


import argparse
import json
from typing import List

from ltp import LTP

from transformers.models.bert.tokenization_bert import BertTokenizer





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


						'''simple docstring'''






						if (
						    (cp >= 0x4_E00 and cp <= 0x9_FFF)
						    or (cp >= 0x3_400 and cp <= 0x4_DBF)  #
						    or (cp >= 0x20_000 and cp <= 0x2A_6DF)  #
						    or (cp >= 0x2A_700 and cp <= 0x2B_73F)  #
						    or (cp >= 0x2B_740 and cp <= 0x2B_81F)  #
						    or (cp >= 0x2B_820 and cp <= 0x2C_EAF)  #
						    or (cp >= 0xF_900 and cp <= 0xF_AFF)
						    or (cp >= 0x2F_800 and cp <= 0x2F_A1F)  #
						):  #
												return True

						return False





def 	_lowerCAmelCase       (    _lowerCAmelCase )						->	Optional[int]:


						'''simple docstring'''






						for char in word:
												__snake_case				=		ord(_lowerCAmelCase )
												if not _is_chinese_char(_lowerCAmelCase ):
																		return 0
						return 1





def 	_lowerCAmelCase       (    _lowerCAmelCase )						->	Dict:


						'''simple docstring'''






						__snake_case				=		set()

						for token in tokens:
												__snake_case				=		len(_lowerCAmelCase ) > 1 and is_chinese(_lowerCAmelCase )
												if chinese_word:
																		word_set.add(_lowerCAmelCase )
						__snake_case				=		list(_lowerCAmelCase )
						return word_list





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


						'''simple docstring'''






						if not chinese_word_set:
												return bert_tokens
						__snake_case				=		max([len(_lowerCAmelCase ) for w in chinese_word_set] )

						__snake_case				=		bert_tokens
						__snake_case		,					__snake_case				=		0, len(_lowerCAmelCase )
						while start < end:
												__snake_case				=		True
												if is_chinese(bert_word[start] ):
																		__snake_case				=		min(end - start      ,	_lowerCAmelCase )
																		for i in range(_lowerCAmelCase      ,	1      ,	-1 ):
																								__snake_case				=		"".join(bert_word[start : start + i] )
																								if whole_word in chinese_word_set:
																														for j in range(start + 1      ,	start + i ):
																																				__snake_case				=		"##" + bert_word[j]
																														__snake_case				=		start + i
																														__snake_case				=		False
																														break
												if single_word:
																		start += 1
						return bert_word





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


						'''simple docstring'''






						__snake_case				=		[]

						for i in range(0      ,	len(_lowerCAmelCase )      ,	100 ):
												__snake_case				=		ltp_tokenizer.pipeline(lines[i : i + 100]      ,	tasks=["cws"] ).cws
												__snake_case				=		[get_chinese_word(_lowerCAmelCase ) for r in res]
												ltp_res.extend(_lowerCAmelCase )
						assert len(_lowerCAmelCase ) == len(_lowerCAmelCase )

						__snake_case				=		[]
						for i in range(0      ,	len(_lowerCAmelCase )      ,	100 ):
												__snake_case				=		bert_tokenizer(lines[i : i + 100]      ,	add_special_tokens=_lowerCAmelCase      ,	truncation=_lowerCAmelCase      ,	max_length=512 )
												bert_res.extend(res["input_ids"] )
						assert len(_lowerCAmelCase ) == len(_lowerCAmelCase )

						__snake_case				=		[]
						for input_ids, chinese_word in zip(_lowerCAmelCase      ,	_lowerCAmelCase ):
												__snake_case				=		[]
												for id in input_ids:
																		__snake_case				=		bert_tokenizer._convert_id_to_token(_lowerCAmelCase )
																		input_tokens.append(_lowerCAmelCase )
												__snake_case				=		add_sub_symbol(_lowerCAmelCase      ,	_lowerCAmelCase )
												__snake_case				=		[]
												# We only save pos of chinese subwords start with ##, which mean is part of a whole word.
												for i, token in enumerate(_lowerCAmelCase ):
																		if token[:2] == "##":
																								__snake_case				=		token[2:]
																								# save chinese tokens' pos
																								if len(_lowerCAmelCase ) == 1 and _is_chinese_char(ord(_lowerCAmelCase ) ):
																														ref_id.append(_lowerCAmelCase )
												ref_ids.append(_lowerCAmelCase )

						assert len(_lowerCAmelCase ) == len(_lowerCAmelCase )

						return ref_ids





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


						'''simple docstring'''






						with open(args.file_name      ,	"r"      ,	encoding="utf-8" ) as f:
												__snake_case				=		f.readlines()
						__snake_case				=		[line.strip() for line in data if len(_lowerCAmelCase ) > 0 and not line.isspace()]  # avoid delimiter like '\u2029'
						__snake_case				=		LTP(args.ltp )  # faster in GPU device
						__snake_case				=		BertTokenizer.from_pretrained(args.bert )

