


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   _lowerCamelCase (					UpperCamelCase			):




       """simple docstring"""
       snake_case      =    VOCAB_FILES_NAMES
       snake_case      =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       snake_case      =    PRETRAINED_VOCAB_FILES_MAP
       snake_case      =    ["input_ids", "attention_mask"]

       snake_case      =    []
       snake_case      =    []



       def __init__(      self				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE="<s>"				,       _SCREAMING_SNAKE_CASE="</s>"				,       _SCREAMING_SNAKE_CASE="</s>"				,       _SCREAMING_SNAKE_CASE="<s>"				,       _SCREAMING_SNAKE_CASE="<unk>"				,       _SCREAMING_SNAKE_CASE="<pad>"				,       _SCREAMING_SNAKE_CASE="<mask>"				,       _SCREAMING_SNAKE_CASE=None				,       _SCREAMING_SNAKE_CASE=None				,       _SCREAMING_SNAKE_CASE=None				,       _SCREAMING_SNAKE_CASE = None				,       _SCREAMING_SNAKE_CASE=None				,       _SCREAMING_SNAKE_CASE=False				,       **_SCREAMING_SNAKE_CASE				,       )->Optional[int]:




         '''simple docstring'''





         A_      :				Optional[Any]		     =					AddedToken(_SCREAMING_SNAKE_CASE				,       lstrip=_SCREAMING_SNAKE_CASE				,       rstrip=_SCREAMING_SNAKE_CASE		) if isinstance(_SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE		) else mask_token

         A_      :				List[Any]		     =					{} if sp_model_kwargs is None else sp_model_kwargs
         A_      :				Dict		     =					legacy_behaviour

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

         A_      :				Optional[int]		     =					spm.SentencePieceProcessor(**self.sp_model_kwargs		)
         self.sp_model.Load(str(_SCREAMING_SNAKE_CASE		)		)
         A_      :				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
         A_      :				List[str]		     =					{'''<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
         A_      :				Tuple		     =					1

         A_      :				int		     =					len(self.sp_model		)
         A_      :				List[Any]		     =					{
             code: self.sp_model_size + i + self.fairseq_offset for i, code in enumerate(_SCREAMING_SNAKE_CASE		)
         }
         A_      :				Tuple		     =					{v: k for k, v in self.lang_code_to_id.items()}
         A_      :				Tuple		     =					len(self.sp_model		) + len(self.lang_code_to_id		) + self.fairseq_offset

         self.fairseq_tokens_to_ids.update(self.lang_code_to_id		)
         A_      :				Optional[int]		     =					{v: k for k, v in self.fairseq_tokens_to_ids.items()}
         A_      :				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]		)

         A_      :				Union[str, Any]		     =					src_lang if src_lang is not None else '''eng_Latn'''
         A_      :				str		     =					self.lang_code_to_id[self._src_lang]
         A_      :				Union[str, Any]		     =					tgt_lang
         self.set_src_lang_special_tokens(self._src_lang		)



       def __getstate__(      self		)->List[Any]:




         '''simple docstring'''





         A_      :				Optional[int]		     =					self.__dict__.copy()
         A_      :				List[Any]		     =					None
         A_      :				List[Any]		     =					self.sp_model.serialized_model_proto()
         return state



       def __setstate__(      self				,       _SCREAMING_SNAKE_CASE		)->Tuple:




         '''simple docstring'''





         A_      :				Dict		     =					d

         # for backward compatibility
         if not hasattr(self				,       '''sp_model_kwargs'''		):
           A_      :				List[Any]		     =					{}

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



       @property
       def        _snake_case       (      self		)->int:




         '''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        _snake_case       (      self		)->str:




         '''simple docstring'''





         return self._src_lang



       @src_lang.setter
       def        _snake_case       (      self				,       _SCREAMING_SNAKE_CASE		)->None:




         '''simple docstring'''





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



       def        _snake_case       (      self				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE = None				,       _SCREAMING_SNAKE_CASE = False		)->List[int]:




         '''simple docstring'''





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

         A_      :				List[Any]		     =					[1] * len(self.prefix_tokens		)
         A_      :				Optional[int]		     =					[1] * len(self.suffix_tokens		)
         if token_ids_a is None:
           return prefix_ones + ([0] * len(_SCREAMING_SNAKE_CASE		)) + suffix_ones
         return prefix_ones + ([0] * len(_SCREAMING_SNAKE_CASE		)) + ([0] * len(_SCREAMING_SNAKE_CASE		)) + suffix_ones



       def        _snake_case       (      self				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE = 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        _snake_case       (      self				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE = None		)->List[int]:




         '''simple docstring'''





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



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




         '''simple docstring'''





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



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




         '''simple docstring'''





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



       def        _snake_case       (      self				,       _SCREAMING_SNAKE_CASE		)->str:




         '''simple docstring'''





         if token in self.fairseq_tokens_to_ids:
           return self.fairseq_tokens_to_ids[token]
         A_      :				Tuple		     =					self.sp_model.PieceToId(_SCREAMING_SNAKE_CASE		)

         # 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        _snake_case       (      self				,       _SCREAMING_SNAKE_CASE		)->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        _snake_case       (      self				,       _SCREAMING_SNAKE_CASE		)->Optional[int]:




         '''simple docstring'''





         A_      :				Dict		     =					''''''.join(_SCREAMING_SNAKE_CASE		).replace(_SCREAMING_SNAKE_CASE				,       ''' '''		).strip()
         return out_string



       def        _snake_case       (      self				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE = None		)->Tuple[str]:




         '''simple docstring'''





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

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

         return (out_vocab_file,)



       def        _snake_case       (      self				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE = "eng_Latn"				,       _SCREAMING_SNAKE_CASE = None				,       _SCREAMING_SNAKE_CASE = "fra_Latn"				,       **_SCREAMING_SNAKE_CASE				,       )->BatchEncoding:




         '''simple docstring'''





         A_      :				Tuple		     =					src_lang
         A_      :				Optional[Any]		     =					tgt_lang
         return super().prepare_seqaseq_batch(_SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       **_SCREAMING_SNAKE_CASE		)



       def        _snake_case       (      self		)->Optional[Any]:




         '''simple docstring'''





         return self.set_src_lang_special_tokens(self.src_lang		)



       def        _snake_case       (      self		)->str:




         '''simple docstring'''





         return self.set_tgt_lang_special_tokens(self.tgt_lang		)



       def        _snake_case       (      self				,       _SCREAMING_SNAKE_CASE		)->None:




         '''simple docstring'''





         A_      :				Optional[Any]		     =					self.lang_code_to_id[src_lang]
         if self.legacy_behaviour:
           A_      :				Tuple		     =					[]
           A_      :				List[str]		     =					[self.eos_token_id, self.cur_lang_code]
         else:
           A_      :				Union[str, Any]		     =					[self.cur_lang_code]
           A_      :				List[Any]		     =					[self.eos_token_id]







       def        _snake_case       (      self				,       _SCREAMING_SNAKE_CASE		)->None:




         '''simple docstring'''





         A_      :				Tuple		     =					self.lang_code_to_id[lang]
         if self.legacy_behaviour:
           A_      :				Optional[Any]		     =					[]
           A_      :				Any		     =					[self.eos_token_id, self.cur_lang_code]
         else:
           A_      :				List[Any]		     =					[self.cur_lang_code]
           A_      :				Tuple		     =					[self.eos_token_id]








from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCamelCase								=	{
    """configuration_roberta""": ["""ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """RobertaConfig""", """RobertaOnnxConfig"""],
    """tokenization_roberta""": ["""RobertaTokenizer"""],
}

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCamelCase								=	["""RobertaTokenizerFast"""]

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCamelCase								=	[
         """ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """RobertaForCausalLM""",
         """RobertaForMaskedLM""",
         """RobertaForMultipleChoice""",
         """RobertaForQuestionAnswering""",
         """RobertaForSequenceClassification""",
         """RobertaForTokenClassification""",
         """RobertaModel""",
         """RobertaPreTrainedModel""",
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCamelCase								=	[
         """TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """TFRobertaForCausalLM""",
         """TFRobertaForMaskedLM""",
         """TFRobertaForMultipleChoice""",
         """TFRobertaForQuestionAnswering""",
         """TFRobertaForSequenceClassification""",
         """TFRobertaForTokenClassification""",
         """TFRobertaMainLayer""",
         """TFRobertaModel""",
         """TFRobertaPreTrainedModel""",
     ]

try:
     if not is_flax_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCamelCase								=	[
         """FlaxRobertaForCausalLM""",
         """FlaxRobertaForMaskedLM""",
         """FlaxRobertaForMultipleChoice""",
         """FlaxRobertaForQuestionAnswering""",
         """FlaxRobertaForSequenceClassification""",
         """FlaxRobertaForTokenClassification""",
         """FlaxRobertaModel""",
         """FlaxRobertaPreTrainedModel""",
     ]


if TYPE_CHECKING:
     from .configuration_roberta import ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, RobertaConfig, RobertaOnnxConfig
     from .tokenization_roberta import RobertaTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_roberta_fast import RobertaTokenizerFast

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_roberta import (
              ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
              RobertaForCausalLM,
              RobertaForMaskedLM,
              RobertaForMultipleChoice,
              RobertaForQuestionAnswering,
              RobertaForSequenceClassification,
              RobertaForTokenClassification,
              RobertaModel,
              RobertaPreTrainedModel,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_roberta import (
              TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
              TFRobertaForCausalLM,
              TFRobertaForMaskedLM,
              TFRobertaForMultipleChoice,
              TFRobertaForQuestionAnswering,
              TFRobertaForSequenceClassification,
              TFRobertaForTokenClassification,
              TFRobertaMainLayer,
              TFRobertaModel,
              TFRobertaPreTrainedModel,
          )

     try:
          if not is_flax_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_flax_roberta import (
              FlaxRobertaForCausalLM,
              FlaxRobertaForMaskedLM,
              FlaxRobertaForMultipleChoice,
              FlaxRobertaForQuestionAnswering,
              FlaxRobertaForSequenceClassification,
              FlaxRobertaForTokenClassification,
              FlaxRobertaModel,
              FlaxRobertaPreTrainedModel,
          )

else:
     import sys

     UpperCamelCase								=	_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




