"""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						=    "\\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						=    "\\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						=    ""

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

# 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						=    {
    "first_domain": "https://huggingface.co/great-new-dataset-first_domain.zip",
    "second_domain": "https://huggingface.co/great-new-dataset-second_domain.zip",
}



class 			_lowerCamelCase      (datasets.GeneratorBasedBuilder  ):
				_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')
				_snake_case  =	[
				    datasets.BuilderConfig(name="first_domain"    ,				version=lowercase__    ,				description="This part of my dataset covers a first domain"  ),
				    datasets.BuilderConfig(name="second_domain"    ,				version=lowercase__    ,				description="This part of my dataset covers a second domain"  ),
				]

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







				def 				__UpperCAmelCase       (			self     :							Optional[int]    ):

					"""simple docstring"""






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







				def 				__UpperCAmelCase       (			self     :							List[Any]      ,       lowerCamelCase_     :							Any    ):

					"""simple docstring"""






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







				def 				__UpperCAmelCase       (			self     :							Optional[Any]      ,       lowerCamelCase_     :							List[str]      ,       lowerCamelCase_     :							Dict    ):

					"""simple docstring"""






					with open(__lowerCamelCase      ,       encoding='utf-8'    ) as f:
						for key, row in enumerate(__lowerCamelCase    ):
							_lowercase			:     List[str]      					=				json.loads(__lowerCamelCase    )
							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     :       Dict		):

      """simple docstring"""




      lowerCamelCase__						:							Optional[Any]       =    len(_A		)
      for _ in range(
          0       ,   int(((no_of_elements - 1) / 2) + 1		)		):  # we don't need to traverse to end of list as
            for j in range(0       ,   no_of_elements - 1		):
                  if (
                      lst[j + 1] < lst[j]
                  ):  # applying bubble sort algorithm from left to right (or forwards)
                        lowerCamelCase__						:							Tuple       =    lst[j + 1]
                        lowerCamelCase__						:							Optional[Any]       =    lst[j]
                        lowerCamelCase__						:							Optional[Any]       =    temp
                  if (
                      lst[no_of_elements - 1 - j] < lst[no_of_elements - 2 - j]
                  ):  # applying bubble sort algorithm from right to left (or backwards)
                        lowerCamelCase__						:							Any       =    lst[no_of_elements - 1 - j]
                        lowerCamelCase__						:							List[str]       =    lst[no_of_elements - 2 - j]
                        lowerCamelCase__						:							str       =    temp
      return lst


if __name__ == "__main__":
    print("enter the list to be sorted")
    A :       Optional[int]						=   [int(x) for x in input().split()]  # inputing elements of the list in one line
    A :       Optional[Any]						=   double_sort(lst)
    print("the sorted list is")
    print(sorted_lst)


