


import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, BatchEncoding, PreTrainedTokenizer
from ...utils import logging


SCREAMING_SNAKE_CASE							    =							logging.get_logger(__name__)

SCREAMING_SNAKE_CASE							    =							'▁'

SCREAMING_SNAKE_CASE							    =							{'vocab_file': 'sentencepiece.bpe.model'}

SCREAMING_SNAKE_CASE							    =							{
    'vocab_file': {
        'facebook/nllb-200-distilled-600M': (
            'https://huggingface.co/facebook/nllb-200-distilled-600M/blob/main/sentencepiece.bpe.model'
        ),
    }
}

SCREAMING_SNAKE_CASE							    =							{
    'facebook/nllb-200-distilled-600M': 1_0_2_4,
}

# fmt: off
SCREAMING_SNAKE_CASE							    =							['ace_Arab', 'ace_Latn', 'acm_Arab', 'acq_Arab', 'aeb_Arab', 'afr_Latn', 'ajp_Arab', 'aka_Latn', 'amh_Ethi', 'apc_Arab', 'arb_Arab', 'ars_Arab', 'ary_Arab', 'arz_Arab', 'asm_Beng', 'ast_Latn', 'awa_Deva', 'ayr_Latn', 'azb_Arab', 'azj_Latn', 'bak_Cyrl', 'bam_Latn', 'ban_Latn', 'bel_Cyrl', 'bem_Latn', 'ben_Beng', 'bho_Deva', 'bjn_Arab', 'bjn_Latn', 'bod_Tibt', 'bos_Latn', 'bug_Latn', 'bul_Cyrl', 'cat_Latn', 'ceb_Latn', 'ces_Latn', 'cjk_Latn', 'ckb_Arab', 'crh_Latn', 'cym_Latn', 'dan_Latn', 'deu_Latn', 'dik_Latn', 'dyu_Latn', 'dzo_Tibt', 'ell_Grek', 'eng_Latn', 'epo_Latn', 'est_Latn', 'eus_Latn', 'ewe_Latn', 'fao_Latn', 'pes_Arab', 'fij_Latn', 'fin_Latn', 'fon_Latn', 'fra_Latn', 'fur_Latn', 'fuv_Latn', 'gla_Latn', 'gle_Latn', 'glg_Latn', 'grn_Latn', 'guj_Gujr', 'hat_Latn', 'hau_Latn', 'heb_Hebr', 'hin_Deva', 'hne_Deva', 'hrv_Latn', 'hun_Latn', 'hye_Armn', 'ibo_Latn', 'ilo_Latn', 'ind_Latn', 'isl_Latn', 'ita_Latn', 'jav_Latn', 'jpn_Jpan', 'kab_Latn', 'kac_Latn', 'kam_Latn', 'kan_Knda', 'kas_Arab', 'kas_Deva', 'kat_Geor', 'knc_Arab', 'knc_Latn', 'kaz_Cyrl', 'kbp_Latn', 'kea_Latn', 'khm_Khmr', 'kik_Latn', 'kin_Latn', 'kir_Cyrl', 'kmb_Latn', 'kon_Latn', 'kor_Hang', 'kmr_Latn', 'lao_Laoo', 'lvs_Latn', 'lij_Latn', 'lim_Latn', 'lin_Latn', 'lit_Latn', 'lmo_Latn', 'ltg_Latn', 'ltz_Latn', 'lua_Latn', 'lug_Latn', 'luo_Latn', 'lus_Latn', 'mag_Deva', 'mai_Deva', 'mal_Mlym', 'mar_Deva', 'min_Latn', 'mkd_Cyrl', 'plt_Latn', 'mlt_Latn', 'mni_Beng', 'khk_Cyrl', 'mos_Latn', 'mri_Latn', 'zsm_Latn', 'mya_Mymr', 'nld_Latn', 'nno_Latn', 'nob_Latn', 'npi_Deva', 'nso_Latn', 'nus_Latn', 'nya_Latn', 'oci_Latn', 'gaz_Latn', 'ory_Orya', 'pag_Latn', 'pan_Guru', 'pap_Latn', 'pol_Latn', 'por_Latn', 'prs_Arab', 'pbt_Arab', 'quy_Latn', 'ron_Latn', 'run_Latn', 'rus_Cyrl', 'sag_Latn', 'san_Deva', 'sat_Beng', 'scn_Latn', 'shn_Mymr', 'sin_Sinh', 'slk_Latn', 'slv_Latn', 'smo_Latn', 'sna_Latn', 'snd_Arab', 'som_Latn', 'sot_Latn', 'spa_Latn', 'als_Latn', 'srd_Latn', 'srp_Cyrl', 'ssw_Latn', 'sun_Latn', 'swe_Latn', 'swh_Latn', 'szl_Latn', 'tam_Taml', 'tat_Cyrl', 'tel_Telu', 'tgk_Cyrl', 'tgl_Latn', 'tha_Thai', 'tir_Ethi', 'taq_Latn', 'taq_Tfng', 'tpi_Latn', 'tsn_Latn', 'tso_Latn', 'tuk_Latn', 'tum_Latn', 'tur_Latn', 'twi_Latn', 'tzm_Tfng', 'uig_Arab', 'ukr_Cyrl', 'umb_Latn', 'urd_Arab', 'uzn_Latn', 'vec_Latn', 'vie_Latn', 'war_Latn', 'wol_Latn', 'xho_Latn', 'ydd_Hebr', 'yor_Latn', 'yue_Hant', 'zho_Hans', 'zho_Hant', 'zul_Latn']


class     __UpperCAmelCase							(       __A							):


