

import os
import sys
import time

from . import transposition_cipher as trans_cipher





def  _snake_case		()							->		None:
  __UpperCAmelCase	:  int		=     'Prehistoric Men.txt'
  __UpperCAmelCase	:  int		=     'Output.txt'
  __UpperCAmelCase	:  Tuple		=     int(input('Enter key: '  )  )
  __UpperCAmelCase	:  Tuple		=     input('Encrypt/Decrypt [e/d]: '  )

  if not os.path.exists(snake_case_  ):
    print(F"File {input_file} does not exist. Quitting..."  )
    sys.exit()
  if os.path.exists(snake_case_  ):
    print(F"Overwrite {output_file}? [y/n]"  )
    __UpperCAmelCase	:  Optional[int]		=     input('> '  )
    if not response.lower().startswith('y'  ):
      sys.exit()

  __UpperCAmelCase	:  Tuple		=     time.time()
  if mode.lower().startswith('e'  ):
    with open(snake_case_  ) as f:
      __UpperCAmelCase	:  Optional[Any]		=     f.read()
    __UpperCAmelCase	:  int		=     trans_cipher.encrypt_message(snake_case_    ,       snake_case_  )
  elif mode.lower().startswith('d'  ):
    with open(snake_case_  ) as f:
      __UpperCAmelCase	:  int		=     f.read()
    __UpperCAmelCase	:  List[str]		=     trans_cipher.decrypt_message(snake_case_    ,       snake_case_  )

  with open(snake_case_    ,       'w'  ) as output_obj:
    output_obj.write(snake_case_  )

  __UpperCAmelCase	:  List[str]		=     round(time.time() - start_time    ,       2  )
  print(('Done (', total_time, 'seconds )')  )


if __name__ == "__main__":
      main()













"""simple docstring"""





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






def 							A(    snake_case_						):
       """simple docstring"""





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

       def white_space_fix(snake_case_						):
              return " ".join(text.split()						)

       def remove_punc(snake_case_						):
              lowercase__:							str         =     set(string.punctuation						)
              return "".join(ch for ch in text if ch not in exclude						)

       def lower(snake_case_						):
              return text.lower()

       return white_space_fix(remove_articles(remove_punc(lower(snake_case_						)						)						)						)






def 							A(    snake_case_		,  snake_case_						):
       """simple docstring"""





       lowercase__:							Dict         =     normalize_answer(snake_case_						).split()
       lowercase__:							List[str]         =     normalize_answer(snake_case_						).split()
       lowercase__:							Optional[int]         =     Counter(snake_case_						) & Counter(snake_case_						)
       lowercase__:							Any         =     sum(common.values()						)
       if num_same == 0:
              return 0
       lowercase__:							Any         =     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 							A(    snake_case_		,  snake_case_						):
       """simple docstring"""





       return normalize_answer(snake_case_						) == normalize_answer(snake_case_						)






def 							A(    snake_case_		,  snake_case_		,  snake_case_						):
       """simple docstring"""





       lowercase__:							int         =     []
       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 							A(    snake_case_		,  snake_case_						):
       """simple docstring"""





       lowercase__:							Optional[int]         =     0
       for article in dataset:
              for paragraph in article["paragraphs"]:
                     for qa in paragraph["qas"]:
                            total += 1
                            if qa["id"] not in predictions:
                                   lowercase__:							Union[str, Any]         =     "Unanswered question " + qa["id"] + " will receive score 0."
                                   print(snake_case_		,  file=sys.stderr						)
                                   continue
                            lowercase__:							List[Any]         =     [x["text"] for x in qa["answers"]]
                            lowercase__:							Optional[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__:							Union[str, Any]         =     1_00.0 * exact_match / total
       lowercase__:							str         =     1_00.0 * fa / total

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


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






