




'''simple docstring'''


from ...processing_utils import ProcessorMixin





class    __lowercase						(  __magic_name__ ):
   _a							=      ["""image_processor""", """feature_extractor"""]
   _a							=      """TvltImageProcessor"""
   _a							=      """TvltFeatureExtractor"""







   def __init__(	self      ,							UpperCamelCase      ,							UpperCamelCase      )				->    int:
     super().__init__(image_processor=UpperCamelCase      ,							feature_extractor=UpperCamelCase      )

     __a			=					image_processor
     __a			=					feature_extractor







   def __call__(	self      ,							UpperCamelCase=None      ,							UpperCamelCase=None      ,							UpperCamelCase=None      ,							UpperCamelCase=None      ,							UpperCamelCase=False      ,							UpperCamelCase=False      ,							*UpperCamelCase      ,							**UpperCamelCase      ,							)				->    Union[str, Any]:

     if images is None and audio is None:
       raise ValueError('You need to specify either an `images` or `audio` input to process.'      )

     __a			=					None
     if images is not None:
       __a			=					self.image_processor(UpperCamelCase      ,							mask_pixel=UpperCamelCase      ,							*UpperCamelCase      ,							**UpperCamelCase      )
     if images_mixed is not None:
       __a			=					self.image_processor(UpperCamelCase      ,							is_mixed=UpperCamelCase      ,							*UpperCamelCase      ,							**UpperCamelCase      )
     if audio is not None:
       __a			=					self.feature_extractor(
           UpperCamelCase      ,							*UpperCamelCase      ,							sampling_rate=UpperCamelCase      ,							mask_audio=UpperCamelCase      ,							**UpperCamelCase      )

     __a			=					{}
     if audio is not None:
       output_dict.update(UpperCamelCase      )
     if images is not None:
       output_dict.update(UpperCamelCase      )
     if images_mixed_dict is not None:
       output_dict.update(UpperCamelCase      )
     return output_dict







   @property
   def 		UpperCamelCase__			(	self      )				->    Union[str, Any]:
     __a			=					self.image_processor.model_input_names
     __a			=					self.feature_extractor.model_input_names
     return list(dict.fromkeys(image_processor_input_names + feature_extractor_input_names      )      )









'''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


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": 10_24,
}

# 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    __lowercase						(  __magic_name__ ):
   _a							=      VOCAB_FILES_NAMES
   _a							=      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   _a							=      PRETRAINED_VOCAB_FILES_MAP
   _a							=      ["""input_ids""", """attention_mask"""]

   _a							=      []
   _a							=      []







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

     __a			=					{} if sp_model_kwargs is None else sp_model_kwargs
     __a			=					legacy_behaviour

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

     __a			=					spm.SentencePieceProcessor(**self.sp_model_kwargs      )
     self.sp_model.Load(str(UpperCamelCase      )      )
     __a			=					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			=					{'<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			=					1

     __a			=					len(self.sp_model      )
     __a			=					{
         code: self.sp_model_size + i + self.fairseq_offset for i, code in enumerate(UpperCamelCase      )
     }
     __a			=					{v: k for k, v in self.lang_code_to_id.items()}
     __a			=					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			=					{v: k for k, v in self.fairseq_tokens_to_ids.items()}
     __a			=					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			=					src_lang if src_lang is not None else 'eng_Latn'
     __a			=					self.lang_code_to_id[self._src_lang]
     __a			=					tgt_lang
     self.set_src_lang_special_tokens(self._src_lang      )







   def __getstate__(	self      )				->    Union[str, Any]:
     __a			=					self.__dict__.copy()
     __a			=					None
     __a			=					self.sp_model.serialized_model_proto()
     return state







   def __setstate__(	self      ,							UpperCamelCase      )				->    str:
     __a			=					d

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

     __a			=					spm.SentencePieceProcessor(**self.sp_model_kwargs      )
     self.sp_model.LoadFromSerializedProto(self.sp_model_proto      )







   @property
   def 		UpperCamelCase__			(	self      )				->    str:
     return len(self.sp_model      ) + len(self.lang_code_to_id      ) + self.fairseq_offset + 1  # Plus 1 for the mask token







   @property
   def 		UpperCamelCase__			(	self      )				->    str:
     return self._src_lang







   @src_lang.setter
   def 		UpperCamelCase__			(	self      ,							UpperCamelCase      )				->    None:
     __a			=					new_src_lang
     self.set_src_lang_special_tokens(self._src_lang      )







   def 		UpperCamelCase__			(	self      ,							UpperCamelCase      ,							UpperCamelCase = None      ,							UpperCamelCase = False      )				->    List[int]:

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

     __a			=					[1] * len(self.prefix_tokens      )
     __a			=					[1] * len(self.suffix_tokens      )
     if token_ids_a is None:
       return prefix_ones + ([0] * len(UpperCamelCase      )) + suffix_ones
     return prefix_ones + ([0] * len(UpperCamelCase      )) + ([0] * len(UpperCamelCase      )) + suffix_ones







   def 		UpperCamelCase__			(	self      ,							UpperCamelCase      ,							UpperCamelCase = None      )				->    List[int]:
     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 		UpperCamelCase__			(	self      ,							UpperCamelCase      ,							UpperCamelCase = None      )				->    List[int]:
     __a			=					[self.sep_token_id]
     __a			=					[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 		UpperCamelCase__			(	self      ,							UpperCamelCase      ,							UpperCamelCase      ,							UpperCamelCase      ,							UpperCamelCase      ,							**UpperCamelCase      )				->    Dict:
     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			=					src_lang
     __a			=					self(UpperCamelCase      ,							add_special_tokens=UpperCamelCase      ,							return_tensors=UpperCamelCase      ,							**UpperCamelCase      )
     __a			=					self.convert_tokens_to_ids(UpperCamelCase      )
     __a			=					tgt_lang_id
     return inputs







   def 		UpperCamelCase__			(	self      )				->    Union[str, Any]:
     __a			=					{self.convert_ids_to_tokens(UpperCamelCase      ): i for i in range(self.vocab_size      )}
     vocab.update(self.added_tokens_encoder      )
     return vocab







   def 		UpperCamelCase__			(	self      ,							UpperCamelCase      )				->    List[str]:
     return self.sp_model.encode(UpperCamelCase      ,							out_type=UpperCamelCase      )







   def 		UpperCamelCase__			(	self      ,							UpperCamelCase      )				->    Optional[Any]:
     if token in self.fairseq_tokens_to_ids:
       return self.fairseq_tokens_to_ids[token]
     __a			=					self.sp_model.PieceToId(UpperCamelCase      )

     # 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 		UpperCamelCase__			(	self      ,							UpperCamelCase      )				->    Any:
     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 		UpperCamelCase__			(	self      ,							UpperCamelCase      )				->    str:
     __a			=					''.join(UpperCamelCase      ).replace(UpperCamelCase      ,							' '      ).strip()
     return out_string







   def 		UpperCamelCase__			(	self      ,							UpperCamelCase      ,							UpperCamelCase = None      )				->    Tuple[str]:
     if not os.path.isdir(UpperCamelCase      ):
       logger.error(f"Vocabulary path ({save_directory}) should be a directory"      )
       return
     __a			=					os.path.join(
         UpperCamelCase      ,							(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']      )

     if os.path.abspath(self.vocab_file      ) != os.path.abspath(UpperCamelCase      ) and os.path.isfile(self.vocab_file      ):
       copyfile(self.vocab_file      ,							UpperCamelCase      )
     elif not os.path.isfile(self.vocab_file      ):
       with open(UpperCamelCase      ,							'wb'      ) as fi:
         __a			=					self.sp_model.serialized_model_proto()
         fi.write(UpperCamelCase      )

     return (out_vocab_file,)







   def 		UpperCamelCase__			(	self      ,							UpperCamelCase      ,							UpperCamelCase = "eng_Latn"      ,							UpperCamelCase = None      ,							UpperCamelCase = "fra_Latn"      ,							**UpperCamelCase      ,							)				->    BatchEncoding:
     __a			=					src_lang
     __a			=					tgt_lang
     return super().prepare_seqaseq_batch(UpperCamelCase      ,							UpperCamelCase      ,							**UpperCamelCase      )







   def 		UpperCamelCase__			(	self      )				->    List[str]:
     return self.set_src_lang_special_tokens(self.src_lang      )







   def 		UpperCamelCase__			(	self      )				->    int:
     return self.set_tgt_lang_special_tokens(self.tgt_lang      )







   def 		UpperCamelCase__			(	self      ,							UpperCamelCase      )				->    None:
     __a			=					self.lang_code_to_id[src_lang]
     if self.legacy_behaviour:
       __a			=					[]
       __a			=					[self.eos_token_id, self.cur_lang_code]
     else:
       __a			=					[self.cur_lang_code]
       __a			=					[self.eos_token_id]







   def 		UpperCamelCase__			(	self      ,							UpperCamelCase      )				->    None:
     __a			=					self.lang_code_to_id[lang]
     if self.legacy_behaviour:
       __a			=					[]
       __a			=					[self.eos_token_id, self.cur_lang_code]
     else:
       __a			=					[self.cur_lang_code]
       __a			=					[self.eos_token_id]



