
from __future__ import annotations

import unittest

from transformers import DebertaConfig, 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 (
		    TFDebertaForMaskedLM,
		    TFDebertaForQuestionAnswering,
		    TFDebertaForSequenceClassification,
		    TFDebertaForTokenClassification,
		    TFDebertaModel,
		)


class 						__magic_name__				:





		def __init__(							self      :		int  ,					lowerCamelCase__      :		List[str]  ,					lowerCamelCase__      :		Dict=1_3  ,					lowerCamelCase__      :		Tuple=7  ,					lowerCamelCase__      :		Optional[int]=True  ,					lowerCamelCase__      :		str=True  ,					lowerCamelCase__      :		Union[str, Any]=True  ,					lowerCamelCase__      :		Any=True  ,					lowerCamelCase__      :		Union[str, Any]=9_9  ,					lowerCamelCase__      :		int=3_2  ,					lowerCamelCase__      :		List[str]=2  ,					lowerCamelCase__      :		Union[str, Any]=4  ,					lowerCamelCase__      :		str=3_7  ,					lowerCamelCase__      :		Optional[int]="gelu"  ,					lowerCamelCase__      :		Any=0.1  ,					lowerCamelCase__      :		str=0.1  ,					lowerCamelCase__      :		int=5_1_2  ,					lowerCamelCase__      :		Optional[Any]=1_6  ,					lowerCamelCase__      :		str=2  ,					lowerCamelCase__      :		Optional[Any]=0.0_2  ,					lowerCamelCase__      :		Any=3  ,					lowerCamelCase__      :		str=4  ,					lowerCamelCase__      :		Optional[int]=None  ,					):
							lowerCAmelCase						:   str							=       parent
							lowerCAmelCase						:   List[Any]							=       1_3
							lowerCAmelCase						:   Tuple							=       7
							lowerCAmelCase						:   List[Any]							=       True
							lowerCAmelCase						:   List[str]							=       True
							lowerCAmelCase						:   List[str]							=       True
							lowerCAmelCase						:   Tuple							=       True
							lowerCAmelCase						:   Optional[Any]							=       9_9
							lowerCAmelCase						:   str							=       3_2
							lowerCAmelCase						:   int							=       2
							lowerCAmelCase						:   Dict							=       4
							lowerCAmelCase						:   Optional[Any]							=       3_7
							lowerCAmelCase						:   str							=       '''gelu'''
							lowerCAmelCase						:   Optional[Any]							=       0.1
							lowerCAmelCase						:   Dict							=       0.1
							lowerCAmelCase						:   str							=       5_1_2
							lowerCAmelCase						:   str							=       1_6
							lowerCAmelCase						:   Optional[int]							=       False
							lowerCAmelCase						:   Tuple							=       -1
							lowerCAmelCase						:   str							=       True
							lowerCAmelCase						:   List[Any]							=       2
							lowerCAmelCase						:   Optional[int]							=       0.0_2
							lowerCAmelCase						:   Optional[Any]							=       3
							lowerCAmelCase						:   str							=       4
							lowerCAmelCase						:   str							=       None





		def  _A							(							self      :		Optional[int]			):
							lowerCAmelCase						:   Optional[int]							=       ids_tensor([self.batch_size, self.seq_length]  ,					self.vocab_size			)

							lowerCAmelCase						:   Dict							=       None
							if self.use_input_mask:
												lowerCAmelCase						:   List[Any]							=       random_attention_mask([self.batch_size, self.seq_length]			)

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

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

							lowerCAmelCase						:   Tuple							=       DebertaConfig(
							    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  ,					max_relative_positions=self.max_relative_positions  ,					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  _A							(							self      :		Optional[int]  ,					lowerCamelCase__      :		Optional[Any]  ,					lowerCamelCase__      :		Any  ,					lowerCamelCase__      :		str  ,					lowerCamelCase__      :		str  ,					lowerCamelCase__      :		List[Any]  ,					lowerCamelCase__      :		List[Any]  ,					lowerCamelCase__      :		Any			):
							lowerCAmelCase						:   str							=       TFDebertaModel(config=lowerCamelCase__			)
							lowerCAmelCase						:   List[str]							=       {'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}

							lowerCAmelCase						:   Dict							=       [input_ids, input_mask]
							lowerCAmelCase						:   List[str]							=       model(lowerCamelCase__			)

							lowerCAmelCase						:   int							=       model(lowerCamelCase__			)

