






'''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


SCREAMING_SNAKE_CASE						=	logging.get_logger(__name__)

SCREAMING_SNAKE_CASE						=	'▁'

SCREAMING_SNAKE_CASE						=	{'vocab_file': 'sentencepiece.bpe.model'}

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

SCREAMING_SNAKE_CASE						=	{
    'facebook/nllb-200-distilled-600M': 1_024,
}

# fmt: off
SCREAMING_SNAKE_CASE						=	['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_								=			[]
     UpperCamelCase_								=			[]






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


      '''simple docstring'''

      lowercase					:   List[Any]				  =AddedToken(lowercase__     ,      lstrip=lowercase__     ,      rstrip=lowercase__		) if isinstance(lowercase__     ,      lowercase__		) else mask_token

      lowercase					:   str				  ={} if sp_model_kwargs is None else sp_model_kwargs
      lowercase					:   Optional[Any]				  =legacy_behaviour

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

      lowercase					:   Optional[Any]				  =spm.SentencePieceProcessor(**self.sp_model_kwargs		)
      self.sp_model.Load(str(lowercase__		)		)
      lowercase					:   Tuple				  =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					:   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					:   Dict				  =1

      lowercase					:   int				  =len(self.sp_model		)
      lowercase					:   Union[str, Any]				  ={
          code: self.sp_model_size + i + self.fairseq_offset for i, code in enumerate(lowercase__		)
      }
      lowercase					:   Optional[Any]				  ={v: k for k, v in self.lang_code_to_id.items()}
      lowercase					:   List[Any]				  =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					:   Tuple				  =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					:   Dict				  =src_lang if src_lang is not None else "eng_Latn"
      lowercase					:   List[Any]				  =self.lang_code_to_id[self._src_lang]
      lowercase					:   List[Any]				  =tgt_lang
      self.set_src_lang_special_tokens(self._src_lang		)






     def __getstate__( self			:		str		)  ->  Tuple:


      '''simple docstring'''

      lowercase					:   str				  =self.__dict__.copy()
      lowercase					:   Optional[Any]				  =None
      lowercase					:   Any				  =self.sp_model.serialized_model_proto()
      return state






     def __setstate__( self			:		Optional[Any]     ,      UpperCAmelCase			:		Optional[int]		)  ->  Dict:


      '''simple docstring'''

      lowercase					:   Union[str, Any]				  =d

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

      lowercase					:   Tuple				  =spm.SentencePieceProcessor(**self.sp_model_kwargs		)
      self.sp_model.LoadFromSerializedProto(self.sp_model_proto		)






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


      '''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			:		Union[str, Any]		)  ->  Dict:


      '''simple docstring'''

      return self._src_lang






     @src_lang.setter
     def 				A__      ( self			:		Dict     ,      UpperCAmelCase			:		List[str]		)  ->  Union[str, Any]:


      '''simple docstring'''

      lowercase					:   Optional[Any]				  =new_src_lang
      self.set_src_lang_special_tokens(self._src_lang		)






     def 				A__      ( self			:		Dict     ,      UpperCAmelCase			:		Any     ,      UpperCAmelCase			:		int = None     ,      UpperCAmelCase			:		Dict = False		)  ->  Union[str, Any]:


      '''simple docstring'''

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

      lowercase					:   List[str]				  =[1] * len(self.prefix_tokens		)
      lowercase					:   Any				  =[1] * len(self.suffix_tokens		)
      if token_ids_a is None:
       return prefix_ones + ([0] * len(lowercase__		)) + suffix_ones
      return prefix_ones + ([0] * len(lowercase__		)) + ([0] * len(lowercase__		)) + suffix_ones






     def 				A__      ( self			:		Optional[int]     ,      UpperCAmelCase			:		Tuple     ,      UpperCAmelCase			:		Any = None		)  ->  Union[str, Any]:


      '''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			:		Optional[Any]     ,      UpperCAmelCase			:		List[Any]     ,      UpperCAmelCase			:		Optional[int] = None		)  ->  List[str]:


      '''simple docstring'''

      lowercase					:   Dict				  =[self.sep_token_id]
      lowercase					:   str				  =[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			:		int     ,      UpperCAmelCase			:		Optional[int]     ,      UpperCAmelCase			:		int     ,      UpperCAmelCase			:		int     ,      UpperCAmelCase			:		Any     ,      **UpperCAmelCase			:		Union[str, Any]		)  ->  Tuple:


      '''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					:   List[Any]				  =src_lang
      lowercase					:   Tuple				  =self(lowercase__     ,      add_special_tokens=lowercase__     ,      return_tensors=lowercase__     ,      **lowercase__		)
      lowercase					:   List[Any]				  =self.convert_tokens_to_ids(lowercase__		)
      lowercase					:   str				  =tgt_lang_id
      return inputs






     def 				A__      ( self			:		Tuple		)  ->  Optional[Any]:


      '''simple docstring'''

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






     def 				A__      ( self			:		str     ,      UpperCAmelCase			:		Any		)  ->  Tuple:


      '''simple docstring'''

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






     def 				A__      ( self			:		Optional[Any]     ,      UpperCAmelCase			:		List[Any]		)  ->  str:


      '''simple docstring'''

      if token in self.fairseq_tokens_to_ids:
       return self.fairseq_tokens_to_ids[token]
      lowercase					:   List[Any]				  =self.sp_model.PieceToId(lowercase__		)

      # 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			:		Any     ,      UpperCAmelCase			:		Union[str, Any]		)  ->  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			:		List[Any]     ,      UpperCAmelCase			:		List[str]		)  ->  Optional[Any]:


      '''simple docstring'''

      lowercase					:   Optional[Any]				  ="".join(lowercase__		).replace(lowercase__     ,      ''' '''		).strip()
      return out_string






     def 				A__      ( self			:		Optional[Any]     ,      UpperCAmelCase			:		Union[str, Any]     ,      UpperCAmelCase			:		Optional[int] = None		)  ->  Dict:


      '''simple docstring'''

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

      if os.path.abspath(self.vocab_file		) != os.path.abspath(lowercase__		) and os.path.isfile(self.vocab_file		):
       copyfile(self.vocab_file     ,      lowercase__		)
      elif not os.path.isfile(self.vocab_file		):
       with open(lowercase__     ,      '''wb'''		) as fi:
        lowercase					:   List[str]				  =self.sp_model.serialized_model_proto()
        fi.write(lowercase__		)

      return (out_vocab_file,)






     def 				A__      ( self			:		Any     ,      UpperCAmelCase			:		Dict     ,      UpperCAmelCase			:		Any = "eng_Latn"     ,      UpperCAmelCase			:		Dict = None     ,      UpperCAmelCase			:		Dict = "fra_Latn"     ,      **UpperCAmelCase			:		Optional[int]     ,      )  ->  Optional[int]:


      '''simple docstring'''

      lowercase					:   str				  =src_lang
      lowercase					:   List[Any]				  =tgt_lang
      return super().prepare_seqaseq_batch(lowercase__     ,      lowercase__     ,      **lowercase__		)






     def 				A__      ( self			:		Optional[int]		)  ->  Tuple:


      '''simple docstring'''

      return self.set_src_lang_special_tokens(self.src_lang		)






     def 				A__      ( self			:		List[Any]		)  ->  Optional[Any]:


      '''simple docstring'''

      return self.set_tgt_lang_special_tokens(self.tgt_lang		)






     def 				A__      ( self			:		Any     ,      UpperCAmelCase			:		Optional[int]		)  ->  Any:


      '''simple docstring'''

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






     def 				A__      ( self			:		Tuple     ,      UpperCAmelCase			:		List[Any]		)  ->  Optional[Any]:


      '''simple docstring'''

      lowercase					:   Any				  =self.lang_code_to_id[lang]
      if self.legacy_behaviour:
       lowercase					:   List[str]				  =[]
       lowercase					:   Union[str, Any]				  =[self.eos_token_id, self.cur_lang_code]
      else:
       lowercase					:   Tuple				  =[self.cur_lang_code]
       lowercase					:   List[str]				  =[self.eos_token_id]











'''simple docstring'''



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


SCREAMING_SNAKE_CASE						=	{
    'configuration_mega': ['MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MegaConfig', 'MegaOnnxConfig'],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE						=	[
          'MEGA_PRETRAINED_MODEL_ARCHIVE_LIST',
          'MegaForCausalLM',
          'MegaForMaskedLM',
          'MegaForMultipleChoice',
          'MegaForQuestionAnswering',
          'MegaForSequenceClassification',
          'MegaForTokenClassification',
          'MegaModel',
          'MegaPreTrainedModel',
      ]

if TYPE_CHECKING:
      from .configuration_mega import MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP, MegaConfig, MegaOnnxConfig

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_mega import (
                MEGA_PRETRAINED_MODEL_ARCHIVE_LIST,
                MegaForCausalLM,
                MegaForMaskedLM,
                MegaForMultipleChoice,
                MegaForQuestionAnswering,
                MegaForSequenceClassification,
                MegaForTokenClassification,
                MegaModel,
                MegaPreTrainedModel,
            )

else:
      import sys

      SCREAMING_SNAKE_CASE						=	_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



