code: 
  """simple docstring"""
  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
  __magic_name__		       =				'''\
  @InProceedings{huggingface:dataset,
  title = {A great new dataset},
  author={huggingface, Inc.
  },
  year={2020}
  }
  '''
  
  # TODO: Add description of the dataset here
  # You can copy an official description
  __magic_name__		       =				'''\
  This new dataset is designed to solve this great NLP task and is crafted with a lot of care.
  '''
  
  # TODO: Add a link to an official homepage for the dataset here
  __magic_name__		       =				''''''
  
  # TODO: Add the licence for the dataset here if you can find it
  __magic_name__		       =				''''''
  
  # 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)
  __magic_name__		       =				{
      '''first_domain''': '''https://huggingface.co/great-new-dataset-first_domain.zip''',
      '''second_domain''': '''https://huggingface.co/great-new-dataset-second_domain.zip''',
  }
  
  
  class    _lowerCAmelCase						(						datasets.GeneratorBasedBuilder						):
  							lowercase_     :     List[str]       =							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')
  							lowercase_     :     Tuple       =							[
  							    datasets.BuilderConfig(name='''first_domain'''    ,       version=lowerCamelCase    ,       description='''This part of my dataset covers a first domain'''						),
  							    datasets.BuilderConfig(name='''second_domain'''    ,       version=lowerCamelCase    ,       description='''This part of my dataset covers a second domain'''						),
  							]
  
  							lowercase_     :     int       =							'''first_domain'''  # It's not mandatory to have a default configuration. Just use one if it make sense.
  
  
  
  							def       _a   (      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
  									_UpperCAmelCase =						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"
  									_UpperCAmelCase =						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=a_ ,						homepage=_HOMEPAGE ,						license=_LICENSE ,						citation=_CITATION ,						)
  
  
  
  							def       _a   (      self ,						a_   )							->							Any:
  								# 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
  								_UpperCAmelCase =						_URLS[self.config.name]
  								_UpperCAmelCase =						dl_manager.download_and_extract(a_   )
  								return [
  								    datasets.SplitGenerator(
  								        name=datasets.Split.TRAIN ,						gen_kwargs={
  								            "filepath": os.path.join(a_ ,						"train.jsonl"   ),
  								            "split": "train",
  								        } ,						),
  								    datasets.SplitGenerator(
  								        name=datasets.Split.VALIDATION ,						gen_kwargs={
  								            "filepath": os.path.join(a_ ,						"dev.jsonl"   ),
  								            "split": "dev",
  								        } ,						),
  								    datasets.SplitGenerator(
  								        name=datasets.Split.TEST ,						gen_kwargs={
  								            "filepath": os.path.join(a_ ,						"test.jsonl"   ),
  								            "split": "test"
  								        } ,						),
  								]
  
  
  
  							def       _a   (      self ,						a_ ,						a_   )							->							Tuple:
  								# 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(a_ ,						encoding="utf-8"   ) as f:
  									for key, row in enumerate(a_   ):
  										_UpperCAmelCase =						json.loads(a_   )
  										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"],
  											}
  
  
  
  
  
style_context: 
  """simple docstring"""
  def     __lowerCamelCase  (		UpperCamelCase__ = 400_0000      ):
  
  
  	"""simple docstring"""
  
  
  
  
  
  
  	_UpperCAmelCase =						1
  	_UpperCAmelCase =						2
  	_UpperCAmelCase =						0
  	while j <= n:
  		if j % 2 == 0:
  			total += j
  		_UpperCAmelCase ,      _UpperCAmelCase =						j, i + j
  
  	return total
  
  
  if __name__ == "__main__":
  			print(f'''{solution() = }''')
  
  
  
  
  