






'''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    _UpperCAmelCase			:




   """simple docstring"""

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





         '''simple docstring'''





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

   def 						lowerCAmelCase (		self :	int		):





         '''simple docstring'''





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

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

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

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

         _A			     =	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 :	Optional[int]      ,      __UpperCAmelCase :	str      ,      __UpperCAmelCase :	Optional[Any]      ,      __UpperCAmelCase :	str      ,      __UpperCAmelCase :	Optional[int]      ,      __UpperCAmelCase :	str      ,      __UpperCAmelCase :	Optional[int]      ,      __UpperCAmelCase :	int		):





         '''simple docstring'''





         _A			     =	TFDebertaVaModel(config=__UpperCAmelCase		)
         _A			     =	{"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}

         _A			     =	[input_ids, input_mask]
         _A			     =	model(__UpperCAmelCase		)

         _A			     =	model(__UpperCAmelCase		)

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

   def 						lowerCAmelCase (		self :	str      ,      __UpperCAmelCase :	Union[str, Any]      ,      __UpperCAmelCase :	List[Any]      ,      __UpperCAmelCase :	Dict      ,      __UpperCAmelCase :	Union[str, Any]      ,      __UpperCAmelCase :	Union[str, Any]      ,      __UpperCAmelCase :	Tuple      ,      __UpperCAmelCase :	Dict		):





         '''simple docstring'''





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

   def 						lowerCAmelCase (		self :	Optional[Any]      ,      __UpperCAmelCase :	List[str]      ,      __UpperCAmelCase :	Any      ,      __UpperCAmelCase :	List[str]      ,      __UpperCAmelCase :	Any      ,      __UpperCAmelCase :	str      ,      __UpperCAmelCase :	List[Any]      ,      __UpperCAmelCase :	Any		):





         '''simple docstring'''





         _A			     =	self.num_labels
         _A			     =	TFDebertaVaForSequenceClassification(config=__UpperCAmelCase		)
         _A			     =	{
             "input_ids": input_ids,
             "attention_mask": input_mask,
             "token_type_ids": token_type_ids,
         }

         _A			     =	model(__UpperCAmelCase		)
         self.parent.assertEqual(result.logits.shape      ,      (self.batch_size, self.num_labels)		)

   def 						lowerCAmelCase (		self :	List[str]      ,      __UpperCAmelCase :	str      ,      __UpperCAmelCase :	Dict      ,      __UpperCAmelCase :	Dict      ,      __UpperCAmelCase :	List[str]      ,      __UpperCAmelCase :	Any      ,      __UpperCAmelCase :	Optional[Any]      ,      __UpperCAmelCase :	Tuple		):





         '''simple docstring'''





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

   def 						lowerCAmelCase (		self :	Tuple      ,      __UpperCAmelCase :	Dict      ,      __UpperCAmelCase :	Union[str, Any]      ,      __UpperCAmelCase :	List[str]      ,      __UpperCAmelCase :	List[str]      ,      __UpperCAmelCase :	Dict      ,      __UpperCAmelCase :	Any      ,      __UpperCAmelCase :	Optional[Any]		):





         '''simple docstring'''





         _A			     =	TFDebertaVaForQuestionAnswering(config=__UpperCAmelCase		)
         _A			     =	{
             "input_ids": input_ids,
             "attention_mask": input_mask,
             "token_type_ids": token_type_ids,
         }

         _A			     =	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]		):





         '''simple docstring'''





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





@require_tf
class    _UpperCAmelCase			(     snake_case_							,    snake_case_							,    unittest.TestCase						):




   """simple docstring"""

   snake_case    				=						(
       (
           TFDebertaVaModel,
           TFDebertaVaForMaskedLM,
           TFDebertaVaForQuestionAnswering,
           TFDebertaVaForSequenceClassification,
           TFDebertaVaForTokenClassification,
       )
       if is_tf_available()
       else ()
   )
   snake_case    				=						(
       {
           '''feature-extraction''': TFDebertaVaModel,
           '''fill-mask''': TFDebertaVaForMaskedLM,
           '''question-answering''': TFDebertaVaForQuestionAnswering,
           '''text-classification''': TFDebertaVaForSequenceClassification,
           '''token-classification''': TFDebertaVaForTokenClassification,
           '''zero-shot''': TFDebertaVaForSequenceClassification,
       }
       if is_tf_available()
       else {}
   )

   snake_case    				=						False
   snake_case    				=						False

   def 						lowerCAmelCase (		self :	int		):





         '''simple docstring'''





         _A			     =	TFDebertaVaModelTester(self		)
         _A			     =	ConfigTester(self      ,      config_class=__UpperCAmelCase      ,      hidden_size=37		)

   def 						lowerCAmelCase (		self :	List[str]		):





         '''simple docstring'''





         self.config_tester.run_common_tests()

   def 						lowerCAmelCase (		self :	List[Any]		):





         '''simple docstring'''





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

   def 						lowerCAmelCase (		self :	Optional[Any]		):





         '''simple docstring'''





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

   def 						lowerCAmelCase (		self :	Optional[Any]		):





         '''simple docstring'''





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

   def 						lowerCAmelCase (		self :	List[Any]		):





         '''simple docstring'''





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

   def 						lowerCAmelCase (		self :	Union[str, Any]		):





         '''simple docstring'''





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

   @slow
   def 						lowerCAmelCase (		self :	Optional[int]		):





         '''simple docstring'''





         _A			     =	TFDebertaVaModel.from_pretrained("kamalkraj/deberta-v2-xlarge"		)
         self.assertIsNotNone(__UpperCAmelCase		)





@require_tf
class    _UpperCAmelCase			(     unittest.TestCase						):




   """simple docstring"""

   @unittest.skip(reason="Model not available yet"		)
   def 						lowerCAmelCase (		self :	str		):





         '''simple docstring'''





         pass

   @slow
   def 						lowerCAmelCase (		self :	List[str]		):





         '''simple docstring'''





         _A			     =	TFDebertaVaModel.from_pretrained("kamalkraj/deberta-v2-xlarge"		)
         _A			     =	tf.constant([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]]		)
         _A			     =	tf.constant([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]		)
         _A			     =	model(__UpperCAmelCase      ,      attention_mask=__UpperCAmelCase		)[0]

         _A			     =	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		)








"""simple docstring"""





from ..utils import DummyObject, requires_backends







class 				SCREAMING_SNAKE_CASE					(		metaclass=a_       ):






					"""simple docstring"""



					lowercase__				 =       ["speech"]







					def __init__(       self			:				Tuple   ,*lowercase_			:				Tuple   ,**lowercase_			:				List[str]		):
						requires_backends(self   ,['''speech''']		)







class 				SCREAMING_SNAKE_CASE					(		metaclass=a_       ):






					"""simple docstring"""



					lowercase__				 =       ["speech"]







					def __init__(       self			:				Union[str, Any]   ,*lowercase_			:				List[str]   ,**lowercase_			:				Any		):
						requires_backends(self   ,['''speech''']		)




