




"""simple docstring"""

from __future__ import annotations

import unittest

import numpy as np

from transformers import LayoutLMConfig, is_tf_available
from transformers.testing_utils import require_tf, 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 tensorflow as tf

		from transformers.models.layoutlm.modeling_tf_layoutlm import (
		    TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFLayoutLMForMaskedLM,
		    TFLayoutLMForQuestionAnswering,
		    TFLayoutLMForSequenceClassification,
		    TFLayoutLMForTokenClassification,
		    TFLayoutLMModel,
		)

class        lowercase      :







				def __init__(							self   :					List[str]      ,     _lowerCamelCase   :					Dict      ,     _lowerCamelCase   :					List[Any]=13      ,     _lowerCamelCase   :					List[str]=7      ,     _lowerCamelCase   :					Optional[int]=True      ,     _lowerCamelCase   :					str=True      ,     _lowerCamelCase   :					Optional[int]=True      ,     _lowerCamelCase   :					str=True      ,     _lowerCamelCase   :					str=99      ,     _lowerCamelCase   :					str=32      ,     _lowerCamelCase   :					List[str]=2      ,     _lowerCamelCase   :					List[str]=4      ,     _lowerCamelCase   :					Union[str, Any]=37      ,     _lowerCamelCase   :					List[str]="gelu"      ,     _lowerCamelCase   :					Dict=0.1      ,     _lowerCamelCase   :					Any=0.1      ,     _lowerCamelCase   :					Tuple=5_12      ,     _lowerCamelCase   :					Optional[int]=16      ,     _lowerCamelCase   :					int=2      ,     _lowerCamelCase   :					Any=0.02      ,     _lowerCamelCase   :					Any=3      ,     _lowerCamelCase   :					Dict=4      ,     _lowerCamelCase   :					List[str]=None      ,     _lowerCamelCase   :					Tuple=10_00      ,     ):



					"""simple docstring"""

					A_  :				Any	   =		parent
					A_  :				List[Any]	   =		batch_size
					A_  :				Dict	   =		seq_length
					A_  :				Tuple	   =		is_training
					A_  :				List[str]	   =		use_input_mask
					A_  :				List[Any]	   =		use_token_type_ids
					A_  :				str	   =		use_labels
					A_  :				Union[str, Any]	   =		vocab_size
					A_  :				Optional[Any]	   =		hidden_size
					A_  :				Union[str, Any]	   =		num_hidden_layers
					A_  :				List[Any]	   =		num_attention_heads
					A_  :				int	   =		intermediate_size
					A_  :				Union[str, Any]	   =		hidden_act
					A_  :				Any	   =		hidden_dropout_prob
					A_  :				Optional[Any]	   =		attention_probs_dropout_prob
					A_  :				int	   =		max_position_embeddings
					A_  :				List[Any]	   =		type_vocab_size
					A_  :				Optional[int]	   =		type_sequence_label_size
					A_  :				Tuple	   =		initializer_range
					A_  :				Tuple	   =		num_labels
					A_  :				Any	   =		num_choices
					A_  :				Union[str, Any]	   =		scope
					A_  :				Union[str, Any]	   =		range_bbox







				def 					a_  (							self   :					int       ):



					"""simple docstring"""

					A_  :				Optional[Any]	   =		ids_tensor([self.batch_size, self.seq_length]      ,     self.vocab_size       )

					# convert bbox to numpy since TF does not support item assignment
					A_  :				str	   =		ids_tensor([self.batch_size, self.seq_length, 4]      ,     self.range_bbox       ).numpy()
					# Ensure that bbox is legal
					for i in range(bbox.shape[0]       ):
						for j in range(bbox.shape[1]       ):
							if bbox[i, j, 3] < bbox[i, j, 1]:
								A_  :				int	   =		bbox[i, j, 3]
								A_  :				Any	   =		bbox[i, j, 1]
								A_  :				List[Any]	   =		t
							if bbox[i, j, 2] < bbox[i, j, 0]:
								A_  :				int	   =		bbox[i, j, 2]
								A_  :				int	   =		bbox[i, j, 0]
								A_  :				List[Any]	   =		t
					A_  :				str	   =		tf.convert_to_tensor(_lowerCamelCase       )

