





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
#
########################################################################


__lowercase				    =      16
__lowercase				    =      32


def        __SCREAMING_SNAKE_CASE	(				__lowercase:		List[str]  ,				__lowercase:		List[str]):



  '''simple docstring'''






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

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

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

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

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

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

  def collate_fn(__lowercase:		Union[str, Any]):
    # On TPU it's best to pad everything to the same length or training will be very slow.
    __lowerCAmelCase								=					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":
      __lowerCAmelCase								=					16
    elif accelerator.mixed_precision != "no":
      __lowerCAmelCase								=					8
    else:
      __lowerCAmelCase								=					None

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

  # Instantiate dataloaders.
  __lowerCAmelCase								=					DataLoader(
      tokenized_datasets['''train''']  ,				shuffle=__lowercase  ,				collate_fn=__lowercase  ,				batch_size=__lowercase)
  __lowerCAmelCase								=					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)
  __lowerCAmelCase								=					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).
  __lowerCAmelCase								=					model.to(accelerator.device)

  # Instantiate optimizer
  __lowerCAmelCase								=					AdamW(params=model.parameters()  ,				lr=__lowercase)

  # Instantiate scheduler
  __lowerCAmelCase								=					get_linear_schedule_with_warmup(
      optimizer=__lowercase  ,				num_warmup_steps=1_00  ,				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.
  __lowerCAmelCase						,    __lowerCAmelCase						,    __lowerCAmelCase						,    __lowerCAmelCase						,    __lowerCAmelCase								=					accelerator.prepare(
      __lowercase  ,				__lowercase  ,				__lowercase  ,				__lowercase  ,				__lowercase)

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

    if "epoch" in training_difference:
      __lowerCAmelCase								=					int(training_difference.replace('''epoch_'''  ,				'''''')) + 1
      __lowerCAmelCase								=					None
    else:
      __lowerCAmelCase								=					int(training_difference.replace('''step_'''  ,				''''''))
      __lowerCAmelCase								=					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:
      __lowerCAmelCase								=					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
      __lowerCAmelCase								=					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
      __lowerCAmelCase								=					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)
      __lowerCAmelCase								=					model(**__lowercase)
      __lowerCAmelCase								=					outputs.loss
      __lowerCAmelCase								=					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):
        __lowerCAmelCase								=					F'''step_{overall_step}'''
        if overall_step % checkpointing_steps == 0:
          if args.output_dir is not None:
            __lowerCAmelCase								=					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():
        __lowerCAmelCase								=					model(**__lowercase)
      __lowerCAmelCase								=					outputs.logits.argmax(dim=-1)
      __lowerCAmelCase						,    __lowerCAmelCase								=					accelerator.gather_for_metrics((predictions, batch['''labels''']))
      metric.add_batch(
          predictions=__lowercase  ,				references=__lowercase  ,				)

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

  if args.with_tracking:
    accelerator.end_training()


def        __SCREAMING_SNAKE_CASE	(				):



  '''simple docstring'''






  __lowerCAmelCase								=					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'''  ,				)
  __lowerCAmelCase								=					parser.parse_args()
  __lowerCAmelCase								=					{'''lr''': 2E-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
  training_function(__lowercase  ,				__lowercase)


if __name__ == "__main__":
      main()







def        __SCREAMING_SNAKE_CASE	(				__lowercase:		int = 10_00):



  '''simple docstring'''






  __lowerCAmelCase								=					0
  for i in range(__lowercase):
    if i % 3 == 0 or i % 5 == 0:
      result += i
  return result


if __name__ == "__main__":
      print(f"""{solution() = }""")
