"""simple docstring"""




import unittest

from transformers import JukeboxTokenizer
from transformers.testing_utils import require_torch
class 						__A							(			unittest.TestCase		):
     _UpperCamelCase							:      Optional[int]		 =		JukeboxTokenizer
     _UpperCamelCase							:      Optional[int]		 =		{
         "artist": "Zac Brown Band",
         "genres": "Country",
         "lyrics": "I met a traveller from an antique land,\n        Who said \"Two vast and trunkless legs of stone\n        Stand in the desert. . . . Near them, on the sand,\n        Half sunk a shattered visage lies, whose frown,\n        And wrinkled lip, and sneer of cold command,\n        Tell that its sculptor well those passions read\n        Which yet survive, stamped on these lifeless things,\n        The hand that mocked them, and the heart that fed;\n        And on the pedestal, these words appear:\n        My name is Ozymandias, King of Kings;\n        Look on my Works, ye Mighty, and despair!\n        Nothing beside remains. Round the decay\n        Of that colossal Wreck, boundless and bare\n        The lone and level sands stretch far away\n        ",
     }






     @require_torch
     def    __A  (		self  ):
       import torch

       _lowerCAmelCase						:	Optional[Any]        =					JukeboxTokenizer.from_pretrained("""openai/jukebox-1b-lyrics"""  )
       _lowerCAmelCase						:	Union[str, Any]        =					tokenizer(**self.metas  )["""input_ids"""]
       # fmt: off
       _lowerCAmelCase						:	Optional[int]        =					[
           torch.tensor([[
               0, 0, 0, 7169, 507, 9, 76, 39, 31, 46, 76, 27,
               76, 46, 44, 27, 48, 31, 38, 38, 31, 44, 76, 32,
               44, 41, 39, 76, 27, 40, 76, 27, 40, 46, 35, 43,
               47, 31, 76, 38, 27, 40, 30, 64, 78, 76, 76, 76,
               76, 76, 76, 76, 76, 23, 34, 41, 76, 45, 27, 35,
               30, 76, 71, 20, 49, 41, 76, 48, 27, 45, 46, 76,
               27, 40, 30, 76, 46, 44, 47, 40, 37, 38, 31, 45,
               45, 76, 38, 31, 33, 45, 76, 41, 32, 76, 45, 46,
               41, 40, 31, 78, 76, 76, 76, 76, 76, 76, 76, 76,
               19, 46, 27, 40, 30, 76, 35, 40, 76, 46, 34, 31,
               76, 30, 31, 45, 31, 44, 46, 63, 76, 63, 76, 63,
               76, 63, 76, 14, 31, 27, 44, 76, 46, 34, 31, 39,
               64, 76, 41, 40, 76, 46, 34, 31, 76, 45, 27, 40,
               30, 64, 78, 76, 76, 76, 76, 76, 76, 76, 76, 8,
               27, 38, 32, 76, 45, 47, 40, 37, 76, 27, 76, 45,
               34, 27, 46, 46, 31, 44, 31, 30, 76, 48, 35, 45,
               27, 33, 31, 76, 38, 35, 31, 45, 64, 76, 49, 34,
               41, 45, 31, 76, 32, 44, 41, 49, 40, 64, 78, 76,
               76, 76, 76, 76, 76, 76, 76, 1, 40, 30, 76, 49,
               44, 35, 40, 37, 38, 31, 30, 76, 38, 35, 42, 64,
               76, 27, 40, 30, 76, 45, 40, 31, 31, 44, 76, 41,
               32, 76, 29, 41, 38, 30, 76, 29, 41, 39, 39, 27,
