
"""simple docstring"""

import unittest

from transformers import ErnieMTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


SCREAMING_SNAKE_CASE_			    =  get_tests_dir('''fixtures/spiece.model''')
@require_sentencepiece
@require_tokenizers
class    _UpperCAmelCase							(						SCREAMING_SNAKE_CASE_   ,	unittest.TestCase							):
      __SCREAMING_SNAKE_CASE      :							int												=		ErnieMTokenizer
      __SCREAMING_SNAKE_CASE      :							List[str]												=		False
      __SCREAMING_SNAKE_CASE      :							Any												=		True
      __SCREAMING_SNAKE_CASE      :							Optional[int]												=		False
      __SCREAMING_SNAKE_CASE      :							List[str]												=		False




      def 			a_					(							self							)  ->    List[str]:
          super().setUp()

          # We have a SentencePiece fixture for testing
          UpperCAmelCase  	=    ErnieMTokenizer(lowercase_      ,				unk_token='<unk>'      ,				pad_token='<pad>'							)
          tokenizer.save_pretrained(self.tmpdirname							)




      def 			a_					(							self      ,				lowercase_							)  ->    str:
          UpperCAmelCase  	=    'this is a test'
          UpperCAmelCase  	=    'this is a test'
          return input_text, output_text




      def 			a_					(							self							)  ->    Union[str, Any]:
          UpperCAmelCase  	=    '<pad>'
          UpperCAmelCase  	=    0

          self.assertEqual(self.get_tokenizer()._convert_token_to_id(lowercase_							)      ,				lowercase_							)
          self.assertEqual(self.get_tokenizer()._convert_id_to_token(lowercase_							)      ,				lowercase_							)




      def 			a_					(							self							)  ->    Optional[int]:
          UpperCAmelCase  	=    list(self.get_tokenizer().get_vocab().keys()							)

          self.assertEqual(vocab_keys[0]      ,				'<pad>'							)
          self.assertEqual(vocab_keys[1]      ,				'<unk>'							)
          self.assertEqual(vocab_keys[-1]      ,				'▁eloquent'							)
          self.assertEqual(len(lowercase_							)      ,				3_0_0_0_0							)




      def 			a_					(							self							)  ->    Tuple:
          self.assertEqual(self.get_tokenizer().vocab_size      ,				3_0_0_0_0							)




      def 			a_					(							self							)  ->    Dict:
          if not self.test_rust_tokenizer:
              return

          UpperCAmelCase  	=    self.get_tokenizer()
          UpperCAmelCase  	=    self.get_rust_tokenizer()

          UpperCAmelCase  	=    'I was born in 92000, and this is falsé.'

          UpperCAmelCase  	=    tokenizer.tokenize(lowercase_							)
          UpperCAmelCase  	=    rust_tokenizer.tokenize(lowercase_							)
          self.assertListEqual(lowercase_      ,				lowercase_							)

          UpperCAmelCase  	=    tokenizer.encode(lowercase_      ,				add_special_tokens=lowercase_							)
          UpperCAmelCase  	=    rust_tokenizer.encode(lowercase_      ,				add_special_tokens=lowercase_							)
          self.assertListEqual(lowercase_      ,				lowercase_							)

          UpperCAmelCase  	=    self.get_rust_tokenizer()
          UpperCAmelCase  	=    tokenizer.encode(lowercase_							)
          UpperCAmelCase  	=    rust_tokenizer.encode(lowercase_							)
          self.assertListEqual(lowercase_      ,				lowercase_							)




      def 			a_					(							self							)  ->    Optional[Any]:
          UpperCAmelCase  	=    ErnieMTokenizer(lowercase_      ,				do_lower_case=lowercase_      ,				unk_token='<unk>'      ,				pad_token='<pad>'							)

          UpperCAmelCase  	=    tokenizer.tokenize('This is a test'							)
          self.assertListEqual(lowercase_      ,				['▁this', '▁is', '▁a', '▁test']							)

          self.assertListEqual(tokenizer.convert_tokens_to_ids(lowercase_							)      ,				[4_8, 2_5, 2_1, 1_2_8_9]							)

          UpperCAmelCase  	=    tokenizer.tokenize('I was born in 92000, and this is falsé.'							)
          # ErnieMTokenizer(paddlenlp implementation) outputs '9' instead of '_9' so to mimic that '_9' is changed to '9'
          self.assertListEqual(
              lowercase_      ,				['▁i', '▁was', '▁born', '▁in', '9', '2000', ',', '▁and', '▁this', '▁is', '▁fal', 's', 'é', '.']							)
          UpperCAmelCase  	=    tokenizer.convert_tokens_to_ids(lowercase_							)
          self.assertListEqual(lowercase_      ,				[3_1, 2_3, 3_8_6, 1_9, 5_1_8, 3_0_5_0, 1_5, 1_7, 4_8, 2_5, 8_2_5_6, 1_8, 1, 9]							)

          UpperCAmelCase  	=    tokenizer.convert_ids_to_tokens(lowercase_							)
          self.assertListEqual(
              lowercase_      ,				['▁i', '▁was', '▁born', '▁in', '9', '2000', ',', '▁and', '▁this', '▁is', '▁fal', 's', '<unk>', '.']      ,				)




      def 			a_					(							self							)  ->    Any:
          UpperCAmelCase  	=    ErnieMTokenizer(lowercase_      ,				unk_token='<unk>'      ,				pad_token='<pad>'							)

          UpperCAmelCase  	=    tokenizer.encode('sequence builders'							)
          UpperCAmelCase  	=    tokenizer.encode('multi-sequence build'							)

