code: import argparse
  import os
  
  # New Code #
  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
  from accelerate.utils import find_executable_batch_size
  
  
  ########################################################################
  # This is a fully working simple example to use Accelerate,
  # specifically showcasing how to combine both the gradient accumulation
  # and automatic batch size finder utilities of Accelerate to perfrom
  # automatic gradient accumulation
  #
  # 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)
  #
  # 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     :			Dict					    =						3_2
  def 		snake_case__  (		lowercase       ,							lowercase = 16				):
     lowerCAmelCase_:       int         =  AutoTokenizer.from_pretrained("bert-base-cased"				)
     lowerCAmelCase_:       Optional[Any]         =  load_dataset("glue"       ,							"mrpc"				)
  
     def tokenize_function(lowercase				):
        # max_length=None => use the model max length (it's actually the default)
        lowerCAmelCase_:       List[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_:       Optional[int]         =  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_:       List[Any]         =  tokenized_datasets.rename_column("label"       ,							"labels"				)
  
     def collate_fn(lowercase				):
        # When using mixed precision we want round multiples of 8/16
        if accelerator.mixed_precision == "fp8":
           lowerCAmelCase_:       Dict         =  16
        elif accelerator.mixed_precision != "no":
           lowerCAmelCase_:       str         =  8
        else:
           lowerCAmelCase_:       int         =  None
  
        return tokenizer.pad(
            lowercase       ,							padding="longest"       ,							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_:       Tuple         =  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     :			Union[str, Any]					    =						mocked_dataloaders  # noqa: F811
  def 		snake_case__  (		lowercase       ,							lowercase				):
     # For testing only
     if os.environ.get("TESTING_MOCKED_DATALOADERS"       ,							lowercase				) == "1":
        lowerCAmelCase_:       List[Any]         =  2
     # Initialize accelerator
     lowerCAmelCase_:       Tuple         =  Accelerator(cpu=args.cpu       ,							mixed_precision=args.mixed_precision				)
     # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
     lowerCAmelCase_:       Dict         =  config["lr"]
     lowerCAmelCase_:       List[str]         =  int(config["num_epochs"]				)
     lowerCAmelCase_:       str         =  int(config["seed"]				)
     lowerCAmelCase_:       Dict         =  int(config["batch_size"]				)
  
     lowerCAmelCase_:       Optional[int]         =  evaluate.load("glue"       ,							"mrpc"				)
  
     # New Code #
     # We use the `find_executable_batch_size` decorator, passing in the desired observed batch size
     # to train on. If a CUDA OOM error occurs, it will retry this loop cutting the batch size in
     # half each time. From this, we can calculate the number of gradient accumulation steps needed
     # and modify the Accelerator object as a result
     @find_executable_batch_size(starting_batch_size=int(lowercase				)				)
     def inner_training_loop(lowercase				):
        # Since we need to modify the outside accelerator object, we need to bring it
        # to the local scope
        nonlocal accelerator
  
        # We can calculate the number of gradient accumulation steps based on the current
        # batch size vs the starting batch size
        lowerCAmelCase_:       Optional[Any]         =  observed_batch_size // batch_size
  
        # And then set it in the Accelerator directly:
        lowerCAmelCase_:       List[str]         =  num_gradient_accumulation_steps
  
        # Next we need to free all of the stored model references in the Accelerator each time
        accelerator.free_memory()
  
        # And set the seed so our results are reproducable each reset
        set_seed(lowercase				)
  
        # Instantiate the model (we build the model here so that the seed also control new weights initialization)
        lowerCAmelCase_:       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_:       List[Any]         =  model.to(accelerator.device				)
  
        # Instantiate optimizer
        lowerCAmelCase_:       str         =  AdamW(params=model.parameters()       ,							lr=lowercase				)
        lowerCAmelCase_     ,  lowerCAmelCase_:       List[str]         =  get_dataloaders(lowercase       ,							lowercase				)
  
        # Instantiate scheduler
        lowerCAmelCase_:       List[Any]         =  get_linear_schedule_with_warmup(
            optimizer=lowercase       ,							num_warmup_steps=100       ,							num_training_steps=(len(lowercase				) * num_epochs)       ,							)
  
        # 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_:       Any         =  accelerator.prepare(
            lowercase       ,							lowercase       ,							lowercase       ,							lowercase       ,							lowercase				)
  
        # Now we train the model
        for epoch in range(lowercase				):
           model.train()
           for step, batch in enumerate(lowercase				):
              # And perform gradient accumulation
              with accelerator.accumulate(lowercase				):
                 # We could avoid this line since we set the accelerator with `device_placement=True`.
                 batch.to(accelerator.device				)
                 lowerCAmelCase_:       Any         =  model(**lowercase				)
                 lowerCAmelCase_:       List[Any]         =  outputs.loss
                 accelerator.backward(lowercase				)
                 optimizer.step()
                 lr_scheduler.step()
                 optimizer.zero_grad()
  
           model.eval()
           for step, batch in enumerate(lowercase				):
              # We could avoid this line since we set the accelerator with `device_placement=True`.
              batch.to(accelerator.device				)
              with torch.no_grad():
                 lowerCAmelCase_:       int         =  model(**lowercase				)
              lowerCAmelCase_:       Optional[int]         =  outputs.logits.argmax(dim=-1				)
              lowerCAmelCase_     ,  lowerCAmelCase_:       Optional[int]         =  accelerator.gather_for_metrics((predictions, batch["labels"])				)
              metric.add_batch(
                  predictions=lowercase       ,							references=lowercase       ,							)
  
           lowerCAmelCase_:       int         =  metric.compute()
           # Use accelerator.print to print only on the main process.
           accelerator.print(F'''epoch {epoch}:'''       ,							lowercase				)
  
      # New Code #
      # And call it at the end with no arguments
      # Note: You could also refactor this outside of your training loop function
     inner_training_loop()
  def 		snake_case__  (		):
     lowerCAmelCase_:       Tuple         =  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."				)
     lowerCAmelCase_:       int         =  parser.parse_args()
     # New Code #
     # We modify the starting batch size to be an observed batch size of 256, to guarentee an initial CUDA OOM
     lowerCAmelCase_:       Dict         =  {"lr": 2E-5, "num_epochs": 3, "seed": 42, "batch_size": 256}
     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