






'''simple docstring'''
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


UpperCamelCase__ =			logging.get_logger(__name__)

UpperCamelCase__ =			"▁"

UpperCamelCase__ =			{"vocab_file": "sentencepiece.bpe.model"}

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

UpperCamelCase__ =			{
    "facebook/nllb-200-distilled-600M": 1024,
}

# fmt: off
UpperCamelCase__ =			["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						(		snake_case			):
    __lowerCamelCase:   Optional[Any]		       =    VOCAB_FILES_NAMES
    __lowerCamelCase:   int		       =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    __lowerCamelCase:   Union[str, Any]		       =    PRETRAINED_VOCAB_FILES_MAP
    __lowerCamelCase:   Dict		       =    ["input_ids", "attention_mask"]

    __lowerCamelCase:   List[int]		       =    []
    __lowerCamelCase:   List[int]		       =    []




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






       '''simple docstring'''






       lowercase_	:    List[str]           =     AddedToken(UpperCamelCase_  ,     lstrip=UpperCamelCase_  ,     rstrip=UpperCamelCase_			) if isinstance(UpperCamelCase_  ,     UpperCamelCase_			) else mask_token

       lowercase_	:    Optional[int]           =     {} if sp_model_kwargs is None else sp_model_kwargs
       lowercase_	:    Any           =     legacy_behaviour

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

       lowercase_	:    List[str]           =     spm.SentencePieceProcessor(**self.sp_model_kwargs			)
       self.sp_model.Load(str(UpperCamelCase_			)			)
       lowercase_	:    Optional[Any]           =     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
       lowercase_	:    List[Any]           =     {"<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
       lowercase_	:    str           =     1

       lowercase_	:    Union[str, Any]           =     len(self.sp_model			)
       lowercase_	:    List[Any]           =     {
           code: self.sp_model_size + i + self.fairseq_offset for i, code in enumerate(UpperCamelCase_			)
       }
       lowercase_	:    Optional[Any]           =     {v: k for k, v in self.lang_code_to_id.items()}
       lowercase_	:    Optional[int]           =     len(self.sp_model			) + len(self.lang_code_to_id			) + self.fairseq_offset

       self.fairseq_tokens_to_ids.update(self.lang_code_to_id			)
       lowercase_	:    Union[str, Any]           =     {v: k for k, v in self.fairseq_tokens_to_ids.items()}
       lowercase_	:    Any           =     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]			)

       lowercase_	:    Optional[Any]           =     src_lang if src_lang is not None else "eng_Latn"
       lowercase_	:    Optional[int]           =     self.lang_code_to_id[self._src_lang]
       lowercase_	:    Tuple           =     tgt_lang
       self.set_src_lang_special_tokens(self._src_lang			)




    def __getstate__(			self  :       Dict			):






       '''simple docstring'''






       lowercase_	:    int           =     self.__dict__.copy()
       lowercase_	:    str           =     None
       lowercase_	:    Any           =     self.sp_model.serialized_model_proto()
       return state




    def __setstate__(			self  :       Dict  ,     a  :       List[Any]			):






       '''simple docstring'''






       lowercase_	:    Optional[Any]           =     d

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

       lowercase_	:    List[Any]           =     spm.SentencePieceProcessor(**self.sp_model_kwargs			)
       self.sp_model.LoadFromSerializedProto(self.sp_model_proto			)




    @property
    def 				lowerCAmelCase__				(			self  :       Optional[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 				lowerCAmelCase__				(			self  :       List[str]			):






       '''simple docstring'''






       return self._src_lang




    @src_lang.setter
    def 				lowerCAmelCase__				(			self  :       List[Any]  ,     a  :       List[str]			):






       '''simple docstring'''






       lowercase_	:    List[Any]           =     new_src_lang
       self.set_src_lang_special_tokens(self._src_lang			)




    def 				lowerCAmelCase__				(			self  :       List[str]  ,     a  :       Any  ,     a  :       Dict = None  ,     a  :       Any = False			):






