



import re
import tempfile
import unittest
from pathlib import Path

from datasets.utils.metadata import DatasetMetadata






def        a( A			:       Optional[int]			)						->			str:
					"""simple docstring"""





					a   	=  min(re.search("^ +"					,	lowerCAmelCase_			).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(lowerCAmelCase_			)]			)


_lowercase:					Dict      =						"\\n---\nlanguage:\n- zh\n- en\ntask_ids:\n- sentiment-classification\n---\n# Begin of markdown\n\nSome cool dataset card\n"

_lowercase:					Any      =						"\\n---\n---\n# Begin of markdown\n\nSome cool dataset card\n"


_lowercase:					Optional[Any]      =						"\\n# Begin of markdown\n\nSome cool dataset card\n"







class       _lowercase				(  unittest.TestCase      ):





	"""simple docstring"""




	def 	UpperCamelCase_			(self						):
						"""simple docstring"""

						with tempfile.TemporaryDirectory() as tmp_dir:
											a   	=  Path(lowerCAmelCase_						) / '''README.md'''
											with open(lowerCAmelCase_						,			"w+"						) as readme_file:
																readme_file.write(lowerCAmelCase_						)
											a   	=  DatasetMetadata.from_readme(lowerCAmelCase_						)
											self.assertDictEqual(lowerCAmelCase_						,			{"language": ["zh", "en"], "task_ids": ["sentiment-classification"]}						)

											with open(lowerCAmelCase_						,			"w+"						) as readme_file:
																readme_file.write(lowerCAmelCase_						)
											a   	=  DatasetMetadata.from_readme(lowerCAmelCase_						)
											self.assertDictEqual(lowerCAmelCase_						,			{}						)

											with open(lowerCAmelCase_						,			"w+"						) as readme_file:
																readme_file.write(lowerCAmelCase_						)
											a   	=  DatasetMetadata.from_readme(lowerCAmelCase_						)
											self.assertEqual(lowerCAmelCase_						,			{}						)




	def 	UpperCamelCase_			(self						):
						"""simple docstring"""

						a   	=  _dedent(
						    "\\n            annotations_creators:\n            - found\n            language_creators:\n            - found\n            language:\n            - en\n            license:\n            - unknown\n            multilinguality:\n            - monolingual\n            pretty_name: Test Dataset\n            size_categories:\n            - 10K<n<100K\n            source_datasets:\n            - extended|other-yahoo-webscope-l6\n            task_categories:\n            - question-answering\n            task_ids:\n            - open-domain-qa\n            "						)
						assert DatasetMetadata.from_yaml_string(lowerCAmelCase_						)

						a   	=  _dedent(
						    "\\n            annotations_creators:\n            - found\n            language:\n            - en\n            license:\n            - unknown\n            multilinguality:\n            - monolingual\n            pretty_name: Test Dataset\n            size_categories:\n            - 10K<n<100K\n            source_datasets:\n            - extended|other-yahoo-webscope-l6\n            task_categories:\n            - question-answering\n            task_ids:\n            - open-domain-qa\n            task_ids:\n            - open-domain-qa\n            "						)
						with self.assertRaises(lowerCAmelCase_						):
											DatasetMetadata.from_yaml_string(lowerCAmelCase_						)

						a   	=  _dedent(
						    "\\n            annotations_creators:\n            - found\n            language_creators:\n            - found\n            language:\n            - en\n            license:\n            - unknown\n            multilinguality:\n            - monolingual\n            pretty_name: Test Dataset\n            size_categories:\n            - 10K<n<100K\n            source_datasets:\n            - extended|other-yahoo-webscope-l6\n            task_categories:\n            - text-classification\n            task_ids:\n            - multi-class-classification\n            paperswithcode_id:\n            - squad\n            configs:\n            - en\n            train-eval-index:\n            - config: en\n              task: text-classification\n              task_id: multi_class_classification\n              splits:\n                train_split: train\n                eval_split: test\n              col_mapping:\n                text: text\n                label: target\n              metrics:\n                - type: accuracy\n                  name: Accuracy\n            extra_gated_prompt: |\n              By clicking on “Access repository” below, you also agree to ImageNet Terms of Access:\n              [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:\n              1. Researcher shall use the Database only for non-commercial research and educational purposes.\n            extra_gated_fields:\n              Company: text\n              Country: text\n              I agree to use this model for non-commerical use ONLY: checkbox\n            "						)
						assert DatasetMetadata.from_yaml_string(lowerCAmelCase_						)

import math







def 						UpperCAmelCase							(		lowerCAmelCase_							)						->						int:
     return sum(int(lowerCAmelCase_							) for digit in str(lowerCAmelCase_							)							)







def 						UpperCAmelCase							(		lowerCAmelCase_ = 3_0							)						->						int:
     a	:     Optional[int]						      =    []
     for digit in range(2    ,   1_0_0							):
          for power in range(2    ,   1_0_0							):
               a	:     List[str]						      =    int(math.pow(lowerCAmelCase_    ,   lowerCAmelCase_							)							)
               if digit == digit_sum(lowerCAmelCase_							):
                    digit_to_powers.append(lowerCAmelCase_							)

     digit_to_powers.sort()
     return digit_to_powers[n - 1]


if __name__ == "__main__":
  print(solution())