






from __future__ import annotations

import inspect
import unittest

import numpy as np

from transformers import DeiTConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

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


if is_tf_available():
		import tensorflow as tf

		from transformers import (
		    TFDeiTForImageClassification,
		    TFDeiTForImageClassificationWithTeacher,
		    TFDeiTForMaskedImageModeling,
		    TFDeiTModel,
		)
		from transformers.models.deit.modeling_tf_deit import TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
		from PIL import Image

		from transformers import DeiTImageProcessor



class     lowerCamelCase :







		'''simple docstring'''



		def __init__(				self							,						_UpperCamelCase							,						_UpperCamelCase=1_3							,						_UpperCamelCase=3_0							,						_UpperCamelCase=2							,						_UpperCamelCase=3							,						_UpperCamelCase=True							,						_UpperCamelCase=True							,						_UpperCamelCase=3_2							,						_UpperCamelCase=2							,						_UpperCamelCase=4							,						_UpperCamelCase=3_7							,						_UpperCamelCase="gelu"							,						_UpperCamelCase=0.1							,						_UpperCamelCase=0.1							,						_UpperCamelCase=1_0							,						_UpperCamelCase=0.02							,						_UpperCamelCase=3							,						_UpperCamelCase=None							,						_UpperCamelCase=2							,						)      ->				Union[str, Any]:
							UpperCAmelCase_       : Tuple				=		parent
							UpperCAmelCase_       : Optional[int]				=		batch_size
							UpperCAmelCase_       : int				=		image_size
							UpperCAmelCase_       : Union[str, Any]				=		patch_size
							UpperCAmelCase_       : List[str]				=		num_channels
							UpperCAmelCase_       : str				=		is_training
							UpperCAmelCase_       : str				=		use_labels
							UpperCAmelCase_       : Dict				=		hidden_size
							UpperCAmelCase_       : int				=		num_hidden_layers
							UpperCAmelCase_       : Any				=		num_attention_heads
							UpperCAmelCase_       : List[str]				=		intermediate_size
							UpperCAmelCase_       : int				=		hidden_act
							UpperCAmelCase_       : Union[str, Any]				=		hidden_dropout_prob
							UpperCAmelCase_       : Any				=		attention_probs_dropout_prob
							UpperCAmelCase_       : int				=		type_sequence_label_size
							UpperCAmelCase_       : Optional[Any]				=		initializer_range
							UpperCAmelCase_       : Any				=		scope
							UpperCAmelCase_       : List[str]				=		encoder_stride

							# in DeiT, the seq length equals the number of patches + 2 (we add 2 for the [CLS] and distilation tokens)
							UpperCAmelCase_       : Tuple				=		(image_size // patch_size) ** 2
							UpperCAmelCase_       : Dict				=		num_patches + 2



		def    __UpperCAmelCase     (				self  )      ->				str:
							UpperCAmelCase_       : List[Any]				=		floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]  )

							UpperCAmelCase_       : int				=		None
							if self.use_labels:
												UpperCAmelCase_       : List[str]				=		ids_tensor([self.batch_size]							,						self.type_sequence_label_size  )

							UpperCAmelCase_       : Optional[Any]				=		self.get_config()

							return config, pixel_values, labels



		def    __UpperCAmelCase     (				self  )      ->				Dict:
							return DeiTConfig(
							    image_size=self.image_size							,						patch_size=self.patch_size							,						num_channels=self.num_channels							,						hidden_size=self.hidden_size							,						num_hidden_layers=self.num_hidden_layers							,						num_attention_heads=self.num_attention_heads							,						intermediate_size=self.intermediate_size							,						hidden_act=self.hidden_act							,						hidden_dropout_prob=self.hidden_dropout_prob							,						attention_probs_dropout_prob=self.attention_probs_dropout_prob							,						is_decoder=lowercase__							,						initializer_range=self.initializer_range							,						encoder_stride=self.encoder_stride							,						)