						__snake_case				=		prepare_ref(_lowerCAmelCase      ,	_lowerCAmelCase      ,	_lowerCAmelCase )

						with open(args.save_path      ,	"w"      ,	encoding="utf-8" ) as f:
												__snake_case				=		[json.dumps(_lowerCAmelCase ) + "\n" for ref in ref_ids]
												f.writelines(_lowerCAmelCase )


if __name__ == "__main__":
	A							:					str     						=     argparse.ArgumentParser(description='prepare_chinese_ref')
	parser.add_argument(
	    '--file_name',
	    required=False,
	    type=str,
	    default='./resources/chinese-demo.txt',
	    help='file need process, same as training data in lm',
	)
	parser.add_argument(
	    '--ltp',
	    required=False,
	    type=str,
	    default='./resources/ltp',
	    help='resources for LTP tokenizer, usually a path',
	)
	parser.add_argument(
	    '--bert',
	    required=False,
	    type=str,
	    default='./resources/robert',
	    help='resources for Bert tokenizer',
	)
	parser.add_argument(
	    '--save_path',
	    required=False,
	    type=str,
	    default='./resources/ref.txt',
	    help='path to save res',
	)

	A							:					int     						=     parser.parse_args()
	main(args)









import unittest

from transformers import DebertaVaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
	import torch

	from transformers import (
	    DebertaVaForMaskedLM,
	    DebertaVaForMultipleChoice,
	    DebertaVaForQuestionAnswering,
	    DebertaVaForSequenceClassification,
	    DebertaVaForTokenClassification,
	    DebertaVaModel,
	)
	from transformers.models.deberta_va.modeling_deberta_va import DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST






class     UpperCamelCase(  _a   ):


					def __init__(						self	:		Dict     ,    SCREAMING_SNAKE_CASE	:		Dict     ,    SCREAMING_SNAKE_CASE	:		Dict=1_3     ,    SCREAMING_SNAKE_CASE	:		str=7     ,    SCREAMING_SNAKE_CASE	:		Optional[int]=True     ,    SCREAMING_SNAKE_CASE	:		str=True     ,    SCREAMING_SNAKE_CASE	:		Union[str, Any]=True     ,    SCREAMING_SNAKE_CASE	:		Dict=True     ,    SCREAMING_SNAKE_CASE	:		Dict=9_9     ,    SCREAMING_SNAKE_CASE	:		str=3_2     ,    SCREAMING_SNAKE_CASE	:		Optional[int]=5     ,    SCREAMING_SNAKE_CASE	:		Dict=4     ,    SCREAMING_SNAKE_CASE	:		Optional[Any]=3_7     ,    SCREAMING_SNAKE_CASE	:		Optional[Any]="gelu"     ,    SCREAMING_SNAKE_CASE	:		Optional[int]=0.1     ,    SCREAMING_SNAKE_CASE	:		int=0.1     ,    SCREAMING_SNAKE_CASE	:		int=5_1_2     ,    SCREAMING_SNAKE_CASE	:		Optional[Any]=1_6     ,    SCREAMING_SNAKE_CASE	:		Union[str, Any]=2     ,    SCREAMING_SNAKE_CASE	:		Optional[int]=0.02     ,    SCREAMING_SNAKE_CASE	:		str=False     ,    SCREAMING_SNAKE_CASE	:		Union[str, Any]=True     ,    SCREAMING_SNAKE_CASE	:		Tuple="None"     ,    SCREAMING_SNAKE_CASE	:		List[str]=3     ,    SCREAMING_SNAKE_CASE	:		str=4     ,    SCREAMING_SNAKE_CASE	:		List[str]=None     ,    )			->	Tuple:




											'''simple docstring'''

