
"""simple docstring"""



import json
import os
import unittest

from transformers import BatchEncoding, LEDTokenizer, LEDTokenizerFast
from transformers.models.led.tokenization_led import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class        _SCREAMING_SNAKE_CASE  (  UpperCAmelCase      ,				unittest.TestCase							):






				'''simple docstring'''



				SCREAMING_SNAKE_CASE_:							Tuple							=					LEDTokenizer
				SCREAMING_SNAKE_CASE_:							str							=					LEDTokenizerFast
				SCREAMING_SNAKE_CASE_:							Optional[int]							=					True


				def 				__lowerCamelCase	(					self	:			Union[str, Any]  )		->  List[str]:







											"""simple docstring"""




											super().setUp()
											_lowerCAmelCase  	=			[
											    'l',
											    'o',
											    'w',
											    'e',
											    'r',
											    's',
											    't',
											    'i',
											    'd',
											    'n',
											    '\u0120',
											    '\u0120l',
											    '\u0120n',
											    '\u0120lo',
											    '\u0120low',
											    'er',
											    '\u0120lowest',
											    '\u0120newer',
											    '\u0120wider',
											    '<unk>',
											]
											_lowerCAmelCase  	=			dict(zip(UpperCAmelCase_				,     range(len(UpperCAmelCase_  )  )  )  )
											_lowerCAmelCase  	=			['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
											_lowerCAmelCase  	=			{'unk_token': '<unk>'}

											_lowerCAmelCase  	=			os.path.join(self.tmpdirname				,     VOCAB_FILES_NAMES['vocab_file']  )
											_lowerCAmelCase  	=			os.path.join(self.tmpdirname				,     VOCAB_FILES_NAMES['merges_file']  )
											with open(self.vocab_file				,     'w'				,     encoding='utf-8'  ) as fp:
																		fp.write(json.dumps(UpperCAmelCase_  ) + '\n'  )
											with open(self.merges_file				,     'w'				,     encoding='utf-8'  ) as fp:
																		fp.write('\n'.join(UpperCAmelCase_  )  )


				def 				__lowerCamelCase	(					self	:			Optional[int]				,     **UpperCAmelCase_	:			Optional[Any]  )		->  Tuple:







											"""simple docstring"""




											kwargs.update(self.special_tokens_map  )
											return self.tokenizer_class.from_pretrained(self.tmpdirname				,     **UpperCAmelCase_  )


				def 				__lowerCamelCase	(					self	:			str				,     **UpperCAmelCase_	:			Any  )		->  Dict:







											"""simple docstring"""




											kwargs.update(self.special_tokens_map  )
											return self.rust_tokenizer_class.from_pretrained(self.tmpdirname				,     **UpperCAmelCase_  )


				def 				__lowerCamelCase	(					self	:			Union[str, Any]				,     UpperCAmelCase_	:			Tuple  )		->  int:







											"""simple docstring"""




											return "lower newer", "lower newer"


				@cached_property
				def 				__lowerCamelCase	(					self	:			Union[str, Any]  )		->  str:







											"""simple docstring"""




											return LEDTokenizer.from_pretrained('allenai/led-base-16384'  )


				@cached_property
				def 				__lowerCamelCase	(					self	:			Any  )		->  Dict:







											"""simple docstring"""




											return LEDTokenizerFast.from_pretrained('allenai/led-base-16384'  )


				@require_torch
				def 				__lowerCamelCase	(					self	:			Optional[int]  )		->  Optional[int]:







											"""simple docstring"""




											_lowerCAmelCase  	=			['A long paragraph for summarization.', 'Another paragraph for summarization.']
											_lowerCAmelCase  	=			[0, 250, 251, 17_818, 13, 39_186, 1_938, 4, 2]

											for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																		_lowerCAmelCase  	=			tokenizer(UpperCAmelCase_				,     max_length=len(UpperCAmelCase_  )				,     padding=UpperCAmelCase_				,     return_tensors='pt'  )
																		self.assertIsInstance(UpperCAmelCase_				,     UpperCAmelCase_  )

																		self.assertEqual((2, 9)				,     batch.input_ids.shape  )
																		self.assertEqual((2, 9)				,     batch.attention_mask.shape  )
																		_lowerCAmelCase  	=			batch.input_ids.tolist()[0]
																		self.assertListEqual(UpperCAmelCase_				,     UpperCAmelCase_  )


