
"""simple docstring"""




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


def   __magic_name__     (				__snake_case    :	List[str]  )			->    str:

       def remove_articles(__snake_case    :	Any  ):
              return re.sub(r"\b(a|an|the)\b"					,			" "					,			__snake_case  )

       def white_space_fix(__snake_case    :	Union[str, Any]  ):
              return " ".join(text.split()  )

       def remove_punc(__snake_case    :	Optional[Any]  ):
              lowercase   :    Dict			=							set(string.punctuation  )
              return "".join(ch for ch in text if ch not in exclude  )

       def lower(__snake_case    :	Dict  ):
              return text.lower()

       return white_space_fix(remove_articles(remove_punc(lower(__snake_case  )  )  )  )


def   __magic_name__     (				__snake_case    :	List[str]					,			__snake_case    :	Dict  )			->    List[str]:
       lowercase   :    List[str]			=							normalize_answer(__snake_case  ).split()
       lowercase   :    Tuple			=							normalize_answer(__snake_case  ).split()
       lowercase   :    Dict			=							Counter(__snake_case  ) & Counter(__snake_case  )
       lowercase   :    Any			=							sum(common.values()  )
       if num_same == 0:
              return 0
       lowercase   :    Dict			=							1.0 * num_same / len(__snake_case  )
       lowercase   :    List[Any]			=							1.0 * num_same / len(__snake_case  )
       lowercase   :    int			=							(2 * precision * recall) / (precision + recall)
       return fa


def   __magic_name__     (				__snake_case    :	Tuple					,			__snake_case    :	Optional[int]  )			->    Tuple:
       return normalize_answer(__snake_case  ) == normalize_answer(__snake_case  )


def   __magic_name__     (				__snake_case    :	Dict					,			__snake_case    :	Tuple					,			__snake_case    :	int  )			->    Optional[Any]:
       lowercase   :    List[str]			=							[]
       for ground_truth in ground_truths:
              lowercase   :    str			=							metric_fn(__snake_case					,			__snake_case  )
              scores_for_ground_truths.append(__snake_case  )
       return max(__snake_case  )


def   __magic_name__     (				__snake_case    :	Dict					,			__snake_case    :	Dict  )			->    Optional[Any]:
       lowercase   :    str			=							0
       for article in dataset:
              for paragraph in article["paragraphs"]:
                     for qa in paragraph["qas"]:
                            total += 1
                            if qa["id"] not in predictions:
                                   lowercase   :    List[str]			=							"Unanswered question " + qa["id"] + " will receive score 0."
                                   print(__snake_case					,			file=sys.stderr  )
                                   continue
                            lowercase   :    Union[str, Any]			=							[x["text"] for x in qa["answers"]]
                            lowercase   :    Any			=							predictions[qa["id"]]
                            exact_match += metric_max_over_ground_truths(__snake_case					,			__snake_case					,			__snake_case  )
                            fa += metric_max_over_ground_truths(__snake_case					,			__snake_case					,			__snake_case  )

       lowercase   :    int			=							1_00.0 * exact_match / total
       lowercase   :    Dict			=							1_00.0 * fa / total

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


if __name__ == "__main__":
   _A      :	Dict       		=	"""1.1"""
   _A      :	Any       		=	argparse.ArgumentParser(description="""Evaluation for SQuAD """ + expected_version)
   parser.add_argument("""dataset_file""", help="""Dataset file""")
   parser.add_argument("""prediction_file""", help="""Prediction File""")
   _A      :	Union[str, Any]       		=	parser.parse_args()
   with open(args.dataset_file) as dataset_file:
      _A      :	Optional[Any]       		=	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,
         )
      _A      :	Dict       		=	dataset_json["""data"""]
   with open(args.prediction_file) as prediction_file:
      _A      :	Optional[int]       		=	json.load(prediction_file)
   print(json.dumps(evaluate(dataset, predictions)))




"""simple docstring"""




from __future__ import annotations

_A      :	List[str]       		=	"""Alexander Joslin"""


def   __magic_name__     (				__snake_case    :	int  )			->    int:

       if number <= 0:
              lowercase   :    Optional[Any]			=							f"""n must be greater than 0. Got n = {number}"""
              raise ValueError(__snake_case  )

       lowercase   :    List[str]			=							[number + 1] * (number + 1)

       # starting position
       lowercase   :    Optional[Any]			=							0
       for i in range(1					,			__snake_case  ):
              lowercase   :    str			=							min(table[i + 1]					,			table[i] + 1  )
              # check if out of bounds
              if i * 2 <= number:
                     lowercase   :    int			=							min(table[i * 2]					,			table[i] + 1  )
              # check if out of bounds
              if i * 3 <= number:
                     lowercase   :    Optional[int]			=							min(table[i * 3]					,			table[i] + 1  )
       return table[number]


if __name__ == "__main__":
   import doctest

   doctest.testmod()


