"""simple docstring"""



from __future__ import annotations

from abc import abstractmethod
from collections.abc import Iterable
from typing import Generic, Protocol, TypeVar

class SCREAMING_SNAKE_CASE     (    UpperCamelCase_	):







	"""simple docstring"""


	@abstractmethod
	def __lt__(   self		:      int					,   _snake_case		:      List[Any]				)				->			bool:




				'''simple docstring'''






				pass


	@abstractmethod
	def __gt__(   self		:      List[str]					,   _snake_case		:      Dict				)				->			bool:




				'''simple docstring'''






				pass



	@abstractmethod
	def __eq__(   self		:      Any					,   _snake_case		:      List[Any]				)				->			bool:




				'''simple docstring'''






				pass
__magic_name__    				=					TypeVar("T", bound=Comparable)

class SCREAMING_SNAKE_CASE     (    Generic[T]	):







	"""simple docstring"""


	def __init__(   self		:      Dict				)				->			None:




				'''simple docstring'''






				a__     				=		[]
				a__     				=		0


	def __repr__(   self		:      Tuple				)				->			str:




				'''simple docstring'''






				return str(self.h				)


	def        _lowerCAmelCase				(   self		:      List[str]					,   _snake_case		:      Optional[Any]				)				->			int | None:




				'''simple docstring'''






				if child_idx > 0:
							return (child_idx - 1) // 2
				return None


	def        _lowerCAmelCase				(   self		:      Optional[int]					,   _snake_case		:      Optional[int]				)				->			int | None:




				'''simple docstring'''






				a__     				=		2 * parent_idx + 1
				if left_child_index < self.heap_size:
							return left_child_index
				return None


	def        _lowerCAmelCase				(   self		:      str					,   _snake_case		:      int				)				->			int | None:




				'''simple docstring'''






				a__     				=		2 * parent_idx + 2
				if right_child_index < self.heap_size:
							return right_child_index
				return None


	def        _lowerCAmelCase				(   self		:      Optional[int]					,   _snake_case		:      List[Any]				)				->			None:




				'''simple docstring'''






				if index < self.heap_size:
							a__     				=		index
							a__     				=		self.left_child_idx(__SCREAMING_SNAKE_CASE				)
							a__     				=		self.right_child_idx(__SCREAMING_SNAKE_CASE				)
							# check which child is larger than its parent
							if left_child is not None and self.h[left_child] > self.h[violation]:
										a__     				=		left_child
							if right_child is not None and self.h[right_child] > self.h[violation]:
										a__     				=		right_child
							# if violation indeed exists
							if violation != index:
										# swap to fix the violation
										a__				,       a__     				=		self.h[index], self.h[violation]
										# fix the subsequent violation recursively if any
										self.max_heapify(__SCREAMING_SNAKE_CASE				)


	def        _lowerCAmelCase				(   self		:      List[Any]					,   _snake_case		:      Any				)				->			None:




				'''simple docstring'''






				a__     				=		list(__SCREAMING_SNAKE_CASE				)
				a__     				=		len(self.h				)
				if self.heap_size > 1:
							# max_heapify from right to left but exclude leaves (last level)
							for i in range(self.heap_size // 2 - 1					,   -1					,   -1				):
										self.max_heapify(__SCREAMING_SNAKE_CASE				)


	def        _lowerCAmelCase				(   self		:      List[Any]				)				->			T:




				'''simple docstring'''






				if self.heap_size >= 2:
							a__     				=		self.h[0]
							a__     				=		self.h.pop(-1				)
							self.heap_size -= 1
							self.max_heapify(0				)
							return me
				elif self.heap_size == 1:
							self.heap_size -= 1
							return self.h.pop(-1				)
				else:
							raise Exception('Empty heap'				)


	def        _lowerCAmelCase				(   self		:      str					,   _snake_case		:      Optional[int]				)				->			None:




				'''simple docstring'''






				self.h.append(__SCREAMING_SNAKE_CASE				)
				a__     				=		(self.heap_size - 1) // 2
				self.heap_size += 1
				while idx >= 0:
							self.max_heapify(__SCREAMING_SNAKE_CASE				)
							a__     				=		(idx - 1) // 2



	def        _lowerCAmelCase				(   self		:      List[str]				)				->			None:




				'''simple docstring'''






				a__     				=		self.heap_size
				for j in range(size - 1					,   0					,   -1				):
							a__				,       a__     				=		self.h[j], self.h[0]
							self.heap_size -= 1
							self.max_heapify(0				)
				a__     				=		size


if __name__ == "__main__":
					import doctest

					# run doc test
					doctest.testmod()

					# demo
					for unsorted in [
					    [0],
					    [2],
					    [3, 5],
					    [5, 3],
					    [5, 5],
					    [0, 0, 0, 0],
					    [1, 1, 1, 1],
					    [2, 2, 3, 5],
					    [0, 2, 2, 3, 5],
					    [2, 5, 3, 0, 2, 3, 0, 3],
					    [6, 1, 2, 7, 9, 3, 4, 5, 10, 8],
					    [103, 9, 1, 7, 11, 15, 25, 201, 209, 107, 5],
					    [-45, -2, -5],
					]:
										print(F'''unsorted array: {unsorted}''')

										__magic_name__    				=					Heap()
										heap.build_max_heap(unsorted)
										print(F'''after build heap: {heap}''')

										print(F'''max value: {heap.extract_max()}''')
										print(F'''after max value removed: {heap}''')

										heap.insert(100)
										print(F'''after new value 100 inserted: {heap}''')

										heap.heap_sort()
										print(F'''heap-sorted array: {heap}\n''')



"""simple docstring"""



import re
import tempfile
import unittest
from pathlib import Path

from datasets.utils.metadata import DatasetMetadata





def   _lowerCamelCase ( UpperCAmelCase__					)							->     str:


   '''simple docstring'''


   a__     				=		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__					)]					)


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

__magic_name__    				=					"\\n---\n---\n# Begin of markdown\n\nSome cool dataset card\n"


__magic_name__    				=					"\\n# Begin of markdown\n\nSome cool dataset card\n"




class SCREAMING_SNAKE_CASE     (    unittest.TestCase	):







 """simple docstring"""


 def        _lowerCAmelCase				(   self		:      Tuple				)				->			Dict:




    '''simple docstring'''






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

       with open(_snake_case					,   'w+'				) as readme_file:
          readme_file.write(_snake_case				)
       a__     				=		DatasetMetadata.from_readme(_snake_case				)
       self.assertDictEqual(_snake_case					,   {}				)

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



 def        _lowerCAmelCase				(   self		:      List[Any]				)				->			List[Any]:




    '''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(_snake_case				)

    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(_snake_case				):
       DatasetMetadata.from_yaml_string(_snake_case				)

    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(_snake_case				)