				@require_torch
				def 				__lowerCamelCase	(					self	:			str  )		->  str:







											"""simple docstring"""




											_lowerCAmelCase  	=			['A long paragraph for summarization.', 'Another paragraph for summarization.']
											for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																		_lowerCAmelCase  	=			tokenizer(UpperCAmelCase_				,     padding=UpperCAmelCase_				,     return_tensors='pt'  )
																		self.assertIn('input_ids'				,     UpperCAmelCase_  )
																		self.assertIn('attention_mask'				,     UpperCAmelCase_  )
																		self.assertNotIn('labels'				,     UpperCAmelCase_  )
																		self.assertNotIn('decoder_attention_mask'				,     UpperCAmelCase_  )


				@require_torch
				def 				__lowerCamelCase	(					self	:			Any  )		->  str:







											"""simple docstring"""




											_lowerCAmelCase  	=			[
											    'Summary of the text.',
											    'Another summary.',
											]
											for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																		_lowerCAmelCase  	=			tokenizer(text_target=UpperCAmelCase_				,     max_length=32				,     padding='max_length'				,     return_tensors='pt'  )
																		self.assertEqual(32				,     targets['input_ids'].shape[1]  )


				@require_torch
				def 				__lowerCamelCase	(					self	:			Union[str, Any]  )		->  int:







											"""simple docstring"""




											for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																		_lowerCAmelCase  	=			tokenizer(
																		    ['I am a small frog' * 1_024, 'I am a small frog']				,     padding=UpperCAmelCase_				,     truncation=UpperCAmelCase_				,     return_tensors='pt'  )
																		self.assertIsInstance(UpperCAmelCase_				,     UpperCAmelCase_  )
																		self.assertEqual(batch.input_ids.shape				,     (2, 5_122)  )


				@require_torch
				def 				__lowerCamelCase	(					self	:			Union[str, Any]  )		->  Union[str, Any]:







											"""simple docstring"""




											_lowerCAmelCase  	=			['A long paragraph for summarization.']
											_lowerCAmelCase  	=			[
											    'Summary of the text.',
											]
											for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																		_lowerCAmelCase  	=			tokenizer(UpperCAmelCase_				,     return_tensors='pt'  )
																		_lowerCAmelCase  	=			tokenizer(text_target=UpperCAmelCase_				,     return_tensors='pt'  )
																		_lowerCAmelCase  	=			inputs['input_ids']
																		_lowerCAmelCase  	=			targets['input_ids']
																		self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item()  )
																		self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item()  )
																		self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item()  )
																		self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item()  )


				@require_torch
				def 				__lowerCamelCase	(					self	:			Optional[Any]  )		->  List[Any]:







											"""simple docstring"""




											for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																		_lowerCAmelCase  	=			['Summary of the text.', 'Another summary.']
																		_lowerCAmelCase  	=			[[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, -1, -1]]

																		_lowerCAmelCase  	=			tokenizer(UpperCAmelCase_				,     padding=UpperCAmelCase_  )
																		_lowerCAmelCase  	=			[[0] * len(UpperCAmelCase_  ) for x in encoded_output['input_ids']]
																		_lowerCAmelCase  	=			tokenizer.pad(UpperCAmelCase_  )
																		self.assertSequenceEqual(outputs['global_attention_mask']				,     UpperCAmelCase_  )


				def 				__lowerCamelCase	(					self	:			Optional[int]  )		->  List[Any]:







											"""simple docstring"""




											pass







				def 				__lowerCamelCase	(					self	:			Tuple  )		->  str:







											"""simple docstring"""




											for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
																		with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""  ):
																									_lowerCAmelCase  	=			self.rust_tokenizer_class.from_pretrained(UpperCAmelCase_				,     **UpperCAmelCase_  )
																									_lowerCAmelCase  	=			self.tokenizer_class.from_pretrained(UpperCAmelCase_				,     **UpperCAmelCase_  )
																									_lowerCAmelCase  	=			'A, <mask> AllenNLP sentence.'
																									_lowerCAmelCase  	=			tokenizer_r.encode_plus(UpperCAmelCase_				,     add_special_tokens=UpperCAmelCase_				,     return_token_type_ids=UpperCAmelCase_  )
																									_lowerCAmelCase  	=			tokenizer_p.encode_plus(UpperCAmelCase_				,     add_special_tokens=UpperCAmelCase_				,     return_token_type_ids=UpperCAmelCase_  )
																									self.assertEqual(sum(tokens_r['token_type_ids']  )				,     sum(tokens_p['token_type_ids']  )  )
																									self.assertEqual(
																									    sum(tokens_r['attention_mask']  ) / len(tokens_r['attention_mask']  )				,     sum(tokens_p['attention_mask']  ) / len(tokens_p['attention_mask']  )				,     )

