




'''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


UpperCamelCase_    	=				logging.get_logger(__name__)

UpperCamelCase_    	=				"▁"

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

UpperCamelCase_    	=				{
    "vocab_file": {
        "facebook/mbart-large-en-ro": (
            "https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/sentencepiece.bpe.model"
        ),
        "facebook/mbart-large-cc25": (
            "https://huggingface.co/facebook/mbart-large-cc25/resolve/main/sentencepiece.bpe.model"
        ),
    }
}

UpperCamelCase_    	=				{
    "facebook/mbart-large-en-ro": 1_0_2_4,
    "facebook/mbart-large-cc25": 1_0_2_4,
}

# fmt: off
UpperCamelCase_    	=				["ar_AR", "cs_CZ", "de_DE", "en_XX", "es_XX", "et_EE", "fi_FI", "fr_XX", "gu_IN", "hi_IN", "it_IT", "ja_XX", "kk_KZ", "ko_KR", "lt_LT", "lv_LV", "my_MM", "ne_NP", "nl_XX", "ro_RO", "ru_RU", "si_LK", "tr_TR", "vi_VN", "zh_CN"]






class 							_a				(			SCREAMING_SNAKE_CASE ):





       '''simple docstring'''






       A						:				List[str]        =						VOCAB_FILES_NAMES
       A						:				str        =						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       A						:				List[str]        =						PRETRAINED_VOCAB_FILES_MAP
       A						:				Dict        =						['''input_ids''', '''attention_mask''']

       A						:				List[int]        =						[]
       A						:				List[int]        =						[]







       def __init__(      self,							A,							A="<s>",							A="</s>",							A="</s>",							A="<s>",							A="<unk>",							A="<pad>",							A="<mask>",							A=None,							A=None,							A=None,							A = None,							A=None,							**A,							):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						AddedToken(A,							lstrip=A,							rstrip=A  ) if isinstance(A,							A  ) else mask_token

             SCREAMING_SNAKE_CASE   :      Tuple    					=						{} if sp_model_kwargs is None else sp_model_kwargs

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

             SCREAMING_SNAKE_CASE   :      int    					=						spm.SentencePieceProcessor(**self.sp_model_kwargs  )
             self.sp_model.Load(str(A  )  )
             SCREAMING_SNAKE_CASE   :      str    					=						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>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
             # spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

             # Mimic fairseq token-to-id alignment for the first 4 token
             SCREAMING_SNAKE_CASE   :      str    					=						{'<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
             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						1

             SCREAMING_SNAKE_CASE   :      Dict    					=						len(self.sp_model  )
             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						{
                 code: self.sp_model_size + i + self.fairseq_offset for i, code in enumerate(A  )
             }
             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						{v: k for k, v in self.lang_code_to_id.items()}
             SCREAMING_SNAKE_CASE   :      int    					=						len(self.sp_model  ) + len(self.lang_code_to_id  ) + self.fairseq_offset

             self.fairseq_tokens_to_ids.update(self.lang_code_to_id  )
             SCREAMING_SNAKE_CASE   :      str    					=						{v: k for k, v in self.fairseq_tokens_to_ids.items()}
             SCREAMING_SNAKE_CASE   :      Tuple    					=						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]  )

             SCREAMING_SNAKE_CASE   :      Dict    					=						src_lang if src_lang is not None else 'en_XX'
             SCREAMING_SNAKE_CASE   :      Tuple    					=						self.lang_code_to_id[self._src_lang]
             SCREAMING_SNAKE_CASE   :      Tuple    					=						tgt_lang
             self.set_src_lang_special_tokens(self._src_lang  )







       def __getstate__(      self  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      List[Any]    					=						self.__dict__.copy()
             SCREAMING_SNAKE_CASE   :      str    					=						None
             SCREAMING_SNAKE_CASE   :      str    					=						self.sp_model.serialized_model_proto()
             return state







       def __setstate__(      self,							A  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      int    					=						d

             # for backward compatibility
             if not hasattr(self,							'sp_model_kwargs'  ):
                   SCREAMING_SNAKE_CASE   :      Tuple    					=						{}

             SCREAMING_SNAKE_CASE   :      Tuple    					=						spm.SentencePieceProcessor(**self.sp_model_kwargs  )
             self.sp_model.LoadFromSerializedProto(self.sp_model_proto  )







       @property
       def        UpperCamelCase_							(      self  ):