											__snake_case				=		parent
											__snake_case				=		batch_size
											__snake_case				=		seq_length
											__snake_case				=		is_training
											__snake_case				=		use_input_mask
											__snake_case				=		use_token_type_ids
											__snake_case				=		use_labels
											__snake_case				=		vocab_size
											__snake_case				=		hidden_size
											__snake_case				=		num_hidden_layers
											__snake_case				=		num_attention_heads
											__snake_case				=		intermediate_size
											__snake_case				=		hidden_act
											__snake_case				=		hidden_dropout_prob
											__snake_case				=		attention_probs_dropout_prob
											__snake_case				=		max_position_embeddings
											__snake_case				=		type_vocab_size
											__snake_case				=		type_sequence_label_size
											__snake_case				=		initializer_range
											__snake_case				=		num_labels
											__snake_case				=		num_choices
											__snake_case				=		relative_attention
											__snake_case				=		position_biased_input
											__snake_case				=		pos_att_type
											__snake_case				=		scope


					def 							SCREAMING_SNAKE_CASE_  (						self	:		List[Any]						)			->	Dict:




											'''simple docstring'''

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

											__snake_case				=		None
											if self.use_input_mask:
																	__snake_case				=		ids_tensor([self.batch_size, self.seq_length]     ,    vocab_size=2						)

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

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

											__snake_case				=		self.get_config()

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


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Any						)			->	int:




											'''simple docstring'''

											return 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     ,    initializer_range=self.initializer_range     ,    relative_attention=self.relative_attention     ,    position_biased_input=self.position_biased_input     ,    pos_att_type=self.pos_att_type     ,    )


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Optional[int]     ,    SCREAMING_SNAKE_CASE	:		str						)			->	Any:




											'''simple docstring'''

											self.parent.assertListEqual(list(result.loss.size()						)     ,    []						)


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




											'''simple docstring'''

											__snake_case				=		DebertaVaModel(config=SCREAMING_SNAKE_CASE						)
											model.to(SCREAMING_SNAKE_CASE						)
											model.eval()
											__snake_case				=		model(SCREAMING_SNAKE_CASE     ,    attention_mask=SCREAMING_SNAKE_CASE     ,    token_type_ids=SCREAMING_SNAKE_CASE						)[0]
											__snake_case				=		model(SCREAMING_SNAKE_CASE     ,    token_type_ids=SCREAMING_SNAKE_CASE						)[0]
											__snake_case				=		model(SCREAMING_SNAKE_CASE						)[0]

											self.parent.assertListEqual(list(sequence_output.size()						)     ,    [self.batch_size, self.seq_length, self.hidden_size]						)


					def 							SCREAMING_SNAKE_CASE_  (						self	:		List[Any]     ,    SCREAMING_SNAKE_CASE	:		Dict     ,    SCREAMING_SNAKE_CASE	:		Optional[int]     ,    SCREAMING_SNAKE_CASE	:		Dict     ,    SCREAMING_SNAKE_CASE	:		Dict     ,    SCREAMING_SNAKE_CASE	:		Union[str, Any]     ,    SCREAMING_SNAKE_CASE	:		Any     ,    SCREAMING_SNAKE_CASE	:		Optional[Any]						)			->	str:




											'''simple docstring'''

											__snake_case				=		DebertaVaForMaskedLM(config=SCREAMING_SNAKE_CASE						)
											model.to(SCREAMING_SNAKE_CASE						)
											model.eval()
											__snake_case				=		model(SCREAMING_SNAKE_CASE     ,    attention_mask=SCREAMING_SNAKE_CASE     ,    token_type_ids=SCREAMING_SNAKE_CASE     ,    labels=SCREAMING_SNAKE_CASE						)

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


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Union[str, Any]     ,    SCREAMING_SNAKE_CASE	:		Tuple     ,    SCREAMING_SNAKE_CASE	:		str     ,    SCREAMING_SNAKE_CASE	:		Union[str, Any]     ,    SCREAMING_SNAKE_CASE	:		int     ,    SCREAMING_SNAKE_CASE	:		Union[str, Any]     ,    SCREAMING_SNAKE_CASE	:		Any     ,    SCREAMING_SNAKE_CASE	:		Tuple						)			->	int:




											'''simple docstring'''

