


'''simple docstring'''

import unittest

import numpy as np
import requests

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
							import torch

							from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
							lowerCAmelCase      :Any    	=			False

if is_vision_available():
							from PIL import Image

							from transformers import PixaStructImageProcessor
class _lowerCamelCase       (						unittest.TestCase							):



		'''simple docstring'''







		def __init__(					self		:						Optional[int]  ,     _A		:						Dict  ,     _A		:						str=7  ,     _A		:						Optional[Any]=3  ,     _A		:						List[str]=18  ,     _A		:						List[str]=30  ,     _A		:						Tuple=400  ,     _A		:						Tuple=None  ,     _A		:						Tuple=True  ,     _A		:						Dict=True  ,     _A		:						Dict=None  ,     )  -> List[Any]:
			__magic_name__       :    Any     	=     size if size is not None else {'''height''': 20, '''width''': 20}
			__magic_name__       :    int     	=     parent
			__magic_name__       :    Any     	=     batch_size
			__magic_name__       :    Optional[int]     	=     num_channels
			__magic_name__       :    Dict     	=     image_size
			__magic_name__       :    List[str]     	=     min_resolution
			__magic_name__       :    List[str]     	=     max_resolution
			__magic_name__       :    str     	=     size
			__magic_name__       :    Dict     	=     do_normalize
			__magic_name__       :    Dict     	=     do_convert_rgb
			__magic_name__       :    Any     	=     [512, 1024, 2048, 4096]
			__magic_name__       :    Dict     	=     patch_size if patch_size is not None else {'''height''': 16, '''width''': 16}







		def    __lowerCAmelCase		(					self		:						str			)  -> Any:
			return {"do_normalize": self.do_normalize, "do_convert_rgb": self.do_convert_rgb}







		def    __lowerCAmelCase		(					self		:						Union[str, Any]			)  -> int:
			__magic_name__       :    str     	=     '''https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/australia.jpg'''
			__magic_name__       :    Any     	=     Image.open(requests.get(_A  ,     stream=_A			).raw			).convert('RGB'			)
			return raw_image







@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11		,     reason="""`Pix2StructImageProcessor` requires `torch>=1.11.0`."""		,     )
@require_torch
@require_vision
class _lowerCamelCase       (						lowercase__		,     unittest.TestCase							):



		'''simple docstring'''







		A_	:			Any          =				PixaStructImageProcessor if is_vision_available() else None







		def    __lowerCAmelCase		(					self		:						List[Any]			)  -> Optional[int]:
			__magic_name__       :    Optional[Any]     	=     PixaStructImageProcessingTester(self			)







		@property
		def    __lowerCAmelCase		(					self		:						Optional[int]			)  -> Union[str, Any]:
			return self.image_processor_tester.prepare_image_processor_dict()







		def    __lowerCAmelCase		(					self		:						List[str]			)  -> Optional[Any]:
			__magic_name__       :    Dict     	=     self.image_processing_class(**self.image_processor_dict			)
			self.assertTrue(hasattr(_A  ,     'do_normalize'			)			)
			self.assertTrue(hasattr(_A  ,     'do_convert_rgb'			)			)







		def    __lowerCAmelCase		(					self		:						List[Any]			)  -> Any:
			__magic_name__       :    int     	=     self.image_processor_tester.prepare_dummy_image()

			__magic_name__       :    Union[str, Any]     	=     self.image_processing_class(**self.image_processor_dict			)
			__magic_name__       :    Any     	=     2048

			__magic_name__       :    Optional[Any]     	=     image_processor(_A  ,     return_tensors='pt'  ,     max_patches=_A			)
			self.assertTrue(torch.allclose(inputs.flattened_patches.mean()  ,     torch.tensor(0.0606			)  ,     atol=1E-3  ,     rtol=1E-3			)			)







		def    __lowerCAmelCase		(					self		:						Union[str, Any]			)  -> Optional[Any]:
			# Initialize image_processor
			__magic_name__       :    Any     	=     self.image_processing_class(**self.image_processor_dict			)
			# create random PIL images
			__magic_name__       :    Union[str, Any]     	=     prepare_image_inputs(self.image_processor_tester  ,     equal_resolution=_A			)
			for image in image_inputs:
				self.assertIsInstance(_A  ,     Image.Image			)

