




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


_UpperCAmelCase						:    Union[str, Any]      =  16
_UpperCAmelCase						:    Tuple      =  32



def    UpperCAmelCase__	(  lowerCamelCase,      lowerCamelCase ):
  # Initialize accelerator
  if args.with_tracking:
    lowercase     :Any   						=    Accelerator(
        cpu=args.cpu,      mixed_precision=args.mixed_precision,      log_with="all",      project_dir=args.project_dir )
  else:
    lowercase     :int   						=    Accelerator(cpu=args.cpu,      mixed_precision=args.mixed_precision )

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

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

  lowercase     :Dict   						=    AutoTokenizer.from_pretrained("bert-base-cased" )
  lowercase     :str   						=    load_dataset("glue",      "mrpc" )
  lowercase     :int   						=    evaluate.load("glue",      "mrpc" )

  def tokenize_function(lowerCamelCase ):
    # max_length=None => use the model max length (it's actually the default)
    lowercase     :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():
    lowercase     :Dict   						=    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
  lowercase     :List[Any]   						=    tokenized_datasets.rename_column("label",      "labels" )

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

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

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

  # Instantiate dataloaders.
  lowercase     :Dict   						=    DataLoader(
      tokenized_datasets["train"],      shuffle=lowerCamelCase,      collate_fn=lowerCamelCase,      batch_size=lowerCamelCase )
  lowercase     :Union[str, Any]   						=    DataLoader(
      tokenized_datasets["validation"],      shuffle=lowerCamelCase,      collate_fn=lowerCamelCase,      batch_size=lowerCamelCase )

  set_seed(lowerCamelCase )

  # Instantiate the model (we build the model here so that the seed also control new weights initialization)
  lowercase     :List[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).
  lowercase     :int   						=    model.to(accelerator.device )

  # Instantiate optimizer
  lowercase     :List[str]   						=    AdamW(params=model.parameters(),      lr=lowerCamelCase )

  # Instantiate scheduler
  lowercase     :Union[str, Any]   						=    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.
  lowercase						,     lowercase						,     lowercase						,     lowercase						,     lowercase     :Tuple   						=    accelerator.prepare(
      lowerCamelCase,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase )

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

    if "epoch" in training_difference:
      lowercase     :Optional[int]   						=    int(training_difference.replace("epoch_",      "" ) ) + 1
      lowercase     :Tuple   						=    None
    else:
      lowercase     :List[Any]   						=    int(training_difference.replace("step_",      "" ) )
      lowercase     :int   						=    resume_step // len(lowerCamelCase )
      resume_step -= starting_epoch * len(lowerCamelCase )

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

      overall_step += 1

      if isinstance(lowerCamelCase,      lowerCamelCase ):
        lowercase     :Any   						=    F"step_{overall_step}"
        if overall_step % checkpointing_steps == 0:
          if args.output_dir is not None:
            lowercase     :int   						=    os.path.join(args.output_dir,      lowerCamelCase )
          accelerator.save_state(lowerCamelCase )

    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():
        lowercase     :int   						=    model(**lowerCamelCase )
      lowercase     :Optional[Any]   						=    outputs.logits.argmax(dim=-1 )
      lowercase						,     lowercase     :Any   						=    accelerator.gather_for_metrics((predictions, batch["labels"]) )
      metric.add_batch(
          predictions=lowerCamelCase,      references=lowerCamelCase,      )

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

    if checkpointing_steps == "epoch":
      lowercase     :Optional[Any]   						=    F"epoch_{epoch}"
      if args.output_dir is not None:
        lowercase     :int   						=    os.path.join(args.output_dir,      lowerCamelCase )
      accelerator.save_state(lowerCamelCase )

  if args.with_tracking:
    accelerator.end_training()



def    UpperCAmelCase__	(  ):
  lowercase     :int   						=    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." )
  parser.add_argument(
      "--checkpointing_steps",      type=lowerCamelCase,      default=lowerCamelCase,      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=lowerCamelCase,      default=lowerCamelCase,      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=lowerCamelCase,      default=".",      help="Optional save directory where all checkpoint folders will be stored. Default is the current working directory.",      )
  parser.add_argument(
      "--project_dir",      type=lowerCamelCase,      default="logs",      help="Location on where to store experiment tracking logs` and relevent project information",      )
  lowercase     :Any   						=    parser.parse_args()
  lowercase     :Any   						=    {"lr": 2e-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
  training_function(lowerCamelCase,      lowerCamelCase )


if __name__ == "__main__":
     main()









def    UpperCAmelCase__	(  lowerCamelCase ):
  lowercase     :Optional[int]   						=    len(lowerCamelCase )
  if n in (0, 1):
    return 0
  return sum(query * (n - i - 1) for i, query in enumerate(sorted(lowerCamelCase ) ) )


if __name__ == "__main__":
     import doctest

     doctest.testmod()