					A_  :				Optional[int]	   =		None
					if self.use_input_mask:
						A_  :				List[Any]	   =		random_attention_mask([self.batch_size, self.seq_length]       )

					A_  :				Optional[Any]	   =		None
					if self.use_token_type_ids:
						A_  :				Optional[Any]	   =		ids_tensor([self.batch_size, self.seq_length]      ,     self.type_vocab_size       )

					A_  :				int	   =		None
					A_  :				Any	   =		None
					A_  :				Optional[Any]	   =		None
					if self.use_labels:
						A_  :				Optional[Any]	   =		ids_tensor([self.batch_size]      ,     self.type_sequence_label_size       )
						A_  :				List[Any]	   =		ids_tensor([self.batch_size, self.seq_length]      ,     self.num_labels       )
						A_  :				List[Any]	   =		ids_tensor([self.batch_size]      ,     self.num_choices       )

					A_  :				Dict	   =		LayoutLMConfig(
					    vocab_size=self.vocab_size      ,     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      ,     max_position_embeddings=self.max_position_embeddings      ,     type_vocab_size=self.type_vocab_size      ,     initializer_range=self.initializer_range      ,     )

					return config, input_ids, bbox, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels







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



					"""simple docstring"""

					A_  :				str	   =		TFLayoutLMModel(config=_lowerCamelCase       )

					A_  :				str	   =		model(_lowerCamelCase      ,     _lowerCamelCase      ,     attention_mask=_lowerCamelCase      ,     token_type_ids=_lowerCamelCase       )
					A_  :				Union[str, Any]	   =		model(_lowerCamelCase      ,     _lowerCamelCase      ,     token_type_ids=_lowerCamelCase       )
					A_  :				Dict	   =		model(_lowerCamelCase      ,     _lowerCamelCase       )
					self.parent.assertEqual(result.last_hidden_state.shape      ,     (self.batch_size, self.seq_length, self.hidden_size)       )
					self.parent.assertEqual(result.pooler_output.shape      ,     (self.batch_size, self.hidden_size)       )







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



					"""simple docstring"""

					A_  :				List[Any]	   =		TFLayoutLMForMaskedLM(config=_lowerCamelCase       )

					A_  :				Union[str, Any]	   =		model(_lowerCamelCase      ,     _lowerCamelCase      ,     attention_mask=_lowerCamelCase      ,     token_type_ids=_lowerCamelCase      ,     labels=_lowerCamelCase       )
					self.parent.assertEqual(result.logits.shape      ,     (self.batch_size, self.seq_length, self.vocab_size)       )







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



					"""simple docstring"""

					A_  :				Union[str, Any]	   =		self.num_labels
					A_  :				Union[str, Any]	   =		TFLayoutLMForSequenceClassification(config=_lowerCamelCase       )

					A_  :				int	   =		model(_lowerCamelCase      ,     _lowerCamelCase      ,     attention_mask=_lowerCamelCase      ,     token_type_ids=_lowerCamelCase       )
					self.parent.assertEqual(result.logits.shape      ,     (self.batch_size, self.num_labels)       )







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



					"""simple docstring"""

					A_  :				List[Any]	   =		self.num_labels
					A_  :				Tuple	   =		TFLayoutLMForTokenClassification(config=_lowerCamelCase       )

					A_  :				str	   =		model(_lowerCamelCase      ,     _lowerCamelCase      ,     attention_mask=_lowerCamelCase      ,     token_type_ids=_lowerCamelCase      ,     labels=_lowerCamelCase       )
					self.parent.assertEqual(result.logits.shape      ,     (self.batch_size, self.seq_length, self.num_labels)       )







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



					"""simple docstring"""

					A_  :				Dict	   =		TFLayoutLMForQuestionAnswering(config=_lowerCamelCase       )

