

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,
# specifically showcasing the checkpointing capability,
# and builds off the `nlp_example.py` script.
#
# 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 help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# 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
#
########################################################################

lowerCamelCase 	=   16
lowerCamelCase 	=   32




def      UpperCAmelCase__							(     _A :					Accelerator      , _A :					int = 16				):





  '''simple docstring'''







  a__    =AutoTokenizer.from_pretrained('''bert-base-cased'''				)
  a__    =load_dataset('''glue'''      , '''mrpc'''				)

  def tokenize_function(_A :					List[str]				):
    # 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'''				)

  def collate_fn(_A :					Optional[Any]				):
    # On TPU it's best to pad everything to the same length or training will be very slow.
    a__    =1_28 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				)

  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

       lowerCamelCase 	=   mocked_dataloaders  # noqa: F811




def      UpperCAmelCase__							(     _A :					str      , _A :					Dict				):





  '''simple docstring'''







  if os.environ.get('''TESTING_MOCKED_DATALOADERS'''      , _A				) == "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''']				)

  # New Code #
  # Parse out whether we are saving every epoch or after a certain number of batches
  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

  set_seed(_A				)

  a__    =get_dataloaders(_A      , _A				)
  a__    =evaluate.load('''glue'''      , '''mrpc'''				)

  # 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

  # 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=1_00      , 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__    =accelerator.prepare(
      _A      , _A      , _A      , _A      , _A				)

  # New Code #
  # 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

  # We need to load the checkpoint back in before training here with `load_state`
  # The total number of epochs is adjusted based on where the state is being loaded from,
  # as we assume continuation of the same training script
  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()
    # New Code #
    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
      accelerator.backward(_A				)
      if step % gradient_accumulation_steps == 0:
        optimizer.step()
        lr_scheduler.step()
        optimizer.zero_grad()
      # New Code #
      overall_step += 1

      # New Code #
      # We save the model, optimizer, lr_scheduler, and seed states by calling `save_state`
      # These are saved to folders named `step_{overall_step}`
      # Will contain files: "pytorch_model.bin", "optimizer.bin", "scheduler.bin", and "random_states.pkl"
      # If mixed precision was used, will also save a "scalar.bin" file
      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` (the default).
      batch.to(accelerator.device				)
      with torch.no_grad():
        a__    =model(**_A				)
      a__    =outputs.logits.argmax(dim=-1				)
      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				)

    # New Code #
    # We save the model, optimizer, lr_scheduler, and seed states by calling `save_state`
    # These are saved to folders named `epoch_{epoch}`
    # Will contain files: "pytorch_model.bin", "optimizer.bin", "scheduler.bin", and "random_states.pkl"
    # If mixed precision was used, will also save a "scalar.bin" file
    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				)




def      UpperCAmelCase__							(     ):





  '''simple docstring'''







  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(
      '''--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(
      '''--resume_from_checkpoint'''      , type=_A      , default=_A      , help='''If the training should continue from a checkpoint folder.'''      , )
  a__    =parser.parse_args()
  a__    ={'lr': 2E-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
  training_function(_A      , _A				)


if __name__ == "__main__":
       main()



def _a					(			SCREAMING_SNAKE_CASE						:     str						)       ->       str:

 """simple docstring"""






 return "".join(chr(ord(SCREAMING_SNAKE_CASE						) + 32						) if 'A' <= char <= 'Z' else char for char in word						)


if __name__ == "__main__":
  from doctest import testmod

  testmod()





