





import argparse

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


_UpperCAmelCase				:       str	   =				16
_UpperCAmelCase				:       str	   =				32


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

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

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

    # Instantiate dataloaders.
    lowerCamelCase__    :      List[str]					 =		DataLoader(
        tokenized_datasets['train']						,	shuffle=_UpperCAmelCase						,	collate_fn=_UpperCAmelCase						,	batch_size=_UpperCAmelCase						,	drop_last=_UpperCAmelCase )
    lowerCamelCase__    :      str					 =		DataLoader(
        tokenized_datasets['validation']						,	shuffle=_UpperCAmelCase						,	collate_fn=_UpperCAmelCase						,	batch_size=_UpperCAmelCase						,	drop_last=(accelerator.mixed_precision == 'fp8')						,	)

    return train_dataloader, eval_dataloader


def        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase						,	_UpperCAmelCase )			->     Optional[Any]:
    # Initialize accelerator
    lowerCamelCase__    :      Union[str, Any]					 =		Accelerator(cpu=args.cpu						,	mixed_precision=args.mixed_precision )
    # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
    lowerCamelCase__    :      int					 =		config['lr']
    lowerCamelCase__    :      Tuple					 =		int(config['num_epochs'] )
    lowerCamelCase__    :      Union[str, Any]					 =		int(config['seed'] )
    lowerCamelCase__    :      List[str]					 =		int(config['batch_size'] )

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

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

    set_seed(_UpperCAmelCase )
    lowerCamelCase__ ,							lowerCamelCase__    :      Dict					 =		get_dataloaders(_UpperCAmelCase						,	_UpperCAmelCase )
    # Instantiate the model (we build the model here so that the seed also control new weights initialization)
    lowerCamelCase__    :      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__    :      Dict					 =		model.to(accelerator.device )
    # Instantiate optimizer
    lowerCamelCase__    :      int					 =		AdamW(params=model.parameters()						,	lr=_UpperCAmelCase )

    # Instantiate scheduler
    lowerCamelCase__    :      Dict					 =		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__    :      int					 =		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__    :      Optional[int]					 =		model(**_UpperCAmelCase )
            lowerCamelCase__    :      Any					 =		outputs.loss
            lowerCamelCase__    :      Optional[Any]					 =		loss / gradient_accumulation_steps
            accelerator.backward(_UpperCAmelCase )
            if step % gradient_accumulation_steps == 0:
                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[Any]					 =		model(**_UpperCAmelCase )
            lowerCamelCase__    :      Any					 =		outputs.logits.argmax(dim=-1 )
            lowerCamelCase__ ,							lowerCamelCase__    :      Optional[int]					 =		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      (       )			->     Optional[Any]:
    lowerCamelCase__    :      str					 =		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__    :      Tuple					 =		parser.parse_args()
    lowerCamelCase__    :      Dict					 =		{'lr': 2e-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
    training_function(_UpperCAmelCase						,	_UpperCAmelCase )


if __name__ == "__main__":
    main()












from __future__ import annotations

import math


def        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase )			->     bool:

    if 1 < number < 4:
        # 2 and 3 are primes
        return True
    elif number < 2 or number % 2 == 0 or number % 3 == 0:
        # Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
        return False

    # All primes number are in format of 6k +/- 1
    for i in range(5						,	int(math.sqrt(_UpperCAmelCase ) + 1 )						,	6 ):
        if number % i == 0 or number % (i + 2) == 0:
            return False
    return True


_UpperCAmelCase				:       Any	   =				[num for num in range(3, 10_00_01, 2) if not is_prime(num)]


def        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase )			->     list[int]:
    if not isinstance(_UpperCAmelCase						,	_UpperCAmelCase ):
        raise ValueError('n must be an integer' )
    if n <= 0:
        raise ValueError('n must be >= 0' )

    lowerCamelCase__    :      int					 =		[]
    for num in range(len(_UpperCAmelCase ) ):
        lowerCamelCase__    :      Union[str, Any]					 =		0
        while 2 * i * i <= odd_composites[num]:
            lowerCamelCase__    :      Dict					 =		odd_composites[num] - 2 * i * i
            if is_prime(_UpperCAmelCase ):
                break
            i += 1
        else:
            list_nums.append(odd_composites[num] )
            if len(_UpperCAmelCase ) == n:
                return list_nums

    return []


def        SCREAMING_SNAKE_CASE      (       )			->     int:
    return compute_nums(1 )[0]


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