					A_  :				Optional[int]	   =		model(_lowerCamelCase      ,     _lowerCamelCase      ,     attention_mask=_lowerCamelCase      ,     token_type_ids=_lowerCamelCase       )
					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 					a_  (							self   :					List[Any]       ):



					"""simple docstring"""

					A_  :				Optional[int]	   =		self.prepare_config_and_inputs()
					(
					    (
					    A_					
					)   , (
					    A_					
					)   , (
					    A_					
					)   , (
					    A_					
					)   , (
					    A_					
					)   , (
					    A_					
					)   , (
					    A_					
					)   , (
					    A_					
					)   , 					
					)  :				Optional[int]	   =		config_and_inputs
					A_  :				Dict	   =		{
					    '''input_ids''': input_ids,
					    '''bbox''': bbox,
					    '''token_type_ids''': token_type_ids,
					    '''attention_mask''': input_mask,
					}
					return config, inputs_dict

@require_tf
class        lowercase      (    __UpperCAmelCase     ,	__UpperCAmelCase     ,	unittest.TestCase		):
				__lowerCAmelCase    :    List[Any]         =   (
				    (
				        TFLayoutLMModel,
				        TFLayoutLMForMaskedLM,
				        TFLayoutLMForTokenClassification,
				        TFLayoutLMForSequenceClassification,
				        TFLayoutLMForQuestionAnswering,
				    )
				    if is_tf_available()
				    else ()
				)
				__lowerCAmelCase    :    List[str]         =   (
				    {
				        'feature-extraction': TFLayoutLMModel,
				        'fill-mask': TFLayoutLMForMaskedLM,
				        'text-classification': TFLayoutLMForSequenceClassification,
				        'token-classification': TFLayoutLMForTokenClassification,
				        'zero-shot': TFLayoutLMForSequenceClassification,
				    }
				    if is_tf_available()
				    else {}
				)
				__lowerCAmelCase    :    List[str]         =   False
				__lowerCAmelCase    :    Optional[int]         =   True
				__lowerCAmelCase    :    Optional[int]         =   10







				def 					a_  (							self   :					str       ):



					"""simple docstring"""

					A_  :				str	   =		TFLayoutLMModelTester(self       )
					A_  :				Optional[Any]	   =		ConfigTester(self      ,     config_class=_lowerCamelCase      ,     hidden_size=37       )







				def 					a_  (							self   :					Optional[int]       ):



					"""simple docstring"""

					self.config_tester.run_common_tests()







				def 					a_  (							self   :					List[str]       ):



					"""simple docstring"""

					A_  :				Optional[Any]	   =		self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_model(*_lowerCamelCase       )







				def 					a_  (							self   :					str       ):



					"""simple docstring"""

					A_  :				Any	   =		self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_masked_lm(*_lowerCamelCase       )







				def 					a_  (							self   :					int       ):



					"""simple docstring"""

					A_  :				List[Any]	   =		self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_sequence_classification(*_lowerCamelCase       )







				def 					a_  (							self   :					Tuple       ):



					"""simple docstring"""

					A_  :				str	   =		self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_token_classification(*_lowerCamelCase       )







				def 					a_  (							self   :					Dict       ):



					"""simple docstring"""

					A_  :				Optional[int]	   =		self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_question_answering(*_lowerCamelCase       )







				@slow
				def 					a_  (							self   :					List[Any]       ):



					"""simple docstring"""

					for model_name in TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
						A_  :				int	   =		TFLayoutLMModel.from_pretrained(_lowerCamelCase       )
						self.assertIsNotNone(_lowerCamelCase       )







				@unittest.skip('''Onnx compliancy broke with TF 2.10'''       )
				def 					a_  (							self   :					Optional[Any]       ):



					"""simple docstring"""

					pass

def 		lowercase_      (		):




	"""simple docstring"""






