


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        UpperCAmelCase__			(   datasets.GeneratorBasedBuilder						):



    """simple docstring"""






    UpperCAmelCase__		:						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')
    UpperCAmelCase__		:						Tuple    =    [
        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__		:						Optional[Any]    =    "first_domain"  # It's not mandatory to have a default configuration. Just use one if it make sense.






    def _a   (     self       )    ->			Dict:
         # 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
              __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=A_   ,  homepage=_HOMEPAGE   ,  license=_LICENSE   ,  citation=_CITATION   ,  )






    def _a   (     self   ,  A_       )    ->			Union[str, 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
         __UpperCamelCase       				=_URLS[self.config.name]
         __UpperCamelCase       				=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   ,  A_   ,  A_       )    ->			Union[str, 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(A_   ,  encoding='utf-8'       ) as f:
              for key, row in enumerate(A_       ):
                   __UpperCamelCase       				=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"],
                        }









def 		_UpperCAmelCase							(     SCREAMING_SNAKE_CASE__     :   list     ,	SCREAMING_SNAKE_CASE__     :   int     ,	SCREAMING_SNAKE_CASE__     :   int     ,	SCREAMING_SNAKE_CASE__     :   int							):
     __UpperCamelCase       				=int((lower_bound + upper_bound) // 2							)
     if array[r] == value:
          return r
     if lower_bound >= upper_bound:
          return -1
     if array[r] < value:
          return binary_search(SCREAMING_SNAKE_CASE__     ,	r + 1     ,	SCREAMING_SNAKE_CASE__     ,	SCREAMING_SNAKE_CASE__							)
     else:
          return binary_search(SCREAMING_SNAKE_CASE__     ,	SCREAMING_SNAKE_CASE__     ,	r - 1     ,	SCREAMING_SNAKE_CASE__							)

def 		_UpperCAmelCase							(     SCREAMING_SNAKE_CASE__     :   int     ,	SCREAMING_SNAKE_CASE__     :   list							):
     __UpperCamelCase       				=0
     if matrix[index][0] == value:
          return [index, 0]
     while index < len(SCREAMING_SNAKE_CASE__							) and matrix[index][0] < value:
          __UpperCamelCase       				=binary_search(matrix[index]     ,	0     ,	len(matrix[index]							) - 1     ,	SCREAMING_SNAKE_CASE__							)
          if r != -1:
               return [index, r]
          index += 1
     return [-1, -1]


if __name__ == "__main__":
   import doctest

   doctest.testmod()





