code: 
  
  
  import argparse
  import os
  
  import evaluate
  import torch
  from datasets import load_dataset
  from torch.optim import AdamW
  from torch.utils.data import DataLoader
  from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
  
  from accelerate import Accelerator, DistributedType
  
  
  ########################################################################
  # This is a fully working simple example to use Accelerate,
  # specifically showcasing the checkpointing capability,
  # and builds off the `nlp_example.py` script.
  #
  # This example trains a Bert base model on GLUE MRPC
  # in any of the following settings (with the same script):
  #   - single CPU or single GPU
  #   - multi GPUS (using PyTorch distributed mode)
  #   - (multi) TPUs
  #   - fp16 (mixed-precision) or fp32 (normal precision)
  #
  # To help focus on the differences in the code, building `DataLoaders`
  # was refactored into its own function.
  # New additions from the base script can be found quickly by
  # looking for the # New Code # tags
  #
  # To run it in each of these various modes, follow the instructions
  # in the readme for examples:
  # https://github.com/huggingface/accelerate/tree/main/examples
  #
  ########################################################################
  
  lowerCAmelCase__   	=				16
  lowerCAmelCase__   	=				32
  def 							_UpperCAmelCase       (UpperCamelCase__  :						Accelerator       ,							UpperCamelCase__  :						int = 16		):
         _A   : Any       					=				AutoTokenizer.from_pretrained("bert-base-cased"		)
         _A   : List[str]       					=				load_dataset("glue"       ,							"mrpc"		)
  
         def tokenize_function(UpperCamelCase__  :						Tuple		):
                # max_length=None => use the model max length (it's actually the default)
                _A   : Tuple       					=				tokenizer(examples["sentence1"]       ,							examples["sentence2"]       ,							truncation=UpperCamelCase__       ,							max_length=UpperCamelCase__		)
                return outputs
  
         # Apply the method we just defined to all the examples in all the splits of the dataset
         # starting with the main process first:
         with accelerator.main_process_first():
                _A   : Tuple       					=				datasets.map(
                    UpperCamelCase__       ,							batched=UpperCamelCase__       ,							remove_columns=["idx", "sentence1", "sentence2"]       ,							)
  
         # We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
         # transformers library
         _A   : Any       					=				tokenized_datasets.rename_column("label"       ,							"labels"		)
  
         def collate_fn(UpperCamelCase__  :						List[str]		):
                # On TPU it's best to pad everything to the same length or training will be very slow.
                _A   : Optional[int]       					=				128 if accelerator.distributed_type == DistributedType.TPU else None
                # When using mixed precision we want round multiples of 8/16
                if accelerator.mixed_precision == "fp8":
                       _A   : List[Any]       					=				16
                elif accelerator.mixed_precision != "no":
                       _A   : Dict       					=				8
                else:
                       _A   : List[Any]       					=				None
  
                return tokenizer.pad(
                    UpperCamelCase__       ,							padding="longest"       ,							max_length=UpperCamelCase__       ,							pad_to_multiple_of=UpperCamelCase__       ,							return_tensors="pt"       ,							)
  
         # Instantiate dataloaders.
         _A   : Any       					=				DataLoader(
             tokenized_datasets["train"]       ,							shuffle=UpperCamelCase__       ,							collate_fn=UpperCamelCase__       ,							batch_size=UpperCamelCase__		)
         _A   : Any       					=				DataLoader(
             tokenized_datasets["validation"]       ,							shuffle=UpperCamelCase__       ,							collate_fn=UpperCamelCase__       ,							batch_size=UpperCamelCase__		)
  
         return train_dataloader, eval_dataloader
  
  
  # For testing only
  if os.environ.get('TESTING_MOCKED_DATALOADERS', None) == "1":
      from accelerate.test_utils.training import mocked_dataloaders
  
      lowerCAmelCase__   	=				mocked_dataloaders  # noqa: F811
  def 							_UpperCAmelCase       (UpperCamelCase__  :						Dict       ,							UpperCamelCase__  :						Dict		):
         # For testing only
         if os.environ.get("TESTING_MOCKED_DATALOADERS"       ,							UpperCamelCase__		) == "1":
                _A   : Union[str, Any]       					=				2
         # Initialize accelerator
         _A   : Dict       					=				Accelerator(cpu=args.cpu       ,							mixed_precision=args.mixed_precision		)
         # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
         _A   : List[str]       					=				config["lr"]
         _A   : int       					=				int(config["num_epochs"]		)
         _A   : str       					=				int(config["seed"]		)
         _A   : Dict       					=				int(config["batch_size"]		)
  
