'''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 the experiment tracking capability,
# 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
#
########################################################################

A     :  Optional[int]					     =       16
A     :  Tuple					     =       32


def      a__ (		__UpperCamelCase     ,      __UpperCamelCase = 1_6				):
      SCREAMING_SNAKE_CASE_          =  AutoTokenizer.from_pretrained("bert-base-cased"				)
      SCREAMING_SNAKE_CASE_          =  load_dataset("glue"     ,      "mrpc"				)

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

      def collate_fn(__UpperCamelCase				):
            # On TPU it's best to pad everything to the same length or training will be very slow.
            SCREAMING_SNAKE_CASE_          =  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":
                  SCREAMING_SNAKE_CASE_          =  1_6
            elif accelerator.mixed_precision != "no":
                  SCREAMING_SNAKE_CASE_          =  8
            else:
                  SCREAMING_SNAKE_CASE_          =  None

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

      # Instantiate dataloaders.
      SCREAMING_SNAKE_CASE_          =  DataLoader(
          tokenized_datasets["train"]     ,      shuffle=__UpperCamelCase     ,      collate_fn=__UpperCamelCase     ,      batch_size=__UpperCamelCase				)
      SCREAMING_SNAKE_CASE_          =  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

      A     :  Any					     =       mocked_dataloaders  # noqa: F811


def      a__ (		__UpperCamelCase     ,      __UpperCamelCase				):
      # For testing only
      if os.environ.get("TESTING_MOCKED_DATALOADERS"     ,      __UpperCamelCase				) == "1":
            SCREAMING_SNAKE_CASE_          =  2
      # Initialize Accelerator

      # New Code #
      # We pass in "all" to `log_with` to grab all available trackers in the environment
      # Note: If using a custom `Tracker` class, should be passed in here such as:
      # >>> log_with = ["all", MyCustomTrackerClassInstance()]
      if args.with_tracking:
            SCREAMING_SNAKE_CASE_          =  Accelerator(
                cpu=args.cpu     ,      mixed_precision=args.mixed_precision     ,      log_with="all"     ,      project_dir=args.project_dir				)
      else:
            SCREAMING_SNAKE_CASE_          =  Accelerator(cpu=args.cpu     ,      mixed_precision=args.mixed_precision				)
      # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
      SCREAMING_SNAKE_CASE_          =  config["lr"]
      SCREAMING_SNAKE_CASE_          =  int(config["num_epochs"]				)
      SCREAMING_SNAKE_CASE_          =  int(config["seed"]				)
      SCREAMING_SNAKE_CASE_          =  int(config["batch_size"]				)
      set_seed(__UpperCamelCase				)

      SCREAMING_SNAKE_CASE_     , SCREAMING_SNAKE_CASE_          =  get_dataloaders(__UpperCamelCase     ,      __UpperCamelCase				)
      SCREAMING_SNAKE_CASE_          =  evaluate.load("glue"     ,      "mrpc"				)

      # If the batch size is too big we use gradient accumulation
      SCREAMING_SNAKE_CASE_          =  1
      if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
            SCREAMING_SNAKE_CASE_          =  batch_size // MAX_GPU_BATCH_SIZE
            SCREAMING_SNAKE_CASE_          =  MAX_GPU_BATCH_SIZE

      # Instantiate the model (we build the model here so that the seed also control new weights initialization)
      SCREAMING_SNAKE_CASE_          =  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).
      SCREAMING_SNAKE_CASE_          =  model.to(accelerator.device				)

      # Instantiate optimizer
      SCREAMING_SNAKE_CASE_          =  AdamW(params=model.parameters()     ,      lr=__UpperCamelCase				)

      # Instantiate scheduler
      SCREAMING_SNAKE_CASE_          =  get_linear_schedule_with_warmup(
          optimizer=__UpperCamelCase     ,      num_warmup_steps=1_0_0     ,      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.
      SCREAMING_SNAKE_CASE_     , SCREAMING_SNAKE_CASE_     , SCREAMING_SNAKE_CASE_     , SCREAMING_SNAKE_CASE_     , SCREAMING_SNAKE_CASE_          =  accelerator.prepare(
          __UpperCamelCase     ,      __UpperCamelCase     ,      __UpperCamelCase     ,      __UpperCamelCase     ,      __UpperCamelCase				)