											__snake_case				=		self.num_labels
											__snake_case				=		DebertaVaForSequenceClassification(SCREAMING_SNAKE_CASE						)
											model.to(SCREAMING_SNAKE_CASE						)
											model.eval()
											__snake_case				=		model(SCREAMING_SNAKE_CASE     ,    attention_mask=SCREAMING_SNAKE_CASE     ,    token_type_ids=SCREAMING_SNAKE_CASE     ,    labels=SCREAMING_SNAKE_CASE						)
											self.parent.assertListEqual(list(result.logits.size()						)     ,    [self.batch_size, self.num_labels]						)
											self.check_loss_output(SCREAMING_SNAKE_CASE						)


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Dict     ,    SCREAMING_SNAKE_CASE	:		Any     ,    SCREAMING_SNAKE_CASE	:		Dict     ,    SCREAMING_SNAKE_CASE	:		Union[str, Any]     ,    SCREAMING_SNAKE_CASE	:		int     ,    SCREAMING_SNAKE_CASE	:		str     ,    SCREAMING_SNAKE_CASE	:		Optional[Any]     ,    SCREAMING_SNAKE_CASE	:		List[Any]						)			->	Union[str, Any]:




											'''simple docstring'''

											__snake_case				=		self.num_labels
											__snake_case				=		DebertaVaForTokenClassification(config=SCREAMING_SNAKE_CASE						)
											model.to(SCREAMING_SNAKE_CASE						)
											model.eval()
											__snake_case				=		model(SCREAMING_SNAKE_CASE     ,    attention_mask=SCREAMING_SNAKE_CASE     ,    token_type_ids=SCREAMING_SNAKE_CASE     ,    labels=SCREAMING_SNAKE_CASE						)
											self.parent.assertEqual(result.logits.shape     ,    (self.batch_size, self.seq_length, self.num_labels)						)


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Dict     ,    SCREAMING_SNAKE_CASE	:		Optional[int]     ,    SCREAMING_SNAKE_CASE	:		Dict     ,    SCREAMING_SNAKE_CASE	:		int     ,    SCREAMING_SNAKE_CASE	:		str     ,    SCREAMING_SNAKE_CASE	:		int     ,    SCREAMING_SNAKE_CASE	:		int     ,    SCREAMING_SNAKE_CASE	:		Union[str, Any]						)			->	int:




											'''simple docstring'''

											__snake_case				=		DebertaVaForQuestionAnswering(config=SCREAMING_SNAKE_CASE						)
											model.to(SCREAMING_SNAKE_CASE						)
											model.eval()
											__snake_case				=		model(
											    SCREAMING_SNAKE_CASE     ,    attention_mask=SCREAMING_SNAKE_CASE     ,    token_type_ids=SCREAMING_SNAKE_CASE     ,    start_positions=SCREAMING_SNAKE_CASE     ,    end_positions=SCREAMING_SNAKE_CASE     ,    )
											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	:		Tuple     ,    SCREAMING_SNAKE_CASE	:		List[Any]     ,    SCREAMING_SNAKE_CASE	:		str     ,    SCREAMING_SNAKE_CASE	:		List[str]     ,    SCREAMING_SNAKE_CASE	:		List[str]     ,    SCREAMING_SNAKE_CASE	:		Optional[Any]     ,    SCREAMING_SNAKE_CASE	:		Any     ,    SCREAMING_SNAKE_CASE	:		Optional[int]						)			->	Union[str, Any]:




											'''simple docstring'''

											__snake_case				=		DebertaVaForMultipleChoice(config=SCREAMING_SNAKE_CASE						)
											model.to(SCREAMING_SNAKE_CASE						)
											model.eval()
											__snake_case				=		input_ids.unsqueeze(1						).expand(-1     ,    self.num_choices     ,    -1						).contiguous()
											__snake_case				=		token_type_ids.unsqueeze(1						).expand(-1     ,    self.num_choices     ,    -1						).contiguous()
											__snake_case				=		input_mask.unsqueeze(1						).expand(-1     ,    self.num_choices     ,    -1						).contiguous()
											__snake_case				=		model(
											    SCREAMING_SNAKE_CASE     ,    attention_mask=SCREAMING_SNAKE_CASE     ,    token_type_ids=SCREAMING_SNAKE_CASE     ,    labels=SCREAMING_SNAKE_CASE     ,    )
											self.parent.assertEqual(result.logits.shape     ,    (self.batch_size, self.num_choices)						)






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




											'''simple docstring'''

