




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 			a__    :






						def __init__(   self		,			_A		,			_A=1_3		,			_A=7		,			_A=True		,			_A=True		,			_A=True		,			_A=True		,			_A=9_9		,			_A=3_2		,			_A=2		,			_A=4		,			_A=3_7		,			_A="gelu"		,			_A=0.1		,			_A=0.1		,			_A=5_1_2		,			_A=1_6		,			_A=2		,			_A=0.02		,			_A=3		,			_A=4		,			_A=None		,			_A=1_0_0_0		,			):




											"""simple docstring"""



											__lowerCAmelCase				   =       parent
											__lowerCAmelCase				   =       batch_size
											__lowerCAmelCase				   =       seq_length
											__lowerCAmelCase				   =       is_training
											__lowerCAmelCase				   =       use_input_mask
											__lowerCAmelCase				   =       use_token_type_ids
											__lowerCAmelCase				   =       use_labels
											__lowerCAmelCase				   =       vocab_size
											__lowerCAmelCase				   =       hidden_size
											__lowerCAmelCase				   =       num_hidden_layers
											__lowerCAmelCase				   =       num_attention_heads
											__lowerCAmelCase				   =       intermediate_size
											__lowerCAmelCase				   =       hidden_act
											__lowerCAmelCase				   =       hidden_dropout_prob
											__lowerCAmelCase				   =       attention_probs_dropout_prob
											__lowerCAmelCase				   =       max_position_embeddings
											__lowerCAmelCase				   =       type_vocab_size
											__lowerCAmelCase				   =       type_sequence_label_size
											__lowerCAmelCase				   =       initializer_range
											__lowerCAmelCase				   =       num_labels
											__lowerCAmelCase				   =       num_choices
											__lowerCAmelCase				   =       scope
											__lowerCAmelCase				   =       range_bbox






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											__lowerCAmelCase				   =       ids_tensor([self.batch_size, self.seq_length]		,			self.vocab_size			)

											# convert bbox to numpy since TF does not support item assignment
											__lowerCAmelCase				   =       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]:
																										__lowerCAmelCase				   =       bbox[i, j, 3]
																										__lowerCAmelCase				   =       bbox[i, j, 1]
																										__lowerCAmelCase				   =       t
																					if bbox[i, j, 2] < bbox[i, j, 0]:
																										__lowerCAmelCase				   =       bbox[i, j, 2]
																										__lowerCAmelCase				   =       bbox[i, j, 0]
																										__lowerCAmelCase				   =       t
											__lowerCAmelCase				   =       tf.convert_to_tensor(_A			)

											__lowerCAmelCase				   =       None
											if self.use_input_mask:
																__lowerCAmelCase				   =       random_attention_mask([self.batch_size, self.seq_length]			)

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

											__lowerCAmelCase				   =       None
											__lowerCAmelCase				   =       None
											__lowerCAmelCase				   =       None
											if self.use_labels:
																__lowerCAmelCase				   =       ids_tensor([self.batch_size]		,			self.type_sequence_label_size			)
																__lowerCAmelCase				   =       ids_tensor([self.batch_size, self.seq_length]		,			self.num_labels			)
																__lowerCAmelCase				   =       ids_tensor([self.batch_size]		,			self.num_choices			)

											__lowerCAmelCase				   =       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 				__SCREAMING_SNAKE_CASE(   self		,			_A		,			_A		,			_A		,			_A		,			_A		,			_A		,			_A		,			_A			):




											"""simple docstring"""



											__lowerCAmelCase				   =       TFLayoutLMModel(config=_A			)

											__lowerCAmelCase				   =       model(_A		,			_A		,			attention_mask=_A		,			token_type_ids=_A			)
											__lowerCAmelCase				   =       model(_A		,			_A		,			token_type_ids=_A			)
											__lowerCAmelCase				   =       model(_A		,			_A			)
											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 				__SCREAMING_SNAKE_CASE(   self		,			_A		,			_A		,			_A		,			_A		,			_A		,			_A		,			_A		,			_A			):




