





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


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to properly calculate the metrics on the
# validation dataset when in a distributed system, 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)
#
# To help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# 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       : Optional[int]			     =       16
_UpperCAmelCase       : List[Any]			     =       32


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

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

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

    # Instantiate dataloaders.
    lowerCamelCase__					:   Union[str, Any]      				=	DataLoader(
        tokenized_datasets['train']		,			shuffle=_UpperCAmelCase		,			collate_fn=_UpperCAmelCase		,			batch_size=_UpperCAmelCase				)
    lowerCamelCase__					:   Union[str, 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       : Optional[int]			     =       mocked_dataloaders  # noqa: F811


def  SCREAMING_SNAKE_CASE   ( _UpperCAmelCase		,			_UpperCAmelCase				)    ->  Dict:
    # For testing only
    if os.environ.get('TESTING_MOCKED_DATALOADERS'		,			_UpperCAmelCase				) == "1":
        lowerCamelCase__					:   Optional[int]      				=	2
    # Initialize accelerator
    lowerCamelCase__					:   List[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__					:   List[str]      				=	config['lr']
    lowerCamelCase__					:   Any      				=	int(config['num_epochs']				)
    lowerCamelCase__					:   str      				=	int(config['seed']				)
    lowerCamelCase__					:   Dict      				=	int(config['batch_size']				)

    lowerCamelCase__					:   List[str]      				=	evaluate.load('glue'		,			'mrpc'				)

    # If the batch size is too big we use gradient accumulation
    lowerCamelCase__					:   Union[str, Any]      				=	1
    if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
        lowerCamelCase__					:   int      				=	batch_size // MAX_GPU_BATCH_SIZE
        lowerCamelCase__					:   int      				=	MAX_GPU_BATCH_SIZE

    set_seed(_UpperCAmelCase				)
    lowerCamelCase__				,							lowerCamelCase__					:   Optional[int]      				=	get_dataloaders(_UpperCAmelCase		,			_UpperCAmelCase				)
    # Instantiate the model (we build the model here so that the seed also control new weights initialization)
    lowerCamelCase__					:   Union[str, Any]      				=	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__					:   Union[str, Any]      				=	model.to(accelerator.device				)

    # Instantiate optimizer
    lowerCamelCase__					:   int      				=	AdamW(params=model.parameters()		,			lr=_UpperCAmelCase				)

    # Instantiate scheduler
    lowerCamelCase__					:   List[Any]      				=	get_linear_schedule_with_warmup(
        optimizer=_UpperCAmelCase		,			num_warmup_steps=100		,			num_training_steps=(len(_UpperCAmelCase				) * num_epochs) // gradient_accumulation_steps		,			)

    # 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__					:   Tuple      				=	model(**_UpperCAmelCase				)
            lowerCamelCase__					:   Any      				=	outputs.loss
            lowerCamelCase__					:   Tuple      				=	loss / gradient_accumulation_steps
            accelerator.backward(_UpperCAmelCase				)
            if step % gradient_accumulation_steps == 0:
                optimizer.step()
                lr_scheduler.step()
                optimizer.zero_grad()

        model.eval()
        lowerCamelCase__					:   List[str]      				=	0
        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[Any]      				=	model(**_UpperCAmelCase				)
            lowerCamelCase__					:   Dict      				=	outputs.logits.argmax(dim=-1				)
            lowerCamelCase__				,							lowerCamelCase__					:   List[Any]      				=	accelerator.gather((predictions, batch['labels'])				)
            # New Code #
            # First we check if it's a distributed system
            if accelerator.use_distributed:
                # Then see if we're on the last batch of our eval dataloader
                if step == len(_UpperCAmelCase				) - 1:
                    # Last batch needs to be truncated on distributed systems as it contains additional samples
                    lowerCamelCase__					:   Tuple      				=	predictions[: len(eval_dataloader.dataset				) - samples_seen]
                    lowerCamelCase__					:   List[Any]      				=	references[: len(eval_dataloader.dataset				) - samples_seen]
                else:
                    # Otherwise we add the number of samples seen
                    samples_seen += references.shape[0]
            # All of this can be avoided if you use `Accelerator.gather_for_metrics` instead of `Accelerator.gather`:
            # accelerator.gather_for_metrics((predictions, batch["labels"]))
            metric.add_batch(
                predictions=_UpperCAmelCase		,			references=_UpperCAmelCase		,			)

        lowerCamelCase__					:   List[Any]      				=	metric.compute()
        # Use accelerator.print to print only on the main process.
        accelerator.print(F"""epoch {epoch}:"""		,			_UpperCAmelCase				)


def  SCREAMING_SNAKE_CASE   ( )    ->  List[Any]:
    lowerCamelCase__					:   int      				=	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__					:   Optional[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 __future__ import annotations

from collections.abc import Generator


def  SCREAMING_SNAKE_CASE   ( )    ->  Generator[int, None, None]:
    lowerCamelCase__					:   dict[int, int]      				=	{}
    lowerCamelCase__					:   Union[str, Any]      				=	2
    while True:
        lowerCamelCase__					:   Optional[int]      				=	factor_map.pop(_UpperCAmelCase		,			_UpperCAmelCase				)
        if factor:
            lowerCamelCase__					:   Optional[Any]      				=	factor + prime
            while x in factor_map:
                x += factor
            lowerCamelCase__					:   Optional[Any]      				=	factor
        else:
            lowerCamelCase__					:   Union[str, Any]      				=	prime
            yield prime
        prime += 1


def  SCREAMING_SNAKE_CASE   ( _UpperCAmelCase = 1e10				)    ->  int:
    lowerCamelCase__					:   Tuple      				=	sieve()

    lowerCamelCase__					:   Dict      				=	1
    while True:
        lowerCamelCase__					:   List[Any]      				=	next(_UpperCAmelCase				)
        if (2 * prime * n) > limit:
            return n
        # Ignore the next prime as the reminder will be 2.
        next(_UpperCAmelCase				)
        n += 2


if __name__ == "__main__":
    print(solution())





