





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
#
########################################################################


SCREAMING_SNAKE_CASE_						       =	1_6
SCREAMING_SNAKE_CASE_						       =	3_2



def        __lowercase       (				_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE = 16     )			->       Tuple:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE		=						AutoTokenizer.from_pretrained("""bert-base-cased"""     )
      SCREAMING_SNAKE_CASE		=						load_dataset("""glue"""			,						"""mrpc"""     )

      def tokenize_function(_SCREAMING_SNAKE_CASE     ):
            # max_length=None => use the model max length (it's actually the default)
            SCREAMING_SNAKE_CASE		=						tokenizer(examples["""sentence1"""]			,						examples["""sentence2"""]			,						truncation=_SCREAMING_SNAKE_CASE			,						max_length=_SCREAMING_SNAKE_CASE     )
            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():
            SCREAMING_SNAKE_CASE		=						datasets.map(
                _SCREAMING_SNAKE_CASE			,						batched=_SCREAMING_SNAKE_CASE			,						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
      SCREAMING_SNAKE_CASE		=						tokenized_datasets.rename_column("""label"""			,						"""labels"""     )

      def collate_fn(_SCREAMING_SNAKE_CASE     ):
            # On TPU it's best to pad everything to the same length or training will be very slow.
            SCREAMING_SNAKE_CASE		=						1_28 if accelerator.distributed_type == DistributedType.TPU else None
            # When using mixed precision we want round multiples of 8/16
            if accelerator.mixed_precision == "fp8":
                  SCREAMING_SNAKE_CASE		=						16
            elif accelerator.mixed_precision != "no":
                  SCREAMING_SNAKE_CASE		=						8
            else:
                  SCREAMING_SNAKE_CASE		=						None

            return tokenizer.pad(
                _SCREAMING_SNAKE_CASE			,						padding="""longest"""			,						max_length=_SCREAMING_SNAKE_CASE			,						pad_to_multiple_of=_SCREAMING_SNAKE_CASE			,						return_tensors="""pt"""			,						)

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

      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

      SCREAMING_SNAKE_CASE_						       =	mocked_dataloaders  # noqa: F811



def        __lowercase       (				_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE     )			->       Tuple:


      '''simple docstring'''


      if os.environ.get("""TESTING_MOCKED_DATALOADERS"""			,						_SCREAMING_SNAKE_CASE     ) == "1":
            SCREAMING_SNAKE_CASE		=						2
      # Initialize accelerator
      SCREAMING_SNAKE_CASE		=						Accelerator(cpu=args.cpu			,						mixed_precision=args.mixed_precision     )
      # 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		=						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=_SCREAMING_SNAKE_CASE     )
      def inner_training_loop(_SCREAMING_SNAKE_CASE     ):
            # 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(_SCREAMING_SNAKE_CASE     )

            # Instantiate the model (we build the model here so that the seed also control new weights initialization)
            SCREAMING_SNAKE_CASE		=						AutoModelForSequenceClassification.from_pretrained("""bert-base-cased"""			,						return_dict=_SCREAMING_SNAKE_CASE     )

            # 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).
            SCREAMING_SNAKE_CASE		=						model.to(accelerator.device     )

            # Instantiate optimizer
            SCREAMING_SNAKE_CASE		=						AdamW(params=model.parameters()			,						lr=_SCREAMING_SNAKE_CASE     )
            SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE		=						get_dataloaders(_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE     )

            # Instantiate scheduler
            SCREAMING_SNAKE_CASE		=						get_linear_schedule_with_warmup(
                optimizer=_SCREAMING_SNAKE_CASE			,						num_warmup_steps=1_00			,						num_training_steps=(len(_SCREAMING_SNAKE_CASE     ) * 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.
            SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE		=						accelerator.prepare(
                _SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE     )

            # Now we train the model
            for epoch in range(_SCREAMING_SNAKE_CASE     ):
                  model.train()
                  for step, batch in enumerate(_SCREAMING_SNAKE_CASE     ):
                        # We could avoid this line since we set the accelerator with `device_placement=True`.
                        batch.to(accelerator.device     )
                        SCREAMING_SNAKE_CASE		=						model(**_SCREAMING_SNAKE_CASE     )
                        SCREAMING_SNAKE_CASE		=						outputs.loss
                        accelerator.backward(_SCREAMING_SNAKE_CASE     )
                        optimizer.step()
                        lr_scheduler.step()
                        optimizer.zero_grad()

                  model.eval()
                  for step, batch in enumerate(_SCREAMING_SNAKE_CASE     ):
                        # 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(**_SCREAMING_SNAKE_CASE     )
                        SCREAMING_SNAKE_CASE		=						outputs.logits.argmax(dim=-1     )
                        SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE		=						accelerator.gather_for_metrics((predictions, batch["""labels"""])     )
                        metric.add_batch(
                            predictions=_SCREAMING_SNAKE_CASE			,						references=_SCREAMING_SNAKE_CASE			,						)

                  SCREAMING_SNAKE_CASE		=						metric.compute()
                  # Use accelerator.print to print only on the main process.
                  accelerator.print(F"""epoch {epoch}:"""			,						_SCREAMING_SNAKE_CASE     )

