






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
__A          = '\\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
__A          = '\\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
__A          = ''

# TODO: Add the licence for the dataset here if you can find it
__A          = ''

# 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)
__A          = {
    'first_domain': 'https://huggingface.co/great-new-dataset-first_domain.zip',
    'second_domain': 'https://huggingface.co/great-new-dataset-second_domain.zip',
}





class   SCREAMING_SNAKE_CASE (       datasets.GeneratorBasedBuilder    ):





							"""simple docstring"""

							A_       =    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_       =    [
							    datasets.BuilderConfig(name="first_domain"     ,		version=snake_case     ,		description="This part of my dataset covers a first domain"    ),
							    datasets.BuilderConfig(name="second_domain"     ,		version=snake_case     ,		description="This part of my dataset covers a second domain"    ),
							]

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

							def 					__A       (     self:      Optional[int]      )			-> 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=__A						,   homepage=_HOMEPAGE						,   license=_LICENSE						,   citation=_CITATION						,   )

							def 					__A       (     self:      List[Any]						,   __A:      str      )			-> str:
												# 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(__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:      Any						,   __A:      List[Any]						,   __A:      Dict      )			-> Dict:
												# 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      ):
																						_A									=       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

from collections.abc import Generator




def    __A    (   _lowercase       ):

					'''simple docstring'''







					if not isinstance(_lowercase    ,	_lowercase       ) or n < 1:
										raise Exception('''Sequence only defined for positive integers'''       )

					yield n
					while n != 1:
										if n % 2 == 0:
															n //= 2
										else:
															_A									=       3 * n + 1
										yield n




def    __A    (   ):

					'''simple docstring'''







					_A									=       43
					_A									=       tuple(collatz_sequence(_lowercase       )       )
					print(_lowercase       )
					print(f"""Collatz sequence from {n} took {len(_lowercase       )} steps."""       )


if __name__ == "__main__":
				main()



