




"""simple docstring"""


from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
     import numpy as np
     import tensorflow as tf

     from transformers import (
         TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
         FlaubertConfig,
         TFFlaubertForMultipleChoice,
         TFFlaubertForQuestionAnsweringSimple,
         TFFlaubertForSequenceClassification,
         TFFlaubertForTokenClassification,
         TFFlaubertModel,
         TFFlaubertWithLMHeadModel,
     )






class  lowercase:







 '''simple docstring'''





 def __init__(    self:		Any,				a_:		Dict,				):




   '''simple docstring'''



   _snake_case							:		Optional[Any]								=  parent
   _snake_case							:		List[str]								=  13
   _snake_case							:		Tuple								=  7
   _snake_case							:		Union[str, Any]								=  True
   _snake_case							:		Dict								=  True
   _snake_case							:		str								=  True
   _snake_case							:		List[str]								=  True
   _snake_case							:		int								=  True
   _snake_case							:		Union[str, Any]								=  False
   _snake_case							:		Dict								=  False
   _snake_case							:		Optional[int]								=  False
   _snake_case							:		str								=  2
   _snake_case							:		List[Any]								=  99
   _snake_case							:		str								=  0
   _snake_case							:		Optional[int]								=  32
   _snake_case							:		Union[str, Any]								=  2
   _snake_case							:		Optional[Any]								=  4
   _snake_case							:		Optional[int]								=  0.1
   _snake_case							:		Union[str, Any]								=  0.1
   _snake_case							:		Dict								=  512
   _snake_case							:		Tuple								=  16
   _snake_case							:		Any								=  2
   _snake_case							:		Union[str, Any]								=  0.02
   _snake_case							:		Optional[Any]								=  3
   _snake_case							:		Tuple								=  4
   _snake_case							:		Tuple								=  """last"""
   _snake_case							:		int								=  True
   _snake_case							:		str								=  None
   _snake_case							:		List[Any]								=  0


 def 			UpperCamelCase_       (    self:		str		):




   '''simple docstring'''



   _snake_case							:		List[Any]								=  ids_tensor([self.batch_size, self.seq_length],				self.vocab_size		)
   _snake_case							:		int								=  random_attention_mask([self.batch_size, self.seq_length],				dtype=tf.floataa		)

   _snake_case							:		Dict								=  None
   if self.use_input_lengths:
     _snake_case							:		Any								=  (
         ids_tensor([self.batch_size],				vocab_size=2		) + self.seq_length - 2
     )  # small variation of seq_length

   _snake_case							:		Tuple								=  None
   if self.use_token_type_ids:
     _snake_case							:		Any								=  ids_tensor([self.batch_size, self.seq_length],				self.n_langs		)

   _snake_case							:		Dict								=  None
   _snake_case							:		int								=  None
   _snake_case							:		str								=  None
   if self.use_labels:
     _snake_case							:		Union[str, Any]								=  ids_tensor([self.batch_size],				self.type_sequence_label_size		)
     _snake_case							:		Optional[Any]								=  ids_tensor([self.batch_size, self.seq_length],				self.num_labels		)
     _snake_case							:		Optional[Any]								=  ids_tensor([self.batch_size],				2,				dtype=tf.floataa		)
     _snake_case							:		Optional[Any]								=  ids_tensor([self.batch_size],				self.num_choices		)

   _snake_case							:		List[Any]								=  FlaubertConfig(
       vocab_size=self.vocab_size,				n_special=self.n_special,				emb_dim=self.hidden_size,				n_layers=self.num_hidden_layers,				n_heads=self.num_attention_heads,				dropout=self.hidden_dropout_prob,				attention_dropout=self.attention_probs_dropout_prob,				gelu_activation=self.gelu_activation,				sinusoidal_embeddings=self.sinusoidal_embeddings,				asm=self.asm,				causal=self.causal,				n_langs=self.n_langs,				max_position_embeddings=self.max_position_embeddings,				initializer_range=self.initializer_range,				summary_type=self.summary_type,				use_proj=self.use_proj,				bos_token_id=self.bos_token_id,				)

   return (
       config,
       input_ids,
       token_type_ids,
       input_lengths,
       sequence_labels,
       token_labels,
       is_impossible_labels,
       choice_labels,
       input_mask,
   )


 def 			UpperCamelCase_       (    self:		Union[str, Any],				a_:		Any,				a_:		Optional[int],				a_:		Any,				a_:		List[Any],				a_:		str,				a_:		Union[str, Any],				a_:		Dict,				a_:		Any,				a_:		Union[str, Any],				):




