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


__lowerCamelCase    :				List[Any]							     =	16
__lowerCamelCase    :				Tuple							     =	32



def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: Accelerator				,  __UpperCamelCase					: int = 16				,  __UpperCamelCase					: str = "bert-base-cased"			)							->      str:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__    							=   AutoTokenizer.from_pretrained(__UpperCamelCase			)
  SCREAMING_SNAKE_CASE__    							=   load_dataset("""glue"""				,  """mrpc"""			)

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

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

  def collate_fn(__UpperCamelCase					: Optional[Any]			):
    # 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(__UpperCamelCase				,  padding="""max_length"""				,  max_length=1_28				,  return_tensors="""pt"""			)
    return tokenizer.pad(__UpperCamelCase				,  padding="""longest"""				,  return_tensors="""pt"""			)

  # Instantiate dataloaders.
  SCREAMING_SNAKE_CASE__    							=   DataLoader(
      tokenized_datasets["""train"""]				,  shuffle=__UpperCamelCase				,  collate_fn=__UpperCamelCase				,  batch_size=__UpperCamelCase			)
  SCREAMING_SNAKE_CASE__    							=   DataLoader(
      tokenized_datasets["""validation"""]				,  shuffle=__UpperCamelCase				,  collate_fn=__UpperCamelCase				,  batch_size=__UpperCamelCase			)

  return train_dataloader, eval_dataloader



def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: Union[str, Any]				,  __UpperCamelCase					: List[str]				,  __UpperCamelCase					: Tuple				,  __UpperCamelCase					: Optional[Any]			)							->      List[Any]:







  """simple docstring"""





  model.eval()
  SCREAMING_SNAKE_CASE__    							=   0
  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():
      SCREAMING_SNAKE_CASE__    							=   model(**__UpperCamelCase			)
    SCREAMING_SNAKE_CASE__    							=   outputs.logits.argmax(dim=-1			)
    # It is slightly faster to call this once, than multiple times
    SCREAMING_SNAKE_CASE__							,  SCREAMING_SNAKE_CASE__    							=   accelerator.gather(
        (predictions, batch["""labels"""])			)  # If we are in a multiprocess environment, the last batch has duplicates
    if accelerator.use_distributed:
      if step == len(__UpperCamelCase			) - 1:
        SCREAMING_SNAKE_CASE__    							=   predictions[: len(eval_dataloader.dataset			) - samples_seen]
        SCREAMING_SNAKE_CASE__    							=   references[: len(eval_dataloader.dataset			) - samples_seen]
      else:
        samples_seen += references.shape[0]
    metric.add_batch(
        predictions=__UpperCamelCase				,  references=__UpperCamelCase				,  )

  SCREAMING_SNAKE_CASE__    							=   metric.compute()
  return eval_metric["accuracy"]



def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: Any				,  __UpperCamelCase					: List[str]			)							->      Tuple:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__    							=   Accelerator()

  # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
  SCREAMING_SNAKE_CASE__    							=   config["""lr"""]
  SCREAMING_SNAKE_CASE__    							=   int(config["""num_epochs"""]			)
  SCREAMING_SNAKE_CASE__    							=   int(config["""seed"""]			)
  SCREAMING_SNAKE_CASE__    							=   int(config["""batch_size"""]			)
  SCREAMING_SNAKE_CASE__    							=   args.model_name_or_path

  set_seed(__UpperCamelCase			)
  SCREAMING_SNAKE_CASE__							,  SCREAMING_SNAKE_CASE__    							=   get_dataloaders(__UpperCamelCase				,  __UpperCamelCase				,  __UpperCamelCase			)

  # Instantiate the model (we build the model here so that the seed also control new weights initialization)
  SCREAMING_SNAKE_CASE__    							=   AutoModelForSequenceClassification.from_pretrained(__UpperCamelCase				,  return_dict=__UpperCamelCase			)

  # Instantiate optimizer
  SCREAMING_SNAKE_CASE__    							=   (
      AdamW
      if accelerator.state.deepspeed_plugin is None
      or """optimizer""" not in accelerator.state.deepspeed_plugin.deepspeed_config
      else DummyOptim
  )
  SCREAMING_SNAKE_CASE__    							=   optimizer_cls(params=model.parameters()				,  lr=__UpperCamelCase			)

  if accelerator.state.deepspeed_plugin is not None:
    SCREAMING_SNAKE_CASE__    							=   accelerator.state.deepspeed_plugin.deepspeed_config[
        """gradient_accumulation_steps"""
    ]
  else:
    SCREAMING_SNAKE_CASE__    							=   1
  SCREAMING_SNAKE_CASE__    							=   (len(__UpperCamelCase			) * num_epochs) // gradient_accumulation_steps

