

"""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			:				Optional[Any]   ,lowercase_			:				List[str]   ,lowercase_			:				List[str]=1_3   ,lowercase_			:				Union[str, Any]=7   ,lowercase_			:				Tuple=True   ,lowercase_			:				List[str]=True   ,lowercase_			:				Union[str, Any]=True   ,lowercase_			:				List[str]=True   ,lowercase_			:				Tuple=9_9   ,lowercase_			:				Optional[Any]=3_2   ,lowercase_			:				Optional[int]=2   ,lowercase_			:				Optional[Any]=4   ,lowercase_			:				int=3_7   ,lowercase_			:				Dict="gelu"   ,lowercase_			:				str=0.1   ,lowercase_			:				str=0.1   ,lowercase_			:				Dict=5_1_2   ,lowercase_			:				Optional[Any]=1_6   ,lowercase_			:				Optional[Any]=2   ,lowercase_			:				int=0.02   ,lowercase_			:				Optional[Any]=False   ,lowercase_			:				List[Any]=True   ,lowercase_			:				Optional[Any]="None"   ,lowercase_			:				int=3   ,lowercase_			:				int=4   ,lowercase_			:				Union[str, Any]=None   ,):
						lowerCAmelCase__	:						int  				=	parent
						lowerCAmelCase__	:						Optional[int]  				=	batch_size
						lowerCAmelCase__	:						Optional[Any]  				=	seq_length
						lowerCAmelCase__	:						Optional[Any]  				=	is_training
						lowerCAmelCase__	:						Dict  				=	use_input_mask
						lowerCAmelCase__	:						str  				=	use_token_type_ids
						lowerCAmelCase__	:						str  				=	use_labels
						lowerCAmelCase__	:						Any  				=	vocab_size
						lowerCAmelCase__	:						Optional[int]  				=	hidden_size
						lowerCAmelCase__	:						Any  				=	num_hidden_layers
						lowerCAmelCase__	:						Optional[Any]  				=	num_attention_heads
						lowerCAmelCase__	:						List[str]  				=	intermediate_size
						lowerCAmelCase__	:						List[Any]  				=	hidden_act
						lowerCAmelCase__	:						Tuple  				=	hidden_dropout_prob
						lowerCAmelCase__	:						List[Any]  				=	attention_probs_dropout_prob
						lowerCAmelCase__	:						List[str]  				=	max_position_embeddings
						lowerCAmelCase__	:						List[Any]  				=	type_vocab_size
						lowerCAmelCase__	:						int  				=	type_sequence_label_size
						lowerCAmelCase__	:						List[Any]  				=	initializer_range
						lowerCAmelCase__	:						List[Any]  				=	num_labels
						lowerCAmelCase__	:						Union[str, Any]  				=	num_choices
						lowerCAmelCase__	:						int  				=	relative_attention
						lowerCAmelCase__	:						str  				=	position_biased_input
						lowerCAmelCase__	:						Optional[Any]  				=	pos_att_type
						lowerCAmelCase__	:						Tuple  				=	scope







					def        __lowerCAmelCase (       self			:				Tuple		):
						lowerCAmelCase__	:						Tuple  				=	ids_tensor([self.batch_size, self.seq_length]   ,self.vocab_size		)

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

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

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

						lowerCAmelCase__	:						int  				=	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=_lowerCamelCase   ,)

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







					def        __lowerCAmelCase (       self			:				Dict   ,lowercase_			:				Union[str, Any]   ,lowercase_			:				Any   ,lowercase_			:				Optional[int]   ,lowercase_			:				str   ,lowercase_			:				Optional[int]   ,lowercase_			:				Tuple   ,lowercase_			:				List[str]		):
						lowerCAmelCase__	:						Optional[int]  				=	TFDebertaVaModel(config=_lowerCamelCase		)
						lowerCAmelCase__	:						Union[str, Any]  				=	{'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}

						lowerCAmelCase__	:						Any  				=	[input_ids, input_mask]
						lowerCAmelCase__	:						List[Any]  				=	model(_lowerCamelCase		)

						lowerCAmelCase__	:						str  				=	model(_lowerCamelCase		)