             '''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        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             return self._src_lang







       @src_lang.setter
       def        UpperCamelCase_							(      self,							A  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						new_src_lang
             self.set_src_lang_special_tokens(self._src_lang  )







       def        UpperCamelCase_							(      self,							A,							A = None,							A = False  ):







             '''simple docstring'''







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

             SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						[1] * len(self.prefix_tokens  )
             SCREAMING_SNAKE_CASE   :      int    					=						[1] * len(self.suffix_tokens  )
             if token_ids_a is None:
                   return prefix_ones + ([0] * len(A  )) + suffix_ones
             return prefix_ones + ([0] * len(A  )) + ([0] * len(A  )) + suffix_ones







       def        UpperCamelCase_							(      self,							A,							A = None  ):







             '''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        UpperCamelCase_							(      self,							A,							A = None  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						[self.sep_token_id]
             SCREAMING_SNAKE_CASE   :      Tuple    					=						[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        UpperCamelCase_							(      self,							A,							A,							A,							A,							**A  ):







             '''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'  )
             SCREAMING_SNAKE_CASE   :      int    					=						src_lang
             SCREAMING_SNAKE_CASE   :      List[str]    					=						self(A,							add_special_tokens=A,							return_tensors=A,							**A  )
             SCREAMING_SNAKE_CASE   :      Any    					=						self.convert_tokens_to_ids(A  )
             SCREAMING_SNAKE_CASE   :      int    					=						tgt_lang_id
             return inputs







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						{self.convert_ids_to_tokens(A  ): i for i in range(self.vocab_size  )}
             vocab.update(self.added_tokens_encoder  )
             return vocab







       def        UpperCamelCase_							(      self,							A  ):







             '''simple docstring'''







             return self.sp_model.encode(A,							out_type=A  )







       def        UpperCamelCase_							(      self,							A  ):







             '''simple docstring'''







             if token in self.fairseq_tokens_to_ids:
                   return self.fairseq_tokens_to_ids[token]
             SCREAMING_SNAKE_CASE   :      List[Any]    					=						self.sp_model.PieceToId(A  )

             # 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        UpperCamelCase_							(      self,							A  ):







             '''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        UpperCamelCase_							(      self,							A  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      str    					=						''.join(A  ).replace(A,							' '  ).strip()
             return out_string







       def        UpperCamelCase_							(      self,							A,							A = None  ):







             '''simple docstring'''







             if not os.path.isdir(A  ):
                   logger.error(F"Vocabulary path ({save_directory}) should be a directory"  )
                   return
             SCREAMING_SNAKE_CASE   :      Dict    					=						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  ) and os.path.isfile(self.vocab_file  ):
                   copyfile(self.vocab_file,							A  )
             elif not os.path.isfile(self.vocab_file  ):
                   with open(A,							'wb'  ) as fi:
                         SCREAMING_SNAKE_CASE   :      Tuple    					=						self.sp_model.serialized_model_proto()
                         fi.write(A  )

             return (out_vocab_file,)







       def        UpperCamelCase_							(      self,							A,							A = "en_XX",							A = None,							A = "ro_RO",							**A,							):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						src_lang
             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						tgt_lang
             return super().prepare_seqaseq_batch(A,							A,							**A  )







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             return self.set_src_lang_special_tokens(self.src_lang  )







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             return self.set_tgt_lang_special_tokens(self.tgt_lang  )







       def        UpperCamelCase_							(      self,							A  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Dict    					=						self.lang_code_to_id[src_lang]
             SCREAMING_SNAKE_CASE   :      int    					=						[]
             SCREAMING_SNAKE_CASE   :      int    					=						[self.eos_token_id, self.cur_lang_code]







       def        UpperCamelCase_							(      self,							A  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      int    					=						self.lang_code_to_id[lang]
             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						[]
             SCREAMING_SNAKE_CASE   :      Dict    					=						[self.eos_token_id, self.cur_lang_code]






import unittest

from transformers import BertGenerationTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


SCREAMING_SNAKE_CASE__					:     Optional[int]       =     '▁'

SCREAMING_SNAKE_CASE__					:     Optional[Any]       =     get_tests_dir('fixtures/test_sentencepiece.model')


