



"""simple docstring"""

import enum
import warnings

from ..tokenization_utils import TruncationStrategy
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_tf_available():
     import tensorflow as tf

     from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING

if is_torch_available():
     from ..models.auto.modeling_auto import MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING

lowercase__    :  Optional[int]							    =	logging.get_logger(__name__)




class        _UpperCAmelCase       (			enum.Enum):
 _lowerCAmelCase   :			List[str]							=		0
 _lowerCAmelCase   :			Optional[int]							=		1





@add_end_docstrings(lowerCAmelCase__)
class        _UpperCAmelCase       (			lowerCAmelCase__):
 _lowerCAmelCase   :			Any							=		"""generated"""

 def __init__(     self		:       Tuple  ,     *lowercase_		:       Optional[Any]  ,     **lowercase_		:       Optional[Any]				):
     super().__init__(*lowercase_  ,     **lowercase_				)

     self.check_model_type(
         TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
         if self.framework == '''tf'''
         else MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING				)

 def 						_snake_case					(     self		:       List[str]  ,     lowercase_		:       Tuple=None  ,     lowercase_		:       str=None  ,     lowercase_		:       int=None  ,     lowercase_		:       Dict=None  ,     lowercase_		:       List[Any]=None  ,     lowercase_		:       Dict=None  ,     **lowercase_		:       Optional[Any]  ,     ):
     snake_case_ :     Union[str, Any]				=	{}
     if truncation is not None:
         snake_case_ :     Dict				=	truncation

     snake_case_ :     Union[str, Any]				=	generate_kwargs

     snake_case_ :     List[Any]				=	{}
     if return_tensors is not None and return_type is None:
         snake_case_ :     Dict				=	ReturnType.TENSORS if return_tensors else ReturnType.TEXT
     if return_type is not None:
         snake_case_ :     List[str]				=	return_type

     if clean_up_tokenization_spaces is not None:
         snake_case_ :     str				=	clean_up_tokenization_spaces

     if stop_sequence is not None:
         snake_case_ :     Optional[int]				=	self.tokenizer.encode(lowercase_  ,     add_special_tokens=lowercase_				)
         if len(lowercase_				) > 1:
             warnings.warn(
                 '''Stopping on a multiple token sequence is not yet supported on transformers. The first token of'''
                 ''' the stop sequence will be used as the stop sequence string in the interim.'''				)
         snake_case_ :     List[Any]				=	stop_sequence_ids[0]

     return preprocess_params, forward_params, postprocess_params

 def 						_snake_case					(     self		:       List[Any]  ,     lowercase_		:       int  ,     lowercase_		:       int  ,     lowercase_		:       int				):
     return True

 def 						_snake_case					(     self		:       Tuple  ,     *lowercase_		:       List[str]  ,     lowercase_		:       Optional[int]				):
     snake_case_ :     str				=	self.model.config.prefix if self.model.config.prefix is not None else ''''''
     if isinstance(args[0]  ,     lowercase_				):
         if self.tokenizer.pad_token_id is None:
             raise ValueError('''Please make sure that the tokenizer has a pad_token_id when using a batch input'''				)
         snake_case_ :     int				=	([prefix + arg for arg in args[0]],)
         snake_case_ :     Optional[int]				=	True

     elif isinstance(args[0]  ,     lowercase_				):
         snake_case_ :     Tuple				=	(prefix + args[0],)
         snake_case_ :     List[str]				=	False
     else:
         raise ValueError(
             f" `args[0]`: {args[0]} have the wrong format. The should be either of type `str` or type `list`"				)
     snake_case_ :     str				=	self.tokenizer(*lowercase_  ,     padding=lowercase_  ,     truncation=lowercase_  ,     return_tensors=self.framework				)
     # This is produced by tokenizers but is an invalid generate kwargs
     if "token_type_ids" in inputs:
         del inputs["token_type_ids"]
     return inputs

