


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


SCREAMING_SNAKE_CASE_      =			16
SCREAMING_SNAKE_CASE_      =			32




def        __lowercase						(      __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE = 16		)				->					Optional[Any]:
     """simple docstring"""




     __a       =   AutoTokenizer.from_pretrained("""bert-base-cased"""		)
     __a       =   load_dataset("""glue"""				,  """mrpc"""		)

     def tokenize_function(__SCREAMING_SNAKE_CASE		):
          # max_length=None => use the model max length (it's actually the default)
          __a       =   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():
          __a       =   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
     __a       =   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.
          __a       =   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":
               __a       =   16
          elif accelerator.mixed_precision != "no":
               __a       =   8
          else:
               __a       =   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.
     __a       =   DataLoader(
         tokenized_datasets["""train"""]				,  shuffle=__SCREAMING_SNAKE_CASE				,  collate_fn=__SCREAMING_SNAKE_CASE				,  batch_size=__SCREAMING_SNAKE_CASE		)
     __a       =   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

 SCREAMING_SNAKE_CASE_      =			mocked_dataloaders  # noqa: F811




def        __lowercase						(      __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE		)				->					int:
     """simple docstring"""




     if os.environ.get("""TESTING_MOCKED_DATALOADERS"""				,  __SCREAMING_SNAKE_CASE		) == "1":
          __a       =   2
     # New Code #
     __a       =   int(args.gradient_accumulation_steps		)
     # Initialize accelerator
     __a       =   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
     __a       =   config["""lr"""]
     __a       =   int(config["""num_epochs"""]		)
     __a       =   int(config["""seed"""]		)
     __a       =   int(config["""batch_size"""]		)

     __a       =   evaluate.load("""glue"""				,  """mrpc"""		)

     set_seed(__SCREAMING_SNAKE_CASE		)
     __a ,						__a       =   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)
     __a       =   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).
     __a       =   model.to(accelerator.device		)

     # Instantiate optimizer
     __a       =   AdamW(params=model.parameters()				,  lr=__SCREAMING_SNAKE_CASE		)

     # Instantiate scheduler
     __a       =   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.
     __a ,						__a ,						__a ,						__a ,						__a       =   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		):
                    __a       =   model(**__SCREAMING_SNAKE_CASE		)
                    __a       =   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():
                    __a       =   model(**__SCREAMING_SNAKE_CASE		)
               __a       =   outputs.logits.argmax(dim=-1		)
               __a ,						__a       =   accelerator.gather_for_metrics((predictions, batch["""labels"""])		)
               metric.add_batch(
                   predictions=__SCREAMING_SNAKE_CASE				,  references=__SCREAMING_SNAKE_CASE				,  )

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




def        __lowercase						(      )				->					int:
     """simple docstring"""




     __a       =   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."""		)
     __a       =   parser.parse_args()
     __a       =   {"""lr""": 2E-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
     training_function(__SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE		)


if __name__ == "__main__":
 main()










'''simple docstring'''







def        __lowercase						(      __SCREAMING_SNAKE_CASE = 100_0000		)				->					int:
     """simple docstring"""




     __a       =   1
     __a       =   1
     __a       =   {1: 1}

     for inputa in range(2				,  __SCREAMING_SNAKE_CASE		):
          __a       =   0
          __a       =   inputa

          while True:
               if number in counters:
                    counter += counters[number]
                    break
               if number % 2 == 0:
                    number //= 2
                    counter += 1
               else:
                    __a       =   (3 * number) + 1
                    counter += 1

          if inputa not in counters:
               __a       =   counter

          if counter > pre_counter:
               __a       =   inputa
               __a       =   counter
     return largest_number


if __name__ == "__main__":
 print(solution(int(input().strip())))






