


from __future__ import annotations




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




def   snake_case_	(    snake_case     ,				snake_case	)    ->							Any:
       lowercase__:  Tuple         =  []
       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   snake_case_	(    snake_case	)    ->							Optional[int]:
       lowercase__:  Optional[int]         =  len(snake_case	)
       if length <= 1:
              return list_ad
       lowercase__:  int         =  length // 2
       return merge(sortlist_ad(list_ad[:middle]	)     ,				sortlist_ad(list_ad[middle:]	)	)




def   snake_case_	(    snake_case	)    ->							list[int]:

       if len(snake_case	) <= 1:
              return collection
       lowercase__:  Optional[int]         =  []
       lowercase__:  Union[str, Any]         =  False
       for i in range(0     ,				len(snake_case	)     ,				2	):
              if i == len(snake_case	) - 1:
                     lowercase__:  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]]	)
       lowercase__:  Dict         =  sortlist_ad(snake_case	)
       lowercase__:  List[str]         =  [i[0] for i in sorted_list_ad]
       result.append(sorted_list_ad[-1][1]	)
       if has_last_odd_item:
              lowercase__:  Dict         =  collection[-1]
              lowercase__:  List[str]         =  binary_search_insertion(snake_case     ,				snake_case	)
       lowercase__:  int         =  False
       for i in range(len(snake_case	) - 1	):
              if result[i] == collection[-1] and has_last_odd_item:
                     lowercase__:  int         =  True
              lowercase__:  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:
                     lowercase__:  Tuple         =  result[: i + 2] + binary_search_insertion(result[i + 2 :]     ,				snake_case	)
              else:
                     lowercase__:  Optional[int]         =  result[: i + 1] + binary_search_insertion(result[i + 1 :]     ,				snake_case	)

       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 datasets

from .evaluate import (
    apply_no_ans_threshold,
    find_all_best_thresh,
    get_raw_scores,
    make_eval_dict,
    make_qid_to_has_ans,
    merge_eval,
)


__lowerCAmelCase									=				'''\
@inproceedings{Rajpurkar2016SQuAD10,
  title={SQuAD: 100, 000+ Questions for Machine Comprehension of Text},
  author={Pranav Rajpurkar and Jian Zhang and Konstantin Lopyrev and Percy Liang},
  booktitle={EMNLP},
  year={2016}
}
'''

__lowerCAmelCase									=				'''
This metric wrap the official scoring script for version 2 of the Stanford Question
Answering Dataset (SQuAD).

Stanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by
crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span,
from the corresponding reading passage, or the question might be unanswerable.

SQuAD2.0 combines the 100,000 questions in SQuAD1.1 with over 50,000 unanswerable questions
written adversarially by crowdworkers to look similar to answerable ones.
To do well on SQuAD2.0, systems must not only answer questions when possible, but also
determine when no answer is supported by the paragraph and abstain from answering.
'''

__lowerCAmelCase									=				'''
Computes SQuAD v2 scores (F1 and EM).
Args:
    predictions: List of triple for question-answers to score with the following elements:
        - the question-answer \'id\' field as given in the references (see below)
        - the text of the answer
        - the probability that the question has no answer
    references: List of question-answers dictionaries with the following key-values:
            - \'id\': id of the question-answer pair (see above),
            - \'answers\': a list of Dict {\'text\': text of the answer as a string}
    no_answer_threshold: float
        Probability threshold to decide that a question has no answer.
Returns:
    \'exact\': Exact match (the normalized answer exactly match the gold answer)
    \'f1\': The F-score of predicted tokens versus the gold answer
    \'total\': Number of score considered
    \'HasAns_exact\': Exact match (the normalized answer exactly match the gold answer)
    \'HasAns_f1\': The F-score of predicted tokens versus the gold answer
    \'HasAns_total\': Number of score considered
    \'NoAns_exact\': Exact match (the normalized answer exactly match the gold answer)
    \'NoAns_f1\': The F-score of predicted tokens versus the gold answer
    \'NoAns_total\': Number of score considered
    \'best_exact\': Best exact match (with varying threshold)
    \'best_exact_thresh\': No-answer probability threshold associated to the best exact match
    \'best_f1\': Best F1 (with varying threshold)
    \'best_f1_thresh\': No-answer probability threshold associated to the best F1
Examples:

    >>> predictions = [{\'prediction_text\': \'1976\', \'id\': \'56e10a3be3433e1400422b22\', \'no_answer_probability\': 0.}]
    >>> references = [{\'answers\': {\'answer_start\': [97], \'text\': [\'1976\']}, \'id\': \'56e10a3be3433e1400422b22\'}]
    >>> squad_v2_metric = datasets.load_metric("squad_v2")
    >>> results = squad_v2_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {\'exact\': 100.0, \'f1\': 100.0, \'total\': 1, \'HasAns_exact\': 100.0, \'HasAns_f1\': 100.0, \'HasAns_total\': 1, \'best_exact\': 100.0, \'best_exact_thresh\': 0.0, \'best_f1\': 100.0, \'best_f1_thresh\': 0.0}
'''





@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION			,			_KWARGS_DESCRIPTION )
class     __a     (       datasets.Metric ):
 def 						SCREAMING_SNAKE_CASE__  (  self					)   ->							Dict:




        '''simple docstring'''
        return datasets.MetricInfo(
            description=_DESCRIPTION							,					citation=_CITATION							,					inputs_description=_KWARGS_DESCRIPTION							,					features=datasets.Features(
                {
                    'predictions': {
                        'id': datasets.Value('string'					),
                        'prediction_text': datasets.Value('string'					),
                        'no_answer_probability': datasets.Value('float32'					),
                    },
                    'references': {
                        'id': datasets.Value('string'					),
                        'answers': datasets.features.Sequence(
                            {'text': datasets.Value('string'					), 'answer_start': datasets.Value('int32'					)}					),
                    },
                }					)							,					codebase_urls=['https://rajpurkar.github.io/SQuAD-explorer/']							,					reference_urls=['https://rajpurkar.github.io/SQuAD-explorer/']							,					)
 def 						SCREAMING_SNAKE_CASE__  (  self							,					lowerCAmelCase__							,					lowerCAmelCase__							,					lowerCAmelCase__=1.0					)   ->							str:




        '''simple docstring'''
        lowercase__:  int         =  {p['id']: p['no_answer_probability'] for p in predictions}
        lowercase__:  int         =  [{'paragraphs': [{'qas': references}]}]
        lowercase__:  Optional[int]         =  {p['id']: p['prediction_text'] for p in predictions}

        lowercase__:  Any         =  make_qid_to_has_ans(lowerCAmelCase__					)  # maps qid to True/False
        lowercase__:  Optional[Any]         =  [k for k, v in qid_to_has_ans.items() if v]
        lowercase__:  Dict         =  [k for k, v in qid_to_has_ans.items() if not v]

        lowercase__     ,		lowercase__:  Dict         =  get_raw_scores(lowerCAmelCase__							,					lowerCAmelCase__					)
        lowercase__:  Optional[int]         =  apply_no_ans_threshold(lowerCAmelCase__							,					lowerCAmelCase__							,					lowerCAmelCase__							,					lowerCAmelCase__					)
        lowercase__:  List[Any]         =  apply_no_ans_threshold(lowerCAmelCase__							,					lowerCAmelCase__							,					lowerCAmelCase__							,					lowerCAmelCase__					)
        lowercase__:  Dict         =  make_eval_dict(lowerCAmelCase__							,					lowerCAmelCase__					)

        if has_ans_qids:
               lowercase__:  List[Any]         =  make_eval_dict(lowerCAmelCase__							,					lowerCAmelCase__							,					qid_list=lowerCAmelCase__					)
               merge_eval(lowerCAmelCase__							,					lowerCAmelCase__							,					'HasAns'					)
        if no_ans_qids:
               lowercase__:  int         =  make_eval_dict(lowerCAmelCase__							,					lowerCAmelCase__							,					qid_list=lowerCAmelCase__					)
               merge_eval(lowerCAmelCase__							,					lowerCAmelCase__							,					'NoAns'					)
        find_all_best_thresh(lowerCAmelCase__							,					lowerCAmelCase__							,					lowerCAmelCase__							,					lowerCAmelCase__							,					lowerCAmelCase__							,					lowerCAmelCase__					)
        return dict(lowerCAmelCase__					)


