"""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
_SCREAMING_SNAKE_CASE	:		Dict   =   "\\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
_SCREAMING_SNAKE_CASE	:		int   =   "\\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
_SCREAMING_SNAKE_CASE	:		List[str]   =   ""

# TODO: Add the licence for the dataset here if you can find it
_SCREAMING_SNAKE_CASE	:		List[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)
_SCREAMING_SNAKE_CASE	:		str   =   {
    "first_domain": "https://huggingface.co/great-new-dataset-first_domain.zip",
    "second_domain": "https://huggingface.co/great-new-dataset-second_domain.zip",
}






class      _SCREAMING_SNAKE_CASE							(			datasets.GeneratorBasedBuilder				):



   '''simple docstring'''



   __UpperCAmelCase	:							List[Any]    =    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	:							Optional[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"				),
   ]

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







   def    _A				(	self	):


      '''simple docstring'''

      if self.config.name == "first_domain":  # This is the name of the configuration selected in BUILDER_CONFIGS above
         __lowerCamelCase    		=					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    		=					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=lowercase_ ,				homepage=_HOMEPAGE ,				license=_LICENSE ,				citation=_CITATION ,				)







   def    _A				(	self ,				_SCREAMING_SNAKE_CASE	):


      '''simple docstring'''

      __lowerCamelCase    		=					_URLS[self.config.name]
      __lowerCamelCase    		=					dl_manager.download_and_extract(lowercase_	)
      return [
          datasets.SplitGenerator(
              name=datasets.Split.TRAIN ,				gen_kwargs={
                  'filepath': os.path.join(lowercase_ ,				'train.jsonl'	),
                  'split': 'train',
              } ,				),
          datasets.SplitGenerator(
              name=datasets.Split.VALIDATION ,				gen_kwargs={
                  'filepath': os.path.join(lowercase_ ,				'dev.jsonl'	),
                  'split': 'dev',
              } ,				),
          datasets.SplitGenerator(
              name=datasets.Split.TEST ,				gen_kwargs={
                  'filepath': os.path.join(lowercase_ ,				'test.jsonl'	),
                  'split': 'test'
              } ,				),
      ]







   def    _A				(	self ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE	):


      '''simple docstring'''

      with open(lowercase_ ,				encoding='utf-8'	) as f:
         for key, row in enumerate(lowercase_	):
            __lowerCamelCase    		=					json.loads(lowercase_	)
            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'''




import os
import unittest

import pytest
from parameterized import parameterized

from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device


# skipping in unittest tests

_lowercase		:					int													=		[(1,)]




def 				lowerCamelCase      ( )     -> Dict:
							lowercase_   :     Union[str, Any]									=			bool(os.getenv("""RUN_SLOW""" , 0					)					)
							if run_slow:
														assert True
							else:
														assert False, "should have been skipped"




def 				lowerCamelCase      ( )     -> List[str]:
							lowercase_   :     Union[str, Any]									=			bool(os.getenv("""RUN_SLOW""" , 0					)					)
							if run_slow and torch_device == "cuda":
														assert True
							else:
														assert False, "should have been skipped"





@require_torch
class        __magic_name__		(       unittest.TestCase):







			@slow
			@require_torch_gpu
			def       SCREAMING_SNAKE_CASE_	(      self							:    Dict       ):
										check_slow_torch_cuda()







			@require_torch_gpu
			@slow
			def       SCREAMING_SNAKE_CASE_	(      self							:    List[str]       ):
										check_slow_torch_cuda()







			@parameterized.expand(lowercase_       )
			@slow
			def       SCREAMING_SNAKE_CASE_	(      self							:    Tuple							,     lowercase_							:    Dict=None       ):
										check_slow()




@slow
@require_torch_gpu
def 				lowerCamelCase      ( )     -> Union[str, Any]:
							check_slow_torch_cuda()




@require_torch_gpu
@slow
def 				lowerCamelCase      ( )     -> Any:
							check_slow_torch_cuda()




@slow
@pytest.mark.parametrize("""param""" , [1]					)
def 				lowerCamelCase      ( UpperCAmelCase__			:   List[str]					)     -> List[Any]:
							check_slow()




@pytest.mark.parametrize("""param""" , [1]					)
@slow
def 				lowerCamelCase      ( UpperCAmelCase__			:   Optional[Any]					)     -> Any:
							check_slow()





