

'''simple docstring'''




from collections.abc import Generator




def 		UpperCamelCase			(					) ->      Generator[int, None, None]:






     '''simple docstring'''
     __magic_name__		  =							0, 1
     while True:
          __magic_name__		  =							b, a + b
          yield b




def 		UpperCamelCase			(					a = 1000					) ->      int:






     '''simple docstring'''
     __magic_name__		  =							1
     __magic_name__		  =							fibonacci_generator()
     while len(str(next(__SCREAMING_SNAKE_CASE					)					)					) < n:
          answer += 1
     return answer + 1


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












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


A_			      =		16
A_			      =		32






def 		UpperCamelCase__      (		__SCREAMING_SNAKE_CASE				,      __SCREAMING_SNAKE_CASE = 16		)    ->   int:
      snake_case__       :   Tuple	      =    AutoTokenizer.from_pretrained('bert-base-cased'		)
      snake_case__       :   Tuple	      =    load_dataset('glue'				,      'mrpc'		)

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

      A_			      =		mocked_dataloaders  # noqa: F811






def 		UpperCamelCase__      (		__SCREAMING_SNAKE_CASE				,      __SCREAMING_SNAKE_CASE		)    ->   Optional[Any]:
      # For testing only
      if os.environ.get('TESTING_MOCKED_DATALOADERS'				,      __SCREAMING_SNAKE_CASE		) == "1":
            snake_case__       :   Optional[int]	      =    2
      # New Code #
      snake_case__       :   List[str]	      =    int(args.gradient_accumulation_steps		)
      # Initialize accelerator
      snake_case__       :   Optional[Any]	      =    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
      snake_case__       :   Optional[Any]	      =    config['lr']
      snake_case__       :   Dict	      =    int(config['num_epochs']		)
      snake_case__       :   Any	      =    int(config['seed']		)
      snake_case__       :   List[str]	      =    int(config['batch_size']		)

      snake_case__       :   Tuple	      =    evaluate.load('glue'				,      'mrpc'		)

      set_seed(__SCREAMING_SNAKE_CASE		)
      snake_case__      ,							snake_case__       :   Any	      =    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)
      snake_case__       :   Dict	      =    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).
      snake_case__       :   Dict	      =    model.to(accelerator.device		)

      # Instantiate optimizer
      snake_case__       :   int	      =    AdamW(params=model.parameters()				,      lr=__SCREAMING_SNAKE_CASE		)

      # Instantiate scheduler
      snake_case__       :   str	      =    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.
      snake_case__      ,							snake_case__      ,							snake_case__      ,							snake_case__      ,							snake_case__       :   str	      =    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		):
                        snake_case__       :   Any	      =    model(**__SCREAMING_SNAKE_CASE		)
                        snake_case__       :   Dict	      =    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():
                        snake_case__       :   Optional[int]	      =    model(**__SCREAMING_SNAKE_CASE		)
                  snake_case__       :   Optional[Any]	      =    outputs.logits.argmax(dim=-1		)
                  snake_case__      ,							snake_case__       :   int	      =    accelerator.gather_for_metrics((predictions, batch['labels'])		)
                  metric.add_batch(
                      predictions=__SCREAMING_SNAKE_CASE				,      references=__SCREAMING_SNAKE_CASE				,      )

            snake_case__       :   int	      =    metric.compute()
            # Use accelerator.print to print only on the main process.
            accelerator.print(f"epoch {epoch}:"				,      __SCREAMING_SNAKE_CASE		)






def 		UpperCamelCase__      (		)    ->   Optional[int]:
      snake_case__       :   Tuple	      =    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.'		)
      snake_case__       :   Any	      =    parser.parse_args()
      snake_case__       :   Dict	      =    {'lr': 2E-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
      training_function(__SCREAMING_SNAKE_CASE				,      __SCREAMING_SNAKE_CASE		)


if __name__ == "__main__":
      main()
