


'''simple docstring'''






import argparse
import json
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
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler


__A :  Any    							=				16
__A :  Tuple    							=				32







def     UpperCamelCase_      (   A__ :       Accelerator							,   A__ :       int = 16							,   A__ :       str = "bert-base-cased" ):
  '''simple docstring'''





  lowerCAmelCase_      :				Tuple      =       AutoTokenizer.from_pretrained(A__ )
  lowerCAmelCase_      :				Tuple      =       load_dataset("""glue"""							,   """mrpc""" )

  def tokenize_function(A__ :       Dict ):
    # max_length=None => use the model max length (it's actually the default)
    lowerCAmelCase_      :				Tuple      =       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
  lowerCAmelCase_      :				Any      =       datasets.map(
      A__							,   batched=A__							,   remove_columns=["""idx""", """sentence1""", """sentence2"""]							,   load_from_cache_file=A__ )

  # We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
  # transformers library
  lowerCAmelCase_      :				Tuple      =       tokenized_datasets.rename_column("""label"""							,   """labels""" )

  def collate_fn(A__ :       List[str] ):
    # On TPU it's best to pad everything to the same length or training will be very slow.
    if accelerator.distributed_type == DistributedType.TPU:
      return tokenizer.pad(A__							,   padding="""max_length"""							,   max_length=1_28							,   return_tensors="""pt""" )
    return tokenizer.pad(A__							,   padding="""longest"""							,   return_tensors="""pt""" )

  # Instantiate dataloaders.
  lowerCAmelCase_      :				Tuple      =       DataLoader(
      tokenized_datasets["""train"""]							,   shuffle=A__							,   collate_fn=A__							,   batch_size=A__ )
  lowerCAmelCase_      :				int      =       DataLoader(
      tokenized_datasets["""validation"""]							,   shuffle=A__							,   collate_fn=A__							,   batch_size=A__ )

  return train_dataloader, eval_dataloader







def     UpperCamelCase_      (   A__ :       List[str]							,   A__ :       Tuple							,   A__ :       int							,   A__ :       Union[str, Any] ):
  '''simple docstring'''





  model.eval()
  lowerCAmelCase_      :				int      =       0
  for step, batch in enumerate(A__ ):
    # We could avoid this line since we set the accelerator with `device_placement=True`.
    batch.to(accelerator.device )
    with torch.no_grad():
      lowerCAmelCase_      :				Any      =       model(**A__ )
    lowerCAmelCase_      :				Tuple      =       outputs.logits.argmax(dim=-1 )
    # It is slightly faster to call this once, than multiple times
    lowerCAmelCase_,							lowerCAmelCase_      :				Dict      =       accelerator.gather(
        (predictions, batch["""labels"""]) )  # If we are in a multiprocess environment, the last batch has duplicates
    if accelerator.use_distributed:
      if step == len(A__ ) - 1:
        lowerCAmelCase_      :				List[str]      =       predictions[: len(eval_dataloader.dataset ) - samples_seen]
        lowerCAmelCase_      :				Union[str, Any]      =       references[: len(eval_dataloader.dataset ) - samples_seen]
      else:
        samples_seen += references.shape[0]
    metric.add_batch(
        predictions=A__							,   references=A__							,   )

  lowerCAmelCase_      :				Optional[int]      =       metric.compute()
  return eval_metric["accuracy"]







def     UpperCamelCase_      (   A__ :       List[str]							,   A__ :       Tuple ):
  '''simple docstring'''





  lowerCAmelCase_      :				List[str]      =       Accelerator()

  # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
  lowerCAmelCase_      :				Union[str, Any]      =       config["""lr"""]
  lowerCAmelCase_      :				List[Any]      =       int(config["""num_epochs"""] )
  lowerCAmelCase_      :				Tuple      =       int(config["""seed"""] )
  lowerCAmelCase_      :				Union[str, Any]      =       int(config["""batch_size"""] )
  lowerCAmelCase_      :				List[str]      =       args.model_name_or_path

  set_seed(A__ )
  lowerCAmelCase_,							lowerCAmelCase_      :				List[Any]      =       get_dataloaders(A__							,   A__							,   A__ )