@require_sentencepiece
class 		_SCREAMING_SNAKE_CASE     (						A							,	unittest.TestCase     ):
     __SCREAMING_SNAKE_CASE     					=    BertGenerationTokenizer
     __SCREAMING_SNAKE_CASE     					=    False
     __SCREAMING_SNAKE_CASE     					=    True


     def        __snake_case(  self    ):
       super().setUp()

       _UpperCAmelCase		:							Optional[int]							=     BertGenerationTokenizer(A_							,					keep_accents=A_    )
       tokenizer.save_pretrained(self.tmpdirname    )


     def        __snake_case(  self    ):
       _UpperCAmelCase		:							Optional[int]							=     """<s>"""
       _UpperCAmelCase		:							Any							=     1

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


     def        __snake_case(  self    ):
       _UpperCAmelCase		:							Optional[int]							=     list(self.get_tokenizer().get_vocab().keys()    )

       self.assertEqual(vocab_keys[0]							,					"""<unk>"""    )
       self.assertEqual(vocab_keys[1]							,					"""<s>"""    )
       self.assertEqual(vocab_keys[-1]							,					"""<pad>"""    )
       self.assertEqual(len(A_    )							,					10_02    )


     def        __snake_case(  self    ):
       self.assertEqual(self.get_tokenizer().vocab_size							,					10_00    )


     def        __snake_case(  self    ):
       _UpperCAmelCase		:							Union[str, Any]							=     BertGenerationTokenizer(A_							,					keep_accents=A_    )

       _UpperCAmelCase		:							List[Any]							=     tokenizer.tokenize("""This is a test"""    )
       self.assertListEqual(A_							,					["""▁This""", """▁is""", """▁a""", """▁t""", """est"""]    )

       self.assertListEqual(
           tokenizer.convert_tokens_to_ids(A_    )							,					[2_85, 46, 10, 1_70, 3_82]							,					)

       _UpperCAmelCase		:							int							=     tokenizer.tokenize("""I was born in 92000, and this is falsé."""    )
       self.assertListEqual(
           A_							,					[
               SPIECE_UNDERLINE + """I""",
               SPIECE_UNDERLINE + """was""",
               SPIECE_UNDERLINE + """b""",
               """or""",
               """n""",
               SPIECE_UNDERLINE + """in""",
               SPIECE_UNDERLINE + """""",
               """9""",
               """2""",
               """0""",
               """0""",
               """0""",
               """,""",
               SPIECE_UNDERLINE + """and""",
               SPIECE_UNDERLINE + """this""",
               SPIECE_UNDERLINE + """is""",
               SPIECE_UNDERLINE + """f""",
               """al""",
               """s""",
               """é""",
               """.""",
           ]							,					)
       _UpperCAmelCase		:							List[str]							=     tokenizer.convert_tokens_to_ids(A_    )
       self.assertListEqual(
           A_							,					[8, 21, 84, 55, 24, 19, 7, 0, 6_02, 3_47, 3_47, 3_47, 3, 12, 66, 46, 72, 80, 6, 0, 4]							,					)

       _UpperCAmelCase		:							Tuple							=     tokenizer.convert_ids_to_tokens(A_    )
       self.assertListEqual(
           A_							,					[
               SPIECE_UNDERLINE + """I""",
               SPIECE_UNDERLINE + """was""",
               SPIECE_UNDERLINE + """b""",
               """or""",
               """n""",
               SPIECE_UNDERLINE + """in""",
               SPIECE_UNDERLINE + """""",
               """<unk>""",
               """2""",
               """0""",
               """0""",
               """0""",
               """,""",
               SPIECE_UNDERLINE + """and""",
               SPIECE_UNDERLINE + """this""",
               SPIECE_UNDERLINE + """is""",
               SPIECE_UNDERLINE + """f""",
               """al""",
               """s""",
               """<unk>""",
               """.""",
           ]							,					)


     @cached_property
     def        __snake_case(  self    ):
       return BertGenerationTokenizer.from_pretrained("""google/bert_for_seq_generation_L-24_bbc_encoder"""    )


     @slow
     def        __snake_case(  self    ):
       _UpperCAmelCase		:							Optional[Any]							=     """Hello World!"""
       _UpperCAmelCase		:							str							=     [1_85_36, 22_60, 1_01]

       self.assertListEqual(A_							,					self.big_tokenizer.encode(A_    )    )