			# Test not batched input
			__magic_name__       :    Dict     	=     (
			    (self.image_processor_tester.patch_size['''height'''] * self.image_processor_tester.patch_size['''width'''])
			    * self.image_processor_tester.num_channels
			) + 2

			for max_patch in self.image_processor_tester.max_patches:
				# Test not batched input
				__magic_name__       :    Optional[Any]     	=     image_processor(
				    image_inputs[0]  ,     return_tensors='pt'  ,     max_patches=_A			).flattened_patches
				self.assertEqual(
				    encoded_images.shape  ,     (1, max_patch, expected_hidden_dim)  ,     )

				# Test batched
				__magic_name__       :    List[Any]     	=     image_processor(
				    _A  ,     return_tensors='pt'  ,     max_patches=_A			).flattened_patches
				self.assertEqual(
				    encoded_images.shape  ,     (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)  ,     )







		def    __lowerCAmelCase		(					self		:						Optional[Any]			)  -> Any:
			# Initialize image_processor
			__magic_name__       :    Optional[Any]     	=     self.image_processing_class(**self.image_processor_dict			)
			# create random PIL images
			__magic_name__       :    Optional[int]     	=     prepare_image_inputs(self.image_processor_tester  ,     equal_resolution=_A			)
			for image in image_inputs:
				self.assertIsInstance(_A  ,     Image.Image			)

			# Test not batched input
			__magic_name__       :    Optional[Any]     	=     (
			    (self.image_processor_tester.patch_size['''height'''] * self.image_processor_tester.patch_size['''width'''])
			    * self.image_processor_tester.num_channels
			) + 2

			__magic_name__       :    Optional[Any]     	=     True

			for max_patch in self.image_processor_tester.max_patches:
				# Test not batched input
				with self.assertRaises(_A			):
					__magic_name__       :    Optional[int]     	=     image_processor(
					    image_inputs[0]  ,     return_tensors='pt'  ,     max_patches=_A			).flattened_patches

				__magic_name__       :    Union[str, Any]     	=     '''Hello'''

				__magic_name__       :    Optional[int]     	=     image_processor(
				    image_inputs[0]  ,     return_tensors='pt'  ,     max_patches=_A  ,     header_text=_A			).flattened_patches
				self.assertEqual(
				    encoded_images.shape  ,     (1, max_patch, expected_hidden_dim)  ,     )

				# Test batched
				__magic_name__       :    int     	=     image_processor(
				    _A  ,     return_tensors='pt'  ,     max_patches=_A  ,     header_text=_A			).flattened_patches
				self.assertEqual(
				    encoded_images.shape  ,     (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)  ,     )







		def    __lowerCAmelCase		(					self		:						List[str]			)  -> str:
			# Initialize image_processor
			__magic_name__       :    Union[str, Any]     	=     self.image_processing_class(**self.image_processor_dict			)
			# create random numpy tensors
			__magic_name__       :    List[Any]     	=     prepare_image_inputs(self.image_processor_tester  ,     equal_resolution=_A  ,     numpify=_A			)
			for image in image_inputs:
				self.assertIsInstance(_A  ,     np.ndarray			)

			__magic_name__       :    Tuple     	=     (
			    (self.image_processor_tester.patch_size['''height'''] * self.image_processor_tester.patch_size['''width'''])
			    * self.image_processor_tester.num_channels
			) + 2

			for max_patch in self.image_processor_tester.max_patches:
				# Test not batched input
				__magic_name__       :    Optional[Any]     	=     image_processor(
				    image_inputs[0]  ,     return_tensors='pt'  ,     max_patches=_A			).flattened_patches
				self.assertEqual(
				    encoded_images.shape  ,     (1, max_patch, expected_hidden_dim)  ,     )

