
'''simple docstring'''





import argparse
import json
import re
import string
import sys

import numpy as np


SCREAMING_SNAKE_CASE    =     0.5







def snake_case_    (				lowercase__      ,					lowercase__		):
  UpperCAmelCase__			:				int												=       ['''.''', ''',''', ''';''', ''':''']

  for token in remove_tokens:
    UpperCAmelCase__			:				int												=       ground_truth.replace(lowercase_      ,					""		)
    UpperCAmelCase__			:				Tuple												=       prediction.replace(lowercase_      ,					""		)

  UpperCAmelCase__			:				List[str]												=       ground_truth.lower(), prediction.lower()
  UpperCAmelCase__			:				List[Any]												=       ground_truth.replace("/"      ,					" "		), prediction.replace("/"      ,					" "		)
  UpperCAmelCase__			:				Tuple												=       set(ground_truth.split(" "		)		), set(prediction.split(" "		)		)

  UpperCAmelCase__			:				Union[str, Any]												=       ground_truth.intersection(lowercase_		)
  UpperCAmelCase__			:				int												=       ground_truth.union(lowercase_		)
  UpperCAmelCase__			:				int												=       len(lowercase_		) / len(lowercase_		)
  return jaccard







def snake_case_    (				lowercase__		):

  def remove_articles(lowercase__		):
    return re.sub(R"\b(a|an|the)\b"      ,					" "      ,					lowercase_		)

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

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

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

  return white_space_fix(remove_articles(remove_punc(lower(lowercase_		)		)		)		)







def snake_case_    (				lowercase__      ,					lowercase__      ,					lowercase__		):
  UpperCAmelCase__			:				Tuple												=       0, 0, 0

  UpperCAmelCase__			:				Optional[int]												=       '''Parties''' in qa_id

  # first check if ground truth is empty
  if len(lowercase_		) == 0:
    if len(lowercase_		) > 0:
      fp += len(lowercase_		)  # false positive for each one
  else:
    for ground_truth in ground_truths:
      assert len(lowercase_		) > 0
      # check if there is a match
      UpperCAmelCase__			:				Optional[Any]												=       False
      for pred in predictions:
        if substr_ok:
          UpperCAmelCase__			:				List[Any]												=       get_jaccard(lowercase_      ,					lowercase_		) >= IOU_THRESH or ground_truth in pred
        else:
          UpperCAmelCase__			:				Any												=       get_jaccard(lowercase_      ,					lowercase_		) >= IOU_THRESH
        if is_match:
          UpperCAmelCase__			:				Union[str, Any]												=       True

      if match_found:
        tp += 1
      else:
        fn += 1

        # now also get any fps by looping through preds
    for pred in predictions:
      # Check if there's a match. if so, don't count (don't want to double count based on the above)
      # but if there's no match, then this is a false positive.
      # (Note: we get the true positives in the above loop instead of this loop so that we don't double count
      # multiple predictions that are matched with the same answer.)
      UpperCAmelCase__			:				Tuple												=       False
      for ground_truth in ground_truths:
        assert len(lowercase_		) > 0
        if substr_ok:
          UpperCAmelCase__			:				str												=       get_jaccard(lowercase_      ,					lowercase_		) >= IOU_THRESH or ground_truth in pred
        else:
          UpperCAmelCase__			:				Dict												=       get_jaccard(lowercase_      ,					lowercase_		) >= IOU_THRESH
        if is_match:
          UpperCAmelCase__			:				Optional[Any]												=       True

      if not match_found:
        fp += 1

  UpperCAmelCase__			:				Dict												=       tp / (tp + fp) if tp + fp > 0 else np.nan
  UpperCAmelCase__			:				str												=       tp / (tp + fn) if tp + fn > 0 else np.nan

  return precision, recall







def snake_case_    (				lowercase__		):
  UpperCAmelCase__			:				Optional[Any]												=       precisions[::-1]
  for i in range(1      ,					len(lowercase_		)		):
    UpperCAmelCase__			:				Any												=       max(precision_best[i - 1]      ,					precision_best[i]		)
  UpperCAmelCase__			:				Tuple												=       precision_best[::-1]
  return precisions







def snake_case_    (				lowercase__      ,					lowercase__		):
  UpperCAmelCase__			:				List[str]												=       process_precisions(lowercase_		)
  UpperCAmelCase__			:				Tuple												=       np.trapz(lowercase_      ,					lowercase_		)
  if np.isnan(lowercase_		):
    return 0
  return aupr