         # New Code #
         # Parse out whether we are saving every epoch or after a certain number of batches
         if hasattr(args.checkpointing_steps       ,							"isdigit"		):
                if args.checkpointing_steps == "epoch":
                       _A   : int       					=				args.checkpointing_steps
                elif args.checkpointing_steps.isdigit():
                       _A   : List[Any]       					=				int(args.checkpointing_steps		)
                else:
                       raise ValueError(
                           f"Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed."		)
         else:
                _A   : List[str]       					=				None
  
         set_seed(UpperCamelCase__		)
  
         _A					,    _A   : Tuple       					=				get_dataloaders(UpperCamelCase__       ,							UpperCamelCase__		)
         _A   : Any       					=				evaluate.load("glue"       ,							"mrpc"		)
  
         # If the batch size is too big we use gradient accumulation
         _A   : List[str]       					=				1
         if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
                _A   : Union[str, Any]       					=				batch_size // MAX_GPU_BATCH_SIZE
                _A   : Tuple       					=				MAX_GPU_BATCH_SIZE
  
         # Instantiate the model (we build the model here so that the seed also control new weights initialization)
         _A   : Any       					=				AutoModelForSequenceClassification.from_pretrained("bert-base-cased"       ,							return_dict=UpperCamelCase__		)
  
         # We could avoid this line since the accelerator is set with `device_placement=True` (default value).
         # Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
         # creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
         _A   : int       					=				model.to(accelerator.device		)
  
         # Instantiate optimizer
         _A   : Union[str, Any]       					=				AdamW(params=model.parameters()       ,							lr=UpperCamelCase__		)
  
         # Instantiate scheduler
         _A   : List[str]       					=				get_linear_schedule_with_warmup(
             optimizer=UpperCamelCase__       ,							num_warmup_steps=100       ,							num_training_steps=(len(UpperCamelCase__		) * num_epochs) // gradient_accumulation_steps       ,							)
  
         # Prepare everything
         # There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
         # prepare method.
         _A					,    _A					,    _A					,    _A					,    _A   : Union[str, Any]       					=				accelerator.prepare(
             UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__		)
  
         # New Code #
         # We need to keep track of how many total steps we have iterated over
         _A   : Tuple       					=				0
         # We also need to keep track of the stating epoch so files are named properly
         _A   : Optional[Any]       					=				0
  
         # We need to load the checkpoint back in before training here with `load_state`
         # The total number of epochs is adjusted based on where the state is being loaded from,
         # as we assume continuation of the same training script
         if args.resume_from_checkpoint:
                if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
                       accelerator.print(f"Resumed from checkpoint: {args.resume_from_checkpoint}"		)
                       accelerator.load_state(args.resume_from_checkpoint		)
                       _A   : int       					=				os.path.basename(args.resume_from_checkpoint		)
                else:
                       # Get the most recent checkpoint
                       _A   : List[str]       					=				[f.name for f in os.scandir(os.getcwd()		) if f.is_dir()]
                       dirs.sort(key=os.path.getctime		)
                       _A   : str       					=				dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
                # Extract `epoch_{i}` or `step_{i}`
                _A   : Optional[int]       					=				os.path.splitext(UpperCamelCase__		)[0]
  
                if "epoch" in training_difference:
                       _A   : List[Any]       					=				int(training_difference.replace("epoch_"       ,							""		)		) + 1
                       _A   : Dict       					=				None
                else:
                       _A   : Tuple       					=				int(training_difference.replace("step_"       ,							""		)		)
                       _A   : Any       					=				resume_step // len(UpperCamelCase__		)
                       resume_step -= starting_epoch * len(UpperCamelCase__		)
  
      # Now we train the model
         for epoch in range(UpperCamelCase__       ,							UpperCamelCase__		):
                model.train()
                # New Code #
                if args.resume_from_checkpoint and epoch == starting_epoch and resume_step is not None:
                       # We need to skip steps until we reach the resumed step
                       _A   : int       					=				accelerator.skip_first_batches(UpperCamelCase__       ,							UpperCamelCase__		)
                       overall_step += resume_step
                else:
                       # After the first iteration though, we need to go back to the original dataloader
                       _A   : Any       					=				train_dataloader
                for step, batch in enumerate(UpperCamelCase__		):
                       # We could avoid this line since we set the accelerator with `device_placement=True`.
                       batch.to(accelerator.device		)
                       _A   : int       					=				model(**UpperCamelCase__		)
                       _A   : Union[str, Any]       					=				outputs.loss
                       _A   : Union[str, Any]       					=				loss / gradient_accumulation_steps
                       accelerator.backward(UpperCamelCase__		)
                       if step % gradient_accumulation_steps == 0:
                              optimizer.step()
                              lr_scheduler.step()
                              optimizer.zero_grad()
                       # New Code #
                       overall_step += 1
  
