





"""simple docstring"""




import argparse
import os

# New Code #
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
from accelerate.utils import find_executable_batch_size


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to ensure out-of-memory errors never
# interrupt training, 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)
#
# 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_     :						Optional[int]						=					16
lowerCamelCase_     :						Dict						=					32





def 						UpperCAmelCase__		(     _UpperCAmelCase ,					_UpperCAmelCase = 16					):






  """simple docstring"""





  A_  :       List[str]										=  AutoTokenizer.from_pretrained('bert-base-cased'					)
  A_  :       int										=  load_dataset('glue' ,					'mrpc'					)

  def tokenize_function(_UpperCAmelCase					):
    # max_length=None => use the model max length (it's actually the default)
    A_  :       Dict										=  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():
    A_  :       Optional[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
  A_  :       Optional[int]										=  tokenized_datasets.rename_column('label' ,					'labels'					)

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

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

  # Instantiate dataloaders.
  A_  :       Union[str, Any]										=  DataLoader(
      tokenized_datasets['train'] ,					shuffle=_UpperCAmelCase ,					collate_fn=_UpperCAmelCase ,					batch_size=_UpperCAmelCase					)
  A_  :       List[Any]										=  DataLoader(
      tokenized_datasets['validation'] ,					shuffle=_UpperCAmelCase ,					collate_fn=_UpperCAmelCase ,					batch_size=_UpperCAmelCase					)

  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_     :						str						=					mocked_dataloaders  # noqa: F811





def 						UpperCAmelCase__		(     _UpperCAmelCase ,					_UpperCAmelCase					):






  """simple docstring"""





  if os.environ.get('TESTING_MOCKED_DATALOADERS' ,					_UpperCAmelCase					) == "1":
    A_  :       List[Any]										=  2
  # Initialize accelerator
  A_  :       List[Any]										=  Accelerator(cpu=args.cpu ,					mixed_precision=args.mixed_precision					)
  # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
  A_  :       List[str]										=  config['lr']
  A_  :       List[str]										=  int(config['num_epochs']					)
  A_  :       Optional[int]										=  int(config['seed']					)
  A_  :       Optional[Any]										=  int(config['batch_size']					)

  A_  :       Optional[int]										=  evaluate.load('glue' ,					'mrpc'					)

  # New Code #
  # We now can define an inner training loop function. It should take a batch size as the only parameter,
  # and build the dataloaders in there.
  # It also gets our decorator
  @find_executable_batch_size(starting_batch_size=_UpperCAmelCase					)
  def inner_training_loop(_UpperCAmelCase					):
    # And now just move everything below under this function
    # We need to bring in the Accelerator object from earlier
    nonlocal accelerator
    # And reset all of its attributes that could hold onto any memory:
    accelerator.free_memory()

    # Then we can declare the model, optimizer, and everything else:
    set_seed(_UpperCAmelCase					)

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

    # Instantiate optimizer
    A_  :       Any										=  AdamW(params=model.parameters() ,					lr=_UpperCAmelCase					)
    A_						,       A_  :       Tuple										=  get_dataloaders(_UpperCAmelCase ,					_UpperCAmelCase					)

    # Instantiate scheduler
    A_  :       Optional[int]										=  get_linear_schedule_with_warmup(
        optimizer=_UpperCAmelCase ,					num_warmup_steps=100 ,					num_training_steps=(len(_UpperCAmelCase					) * num_epochs) ,					)

    # 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_  :       Tuple										=  accelerator.prepare(
        _UpperCAmelCase ,					_UpperCAmelCase ,					_UpperCAmelCase ,					_UpperCAmelCase ,					_UpperCAmelCase					)

    # Now we train the model
    for epoch in range(_UpperCAmelCase					):
      model.train()
      for step, batch in enumerate(_UpperCAmelCase					):
        # We could avoid this line since we set the accelerator with `device_placement=True`.
        batch.to(accelerator.device					)
        A_  :       Optional[int]										=  model(**_UpperCAmelCase					)
        A_  :       Tuple										=  outputs.loss
        accelerator.backward(_UpperCAmelCase					)
        optimizer.step()
        lr_scheduler.step()
        optimizer.zero_grad()