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







					def        __lowerCAmelCase (       self			:				int   ,lowercase_			:				int   ,lowercase_			:				Union[str, Any]   ,lowercase_			:				Tuple   ,lowercase_			:				Optional[Any]   ,lowercase_			:				Optional[int]   ,lowercase_			:				int   ,lowercase_			:				Optional[int]		):
						lowerCAmelCase__	:						Tuple  				=	TFDebertaVaForMaskedLM(config=_lowerCamelCase		)
						lowerCAmelCase__	:						Tuple  				=	{
						    '''input_ids''': input_ids,
						    '''attention_mask''': input_mask,
						    '''token_type_ids''': token_type_ids,
						}
						lowerCAmelCase__	:						Dict  				=	model(_lowerCamelCase		)
						self.parent.assertEqual(result.logits.shape   ,(self.batch_size, self.seq_length, self.vocab_size)		)







					def        __lowerCAmelCase (       self			:				Any   ,lowercase_			:				Optional[int]   ,lowercase_			:				Union[str, Any]   ,lowercase_			:				Union[str, Any]   ,lowercase_			:				Tuple   ,lowercase_			:				Any   ,lowercase_			:				Optional[int]   ,lowercase_			:				Any		):
						lowerCAmelCase__	:						List[str]  				=	self.num_labels
						lowerCAmelCase__	:						List[str]  				=	TFDebertaVaForSequenceClassification(config=_lowerCamelCase		)
						lowerCAmelCase__	:						Optional[Any]  				=	{
						    '''input_ids''': input_ids,
						    '''attention_mask''': input_mask,
						    '''token_type_ids''': token_type_ids,
						}

						lowerCAmelCase__	:						Tuple  				=	model(_lowerCamelCase		)
						self.parent.assertEqual(result.logits.shape   ,(self.batch_size, self.num_labels)		)







					def        __lowerCAmelCase (       self			:				Union[str, Any]   ,lowercase_			:				str   ,lowercase_			:				List[Any]   ,lowercase_			:				Any   ,lowercase_			:				Union[str, Any]   ,lowercase_			:				int   ,lowercase_			:				Tuple   ,lowercase_			:				Optional[int]		):
						lowerCAmelCase__	:						Dict  				=	self.num_labels
						lowerCAmelCase__	:						Optional[int]  				=	TFDebertaVaForTokenClassification(config=_lowerCamelCase		)
						lowerCAmelCase__	:						Optional[int]  				=	{
						    '''input_ids''': input_ids,
						    '''attention_mask''': input_mask,
						    '''token_type_ids''': token_type_ids,
						}
						lowerCAmelCase__	:						int  				=	model(_lowerCamelCase		)
						self.parent.assertEqual(result.logits.shape   ,(self.batch_size, self.seq_length, self.num_labels)		)







					def        __lowerCAmelCase (       self			:				List[Any]   ,lowercase_			:				Optional[int]   ,lowercase_			:				str   ,lowercase_			:				Optional[int]   ,lowercase_			:				Dict   ,lowercase_			:				Union[str, Any]   ,lowercase_			:				int   ,lowercase_			:				List[str]		):
						lowerCAmelCase__	:						Tuple  				=	TFDebertaVaForQuestionAnswering(config=_lowerCamelCase		)
						lowerCAmelCase__	:						List[Any]  				=	{
						    '''input_ids''': input_ids,
						    '''attention_mask''': input_mask,
						    '''token_type_ids''': token_type_ids,
						}

						lowerCAmelCase__	:						Union[str, Any]  				=	model(_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        __lowerCAmelCase (       self			:				Tuple		):
						lowerCAmelCase__	:						Tuple  				=	self.prepare_config_and_inputs()
						(
						    (
						    lowerCAmelCase__    
						)				,(
						    lowerCAmelCase__    
						)				,(
						    lowerCAmelCase__    
						)				,(
						    lowerCAmelCase__    
						)				,(
						    lowerCAmelCase__    
						)				,(
						    lowerCAmelCase__    
						)				,(
						    lowerCAmelCase__    
						)				,    
						)	:						List[str]  				=	config_and_inputs
						lowerCAmelCase__	:						Union[str, Any]  				=	{'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
						return config, inputs_dict







