from __future__ import annotations

from collections.abc import Callable
from typing import Generic, TypeVar

UpperCamelCase      			=     TypeVar('T')
UpperCamelCase      			=     TypeVar('U')


class _a       (	Generic[T, U] ):

	'''simple docstring'''



	def __init__(       self							,			__UpperCAmelCase							,			__UpperCAmelCase   ):
			__A							:   Dict	  =				key
			__A							:   List[str]	  =				val
			__A							:   int	  =				0
			__A							:   DoubleLinkedListNode[T, U] | None	  =				None
			__A							:   DoubleLinkedListNode[T, U] | None	  =				None







	def __repr__(       self   ):
			return "Node: key: {}, val: {}, freq: {}, has next: {}, has prev: {}".format(
			    self.key							,			self.val							,			self.freq							,			self.next is not None							,			self.prev is not None   )


class _a       (	Generic[T, U] ):

	'''simple docstring'''



	def __init__(       self   ):
			__A							:   DoubleLinkedListNode[T, U]	  =				DoubleLinkedListNode(__UpperCAmelCase							,			__UpperCAmelCase   )
			__A							:   DoubleLinkedListNode[T, U]	  =				DoubleLinkedListNode(__UpperCAmelCase							,			__UpperCAmelCase   )
			__A  ,       __A							:   Any	  =				self.rear, self.head

	def __repr__(       self   ):
			__A							:   Any	  =				["DoubleLinkedList"]
			__A							:   List[str]	  =				self.head
			while node.next is not None:
					rep.append(str(__UpperCAmelCase   )   )
					__A							:   str	  =				node.next
			rep.append(str(self.rear   )   )
			return ",\n    ".join(__UpperCAmelCase   )

	def 					__UpperCAmelCase(       self							,			__UpperCAmelCase   ):
			__A							:   Any	  =				self.rear.prev

			# All nodes other than self.head are guaranteed to have non-None previous
			assert previous is not None

			__A							:   Optional[int]	  =				node
			__A							:   Any	  =				previous
			__A							:   List[str]	  =				node
			__A							:   Tuple	  =				self.rear
			node.freq += 1
			self._position_node(__UpperCAmelCase   )

	def 					__UpperCAmelCase(       self							,			__UpperCAmelCase   ):

			while node.prev is not None and node.prev.freq > node.freq:
					# swap node with previous node
					__A							:   List[Any]	  =				node.prev

					__A							:   Any	  =				previous_node.prev
					__A							:   Dict	  =				node.prev
					__A							:   Any	  =				previous_node
					__A							:   List[Any]	  =				node







	def 					__UpperCAmelCase(       self							,			__UpperCAmelCase   ):

			if node.prev is None or node.next is None:
					return None

			__A							:   List[Any]	  =				node.next
			__A							:   int	  =				node.prev
			__A							:   str	  =				None
			__A							:   List[str]	  =				None
			return node


class _a       (	Generic[T, U] ):

	'''simple docstring'''



	lowerCamelCase_   :      dict[Callable[[T], U], LFUCache[T, U]]       =	{}

	def __init__(       self							,			__UpperCAmelCase   ):
			__A							:   DoubleLinkedList[T, U]	  =				DoubleLinkedList()
			__A							:   Dict	  =				capacity
			__A							:   List[Any]	  =				0
			__A							:   List[Any]	  =				0
			__A							:   str	  =				0
			__A							:   dict[T, DoubleLinkedListNode[T, U]]	  =				{}

	def __repr__(       self   ):
			return (
			    F"CacheInfo(hits={self.hits}, misses={self.miss}, "
			    F"capacity={self.capacity}, current_size={self.num_keys})"
			)

	def __contains__(       self							,			__UpperCAmelCase   ):
			return key in self.cache

	def 					__UpperCAmelCase(       self							,			__UpperCAmelCase   ):

			if key in self.cache:
					self.hits += 1
					__A							:   DoubleLinkedListNode[T, U]	  =				self.cache[key]
					__A							:   List[str]	  =				self.list.remove(self.cache[key]   )
					assert node == value_node

					# node is guaranteed not None because it is in self.cache
					assert node is not None
					self.list.add(__UpperCAmelCase   )
					return node.val
			self.miss += 1
			return None

	def 					__UpperCAmelCase(       self							,			__UpperCAmelCase							,			__UpperCAmelCase   ):

			if key not in self.cache:
					if self.num_keys >= self.capacity:
							# delete first node when over capacity
							__A							:   Dict	  =				self.list.head.next

							# guaranteed to have a non-None first node when num_keys > 0
							# explain to type checker via assertions
							assert first_node is not None
							assert first_node.key is not None
							assert self.list.remove(__UpperCAmelCase   ) is not None
							# first_node guaranteed to be in list

							del self.cache[first_node.key]
							self.num_keys -= 1
					__A							:   List[Any]	  =				DoubleLinkedListNode(__UpperCAmelCase							,			__UpperCAmelCase   )
					self.list.add(self.cache[key]   )
					self.num_keys += 1

			else:
					__A							:   Union[str, Any]	  =				self.list.remove(self.cache[key]   )
					assert node is not None  # node guaranteed to be in list
					__A							:   str	  =				value
					self.list.add(__UpperCAmelCase   )







	@classmethod
	def 					__UpperCAmelCase(       cls							,			__UpperCAmelCase = 128   ):

			def cache_decorator_inner(__UpperCAmelCase   ) -> Callable[..., U]:
					def cache_decorator_wrapper(*__UpperCAmelCase   ) -> U:
							if func not in cls.decorator_function_to_instance_map:
									__A							:   Tuple	  =				LFUCache(__UpperCAmelCase   )

							__A							:   Any	  =				cls.decorator_function_to_instance_map[func].get(args[0]   )
							if result is None:
									__A							:   Any	  =				func(*__UpperCAmelCase   )
									cls.decorator_function_to_instance_map[func].put(args[0]							,			__UpperCAmelCase   )
							return result

					def cache_info() -> LFUCache[T, U]:
							return cls.decorator_function_to_instance_map[func]

					setattr(__UpperCAmelCase							,			"cache_info"							,			__UpperCAmelCase   )  # noqa: B010

					return cache_decorator_wrapper

			return cache_decorator_inner


if __name__ == "__main__":
					import doctest

					doctest.testmod()






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   )





