



"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowercase__		:				str          =	{"""configuration_opt""": ["""OPT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """OPTConfig"""]}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowercase__		:				Any          =	[
         """OPT_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """OPTForCausalLM""",
         """OPTModel""",
         """OPTPreTrainedModel""",
         """OPTForSequenceClassification""",
         """OPTForQuestionAnswering""",
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowercase__		:				List[str]          =	["""TFOPTForCausalLM""", """TFOPTModel""", """TFOPTPreTrainedModel"""]

try:
     if not is_flax_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowercase__		:				str          =	[
         """FlaxOPTForCausalLM""",
         """FlaxOPTModel""",
         """FlaxOPTPreTrainedModel""",
     ]


if TYPE_CHECKING:
     from .configuration_opt import OPT_PRETRAINED_CONFIG_ARCHIVE_MAP, OPTConfig

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_opt import (
              OPT_PRETRAINED_MODEL_ARCHIVE_LIST,
              OPTForCausalLM,
              OPTForQuestionAnswering,
              OPTForSequenceClassification,
              OPTModel,
              OPTPreTrainedModel,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_opt import TFOPTForCausalLM, TFOPTModel, TFOPTPreTrainedModel

     try:
          if not is_flax_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_flax_opt import FlaxOPTForCausalLM, FlaxOPTModel, FlaxOPTPreTrainedModel

else:
     import sys

     lowercase__		:				Optional[Any]          =	_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)










"""simple docstring"""







import pickle
import shutil
import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, XLMRobertaTokenizer, XLMRobertaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


lowercase__		:				Dict          =	get_tests_dir("""fixtures/test_sentencepiece.model""")







@require_sentencepiece
@require_tokenizers
class       UpperCamelCase__   (	lowercase_,					unittest.TestCase  ):




      """simple docstring"""




      _SCREAMING_SNAKE_CASE  				=     XLMRobertaTokenizer
      _SCREAMING_SNAKE_CASE  				=     XLMRobertaTokenizerFast
      _SCREAMING_SNAKE_CASE  				=     True
      _SCREAMING_SNAKE_CASE  				=     True



      def      SCREAMING_SNAKE_CASE__ (      self				:      List[str]       ):
        super().setUp()

        # We have a SentencePiece fixture for testing
        lowerCAmelCase_   :				Any								= XLMRobertaTokenizer(SCREAMING_SNAKE_CASE_    ,			keep_accents=SCREAMING_SNAKE_CASE_       )
        tokenizer.save_pretrained(self.tmpdirname       )



      def      SCREAMING_SNAKE_CASE__ (      self				:      List[Any]       ):
        lowerCAmelCase_   :				Any								= '<pad>'
        lowerCAmelCase_   :				int								= 1

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



      def      SCREAMING_SNAKE_CASE__ (      self				:      Union[str, Any]       ):
        lowerCAmelCase_   :				int								= 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(SCREAMING_SNAKE_CASE_       )    ,			1_0_0_2       )



      def      SCREAMING_SNAKE_CASE__ (      self				:      Optional[Any]       ):
        self.assertEqual(self.get_tokenizer().vocab_size    ,			1_0_0_2       )



      def      SCREAMING_SNAKE_CASE__ (      self				:      Optional[Any]       ):
        lowerCAmelCase_   :				int								= XLMRobertaTokenizer(SCREAMING_SNAKE_CASE_    ,			keep_accents=SCREAMING_SNAKE_CASE_       )

        lowerCAmelCase_   :				Optional[Any]								= tokenizer.tokenize('This is a test'       )
        self.assertListEqual(SCREAMING_SNAKE_CASE_    ,			['▁This', '▁is', '▁a', '▁t', 'est']       )

        self.assertListEqual(
            tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE_       )    ,			[value + tokenizer.fairseq_offset for value in [2_8_5, 4_6, 1_0, 1_7_0, 3_8_2]]    ,			)

        lowerCAmelCase_   :				int								= tokenizer.tokenize('I was born in 92000, and this is falsé.'       )
        self.assertListEqual(
            SCREAMING_SNAKE_CASE_    ,			[
                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',
                '.',
            ]    ,			)
        lowerCAmelCase_   :				Any								= tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE_       )
        self.assertListEqual(
            SCREAMING_SNAKE_CASE_    ,			[
                value + tokenizer.fairseq_offset
                for value in [8, 2_1, 8_4, 5_5, 2_4, 1_9, 7, 2, 6_0_2, 3_4_7, 3_4_7, 3_4_7, 3, 1_2, 6_6, 4_6, 7_2, 8_0, 6, 2, 4]
                #                                       ^ unk: 2 + 1 = 3                  unk: 2 + 1 = 3 ^
            ]    ,			)

