

"""simple docstring"""


def  _lowercase					(    __lowerCAmelCase							)       ->				str:
      if not all(char in """01""" for char in bin_string							):
            raise ValueError("""Non-binary value was passed to the function"""							)
      if not bin_string:
            raise ValueError("""Empty string was passed to the function"""							)
      SCREAMING_SNAKE_CASE__   :      Tuple      		=	""""""
      while len(__lowerCAmelCase							) % 3 != 0:
            SCREAMING_SNAKE_CASE__   :      str      		=	"""0""" + bin_string
      SCREAMING_SNAKE_CASE__   :      List[str]      		=	[
          bin_string[index : index + 3]
          for index in range(len(__lowerCAmelCase							)							)
          if index % 3 == 0
      ]
      for bin_group in bin_string_in_3_list:
            SCREAMING_SNAKE_CASE__   :      List[Any]      		=	0
            for index, val in enumerate(__lowerCAmelCase							):
                  oct_val += int(2 ** (2 - index) * int(__lowerCAmelCase							)							)
            oct_string += str(__lowerCAmelCase							)
      return oct_string


if __name__ == "__main__":
  from doctest import testmod

  testmod()







"""simple docstring"""


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

from tokenizers import processors

from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
  from .tokenization_nllb import NllbTokenizer
else:
  a					:Optional[int]    = None


a					:Optional[Any]    = logging.get_logger(__name__)


a					:Optional[Any]    = {"vocab_file": "sentencepiece.bpe.model", "tokenizer_file": "tokenizer.json"}

a					:Union[str, Any]    = {
    "vocab_file": {
        "facebook/nllb-200-distilled-600M": (
            "https://huggingface.co/facebook/nllb-200-distilled-600M/resolve/main/sentencepiece.bpe.model"
        ),
    },
    "tokenizer_file": {
        "facebook/nllb-200-distilled-600M": (
            "https://huggingface.co/facebook/nllb-200-distilled-600M/resolve/main/tokenizer.json"
        ),
    },
}

a					:Any    = {
    "facebook/nllb-large-en-ro": 1_024,
    "facebook/nllb-200-distilled-600M": 1_024,
}

# fmt: off
a					:Tuple    = ["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      __a							(UpperCamelCase_):






     '''simple docstring'''
     _SCREAMING_SNAKE_CASE						:Optional[Any]       = VOCAB_FILES_NAMES
     _SCREAMING_SNAKE_CASE						:List[str]       = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     _SCREAMING_SNAKE_CASE						:str       = PRETRAINED_VOCAB_FILES_MAP
     _SCREAMING_SNAKE_CASE						:int       = ["""input_ids""", """attention_mask"""]
     _SCREAMING_SNAKE_CASE						:Tuple       = NllbTokenizer

     _SCREAMING_SNAKE_CASE						:List[int]       = []
     _SCREAMING_SNAKE_CASE						:List[int]       = []

     def __init__(						self	,    _a=None	,    _a=None	,    _a="<s>"	,    _a="</s>"	,    _a="</s>"	,    _a="<s>"	,    _a="<unk>"	,    _a="<pad>"	,    _a="<mask>"	,    _a=None	,    _a=None	,    _a=None	,    _a=False	,    **_a	,    )  ->      str:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	AddedToken(_a	,    lstrip=_a	,    rstrip=_a  ) if isinstance(_a	,    _a  ) else mask_token
           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	legacy_behaviour
           super().__init__(
               vocab_file=_a	,    tokenizer_file=_a	,    bos_token=_a	,    eos_token=_a	,    sep_token=_a	,    cls_token=_a	,    unk_token=_a	,    pad_token=_a	,    mask_token=_a	,    src_lang=_a	,    tgt_lang=_a	,    additional_special_tokens=_a	,    legacy_behaviour=_a	,    **_a	,    )

           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	vocab_file
           SCREAMING_SNAKE_CASE__   :      str      		=	False if not self.vocab_file else True

           SCREAMING_SNAKE_CASE__   :      Dict      		=	FAIRSEQ_LANGUAGE_CODES.copy()

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

           self.add_special_tokens({"""additional_special_tokens""": _additional_special_tokens}  )
           SCREAMING_SNAKE_CASE__   :      List[str]      		=	{
               lang_code: self.convert_tokens_to_ids(_a  ) for lang_code in FAIRSEQ_LANGUAGE_CODES
           }

           SCREAMING_SNAKE_CASE__   :      Dict      		=	src_lang if src_lang is not None else """eng_Latn"""
           SCREAMING_SNAKE_CASE__   :      List[str]      		=	self.convert_tokens_to_ids(self._src_lang  )
           SCREAMING_SNAKE_CASE__   :      Dict      		=	tgt_lang
           self.set_src_lang_special_tokens(self._src_lang  )