	A_  :				Any	   =		tf.convert_to_tensor([[101,1019,1014,1016,1037,1_2849,4747,1004,1_4246,2278,5439,4524,5002,2930,2193,2930,4341,3208,1005,1055,2171,2848,1_1300,3531,102],[101,4070,4034,7020,1024,3058,1015,1013,2861,1013,6070,1_9274,2772,6205,2_7814,1_6147,1_6147,4343,2047,1_0283,1_0969,1_4389,1012,2338,102]]	)  # noqa: E231
	A_  :				Union[str, Any]	   =		tf.convert_to_tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],]	)  # noqa: E231
	A_  :				Tuple	   =		tf.convert_to_tensor([[[0,0,0,0],[423,237,440,251],[427,272,441,287],[419,115,437,129],[961,885,992,912],[256,38,330,58],[256,38,330,58],[336,42,353,57],[360,39,401,56],[360,39,401,56],[411,39,471,59],[479,41,528,59],[533,39,630,60],[67,113,134,131],[141,115,209,132],[68,149,133,166],[141,149,187,164],[195,148,287,165],[195,148,287,165],[195,148,287,165],[295,148,349,165],[441,149,492,166],[497,149,546,164],[64,201,125,218],[1000,1000,1000,1000]],[[0,0,0,0],[662,150,754,166],[665,199,742,211],[519,213,554,228],[519,213,554,228],[134,433,187,454],[130,467,204,480],[130,467,204,480],[130,467,204,480],[130,467,204,480],[130,467,204,480],[314,469,376,482],[504,684,582,706],[941,825,973,900],[941,825,973,900],[941,825,973,900],[941,825,973,900],[610,749,652,765],[130,659,168,672],[176,657,237,672],[238,657,312,672],[443,653,628,672],[443,653,628,672],[716,301,825,317],[1000,1000,1000,1000]]]	)  # noqa: E231
	A_  :				Dict	   =		tf.convert_to_tensor([[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]]	)  # noqa: E231
	# these are sequence labels (i.e. at the token level)
	A_  :				Dict	   =		tf.convert_to_tensor([[-100,10,10,10,9,1,-100,7,7,-100,7,7,4,2,5,2,8,8,-100,-100,5,0,3,2,-100],[-100,12,12,12,-100,12,10,-100,-100,-100,-100,10,12,9,-100,-100,-100,10,10,10,9,12,-100,10,-100]]	)  # noqa: E231
	# fmt: on

	return input_ids, attention_mask, bbox, token_type_ids, labels

@require_tf
class        lowercase      (    unittest.TestCase		):







				@slow
				def 					a_  (							self   :					Tuple       ):



					"""simple docstring"""

					A_  :				Optional[Any]	   =		TFLayoutLMModel.from_pretrained('''microsoft/layoutlm-base-uncased'''       )

					A_   , A_   , A_   , A_   , A_  :				List[str]	   =		prepare_layoutlm_batch_inputs()

					# forward pass
					A_  :				Optional[Any]	   =		model(input_ids=_lowerCamelCase      ,     bbox=_lowerCamelCase      ,     attention_mask=_lowerCamelCase      ,     token_type_ids=_lowerCamelCase       )

					# test the sequence output on [0, :3, :3]
					A_  :				str	   =		tf.convert_to_tensor(
					    [[0.1785, -0.1947, -0.0425], [-0.3254, -0.2807, 0.2553], [-0.5391, -0.3322, 0.3364]]      ,     )

					self.assertTrue(np.allclose(outputs.last_hidden_state[0, :3, :3]      ,     _lowerCamelCase      ,     atol=1E-3       )       )

					# test the pooled output on [1, :3]
					A_  :				Optional[int]	   =		tf.convert_to_tensor([-0.6580, -0.0214, 0.8552]       )

					self.assertTrue(np.allclose(outputs.pooler_output[1, :3]      ,     _lowerCamelCase      ,     atol=1E-3       )       )







				@slow
				def 					a_  (							self   :					List[Any]       ):



					"""simple docstring"""

					A_  :				Optional[int]	   =		TFLayoutLMForSequenceClassification.from_pretrained('''microsoft/layoutlm-base-uncased'''      ,     num_labels=2       )

					A_   , A_   , A_   , A_   , A_  :				Optional[Any]	   =		prepare_layoutlm_batch_inputs()