        lowerCAmelCase_   :				Union[str, Any]								= tokenizer.convert_ids_to_tokens(SCREAMING_SNAKE_CASE_       )
        self.assertListEqual(
            SCREAMING_SNAKE_CASE_    ,			[
                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>',
                '.',
            ]    ,			)



      def      SCREAMING_SNAKE_CASE__ (      self				:      int       ):
        if not self.test_slow_tokenizer:
          # as we don't have a slow version, we can't compare the outputs between slow and fast versions
          return

        lowerCAmelCase_   :				List[str]								= (self.rust_tokenizer_class, 'hf-internal-testing/tiny-xlm-roberta', {})
        for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
          with self.subTest(F"{tokenizer.__class__.__name__} ({pretrained_name})"       ):
            lowerCAmelCase_   :				str								= self.rust_tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE_    ,			**SCREAMING_SNAKE_CASE_       )
            lowerCAmelCase_   :				Optional[int]								= self.tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE_    ,			**SCREAMING_SNAKE_CASE_       )

            lowerCAmelCase_   :				str								= tempfile.mkdtemp()

            lowerCAmelCase_   :				int								= tokenizer_r.save_pretrained(SCREAMING_SNAKE_CASE_       )
            lowerCAmelCase_   :				Optional[int]								= tokenizer_p.save_pretrained(SCREAMING_SNAKE_CASE_       )

            # Checks it save with the same files + the tokenizer.json file for the fast one
            self.assertTrue(any('tokenizer.json' in f for f in tokenizer_r_files       )       )
            lowerCAmelCase_   :				Optional[Any]								= tuple(f for f in tokenizer_r_files if 'tokenizer.json' not in f       )
            self.assertSequenceEqual(SCREAMING_SNAKE_CASE_    ,			SCREAMING_SNAKE_CASE_       )

            # Checks everything loads correctly in the same way
            lowerCAmelCase_   :				int								= tokenizer_r.from_pretrained(SCREAMING_SNAKE_CASE_       )
            lowerCAmelCase_   :				Optional[int]								= tokenizer_p.from_pretrained(SCREAMING_SNAKE_CASE_       )

            # Check special tokens are set accordingly on Rust and Python
            for key in tokenizer_pp.special_tokens_map:
              self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_    ,			SCREAMING_SNAKE_CASE_       )       )
              # self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
              # self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))

            shutil.rmtree(SCREAMING_SNAKE_CASE_       )

            # Save tokenizer rust, legacy_format=True
            lowerCAmelCase_   :				str								= tempfile.mkdtemp()

            lowerCAmelCase_   :				List[str]								= tokenizer_r.save_pretrained(SCREAMING_SNAKE_CASE_    ,			legacy_format=SCREAMING_SNAKE_CASE_       )
            lowerCAmelCase_   :				str								= tokenizer_p.save_pretrained(SCREAMING_SNAKE_CASE_       )

            # Checks it save with the same files
            self.assertSequenceEqual(SCREAMING_SNAKE_CASE_    ,			SCREAMING_SNAKE_CASE_       )

            # Checks everything loads correctly in the same way
            lowerCAmelCase_   :				str								= tokenizer_r.from_pretrained(SCREAMING_SNAKE_CASE_       )
            lowerCAmelCase_   :				Union[str, Any]								= tokenizer_p.from_pretrained(SCREAMING_SNAKE_CASE_       )

            # Check special tokens are set accordingly on Rust and Python
            for key in tokenizer_pp.special_tokens_map:
              self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_    ,			SCREAMING_SNAKE_CASE_       )       )

            shutil.rmtree(SCREAMING_SNAKE_CASE_       )

            # Save tokenizer rust, legacy_format=False
            lowerCAmelCase_   :				int								= tempfile.mkdtemp()

            lowerCAmelCase_   :				Optional[Any]								= tokenizer_r.save_pretrained(SCREAMING_SNAKE_CASE_    ,			legacy_format=SCREAMING_SNAKE_CASE_       )
            lowerCAmelCase_   :				int								= tokenizer_p.save_pretrained(SCREAMING_SNAKE_CASE_       )

            # Checks it saved the tokenizer.json file
            self.assertTrue(any('tokenizer.json' in f for f in tokenizer_r_files       )       )

