





'''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


__snake_case:  Dict								=			16
__snake_case:  Tuple								=			32






def 	_snake_case						(      A_       :    Accelerator     ,		A_       :    int = 16     ,		A_       :    str = "bert-base-cased"   ):
  """simple docstring"""







  a_      :	List[Any]      =							AutoTokenizer.from_pretrained(A_   )
  a_      :	int      =							load_dataset("""glue"""     ,		"""mrpc"""   )

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

  def collate_fn(A_       :    int   ):
    # 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=128     ,		return_tensors="""pt"""   )
    return tokenizer.pad(A_     ,		padding="""longest"""     ,		return_tensors="""pt"""   )

  # Instantiate dataloaders.
  a_      :	Tuple      =							DataLoader(
      tokenized_datasets["""train"""]     ,		shuffle=A_     ,		collate_fn=A_     ,		batch_size=A_   )
  a_      :	Optional[Any]      =							DataLoader(
      tokenized_datasets["""validation"""]     ,		shuffle=A_     ,		collate_fn=A_     ,		batch_size=A_   )

  return train_dataloader, eval_dataloader






def 	_snake_case						(      A_       :    List[Any]     ,		A_       :    Optional[Any]   ):
  """simple docstring"""







  a_      :	str      =							Accelerator()

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

  set_seed(A_   )
  a_      :	List[str]      =							get_dataloaders(A_     ,		A_     ,		A_   )

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

  # Instantiate optimizer
  a_      :	Dict      =							(
      AdamW
      if accelerator.state.deepspeed_plugin is None
      or """optimizer""" not in accelerator.state.deepspeed_plugin.deepspeed_config
      else DummyOptim
  )
  a_      :	List[str]      =							optimizer_cls(params=model.parameters()     ,		lr=A_   )

  if accelerator.state.deepspeed_plugin is not None:
    a_      :	Optional[Any]      =							accelerator.state.deepspeed_plugin.deepspeed_config[
        """gradient_accumulation_steps"""
    ]
  else:
    a_      :	Union[str, Any]      =							1
  a_      :	List[str]      =							(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
  ):
    a_      :	Dict      =							get_linear_schedule_with_warmup(
        optimizer=A_     ,		num_warmup_steps=0     ,		num_training_steps=A_     ,		)
  else:
    a_      :	Dict      =							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.
  a_      :	Union[str, Any]      =							accelerator.prepare(
      A_     ,		A_     ,		A_     ,		A_     ,		A_   )

  # We need to keep track of how many total steps we have iterated over
  a_      :	int      =							0
  # We also need to keep track of the stating epoch so files are named properly
  a_      :	Tuple      =							0

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

      overall_step += 1

    model.eval()
    a_      :	List[str]      =							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():
        a_      :	List[Any]      =							model(**A_   )
      a_      :	int      =							outputs.logits.argmax(dim=-1   )
      # It is slightly faster to call this once, than multiple times
      a_      :	Tuple      =							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:
          a_      :	Optional[int]      =							predictions[: len(eval_dataloader.dataset   ) - samples_seen]
          a_      :	Any      =							references[: len(eval_dataloader.dataset   ) - samples_seen]
        else:
          samples_seen += references.shape[0]
      metric.add_batch(
          predictions=A_     ,		references=A_     ,		)

    a_      :	Dict      =							metric.compute()
    # Use accelerator.print to print only on the main process.
    accelerator.print(f'''epoch {epoch}:'''     ,		A_   )
    a_      :	str      =							eval_metric["""accuracy"""]

    if best_performance < eval_metric["accuracy"]:
      a_      :	Union[str, Any]      =							eval_metric["""accuracy"""]

  if args.performance_lower_bound is not None:
    assert (
        args.performance_lower_bound <= best_performance
    ), f'''Best performance metric {best_performance} is lower than the lower bound {args.performance_lower_bound}'''

  accelerator.wait_for_everyone()
  if accelerator.is_main_process:
    with open(os.path.join(args.output_dir     ,		"""all_results.json"""   )     ,		"""w"""   ) as f:
      json.dump(A_     ,		A_   )






def 	_snake_case						(      ):
  """simple docstring"""







  a_      :	Tuple      =							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(
      """--performance_lower_bound"""     ,		type=A_     ,		default=A_     ,		help="""Optional lower bound for the performance metric. If set, the training will throw error when the performance metric drops below this value."""     ,		)
  parser.add_argument(
      """--num_epochs"""     ,		type=A_     ,		default=3     ,		help="""Number of train epochs."""     ,		)
  a_      :	Optional[int]      =							parser.parse_args()
  a_      :	Union[str, Any]      =							{"""lr""": 2E-5, """num_epochs""": args.num_epochs, """seed""": 42, """batch_size""": 16}
  training_function(A_     ,		A_   )


if __name__ == "__main__":
    main()




'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


__snake_case:  Dict								=			{"configuration_encoder_decoder": ["EncoderDecoderConfig"]}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __snake_case:  List[str]								=			["EncoderDecoderModel"]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __snake_case:  Any								=			["TFEncoderDecoderModel"]

try:
    if not is_flax_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __snake_case:  List[str]								=			["FlaxEncoderDecoderModel"]

if TYPE_CHECKING:
    from .configuration_encoder_decoder import EncoderDecoderConfig

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_encoder_decoder import EncoderDecoderModel

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_encoder_decoder import TFEncoderDecoderModel

    try:
        if not is_flax_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_flax_encoder_decoder import FlaxEncoderDecoderModel

else:
    import sys

    __snake_case:  Tuple								=			_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


