

'''simple docstring'''

from __future__ import annotations

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

lowercase__            =     TypeVar("T", bound=bool)




class 			A_					(							Generic[T]				):






     '''simple docstring'''



     def __init__(						self					:	Tuple	,     lowercase_					:	T			)		->      None:
      UpperCAmelCase			:      Any		   =					value
      UpperCAmelCase			:      List[str]		   =					None
      UpperCAmelCase			:      int		   =					None


     @property
     def   UpperCAmelCase_			(						self					:	int			)		->      T:
      return self._value




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

      if not roota:
       return roota

      if roota.value > roota.value:
       UpperCAmelCase   ,					UpperCAmelCase			:      Tuple		   =					roota, roota

      if random.choice([True, False]			):
       UpperCAmelCase   ,					UpperCAmelCase			:      int		   =					roota.right, roota.left

      UpperCAmelCase			:      List[Any]		   =					RandomizedHeapNode.merge(roota.left	,     lowerCAmelCase__			)

      return roota




class 			A_					(							Generic[T]				):






     '''simple docstring'''



     def __init__(						self					:	Union[str, Any]	,     lowercase_					:	Iterable[T] | None = ()			)		->      None:
      UpperCAmelCase			:      Union[str, Any]		   =					None

      if data:
       for item in data:
        self.insert(lowerCAmelCase__			)


     def   UpperCAmelCase_			(						self					:	Optional[int]	,     lowercase_					:	T			)		->      None:
      UpperCAmelCase			:      List[str]		   =					RandomizedHeapNode.merge(self._root	,     RandomizedHeapNode(lowerCAmelCase__			)			)


     def   UpperCAmelCase_			(						self					:	Optional[int]			)		->      T | None:
      UpperCAmelCase			:      Any		   =					self.top()

      if self._root is None:
       return None

      UpperCAmelCase			:      Union[str, Any]		   =					RandomizedHeapNode.merge(self._root.left	,     self._root.right			)

      return result


     def   UpperCAmelCase_			(						self					:	Tuple			)		->      T:
      if not self._root:
       raise IndexError('Can\'t get top element for the empty heap.'			)
      return self._root.value


     def   UpperCAmelCase_			(						self					:	Optional[int]			)		->      None:
      UpperCAmelCase			:      Tuple		   =					None


     def   UpperCAmelCase_			(						self					:	List[str]			)		->      list[Any]:
      UpperCAmelCase			:      Union[str, Any]		   =					[]
      while self:
       result.append(self.pop()			)

      return result




     def __bool__(						self					:	Optional[int]			)		->      bool:
      return self._root is not None


if __name__ == "__main__":
 import doctest

 doctest.testmod()












import json
import os
import tempfile
import unittest

from transformers.modelcard import ModelCard
class 	a_(		unittest.TestCase	):



     """simple docstring"""



     def     __UpperCamelCase						(		self					:	Optional[int])							->	List[str]:

      """simple docstring"""
      SCREAMING_SNAKE_CASE				    =		{
          'model_details': {
              'Organization': 'testing',
              'Model date': 'today',
              'Model version': 'v2.1, Developed by Test Corp in 2019.',
              'Architecture': 'Convolutional Neural Network.',
          },
          'metrics': 'BLEU and ROUGE-1',
          'evaluation_data': {
              'Datasets': {'BLEU': 'My-great-dataset-v1', 'ROUGE-1': 'My-short-dataset-v2.1'},
              'Preprocessing': 'See details on https://arxiv.org/pdf/1810.03993.pdf',
          },
          'training_data': {
              'Dataset': 'English Wikipedia dump dated 2018-12-01',
              'Preprocessing': (
                  'Using SentencePiece vocabulary of size 52k tokens. See details on'
                  ' https://arxiv.org/pdf/1810.03993.pdf'
              ),
          },
          'quantitative_analyses': {'BLEU': 55.1, 'ROUGE-1': 7_6},
      }





     def     __UpperCamelCase						(		self					:	Optional[Any])							->	Any:

      """simple docstring"""
      SCREAMING_SNAKE_CASE				    =		ModelCard.from_dict(self.inputs_dict)
      self.assertTrue(hasattr(lowerCAmelCase__  ,  'model_details'))
      self.assertTrue(hasattr(lowerCAmelCase__  ,  'intended_use'))
      self.assertTrue(hasattr(lowerCAmelCase__  ,  'factors'))
      self.assertTrue(hasattr(lowerCAmelCase__  ,  'metrics'))
      self.assertTrue(hasattr(lowerCAmelCase__  ,  'evaluation_data'))
      self.assertTrue(hasattr(lowerCAmelCase__  ,  'training_data'))
      self.assertTrue(hasattr(lowerCAmelCase__  ,  'quantitative_analyses'))
      self.assertTrue(hasattr(lowerCAmelCase__  ,  'ethical_considerations'))
      self.assertTrue(hasattr(lowerCAmelCase__  ,  'caveats_and_recommendations'))





     def     __UpperCamelCase						(		self					:	Any)							->	Any:

      """simple docstring"""
      SCREAMING_SNAKE_CASE				    =		ModelCard.from_dict(self.inputs_dict)
      SCREAMING_SNAKE_CASE				    =		json.loads(modelcard.to_json_string())
      for key, value in self.inputs_dict.items():
       self.assertEqual(obj[key]  ,  lowerCAmelCase__)





     def     __UpperCamelCase						(		self					:	int)							->	Optional[Any]:

      """simple docstring"""
      SCREAMING_SNAKE_CASE				    =		ModelCard.from_dict(self.inputs_dict)

      with tempfile.TemporaryDirectory() as tmpdirname:
       SCREAMING_SNAKE_CASE				    =		os.path.join(lowerCAmelCase__  ,  'modelcard.json')
       model_card_first.to_json_file(lowerCAmelCase__)
       SCREAMING_SNAKE_CASE				    =		ModelCard.from_json_file(lowerCAmelCase__)

      self.assertEqual(model_card_second.to_dict()  ,  model_card_first.to_dict())





     def     __UpperCamelCase						(		self					:	Optional[int])							->	Any:

      """simple docstring"""
      SCREAMING_SNAKE_CASE				    =		ModelCard.from_dict(self.inputs_dict)

      with tempfile.TemporaryDirectory() as tmpdirname:
       model_card_first.save_pretrained(lowerCAmelCase__)
       SCREAMING_SNAKE_CASE				    =		ModelCard.from_pretrained(lowerCAmelCase__)

      self.assertEqual(model_card_second.to_dict()  ,  model_card_first.to_dict())