   '''simple docstring'''



   _snake_case							:		List[Any]								=  TFFlaubertModel(config=a_		)
   _snake_case							:		List[Any]								=  {"""input_ids""": input_ids, """lengths""": input_lengths, """langs""": token_type_ids}
   _snake_case							:		Dict								=  model(a_		)

   _snake_case							:		str								=  [input_ids, input_mask]
   _snake_case							:		str								=  model(a_		)
   self.parent.assertEqual(result.last_hidden_state.shape,				(self.batch_size, self.seq_length, self.hidden_size)		)


 def 			UpperCamelCase_       (    self:		List[Any],				a_:		Any,				a_:		Any,				a_:		Union[str, Any],				a_:		int,				a_:		Optional[Any],				a_:		str,				a_:		List[Any],				a_:		List[Any],				a_:		Union[str, Any],				):




   '''simple docstring'''



   _snake_case							:		List[Any]								=  TFFlaubertWithLMHeadModel(a_		)

   _snake_case							:		Optional[int]								=  {"""input_ids""": input_ids, """lengths""": input_lengths, """langs""": token_type_ids}
   _snake_case							:		Union[str, Any]								=  model(a_		)

   self.parent.assertEqual(result.logits.shape,				(self.batch_size, self.seq_length, self.vocab_size)		)


 def 			UpperCamelCase_       (    self:		Union[str, Any],				a_:		Any,				a_:		Union[str, Any],				a_:		Union[str, Any],				a_:		Dict,				a_:		Any,				a_:		List[str],				a_:		Any,				a_:		Any,				a_:		List[str],				):




   '''simple docstring'''



   _snake_case							:		Tuple								=  TFFlaubertForQuestionAnsweringSimple(a_		)

   _snake_case							:		Optional[int]								=  {"""input_ids""": input_ids, """lengths""": input_lengths}

   _snake_case							:		Optional[int]								=  model(a_		)

   self.parent.assertEqual(result.start_logits.shape,				(self.batch_size, self.seq_length)		)
   self.parent.assertEqual(result.end_logits.shape,				(self.batch_size, self.seq_length)		)


 def 			UpperCamelCase_       (    self:		Optional[int],				a_:		List[str],				a_:		Optional[Any],				a_:		Optional[int],				a_:		List[str],				a_:		Tuple,				a_:		List[str],				a_:		Optional[Any],				a_:		int,				a_:		Optional[Any],				):




   '''simple docstring'''



   _snake_case							:		Tuple								=  TFFlaubertForSequenceClassification(a_		)

   _snake_case							:		Tuple								=  {"""input_ids""": input_ids, """lengths""": input_lengths}

   _snake_case							:		Tuple								=  model(a_		)

   self.parent.assertEqual(result.logits.shape,				(self.batch_size, self.type_sequence_label_size)		)


 def 			UpperCamelCase_       (    self:		Tuple,				a_:		Tuple,				a_:		Optional[Any],				a_:		List[str],				a_:		List[str],				a_:		Optional[Any],				a_:		int,				a_:		Optional[Any],				a_:		Tuple,				a_:		Dict,				):




   '''simple docstring'''



   _snake_case							:		Union[str, Any]								=  self.num_labels
   _snake_case							:		Optional[int]								=  TFFlaubertForTokenClassification(config=a_		)
   _snake_case							:		Any								=  {"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}
   _snake_case							:		Optional[int]								=  model(a_		)
   self.parent.assertEqual(result.logits.shape,				(self.batch_size, self.seq_length, self.num_labels)		)


 def 			UpperCamelCase_       (    self:		int,				a_:		Optional[int],				a_:		int,				a_:		Dict,				a_:		Optional[int],				a_:		Optional[Any],				a_:		Optional[Any],				a_:		int,				a_:		Dict,				a_:		Optional[int],				):




   '''simple docstring'''



