




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:
  _lowerCamelCase     :		Any								=       None


_lowerCamelCase     :		int								=       logging.get_logger(__name__)


_lowerCamelCase     :		int								=       {"""vocab_file""": """sentencepiece.bpe.model""", """tokenizer_file""": """tokenizer.json"""}

_lowerCamelCase     :		int								=       {
    """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"""
        ),
    },
}

_lowerCamelCase     :		int								=       {
    """facebook/nllb-large-en-ro""": 1024,
    """facebook/nllb-200-distilled-600M""": 1024,
}

# fmt: off
_lowerCamelCase     :		List[Any]								=       ["""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_   (			UpperCAmelCase__					):

    '''simple docstring'''






    UpperCAmelCase__							  =				VOCAB_FILES_NAMES
    UpperCAmelCase__							  =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    UpperCAmelCase__							  =				PRETRAINED_VOCAB_FILES_MAP
    UpperCAmelCase__							  =				['''input_ids''', '''attention_mask''']
    UpperCAmelCase__							  =				NllbTokenizer

    UpperCAmelCase__							  =				[]
    UpperCAmelCase__							  =				[]


    def __init__(      self				:     int ,			UpperCAmelCase__				:     Tuple=None ,			UpperCAmelCase__				:     Tuple=None ,			UpperCAmelCase__				:     Any="<s>" ,			UpperCAmelCase__				:     Any="</s>" ,			UpperCAmelCase__				:     Dict="</s>" ,			UpperCAmelCase__				:     Optional[int]="<s>" ,			UpperCAmelCase__				:     str="<unk>" ,			UpperCAmelCase__				:     Dict="<pad>" ,			UpperCAmelCase__				:     Union[str, Any]="<mask>" ,			UpperCAmelCase__				:     Dict=None ,			UpperCAmelCase__				:     Optional[int]=None ,			UpperCAmelCase__				:     List[Any]=None ,			UpperCAmelCase__				:     Union[str, Any]=False ,			**UpperCAmelCase__				:     Optional[Any] ,			)						->Union[str, Any]:





           '''simple docstring'''

           A__							=   AddedToken(UpperCAmelCase__ ,			lstrip=UpperCAmelCase__ ,			rstrip=UpperCAmelCase__) if isinstance(UpperCAmelCase__ ,			UpperCAmelCase__) else mask_token
           A__							=   legacy_behaviour
           super().__init__(
               vocab_file=UpperCAmelCase__ ,			tokenizer_file=UpperCAmelCase__ ,			bos_token=UpperCAmelCase__ ,			eos_token=UpperCAmelCase__ ,			sep_token=UpperCAmelCase__ ,			cls_token=UpperCAmelCase__ ,			unk_token=UpperCAmelCase__ ,			pad_token=UpperCAmelCase__ ,			mask_token=UpperCAmelCase__ ,			src_lang=UpperCAmelCase__ ,			tgt_lang=UpperCAmelCase__ ,			additional_special_tokens=UpperCAmelCase__ ,			legacy_behaviour=UpperCAmelCase__ ,			**UpperCAmelCase__ ,			)

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

           A__							=   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})
           A__							=   {
               lang_code: self.convert_tokens_to_ids(UpperCAmelCase__) for lang_code in FAIRSEQ_LANGUAGE_CODES
           }

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


    @property
    def 	SCREAMING_SNAKE_CASE							(      self				:     str)						->str:





           '''simple docstring'''

           return self._src_lang


    @src_lang.setter
    def 	SCREAMING_SNAKE_CASE							(      self				:     Tuple ,			UpperCAmelCase__				:     str)						->None:





           '''simple docstring'''

           A__							=   new_src_lang
           self.set_src_lang_special_tokens(self._src_lang)


    def 	SCREAMING_SNAKE_CASE							(      self				:     str ,			UpperCAmelCase__				:     List[int] ,			UpperCAmelCase__				:     Optional[List[int]] = 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 	SCREAMING_SNAKE_CASE							(      self				:     List[Any] ,			UpperCAmelCase__				:     List[int] ,			UpperCAmelCase__				:     Optional[List[int]] = None)						->List[int]:





           '''simple docstring'''

           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 	SCREAMING_SNAKE_CASE							(      self				:     Tuple ,			UpperCAmelCase__				:     int ,			UpperCAmelCase__				:     str ,			UpperCAmelCase__				:     Optional[str] ,			UpperCAmelCase__				:     Optional[str] ,			**UpperCAmelCase__				:     Optional[Any])						->Union[str, Any]:





           '''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''')
           A__							=   src_lang
           A__							=   self(UpperCAmelCase__ ,			add_special_tokens=UpperCAmelCase__ ,			return_tensors=UpperCAmelCase__ ,			**UpperCAmelCase__)
           A__							=   self.convert_tokens_to_ids(UpperCAmelCase__)
           A__							=   tgt_lang_id
           return inputs


    def 	SCREAMING_SNAKE_CASE							(      self				:     Optional[Any] ,			UpperCAmelCase__				:     List[str] ,			UpperCAmelCase__				:     str = "eng_Latn" ,			UpperCAmelCase__				:     Optional[List[str]] = None ,			UpperCAmelCase__				:     str = "fra_Latn" ,			**UpperCAmelCase__				:     int ,			)						->BatchEncoding:





           '''simple docstring'''

           A__							=   src_lang
           A__							=   tgt_lang
           return super().prepare_seqaseq_batch(UpperCAmelCase__ ,			UpperCAmelCase__ ,			**UpperCAmelCase__)


    def 	SCREAMING_SNAKE_CASE							(      self				:     Any)						->List[str]:





           '''simple docstring'''

           return self.set_src_lang_special_tokens(self.src_lang)


    def 	SCREAMING_SNAKE_CASE							(      self				:     List[Any])						->List[Any]:





           '''simple docstring'''

           return self.set_tgt_lang_special_tokens(self.tgt_lang)


    def 	SCREAMING_SNAKE_CASE							(      self				:     Optional[Any] ,			UpperCAmelCase__				:     Optional[Any])						->None:





           '''simple docstring'''

           A__							=   self.convert_tokens_to_ids(UpperCAmelCase__)

           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]

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

           A__							=   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 	SCREAMING_SNAKE_CASE							(      self				:     Union[str, Any] ,			UpperCAmelCase__				:     str)						->None:





           '''simple docstring'''

           A__							=   self.convert_tokens_to_ids(UpperCAmelCase__)
           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]

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

           A__							=   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 	SCREAMING_SNAKE_CASE							(      self				:     int ,			UpperCAmelCase__				:     str ,			UpperCAmelCase__				:     Optional[str] = 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(UpperCAmelCase__):
                  logger.error(f"""Vocabulary path ({save_directory}) should be a directory.""")
                  return
           A__							=   os.path.join(
               UpperCAmelCase__ ,			(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''])

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

           return (out_vocab_file,)








from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


_lowerCamelCase     :		Any								=       {
    """configuration_nezha""": ["""NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """NezhaConfig"""],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _lowerCamelCase     :		str								=       [
      """NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """NezhaForNextSentencePrediction""",
      """NezhaForMaskedLM""",
      """NezhaForPreTraining""",
      """NezhaForMultipleChoice""",
      """NezhaForQuestionAnswering""",
      """NezhaForSequenceClassification""",
      """NezhaForTokenClassification""",
      """NezhaModel""",
      """NezhaPreTrainedModel""",
  ]


if TYPE_CHECKING:
  from .configuration_nezha import NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP, NezhaConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_nezha import (
        NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST,
        NezhaForMaskedLM,
        NezhaForMultipleChoice,
        NezhaForNextSentencePrediction,
        NezhaForPreTraining,
        NezhaForQuestionAnswering,
        NezhaForSequenceClassification,
        NezhaForTokenClassification,
        NezhaModel,
        NezhaPreTrainedModel,
    )


else:
  import sys

  _lowerCamelCase     :		List[str]								=       _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