				# Test batched
				__magic_name__       :    Tuple     	=     image_processor(
				    _A  ,     return_tensors='pt'  ,     max_patches=_A			).flattened_patches
				self.assertEqual(
				    encoded_images.shape  ,     (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)  ,     )







		def    __lowerCAmelCase		(					self		:						Optional[int]			)  -> List[Any]:
			# Initialize image_processor
			__magic_name__       :    Union[str, Any]     	=     self.image_processing_class(**self.image_processor_dict			)
			# create random PyTorch tensors
			__magic_name__       :    Any     	=     prepare_image_inputs(self.image_processor_tester  ,     equal_resolution=_A  ,     torchify=_A			)
			for image in image_inputs:
				self.assertIsInstance(_A  ,     torch.Tensor			)

			# Test not batched input
			__magic_name__       :    str     	=     (
			    (self.image_processor_tester.patch_size['''height'''] * self.image_processor_tester.patch_size['''width'''])
			    * self.image_processor_tester.num_channels
			) + 2

			for max_patch in self.image_processor_tester.max_patches:
				# Test not batched input
				__magic_name__       :    Optional[int]     	=     image_processor(
				    image_inputs[0]  ,     return_tensors='pt'  ,     max_patches=_A			).flattened_patches
				self.assertEqual(
				    encoded_images.shape  ,     (1, max_patch, expected_hidden_dim)  ,     )

				# Test batched
				__magic_name__       :    Union[str, Any]     	=     image_processor(
				    _A  ,     return_tensors='pt'  ,     max_patches=_A			).flattened_patches
				self.assertEqual(
				    encoded_images.shape  ,     (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)  ,     )







@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11		,     reason="""`Pix2StructImageProcessor` requires `torch>=1.11.0`."""		,     )
@require_torch
@require_vision
class _lowerCamelCase       (						lowercase__		,     unittest.TestCase							):



		'''simple docstring'''







		A_	:			Optional[Any]          =				PixaStructImageProcessor if is_vision_available() else None







		def    __lowerCAmelCase		(					self		:						Any			)  -> Union[str, Any]:
			__magic_name__       :    Any     	=     PixaStructImageProcessingTester(self  ,     num_channels=4			)
			__magic_name__       :    Optional[Any]     	=     3







		@property
		def    __lowerCAmelCase		(					self		:						Optional[Any]			)  -> str:
			return self.image_processor_tester.prepare_image_processor_dict()







		def    __lowerCAmelCase		(					self		:						Dict			)  -> List[str]:
			__magic_name__       :    str     	=     self.image_processing_class(**self.image_processor_dict			)
			self.assertTrue(hasattr(_A  ,     'do_normalize'			)			)
			self.assertTrue(hasattr(_A  ,     'do_convert_rgb'			)			)







		def    __lowerCAmelCase		(					self		:						Union[str, Any]			)  -> Union[str, Any]:
			# Initialize image_processor
			__magic_name__       :    Dict     	=     self.image_processing_class(**self.image_processor_dict			)
			# create random PIL images
			__magic_name__       :    Dict     	=     prepare_image_inputs(self.image_processor_tester  ,     equal_resolution=_A			)
			for image in image_inputs:
				self.assertIsInstance(_A  ,     Image.Image			)

			# Test not batched input
			__magic_name__       :    Optional[Any]     	=     (
			    (self.image_processor_tester.patch_size['''height'''] * self.image_processor_tester.patch_size['''width'''])
			    * (self.image_processor_tester.num_channels - 1)
			) + 2

			for max_patch in self.image_processor_tester.max_patches:
				# Test not batched input
				__magic_name__       :    Optional[Any]     	=     image_processor(
				    image_inputs[0]  ,     return_tensors='pt'  ,     max_patches=_A			).flattened_patches
				self.assertEqual(
				    encoded_images.shape  ,     (1, max_patch, expected_hidden_dim)  ,     )

				# Test batched
				__magic_name__       :    Any     	=     image_processor(
				    _A  ,     return_tensors='pt'  ,     max_patches=_A			).flattened_patches
				self.assertEqual(
				    encoded_images.shape  ,     (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)  ,     )




