

'''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,
# specifically showcasing how to properly calculate the metrics on the
# validation dataset when in a distributed system, and builds off the
# `nlp_example.py` script.
#
# 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 help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# 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
#
########################################################################


_snake_case     =  16
_snake_case     =  32







def 					_A		(	snake_case		,							snake_case = 16     )			->	str:
   _lowercase			:      int			     =   AutoTokenizer.from_pretrained("bert-base-cased"     )
   _lowercase			:      Any			     =   load_dataset("glue"		,							"mrpc"     )

   def tokenize_function(snake_case     ):
      # max_length=None => use the model max length (it's actually the default)
      _lowercase			:      List[Any]			     =   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():
      _lowercase			:      Optional[Any]			     =   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
   _lowercase			:      int			     =   tokenized_datasets.rename_column("label"		,							"labels"     )

   def collate_fn(snake_case     ):
      # On TPU it's best to pad everything to the same length or training will be very slow.
      _lowercase			:      Optional[Any]			     =   1_28 if accelerator.distributed_type == DistributedType.TPU else None
      # When using mixed precision we want round multiples of 8/16
      if accelerator.mixed_precision == "fp8":
         _lowercase			:      str			     =   16
      elif accelerator.mixed_precision != "no":
         _lowercase			:      Any			     =   8
      else:
         _lowercase			:      List[Any]			     =   None

      return tokenizer.pad(
          __UpperCAmelCase		,							padding="longest"		,							max_length=__UpperCAmelCase		,							pad_to_multiple_of=__UpperCAmelCase		,							return_tensors="pt"		,							)

   # Instantiate dataloaders.
   _lowercase			:      Optional[Any]			     =   DataLoader(
       tokenized_datasets["train"]		,							shuffle=__UpperCAmelCase		,							collate_fn=__UpperCAmelCase		,							batch_size=__UpperCAmelCase     )
   _lowercase			:      Dict			     =   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

    _snake_case     =  mocked_dataloaders  # noqa: F811







def 					_A		(	snake_case		,							snake_case     )			->	List[str]:
   # For testing only
   if os.environ.get("TESTING_MOCKED_DATALOADERS"		,							__UpperCAmelCase     ) == "1":
      _lowercase			:      Union[str, Any]			     =   2
   # Initialize accelerator
   _lowercase			:      List[str]			     =   Accelerator(cpu=args.cpu		,							mixed_precision=args.mixed_precision     )
   # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
   _lowercase			:      Tuple			     =   config['''lr''']
   _lowercase			:      Tuple			     =   int(config["num_epochs"]     )
   _lowercase			:      str			     =   int(config["seed"]     )
   _lowercase			:      Dict			     =   int(config["batch_size"]     )

   _lowercase			:      str			     =   evaluate.load("glue"		,							"mrpc"     )

   # If the batch size is too big we use gradient accumulation
   _lowercase			:      Optional[Any]			     =   1
   if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
      _lowercase			:      int			     =   batch_size // MAX_GPU_BATCH_SIZE
      _lowercase			:      List[str]			     =   MAX_GPU_BATCH_SIZE

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

   # Instantiate optimizer
   _lowercase			:      int			     =   AdamW(params=model.parameters()		,							lr=__UpperCAmelCase     )

   # Instantiate scheduler
   _lowercase			:      Any			     =   get_linear_schedule_with_warmup(
       optimizer=__UpperCAmelCase		,							num_warmup_steps=1_00		,							num_training_steps=(len(__UpperCAmelCase     ) * num_epochs) // gradient_accumulation_steps		,							)

   # 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.
   _lowercase			:      List[str]			     =   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     )
         _lowercase			:      Union[str, Any]			     =   model(**__UpperCAmelCase     )
         _lowercase			:      Any			     =   outputs.loss
         _lowercase			:      Optional[Any]			     =   loss / gradient_accumulation_steps
         accelerator.backward(__UpperCAmelCase     )
         if step % gradient_accumulation_steps == 0:
            optimizer.step()
            lr_scheduler.step()
            optimizer.zero_grad()

      model.eval()
      _lowercase			:      List[str]			     =   0
      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():
            _lowercase			:      str			     =   model(**__UpperCAmelCase     )
         _lowercase			:      List[Any]			     =   outputs.logits.argmax(dim=-1     )
         _lowercase			:      Optional[Any]			     =   accelerator.gather((predictions, batch["labels"])     )
         # New Code #
         # First we check if it's a distributed system
         if accelerator.use_distributed:
            # Then see if we're on the last batch of our eval dataloader
            if step == len(__UpperCAmelCase     ) - 1:
               # Last batch needs to be truncated on distributed systems as it contains additional samples
               _lowercase			:      Dict			     =   predictions[: len(eval_dataloader.dataset     ) - samples_seen]
               _lowercase			:      Tuple			     =   references[: len(eval_dataloader.dataset     ) - samples_seen]
            else:
               # Otherwise we add the number of samples seen
               samples_seen += references.shape[0]
            # All of this can be avoided if you use `Accelerator.gather_for_metrics` instead of `Accelerator.gather`:
            # accelerator.gather_for_metrics((predictions, batch["labels"]))
         metric.add_batch(
             predictions=__UpperCAmelCase		,							references=__UpperCAmelCase		,							)

      _lowercase			:      Optional[int]			     =   metric.compute()
      # Use accelerator.print to print only on the main process.
      accelerator.print(F'''epoch {epoch}:'''		,							__UpperCAmelCase     )







def 					_A		(	)			->	List[str]:
   _lowercase			:      Tuple			     =   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."		,							)
   parser.add_argument("--cpu"		,							action="store_true"		,							help="If passed, will train on the CPU."     )
   _lowercase			:      Optional[Any]			     =   parser.parse_args()
   _lowercase			:      List[Any]			     =   {'''lr''': 2E-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
   training_function(__UpperCAmelCase		,							__UpperCAmelCase     )


if __name__ == "__main__":
    main()







"""simple docstring"""






def SCREAMING_SNAKE_CASE__			(   __UpperCAmelCase )  ->  int:
   if n == 1 or not isinstance(__UpperCAmelCase       ,    __UpperCAmelCase ):
      return 0
   elif n == 2:
      return 1
   else:
      lowercase__:   List[Any]					=      [0, 1]
      for i in range(2       ,    n + 1 ):
         sequence.append(sequence[i - 1] + sequence[i - 2] )

      return sequence[n]



def SCREAMING_SNAKE_CASE__			(   __UpperCAmelCase )  ->  int:
   lowercase__:   Union[str, Any]					=      0
   lowercase__:   List[Any]					=      2

   while digits < n:
      index += 1
      lowercase__:   Dict					=      len(str(fibonacci(__UpperCAmelCase ) ) )

   return index



def SCREAMING_SNAKE_CASE__			(   __UpperCAmelCase = 1_0_0_0 )  ->  int:
   return fibonacci_digits_index(__UpperCAmelCase )


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