               40, 30, 64, 78, 76, 76, 76, 76, 76, 76, 76, 76,
               20, 31, 38, 38, 76, 46, 34, 27, 46, 76, 35, 46,
               45, 76, 45, 29, 47, 38, 42, 46, 41, 44, 76, 49,
               31, 38, 38, 76, 46, 34, 41, 45, 31, 76, 42, 27,
               45, 45, 35, 41, 40, 45, 76, 44, 31, 27, 30, 78,
               76, 76, 76, 76, 76, 76, 76, 76, 23, 34, 35, 29,
               34, 76, 51, 31, 46, 76, 45, 47, 44, 48, 35, 48,
               31, 64, 76, 45, 46, 27, 39, 42, 31, 30, 76, 41,
               40, 76, 46, 34, 31, 45, 31, 76, 38, 35, 32, 31,
               38, 31, 45, 45, 76, 46, 34, 35, 40, 33, 45, 64,
               78, 76, 76, 76, 76, 76, 76, 76, 76, 20, 34, 31,
               76, 34, 27, 40, 30, 76, 46, 34, 27, 46, 76, 39,
               41, 29, 37, 31, 30, 76, 46, 34, 31, 39, 64, 76,
               27, 40, 30, 76, 46, 34, 31, 76, 34, 31, 27, 44,
               46, 76, 46, 34, 27, 46, 76, 32, 31, 30, 66, 78,
               76, 76, 76, 76, 76, 76, 76, 76, 1, 40, 30, 76,
               41, 40, 76, 46, 34, 31, 76, 42, 31, 30, 31, 45,
               46, 27, 38, 64, 76, 46, 34, 31, 45, 31, 76, 49,
               41, 44, 30, 45, 76, 27, 42, 42, 31, 27, 44, 65,
               78, 76, 76, 76, 76, 76, 76, 76, 76, 13, 51, 76,
               40, 27, 39, 31, 76, 35, 45, 76, 15, 52, 51, 39,
               27, 40, 30, 35, 27, 45, 64, 76, 11, 35, 40, 33,
               76, 41, 32, 76, 11, 35, 40, 33, 45, 66, 78, 76,
               76, 76, 76, 76, 76, 76, 76, 12, 41, 41, 37, 76,
               41, 40, 76, 39, 51, 76, 23, 41, 44, 37, 45, 64,
               76, 51, 31, 76, 13, 35, 33, 34, 46, 51, 64, 76,
               27, 40, 30, 76, 30, 31, 45, 42, 27, 35, 44, 67,
               78, 76, 76, 76, 76, 76, 76, 76, 76, 14, 41, 46,
               34, 35, 40, 33, 76, 28, 31, 45, 35, 30, 31, 76,
               44, 31, 39, 27, 35, 40, 45, 63, 76, 18, 41, 47,
               40, 30, 76, 46, 34, 31, 76, 30, 31, 29, 27, 51,
               78, 76, 76, 76, 76, 76, 76, 76, 76, 15, 32, 76,
               46, 34, 27, 46, 76, 29, 41, 38, 41, 45, 45, 27,
               38, 76, 23, 44, 31, 29, 37, 64, 76, 28, 41, 47,
               40, 30, 38, 31, 45, 45, 76, 27, 40, 30, 76, 28,
               27, 44, 31, 78, 76, 76, 76, 76, 76, 76, 76, 76,
               20, 34, 31, 76, 38, 41, 40, 31, 76, 27, 40, 30,
               76, 38, 31, 48, 31, 38, 76, 45, 27, 40, 30, 45,
               76, 45, 46, 44, 31, 46, 29, 34, 76, 32, 27, 44,
               76, 27, 49, 27, 51, 78, 76, 76, 76, 76, 76, 76,
               76, 76]]  ),
           torch.tensor([[0, 0, 0, 1069, 11]]  ),
           torch.tensor([[0, 0, 0, 1069, 11]]  ),
       ]
       # fmt: on
       self.assertTrue(torch.allclose(tokens[0]    ,					EXPECTED_OUTPUT[0]  )  )
       self.assertTrue(torch.allclose(tokens[1]    ,					EXPECTED_OUTPUT[1]  )  )
       self.assertTrue(torch.allclose(tokens[2]    ,					EXPECTED_OUTPUT[2]  )  )