       '''simple docstring'''






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

       lowercase_	:    Optional[int]           =     [1] * len(self.prefix_tokens			)
       lowercase_	:    str           =     [1] * len(self.suffix_tokens			)
       if token_ids_a is None:
          return prefix_ones + ([0] * len(UpperCamelCase_			)) + suffix_ones
       return prefix_ones + ([0] * len(UpperCamelCase_			)) + ([0] * len(UpperCamelCase_			)) + suffix_ones




    def 				lowerCAmelCase__				(			self  :       List[Any]  ,     a  :       Any  ,     a  :       Optional[int] = None			):






       '''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 				lowerCAmelCase__				(			self  :       Dict  ,     a  :       Optional[Any]  ,     a  :       Dict = None			):






       '''simple docstring'''






       lowercase_	:    Optional[Any]           =     [self.sep_token_id]
       lowercase_	:    Dict           =     [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 				lowerCAmelCase__				(			self  :       Union[str, Any]  ,     a  :       Tuple  ,     a  :       Any  ,     a  :       Tuple  ,     a  :       List[Any]  ,     **a  :       str			):






       '''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"			)
       lowercase_	:    Union[str, Any]           =     src_lang
       lowercase_	:    str           =     self(UpperCamelCase_  ,     add_special_tokens=UpperCamelCase_  ,     return_tensors=UpperCamelCase_  ,     **UpperCamelCase_			)
       lowercase_	:    Union[str, Any]           =     self.convert_tokens_to_ids(UpperCamelCase_			)
       lowercase_	:    Optional[int]           =     tgt_lang_id
       return inputs




    def 				lowerCAmelCase__				(			self  :       Tuple			):






       '''simple docstring'''






       lowercase_	:    Optional[Any]           =     {self.convert_ids_to_tokens(UpperCamelCase_			): i for i in range(self.vocab_size			)}
       vocab.update(self.added_tokens_encoder			)
       return vocab




    def 				lowerCAmelCase__				(			self  :       List[Any]  ,     a  :       Optional[Any]			):






       '''simple docstring'''






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




    def 				lowerCAmelCase__				(			self  :       Union[str, Any]  ,     a  :       List[Any]			):






       '''simple docstring'''






       if token in self.fairseq_tokens_to_ids:
          return self.fairseq_tokens_to_ids[token]
       lowercase_	:    Union[str, Any]           =     self.sp_model.PieceToId(UpperCamelCase_			)

       # 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 				lowerCAmelCase__				(			self  :       Optional[int]  ,     a  :       List[Any]			):






       '''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 				lowerCAmelCase__				(			self  :       int  ,     a  :       Tuple			):






       '''simple docstring'''






       lowercase_	:    str           =     "".join(UpperCamelCase_			).replace(UpperCamelCase_  ,     " "			).strip()
       return out_string




    def 				lowerCAmelCase__				(			self  :       Dict  ,     a  :       Dict  ,     a  :       int = None			):






       '''simple docstring'''






       if not os.path.isdir(UpperCamelCase_			):
          logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""			)
          return
       lowercase_	:    List[str]           =     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_			) and os.path.isfile(self.vocab_file			):
          copyfile(self.vocab_file  ,     UpperCamelCase_			)
       elif not os.path.isfile(self.vocab_file			):
          with open(UpperCamelCase_  ,     "wb"			) as fi:
             lowercase_	:    Dict           =     self.sp_model.serialized_model_proto()
             fi.write(UpperCamelCase_			)

       return (out_vocab_file,)




    def 				lowerCAmelCase__				(			self  :       List[str]  ,     a  :       Union[str, Any]  ,     a  :       str = "eng_Latn"  ,     a  :       List[Any] = None  ,     a  :       int = "fra_Latn"  ,     **a  :       Any  ,     ):