@require_tf
class 				SCREAMING_SNAKE_CASE					(		_lowerCAmelCase    ,							_lowerCAmelCase    ,							unittest.TestCase       ):






					"""simple docstring"""



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

					lowercase__				 =       False
					lowercase__				 =       False







					def        __lowerCAmelCase (       self			:				Any		):
						lowerCAmelCase__	:						Optional[int]  				=	TFDebertaVaModelTester(self		)
						lowerCAmelCase__	:						Dict  				=	ConfigTester(self   ,config_class=_lowerCamelCase   ,hidden_size=3_7		)







					def        __lowerCAmelCase (       self			:				Dict		):
						self.config_tester.run_common_tests()







					def        __lowerCAmelCase (       self			:				List[str]		):
						lowerCAmelCase__	:						Dict  				=	self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*_lowerCamelCase		)







					def        __lowerCAmelCase (       self			:				Optional[int]		):
						lowerCAmelCase__	:						List[Any]  				=	self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_masked_lm(*_lowerCamelCase		)







					def        __lowerCAmelCase (       self			:				int		):
						lowerCAmelCase__	:						Optional[int]  				=	self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_question_answering(*_lowerCamelCase		)







					def        __lowerCAmelCase (       self			:				List[Any]		):
						lowerCAmelCase__	:						Optional[Any]  				=	self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_sequence_classification(*_lowerCamelCase		)







					def        __lowerCAmelCase (       self			:				List[str]		):
						lowerCAmelCase__	:						Optional[int]  				=	self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_token_classification(*_lowerCamelCase		)







					@slow
					def        __lowerCAmelCase (       self			:				List[str]		):
						lowerCAmelCase__	:						List[Any]  				=	TFDebertaVaModel.from_pretrained('''kamalkraj/deberta-v2-xlarge'''		)
						self.assertIsNotNone(_lowerCamelCase		)







@require_tf
class 				SCREAMING_SNAKE_CASE					(		unittest.TestCase       ):






					"""simple docstring"""



					@unittest.skip(reason='''Model not available yet'''		)
					def        __lowerCAmelCase (       self			:				int		):
						pass







					@slow
					def        __lowerCAmelCase (       self			:				Tuple		):
						lowerCAmelCase__	:						str  				=	TFDebertaVaModel.from_pretrained('''kamalkraj/deberta-v2-xlarge'''		)
						lowerCAmelCase__	:						Optional[int]  				=	tf.constant([[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]]		)
						lowerCAmelCase__	:						str  				=	tf.constant([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]		)
						lowerCAmelCase__	:						List[str]  				=	model(_lowerCamelCase   ,attention_mask=_lowerCamelCase		)[0]

						lowerCAmelCase__	:						Any  				=	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]   ,_lowerCamelCase   ,atol=1E-4		)












"""simple docstring"""


from __future__ import annotations


class 					snake_case		:


   '''simple docstring'''

   def __init__(	self :     int,       _lowerCamelCase :     List[Any]=None  ):

     '''simple docstring'''
     __A									= data
     __A									= None






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

     '''simple docstring'''
     __A									= []
     __A									= self
     while temp:
       string_rep.append(f'{temp.data}'  )
       __A									= temp.next
     return "->".join(_lowerCamelCase  )





def 					lowerCAmelCase				(    __UpperCamelCase ):




  """simple docstring"""







  if not elements_list:
    raise Exception('''The Elements List is empty''' )

  __A									= __A									= Node(elements_list[0] )
  for i in range(1		,       len(__UpperCamelCase ) ):
    __A									= Node(elements_list[i] )
    __A									= current.next
  return head



def 					lowerCAmelCase				(    __UpperCamelCase ):




  """simple docstring"""







  if head_node is not None and isinstance(__UpperCamelCase		,       __UpperCamelCase ):
    print_reverse(head_node.next )
    print(head_node.data )



def 					lowerCAmelCase				(    ):




  """simple docstring"""







  from doctest import testmod

  testmod()

  __A									= make_linked_list([1_4, 5_2, 1_4, 1_2, 4_3] )
  print('''Linked List:''' )
  print(__UpperCamelCase )
  print('''Elements in Reverse:''' )
  print_reverse(__UpperCamelCase )


if __name__ == "__main__":
       main()




