
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_       :Tuple				  =				"\\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_       :Union[str, 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
a_       :int				  =				""

# TODO: Add the licence for the dataset here if you can find it
a_       :str				  =				""

# 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_       :Optional[int]				  =				{
    "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            =     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            =     [
         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"""					),
     ]

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






     def        lowercase_       (	self  :  Optional[int] )       ->List[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
                   snake_case__							:     Any            =     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"
                   snake_case__							:     Any            =     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=_snake_case,			homepage=_HOMEPAGE,			license=_LICENSE,			citation=_CITATION,			)






     def        lowercase_       (	self  :  List[Any],			_snake_case  :  Dict )       ->List[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
            snake_case__							:     Union[str, Any]            =     _URLS[self.config.name]
            snake_case__							:     Optional[int]            =     dl_manager.download_and_extract(_snake_case )
            return [
                datasets.SplitGenerator(
                    name=datasets.Split.TRAIN,			gen_kwargs={
                        'filepath': os.path.join(_snake_case,			'train.jsonl' ),
                        'split': 'train',
                    },			),
                datasets.SplitGenerator(
                    name=datasets.Split.VALIDATION,			gen_kwargs={
                        'filepath': os.path.join(_snake_case,			'dev.jsonl' ),
                        'split': 'dev',
                    },			),
                datasets.SplitGenerator(
                    name=datasets.Split.TEST,			gen_kwargs={
                        'filepath': os.path.join(_snake_case,			'test.jsonl' ),
                        'split': 'test'
                    },			),
            ]






     def        lowercase_       (	self  :  Optional[Any],			_snake_case  :  Any,			_snake_case  :  Dict )       ->str:
            # 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(_snake_case,			encoding='utf-8' ) as f:
                   for key, row in enumerate(_snake_case ):
                          snake_case__							:     List[str]            =     json.loads(_snake_case )
                          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 	lowercase_							(A			:			int		, A			:			int				):
       return (input_a, input_a).count(0				) % 2
def 	lowercase_							():
       assert xor_gate(0		, 0				) == 0
       assert xor_gate(0		, 1				) == 1
       assert xor_gate(1		, 0				) == 1
       assert xor_gate(1		, 1				) == 0


if __name__ == "__main__":
 print(xor_gate(0, 0))
 print(xor_gate(0, 1))






