


"""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				,       a_				,       a_=13				,       a_=7				,       a_=True				,       a_=True				,       a_=True				,       a_=True				,       a_=99				,       a_=32				,       a_=2				,       a_=4				,       a_=37				,       a_="gelu"				,       a_=0.1				,       a_=0.1				,       a_=5_12				,       a_=16				,       a_=2				,       a_=0.02				,       a_=False				,       a_=True				,       a_="None"				,       a_=3				,       a_=4				,       a_=None				,       ):





			'''simple docstring'''

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



		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

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

			__snake_case	:						List[str]						=				None
			if self.use_input_mask:
				__snake_case	:						Optional[int]						=				random_attention_mask([self.batch_size, self.seq_length]					)

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

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

			__snake_case	:						Dict						=				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=a_				,       )

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



		def 				SCREAMING_SNAKE_CASE  (self				,       a_				,       a_				,       a_				,       a_				,       a_				,       a_				,       a_					):





			'''simple docstring'''

			__snake_case	:						int						=				TFDebertaVaModel(config=a_					)
			__snake_case	:						Dict						=				{'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}

			__snake_case	:						int						=				[input_ids, input_mask]
			__snake_case	:						List[str]						=				model(a_					)

			__snake_case	:						List[Any]						=				model(a_					)

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



		def 				SCREAMING_SNAKE_CASE  (self				,       a_				,       a_				,       a_				,       a_				,       a_				,       a_				,       a_					):





			'''simple docstring'''

			__snake_case	:						List[Any]						=				TFDebertaVaForMaskedLM(config=a_					)
			__snake_case	:						str						=				{
			    '''input_ids''': input_ids,
			    '''attention_mask''': input_mask,
			    '''token_type_ids''': token_type_ids,
			}
			__snake_case	:						Optional[Any]						=				model(a_					)
			self.parent.assertEqual(result.logits.shape				,       (self.batch_size, self.seq_length, self.vocab_size)					)



		def 				SCREAMING_SNAKE_CASE  (self				,       a_				,       a_				,       a_				,       a_				,       a_				,       a_				,       a_					):





			'''simple docstring'''

			__snake_case	:						Union[str, Any]						=				self.num_labels
			__snake_case	:						List[Any]						=				TFDebertaVaForSequenceClassification(config=a_					)
			__snake_case	:						Optional[Any]						=				{
			    '''input_ids''': input_ids,
			    '''attention_mask''': input_mask,
			    '''token_type_ids''': token_type_ids,
			}

			__snake_case	:						int						=				model(a_					)
			self.parent.assertEqual(result.logits.shape				,       (self.batch_size, self.num_labels)					)



		def 				SCREAMING_SNAKE_CASE  (self				,       a_				,       a_				,       a_				,       a_				,       a_				,       a_				,       a_					):





			'''simple docstring'''

			__snake_case	:						Tuple						=				self.num_labels
			__snake_case	:						Any						=				TFDebertaVaForTokenClassification(config=a_					)
			__snake_case	:						str						=				{
			    '''input_ids''': input_ids,
			    '''attention_mask''': input_mask,
			    '''token_type_ids''': token_type_ids,
			}
			__snake_case	:						Dict						=				model(a_					)
			self.parent.assertEqual(result.logits.shape				,       (self.batch_size, self.seq_length, self.num_labels)					)



		def 				SCREAMING_SNAKE_CASE  (self				,       a_				,       a_				,       a_				,       a_				,       a_				,       a_				,       a_					):





			'''simple docstring'''

			__snake_case	:						int						=				TFDebertaVaForQuestionAnswering(config=a_					)
			__snake_case	:						Optional[Any]						=				{
			    '''input_ids''': input_ids,
			    '''attention_mask''': input_mask,
			    '''token_type_ids''': token_type_ids,
			}

			__snake_case	:						int						=				model(a_					)
			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					):





			'''simple docstring'''

			__snake_case	:						int						=				self.prepare_config_and_inputs()
			(
			    (
			    __snake_case
			)	,							(
			    __snake_case
			)	,							(
			    __snake_case
			)	,							(
			    __snake_case
			)	,							(
			    __snake_case
			)	,							(
			    __snake_case
			)	,							(
			    __snake_case
			)	,							
			)	:						List[str]						=				config_and_inputs
			__snake_case	:						Dict						=				{'''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'''

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

		lowerCamelCase__				=False
		lowerCamelCase__				=False



		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			__snake_case	:						Dict						=				TFDebertaVaModelTester(self					)
			__snake_case	:						Optional[Any]						=				ConfigTester(self				,       config_class=a_				,       hidden_size=37					)



		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			self.config_tester.run_common_tests()



		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			__snake_case	:						Optional[int]						=				self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_model(*a_					)



		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			__snake_case	:						Optional[Any]						=				self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_for_masked_lm(*a_					)



		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			__snake_case	:						Dict						=				self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_for_question_answering(*a_					)



		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			__snake_case	:						Tuple						=				self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_for_sequence_classification(*a_					)



		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			__snake_case	:						Union[str, Any]						=				self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_for_token_classification(*a_					)



		@slow
		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			__snake_case	:						Dict						=				TFDebertaVaModel.from_pretrained('''kamalkraj/deberta-v2-xlarge'''					)
			self.assertIsNotNone(a_					)



@require_tf
class 			_UpperCAmelCase (							unittest.TestCase				):






		'''simple docstring'''

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





			'''simple docstring'''

			pass



		@slow
		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			__snake_case	:						Dict						=				TFDebertaVaModel.from_pretrained('''kamalkraj/deberta-v2-xlarge'''					)
			__snake_case	:						Tuple						=				tf.constant([[0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69, 4_60_78, 15_88, 2]]					)
			__snake_case	:						Tuple						=				tf.constant([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]					)
			__snake_case	:						str						=				model(a_				,       attention_mask=a_					)[0]

			__snake_case	:						List[str]						=				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]				,       a_				,       atol=1E-4					)













def 						_a    (      a :int = 100 )							->			int:
 a		  =      n * (n + 1) * (2 * n + 1) / 6
 a		  =      (n * (n + 1) / 2) ** 2
 return int(square_of_sum - sum_of_squares )


if __name__ == "__main__":
     print(f"""{solution() = }""")


