




"""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:
  _A													=      None


_A													=      logging.get_logger(__name__)


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

_A													=      {
    """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"""
        ),
    },
}

_A													=      {
    """facebook/nllb-large-en-ro""": 1_0_2_4,
    """facebook/nllb-200-distilled-600M""": 1_0_2_4,
}

# fmt: off
_A													=      ["""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    (					a__						):
   _lowerCamelCase    :Optional[int]     				=     VOCAB_FILES_NAMES
   _lowerCamelCase    :Dict     				=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   _lowerCamelCase    :Tuple     				=     PRETRAINED_VOCAB_FILES_MAP
   _lowerCamelCase    :Optional[Any]     				=     ["input_ids", "attention_mask"]
   _lowerCamelCase    :Union[str, Any]     				=     NllbTokenizer

   _lowerCamelCase    :Any     				=     []
   _lowerCamelCase    :List[Any]     				=     []






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

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

         lowerCAmelCase__      :			str   =	vocab_file
         lowerCAmelCase__      :			int   =	False if not self.vocab_file else True

         lowerCAmelCase__      :			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}   )
         lowerCAmelCase__      :			str   =	{
             lang_code: self.convert_tokens_to_ids(_lowerCamelCase   ) for lang_code in FAIRSEQ_LANGUAGE_CODES
         }

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






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

         """simple docstring"""
         return self._src_lang






   @src_lang.setter
   def 	_lowerCAmelCase   (		self       :		int   ,     UpperCamelCase       :		str   )			->						Dict:

         """simple docstring"""
         lowerCAmelCase__      :			Dict   =	new_src_lang
         self.set_src_lang_special_tokens(self._src_lang   )






   def 	_lowerCAmelCase   (		self       :		int   ,     UpperCamelCase       :		List[int]   ,     UpperCamelCase       :		Optional[List[int]] = None   )			->						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 	_lowerCAmelCase   (		self       :		Optional[int]   ,     UpperCamelCase       :		List[int]   ,     UpperCamelCase       :		Optional[List[int]] = None   )			->						int:

         """simple docstring"""
         lowerCAmelCase__      :			Dict   =	[self.sep_token_id]
         lowerCAmelCase__      :			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 	_lowerCAmelCase   (		self       :		Optional[Any]   ,     UpperCamelCase       :		List[str]   ,     UpperCamelCase       :		str   ,     UpperCamelCase       :		Optional[str]   ,     UpperCamelCase       :		Optional[str]   ,     **UpperCamelCase       :		str   )			->						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"""   )
         lowerCAmelCase__      :			List[Any]   =	src_lang
         lowerCAmelCase__      :			List[Any]   =	self(_lowerCamelCase   ,     add_special_tokens=_lowerCamelCase   ,     return_tensors=_lowerCamelCase   ,     **_lowerCamelCase   )
         lowerCAmelCase__      :			str   =	self.convert_tokens_to_ids(_lowerCamelCase   )
         lowerCAmelCase__      :			Tuple   =	tgt_lang_id
         return inputs






   def 	_lowerCAmelCase   (		self       :		Any   ,     UpperCamelCase       :		List[str]   ,     UpperCamelCase       :		str = "eng_Latn"   ,     UpperCamelCase       :		Optional[List[str]] = None   ,     UpperCamelCase       :		str = "fra_Latn"   ,     **UpperCamelCase       :		List[Any]   ,     )			->						Tuple:

         """simple docstring"""
         lowerCAmelCase__      :			Dict   =	src_lang
         lowerCAmelCase__      :			Any   =	tgt_lang
         return super().prepare_seqaseq_batch(_lowerCamelCase   ,     _lowerCamelCase   ,     **_lowerCamelCase   )






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

         """simple docstring"""
         return self.set_src_lang_special_tokens(self.src_lang   )






   def 	_lowerCAmelCase   (		self       :		Optional[int]   )			->						int:

         """simple docstring"""
         return self.set_tgt_lang_special_tokens(self.tgt_lang   )






   def 	_lowerCAmelCase   (		self       :		Any   ,     UpperCamelCase       :		Optional[Any]   )			->						Dict:

         """simple docstring"""
         lowerCAmelCase__      :			Optional[int]   =	self.convert_tokens_to_ids(_lowerCamelCase   )

         if self.legacy_behaviour:
               lowerCAmelCase__      :			Tuple   =	[]
               lowerCAmelCase__      :			int   =	[self.eos_token_id, self.cur_lang_code]
         else:
               lowerCAmelCase__      :			Any   =	[self.cur_lang_code]
               lowerCAmelCase__      :			str   =	[self.eos_token_id]

         lowerCAmelCase__      :			Dict   =	self.convert_ids_to_tokens(self.prefix_tokens   )
         lowerCAmelCase__      :			str   =	self.convert_ids_to_tokens(self.suffix_tokens   )

         lowerCAmelCase__      :			Tuple   =	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 	_lowerCAmelCase   (		self       :		Union[str, Any]   ,     UpperCamelCase       :		str   )			->						Any:

         """simple docstring"""
         lowerCAmelCase__      :			str   =	self.convert_tokens_to_ids(_lowerCamelCase   )
         if self.legacy_behaviour:
               lowerCAmelCase__      :			Tuple   =	[]
               lowerCAmelCase__      :			Optional[Any]   =	[self.eos_token_id, self.cur_lang_code]
         else:
               lowerCAmelCase__      :			Dict   =	[self.cur_lang_code]
               lowerCAmelCase__      :			Tuple   =	[self.eos_token_id]

         lowerCAmelCase__      :			List[Any]   =	self.convert_ids_to_tokens(self.prefix_tokens   )
         lowerCAmelCase__      :			int   =	self.convert_ids_to_tokens(self.suffix_tokens   )

         lowerCAmelCase__      :			Union[str, Any]   =	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 	_lowerCAmelCase   (		self       :		Tuple   ,     UpperCamelCase       :		str   ,     UpperCamelCase       :		Optional[str] = None   )			->						Dict:

