

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
#
# 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)
#
# This example also demonstrates the checkpointing and sharding capabilities
#
# 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(   lowercase_     ,   lowercase_				)      ->							Tuple:





  '''simple docstring'''





  if args.with_tracking:
    snake_case_					= Accelerator(
        cpu=args.cpu     ,   mixed_precision=args.mixed_precision     ,   log_with="""all"""     ,   project_dir=args.project_dir				)
  else:
    snake_case_					= Accelerator(cpu=args.cpu     ,   mixed_precision=args.mixed_precision				)

  if hasattr(args.checkpointing_steps     ,   """isdigit"""				):
    if args.checkpointing_steps == "epoch":
      snake_case_					= args.checkpointing_steps
    elif args.checkpointing_steps.isdigit():
      snake_case_					= int(args.checkpointing_steps				)
    else:
      raise ValueError(
          f'''Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed.'''				)
  else:
    snake_case_					= None
  # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
  snake_case_					= config["""lr"""]
  snake_case_					= int(config["""num_epochs"""]				)
  snake_case_					= int(config["""seed"""]				)
  snake_case_					= int(config["""batch_size"""]				)

  # We need to initialize the trackers we use, and also store our configuration
  if args.with_tracking:
    snake_case_					= os.path.split(lowercase_				)[-1].split("""."""				)[0]
    accelerator.init_trackers(lowercase_     ,   lowercase_				)

  snake_case_					= AutoTokenizer.from_pretrained("""bert-base-cased"""				)
  snake_case_					= load_dataset("""glue"""     ,   """mrpc"""				)
  snake_case_					= evaluate.load("""glue"""     ,   """mrpc"""				)

  def tokenize_function(lowercase_				):
    # max_length=None => use the model max length (it's actually the default)
    snake_case_					= 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():
    snake_case_					= 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
  snake_case_					= tokenized_datasets.rename_column("""label"""     ,   """labels"""				)

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

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

    return tokenizer.pad(
        lowercase_     ,   padding="""longest"""     ,   max_length=lowercase_     ,   pad_to_multiple_of=lowercase_     ,   return_tensors="""pt"""     ,   )

  # Instantiate dataloaders.
  snake_case_					= DataLoader(
      tokenized_datasets["""train"""]     ,   shuffle=lowercase_     ,   collate_fn=lowercase_     ,   batch_size=lowercase_				)
  snake_case_					= DataLoader(
      tokenized_datasets["""validation"""]     ,   shuffle=lowercase_     ,   collate_fn=lowercase_     ,   batch_size=lowercase_				)

  set_seed(lowercase_				)

  # Instantiate the model (we build the model here so that the seed also control new weights initialization)
  snake_case_					= 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).
  snake_case_					= model.to(accelerator.device				)

  # Instantiate optimizer
  snake_case_					= AdamW(params=model.parameters()     ,   lr=lowercase_				)

  # Instantiate scheduler
  snake_case_					= 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.
  snake_case_		,							snake_case_		,							snake_case_		,							snake_case_		,							snake_case_					= accelerator.prepare(
      lowercase_     ,   lowercase_     ,   lowercase_     ,   lowercase_     ,   lowercase_				)

  # We need to keep track of how many total steps we have iterated over
  snake_case_					= 0
  # We also need to keep track of the stating epoch so files are named properly
  snake_case_					= 0

  # Potentially load in the weights and states from a previous save
  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				)
      snake_case_					= os.path.basename(args.resume_from_checkpoint				)
    else:
      # Get the most recent checkpoint
      snake_case_					= [f.name for f in os.scandir(os.getcwd()				) if f.is_dir()]
      dirs.sort(key=os.path.getctime				)
      snake_case_					= dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
    # Extract `epoch_{i}` or `step_{i}`
    snake_case_					= os.path.splitext(lowercase_				)[0]

    if "epoch" in training_difference:
      snake_case_					= int(training_difference.replace("""epoch_"""     ,   """"""				)				) + 1
      snake_case_					= None
    else:
      snake_case_					= int(training_difference.replace("""step_"""     ,   """"""				)				)
      snake_case_					= resume_step // len(lowercase_				)
      resume_step -= starting_epoch * len(lowercase_				)

