





"""simple docstring"""






def       __lowerCamelCase    (       SCREAMING_SNAKE_CASE							)		->       bool:






       """simple docstring"""




       if not isinstance(SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE							):
              _UpperCAmelCase     =     F"""Input value of [number={number}] must be an integer"""
              raise TypeError(SCREAMING_SNAKE_CASE							)
       if number < 0:
              return False
       _UpperCAmelCase     =     number * number
       while number > 0:
              if number % 10 != number_square % 10:
                     return False
              number //= 10
              number_square //= 10
       return True


if __name__ == "__main__":
    import doctest

    doctest.testmod()













"""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


########################################################################
# This is a fully working simple example to use Accelerate
# and perform 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
#
########################################################################


lowerCAmelCase_			=							16
lowerCAmelCase_			=							32

def       __lowerCamelCase    (       SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE = 16							)		->       int:






       """simple docstring"""




       _UpperCAmelCase     =     AutoTokenizer.from_pretrained('bert-base-cased'							)
       _UpperCAmelCase     =     load_dataset('glue','mrpc'							)

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

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

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

       # Instantiate dataloaders.
       _UpperCAmelCase     =     DataLoader(
           tokenized_datasets['train'],shuffle=SCREAMING_SNAKE_CASE,collate_fn=SCREAMING_SNAKE_CASE,batch_size=SCREAMING_SNAKE_CASE							)
       _UpperCAmelCase     =     DataLoader(
           tokenized_datasets['validation'],shuffle=SCREAMING_SNAKE_CASE,collate_fn=SCREAMING_SNAKE_CASE,batch_size=SCREAMING_SNAKE_CASE							)

       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

    lowerCAmelCase_			=							mocked_dataloaders  # noqa: F811

def       __lowerCamelCase    (       SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE							)		->       Union[str, Any]:






       """simple docstring"""




       if os.environ.get('TESTING_MOCKED_DATALOADERS',SCREAMING_SNAKE_CASE							) == "1":
              _UpperCAmelCase     =     2
       # New Code #
       _UpperCAmelCase     =     int(args.gradient_accumulation_steps							)
       # Initialize accelerator
       _UpperCAmelCase     =     Accelerator(
           cpu=args.cpu,mixed_precision=args.mixed_precision,gradient_accumulation_steps=SCREAMING_SNAKE_CASE							)
       if accelerator.distributed_type == DistributedType.TPU and gradient_accumulation_steps > 1:
              raise NotImplementedError(
                  'Gradient accumulation on TPUs is currently not supported. Pass `gradient_accumulation_steps=1`'							)
       # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
       _UpperCAmelCase     =     config['lr']
       _UpperCAmelCase     =     int(config['num_epochs']							)
       _UpperCAmelCase     =     int(config['seed']							)
       _UpperCAmelCase     =     int(config['batch_size']							)

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

       set_seed(SCREAMING_SNAKE_CASE							)
       _UpperCAmelCase    ,							_UpperCAmelCase     =     get_dataloaders(SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE							)
       # Instantiate the model (we build the model here so that the seed also control new weights initialization)
       _UpperCAmelCase     =     AutoModelForSequenceClassification.from_pretrained('bert-base-cased',return_dict=SCREAMING_SNAKE_CASE							)

       # 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).
       _UpperCAmelCase     =     model.to(accelerator.device							)

       # Instantiate optimizer
       _UpperCAmelCase     =     AdamW(params=model.parameters(),lr=SCREAMING_SNAKE_CASE							)

       # Instantiate scheduler
       _UpperCAmelCase     =     get_linear_schedule_with_warmup(
           optimizer=SCREAMING_SNAKE_CASE,num_warmup_steps=100,num_training_steps=(len(SCREAMING_SNAKE_CASE							) * 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.
       _UpperCAmelCase    ,							_UpperCAmelCase    ,							_UpperCAmelCase    ,							_UpperCAmelCase    ,							_UpperCAmelCase     =     accelerator.prepare(
           SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE							)

       # Now we train the model
       for epoch in range(SCREAMING_SNAKE_CASE							):
              model.train()
              for step, batch in enumerate(SCREAMING_SNAKE_CASE							):
                     # 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(SCREAMING_SNAKE_CASE							):
                            _UpperCAmelCase     =     model(**SCREAMING_SNAKE_CASE							)
                            _UpperCAmelCase     =     output.loss
                            accelerator.backward(SCREAMING_SNAKE_CASE							)
                            optimizer.step()
                            lr_scheduler.step()
                            optimizer.zero_grad()

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

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

def       __lowerCamelCase    (       )		->       str:






       """simple docstring"""




       _UpperCAmelCase     =     argparse.ArgumentParser(description='Simple example of training script.'							)
       parser.add_argument(
           '--mixed_precision',type=SCREAMING_SNAKE_CASE,default=SCREAMING_SNAKE_CASE,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=SCREAMING_SNAKE_CASE,default=1,help='The number of minibatches to be ran before gradients are accumulated.',)
       parser.add_argument('--cpu',action='store_true',help='If passed, will train on the CPU.'							)
       _UpperCAmelCase     =     parser.parse_args()
       _UpperCAmelCase     =     {'lr': 2E-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
       training_function(SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE							)


if __name__ == "__main__":
    main()