  # Instantiate the model (we build the model here so that the seed also control new weights initialization)
  lowerCAmelCase_      :				int      =       AutoModelForSequenceClassification.from_pretrained(A__							,   return_dict=A__ )

  # Instantiate optimizer
  lowerCAmelCase_      :				str      =       (
      AdamW
      if accelerator.state.deepspeed_plugin is None
      or """optimizer""" not in accelerator.state.deepspeed_plugin.deepspeed_config
      else DummyOptim
  )
  lowerCAmelCase_      :				Union[str, Any]      =       optimizer_cls(params=model.parameters()							,   lr=A__ )

  if accelerator.state.deepspeed_plugin is not None:
    lowerCAmelCase_      :				Optional[Any]      =       accelerator.state.deepspeed_plugin.deepspeed_config[
        """gradient_accumulation_steps"""
    ]
  else:
    lowerCAmelCase_      :				Union[str, Any]      =       1
  lowerCAmelCase_      :				List[Any]      =       (len(A__ ) * num_epochs) // gradient_accumulation_steps

  # Instantiate scheduler
  if (
      accelerator.state.deepspeed_plugin is None
      or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
  ):
    lowerCAmelCase_      :				List[str]      =       get_linear_schedule_with_warmup(
        optimizer=A__							,   num_warmup_steps=0							,   num_training_steps=A__							,   )
  else:
    lowerCAmelCase_      :				List[Any]      =       DummyScheduler(A__							,   total_num_steps=A__							,   warmup_num_steps=0 )

  # 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_      :				Optional[int]      =       accelerator.prepare(
      A__							,   A__							,   A__							,   A__							,   A__ )

  # We need to keep track of how many total steps we have iterated over
  lowerCAmelCase_      :				str      =       0
  # We also need to keep track of the stating epoch so files are named properly
  lowerCAmelCase_      :				List[str]      =       0
  lowerCAmelCase_      :				Any      =       evaluate.load("""glue"""							,   """mrpc""" )
  lowerCAmelCase_      :				List[Any]      =       num_epochs

  if args.partial_train_epoch is not None:
    lowerCAmelCase_      :				Dict      =       args.partial_train_epoch

  if args.resume_from_checkpoint:
    accelerator.load_state(args.resume_from_checkpoint )
    lowerCAmelCase_      :				List[str]      =       args.resume_from_checkpoint.split("""epoch_""" )[1]
    lowerCAmelCase_      :				List[str]      =       """"""
    for char in epoch_string:
      if char.isdigit():
        state_epoch_num += char
      else:
        break
    lowerCAmelCase_      :				Optional[int]      =       int(A__ ) + 1
    lowerCAmelCase_      :				List[Any]      =       evaluation_loop(A__							,   A__							,   A__							,   A__ )
    accelerator.print("""resumed checkpoint performance:"""							,   A__ )
    accelerator.print("""resumed checkpoint's scheduler's lr:"""							,   lr_scheduler.get_lr()[0] )
    accelerator.print("""resumed optimizers's lr:"""							,   optimizer.param_groups[0]["""lr"""] )
    with open(os.path.join(args.output_dir							,   f'state_{starting_epoch-1}.json' )							,   """r""" ) as f:
      lowerCAmelCase_      :				Any      =       json.load(A__ )
      assert resumed_state["accuracy"] == accuracy, "Accuracy mismatch, loading from checkpoint failed"
      assert (
          resumed_state["lr"] == lr_scheduler.get_lr()[0]
      ), "Scheduler learning rate mismatch, loading from checkpoint failed"
      assert (
          resumed_state["optimizer_lr"] == optimizer.param_groups[0]["lr"]
      ), "Optimizer learning rate mismatch, loading from checkpoint failed"
      assert resumed_state["epoch"] == starting_epoch - 1, "Epoch mismatch, loading from checkpoint failed"
      return

