
"""simple docstring"""

import argparse
import json
import re
import string
import sys
from collections import Counter




def       lowercase__  (   lowerCAmelCase			:   Union[str, Any]	)	-> Dict:






    """simple docstring"""




    def remove_articles(lowerCAmelCase			:   Optional[Any]	):
        return re.sub(r'\b(a|an|the)\b'	,			' '	,			lowerCAmelCase	)

    def white_space_fix(lowerCAmelCase			:   Optional[Any]	):
        return " ".join(text.split()	)

    def remove_punc(lowerCAmelCase			:   Optional[int]	):
        UpperCAmelCase       =   set(string.punctuation	)
        return "".join(ch for ch in text if ch not in exclude	)

    def lower(lowerCAmelCase			:   Any	):
        return text.lower()

    return white_space_fix(remove_articles(remove_punc(lower(lowerCAmelCase	)	)	)	)




def       lowercase__  (   lowerCAmelCase			:   Optional[int]	,			lowerCAmelCase			:   Optional[Any]	)	-> Union[str, Any]:






    """simple docstring"""




    UpperCAmelCase       =   normalize_answer(lowerCAmelCase	).split()
    UpperCAmelCase       =   normalize_answer(lowerCAmelCase	).split()
    UpperCAmelCase       =   Counter(lowerCAmelCase	) & Counter(lowerCAmelCase	)
    UpperCAmelCase       =   sum(common.values()	)
    if num_same == 0:
        return 0
    UpperCAmelCase       =   1.0 * num_same / len(lowerCAmelCase	)
    UpperCAmelCase       =   1.0 * num_same / len(lowerCAmelCase	)
    UpperCAmelCase       =   (2 * precision * recall) / (precision + recall)
    return fa




def       lowercase__  (   lowerCAmelCase			:   Union[str, Any]	,			lowerCAmelCase			:   str	)	-> Union[str, Any]:






    """simple docstring"""




    return normalize_answer(lowerCAmelCase	) == normalize_answer(lowerCAmelCase	)




def       lowercase__  (   lowerCAmelCase			:   Any	,			lowerCAmelCase			:   Optional[Any]	,			lowerCAmelCase			:   Union[str, Any]	)	-> List[Any]:






    """simple docstring"""




    UpperCAmelCase       =   []
    for ground_truth in ground_truths:
        UpperCAmelCase       =   metric_fn(lowerCAmelCase	,			lowerCAmelCase	)
        scores_for_ground_truths.append(lowerCAmelCase	)
    return max(lowerCAmelCase	)




def       lowercase__  (   lowerCAmelCase			:   Optional[int]	,			lowerCAmelCase			:   int	)	-> List[Any]:






    """simple docstring"""




    UpperCAmelCase       =   UpperCAmelCase       =   UpperCAmelCase       =   0
    for article in dataset:
        for paragraph in article["paragraphs"]:
            for qa in paragraph["qas"]:
                total += 1
                if qa["id"] not in predictions:
                    UpperCAmelCase       =   'Unanswered question ' + qa['id'] + ' will receive score 0.'
                    print(lowerCAmelCase	,			file=sys.stderr	)
                    continue
                UpperCAmelCase       =   [x['text'] for x in qa['answers']]
                UpperCAmelCase       =   predictions[qa['id']]
                exact_match += metric_max_over_ground_truths(lowerCAmelCase	,			lowerCAmelCase	,			lowerCAmelCase	)
                fa += metric_max_over_ground_truths(lowerCAmelCase	,			lowerCAmelCase	,			lowerCAmelCase	)

    UpperCAmelCase       =   100.0 * exact_match / total
    UpperCAmelCase       =   100.0 * fa / total

    return {"exact_match": exact_match, "f1": fa}


if __name__ == "__main__":
   SCREAMING_SNAKE_CASE_         =  '''1.1'''
   SCREAMING_SNAKE_CASE_         =  argparse.ArgumentParser(description='''Evaluation for SQuAD ''' + expected_version)
   parser.add_argument('''dataset_file''', help='''Dataset file''')
   parser.add_argument('''prediction_file''', help='''Prediction File''')
   SCREAMING_SNAKE_CASE_         =  parser.parse_args()
   with open(args.dataset_file) as dataset_file:
      SCREAMING_SNAKE_CASE_         =  json.load(dataset_file)
      if dataset_json["version"] != expected_version:
         print(
             '''Evaluation expects v-''' + expected_version + ''', but got dataset with v-''' + dataset_json['''version'''],
             file=sys.stderr,
         )
      SCREAMING_SNAKE_CASE_         =  dataset_json['''data''']
   with open(args.prediction_file) as prediction_file:
      SCREAMING_SNAKE_CASE_         =  json.load(prediction_file)
   print(json.dumps(evaluate(dataset, predictions)))





"""simple docstring"""

import heapq




def       lowercase__  (   lowerCAmelCase			:   str	,			lowerCAmelCase			:   int	,			lowerCAmelCase			:   Optional[Any]	)	-> Union[str, Any]:






    """simple docstring"""




    UpperCAmelCase       =   [(0, start)]  # cost from start node,end node
    UpperCAmelCase       =   set()
    while heap:
        ((UpperCAmelCase)     ,							(UpperCAmelCase))       =   heapq.heappop(lowerCAmelCase	)
        if u in visited:
            continue
        visited.add(lowerCAmelCase	)
        if u == end:
            return cost
        for v, c in graph[u]:
            if v in visited:
                continue
            UpperCAmelCase       =   cost + c
            heapq.heappush(lowerCAmelCase	,			(next_item, v)	)
    return -1


SCREAMING_SNAKE_CASE_         =  {
    '''A''': [['''B''', 2], ['''C''', 5]],
    '''B''': [['''A''', 2], ['''D''', 3], ['''E''', 1], ['''F''', 1]],
    '''C''': [['''A''', 5], ['''F''', 3]],
    '''D''': [['''B''', 3]],
    '''E''': [['''B''', 4], ['''F''', 3]],
    '''F''': [['''C''', 3], ['''E''', 3]],
}
SCREAMING_SNAKE_CASE_         =  {
    '''B''': [['''C''', 1]],
    '''C''': [['''D''', 1]],
    '''D''': [['''F''', 1]],
    '''E''': [['''B''', 1], ['''F''', 3]],
    '''F''': [],
}
SCREAMING_SNAKE_CASE_         =  {
    '''B''': [['''C''', 1]],
    '''C''': [['''D''', 1]],
    '''D''': [['''F''', 1]],
    '''E''': [['''B''', 1], ['''G''', 2]],
    '''F''': [],
    '''G''': [['''F''', 1]],
}

SCREAMING_SNAKE_CASE_         =  dijkstra(G, '''E''', '''C''')
print(short_distance)  # E -- 3 --> F -- 3 --> C == 6

SCREAMING_SNAKE_CASE_         =  dijkstra(Ga, '''E''', '''F''')
print(short_distance)  # E -- 3 --> F == 3

SCREAMING_SNAKE_CASE_         =  dijkstra(Ga, '''E''', '''F''')
print(short_distance)  # E -- 2 --> G -- 1 --> F == 3

if __name__ == "__main__":
   import doctest

   doctest.testmod()



