




'''simple docstring'''






import re
import tempfile
import unittest
from pathlib import Path

from datasets.utils.metadata import DatasetMetadata

def      __lowerCAmelCase   (       snake_case__		):
			__UpperCamelCase :     Optional[Any]										=  min(re.search("^ +"   , snake_case__		).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(snake_case__		)]		)


_lowerCAmelCase       =   '''\
---
language:
- zh
- en
task_ids:
- sentiment-classification
---
# Begin of markdown

Some cool dataset card
'''

_lowerCAmelCase       =   '''\
---
---
# Begin of markdown

Some cool dataset card
'''


_lowerCAmelCase       =   '''\
# Begin of markdown

Some cool dataset card
'''




class    A						(					unittest.TestCase						):




				'''simple docstring'''





				def 			a_			(self     )      ->			List[Any]:
							with tempfile.TemporaryDirectory() as tmp_dir:
										__UpperCamelCase :     List[str]										=  Path(_UpperCAmelCase     ) / "README.md"
										with open(_UpperCAmelCase					,    "w+"     ) as readme_file:
													readme_file.write(_UpperCAmelCase     )
										__UpperCamelCase :     Any										=  DatasetMetadata.from_readme(_UpperCAmelCase     )
										self.assertDictEqual(_UpperCAmelCase					,    {"language": ["zh", "en"], "task_ids": ["sentiment-classification"]}     )

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

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





				def 			a_			(self     )      ->			Dict:
							__UpperCamelCase :     str										=  _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(_UpperCAmelCase     )

							__UpperCamelCase :     Dict										=  _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(_UpperCAmelCase     ):
										DatasetMetadata.from_yaml_string(_UpperCAmelCase     )

							__UpperCamelCase :     Union[str, Any]										=  _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(_UpperCAmelCase     )









'''simple docstring'''






import itertools

def      __lowerCAmelCase   (       snake_case__		):
			return (
			    int("".join(combination[0:2]		)		) * int("".join(combination[2:5]		)		)
			    == int("".join(combination[5:9]		)		)
			) or (
			    int("".join(combination[0]		)		) * int("".join(combination[1:5]		)		)
			    == int("".join(combination[5:9]		)		)
			)

def      __lowerCAmelCase   (       ):
			return sum(
			    {
			        int("".join(pandigital[5:9]		)		)
			        for pandigital in itertools.permutations("123456789"		)
			        if is_combination_valid(snake_case__		)
			    }		)


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



