

import csv
import json
import os

import datasets


# TODO: Add BibTeX citation
# Find for instance the citation on arxiv or on the dataset repo/website
_UpperCAmelCase    	=			"\\n@InProceedings{huggingface:dataset,\ntitle = {A great new dataset},\nauthor={huggingface, Inc.\n},\nyear={2020}\n}\n"

# TODO: Add description of the dataset here
# You can copy an official description
_UpperCAmelCase    	=			"\\nThis new dataset is designed to solve this great NLP task and is crafted with a lot of care.\n"

# TODO: Add a link to an official homepage for the dataset here
_UpperCAmelCase    	=			""

# TODO: Add the licence for the dataset here if you can find it
_UpperCAmelCase    	=			""

# TODO: Add link to the official dataset URLs here
# The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
_UpperCAmelCase    	=			{
    "first_domain": "https://huggingface.co/great-new-dataset-first_domain.zip",
    "second_domain": "https://huggingface.co/great-new-dataset-second_domain.zip",
}



class        _UpperCAmelCase (   datasets.GeneratorBasedBuilder			):
  '''simple docstring'''







  SCREAMING_SNAKE_CASE							:				Optional[Any]					      =				datasets.Version('''1.1.0'''			)

  # This is an example of a dataset with multiple configurations.
  # If you don't want/need to define several sub-sets in your dataset,
  # just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes.

  # If you need to make complex sub-parts in the datasets with configurable options
  # You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig
  # BUILDER_CONFIG_CLASS = MyBuilderConfig

  # You will be able to load one or the other configurations in the following list with
  # data = datasets.load_dataset('my_dataset', 'first_domain')
  # data = datasets.load_dataset('my_dataset', 'second_domain')
  SCREAMING_SNAKE_CASE							:				int					      =				[
      datasets.BuilderConfig(name='''first_domain'''     ,				version=__lowercase     ,				description='''This part of my dataset covers a first domain'''			),
      datasets.BuilderConfig(name='''second_domain'''     ,				version=__lowercase     ,				description='''This part of my dataset covers a second domain'''			),
  ]

  SCREAMING_SNAKE_CASE							:				Dict					      =				'''first_domain'''  # It's not mandatory to have a default configuration. Just use one if it make sense.







  def 	UpperCamelCase       ( self	: Union[str, Any] ):
        # TODO: This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset
        if self.config.name == "first_domain":  # This is the name of the configuration selected in BUILDER_CONFIGS above
              A    =				datasets.Features(
                  {
                      'sentence': datasets.Value('string' ),
                      'option1': datasets.Value('string' ),
                      'answer': datasets.Value('string' )
                      # These are the features of your dataset like images, labels ...
                  } )
        else:  # This is an example to show how to have different features for "first_domain" and "second_domain"
              A    =				datasets.Features(
                  {
                      'sentence': datasets.Value('string' ),
                      'option2': datasets.Value('string' ),
                      'second_domain_answer': datasets.Value('string' )
                      # These are the features of your dataset like images, labels ...
                  } )
        return datasets.DatasetInfo(
            # This is the description that will appear on the datasets page.
            description=_DESCRIPTION		,						features=UpperCamelCase__		,						homepage=_HOMEPAGE		,						license=_LICENSE		,						citation=_CITATION		,						)







  def 	UpperCamelCase       ( self	: str		,						UpperCamelCase__	: Tuple ):
        # TODO: This method is tasked with downloading/extracting the data and defining the splits depending on the configuration
        # If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name

        # dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLS
        # It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
        # By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
        A    =				_URLS[self.config.name]
        A    =				dl_manager.download_and_extract(UpperCamelCase__ )
        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN		,						gen_kwargs={
                    'filepath': os.path.join(UpperCamelCase__		,						'train.jsonl' ),
                    'split': 'train',
                }		,						),
            datasets.SplitGenerator(
                name=datasets.Split.VALIDATION		,						gen_kwargs={
                    'filepath': os.path.join(UpperCamelCase__		,						'dev.jsonl' ),
                    'split': 'dev',
                }		,						),
            datasets.SplitGenerator(
                name=datasets.Split.TEST		,						gen_kwargs={
                    'filepath': os.path.join(UpperCamelCase__		,						'test.jsonl' ),
                    'split': 'test'
                }		,						),
        ]







  def 	UpperCamelCase       ( self	: List[str]		,						UpperCamelCase__	: Optional[int]		,						UpperCamelCase__	: List[Any] ):
        # TODO: This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
        # The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example.
        with open(UpperCamelCase__		,						encoding='utf-8' ) as f:
              for key, row in enumerate(UpperCamelCase__ ):
                    A    =				json.loads(UpperCamelCase__ )
                    if self.config.name == "first_domain":
                          # Yields examples as (key, example) tuples
                          yield key, {
                              "sentence": data["sentence"],
                              "option1": data["option1"],
                              "answer": "" if split == "test" else data["answer"],
                          }
                    else:
                          yield key, {
                              "sentence": data["sentence"],
                              "option2": data["option2"],
                              "second_domain_answer": "" if split == "test" else data["second_domain_answer"],
                          }






from math import floor, sqrt


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


def 			__UpperCamelCase    (lowerCAmelCase					:  int = 10_000     )     ->  int:
      A    =				0
      for i in range(2,    n + 1     ):
            A    =				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()))}''')