											"""simple docstring"""



											__lowerCAmelCase				   =       TFLayoutLMForMaskedLM(config=_A			)

											__lowerCAmelCase				   =       model(_A		,			_A		,			attention_mask=_A		,			token_type_ids=_A		,			labels=_A			)
											self.parent.assertEqual(result.logits.shape		,			(self.batch_size, self.seq_length, self.vocab_size)			)






						def 				__SCREAMING_SNAKE_CASE(   self		,			_A		,			_A		,			_A		,			_A		,			_A		,			_A		,			_A		,			_A			):




											"""simple docstring"""



											__lowerCAmelCase				   =       self.num_labels
											__lowerCAmelCase				   =       TFLayoutLMForSequenceClassification(config=_A			)

											__lowerCAmelCase				   =       model(_A		,			_A		,			attention_mask=_A		,			token_type_ids=_A			)
											self.parent.assertEqual(result.logits.shape		,			(self.batch_size, self.num_labels)			)






						def 				__SCREAMING_SNAKE_CASE(   self		,			_A		,			_A		,			_A		,			_A		,			_A		,			_A		,			_A		,			_A			):




											"""simple docstring"""



											__lowerCAmelCase				   =       self.num_labels
											__lowerCAmelCase				   =       TFLayoutLMForTokenClassification(config=_A			)

											__lowerCAmelCase				   =       model(_A		,			_A		,			attention_mask=_A		,			token_type_ids=_A		,			labels=_A			)
											self.parent.assertEqual(result.logits.shape		,			(self.batch_size, self.seq_length, self.num_labels)			)






						def 				__SCREAMING_SNAKE_CASE(   self		,			_A		,			_A		,			_A		,			_A		,			_A		,			_A		,			_A		,			_A			):




											"""simple docstring"""



											__lowerCAmelCase				   =       TFLayoutLMForQuestionAnswering(config=_A			)

											__lowerCAmelCase				   =       model(_A		,			_A		,			attention_mask=_A		,			token_type_ids=_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 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											__lowerCAmelCase				   =       self.prepare_config_and_inputs()
											(
											    (
											    __lowerCAmelCase
											)   ,							(
											    __lowerCAmelCase
											)   ,							(
											    __lowerCAmelCase
											)   ,							(
											    __lowerCAmelCase
											)   ,							(
											    __lowerCAmelCase
											)   ,							(
											    __lowerCAmelCase
											)   ,							(
											    __lowerCAmelCase
											)   ,							(
											    __lowerCAmelCase
											)   ,							
											)				   =       config_and_inputs
											__lowerCAmelCase				   =       {
											    "input_ids": input_ids,
											    "bbox": bbox,
											    "token_type_ids": token_type_ids,
											    "attention_mask": input_mask,
											}
											return config, inputs_dict






@require_tf
class 			a__    (					snake_case__ ,				snake_case__ ,				unittest.TestCase      ):
						_a							:    List[Any]					=    (
						    (
						        TFLayoutLMModel,
						        TFLayoutLMForMaskedLM,
						        TFLayoutLMForTokenClassification,
						        TFLayoutLMForSequenceClassification,
						        TFLayoutLMForQuestionAnswering,
						    )
						    if is_tf_available()
						    else ()
						)
						_a							:    Optional[int]					=    (
						    {
						        """feature-extraction""": TFLayoutLMModel,
						        """fill-mask""": TFLayoutLMForMaskedLM,
						        """text-classification""": TFLayoutLMForSequenceClassification,
						        """token-classification""": TFLayoutLMForTokenClassification,
						        """zero-shot""": TFLayoutLMForSequenceClassification,
						    }
						    if is_tf_available()
						    else {}
						)
						_a							:    Optional[Any]					=    False
						_a							:    Dict					=    True
						_a							:    List[str]					=    1_0






