

import re
import tempfile
import unittest
from pathlib import Path

from datasets.utils.metadata import DatasetMetadata







def  UpperCAmelCase_   (_lowerCAmelCase :		str		):
			__UpperCamelCase    :    Optional[int]								=      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     :       Union[str, Any]        =       "\\n---\nlanguage:\n- zh\n- en\ntask_ids:\n- sentiment-classification\n---\n# Begin of markdown\n\nSome cool dataset card\n"

lowercase     :       Dict        =       "\\n---\n---\n# Begin of markdown\n\nSome cool dataset card\n"


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

class 		SCREAMING_SNAKE_CASE__					(		unittest.TestCase   ):







						"""simple docstring"""




						def __lowerCamelCase					(				self    )							->	Optional[int]:




									'''simple docstring'''


									with tempfile.TemporaryDirectory() as tmp_dir:
												__UpperCamelCase    :    int								=      Path(__UpperCamelCase    ) / "README.md"
												with open(__UpperCamelCase , "w+"    ) as readme_file:
															readme_file.write(__UpperCamelCase    )
												__UpperCamelCase    :    Optional[int]								=      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    :    Dict								=      DatasetMetadata.from_readme(__UpperCamelCase    )
												self.assertDictEqual(__UpperCamelCase , {}    )

												with open(__UpperCamelCase , "w+"    ) as readme_file:
															readme_file.write(__UpperCamelCase    )
												__UpperCamelCase    :    List[str]								=      DatasetMetadata.from_readme(__UpperCamelCase    )
												self.assertEqual(__UpperCamelCase , {}    )




						def __lowerCamelCase					(				self    )							->	str:




									'''simple docstring'''


									__UpperCamelCase    :    Optional[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            - question-answering\n            task_ids:\n            - open-domain-qa\n            "    )
									assert DatasetMetadata.from_yaml_string(__UpperCamelCase    )

									__UpperCamelCase    :    int								=      _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    :    Dict								=      _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    )


from __future__ import annotations







def  UpperCAmelCase_   (_lowerCAmelCase :		int     ,			_lowerCAmelCase :		list[int]     ,			_lowerCAmelCase :		list[int]     ,			_lowerCAmelCase :		int		):

			# Base Case
			if counter == 0 or capacity == 0:
						return 0

			# If weight of the nth item is more than Knapsack of capacity,
			#   then this item cannot be included in the optimal solution,
			# else return the maximum of two cases:
			#   (1) nth item included
			#   (2) not included
			if weights[counter - 1] > capacity:
						return knapsack(_lowerCAmelCase     ,			_lowerCAmelCase     ,			_lowerCAmelCase     ,			counter - 1		)
			else:
						__UpperCamelCase    :    Tuple								=      capacity - weights[counter - 1]
						__UpperCamelCase    :    List[str]								=      values[counter - 1] + knapsack(
						    _lowerCAmelCase     ,			_lowerCAmelCase     ,			_lowerCAmelCase     ,			counter - 1		)
						__UpperCamelCase    :    Dict								=      knapsack(_lowerCAmelCase     ,			_lowerCAmelCase     ,			_lowerCAmelCase     ,			counter - 1		)
						return max(_lowerCAmelCase     ,			_lowerCAmelCase		)


if __name__ == "__main__":
					import doctest

					doctest.testmod()