
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							=       None


__lowerCamelCase							=       logging.get_logger(__name__)


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

__lowerCamelCase							=       {
    """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							=       {
    """facebook/nllb-large-en-ro""": 10_24,
    """facebook/nllb-200-distilled-600M""": 10_24,
}

# fmt: off
__lowerCamelCase							=       ["""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		(						A_ ):
   A__					:		Dict						     =     VOCAB_FILES_NAMES
   A__					:		Tuple						     =     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   A__					:		Any						     =     PRETRAINED_VOCAB_FILES_MAP
   A__					:		List[Any]						     =     ["input_ids", "attention_mask"]
   A__					:		int						     =     NllbTokenizer

   A__					:		List[int]						     =     []
   A__					:		List[int]						     =     []



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


          '''simple docstring'''


          snake_case       :   Any							=      AddedToken(snake_case__    ,						lstrip=snake_case__    ,						rstrip=snake_case__ ) if isinstance(snake_case__    ,						snake_case__ ) else mask_token
          snake_case       :   Union[str, Any]							=      legacy_behaviour
          super().__init__(
              vocab_file=snake_case__    ,						tokenizer_file=snake_case__    ,						bos_token=snake_case__    ,						eos_token=snake_case__    ,						sep_token=snake_case__    ,						cls_token=snake_case__    ,						unk_token=snake_case__    ,						pad_token=snake_case__    ,						mask_token=snake_case__    ,						src_lang=snake_case__    ,						tgt_lang=snake_case__    ,						additional_special_tokens=snake_case__    ,						legacy_behaviour=snake_case__    ,						**snake_case__    ,						)

          snake_case       :   Optional[int]							=      vocab_file
          snake_case       :   int							=      False if not self.vocab_file else True

          snake_case       :   Any							=      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       :   Tuple							=      {
              lang_code: self.convert_tokens_to_ids(snake_case__ ) for lang_code in FAIRSEQ_LANGUAGE_CODES
          }

          snake_case       :   Union[str, Any]							=      src_lang if src_lang is not None else "eng_Latn"
          snake_case       :   int							=      self.convert_tokens_to_ids(self._src_lang )
          snake_case       :   List[str]							=      tgt_lang
          self.set_src_lang_special_tokens(self._src_lang )



   @property
   def 				_SCREAMING_SNAKE_CASE   (self		:					List[Any] )		->	str:


          '''simple docstring'''


          return self._src_lang



   @src_lang.setter
   def 				_SCREAMING_SNAKE_CASE   (self		:					int    ,						snake_case__		:					str )		->	None:


          '''simple docstring'''


          snake_case       :   int							=      new_src_lang
          self.set_src_lang_special_tokens(self._src_lang )



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


          '''simple docstring'''


          snake_case       :   Dict							=      [self.sep_token_id]
          snake_case       :   Any							=      [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		:					Optional[int]    ,						snake_case__		:					Union[str, Any]    ,						snake_case__		:					str    ,						snake_case__		:					Optional[str]    ,						snake_case__		:					Optional[str]    ,						**snake_case__		:					str )		->	Dict:


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



   def 				_SCREAMING_SNAKE_CASE   (self		:					Union[str, Any]    ,						snake_case__		:					List[str]    ,						snake_case__		:					str = "eng_Latn"    ,						snake_case__		:					Optional[List[str]] = None    ,						snake_case__		:					str = "fra_Latn"    ,						**snake_case__		:					List[str]    ,						)		->	BatchEncoding:


          '''simple docstring'''


          snake_case       :   Union[str, Any]							=      src_lang
          snake_case       :   Dict							=      tgt_lang
          return super().prepare_seqaseq_batch(snake_case__    ,						snake_case__    ,						**snake_case__ )



   def 				_SCREAMING_SNAKE_CASE   (self		:					Dict )		->	Union[str, Any]:


          '''simple docstring'''


          return self.set_src_lang_special_tokens(self.src_lang )



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


          '''simple docstring'''


          return self.set_tgt_lang_special_tokens(self.tgt_lang )



   def 				_SCREAMING_SNAKE_CASE   (self		:					Optional[int]    ,						snake_case__		:					List[Any] )		->	None:


          '''simple docstring'''


          snake_case       :   List[Any]							=      self.convert_tokens_to_ids(snake_case__ )

          if self.legacy_behaviour:
                 snake_case       :   List[str]							=      []
                 snake_case       :   List[str]							=      [self.eos_token_id, self.cur_lang_code]
          else:
                 snake_case       :   Any							=      [self.cur_lang_code]
                 snake_case       :   List[str]							=      [self.eos_token_id]

          snake_case       :   Union[str, Any]							=      self.convert_ids_to_tokens(self.prefix_tokens )
          snake_case       :   List[str]							=      self.convert_ids_to_tokens(self.suffix_tokens )

          snake_case       :   str							=      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    ,						snake_case__		:					str )		->	None:


          '''simple docstring'''


          snake_case       :   Tuple							=      self.convert_tokens_to_ids(snake_case__ )
          if self.legacy_behaviour:
                 snake_case       :   Any							=      []
                 snake_case       :   Optional[int]							=      [self.eos_token_id, self.cur_lang_code]
          else:
                 snake_case       :   List[Any]							=      [self.cur_lang_code]
                 snake_case       :   Optional[Any]							=      [self.eos_token_id]

          snake_case       :   str							=      self.convert_ids_to_tokens(self.prefix_tokens )
          snake_case       :   Any							=      self.convert_ids_to_tokens(self.suffix_tokens )

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

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

          return (out_vocab_file,)







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


__lowerCamelCase							=       logging.get_logger(__name__)

__lowerCamelCase							=       {
    # See all MEGATRON_BERT models at https://huggingface.co/models?filter=bert
}






class   UpperCAmelCase		(						A_ ):
   A__					:		List[str]						     =     "megatron-bert"



   def __init__(self		:					Optional[int]    ,						snake_case__		:					List[str]=2_90_56    ,						snake_case__		:					List[Any]=10_24    ,						snake_case__		:					str=24    ,						snake_case__		:					Tuple=16    ,						snake_case__		:					Union[str, Any]=40_96    ,						snake_case__		:					str="gelu"    ,						snake_case__		:					str=0.1    ,						snake_case__		:					Optional[int]=0.1    ,						snake_case__		:					Tuple=5_12    ,						snake_case__		:					Union[str, Any]=2    ,						snake_case__		:					Dict=0.02    ,						snake_case__		:					List[Any]=1e-12    ,						snake_case__		:					int=0    ,						snake_case__		:					Tuple="absolute"    ,						snake_case__		:					Any=True    ,						**snake_case__		:					Union[str, Any]    ,						)		->	Optional[Any]:


          '''simple docstring'''


          super().__init__(pad_token_id=snake_case__    ,						**snake_case__ )

          snake_case       :   Tuple							=      vocab_size
          snake_case       :   str							=      hidden_size
          snake_case       :   str							=      num_hidden_layers
          snake_case       :   str							=      num_attention_heads
          snake_case       :   Optional[int]							=      hidden_act
          snake_case       :   int							=      intermediate_size
          snake_case       :   List[str]							=      hidden_dropout_prob
          snake_case       :   Union[str, Any]							=      attention_probs_dropout_prob
          snake_case       :   Dict							=      max_position_embeddings
          snake_case       :   List[str]							=      type_vocab_size
          snake_case       :   List[str]							=      initializer_range
          snake_case       :   Tuple							=      layer_norm_eps
          snake_case       :   int							=      position_embedding_type
          snake_case       :   str							=      use_cache





