'''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
#
########################################################################


snake_case__					:      List[str]				      =  16
snake_case__					:      Optional[Any]				      =  32


def  _lowerCamelCase   (				lowerCamelCase_ :							Accelerator     ,					lowerCamelCase_ :							int = 16       ):



     """simple docstring"""





     UpperCAmelCase_							:       Tuple       =				AutoTokenizer.from_pretrained('bert-base-cased'       )
     UpperCAmelCase_							:       Optional[Any]       =				load_dataset('glue'     ,					'mrpc'       )

     def tokenize_function(lowerCamelCase_ :							str       ):
          # max_length=None => use the model max length (it's actually the default)
          UpperCAmelCase_							:       Union[str, 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():
          UpperCAmelCase_							:       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
     UpperCAmelCase_							:       int       =				tokenized_datasets.rename_column('label'     ,					'labels'       )

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

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

     # Instantiate dataloaders.
     UpperCAmelCase_							:       Optional[Any]       =				DataLoader(
         tokenized_datasets['train']     ,					shuffle=lowerCamelCase_     ,					collate_fn=lowerCamelCase_     ,					batch_size=lowerCamelCase_       )
     UpperCAmelCase_							:       Union[str, Any]       =				DataLoader(
         tokenized_datasets['validation']     ,					shuffle=lowerCamelCase_     ,					collate_fn=lowerCamelCase_     ,					batch_size=lowerCamelCase_       )

     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

 snake_case__					:      int				      =  mocked_dataloaders  # noqa: F811


def  _lowerCamelCase   (				lowerCamelCase_ :							Optional[int]     ,					lowerCamelCase_ :							int       ):



     """simple docstring"""





     if os.environ.get('TESTING_MOCKED_DATALOADERS'     ,					lowerCamelCase_       ) == "1":
          UpperCAmelCase_							:       Optional[Any]       =				2
     # New Code #
     UpperCAmelCase_							:       Tuple       =				int(args.gradient_accumulation_steps       )
     # Initialize accelerator
     UpperCAmelCase_							:       List[str]       =				Accelerator(
         cpu=args.cpu     ,					mixed_precision=args.mixed_precision     ,					gradient_accumulation_steps=lowerCamelCase_       )
     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_							:       Dict       =				config['lr']
     UpperCAmelCase_							:       List[Any]       =				int(config['num_epochs']       )
     UpperCAmelCase_							:       Dict       =				int(config['seed']       )
     UpperCAmelCase_							:       List[Any]       =				int(config['batch_size']       )

     UpperCAmelCase_							:       Optional[Any]       =				evaluate.load('glue'     ,					'mrpc'       )

     set_seed(lowerCamelCase_       )
     UpperCAmelCase_       ,      UpperCAmelCase_							:       int       =				get_dataloaders(lowerCamelCase_     ,					lowerCamelCase_       )
     # Instantiate the model (we build the model here so that the seed also control new weights initialization)
     UpperCAmelCase_							:       int       =				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).
     UpperCAmelCase_							:       str       =				model.to(accelerator.device       )

     # Instantiate optimizer
     UpperCAmelCase_							:       Any       =				AdamW(params=model.parameters()     ,					lr=lowerCamelCase_       )

     # Instantiate scheduler
     UpperCAmelCase_							:       str       =				get_linear_schedule_with_warmup(
         optimizer=lowerCamelCase_     ,					num_warmup_steps=100     ,					num_training_steps=(len(lowerCamelCase_       ) * 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_							:       List[str]       =				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       )
               # 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(lowerCamelCase_       ):
                    UpperCAmelCase_							:       Any       =				model(**lowerCamelCase_       )
                    UpperCAmelCase_							:       Any       =				output.loss
                    accelerator.backward(lowerCamelCase_       )
                    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():
                    UpperCAmelCase_							:       Union[str, Any]       =				model(**lowerCamelCase_       )
               UpperCAmelCase_							:       List[Any]       =				outputs.logits.argmax(dim=-1       )
               UpperCAmelCase_       ,      UpperCAmelCase_							:       Any       =				accelerator.gather_for_metrics((predictions, batch['labels'])       )
               metric.add_batch(
                   predictions=lowerCamelCase_     ,					references=lowerCamelCase_     ,					)

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


def  _lowerCamelCase   (				):



     """simple docstring"""





     UpperCAmelCase_							:       Tuple       =				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.'     ,					)
     # New Code #
     parser.add_argument(
         '--gradient_accumulation_steps'     ,					type=lowerCamelCase_     ,					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_							:       Any       =				parser.parse_args()
     UpperCAmelCase_							:       List[Any]       =				{'lr': 2e-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
     training_function(lowerCamelCase_     ,					lowerCamelCase_       )


if __name__ == "__main__":
 main()







'''simple docstring'''


def  _lowerCamelCase   (				lowerCamelCase_ :							str     ,					lowerCamelCase_ :							str = " "       ):



     """simple docstring"""





     UpperCAmelCase_							:       str       =				[]

     UpperCAmelCase_							:       List[Any]       =				0
     for index, char in enumerate(lowerCamelCase_       ):
          if char == separator:
               split_words.append(string[last_index:index]       )
               UpperCAmelCase_							:       Optional[Any]       =				index + 1
          elif index + 1 == len(lowerCamelCase_       ):
               split_words.append(string[last_index : index + 1]       )
     return split_words


if __name__ == "__main__":
 from doctest import testmod

 testmod()