																									_lowerCAmelCase  	=			tokenizer_r.convert_ids_to_tokens(tokens_r['input_ids']  )
																									_lowerCAmelCase  	=			tokenizer_p.convert_ids_to_tokens(tokens_p['input_ids']  )
																									self.assertSequenceEqual(tokens_p['input_ids']				,     [0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2]  )
																									self.assertSequenceEqual(tokens_r['input_ids']				,     [0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2]  )

																									self.assertSequenceEqual(
																									    UpperCAmelCase_				,     ['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>']  )
																									self.assertSequenceEqual(
																									    UpperCAmelCase_				,     ['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>']  )








"""simple docstring"""



from __future__ import annotations

import unittest

from transformers import DebertaVaConfig, 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 import (
	    TFDebertaVaForMaskedLM,
	    TFDebertaVaForQuestionAnswering,
	    TFDebertaVaForSequenceClassification,
	    TFDebertaVaForTokenClassification,
	    TFDebertaVaModel,
	)
class        _SCREAMING_SNAKE_CASE  :






				'''simple docstring'''



				def __init__(					self	:			Dict				,     UpperCAmelCase_	:			Tuple				,     UpperCAmelCase_	:			Optional[int]=13				,     UpperCAmelCase_	:			Tuple=7				,     UpperCAmelCase_	:			Union[str, Any]=True				,     UpperCAmelCase_	:			int=True				,     UpperCAmelCase_	:			int=True				,     UpperCAmelCase_	:			Optional[int]=True				,     UpperCAmelCase_	:			Dict=99				,     UpperCAmelCase_	:			Tuple=32				,     UpperCAmelCase_	:			str=2				,     UpperCAmelCase_	:			int=4				,     UpperCAmelCase_	:			Any=37				,     UpperCAmelCase_	:			str="gelu"				,     UpperCAmelCase_	:			Union[str, Any]=0.1				,     UpperCAmelCase_	:			str=0.1				,     UpperCAmelCase_	:			int=512				,     UpperCAmelCase_	:			str=16				,     UpperCAmelCase_	:			str=2				,     UpperCAmelCase_	:			Union[str, Any]=0.02				,     UpperCAmelCase_	:			List[str]=False				,     UpperCAmelCase_	:			List[Any]=True				,     UpperCAmelCase_	:			Optional[int]="None"				,     UpperCAmelCase_	:			List[Any]=3				,     UpperCAmelCase_	:			Any=4				,     UpperCAmelCase_	:			Tuple=None				,     )		->  Optional[Any]:







											"""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  	=			relative_attention
											_lowerCAmelCase  	=			position_biased_input
											_lowerCAmelCase  	=			pos_att_type
											_lowerCAmelCase  	=			scope


				def 				__lowerCamelCase	(					self	:			Tuple  )		->  Optional[Any]:







											"""simple docstring"""




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

											_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  	=			DebertaVaConfig(
											    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				,     relative_attention=self.relative_attention				,     position_biased_input=self.position_biased_input				,     initializer_range=self.initializer_range				,     return_dict=UpperCAmelCase_				,     )

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


				def 				__lowerCamelCase	(					self	:			Tuple				,     UpperCAmelCase_	:			Any				,     UpperCAmelCase_	:			Union[str, Any]				,     UpperCAmelCase_	:			Any				,     UpperCAmelCase_	:			List[Any]				,     UpperCAmelCase_	:			Tuple				,     UpperCAmelCase_	:			int				,     UpperCAmelCase_	:			Tuple  )		->  int:







											"""simple docstring"""




											_lowerCAmelCase  	=			TFDebertaVaModel(config=UpperCAmelCase_  )
											_lowerCAmelCase  	=			{'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}

											_lowerCAmelCase  	=			[input_ids, input_mask]
											_lowerCAmelCase  	=			model(UpperCAmelCase_  )