					# forward pass
					A_  :				Optional[int]	   =		model(
					    input_ids=_lowerCamelCase      ,     bbox=_lowerCamelCase      ,     attention_mask=_lowerCamelCase      ,     token_type_ids=_lowerCamelCase      ,     labels=tf.convert_to_tensor([1, 1]       )      ,     )

					# test whether we get a loss as a scalar
					A_  :				Optional[Any]	   =		outputs.loss
					A_  :				int	   =		(2,)
					self.assertEqual(loss.shape      ,     _lowerCamelCase       )

					# test the shape of the logits
					A_  :				Optional[Any]	   =		outputs.logits
					A_  :				List[Any]	   =		(2, 2)
					self.assertEqual(logits.shape      ,     _lowerCamelCase       )







				@slow
				def 					a_  (							self   :					Optional[Any]       ):



					"""simple docstring"""

					A_  :				List[str]	   =		TFLayoutLMForTokenClassification.from_pretrained('''microsoft/layoutlm-base-uncased'''      ,     num_labels=13       )

					A_   , A_   , A_   , A_   , A_  :				List[Any]	   =		prepare_layoutlm_batch_inputs()

					# forward pass
					A_  :				Optional[Any]	   =		model(
					    input_ids=_lowerCamelCase      ,     bbox=_lowerCamelCase      ,     attention_mask=_lowerCamelCase      ,     token_type_ids=_lowerCamelCase      ,     labels=_lowerCamelCase       )

					# test the shape of the logits
					A_  :				int	   =		outputs.logits
					A_  :				List[str]	   =		tf.convert_to_tensor((2, 25, 13)       )
					self.assertEqual(logits.shape      ,     _lowerCamelCase       )







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



					"""simple docstring"""

					A_  :				List[str]	   =		TFLayoutLMForQuestionAnswering.from_pretrained('''microsoft/layoutlm-base-uncased'''       )

					A_   , A_   , A_   , A_   , A_  :				Any	   =		prepare_layoutlm_batch_inputs()

					# forward pass
					A_  :				Optional[Any]	   =		model(input_ids=_lowerCamelCase      ,     bbox=_lowerCamelCase      ,     attention_mask=_lowerCamelCase      ,     token_type_ids=_lowerCamelCase       )

					# test the shape of the logits
					A_  :				int	   =		tf.convert_to_tensor((2, 25)       )
					self.assertEqual(outputs.start_logits.shape      ,     _lowerCamelCase       )
					self.assertEqual(outputs.end_logits.shape      ,     _lowerCamelCase       )











"""simple docstring"""

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  :			Tuple        =       get_tests_dir('fixtures/test_sentencepiece.model')

@require_sentencepiece
@require_tokenizers
class        lowercase      (    __UpperCAmelCase     ,	unittest.TestCase):
				__lowerCAmelCase    :    Tuple         =   XLNetTokenizer
				__lowerCAmelCase    :    Optional[int]         =   XLNetTokenizerFast
				__lowerCAmelCase    :    Union[str, Any]         =   True
				__lowerCAmelCase    :    Any         =   True







				def 					a_  (							self   :					List[str]       ):



					"""simple docstring"""

					super().setUp()

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







				def 					a_  (							self   :					List[str]       ):



					"""simple docstring"""

					A_  :				Optional[int]	   =		'''<s>'''
					A_  :				int	   =		1

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







				def 					a_  (							self   :					str       ):



					"""simple docstring"""

					A_  :				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(_lowerCamelCase       )      ,     10_06       )







				def 					a_  (							self   :					Optional[int]       ):



					"""simple docstring"""

					self.assertEqual(self.get_tokenizer().vocab_size      ,     10_00       )







				def 					a_  (							self   :					Optional[Any]       ):



					"""simple docstring"""

					A_  :				Optional[int]	   =		XLNetTokenizer(_lowerCamelCase      ,     keep_accents=_lowerCamelCase       )

					A_  :				Union[str, Any]	   =		tokenizer.tokenize('''This is a test'''       )
					self.assertListEqual(_lowerCamelCase      ,     ['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''']       )

