

def 							__a       (					__A = 1000	)     ->   Union[str, Any]:
    SCREAMING_SNAKE_CASE__					     =						[0, 3, 3, 5, 4, 4, 3, 5, 5, 4, 3, 6, 6, 8, 8, 7, 7, 9, 8, 8]
    # number of letters in twenty, thirty, ..., ninety (0 for numbers less than
    # 20 due to inconsistency in teens)
    SCREAMING_SNAKE_CASE__					     =						[0, 0, 6, 6, 5, 5, 5, 7, 6, 6]

    SCREAMING_SNAKE_CASE__					     =						0

    for i in range(1 ,       n + 1	):
        if i < 1000:
            if i >= 100:
                # add number of letters for "n hundred"
                count += ones_counts[i // 100] + 7

                if i % 100 != 0:
                    # add number of letters for "and" if number is not multiple
                    # of 100
                    count += 3

            if 0 < i % 100 < 20:
                # add number of letters for one, two, three, ..., nineteen
                # (could be combined with below if not for inconsistency in
                # teens)
                count += ones_counts[i % 100]
            else:
                # add number of letters for twenty, twenty one, ..., ninety
                # nine
                count += ones_counts[i % 10]
                count += tens_counts[(i % 100 - i % 10) // 10]
        else:
            count += ones_counts[i // 1000] + 8
    return count


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





import argparse

import torch
from flax.training.checkpoints import restore_checkpoint

from transformers import FNetConfig, FNetForPreTraining
from transformers.utils import logging


logging.set_verbosity_info()





def 							__a       (					__A ,       __A ,       __A	):
    # Initialise PyTorch model
    SCREAMING_SNAKE_CASE__					     =						FNetConfig.from_json_file(__A	)
    print(f"Building PyTorch model from configuration: {config}"	)
    SCREAMING_SNAKE_CASE__					     =						FNetForPreTraining(__A	)

    SCREAMING_SNAKE_CASE__					     =						restore_checkpoint(__A ,       __A	)
    SCREAMING_SNAKE_CASE__					     =						checkpoint_dict["target"]

    # Embeddings
    # Position IDs
    SCREAMING_SNAKE_CASE__					     =						fnet_pretraining_model.state_dict()

    SCREAMING_SNAKE_CASE__					     =						state_dict["fnet.embeddings.position_ids"]
    SCREAMING_SNAKE_CASE__					     =						{"fnet.embeddings.position_ids": position_ids}
    # Embedding Layers
    SCREAMING_SNAKE_CASE__					     =						torch.tensor(
        pretrained_model_params["encoder"]["embedder"]["word"]["embedding"]	)
    SCREAMING_SNAKE_CASE__					     =						torch.tensor(
        pretrained_model_params["encoder"]["embedder"]["position"]["embedding"][0]	)
    SCREAMING_SNAKE_CASE__					     =						torch.tensor(
        pretrained_model_params["encoder"]["embedder"]["type"]["embedding"]	)
    SCREAMING_SNAKE_CASE__					     =						torch.tensor(
        pretrained_model_params["encoder"]["embedder"]["hidden_mapping_in"]["kernel"]	).T
    SCREAMING_SNAKE_CASE__					     =						torch.tensor(
        pretrained_model_params["encoder"]["embedder"]["hidden_mapping_in"]["bias"]	)
    SCREAMING_SNAKE_CASE__					     =						torch.tensor(
        pretrained_model_params["encoder"]["embedder"]["layer_norm"]["scale"]	)
    SCREAMING_SNAKE_CASE__					     =						torch.tensor(
        pretrained_model_params["encoder"]["embedder"]["layer_norm"]["bias"]	)

    # Encoder Layers
    for layer in range(config.num_hidden_layers	):
        SCREAMING_SNAKE_CASE__					     =						torch.tensor(
            pretrained_model_params["encoder"][f"encoder_{layer}"]["mixing_layer_norm"]["scale"]	)
        SCREAMING_SNAKE_CASE__					     =						torch.tensor(
            pretrained_model_params["encoder"][f"encoder_{layer}"]["mixing_layer_norm"]["bias"]	)

        SCREAMING_SNAKE_CASE__					     =						torch.tensor(
            pretrained_model_params["encoder"][f"feed_forward_{layer}"]["intermediate"]["kernel"]	).T
        SCREAMING_SNAKE_CASE__					     =						torch.tensor(
            pretrained_model_params["encoder"][f"feed_forward_{layer}"]["intermediate"]["bias"]	)

        SCREAMING_SNAKE_CASE__					     =						torch.tensor(
            pretrained_model_params["encoder"][f"feed_forward_{layer}"]["output"]["kernel"]	).T
        SCREAMING_SNAKE_CASE__					     =						torch.tensor(
            pretrained_model_params["encoder"][f"feed_forward_{layer}"]["output"]["bias"]	)

        SCREAMING_SNAKE_CASE__					     =						torch.tensor(
            pretrained_model_params["encoder"][f"encoder_{layer}"]["output_layer_norm"]["scale"]	)
        SCREAMING_SNAKE_CASE__					     =						torch.tensor(
            pretrained_model_params["encoder"][f"encoder_{layer}"]["output_layer_norm"]["bias"]	)

    # Pooler Layers
    SCREAMING_SNAKE_CASE__					     =						torch.tensor(pretrained_model_params["encoder"]["pooler"]["kernel"]	).T
    SCREAMING_SNAKE_CASE__					     =						torch.tensor(pretrained_model_params["encoder"]["pooler"]["bias"]	)

    # Masked LM Layers
    SCREAMING_SNAKE_CASE__					     =						torch.tensor(
        pretrained_model_params["predictions_dense"]["kernel"]	).T
    SCREAMING_SNAKE_CASE__					     =						torch.tensor(
        pretrained_model_params["predictions_dense"]["bias"]	)
    SCREAMING_SNAKE_CASE__					     =						torch.tensor(
        pretrained_model_params["predictions_layer_norm"]["scale"]	)
    SCREAMING_SNAKE_CASE__					     =						torch.tensor(
        pretrained_model_params["predictions_layer_norm"]["bias"]	)
    SCREAMING_SNAKE_CASE__					     =						torch.tensor(
        pretrained_model_params["encoder"]["embedder"]["word"]["embedding"]	)
    SCREAMING_SNAKE_CASE__					     =						torch.tensor(
        pretrained_model_params["predictions_output"]["output_bias"]	)
    SCREAMING_SNAKE_CASE__					     =						torch.tensor(pretrained_model_params["predictions_output"]["output_bias"]	)

    # Seq Relationship Layers
    SCREAMING_SNAKE_CASE__					     =						torch.tensor(
        pretrained_model_params["classification"]["output_kernel"]	)
    SCREAMING_SNAKE_CASE__					     =						torch.tensor(
        pretrained_model_params["classification"]["output_bias"]	)

    # Load State Dict
    fnet_pretraining_model.load_state_dict(__A	)

    # Save PreTrained
    print(f"Saving pretrained model to {save_path}"	)
    fnet_pretraining_model.save_pretrained(__A	)


if __name__ == "__main__":
  A__        =			argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      "--flax_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
  )
  parser.add_argument(
      "--fnet_config_file",
      default=None,
      type=str,
      required=True,
      help=(
          "The config json file corresponding to the pre-trained FNet model. \n"
          "This specifies the model architecture."
      ),
  )
  parser.add_argument("--save_path", default=None, type=str, required=True, help="Path to the output model.")
  A__        =			parser.parse_args()
  convert_flax_checkpoint_to_pytorch(args.flax_checkpoint_path, args.fnet_config_file, args.save_path)


