

"""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										=		16
__lowerCamelCase										=		32





def 	lowercase      (   __UpperCamelCase      ,   __UpperCamelCase = 16						) ->      Union[str, Any]:
 __magic_name__     =      AutoTokenizer.from_pretrained('''bert-base-cased'''						)
 __magic_name__     =      load_dataset('''glue'''      ,   '''mrpc'''						)

 def tokenize_function(__UpperCamelCase						):
  # max_length=None => use the model max length (it's actually the default)
  __magic_name__     =      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():
  __magic_name__     =      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
 __magic_name__     =      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.
  __magic_name__     =      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":
   __magic_name__     =      16
  elif accelerator.mixed_precision != "no":
   __magic_name__     =      8
  else:
   __magic_name__     =      None

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

 # Instantiate dataloaders.
 __magic_name__     =      DataLoader(
     tokenized_datasets['''train''']      ,   shuffle=__UpperCamelCase      ,   collate_fn=__UpperCamelCase      ,   batch_size=__UpperCamelCase						)
 __magic_name__     =      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										=		mocked_dataloaders  # noqa: F811





def 	lowercase      (   __UpperCamelCase      ,   __UpperCamelCase						) ->      int:
 # For testing only
 if os.environ.get('''TESTING_MOCKED_DATALOADERS'''      ,   __UpperCamelCase						) == "1":
  __magic_name__     =      2
 # Initialize accelerator
 __magic_name__     =      Accelerator(cpu=args.cpu      ,   mixed_precision=args.mixed_precision						)
 # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
 __magic_name__     =      config['''lr''']
 __magic_name__     =      int(config['''num_epochs''']						)
 __magic_name__     =      int(config['''seed''']						)
 __magic_name__     =      int(config['''batch_size''']						)

 __magic_name__     =      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)
  __magic_name__     =      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).
  __magic_name__     =      model.to(accelerator.device						)

  # Instantiate optimizer
  __magic_name__     =      AdamW(params=model.parameters()      ,   lr=__UpperCamelCase						)
  __magic_name__   ,						__magic_name__     =      get_dataloaders(__UpperCamelCase      ,   __UpperCamelCase						)

  # Instantiate scheduler
  __magic_name__     =      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.
  __magic_name__   ,						__magic_name__   ,						__magic_name__   ,						__magic_name__   ,						__magic_name__     =      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						)
    __magic_name__     =      model(**__UpperCamelCase						)
    __magic_name__     =      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():
     __magic_name__     =      model(**__UpperCamelCase						)
    __magic_name__     =      outputs.logits.argmax(dim=-1						)
    __magic_name__   ,						__magic_name__     =      accelerator.gather_for_metrics((predictions, batch['''labels'''])						)
    metric.add_batch(
        predictions=__UpperCamelCase      ,   references=__UpperCamelCase      ,   )

   __magic_name__     =      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 	lowercase      (   ) ->      Union[str, Any]:
 __magic_name__     =      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.'''						)
 __magic_name__     =      parser.parse_args()
 __magic_name__     =      {'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
 training_function(__UpperCamelCase      ,   __UpperCamelCase						)


if __name__ == "__main__":
       main()




"""simple docstring"""






import pytest

import datasets


# Import fixture modules as plugins
__lowerCamelCase										=		["tests.fixtures.files", "tests.fixtures.hub", "tests.fixtures.fsspec"]





def 	lowercase      (   __UpperCamelCase      ,   __UpperCamelCase						) ->      str:
 # Mark tests as "unit" by default if not marked as "integration" (or already marked as "unit")
 for item in items:
  if any(marker in item.keywords for marker in ['''integration''', '''unit''']						):
   continue
  item.add_marker(pytest.mark.unit						)





def 	lowercase      (   __UpperCamelCase						) ->      Union[str, Any]:
 config.addinivalue_line('''markers'''      ,   '''torchaudio_latest: mark test to run with torchaudio>=0.12'''						)





@pytest.fixture(autouse=__UpperCamelCase						)
def 	lowercase      (   __UpperCamelCase      ,   __UpperCamelCase						) ->      Dict:
 # test_hf_cache_home = tmp_path_factory.mktemp("cache")  # TODO: why a cache dir per test function does not work?
 __magic_name__     =      tmp_path_factory.getbasetemp() / '''cache'''
 __magic_name__     =      test_hf_cache_home / '''datasets'''
 __magic_name__     =      test_hf_cache_home / '''metrics'''
 __magic_name__     =      test_hf_cache_home / '''modules'''
 monkeypatch.setattr('''datasets.config.HF_DATASETS_CACHE'''      ,   str(__UpperCamelCase						)						)
 monkeypatch.setattr('''datasets.config.HF_METRICS_CACHE'''      ,   str(__UpperCamelCase						)						)
 monkeypatch.setattr('''datasets.config.HF_MODULES_CACHE'''      ,   str(__UpperCamelCase						)						)
 __magic_name__     =      test_hf_datasets_cache / '''downloads'''
 monkeypatch.setattr('''datasets.config.DOWNLOADED_DATASETS_PATH'''      ,   str(__UpperCamelCase						)						)
 __magic_name__     =      test_hf_datasets_cache / '''downloads''' / '''extracted'''
 monkeypatch.setattr('''datasets.config.EXTRACTED_DATASETS_PATH'''      ,   str(__UpperCamelCase						)						)





@pytest.fixture(autouse=__UpperCamelCase      ,   scope='''session'''						)
def 	lowercase      (   ) ->      Any:
 datasets.disable_progress_bar()





@pytest.fixture(autouse=__UpperCamelCase						)
def 	lowercase      (   __UpperCamelCase						) ->      str:
 # don't take tests into account when counting downloads
 monkeypatch.setattr('''datasets.config.HF_UPDATE_DOWNLOAD_COUNTS'''      ,   __UpperCamelCase						)





@pytest.fixture
def 	lowercase      (   __UpperCamelCase						) ->      int:
 # Required to suppress RemovedIn20Warning when feature(s) are not compatible with SQLAlchemy 2.0
 # To be removed once SQLAlchemy 2.0 supported
 monkeypatch.setattr('''sqlalchemy.util.deprecations.SILENCE_UBER_WARNING'''      ,   __UpperCamelCase						)