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





		def  _A							(							self      :		Union[str, Any]  ,					lowerCamelCase__      :		Any  ,					lowerCamelCase__      :		List[Any]  ,					lowerCamelCase__      :		str  ,					lowerCamelCase__      :		int  ,					lowerCamelCase__      :		str  ,					lowerCamelCase__      :		str  ,					lowerCamelCase__      :		Dict			):
							lowerCAmelCase						:   int							=       TFDebertaForMaskedLM(config=lowerCamelCase__			)
							lowerCAmelCase						:   List[str]							=       {
							    '''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.seq_length, self.vocab_size)			)





		def  _A							(							self      :		str  ,					lowerCamelCase__      :		List[str]  ,					lowerCamelCase__      :		Tuple  ,					lowerCamelCase__      :		Optional[Any]  ,					lowerCamelCase__      :		Optional[Any]  ,					lowerCamelCase__      :		List[str]  ,					lowerCamelCase__      :		Union[str, Any]  ,					lowerCamelCase__      :		Dict			):
							lowerCAmelCase						:   List[Any]							=       self.num_labels
							lowerCAmelCase						:   int							=       TFDebertaForSequenceClassification(config=lowerCamelCase__			)
							lowerCAmelCase						:   Dict							=       {
							    '''input_ids''': input_ids,
							    '''attention_mask''': input_mask,
							    '''token_type_ids''': token_type_ids,
							}

							lowerCAmelCase						:   Optional[int]							=       model(lowerCamelCase__			)
							self.parent.assertEqual(result.logits.shape  ,					(self.batch_size, self.num_labels)			)





		def  _A							(							self      :		Optional[Any]  ,					lowerCamelCase__      :		Any  ,					lowerCamelCase__      :		Tuple  ,					lowerCamelCase__      :		List[str]  ,					lowerCamelCase__      :		str  ,					lowerCamelCase__      :		Optional[Any]  ,					lowerCamelCase__      :		str  ,					lowerCamelCase__      :		Optional[int]			):
							lowerCAmelCase						:   Optional[int]							=       self.num_labels
							lowerCAmelCase						:   int							=       TFDebertaForTokenClassification(config=lowerCamelCase__			)
							lowerCAmelCase						:   Union[str, Any]							=       {
							    '''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.num_labels)			)





		def  _A							(							self      :		List[str]  ,					lowerCamelCase__      :		List[str]  ,					lowerCamelCase__      :		Any  ,					lowerCamelCase__      :		Dict  ,					lowerCamelCase__      :		Dict  ,					lowerCamelCase__      :		List[str]  ,					lowerCamelCase__      :		Optional[int]  ,					lowerCamelCase__      :		Optional[Any]			):
							lowerCAmelCase						:   Optional[int]							=       TFDebertaForQuestionAnswering(config=lowerCamelCase__			)
							lowerCAmelCase						:   Union[str, Any]							=       {
							    '''input_ids''': input_ids,
							    '''attention_mask''': input_mask,
							    '''token_type_ids''': token_type_ids,
							}

							lowerCAmelCase						:   Tuple							=       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  _A							(							self      :		Dict			):
							lowerCAmelCase						:   str							=       self.prepare_config_and_inputs()
							(
							    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 						__magic_name__				(  snake_case,			snake_case,			unittest.TestCase      ):
		_lowerCAmelCase						=		(
		    (
		        TFDebertaModel,
		        TFDebertaForMaskedLM,
		        TFDebertaForQuestionAnswering,
		        TFDebertaForSequenceClassification,
		        TFDebertaForTokenClassification,
		    )
		    if is_tf_available()
		    else ()
		)
		_lowerCAmelCase						=		(
		    {
		        "feature-extraction": TFDebertaModel,
		        "fill-mask": TFDebertaForMaskedLM,
		        "question-answering": TFDebertaForQuestionAnswering,
		        "text-classification": TFDebertaForSequenceClassification,
		        "token-classification": TFDebertaForTokenClassification,
		        "zero-shot": TFDebertaForSequenceClassification,
		    }
		    if is_tf_available()
		    else {}
		)