  # Instantiate scheduler
  if (
      accelerator.state.deepspeed_plugin is None
      or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
  ):
    SCREAMING_SNAKE_CASE__    							=   get_linear_schedule_with_warmup(
        optimizer=__UpperCamelCase				,  num_warmup_steps=0				,  num_training_steps=__UpperCamelCase				,  )
  else:
    SCREAMING_SNAKE_CASE__    							=   DummyScheduler(__UpperCamelCase				,  total_num_steps=__UpperCamelCase				,  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.
  SCREAMING_SNAKE_CASE__							,  SCREAMING_SNAKE_CASE__							,  SCREAMING_SNAKE_CASE__							,  SCREAMING_SNAKE_CASE__							,  SCREAMING_SNAKE_CASE__    							=   accelerator.prepare(
      __UpperCamelCase				,  __UpperCamelCase				,  __UpperCamelCase				,  __UpperCamelCase				,  __UpperCamelCase			)

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

  if args.partial_train_epoch is not None:
    SCREAMING_SNAKE_CASE__    							=   args.partial_train_epoch

  if args.resume_from_checkpoint:
    accelerator.load_state(args.resume_from_checkpoint			)
    SCREAMING_SNAKE_CASE__    							=   args.resume_from_checkpoint.split("""epoch_"""			)[1]
    SCREAMING_SNAKE_CASE__    							=   """"""
    for char in epoch_string:
      if char.isdigit():
        state_epoch_num += char
      else:
        break
    SCREAMING_SNAKE_CASE__    							=   int(__UpperCamelCase			) + 1
    SCREAMING_SNAKE_CASE__    							=   evaluation_loop(__UpperCamelCase				,  __UpperCamelCase				,  __UpperCamelCase				,  __UpperCamelCase			)
    accelerator.print("""resumed checkpoint performance:"""				,  __UpperCamelCase			)
    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:
      SCREAMING_SNAKE_CASE__    							=   json.load(__UpperCamelCase			)
      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
  SCREAMING_SNAKE_CASE__    							=   {}
  for epoch in range(__UpperCamelCase				,  __UpperCamelCase			):
    model.train()
    for step, batch in enumerate(__UpperCamelCase			):
      SCREAMING_SNAKE_CASE__    							=   model(**__UpperCamelCase			)
      SCREAMING_SNAKE_CASE__    							=   outputs.loss
      SCREAMING_SNAKE_CASE__    							=   loss / gradient_accumulation_steps
      accelerator.backward(__UpperCamelCase			)
      if step % gradient_accumulation_steps == 0:
        optimizer.step()
        lr_scheduler.step()
        optimizer.zero_grad()

      overall_step += 1
    SCREAMING_SNAKE_CASE__    							=   f"""epoch_{epoch}"""
    SCREAMING_SNAKE_CASE__    							=   os.path.join(args.output_dir				,  __UpperCamelCase			)
    accelerator.save_state(__UpperCamelCase			)
    SCREAMING_SNAKE_CASE__    							=   evaluation_loop(__UpperCamelCase				,  __UpperCamelCase				,  __UpperCamelCase				,  __UpperCamelCase			)
    SCREAMING_SNAKE_CASE__    							=   accuracy
    SCREAMING_SNAKE_CASE__    							=   lr_scheduler.get_lr()[0]
    SCREAMING_SNAKE_CASE__    							=   optimizer.param_groups[0]["""lr"""]
    SCREAMING_SNAKE_CASE__    							=   epoch
    SCREAMING_SNAKE_CASE__    							=   overall_step
    accelerator.print(f"""epoch {epoch}:"""				,  __UpperCamelCase			)

    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(__UpperCamelCase				,  __UpperCamelCase			)



def 			__SCREAMING_SNAKE_CASE						(      )							->      List[str]:







  """simple docstring"""





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

  training_function(__UpperCamelCase				,  __UpperCamelCase			)


if __name__ == "__main__":
   main()

def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: list				,  __UpperCamelCase					: list				,  __UpperCamelCase					: int				,  __UpperCamelCase					: int				,  __UpperCamelCase					: int			)							->      int:







  """simple docstring"""





  if index == number_of_items:
    return 0
  SCREAMING_SNAKE_CASE__    							=   0
  SCREAMING_SNAKE_CASE__    							=   0
  SCREAMING_SNAKE_CASE__    							=   knapsack(__UpperCamelCase				,  __UpperCamelCase				,  __UpperCamelCase				,  __UpperCamelCase				,  index + 1			)
  if weights[index] <= max_weight:
    SCREAMING_SNAKE_CASE__    							=   values[index] + knapsack(
        __UpperCamelCase				,  __UpperCamelCase				,  __UpperCamelCase				,  max_weight - weights[index]				,  index + 1			)
  return max(__UpperCamelCase				,  __UpperCamelCase			)


if __name__ == "__main__":
   import doctest

   doctest.testmod()
