





"""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
lowerCAmelCase__        =      '''\\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
lowerCAmelCase__        =      '''\\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
lowerCAmelCase__        =      ''''''

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

# 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)
lowerCAmelCase__        =      {
    '''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			):
						UpperCAmelCase_			=      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')
						UpperCAmelCase_			=      [
						    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"			),
						]

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


						def      snake_case_ (self		)			-> List[Any]:






								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=lowerCamelCase_ ,				homepage=_HOMEPAGE ,				license=_LICENSE ,				citation=_CITATION ,				)


						def      snake_case_ (self ,				__a		)			-> Dict:
								UpperCamelCase =						_URLS[self.config.name]
								UpperCamelCase =						dl_manager.download_and_extract(lowerCamelCase_		)
								return [
								    datasets.SplitGenerator(
								        name=datasets.Split.TRAIN ,				gen_kwargs={
								            "filepath": os.path.join(lowerCamelCase_ ,				"train.jsonl"		),
								            "split": "train",
								        } ,				),
								    datasets.SplitGenerator(
								        name=datasets.Split.VALIDATION ,				gen_kwargs={
								            "filepath": os.path.join(lowerCamelCase_ ,				"dev.jsonl"		),
								            "split": "dev",
								        } ,				),
								    datasets.SplitGenerator(
								        name=datasets.Split.TEST ,				gen_kwargs={
								            "filepath": os.path.join(lowerCamelCase_ ,				"test.jsonl"		),
								            "split": "test"
								        } ,				),
								]




						def      snake_case_ (self ,				__a ,				__a		)			-> Tuple:






								with open(lowerCamelCase_ ,				encoding="utf-8"		) as f:
										for key, row in enumerate(lowerCamelCase_		):
												UpperCamelCase =						json.loads(lowerCamelCase_		)
												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"""







from __future__ import annotations

import random
from collections.abc import Iterable
from typing import Any, Generic, TypeVar

SCREAMING_SNAKE_CASE						=    TypeVar('T', bound=bool)



class 			_lowerCamelCase      (Generic[T]  ):







				def __init__(			self     :							Any      ,       lowerCamelCase_     :							T    ):

					"""simple docstring"""






					_lowercase			:     T      					=				value
					_lowercase			:     RandomizedHeapNode[T] | None      					=				None
					_lowercase			:     RandomizedHeapNode[T] | None      					=				None







				@property
				def 				__UpperCAmelCase       (			self     :							int    ):

					"""simple docstring"""






					return self._value







				@staticmethod
				def 				__UpperCAmelCase       (			lowerCamelCase_     :							RandomizedHeapNode[T] | None      ,       lowerCamelCase_     :							RandomizedHeapNode[T] | None    ):

					"""simple docstring"""






					if not roota:
						return roota

					if not roota:
						return roota

					if roota.value > roota.value:
						_lowercase					,   _lowercase			:     List[Any]      					=				roota, roota

					if random.choice([True, False]    ):
						_lowercase					,   _lowercase			:     Optional[int]      					=				roota.right, roota.left

					_lowercase			:     Tuple      					=				RandomizedHeapNode.merge(roota.left      ,       lowerCamelCase_    )

					return roota



class 			_lowerCamelCase      (Generic[T]  ):







				def __init__(			self     :							Optional[Any]      ,       lowerCamelCase_     :							Iterable[T] | None = ()    ):

					"""simple docstring"""






					_lowercase			:     RandomizedHeapNode[T] | None      					=				None

					if data:
						for item in data:
							self.insert(lowerCamelCase_    )







				def 				__UpperCAmelCase       (			self     :							Tuple      ,       lowerCamelCase_     :							T    ):

					"""simple docstring"""






					_lowercase			:     str      					=				RandomizedHeapNode.merge(self._root      ,       RandomizedHeapNode(lowerCamelCase_    )    )







				def 				__UpperCAmelCase       (			self     :							int    ):

					"""simple docstring"""






					_lowercase			:     Any      					=				self.top()

					if self._root is None:
						return None

					_lowercase			:     List[Any]      					=				RandomizedHeapNode.merge(self._root.left      ,       self._root.right    )

					return result







				def 				__UpperCAmelCase       (			self     :							List[Any]    ):

					"""simple docstring"""






					if not self._root:
						raise IndexError('Can\'t get top element for the empty heap.'    )
					return self._root.value







				def 				__UpperCAmelCase       (			self     :							Any    ):

					"""simple docstring"""






					_lowercase			:     List[Any]      					=				None







				def 				__UpperCAmelCase       (			self     :							Union[str, Any]    ):

					"""simple docstring"""






					_lowercase			:     List[Any]      					=				[]
					while self:
						result.append(self.pop()    )

					return result







				def __bool__(			self     :							List[str]    ):

					"""simple docstring"""






					return self._root is not None


if __name__ == "__main__":
						import doctest

						doctest.testmod()



