





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


_UpperCAmelCase				:       Tuple	   =				16
_UpperCAmelCase				:       Any	   =				32


def        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase						,	_UpperCAmelCase = 16 )			->     Tuple:
    lowerCamelCase__    :      List[str]					 =		AutoTokenizer.from_pretrained('bert-base-cased' )
    lowerCamelCase__    :      int					 =		load_dataset('glue'						,	'mrpc' )

    def tokenize_function(_UpperCAmelCase ):
        # max_length=None => use the model max length (it's actually the default)
        lowerCamelCase__    :      Optional[Any]					 =		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():
        lowerCamelCase__    :      List[str]					 =		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
    lowerCamelCase__    :      Tuple					 =		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.
        lowerCamelCase__    :      Union[str, Any]					 =		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":
            lowerCamelCase__    :      Dict					 =		16
        elif accelerator.mixed_precision != "no":
            lowerCamelCase__    :      List[Any]					 =		8
        else:
            lowerCamelCase__    :      Optional[int]					 =		None

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

    # Instantiate dataloaders.
    lowerCamelCase__    :      Tuple					 =		DataLoader(
        tokenized_datasets['train']						,	shuffle=_UpperCAmelCase						,	collate_fn=_UpperCAmelCase						,	batch_size=_UpperCAmelCase )
    lowerCamelCase__    :      Any					 =		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

    _UpperCAmelCase				:       Tuple	   =				mocked_dataloaders  # noqa: F811


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

    lowerCamelCase__    :      str					 =		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)
        lowerCamelCase__    :      Tuple					 =		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).
        lowerCamelCase__    :      List[Any]					 =		model.to(accelerator.device )

        # Instantiate optimizer
        lowerCamelCase__    :      List[Any]					 =		AdamW(params=model.parameters()						,	lr=_UpperCAmelCase )
        lowerCamelCase__ ,							lowerCamelCase__    :      Tuple					 =		get_dataloaders(_UpperCAmelCase						,	_UpperCAmelCase )

        # Instantiate scheduler
        lowerCamelCase__    :      Union[str, Any]					 =		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.
        lowerCamelCase__ ,							lowerCamelCase__ ,							lowerCamelCase__ ,							lowerCamelCase__ ,							lowerCamelCase__    :      Optional[Any]					 =		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 )
                lowerCamelCase__    :      List[str]					 =		model(**_UpperCAmelCase )
                lowerCamelCase__    :      Union[str, Any]					 =		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():
                    lowerCamelCase__    :      List[str]					 =		model(**_UpperCAmelCase )
                lowerCamelCase__    :      Union[str, Any]					 =		outputs.logits.argmax(dim=-1 )
                lowerCamelCase__ ,							lowerCamelCase__    :      int					 =		accelerator.gather_for_metrics((predictions, batch['labels']) )
                metric.add_batch(
                    predictions=_UpperCAmelCase						,	references=_UpperCAmelCase						,	)

            lowerCamelCase__    :      Tuple					 =		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        SCREAMING_SNAKE_CASE      (       )			->     Dict:
    lowerCamelCase__    :      Dict					 =		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.' )
    lowerCamelCase__    :      Union[str, Any]					 =		parser.parse_args()
    lowerCamelCase__    :      int					 =		{'lr': 2e-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
    training_function(_UpperCAmelCase						,	_UpperCAmelCase )


if __name__ == "__main__":
    main()












from itertools import count


def        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase = 50 )			->     int:
    lowerCamelCase__    :      Optional[Any]					 =		[1] * min_block_length

    for n in count(_UpperCAmelCase ):
        fill_count_functions.append(1 )

        for block_length in range(_UpperCAmelCase						,	n + 1 ):
            for block_start in range(n - block_length ):
                fill_count_functions[n] += fill_count_functions[
                    n - block_start - block_length - 1
                ]

            fill_count_functions[n] += 1

        if fill_count_functions[n] > 100_0000:
            break

    return n


if __name__ == "__main__":
    print(F"""{solution() = }""")





