
"""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


SCREAMING_SNAKE_CASE      =     logging.get_logger(__name__)

SCREAMING_SNAKE_CASE      =     """▁"""

SCREAMING_SNAKE_CASE      =     {"""vocab_file""": """sentencepiece.bpe.model"""}

SCREAMING_SNAKE_CASE      =     {
    """vocab_file""": {
        """facebook/nllb-200-distilled-600M""": (
            """https://huggingface.co/facebook/nllb-200-distilled-600M/blob/main/sentencepiece.bpe.model"""
        ),
    }
}

SCREAMING_SNAKE_CASE      =     {
    """facebook/nllb-200-distilled-600M""": 1_024,
}

# fmt: off
SCREAMING_SNAKE_CASE      =     ["""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  (      _lowerCAmelCase	):
       UpperCamelCase_		:					Any   =			VOCAB_FILES_NAMES
       UpperCamelCase_		:					Optional[Any]   =			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       UpperCamelCase_		:					Tuple   =			PRETRAINED_VOCAB_FILES_MAP
       UpperCamelCase_		:					Optional[Any]   =			['''input_ids''', '''attention_mask''']

       UpperCamelCase_		:					List[int]   =			[]
       UpperCamelCase_		:					List[int]   =			[]






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







              """simple docstring"""
              # Mask token behave like a normal word, i.e. include the space before it
              UpperCamelCase					 =		AddedToken(UpperCAmelCase_       ,      lstrip=UpperCAmelCase_       ,      rstrip=UpperCAmelCase_			) if isinstance(UpperCAmelCase_       ,      UpperCAmelCase_			) else mask_token

              UpperCamelCase					 =		{} if sp_model_kwargs is None else sp_model_kwargs
              UpperCamelCase					 =		legacy_behaviour

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

              UpperCamelCase					 =		spm.SentencePieceProcessor(**self.sp_model_kwargs			)
              self.sp_model.Load(str(UpperCAmelCase_			)			)
              UpperCamelCase					 =		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					 =		{"<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					 =		1

              UpperCamelCase					 =		len(self.sp_model			)
              UpperCamelCase					 =		{
                  code: self.sp_model_size + i + self.fairseq_offset for i, code in enumerate(UpperCAmelCase_			)
              }
              UpperCamelCase					 =		{v: k for k, v in self.lang_code_to_id.items()}
              UpperCamelCase					 =		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					 =		{v: k for k, v in self.fairseq_tokens_to_ids.items()}
              UpperCamelCase					 =		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					 =		src_lang if src_lang is not None else "eng_Latn"
              UpperCamelCase					 =		self.lang_code_to_id[self._src_lang]
              UpperCamelCase					 =		tgt_lang
              self.set_src_lang_special_tokens(self._src_lang			)






       def __getstate__(  self :							Dict			)->  Any:







              """simple docstring"""
              UpperCamelCase					 =		self.__dict__.copy()
              UpperCamelCase					 =		None
              UpperCamelCase					 =		self.sp_model.serialized_model_proto()
              return state






       def __setstate__(  self :							Any       ,      UpperCAmelCase_ :							Optional[Any]			)->  int:







              """simple docstring"""
              UpperCamelCase					 =		d

              # for backward compatibility
              if not hasattr(self       ,      "sp_model_kwargs"			):
                     UpperCamelCase					 =		{}

              UpperCamelCase					 =		spm.SentencePieceProcessor(**self.sp_model_kwargs			)
              self.sp_model.LoadFromSerializedProto(self.sp_model_proto			)






       @property
       def 	_SCREAMING_SNAKE_CASE       (  self :							Optional[Any]			)->  Tuple:







              """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 	_SCREAMING_SNAKE_CASE       (  self :							List[str]			)->  str:







              """simple docstring"""
              return self._src_lang






       @src_lang.setter
       def 	_SCREAMING_SNAKE_CASE       (  self :							List[Any]       ,      UpperCAmelCase_ :							str			)->  None:







              """simple docstring"""
              UpperCamelCase					 =		new_src_lang
              self.set_src_lang_special_tokens(self._src_lang			)






       def 	_SCREAMING_SNAKE_CASE       (  self :							Optional[Any]       ,      UpperCAmelCase_ :							List[int]       ,      UpperCAmelCase_ :							Optional[List[int]] = None       ,      UpperCAmelCase_ :							bool = False			)->  List[int]:







              """simple docstring"""

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

              UpperCamelCase					 =		[1] * len(self.prefix_tokens			)
              UpperCamelCase					 =		[1] * len(self.suffix_tokens			)
              if token_ids_a is None:
                     return prefix_ones + ([0] * len(UpperCAmelCase_			)) + suffix_ones
              return prefix_ones + ([0] * len(UpperCAmelCase_			)) + ([0] * len(UpperCAmelCase_			)) + suffix_ones






       def 	_SCREAMING_SNAKE_CASE       (  self :							int       ,      UpperCAmelCase_ :							List[int]       ,      UpperCAmelCase_ :							Optional[List[int]] = None			)->  List[int]:







              """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 	_SCREAMING_SNAKE_CASE       (  self :							int       ,      UpperCAmelCase_ :							List[int]       ,      UpperCAmelCase_ :							Optional[List[int]] = None			)->  List[int]:







              """simple docstring"""
              UpperCamelCase					 =		[self.sep_token_id]
              UpperCamelCase					 =		[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 	_SCREAMING_SNAKE_CASE       (  self :							Any       ,      UpperCAmelCase_ :							List[Any]       ,      UpperCAmelCase_ :							str       ,      UpperCAmelCase_ :							Optional[str]       ,      UpperCAmelCase_ :							Optional[str]       ,      **UpperCAmelCase_ :							List[Any]			)->  Any:







              """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					 =		src_lang
              UpperCamelCase					 =		self(UpperCAmelCase_       ,      add_special_tokens=UpperCAmelCase_       ,      return_tensors=UpperCAmelCase_       ,      **UpperCAmelCase_			)
              UpperCamelCase					 =		self.convert_tokens_to_ids(UpperCAmelCase_			)
              UpperCamelCase					 =		tgt_lang_id
              return inputs






       def 	_SCREAMING_SNAKE_CASE       (  self :							str			)->  Union[str, Any]:







              """simple docstring"""
              UpperCamelCase					 =		{self.convert_ids_to_tokens(UpperCAmelCase_			): i for i in range(self.vocab_size			)}
              vocab.update(self.added_tokens_encoder			)
              return vocab






       def 	_SCREAMING_SNAKE_CASE       (  self :							int       ,      UpperCAmelCase_ :							str			)->  List[str]:







              """simple docstring"""
              return self.sp_model.encode(UpperCAmelCase_       ,      out_type=UpperCAmelCase_			)






       def 	_SCREAMING_SNAKE_CASE       (  self :							Optional[int]       ,      UpperCAmelCase_ :							Union[str, Any]			)->  int:







              """simple docstring"""
              if token in self.fairseq_tokens_to_ids:
                     return self.fairseq_tokens_to_ids[token]
              UpperCamelCase					 =		self.sp_model.PieceToId(UpperCAmelCase_			)

              # 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 	_SCREAMING_SNAKE_CASE       (  self :							List[str]       ,      UpperCAmelCase_ :							Optional[Any]			)->  Dict:







              """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 	_SCREAMING_SNAKE_CASE       (  self :							Optional[Any]       ,      UpperCAmelCase_ :							Union[str, Any]			)->  Optional[Any]:







              """simple docstring"""
              UpperCamelCase					 =		"".join(UpperCAmelCase_			).replace(UpperCAmelCase_       ,      " "			).strip()
              return out_string






       def 	_SCREAMING_SNAKE_CASE       (  self :							Optional[int]       ,      UpperCAmelCase_ :							str       ,      UpperCAmelCase_ :							Optional[str] = None			)->  Tuple[str]:







              """simple docstring"""
              if not os.path.isdir(UpperCAmelCase_			):
                     logger.error(f"Vocabulary path ({save_directory}) should be a directory"			)
                     return
              UpperCamelCase					 =		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_			) and os.path.isfile(self.vocab_file			):
                     copyfile(self.vocab_file       ,      UpperCAmelCase_			)
              elif not os.path.isfile(self.vocab_file			):
                     with open(UpperCAmelCase_       ,      "wb"			) as fi:
                            UpperCamelCase					 =		self.sp_model.serialized_model_proto()
                            fi.write(UpperCAmelCase_			)

              return (out_vocab_file,)






       def 	_SCREAMING_SNAKE_CASE       (  self :							Dict       ,      UpperCAmelCase_ :							List[str]       ,      UpperCAmelCase_ :							str = "eng_Latn"       ,      UpperCAmelCase_ :							Optional[List[str]] = None       ,      UpperCAmelCase_ :							str = "fra_Latn"       ,      **UpperCAmelCase_ :							Any       ,      )->  BatchEncoding:







              """simple docstring"""
              UpperCamelCase					 =		src_lang
              UpperCamelCase					 =		tgt_lang
              return super().prepare_seqaseq_batch(UpperCAmelCase_       ,      UpperCAmelCase_       ,      **UpperCAmelCase_			)






       def 	_SCREAMING_SNAKE_CASE       (  self :							Any			)->  List[str]:







              """simple docstring"""
              return self.set_src_lang_special_tokens(self.src_lang			)






       def 	_SCREAMING_SNAKE_CASE       (  self :							Optional[int]			)->  List[str]:







              """simple docstring"""
              return self.set_tgt_lang_special_tokens(self.tgt_lang			)






       def 	_SCREAMING_SNAKE_CASE       (  self :							Dict       ,      UpperCAmelCase_ :							Optional[int]			)->  None:







              """simple docstring"""
              UpperCamelCase					 =		self.lang_code_to_id[src_lang]
              if self.legacy_behaviour:
                     UpperCamelCase					 =		[]
                     UpperCamelCase					 =		[self.eos_token_id, self.cur_lang_code]
              else:
                     UpperCamelCase					 =		[self.cur_lang_code]
                     UpperCamelCase					 =		[self.eos_token_id]






       def 	_SCREAMING_SNAKE_CASE       (  self :							Union[str, Any]       ,      UpperCAmelCase_ :							str			)->  None:







              """simple docstring"""
              UpperCamelCase					 =		self.lang_code_to_id[lang]
              if self.legacy_behaviour:
                     UpperCamelCase					 =		[]
                     UpperCamelCase					 =		[self.eos_token_id, self.cur_lang_code]
              else:
                     UpperCamelCase					 =		[self.cur_lang_code]
                     UpperCamelCase					 =		[self.eos_token_id]







"""simple docstring"""


import unittest

from transformers import BarthezTokenizer, BarthezTokenizerFast, BatchEncoding
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow

from ...test_tokenization_common import TokenizerTesterMixin



@require_tokenizers
@require_sentencepiece
@slow  # see https://github.com/huggingface/transformers/issues/11457
class   __a  (      _lowerCAmelCase    ,     unittest.TestCase	):
       UpperCamelCase_		:					Any   =			BarthezTokenizer
       UpperCamelCase_		:					Tuple   =			BarthezTokenizerFast
       UpperCamelCase_		:					str   =			True
       UpperCamelCase_		:					Dict   =			True






       def 	_SCREAMING_SNAKE_CASE       (  self :							List[Any]			)->  int:







              """simple docstring"""
              super().setUp()

              UpperCamelCase					 =		BarthezTokenizerFast.from_pretrained("moussaKam/mbarthez"			)
              tokenizer.save_pretrained(self.tmpdirname			)
              tokenizer.save_pretrained(self.tmpdirname       ,      legacy_format=UpperCAmelCase_			)
              UpperCamelCase					 =		tokenizer






       def 	_SCREAMING_SNAKE_CASE       (  self :							Dict			)->  Optional[Any]:







              """simple docstring"""
              UpperCamelCase					 =		"<pad>"
              UpperCamelCase					 =		1

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






       def 	_SCREAMING_SNAKE_CASE       (  self :							Optional[int]			)->  int:







              """simple docstring"""
              UpperCamelCase					 =		list(self.get_tokenizer().get_vocab().keys()			)

              self.assertEqual(vocab_keys[0]       ,      "<s>"			)
              self.assertEqual(vocab_keys[1]       ,      "<pad>"			)
              self.assertEqual(vocab_keys[-1]       ,      "<mask>"			)
              self.assertEqual(len(UpperCAmelCase_			)       ,      101_122			)






       def 	_SCREAMING_SNAKE_CASE       (  self :							List[Any]			)->  List[str]:







              """simple docstring"""
              self.assertEqual(self.get_tokenizer().vocab_size       ,      101_122			)






       @require_torch
       def 	_SCREAMING_SNAKE_CASE       (  self :							Union[str, Any]			)->  int:







              """simple docstring"""
              UpperCamelCase					 =		["A long paragraph for summarization.", "Another paragraph for summarization."]
              UpperCamelCase					 =		[0, 57, 3_018, 70_307, 91, 2]

              UpperCamelCase					 =		self.tokenizer(
                  UpperCAmelCase_       ,      max_length=len(UpperCAmelCase_			)       ,      padding=UpperCAmelCase_       ,      truncation=UpperCAmelCase_       ,      return_tensors="pt"			)
              self.assertIsInstance(UpperCAmelCase_       ,      UpperCAmelCase_			)

              self.assertEqual((2, 6)       ,      batch.input_ids.shape			)
              self.assertEqual((2, 6)       ,      batch.attention_mask.shape			)
              UpperCamelCase					 =		batch.input_ids.tolist()[0]
              self.assertListEqual(UpperCAmelCase_       ,      UpperCAmelCase_			)






       def 	_SCREAMING_SNAKE_CASE       (  self :							Optional[int]			)->  Optional[int]:







              """simple docstring"""
              if not self.test_rust_tokenizer:
                     return

              UpperCamelCase					 =		self.get_tokenizer()
              UpperCamelCase					 =		self.get_rust_tokenizer()

              UpperCamelCase					 =		"I was born in 92000, and this is falsé."

              UpperCamelCase					 =		tokenizer.tokenize(UpperCAmelCase_			)
              UpperCamelCase					 =		rust_tokenizer.tokenize(UpperCAmelCase_			)
              self.assertListEqual(UpperCAmelCase_       ,      UpperCAmelCase_			)

              UpperCamelCase					 =		tokenizer.encode(UpperCAmelCase_       ,      add_special_tokens=UpperCAmelCase_			)
              UpperCamelCase					 =		rust_tokenizer.encode(UpperCAmelCase_       ,      add_special_tokens=UpperCAmelCase_			)
              self.assertListEqual(UpperCAmelCase_       ,      UpperCAmelCase_			)

              UpperCamelCase					 =		self.get_rust_tokenizer()
              UpperCamelCase					 =		tokenizer.encode(UpperCAmelCase_			)
              UpperCamelCase					 =		rust_tokenizer.encode(UpperCAmelCase_			)
              self.assertListEqual(UpperCAmelCase_       ,      UpperCAmelCase_			)






       @slow
       def 	_SCREAMING_SNAKE_CASE       (  self :							int			)->  Union[str, Any]:







              """simple docstring"""
              # fmt: off
              UpperCamelCase					 =		{"input_ids": [[0, 490, 14_328, 4_507, 354, 47, 43_669, 95, 25, 78_117, 20_215, 19_779, 190, 22, 400, 4, 35_343, 80_310, 603, 86, 24_937, 105, 33_438, 94_762, 196, 39_642, 7, 15, 15_933, 173, 2, 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, 10_534, 87, 25, 66, 3_358, 196, 55_289, 8, 82_961, 81, 2_204, 75_203, 7, 15, 763, 12_956, 216, 178, 14_328, 9_595, 1_377, 69_693, 7, 448, 71_021, 196, 18_106, 1_437, 13_974, 108, 9_083, 4, 49_315, 7, 39, 86, 1_326, 2_793, 46_333, 4, 448, 196, 74_588, 7, 49_315, 7, 39, 21, 822, 38_470, 74, 21, 66_723, 62_480, 8, 22_050, 5, 2]], "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, 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, 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]]}  # noqa: E501
              # fmt: on

              # moussaKam/mbarthez is a french model. So we also use french texts.
              UpperCamelCase					 =		[
                  "Le transformeur est un modèle d'apprentissage profond introduit en 2017, "
                  "utilisé principalement dans le domaine du traitement automatique des langues (TAL).",
                  "À l'instar des réseaux de neurones récurrents (RNN), les transformeurs sont conçus "
                  "pour gérer des données séquentielles, telles que le langage naturel, pour des tâches "
                  "telles que la traduction et la synthèse de texte.",
              ]

              self.tokenizer_integration_test_util(
                  expected_encoding=UpperCAmelCase_       ,      model_name="moussaKam/mbarthez"       ,      revision="c2e4ecbca5e3cd2c37fe1ac285ca4fbdf1366fb6"       ,      sequences=UpperCAmelCase_       ,      )





