



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								=     1_6
__lowerCamelCase								=     3_2




def       UpperCAmelCase__	(							__snake_case  ,			__snake_case							)				->	Tuple:



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

  if hasattr(args.checkpointing_steps  ,			'''isdigit'''							):
    if args.checkpointing_steps == "epoch":
      _A          =      args.checkpointing_steps
    elif args.checkpointing_steps.isdigit():
      _A          =      int(args.checkpointing_steps							)
    else:
      raise ValueError(
          F'''Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed.'''							)
  else:
    _A          =      None
  # 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''']							)

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

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

  def tokenize_function(__snake_case							):
    # max_length=None => use the model max length (it's actually the default)
    _A          =      tokenizer(examples['''sentence1''']  ,			examples['''sentence2''']  ,			truncation=__A  ,			max_length=__A							)
    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(
        __A  ,			batched=__A  ,			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'''							)

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

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

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

  # Instantiate dataloaders.
  _A          =      DataLoader(
      tokenized_datasets['''train''']  ,			shuffle=__A  ,			collate_fn=__A  ,			batch_size=__A							)
  _A          =      DataLoader(
      tokenized_datasets['''validation''']  ,			shuffle=__A  ,			collate_fn=__A  ,			batch_size=__A							)

  set_seed(__A							)

  # 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=__A							)

  # 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=__A							)

  # Instantiate scheduler
  _A          =      get_linear_schedule_with_warmup(
      optimizer=__A  ,			num_warmup_steps=100  ,			num_training_steps=(len(__A							) * 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.
  _A      ,				_A      ,				_A      ,				_A      ,				_A          =      accelerator.prepare(
      __A  ,			__A  ,			__A  ,			__A  ,			__A							)

  # We need to keep track of how many total steps we have iterated over
  _A          =      0
  # We also need to keep track of the stating epoch so files are named properly
  _A          =      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							)
      _A          =      os.path.basename(args.resume_from_checkpoint							)
    else:
      # Get the most recent checkpoint
      _A          =      [f.name for f in os.scandir(os.getcwd()							) if f.is_dir()]
      dirs.sort(key=os.path.getctime							)
      _A          =      dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
    # Extract `epoch_{i}` or `step_{i}`
    _A          =      os.path.splitext(__A							)[0]

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

    # Now we train the model
  for epoch in range(__A  ,			__A							):
    model.train()
    if args.with_tracking:
      _A          =      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
      _A          =      accelerator.skip_first_batches(__A  ,			__A							)
      overall_step += resume_step
    else:
      # After the first iteration though, we need to go back to the original dataloader
      _A          =      train_dataloader
    for step, batch in enumerate(__A							):
      # We could avoid this line since we set the accelerator with `device_placement=True`.
      batch.to(accelerator.device							)
      _A          =      model(**__A							)
      _A          =      outputs.loss
      _A          =      loss / gradient_accumulation_steps
      # We keep track of the loss at each epoch
      if args.with_tracking:
        total_loss += loss.detach().float()
      accelerator.backward(__A							)
      if step % gradient_accumulation_steps == 0:
        optimizer.step()
        lr_scheduler.step()
        optimizer.zero_grad()

      overall_step += 1

      if isinstance(__A  ,			__A							):
        _A          =      F'''step_{overall_step}'''
        if overall_step % checkpointing_steps == 0:
          if args.output_dir is not None:
            _A          =      os.path.join(args.output_dir  ,			__A							)
          accelerator.save_state(__A							)

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

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

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

  if args.with_tracking:
    accelerator.end_training()




def       UpperCAmelCase__	(							)				->	List[Any]:
  _A          =      argparse.ArgumentParser(description='''Simple example of training script.'''							)
  parser.add_argument(
      '''--mixed_precision'''  ,			type=__A  ,			default=__A  ,			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=__A  ,			default=__A  ,			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=__A  ,			default=__A  ,			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=__A  ,			default='''.'''  ,			help='''Optional save directory where all checkpoint folders will be stored. Default is the current working directory.'''  ,			)
  parser.add_argument(
      '''--project_dir'''  ,			type=__A  ,			default='''logs'''  ,			help='''Location on where to store experiment tracking logs` and relevent project information'''  ,			)
  _A          =      parser.parse_args()
  _A          =      {'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
  training_function(__A  ,			__A							)


if __name__ == "__main__":
    main()




import math
from timeit import timeit




def       UpperCAmelCase__	(							__snake_case							)				->	int:
  _A          =      0
  _A          =      abs(__snake_case							)
  while True:
    _A          =      n // 10
    digits += 1
    if n == 0:
      break
  return digits




def       UpperCAmelCase__	(							__snake_case							)				->	int:
  return 1 if n == 0 else math.floor(math.log(abs(__snake_case							)  ,			10							) + 1							)




def       UpperCAmelCase__	(							__snake_case							)				->	int:
  return len(str(abs(__snake_case							)							)							)




def       UpperCAmelCase__	(							)				->	None:
  from collections.abc import Callable

  def benchmark_a_function(__snake_case  ,			__snake_case							) -> None:
    _A          =      F'''{func.__name__}({value})'''
    _A          =      timeit(F'''__main__.{call}'''  ,			setup='''import __main__'''							)
    print(F'''{call}: {func(__snake_case							)} -- {timing} seconds'''							)

  for value in (262_144, 1_125_899_906_842_624, 1_267_650_600_228_229_401_496_703_205_376):
    for func in (num_digits, num_digits_fast, num_digits_faster):
      benchmark_a_function(__snake_case  ,			__snake_case							)
    print()


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    benchmark()