   _snake_case							:		Optional[Any]								=  self.num_choices
   _snake_case							:		Union[str, Any]								=  TFFlaubertForMultipleChoice(config=a_		)
   _snake_case							:		Union[str, Any]								=  tf.tile(tf.expand_dims(a_,				1		),				(1, self.num_choices, 1)		)
   _snake_case							:		List[Any]								=  tf.tile(tf.expand_dims(a_,				1		),				(1, self.num_choices, 1)		)
   _snake_case							:		Optional[Any]								=  tf.tile(tf.expand_dims(a_,				1		),				(1, self.num_choices, 1)		)
   _snake_case							:		Optional[int]								=  {
       """input_ids""": multiple_choice_inputs_ids,
       """attention_mask""": multiple_choice_input_mask,
       """token_type_ids""": multiple_choice_token_type_ids,
   }
   _snake_case							:		Union[str, Any]								=  model(a_		)
   self.parent.assertEqual(result.logits.shape,				(self.batch_size, self.num_choices)		)


 def 			UpperCamelCase_       (    self:		Tuple		):




   '''simple docstring'''



   _snake_case							:		List[Any]								=  self.prepare_config_and_inputs()
   (
       (
       _snake_case
   )		, (
       _snake_case
   )		, (
       _snake_case
   )		, (
       _snake_case
   )		, (
       _snake_case
   )		, (
       _snake_case
   )		, (
       _snake_case
   )		, (
       _snake_case
   )		, (
       _snake_case
   )		, 
   )							:		int								=  config_and_inputs
   _snake_case							:		str								=  {
       """input_ids""": input_ids,
       """token_type_ids""": token_type_ids,
       """langs""": token_type_ids,
       """lengths""": input_lengths,
   }
   return config, inputs_dict







@require_tf
class  lowercase(					__a	,      __a	,      unittest.TestCase			):







 '''simple docstring'''





 lowercase__			 = (
     (
         TFFlaubertModel,
         TFFlaubertWithLMHeadModel,
         TFFlaubertForSequenceClassification,
         TFFlaubertForQuestionAnsweringSimple,
         TFFlaubertForTokenClassification,
         TFFlaubertForMultipleChoice,
     )
     if is_tf_available()
     else ()
 )
 lowercase__			 = (
     (TFFlaubertWithLMHeadModel,) if is_tf_available() else ()
 )  # TODO (PVP): Check other models whether language generation is also applicable
 lowercase__			 = (
     {
         "feature-extraction": TFFlaubertModel,
         "fill-mask": TFFlaubertWithLMHeadModel,
         "question-answering": TFFlaubertForQuestionAnsweringSimple,
         "text-classification": TFFlaubertForSequenceClassification,
         "token-classification": TFFlaubertForTokenClassification,
         "zero-shot": TFFlaubertForSequenceClassification,
     }
     if is_tf_available()
     else {}
 )
 lowercase__			 = False
 lowercase__			 = False


 def 			UpperCamelCase_       (    self:		int,				a_:		str,				a_:		List[str],				a_:		Optional[int],				a_:		List[str],				a_:		str		):




   '''simple docstring'''



   if (
       pipeline_test_casse_name == "QAPipelineTests"
       and tokenizer_name is not None
       and not tokenizer_name.endswith("""Fast"""		)
   ):
     # `QAPipelineTests` fails for a few models when the slower tokenizer are used.
     # (The slower tokenizers were never used for pipeline tests before the pipeline testing rework)
     # TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer
     return True

   return False


 def 			UpperCamelCase_       (    self:		List[Any]		):




   '''simple docstring'''



   _snake_case							:		Dict								=  TFFlaubertModelTester(self		)
   _snake_case							:		int								=  ConfigTester(self,				config_class=a_,				emb_dim=37		)


 def 			UpperCamelCase_       (    self:		Dict		):




   '''simple docstring'''



   self.config_tester.run_common_tests()


 def 			UpperCamelCase_       (    self:		str		):




   '''simple docstring'''



   _snake_case							:		List[Any]								=  self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_flaubert_model(*a_		)


 def 			UpperCamelCase_       (    self:		Dict		):




   '''simple docstring'''



   _snake_case							:		List[str]								=  self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_flaubert_lm_head(*a_		)


 def 			UpperCamelCase_       (    self:		Union[str, Any]		):




   '''simple docstring'''



   _snake_case							:		Dict								=  self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_flaubert_qa(*a_		)


 def 			UpperCamelCase_       (    self:		int		):




   '''simple docstring'''



   _snake_case							:		int								=  self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_flaubert_sequence_classif(*a_		)


 def 			UpperCamelCase_       (    self:		Any		):




   '''simple docstring'''



