


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


SCREAMING_SNAKE_CASE       :      Optional[int]												= 16
SCREAMING_SNAKE_CASE       :      List[Any]												= 32



def 				UpperCamelCase_(      lowerCamelCase_							, lowerCamelCase_ = 16 )    ->						Optional[Any]:
    _lowercase       :							Optional[Any]      		=		AutoTokenizer.from_pretrained('bert-base-cased' )
    _lowercase       :							Any      		=		load_dataset('glue'							, 'mrpc' )

    def tokenize_function(lowerCamelCase_ ):
        # max_length=None => use the model max length (it's actually the default)
        _lowercase       :							Any      		=		tokenizer(examples['sentence1']							, examples['sentence2']							, truncation=lowerCamelCase_							, max_length=lowerCamelCase_ )
        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():
        _lowercase       :							Union[str, Any]      		=		datasets.map(
            lowerCamelCase_							, batched=lowerCamelCase_							, 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
    _lowercase       :							Tuple      		=		tokenized_datasets.rename_column('label'							, 'labels' )

    def collate_fn(lowerCamelCase_ ):
        # On TPU it's best to pad everything to the same length or training will be very slow.
        _lowercase       :							str      		=		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":
            _lowercase       :							Union[str, Any]      		=		16
        elif accelerator.mixed_precision != "no":
            _lowercase       :							Any      		=		8
        else:
            _lowercase       :							List[Any]      		=		None

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

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

    return train_dataloader, eval_dataloader



def 				UpperCamelCase_(      lowerCamelCase_							, lowerCamelCase_ )    ->						Optional[int]:
    # Initialize accelerator
    _lowercase       :							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
    _lowercase       :							Tuple      		=		config['lr']
    _lowercase       :							Any      		=		int(config['num_epochs'] )
    _lowercase       :							Optional[int]      		=		int(config['seed'] )
    _lowercase       :							List[str]      		=		int(config['batch_size'] )

    _lowercase       :							List[Any]      		=		evaluate.load('glue'							, 'mrpc' )

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

    set_seed(lowerCamelCase_ )
    _lowercase		,				_lowercase       :							Optional[int]      		=		get_dataloaders(lowerCamelCase_							, lowerCamelCase_ )
    # Instantiate the model (we build the model here so that the seed also control new weights initialization)
    _lowercase       :							List[Any]      		=		AutoModelForSequenceClassification.from_pretrained('bert-base-cased'							, return_dict=lowerCamelCase_ )

    # 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).
    _lowercase       :							Optional[Any]      		=		model.to(accelerator.device )
    # Instantiate optimizer
    _lowercase       :							str      		=		AdamW(params=model.parameters()							, lr=lowerCamelCase_ )

    # Instantiate scheduler
    _lowercase       :							Optional[Any]      		=		get_linear_schedule_with_warmup(
        optimizer=lowerCamelCase_							, num_warmup_steps=100							, num_training_steps=(len(lowerCamelCase_ ) * 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.

    _lowercase		,				_lowercase		,				_lowercase		,				_lowercase		,				_lowercase       :							int      		=		accelerator.prepare(
        lowerCamelCase_							, lowerCamelCase_							, lowerCamelCase_							, lowerCamelCase_							, lowerCamelCase_ )

    # Now we train the model
    for epoch in range(lowerCamelCase_ ):
        model.train()
        for step, batch in enumerate(lowerCamelCase_ ):
            # We could avoid this line since we set the accelerator with `device_placement=True`.
            batch.to(accelerator.device )
            _lowercase       :							str      		=		model(**lowerCamelCase_ )
            _lowercase       :							Tuple      		=		outputs.loss
            _lowercase       :							str      		=		loss / gradient_accumulation_steps
            accelerator.backward(lowerCamelCase_ )
            if step % gradient_accumulation_steps == 0:
                optimizer.step()
                lr_scheduler.step()
                optimizer.zero_grad()

        model.eval()
        for step, batch in enumerate(lowerCamelCase_ ):
            # We could avoid this line since we set the accelerator with `device_placement=True`.
            batch.to(accelerator.device )
            with torch.no_grad():
                _lowercase       :							List[str]      		=		model(**lowerCamelCase_ )
            _lowercase       :							Dict      		=		outputs.logits.argmax(dim=-1 )
            _lowercase		,				_lowercase       :							str      		=		accelerator.gather_for_metrics((predictions, batch['labels']) )
            metric.add_batch(
                predictions=lowerCamelCase_							, references=lowerCamelCase_							, )

        _lowercase       :							Any      		=		metric.compute()
        # Use accelerator.print to print only on the main process.
        accelerator.print(F'''epoch {epoch}:'''							, lowerCamelCase_ )



def 				UpperCamelCase_(      )    ->						Tuple:
    _lowercase       :							List[Any]      		=		argparse.ArgumentParser(description='Simple example of training script.' )
    parser.add_argument(
        '--mixed_precision'							, type=lowerCamelCase_							, default=lowerCamelCase_							, 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.' )
    _lowercase       :							Optional[int]      		=		parser.parse_args()
    _lowercase       :							Dict      		=		{'lr': 2e-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
    training_function(lowerCamelCase_							, lowerCamelCase_ )


if __name__ == "__main__":
 main()





from abc import ABC, abstractmethod
from argparse import ArgumentParser







class  lowercase				(       lowercase_			):




				@staticmethod
				@abstractmethod
				def     a							(					snake_case       ):
											raise NotImplementedError()




				@abstractmethod
				def     a							(					self       ):
											raise NotImplementedError()