 def __call__(     self		:       int  ,     *lowercase_		:       Optional[Any]  ,     **lowercase_		:       str				):
     snake_case_ :     Any				=	super().__call__(*lowercase_  ,     **lowercase_				)
     if (
         isinstance(args[0]  ,     lowercase_				)
         and all(isinstance(lowercase_  ,     lowercase_				) for el in args[0]				)
         and all(len(lowercase_				) == 1 for res in result				)
     ):
         return [res[0] for res in result]
     return result

 def 						_snake_case					(     self		:       Optional[int]  ,     lowercase_		:       List[Any]  ,     lowercase_		:       Optional[int]=TruncationStrategy.DO_NOT_TRUNCATE  ,     **lowercase_		:       Dict				):
     snake_case_ :     Tuple				=	self._parse_and_tokenize(lowercase_  ,     truncation=lowercase_  ,     **lowercase_				)
     return inputs

 def 						_snake_case					(     self		:       Optional[Any]  ,     lowercase_		:       List[Any]  ,     **lowercase_		:       Tuple				):
     if self.framework == "pt":
         snake_case_,	snake_case_ :     List[Any]				=	model_inputs['''input_ids'''].shape
     elif self.framework == "tf":
         snake_case_,	snake_case_ :     List[Any]				=	tf.shape(model_inputs['''input_ids''']				).numpy()

     snake_case_ :     Union[str, Any]				=	generate_kwargs.get('''min_length'''  ,     self.model.config.min_length				)
     snake_case_ :     Optional[Any]				=	generate_kwargs.get('''max_length'''  ,     self.model.config.max_length				)
     self.check_inputs(lowercase_  ,     generate_kwargs['''min_length''']  ,     generate_kwargs['''max_length''']				)
     snake_case_ :     List[str]				=	self.model.generate(**lowercase_  ,     **lowercase_				)
     snake_case_ :     Tuple				=	output_ids.shape[0]
     if self.framework == "pt":
         snake_case_ :     Dict				=	output_ids.reshape(lowercase_  ,     out_b // in_b  ,     *output_ids.shape[1:]				)
     elif self.framework == "tf":
         snake_case_ :     Optional[Any]				=	tf.reshape(lowercase_  ,     (in_b, out_b // in_b, *output_ids.shape[1:])				)
     return {"output_ids": output_ids}






 def 						_snake_case					(     self		:       Any  ,     lowercase_		:       Any  ,     lowercase_		:       Union[str, Any]=ReturnType.TEXT  ,     lowercase_		:       List[Any]=False				):
     snake_case_ :     Union[str, Any]				=	[]
     for output_ids in model_outputs["output_ids"][0]:
         if return_type == ReturnType.TENSORS:
             snake_case_ :     Union[str, Any]				=	{f"{self.return_name}_token_ids": output_ids}
         elif return_type == ReturnType.TEXT:
             snake_case_ :     List[Any]				=	{
                 f"{self.return_name}_text": self.tokenizer.decode(
                     lowercase_  ,     skip_special_tokens=lowercase_  ,     clean_up_tokenization_spaces=lowercase_  ,     )
             }
         records.append(lowercase_				)
     return records





@add_end_docstrings(lowerCAmelCase__)
class        _UpperCAmelCase       (			lowerCAmelCase__):
 _lowerCAmelCase   :			Dict							=		"""summary"""

 def __call__(     self		:       Tuple  ,     *lowercase_		:       Any  ,     **lowercase_		:       int				):
     return super().__call__(*lowercase_  ,     **lowercase_				)






 def 						_snake_case					(     self		:       Dict  ,     lowercase_		:       int  ,     lowercase_		:       int  ,     lowercase_		:       int				):
     if max_length < min_length:
         logger.warning(f"Your min_length={min_length} must be inferior than your max_length={max_length}."				)

     if input_length < max_length:
         logger.warning(
             f"Your max_length is set to {max_length}, but your input_length is only {input_length}. Since this is "
             '''a summarization task, where outputs shorter than the input are typically wanted, you might '''
             f"consider decreasing max_length manually, e.g. summarizer('...', max_length={input_length//2})"				)





