




'''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:
   UpperCamelCase_						:      int       =						None


UpperCamelCase_						:      int       =						logging.get_logger(__name__)


UpperCamelCase_						:      Dict       =						{'vocab_file': 'sentencepiece.bpe.model', 'tokenizer_file': 'tokenizer.json'}

UpperCamelCase_						:      str       =						{
    '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'
        ),
    },
}

UpperCamelCase_						:      Any       =						{
    'facebook/nllb-large-en-ro': 1024,
    'facebook/nllb-200-distilled-600M': 1024,
}

# fmt: off
UpperCamelCase_						:      int       =						['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    (						__SCREAMING_SNAKE_CASE							):
     SCREAMING_SNAKE_CASE_							:   int       =       VOCAB_FILES_NAMES
     SCREAMING_SNAKE_CASE_							:   Any       =       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     SCREAMING_SNAKE_CASE_							:   Union[str, Any]       =       PRETRAINED_VOCAB_FILES_MAP
     SCREAMING_SNAKE_CASE_							:   Dict       =       ["""input_ids""", """attention_mask"""]
     SCREAMING_SNAKE_CASE_							:   Optional[Any]       =       NllbTokenizer

     SCREAMING_SNAKE_CASE_							:   Optional[int]       =       []
     SCREAMING_SNAKE_CASE_							:   List[str]       =       []






     def __init__(			self ,_SCREAMING_SNAKE_CASE=None ,_SCREAMING_SNAKE_CASE=None ,_SCREAMING_SNAKE_CASE="<s>" ,_SCREAMING_SNAKE_CASE="</s>" ,_SCREAMING_SNAKE_CASE="</s>" ,_SCREAMING_SNAKE_CASE="<s>" ,_SCREAMING_SNAKE_CASE="<unk>" ,_SCREAMING_SNAKE_CASE="<pad>" ,_SCREAMING_SNAKE_CASE="<mask>" ,_SCREAMING_SNAKE_CASE=None ,_SCREAMING_SNAKE_CASE=None ,_SCREAMING_SNAKE_CASE=None ,_SCREAMING_SNAKE_CASE=False ,**_SCREAMING_SNAKE_CASE ,)							->      Optional[int]:
          _snake_case							=				AddedToken(_SCREAMING_SNAKE_CASE ,lstrip=_SCREAMING_SNAKE_CASE ,rstrip=_SCREAMING_SNAKE_CASE   ) if isinstance(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   ) else mask_token
          _snake_case							=				legacy_behaviour
          super().__init__(
              vocab_file=_SCREAMING_SNAKE_CASE ,tokenizer_file=_SCREAMING_SNAKE_CASE ,bos_token=_SCREAMING_SNAKE_CASE ,eos_token=_SCREAMING_SNAKE_CASE ,sep_token=_SCREAMING_SNAKE_CASE ,cls_token=_SCREAMING_SNAKE_CASE ,unk_token=_SCREAMING_SNAKE_CASE ,pad_token=_SCREAMING_SNAKE_CASE ,mask_token=_SCREAMING_SNAKE_CASE ,src_lang=_SCREAMING_SNAKE_CASE ,tgt_lang=_SCREAMING_SNAKE_CASE ,additional_special_tokens=_SCREAMING_SNAKE_CASE ,legacy_behaviour=_SCREAMING_SNAKE_CASE ,**_SCREAMING_SNAKE_CASE ,)

          _snake_case							=				vocab_file
          _snake_case							=				False if not self.vocab_file else True

          _snake_case							=				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}   )
          _snake_case							=				{
              lang_code: self.convert_tokens_to_ids(_SCREAMING_SNAKE_CASE   ) for lang_code in FAIRSEQ_LANGUAGE_CODES
          }

          _snake_case							=				src_lang if src_lang is not None else "eng_Latn"
          _snake_case							=				self.convert_tokens_to_ids(self._src_lang   )
          _snake_case							=				tgt_lang
          self.set_src_lang_special_tokens(self._src_lang   )






