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
  #
  ########################################################################
  
  a     :			Optional[int]					    =						1_6
  a     :			List[Any]					    =						3_2
  def 		snake_case__  (		lowercase       ,							lowercase = 16				):
     lowerCAmelCase_:       str         =  AutoTokenizer.from_pretrained("bert-base-cased"				)
     lowerCAmelCase_:       str         =  load_dataset("glue"       ,							"mrpc"				)
  
     def tokenize_function(lowercase				):
        # max_length=None => use the model max length (it's actually the default)
        lowerCAmelCase_:       Optional[Any]         =  tokenizer(examples["sentence1"]       ,							examples["sentence2"]       ,							truncation=lowercase       ,							max_length=lowercase				)
        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():
        lowerCAmelCase_:       List[str]         =  datasets.map(
            lowercase       ,							batched=lowercase       ,							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
     lowerCAmelCase_:       Dict         =  tokenized_datasets.rename_column("label"       ,							"labels"				)
  
     def collate_fn(lowercase				):
        # On TPU it's best to pad everything to the same length or training will be very slow.
        lowerCAmelCase_:       Union[str, Any]         =  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":
           lowerCAmelCase_:       int         =  16
        elif accelerator.mixed_precision != "no":
           lowerCAmelCase_:       Dict         =  8
        else:
           lowerCAmelCase_:       str         =  None
  
        return tokenizer.pad(
            lowercase       ,							padding="longest"       ,							max_length=lowercase       ,							pad_to_multiple_of=lowercase       ,							return_tensors="pt"       ,							)
  
     # Instantiate dataloaders.
     lowerCAmelCase_:       Any         =  DataLoader(
         tokenized_datasets["train"]       ,							shuffle=lowercase       ,							collate_fn=lowercase       ,							batch_size=lowercase				)
     lowerCAmelCase_:       Any         =  DataLoader(
         tokenized_datasets["validation"]       ,							shuffle=lowercase       ,							collate_fn=lowercase       ,							batch_size=lowercase				)
  
     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
  
   a     :			Tuple					    =						mocked_dataloaders  # noqa: F811
  def 		snake_case__  (		lowercase       ,							lowercase				):
     # For testing only
     if os.environ.get("TESTING_MOCKED_DATALOADERS"       ,							lowercase				) == "1":
        lowerCAmelCase_:       Tuple         =  2
     # Initialize accelerator
     lowerCAmelCase_:       int         =  Accelerator(cpu=args.cpu       ,							mixed_precision=args.mixed_precision				)
     # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
     lowerCAmelCase_:       str         =  config["lr"]
     lowerCAmelCase_:       List[Any]         =  int(config["num_epochs"]				)
     lowerCAmelCase_:       Tuple         =  int(config["seed"]				)
     lowerCAmelCase_:       List[Any]         =  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":
           lowerCAmelCase_:       Optional[int]         =  args.checkpointing_steps
        elif args.checkpointing_steps.isdigit():
           lowerCAmelCase_:       Union[str, Any]         =  int(args.checkpointing_steps				)
        else:
           raise ValueError(
               F'''Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed.'''				)
     else:
        lowerCAmelCase_:       List[Any]         =  None
  
     set_seed(lowercase				)
  
     lowerCAmelCase_     ,  lowerCAmelCase_:       Optional[int]         =  get_dataloaders(lowercase       ,							lowercase				)
     lowerCAmelCase_:       str         =  evaluate.load("glue"       ,							"mrpc"				)
  
     # If the batch size is too big we use gradient accumulation
     lowerCAmelCase_:       List[str]         =  1
     if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
        lowerCAmelCase_:       Optional[int]         =  batch_size // MAX_GPU_BATCH_SIZE
        lowerCAmelCase_:       Optional[Any]         =  MAX_GPU_BATCH_SIZE
  
     # Instantiate the model (we build the model here so that the seed also control new weights initialization)
     lowerCAmelCase_:       List[str]         =  AutoModelForSequenceClassification.from_pretrained("bert-base-cased"       ,							return_dict=lowercase				)
  
     # 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).
     lowerCAmelCase_:       Optional[int]         =  model.to(accelerator.device				)
  
     # Instantiate optimizer
     lowerCAmelCase_:       Optional[Any]         =  AdamW(params=model.parameters()       ,							lr=lowercase				)
  
     # Instantiate scheduler
     lowerCAmelCase_:       List[Any]         =  get_linear_schedule_with_warmup(
         optimizer=lowercase       ,							num_warmup_steps=100       ,							num_training_steps=(len(lowercase				) * 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.
     lowerCAmelCase_     ,  lowerCAmelCase_     ,  lowerCAmelCase_     ,  lowerCAmelCase_     ,  lowerCAmelCase_:       int         =  accelerator.prepare(
         lowercase       ,							lowercase       ,							lowercase       ,							lowercase       ,							lowercase				)
  
     # New Code #
     # We need to keep track of how many total steps we have iterated over
     lowerCAmelCase_:       int         =  0
     # We also need to keep track of the stating epoch so files are named properly
     lowerCAmelCase_:       Optional[int]         =  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				)
           lowerCAmelCase_:       Any         =  os.path.basename(args.resume_from_checkpoint				)
        else:
           # Get the most recent checkpoint
           lowerCAmelCase_:       List[Any]         =  [f.name for f in os.scandir(os.getcwd()				) if f.is_dir()]
           dirs.sort(key=os.path.getctime				)
           lowerCAmelCase_:       int         =  dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
        # Extract `epoch_{i}` or `step_{i}`
        lowerCAmelCase_:       Optional[Any]         =  os.path.splitext(lowercase				)[0]
  
        if "epoch" in training_difference:
           lowerCAmelCase_:       List[Any]         =  int(training_difference.replace("epoch_"       ,							""				)				) + 1
           lowerCAmelCase_:       Optional[Any]         =  None
        else:
           lowerCAmelCase_:       Tuple         =  int(training_difference.replace("step_"       ,							""				)				)
           lowerCAmelCase_:       Tuple         =  resume_step // len(lowercase				)
           resume_step -= starting_epoch * len(lowercase				)
  