            # Checks everything loads correctly in the same way
            lowerCAmelCase_   :				List[str]								= tokenizer_r.from_pretrained(SCREAMING_SNAKE_CASE_       )
            lowerCAmelCase_   :				Optional[int]								= tokenizer_p.from_pretrained(SCREAMING_SNAKE_CASE_       )

            # Check special tokens are set accordingly on Rust and Python
            for key in tokenizer_pp.special_tokens_map:
              self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_    ,			SCREAMING_SNAKE_CASE_       )       )

            shutil.rmtree(SCREAMING_SNAKE_CASE_       )



      @cached_property
      def      SCREAMING_SNAKE_CASE__ (      self				:      Optional[Any]       ):
        return XLMRobertaTokenizer.from_pretrained('xlm-roberta-base'       )



      def      SCREAMING_SNAKE_CASE__ (      self				:      int       ):
        with tempfile.NamedTemporaryFile() as f:
          shutil.copyfile(SCREAMING_SNAKE_CASE_    ,			f.name       )
          lowerCAmelCase_   :				Tuple								= XLMRobertaTokenizer(f.name    ,			keep_accents=SCREAMING_SNAKE_CASE_       )
          lowerCAmelCase_   :				Dict								= pickle.dumps(SCREAMING_SNAKE_CASE_       )
        pickle.loads(SCREAMING_SNAKE_CASE_       )



      def      SCREAMING_SNAKE_CASE__ (      self				:      Union[str, Any]       ):
        if not self.test_rust_tokenizer:
          return

        lowerCAmelCase_   :				Union[str, Any]								= self.get_tokenizer()
        lowerCAmelCase_   :				Dict								= self.get_rust_tokenizer()

        lowerCAmelCase_   :				Tuple								= 'I was born in 92000, and this is falsé.'

        lowerCAmelCase_   :				Union[str, Any]								= tokenizer.tokenize(SCREAMING_SNAKE_CASE_       )
        lowerCAmelCase_   :				Dict								= rust_tokenizer.tokenize(SCREAMING_SNAKE_CASE_       )
        self.assertListEqual(SCREAMING_SNAKE_CASE_    ,			SCREAMING_SNAKE_CASE_       )

        lowerCAmelCase_   :				Any								= tokenizer.encode(SCREAMING_SNAKE_CASE_    ,			add_special_tokens=SCREAMING_SNAKE_CASE_       )
        lowerCAmelCase_   :				List[str]								= rust_tokenizer.encode(SCREAMING_SNAKE_CASE_    ,			add_special_tokens=SCREAMING_SNAKE_CASE_       )
        self.assertListEqual(SCREAMING_SNAKE_CASE_    ,			SCREAMING_SNAKE_CASE_       )

        lowerCAmelCase_   :				int								= self.get_rust_tokenizer()
        lowerCAmelCase_   :				Any								= tokenizer.encode(SCREAMING_SNAKE_CASE_       )
        lowerCAmelCase_   :				List[Any]								= rust_tokenizer.encode(SCREAMING_SNAKE_CASE_       )
        self.assertListEqual(SCREAMING_SNAKE_CASE_    ,			SCREAMING_SNAKE_CASE_       )



      @slow
      def      SCREAMING_SNAKE_CASE__ (      self				:      Any       ):
        lowerCAmelCase_   :				Any								= 'Hello World!'
        lowerCAmelCase_   :				Union[str, Any]								= [0, 3_5_3_7_8, 6_6_6_1, 3_8, 2]
        # xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')  # xlmr.large has same tokenizer
        # xlmr.eval()
        # xlmr.encode(symbols)

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



      @slow
      def      SCREAMING_SNAKE_CASE__ (      self				:      Union[str, Any]       ):
        lowerCAmelCase_   :				Tuple								= (
            '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'
        )
        lowerCAmelCase_   :				int								= [
            0,
            3_2_9_3,
            8_3,
            1_0,
            4_5_5_2,
            4_9_8_9,
            7_9_8_6,
            6_7_8,
            1_0,
            5_9_1_5,
            1_1_1,
            1_7_9_4_5_9,
            1_2_4_8_5_0,
            4,
            6_0_4_4,
            2_3_7,
            1_2,
            6,
            5,
            6,
            4,
            6_7_8_0,
            7_0_5,
            1_5,
            1_3_8_8,
            4_4,
            3_7_8,
            1_0_1_1_4,
            7_1_1,
            1_5_2,
            2_0,
            6,
            5,
            2_2_3_7_6,
            6_4_2,
            1_2_2_1,
            1_5_1_9_0,
            3_4_1_5_3,
            4_5_0,
            5_6_0_8,
            9_5_9,
            1_1_1_9,
            5_7_7_0_2,
            1_3_6,
            1_8_6,
            4_7,
            1_0_9_8,
            2_9_3_6_7,
            4_7,
            # 4426, # What fairseq tokenizes from "<unk>": "_<"
            # 3678, # What fairseq tokenizes from "<unk>": "unk"
            # 2740, # What fairseq tokenizes from "<unk>": ">"
            3,  # What we tokenize from "<unk>": "<unk>"
            6,  # Residue from the tokenization: an extra sentencepiece underline
            4,
            6_0_4_4,
            2_3_7,
            6_2_8_4,
            5_0_9_0_1,
            5_2_8,
            3_1,
            9_0,
            3_4,
            9_2_7,
            2,
        ]
        # xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')  # xlmr.large has same tokenizer
        # xlmr.eval()
        # xlmr.encode(symbols)

