




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_keras_nlp_available,
    is_tensorflow_text_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCamelCase								=     {
    'configuration_gpt2': ['GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GPT2Config', 'GPT2OnnxConfig'],
    'tokenization_gpt2': ['GPT2Tokenizer'],
}

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCamelCase								=     ['GPT2TokenizerFast']

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCamelCase								=     [
         'GPT2_PRETRAINED_MODEL_ARCHIVE_LIST',
         'GPT2DoubleHeadsModel',
         'GPT2ForQuestionAnswering',
         'GPT2ForSequenceClassification',
         'GPT2ForTokenClassification',
         'GPT2LMHeadModel',
         'GPT2Model',
         'GPT2PreTrainedModel',
         'load_tf_weights_in_gpt2',
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCamelCase								=     [
         'TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST',
         'TFGPT2DoubleHeadsModel',
         'TFGPT2ForSequenceClassification',
         'TFGPT2LMHeadModel',
         'TFGPT2MainLayer',
         'TFGPT2Model',
         'TFGPT2PreTrainedModel',
     ]

try:
     if not is_keras_nlp_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCamelCase								=     ['TFGPT2Tokenizer']

try:
     if not is_flax_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCamelCase								=     ['FlaxGPT2LMHeadModel', 'FlaxGPT2Model', 'FlaxGPT2PreTrainedModel']

if TYPE_CHECKING:
     from .configuration_gpta import GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTaConfig, GPTaOnnxConfig
     from .tokenization_gpta import GPTaTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_gpta_fast import GPTaTokenizerFast

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_gpta import (
              GPT2_PRETRAINED_MODEL_ARCHIVE_LIST,
              GPTaDoubleHeadsModel,
              GPTaForQuestionAnswering,
              GPTaForSequenceClassification,
              GPTaForTokenClassification,
              GPTaLMHeadModel,
              GPTaModel,
              GPTaPreTrainedModel,
              load_tf_weights_in_gpta,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_gpta import (
              TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST,
              TFGPTaDoubleHeadsModel,
              TFGPTaForSequenceClassification,
              TFGPTaLMHeadModel,
              TFGPTaMainLayer,
              TFGPTaModel,
              TFGPTaPreTrainedModel,
          )

     try:
          if not is_keras_nlp_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_gpta_tf import TFGPTaTokenizer

     try:
          if not is_flax_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_flax_gpta import FlaxGPTaLMHeadModel, FlaxGPTaModel, FlaxGPTaPreTrainedModel

else:
     import sys

     UpperCamelCase								=     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)






from math import floor, sqrt



def 							_A   (						lowerCAmelCase_     :					int			):
     """simple docstring"""

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



def 							_A   (						lowerCAmelCase_     :					int = 1_0000			):
     """simple docstring"""

     lowerCAmelCase__		= 0
     for i in range(2   ,				n + 1			):
          lowerCAmelCase__		= 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()))}""")
