from __future__ import annotations


def       a__      (					a  ,   a							)		->	Dict:
       A_							:       List[str]	      =				0
       A_							:       Optional[Any]	      =				len(a							) - 1
       while left <= right:
              A_							:       Optional[int]	      =				(left + right) // 2
              if left == right:
                     if sorted_list[middle] < item:
                            A_							:       Tuple	      =				middle + 1
                     break
              elif sorted_list[middle] < item:
                     A_							:       int	      =				middle + 1
              else:
                     A_							:       Optional[int]	      =				middle - 1
       sorted_list.insert(a  ,   a							)
       return sorted_list


def       a__      (					a  ,   a							)		->	Dict:
       A_							:       int	      =				[]
       while left and right:
              if left[0][0] < right[0][0]:
                     result.append(left.pop(0							)							)
              else:
                     result.append(right.pop(0							)							)
       return result + left + right


def       a__      (					a							)		->	List[str]:
       A_							:       str	      =				len(a							)
       if length <= 1:
              return list_ad
       A_							:       int	      =				length // 2
       return merge(sortlist_ad(list_ad[:middle]							)  ,   sortlist_ad(list_ad[middle:]							)							)


def       a__      (					a							)		->	list[int]:

       if len(a							) <= 1:
              return collection
       A_							:       Any	      =				[]
       A_							:       Union[str, Any]	      =				False
       for i in range(0  ,   len(a							)  ,   2							):
              if i == len(a							) - 1:
                     A_							:       int	      =				True
              else:
                     if collection[i] < collection[i + 1]:
                            two_paired_list.append([collection[i], collection[i + 1]]							)
                     else:
                            two_paired_list.append([collection[i + 1], collection[i]]							)
       A_							:       Any	      =				sortlist_ad(a							)
       A_							:       Optional[int]	      =				[i[0] for i in sorted_list_ad]
       result.append(sorted_list_ad[-1][1]							)
       if has_last_odd_item:
              A_							:       int	      =				collection[-1]
              A_							:       Any	      =				binary_search_insertion(a  ,   a							)
       A_							:       Optional[Any]	      =				False
       for i in range(len(a							) - 1							):
              if result[i] == collection[-1] and has_last_odd_item:
                     A_							:       Optional[int]	      =				True
              A_							:       Optional[Any]	      =				sorted_list_ad[i][1]
              # If last_odd_item is inserted before the item's index,
              # you should forward index one more.
              if is_last_odd_item_inserted_before_this_index:
                     A_							:       str	      =				result[: i + 2] + binary_search_insertion(result[i + 2 :]  ,   a							)
              else:
                     A_							:       int	      =				result[: i + 1] + binary_search_insertion(result[i + 1 :]  ,   a							)

       return result


if __name__ == "__main__":
    _lowerCAmelCase        =       input('Enter numbers separated by a comma:\n').strip()
    _lowerCAmelCase        =       [int(item) for item in user_input.split(',')]
    print(merge_insertion_sort(unsorted))



import json
import os
import tempfile
import unittest

from transformers.modelcard import ModelCard






class     __UpperCAmelCase(    unittest.TestCase				):






 """simple docstring"""




 def  UpperCAmelCase   (    self      ):

        """simple docstring"""

        A_							:       Optional[int]	      =				{
            '''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''': 76},
        }
 def  UpperCAmelCase   (    self      ):

        """simple docstring"""

        A_							:       Tuple	      =				ModelCard.from_dict(self.inputs_dict      )
        self.assertTrue(hasattr(__magic_name__ ,       '''model_details'''      )      )
        self.assertTrue(hasattr(__magic_name__ ,       '''intended_use'''      )      )
        self.assertTrue(hasattr(__magic_name__ ,       '''factors'''      )      )
        self.assertTrue(hasattr(__magic_name__ ,       '''metrics'''      )      )
        self.assertTrue(hasattr(__magic_name__ ,       '''evaluation_data'''      )      )
        self.assertTrue(hasattr(__magic_name__ ,       '''training_data'''      )      )
        self.assertTrue(hasattr(__magic_name__ ,       '''quantitative_analyses'''      )      )
        self.assertTrue(hasattr(__magic_name__ ,       '''ethical_considerations'''      )      )
        self.assertTrue(hasattr(__magic_name__ ,       '''caveats_and_recommendations'''      )      )
 def  UpperCAmelCase   (    self      ):

        """simple docstring"""

        A_							:       Optional[Any]	      =				ModelCard.from_dict(self.inputs_dict      )
        A_							:       List[Any]	      =				json.loads(modelcard.to_json_string()      )
        for key, value in self.inputs_dict.items():
               self.assertEqual(obj[key] ,       __magic_name__      )
 def  UpperCAmelCase   (    self      ):

        """simple docstring"""

        A_							:       Dict	      =				ModelCard.from_dict(self.inputs_dict      )

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

        self.assertEqual(model_card_second.to_dict() ,       model_card_first.to_dict()      )
 def  UpperCAmelCase   (    self      ):

        """simple docstring"""

        A_							:       List[str]	      =				ModelCard.from_dict(self.inputs_dict      )

        with tempfile.TemporaryDirectory() as tmpdirname:
               model_card_first.save_pretrained(__magic_name__      )
               A_							:       Optional[int]	      =				ModelCard.from_pretrained(__magic_name__      )

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