		def    __UpperCAmelCase     (				self							,						_UpperCamelCase							,						_UpperCamelCase							,						_UpperCamelCase  )      ->				Any:
							UpperCAmelCase_       : Tuple				=		TFDeiTModel(config=lowercase__  )
							UpperCAmelCase_       : Tuple				=		model(lowercase__  )
							self.parent.assertEqual(result.last_hidden_state.shape							,						(self.batch_size, self.seq_length, self.hidden_size)  )



		def    __UpperCAmelCase     (				self							,						_UpperCamelCase							,						_UpperCamelCase							,						_UpperCamelCase  )      ->				str:
							UpperCAmelCase_       : int				=		TFDeiTForMaskedImageModeling(config=lowercase__  )
							UpperCAmelCase_       : Optional[int]				=		model(lowercase__  )
							self.parent.assertEqual(
							    result.reconstruction.shape							,						(self.batch_size, self.num_channels, self.image_size, self.image_size)  )

							# test greyscale images
							UpperCAmelCase_       : List[Any]				=		1
							UpperCAmelCase_       : Union[str, Any]				=		TFDeiTForMaskedImageModeling(lowercase__  )

							UpperCAmelCase_       : Any				=		floats_tensor([self.batch_size, 1, self.image_size, self.image_size]  )
							UpperCAmelCase_       : Tuple				=		model(lowercase__  )
							self.parent.assertEqual(result.reconstruction.shape							,						(self.batch_size, 1, self.image_size, self.image_size)  )



		def    __UpperCAmelCase     (				self							,						_UpperCamelCase							,						_UpperCamelCase							,						_UpperCamelCase  )      ->				Any:
							UpperCAmelCase_       : Optional[Any]				=		self.type_sequence_label_size
							UpperCAmelCase_       : Union[str, Any]				=		TFDeiTForImageClassification(lowercase__  )
							UpperCAmelCase_       : int				=		model(lowercase__							,						labels=lowercase__  )
							self.parent.assertEqual(result.logits.shape							,						(self.batch_size, self.type_sequence_label_size)  )

							# test greyscale images
							UpperCAmelCase_       : Union[str, Any]				=		1
							UpperCAmelCase_       : Optional[Any]				=		TFDeiTForImageClassification(lowercase__  )

							UpperCAmelCase_       : Union[str, Any]				=		floats_tensor([self.batch_size, 1, self.image_size, self.image_size]  )
							UpperCAmelCase_       : Union[str, Any]				=		model(lowercase__							,						labels=lowercase__  )
							self.parent.assertEqual(result.logits.shape							,						(self.batch_size, self.type_sequence_label_size)  )



		def    __UpperCAmelCase     (				self  )      ->				Any:
							UpperCAmelCase_       : Dict				=		self.prepare_config_and_inputs()
							UpperCAmelCase_       : str				=		config_and_inputs
							UpperCAmelCase_       : Any				=		{'''pixel_values''': pixel_values}
							return config, inputs_dict





@require_tf
class     lowerCamelCase (_UpperCamelCase					,    _UpperCamelCase					,    unittest.TestCase     ):







		'''simple docstring'''



		_snake_case       :       Optional[int]					=    (
		    (
		        TFDeiTModel,
		        TFDeiTForImageClassification,
		        TFDeiTForImageClassificationWithTeacher,
		        TFDeiTForMaskedImageModeling,
		    )
		    if is_tf_available()
		    else ()
		)
		_snake_case       :       Dict					=    (
		    {
		        '''feature-extraction''': TFDeiTModel,
		        '''image-classification''': (TFDeiTForImageClassification, TFDeiTForImageClassificationWithTeacher),
		    }
		    if is_tf_available()
		    else {}
		)

