




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






def 				UpperCAmelCase__  (							_A			):






  """simple docstring"""


  def remove_articles(_A			):
    return re.sub(R'''\b(a|an|the)\b'''      ,      ''' '''      ,      _A			)

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

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

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

  return white_space_fix(remove_articles(remove_punc(lower(_A			)			)			)			)






def 				UpperCAmelCase__  (							_A      ,      _A			):






  """simple docstring"""


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






def 				UpperCAmelCase__  (							_A      ,      _A			):






  """simple docstring"""


  return normalize_answer(_A			) == normalize_answer(_A			)






def 				UpperCAmelCase__  (							_A      ,      _A      ,      _A			):






  """simple docstring"""


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






def 				UpperCAmelCase__  (							_A      ,      _A			):






  """simple docstring"""


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

  a_		    =  1_00.0 * exact_match / total
  a_		    =  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)))






def 				UpperCAmelCase__  (							_A      ,      _A			):






  """simple docstring"""


  if not root or root not in binary_tree_mirror_dictionary:
    return
  a_    , a_		    =  binary_tree_mirror_dictionary[root][:2]
  a_		    =  [right_child, left_child]
  binary_tree_mirror_dict(_A      ,      _A			)
  binary_tree_mirror_dict(_A      ,      _A			)






def 				UpperCAmelCase__  (							_A      ,      _A = 1			):






  """simple docstring"""


  if not binary_tree:
    raise ValueError('''binary tree cannot be empty'''			)
  if root not in binary_tree:
    a_		    =  f"root {root} is not present in the binary_tree"
    raise ValueError(_A			)
  a_		    =  dict(_A			)
  binary_tree_mirror_dict(_A      ,      _A			)
  return binary_tree_mirror_dictionary


if __name__ == "__main__":
  UpperCamelCase__       				=	{1: [2, 3], 2: [4, 5], 3: [6, 7], 7: [8, 9]}
  print(F"""Binary tree: {binary_tree}""")
  UpperCamelCase__       				=	binary_tree_mirror(binary_tree, 5)
  print(F"""Binary tree mirror: {binary_tree_mirror_dictionary}""")