@add_end_docstrings(lowerCAmelCase__)
class        _UpperCAmelCase       (			lowerCAmelCase__):
 _lowerCAmelCase   :			Union[str, Any]							=		"""translation"""

 def 						_snake_case					(     self		:       List[Any]  ,     lowercase_		:       int  ,     lowercase_		:       int  ,     lowercase_		:       int				):
     if input_length > 0.9 * max_length:
         logger.warning(
             f"Your input_length: {input_length} is bigger than 0.9 * max_length: {max_length}. You might consider "
             '''increasing your max_length manually, e.g. translator(\'...\', max_length=400)'''				)
     return True

 def 						_snake_case					(     self		:       str  ,     *lowercase_		:       List[str]  ,     lowercase_		:       List[str]=TruncationStrategy.DO_NOT_TRUNCATE  ,     lowercase_		:       Optional[int]=None  ,     lowercase_		:       List[str]=None				):
     if getattr(self.tokenizer  ,     '''_build_translation_inputs'''  ,     lowercase_				):
         return self.tokenizer._build_translation_inputs(
             *lowercase_  ,     return_tensors=self.framework  ,     truncation=lowercase_  ,     src_lang=lowercase_  ,     tgt_lang=lowercase_				)
     else:
         return super()._parse_and_tokenize(*lowercase_  ,     truncation=lowercase_				)

 def 						_snake_case					(     self		:       Optional[Any]  ,     lowercase_		:       Optional[Any]=None  ,     lowercase_		:       Any=None  ,     **lowercase_		:       Any				):
     snake_case_,	snake_case_,	snake_case_ :     int				=	super()._sanitize_parameters(**lowercase_				)
     if src_lang is not None:
         snake_case_ :     int				=	src_lang
     if tgt_lang is not None:
         snake_case_ :     List[str]				=	tgt_lang
     if src_lang is None and tgt_lang is None:
         # Backward compatibility, direct arguments use is preferred.
         snake_case_ :     Dict				=	kwargs.get('''task'''  ,     self.task				)
         snake_case_ :     Any				=	task.split('''_'''				)
         if task and len(lowercase_				) == 4:
             # translation, XX, to YY
             snake_case_ :     Union[str, Any]				=	items[1]
             snake_case_ :     Any				=	items[3]
     return preprocess_params, forward_params, postprocess_params






 def __call__(     self		:       Optional[int]  ,     *lowercase_		:       Union[str, Any]  ,     **lowercase_		:       Dict				):
     return super().__call__(*lowercase_  ,     **lowercase_				)










"""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       (			lowerCAmelCase__    ,     unittest.TestCase):
 _lowerCAmelCase   :			str							=		XLMRobertaTokenizer
 _lowerCAmelCase   :			int							=		XLMRobertaTokenizerFast
 _lowerCAmelCase   :			str							=		True
 _lowerCAmelCase   :			Dict							=		True

 def 						_snake_case					(     self		:       List[Any]				):
     super().setUp()

     # We have a SentencePiece fixture for testing
     snake_case_ :     List[str]				=	XLMRobertaTokenizer(lowercase_  ,     keep_accents=lowercase_				)
     tokenizer.save_pretrained(self.tmpdirname				)

 def 						_snake_case					(     self		:       str				):
     snake_case_ :     List[Any]				=	'''<pad>'''
     snake_case_ :     Optional[int]				=	1

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

 def 						_snake_case					(     self		:       Union[str, Any]				):
     snake_case_ :     Dict				=	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(lowercase_				)  ,     1002				)

 def 						_snake_case					(     self		:       Union[str, Any]				):
     self.assertEqual(self.get_tokenizer().vocab_size  ,     1002				)

 def 						_snake_case					(     self		:       Dict				):
     snake_case_ :     Optional[Any]				=	XLMRobertaTokenizer(lowercase_  ,     keep_accents=lowercase_				)

     snake_case_ :     Dict				=	tokenizer.tokenize('''This is a test'''				)
     self.assertListEqual(lowercase_  ,     ['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''']				)

