


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


SCREAMING_SNAKE_CASE :   Optional[Any]							      =				logging.get_logger(__name__)

SCREAMING_SNAKE_CASE :   int							      =				{
    "unc-nlp/lxmert-base-uncased": "https://huggingface.co/unc-nlp/lxmert-base-uncased/resolve/main/config.json",
}






class lowerCamelCase(		_a     ):
       lowercase_				:			Optional[int]									=    """lxmert"""
       lowercase_				:			Dict									=    {}
       def __init__(      self,   lowerCamelCase=3_05_22,   lowerCamelCase=7_68,   lowerCamelCase=12,   lowerCamelCase=95_00,   lowerCamelCase=16_00,   lowerCamelCase=4_00,   lowerCamelCase=30_72,   lowerCamelCase="gelu",   lowerCamelCase=0.1,   lowerCamelCase=0.1,   lowerCamelCase=5_12,   lowerCamelCase=2,   lowerCamelCase=0.0_2,   lowerCamelCase=1E-12,   lowerCamelCase=9,   lowerCamelCase=5,   lowerCamelCase=5,   lowerCamelCase=20_48,   lowerCamelCase=4,   lowerCamelCase=6.6_7,   lowerCamelCase=True,   lowerCamelCase=True,   lowerCamelCase=True,   lowerCamelCase=True,   lowerCamelCase=True,   lowerCamelCase=True,   lowerCamelCase=True,   **lowerCamelCase,   ) ->     Any:




           """simple docstring"""

           _lowercase :							Tuple   	=	vocab_size
           _lowercase :							str   	=	hidden_size
           _lowercase :							Any   	=	num_attention_heads
           _lowercase :							Union[str, Any]   	=	hidden_act
           _lowercase :							Dict   	=	intermediate_size
           _lowercase :							Union[str, Any]   	=	hidden_dropout_prob
           _lowercase :							Any   	=	attention_probs_dropout_prob
           _lowercase :							List[str]   	=	max_position_embeddings
           _lowercase :							Optional[Any]   	=	type_vocab_size
           _lowercase :							List[str]   	=	initializer_range
           _lowercase :							Any   	=	layer_norm_eps
           _lowercase :							List[Any]   	=	num_qa_labels
           _lowercase :							Union[str, Any]   	=	num_object_labels
           _lowercase :							Dict   	=	num_attr_labels
           _lowercase :							Dict   	=	l_layers
           _lowercase :							int   	=	x_layers
           _lowercase :							Tuple   	=	r_layers
           _lowercase :							Union[str, Any]   	=	visual_feat_dim
           _lowercase :							Optional[Any]   	=	visual_pos_dim
           _lowercase :							List[str]   	=	visual_loss_normalizer
           _lowercase :							Dict   	=	task_matched
           _lowercase :							Optional[Any]   	=	task_mask_lm
           _lowercase :							Any   	=	task_obj_predict
           _lowercase :							Dict   	=	task_qa
           _lowercase :							Optional[int]   	=	visual_obj_loss
           _lowercase :							Optional[Any]   	=	visual_attr_loss
           _lowercase :							Union[str, Any]   	=	visual_feat_loss
           _lowercase :							Optional[int]   	=	{'vision': r_layers, 'cross_encoder': x_layers, 'language': l_layers}
           super().__init__(**lowerCamelCase)






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 _lowerCamelCase(		_a     ):
       def __init__(      self,   lowerCamelCase,   lowerCamelCase=13,   lowerCamelCase=7,   lowerCamelCase=True,   lowerCamelCase=True,   lowerCamelCase=True,   lowerCamelCase=True,   lowerCamelCase=99,   lowerCamelCase=32,   lowerCamelCase=5,   lowerCamelCase=4,   lowerCamelCase=37,   lowerCamelCase="gelu",   lowerCamelCase=0.1,   lowerCamelCase=0.1,   lowerCamelCase=5_12,   lowerCamelCase=16,   lowerCamelCase=2,   lowerCamelCase=0.0_2,   lowerCamelCase=False,   lowerCamelCase=True,   lowerCamelCase="None",   lowerCamelCase=3,   lowerCamelCase=4,   lowerCamelCase=None,   ) ->     Optional[Any]:




           """simple docstring"""

