





from collections import defaultdict
from math import gcd






def    _A	(   lowerCamelCase = 150_0000 ):
   a__						:    defaultdict   		=	defaultdict(lowerCamelCase )
   a__						:    Optional[Any]   		=	2
   while 2 * euclid_m * (euclid_m + 1) <= limit:
      for euclid_n in range((euclid_m % 2) + 1				,			lowerCamelCase				,			2 ):
         if gcd(lowerCamelCase				,			lowerCamelCase ) > 1:
            continue
         a__						:    Any   		=	2 * euclid_m * (euclid_m + euclid_n)
         for perimeter in range(lowerCamelCase				,			limit + 1				,			lowerCamelCase ):
            frequencies[perimeter] += 1
      euclid_m += 1
   return sum(1 for frequency in frequencies.values() if frequency == 1 )


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








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 	__lowerCAmelCase      :





   def __init__(			self       ,      snake_case       ,      snake_case=13       ,      snake_case=7       ,      snake_case=True       ,      snake_case=True       ,      snake_case=True       ,      snake_case=True       ,      snake_case=99       ,      snake_case=32       ,      snake_case=2       ,      snake_case=4       ,      snake_case=37       ,      snake_case="gelu"       ,      snake_case=0.1       ,      snake_case=0.1       ,      snake_case=512       ,      snake_case=16       ,      snake_case=2       ,      snake_case=0.02       ,      snake_case=False       ,      snake_case=True       ,      snake_case="None"       ,      snake_case=3       ,      snake_case=4       ,      snake_case=None       ,      )					->	int:
      """simple docstring"""





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





   def 						_snake_case			(			self   )					->	Tuple:
      """simple docstring"""





      a__						:    Union[str, Any]   		=	ids_tensor([self.batch_size, self.seq_length]       ,      self.vocab_size   )

      a__						:    Union[str, Any]   		=	None
      if self.use_input_mask:
         a__						:    Any   		=	random_attention_mask([self.batch_size, self.seq_length]   )

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

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

      a__						:    List[str]   		=	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=snake_case       ,      )

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





   def 						_snake_case			(			self       ,      snake_case       ,      snake_case       ,      snake_case       ,      snake_case       ,      snake_case       ,      snake_case       ,      snake_case   )					->	List[Any]:
      """simple docstring"""





      a__						:    Dict   		=	TFDebertaVaModel(config=snake_case   )
      a__						:    Union[str, Any]   		=	{"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}

      a__						:    Union[str, Any]   		=	[input_ids, input_mask]
      a__						:    Any   		=	model(snake_case   )

      a__						:    Optional[Any]   		=	model(snake_case   )

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





   def 						_snake_case			(			self       ,      snake_case       ,      snake_case       ,      snake_case       ,      snake_case       ,      snake_case       ,      snake_case       ,      snake_case   )					->	Any:
      """simple docstring"""





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





   def 						_snake_case			(			self       ,      snake_case       ,      snake_case       ,      snake_case       ,      snake_case       ,      snake_case       ,      snake_case       ,      snake_case   )					->	Optional[Any]:
      """simple docstring"""





      a__						:    List[Any]   		=	self.num_labels
      a__						:    int   		=	TFDebertaVaForSequenceClassification(config=snake_case   )
      a__						:    Optional[Any]   		=	{
          "input_ids": input_ids,
          "attention_mask": input_mask,
          "token_type_ids": token_type_ids,
      }

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





   def 						_snake_case			(			self       ,      snake_case       ,      snake_case       ,      snake_case       ,      snake_case       ,      snake_case       ,      snake_case       ,      snake_case   )					->	Dict:
      """simple docstring"""





      a__						:    Dict   		=	self.num_labels
      a__						:    str   		=	TFDebertaVaForTokenClassification(config=snake_case   )
      a__						:    Dict   		=	{
          "input_ids": input_ids,
          "attention_mask": input_mask,
          "token_type_ids": token_type_ids,
      }
      a__						:    Tuple   		=	model(snake_case   )
      self.parent.assertEqual(result.logits.shape       ,      (self.batch_size, self.seq_length, self.num_labels)   )





   def 						_snake_case			(			self       ,      snake_case       ,      snake_case       ,      snake_case       ,      snake_case       ,      snake_case       ,      snake_case       ,      snake_case   )					->	List[str]:
      """simple docstring"""