"""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


_lowerCAmelCase    :Tuple     =					logging.getLogger(__name__)





def  lowerCamelCase_						(UpperCamelCase__		:		List[Any]=2     ,       UpperCamelCase__		:		List[Any]=3     ,       UpperCamelCase__		:		List[Any]=16     ,       UpperCamelCase__		:		int = 10     ,       UpperCamelCase__		:		int = 2							):

    def get_dataset(UpperCamelCase__		:		List[str]							):
        _UpperCAmelCase							:  Optional[Any]											=    torch.randn(batch_size * n_batches     ,       1							)
        return TensorDataset(UpperCamelCase__     ,       a * x + b + 0.1 * torch.randn(batch_size * n_batches     ,       1							)							)

    _UpperCAmelCase							:  Optional[Any]											=    get_dataset(UpperCamelCase__							)
    _UpperCAmelCase							:  Optional[Any]											=    get_dataset(UpperCamelCase__							)
    _UpperCAmelCase							:  List[str]											=    DataLoader(UpperCamelCase__     ,       shuffle=UpperCamelCase__     ,       batch_size=UpperCamelCase__     ,       num_workers=4							)
    _UpperCAmelCase							:  List[str]											=    DataLoader(UpperCamelCase__     ,       shuffle=UpperCamelCase__     ,       batch_size=UpperCamelCase__     ,       num_workers=4							)
    return (train_dataloader, valid_dataloader)





def  lowerCamelCase_						(UpperCamelCase__		:		Optional[int]     ,       UpperCamelCase__		:		List[Any]     ,       UpperCamelCase__		:		int     ,       UpperCamelCase__		:		Any     ,       UpperCamelCase__		:		Optional[Any]     ,       UpperCamelCase__		:		Tuple=None							):
    _UpperCAmelCase							:  Tuple											=    []
    for epoch in range(UpperCamelCase__							):
        # Train quickly
        model.train()
        for batch in dataloader:
            _UpperCAmelCase							,				_UpperCAmelCase							:  Dict											=    batch
            _UpperCAmelCase							:  int											=    model(UpperCamelCase__							)
            _UpperCAmelCase							:  Dict											=    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 							_UpperCAmelCase							( nn.Module      ):





     '''simple docstring'''



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





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



class 							_UpperCAmelCase							( unittest.TestCase      ):





     '''simple docstring'''



     def  __lowerCAmelCase	(					self		)							->					Any:
         with tempfile.TemporaryDirectory() as tmpdir:
             set_seed(4_2		)
             _UpperCAmelCase							:  int											=    DummyModel()
             _UpperCAmelCase							:  str											=    torch.optim.Adam(params=model.parameters()       ,	lr=1E-3		)
             _UpperCAmelCase							,				_UpperCAmelCase							:  List[Any]											=    dummy_dataloaders()
             _UpperCAmelCase							:  Any											=    ProjectConfiguration(total_limit=1       ,	project_dir=A       ,	automatic_checkpoint_naming=A		)
             # Train baseline
             _UpperCAmelCase							:  Union[str, Any]											=    Accelerator(project_config=A		)
             _UpperCAmelCase							,				_UpperCAmelCase							,				_UpperCAmelCase							,				_UpperCAmelCase							:  int											=    accelerator.prepare(
                 A       ,	A       ,	A       ,	A		)
             # Save initial
             accelerator.save_state()

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





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

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

             _UpperCAmelCase							:  Union[str, Any]											=    train(2       ,	A       ,	A       ,	A       ,	A		)
             # Save everything
             _UpperCAmelCase							:  List[str]											=    os.path.join(A       ,	'''checkpoint'''		)
             accelerator.save_state(A		)

