






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       ,			)       ->    Optional[int]:
								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 							_UpperCamelCase			(       self					)       ->    int:
								snake_case_				=							ids_tensor([self.batch_size, self.seq_length]       ,			self.vocab_size					)

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

								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_				=							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 							_UpperCamelCase			(       self       ,			a       ,			a       ,			a       ,			a       ,			a       ,			a       ,			a					)       ->    Tuple:
								snake_case_				=							TFDebertaVaModel(config=__lowerCAmelCase					)
								snake_case_				=							{'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}

								snake_case_				=							[input_ids, input_mask]
								snake_case_				=							model(__lowerCAmelCase					)

								snake_case_				=							model(__lowerCAmelCase					)

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




	def 							_UpperCamelCase			(       self       ,			a       ,			a       ,			a       ,			a       ,			a       ,			a       ,			a					)       ->    Tuple:
								snake_case_				=							TFDebertaVaForMaskedLM(config=__lowerCAmelCase					)
								snake_case_				=							{
								    'input_ids': input_ids,
								    'attention_mask': input_mask,
								    'token_type_ids': token_type_ids,
								}
								snake_case_				=							model(__lowerCAmelCase					)
								self.parent.assertEqual(result.logits.shape       ,			(self.batch_size, self.seq_length, self.vocab_size)					)




	def 							_UpperCamelCase			(       self       ,			a       ,			a       ,			a       ,			a       ,			a       ,			a       ,			a					)       ->    Dict:
								snake_case_				=							self.num_labels
								snake_case_				=							TFDebertaVaForSequenceClassification(config=__lowerCAmelCase					)
								snake_case_				=							{
								    'input_ids': input_ids,
								    'attention_mask': input_mask,
								    'token_type_ids': token_type_ids,
								}

								snake_case_				=							model(__lowerCAmelCase					)
								self.parent.assertEqual(result.logits.shape       ,			(self.batch_size, self.num_labels)					)




	def 							_UpperCamelCase			(       self       ,			a       ,			a       ,			a       ,			a       ,			a       ,			a       ,			a					)       ->    Union[str, Any]:
								snake_case_				=							self.num_labels
								snake_case_				=							TFDebertaVaForTokenClassification(config=__lowerCAmelCase					)
								snake_case_				=							{
								    'input_ids': input_ids,
								    'attention_mask': input_mask,
								    'token_type_ids': token_type_ids,
								}
								snake_case_				=							model(__lowerCAmelCase					)
								self.parent.assertEqual(result.logits.shape       ,			(self.batch_size, self.seq_length, self.num_labels)					)




	def 							_UpperCamelCase			(       self       ,			a       ,			a       ,			a       ,			a       ,			a       ,			a       ,			a					)       ->    int:
								snake_case_				=							TFDebertaVaForQuestionAnswering(config=__lowerCAmelCase					)
								snake_case_				=							{
								    'input_ids': input_ids,
								    'attention_mask': input_mask,
								    'token_type_ids': token_type_ids,
								}

								snake_case_				=							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 							_UpperCamelCase			(       self					)       ->    Union[str, Any]:
								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_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 							_UpperCamelCase			(       self					)       ->    int:
								snake_case_				=							TFDebertaVaModelTester(self					)
								snake_case_				=							ConfigTester(self       ,			config_class=__lowerCAmelCase       ,			hidden_size=37					)




	def 							_UpperCamelCase			(       self					)       ->    Dict:
								self.config_tester.run_common_tests()




	def 							_UpperCamelCase			(       self					)       ->    Dict:
								snake_case_				=							self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*__lowerCAmelCase					)




	def 							_UpperCamelCase			(       self					)       ->    int:
								snake_case_				=							self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_masked_lm(*__lowerCAmelCase					)




	def 							_UpperCamelCase			(       self					)       ->    Optional[Any]:
								snake_case_				=							self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_question_answering(*__lowerCAmelCase					)




	def 							_UpperCamelCase			(       self					)       ->    List[Any]:
								snake_case_				=							self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_sequence_classification(*__lowerCAmelCase					)




	def 							_UpperCamelCase			(       self					)       ->    List[str]:
								snake_case_				=							self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_token_classification(*__lowerCAmelCase					)






	@slow
	def 							_UpperCamelCase			(       self					)       ->    str:
								snake_case_				=							TFDebertaVaModel.from_pretrained('kamalkraj/deberta-v2-xlarge'					)
								self.assertIsNotNone(__lowerCAmelCase					)






@require_tf
class  UpperCamelCase_      (			unittest.TestCase				):



	'''simple docstring'''




	@unittest.skip(reason='Model not available yet'					)
	def 							_UpperCamelCase			(       self					)       ->    Optional[int]:
								pass






	@slow
	def 							_UpperCamelCase			(       self					)       ->    Optional[int]:
								snake_case_				=							TFDebertaVaModel.from_pretrained('kamalkraj/deberta-v2-xlarge'					)
								snake_case_				=							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_				=							tf.constant([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]					)
								snake_case_				=							model(__lowerCAmelCase       ,			attention_mask=__lowerCAmelCase					)[0]

								snake_case_				=							tf.constant(
								    [[[0.2_356, 0.1_948, 0.0_369], [-0.1_063, 0.3_586, -0.5_152], [-0.6_399, -0.0_259, -0.2_525]]]					)
								tf.debugging.assert_near(output[:, 1:4, 1:4]       ,			__lowerCAmelCase       ,			atol=1E-4					)












def    __lowerCamelCase					(							__a   :int = 1_0_0_0_0_0_0    )		->				int:






		"""simple docstring"""

		A__        =   limit + 1
		A__        =   [0] * limit
		for first_term in range(1    ,     __a    ):
				for n in range(__a    ,     __a    ,     __a    ):
						A__        =   first_term + n / first_term
						if common_difference % 4:  # d must be divisble by 4
								continue
						else:
								common_difference /= 4
								if (
								    first_term > common_difference
								    and first_term < 4 * common_difference
								):  # since x,y,z are positive integers
										frequency[n] += 1  # so z>0 and a>d ,also 4d<a

		A__        =   sum(1 for x in frequency[1:limit] if x == 1_0    )

		return count


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