     @property
     def 					_a			(						self  )  ->      str:


           """simple docstring"""


           return self._src_lang

     @src_lang.setter
     def 					_a			(						self	,    _a  )  ->      None:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      int      		=	new_src_lang
           self.set_src_lang_special_tokens(self._src_lang  )

     def 					_a			(						self	,    _a	,    _a = None  )  ->      List[int]:


           """simple docstring"""


           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 					_a			(						self	,    _a	,    _a = None  )  ->      List[int]:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      int      		=	[self.sep_token_id]
           SCREAMING_SNAKE_CASE__   :      str      		=	[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 					_a			(						self	,    _a	,    _a	,    _a	,    _a	,    **_a  )  ->      Tuple:


           """simple docstring"""


           if src_lang is None or tgt_lang is None:
                 raise ValueError("""Translation requires a `src_lang` and a `tgt_lang` for this model"""  )
           SCREAMING_SNAKE_CASE__   :      Dict      		=	src_lang
           SCREAMING_SNAKE_CASE__   :      Dict      		=	self(_a	,    add_special_tokens=_a	,    return_tensors=_a	,    **_a  )
           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	self.convert_tokens_to_ids(_a  )
           SCREAMING_SNAKE_CASE__   :      List[Any]      		=	tgt_lang_id
           return inputs

     def 					_a			(						self	,    _a	,    _a = "eng_Latn"	,    _a = None	,    _a = "fra_Latn"	,    **_a	,    )  ->      BatchEncoding:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Tuple      		=	src_lang
           SCREAMING_SNAKE_CASE__   :      Dict      		=	tgt_lang
           return super().prepare_seqaseq_batch(_a	,    _a	,    **_a  )

     def 					_a			(						self  )  ->      Optional[Any]:


           """simple docstring"""


           return self.set_src_lang_special_tokens(self.src_lang  )

     def 					_a			(						self  )  ->      str:


           """simple docstring"""


           return self.set_tgt_lang_special_tokens(self.tgt_lang  )

     def 					_a			(						self	,    _a  )  ->      None:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      List[Any]      		=	self.convert_tokens_to_ids(_a  )

           if self.legacy_behaviour:
                 SCREAMING_SNAKE_CASE__   :      str      		=	[]
                 SCREAMING_SNAKE_CASE__   :      Dict      		=	[self.eos_token_id, self.cur_lang_code]
           else:
                 SCREAMING_SNAKE_CASE__   :      Dict      		=	[self.cur_lang_code]
                 SCREAMING_SNAKE_CASE__   :      Dict      		=	[self.eos_token_id]

           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	self.convert_ids_to_tokens(self.prefix_tokens  )
           SCREAMING_SNAKE_CASE__   :      int      		=	self.convert_ids_to_tokens(self.suffix_tokens  )

           SCREAMING_SNAKE_CASE__   :      int      		=	processors.TemplateProcessing(
               single=prefix_tokens_str + ["""$A"""] + suffix_tokens_str	,    pair=prefix_tokens_str + ["""$A""", """$B"""] + suffix_tokens_str	,    special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str	,    self.prefix_tokens + self.suffix_tokens  )  )	,    )

     def 					_a			(						self	,    _a  )  ->      None:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      str      		=	self.convert_tokens_to_ids(_a  )
           if self.legacy_behaviour:
                 SCREAMING_SNAKE_CASE__   :      List[Any]      		=	[]
                 SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	[self.eos_token_id, self.cur_lang_code]
           else:
                 SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	[self.cur_lang_code]
                 SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	[self.eos_token_id]

           SCREAMING_SNAKE_CASE__   :      Any      		=	self.convert_ids_to_tokens(self.prefix_tokens  )
           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	self.convert_ids_to_tokens(self.suffix_tokens  )

           SCREAMING_SNAKE_CASE__   :      Tuple      		=	processors.TemplateProcessing(
               single=prefix_tokens_str + ["""$A"""] + suffix_tokens_str	,    pair=prefix_tokens_str + ["""$A""", """$B"""] + suffix_tokens_str	,    special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str	,    self.prefix_tokens + self.suffix_tokens  )  )	,    )





     def 					_a			(						self	,    _a	,    _a = None  )  ->      Tuple[str]:


           """simple docstring"""


           if not self.can_save_slow_tokenizer:
                 raise ValueError(
                     """Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """
                     """tokenizer."""  )

           if not os.path.isdir(_a  ):
                 logger.error(f'''Vocabulary path ({save_directory}) should be a directory.'''  )
                 return
           SCREAMING_SNAKE_CASE__   :      Dict      		=	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  ):
                 copyfile(self.vocab_file	,    _a  )

           return (out_vocab_file,)




