
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
__snake_case					:							Dict           =							'\\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
__snake_case					:							Optional[int]           =							'\\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
__snake_case					:							Union[str, Any]           =							''

# TODO: Add the licence for the dataset here if you can find it
__snake_case					:							Optional[Any]           =							''

# 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)
__snake_case					:							Union[str, Any]           =							{
    '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'''



						__a     :    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')
						__a     :    Any												=			[
						    datasets.BuilderConfig(name="""first_domain"""						,			version=UpperCAmelCase__						,			description="""This part of my dataset covers a first domain"""),
						    datasets.BuilderConfig(name="""second_domain"""						,			version=UpperCAmelCase__						,			description="""This part of my dataset covers a second domain"""),
						]

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


						def    A__       (  self	)  ->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__		:Tuple 		=    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__		:Optional[int] 		=    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	)  ->int:
									# 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__		:Dict 		=    _URLS[self.config.name]
									UpperCAmelCase__		:str 		=    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__		:Optional[int] 		=    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"],
																		}







from __future__ import annotations




def 		A						(  SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE     ,						):




			"""simple docstring"""


			if not all((nth_term, start_term_a, common_ratio_r)							):
						return []
			UpperCAmelCase__		:list[float | int] 		=    []
			UpperCAmelCase__		:str 		=    1
			UpperCAmelCase__		:Any 		=    common_ratio_r
			for _ in range(int(SCREAMING_SNAKE_CASE							)							):
						if not series:
									series.append(SCREAMING_SNAKE_CASE							)
						else:
									power += 1
									series.append(float(start_term_a * multiple							)							)
									UpperCAmelCase__		:str 		=    pow(float(SCREAMING_SNAKE_CASE							)     ,						SCREAMING_SNAKE_CASE							)
			return series


if __name__ == "__main__":
						import doctest

						doctest.testmod()

						__snake_case					:							Optional[int]           =							float(input('Enter the last number (n term) of the Geometric Series'))
						__snake_case					:							List[str]           =							float(input('Enter the starting term (a) of the Geometric Series'))
						__snake_case					:							Optional[int]           =							float(
						    input('Enter the common ratio between two terms (r) of the Geometric Series')
						)
						print('Formula of Geometric Series => a + ar + ar^2 ... +ar^n')
						print(geometric_series(nth_term, start_term_a, common_ratio_r))





