"""simple docstring"""




import logging
import os
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union

from filelock import FileLock

from transformers import PreTrainedTokenizer, is_tf_available, is_torch_available


_a				:		int	   =       logging.getLogger(__name__)
@dataclass
class 						__A							:
     _UpperCamelCase							:      str
     _UpperCamelCase							:      List[str]
     _UpperCamelCase							:      Optional[List[str]]







@dataclass
class 						__A							:
     _UpperCamelCase							:      List[int]
     _UpperCamelCase							:      List[int]
     _UpperCamelCase							:      Optional[List[int]]		 =		None
     _UpperCamelCase							:      Optional[List[int]]		 =		None







class 						__A							(			SCREAMING_SNAKE_CASE_		):
     _UpperCamelCase							:      Optional[Any]		 =		"train"
     _UpperCamelCase							:      int		 =		"dev"
     _UpperCamelCase							:      Any		 =		"test"







class 						__A							:






     @staticmethod
     def    __A  (		a__    ,					a__  ):
       raise NotImplementedError






     @staticmethod
     def    __A  (		a__  ):
       raise NotImplementedError






     @staticmethod
     def    __A  (		a__    ,					a__    ,					a__    ,					a__    ,					a__=False    ,					a__="[CLS]"    ,					a__=1    ,					a__="[SEP]"    ,					a__=False    ,					a__=False    ,					a__=0    ,					a__=0    ,					a__=-100    ,					a__=0    ,					a__=True    ,					):
       _lowerCAmelCase						:	int        =					{label: i for i, label in enumerate(a__  )}

       _lowerCAmelCase						:	Optional[int]        =					[]
       for ex_index, example in enumerate(a__  ):
         if ex_index % 10000 == 0:
           logger.info("""Writing example %d of %d"""    ,					a__    ,					len(a__  )  )

         _lowerCAmelCase						:	str        =					[]
         _lowerCAmelCase						:	Tuple        =					[]
         for word, label in zip(example.words    ,					example.labels  ):
           _lowerCAmelCase						:	Optional[int]        =					tokenizer.tokenize(a__  )

           # bert-base-multilingual-cased sometimes output "nothing ([]) when calling tokenize with just a space.
           if len(a__  ) > 0:
             tokens.extend(a__  )
             # Use the real label id for the first token of the word, and padding ids for the remaining tokens
             label_ids.extend([label_map[label]] + [pad_token_label_id] * (len(a__  ) - 1)  )

            # Account for [CLS] and [SEP] with "- 2" and with "- 3" for RoBERTa.
         _lowerCAmelCase						:	Optional[Any]        =					tokenizer.num_special_tokens_to_add()
         if len(a__  ) > max_seq_length - special_tokens_count:
           _lowerCAmelCase						:	Tuple        =					tokens[: (max_seq_length - special_tokens_count)]
           _lowerCAmelCase						:	Tuple        =					label_ids[: (max_seq_length - special_tokens_count)]

         # The convention in BERT is:
         # (a) For sequence pairs:
         #  tokens:   [CLS] is this jack ##son ##ville ? [SEP] no it is not . [SEP]
         #  type_ids:   0   0  0    0    0     0       0   0   1  1  1  1   1   1
         # (b) For single sequences:
         #  tokens:   [CLS] the dog is hairy . [SEP]
         #  type_ids:   0   0   0   0  0     0   0
         #
         # Where "type_ids" are used to indicate whether this is the first
         # sequence or the second sequence. The embedding vectors for `type=0` and
         # `type=1` were learned during pre-training and are added to the wordpiece
         # embedding vector (and position vector). This is not *strictly* necessary
         # since the [SEP] token unambiguously separates the sequences, but it makes
         # it easier for the model to learn the concept of sequences.
         #
         # For classification tasks, the first vector (corresponding to [CLS]) is
         # used as the "sentence vector". Note that this only makes sense because
         # the entire model is fine-tuned.
         tokens += [sep_token]
         label_ids += [pad_token_label_id]
         if sep_token_extra:
           # roberta uses an extra separator b/w pairs of sentences
           tokens += [sep_token]
           label_ids += [pad_token_label_id]
         _lowerCAmelCase						:	str        =					[sequence_a_segment_id] * len(a__  )

         if cls_token_at_end:
           tokens += [cls_token]
           label_ids += [pad_token_label_id]
           segment_ids += [cls_token_segment_id]
         else:
           _lowerCAmelCase						:	List[Any]        =					[cls_token] + tokens
           _lowerCAmelCase						:	Union[str, Any]        =					[pad_token_label_id] + label_ids
           _lowerCAmelCase						:	List[Any]        =					[cls_token_segment_id] + segment_ids