     @property
     def     _lowercase     (			self   )							->      str:
          return self._src_lang






     @src_lang.setter
     def     _lowercase     (			self ,_SCREAMING_SNAKE_CASE   )							->      None:
          _snake_case							=				new_src_lang
          self.set_src_lang_special_tokens(self._src_lang   )






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

          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     _lowercase     (			self ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE = None   )							->      List[int]:
          _snake_case							=				[self.sep_token_id]
          _snake_case							=				[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     _lowercase     (			self ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,**_SCREAMING_SNAKE_CASE   )							->      Dict:

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






     def     _lowercase     (			self ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE = "eng_Latn" ,_SCREAMING_SNAKE_CASE = None ,_SCREAMING_SNAKE_CASE = "fra_Latn" ,**_SCREAMING_SNAKE_CASE ,)							->      BatchEncoding:
          _snake_case							=				src_lang
          _snake_case							=				tgt_lang
          return super().prepare_seqaseq_batch(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,**_SCREAMING_SNAKE_CASE   )






     def     _lowercase     (			self   )							->      Optional[Any]:
          return self.set_src_lang_special_tokens(self.src_lang   )






     def     _lowercase     (			self   )							->      List[str]:
          return self.set_tgt_lang_special_tokens(self.tgt_lang   )






     def     _lowercase     (			self ,_SCREAMING_SNAKE_CASE   )							->      None:
          _snake_case							=				self.convert_tokens_to_ids(_SCREAMING_SNAKE_CASE   )

          if self.legacy_behaviour:
               _snake_case							=				[]
               _snake_case							=				[self.eos_token_id, self.cur_lang_code]
          else:
               _snake_case							=				[self.cur_lang_code]
               _snake_case							=				[self.eos_token_id]

          _snake_case							=				self.convert_ids_to_tokens(self.prefix_tokens   )
          _snake_case							=				self.convert_ids_to_tokens(self.suffix_tokens   )

          _snake_case							=				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     _lowercase     (			self ,_SCREAMING_SNAKE_CASE   )							->      None:
          _snake_case							=				self.convert_tokens_to_ids(_SCREAMING_SNAKE_CASE   )
          if self.legacy_behaviour:
               _snake_case							=				[]
               _snake_case							=				[self.eos_token_id, self.cur_lang_code]
          else:
               _snake_case							=				[self.cur_lang_code]
               _snake_case							=				[self.eos_token_id]

          _snake_case							=				self.convert_ids_to_tokens(self.prefix_tokens   )
          _snake_case							=				self.convert_ids_to_tokens(self.suffix_tokens   )

          _snake_case							=				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     _lowercase     (			self ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE = None   )							->      Tuple[str]:

          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(_SCREAMING_SNAKE_CASE   ):
               logger.error(f"""Vocabulary path ({save_directory}) should be a directory."""   )
               return
          _snake_case							=				os.path.join(
              _SCREAMING_SNAKE_CASE ,(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]   )

          if os.path.abspath(self.vocab_file   ) != os.path.abspath(_SCREAMING_SNAKE_CASE   ):
               copyfile(self.vocab_file ,_SCREAMING_SNAKE_CASE   )

          return (out_vocab_file,)








'''simple docstring'''





def      __a     (       _UpperCamelCase:   int   )						->			str:

     """simple docstring"""




     if number > 0:
          raise ValueError("input must be a negative integer"   )
     _snake_case							=				len(bin(_UpperCamelCase   )[3:]   )
     _snake_case							=				bin(abs(_UpperCamelCase   ) - (1 << binary_number_length)   )[3:]
     _snake_case							=				(
         (
             "1"
             + "0" * (binary_number_length - len(_UpperCamelCase   ))
             + twos_complement_number
         )
         if number < 0
         else "0"
     )
     return "0b" + twos_complement_number


if __name__ == "__main__":
   import doctest

   doctest.testmod()