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






@require_torch
class     UpperCamelCase(  _a     ,							_a     ,							unittest.TestCase   ):
					snake_case_		:				Union[str, Any]										=	(
					    (
					        DebertaVaModel,
					        DebertaVaForMaskedLM,
					        DebertaVaForSequenceClassification,
					        DebertaVaForTokenClassification,
					        DebertaVaForQuestionAnswering,
					        DebertaVaForMultipleChoice,
					    )
					    if is_torch_available()
					    else ()
					)
					snake_case_		:				Optional[Any]										=	(
					    {
					        """feature-extraction""": DebertaVaModel,
					        """fill-mask""": DebertaVaForMaskedLM,
					        """question-answering""": DebertaVaForQuestionAnswering,
					        """text-classification""": DebertaVaForSequenceClassification,
					        """token-classification""": DebertaVaForTokenClassification,
					        """zero-shot""": DebertaVaForSequenceClassification,
					    }
					    if is_torch_available()
					    else {}
					)

					snake_case_		:				Dict										=	True
					snake_case_		:				Union[str, Any]										=	False
					snake_case_		:				Tuple										=	False
					snake_case_		:				List[str]										=	False
					snake_case_		:				List[Any]										=	False


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Dict						)			->	Dict:




											'''simple docstring'''

											__snake_case				=		DebertaVaModelTester(self						)
											__snake_case				=		ConfigTester(self     ,    config_class=SCREAMING_SNAKE_CASE     ,    hidden_size=3_7						)


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Any						)			->	Dict:




											'''simple docstring'''

											self.config_tester.run_common_tests()


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Any						)			->	str:




											'''simple docstring'''

											__snake_case				=		self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_deberta_model(*SCREAMING_SNAKE_CASE						)


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




											'''simple docstring'''

											__snake_case				=		self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_deberta_for_sequence_classification(*SCREAMING_SNAKE_CASE						)


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Any						)			->	Optional[Any]:




											'''simple docstring'''

											__snake_case				=		self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_deberta_for_masked_lm(*SCREAMING_SNAKE_CASE						)


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Tuple						)			->	List[str]:




											'''simple docstring'''

											__snake_case				=		self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_deberta_for_question_answering(*SCREAMING_SNAKE_CASE						)


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Optional[Any]						)			->	str:




											'''simple docstring'''

											__snake_case				=		self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_deberta_for_token_classification(*SCREAMING_SNAKE_CASE						)


					def 							SCREAMING_SNAKE_CASE_  (						self	:		int						)			->	Dict:




											'''simple docstring'''

											__snake_case				=		self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_deberta_for_multiple_choice(*SCREAMING_SNAKE_CASE						)






					@slow
					def 							SCREAMING_SNAKE_CASE_  (						self	:		str						)			->	Union[str, Any]:




											'''simple docstring'''

											for model_name in DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																	__snake_case				=		DebertaVaModel.from_pretrained(SCREAMING_SNAKE_CASE						)
																	self.assertIsNotNone(SCREAMING_SNAKE_CASE						)






@require_torch
@require_sentencepiece
@require_tokenizers
class     UpperCamelCase(  unittest.TestCase   ):


					@unittest.skip(reason="Model not available yet"						)
					def 							SCREAMING_SNAKE_CASE_  (						self	:		Optional[int]						)			->	str:




											'''simple docstring'''

											pass






					@slow
					def 							SCREAMING_SNAKE_CASE_  (						self	:		str						)			->	Tuple:




											'''simple docstring'''

											__snake_case				=		DebertaVaModel.from_pretrained("microsoft/deberta-v2-xlarge"						)

											__snake_case				=		torch.tensor([[0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9, 4_6_0_7_8, 1_5_8_8, 2]]						)
											__snake_case				=		torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]						)
											with torch.no_grad():
																	__snake_case				=		model(SCREAMING_SNAKE_CASE     ,    attention_mask=SCREAMING_SNAKE_CASE						)[0]
											# compare the actual values for a slice.
											__snake_case				=		torch.tensor(
											    [[[0.2356, 0.1948, 0.0369], [-0.1063, 0.3586, -0.5152], [-0.6399, -0.0259, -0.2525]]]						)
											self.assertTrue(torch.allclose(output[:, 1:4, 1:4]     ,    SCREAMING_SNAKE_CASE     ,    atol=1e-4						)     ,    f'''{output[:, 1:4, 1:4]}'''						)





