



import argparse
import json
import re
import string
import sys

import numpy as np


__lowerCamelCase								=      0.5







def  _snake_case			(							__snake_case       ,	__snake_case     )  ->  Optional[Any]:






      '''simple docstring'''







      UpperCAmelCase_	:						List[str]					   =		[".", ",", ";", ":"]

      for token in remove_tokens:
            UpperCAmelCase_	:						Optional[Any]					   =		ground_truth.replace(__snake_case       ,	""     )
            UpperCAmelCase_	:						Any					   =		prediction.replace(__snake_case       ,	""     )

      UpperCAmelCase_   ,   UpperCAmelCase_	:						Optional[int]					   =		ground_truth.lower(), prediction.lower()
      UpperCAmelCase_   ,   UpperCAmelCase_	:						str					   =		ground_truth.replace("/"       ,	" "     ), prediction.replace("/"       ,	" "     )
      UpperCAmelCase_   ,   UpperCAmelCase_	:						Optional[int]					   =		set(ground_truth.split(" "     )     ), set(prediction.split(" "     )     )

      UpperCAmelCase_	:						int					   =		ground_truth.intersection(__snake_case     )
      UpperCAmelCase_	:						int					   =		ground_truth.union(__snake_case     )
      UpperCAmelCase_	:						int					   =		len(__snake_case     ) / len(__snake_case     )
      return jaccard







def  _snake_case			(							__snake_case     )  ->  List[Any]:






      '''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     ):
            UpperCAmelCase_	:						List[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  _snake_case			(							__snake_case       ,	__snake_case       ,	__snake_case     )  ->  List[str]:






      '''simple docstring'''







      UpperCAmelCase_   ,   UpperCAmelCase_   ,   UpperCAmelCase_	:						Optional[Any]					   =		0, 0, 0

      UpperCAmelCase_	:						List[Any]					   =		"Parties" in qa_id

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

                  if not match_found:
                        fp += 1

      UpperCAmelCase_	:						str					   =		tp / (tp + fp) if tp + fp > 0 else np.nan
      UpperCAmelCase_	:						Optional[int]					   =		tp / (tp + fn) if tp + fn > 0 else np.nan

      return precision, recall







def  _snake_case			(							__snake_case     )  ->  Tuple:






      '''simple docstring'''







      UpperCAmelCase_	:						str					   =		precisions[::-1]
      for i in range(1       ,	len(__snake_case     )     ):
            UpperCAmelCase_	:						List[str]					   =		max(precision_best[i - 1]       ,	precision_best[i]     )
      UpperCAmelCase_	:						str					   =		precision_best[::-1]
      return precisions







def  _snake_case			(							__snake_case       ,	__snake_case     )  ->  Tuple:






      '''simple docstring'''







      UpperCAmelCase_	:						str					   =		process_precisions(__snake_case     )
      UpperCAmelCase_	:						str					   =		np.trapz(__snake_case       ,	__snake_case     )
      if np.isnan(__snake_case     ):
            return 0
      return aupr







def  _snake_case			(							__snake_case       ,	__snake_case       ,	__snake_case     )  ->  int:






      '''simple docstring'''







      UpperCAmelCase_	:						Optional[int]					   =		process_precisions(__snake_case     )
      UpperCAmelCase_	:						int					   =		0
      for prec, recall in zip(__snake_case       ,	__snake_case     ):
            if recall >= recall_thresh:
                  UpperCAmelCase_	:						List[Any]					   =		prec
                  break
      return prec_at_recall







def  _snake_case			(							__snake_case       ,	__snake_case     )  ->  Any:






      '''simple docstring'''







      return normalize_answer(__snake_case     ) == normalize_answer(__snake_case     )







def  _snake_case			(							__snake_case       ,	__snake_case       ,	__snake_case     )  ->  Union[str, Any]:






      '''simple docstring'''







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







def  _snake_case			(							__snake_case       ,	__snake_case     )  ->  List[Any]:






      '''simple docstring'''







      UpperCAmelCase_	:						Optional[Any]					   =		0
      UpperCAmelCase_	:						Optional[Any]					   =		[]
      UpperCAmelCase_	:						Union[str, Any]					   =		[]
      for article in dataset:
            for paragraph in article["paragraphs"]:
                  for qa in paragraph["qas"]:
                        total += 1
                        if qa["id"] not in predictions:
                              UpperCAmelCase_	:						Optional[Any]					   =		"Unanswered question " + qa["id"] + " will receive score 0."
                              print(__snake_case       ,	file=sys.stderr     )
                              continue
                        UpperCAmelCase_	:						Dict					   =		[x["text"] for x in qa["answers"]]
                        UpperCAmelCase_	:						int					   =		predictions[qa["id"]]
                        UpperCAmelCase_   ,   UpperCAmelCase_	:						Dict					   =		compute_precision_recall(__snake_case       ,	__snake_case       ,	qa["id"]     )

                        precisions.append(__snake_case     )
                        recalls.append(__snake_case     )

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

                        exact_match += metric_max_over_ground_truths(__snake_case       ,	__snake_case       ,	__snake_case     )

      UpperCAmelCase_	:						Any					   =		[x for _, x in sorted(zip(__snake_case       ,	__snake_case     )     )]
      recalls.sort()

      UpperCAmelCase_	:						List[str]					   =		100.0 * fa / total
      UpperCAmelCase_	:						List[Any]					   =		100.0 * exact_match / total
      UpperCAmelCase_	:						Tuple					   =		get_aupr(__snake_case       ,	__snake_case     )

      UpperCAmelCase_	:						str					   =		get_prec_at_recall(__snake_case       ,	__snake_case       ,	recall_thresh=0.9     )
      UpperCAmelCase_	:						str					   =		get_prec_at_recall(__snake_case       ,	__snake_case       ,	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  _snake_case			(							)  ->  str:






      '''simple docstring'''







      UpperCAmelCase_	:						Optional[Any]					   =		bool(os.getenv("RUN_SLOW"       ,	0     )     )
      if run_slow:
            assert True
      else:
            assert False, "should have been skipped"







def  _snake_case			(							)  ->  Tuple:






      '''simple docstring'''







      UpperCAmelCase_	:						Union[str, Any]					   =		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 	snake_case_			(unittest.TestCase	):




       """simple docstring"""






       @slow
       @require_torch_gpu
       def 			A_ (  self):

             """simple docstring"""





             check_slow_torch_cuda()



       @require_torch_gpu
       @slow
       def 			A_ (  self):

             """simple docstring"""





             check_slow_torch_cuda()



       @parameterized.expand(lowercase)
       @slow
       def 			A_ (  self  ,lowercase=None):

             """simple docstring"""





             check_slow()







@slow
@require_torch_gpu
def  _snake_case			(							)  ->  str:






      '''simple docstring'''







      check_slow_torch_cuda()







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






      '''simple docstring'''







      check_slow_torch_cuda()







@slow
@pytest.mark.parametrize("param"       ,	[1]     )
def  _snake_case			(							__snake_case     )  ->  Optional[Any]:






      '''simple docstring'''







      check_slow()







@pytest.mark.parametrize("param"       ,	[1]     )
@slow
def  _snake_case			(							__snake_case     )  ->  Any:






      '''simple docstring'''







      check_slow()
