





import argparse
from pathlib import Path

import fairseq
import torch
from fairseq.models.xmod import XMODModel as FairseqXmodModel
from packaging import version

from transformers import XmodConfig, XmodForMaskedLM, XmodForSequenceClassification
from transformers.utils import logging


if version.parse(fairseq.__version__) < version.parse("""0.12.2"""):
    raise Exception("""requires fairseq >= 0.12.2""")
if version.parse(fairseq.__version__) > version.parse("""2"""):
    raise Exception("""requires fairseq < v2""")

logging.set_verbosity_info()
_UpperCAmelCase				:       str	   =				logging.get_logger(__name__)

_UpperCAmelCase				:       Dict	   =				"""Hello, World!"""
_UpperCAmelCase				:       Any	   =				"""en_XX"""


def        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase )			->     Any:
    lowerCamelCase__    :      Optional[Any]					 =		Path('data_bin' )
    lowerCamelCase__    :      Optional[Any]					 =		FairseqXmodModel.from_pretrained(
        model_name_or_path=str(Path(_UpperCAmelCase ).parent )						,	checkpoint_file=Path(_UpperCAmelCase ).name						,	_name='xmod_base'						,	arch='xmod_base'						,	task='multilingual_masked_lm'						,	data_name_or_path=str(_UpperCAmelCase )						,	bpe='sentencepiece'						,	sentencepiece_model=str(Path(_UpperCAmelCase ).parent / 'sentencepiece.bpe.model' )						,	src_dict=str(data_dir / 'dict.txt' )						,	)
    xmod.eval()  # disable dropout
    print(_UpperCAmelCase )

    lowerCamelCase__    :      Any					 =		xmod.model.encoder.sentence_encoder
    lowerCamelCase__    :      Optional[int]					 =		XmodConfig(
        vocab_size=xmod_sent_encoder.embed_tokens.num_embeddings						,	hidden_size=xmod.cfg.model.encoder_embed_dim						,	num_hidden_layers=xmod.cfg.model.encoder_layers						,	num_attention_heads=xmod.cfg.model.encoder_attention_heads						,	intermediate_size=xmod.cfg.model.encoder_ffn_embed_dim						,	max_position_embeddings=514						,	type_vocab_size=1						,	layer_norm_eps=1e-5						,	pre_norm=xmod.cfg.model.encoder_normalize_before						,	adapter_reduction_factor=getattr(xmod.cfg.model						,	'bottleneck'						,	2 )						,	adapter_layer_norm=xmod.cfg.model.adapter_layer_norm						,	adapter_reuse_layer_norm=xmod.cfg.model.adapter_reuse_layer_norm						,	ln_before_adapter=xmod.cfg.model.ln_before_adapter						,	languages=xmod.cfg.model.languages						,	)
    if classification_head:
        lowerCamelCase__    :      Optional[int]					 =		xmod.model.classification_heads['mnli'].out_proj.weight.shape[0]

    print('Our X-MOD config:'						,	_UpperCAmelCase )

    lowerCamelCase__    :      Union[str, Any]					 =		XmodForSequenceClassification(_UpperCAmelCase ) if classification_head else XmodForMaskedLM(_UpperCAmelCase )
    model.eval()

    # Now let's copy all the weights.
    # Embeddings
    lowerCamelCase__    :      Any					 =		xmod_sent_encoder.embed_tokens.weight
    lowerCamelCase__    :      str					 =		xmod_sent_encoder.embed_positions.weight
    lowerCamelCase__    :      Optional[int]					 =		torch.zeros_like(
        model.roberta.embeddings.token_type_embeddings.weight )  # just zero them out b/c xmod doesn't use them.

    lowerCamelCase__    :      Any					 =		xmod_sent_encoder.layernorm_embedding.weight
    lowerCamelCase__    :      str					 =		xmod_sent_encoder.layernorm_embedding.bias

    for i in range(config.num_hidden_layers ):
        # Encoder: start of layer
        lowerCamelCase__    :      List[str]					 =		model.roberta.encoder.layer[i]
        lowerCamelCase__    :      Dict					 =		xmod_sent_encoder.layers[i]