           _lowercase :							Union[str, Any]   	=	parent
           _lowercase :							Optional[Any]   	=	batch_size
           _lowercase :							Optional[Any]   	=	seq_length
           _lowercase :							Dict   	=	is_training
           _lowercase :							Optional[Any]   	=	use_input_mask
           _lowercase :							Optional[int]   	=	use_token_type_ids
           _lowercase :							str   	=	use_labels
           _lowercase :							List[Any]   	=	vocab_size
           _lowercase :							Dict   	=	hidden_size
           _lowercase :							Any   	=	num_hidden_layers
           _lowercase :							Union[str, Any]   	=	num_attention_heads
           _lowercase :							int   	=	intermediate_size
           _lowercase :							List[str]   	=	hidden_act
           _lowercase :							Tuple   	=	hidden_dropout_prob
           _lowercase :							Optional[Any]   	=	attention_probs_dropout_prob
           _lowercase :							int   	=	max_position_embeddings
           _lowercase :							Any   	=	type_vocab_size
           _lowercase :							Tuple   	=	type_sequence_label_size
           _lowercase :							List[Any]   	=	initializer_range
           _lowercase :							Optional[Any]   	=	num_labels
           _lowercase :							Tuple   	=	num_choices
           _lowercase :							Dict   	=	relative_attention
           _lowercase :							Optional[int]   	=	position_biased_input
           _lowercase :							str   	=	pos_att_type
           _lowercase :							Optional[Any]   	=	scope
       def     UpperCamelCase  (      self) ->     Optional[int]:




           """simple docstring"""

           _lowercase :							int   	=	ids_tensor([self.batch_size, self.seq_length],   self.vocab_size)

           _lowercase :							Union[str, Any]   	=	None
           if self.use_input_mask:
               _lowercase :							int   	=	ids_tensor([self.batch_size, self.seq_length],   vocab_size=2)

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

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

           _lowercase :							Union[str, Any]   	=	self.get_config()

           return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
       def     UpperCamelCase  (      self) ->     Optional[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     UpperCamelCase  (      self,   lowerCamelCase) ->     int:




           """simple docstring"""

           self.parent.assertListEqual(list(result.loss.size()),   [])
       def     UpperCamelCase  (      self,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase) ->     Union[str, Any]:




           """simple docstring"""

           _lowercase :							List[str]   	=	DebertaVaModel(config=lowerCamelCase)
           model.to(lowerCamelCase)
           model.eval()
           _lowercase :							int   	=	model(lowerCamelCase,   attention_mask=lowerCamelCase,   token_type_ids=lowerCamelCase)[0]
           _lowercase :							Optional[int]   	=	model(lowerCamelCase,   token_type_ids=lowerCamelCase)[0]
           _lowercase :							Dict   	=	model(lowerCamelCase)[0]

           self.parent.assertListEqual(list(sequence_output.size()),   [self.batch_size, self.seq_length, self.hidden_size])
       def     UpperCamelCase  (      self,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase) ->     List[Any]:




           """simple docstring"""

           _lowercase :							List[Any]   	=	DebertaVaForMaskedLM(config=lowerCamelCase)
           model.to(lowerCamelCase)
           model.eval()
           _lowercase :							str   	=	model(lowerCamelCase,   attention_mask=lowerCamelCase,   token_type_ids=lowerCamelCase,   labels=lowerCamelCase)

           self.parent.assertEqual(result.logits.shape,   (self.batch_size, self.seq_length, self.vocab_size))
       def     UpperCamelCase  (      self,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase) ->     List[str]:




           """simple docstring"""

           _lowercase :							Optional[int]   	=	self.num_labels
           _lowercase :							Any   	=	DebertaVaForSequenceClassification(lowerCamelCase)
           model.to(lowerCamelCase)
           model.eval()
           _lowercase :							Any   	=	model(lowerCamelCase,   attention_mask=lowerCamelCase,   token_type_ids=lowerCamelCase,   labels=lowerCamelCase)
           self.parent.assertListEqual(list(result.logits.size()),   [self.batch_size, self.num_labels])
           self.check_loss_output(lowerCamelCase)
       def     UpperCamelCase  (      self,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase) ->     Optional[int]:




           """simple docstring"""

           _lowercase :							Union[str, Any]   	=	self.num_labels
           _lowercase :							Optional[int]   	=	DebertaVaForTokenClassification(config=lowerCamelCase)
           model.to(lowerCamelCase)
           model.eval()
           _lowercase :							Optional[Any]   	=	model(lowerCamelCase,   attention_mask=lowerCamelCase,   token_type_ids=lowerCamelCase,   labels=lowerCamelCase)
           self.parent.assertEqual(result.logits.shape,   (self.batch_size, self.seq_length, self.num_labels))
       def     UpperCamelCase  (      self,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase) ->     Union[str, Any]:




           """simple docstring"""

           _lowercase :							Union[str, Any]   	=	DebertaVaForQuestionAnswering(config=lowerCamelCase)
           model.to(lowerCamelCase)
           model.eval()
           _lowercase :							Union[str, Any]   	=	model(
               lowerCamelCase,   attention_mask=lowerCamelCase,   token_type_ids=lowerCamelCase,   start_positions=lowerCamelCase,   end_positions=lowerCamelCase,   )
           self.parent.assertEqual(result.start_logits.shape,   (self.batch_size, self.seq_length))
           self.parent.assertEqual(result.end_logits.shape,   (self.batch_size, self.seq_length))
       def     UpperCamelCase  (      self,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase) ->     Optional[int]:




           """simple docstring"""

           _lowercase :							Any   	=	DebertaVaForMultipleChoice(config=lowerCamelCase)
           model.to(lowerCamelCase)
           model.eval()
           _lowercase :							Optional[int]   	=	input_ids.unsqueeze(1).expand(-1,   self.num_choices,   -1).contiguous()
           _lowercase :							List[Any]   	=	token_type_ids.unsqueeze(1).expand(-1,   self.num_choices,   -1).contiguous()
           _lowercase :							int   	=	input_mask.unsqueeze(1).expand(-1,   self.num_choices,   -1).contiguous()
           _lowercase :							str   	=	model(
               lowerCamelCase,   attention_mask=lowerCamelCase,   token_type_ids=lowerCamelCase,   labels=lowerCamelCase,   )
           self.parent.assertEqual(result.logits.shape,   (self.batch_size, self.num_choices))




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




           """simple docstring"""

           _lowercase :							Any   	=	self.prepare_config_and_inputs()
           (
               (
               _lowercase
           )	,       (
               _lowercase
           )	,       (
               _lowercase
           )	,       (
               _lowercase
           )	,       (
               _lowercase
           )	,       (
               _lowercase
           )	,       (
               _lowercase
           )	,       
           ) :							List[str]   	=	config_and_inputs
           _lowercase :							int   	=	{'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
           return config, inputs_dict






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

       lowercase_				:			int									=    True
       lowercase_				:			str									=    False
       lowercase_				:			str									=    False
       lowercase_				:			str									=    False
       lowercase_				:			List[Any]									=    False
       def     UpperCamelCase  (      self) ->     str:




           """simple docstring"""

           _lowercase :							List[Any]   	=	DebertaVaModelTester(self)
           _lowercase :							List[Any]   	=	ConfigTester(self,   config_class=lowerCamelCase,   hidden_size=37)
       def     UpperCamelCase  (      self) ->     Dict:




           """simple docstring"""

           self.config_tester.run_common_tests()
       def     UpperCamelCase  (      self) ->     Union[str, Any]:




           """simple docstring"""

           _lowercase :							Tuple   	=	self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_deberta_model(*lowerCamelCase)
       def     UpperCamelCase  (      self) ->     Optional[Any]:




           """simple docstring"""

           _lowercase :							int   	=	self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_deberta_for_sequence_classification(*lowerCamelCase)
       def     UpperCamelCase  (      self) ->     int:




           """simple docstring"""

           _lowercase :							Union[str, Any]   	=	self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_deberta_for_masked_lm(*lowerCamelCase)
       def     UpperCamelCase  (      self) ->     List[str]:




           """simple docstring"""

           _lowercase :							List[Any]   	=	self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_deberta_for_question_answering(*lowerCamelCase)
       def     UpperCamelCase  (      self) ->     Tuple:




           """simple docstring"""

           _lowercase :							Union[str, Any]   	=	self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_deberta_for_token_classification(*lowerCamelCase)
       def     UpperCamelCase  (      self) ->     Optional[Any]:




           """simple docstring"""

           _lowercase :							Tuple   	=	self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_deberta_for_multiple_choice(*lowerCamelCase)




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




           """simple docstring"""

           for model_name in DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               _lowercase :							Dict   	=	DebertaVaModel.from_pretrained(lowerCamelCase)
               self.assertIsNotNone(lowerCamelCase)






@require_torch
@require_sentencepiece
@require_tokenizers
class _lowerCamelCase(		unittest.TestCase     ):
       @unittest.skip(reason='Model not available yet')
       def     UpperCamelCase  (      self) ->     Tuple:




           """simple docstring"""

           pass




       @slow
       def     UpperCamelCase  (      self) ->     Optional[int]:




           """simple docstring"""

           _lowercase :							Dict   	=	DebertaVaModel.from_pretrained('microsoft/deberta-v2-xlarge')

           _lowercase :							str   	=	torch.tensor([[0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69, 4_60_78, 15_88, 2]])
           _lowercase :							Any   	=	torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])
           with torch.no_grad():
               _lowercase :							Tuple   	=	model(lowerCamelCase,   attention_mask=lowerCamelCase)[0]
           # compare the actual values for a slice.
           _lowercase :							int   	=	torch.tensor(
               [[[0.2_3_5_6, 0.1_9_4_8, 0.0_3_6_9], [-0.1_0_6_3, 0.3_5_8_6, -0.5_1_5_2], [-0.6_3_9_9, -0.0_2_5_9, -0.2_5_2_5]]])
           self.assertTrue(torch.allclose(output[:, 1:4, 1:4],   lowerCamelCase,   atol=1E-4),   F'''{output[:, 1:4, 1:4]}''')


