





"""simple docstring"""




import tempfile
import unittest
from pathlib import Path
from shutil import copyfile

from transformers import BatchEncoding, MarianTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import is_sentencepiece_available, is_tf_available, is_torch_available


if is_sentencepiece_available():
		from transformers.models.marian.tokenization_marian import VOCAB_FILES_NAMES, save_json

from ...test_tokenization_common import TokenizerTesterMixin


a		:				int  			=    get_tests_dir('''fixtures/test_sentencepiece.model''')

a		:				int  			=    {'''target_lang''': '''fi''', '''source_lang''': '''en'''}
a		:				Optional[int]  			=    '''>>zh<<'''
a		:				Any  			=    '''Helsinki-NLP/'''

if is_torch_available():
		a		:				Optional[Any]  			=    '''pt'''
elif is_tf_available():
		a		:				Any  			=    '''tf'''
else:
		a		:				int  			=    '''jax'''


@require_sentencepiece
class  __UpperCamelCase       (		a__	, unittest.TestCase		):
							lowerCamelCase     :      int         =MarianTokenizer
							lowerCamelCase     :      str         =False
							lowerCamelCase     :      str         =True






							def 					__a     (     self   )			->				Tuple:
								super().setUp()
								a   :	List[Any]			=       ["</s>", "<unk>", "▁This", "▁is", "▁a", "▁t", "est", "\u0120", "<pad>"]
								a   :	Dict			=       dict(zip(lowerCAmelCase__   ,      range(len(lowerCAmelCase__   )   )   )   )
								a   :	Dict			=       Path(self.tmpdirname   )
								save_json(lowerCAmelCase__   ,      save_dir / VOCAB_FILES_NAMES["vocab"]   )
								save_json(lowerCAmelCase__   ,      save_dir / VOCAB_FILES_NAMES["tokenizer_config_file"]   )
								if not (save_dir / VOCAB_FILES_NAMES["source_spm"]).exists():
									copyfile(lowerCAmelCase__   ,      save_dir / VOCAB_FILES_NAMES["source_spm"]   )
									copyfile(lowerCAmelCase__   ,      save_dir / VOCAB_FILES_NAMES["target_spm"]   )

								a   :	Dict			=       MarianTokenizer.from_pretrained(self.tmpdirname   )
								tokenizer.save_pretrained(self.tmpdirname   )






							def 					__a     (     self   ,      **lowerCAmelCase__   )			->				MarianTokenizer:
								return MarianTokenizer.from_pretrained(self.tmpdirname   ,      **lowerCAmelCase__   )






							def 					__a     (     self   ,      lowerCAmelCase__   )			->				Union[str, Any]:
								return (
								    "This is a test",
								    "This is a test",
								)






							def 					__a     (     self   )			->				Any:
								a   :	List[Any]			=       "</s>"
								a   :	int			=       0

								self.assertEqual(self.get_tokenizer()._convert_token_to_id(lowerCAmelCase__   )   ,      lowerCAmelCase__   )
								self.assertEqual(self.get_tokenizer()._convert_id_to_token(lowerCAmelCase__   )   ,      lowerCAmelCase__   )






							def 					__a     (     self   )			->				List[str]:
								a   :	Dict			=       list(self.get_tokenizer().get_vocab().keys()   )

								self.assertEqual(vocab_keys[0]   ,      "</s>"   )
								self.assertEqual(vocab_keys[1]   ,      "<unk>"   )
								self.assertEqual(vocab_keys[-1]   ,      "<pad>"   )
								self.assertEqual(len(lowerCAmelCase__   )   ,      9   )






							def 					__a     (     self   )			->				int:
								self.assertEqual(self.get_tokenizer().vocab_size   ,      9   )






							def 					__a     (     self   )			->				List[str]:
								a   :	Dict			=       MarianTokenizer.from_pretrained(f"""{ORG_NAME}opus-mt-en-de"""   )
								a   :	Any			=       en_de_tokenizer(["I am a small frog"]   ,      return_tensors=lowerCAmelCase__   )
								self.assertIsInstance(lowerCAmelCase__   ,      lowerCAmelCase__   )
								a   :	Optional[Any]			=       [38, 121, 14, 697, 3_8848, 0]
								self.assertListEqual(lowerCAmelCase__   ,      batch.input_ids[0]   )