      # New Code #
      # We need to initialize the trackers we use. Overall configurations can also be stored
      if args.with_tracking:
            SCREAMING_SNAKE_CASE_          =  os.path.split(__UpperCamelCase				)[-1].split("."				)[0]
            accelerator.init_trackers(__UpperCamelCase     ,      __UpperCamelCase				)

      # Now we train the model
      for epoch in range(__UpperCamelCase				):
            model.train()
            # New Code #
            # For our tracking example, we will log the total loss of each epoch
            if args.with_tracking:
                  SCREAMING_SNAKE_CASE_          =  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				)
                  SCREAMING_SNAKE_CASE_          =  model(**__UpperCamelCase				)
                  SCREAMING_SNAKE_CASE_          =  outputs.loss
                  # New Code #
                  if args.with_tracking:
                        total_loss += loss.detach().float()
                  SCREAMING_SNAKE_CASE_          =  loss / gradient_accumulation_steps
                  accelerator.backward(__UpperCamelCase				)
                  if step % gradient_accumulation_steps == 0:
                        optimizer.step()
                        lr_scheduler.step()
                        optimizer.zero_grad()

            model.eval()
            for step, batch in enumerate(__UpperCamelCase				):
                  # We could avoid this line since we set the accelerator with `device_placement=True` (the default).
                  batch.to(accelerator.device				)
                  with torch.no_grad():
                        SCREAMING_SNAKE_CASE_          =  model(**__UpperCamelCase				)
                  SCREAMING_SNAKE_CASE_          =  outputs.logits.argmax(dim=-1				)
                  SCREAMING_SNAKE_CASE_     , SCREAMING_SNAKE_CASE_          =  accelerator.gather_for_metrics((predictions, batch["labels"])				)
                  metric.add_batch(
                      predictions=__UpperCamelCase     ,      references=__UpperCamelCase     ,      )

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

            # New Code #
            # To actually log, we call `Accelerator.log`
            # The values passed can be of `str`, `int`, `float` or `dict` of `str` to `float`/`int`
            if args.with_tracking:
                  accelerator.log(
                      {
                          "accuracy": eval_metric["accuracy"],
                          "f1": eval_metric["f1"],
                          "train_loss": total_loss.item() / len(__UpperCamelCase				),
                          "epoch": epoch,
                      }     ,      step=__UpperCamelCase     ,      )

    # New Code #
    # When a run is finished, you should call `accelerator.end_training()`
    # to close all of the open trackers
      if args.with_tracking:
            accelerator.end_training()


def      a__ (		):
      SCREAMING_SNAKE_CASE_          =  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."				)
      parser.add_argument(
          "--with_tracking"     ,      action="store_true"     ,      help="Whether to load in all available experiment trackers from the environment and use them for logging."     ,      )
      parser.add_argument(
          "--project_dir"     ,      type=__UpperCamelCase     ,      default="logs"     ,      help="Location on where to store experiment tracking logs` and relevent project information"     ,      )
      SCREAMING_SNAKE_CASE_          =  parser.parse_args()
      SCREAMING_SNAKE_CASE_          =  {"lr": 2E-5, "num_epochs": 3, "seed": 4_2, "batch_size": 1_6}
      training_function(__UpperCamelCase     ,      __UpperCamelCase				)


if __name__ == "__main__":
      main()



from __future__ import annotations


def      a__ (		__UpperCamelCase				):
      SCREAMING_SNAKE_CASE_          =  str(__UpperCamelCase				)
      return n == n[::-1]


def      a__ (		__UpperCamelCase = 1_0_0_0_0_0_0				):
      SCREAMING_SNAKE_CASE_          =  0

      for i in range(1     ,      __UpperCamelCase				):
            if is_palindrome(__UpperCamelCase				) and is_palindrome(bin(__UpperCamelCase				).split("b"				)[1]				):
                  total += i
      return total


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