     @slow
     def        __snake_case(  self    ):
       _UpperCAmelCase		:							List[str]							=     (
           """This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) \" [ ] ! : - . Also we will"""
           """ add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth"""
       )
       _UpperCAmelCase		:							List[Any]							=     [
           8_71,
           4_19,
           3_58,
           9_46,
           9_91,
           25_21,
           4_52,
           3_58,
           13_57,
           3_87,
           77_51,
           35_36,
           1_12,
           9_85,
           4_56,
           1_26,
           8_65,
           9_38,
           54_00,
           57_34,
           4_58,
           13_68,
           4_67,
           7_86,
           24_62,
           52_46,
           11_59,
           6_33,
           8_65,
           45_19,
           4_57,
           5_82,
           8_52,
           25_57,
           4_27,
           9_16,
           5_08,
           4_05,
           3_43_24,
           4_97,
           3_91,
           4_08,
           1_13_42,
           12_44,
           3_85,
           1_00,
           9_38,
           9_85,
           4_56,
           5_74,
           3_62,
           1_25_97,
           32_00,
           31_29,
           11_72,
       ]

       self.assertListEqual(A_							,					self.big_tokenizer.encode(A_    )    )


     @require_torch
     @slow
     def        __snake_case(  self    ):
       import torch

       from transformers import BertGenerationConfig, BertGenerationEncoder

       # Build sequence
       _UpperCAmelCase		:							Optional[Any]							=     list(self.big_tokenizer.get_vocab().keys()    )[:10]
       _UpperCAmelCase		:							Tuple							=     """ """.join(A_    )
       _UpperCAmelCase		:							List[Any]							=     self.big_tokenizer.encode_plus(A_							,					return_tensors="""pt"""							,					return_token_type_ids=A_    )
       _UpperCAmelCase		:							Any							=     self.big_tokenizer.batch_encode_plus(
           [sequence + """ """ + sequence]							,					return_tensors="""pt"""							,					return_token_type_ids=A_    )

       _UpperCAmelCase		:							int							=     BertGenerationConfig()
       _UpperCAmelCase		:							Dict							=     BertGenerationEncoder(A_    )

       assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size

       with torch.no_grad():
         model(**A_    )
         model(**A_    )




     @slow
     def        __snake_case(  self    ):
       # fmt: off
       _UpperCAmelCase		:							Optional[int]							=     {"""input_ids""": [[3_92_86, 4_58, 3_63_35, 20_01, 4_56, 1_30_73, 1_32_66, 4_55, 1_13, 77_46, 17_41, 1_11_57, 3_91, 1_30_73, 1_32_66, 4_55, 1_13, 39_67, 3_54_12, 1_13, 49_36, 1_09, 38_70, 23_77, 1_13, 3_00_84, 4_57_20, 4_58, 1_34, 1_74_96, 1_12, 5_03, 1_16_72, 1_13, 1_18, 1_12, 56_65, 1_33_47, 3_86_87, 1_12, 14_96, 3_13_89, 1_12, 32_68, 4_72_64, 1_34, 9_62, 1_12, 1_63_77, 80_35, 2_31_30, 4_30, 1_21_69, 1_55_18, 2_85_92, 4_58, 1_46, 4_16_97, 1_09, 3_91, 1_21_69, 1_55_18, 1_66_89, 4_58, 1_46, 4_13_58, 1_09, 4_52, 7_26, 40_34, 1_11, 7_63, 3_54_12, 50_82, 3_88, 19_03, 1_11, 90_51, 3_91, 28_70, 4_89_18, 19_00, 11_23, 5_50, 9_98, 1_12, 95_86, 1_59_85, 4_55, 3_91, 4_10, 2_29_55, 3_76_36, 1_14], [4_48, 1_74_96, 4_19, 36_63, 3_85, 7_63, 1_13, 2_75_33, 28_70, 32_83, 1_30_43, 16_39, 2_47_13, 5_23, 6_56, 2_40_13, 1_85_50, 25_21, 5_17, 2_70_14, 2_12_44, 4_20, 12_12, 14_65, 3_91, 9_27, 48_33, 3_88, 5_78, 1_17_86, 1_14, 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], [4_84, 21_69, 76_87, 2_19_32, 1_81_46, 7_26, 3_63, 1_70_32, 33_91, 1_14, 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]], """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, 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, 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=A_							,					model_name="""google/bert_for_seq_generation_L-24_bbc_encoder"""							,					revision="""c817d1fd1be2ffa69431227a1fe320544943d4db"""							,					)