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

					A_  :				List[str]	   =		tokenizer.tokenize('''I was born in 92000, and this is falsé.'''       )
					self.assertListEqual(
					    _lowerCamelCase      ,     [
					        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''',
					        '''.''',
					    ]      ,     )
					A_  :				int	   =		tokenizer.convert_tokens_to_ids(_lowerCamelCase       )
					self.assertListEqual(_lowerCamelCase      ,     [8, 21, 84, 55, 24, 19, 7, 0, 6_02, 3_47, 3_47, 3_47, 3, 12, 66, 46, 72, 80, 6, 0, 4]       )

					A_  :				Union[str, Any]	   =		tokenizer.convert_ids_to_tokens(_lowerCamelCase       )
					self.assertListEqual(
					    _lowerCamelCase      ,     [
					        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   :					List[str]       ):



					"""simple docstring"""

					A_  :				Optional[Any]	   =		XLNetTokenizer(_lowerCamelCase      ,     do_lower_case=_lowerCamelCase       )
					A_  :				str	   =		tokenizer.tokenize('''I was born in 92000, and this is falsé.'''       )
					self.assertListEqual(
					    _lowerCamelCase      ,     [
					        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   :					Optional[Any]       ):



					"""simple docstring"""

					A_  :				Optional[int]	   =		XLNetTokenizer(_lowerCamelCase      ,     do_lower_case=_lowerCamelCase       )
					A_  :				Tuple	   =		tokenizer.tokenize('''I was born in 92000, and this is falsé.'''       )
					self.assertListEqual(
					    _lowerCamelCase      ,     [
					        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   :					Dict       ):



					"""simple docstring"""

					A_  :				int	   =		XLNetTokenizer.from_pretrained('''xlnet-base-cased'''       )

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

					A_  :				Tuple	   =		tokenizer.build_inputs_with_special_tokens(_lowerCamelCase       )
					A_  :				Union[str, Any]	   =		tokenizer.build_inputs_with_special_tokens(_lowerCamelCase      ,     _lowerCamelCase       )

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







				@slow
				def 					a_  (							self   :					List[str]       ):



					"""simple docstring"""

					A_  :				Dict	   =		{'''input_ids''': [[17, 2_14_42, 2_70, 17, 10, 1_46_45, 3_18, 34, 17, 45_46, 31_45, 7_87, 13, 77_52, 2_20_18, 23, 21, 17, 45_46, 31_45, 7_87, 13, 33_52, 1_44_31, 13, 55_00, 11, 11_76, 5_80, 13, 1_68_19, 47_97, 23, 17, 10, 1_71_35, 6_58, 19, 4_57, 79_32, 13, 1_84, 19, 31_54, 1_71_35, 64_68, 19, 14_04, 1_22_69, 19, 42_29, 53_56, 1_62_64, 46, 19, 17, 2_05_45, 1_03_95, 9, 9, 9, 11, 28, 64_21, 95_31, 2_07_29, 17, 10, 3_53, 1_70_22, 11, 21, 64_21, 95_31, 1_69_49, 17, 10, 1_15_09, 7_53, 11, 33, 95, 24_21, 73_85, 9_56, 1_44_31, 26_26, 25, 8_42, 73_85, 48_36, 21, 14_29, 22_72, 98_55, 31_20, 1_61, 2_47_38, 19, 1_32_03, 6_58, 2_18, 7_87, 21, 4_30, 1_84_82, 8_47, 26_37, 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_22, 2_21_78, 27, 10_64, 22, 9_56, 13, 1_11_01, 14_29, 58_54, 2_43_13, 1_89_53, 40, 4_22, 2_43_66, 68, 17_58, 37, 1_04_83, 1_42_57, 31, 2_07, 2_63, 21, 2_03, 37_73, 25, 71, 97_35, 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, 32, 20_49, 34_42, 17, 1_38_94, 33_80, 23, 95, 18, 1_76_34, 22_88, 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=_lowerCamelCase      ,     model_name='''xlnet-base-cased'''      ,     revision='''c841166438c31ec7ca9a106dee7bb312b73ae511'''      ,     )