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											__lowerCAmelCase				   =       TFLayoutLMModelTester(self			)
											__lowerCAmelCase				   =       ConfigTester(self		,			config_class=_A		,			hidden_size=3_7			)






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											self.config_tester.run_common_tests()






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											__lowerCAmelCase				   =       self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_model(*_A			)






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											__lowerCAmelCase				   =       self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_masked_lm(*_A			)






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											__lowerCAmelCase				   =       self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_sequence_classification(*_A			)






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											__lowerCAmelCase				   =       self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_token_classification(*_A			)






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											__lowerCAmelCase				   =       self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_question_answering(*_A			)






						@slow
						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											for model_name in TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																__lowerCAmelCase				   =       TFLayoutLMModel.from_pretrained(_A			)
																self.assertIsNotNone(_A			)






						@unittest.skip("Onnx compliancy broke with TF 2.10"			)
						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											pass







def 	_a      (						):
					# Here we prepare a batch of 2 sequences to test a LayoutLM forward pass on:
					# fmt: off
					__lowerCAmelCase				   =       tf.convert_to_tensor([[1_01,10_19,10_14,10_16,10_37,1_28_49,47_47,10_04,1_42_46,22_78,54_39,45_24,50_02,29_30,21_93,29_30,43_41,32_08,10_05,10_55,21_71,28_48,1_13_00,35_31,1_02],[1_01,40_70,40_34,70_20,10_24,30_58,10_15,10_13,28_61,10_13,60_70,1_92_74,27_72,62_05,2_78_14,1_61_47,1_61_47,43_43,20_47,1_02_83,1_09_69,1_43_89,10_12,23_38,1_02]]     )  # noqa: E231
					__lowerCAmelCase				   =       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
					__lowerCAmelCase				   =       tf.convert_to_tensor([[[0,0,0,0],[4_23,2_37,4_40,2_51],[4_27,2_72,4_41,2_87],[4_19,1_15,4_37,1_29],[9_61,8_85,9_92,9_12],[2_56,38,3_30,58],[2_56,38,3_30,58],[3_36,42,3_53,57],[3_60,39,4_01,56],[3_60,39,4_01,56],[4_11,39,4_71,59],[4_79,41,5_28,59],[5_33,39,6_30,60],[67,1_13,1_34,1_31],[1_41,1_15,2_09,1_32],[68,1_49,1_33,1_66],[1_41,1_49,1_87,1_64],[1_95,1_48,2_87,1_65],[1_95,1_48,2_87,1_65],[1_95,1_48,2_87,1_65],[2_95,1_48,3_49,1_65],[4_41,1_49,4_92,1_66],[4_97,1_49,5_46,1_64],[64,2_01,1_25,2_18],[10_00,10_00,10_00,10_00]],[[0,0,0,0],[6_62,1_50,7_54,1_66],[6_65,1_99,7_42,2_11],[5_19,2_13,5_54,2_28],[5_19,2_13,5_54,2_28],[1_34,4_33,1_87,4_54],[1_30,4_67,2_04,4_80],[1_30,4_67,2_04,4_80],[1_30,4_67,2_04,4_80],[1_30,4_67,2_04,4_80],[1_30,4_67,2_04,4_80],[3_14,4_69,3_76,4_82],[5_04,6_84,5_82,7_06],[9_41,8_25,9_73,9_00],[9_41,8_25,9_73,9_00],[9_41,8_25,9_73,9_00],[9_41,8_25,9_73,9_00],[6_10,7_49,6_52,7_65],[1_30,6_59,1_68,6_72],[1_76,6_57,2_37,6_72],[2_38,6_57,3_12,6_72],[4_43,6_53,6_28,6_72],[4_43,6_53,6_28,6_72],[7_16,3_01,8_25,3_17],[10_00,10_00,10_00,10_00]]]     )  # noqa: E231
					__lowerCAmelCase				   =       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)
					__lowerCAmelCase				   =       tf.convert_to_tensor([[-1_00,10,10,10,9,1,-1_00,7,7,-1_00,7,7,4,2,5,2,8,8,-1_00,-1_00,5,0,3,2,-1_00],[-1_00,12,12,12,-1_00,12,10,-1_00,-1_00,-1_00,-1_00,10,12,9,-1_00,-1_00,-1_00,10,10,10,9,12,-1_00,10,-1_00]]     )  # noqa: E231
					# fmt: on

