




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


lowerCamelCase    :		int			   =       logging.get_logger(__name__)

lowerCamelCase    :		Any			   =       "▁"

lowerCamelCase    :		int			   =       {"vocab_file": "sentencepiece.bpe.model"}

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

lowerCamelCase    :		Any			   =       {
    "facebook/nllb-200-distilled-600M": 1_024,
}

# fmt: off
lowerCamelCase    :		Union[str, 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   A(						__snake_case       ):



 '''simple docstring'''


 UpperCamelCase						       =						VOCAB_FILES_NAMES
 UpperCamelCase						       =						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 UpperCamelCase						       =						PRETRAINED_VOCAB_FILES_MAP
 UpperCamelCase						       =						["input_ids", "attention_mask"]

 UpperCamelCase						       =						[]
 UpperCamelCase						       =						[]

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

       """simple docstring"""






       lowerCamelCase_							=   AddedToken(lowerCamelCase_						,							lstrip=lowerCamelCase_						,							rstrip=lowerCamelCase_  ) if isinstance(lowerCamelCase_						,							lowerCamelCase_  ) else mask_token

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

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

       lowerCamelCase_							=   spm.SentencePieceProcessor(**self.sp_model_kwargs  )
       self.sp_model.Load(str(lowerCamelCase_  )  )
       lowerCamelCase_							=   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
       lowerCamelCase_							=   {"""<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
       lowerCamelCase_							=   1

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

       self.fairseq_tokens_to_ids.update(self.lang_code_to_id  )
       lowerCamelCase_							=   {v: k for k, v in self.fairseq_tokens_to_ids.items()}
       lowerCamelCase_							=   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]  )

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

 def __getstate__(    self :  str  )       ->     Optional[Any]:

       """simple docstring"""






       lowerCamelCase_							=   self.__dict__.copy()
       lowerCamelCase_							=   None
       lowerCamelCase_							=   self.sp_model.serialized_model_proto()
       return state

 def __setstate__(    self :  List[str]						,							A_ :  Union[str, Any]  )       ->     Union[str, Any]:

       """simple docstring"""






       lowerCamelCase_							=   d

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

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

 @property
 def     a__			(    self :  Any  )       ->     Any:

       """simple docstring"""






       return len(self.sp_model  ) + len(self.lang_code_to_id  ) + self.fairseq_offset + 1  # Plus 1 for the mask token

 @property
 def     a__			(    self :  Optional[Any]  )       ->     str:

       """simple docstring"""






       return self._src_lang

 @src_lang.setter
 def     a__			(    self :  str						,							A_ :  str  )       ->     None:

       """simple docstring"""






       lowerCamelCase_							=   new_src_lang
       self.set_src_lang_special_tokens(self._src_lang  )

 def     a__			(    self :  Tuple						,							A_ :  List[int]						,							A_ :  Optional[List[int]] = None						,							A_ :  bool = False  )       ->     List[int]:

       """simple docstring"""






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

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

 def     a__			(    self :  Any						,							A_ :  List[int]						,							A_ :  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     a__			(    self :  Tuple						,							A_ :  List[int]						,							A_ :  Optional[List[int]] = None  )       ->     List[int]:

       """simple docstring"""






       lowerCamelCase_							=   [self.sep_token_id]
       lowerCamelCase_							=   [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 :  List[Any]						,							A_ :  Union[str, Any]						,							A_ :  str						,							A_ :  Optional[str]						,							A_ :  Optional[str]						,							**A_ :  Dict  )       ->     Optional[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'  )
       lowerCamelCase_							=   src_lang
       lowerCamelCase_							=   self(lowerCamelCase_						,							add_special_tokens=lowerCamelCase_						,							return_tensors=lowerCamelCase_						,							**lowerCamelCase_  )
       lowerCamelCase_							=   self.convert_tokens_to_ids(lowerCamelCase_  )
       lowerCamelCase_							=   tgt_lang_id
       return inputs

 def     a__			(    self :  str  )       ->     List[str]:

       """simple docstring"""






       lowerCamelCase_							=   {self.convert_ids_to_tokens(lowerCamelCase_  ): i for i in range(self.vocab_size  )}
       vocab.update(self.added_tokens_encoder  )
       return vocab

 def     a__			(    self :  int						,							A_ :  str  )       ->     List[str]:

       """simple docstring"""






       return self.sp_model.encode(lowerCamelCase_						,							out_type=lowerCamelCase_  )

 def     a__			(    self :  Optional[Any]						,							A_ :  Union[str, Any]  )       ->     Union[str, Any]:

       """simple docstring"""






       if token in self.fairseq_tokens_to_ids:
             return self.fairseq_tokens_to_ids[token]
       lowerCamelCase_							=   self.sp_model.PieceToId(lowerCamelCase_  )

       # 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     a__			(    self :  Optional[Any]						,							A_ :  Optional[int]  )       ->     List[str]:

       """simple docstring"""






       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     a__			(    self :  Tuple						,							A_ :  Union[str, Any]  )       ->     Tuple:

       """simple docstring"""






       lowerCamelCase_							=   """""".join(lowerCamelCase_  ).replace(lowerCamelCase_						,							' '  ).strip()
       return out_string

 def     a__			(    self :  Dict						,							A_ :  str						,							A_ :  Optional[str] = None  )       ->     Tuple[str]:

       """simple docstring"""






       if not os.path.isdir(lowerCamelCase_  ):
             logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""  )
             return
       lowerCamelCase_							=   os.path.join(
           lowerCamelCase_						,							(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']  )

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

       return (out_vocab_file,)

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

       """simple docstring"""






       lowerCamelCase_							=   src_lang
       lowerCamelCase_							=   tgt_lang
       return super().prepare_seqaseq_batch(lowerCamelCase_						,							lowerCamelCase_						,							**lowerCamelCase_  )

 def     a__			(    self :  Optional[int]  )       ->     Dict:

       """simple docstring"""






       return self.set_src_lang_special_tokens(self.src_lang  )

 def     a__			(    self :  Optional[int]  )       ->     Any:

       """simple docstring"""






       return self.set_tgt_lang_special_tokens(self.tgt_lang  )

 def     a__			(    self :  Optional[Any]						,							A_ :  str  )       ->     None:

       """simple docstring"""






       lowerCamelCase_							=   self.lang_code_to_id[src_lang]
       if self.legacy_behaviour:
             lowerCamelCase_							=   []
             lowerCamelCase_							=   [self.eos_token_id, self.cur_lang_code]
       else:
             lowerCamelCase_							=   [self.cur_lang_code]
             lowerCamelCase_							=   [self.eos_token_id]





 def     a__			(    self :  Optional[int]						,							A_ :  str  )       ->     None:

       """simple docstring"""






       lowerCamelCase_							=   self.lang_code_to_id[lang]
       if self.legacy_behaviour:
             lowerCamelCase_							=   []
             lowerCamelCase_							=   [self.eos_token_id, self.cur_lang_code]
       else:
             lowerCamelCase_							=   [self.cur_lang_code]
             lowerCamelCase_							=   [self.eos_token_id]









from ..utils import DummyObject, requires_backends







class   A(						metaclass=UpperCamelCase       ):



 '''simple docstring'''


 UpperCamelCase						       =						['''transformers''', '''torch''', '''note_seq''']

 def __init__(    self :  Any						,							*A_ :  Any						,							**A_ :  Tuple  )       ->     List[Any]:

       """simple docstring"""






       requires_backends(self						,							['transformers', 'torch', 'note_seq']  )

 @classmethod
 def     a__			(    cls :  Tuple						,							*A_ :  Dict						,							**A_ :  Optional[Any]  )       ->     Union[str, Any]:

       """simple docstring"""






       requires_backends(cls						,							['transformers', 'torch', 'note_seq']  )





 @classmethod
 def     a__			(    cls :  int						,							*A_ :  Dict						,							**A_ :  List[str]  )       ->     Tuple:

       """simple docstring"""






       requires_backends(cls						,							['transformers', 'torch', 'note_seq']  )



