


'''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
lowerCamelCase				:   List[str]      				=  "\\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				:   str      				=  "\\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				:   Tuple      				=  ""

# TODO: Add the licence for the dataset here if you can find it
lowerCamelCase				:   Union[str, 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)
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 							A__					( datasets.GeneratorBasedBuilder						):
  A__					=       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')
  A__					=       [
      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'						),
  ]

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





  def 		A						(	self   :			List[Any]				)			->					Union[str, Any]:





       '''simple docstring'''






       if self.config.name == "first_domain":  # This is the name of the configuration selected in BUILDER_CONFIGS above
            _SCREAMING_SNAKE_CASE												=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"
            _SCREAMING_SNAKE_CASE												=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   :			Any							,      _a   :			Tuple				)			->					Optional[Any]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE												=_URLS[self.config.name]
       _SCREAMING_SNAKE_CASE												=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   :			Tuple							,      _a   :			int							,      _a   :			str				)			->					List[str]:





       '''simple docstring'''






       with open(_a							,      encoding='utf-8'				) as f:
            for key, row in enumerate(_a				):
                 _SCREAMING_SNAKE_CASE												=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"],
                      }



'''simple docstring'''
lowerCamelCase				:   int      				=  {
    0: "underwear",
    1: "pants",
    2: "belt",
    3: "suit",
    4: "shoe",
    5: "socks",
    6: "shirt",
    7: "tie",
    8: "watch",
}

lowerCamelCase				:   Union[str, Any]      				=  [[1, 4], [2, 4], [3], [], [], [4], [2, 7], [3], []]

lowerCamelCase				:   int      				=  [0 for x in range(len(graph))]
lowerCamelCase				:   Optional[Any]      				=  []


def  _lowerCAmelCase      (		_UpperCamelCase		:  List[str]							,				_UpperCamelCase		:  Union[str, Any]       )   ->    Union[str, Any]:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE												=1
     while stack:
          _SCREAMING_SNAKE_CASE												=stack.pop()
          print(_UpperCamelCase							,				clothes[current_clothing]       )
          order += 1


def  _lowerCAmelCase      (		_UpperCamelCase		:  str							,				_UpperCamelCase		:  Optional[Any]							,				_UpperCamelCase		:  Dict       )   ->    Optional[Any]:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE												=1
     for v in graph[u]:
          if not visited[v]:
               depth_first_search(_UpperCamelCase							,				_UpperCamelCase							,				_UpperCamelCase       )

     stack.append(_UpperCamelCase       )


def  _lowerCAmelCase      (		_UpperCamelCase		:  Dict							,				_UpperCamelCase		:  List[str]       )   ->    Optional[int]:
     """simple docstring"""




     for v in range(len(_UpperCamelCase       )       ):
          if not visited[v]:
               depth_first_search(_UpperCamelCase							,				_UpperCamelCase							,				_UpperCamelCase       )


if __name__ == "__main__":
     topological_sort(graph, visited)
     print(stack)
     print_stack(stack, clothes)