					return input_ids, attention_mask, bbox, token_type_ids, labels


@require_tf
class 			a__    (					unittest.TestCase      ):






						@slow
						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											__lowerCAmelCase				   =       TFLayoutLMModel.from_pretrained("microsoft/layoutlm-base-uncased"			)

											__lowerCAmelCase   ,							__lowerCAmelCase   ,							__lowerCAmelCase   ,							__lowerCAmelCase   ,							__lowerCAmelCase				   =       prepare_layoutlm_batch_inputs()

											# forward pass
											__lowerCAmelCase				   =       model(input_ids=_A		,			bbox=_A		,			attention_mask=_A		,			token_type_ids=_A			)

											# test the sequence output on [0, :3, :3]
											__lowerCAmelCase				   =       tf.convert_to_tensor(
											    [[0.17_85, -0.19_47, -0.04_25], [-0.32_54, -0.28_07, 0.25_53], [-0.53_91, -0.33_22, 0.33_64]]		,			)

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

											# test the pooled output on [1, :3]
											__lowerCAmelCase				   =       tf.convert_to_tensor([-0.65_80, -0.02_14, 0.85_52]			)

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






						@slow
						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											__lowerCAmelCase				   =       TFLayoutLMForSequenceClassification.from_pretrained("microsoft/layoutlm-base-uncased"		,			num_labels=2			)

											__lowerCAmelCase   ,							__lowerCAmelCase   ,							__lowerCAmelCase   ,							__lowerCAmelCase   ,							__lowerCAmelCase				   =       prepare_layoutlm_batch_inputs()

											# forward pass
											__lowerCAmelCase				   =       model(
											    input_ids=_A		,			bbox=_A		,			attention_mask=_A		,			token_type_ids=_A		,			labels=tf.convert_to_tensor([1, 1]			)		,			)

											# test whether we get a loss as a scalar
											__lowerCAmelCase				   =       outputs.loss
											__lowerCAmelCase				   =       (2,)
											self.assertEqual(loss.shape		,			_A			)

											# test the shape of the logits
											__lowerCAmelCase				   =       outputs.logits
											__lowerCAmelCase				   =       (2, 2)
											self.assertEqual(logits.shape		,			_A			)






						@slow
						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											__lowerCAmelCase				   =       TFLayoutLMForTokenClassification.from_pretrained("microsoft/layoutlm-base-uncased"		,			num_labels=1_3			)

											__lowerCAmelCase   ,							__lowerCAmelCase   ,							__lowerCAmelCase   ,							__lowerCAmelCase   ,							__lowerCAmelCase				   =       prepare_layoutlm_batch_inputs()

											# forward pass
											__lowerCAmelCase				   =       model(
											    input_ids=_A		,			bbox=_A		,			attention_mask=_A		,			token_type_ids=_A		,			labels=_A			)

											# test the shape of the logits
											__lowerCAmelCase				   =       outputs.logits
											__lowerCAmelCase				   =       tf.convert_to_tensor((2, 2_5, 1_3)			)
											self.assertEqual(logits.shape		,			_A			)






						@slow
						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											__lowerCAmelCase				   =       TFLayoutLMForQuestionAnswering.from_pretrained("microsoft/layoutlm-base-uncased"			)

											__lowerCAmelCase   ,							__lowerCAmelCase   ,							__lowerCAmelCase   ,							__lowerCAmelCase   ,							__lowerCAmelCase				   =       prepare_layoutlm_batch_inputs()

