



"""simple docstring"""







import argparse

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
#
# 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 run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################


lowercase__		:    Any				   =     1_6
lowercase__		:    str				   =     3_2

def        UpperCamelCase_   (   lowerCAmelCase__				:    Accelerator   ,       lowerCAmelCase__				:    int = 16						)						->       Optional[Any]:

  """simple docstring"""




  lowerCAmelCase_       :    Any								=			AutoTokenizer.from_pretrained('bert-base-cased'						)
  lowerCAmelCase_       :    Optional[Any]								=			load_dataset('glue'   ,       'mrpc'						)

  def tokenize_function(lowerCAmelCase__				:    Dict						):
    # max_length=None => use the model max length (it's actually the default)
    lowerCAmelCase_       :    str								=			tokenizer(examples['sentence1']   ,       examples['sentence2']   ,       truncation=lowerCAmelCase__   ,       max_length=lowerCAmelCase__						)
    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_       :    str								=			datasets.map(
        lowerCAmelCase__   ,       batched=lowerCAmelCase__   ,       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_       :    Union[str, Any]								=			tokenized_datasets.rename_column('label'   ,       'labels'						)

  def collate_fn(lowerCAmelCase__				:    Any						):
    # On TPU it's best to pad everything to the same length or training will be very slow.
    lowerCAmelCase_       :    Tuple								=			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_       :    str								=			16
    elif accelerator.mixed_precision != "no":
      lowerCAmelCase_       :    str								=			8
    else:
      lowerCAmelCase_       :    Tuple								=			None

    return tokenizer.pad(
        lowerCAmelCase__   ,       padding='longest'   ,       max_length=lowerCAmelCase__   ,       pad_to_multiple_of=lowerCAmelCase__   ,       return_tensors='pt'   ,       )

  # Instantiate dataloaders.
  lowerCAmelCase_       :    Tuple								=			DataLoader(
      tokenized_datasets['train']   ,       shuffle=lowerCAmelCase__   ,       collate_fn=lowerCAmelCase__   ,       batch_size=lowerCAmelCase__   ,       drop_last=lowerCAmelCase__						)
  lowerCAmelCase_       :    Optional[Any]								=			DataLoader(
      tokenized_datasets['validation']   ,       shuffle=lowerCAmelCase__   ,       collate_fn=lowerCAmelCase__   ,       batch_size=lowerCAmelCase__   ,       drop_last=(accelerator.mixed_precision == 'fp8')   ,       )

  return train_dataloader, eval_dataloader

def        UpperCamelCase_   (   lowerCAmelCase__				:    List[Any]   ,       lowerCAmelCase__				:    Any						)						->       List[str]:

  """simple docstring"""




  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_       :    Dict								=			int(config['num_epochs']						)
  lowerCAmelCase_       :    Optional[Any]								=			int(config['seed']						)
  lowerCAmelCase_       :    Any								=			int(config['batch_size']						)

  lowerCAmelCase_       :    Optional[int]								=			evaluate.load('glue'   ,       'mrpc'						)

  # If the batch size is too big we use gradient accumulation
  lowerCAmelCase_       :    Tuple								=			1
  if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
    lowerCAmelCase_       :    Any								=			batch_size // MAX_GPU_BATCH_SIZE
    lowerCAmelCase_       :    Any								=			MAX_GPU_BATCH_SIZE

  set_seed(lowerCAmelCase__						)
  lowerCAmelCase_						,lowerCAmelCase_       :    str								=			get_dataloaders(lowerCAmelCase__   ,       lowerCAmelCase__						)
  # Instantiate the model (we build the model here so that the seed also control new weights initialization)
  lowerCAmelCase_       :    Union[str, Any]								=			AutoModelForSequenceClassification.from_pretrained('bert-base-cased'   ,       return_dict=lowerCAmelCase__						)

  # 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_       :    str								=			model.to(accelerator.device						)
  # Instantiate optimizer
  lowerCAmelCase_       :    Tuple								=			AdamW(params=model.parameters()   ,       lr=lowerCAmelCase__						)

  # Instantiate scheduler
  lowerCAmelCase_       :    Tuple								=			get_linear_schedule_with_warmup(
      optimizer=lowerCAmelCase__   ,       num_warmup_steps=100   ,       num_training_steps=(len(lowerCAmelCase__						) * 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_       :    Optional[int]								=			accelerator.prepare(
      lowerCAmelCase__   ,       lowerCAmelCase__   ,       lowerCAmelCase__   ,       lowerCAmelCase__   ,       lowerCAmelCase__						)

  # Now we train the model
  for epoch in range(lowerCAmelCase__						):
    model.train()
    for step, batch in enumerate(lowerCAmelCase__						):
      # We could avoid this line since we set the accelerator with `device_placement=True`.
      batch.to(accelerator.device						)
      lowerCAmelCase_       :    List[str]								=			model(**lowerCAmelCase__						)
      lowerCAmelCase_       :    List[str]								=			outputs.loss
      lowerCAmelCase_       :    int								=			loss / gradient_accumulation_steps
      accelerator.backward(lowerCAmelCase__						)
      if step % gradient_accumulation_steps == 0:
        optimizer.step()
        lr_scheduler.step()
        optimizer.zero_grad()

    model.eval()
    for step, batch in enumerate(lowerCAmelCase__						):
      # We could avoid this line since we set the accelerator with `device_placement=True`.
      batch.to(accelerator.device						)
      with torch.no_grad():
        lowerCAmelCase_       :    Dict								=			model(**lowerCAmelCase__						)
      lowerCAmelCase_       :    Union[str, Any]								=			outputs.logits.argmax(dim=-1						)
      lowerCAmelCase_						,lowerCAmelCase_       :    int								=			accelerator.gather_for_metrics((predictions, batch['labels'])						)
      metric.add_batch(
          predictions=lowerCAmelCase__   ,       references=lowerCAmelCase__   ,       )

    lowerCAmelCase_       :    Optional[Any]								=			metric.compute()
    # Use accelerator.print to print only on the main process.
    accelerator.print(f"epoch {epoch}:"   ,       lowerCAmelCase__						)

def        UpperCamelCase_   (   )						->       Tuple:

  """simple docstring"""




  lowerCAmelCase_       :    Optional[Any]								=			argparse.ArgumentParser(description='Simple example of training script.'						)
  parser.add_argument(
      '--mixed_precision'   ,       type=lowerCAmelCase__   ,       default=lowerCAmelCase__   ,       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_       :    Optional[Any]								=			parser.parse_args()
  lowerCAmelCase_       :    Tuple								=			{'lr': 2e-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
  training_function(lowerCAmelCase__   ,       lowerCAmelCase__						)


if __name__ == "__main__":
     main()










"""simple docstring"""







import baseaa

def     UpperCamelCase_   (   lowerCAmelCase__				:    str						)						->       bytes:

  """simple docstring"""




  return baseaa.aaaencode(string.encode('utf-8'						)						)

def     UpperCamelCase_   (   lowerCAmelCase__				:    bytes						)						->       str:

  """simple docstring"""




  return baseaa.aaadecode(lowerCAmelCase__						).decode('utf-8'						)


if __name__ == "__main__":
     import doctest

     doctest.testmod()





