code: 
  
  
  """simple docstring"""
  
  
  
  
  
  
  
  import re
  import tempfile
  import unittest
  from pathlib import Path
  
  from datasets.utils.metadata import DatasetMetadata
  def _A		(		lowercase	):
  
  
  
  
  
  
  
      """simple docstring"""
      a					=min(re.search('''^ +''' ,					lowercase	).end() if t.startswith(''' '''	) else 0 for t in string.splitlines()	)
      return "\n".join([line[indent_level:] for line in string.splitlines() if indent_level < len(lowercase	)]	)
  
  
  lowerCamelCase_     :     Optional[int]    		=						"""\
  ---
  language:
  - zh
  - en
  task_ids:
  - sentiment-classification
  ---
  # Begin of markdown
  
  Some cool dataset card
  """
  
  lowerCamelCase_     :     Optional[int]    		=						"""\
  ---
  ---
  # Begin of markdown
  
  Some cool dataset card
  """
  
  
  lowerCamelCase_     :     List[str]    		=						"""\
  # Begin of markdown
  
  Some cool dataset card
  """
  
  
  
  
  
  
  class    __A       ( unittest.TestCase    ):
  
  
  
  
  
  
   """simple docstring"""
  
  
  
  
   def    SCREAMING_SNAKE_CASE							(	self		)      ->  Union[str, Any]:
       with tempfile.TemporaryDirectory() as tmp_dir:
           a					=Path(__A		) / '''README.md'''
           with open(__A				,					'''w+'''		) as readme_file:
               readme_file.write(__A		)
           a					=DatasetMetadata.from_readme(__A		)
           self.assertDictEqual(__A				,					{'''language''': ['''zh''', '''en'''], '''task_ids''': ['''sentiment-classification''']}		)
  
           with open(__A				,					'''w+'''		) as readme_file:
               readme_file.write(__A		)
           a					=DatasetMetadata.from_readme(__A		)
           self.assertDictEqual(__A				,					{}		)
  
           with open(__A				,					'''w+'''		) as readme_file:
               readme_file.write(__A		)
           a					=DatasetMetadata.from_readme(__A		)
           self.assertEqual(__A				,					{}		)
  
  
  
  
  
   def    SCREAMING_SNAKE_CASE							(	self		)      ->  Optional[int]:
       a					=_dedent(
           '''\
              annotations_creators:
              - found
              language_creators:
              - found
              language:
              - en
              license:
              - unknown
              multilinguality:
              - monolingual
              pretty_name: Test Dataset
              size_categories:
              - 10K<n<100K
              source_datasets:
              - extended|other-yahoo-webscope-l6
              task_categories:
              - question-answering
              task_ids:
              - open-domain-qa
              '''		)
       assert DatasetMetadata.from_yaml_string(__A		)
  
       a					=_dedent(
           '''\
              annotations_creators:
              - found
              language:
              - en
              license:
              - unknown
              multilinguality:
              - monolingual
              pretty_name: Test Dataset
              size_categories:
              - 10K<n<100K
              source_datasets:
              - extended|other-yahoo-webscope-l6
              task_categories:
              - question-answering
              task_ids:
              - open-domain-qa
              task_ids:
              - open-domain-qa
              '''		)
       with self.assertRaises(__A		):
           DatasetMetadata.from_yaml_string(__A		)
  
       a					=_dedent(
           '''\
              annotations_creators:
              - found
              language_creators:
              - found
              language:
              - en
              license:
              - unknown
              multilinguality:
              - monolingual
              pretty_name: Test Dataset
              size_categories:
              - 10K<n<100K
              source_datasets:
              - extended|other-yahoo-webscope-l6
              task_categories:
              - text-classification
              task_ids:
              - multi-class-classification
              paperswithcode_id:
              - squad
              configs:
              - en
              train-eval-index:
              - config: en
                task: text-classification
                task_id: multi_class_classification
                splits:
                  train_split: train
                  eval_split: test
                col_mapping:
                  text: text
                  label: target
                metrics:
                  - type: accuracy
                    name: Accuracy
              extra_gated_prompt: |
                By clicking on “Access repository” below, you also agree to ImageNet Terms of Access:
                [RESEARCHER_FULLNAME] (the "Researcher") has requested permission to use the ImageNet database (the "Database") at Princeton University and Stanford University. In exchange for such permission, Researcher hereby agrees to the following terms and conditions:
                1. Researcher shall use the Database only for non-commercial research and educational purposes.
              extra_gated_fields:
                Company: text
                Country: text
                I agree to use this model for non-commerical use ONLY: checkbox
              '''		)
       assert DatasetMetadata.from_yaml_string(__A		)
style_context: 
  
  
  """simple docstring"""
  
  
  
  
  
  
  
  from __future__ import annotations
  
  from random import randrange
  def _A		(		lowercase	):
  
  
  
  
  
  
  
      """simple docstring"""
      if len(lowercase	) < 2:
          return collection
      a					=randrange(len(lowercase	)	)  # Use random element as pivot
      a					=collection[pivot_index]
      a					=[]  # All elements greater than pivot
      a					=[]  # All elements less than or equal to pivot
  
      for element in collection[:pivot_index]:
          (greater if element > pivot else lesser).append(lowercase	)
  
      for element in collection[pivot_index + 1 :]:
          (greater if element > pivot else lesser).append(lowercase	)
  
      return [*quick_sort(lowercase	), pivot, *quick_sort(lowercase	)]
  
  
  if __name__ == "__main__":
         lowerCamelCase_     :     Union[str, Any]    		=						input("""Enter numbers separated by a comma:\n""").strip()
         lowerCamelCase_     :     Union[str, Any]    		=						[int(item) for item in user_input.split(""",""")]
         print(quick_sort(unsorted))