        # self attention
        lowerCamelCase__    :      Union[str, Any]					 =		layer.attention.self
        if not (
            xmod_layer.self_attn.k_proj.weight.data.shape
            == xmod_layer.self_attn.q_proj.weight.data.shape
            == xmod_layer.self_attn.v_proj.weight.data.shape
            == torch.Size((config.hidden_size, config.hidden_size) )
        ):
            raise AssertionError('Dimensions of self-attention weights do not match.' )

        lowerCamelCase__    :      Optional[int]					 =		xmod_layer.self_attn.q_proj.weight
        lowerCamelCase__    :      Any					 =		xmod_layer.self_attn.q_proj.bias
        lowerCamelCase__    :      Tuple					 =		xmod_layer.self_attn.k_proj.weight
        lowerCamelCase__    :      Tuple					 =		xmod_layer.self_attn.k_proj.bias
        lowerCamelCase__    :      int					 =		xmod_layer.self_attn.v_proj.weight
        lowerCamelCase__    :      Any					 =		xmod_layer.self_attn.v_proj.bias

        # self-attention output
        lowerCamelCase__    :      str					 =		layer.attention.output
        if self_output.dense.weight.shape != xmod_layer.self_attn.out_proj.weight.shape:
            raise AssertionError('Dimensions of self-attention output weights do not match.' )
        lowerCamelCase__    :      Optional[Any]					 =		xmod_layer.self_attn.out_proj.weight
        lowerCamelCase__    :      Tuple					 =		xmod_layer.self_attn.out_proj.bias
        lowerCamelCase__    :      Dict					 =		xmod_layer.self_attn_layer_norm.weight
        lowerCamelCase__    :      Tuple					 =		xmod_layer.self_attn_layer_norm.bias

        # intermediate
        lowerCamelCase__    :      Dict					 =		layer.intermediate
        if intermediate.dense.weight.shape != xmod_layer.fca.weight.shape:
            raise AssertionError('Dimensions of intermediate weights do not match.' )
        lowerCamelCase__    :      Tuple					 =		xmod_layer.fca.weight
        lowerCamelCase__    :      List[str]					 =		xmod_layer.fca.bias

        # output
        lowerCamelCase__    :      List[str]					 =		layer.output
        if bert_output.dense.weight.shape != xmod_layer.fca.weight.shape:
            raise AssertionError('Dimensions of feed-forward weights do not match.' )
        lowerCamelCase__    :      Union[str, Any]					 =		xmod_layer.fca.weight
        lowerCamelCase__    :      int					 =		xmod_layer.fca.bias
        lowerCamelCase__    :      List[Any]					 =		xmod_layer.final_layer_norm.weight
        lowerCamelCase__    :      Union[str, Any]					 =		xmod_layer.final_layer_norm.bias
        if bert_output.adapter_layer_norm is not None:
            lowerCamelCase__    :      Dict					 =		xmod_layer.adapter_layer_norm.weight
            lowerCamelCase__    :      Tuple					 =		xmod_layer.adapter_layer_norm.bias

        if sorted(bert_output.adapter_modules.keys() ) != sorted(xmod_layer.adapter_modules.keys() ):
            raise AssertionError('Lists of language adapters do not match.' )
        for lang_code, adapter in xmod_layer.adapter_modules.items():
            lowerCamelCase__    :      Optional[int]					 =		bert_output.adapter_modules[lang_code]
            lowerCamelCase__    :      List[Any]					 =		xmod_layer.adapter_modules[lang_code]
            lowerCamelCase__    :      Dict					 =		from_adapter.fca.weight
            lowerCamelCase__    :      Any					 =		from_adapter.fca.bias
            lowerCamelCase__    :      Optional[Any]					 =		from_adapter.fca.weight
            lowerCamelCase__    :      List[str]					 =		from_adapter.fca.bias

