
"""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"],
											}






"""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() = }''')