   _snake_case							:		Any								=  self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_flaubert_for_token_classification(*a_		)


 def 			UpperCamelCase_       (    self:		Union[str, Any]		):




   '''simple docstring'''



   _snake_case							:		Union[str, Any]								=  self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_flaubert_for_multiple_choice(*a_		)


 @slow
 def 			UpperCamelCase_       (    self:		Union[str, Any]		):




   '''simple docstring'''



   for model_name in TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
     _snake_case							:		str								=  TFFlaubertModel.from_pretrained(a_		)
     self.assertIsNotNone(a_		)







@require_tf
@require_sentencepiece
@require_tokenizers
class  lowercase(					unittest.TestCase			):







 '''simple docstring'''





 @slow
 def 			UpperCamelCase_       (    self:		Any		):




   '''simple docstring'''



   _snake_case							:		int								=  TFFlaubertModel.from_pretrained("""jplu/tf-flaubert-small-cased"""		)

   _snake_case							:		str								=  tf.convert_to_tensor(
       [[0, 158, 735, 2_592, 1_424, 6_727, 82, 1]],				dtype=tf.intaa,				)  # "J'aime flaubert !"

   _snake_case							:		Optional[int]								=  model(a_		)[0]
   _snake_case							:		Union[str, Any]								=  tf.TensorShape((1, 8, 512)		)
   self.assertEqual(output.shape,				a_		)
   # compare the actual values for a slice.
   _snake_case							:		List[str]								=  tf.convert_to_tensor(
       [
           [
               [-1.8_768_773, -1.566_555, 0.27_072_418],
               [-1.6_920_038, -0.5_873_505, 1.9_329_599],
               [-2.9_563_985, -1.6_993_835, 1.7_972_052],
           ]
       ],				dtype=tf.floataa,				)

   self.assertTrue(np.allclose(output[:, :3, :3].numpy(),				expected_slice.numpy(),				atol=1E-4		)		)








"""simple docstring"""


import unittest

from transformers import AlbertTokenizer, AlbertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


A_      =    get_tests_dir('''fixtures/spiece.model''')






@require_sentencepiece
@require_tokenizers
class  lowercase(					__a	,      unittest.TestCase			):







 '''simple docstring'''





 lowercase__			 = AlbertTokenizer
 lowercase__			 = AlbertTokenizerFast
 lowercase__			 = True
 lowercase__			 = True
 lowercase__			 = True


 def 			UpperCamelCase_       (    self:		str		):




   '''simple docstring'''



   super().setUp()

   # We have a SentencePiece fixture for testing
   _snake_case							:		Optional[int]								=  AlbertTokenizer(a_		)
   tokenizer.save_pretrained(self.tmpdirname		)


 def 			UpperCamelCase_       (    self:		Optional[int],				a_:		int		):




   '''simple docstring'''



   _snake_case							:		Dict								=  """this is a test"""
   _snake_case							:		Optional[int]								=  """this is a test"""
   return input_text, output_text


 def 			UpperCamelCase_       (    self:		str		):




   '''simple docstring'''



   _snake_case							:		str								=  """<pad>"""
   _snake_case							:		List[Any]								=  0

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


 def 			UpperCamelCase_       (    self:		Dict		):




   '''simple docstring'''



   _snake_case							:		Union[str, Any]								=  list(self.get_tokenizer().get_vocab().keys()		)

   self.assertEqual(vocab_keys[0],				"""<pad>"""		)
   self.assertEqual(vocab_keys[1],				"""<unk>"""		)
   self.assertEqual(vocab_keys[-1],				"""▁eloquent"""		)
   self.assertEqual(len(a_		),				30_000		)


 def 			UpperCamelCase_       (    self:		Optional[int]		):




   '''simple docstring'''



   self.assertEqual(self.get_tokenizer().vocab_size,				30_000		)


 def 			UpperCamelCase_       (    self:		List[str]		):




   '''simple docstring'''



   if not self.test_rust_tokenizer:
     return

   _snake_case							:		Tuple								=  self.get_tokenizer()
   _snake_case							:		List[str]								=  self.get_rust_tokenizer()

   _snake_case							:		Optional[int]								=  """I was born in 92000, and this is falsé."""