def snake_case_    (				lowercase__      ,					lowercase__      ,					lowercase__		):
  UpperCAmelCase__			:				Optional[int]												=       process_precisions(lowercase_		)
  UpperCAmelCase__			:				Dict												=       0
  for prec, recall in zip(lowercase_      ,					lowercase_		):
    if recall >= recall_thresh:
      UpperCAmelCase__			:				Optional[Any]												=       prec
      break
  return prec_at_recall







def snake_case_    (				lowercase__      ,					lowercase__		):
  return normalize_answer(lowercase_		) == normalize_answer(lowercase_		)







def snake_case_    (				lowercase__      ,					lowercase__      ,					lowercase__		):
  UpperCAmelCase__			:				Any												=       0
  for pred in predictions:
    for ground_truth in ground_truths:
      UpperCAmelCase__			:				Optional[int]												=       metric_fn(lowercase_      ,					lowercase_		)
      if score == 1:  # break the loop when one prediction matches the ground truth
        break
    if score == 1:
      break
  return score







def snake_case_    (				lowercase__      ,					lowercase__		):
  UpperCAmelCase__			:				List[Any]												=       0
  UpperCAmelCase__			:				Any												=       []
  UpperCAmelCase__			:				int												=       []
  for article in dataset:
    for paragraph in article["paragraphs"]:
      for qa in paragraph["qas"]:
        total += 1
        if qa["id"] not in predictions:
          UpperCAmelCase__			:				int												=       '''Unanswered question ''' + qa['''id'''] + ''' will receive score 0.'''
          print(lowercase_      ,					file=sys.stderr		)
          continue
        UpperCAmelCase__			:				List[str]												=       [x['''text'''] for x in qa['''answers''']]
        UpperCAmelCase__			:				str												=       predictions[qa['''id''']]
        UpperCAmelCase__			:				List[str]												=       compute_precision_recall(lowercase_      ,					lowercase_      ,					qa["id"]		)

        precisions.append(lowercase_		)
        recalls.append(lowercase_		)

        if precision == 0 and recall == 0:
          fa += 0
        else:
          fa += 2 * (precision * recall) / (precision + recall)

        exact_match += metric_max_over_ground_truths(lowercase_      ,					lowercase_      ,					lowercase_		)

  UpperCAmelCase__			:				int												=       [x for _, x in sorted(zip(lowercase_      ,					lowercase_		)		)]
  recalls.sort()

  UpperCAmelCase__			:				Dict												=       1_0_0.0 * fa / total
  UpperCAmelCase__			:				str												=       1_0_0.0 * exact_match / total
  UpperCAmelCase__			:				Tuple												=       get_aupr(lowercase_      ,					lowercase_		)

  UpperCAmelCase__			:				Optional[Any]												=       get_prec_at_recall(lowercase_      ,					lowercase_      ,					recall_thresh=0.9		)
  UpperCAmelCase__			:				Dict												=       get_prec_at_recall(lowercase_      ,					lowercase_      ,					recall_thresh=0.8		)

  return {
      "exact_match": exact_match,
      "f1": fa,
      "aupr": aupr,
      "prec_at_80_recall": prec_at_aa_recall,
      "prec_at_90_recall": prec_at_aa_recall,
  }


if __name__ == "__main__":
      SCREAMING_SNAKE_CASE    =     argparse.ArgumentParser(description="""Evaluation for CUAD""")
      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)
            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'''





from doctest import testmod
from math import sqrt







def snake_case_    (				lowercase__		):
  UpperCAmelCase__			:				list[int]												=       []
  if num < 1:
    return facs
  facs.append(1		)
  if num == 1:
    return facs
  facs.append(lowercase__		)
  for i in range(2      ,					int(sqrt(lowercase__		)		) + 1		):
    if num % i == 0:  # If i is a factor of num
      facs.append(lowercase__		)
      UpperCAmelCase__			:				Tuple												=       num // i  # num//i is the other factor of num
      if d != i:  # If d and i are distinct
        facs.append(lowercase__		)  # we have found another factor
  facs.sort()
  return facs


if __name__ == "__main__":
      testmod(name="""factors_of_a_number""", verbose=True)