    # 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       (				)			->       Optional[int]:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE		=						argparse.ArgumentParser(description="""Simple example of training script."""     )
      parser.add_argument(
          """--mixed_precision"""			,						type=_SCREAMING_SNAKE_CASE			,						default=_SCREAMING_SNAKE_CASE			,						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."""     )
      SCREAMING_SNAKE_CASE		=						parser.parse_args()
      SCREAMING_SNAKE_CASE		=						{"""lr""": 2E-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
      training_function(_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE     )


if __name__ == "__main__":
      main()









from typing import List, Optional, TypeVar

from .arrow_dataset import Dataset, _concatenate_map_style_datasets, _interleave_map_style_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .info import DatasetInfo
from .iterable_dataset import IterableDataset, _concatenate_iterable_datasets, _interleave_iterable_datasets
from .splits import NamedSplit
from .utils import logging
from .utils.py_utils import Literal


SCREAMING_SNAKE_CASE_						       =	logging.get_logger(__name__)


SCREAMING_SNAKE_CASE_						       =	TypeVar("""DatasetType""", Dataset, IterableDataset)



def        __lowercase       (				_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE = None			,						_SCREAMING_SNAKE_CASE = None			,						_SCREAMING_SNAKE_CASE = None			,						_SCREAMING_SNAKE_CASE = None			,						_SCREAMING_SNAKE_CASE = "first_exhausted"			,						)			->       DatasetType:


      '''simple docstring'''


      from .arrow_dataset import Dataset
      from .iterable_dataset import IterableDataset

      if not datasets:
            raise ValueError("""Unable to interleave an empty list of datasets."""     )
      for i, dataset in enumerate(_SCREAMING_SNAKE_CASE     ):
            if not isinstance(_SCREAMING_SNAKE_CASE			,						(Dataset, IterableDataset)     ):
                  if isinstance(_SCREAMING_SNAKE_CASE			,						(DatasetDict, IterableDatasetDict)     ):
                        if not dataset:
                              raise ValueError(
                                  F"""Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} """
                                  """is an empty dataset dictionary."""     )
                        raise ValueError(
                            F"""Dataset at position {i} has at least one split: {list(_SCREAMING_SNAKE_CASE     )}\n"""
                            F"""Please pick one to interleave with the other datasets, for example: dataset['{next(iter(_SCREAMING_SNAKE_CASE     )     )}']"""     )
                  raise ValueError(
                      F"""Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(_SCREAMING_SNAKE_CASE     ).__name__}."""     )
            if i == 0:
                  SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE		=						(
                      (Dataset, IterableDataset) if isinstance(_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE     ) else (IterableDataset, Dataset)
                  )
            elif not isinstance(_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE     ):
                  raise ValueError(
                      F"""Unable to interleave a {dataset_type.__name__} (at position 0) with a {other_type.__name__} (at position {i}). Expected a list of Dataset objects or a list of IterableDataset objects."""     )
      if stopping_strategy not in ["first_exhausted", "all_exhausted"]:
            raise ValueError(F"""{stopping_strategy} is not supported. Please enter a valid stopping_strategy."""     )
      if dataset_type is Dataset:
            return _interleave_map_style_datasets(
                _SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						info=_SCREAMING_SNAKE_CASE			,						split=_SCREAMING_SNAKE_CASE			,						stopping_strategy=_SCREAMING_SNAKE_CASE     )
      else:
            return _interleave_iterable_datasets(
                _SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						info=_SCREAMING_SNAKE_CASE			,						split=_SCREAMING_SNAKE_CASE			,						stopping_strategy=_SCREAMING_SNAKE_CASE     )



def        __lowercase       (				_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE = None			,						_SCREAMING_SNAKE_CASE = None			,						_SCREAMING_SNAKE_CASE = 0			,						)			->       DatasetType:


      '''simple docstring'''


      if not dsets:
            raise ValueError("""Unable to concatenate an empty list of datasets."""     )
      for i, dataset in enumerate(_SCREAMING_SNAKE_CASE     ):
            if not isinstance(_SCREAMING_SNAKE_CASE			,						(Dataset, IterableDataset)     ):
                  if isinstance(_SCREAMING_SNAKE_CASE			,						(DatasetDict, IterableDatasetDict)     ):
                        if not dataset:
                              raise ValueError(
                                  F"""Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} """
                                  """is an empty dataset dictionary."""     )
                        raise ValueError(
                            F"""Dataset at position {i} has at least one split: {list(_SCREAMING_SNAKE_CASE     )}\n"""
                            F"""Please pick one to interleave with the other datasets, for example: dataset['{next(iter(_SCREAMING_SNAKE_CASE     )     )}']"""     )
                  raise ValueError(
                      F"""Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(_SCREAMING_SNAKE_CASE     ).__name__}."""     )
            if i == 0:
                  SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE		=						(
                      (Dataset, IterableDataset) if isinstance(_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE     ) else (IterableDataset, Dataset)
                  )
            elif not isinstance(_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE     ):
                  raise ValueError(
                      F"""Unable to interleave a {dataset_type.__name__} (at position 0) with a {other_type.__name__} (at position {i}). Expected a list of Dataset objects or a list of IterableDataset objects."""     )
      if dataset_type is Dataset:
            return _concatenate_map_style_datasets(_SCREAMING_SNAKE_CASE			,						info=_SCREAMING_SNAKE_CASE			,						split=_SCREAMING_SNAKE_CASE			,						axis=_SCREAMING_SNAKE_CASE     )
      else:
            return _concatenate_iterable_datasets(_SCREAMING_SNAKE_CASE			,						info=_SCREAMING_SNAKE_CASE			,						split=_SCREAMING_SNAKE_CASE			,						axis=_SCREAMING_SNAKE_CASE     )