     @require_torch
     def    __A  (		self  ):
       import torch

       _lowerCAmelCase						:	Any        =					JukeboxTokenizer.from_pretrained("""openai/jukebox-5b-lyrics"""  )
       _lowerCAmelCase						:	Optional[int]        =					tokenizer(**self.metas  )["""input_ids"""]
       # fmt: off
       _lowerCAmelCase						:	List[str]        =					[
           torch.tensor([[
               0, 0, 0, 1069, 11, -1, -1, -1, -1, 9, 77, 39,
               31, 46, 77, 27, 77, 46, 44, 27, 48, 31, 38, 38,
               31, 44, 77, 32, 44, 41, 39, 77, 27, 40, 77, 27,
               40, 46, 35, 43, 47, 31, 77, 38, 27, 40, 30, 64,
               79, 77, 77, 77, 77, 77, 77, 77, 77, 23, 34, 41,
               77, 45, 27, 35, 30, 77, 72, 20, 49, 41, 77, 48,
               27, 45, 46, 77, 27, 40, 30, 77, 46, 44, 47, 40,
               37, 38, 31, 45, 45, 77, 38, 31, 33, 45, 77, 41,
               32, 77, 45, 46, 41, 40, 31, 79, 77, 77, 77, 77,
               77, 77, 77, 77, 19, 46, 27, 40, 30, 77, 35, 40,
               77, 46, 34, 31, 77, 30, 31, 45, 31, 44, 46, 63,
               77, 63, 77, 63, 77, 63, 77, 14, 31, 27, 44, 77,
               46, 34, 31, 39, 64, 77, 41, 40, 77, 46, 34, 31,
               77, 45, 27, 40, 30, 64, 79, 77, 77, 77, 77, 77,
               77, 77, 77, 8, 27, 38, 32, 77, 45, 47, 40, 37,
               77, 27, 77, 45, 34, 27, 46, 46, 31, 44, 31, 30,
               77, 48, 35, 45, 27, 33, 31, 77, 38, 35, 31, 45,
               64, 77, 49, 34, 41, 45, 31, 77, 32, 44, 41, 49,
               40, 64, 79, 77, 77, 77, 77, 77, 77, 77, 77, 1,
               40, 30, 77, 49, 44, 35, 40, 37, 38, 31, 30, 77,
               38, 35, 42, 64, 77, 27, 40, 30, 77, 45, 40, 31,
               31, 44, 77, 41, 32, 77, 29, 41, 38, 30, 77, 29,
               41, 39, 39, 27, 40, 30, 64, 79, 77, 77, 77, 77,
               77, 77, 77, 77, 20, 31, 38, 38, 77, 46, 34, 27,
               46, 77, 35, 46, 45, 77, 45, 29, 47, 38, 42, 46,
               41, 44, 77, 49, 31, 38, 38, 77, 46, 34, 41, 45,
               31, 77, 42, 27, 45, 45, 35, 41, 40, 45, 77, 44,
               31, 27, 30, 79, 77, 77, 77, 77, 77, 77, 77, 77,
               23, 34, 35, 29, 34, 77, 51, 31, 46, 77, 45, 47,
               44, 48, 35, 48, 31, 64, 77, 45, 46, 27, 39, 42,
               31, 30, 77, 41, 40, 77, 46, 34, 31, 45, 31, 77,
               38, 35, 32, 31, 38, 31, 45, 45, 77, 46, 34, 35,
               40, 33, 45, 64, 79, 77, 77, 77, 77, 77, 77, 77,
               77, 20, 34, 31, 77, 34, 27, 40, 30, 77, 46, 34,
               27, 46, 77, 39, 41, 29, 37, 31, 30, 77, 46, 34,
               31, 39, 64, 77, 27, 40, 30, 77, 46, 34, 31, 77,
               34, 31, 27, 44, 46, 77, 46, 34, 27, 46, 77, 32,
               31, 30, 66, 79, 77, 77, 77, 77, 77, 77, 77, 77,
               1, 40, 30, 77, 41, 40, 77, 46, 34, 31, 77, 42,
               31, 30, 31, 45, 46, 27, 38, 64, 77, 46, 34, 31,
               45, 31, 77, 49, 41, 44, 30, 45, 77, 27, 42, 42,
               31, 27, 44, 65, 79, 77, 77, 77, 77, 77, 77, 77,
               77, 13, 51, 77, 40, 27, 39, 31, 77, 35, 45, 77,
               15, 52, 51, 39, 27, 40, 30, 35, 27, 45, 64, 77,
               11, 35, 40, 33, 77, 41, 32, 77, 11, 35, 40, 33,
               45, 66, 79, 77, 77, 77, 77, 77, 77, 77, 77, 12,
               41, 41, 37, 77, 41, 40, 77, 39, 51, 77, 23, 41,
               44, 37, 45, 64, 77, 51, 31, 77, 13, 35, 33, 34,
               46, 51, 64, 77, 27, 40, 30, 77, 30, 31, 45, 42,
               27, 35, 44, 67, 79, 77, 77, 77, 77, 77, 77, 77,
               77, 14, 41, 46, 34, 35, 40, 33, 77, 28, 31, 45,
               35, 30, 31, 77, 44, 31, 39, 27, 35, 40, 45, 63,
               77, 18, 41, 47, 40, 30, 77, 46, 34, 31, 77, 30,
               31, 29, 27, 51, 79, 77, 77, 77, 77, 77, 77, 77,
               77, 15, 32, 77, 46, 34, 27, 46, 77, 29, 41, 38,
               41, 45, 45, 27, 38, 77, 23, 44, 31, 29, 37, 64,
               77, 28, 41, 47, 40, 30, 38, 31, 45, 45, 77, 27,
               40, 30, 77, 28, 27, 44, 31, 79, 77, 77, 77, 77,
               77, 77, 77, 77, 20, 34, 31, 77, 38, 41, 40, 31,
               77, 27, 40, 30, 77, 38, 31, 48, 31, 38, 77, 45,
               27, 40, 30, 45, 77, 45, 46, 44, 31, 46, 29, 34,
               77, 32, 27, 44, 77, 27, 49, 27, 51, 79, 77, 77,
               77, 77, 77, 77, 77, 77]]  ),
           torch.tensor([[0, 0, 0, 1069, 11, -1, -1, -1, -1]]  ),
           torch.tensor([[0, 0, 0, 1069, 11, -1, -1, -1, -1]]  ),
       ]
       # fmt: on
       self.assertTrue(torch.allclose(tokens[0]    ,					EXPECTED_OUTPUT[0]  )  )
       self.assertTrue(torch.allclose(tokens[1]    ,					EXPECTED_OUTPUT[1]  )  )
       self.assertTrue(torch.allclose(tokens[2]    ,					EXPECTED_OUTPUT[2]  )  )





"""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,)




