


import functools
import operator

from ...configuration_utils import PretrainedConfig
from ...utils import logging


A							:					Any     						=     logging.get_logger(__name__)

A							:					List[str]     						=     {
    'facebook/wav2vec2-base-960h': 'https://huggingface.co/facebook/wav2vec2-base-960h/resolve/main/config.json',
    # See all Wav2Vec2 models at https://huggingface.co/models?filter=wav2vec2
}






class     UpperCamelCase(  _a   ):
					snake_case_		:				List[Any]										=	"""wav2vec2"""


					def __init__(						self	:		Optional[int]     ,    SCREAMING_SNAKE_CASE	:		Any=3_2     ,    SCREAMING_SNAKE_CASE	:		Union[str, Any]=7_6_8     ,    SCREAMING_SNAKE_CASE	:		str=1_2     ,    SCREAMING_SNAKE_CASE	:		Union[str, Any]=1_2     ,    SCREAMING_SNAKE_CASE	:		Dict=3_0_7_2     ,    SCREAMING_SNAKE_CASE	:		Tuple="gelu"     ,    SCREAMING_SNAKE_CASE	:		Union[str, Any]=0.1     ,    SCREAMING_SNAKE_CASE	:		List[Any]=0.1     ,    SCREAMING_SNAKE_CASE	:		List[Any]=0.1     ,    SCREAMING_SNAKE_CASE	:		Optional[Any]=0.0     ,    SCREAMING_SNAKE_CASE	:		Union[str, Any]=0.0     ,    SCREAMING_SNAKE_CASE	:		Dict=0.1     ,    SCREAMING_SNAKE_CASE	:		Union[str, Any]=0.1     ,    SCREAMING_SNAKE_CASE	:		Union[str, Any]=0.02     ,    SCREAMING_SNAKE_CASE	:		Optional[Any]=1e-5     ,    SCREAMING_SNAKE_CASE	:		Optional[int]="group"     ,    SCREAMING_SNAKE_CASE	:		Optional[Any]="gelu"     ,    SCREAMING_SNAKE_CASE	:		List[str]=(5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2)     ,    SCREAMING_SNAKE_CASE	:		Optional[int]=(5, 2, 2, 2, 2, 2, 2)     ,    SCREAMING_SNAKE_CASE	:		Any=(1_0, 3, 3, 3, 3, 2, 2)     ,    SCREAMING_SNAKE_CASE	:		Any=False     ,    SCREAMING_SNAKE_CASE	:		Optional[Any]=1_2_8     ,    SCREAMING_SNAKE_CASE	:		str=1_6     ,    SCREAMING_SNAKE_CASE	:		Dict=False     ,    SCREAMING_SNAKE_CASE	:		List[str]=True     ,    SCREAMING_SNAKE_CASE	:		Any=0.05     ,    SCREAMING_SNAKE_CASE	:		Optional[int]=1_0     ,    SCREAMING_SNAKE_CASE	:		List[Any]=2     ,    SCREAMING_SNAKE_CASE	:		Dict=0.0     ,    SCREAMING_SNAKE_CASE	:		Union[str, Any]=1_0     ,    SCREAMING_SNAKE_CASE	:		int=0     ,    SCREAMING_SNAKE_CASE	:		Any=3_2_0     ,    SCREAMING_SNAKE_CASE	:		Dict=2     ,    SCREAMING_SNAKE_CASE	:		Optional[int]=0.1     ,    SCREAMING_SNAKE_CASE	:		List[str]=1_0_0     ,    SCREAMING_SNAKE_CASE	:		Any=2_5_6     ,    SCREAMING_SNAKE_CASE	:		Tuple=2_5_6     ,    SCREAMING_SNAKE_CASE	:		List[str]=0.1     ,    SCREAMING_SNAKE_CASE	:		int="sum"     ,    SCREAMING_SNAKE_CASE	:		Optional[Any]=False     ,    SCREAMING_SNAKE_CASE	:		Optional[Any]=False     ,    SCREAMING_SNAKE_CASE	:		List[str]=2_5_6     ,    SCREAMING_SNAKE_CASE	:		List[Any]=(5_1_2, 5_1_2, 5_1_2, 5_1_2, 1_5_0_0)     ,    SCREAMING_SNAKE_CASE	:		Union[str, Any]=(5, 3, 3, 1, 1)     ,    SCREAMING_SNAKE_CASE	:		Optional[Any]=(1, 2, 3, 1, 1)     ,    SCREAMING_SNAKE_CASE	:		List[str]=5_1_2     ,    SCREAMING_SNAKE_CASE	:		Dict=0     ,    SCREAMING_SNAKE_CASE	:		Tuple=1     ,    SCREAMING_SNAKE_CASE	:		str=2     ,    SCREAMING_SNAKE_CASE	:		List[Any]=False     ,    SCREAMING_SNAKE_CASE	:		int=3     ,    SCREAMING_SNAKE_CASE	:		List[str]=2     ,    SCREAMING_SNAKE_CASE	:		List[str]=3     ,    SCREAMING_SNAKE_CASE	:		List[str]=None     ,    SCREAMING_SNAKE_CASE	:		Optional[Any]=None     ,    **SCREAMING_SNAKE_CASE	:		Optional[int]     ,    )			->	int:




											'''simple docstring'''

											super().__init__(**SCREAMING_SNAKE_CASE     ,    pad_token_id=SCREAMING_SNAKE_CASE     ,    bos_token_id=SCREAMING_SNAKE_CASE     ,    eos_token_id=SCREAMING_SNAKE_CASE						)
											__snake_case				=		hidden_size
											__snake_case				=		feat_extract_norm
											__snake_case				=		feat_extract_activation
											__snake_case				=		list(SCREAMING_SNAKE_CASE						)
											__snake_case				=		list(SCREAMING_SNAKE_CASE						)
											__snake_case				=		list(SCREAMING_SNAKE_CASE						)
											__snake_case				=		conv_bias
											__snake_case				=		num_conv_pos_embeddings
											__snake_case				=		num_conv_pos_embedding_groups
											__snake_case				=		len(self.conv_dim						)
											__snake_case				=		num_hidden_layers
											__snake_case				=		intermediate_size
											__snake_case				=		hidden_act
											__snake_case				=		num_attention_heads
											__snake_case				=		hidden_dropout
											__snake_case				=		attention_dropout
											__snake_case				=		activation_dropout
											__snake_case				=		feat_proj_dropout
											__snake_case				=		final_dropout
											__snake_case				=		layerdrop
											__snake_case				=		layer_norm_eps
											__snake_case				=		initializer_range
											__snake_case				=		vocab_size
											__snake_case				=		do_stable_layer_norm
											__snake_case				=		use_weighted_layer_sum

											if (
											    (len(self.conv_stride						) != self.num_feat_extract_layers)
											    or (len(self.conv_kernel						) != self.num_feat_extract_layers)
											    or (len(self.conv_dim						) != self.num_feat_extract_layers)
											):
																	raise ValueError(
																	    "Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =="
																	    " `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ="
																	    f''' {len(self.conv_dim						)}`, `len(config.conv_stride) = {len(self.conv_stride						)}`,'''
																	    f''' `len(config.conv_kernel) = {len(self.conv_kernel						)}`.'''						)

											# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
											__snake_case				=		apply_spec_augment
											__snake_case				=		mask_time_prob
											__snake_case				=		mask_time_length
											__snake_case				=		mask_time_min_masks
											__snake_case				=		mask_feature_prob
											__snake_case				=		mask_feature_length
											__snake_case				=		mask_feature_min_masks

											# parameters for pretraining with codevector quantized representations
											__snake_case				=		num_codevectors_per_group
											__snake_case				=		num_codevector_groups
											__snake_case				=		contrastive_logits_temperature
											__snake_case				=		feat_quantizer_dropout
											__snake_case				=		num_negatives
											__snake_case				=		codevector_dim
											__snake_case				=		proj_codevector_dim
											__snake_case				=		diversity_loss_weight

											# ctc loss
											__snake_case				=		ctc_loss_reduction
											__snake_case				=		ctc_zero_infinity

											# adapter
											__snake_case				=		add_adapter
											__snake_case				=		adapter_kernel_size
											__snake_case				=		adapter_stride
											__snake_case				=		num_adapter_layers
											__snake_case				=		output_hidden_size or hidden_size
											__snake_case				=		adapter_attn_dim

											# SequenceClassification-specific parameter. Feel free to ignore for other classes.
											__snake_case				=		classifier_proj_size

											# XVector-specific parameters. Feel free to ignore for other classes.
											__snake_case				=		list(SCREAMING_SNAKE_CASE						)
											__snake_case				=		list(SCREAMING_SNAKE_CASE						)
											__snake_case				=		list(SCREAMING_SNAKE_CASE						)
											__snake_case				=		xvector_output_dim






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




											'''simple docstring'''

											return functools.reduce(operator.mul     ,    self.conv_stride     ,    1						)









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]}'''						)