    # Now we train the model
  lowerCAmelCase_      :				int      =       {}
  for epoch in range(A__							,   A__ ):
    model.train()
    for step, batch in enumerate(A__ ):
      lowerCAmelCase_      :				Optional[int]      =       model(**A__ )
      lowerCAmelCase_      :				Dict      =       outputs.loss
      lowerCAmelCase_      :				Union[str, Any]      =       loss / gradient_accumulation_steps
      accelerator.backward(A__ )
      if step % gradient_accumulation_steps == 0:
        optimizer.step()
        lr_scheduler.step()
        optimizer.zero_grad()

      overall_step += 1
    lowerCAmelCase_      :				str      =       f'epoch_{epoch}'
    lowerCAmelCase_      :				Union[str, Any]      =       os.path.join(args.output_dir							,   A__ )
    accelerator.save_state(A__ )
    lowerCAmelCase_      :				Any      =       evaluation_loop(A__							,   A__							,   A__							,   A__ )
    lowerCAmelCase_      :				Any      =       accuracy
    lowerCAmelCase_      :				Optional[Any]      =       lr_scheduler.get_lr()[0]
    lowerCAmelCase_      :				Tuple      =       optimizer.param_groups[0]["""lr"""]
    lowerCAmelCase_      :				Any      =       epoch
    lowerCAmelCase_      :				Union[str, Any]      =       overall_step
    accelerator.print(f'epoch {epoch}:'							,   A__ )

    accelerator.wait_for_everyone()
    if accelerator.is_main_process:
      with open(os.path.join(args.output_dir							,   f'state_{epoch}.json' )							,   """w""" ) as f:
        json.dump(A__							,   A__ )







def     UpperCamelCase_      (   ):
  '''simple docstring'''





  lowerCAmelCase_      :				Optional[Any]      =       argparse.ArgumentParser(description="""Simple example of training script tracking peak GPU memory usage.""" )
  parser.add_argument(
      """--model_name_or_path"""							,   type=A__							,   default="""bert-base-cased"""							,   help="""Path to pretrained model or model identifier from huggingface.co/models."""							,   required=A__							,   )
  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."""							,   )
  parser.add_argument(
      """--partial_train_epoch"""							,   type=A__							,   default=A__							,   help="""If passed, the training will stop after this number of epochs."""							,   )
  parser.add_argument(
      """--num_epochs"""							,   type=A__							,   default=2							,   help="""Number of train epochs."""							,   )
  lowerCAmelCase_      :				List[Any]      =       parser.parse_args()
  lowerCAmelCase_      :				Optional[int]      =       {"""lr""": 2E-5, """num_epochs""": args.num_epochs, """seed""": 42, """batch_size""": 16}

  training_function(A__							,   A__ )


if __name__ == "__main__":
  main()







'''simple docstring'''






from ..utils import DummyObject, requires_backends






class        __snake_case	(       metaclass=_SCREAMING_SNAKE_CASE):







    """simple docstring"""



    lowercase       			= ['note_seq']

    def __init__(       self   :  Tuple   ,						*lowerCamelCase   :  Optional[int]   ,						**lowerCamelCase   :  Tuple		)       ->  Any:
      requires_backends(self   ,						["""note_seq"""]		)

    @classmethod
    def __lowercase       (       cls   :  Optional[Any]   ,						*lowerCamelCase   :  List[Any]   ,						**lowerCamelCase   :  Dict		)       ->  List[Any]:
      requires_backends(cls   ,						["""note_seq"""]		)





    @classmethod
    def __lowercase       (       cls   :  Union[str, Any]   ,						*lowerCamelCase   :  Union[str, Any]   ,						**lowerCamelCase   :  List[Any]		)       ->  Optional[int]:
      requires_backends(cls   ,						["""note_seq"""]		)