											# forward pass
											__lowerCAmelCase				   =       model(input_ids=_A		,			bbox=_A		,			attention_mask=_A		,			token_type_ids=_A			)

											# test the shape of the logits
											__lowerCAmelCase				   =       tf.convert_to_tensor((2, 2_5)			)
											self.assertEqual(outputs.start_logits.shape		,			_A			)
											self.assertEqual(outputs.end_logits.shape		,			_A			)










import unittest

from transformers import DebertaVaTokenizer, DebertaVaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


UpperCamelCase__    			=			get_tests_dir("""fixtures/spiece.model""")
@require_sentencepiece
@require_tokenizers
class 			a__    (					snake_case__ ,				unittest.TestCase      ):
						_a							:    Optional[Any]					=    DebertaVaTokenizer
						_a							:    Optional[Any]					=    DebertaVaTokenizerFast
						_a							:    List[str]					=    True
						_a							:    Optional[Any]					=    True






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											super().setUp()

											# We have a SentencePiece fixture for testing
											__lowerCAmelCase				   =       DebertaVaTokenizer(_A		,			unk_token="<unk>"			)
											tokenizer.save_pretrained(self.tmpdirname			)






						def 				__SCREAMING_SNAKE_CASE(   self		,			_A			):




											"""simple docstring"""



											__lowerCAmelCase				   =       "this is a test"
											__lowerCAmelCase				   =       "this is a test"
											return input_text, output_text






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											__lowerCAmelCase				   =       "<pad>"
											__lowerCAmelCase				   =       0

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






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											__lowerCAmelCase				   =       list(self.get_tokenizer().get_vocab().keys()			)
											self.assertEqual(vocab_keys[0]		,			"<pad>"			)
											self.assertEqual(vocab_keys[1]		,			"<unk>"			)
											self.assertEqual(vocab_keys[-1]		,			"[PAD]"			)
											self.assertEqual(len(_A			)		,			3_0_0_0_1			)






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											self.assertEqual(self.get_tokenizer().vocab_size		,			3_0_0_0_0			)






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											__lowerCAmelCase				   =       " \tHeLLo!how  \n Are yoU?  "
											__lowerCAmelCase				   =       ["▁hello", "!", "how", "▁are", "▁you", "?"]
											# fmt: on

											__lowerCAmelCase				   =       DebertaVaTokenizer(_A		,			do_lower_case=_A			)
											__lowerCAmelCase				   =       tokenizer.convert_ids_to_tokens(tokenizer.encode(_A		,			add_special_tokens=_A			)			)

											self.assertListEqual(_A		,			_A			)

											__lowerCAmelCase				   =       DebertaVaTokenizerFast(_A		,			do_lower_case=_A			)
											__lowerCAmelCase				   =       rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(_A		,			add_special_tokens=_A			)			)

											self.assertListEqual(_A		,			_A			)






						@unittest.skip("There is an inconsistency between slow and fast tokenizer due to a bug in the fast one."			)
						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											pass






						@unittest.skip("There is an inconsistency between slow and fast tokenizer due to a bug in the fast one."			)
						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											pass






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											__lowerCAmelCase				   =       "I was born in 92000, and this is falsé."
											__lowerCAmelCase				   =       ["▁", "<unk>", "▁was", "▁born", "▁in", "▁9", "2000", "▁", ",", "▁and", "▁this", "▁is", "▁fal", "s", "<unk>", "▁", ".", ]
											# fmt: on

											__lowerCAmelCase				   =       DebertaVaTokenizer(_A		,			split_by_punct=_A			)
											__lowerCAmelCase				   =       tokenizer.convert_ids_to_tokens(tokenizer.encode(_A		,			add_special_tokens=_A			)			)

											self.assertListEqual(_A		,			_A			)