    # Now we train the model
  for epoch in range(lowercase_     ,   lowercase_				):
    model.train()
    if args.with_tracking:
      snake_case_					= 0
    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
      snake_case_					= 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
      snake_case_					= 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				)
      snake_case_					= model(**lowercase_				)
      snake_case_					= outputs.loss
      snake_case_					= loss / gradient_accumulation_steps
      # We keep track of the loss at each epoch
      if args.with_tracking:
        total_loss += loss.detach().float()
      accelerator.backward(lowercase_				)
      if step % gradient_accumulation_steps == 0:
        optimizer.step()
        lr_scheduler.step()
        optimizer.zero_grad()

      overall_step += 1

      if isinstance(lowercase_     ,   lowercase_				):
        snake_case_					= f'''step_{overall_step}'''
        if overall_step % checkpointing_steps == 0:
          if args.output_dir is not None:
            snake_case_					= 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`.
      batch.to(accelerator.device				)
      with torch.no_grad():
        snake_case_					= model(**lowercase_				)
      snake_case_					= outputs.logits.argmax(dim=-1				)
      snake_case_		,							snake_case_					= accelerator.gather_for_metrics((predictions, batch["""labels"""])				)
      metric.add_batch(
          predictions=lowercase_     ,   references=lowercase_     ,   )

    snake_case_					= metric.compute()
    # Use accelerator.print to print only on the main process.
    accelerator.print(f'''epoch {epoch}:'''     ,   lowercase_				)
    if args.with_tracking:
      accelerator.log(
          {
              """accuracy""": eval_metric["""accuracy"""],
              """f1""": eval_metric["""f1"""],
              """train_loss""": total_loss.item() / len(lowercase_				),
              """epoch""": epoch,
          }     ,   step=lowercase_     ,   )

    if checkpointing_steps == "epoch":
      snake_case_					= f'''epoch_{epoch}'''
      if args.output_dir is not None:
        snake_case_					= os.path.join(args.output_dir     ,   lowercase_				)
      accelerator.save_state(lowercase_				)

  if args.with_tracking:
    accelerator.end_training()



def   UpperCamelCase(   )      ->							Any:





  '''simple docstring'''





  snake_case_					= 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(
      """--resume_from_checkpoint"""     ,   type=lowercase_     ,   default=lowercase_     ,   help="""If the training should continue from a checkpoint folder."""     ,   )
  parser.add_argument(
      """--with_tracking"""     ,   action="""store_true"""     ,   help="""Whether to load in all available experiment trackers from the environment and use them for logging."""     ,   )
  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(
      """--project_dir"""     ,   type=lowercase_     ,   default="""logs"""     ,   help="""Location on where to store experiment tracking logs` and relevent project information"""     ,   )
  snake_case_					= parser.parse_args()
  snake_case_					= {"""lr""": 2e-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
  training_function(lowercase_     ,   lowercase_				)


if __name__ == "__main__":
    main()


def   UpperCamelCase(   lowercase_     ,   lowercase_				)      ->							Union[str, Any]:





  '''simple docstring'''





  if len(str(lowercase_				)				) == 1 or len(str(lowercase_				)				) == 1:
    return a * b

  snake_case_					= max(len(str(lowercase_				)				)     ,   len(str(lowercase_				)				)				)
  snake_case_					= ma // 2

  snake_case_		,							snake_case_					= divmod(lowercase_     ,   10**ma				)
  snake_case_		,							snake_case_					= divmod(lowercase_     ,   10**ma				)

  snake_case_					= karatsuba(lowercase_     ,   lowercase_				)
  snake_case_					= karatsuba((aa + aa)     ,   (ba + ba)				)
  snake_case_					= karatsuba(lowercase_     ,   lowercase_				)

  return (z * 10 ** (2 * ma)) + ((y - z - x) * 10 ** (ma)) + (x)



def   UpperCamelCase(   )      ->							Dict:





  '''simple docstring'''





  print(karatsuba(15463     ,   23489				)				)


if __name__ == "__main__":
    main()