'''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:
    SCREAMING_SNAKE_CASE_:     Optional[int]  =None


SCREAMING_SNAKE_CASE_:     Dict  =logging.get_logger(__name__)


SCREAMING_SNAKE_CASE_:     str  ={'vocab_file': 'sentencepiece.bpe.model', 'tokenizer_file': 'tokenizer.json'}

SCREAMING_SNAKE_CASE_:     List[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'
        ),
    },
}

SCREAMING_SNAKE_CASE_:     List[str]  ={
    'facebook/nllb-large-en-ro': 10_24,
    'facebook/nllb-200-distilled-600M': 10_24,
}

# fmt: off
SCREAMING_SNAKE_CASE_:     Dict  =['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__	):
    a__       :						Dict          =  VOCAB_FILES_NAMES
    a__       :						Tuple          =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    a__       :						List[Any]          =  PRETRAINED_VOCAB_FILES_MAP
    a__       :						Tuple          =  ["""input_ids""", """attention_mask"""]
    a__       :						Any          =  NllbTokenizer

    a__       :						List[int]          =  []
    a__       :						List[int]          =  []



    def __init__(self    :    Tuple   ,			__a    :    Optional[int]=None   ,			__a    :    Union[str, Any]=None   ,			__a    :    int="<s>"   ,			__a    :    Union[str, Any]="</s>"   ,			__a    :    List[str]="</s>"   ,			__a    :    List[str]="<s>"   ,			__a    :    List[Any]="<unk>"   ,			__a    :    List[Any]="<pad>"   ,			__a    :    Any="<mask>"   ,			__a    :    Tuple=None   ,			__a    :    int=None   ,			__a    :    str=None   ,			__a    :    Any=False   ,			**__a    :    str   ,			):
         # Mask token behave like a normal word, i.e. include the space before it
         UpperCAmelCase_           =   AddedToken(__a   ,			lstrip=__a   ,			rstrip=__a ) if isinstance(__a   ,			__a ) else mask_token
         UpperCAmelCase_           =   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   ,			)

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

         UpperCAmelCase_           =   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} )
         UpperCAmelCase_           =   {
             lang_code: self.convert_tokens_to_ids(__a ) for lang_code in FAIRSEQ_LANGUAGE_CODES
         }

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



    @property
    def 					_lowercase						(self    :    List[Any] ):
         return self._src_lang



    @src_lang.setter
    def 					_lowercase						(self    :    List[Any]   ,			__a    :    str ):
         UpperCAmelCase_           =   new_src_lang
         self.set_src_lang_special_tokens(self._src_lang )



    def 					_lowercase						(self    :    Union[str, Any]   ,			__a    :    List[int]   ,			__a    :    Optional[List[int]] = None ):
         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    :    List[Any]   ,			__a    :    List[int]   ,			__a    :    Optional[List[int]] = None ):
         UpperCAmelCase_           =   [self.sep_token_id]
         UpperCAmelCase_           =   [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    :    Optional[int]   ,			__a    :    List[Any]   ,			__a    :    str   ,			__a    :    Optional[str]   ,			__a    :    Optional[str]   ,			**__a    :    Any ):
         if src_lang is None or tgt_lang is None:
              raise ValueError("Translation requires a `src_lang` and a `tgt_lang` for this model" )
         UpperCAmelCase_           =   src_lang
         UpperCAmelCase_           =   self(__a   ,			add_special_tokens=__a   ,			return_tensors=__a   ,			**__a )
         UpperCAmelCase_           =   self.convert_tokens_to_ids(__a )
         UpperCAmelCase_           =   tgt_lang_id
         return inputs



    def 					_lowercase						(self    :    Optional[int]   ,			__a    :    List[str]   ,			__a    :    str = "eng_Latn"   ,			__a    :    Optional[List[str]] = None   ,			__a    :    str = "fra_Latn"   ,			**__a    :    Tuple   ,			):
         UpperCAmelCase_           =   src_lang
         UpperCAmelCase_           =   tgt_lang
         return super().prepare_seqaseq_batch(__a   ,			__a   ,			**__a )



    def 					_lowercase						(self    :    str ):
         return self.set_src_lang_special_tokens(self.src_lang )



    def 					_lowercase						(self    :    Any ):
         return self.set_tgt_lang_special_tokens(self.tgt_lang )



    def 					_lowercase						(self    :    int   ,			__a    :    Optional[int] ):
         UpperCAmelCase_           =   self.convert_tokens_to_ids(__a )