								a   :	Dict			=       tempfile.mkdtemp()
								en_de_tokenizer.save_pretrained(lowerCAmelCase__   )
								a   :	Union[str, Any]			=       [x.name for x in Path(lowerCAmelCase__   ).glob("*"   )]
								self.assertIn("source.spm"   ,      lowerCAmelCase__   )
								MarianTokenizer.from_pretrained(lowerCAmelCase__   )






							def 					__a     (     self   )			->				Union[str, Any]:
								a   :	Any			=       self.get_tokenizer()

								a   :	List[Any]			=       tok(
								    ["I am a small frog" * 1000, "I am a small frog"]   ,      padding=lowerCAmelCase__   ,      truncation=lowerCAmelCase__   ,      return_tensors=lowerCAmelCase__   )
								self.assertIsInstance(lowerCAmelCase__   ,      lowerCAmelCase__   )
								self.assertEqual(batch.input_ids.shape   ,      (2, 512)   )






							def 					__a     (     self   )			->				int:
								a   :	str			=       self.get_tokenizer()
								a   :	Tuple			=       tok(["I am a tiny frog", "I am a small frog"]   ,      padding=lowerCAmelCase__   ,      return_tensors=lowerCAmelCase__   )
								self.assertIsInstance(lowerCAmelCase__   ,      lowerCAmelCase__   )
								self.assertEqual(batch_smaller.input_ids.shape   ,      (2, 10)   )






							@slow
							def 					__a     (     self   )			->				Dict:
								# fmt: off
								a   :	List[Any]			=       {"input_ids": [[4_3495, 462, 20, 4_2164, 1369, 52, 464, 132, 1703, 492, 13, 7491, 3_8999, 6, 8, 464, 132, 1703, 492, 13, 4669, 3_7867, 13, 7525, 27, 1593, 988, 13, 3_3972, 7029, 6, 20, 8251, 383, 2, 270, 5866, 3788, 2, 2353, 8251, 1_2338, 2, 1_3958, 387, 2, 3629, 6953, 188, 2900, 2, 1_3958, 8011, 1_1501, 23, 8460, 4073, 3_4009, 20, 435, 1_1439, 27, 8, 8460, 4073, 6004, 20, 9988, 375, 27, 33, 266, 1945, 1076, 1350, 3_7867, 3288, 5, 577, 1076, 4374, 8, 5082, 5, 2_6453, 257, 556, 403, 2, 242, 132, 383, 316, 492, 8, 1_0767, 6, 316, 304, 4239, 3, 0], [148, 1_5722, 19, 1839, 12, 1350, 13, 2_2327, 5082, 5418, 4_7567, 3_5938, 59, 318, 1_9552, 108, 2183, 54, 1_4976, 4835, 32, 547, 1114, 8, 315, 2417, 5, 92, 1_9088, 3, 0, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100], [36, 6395, 1_2570, 3_9147, 1_1597, 6, 266, 4, 4_5405, 7296, 3, 0, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100]], "attention_mask": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
								# fmt: on

								self.tokenizer_integration_test_util(
								    expected_encoding=lowerCAmelCase__   ,      model_name="Helsinki-NLP/opus-mt-en-de"   ,      revision="1a8c2263da11e68e50938f97e10cd57820bd504c"   ,      decode_kwargs={"use_source_tokenizer": True}   ,      )






							def 					__a     (     self   )			->				int:
								a   :	str			=       MarianTokenizer.from_pretrained("hf-internal-testing/test-marian-two-vocabs"   )

								a   :	Union[str, Any]			=       "Tämä on testi"
								a   :	Any			=       "This is a test"

								a   :	Optional[Any]			=       [76, 7, 2047, 2]
								a   :	Dict			=       [69, 12, 11, 940, 2]

								a   :	int			=       tokenizer(lowerCAmelCase__   ).input_ids
								self.assertListEqual(lowerCAmelCase__   ,      lowerCAmelCase__   )

								a   :	int			=       tokenizer(text_target=lowerCAmelCase__   ).input_ids
								self.assertListEqual(lowerCAmelCase__   ,      lowerCAmelCase__   )

								a   :	str			=       tokenizer.decode(lowerCAmelCase__   ,      skip_special_tokens=lowerCAmelCase__   )
								self.assertEqual(lowerCAmelCase__   ,      lowerCAmelCase__   )









