # Save initial
             accelerator.save_state()
             UpperCamelCase_			  =						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 	UpperCAmelCase_				(				self	)->     str:
         with tempfile.TemporaryDirectory() as tmpdir:
             set_seed(42	)
             UpperCamelCase_			  =						DummyModel()
             UpperCamelCase_			  =						ProjectConfiguration(automatic_checkpoint_naming=_lowercase		,						total_limit=2	)
             # Train baseline
             UpperCamelCase_			  =						Accelerator(project_dir=_lowercase		,						project_config=_lowercase	)
             UpperCamelCase_			  =						accelerator.prepare(_lowercase	)
             # Save 3 states:
             for _ in range(11	):
                 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 	UpperCAmelCase_				(				self	)->     int:
         UpperCamelCase_			  =						["torchrun", F"--nproc_per_node={torch.cuda.device_count()}", inspect.getfile(self.__class__	)]
         execute_subprocess_async(_lowercase		,						env=os.environ.copy()	)


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