


'''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''': 1_0_2_4,
}

# 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_   (	__lowercase      ):
      lowerCAmelCase__		     =     VOCAB_FILES_NAMES
      lowerCAmelCase__		     =     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      lowerCAmelCase__		     =     PRETRAINED_VOCAB_FILES_MAP
      lowerCAmelCase__		     =     ["input_ids", "attention_mask"]

      lowerCAmelCase__		     =     []
      lowerCAmelCase__		     =     []






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


          '''simple docstring'''


          UpperCAmelCase__			:		Dict													=				AddedToken(_a						,						lstrip=_a						,						rstrip=_a ) if isinstance(_a						,						_a ) else mask_token

          UpperCAmelCase__			:		Optional[int]													=				{} if sp_model_kwargs is None else sp_model_kwargs
          UpperCAmelCase__			:		Optional[Any]													=				legacy_behaviour

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

          UpperCAmelCase__			:		Union[str, Any]													=				spm.SentencePieceProcessor(**self.sp_model_kwargs )
          self.sp_model.Load(str(_a ) )
          UpperCAmelCase__			:		Dict													=				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
          UpperCAmelCase__			:		Optional[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
          UpperCAmelCase__			:		Dict													=				1

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

          self.fairseq_tokens_to_ids.update(self.lang_code_to_id )
          UpperCAmelCase__			:		List[Any]													=				{v: k for k, v in self.fairseq_tokens_to_ids.items()}
          UpperCAmelCase__			:		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] )

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






      def __getstate__(					self				:    List[Any] ):


          '''simple docstring'''


          UpperCAmelCase__			:		str													=				self.__dict__.copy()
          UpperCAmelCase__			:		int													=				None
          UpperCAmelCase__			:		str													=				self.sp_model.serialized_model_proto()
          return state






      def __setstate__(					self				:    Any						,						_A				:    List[str] ):


          '''simple docstring'''


          UpperCAmelCase__			:		int													=				d

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

          UpperCAmelCase__			:		Optional[int]													=				spm.SentencePieceProcessor(**self.sp_model_kwargs )
          self.sp_model.LoadFromSerializedProto(self.sp_model_proto )






      @property
      def lowercase_							(					self				:    Optional[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 lowercase_							(					self				:    List[Any] ):


          '''simple docstring'''


          return self._src_lang






      @src_lang.setter
      def lowercase_							(					self				:    Optional[int]						,						_A				:    Any ):


          '''simple docstring'''


          UpperCAmelCase__			:		List[Any]													=				new_src_lang
          self.set_src_lang_special_tokens(self._src_lang )






      def lowercase_							(					self				:    Union[str, Any]						,						_A				:    int						,						_A				:    Tuple = None						,						_A				:    Dict = False ):


          '''simple docstring'''


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

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






      def lowercase_							(					self				:    Union[str, Any]						,						_A				:    Union[str, Any]						,						_A				:    Optional[Any] = None ):


          '''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 lowercase_							(					self				:    List[str]						,						_A				:    str						,						_A				:    str = None ):


          '''simple docstring'''


          UpperCAmelCase__			:		Dict													=				[self.sep_token_id]
          UpperCAmelCase__			:		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 lowercase_							(					self				:    Any						,						_A				:    List[str]						,						_A				:    Any						,						_A				:    List[Any]						,						_A				:    Optional[int]						,						**_A				:    int ):


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






      def lowercase_							(					self				:    List[Any] ):


          '''simple docstring'''


          UpperCAmelCase__			:		List[str]													=				{self.convert_ids_to_tokens(_a ): i for i in range(self.vocab_size )}
          vocab.update(self.added_tokens_encoder )
          return vocab






      def lowercase_							(					self				:    Any						,						_A				:    Optional[Any] ):


          '''simple docstring'''


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






      def lowercase_							(					self				:    Optional[int]						,						_A				:    str ):


          '''simple docstring'''


          if token in self.fairseq_tokens_to_ids:
              return self.fairseq_tokens_to_ids[token]
          UpperCAmelCase__			:		Optional[int]													=				self.sp_model.PieceToId(_a )

          # 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 lowercase_							(					self				:    Optional[Any]						,						_A				:    int ):


          '''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 lowercase_							(					self				:    Optional[Any]						,						_A				:    Optional[int] ):


          '''simple docstring'''


