


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


lowercase							=16
lowercase							=32





def lowerCamelCase__      (						__lowerCamelCase	:							Accelerator	,   __lowerCamelCase	:							int = 1_6							):







   '''simple docstring'''




   _UpperCAmelCase       :       Union[str, Any]							   =AutoTokenizer.from_pretrained('bert-base-cased'							)
   _UpperCAmelCase       :       str							   =load_dataset('glue'	,   'mrpc'							)

   def tokenize_function(__lowerCamelCase	:							Optional[int]							):
      # max_length=None => use the model max length (it's actually the default)
      _UpperCAmelCase       :       Optional[int]							   =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       :       Optional[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       :       Tuple							   =tokenized_datasets.rename_column('label'	,   'labels'							)

   def collate_fn(__lowerCamelCase	:							Union[str, Any]							):
      # On TPU it's best to pad everything to the same length or training will be very slow.
      _UpperCAmelCase       :       List[Any]							   =1_2_8 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]							   =1_6
      elif accelerator.mixed_precision != "no":
         _UpperCAmelCase       :       int							   =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       :       Dict							   =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

 lowercase							=mocked_dataloaders  # noqa: F811





def lowerCamelCase__      (						__lowerCamelCase	:							Optional[Any]	,   __lowerCamelCase	:							List[Any]							):







   '''simple docstring'''




   if os.environ.get('TESTING_MOCKED_DATALOADERS'	,   __lowerCamelCase							) == "1":
      _UpperCAmelCase       :       Dict							   =2
   # New Code #
   _UpperCAmelCase       :       int							   =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       :       str							   =config['lr']
   _UpperCAmelCase       :       List[str]							   =int(config['num_epochs']							)
   _UpperCAmelCase       :       Tuple							   =int(config['seed']							)
   _UpperCAmelCase       :       Optional[Any]							   =int(config['batch_size']							)

   _UpperCAmelCase       :       Optional[int]							   =evaluate.load('glue'	,   'mrpc'							)

   set_seed(__lowerCamelCase							)
   _UpperCAmelCase   ,	_UpperCAmelCase       :       str							   =get_dataloaders(__lowerCamelCase	,   __lowerCamelCase							)
   # Instantiate the model (we build the model here so that the seed also control new weights initialization)
   _UpperCAmelCase       :       Union[str, 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).
   _UpperCAmelCase       :       Tuple							   =model.to(accelerator.device							)

   # Instantiate optimizer
   _UpperCAmelCase       :       Dict							   =AdamW(params=model.parameters()	,   lr=__lowerCamelCase							)

   # Instantiate scheduler
   _UpperCAmelCase       :       int							   =get_linear_schedule_with_warmup(
       optimizer=__lowerCamelCase	,   num_warmup_steps=1_0_0	,   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       :       Union[str, Any]							   =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       :       int							   =model(**__lowerCamelCase							)
            _UpperCAmelCase       :       List[str]							   =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       :       Optional[int]							   =model(**__lowerCamelCase							)
         _UpperCAmelCase       :       Union[str, Any]							   =outputs.logits.argmax(dim=-1							)
         _UpperCAmelCase   ,	_UpperCAmelCase       :       str							   =accelerator.gather_for_metrics((predictions, batch['labels'])							)
         metric.add_batch(
             predictions=__lowerCamelCase	,   references=__lowerCamelCase	,   )

      _UpperCAmelCase       :       Optional[Any]							   =metric.compute()
      # Use accelerator.print to print only on the main process.
      accelerator.print(f"epoch {epoch}:"	,   __lowerCamelCase							)





def lowerCamelCase__      (						):







   '''simple docstring'''




   _UpperCAmelCase       :       Optional[int]							   =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       :       List[str]							   =parser.parse_args()
   _UpperCAmelCase       :       Optional[int]							   ={'lr': 2e-5, 'num_epochs': 3, 'seed': 4_2, 'batch_size': 1_6}
   training_function(__lowerCamelCase	,   __lowerCamelCase							)


if __name__ == "__main__":
 main()





'''simple docstring'''






from __future__ import annotations

from random import choice





def lowerCamelCase__      (						__lowerCamelCase	:							Optional[int]							):







   '''simple docstring'''




   return choice(__lowerCamelCase							)





def lowerCamelCase__      (						__lowerCamelCase	:							list[int]	,   __lowerCamelCase	:							int							):







   '''simple docstring'''




   _UpperCAmelCase       :       int							   =random_pivot(__lowerCamelCase							)

   # partition based on pivot
   # linear time
   _UpperCAmelCase       :       str							   =[e for e in lst if e < pivot]
   _UpperCAmelCase       :       Dict							   =[e for e in lst if e > pivot]

   # if we get lucky, pivot might be the element we want.
   # we can easily see this:
   # small (elements smaller than k)
   # + pivot (kth element)
   # + big (elements larger than k)
   if len(__lowerCamelCase							) == k - 1:
      return pivot
   # pivot is in elements bigger than k
   elif len(__lowerCamelCase							) < k - 1:
      return kth_number(__lowerCamelCase	,   k - len(__lowerCamelCase							) - 1							)
   # pivot is in elements smaller than k
   else:
      return kth_number(__lowerCamelCase	,   __lowerCamelCase							)


if __name__ == "__main__":
 import doctest

 doctest.testmod()

