

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
from accelerate.utils import find_executable_batch_size


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to combine both the gradient accumulation
# and automatic batch size finder utilities of Accelerate to perfrom
# automatic gradient accumulation
#
# 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
#
########################################################################

UpperCamelCase__					: List[Any]								=      32


def    _UpperCAmelCase    (					_SCREAMING_SNAKE_CASE   :						Optional[Any]  ,				_SCREAMING_SNAKE_CASE   :						str = 16    ):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_													=  AutoTokenizer.from_pretrained('bert-base-cased'    )
    SCREAMING_SNAKE_CASE_													=  load_dataset('glue'  ,				'mrpc'    )

    def tokenize_function(_SCREAMING_SNAKE_CASE   :						Union[str, Any]    ):
        # 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   :						int    ):
        # 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'  ,				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

       UpperCamelCase__					: Tuple								=      mocked_dataloaders  # noqa: F811


def    _UpperCAmelCase    (					_SCREAMING_SNAKE_CASE   :						int  ,				_SCREAMING_SNAKE_CASE   :						List[str]    ):





    """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 use the `find_executable_batch_size` decorator, passing in the desired observed batch size
    # to train on. If a CUDA OOM error occurs, it will retry this loop cutting the batch size in
    # half each time. From this, we can calculate the number of gradient accumulation steps needed
    # and modify the Accelerator object as a result
    @find_executable_batch_size(starting_batch_size=int(_SCREAMING_SNAKE_CASE    )    )
    def inner_training_loop(_SCREAMING_SNAKE_CASE   :						Optional[int]    ):
        # Since we need to modify the outside accelerator object, we need to bring it
        # to the local scope
        nonlocal accelerator

        # We can calculate the number of gradient accumulation steps based on the current
        # batch size vs the starting batch size
        SCREAMING_SNAKE_CASE_													=  observed_batch_size // batch_size

        # And then set it in the Accelerator directly:
        SCREAMING_SNAKE_CASE_													=  num_gradient_accumulation_steps

        # Next we need to free all of the stored model references in the Accelerator each time
        accelerator.free_memory()

        # And set the seed so our results are reproducable each reset
        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_													=  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=100  ,				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_													=  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    ):
                # And perform gradient accumulation
                with accelerator.accumulate(_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_													=  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    _UpperCAmelCase    (					):





    """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()
    # New Code #
    # We modify the starting batch size to be an observed batch size of 256, to guarentee an initial CUDA OOM
    SCREAMING_SNAKE_CASE_													=  {'lr': 2E-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 256}
    training_function(_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE    )


if __name__ == "__main__":
       main()





def    _UpperCAmelCase    (					_SCREAMING_SNAKE_CASE   :						int	,				_SCREAMING_SNAKE_CASE   :						int	,				_SCREAMING_SNAKE_CASE   :						int    ):





    """simple docstring"""


    if power < 0:
        return -1
    base %= mod
    SCREAMING_SNAKE_CASE_													=  1

    while power > 0:
        if power & 1:
            SCREAMING_SNAKE_CASE_													=  (result * base) % mod
        SCREAMING_SNAKE_CASE_													=  power >> 1
        SCREAMING_SNAKE_CASE_													=  (base * base) % mod

    return result


def    _UpperCAmelCase    (					):





    """simple docstring"""


    print(modular_exponential(3	,				200	,				13    )    )


if __name__ == "__main__":
       import doctest

       doctest.testmod()

       main()