		_snake_case       :       Any					=    False
		_snake_case       :       Union[str, Any]					=    False
		_snake_case       :       Any					=    False
		_snake_case       :       Optional[Any]					=    False



		def    __UpperCAmelCase     (				self  )      ->				Tuple:
							UpperCAmelCase_       : Tuple				=		TFDeiTModelTester(self  )
							UpperCAmelCase_       : str				=		ConfigTester(self							,						config_class=lowercase__							,						has_text_modality=lowercase__							,						hidden_size=3_7  )



		def    __UpperCAmelCase     (				self  )      ->				Optional[int]:
							self.config_tester.run_common_tests()



		@unittest.skip(reason='DeiT does not use inputs_embeds'  )
		def    __UpperCAmelCase     (				self  )      ->				Optional[Any]:
							pass



		def    __UpperCAmelCase     (				self  )      ->				List[str]:
							UpperCAmelCase_       : List[Any]				=		self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
												UpperCAmelCase_       : Any				=		model_class(lowercase__  )
												self.assertIsInstance(model.get_input_embeddings()							,						(tf.keras.layers.Layer)  )
												UpperCAmelCase_       : Any				=		model.get_output_embeddings()
												self.assertTrue(x is None or isinstance(lowercase__							,						tf.keras.layers.Dense  )  )



		def    __UpperCAmelCase     (				self  )      ->				Union[str, Any]:
							UpperCAmelCase_       : Union[str, Any]				=		self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
												UpperCAmelCase_       : List[Any]				=		model_class(lowercase__  )
												UpperCAmelCase_       : Union[str, Any]				=		inspect.signature(model.call  )
												# signature.parameters is an OrderedDict => so arg_names order is deterministic
												UpperCAmelCase_       : Dict				=		[*signature.parameters.keys()]

												UpperCAmelCase_       : int				=		['''pixel_values''']
												self.assertListEqual(arg_names[:1]							,						lowercase__  )



		def    __UpperCAmelCase     (				self  )      ->				int:
							UpperCAmelCase_       : int				=		self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*lowercase__  )



		def    __UpperCAmelCase     (				self  )      ->				List[Any]:
							UpperCAmelCase_       : Optional[Any]				=		self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_masked_image_modeling(*lowercase__  )



		def    __UpperCAmelCase     (				self  )      ->				str:
							UpperCAmelCase_       : Any				=		self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_image_classification(*lowercase__  )



		def    __UpperCAmelCase     (				self							,						_UpperCamelCase							,						_UpperCamelCase							,						_UpperCamelCase=False  )      ->				Union[str, Any]:
							UpperCAmelCase_       : Tuple				=		super()._prepare_for_class(lowercase__							,						lowercase__							,						return_labels=lowercase__  )

							if return_labels:
												if "labels" in inputs_dict and "labels" not in inspect.signature(model_class.call  ).parameters:
																	del inputs_dict["labels"]

							return inputs_dict



		@slow
		def    __UpperCAmelCase     (				self  )      ->				Union[str, Any]:
							for model_name in TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
												UpperCAmelCase_       : List[str]				=		TFDeiTModel.from_pretrained(lowercase__  )
												self.assertIsNotNone(lowercase__  )





def 	lowercase__ (	):


					'''simple docstring'''


					UpperCAmelCase_       : int				=		Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'    )
					return image






@require_tf
@require_vision
class     lowerCamelCase (unittest.TestCase     ):







		'''simple docstring'''



		@cached_property
		def    __UpperCAmelCase     (				self  )      ->				Tuple:
							return (
							    DeiTImageProcessor.from_pretrained('facebook/deit-base-distilled-patch16-224'  )
							    if is_vision_available()
							    else None
							)



		@slow
		def    __UpperCAmelCase     (				self  )      ->				Union[str, Any]:
							UpperCAmelCase_       : str				=		TFDeiTForImageClassificationWithTeacher.from_pretrained('facebook/deit-base-distilled-patch16-224'  )

							UpperCAmelCase_       : Union[str, Any]				=		self.default_image_processor
							UpperCAmelCase_       : int				=		prepare_img()
							UpperCAmelCase_       : Any				=		image_processor(images=lowercase__							,						return_tensors='tf'  )