		_lowerCAmelCase						=		False
		_lowerCAmelCase						=		False





		def  _A							(							self      :		Any			):
							lowerCAmelCase						:   List[str]							=       TFDebertaModelTester(self			)
							lowerCAmelCase						:   Dict							=       ConfigTester(self  ,					config_class=lowerCamelCase__  ,					hidden_size=3_7			)





		def  _A							(							self      :		str			):
							self.config_tester.run_common_tests()





		def  _A							(							self      :		Any			):
							lowerCAmelCase						:   str							=       self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*lowerCamelCase__			)





		def  _A							(							self      :		Union[str, Any]			):
							lowerCAmelCase						:   Dict							=       self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_masked_lm(*lowerCamelCase__			)





		def  _A							(							self      :		Any			):
							lowerCAmelCase						:   List[str]							=       self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_question_answering(*lowerCamelCase__			)





		def  _A							(							self      :		Tuple			):
							lowerCAmelCase						:   List[Any]							=       self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_sequence_classification(*lowerCamelCase__			)





		def  _A							(							self      :		str			):
							lowerCAmelCase						:   Optional[Any]							=       self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_token_classification(*lowerCamelCase__			)





		@slow
		def  _A							(							self      :		str			):
							lowerCAmelCase						:   Optional[int]							=       TFDebertaModel.from_pretrained('''kamalkraj/deberta-base'''			)
							self.assertIsNotNone(lowerCamelCase__			)



@require_tf
class 						__magic_name__				(  unittest.TestCase      ):





		@unittest.skip(reason='''Model not available yet'''			)
		def  _A							(							self      :		Union[str, Any]			):
							pass





		@slow
		def  _A							(							self      :		Optional[int]			):
							lowerCAmelCase						:   int							=       TFDebertaModel.from_pretrained('''kamalkraj/deberta-base'''			)
							lowerCAmelCase						:   List[Any]							=       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						:   Optional[Any]							=       tf.constant([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]			)
							lowerCAmelCase						:   Tuple							=       model(lowerCamelCase__  ,					attention_mask=lowerCamelCase__			)[0]

							lowerCAmelCase						:   List[str]							=       tf.constant(
							    [
							        [
							            [-0.5_9_8_5_5_8_9_6, -0.8_0_5_5_2_5_6_6, -0.8_4_6_2_1_3_5],
							            [1.4_4_8_4_0_2_5, -0.9_3_4_8_3_7_9_4, -0.8_0_5_9_3_0_8_5],
							            [0.3_1_2_2_7_4_1, 0.0_0_3_1_6_0_5_9, -1.4_1_3_1_3_7_7],
							        ]
							    ]			)
							tf.debugging.assert_near(output[:, 1:4, 1:4]  ,					lowerCamelCase__  ,					atol=1E-4			)


from math import floor, sqrt
def       UpperCAmelCase__							(    __magic_name__  :       int					):


					'''simple docstring'''

					lowerCAmelCase						:   int							=       0.0
					lowerCAmelCase						:   int							=       1.0
					lowerCAmelCase						:   Any							=       int(sqrt(__magic_name__					)					)
					lowerCAmelCase						:   Optional[int]							=       root
					lowerCAmelCase						:   Dict							=       0
					while integer_part != 2 * root:
										lowerCAmelCase						:   str							=       denominator * integer_part - numerator
										lowerCAmelCase						:   Dict							=       (n - numerator**2) / denominator
										lowerCAmelCase						:   str							=       int((root + numerator) / denominator					)
										period += 1
					return period
def       UpperCAmelCase__							(    __magic_name__  :       int = 1_00_00					):


					'''simple docstring'''

					lowerCAmelCase						:   List[str]							=       0
					for i in range(2							,					n + 1					):
										lowerCAmelCase						:   Any							=       sqrt(__magic_name__					)
										if sr - floor(__magic_name__					) != 0 and continuous_fraction_period(__magic_name__					) % 2 == 1:
															count_odd_periods += 1
					return count_odd_periods


if __name__ == "__main__":
		print(f"""{solution(int(input().strip()))}""")
