


'''simple docstring'''

import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class _lowerCamelCase       (						lowercase__							):



		'''simple docstring'''







		A_	:			Tuple          =				["""image_processor""", """tokenizer"""]
		A_	:			List[str]          =				"""LayoutLMv3ImageProcessor"""
		A_	:			Any          =				("""LayoutLMv3Tokenizer""", """LayoutLMv3TokenizerFast""")







		def __init__(					self		:						List[Any]  ,     _A		:						str=None  ,     _A		:						int=None  ,     **_A		:						Tuple			)  -> Dict:
			__magic_name__       :    Optional[int]     	=     None
			if "feature_extractor" in kwargs:
				warnings.warn(
				    'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
				    ' instead.'  ,     _A  ,     )
				__magic_name__       :    Any     	=     kwargs.pop('feature_extractor'			)

			__magic_name__       :    Union[str, Any]     	=     image_processor if image_processor is not None else feature_extractor
			if image_processor is None:
				raise ValueError('You need to specify an `image_processor`.'			)
			if tokenizer is None:
				raise ValueError('You need to specify a `tokenizer`.'			)

			super().__init__(_A  ,     _A			)







		def __call__(					self		:						Optional[Any]  ,     _A		:						str  ,     _A		:						int = None  ,     _A		:						Optional[Any] = None  ,     _A		:						List[str] = None  ,     _A		:						Optional[int] = None  ,     _A		:						List[Any] = True  ,     _A		:						Any = False  ,     _A		:						Optional[Any] = None  ,     _A		:						Any = None  ,     _A		:						List[Any] = 0  ,     _A		:						List[str] = None  ,     _A		:						List[str] = None  ,     _A		:						Dict = None  ,     _A		:						Any = False  ,     _A		:						str = False  ,     _A		:						int = False  ,     _A		:						Optional[int] = False  ,     _A		:						List[str] = True  ,     _A		:						Tuple = None  ,     **_A		:						Any  ,     )  -> BatchEncoding:
			# verify input
			if self.image_processor.apply_ocr and (boxes is not None):
				raise ValueError(
				    'You cannot provide bounding boxes if you initialized the image processor with apply_ocr set to True.'			)

			if self.image_processor.apply_ocr and (word_labels is not None):
				raise ValueError(
				    'You cannot provide word labels if you initialized the image processor with apply_ocr set to True.'			)

			# first, apply the image processor
			__magic_name__       :    str     	=     self.image_processor(images=_A  ,     return_tensors=_A			)

			# second, apply the tokenizer
			if text is not None and self.image_processor.apply_ocr and text_pair is None:
				if isinstance(_A  ,     _A			):
					__magic_name__       :    Dict     	=     [text]  # add batch dimension (as the image processor always adds a batch dimension)
				__magic_name__       :    Tuple     	=     features['''words''']

			__magic_name__       :    List[Any]     	=     self.tokenizer(
			    text=text if text is not None else features['words']  ,     text_pair=text_pair if text_pair is not None else None  ,     boxes=boxes if boxes is not None else features['boxes']  ,     word_labels=_A  ,     add_special_tokens=_A  ,     padding=_A  ,     truncation=_A  ,     max_length=_A  ,     stride=_A  ,     pad_to_multiple_of=_A  ,     return_token_type_ids=_A  ,     return_attention_mask=_A  ,     return_overflowing_tokens=_A  ,     return_special_tokens_mask=_A  ,     return_offsets_mapping=_A  ,     return_length=_A  ,     verbose=_A  ,     return_tensors=_A  ,     **_A  ,     )

			# add pixel values
			__magic_name__       :    Union[str, Any]     	=     features.pop('pixel_values'			)
			if return_overflowing_tokens is True:
				__magic_name__       :    Optional[int]     	=     self.get_overflowing_images(_A  ,     encoded_inputs['overflow_to_sample_mapping']			)
			__magic_name__       :    Optional[int]     	=     images

			return encoded_inputs







		def    __lowerCAmelCase		(					self		:						Optional[int]  ,     _A		:						List[Any]  ,     _A		:						List[str]			)  -> List[str]:
			# in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
			__magic_name__       :    Optional[int]     	=     []
			for sample_idx in overflow_to_sample_mapping:
				images_with_overflow.append(images[sample_idx]			)

			if len(_A			) != len(_A			):
				raise ValueError(
				    'Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got'
				    F' {len(_A			)} and {len(_A			)}'			)

			return images_with_overflow







		def    __lowerCAmelCase		(					self		:						str  ,     *_A		:						Tuple  ,     **_A		:						str			)  -> Optional[Any]:
			return self.tokenizer.batch_decode(*_A  ,     **_A			)







		def    __lowerCAmelCase		(					self		:						int  ,     *_A		:						Dict  ,     **_A		:						Any			)  -> Tuple:
			return self.tokenizer.decode(*_A  ,     **_A			)







		@property
		def    __lowerCAmelCase		(					self		:						int			)  -> Tuple:
			return ["input_ids", "bbox", "attention_mask", "pixel_values"]







		@property
		def    __lowerCAmelCase		(					self		:						Any			)  -> str:
			warnings.warn(
			    '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'  ,     _A  ,     )
			return self.image_processor_class







		@property
		def    __lowerCAmelCase		(					self		:						List[Any]			)  -> Optional[int]:
			warnings.warn(
			    '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'  ,     _A  ,     )
			return self.image_processor




"""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()