      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():
          A_  :       str										=  model(**_UpperCAmelCase					)
        A_  :       int										=  outputs.logits.argmax(dim=-1					)
        A_						,       A_  :       List[str]										=  accelerator.gather_for_metrics((predictions, batch['labels'])					)
        metric.add_batch(
            predictions=_UpperCAmelCase ,					references=_UpperCAmelCase ,					)

      A_  :       Union[str, Any]										=  metric.compute()
      # Use accelerator.print to print only on the main process.
      accelerator.print(f"""epoch {epoch}:""" ,					_UpperCAmelCase					)

    # New Code #
    # And call it at the end with no arguments
    # Note: You could also refactor this outside of your training loop function
  inner_training_loop()





def 						UpperCAmelCase__		(     ):






  """simple docstring"""





  A_  :       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.'					)
  A_  :       str										=  parser.parse_args()
  A_  :       Tuple										=  {'lr': 2E-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
  training_function(_UpperCAmelCase ,					_UpperCAmelCase					)


if __name__ == "__main__":
   main()






"""simple docstring"""




from copy import deepcopy

class  _UpperCAmelCase  :



      '''simple docstring'''

      def __init__(     self					,      snake_case_ = None					,      snake_case_ = None  ):

        """simple docstring"""

        if arr is None and size is not None:
          A_  :       Union[str, Any]										=  size
          A_  :       List[str]										=  [0] * size
        elif arr is not None:
          self.init(snake_case_  )
        else:
          raise ValueError('Either arr or size must be specified'  )

      def 					lowerCamelCase_			(     self					,      snake_case_  ):

        """simple docstring"""

        A_  :       Union[str, Any]										=  len(snake_case_  )
        A_  :       Optional[int]										=  deepcopy(snake_case_  )
        for i in range(1					,      self.size  ):
          A_  :       Optional[Any]										=  self.next_(snake_case_  )
          if j < self.size:
            self.tree[j] += self.tree[i]

      def 					lowerCamelCase_			(     self  ):

        """simple docstring"""

        A_  :       int										=  self.tree[:]
        for i in range(self.size - 1					,      0					,      -1  ):
          A_  :       Optional[int]										=  self.next_(snake_case_  )
          if j < self.size:
            arr[j] -= arr[i]
        return arr

      @staticmethod
      def 					lowerCamelCase_			(     snake_case_  ):

        """simple docstring"""

        return index + (index & (-index))

      @staticmethod
      def 					lowerCamelCase_			(     snake_case_  ):

        """simple docstring"""

        return index - (index & (-index))

      def 					lowerCamelCase_			(     self					,      snake_case_					,      snake_case_  ):

        """simple docstring"""

        if index == 0:
          self.tree[0] += value
          return
        while index < self.size:
          self.tree[index] += value
          A_  :       List[str]										=  self.next_(snake_case_  )

      def 					lowerCamelCase_			(     self					,      snake_case_					,      snake_case_  ):

        """simple docstring"""

        self.add(snake_case_					,      value - self.get(snake_case_  )  )

      def 					lowerCamelCase_			(     self					,      snake_case_  ):

        """simple docstring"""

        if right == 0:
          return 0
        A_  :       Any										=  self.tree[0]
        right -= 1  # make right inclusive
        while right > 0:
          result += self.tree[right]
          A_  :       Tuple										=  self.prev(snake_case_  )
        return result

      def 					lowerCamelCase_			(     self					,      snake_case_					,      snake_case_  ):

        """simple docstring"""

        return self.prefix(snake_case_  ) - self.prefix(snake_case_  )

      def 					lowerCamelCase_			(     self					,      snake_case_  ):

        """simple docstring"""

        return self.query(snake_case_					,      index + 1  )

      def 					lowerCamelCase_			(     self					,      snake_case_  ):

        """simple docstring"""

        value -= self.tree[0]
        if value < 0:
          return -1

        A_  :       List[Any]										=  1  # Largest power of 2 <= size
        while j * 2 < self.size:
          j *= 2

        A_  :       Tuple										=  0

        while j > 0:
          if i + j < self.size and self.tree[i + j] <= value:
            value -= self.tree[i + j]
            i += j
          j //= 2
        return i


if __name__ == "__main__":
   import doctest

   doctest.testmod()