							# forward pass
							UpperCAmelCase_       : Any				=		model(**lowercase__  )

							# verify the logits
							UpperCAmelCase_       : List[Any]				=		tf.TensorShape((1, 1_0_0_0)  )
							self.assertEqual(outputs.logits.shape							,						lowercase__  )

							UpperCAmelCase_       : Tuple				=		tf.constant([-1.02_66, 0.19_12, -1.28_61]  )

							self.assertTrue(np.allclose(outputs.logits[0, :3]							,						lowercase__							,						atol=1E-4  )  )










import unittest

from transformers import SPIECE_UNDERLINE, XLNetTokenizer, XLNetTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


lowerCAmelCase       		=    get_tests_dir("""fixtures/test_sentencepiece.model""")




@require_sentencepiece
@require_tokenizers
class 							lowerCamelCase   (  _UpperCamelCase							,						unittest.TestCase ):
	_lowerCAmelCase     :		Union[str, Any]										=						XLNetTokenizer
	_lowerCAmelCase     :		int										=						XLNetTokenizerFast
	_lowerCAmelCase     :		Optional[Any]										=						True
	_lowerCAmelCase     :		Optional[Any]										=						True


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

							# We have a SentencePiece fixture for testing
							__UpperCAmelCase			:							Optional[Any]	    =	XLNetTokenizer(lowercase__				,     keep_accents=lowercase__)
							tokenizer.sanitize_special_tokens()
							tokenizer.save_pretrained(self.tmpdirname)


	def  A(    self):
							__UpperCAmelCase			:							Tuple	    =	'''<s>'''
							__UpperCAmelCase			:							List[str]	    =	1

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


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

							self.assertEqual(vocab_keys[0]				,     '''<unk>''')
							self.assertEqual(vocab_keys[1]				,     '''<s>''')
							self.assertEqual(vocab_keys[-1]				,     '''<eod>''')
							self.assertEqual(len(lowercase__)				,     1_0_0_6)


	def  A(    self):
							self.assertEqual(self.get_tokenizer().vocab_size				,     1_0_0_0)


	def  A(    self):
							__UpperCAmelCase			:							List[str]	    =	XLNetTokenizer(lowercase__				,     keep_accents=lowercase__)

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

							self.assertListEqual(tokenizer.convert_tokens_to_ids(lowercase__)				,     [2_8_5, 4_6, 1_0, 1_7_0, 3_8_2])

							__UpperCAmelCase			:							Tuple	    =	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''',
							        '''.''',
							    ]				,     )
							__UpperCAmelCase			:							Union[str, Any]	    =	tokenizer.convert_tokens_to_ids(lowercase__)
							self.assertListEqual(lowercase__				,     [8, 2_1, 8_4, 5_5, 2_4, 1_9, 7, 0, 6_0_2, 3_4_7, 3_4_7, 3_4_7, 3, 1_2, 6_6, 4_6, 7_2, 8_0, 6, 0, 4])

							__UpperCAmelCase			:							int	    =	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  A(    self):
							__UpperCAmelCase			:							str	    =	XLNetTokenizer(lowercase__				,     do_lower_case=lowercase__)
							__UpperCAmelCase			:							int	    =	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''',
							        '''se''',
							        '''.''',
							    ]				,     )
							self.assertListEqual(tokenizer.tokenize('''H\u00E9llo''')				,     ['''▁he''', '''ll''', '''o'''])


	def  A(    self):
							__UpperCAmelCase			:							Optional[Any]	    =	XLNetTokenizer(lowercase__				,     do_lower_case=lowercase__)
							__UpperCAmelCase			:							Union[str, Any]	    =	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''',
							        '''se''',
							        '''.''',
							    ]				,     )


	@slow
	def  A(    self):
							__UpperCAmelCase			:							Tuple	    =	XLNetTokenizer.from_pretrained('''xlnet-base-cased''')