"""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		:				int  			=    logging.get_logger(__name__)


a		:				int  			=    {'''vocab_file''': '''sentencepiece.bpe.model''', '''tokenizer_file''': '''tokenizer.json'''}

a		:				Optional[int]  			=    {
    '''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''': 1024,
    '''facebook/nllb-200-distilled-600M''': 1024,
}

# fmt: off
a		:				List[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  __UpperCamelCase       (		a__		):
							lowerCamelCase     :      Optional[Any]         =VOCAB_FILES_NAMES
							lowerCamelCase     :      str         =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							lowerCamelCase     :      Dict         =PRETRAINED_VOCAB_FILES_MAP
							lowerCamelCase     :      List[Any]         =["""input_ids""", """attention_mask"""]
							lowerCamelCase     :      Union[str, Any]         =NllbTokenizer

							lowerCamelCase     :      List[int]         =[]
							lowerCamelCase     :      List[int]         =[]






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

								a   :	int			=       vocab_file
								a   :	Any			=       False if not self.vocab_file else True

								a   :	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}   )
								a   :	str			=       {
								    lang_code: self.convert_tokens_to_ids(lowerCAmelCase__   ) for lang_code in FAIRSEQ_LANGUAGE_CODES
								}

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






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






							@src_lang.setter
							def 					__a     (     self   ,      lowerCAmelCase__   )			->				None:
								a   :	List[str]			=       new_src_lang
								self.set_src_lang_special_tokens(self._src_lang   )






							def 					__a     (     self   ,      lowerCAmelCase__   ,      lowerCAmelCase__ = 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   ,      lowerCAmelCase__   ,      lowerCAmelCase__ = None   )			->				List[int]:
								a   :	str			=       [self.sep_token_id]
								a   :	Union[str, 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 					__a     (     self   ,      lowerCAmelCase__   ,      lowerCAmelCase__   ,      lowerCAmelCase__   ,      lowerCAmelCase__   ,      **lowerCAmelCase__   )			->				Any:
								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   :	Dict			=       src_lang
								a   :	int			=       self(lowerCAmelCase__   ,      add_special_tokens=lowerCAmelCase__   ,      return_tensors=lowerCAmelCase__   ,      **lowerCAmelCase__   )
								a   :	Dict			=       self.convert_tokens_to_ids(lowerCAmelCase__   )
								a   :	Any			=       tgt_lang_id
								return inputs






							def 					__a     (     self   ,      lowerCAmelCase__   ,      lowerCAmelCase__ = "eng_Latn"   ,      lowerCAmelCase__ = None   ,      lowerCAmelCase__ = "fra_Latn"   ,      **lowerCAmelCase__   ,      )			->				BatchEncoding:
								a   :	Optional[int]			=       src_lang
								a   :	int			=       tgt_lang
								return super().prepare_seqaseq_batch(lowerCAmelCase__   ,      lowerCAmelCase__   ,      **lowerCAmelCase__   )






							def 					__a     (     self   )			->				Tuple:
								return self.set_src_lang_special_tokens(self.src_lang   )






							def 					__a     (     self   )			->				str:
								return self.set_tgt_lang_special_tokens(self.tgt_lang   )






							def 					__a     (     self   ,      lowerCAmelCase__   )			->				None:
								a   :	int			=       self.convert_tokens_to_ids(lowerCAmelCase__   )

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

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

								a   :	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   ,      lowerCAmelCase__   )			->				None:
								a   :	str			=       self.convert_tokens_to_ids(lowerCAmelCase__   )
								if self.legacy_behaviour:
									a   :	Optional[Any]			=       []
									a   :	int			=       [self.eos_token_id, self.cur_lang_code]
								else:
									a   :	List[Any]			=       [self.cur_lang_code]
									a   :	List[Any]			=       [self.eos_token_id]

								a   :	int			=       self.convert_ids_to_tokens(self.prefix_tokens   )
								a   :	int			=       self.convert_ids_to_tokens(self.suffix_tokens   )

								a   :	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   ,      lowerCAmelCase__   ,      lowerCAmelCase__ = 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(lowerCAmelCase__   ):
									logger.error(f"""Vocabulary path ({save_directory}) should be a directory."""   )
									return
								a   :	Any			=       os.path.join(
								    lowerCAmelCase__   ,      (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]   )

								if os.path.abspath(self.vocab_file   ) != os.path.abspath(lowerCAmelCase__   ):
									copyfile(self.vocab_file   ,      lowerCAmelCase__   )

								return (out_vocab_file,)