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

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

         UpperCAmelCase_           =   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    :    List[str]   ,			__a    :    str ):
         UpperCAmelCase_           =   self.convert_tokens_to_ids(__a )
         if self.legacy_behaviour:
              UpperCAmelCase_           =   []
              UpperCAmelCase_           =   [self.eos_token_id, self.cur_lang_code]
         else:
              UpperCAmelCase_           =   [self.cur_lang_code]
              UpperCAmelCase_           =   [self.eos_token_id]

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

         UpperCAmelCase_           =   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    :    Optional[int]   ,			__a    :    str   ,			__a    :    Optional[str] = None ):
         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
         UpperCAmelCase_           =   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,)





'''simple docstring'''





from ...configuration_utils import PretrainedConfig
from ...utils import logging


SCREAMING_SNAKE_CASE_:     Dict  =logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_:     Tuple  ={}




class        __A      ( UpperCamelCase__	):
    a__       :						int          =  """llama"""
    a__       :						Any          =  ["""past_key_values"""]



    def __init__(self    :    List[str]   ,			__a    :    List[str]=32000   ,			__a    :    Tuple=4096   ,			__a    :    List[Any]=11008   ,			__a    :    Dict=32   ,			__a    :    Tuple=32   ,			__a    :    Any=None   ,			__a    :    Any="silu"   ,			__a    :    List[Any]=2048   ,			__a    :    List[Any]=0.02   ,			__a    :    str=1E-6   ,			__a    :    Optional[Any]=True   ,			__a    :    Union[str, Any]=0   ,			__a    :    Any=1   ,			__a    :    Dict=2   ,			__a    :    Dict=1   ,			__a    :    str=False   ,			__a    :    str=None   ,			**__a    :    Optional[Any]   ,			):
         UpperCAmelCase_           =   vocab_size
         UpperCAmelCase_           =   max_position_embeddings
         UpperCAmelCase_           =   hidden_size
         UpperCAmelCase_           =   intermediate_size
         UpperCAmelCase_           =   num_hidden_layers
         UpperCAmelCase_           =   num_attention_heads

         # for backward compatibility
         if num_key_value_heads is None:
              UpperCAmelCase_           =   num_attention_heads

         UpperCAmelCase_           =   num_key_value_heads
         UpperCAmelCase_           =   hidden_act
         UpperCAmelCase_           =   initializer_range
         UpperCAmelCase_           =   rms_norm_eps
         UpperCAmelCase_           =   pretraining_tp
         UpperCAmelCase_           =   use_cache
         UpperCAmelCase_           =   rope_scaling
         self._rope_scaling_validation()

         super().__init__(
             pad_token_id=__a   ,			bos_token_id=__a   ,			eos_token_id=__a   ,			tie_word_embeddings=__a   ,			**__a   ,			)






    def 					_lowercase						(self    :    List[str] ):
         if self.rope_scaling is None:
              return

         if not isinstance(self.rope_scaling   ,			__a ) or len(self.rope_scaling ) != 2:
              raise ValueError(
                  "`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, "
                  f"""got {self.rope_scaling}""" )
         UpperCAmelCase_           =   self.rope_scaling.get("type"   ,			__a )
         UpperCAmelCase_           =   self.rope_scaling.get("factor"   ,			__a )
         if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
              raise ValueError(
                  f"""`rope_scaling`'s name field must be one of ['linear', 'dynamic'], got {rope_scaling_type}""" )
         if rope_scaling_factor is None or not isinstance(__a   ,			__a ) or rope_scaling_factor <= 1.0:
              raise ValueError(f"""`rope_scaling`'s factor field must be an float > 1, got {rope_scaling_factor}""" )