         _lowerCAmelCase						:	List[Any]        =					tokenizer.convert_tokens_to_ids(a__  )

         # The mask has 1 for real tokens and 0 for padding tokens. Only real
         # tokens are attended to.
         _lowerCAmelCase						:	Optional[Any]        =					[1 if mask_padding_with_zero else 0] * len(a__  )

         # Zero-pad up to the sequence length.
         _lowerCAmelCase						:	Optional[Any]        =					max_seq_length - len(a__  )
         if pad_on_left:
           _lowerCAmelCase						:	Union[str, Any]        =					([pad_token] * padding_length) + input_ids
           _lowerCAmelCase						:	List[Any]        =					([0 if mask_padding_with_zero else 1] * padding_length) + input_mask
           _lowerCAmelCase						:	List[Any]        =					([pad_token_segment_id] * padding_length) + segment_ids
           _lowerCAmelCase						:	Union[str, Any]        =					([pad_token_label_id] * padding_length) + label_ids
         else:
           input_ids += [pad_token] * padding_length
           input_mask += [0 if mask_padding_with_zero else 1] * padding_length
           segment_ids += [pad_token_segment_id] * padding_length
           label_ids += [pad_token_label_id] * padding_length

         assert len(a__  ) == max_seq_length
         assert len(a__  ) == max_seq_length
         assert len(a__  ) == max_seq_length
         assert len(a__  ) == max_seq_length

         if ex_index < 5:
           logger.info("""*** Example ***"""  )
           logger.info("""guid: %s"""    ,					example.guid  )
           logger.info("""tokens: %s"""    ,					""" """.join([str(a__  ) for x in tokens]  )  )
           logger.info("""input_ids: %s"""    ,					""" """.join([str(a__  ) for x in input_ids]  )  )
           logger.info("""input_mask: %s"""    ,					""" """.join([str(a__  ) for x in input_mask]  )  )
           logger.info("""segment_ids: %s"""    ,					""" """.join([str(a__  ) for x in segment_ids]  )  )
           logger.info("""label_ids: %s"""    ,					""" """.join([str(a__  ) for x in label_ids]  )  )

         if "token_type_ids" not in tokenizer.model_input_names:
           _lowerCAmelCase						:	Tuple        =					None

         features.append(
             InputFeatures(
                 input_ids=a__    ,					attention_mask=a__    ,					token_type_ids=a__    ,					label_ids=a__  )  )
       return features


if is_torch_available():
     import torch
     from torch import nn
     from torch.utils.data import Dataset
     class 						__A							(			SCREAMING_SNAKE_CASE_		):
          _UpperCamelCase							:      List[InputFeatures]
          _UpperCamelCase							:      int		 =		nn.CrossEntropyLoss().ignore_index






          def __init__(		self    ,					a__    ,					a__    ,					a__    ,					a__    ,					a__    ,					a__ = None    ,					a__=False    ,					a__ = Split.train    ,					):
            # Load data features from cache or dataset file
            _lowerCAmelCase						:	Optional[int]        =					os.path.join(
                a__    ,					"""cached_{}_{}_{}""".format(mode.value    ,					tokenizer.__class__.__name__    ,					str(a__  )  )    ,					)

            # Make sure only the first process in distributed training processes the dataset,
            # and the others will use the cache.
            _lowerCAmelCase						:	Optional[Any]        =					cached_features_file + """.lock"""
            with FileLock(a__  ):
              if os.path.exists(a__  ) and not overwrite_cache:
                logger.info(F"Loading features from cached file {cached_features_file}"  )
                _lowerCAmelCase						:	Optional[Any]        =					torch.load(a__  )
              else:
                logger.info(F"Creating features from dataset file at {data_dir}"  )
                _lowerCAmelCase						:	Optional[int]        =					token_classification_task.read_examples_from_file(a__    ,					a__  )
                # TODO clean up all this to leverage built-in features of tokenizers
                _lowerCAmelCase						:	Any        =					token_classification_task.convert_examples_to_features(
                    a__    ,					a__    ,					a__    ,					a__    ,					cls_token_at_end=bool(model_type in ["""xlnet"""]  )    ,					cls_token=tokenizer.cls_token    ,					cls_token_segment_id=2 if model_type in ["""xlnet"""] else 0    ,					sep_token=tokenizer.sep_token    ,					sep_token_extra=a__    ,					pad_on_left=bool(tokenizer.padding_side == """left"""  )    ,					pad_token=tokenizer.pad_token_id    ,					pad_token_segment_id=tokenizer.pad_token_type_id    ,					pad_token_label_id=self.pad_token_label_id    ,					)
                logger.info(F"Saving features into cached file {cached_features_file}"  )
                torch.save(self.features    ,					a__  )






          def __len__(		self  ):
            return len(self.features  )