      a__						:    Union[str, Any]   		=	TFDebertaVaForQuestionAnswering(config=snake_case   )
      a__						:    Optional[int]   		=	{
          "input_ids": input_ids,
          "attention_mask": input_mask,
          "token_type_ids": token_type_ids,
      }

      a__						:    List[Any]   		=	model(snake_case   )
      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 						_snake_case			(			self   )					->	List[str]:
      """simple docstring"""





      a__						:    List[str]   		=	self.prepare_config_and_inputs()
      (
          (
          a__
      )		,  (
          a__
      )		,  (
          a__
      )		,  (
          a__
      )		,  (
          a__
      )		,  (
          a__
      )		,  (
          a__
      )		,  
      )						:    int   		=	config_and_inputs
      a__						:    Any   		=	{"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
      return config, inputs_dict







@require_tf
class 	__lowerCAmelCase      (       _UpperCamelCase		,_UpperCamelCase		,unittest.TestCase	):
   _UpperCamelCase			:    int  		=					(
       (
           TFDebertaVaModel,
           TFDebertaVaForMaskedLM,
           TFDebertaVaForQuestionAnswering,
           TFDebertaVaForSequenceClassification,
           TFDebertaVaForTokenClassification,
       )
       if is_tf_available()
       else ()
   )
   _UpperCamelCase			:    List[str]  		=					(
       {
           """feature-extraction""": TFDebertaVaModel,
           """fill-mask""": TFDebertaVaForMaskedLM,
           """question-answering""": TFDebertaVaForQuestionAnswering,
           """text-classification""": TFDebertaVaForSequenceClassification,
           """token-classification""": TFDebertaVaForTokenClassification,
           """zero-shot""": TFDebertaVaForSequenceClassification,
       }
       if is_tf_available()
       else {}
   )

   _UpperCamelCase			:    str  		=					False
   _UpperCamelCase			:    List[str]  		=					False





   def 						_snake_case			(			self   )					->	Dict:
      """simple docstring"""





      a__						:    Any   		=	TFDebertaVaModelTester(self   )
      a__						:    int   		=	ConfigTester(self       ,      config_class=snake_case       ,      hidden_size=37   )





   def 						_snake_case			(			self   )					->	Union[str, Any]:
      """simple docstring"""





      self.config_tester.run_common_tests()





   def 						_snake_case			(			self   )					->	Any:
      """simple docstring"""





      a__						:    Dict   		=	self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*snake_case   )





   def 						_snake_case			(			self   )					->	str:
      """simple docstring"""





      a__						:    List[str]   		=	self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_masked_lm(*snake_case   )





   def 						_snake_case			(			self   )					->	List[Any]:
      """simple docstring"""





      a__						:    Tuple   		=	self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_question_answering(*snake_case   )





   def 						_snake_case			(			self   )					->	List[Any]:
      """simple docstring"""





      a__						:    Any   		=	self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_sequence_classification(*snake_case   )





   def 						_snake_case			(			self   )					->	List[Any]:
      """simple docstring"""





      a__						:    int   		=	self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_token_classification(*snake_case   )





   @slow
   def 						_snake_case			(			self   )					->	List[str]:
      """simple docstring"""





      a__						:    Tuple   		=	TFDebertaVaModel.from_pretrained("kamalkraj/deberta-v2-xlarge"   )
      self.assertIsNotNone(snake_case   )







@require_tf
class 	__lowerCAmelCase      (       unittest.TestCase	):





   @unittest.skip(reason="Model not available yet"   )
   def 						_snake_case			(			self   )					->	Dict:
      """simple docstring"""





      pass





   @slow
   def 						_snake_case			(			self   )					->	List[Any]:
      """simple docstring"""





      a__						:    Optional[Any]   		=	TFDebertaVaModel.from_pretrained("kamalkraj/deberta-v2-xlarge"   )
      a__						:    Any   		=	tf.constant([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]]   )
      a__						:    List[str]   		=	tf.constant([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]   )
      a__						:    Tuple   		=	model(snake_case       ,      attention_mask=snake_case   )[0]

      a__						:    List[Any]   		=	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]       ,      snake_case       ,      atol=1E-4   )