          UpperCAmelCase  	=    tokenizer.build_inputs_with_special_tokens(lowercase_							)
          UpperCAmelCase  	=    tokenizer.build_inputs_with_special_tokens(lowercase_      ,				lowercase_							)

          assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]
          assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + [
              tokenizer.sep_token_id
          ] + text_a + [tokenizer.sep_token_id]




      @slow
      def 			a_					(							self							)  ->    int:
          # fmt: off
          UpperCAmelCase  	=    {'input_ids': [[0, 1_1_0_6_2, 8_2_7_7_2, 7, 1_5, 8_2_7_7_2, 5_3_8, 5_1_5_2_9, 2_3_7, 1_7_1_9_8, 1_2_9_0, 2_0_6, 9, 2_1_5_1_7_5, 1_3_1_4, 1_3_6, 1_7_1_9_8, 1_2_9_0, 2_0_6, 9, 5_6_3_5_9, 4_2, 1_2_2_0_0_9, 9, 1_6_4_6_6, 1_6, 8_7_3_4_4, 4_5_3_7, 9, 4_7_1_7, 7_8_3_8_1, 6, 1_5_9_9_5_8, 7, 1_5, 2_4_4_8_0, 6_1_8, 4, 5_2_7, 2_2_6_9_3, 9, 3_0_4, 4, 2_7_7_7, 2_4_4_8_0, 9_8_7_4, 4, 4_3_5_2_3, 5_9_4, 4, 8_0_3, 1_8_3_9_2, 3_3_1_8_9, 1_8, 4, 4_3_5_2_3, 2_4_4_4_7, 5, 5, 5, 1_6, 1_0_0, 2_4_9_5_5, 8_3_6_5_8, 9_6_2_6, 1_4_4_0_5_7, 1_5, 8_3_9, 2_2_3_3_5, 1_6, 1_3_6, 2_4_9_5_5, 8_3_6_5_8, 8_3_4_7_9, 1_5, 3_9_1_0_2, 7_2_4, 1_6, 6_7_8, 6_4_5, 6_4_6_0, 1_3_2_8, 4_5_8_9, 4_2, 1_2_2_0_0_9, 1_1_5_7_7_4, 2_3, 3_5_5_9, 1_3_2_8, 4_6_8_7_6, 7, 1_3_6, 5_3_8_9_4, 1_9_4_0, 4_2_2_2_7, 4_1_1_5_9, 1_7_7_2_1, 8_2_3, 4_2_5, 4, 2_7_5_1_2, 9_8_7_2_2, 2_0_6, 1_3_6, 5_5_3_1, 4_9_7_0, 9_1_9, 1_7_3_3_6, 5, 2], [0, 2_0_0_8_0, 6_1_8, 8_3, 8_2_7_7_5, 4_7, 4_7_9, 9, 1_5_1_7, 7_3, 5_3_8_9_4, 3_3_3, 8_0_5_8_1, 1_1_0_1_1_7, 1_8_8_1_1, 5_2_5_6, 1_2_9_5, 5_1, 1_5_2_5_2_6, 2_9_7, 7_9_8_6, 3_9_0, 1_2_4_4_1_6, 5_3_8, 3_5_4_3_1, 2_1_4, 9_8, 1_5_0_4_4, 2_5_7_3_7, 1_3_6, 7_1_0_8, 4_3_7_0_1, 2_3, 7_5_6, 1_3_5_3_5_5, 7, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 5_8_1, 6_3_7_7_3, 1_1_9_4_5_5, 6, 1_4_7_7_9_7, 8_8_2_0_3, 7, 6_4_5, 7_0, 2_1, 3_2_8_5, 1_0_2_6_9, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}
          # fmt: on

          self.tokenizer_integration_test_util(
              expected_encoding=lowercase_      ,				model_name='susnato/ernie-m-base_pytorch'      ,				sequences=[
                  'Transformers (formerly known as pytorch-transformers and pytorch-pretrained-bert) provides '
                  'general-purpose architectures (BERT, GPT-2, RoBERTa, XLM, DistilBert, XLNet...) for Natural '
                  'Language Understanding (NLU) and Natural Language Generation (NLG) with over32+ pretrained '
                  'models in100+ languages and deep interoperability between Jax, PyTorch and TensorFlow.',
                  'BERT is designed to pre-train deep bidirectional representations from unlabeled text by jointly '
                  'conditioning on both left and right context in all layers.',
                  'The quick brown fox jumps over the lazy dog.',
              ]      ,				)





"""simple docstring"""

from math import floor, sqrt




def 			lowercase__  (    lowerCAmelCase :    int       )       ->					int:






    """simple docstring"""




    UpperCAmelCase  	=    0.0
    UpperCAmelCase  	=    1.0
    UpperCAmelCase  	=    int(sqrt(lowerCAmelCase       )       )
    UpperCAmelCase  	=    root
    UpperCAmelCase  	=    0
    while integer_part != 2 * root:
        UpperCAmelCase  	=    denominator * integer_part - numerator
        UpperCAmelCase  	=    (n - numerator**2) / denominator
        UpperCAmelCase  	=    int((root + numerator) / denominator       )
        period += 1
    return period




def 			lowercase__  (    lowerCAmelCase :    int = 10_000       )       ->					int:






    """simple docstring"""




    UpperCAmelCase  	=    0
    for i in range(2       , n + 1       ):
        UpperCAmelCase  	=    sqrt(lowerCAmelCase       )
        if sr - floor(lowerCAmelCase       ) != 0 and continuous_fraction_period(lowerCAmelCase       ) % 2 == 1:
            count_odd_periods += 1
    return count_odd_periods


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