     self.assertListEqual(
         tokenizer.convert_tokens_to_ids(lowercase_				)  ,     [value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]]  ,     )

     snake_case_ :     Dict				=	tokenizer.tokenize('''I was born in 92000, and this is falsé.'''				)
     self.assertListEqual(
         lowercase_  ,     [
             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''',
             '''.''',
         ]  ,     )
     snake_case_ :     List[Any]				=	tokenizer.convert_tokens_to_ids(lowercase_				)
     self.assertListEqual(
         lowercase_  ,     [
             value + tokenizer.fairseq_offset
             for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
             #                                       ^ unk: 2 + 1 = 3                  unk: 2 + 1 = 3 ^
         ]  ,     )

     snake_case_ :     List[str]				=	tokenizer.convert_ids_to_tokens(lowercase_				)
     self.assertListEqual(
         lowercase_  ,     [
             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 						_snake_case					(     self		:       List[str]				):
     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

     snake_case_ :     int				=	(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})"				):
             snake_case_ :     Union[str, Any]				=	self.rust_tokenizer_class.from_pretrained(lowercase_  ,     **lowercase_				)
             snake_case_ :     int				=	self.tokenizer_class.from_pretrained(lowercase_  ,     **lowercase_				)

             snake_case_ :     Optional[Any]				=	tempfile.mkdtemp()

             snake_case_ :     Tuple				=	tokenizer_r.save_pretrained(lowercase_				)
             snake_case_ :     List[str]				=	tokenizer_p.save_pretrained(lowercase_				)

             # 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				)				)
             snake_case_ :     str				=	tuple(f for f in tokenizer_r_files if '''tokenizer.json''' not in f				)
             self.assertSequenceEqual(lowercase_  ,     lowercase_				)

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

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

             shutil.rmtree(lowercase_				)

             # Save tokenizer rust, legacy_format=True
             snake_case_ :     Optional[Any]				=	tempfile.mkdtemp()

             snake_case_ :     List[str]				=	tokenizer_r.save_pretrained(lowercase_  ,     legacy_format=lowercase_				)
             snake_case_ :     List[str]				=	tokenizer_p.save_pretrained(lowercase_				)

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

             # Checks everything loads correctly in the same way
             snake_case_ :     List[Any]				=	tokenizer_r.from_pretrained(lowercase_				)
             snake_case_ :     List[str]				=	tokenizer_p.from_pretrained(lowercase_				)

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

             shutil.rmtree(lowercase_				)

             # Save tokenizer rust, legacy_format=False
             snake_case_ :     Optional[Any]				=	tempfile.mkdtemp()

             snake_case_ :     List[Any]				=	tokenizer_r.save_pretrained(lowercase_  ,     legacy_format=lowercase_				)
             snake_case_ :     Tuple				=	tokenizer_p.save_pretrained(lowercase_				)

             # 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
             snake_case_ :     Optional[Any]				=	tokenizer_r.from_pretrained(lowercase_				)
             snake_case_ :     Dict				=	tokenizer_p.from_pretrained(lowercase_				)

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

             shutil.rmtree(lowercase_				)

 @cached_property
 def 						_snake_case					(     self		:       List[str]				):
     return XLMRobertaTokenizer.from_pretrained('''xlm-roberta-base'''				)

 def 						_snake_case					(     self		:       Optional[Any]				):
     with tempfile.NamedTemporaryFile() as f:
         shutil.copyfile(lowercase_  ,     f.name				)
         snake_case_ :     Any				=	XLMRobertaTokenizer(f.name  ,     keep_accents=lowercase_				)
         snake_case_ :     List[Any]				=	pickle.dumps(lowercase_				)
     pickle.loads(lowercase_				)

 def 						_snake_case					(     self		:       Tuple				):
     if not self.test_rust_tokenizer:
         return

     snake_case_ :     List[str]				=	self.get_tokenizer()
     snake_case_ :     Optional[int]				=	self.get_rust_tokenizer()

     snake_case_ :     Dict				=	'''I was born in 92000, and this is falsé.'''