        # end of layer

    if xmod_sent_encoder.layer_norm is not None:
        lowerCamelCase__    :      List[Any]					 =		xmod_sent_encoder.layer_norm.weight
        lowerCamelCase__    :      List[str]					 =		xmod_sent_encoder.layer_norm.bias

    if classification_head:
        lowerCamelCase__    :      Union[str, Any]					 =		xmod.model.classification_heads['mnli'].dense.weight
        lowerCamelCase__    :      Any					 =		xmod.model.classification_heads['mnli'].dense.bias
        lowerCamelCase__    :      int					 =		xmod.model.classification_heads['mnli'].out_proj.weight
        lowerCamelCase__    :      Union[str, Any]					 =		xmod.model.classification_heads['mnli'].out_proj.bias
    else:
        # LM Head
        lowerCamelCase__    :      Optional[int]					 =		xmod.model.encoder.lm_head.dense.weight
        lowerCamelCase__    :      Any					 =		xmod.model.encoder.lm_head.dense.bias
        lowerCamelCase__    :      Dict					 =		xmod.model.encoder.lm_head.layer_norm.weight
        lowerCamelCase__    :      str					 =		xmod.model.encoder.lm_head.layer_norm.bias
        lowerCamelCase__    :      str					 =		xmod.model.encoder.lm_head.weight
        lowerCamelCase__    :      Optional[Any]					 =		xmod.model.encoder.lm_head.bias

    # Let's check that we get the same results.
    lowerCamelCase__    :      Optional[Any]					 =		xmod.encode(_UpperCAmelCase ).unsqueeze(0 )  # batch of size 1
    model.roberta.set_default_language(_UpperCAmelCase )

    lowerCamelCase__    :      Union[str, Any]					 =		model(_UpperCAmelCase )[0]
    if classification_head:
        lowerCamelCase__    :      int					 =		xmod.model.classification_heads['mnli'](xmod.extract_features(_UpperCAmelCase ) )
    else:
        lowerCamelCase__    :      Any					 =		xmod.model(_UpperCAmelCase						,	lang_id=[SAMPLE_LANGUAGE] )[0]
    print(our_output.shape						,	their_output.shape )
    lowerCamelCase__    :      Any					 =		torch.max(torch.abs(our_output - their_output ) ).item()
    print(F"""max_absolute_diff = {max_absolute_diff}""" )  # ~ 1e-7
    lowerCamelCase__    :      Any					 =		torch.allclose(_UpperCAmelCase						,	_UpperCAmelCase						,	atol=1e-3 )
    print('Do both models output the same tensors?'						,	'🔥' if success else '💩' )
    if not success:
        raise Exception('Something went wRoNg' )

    Path(_UpperCAmelCase ).mkdir(parents=_UpperCAmelCase						,	exist_ok=_UpperCAmelCase )
    print(F"""Saving model to {pytorch_dump_folder_path}""" )
    model.save_pretrained(_UpperCAmelCase )


if __name__ == "__main__":
    _UpperCAmelCase				:       List[str]	   =				argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        """--xmod_checkpoint_path""", default=None, type=str, required=True, help="""Path the official PyTorch dump."""
    )
    parser.add_argument(
        """--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
    )
    parser.add_argument(
        """--classification_head""", action="""store_true""", help="""Whether to convert a final classification head."""
    )
    _UpperCAmelCase				:       Tuple	   =				parser.parse_args()
    convert_xmod_checkpoint_to_pytorch(
        args.xmod_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
    )












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:
    _UpperCAmelCase				:       int	   =				None


_UpperCAmelCase				:       Dict	   =				logging.get_logger(__name__)


_UpperCAmelCase				:       Optional[int]	   =				{"""vocab_file""": """sentencepiece.bpe.model""", """tokenizer_file""": """tokenizer.json"""}

_UpperCAmelCase				:       List[Any]	   =				{
    """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"""
        ),
    },
}

_UpperCAmelCase				:       List[str]	   =				{
    """facebook/nllb-large-en-ro""": 10_24,
    """facebook/nllb-200-distilled-600M""": 10_24,
}

# fmt: off
_UpperCAmelCase				:       Optional[int]	   =				["""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			):
 UpperCAmelCase__													=		VOCAB_FILES_NAMES
 UpperCAmelCase__													=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 UpperCAmelCase__													=		PRETRAINED_VOCAB_FILES_MAP
 UpperCAmelCase__													=		["""input_ids""", """attention_mask"""]
 UpperCAmelCase__													=		NllbTokenizer