											__lowerCAmelCase				   =       DebertaVaTokenizerFast(_A		,			split_by_punct=_A			)
											__lowerCAmelCase				   =       rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(_A		,			add_special_tokens=_A			)			)

											self.assertListEqual(_A		,			_A			)






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											__lowerCAmelCase				   =       "I was born in 92000, and this is falsé."
											__lowerCAmelCase				   =       ["▁i", "▁was", "▁born", "▁in", "▁9", "2000", "▁", ",", "▁and", "▁this", "▁is", "▁fal", "s", "<unk>", "▁", ".", ]
											# fmt: on

											__lowerCAmelCase				   =       DebertaVaTokenizer(_A		,			do_lower_case=_A		,			split_by_punct=_A			)
											__lowerCAmelCase				   =       tokenizer.convert_ids_to_tokens(tokenizer.encode(_A		,			add_special_tokens=_A			)			)
											self.assertListEqual(_A		,			_A			)

											__lowerCAmelCase				   =       DebertaVaTokenizerFast(_A		,			do_lower_case=_A		,			split_by_punct=_A			)
											__lowerCAmelCase				   =       rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(_A		,			add_special_tokens=_A			)			)
											self.assertListEqual(_A		,			_A			)






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											__lowerCAmelCase				   =       "I was born in 92000, and this is falsé."
											__lowerCAmelCase				   =       ["▁i", "▁was", "▁born", "▁in", "▁9", "2000", ",", "▁and", "▁this", "▁is", "▁fal", "s", "<unk>", ".", ]
											# fmt: on

											__lowerCAmelCase				   =       DebertaVaTokenizer(_A		,			do_lower_case=_A		,			split_by_punct=_A			)
											__lowerCAmelCase				   =       tokenizer.convert_ids_to_tokens(tokenizer.encode(_A		,			add_special_tokens=_A			)			)

											self.assertListEqual(_A		,			_A			)

											__lowerCAmelCase				   =       DebertaVaTokenizerFast(_A		,			do_lower_case=_A		,			split_by_punct=_A			)
											__lowerCAmelCase				   =       rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(_A		,			add_special_tokens=_A			)			)

											self.assertListEqual(_A		,			_A			)






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											__lowerCAmelCase				   =       "I was born in 92000, and this is falsé."
											__lowerCAmelCase				   =       ["▁", "<unk>", "▁was", "▁born", "▁in", "▁9", "2000", "▁", ",", "▁and", "▁this", "▁is", "▁fal", "s", "<unk>", "▁", ".", ]
											# fmt: on

											__lowerCAmelCase				   =       DebertaVaTokenizer(_A		,			do_lower_case=_A		,			split_by_punct=_A			)
											__lowerCAmelCase				   =       tokenizer.convert_ids_to_tokens(tokenizer.encode(_A		,			add_special_tokens=_A			)			)

											self.assertListEqual(_A		,			_A			)

											__lowerCAmelCase				   =       DebertaVaTokenizerFast(_A		,			do_lower_case=_A		,			split_by_punct=_A			)
											__lowerCAmelCase				   =       rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(_A		,			add_special_tokens=_A			)			)

											self.assertListEqual(_A		,			_A			)






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											__lowerCAmelCase				   =       " \tHeLLo!how  \n Are yoU?  "
											__lowerCAmelCase				   =       ["▁", "<unk>", "e", "<unk>", "o", "!", "how", "▁", "<unk>", "re", "▁yo", "<unk>", "?"]
											# fmt: on

											__lowerCAmelCase				   =       DebertaVaTokenizer(_A		,			do_lower_case=_A		,			split_by_punct=_A			)
											__lowerCAmelCase				   =       tokenizer.convert_ids_to_tokens(tokenizer.encode(_A		,			add_special_tokens=_A			)			)

											self.assertListEqual(_A		,			_A			)