							__UpperCAmelCase			:							Optional[int]	    =	tokenizer.encode('''sequence builders'''				,     add_special_tokens=lowercase__)
							__UpperCAmelCase			:							Optional[int]	    =	tokenizer.encode('''multi-sequence build'''				,     add_special_tokens=lowercase__)

							__UpperCAmelCase			:							Union[str, Any]	    =	tokenizer.build_inputs_with_special_tokens(lowercase__)
							__UpperCAmelCase			:							Optional[Any]	    =	tokenizer.build_inputs_with_special_tokens(lowercase__				,     lowercase__)

							assert encoded_sentence == text + [4, 3]
							assert encoded_pair == text + [4] + text_a + [4, 3]


	@slow
	def  A(    self):
							# fmt: off
							__UpperCAmelCase			:							Optional[Any]	    =	{'''input_ids''': [[1_7, 2_1_4_4_2, 2_7_0, 1_7, 1_0, 1_4_6_4_5, 3_1_8, 3_4, 1_7, 4_5_4_6, 3_1_4_5, 7_8_7, 1_3, 7_7_5_2, 2_2_0_1_8, 2_3, 2_1, 1_7, 4_5_4_6, 3_1_4_5, 7_8_7, 1_3, 3_3_5_2, 1_4_4_3_1, 1_3, 5_5_0_0, 1_1, 1_1_7_6, 5_8_0, 1_3, 1_6_8_1_9, 4_7_9_7, 2_3, 1_7, 1_0, 1_7_1_3_5, 6_5_8, 1_9, 4_5_7, 7_9_3_2, 1_3, 1_8_4, 1_9, 3_1_5_4, 1_7_1_3_5, 6_4_6_8, 1_9, 1_4_0_4, 1_2_2_6_9, 1_9, 4_2_2_9, 5_3_5_6, 1_6_2_6_4, 4_6, 1_9, 1_7, 2_0_5_4_5, 1_0_3_9_5, 9, 9, 9, 1_1, 2_8, 6_4_2_1, 9_5_3_1, 2_0_7_2_9, 1_7, 1_0, 3_5_3, 1_7_0_2_2, 1_1, 2_1, 6_4_2_1, 9_5_3_1, 1_6_9_4_9, 1_7, 1_0, 1_1_5_0_9, 7_5_3, 1_1, 3_3, 9_5, 2_4_2_1, 7_3_8_5, 9_5_6, 1_4_4_3_1, 2_6_2_6, 2_5, 8_4_2, 7_3_8_5, 4_8_3_6, 2_1, 1_4_2_9, 2_2_7_2, 9_8_5_5, 3_1_2_0, 1_6_1, 2_4_7_3_8, 1_9, 1_3_2_0_3, 6_5_8, 2_1_8, 7_8_7, 2_1, 4_3_0, 1_8_4_8_2, 8_4_7, 2_6_3_7, 9, 4, 3], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 3_2_2, 2_2_1_7_8, 2_7, 1_0_6_4, 2_2, 9_5_6, 1_3, 1_1_1_0_1, 1_4_2_9, 5_8_5_4, 2_4_3_1_3, 1_8_9_5_3, 4_0, 4_2_2, 2_4_3_6_6, 6_8, 1_7_5_8, 3_7, 1_0_4_8_3, 1_4_2_5_7, 3_1, 2_0_7, 2_6_3, 2_1, 2_0_3, 3_7_7_3, 2_5, 7_1, 9_7_3_5, 9, 4, 3], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 3_2, 2_0_4_9, 3_4_4_2, 1_7, 1_3_8_9_4, 3_3_8_0, 2_3, 9_5, 1_8, 1_7_6_3_4, 2_2_8_8, 9, 4, 3]], '''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, 2], [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 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, 2], [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2]], '''attention_mask''': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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]]}  # noqa: E501
							# fmt: on

							self.tokenizer_integration_test_util(
							    expected_encoding=lowercase__				,     model_name='''xlnet-base-cased'''				,     revision='''c841166438c31ec7ca9a106dee7bb312b73ae511'''				,     )

