





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


UpperCAmelCase					:		Tuple		=							1_6
UpperCAmelCase					:		Dict		=							3_2




def    _a    (							lowerCAmelCase_			,      lowerCAmelCase_ = 16	):





      """simple docstring"""



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

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

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

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

      # Instantiate dataloaders.
      _snake_case		:	List[Any]					     =     DataLoader(
          tokenized_datasets['''train''']			,      shuffle=lowerCAmelCase_			,      collate_fn=lowerCAmelCase_			,      batch_size=lowerCAmelCase_	)
      _snake_case		:	str					     =     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

   UpperCAmelCase					:		Dict		=							mocked_dataloaders  # noqa: F811




def    _a    (							lowerCAmelCase_			,      lowerCAmelCase_	):





      """simple docstring"""



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

      _snake_case		:	List[str]					     =     evaluate.load('''glue'''			,      '''mrpc'''	)

      set_seed(lowerCAmelCase_	)
      _snake_case		:	Any					     =     get_dataloaders(lowerCAmelCase_			,      lowerCAmelCase_	)
      # Instantiate the model (we build the model here so that the seed also control new weights initialization)
      _snake_case		:	Tuple					     =     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).
      _snake_case		:	Optional[Any]					     =     model.to(accelerator.device	)

      # Instantiate optimizer
      _snake_case		:	Any					     =     AdamW(params=model.parameters()			,      lr=lowerCAmelCase_	)

      # Instantiate scheduler
      _snake_case		:	List[Any]					     =     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.
      _snake_case		:	Tuple					     =     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_	):
                        _snake_case		:	Union[str, Any]					     =     model(**lowerCAmelCase_	)
                        _snake_case		:	Optional[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():
                        _snake_case		:	Union[str, Any]					     =     model(**lowerCAmelCase_	)
                  _snake_case		:	List[Any]					     =     outputs.logits.argmax(dim=-1	)
                  _snake_case		:	str					     =     accelerator.gather_for_metrics((predictions, batch['''labels'''])	)
                  metric.add_batch(
                      predictions=lowerCAmelCase_			,      references=lowerCAmelCase_			,      )

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




def    _a    (							):





      """simple docstring"""



      _snake_case		:	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.'''	)
      _snake_case		:	List[Any]					     =     parser.parse_args()
      _snake_case		:	Union[str, Any]					     =     {'''lr''': 2E-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
      training_function(lowerCAmelCase_			,      lowerCAmelCase_	)


if __name__ == "__main__":
   main()









'''simple docstring'''

def    _a    (							lowerCAmelCase_	):





      """simple docstring"""



      if n == 1 or not isinstance(lowerCAmelCase_			,      lowerCAmelCase_	):
            return 0
      elif n == 2:
            return 1
      else:
            _snake_case		:	Union[str, Any]					     =     [0, 1]
            for i in range(2			,      n + 1	):
                  sequence.append(sequence[i - 1] + sequence[i - 2]	)

            return sequence[n]




def    _a    (							lowerCAmelCase_	):





      """simple docstring"""



      _snake_case		:	Optional[int]					     =     0
      _snake_case		:	int					     =     2

      while digits < n:
            index += 1
            _snake_case		:	Tuple					     =     len(str(fibonacci(lowerCAmelCase_	)	)	)

      return index




def    _a    (							lowerCAmelCase_ = 1_000	):





      """simple docstring"""



      return fibonacci_digits_index(lowerCAmelCase_	)


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


