






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_						:						Optional[Any]						      =					16
A_						:						List[Any]						      =					32

def     snake_case   (UpperCAmelCase__      ,       UpperCAmelCase__ = 1_6      ,       UpperCAmelCase__ = "bert-base-cased"    )			->							Union[str, Any]:
  UpperCamelCase_:   str        =							AutoTokenizer.from_pretrained(UpperCAmelCase__    )
  UpperCamelCase_:   int        =							load_dataset('glue'      ,       'mrpc'    )

  def tokenize_function(UpperCAmelCase__    ):
    # max_length=None => use the model max length (it's actually the default)
    UpperCamelCase_:   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
  UpperCamelCase_:   Tuple        =							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
  UpperCamelCase_:   Any        =							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.
    if accelerator.distributed_type == DistributedType.TPU:
      return tokenizer.pad(UpperCAmelCase__      ,       padding='max_length'      ,       max_length=1_2_8      ,       return_tensors='pt'    )
    return tokenizer.pad(UpperCAmelCase__      ,       padding='longest'      ,       return_tensors='pt'    )

  # Instantiate dataloaders.
  UpperCamelCase_:   Dict        =							DataLoader(
      tokenized_datasets['train']      ,       shuffle=UpperCAmelCase__      ,       collate_fn=UpperCAmelCase__      ,       batch_size=UpperCAmelCase__    )
  UpperCamelCase_:   Tuple        =							DataLoader(
      tokenized_datasets['validation']      ,       shuffle=UpperCAmelCase__      ,       collate_fn=UpperCAmelCase__      ,       batch_size=UpperCAmelCase__    )

  return train_dataloader, eval_dataloader

def     snake_case   (UpperCAmelCase__      ,       UpperCAmelCase__    )			->							List[Any]:
  # Initialize accelerator
  UpperCamelCase_:   List[Any]        =							Accelerator()

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

  set_seed(UpperCAmelCase__    )
  UpperCamelCase_ ,UpperCamelCase_:   int        =							get_dataloaders(UpperCAmelCase__      ,       UpperCAmelCase__      ,       UpperCAmelCase__    )

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

  # Instantiate optimizer
  UpperCamelCase_:   Optional[int]        =							(
      AdamW
      if accelerator.state.deepspeed_plugin is None
      or 'optimizer' not in accelerator.state.deepspeed_plugin.deepspeed_config
      else DummyOptim
  )
  UpperCamelCase_:   Optional[int]        =							optimizer_cls(params=model.parameters()      ,       lr=UpperCAmelCase__    )

  if accelerator.state.deepspeed_plugin is not None:
    UpperCamelCase_:   Optional[int]        =							accelerator.state.deepspeed_plugin.deepspeed_config[
        'gradient_accumulation_steps'
    ]
  else:
    UpperCamelCase_:   Dict        =							1
  UpperCamelCase_:   int        =							(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
  ):
    UpperCamelCase_:   Dict        =							get_linear_schedule_with_warmup(
        optimizer=UpperCAmelCase__      ,       num_warmup_steps=0      ,       num_training_steps=UpperCAmelCase__      ,       )
  else:
    UpperCamelCase_:   int        =							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.
  UpperCamelCase_ ,UpperCamelCase_ ,UpperCamelCase_ ,UpperCamelCase_ ,UpperCamelCase_:   Tuple        =							accelerator.prepare(
      UpperCAmelCase__      ,       UpperCAmelCase__      ,       UpperCAmelCase__      ,       UpperCAmelCase__      ,       UpperCAmelCase__    )

  # We need to keep track of how many total steps we have iterated over
  UpperCamelCase_:   List[str]        =							0
  # We also need to keep track of the stating epoch so files are named properly
  UpperCamelCase_:   List[str]        =							0

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

      overall_step += 1

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

    UpperCamelCase_:   Any        =							metric.compute()
    # Use accelerator.print to print only on the main process.
    accelerator.print(F'''epoch {epoch}:'''      ,       UpperCAmelCase__    )
    UpperCamelCase_:   Optional[Any]        =							eval_metric['accuracy']

    if best_performance < eval_metric["accuracy"]:
      UpperCamelCase_:   List[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(UpperCAmelCase__      ,       UpperCAmelCase__    )

def     snake_case   ()			->							int:
  UpperCamelCase_:   str        =							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(
      '--performance_lower_bound'      ,       type=UpperCAmelCase__      ,       default=UpperCAmelCase__      ,       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=UpperCAmelCase__      ,       default=3      ,       help='Number of train epochs.'      ,       )
  UpperCamelCase_:   int        =							parser.parse_args()
  UpperCamelCase_:   List[Any]        =							{'lr': 2E-5, 'num_epochs': args.num_epochs, 'seed': 4_2, 'batch_size': 1_6}
  training_function(UpperCAmelCase__      ,       UpperCAmelCase__    )


if __name__ == "__main__":
   main()






import os

import pytest

from transformers.dynamic_module_utils import get_imports


UpperCamelCase__           =			"\nimport os\n"

UpperCamelCase__           =			"\ndef foo():\n    import os\n    return False\n"

UpperCamelCase__           =			"\ndef foo():\n    def bar():\n        if True:\n            import os\n        return False\n    return bar()\n"

UpperCamelCase__           =			"\nimport os\n\ntry:\n    import bar\nexcept ImportError:\n    raise ValueError()\n"

UpperCamelCase__           =			"\nimport os\n\ndef foo():\n    try:\n        import bar\n    except ImportError:\n        raise ValueError()\n"

UpperCamelCase__           =			"\nimport os\n\ntry:\n    import bar\nexcept (ImportError, AttributeError):\n    raise ValueError()\n"

UpperCamelCase__           =			"\nimport os\n\ntry:\n    import bar\nexcept ImportError as e:\n    raise ValueError()\n"

UpperCamelCase__           =			"\nimport os\n\ntry:\n    import bar\nexcept:\n    raise ValueError()\n"

UpperCamelCase__           =			"\nimport os\n\ntry:\n    import bar\n    import baz\nexcept ImportError:\n    raise ValueError()\n"

UpperCamelCase__           =			"\nimport os\n\ntry:\n    import bar\n    import baz\nexcept ImportError:\n    x = 1\n    raise ValueError()\n"

UpperCamelCase__           =			[
    TOP_LEVEL_IMPORT,
    IMPORT_IN_FUNCTION,
    DEEPLY_NESTED_IMPORT,
    TOP_LEVEL_TRY_IMPORT,
    GENERIC_EXCEPT_IMPORT,
    MULTILINE_TRY_IMPORT,
    MULTILINE_BOTH_IMPORT,
    MULTIPLE_EXCEPTS_IMPORT,
    EXCEPT_AS_IMPORT,
    TRY_IMPORT_IN_FUNCTION,
]





@pytest.mark.parametrize("""case""" ,      a__							)
def      _UpperCamelCase  (a__ :Any ,      a__ :Optional[Any]							):






							"""simple docstring"""





							UpperCamelCase__					  =      os.path.join(a__ ,      """test_file.py"""							)
							with open(a__ ,      """w"""							) as _tmp_file:
														_tmp_file.write(a__							)

							UpperCamelCase__					  =      get_imports(a__							)
							assert parsed_imports == ["os"]