     """simple docstring"""
     _lowerCamelCase   =      VOCAB_FILES_NAMES
     _lowerCamelCase   =      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     _lowerCamelCase   =      PRETRAINED_VOCAB_FILES_MAP
     _lowerCamelCase   =      ["""input_ids""", """attention_mask"""]

     _lowerCamelCase   =      []
     _lowerCamelCase   =      []


     def __init__(	self  ,       __A  ,       __A="<s>"  ,       __A="</s>"  ,       __A="</s>"  ,       __A="<s>"  ,       __A="<unk>"  ,       __A="<pad>"  ,       __A="<mask>"  ,       __A=None  ,       __A=None  ,       __A=None  ,       __A = None  ,       __A=None  ,       __A=False  ,       **__A  ,       ):
        # Mask token behave like a normal word, i.e. include the space before it
        __a					=  AddedToken(__A  ,       lstrip=__A  ,       rstrip=__A    ) if isinstance(__A  ,       __A    ) else mask_token

        __a					=  {} if sp_model_kwargs is None else sp_model_kwargs
        __a					=  legacy_behaviour

        super().__init__(
            bos_token=__A  ,       eos_token=__A  ,       unk_token=__A  ,       sep_token=__A  ,       cls_token=__A  ,       pad_token=__A  ,       mask_token=__A  ,       tokenizer_file=__A  ,       src_lang=__A  ,       tgt_lang=__A  ,       additional_special_tokens=__A  ,       sp_model_kwargs=self.sp_model_kwargs  ,       legacy_behaviour=__A  ,       **__A  ,       )

        __a					=  spm.SentencePieceProcessor(**self.sp_model_kwargs    )
        self.sp_model.Load(str(__A    )    )
        __a					=  vocab_file
        # Original fairseq vocab and spm vocab must be "aligned":
        # Vocab    |    0    |    1    |   2    |    3    |  4   |  5   |  6   |   7  |   8  |  9
        # -------- | ------- | ------- | ------ | ------- | ---- | ---- | ---- | ---- | ---- | ----
        # fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | 'an' | '▁n' | '▁m' | '▁t' | '▁k' | '▁a'
        # spm      | '<unk>' | '<s>'   | '</s>' | 'an'    | '▁n' | '▁m' | '▁t' | '▁k' | '▁a' | '▁s'

        # Mimic fairseq token-to-id alignment for the first 4 token
        __a					=  {"""<s>""": 0, """<pad>""": 1, """</s>""": 2, """<unk>""": 3}

        # The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
        __a					=  1

        __a					=  len(self.sp_model    )
        __a					=  {
            code: self.sp_model_size + i + self.fairseq_offset for i, code in enumerate(__A    )
        }
        __a					=  {v: k for k, v in self.lang_code_to_id.items()}
        __a					=  len(self.sp_model    ) + len(self.lang_code_to_id    ) + self.fairseq_offset

        self.fairseq_tokens_to_ids.update(self.lang_code_to_id    )
        __a					=  {v: k for k, v in self.fairseq_tokens_to_ids.items()}
        __a					=  list(self.lang_code_to_id.keys()    )

        if additional_special_tokens is not None:
           # Only add those special tokens if they are not already there.
           self._additional_special_tokens.extend(
               [t for t in additional_special_tokens if t not in self._additional_special_tokens]    )

        __a					=  src_lang if src_lang is not None else """eng_Latn"""
        __a					=  self.lang_code_to_id[self._src_lang]
        __a					=  tgt_lang
        self.set_src_lang_special_tokens(self._src_lang    )


     def __getstate__(	self    ):
        __a					=  self.__dict__.copy()
        __a					=  None
        __a					=  self.sp_model.serialized_model_proto()
        return state


     def __setstate__(	self  ,       __A    ):
        __a					=  d

        # for backward compatibility
        if not hasattr(self  ,       """sp_model_kwargs"""    ):
           __a					=  {}

        __a					=  spm.SentencePieceProcessor(**self.sp_model_kwargs    )
        self.sp_model.LoadFromSerializedProto(self.sp_model_proto    )


     @property
     def 	snake_case_  (	self    ):
        return len(self.sp_model    ) + len(self.lang_code_to_id    ) + self.fairseq_offset + 1  # Plus 1 for the mask token


     @property
     def 	snake_case_  (	self    ):
        return self._src_lang


     @src_lang.setter
     def 	snake_case_  (	self  ,       __A    ):
        __a					=  new_src_lang
        self.set_src_lang_special_tokens(self._src_lang    )


     def 	snake_case_  (	self  ,       __A  ,       __A = None  ,       __A = False    ):

        if already_has_special_tokens:
           return super().get_special_tokens_mask(
               token_ids_a=__A  ,       token_ids_a=__A  ,       already_has_special_tokens=__A    )