											_lowerCAmelCase  	=			model(UpperCAmelCase_  )

											self.parent.assertEqual(result.last_hidden_state.shape				,     (self.batch_size, self.seq_length, self.hidden_size)  )


				def 				__lowerCamelCase	(					self	:			Tuple				,     UpperCAmelCase_	:			Dict				,     UpperCAmelCase_	:			List[Any]				,     UpperCAmelCase_	:			Any				,     UpperCAmelCase_	:			Optional[Any]				,     UpperCAmelCase_	:			Union[str, Any]				,     UpperCAmelCase_	:			Optional[Any]				,     UpperCAmelCase_	:			Any  )		->  Optional[int]:







											"""simple docstring"""




											_lowerCAmelCase  	=			TFDebertaVaForMaskedLM(config=UpperCAmelCase_  )
											_lowerCAmelCase  	=			{
											    'input_ids': input_ids,
											    'attention_mask': input_mask,
											    'token_type_ids': token_type_ids,
											}
											_lowerCAmelCase  	=			model(UpperCAmelCase_  )
											self.parent.assertEqual(result.logits.shape				,     (self.batch_size, self.seq_length, self.vocab_size)  )


				def 				__lowerCamelCase	(					self	:			Any				,     UpperCAmelCase_	:			List[str]				,     UpperCAmelCase_	:			Any				,     UpperCAmelCase_	:			List[Any]				,     UpperCAmelCase_	:			List[str]				,     UpperCAmelCase_	:			Dict				,     UpperCAmelCase_	:			Optional[Any]				,     UpperCAmelCase_	:			int  )		->  List[str]:







											"""simple docstring"""




											_lowerCAmelCase  	=			self.num_labels
											_lowerCAmelCase  	=			TFDebertaVaForSequenceClassification(config=UpperCAmelCase_  )
											_lowerCAmelCase  	=			{
											    'input_ids': input_ids,
											    'attention_mask': input_mask,
											    'token_type_ids': token_type_ids,
											}

											_lowerCAmelCase  	=			model(UpperCAmelCase_  )
											self.parent.assertEqual(result.logits.shape				,     (self.batch_size, self.num_labels)  )


				def 				__lowerCamelCase	(					self	:			int				,     UpperCAmelCase_	:			int				,     UpperCAmelCase_	:			str				,     UpperCAmelCase_	:			List[Any]				,     UpperCAmelCase_	:			List[Any]				,     UpperCAmelCase_	:			Any				,     UpperCAmelCase_	:			List[str]				,     UpperCAmelCase_	:			Optional[Any]  )		->  Tuple:







											"""simple docstring"""




											_lowerCAmelCase  	=			self.num_labels
											_lowerCAmelCase  	=			TFDebertaVaForTokenClassification(config=UpperCAmelCase_  )
											_lowerCAmelCase  	=			{
											    'input_ids': input_ids,
											    'attention_mask': input_mask,
											    'token_type_ids': token_type_ids,
											}
											_lowerCAmelCase  	=			model(UpperCAmelCase_  )
											self.parent.assertEqual(result.logits.shape				,     (self.batch_size, self.seq_length, self.num_labels)  )


				def 				__lowerCamelCase	(					self	:			Tuple				,     UpperCAmelCase_	:			Union[str, Any]				,     UpperCAmelCase_	:			Any				,     UpperCAmelCase_	:			int				,     UpperCAmelCase_	:			Union[str, Any]				,     UpperCAmelCase_	:			Tuple				,     UpperCAmelCase_	:			List[Any]				,     UpperCAmelCase_	:			List[str]  )		->  List[Any]:







											"""simple docstring"""




											_lowerCAmelCase  	=			TFDebertaVaForQuestionAnswering(config=UpperCAmelCase_  )
											_lowerCAmelCase  	=			{
											    'input_ids': input_ids,
											    'attention_mask': input_mask,
											    'token_type_ids': token_type_ids,
											}

											_lowerCAmelCase  	=			model(UpperCAmelCase_  )
											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 				__lowerCamelCase	(					self	:			List[Any]  )		->  List[Any]:







											"""simple docstring"""




											_lowerCAmelCase  	=			self.prepare_config_and_inputs()
											(
											    (
											    _lowerCAmelCase
											)	,		(
											    _lowerCAmelCase
											)	,		(
											    _lowerCAmelCase
											)	,		(
											    _lowerCAmelCase
											)	,		(
											    _lowerCAmelCase
											)	,		(
											    _lowerCAmelCase
											)	,		(
											    _lowerCAmelCase
											)	,		
											)  	=			config_and_inputs
											_lowerCAmelCase  	=			{'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
											return config, inputs_dict






