




from __future__ import annotations

import math
from itertools import permutations



def    lowerCamelCase_   (  _lowercase    )							->	bool:





		if 1 < number < 4:
				# 2 and 3 are primes
				return True
		elif number < 2 or number % 2 == 0 or number % 3 == 0:
				# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
				return False

		# All primes number are in format of 6k +/- 1
		for i in range(5 ,     int(math.sqrt(_lowerCamelCase    ) + 1    ) ,     6    ):
				if number % i == 0 or number % (i + 2) == 0:
						return False
		return True



def    lowerCamelCase_   (  _lowercase = 7    )							->	int:
		__A							:   str	  =				"".join(str(_lowerCamelCase    ) for i in range(1 ,     n + 1    )    )
		__A							:   List[str]	  =				[int("".join(_lowerCamelCase    )    ) for i in permutations(_lowerCamelCase ,     _lowerCamelCase    )]
		__A							:   Optional[int]	  =				[num for num in perm_list if is_prime(_lowerCamelCase    )]
		return max(_lowerCamelCase    ) if pandigitals else 0


if __name__ == "__main__":
					print(F'''{solution() = }''')






import re
import tempfile
import unittest
from pathlib import Path

from datasets.utils.metadata import DatasetMetadata



def    lowerCamelCase_   (  _lowercase    )							->	str:
		__A							:   Optional[int]	  =				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    )]    )


UpperCamelCase      			=     '\\n---\nlanguage:\n- zh\n- en\ntask_ids:\n- sentiment-classification\n---\n# Begin of markdown\n\nSome cool dataset card\n'

UpperCamelCase      			=     '\\n---\n---\n# Begin of markdown\n\nSome cool dataset card\n'


UpperCamelCase      			=     '\\n# Begin of markdown\n\nSome cool dataset card\n'





class _a       (	unittest.TestCase ):

	'''simple docstring'''



	def 					__UpperCAmelCase(       self   ):
			with tempfile.TemporaryDirectory() as tmp_dir:
					__A							:   List[Any]	  =				Path(__UpperCAmelCase   ) / "README.md"
					with open(__UpperCAmelCase							,			"w+"   ) as readme_file:
							readme_file.write(__UpperCAmelCase   )
					__A							:   Tuple	  =				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   )
					__A							:   Any	  =				DatasetMetadata.from_readme(__UpperCAmelCase   )
					self.assertDictEqual(__UpperCAmelCase							,			{}   )

					with open(__UpperCAmelCase							,			"w+"   ) as readme_file:
							readme_file.write(__UpperCAmelCase   )
					__A							:   Union[str, Any]	  =				DatasetMetadata.from_readme(__UpperCAmelCase   )
					self.assertEqual(__UpperCAmelCase							,			{}   )







	def 					__UpperCAmelCase(       self   ):
			__A							:   List[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   )

			__A							:   List[Any]	  =				_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   )

			__A							:   Optional[int]	  =				_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   )





