

import argparse
import json
import re
import string
import sys

import numpy as np


lowerCamelCase									= 0.5




def       UpperCAmelCase__    (      _A				:			Optional[Any]     ,				_A				:			Tuple							):





  '''simple docstring'''







  a__  =['''.''', ''',''', ''';''', ''':''']

  for token in remove_tokens:
    a__  =ground_truth.replace(_A     ,				''''''							)
    a__  =prediction.replace(_A     ,				''''''							)

  a__,				a__  =ground_truth.lower(), prediction.lower()
  a__,				a__  =ground_truth.replace('''/'''     ,				''' '''							), prediction.replace('''/'''     ,				''' '''							)
  a__,				a__  =set(ground_truth.split(''' '''							)							), set(prediction.split(''' '''							)							)

  a__  =ground_truth.intersection(_A							)
  a__  =ground_truth.union(_A							)
  a__  =len(_A							) / len(_A							)
  return jaccard




def       UpperCAmelCase__    (      _A				:			Optional[Any]							):





  '''simple docstring'''







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

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

  def remove_punc(_A				:			List[Any]							):
    a__  =set(string.punctuation							)
    return "".join(ch for ch in text if ch not in exclude							)

  def lower(_A				:			Optional[Any]							):
    return text.lower()

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




def       UpperCAmelCase__    (      _A				:			str     ,				_A				:			int     ,				_A				:			str							):





  '''simple docstring'''







  a__,				a__,				a__  =0, 0, 0

  a__  ='''Parties''' in qa_id

  # first check if ground truth is empty
  if len(_A							) == 0:
    if len(_A							) > 0:
      fp += len(_A							)  # false positive for each one
  else:
    for ground_truth in ground_truths:
      assert len(_A							) > 0
      # check if there is a match
      a__  =False
      for pred in predictions:
        if substr_ok:
          a__  =get_jaccard(_A     ,				_A							) >= IOU_THRESH or ground_truth in pred
        else:
          a__  =get_jaccard(_A     ,				_A							) >= IOU_THRESH
        if is_match:
          a__  =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.)
      a__  =False
      for ground_truth in ground_truths:
        assert len(_A							) > 0
        if substr_ok:
          a__  =get_jaccard(_A     ,				_A							) >= IOU_THRESH or ground_truth in pred
        else:
          a__  =get_jaccard(_A     ,				_A							) >= IOU_THRESH
        if is_match:
          a__  =True

      if not match_found:
        fp += 1

  a__  =tp / (tp + fp) if tp + fp > 0 else np.nan
  a__  =tp / (tp + fn) if tp + fn > 0 else np.nan

  return precision, recall




def       UpperCAmelCase__    (      _A				:			Optional[int]							):





  '''simple docstring'''







  a__  =precisions[::-1]
  for i in range(1     ,				len(_A							)							):
    a__  =max(precision_best[i - 1]     ,				precision_best[i]							)
  a__  =precision_best[::-1]
  return precisions




def       UpperCAmelCase__    (      _A				:			Optional[Any]     ,				_A				:			List[Any]							):





  '''simple docstring'''







  a__  =process_precisions(_A							)
  a__  =np.trapz(_A     ,				_A							)
  if np.isnan(_A							):
    return 0
  return aupr




def       UpperCAmelCase__    (      _A				:			Dict     ,				_A				:			int     ,				_A				:			Union[str, Any]							):





  '''simple docstring'''







  a__  =process_precisions(_A							)
  a__  =0
  for prec, recall in zip(_A     ,				_A							):
    if recall >= recall_thresh:
      a__  =prec
      break
  return prec_at_recall




def       UpperCAmelCase__    (      _A				:			Tuple     ,				_A				:			Tuple							):





  '''simple docstring'''







  return normalize_answer(_A							) == normalize_answer(_A							)




def       UpperCAmelCase__    (      _A				:			List[Any]     ,				_A				:			Dict     ,				_A				:			Optional[Any]							):





  '''simple docstring'''







  a__  =0
  for pred in predictions:
    for ground_truth in ground_truths:
      a__  =metric_fn(_A     ,				_A							)
      if score == 1:  # break the loop when one prediction matches the ground truth
        break
    if score == 1:
      break
  return score




def       UpperCAmelCase__    (      _A				:			Dict     ,				_A				:			Dict							):





  '''simple docstring'''







  a__  =a__  =a__  =0
  a__  =[]
  a__  =[]
  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''']]
        a__,				a__  =compute_precision_recall(_A     ,				_A     ,				qa['''id''']							)

        precisions.append(_A							)
        recalls.append(_A							)

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

        exact_match += metric_max_over_ground_truths(_A     ,				_A     ,				_A							)

  a__  =[x for _, x in sorted(zip(_A     ,				_A							)							)]
  recalls.sort()

  a__  =100.0 * fa / total
  a__  =100.0 * exact_match / total
  a__  =get_aupr(_A     ,				_A							)

  a__  =get_prec_at_recall(_A     ,				_A     ,				recall_thresh=0.9							)
  a__  =get_prec_at_recall(_A     ,				_A     ,				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__":
       lowerCamelCase									= argparse.ArgumentParser(description='''Evaluation for CUAD''')
       parser.add_argument('''dataset_file''', help='''Dataset file''')
       parser.add_argument('''prediction_file''', help='''Prediction File''')
       lowerCamelCase									= parser.parse_args()
       with open(args.dataset_file) as dataset_file:
              lowerCamelCase									= json.load(dataset_file)
              lowerCamelCase									= dataset_json['''data''']
       with open(args.prediction_file) as prediction_file:
              lowerCamelCase									= json.load(prediction_file)
       print(json.dumps(evaluate(dataset, predictions)))



import os
import unittest

import pytest
from parameterized import parameterized

from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device


# skipping in unittest tests

lowerCamelCase									= [(1,)]




def       UpperCAmelCase__    (      ):





  '''simple docstring'''







  a__  =bool(os.getenv('''RUN_SLOW'''     ,				0							)							)
  if run_slow:
    assert True
  else:
    assert False, "should have been skipped"




def       UpperCAmelCase__    (      ):





  '''simple docstring'''







  a__  =bool(os.getenv('''RUN_SLOW'''     ,				0							)							)
  if run_slow and torch_device == "cuda":
    assert True
  else:
    assert False, "should have been skipped"







@require_torch
class 						__magic_name__      (							unittest.TestCase							):

   '''simple docstring'''




   @slow
   @require_torch_gpu
   def 			_UpperCAmelCase			(    self	)							->   Union[str, Any]:






     """simple docstring"""






     check_slow_torch_cuda()



   @require_torch_gpu
   @slow
   def 			_UpperCAmelCase			(    self	)							->   List[str]:






     """simple docstring"""






     check_slow_torch_cuda()



   @parameterized.expand(lowercase_	)
   @slow
   def 			_UpperCAmelCase			(    self,   lowercase_=None	)							->   Tuple:






     """simple docstring"""






     check_slow()





@slow
@require_torch_gpu
def       UpperCAmelCase__    (      ):





  '''simple docstring'''







  check_slow_torch_cuda()




@require_torch_gpu
@slow
def       UpperCAmelCase__    (      ):





  '''simple docstring'''







  check_slow_torch_cuda()




@slow
@pytest.mark.parametrize('''param'''     ,				[1]							)
def       UpperCAmelCase__    (      _A				:			str							):





  '''simple docstring'''







  check_slow()




@pytest.mark.parametrize('''param'''     ,				[1]							)
@slow
def       UpperCAmelCase__    (      _A				:			Tuple							):





  '''simple docstring'''







  check_slow()
