





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


_snake_case  :    List[Any]						    =  16
_snake_case  :    Any						    =  32

def   a_    ( lowerCAmelCase_      :			Accelerator,	lowerCAmelCase_      :			int = 16	):
   __lowerCAmelCase								=	AutoTokenizer.from_pretrained('bert-base-cased'	)
   __lowerCAmelCase								=	load_dataset('glue',	'mrpc'	)

   def tokenize_function(lowerCAmelCase_      :			int	):
      # max_length=None => use the model max length (it's actually the default)
      __lowerCAmelCase								=	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():
      __lowerCAmelCase								=	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
   __lowerCAmelCase								=	tokenized_datasets.rename_column('label',	'labels'	)

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

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

   # Instantiate dataloaders.
   __lowerCAmelCase								=	DataLoader(
       tokenized_datasets['train'],	shuffle=lowerCAmelCase_,	collate_fn=lowerCAmelCase_,	batch_size=lowerCAmelCase_,	drop_last=lowerCAmelCase_	)
   __lowerCAmelCase								=	DataLoader(
       tokenized_datasets['validation'],	shuffle=lowerCAmelCase_,	collate_fn=lowerCAmelCase_,	batch_size=lowerCAmelCase_,	drop_last=(accelerator.mixed_precision == 'fp8'),	)

   return train_dataloader, eval_dataloader

def   a_    ( lowerCAmelCase_      :			Any,	lowerCAmelCase_      :			int	):
   # Initialize accelerator
   __lowerCAmelCase								=	Accelerator(cpu=args.cpu,	mixed_precision=args.mixed_precision	)
   # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
   __lowerCAmelCase								=	config['lr']
   __lowerCAmelCase								=	int(config['num_epochs']	)
   __lowerCAmelCase								=	int(config['seed']	)
   __lowerCAmelCase								=	int(config['batch_size']	)

   __lowerCAmelCase								=	evaluate.load('glue',	'mrpc'	)

   # If the batch size is too big we use gradient accumulation
   __lowerCAmelCase								=	1
   if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
      __lowerCAmelCase								=	batch_size // MAX_GPU_BATCH_SIZE
      __lowerCAmelCase								=	MAX_GPU_BATCH_SIZE

   set_seed(lowerCAmelCase_	)
   __lowerCAmelCase	,	__lowerCAmelCase								=	get_dataloaders(lowerCAmelCase_,	lowerCAmelCase_	)
   # Instantiate the model (we build the model here so that the seed also control new weights initialization)
   __lowerCAmelCase								=	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).
   __lowerCAmelCase								=	model.to(accelerator.device	)
   # Instantiate optimizer
   __lowerCAmelCase								=	AdamW(params=model.parameters(),	lr=lowerCAmelCase_	)

   # Instantiate scheduler
   __lowerCAmelCase								=	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.

   __lowerCAmelCase	,	__lowerCAmelCase	,	__lowerCAmelCase	,	__lowerCAmelCase	,	__lowerCAmelCase								=	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	)
         __lowerCAmelCase								=	model(**lowerCAmelCase_	)
         __lowerCAmelCase								=	outputs.loss
         __lowerCAmelCase								=	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():
            __lowerCAmelCase								=	model(**lowerCAmelCase_	)
         __lowerCAmelCase								=	outputs.logits.argmax(dim=-1	)
         __lowerCAmelCase	,	__lowerCAmelCase								=	accelerator.gather_for_metrics((predictions, batch['labels'])	)
         metric.add_batch(
             predictions=lowerCAmelCase_,	references=lowerCAmelCase_,	)

      __lowerCAmelCase								=	metric.compute()
      # Use accelerator.print to print only on the main process.
      accelerator.print(F"""epoch {epoch}:""",	lowerCAmelCase_	)

def   a_    ( ):
   __lowerCAmelCase								=	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.'	)
   __lowerCAmelCase								=	parser.parse_args()
   __lowerCAmelCase								=	{'lr': 2E-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
   training_function(lowerCAmelCase_,	lowerCAmelCase_	)


if __name__ == "__main__":
     main()








def   a_    ( lowerCAmelCase_      :			int	):
   if number < 0:
      raise ValueError('number must not be negative'	)
   return number & (number - 1) == 0


if __name__ == "__main__":
     import doctest

     doctest.testmod()