											__lowerCAmelCase				   =       DebertaVaTokenizerFast(_A		,			do_lower_case=_A		,			split_by_punct=_A			)
											__lowerCAmelCase				   =       rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(_A		,			add_special_tokens=_A			)			)

											self.assertListEqual(_A		,			_A			)






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											__lowerCAmelCase				   =       self.get_tokenizer()
											__lowerCAmelCase				   =       self.get_rust_tokenizer()

											__lowerCAmelCase				   =       "I was born in 92000, and this is falsé."

											__lowerCAmelCase				   =       tokenizer.convert_ids_to_tokens(tokenizer.encode(_A		,			add_special_tokens=_A			)			)
											__lowerCAmelCase				   =       rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(_A		,			add_special_tokens=_A			)			)
											self.assertListEqual(_A		,			_A			)

											__lowerCAmelCase				   =       tokenizer.encode(_A		,			add_special_tokens=_A			)
											__lowerCAmelCase				   =       rust_tokenizer.encode(_A		,			add_special_tokens=_A			)
											self.assertListEqual(_A		,			_A			)

											__lowerCAmelCase				   =       self.get_rust_tokenizer()
											__lowerCAmelCase				   =       tokenizer.encode(_A			)
											__lowerCAmelCase				   =       rust_tokenizer.encode(_A			)
											self.assertListEqual(_A		,			_A			)






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											__lowerCAmelCase				   =       "This is a test"
											__lowerCAmelCase				   =       [1_3, 1, 4_3_9_8, 2_5, 2_1, 1_2_8_9]
											__lowerCAmelCase				   =       ["▁", "T", "his", "▁is", "▁a", "▁test"]
											__lowerCAmelCase				   =       ["▁", "<unk>", "his", "▁is", "▁a", "▁test"]

											__lowerCAmelCase				   =       DebertaVaTokenizer(_A		,			keep_accents=_A			)
											__lowerCAmelCase				   =       DebertaVaTokenizerFast(_A		,			keep_accents=_A			)

											__lowerCAmelCase				   =       tokenizer.encode(_A		,			add_special_tokens=_A			)
											self.assertListEqual(_A		,			_A			)
											__lowerCAmelCase				   =       tokenizer.tokenize(_A			)
											self.assertListEqual(_A		,			_A			)
											__lowerCAmelCase				   =       tokenizer.convert_ids_to_tokens(_A			)
											self.assertListEqual(_A		,			_A			)

											__lowerCAmelCase				   =       rust_tokenizer.encode(_A		,			add_special_tokens=_A			)
											self.assertListEqual(_A		,			_A			)
											__lowerCAmelCase				   =       rust_tokenizer.tokenize(_A			)
											self.assertListEqual(_A		,			_A			)
											__lowerCAmelCase				   =       rust_tokenizer.convert_ids_to_tokens(_A			)
											self.assertListEqual(_A		,			_A			)

											# fmt: off
											__lowerCAmelCase				   =       "I was born in 92000, and this is falsé."
											__lowerCAmelCase				   =       [1_3, 1, 2_3, 3_8_6, 1_9, 5_6_1, 3_0_5_0, 1_5, 1_7, 4_8, 2_5, 8_2_5_6, 1_8, 1, 9]
											__lowerCAmelCase				   =       ["▁", "I", "▁was", "▁born", "▁in", "▁9", "2000", ",", "▁and", "▁this", "▁is", "▁fal", "s", "é", ".", ]
											__lowerCAmelCase				   =       ["▁", "<unk>", "▁was", "▁born", "▁in", "▁9", "2000", ",", "▁and", "▁this", "▁is", "▁fal", "s", "<unk>", ".", ]
											# fmt: on

											__lowerCAmelCase				   =       tokenizer.encode(_A		,			add_special_tokens=_A			)
											self.assertListEqual(_A		,			_A			)
											__lowerCAmelCase				   =       tokenizer.tokenize(_A			)
											self.assertListEqual(_A		,			_A			)
											__lowerCAmelCase				   =       tokenizer.convert_ids_to_tokens(_A			)
											self.assertListEqual(_A		,			_A			)

