code: 
  
  
  
  
  """simple docstring"""
  
  
  
  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
  #
  ########################################################################
  
  SCREAMING_SNAKE_CASE       = 32
  
  
  
  
  
  
  
  def 						_SCREAMING_SNAKE_CASE     (							lowercase_    ,				lowercase_ = 16		)		->	Optional[int]:
   A__ =       AutoTokenizer.from_pretrained("bert-base-cased"		)
   A__ =       load_dataset("glue"    ,				"mrpc"		)
  
   def tokenize_function(lowercase_		):
    # max_length=None => use the model max length (it's actually the default)
    A__ =       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():
    A__ =       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
   A__ =       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":
     A__ =       16
    elif accelerator.mixed_precision != "no":
     A__ =       8
    else:
     A__ =       None
  
    return tokenizer.pad(
        lowercase_    ,				padding="longest"    ,				pad_to_multiple_of=lowercase_    ,				return_tensors="pt"    ,				)
  
   # Instantiate dataloaders.
   A__ =       DataLoader(
       tokenized_datasets["train"]    ,				shuffle=lowercase_    ,				collate_fn=lowercase_    ,				batch_size=lowercase_		)
   A__ =       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
  
        SCREAMING_SNAKE_CASE       = mocked_dataloaders  # noqa: F811
  
  
  
  
  
  
  
  def 						_SCREAMING_SNAKE_CASE     (							lowercase_    ,				lowercase_		)		->	Tuple:
   # For testing only
   if os.environ.get("TESTING_MOCKED_DATALOADERS"    ,				lowercase_		) == "1":
    A__ =       2
   # Initialize accelerator
   A__ =       Accelerator(cpu=args.cpu    ,				mixed_precision=args.mixed_precision		)
   # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
   A__ =       config["lr"]
   A__ =       int(config["num_epochs"]		)
   A__ =       int(config["seed"]		)
   A__ =       int(config["batch_size"]		)
  
   A__ =       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
    A__ =       observed_batch_size // batch_size
  
    # And then set it in the Accelerator directly:
    A__ =       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)
    A__ =       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).
    A__ =       model.to(accelerator.device		)
  
    # Instantiate optimizer
    A__ =       AdamW(params=model.parameters()    ,				lr=lowercase_		)
    A__,   A__ =       get_dataloaders(lowercase_    ,				lowercase_		)
  
    # Instantiate scheduler
    A__ =       get_linear_schedule_with_warmup(
        optimizer=lowercase_    ,				num_warmup_steps=1_00    ,				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.
    A__,   A__,   A__,   A__,   A__ =       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		)
       A__ =       model(**lowercase_		)
       A__ =       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():
       A__ =       model(**lowercase_		)
      A__ =       outputs.logits.argmax(dim=-1		)
      A__,   A__ =       accelerator.gather_for_metrics((predictions, batch["labels"])		)
      metric.add_batch(
          predictions=lowercase_    ,				references=lowercase_    ,				)
  
     A__ =       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 						_SCREAMING_SNAKE_CASE     (							)		->	List[Any]:
   A__ =       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."		)
   A__ =       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
   A__ =       {"lr": 2E-5, "num_epochs": 3, "seed": 42, "batch_size": 2_56}
   training_function(lowercase_    ,				lowercase_		)
  
  
  if __name__ == "__main__":
        main()
  
  
  
  
  
  
style_context: 
  
  
  
  
  """simple docstring"""
  
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import _LazyModule
  
  
  SCREAMING_SNAKE_CASE       = {"tokenization_bert_japanese": ["BertJapaneseTokenizer", "CharacterTokenizer", "MecabTokenizer"]}
  
  
  if TYPE_CHECKING:
        from .tokenization_bert_japanese import BertJapaneseTokenizer, CharacterTokenizer, MecabTokenizer
  
  else:
        import sys
  
        SCREAMING_SNAKE_CASE       = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
  
  
  
  
  
  