     snake_case_ :     Optional[int]				=	tokenizer.tokenize(lowercase_				)
     snake_case_ :     Tuple				=	rust_tokenizer.tokenize(lowercase_				)
     self.assertListEqual(lowercase_  ,     lowercase_				)

     snake_case_ :     List[str]				=	tokenizer.encode(lowercase_  ,     add_special_tokens=lowercase_				)
     snake_case_ :     str				=	rust_tokenizer.encode(lowercase_  ,     add_special_tokens=lowercase_				)
     self.assertListEqual(lowercase_  ,     lowercase_				)

     snake_case_ :     int				=	self.get_rust_tokenizer()
     snake_case_ :     Any				=	tokenizer.encode(lowercase_				)
     snake_case_ :     int				=	rust_tokenizer.encode(lowercase_				)
     self.assertListEqual(lowercase_  ,     lowercase_				)

 @slow
 def 						_snake_case					(     self		:       Tuple				):
     snake_case_ :     int				=	'''Hello World!'''
     snake_case_ :     int				=	[0, 35378, 6661, 38, 2]
     # xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')  # xlmr.large has same tokenizer
     # xlmr.eval()
     # xlmr.encode(symbols)

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

 @slow
 def 						_snake_case					(     self		:       List[Any]				):
     snake_case_ :     Any				=	(
         '''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'''
     )
     snake_case_ :     Optional[int]				=	[
         0,
         3293,
         83,
         10,
         4552,
         4989,
         7986,
         678,
         10,
         5915,
         111,
         179459,
         124850,
         4,
         6044,
         237,
         12,
         6,
         5,
         6,
         4,
         6780,
         705,
         15,
         1388,
         44,
         378,
         10114,
         711,
         152,
         20,
         6,
         5,
         22376,
         642,
         1221,
         15190,
         34153,
         450,
         5608,
         959,
         1119,
         57702,
         136,
         186,
         47,
         1098,
         29367,
         47,
         # 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,
         6044,
         237,
         6284,
         50901,
         528,
         31,
         90,
         34,
         927,
         2,
     ]
     # xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')  # xlmr.large has same tokenizer
     # xlmr.eval()
     # xlmr.encode(symbols)

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






 @slow
 def 						_snake_case					(     self		:       Dict				):
     # fmt: off
     snake_case_ :     int				=	{'''input_ids''': [[0, 11062, 82772, 7, 15, 82772, 538, 51529, 237, 17198, 1290, 206, 9, 215175, 1314, 136, 17198, 1290, 206, 9, 56359, 42, 122009, 9, 16466, 16, 87344, 4537, 9, 4717, 78381, 6, 159958, 7, 15, 24480, 618, 4, 527, 22693, 5428, 4, 2777, 24480, 9874, 4, 43523, 594, 4, 803, 18392, 33189, 18, 4, 43523, 24447, 12399, 100, 24955, 83658, 9626, 144057, 15, 839, 22335, 16, 136, 24955, 83658, 83479, 15, 39102, 724, 16, 678, 645, 2789, 1328, 4589, 42, 122009, 115774, 23, 805, 1328, 46876, 7, 136, 53894, 1940, 42227, 41159, 17721, 823, 425, 4, 27512, 98722, 206, 136, 5531, 4970, 919, 17336, 5, 2], [0, 20080, 618, 83, 82775, 47, 479, 9, 1517, 73, 53894, 333, 80581, 110117, 18811, 5256, 1295, 51, 152526, 297, 7986, 390, 124416, 538, 35431, 214, 98, 15044, 25737, 136, 7108, 43701, 23, 756, 135355, 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, 581, 63773, 119455, 6, 147797, 88203, 7, 645, 70, 21, 3285, 10269, 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=lowercase_  ,     model_name='''xlm-roberta-base'''  ,     revision='''d9d8a8ea5eb94b1c6654ae9249df7793cd2933d3'''  ,     )