             # Load everything back in and make sure all states work
             accelerator.load_state(A		)
             test_rands += train(1       ,	A       ,	A       ,	A       ,	A		)
             ((_UpperCAmelCase)							,				(_UpperCAmelCase))							:  Dict											=    model.a.item(), model.b.item()
             _UpperCAmelCase							:  Dict											=    optimizer.state_dict()
             self.assertEqual(A       ,	A		)
             self.assertEqual(A       ,	A		)
             self.assertEqual(A       ,	A		)
             self.assertEqual(A       ,	A		)





     def  __lowerCAmelCase	(					self		)							->					int:
         with tempfile.TemporaryDirectory() as tmpdir:
             set_seed(4_2		)
             _UpperCAmelCase							:  List[Any]											=    DummyModel()
             _UpperCAmelCase							:  List[str]											=    torch.optim.Adam(params=model.parameters()       ,	lr=1E-3		)
             _UpperCAmelCase							,				_UpperCAmelCase							:  List[Any]											=    dummy_dataloaders()
             _UpperCAmelCase							:  List[str]											=    ProjectConfiguration(automatic_checkpoint_naming=A		)

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

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

             _UpperCAmelCase							:  List[str]											=    train(2       ,	A       ,	A       ,	A       ,	A		)
             # Save everything
             accelerator.save_state()

             # Load everything back in and make sure all states work
             accelerator.load_state(os.path.join(A       ,	'''checkpoints'''       ,	'''checkpoint_1'''		)		)
             test_rands += train(1       ,	A       ,	A       ,	A       ,	A		)
             ((_UpperCAmelCase)							,				(_UpperCAmelCase))							:  List[str]											=    model.a.item(), model.b.item()
             _UpperCAmelCase							:  Tuple											=    optimizer.state_dict()
             self.assertEqual(A       ,	A		)
             self.assertEqual(A       ,	A		)
             self.assertEqual(A       ,	A		)
             self.assertEqual(A       ,	A		)





     def  __lowerCAmelCase	(					self		)							->					Dict:
         _UpperCAmelCase							:  List[Any]											=    torch.tensor([1, 2, 3]		)
         _UpperCAmelCase							:  List[str]											=    torch.tensor([2, 3, 4]		)
         _UpperCAmelCase							:  Optional[int]											=    DummyModel()
         _UpperCAmelCase							:  Dict											=    torch.optim.Adam(net.parameters()		)
         _UpperCAmelCase							:  Optional[int]											=    Accelerator()
         with self.assertRaises(A		) as ve:
             accelerator.register_for_checkpointing(A       ,	A       ,	A       ,	A		)
         _UpperCAmelCase							:  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  __lowerCAmelCase	(					self		)							->					Tuple:
         with tempfile.TemporaryDirectory() as tmpdir:
             set_seed(4_2		)
             _UpperCAmelCase							:  Tuple											=    DummyModel()
             _UpperCAmelCase							:  List[Any]											=    torch.optim.Adam(params=model.parameters()       ,	lr=1E-3		)
             _UpperCAmelCase							:  Optional[int]											=    torch.optim.lr_scheduler.StepLR(A       ,	step_size=1       ,	gamma=0.99		)
             _UpperCAmelCase							,				_UpperCAmelCase							:  str											=    dummy_dataloaders()
             _UpperCAmelCase							:  List[str]											=    ProjectConfiguration(automatic_checkpoint_naming=A		)
             # Train baseline
             _UpperCAmelCase							:  int											=    Accelerator(project_dir=A       ,	project_config=A		)
             _UpperCAmelCase							,				_UpperCAmelCase							,				_UpperCAmelCase							,				_UpperCAmelCase							,				_UpperCAmelCase							:  str											=    accelerator.prepare(
                 A       ,	A       ,	A       ,	A       ,	A		)
             # Save initial
             accelerator.save_state()
             _UpperCAmelCase							:  List[str]											=    scheduler.state_dict()
             train(3       ,	A       ,	A       ,	A       ,	A       ,	A		)
             self.assertNotEqual(A       ,	scheduler.state_dict()		)

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





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





     @require_cuda
     def  __lowerCAmelCase	(					self		)							->					Dict:
         _UpperCAmelCase							:  str											=    ['''torchrun''', f'--nproc_per_node={torch.cuda.device_count()}', inspect.getfile(self.__class__		)]
         execute_subprocess_async(A       ,	env=os.environ.copy()		)


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


