




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		:    List[Any]	      =		'\\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		:    Any	      =		'\\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		:    List[Any]	      =		''

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

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




class    snake_case__							(datasets.GeneratorBasedBuilder       ):
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_	:							Tuple										= 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')
				SCREAMING_SNAKE_CASE_	:							List[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"""       ),
				]

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





				def 							__UpperCAmelCase							(				self    :					List[str]							) ->					Tuple:
							# 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=__lowerCamelCase ,				homepage=_HOMEPAGE ,				license=_LICENSE ,				citation=_CITATION ,				)





				def 							__UpperCAmelCase							(				self    :					Dict ,				__lowerCamelCase    :					str							) ->					Tuple:
							# 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(__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 							__UpperCAmelCase							(				self    :					Union[str, Any] ,				__lowerCamelCase    :					Any ,				__lowerCamelCase    :					Optional[int]							) ->					Any:
							# 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(__lowerCamelCase ,				encoding="utf-8"							) as f:
										for key, row in enumerate(__lowerCamelCase							):
													a   =      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"],
																}












def       __magic_name__    (			A		: float       ):






			'''simple docstring'''







			return -num if num < 0 else num




def       __magic_name__    (			A		: list[int]       ):






			'''simple docstring'''







			if len(A       ) == 0:
						raise ValueError("abs_min() arg is an empty sequence"       )
			a   =      x[0]
			for i in x:
						if abs_val(A       ) < abs_val(A       ):
									a   =      i
			return j




def       __magic_name__    (			A		: list[int]       ):






			'''simple docstring'''







			if len(A       ) == 0:
						raise ValueError("abs_max() arg is an empty sequence"       )
			a   =      x[0]
			for i in x:
						if abs(A       ) > abs(A       ):
									a   =      i
			return j




def       __magic_name__    (			A		: list[int]       ):






			'''simple docstring'''







			if len(A       ) == 0:
						raise ValueError("abs_max_sort() arg is an empty sequence"       )
			return sorted(A,  key=A       )[-1]




def       __magic_name__    (			):






			'''simple docstring'''







			assert abs_val(0       ) == 0
			assert abs_val(34       ) == 34
			assert abs_val(-100000000000       ) == 100000000000

			a   =      [-3, -1, 2, -11]
			assert abs_max(A       ) == -11
			assert abs_max_sort(A       ) == -11
			assert abs_min(A       ) == -1


if __name__ == "__main__":
		import doctest

		doctest.testmod()

		test_abs_val()
		print(abs_val(-34))  # --> 34






