





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


UpperCAmelCase_      							=     16
UpperCAmelCase_      							=     32





def 						lowerCamelCase__     (     UpperCamelCase__			:       Accelerator       ,    UpperCamelCase__			:       int = 16						)			-> int:



     '''simple docstring'''




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

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

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

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

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

     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

     UpperCAmelCase_      							=     mocked_dataloaders  # noqa: F811





def 						lowerCamelCase__     (     UpperCamelCase__			:       Tuple       ,    UpperCamelCase__			:       Optional[int]						)			-> Dict:



     '''simple docstring'''




     if os.environ.get('TESTING_MOCKED_DATALOADERS'       ,    UpperCamelCase__						) == "1":
          _snake_case									=				2
     # New Code #
     _snake_case									=				int(args.gradient_accumulation_steps						)
     # Initialize accelerator
     _snake_case									=				Accelerator(
         cpu=args.cpu       ,    mixed_precision=args.mixed_precision       ,    gradient_accumulation_steps=UpperCamelCase__						)
     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
     _snake_case									=				config['lr']
     _snake_case									=				int(config['num_epochs']						)
     _snake_case									=				int(config['seed']						)
     _snake_case									=				int(config['batch_size']						)

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

     set_seed(UpperCamelCase__						)
     _snake_case							,     _snake_case									=				get_dataloaders(UpperCamelCase__       ,    UpperCamelCase__						)
     # Instantiate the model (we build the model here so that the seed also control new weights initialization)
     _snake_case									=				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).
     _snake_case									=				model.to(accelerator.device						)

     # Instantiate optimizer
     _snake_case									=				AdamW(params=model.parameters()       ,    lr=UpperCamelCase__						)

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

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





def 						lowerCamelCase__     (     )			-> Tuple:



     '''simple docstring'''




     _snake_case									=				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.'       ,    )
     # New Code #
     parser.add_argument(
         '--gradient_accumulation_steps'       ,    type=UpperCamelCase__       ,    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.'						)
     _snake_case									=				parser.parse_args()
     _snake_case									=				{'lr': 2e-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
     training_function(UpperCamelCase__       ,    UpperCamelCase__						)


if __name__ == "__main__":
     main()










import random





def 						lowerCamelCase__     (     UpperCamelCase__			:       list       ,    UpperCamelCase__			:       List[Any]						)			-> tuple:



					'''simple docstring'''




					_snake_case							,     _snake_case							,     _snake_case									=				[], [], []
					for element in data:
										if element < pivot:
															less.append(UpperCamelCase__						)
										elif element > pivot:
															greater.append(UpperCamelCase__						)
										else:
															equal.append(UpperCamelCase__						)
					return less, equal, greater





def 						lowerCamelCase__     (     UpperCamelCase__			:       list       ,    UpperCamelCase__			:       int						)			-> Dict:



					'''simple docstring'''




					if index >= len(UpperCamelCase__						) or index < 0:
										return None

					_snake_case									=				items[random.randint(0       ,    len(UpperCamelCase__						) - 1						)]
					_snake_case									=				0
					_snake_case							,     _snake_case							,     _snake_case									=				_partition(UpperCamelCase__       ,    UpperCamelCase__						)
					_snake_case									=				len(UpperCamelCase__						)
					_snake_case									=				len(UpperCamelCase__						)

					# index is the pivot
					if m <= index < m + count:
										return pivot
					# must be in smaller
					elif m > index:
										return quick_select(UpperCamelCase__       ,    UpperCamelCase__						)
					# must be in larger
					else:
										return quick_select(UpperCamelCase__       ,    index - (m + count)						)