          def __getitem__(		self    ,					a__  ):
            return self.features[i]


if is_tf_available():
     import tensorflow as tf
     class 						__A							:
          _UpperCamelCase							:      List[InputFeatures]
          _UpperCamelCase							:      int		 =		-100






          def __init__(		self    ,					a__    ,					a__    ,					a__    ,					a__    ,					a__    ,					a__ = None    ,					a__=False    ,					a__ = Split.train    ,					):
            _lowerCAmelCase						:	Optional[Any]        =					token_classification_task.read_examples_from_file(a__    ,					a__  )
            # TODO clean up all this to leverage built-in features of tokenizers
            _lowerCAmelCase						:	Tuple        =					token_classification_task.convert_examples_to_features(
                a__    ,					a__    ,					a__    ,					a__    ,					cls_token_at_end=bool(model_type in ["""xlnet"""]  )    ,					cls_token=tokenizer.cls_token    ,					cls_token_segment_id=2 if model_type in ["""xlnet"""] else 0    ,					sep_token=tokenizer.sep_token    ,					sep_token_extra=a__    ,					pad_on_left=bool(tokenizer.padding_side == """left"""  )    ,					pad_token=tokenizer.pad_token_id    ,					pad_token_segment_id=tokenizer.pad_token_type_id    ,					pad_token_label_id=self.pad_token_label_id    ,					)

            def gen():
              for ex in self.features:
                if ex.token_type_ids is None:
                  yield (
                      {"input_ids": ex.input_ids, "attention_mask": ex.attention_mask},
                      ex.label_ids,
                  )
                else:
                  yield (
                      {
                          "input_ids": ex.input_ids,
                          "attention_mask": ex.attention_mask,
                          "token_type_ids": ex.token_type_ids,
                      },
                      ex.label_ids,
                  )

            if "token_type_ids" not in tokenizer.model_input_names:
              _lowerCAmelCase						:	Union[str, Any]        =					tf.data.Dataset.from_generator(
                  a__    ,					({"""input_ids""": tf.intaa, """attention_mask""": tf.intaa}, tf.intaa)    ,					(
                      {"""input_ids""": tf.TensorShape([None]  ), """attention_mask""": tf.TensorShape([None]  )},
                      tf.TensorShape([None]  ),
                  )    ,					)
            else:
              _lowerCAmelCase						:	int        =					tf.data.Dataset.from_generator(
                  a__    ,					({"""input_ids""": tf.intaa, """attention_mask""": tf.intaa, """token_type_ids""": tf.intaa}, tf.intaa)    ,					(
                      {
                          """input_ids""": tf.TensorShape([None]  ),
                          """attention_mask""": tf.TensorShape([None]  ),
                          """token_type_ids""": tf.TensorShape([None]  ),
                      },
                      tf.TensorShape([None]  ),
                  )    ,					)






          def    __A  (		self  ):
            _lowerCAmelCase						:	Any        =					self.dataset.apply(tf.data.experimental.assert_cardinality(len(self.features  )  )  )

            return self.dataset






          def __len__(		self  ):
            return len(self.features  )






          def __getitem__(		self    ,					a__  ):
            return self.features[i]





"""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				:		Tuple	   =       None


_a				:		str	   =       logging.get_logger(__name__)


_a				:		str	   =       {'vocab_file': 'sentencepiece.bpe.model', 'tokenizer_file': 'tokenizer.json'}

_a				:		Tuple	   =       {
    '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				:		int	   =       {
    'facebook/nllb-large-en-ro': 1_024,
    'facebook/nllb-200-distilled-600M': 1_024,
}

# fmt: off
_a				:		Union[str, Any]	   =       ['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 						__A							(			SCREAMING_SNAKE_CASE_		):
     _UpperCamelCase							:      Union[str, Any]		 =		VOCAB_FILES_NAMES
     _UpperCamelCase							:      Union[str, Any]		 =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     _UpperCamelCase							:      Dict		 =		PRETRAINED_VOCAB_FILES_MAP
     _UpperCamelCase							:      str		 =		["input_ids", "attention_mask"]
     _UpperCamelCase							:      Tuple		 =		NllbTokenizer