   _snake_case							:		Optional[Any]								=  tokenizer.tokenize(a_		)
   _snake_case							:		List[str]								=  rust_tokenizer.tokenize(a_		)
   self.assertListEqual(a_,				a_		)

   _snake_case							:		Any								=  tokenizer.encode(a_,				add_special_tokens=a_		)
   _snake_case							:		Any								=  rust_tokenizer.encode(a_,				add_special_tokens=a_		)
   self.assertListEqual(a_,				a_		)

   _snake_case							:		int								=  self.get_rust_tokenizer()
   _snake_case							:		Dict								=  tokenizer.encode(a_		)
   _snake_case							:		Optional[int]								=  rust_tokenizer.encode(a_		)
   self.assertListEqual(a_,				a_		)


 def 			UpperCamelCase_       (    self:		Optional[Any]		):




   '''simple docstring'''



   _snake_case							:		Any								=  AlbertTokenizer(a_,				keep_accents=a_		)

   _snake_case							:		Any								=  tokenizer.tokenize("""This is a test"""		)
   self.assertListEqual(a_,				["""▁this""", """▁is""", """▁a""", """▁test"""]		)

   self.assertListEqual(tokenizer.convert_tokens_to_ids(a_		),				[48, 25, 21, 1_289]		)

   _snake_case							:		Dict								=  tokenizer.tokenize("""I was born in 92000, and this is falsé."""		)
   self.assertListEqual(
       a_,				["""▁i""", """▁was""", """▁born""", """▁in""", """▁9""", """2000""", """,""", """▁and""", """▁this""", """▁is""", """▁fal""", """s""", """é""", """."""]		)
   _snake_case							:		str								=  tokenizer.convert_tokens_to_ids(a_		)
   self.assertListEqual(a_,				[31, 23, 386, 19, 561, 3_050, 15, 17, 48, 25, 8_256, 18, 1, 9]		)

   _snake_case							:		Tuple								=  tokenizer.convert_ids_to_tokens(a_		)
   self.assertListEqual(
       a_,				["""▁i""", """▁was""", """▁born""", """▁in""", """▁9""", """2000""", """,""", """▁and""", """▁this""", """▁is""", """▁fal""", """s""", """<unk>""", """."""],				)


 def 			UpperCamelCase_       (    self:		Optional[Any]		):




   '''simple docstring'''



   _snake_case							:		Optional[Any]								=  AlbertTokenizer(a_		)

   _snake_case							:		int								=  tokenizer.encode("""sequence builders"""		)
   _snake_case							:		Optional[int]								=  tokenizer.encode("""multi-sequence build"""		)

   _snake_case							:		Any								=  tokenizer.build_inputs_with_special_tokens(a_		)
   _snake_case							:		Union[str, Any]								=  tokenizer.build_inputs_with_special_tokens(a_,				a_		)

   assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]
   assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + text_a + [
       tokenizer.sep_token_id
   ]


 @slow
 def 			UpperCamelCase_       (    self:		Dict		):




   '''simple docstring'''



   _snake_case							:		Any								=  {"""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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], """input_ids""": [[2, 21_970, 13, 5, 6_092, 167, 28, 7_103, 2_153, 673, 8, 7_028, 12_051, 18, 17, 7_103, 2_153, 673, 8, 3_515, 18_684, 8, 4_461, 6, 1_927, 297, 8, 12_060, 2_607, 18, 13, 5, 4_461, 15, 10_538, 38, 8, 135, 15, 822, 58, 15, 993, 10_363, 15, 1_460, 8_005, 4_461, 15, 993, 255, 2_328, 9, 9, 9, 6, 26, 1_112, 816, 3_260, 13, 5, 103, 2_377, 6, 17, 1_112, 816, 2_782, 13, 5, 103, 10_641, 6, 29, 84, 2_512, 2_430, 782, 18_684, 2_761, 19, 808, 2_430, 2_556, 17, 855, 1_480, 9_477, 4_091, 128, 11_712, 15, 7_103, 2_153, 673, 17, 24_883, 9_990, 9, 3], [2, 11_502, 25, 1_006, 20, 782, 8, 11_809, 855, 1_732, 19_393, 18_667, 37, 367, 21_018, 69, 1_854, 34, 11_860, 19_124, 27, 156, 225, 17, 193, 4_141, 19, 65, 9_124, 9, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [2, 14, 2_231, 886, 2_385, 17_659, 84, 14, 16_792, 1_952, 9, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], """token_type_ids""": [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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="""albert-base-v2""",				revision="""6b6560eaf5ff2e250b00c50f380c5389a9c2d82e""",				)


