

'''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  UpperCAmelCase__      ( UpperCAmelCase__,    UpperCAmelCase__ = 16			)     ->     Dict:
       A_		   = AutoTokenizer.from_pretrained("""bert-base-cased"""			)
       A_		   = load_dataset("""glue""",    """mrpc"""			)

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

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

       # Instantiate dataloaders.
       A_		   = DataLoader(
           tokenized_datasets["""train"""],    shuffle=_UpperCAmelCase,    collate_fn=_UpperCAmelCase,    batch_size=_UpperCAmelCase			)
       A_		   = 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  UpperCAmelCase__      ( UpperCAmelCase__,    UpperCAmelCase__			)     ->     Tuple:
       # For testing only
       if os.environ.get("""TESTING_MOCKED_DATALOADERS""",    _UpperCAmelCase			) == "1":
              A_		   = 2
       # Initialize accelerator
       A_		   = Accelerator(cpu=args.cpu,    mixed_precision=args.mixed_precision			)
       # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
       A_		   = config["lr"]
       A_		   = int(config["""num_epochs"""]			)
       A_		   = int(config["""seed"""]			)
       A_		   = int(config["""batch_size"""]			)

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

              # Instantiate optimizer
              A_		   = AdamW(params=model.parameters(),    lr=_UpperCAmelCase			)
              A_		   = get_dataloaders(_UpperCAmelCase,    _UpperCAmelCase			)

              # Instantiate scheduler
              A_		   = get_linear_schedule_with_warmup(
                  optimizer=_UpperCAmelCase,    num_warmup_steps=1_00,    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.
              A_		   = 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			)
                            A_		   = model(**_UpperCAmelCase			)
                            A_		   = 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():
                                   A_		   = model(**_UpperCAmelCase			)
                            A_		   = outputs.logits.argmax(dim=-1			)
                            A_		   = accelerator.gather_for_metrics((predictions, batch["""labels"""])			)
                            metric.add_batch(
                                predictions=_UpperCAmelCase,    references=_UpperCAmelCase,    )

                     A_		   = 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  UpperCAmelCase__      ( )     ->     Any:
       A_		   = 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."""			)
       A_		   = parser.parse_args()
       A_		   = {"lr": 2e-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
       training_function(_UpperCAmelCase,    _UpperCAmelCase			)


if __name__ == "__main__":
      main()






'''simple docstring'''







def  UpperCAmelCase__      ( UpperCAmelCase__			)     ->     float:

       if edge <= 0 or not isinstance(UpperCAmelCase__,    UpperCAmelCase__			):
              raise ValueError("""Length must be a positive."""			)
       return 3 * ((25 + 10 * (5 ** (1 / 2))) ** (1 / 2)) * (edge**2)
def  UpperCAmelCase__      ( UpperCAmelCase__			)     ->     float:

       if edge <= 0 or not isinstance(UpperCAmelCase__,    UpperCAmelCase__			):
              raise ValueError("""Length must be a positive."""			)
       return ((15 + (7 * (5 ** (1 / 2)))) / 4) * (edge**3)


if __name__ == "__main__":
      import doctest

      doctest.testmod()