@require_tf
class        _SCREAMING_SNAKE_CASE  (  UpperCAmelCase      ,				UpperCAmelCase      ,				unittest.TestCase							):






				'''simple docstring'''



				SCREAMING_SNAKE_CASE_:							List[str]							=					(
				    (
				        TFDebertaVaModel,
				        TFDebertaVaForMaskedLM,
				        TFDebertaVaForQuestionAnswering,
				        TFDebertaVaForSequenceClassification,
				        TFDebertaVaForTokenClassification,
				    )
				    if is_tf_available()
				    else ()
				)
				SCREAMING_SNAKE_CASE_:							str							=					(
				    {
				        "feature-extraction": TFDebertaVaModel,
				        "fill-mask": TFDebertaVaForMaskedLM,
				        "question-answering": TFDebertaVaForQuestionAnswering,
				        "text-classification": TFDebertaVaForSequenceClassification,
				        "token-classification": TFDebertaVaForTokenClassification,
				        "zero-shot": TFDebertaVaForSequenceClassification,
				    }
				    if is_tf_available()
				    else {}
				)

				SCREAMING_SNAKE_CASE_:							str							=					False
				SCREAMING_SNAKE_CASE_:							List[str]							=					False


				def 				__lowerCamelCase	(					self	:			Optional[int]  )		->  List[str]:







											"""simple docstring"""




											_lowerCAmelCase  	=			TFDebertaVaModelTester(self  )
											_lowerCAmelCase  	=			ConfigTester(self				,     config_class=UpperCAmelCase_				,     hidden_size=37  )


				def 				__lowerCamelCase	(					self	:			Union[str, Any]  )		->  Any:







											"""simple docstring"""




											self.config_tester.run_common_tests()


				def 				__lowerCamelCase	(					self	:			Optional[int]  )		->  List[str]:







											"""simple docstring"""




											_lowerCAmelCase  	=			self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_model(*UpperCAmelCase_  )


				def 				__lowerCamelCase	(					self	:			List[Any]  )		->  Optional[int]:







											"""simple docstring"""




											_lowerCAmelCase  	=			self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_masked_lm(*UpperCAmelCase_  )


				def 				__lowerCamelCase	(					self	:			Dict  )		->  List[str]:







											"""simple docstring"""




											_lowerCAmelCase  	=			self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_question_answering(*UpperCAmelCase_  )


				def 				__lowerCamelCase	(					self	:			Any  )		->  int:







											"""simple docstring"""




											_lowerCAmelCase  	=			self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_sequence_classification(*UpperCAmelCase_  )


				def 				__lowerCamelCase	(					self	:			Tuple  )		->  Optional[int]:







											"""simple docstring"""




											_lowerCAmelCase  	=			self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_token_classification(*UpperCAmelCase_  )







				@slow
				def 				__lowerCamelCase	(					self	:			List[str]  )		->  List[Any]:







											"""simple docstring"""




											_lowerCAmelCase  	=			TFDebertaVaModel.from_pretrained('kamalkraj/deberta-v2-xlarge'  )
											self.assertIsNotNone(UpperCAmelCase_  )






@require_tf
class        _SCREAMING_SNAKE_CASE  (  unittest.TestCase							):






				'''simple docstring'''



				@unittest.skip(reason='Model not available yet'  )
				def 				__lowerCamelCase	(					self	:			Optional[Any]  )		->  Tuple:







											"""simple docstring"""




											pass







				@slow
				def 				__lowerCamelCase	(					self	:			Any  )		->  int:







											"""simple docstring"""




											_lowerCAmelCase  	=			TFDebertaVaModel.from_pretrained('kamalkraj/deberta-v2-xlarge'  )
											_lowerCAmelCase  	=			tf.constant([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]]  )
											_lowerCAmelCase  	=			tf.constant([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]  )
											_lowerCAmelCase  	=			model(UpperCAmelCase_				,     attention_mask=UpperCAmelCase_  )[0]

											_lowerCAmelCase  	=			tf.constant(
											    [[[0.2356, 0.1948, 0.0369], [-0.1063, 0.3586, -0.5152], [-0.6399, -0.0259, -0.2525]]]  )
											tf.debugging.assert_near(output[:, 1:4, 1:4]				,     UpperCAmelCase_				,     atol=1E-4  )