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

     lowerCamelCase__    :      List[Any]					 =		vocab_file
     lowerCamelCase__    :      Dict					 =		False if not self.vocab_file else True

     lowerCamelCase__    :      Optional[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(UpperCAmelCase	) for lang_code in FAIRSEQ_LANGUAGE_CODES
     }

     lowerCamelCase__    :      int					 =		src_lang if src_lang is not None else 'eng_Latn'
     lowerCamelCase__    :      List[Any]					 =		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  :      int	)			->	str:
     return self._src_lang
 @src_lang.setter
 def      A_	(      self  :      List[Any]			,     UpperCAmelCase  :      str	)			->	None:
     lowerCamelCase__    :      Any					 =		new_src_lang
     self.set_src_lang_special_tokens(self._src_lang	)
 def      A_	(      self  :      Optional[Any]			,     UpperCAmelCase  :      List[int]			,     UpperCAmelCase  :      Optional[List[int]] = 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      A_	(      self  :      Optional[Any]			,     UpperCAmelCase  :      List[int]			,     UpperCAmelCase  :      Optional[List[int]] = None	)			->	List[int]:
     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      A_	(      self  :      int			,     UpperCAmelCase  :      int			,     UpperCAmelCase  :      str			,     UpperCAmelCase  :      Optional[str]			,     UpperCAmelCase  :      Optional[str]			,     **UpperCAmelCase  :      List[str]	)			->	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'	)
     lowerCamelCase__    :      Optional[int]					 =		src_lang
     lowerCamelCase__    :      Optional[int]					 =		self(UpperCAmelCase			,     add_special_tokens=UpperCAmelCase			,     return_tensors=UpperCAmelCase			,     **UpperCAmelCase	)
     lowerCamelCase__    :      Optional[Any]					 =		self.convert_tokens_to_ids(UpperCAmelCase	)
     lowerCamelCase__    :      Union[str, Any]					 =		tgt_lang_id
     return inputs
 def      A_	(      self  :      Dict			,     UpperCAmelCase  :      List[str]			,     UpperCAmelCase  :      str = "eng_Latn"			,     UpperCAmelCase  :      Optional[List[str]] = None			,     UpperCAmelCase  :      str = "fra_Latn"			,     **UpperCAmelCase  :      Dict			,     )			->	BatchEncoding:
     lowerCamelCase__    :      Any					 =		src_lang
     lowerCamelCase__    :      int					 =		tgt_lang
     return super().prepare_seqaseq_batch(UpperCAmelCase			,     UpperCAmelCase			,     **UpperCAmelCase	)
 def      A_	(      self  :      Union[str, Any]	)			->	Optional[int]:
     return self.set_src_lang_special_tokens(self.src_lang	)
 def      A_	(      self  :      Any	)			->	Union[str, Any]:
     return self.set_tgt_lang_special_tokens(self.tgt_lang	)
 def      A_	(      self  :      str			,     UpperCAmelCase  :      Optional[Any]	)			->	None:
     lowerCamelCase__    :      int					 =		self.convert_tokens_to_ids(UpperCAmelCase	)

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

     lowerCamelCase__    :      Any					 =		self.convert_ids_to_tokens(self.prefix_tokens	)
     lowerCamelCase__    :      Optional[Any]					 =		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  :      int			,     UpperCAmelCase  :      str	)			->	None:
     lowerCamelCase__    :      Union[str, Any]					 =		self.convert_tokens_to_ids(UpperCAmelCase	)
     if self.legacy_behaviour:
         lowerCamelCase__    :      Dict					 =		[]
         lowerCamelCase__    :      Union[str, Any]					 =		[self.eos_token_id, self.cur_lang_code]
     else:
         lowerCamelCase__    :      Any					 =		[self.cur_lang_code]
         lowerCamelCase__    :      Optional[Any]					 =		[self.eos_token_id]

     lowerCamelCase__    :      Union[str, Any]					 =		self.convert_ids_to_tokens(self.prefix_tokens	)
     lowerCamelCase__    :      List[Any]					 =		self.convert_ids_to_tokens(self.suffix_tokens	)

     lowerCamelCase__    :      Optional[int]					 =		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  :      Union[str, Any]			,     UpperCAmelCase  :      str			,     UpperCAmelCase  :      Optional[str] = None	)			->	Tuple[str]:
     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(UpperCAmelCase	):
         logger.error(F"""Vocabulary path ({save_directory}) should be a directory."""	)
         return
     lowerCamelCase__    :      int					 =		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	):
         copyfile(self.vocab_file			,     UpperCAmelCase	)

     return (out_vocab_file,)