                       # New Code #
                       # We save the model, optimizer, lr_scheduler, and seed states by calling `save_state`
                       # These are saved to folders named `step_{overall_step}`
                       # Will contain files: "pytorch_model.bin", "optimizer.bin", "scheduler.bin", and "random_states.pkl"
                       # If mixed precision was used, will also save a "scalar.bin" file
                       if isinstance(UpperCamelCase__       ,							UpperCamelCase__		):
                              _A   : Optional[Any]       					=				f"step_{overall_step}"
                              if overall_step % checkpointing_steps == 0:
                                     if args.output_dir is not None:
                                            _A   : Optional[Any]       					=				os.path.join(args.output_dir       ,							UpperCamelCase__		)
                                     accelerator.save_state(UpperCamelCase__		)
  
                model.eval()
                for step, batch in enumerate(UpperCamelCase__		):
                       # We could avoid this line since we set the accelerator with `device_placement=True` (the default).
                       batch.to(accelerator.device		)
                       with torch.no_grad():
                              _A   : int       					=				model(**UpperCamelCase__		)
                       _A   : Dict       					=				outputs.logits.argmax(dim=-1		)
                       _A					,    _A   : Dict       					=				accelerator.gather_for_metrics((predictions, batch["labels"])		)
                       metric.add_batch(
                           predictions=UpperCamelCase__       ,							references=UpperCamelCase__       ,							)
  
                _A   : int       					=				metric.compute()
                # Use accelerator.print to print only on the main process.
                accelerator.print(f"epoch {epoch}:"       ,							UpperCamelCase__		)
  
                # New Code #
                # We save the model, optimizer, lr_scheduler, and seed states by calling `save_state`
                # These are saved to folders named `epoch_{epoch}`
                # Will contain files: "pytorch_model.bin", "optimizer.bin", "scheduler.bin", and "random_states.pkl"
                # If mixed precision was used, will also save a "scalar.bin" file
                if checkpointing_steps == "epoch":
                       _A   : Any       					=				f"epoch_{epoch}"
                       if args.output_dir is not None:
                              _A   : Tuple       					=				os.path.join(args.output_dir       ,							UpperCamelCase__		)
                       accelerator.save_state(UpperCamelCase__		)
  def 							_UpperCAmelCase       ():
         _A   : Dict       					=				argparse.ArgumentParser(description="Simple example of training script."		)
         parser.add_argument(
             "--mixed_precision"       ,							type=UpperCamelCase__       ,							default=UpperCamelCase__       ,							choices=["no", "fp16", "bf16", "fp8"]       ,							help="Whether to use mixed precision. Choose"
             "between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."
             "and an Nvidia Ampere GPU."       ,							)
         parser.add_argument("--cpu"       ,							action="store_true"       ,							help="If passed, will train on the CPU."		)
         parser.add_argument(
             "--checkpointing_steps"       ,							type=UpperCamelCase__       ,							default=UpperCamelCase__       ,							help="Whether the various states should be saved at the end of every n steps, or 'epoch' for each epoch."       ,							)
         parser.add_argument(
             "--output_dir"       ,							type=UpperCamelCase__       ,							default="."       ,							help="Optional save directory where all checkpoint folders will be stored. Default is the current working directory."       ,							)
         parser.add_argument(
             "--resume_from_checkpoint"       ,							type=UpperCamelCase__       ,							default=UpperCamelCase__       ,							help="If the training should continue from a checkpoint folder."       ,							)
         _A   : Optional[Any]       					=				parser.parse_args()
         _A   : Optional[int]       					=				{"lr": 2E-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
         training_function(UpperCamelCase__       ,							UpperCamelCase__		)
  
  
  if __name__ == "__main__":
      main()
  
style_context: 
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
  
  
  lowerCAmelCase__   	=				{
      'configuration_deta': ['DETA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DetaConfig'],
  }
  
  try:
      if not is_vision_available():
          raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
      pass
  else:
      lowerCAmelCase__   	=				['DetaImageProcessor']
  
  try:
      if not is_torch_available():
          raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
      pass
  else:
      lowerCAmelCase__   	=				[
          'DETA_PRETRAINED_MODEL_ARCHIVE_LIST',
          'DetaForObjectDetection',
          'DetaModel',
          'DetaPreTrainedModel',
      ]
  
  
  if TYPE_CHECKING:
      from .configuration_deta import DETA_PRETRAINED_CONFIG_ARCHIVE_MAP, DetaConfig
  
      try:
          if not is_vision_available():
              raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
          pass
      else:
          from .image_processing_deta import DetaImageProcessor
  
      try:
          if not is_torch_available():
              raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
          pass
      else:
          from .modeling_deta import (
              DETA_PRETRAINED_MODEL_ARCHIVE_LIST,
              DetaForObjectDetection,
              DetaModel,
              DetaPreTrainedModel,
          )
  
  else:
      import sys
  
      lowerCAmelCase__   	=				_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
  