											__lowerCAmelCase				   =       rust_tokenizer.encode(_A		,			add_special_tokens=_A			)
											self.assertListEqual(_A		,			_A			)
											__lowerCAmelCase				   =       rust_tokenizer.tokenize(_A			)
											self.assertListEqual(_A		,			_A			)
											__lowerCAmelCase				   =       rust_tokenizer.convert_ids_to_tokens(_A			)
											self.assertListEqual(_A		,			_A			)






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											__lowerCAmelCase				   =       DebertaVaTokenizer(_A			)

											__lowerCAmelCase				   =       tokenizer.encode("sequence builders"			)
											__lowerCAmelCase				   =       tokenizer.encode("multi-sequence build"			)

											__lowerCAmelCase				   =       tokenizer.build_inputs_with_special_tokens(_A			)
											__lowerCAmelCase				   =       tokenizer.build_inputs_with_special_tokens(_A		,			_A			)

											self.assertEqual([tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]		,			_A			)
											self.assertEqual(
											    [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + text_a + [tokenizer.sep_token_id]		,			_A		,			)






						@slow
						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											__lowerCAmelCase				   =       {"input_ids": [[1, 3_9_8_6_7, 3_6, 1_9_3_9_0, 4_8_6, 2_7, 3_5_0_5_2, 8_1_4_3_6, 1_8, 6_0_6_8_5, 1_2_2_5, 7, 3_5_0_5_2, 8_1_4_3_6, 1_8, 9_3_6_7, 1_6_8_9_9, 1_8, 1_5_9_3_7, 5_3, 5_9_4, 7_7_3, 1_8, 1_6_2_8_7, 3_0_4_6_5, 3_6, 1_5_9_3_7, 6, 4_1_1_3_9, 3_8, 3_6_9_7_9, 6_0_7_6_3, 1_9_1, 6, 3_4_1_3_2, 9_9, 6, 5_0_5_3_8, 3_9_0, 4_3_2_3_0, 6, 3_4_1_3_2, 2_7_7_9, 2_0_8_5_0, 1_4, 6_9_9, 1_0_7_2, 1_1_9_4, 3_6, 3_8_2, 1_0_9_0_1, 5_3, 7, 6_9_9, 1_0_7_2, 2_0_8_4, 3_6, 2_0_4_2_2, 6_3_0, 5_3, 1_9, 1_0_5, 3_0_4_9, 1_8_9_6, 1_0_5_3, 1_6_8_9_9, 1_5_0_6, 1_1, 3_7_9_7_8, 4_2_4_3, 7, 1_2_3_7, 3_1_8_6_9, 2_0_0, 1_6_5_6_6, 6_5_4, 6, 3_5_0_5_2, 8_1_4_3_6, 7, 5_5_6_3_0, 1_3_5_9_3, 4, 2], [1, 2_6, 1_5_0_1_1, 1_3, 6_6_7, 8, 1_0_5_3, 1_8, 2_3_6_1_1, 1_2_3_7, 7_2_3_5_6, 1_2_8_2_0, 3_4, 1_0_4_1_3_4, 1_2_0_9, 3_5, 1_3_3_1_3, 6_6_2_7, 2_1, 2_0_2, 3_4_7, 7, 1_6_4, 2_3_9_9, 1_1, 4_6, 4_4_8_5, 4, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 5, 1_2_3_2, 2_8_6_4, 1_5_7_8_5, 1_4_9_5_1, 1_0_5, 5, 8_5_8_1, 1_2_5_0, 4, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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]], "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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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]]}  # noqa: E501
											# fmt: on

											self.tokenizer_integration_test_util(
											    expected_encoding=_A		,			model_name="microsoft/deberta-v2-xlarge"		,			revision="ad6e42c1532ddf3a15c39246b63f5559d558b670"		,			)