          UpperCAmelCase__			:		Dict													=				''''''.join(_a ).replace(_a						,						''' ''' ).strip()
          return out_string






      def lowercase_							(					self				:    Tuple						,						_A				:    Optional[Any]						,						_A				:    Any = None ):


          '''simple docstring'''


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

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

          return (out_vocab_file,)






      def lowercase_							(					self				:    Union[str, Any]						,						_A				:    Optional[int]						,						_A				:    List[str] = "eng_Latn"						,						_A				:    int = None						,						_A				:    Tuple = "fra_Latn"						,						**_A				:    str						,						):


          '''simple docstring'''


          UpperCAmelCase__			:		Optional[int]													=				src_lang
          UpperCAmelCase__			:		Tuple													=				tgt_lang
          return super().prepare_seqaseq_batch(_a						,						_a						,						**_a )






      def lowercase_							(					self				:    Optional[Any] ):


          '''simple docstring'''


          return self.set_src_lang_special_tokens(self.src_lang )






      def lowercase_							(					self				:    Optional[int] ):


          '''simple docstring'''


          return self.set_tgt_lang_special_tokens(self.tgt_lang )






      def lowercase_							(					self				:    List[str]						,						_A				:    List[Any] ):


          '''simple docstring'''


          UpperCAmelCase__			:		str													=				self.lang_code_to_id[src_lang]
          if self.legacy_behaviour:
              UpperCAmelCase__			:		Dict													=				[]
              UpperCAmelCase__			:		List[Any]													=				[self.eos_token_id, self.cur_lang_code]
          else:
              UpperCAmelCase__			:		Optional[int]													=				[self.cur_lang_code]
              UpperCAmelCase__			:		List[Any]													=				[self.eos_token_id]






      def lowercase_							(					self				:    str						,						_A				:    List[Any] ):


          '''simple docstring'''


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



import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding

class 				UpperCAmelCase_		(			__lowercase   ):
							"""simple docstring"""







							UpperCAmelCase__       :						Union[str, Any]	    =    ["image_processor", "tokenizer"]
							UpperCAmelCase__       :						Dict	    =    "ChineseCLIPImageProcessor"
							UpperCAmelCase__       :						List[str]	    =    ("BertTokenizer", "BertTokenizerFast")


							def __init__(			self       ,							_a=None       ,							_a=None       ,							**_a       )    ->    Any:
												_a	:    Optional[Any]         =		None
												if "feature_extractor" in kwargs:
																	warnings.warn(
																	    '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
																	    ''' instead.'''       ,							_a       ,							)
																	_a	:    Tuple         =		kwargs.pop('''feature_extractor'''       )

												_a	:    Tuple         =		image_processor if image_processor is not None else feature_extractor
												if image_processor is None:
																	raise ValueError('''You need to specify an `image_processor`.'''       )
												if tokenizer is None:
																	raise ValueError('''You need to specify a `tokenizer`.'''       )

												super().__init__(_a       ,							_a       )
												_a	:    List[str]         =		self.image_processor


							def __call__(			self       ,							_a=None       ,							_a=None       ,							_a=None       ,							**_a       )    ->    int:

												if text is None and images is None:
																	raise ValueError('''You have to specify either text or images. Both cannot be none.'''       )

												if text is not None:
																	_a	:    List[str]         =		self.tokenizer(_a       ,							return_tensors=_a       ,							**_a       )

												if images is not None:
																	_a	:    Optional[Any]         =		self.image_processor(_a       ,							return_tensors=_a       ,							**_a       )

												if text is not None and images is not None:
																	_a	:    Union[str, Any]         =		image_features.pixel_values
																	return encoding
												elif text is not None:
																	return encoding
												else:
																	return BatchEncoding(data=dict(**_a       )       ,							tensor_type=_a       )


							def 				__lowercase				(			self       ,							*_a       ,							**_a       )    ->    Union[str, Any]:
												return self.tokenizer.batch_decode(*_a       ,							**_a       )


							def 				__lowercase				(			self       ,							*_a       ,							**_a       )    ->    Any:
												return self.tokenizer.decode(*_a       ,							**_a       )


							@property
							def 				__lowercase				(			self       )    ->    Optional[Any]:
												_a	:    Any         =		self.tokenizer.model_input_names
												_a	:    Dict         =		self.image_processor.model_input_names
												return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names       )       )




							@property
							def 				__lowercase				(			self       )    ->    Dict:
												warnings.warn(
												    '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''       ,							_a       ,							)
												return self.image_processor_class



