




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__						:       Dict							=    16
UpperCAmelCase__						:       Tuple							=    32


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





  """simple docstring"""





  if args.with_tracking:
    _lowercase: Union[str, Any]						     =    Accelerator(
        cpu=args.cpu					,     mixed_precision=args.mixed_precision					,     log_with='all'					,     project_dir=args.project_dir							)
  else:
    _lowercase: str						     =    Accelerator(cpu=args.cpu					,     mixed_precision=args.mixed_precision							)

  if hasattr(args.checkpointing_steps					,     'isdigit'							):
    if args.checkpointing_steps == "epoch":
      _lowercase: List[Any]						     =    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: Optional[int]						     =    None
  # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
  _lowercase: List[str]						     =    config['lr']
  _lowercase: List[str]						     =    int(config['num_epochs']							)
  _lowercase: Optional[Any]						     =    int(config['seed']							)
  _lowercase: Dict						     =    int(config['batch_size']							)

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

  _lowercase: Tuple						     =    AutoTokenizer.from_pretrained('bert-base-cased'							)
  _lowercase: Any						     =    load_dataset('glue'					,     'mrpc'							)
  _lowercase: Union[str, Any]						     =    evaluate.load('glue'					,     'mrpc'							)

  def tokenize_function(UpperCamelCase							):
    # max_length=None => use the model max length (it's actually the default)
    _lowercase: Optional[int]						     =    tokenizer(examples['sentence1']					,     examples['sentence2']					,     truncation=UpperCamelCase					,     max_length=UpperCamelCase							)
    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: Union[str, Any]						     =    datasets.map(
        UpperCamelCase					,     batched=UpperCamelCase					,     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: Optional[Any]						     =    tokenized_datasets.rename_column('label'					,     'labels'							)

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

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

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

  # Instantiate dataloaders.
  _lowercase: List[str]						     =    DataLoader(
      tokenized_datasets['train']					,     shuffle=UpperCamelCase					,     collate_fn=UpperCamelCase					,     batch_size=UpperCamelCase							)
  _lowercase: str						     =    DataLoader(
      tokenized_datasets['validation']					,     shuffle=UpperCamelCase					,     collate_fn=UpperCamelCase					,     batch_size=UpperCamelCase							)

  set_seed(UpperCamelCase							)

  # 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=UpperCamelCase							)

  # 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: Dict						     =    model.to(accelerator.device							)

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

  # Instantiate scheduler
  _lowercase: int						     =    get_linear_schedule_with_warmup(
      optimizer=UpperCamelCase					,     num_warmup_steps=100					,     num_training_steps=(len(UpperCamelCase							) * 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: List[Any]						     =    accelerator.prepare(
      UpperCamelCase					,     UpperCamelCase					,     UpperCamelCase					,     UpperCamelCase					,     UpperCamelCase							)

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

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

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

      overall_step += 1

      if isinstance(UpperCamelCase					,     UpperCamelCase							):
        _lowercase: Optional[int]						     =    F'step_{overall_step}'
        if overall_step % checkpointing_steps == 0:
          if args.output_dir is not None:
            _lowercase: List[str]						     =    os.path.join(args.output_dir					,     UpperCamelCase							)
          accelerator.save_state(UpperCamelCase							)

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

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

    if checkpointing_steps == "epoch":
      _lowercase: List[Any]						     =    F'epoch_{epoch}'
      if args.output_dir is not None:
        _lowercase: Dict						     =    os.path.join(args.output_dir					,     UpperCamelCase							)
      accelerator.save_state(UpperCamelCase							)

  if args.with_tracking:
    accelerator.end_training()


def 		_lowerCamelCase				(					)			->		Tuple:





  """simple docstring"""





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


if __name__ == "__main__":
   main()






from cva import destroyAllWindows, imread, imshow, waitKey


def 		_lowerCamelCase				(					UpperCamelCase					,     UpperCamelCase							)			->		List[str]:





  """simple docstring"""





  _lowercase     ,					_lowercase: Tuple						     =    img.shape[0], img.shape[1]

  def to_grayscale(UpperCamelCase					,     UpperCamelCase					,     UpperCamelCase							):
    return 0.2_126 * red + 0.587 * green + 0.114 * blue

  def normalize(UpperCamelCase							):
    return min(UpperCamelCase					,     255							)

  for i in range(UpperCamelCase							):
    for j in range(UpperCamelCase							):
      _lowercase: Any						     =    int(to_grayscale(*img[i][j]							)							)
      _lowercase: str						     =    [
          normalize(UpperCamelCase							),
          normalize(greyscale + factor							),
          normalize(greyscale + 2 * factor							),
      ]

  return img


if __name__ == "__main__":
   # read original image
   UpperCAmelCase__						:       Optional[Any]							=    {
       percentage: imread('image_data/lena.jpg', 1) for percentage in (10, 20, 30, 40)
   }

   for percentage, img in images.items():
      make_sepia(img, percentage)

   for percentage, img in images.items():
      imshow(f'''Original image with sepia (factor: {percentage})''', img)

   waitKey(0)
   destroyAllWindows()