       '''simple docstring'''






       lowercase_	:    Tuple           =     src_lang
       lowercase_	:    Dict           =     tgt_lang
       return super().prepare_seqaseq_batch(UpperCamelCase_  ,     UpperCamelCase_  ,     **UpperCamelCase_			)




    def 				lowerCAmelCase__				(			self  :       List[str]			):






       '''simple docstring'''






       return self.set_src_lang_special_tokens(self.src_lang			)




    def 				lowerCAmelCase__				(			self  :       Dict			):






       '''simple docstring'''






       return self.set_tgt_lang_special_tokens(self.tgt_lang			)




    def 				lowerCAmelCase__				(			self  :       Dict  ,     a  :       str			):






       '''simple docstring'''






       lowercase_	:    int           =     self.lang_code_to_id[src_lang]
       if self.legacy_behaviour:
          lowercase_	:    Union[str, Any]           =     []
          lowercase_	:    Optional[int]           =     [self.eos_token_id, self.cur_lang_code]
       else:
          lowercase_	:    Dict           =     [self.cur_lang_code]
          lowercase_	:    Any           =     [self.eos_token_id]





    def 				lowerCAmelCase__				(			self  :       Union[str, Any]  ,     a  :       str			):






       '''simple docstring'''






       lowercase_	:    int           =     self.lang_code_to_id[lang]
       if self.legacy_behaviour:
          lowercase_	:    Union[str, Any]           =     []
          lowercase_	:    Optional[int]           =     [self.eos_token_id, self.cur_lang_code]
       else:
          lowercase_	:    Tuple           =     [self.cur_lang_code]
          lowercase_	:    Any           =     [self.eos_token_id]












'''simple docstring'''
from __future__ import annotations

UpperCamelCase__ =			[-10, -5, 0, 5, 5.1, 11, 13, 21, 3, 4, -21, -10, -5, -1, 0]
UpperCamelCase__ =			[-5, 0, 5, 5.1, 11, 13, 21, -1, 4, -1, -10, -5, -1, 0, -1]




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase							):


   """simple docstring"""




   lowercase_	:    str           =     []
   lowercase_	:    List[str]           =     len(_UpperCamelCase							)

   for i in range(_UpperCamelCase							):
      lowercase_	:    float           =     -1
      for j in range(i + 1						,		_UpperCamelCase							):
         if arr[i] < arr[j]:
            lowercase_	:    Union[str, Any]           =     arr[j]
            break
      result.append(_UpperCamelCase							)
   return result




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase							):


   """simple docstring"""




   lowercase_	:    List[str]           =     []
   for i, outer in enumerate(_UpperCamelCase							):
      lowercase_	:    float           =     -1
      for inner in arr[i + 1 :]:
         if outer < inner:
            lowercase_	:    Optional[Any]           =     inner
            break
      result.append(_UpperCamelCase							)
   return result




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase							):


   """simple docstring"""




   lowercase_	:    List[str]           =     len(_UpperCamelCase							)
   lowercase_	:    list[float]           =     []
   lowercase_	:    list[float]           =     [-1] * arr_size

   for index in reversed(range(_UpperCamelCase							)							):
      if stack:
         while stack[-1] <= arr[index]:
            stack.pop()
            if not stack:
               break
      if stack:
         lowercase_	:    Optional[Any]           =     stack[-1]
      stack.append(arr[index]							)
   return result


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

 testmod()
 print(next_greatest_element_slow(arr))
 print(next_greatest_element_fast(arr))
 print(next_greatest_element(arr))

 UpperCamelCase__ =			(
     'from __main__ import arr, next_greatest_element_slow, '
     'next_greatest_element_fast, next_greatest_element'
 )
 print(
     'next_greatest_element_slow():',
     timeit('next_greatest_element_slow(arr)', setup=setup),
 )
 print(
     'next_greatest_element_fast():',
     timeit('next_greatest_element_fast(arr)', setup=setup),
 )
 print(
     '     next_greatest_element():',
     timeit('next_greatest_element(arr)', setup=setup),
 )