      # Now we train the model
     for epoch in range(lowercase       ,							lowercase				):
        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
           lowerCAmelCase_:       Optional[int]         =  accelerator.skip_first_batches(lowercase       ,							lowercase				)
           overall_step += resume_step
        else:
           # After the first iteration though, we need to go back to the original dataloader
           lowerCAmelCase_:       List[Any]         =  train_dataloader
        for step, batch in enumerate(lowercase				):
           # We could avoid this line since we set the accelerator with `device_placement=True`.
           batch.to(accelerator.device				)
           lowerCAmelCase_:       Any         =  model(**lowercase				)
           lowerCAmelCase_:       int         =  outputs.loss
           lowerCAmelCase_:       Optional[Any]         =  loss / gradient_accumulation_steps
           accelerator.backward(lowercase				)
           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(lowercase       ,							lowercase				):
              lowerCAmelCase_:       int         =  F'''step_{overall_step}'''
              if overall_step % checkpointing_steps == 0:
                 if args.output_dir is not None:
                    lowerCAmelCase_:       str         =  os.path.join(args.output_dir       ,							lowercase				)
                 accelerator.save_state(lowercase				)
  
        model.eval()
        for step, batch in enumerate(lowercase				):
           # We could avoid this line since we set the accelerator with `device_placement=True` (the default).
           batch.to(accelerator.device				)
           with torch.no_grad():
              lowerCAmelCase_:       Any         =  model(**lowercase				)
           lowerCAmelCase_:       List[str]         =  outputs.logits.argmax(dim=-1				)
           lowerCAmelCase_     ,  lowerCAmelCase_:       str         =  accelerator.gather_for_metrics((predictions, batch["labels"])				)
           metric.add_batch(
               predictions=lowercase       ,							references=lowercase       ,							)
  
        lowerCAmelCase_:       Tuple         =  metric.compute()
        # Use accelerator.print to print only on the main process.
        accelerator.print(F'''epoch {epoch}:'''       ,							lowercase				)
  
        # 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":
           lowerCAmelCase_:       List[str]         =  F'''epoch_{epoch}'''
           if args.output_dir is not None:
              lowerCAmelCase_:       int         =  os.path.join(args.output_dir       ,							lowercase				)
           accelerator.save_state(lowercase				)
  def 		snake_case__  (		):
     lowerCAmelCase_:       List[str]         =  argparse.ArgumentParser(description="Simple example of training script."				)
     parser.add_argument(
         "--mixed_precision"       ,							type=lowercase       ,							default=lowercase       ,							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=lowercase       ,							default=lowercase       ,							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=lowercase       ,							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=lowercase       ,							default=lowercase       ,							help="If the training should continue from a checkpoint folder."       ,							)
     lowerCAmelCase_:       Dict         =  parser.parse_args()
     lowerCAmelCase_:       str         =  {"lr": 2E-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
     training_function(lowercase       ,							lowercase				)
  
  
  if __name__ == "__main__":
   main()
style_context: from ctypes import c_float, sizeof
  from enum import Enum
  from typing import TYPE_CHECKING, Optional, Union
  
  
  if TYPE_CHECKING:
   from .. import AutoFeatureExtractor, AutoProcessor, AutoTokenizer  # tests_ignore
  
  class 				_lowercase						(    UpperCAmelCase__ ):
  
  
  
  
  
  
      '''simple docstring'''
      SCREAMING_SNAKE_CASE:							Union[str, Any]        =						c_float
  
  
  
  
      @property
      def     _a						(						self   ):
         return sizeof(self.value   )
  
  
  
  
  
  def 		snake_case__  (		lowercase       ,							lowercase       ,							lowercase = 0				):
     # < 0 is possible if using a dynamic axis
     if dimension <= 0:
        lowerCAmelCase_:       int         =  fixed_dimension
  
     dimension -= num_token_to_add
     return dimension
  def 		snake_case__  (		lowercase       ,							lowercase				):
     return num_parameters * dtype.size
  
  
  
  
  
  
  
  def 		snake_case__  (		lowercase				):
     from .. import AutoFeatureExtractor, AutoProcessor, AutoTokenizer  # tests_ignore
  
     try:
        return AutoProcessor.from_pretrained(lowercase				)
     except (ValueError, OSError, KeyError):
        lowerCAmelCase_     ,  lowerCAmelCase_:       int         =  None, None
        try:
           lowerCAmelCase_:       Dict         =  AutoTokenizer.from_pretrained(lowercase				)
        except (OSError, KeyError):
           pass
        try:
           lowerCAmelCase_:       Optional[int]         =  AutoFeatureExtractor.from_pretrained(lowercase				)
        except (OSError, KeyError):
           pass
  
        if tokenizer is not None and feature_extractor is not None:
           raise ValueError(
               F'''Couldn\'t auto-detect preprocessor for {model_name}. Found both a tokenizer and a feature extractor.'''				)
        elif tokenizer is None and feature_extractor is None:
           return None
        elif tokenizer is not None:
           return tokenizer
        else:
           return feature_extractor