


"""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
snake_case__		:  Optional[int]     					=					'''\
@InProceedings{huggingface:dataset,
title = {A great new dataset},
author={huggingface, Inc.
},
year={2020}
}
'''

# TODO: Add description of the dataset here
# You can copy an official description
snake_case__		:  List[str]     					=					'''\
This new dataset is designed to solve this great NLP task and is crafted with a lot of care.
'''

# TODO: Add a link to an official homepage for the dataset here
snake_case__		:  int     					=					''''''

# 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__		:  Tuple     					=					{
    '''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      ):
     __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=a__				,       description='''This part of my dataset covers a first domain'''      ),
         datasets.BuilderConfig(name='''second_domain'''				,       version=a__				,       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   lowerCamelCase__  (						self	:       Union[str, 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
                 lowerCAmelCase       :						int							= 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"
                 lowerCAmelCase       :						Union[str, 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=UpperCamelCase_       ,					homepage=_HOMEPAGE       ,					license=_LICENSE       ,					citation=_CITATION       ,					)






     def   lowerCamelCase__  (						self	:       str       ,					UpperCamelCase_	:       Optional[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
           lowerCAmelCase       :						List[str]							= _URLS[self.config.name]
           lowerCAmelCase       :						Optional[int]							= dl_manager.download_and_extract(UpperCamelCase_					)
           return [
               datasets.SplitGenerator(
                   name=datasets.Split.TRAIN       ,					gen_kwargs={
                       '''filepath''': os.path.join(UpperCamelCase_       ,					'''train.jsonl'''					),
                       '''split''': '''train''',
                   }       ,					),
               datasets.SplitGenerator(
                   name=datasets.Split.VALIDATION       ,					gen_kwargs={
                       '''filepath''': os.path.join(UpperCamelCase_       ,					'''dev.jsonl'''					),
                       '''split''': '''dev''',
                   }       ,					),
               datasets.SplitGenerator(
                   name=datasets.Split.TEST       ,					gen_kwargs={
                       '''filepath''': os.path.join(UpperCamelCase_       ,					'''test.jsonl'''					),
                       '''split''': '''test'''
                   }       ,					),
           ]






     def   lowerCamelCase__  (						self	:       List[Any]       ,					UpperCamelCase_	:       Any       ,					UpperCamelCase_	:       Optional[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(UpperCamelCase_       ,					encoding='''utf-8'''					) as f:
                 for key, row in enumerate(UpperCamelCase_					):
                       lowerCAmelCase       :						List[Any]							= json.loads(UpperCamelCase_					)
                       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"""





def   _snake_case    (    _snake_case					:      int      ):
      if not isinstance(_snake_case			, _snake_case      ):
            lowerCAmelCase       :						int							= f'''Input value of [number={number}] must be an integer'''
            raise TypeError(_snake_case      )
      if number < 0 or number % 2 == 1:
            return False
      lowerCAmelCase       :						int							= int(number**0.5      )
      return number == number_sqrt * (number_sqrt + 1)


if __name__ == "__main__":
 import doctest

 doctest.testmod()