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

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

         return (out_vocab_file,)











"""simple docstring"""


from typing import List, Optional, Union

from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType


class _lowerCamelCase    (					a_						):
   _lowerCamelCase    :Dict     				=     ["image_processor", "tokenizer"]
   _lowerCamelCase    :Dict     				=     "BlipImageProcessor"
   _lowerCamelCase    :Any     				=     ("BertTokenizer", "BertTokenizerFast")






   def __init__(		self       :		Dict   ,     UpperCamelCase       :		Optional[int]   ,     UpperCamelCase       :		Union[str, Any]   )			->						str:

         """simple docstring"""
         lowerCAmelCase__      :			Optional[Any]   =	False
         super().__init__(UpperCamelCase   ,     UpperCamelCase   )
         lowerCAmelCase__      :			List[Any]   =	self.image_processor






   def __call__(		self       :		int   ,     UpperCamelCase       :		ImageInput = None   ,     UpperCamelCase       :		Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None   ,     UpperCamelCase       :		bool = True   ,     UpperCamelCase       :		Union[bool, str, PaddingStrategy] = False   ,     UpperCamelCase       :		Union[bool, str, TruncationStrategy] = None   ,     UpperCamelCase       :		Optional[int] = None   ,     UpperCamelCase       :		int = 0   ,     UpperCamelCase       :		Optional[int] = None   ,     UpperCamelCase       :		Optional[bool] = None   ,     UpperCamelCase       :		bool = False   ,     UpperCamelCase       :		bool = False   ,     UpperCamelCase       :		bool = False   ,     UpperCamelCase       :		bool = False   ,     UpperCamelCase       :		bool = False   ,     UpperCamelCase       :		bool = True   ,     UpperCamelCase       :		Optional[Union[str, TensorType]] = None   ,     **UpperCamelCase       :		Optional[int]   ,     )			->						BatchEncoding:

         """simple docstring"""
         if images is None and text is None:
               raise ValueError("""You have to specify either images or text."""   )

         # Get only text
         if images is None:
               lowerCAmelCase__      :			Any   =	self.tokenizer
               lowerCAmelCase__      :			Optional[int]   =	self.tokenizer(
                   text=UpperCamelCase   ,     add_special_tokens=UpperCamelCase   ,     padding=UpperCamelCase   ,     truncation=UpperCamelCase   ,     max_length=UpperCamelCase   ,     stride=UpperCamelCase   ,     pad_to_multiple_of=UpperCamelCase   ,     return_attention_mask=UpperCamelCase   ,     return_overflowing_tokens=UpperCamelCase   ,     return_special_tokens_mask=UpperCamelCase   ,     return_offsets_mapping=UpperCamelCase   ,     return_token_type_ids=UpperCamelCase   ,     return_length=UpperCamelCase   ,     verbose=UpperCamelCase   ,     return_tensors=UpperCamelCase   ,     **UpperCamelCase   ,     )
               return text_encoding

         # add pixel_values
         lowerCAmelCase__      :			Tuple   =	self.image_processor(UpperCamelCase   ,     return_tensors=UpperCamelCase   )

         if text is not None:
               lowerCAmelCase__      :			Optional[int]   =	self.tokenizer(
                   text=UpperCamelCase   ,     add_special_tokens=UpperCamelCase   ,     padding=UpperCamelCase   ,     truncation=UpperCamelCase   ,     max_length=UpperCamelCase   ,     stride=UpperCamelCase   ,     pad_to_multiple_of=UpperCamelCase   ,     return_attention_mask=UpperCamelCase   ,     return_overflowing_tokens=UpperCamelCase   ,     return_special_tokens_mask=UpperCamelCase   ,     return_offsets_mapping=UpperCamelCase   ,     return_token_type_ids=UpperCamelCase   ,     return_length=UpperCamelCase   ,     verbose=UpperCamelCase   ,     return_tensors=UpperCamelCase   ,     **UpperCamelCase   ,     )
         else:
               lowerCAmelCase__      :			Tuple   =	None

         if text_encoding is not None:
               encoding_image_processor.update(UpperCamelCase   )

         return encoding_image_processor






   def 	_lowerCAmelCase   (		self       :		int   ,     *UpperCamelCase       :		Union[str, Any]   ,     **UpperCamelCase       :		List[str]   )			->						Dict:

         """simple docstring"""
         return self.tokenizer.batch_decode(*UpperCamelCase   ,     **UpperCamelCase   )






   def 	_lowerCAmelCase   (		self       :		List[Any]   ,     *UpperCamelCase       :		Tuple   ,     **UpperCamelCase       :		List[str]   )			->						List[Any]:

         """simple docstring"""
         return self.tokenizer.decode(*UpperCamelCase   ,     **UpperCamelCase   )






   @property
   def 	_lowerCAmelCase   (		self       :		List[Any]   )			->						Tuple:

         """simple docstring"""
         lowerCAmelCase__      :			Optional[int]   =	self.tokenizer.model_input_names
         lowerCAmelCase__      :			Union[str, Any]   =	self.image_processor.model_input_names
         return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names   )   )