        __a					=  [1] * len(self.prefix_tokens    )
        __a					=  [1] * len(self.suffix_tokens    )
        if token_ids_a is None:
           return prefix_ones + ([0] * len(__A    )) + suffix_ones
        return prefix_ones + ([0] * len(__A    )) + ([0] * len(__A    )) + suffix_ones


     def 	snake_case_  (	self  ,       __A  ,       __A = None    ):
        if token_ids_a is None:
           return self.prefix_tokens + token_ids_a + self.suffix_tokens
        # We don't expect to process pairs, but leave the pair logic for API consistency
        return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens


     def 	snake_case_  (	self  ,       __A  ,       __A = None    ):
        __a					=  [self.sep_token_id]
        __a					=  [self.cls_token_id]

        if token_ids_a is None:
           return len(cls + token_ids_a + sep    ) * [0]
        return len(cls + token_ids_a + sep + sep + token_ids_a + sep    ) * [0]


     def 	snake_case_  (	self  ,       __A  ,       __A  ,       __A  ,       __A  ,       **__A    ):
        if src_lang is None or tgt_lang is None:
           raise ValueError("""Translation requires a `src_lang` and a `tgt_lang` for this model"""    )
        __a					=  src_lang
        __a					=  self(__A  ,       add_special_tokens=__A  ,       return_tensors=__A  ,       **__A    )
        __a					=  self.convert_tokens_to_ids(__A    )
        __a					=  tgt_lang_id
        return inputs


     def 	snake_case_  (	self    ):
        __a					=  {self.convert_ids_to_tokens(__A    ): i for i in range(self.vocab_size    )}
        vocab.update(self.added_tokens_encoder    )
        return vocab


     def 	snake_case_  (	self  ,       __A    ):
        return self.sp_model.encode(__A  ,       out_type=__A    )


     def 	snake_case_  (	self  ,       __A    ):
        if token in self.fairseq_tokens_to_ids:
           return self.fairseq_tokens_to_ids[token]
        __a					=  self.sp_model.PieceToId(__A    )

        # Need to return unknown token if the SP model returned 0
        return spm_id + self.fairseq_offset if spm_id else self.unk_token_id


     def 	snake_case_  (	self  ,       __A    ):
        if index in self.fairseq_ids_to_tokens:
           return self.fairseq_ids_to_tokens[index]
        return self.sp_model.IdToPiece(index - self.fairseq_offset    )


     def 	snake_case_  (	self  ,       __A    ):
        __a					=  """""".join(__A    ).replace(__A  ,       """ """    ).strip()
        return out_string


     def 	snake_case_  (	self  ,       __A  ,       __A = None    ):
        if not os.path.isdir(__A    ):
           logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''    )
           return
        __a					=  os.path.join(
            __A  ,       (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]    )

        if os.path.abspath(self.vocab_file    ) != os.path.abspath(__A    ) and os.path.isfile(self.vocab_file    ):
           copyfile(self.vocab_file  ,       __A    )
        elif not os.path.isfile(self.vocab_file    ):
           with open(__A  ,       """wb"""    ) as fi:
              __a					=  self.sp_model.serialized_model_proto()
              fi.write(__A    )

        return (out_vocab_file,)


     def 	snake_case_  (	self  ,       __A  ,       __A = "eng_Latn"  ,       __A = None  ,       __A = "fra_Latn"  ,       **__A  ,       ):
        __a					=  src_lang
        __a					=  tgt_lang
        return super().prepare_seqaseq_batch(__A  ,       __A  ,       **__A    )


     def 	snake_case_  (	self    ):
        return self.set_src_lang_special_tokens(self.src_lang    )


     def 	snake_case_  (	self    ):
        return self.set_tgt_lang_special_tokens(self.tgt_lang    )


     def 	snake_case_  (	self  ,       __A    ):
        __a					=  self.lang_code_to_id[src_lang]
        if self.legacy_behaviour:
           __a					=  []
           __a					=  [self.eos_token_id, self.cur_lang_code]
        else:
           __a					=  [self.cur_lang_code]
           __a					=  [self.eos_token_id]


     def 	snake_case_  (	self  ,       __A    ):
        __a					=  self.lang_code_to_id[lang]
        if self.legacy_behaviour:
           __a					=  []
           __a					=  [self.eos_token_id, self.cur_lang_code]
        else:
           __a					=  [self.cur_lang_code]
           __a					=  [self.eos_token_id]







def  a   (lowerCAmelCase__ = 1_000_000   ):
   __a					=  1
   __a					=  1
   __a					=  {1: 1}

   for inputa in range(2		,				lowerCAmelCase__   ):
      __a					=  0
      __a					=  inputa

      while True:
         if number in counters:
            counter += counters[number]
            break
         if number % 2 == 0:
            number //= 2
            counter += 1
         else:
            __a					=  (3 * number) + 1
            counter += 1

      if inputa not in counters:
         __a					=  counter

      if counter > pre_counter:
         __a					=  inputa
         __a					=  counter
   return largest_number


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