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





      @slow
      def      SCREAMING_SNAKE_CASE__ (      self				:      int       ):
        # fmt: off
        lowerCAmelCase_   :				List[str]								= {'input_ids': [[0, 1_1_0_6_2, 8_2_7_7_2, 7, 1_5, 8_2_7_7_2, 5_3_8, 5_1_5_2_9, 2_3_7, 1_7_1_9_8, 1_2_9_0, 2_0_6, 9, 2_1_5_1_7_5, 1_3_1_4, 1_3_6, 1_7_1_9_8, 1_2_9_0, 2_0_6, 9, 5_6_3_5_9, 4_2, 1_2_2_0_0_9, 9, 1_6_4_6_6, 1_6, 8_7_3_4_4, 4_5_3_7, 9, 4_7_1_7, 7_8_3_8_1, 6, 1_5_9_9_5_8, 7, 1_5, 2_4_4_8_0, 6_1_8, 4, 5_2_7, 2_2_6_9_3, 5_4_2_8, 4, 2_7_7_7, 2_4_4_8_0, 9_8_7_4, 4, 4_3_5_2_3, 5_9_4, 4, 8_0_3, 1_8_3_9_2, 3_3_1_8_9, 1_8, 4, 4_3_5_2_3, 2_4_4_4_7, 1_2_3_9_9, 1_0_0, 2_4_9_5_5, 8_3_6_5_8, 9_6_2_6, 1_4_4_0_5_7, 1_5, 8_3_9, 2_2_3_3_5, 1_6, 1_3_6, 2_4_9_5_5, 8_3_6_5_8, 8_3_4_7_9, 1_5, 3_9_1_0_2, 7_2_4, 1_6, 6_7_8, 6_4_5, 2_7_8_9, 1_3_2_8, 4_5_8_9, 4_2, 1_2_2_0_0_9, 1_1_5_7_7_4, 2_3, 8_0_5, 1_3_2_8, 4_6_8_7_6, 7, 1_3_6, 5_3_8_9_4, 1_9_4_0, 4_2_2_2_7, 4_1_1_5_9, 1_7_7_2_1, 8_2_3, 4_2_5, 4, 2_7_5_1_2, 9_8_7_2_2, 2_0_6, 1_3_6, 5_5_3_1, 4_9_7_0, 9_1_9, 1_7_3_3_6, 5, 2], [0, 2_0_0_8_0, 6_1_8, 8_3, 8_2_7_7_5, 4_7, 4_7_9, 9, 1_5_1_7, 7_3, 5_3_8_9_4, 3_3_3, 8_0_5_8_1, 1_1_0_1_1_7, 1_8_8_1_1, 5_2_5_6, 1_2_9_5, 5_1, 1_5_2_5_2_6, 2_9_7, 7_9_8_6, 3_9_0, 1_2_4_4_1_6, 5_3_8, 3_5_4_3_1, 2_1_4, 9_8, 1_5_0_4_4, 2_5_7_3_7, 1_3_6, 7_1_0_8, 4_3_7_0_1, 2_3, 7_5_6, 1_3_5_3_5_5, 7, 5, 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, 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, 5_8_1, 6_3_7_7_3, 1_1_9_4_5_5, 6, 1_4_7_7_9_7, 8_8_2_0_3, 7, 6_4_5, 7_0, 2_1, 3_2_8_5, 1_0_2_6_9, 5, 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, 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]], '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, 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], [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, 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=SCREAMING_SNAKE_CASE_    ,			model_name='xlm-roberta-base'    ,			revision='d9d8a8ea5eb94b1c6654ae9249df7793cd2933d3'    ,			)





