
"""simple docstring"""




from __future__ import annotations



def     __magic_name__		(					UpperCamelCase     :		str							,		UpperCamelCase     :		list[str] | None = None     )     ->						list[list[str]]:
    a__		=			word_bank or []
    # create a table
    a__		=			len(UpperCamelCase     ) + 1

    a__		=			[]
    for _ in range(UpperCamelCase     ):
        table.append([]     )
    # seed value
    a__		=			[[]]  # because empty string has empty combination

    # iterate through the indices
    for i in range(UpperCamelCase     ):
        # condition
        if table[i] != []:
            for word in word_bank:
                # slice condition
                if target[i : i + len(UpperCamelCase     )] == word:
                    a__		=			[
                        [word, *way] for way in table[i]
                    ]
                    # adds the word to every combination the current position holds
                    # now,push that combination to the table[i+len(word)]
                    table[i + len(UpperCamelCase     )] += new_combinations

    # combinations are in reverse order so reverse for better output
    for combination in table[len(UpperCamelCase     )]:
        combination.reverse()

    return table[len(UpperCamelCase     )]


if __name__ == "__main__":
    print(all_construct('jwajalapa', ['jwa', 'j', 'w', 'a', 'la', 'lapa']))
    print(all_construct('rajamati', ['s', 'raj', 'amat', 'raja', 'ma', 'i', 't']))
    print(
        all_construct(
            'hexagonosaurus',
            ['h', 'ex', 'hex', 'ag', 'ago', 'ru', 'auru', 'rus', 'go', 'no', 'o', 's'],
        )
    )




"""simple docstring"""




import argparse
import os

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.local_sgd import LocalSGD


########################################################################
# This is a fully working simple example to use Accelerate
# with LocalSGD, which is a method to synchronize model
# parameters every K batches. It is different, but complementary
# to 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)
#
# 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
#
########################################################################


a		:       List[str]     						=       16
a		:       str     						=       32



def     __magic_name__		(					UpperCamelCase     :		Accelerator							,		UpperCamelCase     :		int = 16     )     ->						Dict:
    a__		=			AutoTokenizer.from_pretrained('bert-base-cased'     )
    a__		=			load_dataset('glue'							,		'mrpc'     )

    def tokenize_function(UpperCamelCase     :		Optional[int]     ):
        # 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     :		List[str]     ):
        # On TPU it's best to pad everything to the same length or training will be very slow.
        a__		=			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":
            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

    a		:       Union[str, Any]     						=       mocked_dataloaders  # noqa: F811



def     __magic_name__		(					UpperCamelCase     :		List[Any]							,		UpperCamelCase     :		Optional[int]     )     ->						Optional[Any]:
    # For testing only
    if os.environ.get('TESTING_MOCKED_DATALOADERS'							,		UpperCamelCase     ) == "1":
        a__		=			2
    # New Code #
    a__		=			int(args.gradient_accumulation_steps     )
    a__		=			int(args.local_sgd_steps     )
    # Initialize accelerator
    a__		=			Accelerator(
        cpu=args.cpu							,		mixed_precision=args.mixed_precision							,		gradient_accumulation_steps=UpperCamelCase     )
    if accelerator.distributed_type not in [DistributedType.NO, DistributedType.MULTI_CPU, DistributedType.MULTI_GPU]:
        raise NotImplementedError('LocalSGD is supported only for CPUs and GPUs (no DeepSpeed or MegatronLM)'     )
    # 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'     )

    set_seed(UpperCamelCase     )
    a__	,     a__		=			get_dataloaders(UpperCamelCase							,		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     )

    # Instantiate scheduler
    a__		=			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.
    a__	,     a__	,     a__	,     a__	,     a__		=			accelerator.prepare(
        UpperCamelCase							,		UpperCamelCase							,		UpperCamelCase							,		UpperCamelCase							,		UpperCamelCase     )

    # Now we train the model
    for epoch in range(UpperCamelCase     ):
        model.train()
        with LocalSGD(
            accelerator=UpperCamelCase							,		model=UpperCamelCase							,		local_sgd_steps=UpperCamelCase							,		enabled=local_sgd_steps is not None     ) as local_sgd:
            for step, batch in enumerate(UpperCamelCase     ):
                # We could avoid this line since we set the accelerator with `device_placement=True`.
                batch.to(accelerator.device     )
                # New code #
                # We use the new `accumulate` context manager to perform gradient accumulation
                # We also currently do not support TPUs nor advise it as bugs were found on the XLA side when running our tests.
                with accelerator.accumulate(UpperCamelCase     ):
                    a__		=			model(**UpperCamelCase     )
                    a__		=			output.loss
                    accelerator.backward(UpperCamelCase     )
                    optimizer.step()
                    lr_scheduler.step()
                    optimizer.zero_grad()
                    # LocalSGD-specific line
                    local_sgd.step()

        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__	,     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     )



def     __magic_name__		(					)     ->						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.'							,		)
    # New Code #
    parser.add_argument(
        '--gradient_accumulation_steps'							,		type=UpperCamelCase							,		default=1							,		help='The number of minibatches to be ran before gradients are accumulated.'							,		)
    parser.add_argument(
        '--local_sgd_steps'							,		type=UpperCamelCase							,		default=8							,		help='Number of local SGD steps or None to disable local SGD'     )
    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()


