


"""simple docstring"""






from __future__ import annotations

import random
from collections.abc import Iterable
from typing import Any, Generic, TypeVar

SCREAMING_SNAKE_CASE__				:  List[Any]						= TypeVar("T", bound=bool)


class 	A_       (     Generic[T]   ):





  """simple docstring"""

  def __init__(						self  ,					__UpperCAmelCase						)				->					None:
     a   :	T						=    value
     a   :	RandomizedHeapNode[T] | None						=    None
     a   :	RandomizedHeapNode[T] | None						=    None
  @property
  def 							lowercase_     (						self						)				->					T:
     return self._value






  @staticmethod
  def 							lowercase_     (						__UpperCAmelCase  ,					__UpperCAmelCase						)				->					RandomizedHeapNode[T] | None:
     if not roota:
        return roota

     if not roota:
        return roota

     if roota.value > roota.value:
        a	, a   :	List[Any]						=    roota, roota

     if random.choice([True, False]						):
        a	, a   :	Union[str, Any]						=    roota.right, roota.left

     a   :	Any						=    RandomizedHeapNode.merge(roota.left  ,					__UpperCAmelCase						)

     return roota



class 	A_       (     Generic[T]   ):





  """simple docstring"""

  def __init__(						self  ,					__UpperCAmelCase = ()						)				->					None:
     a   :	RandomizedHeapNode[T] | None						=    None

     if data:
        for item in data:
           self.insert(__UpperCAmelCase						)
  def 							lowercase_     (						self  ,					__UpperCAmelCase						)				->					None:
     a   :	Union[str, Any]						=    RandomizedHeapNode.merge(self._root  ,					RandomizedHeapNode(__UpperCAmelCase						)						)
  def 							lowercase_     (						self						)				->					T | None:
     a   :	Any						=    self.top()

     if self._root is None:
        return None

     a   :	str						=    RandomizedHeapNode.merge(self._root.left  ,					self._root.right						)

     return result
  def 							lowercase_     (						self						)				->					T:
     if not self._root:
        raise IndexError('Can\'t get top element for the empty heap.'						)
     return self._root.value
  def 							lowercase_     (						self						)				->					None:
     a   :	Union[str, Any]						=    None
  def 							lowercase_     (						self						)				->					list[Any]:
     a   :	Dict						=    []
     while self:
        result.append(self.pop()						)

     return result






  def __bool__(						self						)				->					bool:
     return self._root is not None


if __name__ == "__main__":
 import doctest

 doctest.testmod()









"""simple docstring"""






import re
import tempfile
import unittest
from pathlib import Path

from datasets.utils.metadata import DatasetMetadata




def        A_					(					UpperCAmelCase__				)     ->     str:
   a   :	Optional[int]						=    min(re.search('^ +'       ,   UpperCAmelCase__				).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(UpperCAmelCase__				)]				)


SCREAMING_SNAKE_CASE__				:  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"

SCREAMING_SNAKE_CASE__				:  List[str]						= "\\n---\n---\n# Begin of markdown\n\nSome cool dataset card\n"


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


class 	A_       (     unittest.TestCase   ):





  """simple docstring"""

  def 							lowercase_     (						self						)				->					Any:
     with tempfile.TemporaryDirectory() as tmp_dir:
        a   :	Optional[Any]						=    Path(__UpperCAmelCase						) / 'README.md'
        with open(__UpperCAmelCase  ,					'w+'						) as readme_file:
           readme_file.write(__UpperCAmelCase						)
        a   :	str						=    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   :	Optional[Any]						=    DatasetMetadata.from_readme(__UpperCAmelCase						)
        self.assertDictEqual(__UpperCAmelCase  ,					{}						)

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






  def 							lowercase_     (						self						)				->					Tuple:
     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   :	Optional[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   :	List[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						)