     _UpperCamelCase							:      List[int]		 =		[]
     _UpperCamelCase							:      List[int]		 =		[]






     def __init__(		self    ,					a__=None    ,					a__=None    ,					a__="<s>"    ,					a__="</s>"    ,					a__="</s>"    ,					a__="<s>"    ,					a__="<unk>"    ,					a__="<pad>"    ,					a__="<mask>"    ,					a__=None    ,					a__=None    ,					a__=None    ,					a__=False    ,					**a__    ,					):
       # Mask token behave like a normal word, i.e. include the space before it
       _lowerCAmelCase						:	Optional[int]        =					AddedToken(a__    ,					lstrip=a__    ,					rstrip=a__  ) if isinstance(a__    ,					a__  ) else mask_token
       _lowerCAmelCase						:	int        =					legacy_behaviour
       super().__init__(
           vocab_file=a__    ,					tokenizer_file=a__    ,					bos_token=a__    ,					eos_token=a__    ,					sep_token=a__    ,					cls_token=a__    ,					unk_token=a__    ,					pad_token=a__    ,					mask_token=a__    ,					src_lang=a__    ,					tgt_lang=a__    ,					additional_special_tokens=a__    ,					legacy_behaviour=a__    ,					**a__    ,					)

       _lowerCAmelCase						:	List[Any]        =					vocab_file
       _lowerCAmelCase						:	List[str]        =					False if not self.vocab_file else True

       _lowerCAmelCase						:	List[str]        =					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						:	Tuple        =					{
           lang_code: self.convert_tokens_to_ids(a__  ) for lang_code in FAIRSEQ_LANGUAGE_CODES
       }

       _lowerCAmelCase						:	List[str]        =					src_lang if src_lang is not None else """eng_Latn"""
       _lowerCAmelCase						:	int        =					self.convert_tokens_to_ids(self._src_lang  )
       _lowerCAmelCase						:	str        =					tgt_lang
       self.set_src_lang_special_tokens(self._src_lang  )






     @property
     def    __A  (		self  ):
       return self._src_lang






     @src_lang.setter
     def    __A  (		self    ,					a__  ):
       _lowerCAmelCase						:	List[Any]        =					new_src_lang
       self.set_src_lang_special_tokens(self._src_lang  )






     def    __A  (		self    ,					a__    ,					a__ = None  ):
       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    ,					a__    ,					a__ = None  ):
       _lowerCAmelCase						:	Tuple        =					[self.sep_token_id]
       _lowerCAmelCase						:	int        =					[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    ,					a__    ,					a__    ,					a__    ,					a__    ,					**a__  ):
       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						:	str        =					src_lang
       _lowerCAmelCase						:	List[Any]        =					self(a__    ,					add_special_tokens=a__    ,					return_tensors=a__    ,					**a__  )
       _lowerCAmelCase						:	Dict        =					self.convert_tokens_to_ids(a__  )
       _lowerCAmelCase						:	Dict        =					tgt_lang_id
       return inputs






     def    __A  (		self    ,					a__    ,					a__ = "eng_Latn"    ,					a__ = None    ,					a__ = "fra_Latn"    ,					**a__    ,					):
       _lowerCAmelCase						:	List[Any]        =					src_lang
       _lowerCAmelCase						:	Optional[int]        =					tgt_lang
       return super().prepare_seqaseq_batch(a__    ,					a__    ,					**a__  )






     def    __A  (		self  ):
       return self.set_src_lang_special_tokens(self.src_lang  )






     def    __A  (		self  ):
       return self.set_tgt_lang_special_tokens(self.tgt_lang  )






     def    __A  (		self    ,					a__  ):
       _lowerCAmelCase						:	Optional[Any]        =					self.convert_tokens_to_ids(a__  )

       if self.legacy_behaviour:
         _lowerCAmelCase						:	Optional[int]        =					[]
         _lowerCAmelCase						:	Union[str, Any]        =					[self.eos_token_id, self.cur_lang_code]
       else:
         _lowerCAmelCase						:	str        =					[self.cur_lang_code]
         _lowerCAmelCase						:	List[Any]        =					[self.eos_token_id]

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

       _lowerCAmelCase						:	Optional[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    __A  (		self    ,					a__  ):
       _lowerCAmelCase						:	Any        =					self.convert_tokens_to_ids(a__  )
       if self.legacy_behaviour:
         _lowerCAmelCase						:	Optional[int]        =					[]
         _lowerCAmelCase						:	str        =					[self.eos_token_id, self.cur_lang_code]
       else:
         _lowerCAmelCase						:	int        =					[self.cur_lang_code]
         _lowerCAmelCase						:	Optional[int]        =					[self.eos_token_id]

       _lowerCAmelCase						:	Union[str, Any]        =					self.convert_ids_to_tokens(self.prefix_tokens  )
       _lowerCAmelCase						:	int        =					self.convert_ids_to_tokens(self.suffix_tokens  )

       _lowerCAmelCase						:	str        =					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    __A  (		self    ,					a__    ,					a__ = None  ):
       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(a__  ):
         logger.error(F"Vocabulary path ({save_directory}) should be a directory."  )
         return
       _lowerCAmelCase						:	Optional[Any]        =					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__  ):
         copyfile(self.vocab_file    ,					a__  )

       return (out_vocab_file,)




