import argparse
import json
import re
import string
import sys

import numpy as np


_SCREAMING_SNAKE_CASE:    Dict     						=							0.5






def  UpperCAmelCase__(    lowerCamelCase_				,						lowerCamelCase_	):



			"""simple docstring"""





			_snake_case														=   [".", ",", ";", ":"]

			for token in remove_tokens:
						_snake_case														=   ground_truth.replace(lowercase_				,						""	)
						_snake_case														=   prediction.replace(lowercase_				,						""	)

			_snake_case							,			_snake_case														=   ground_truth.lower(), prediction.lower()
			_snake_case							,			_snake_case														=   ground_truth.replace("/"				,						" "	), prediction.replace("/"				,						" "	)
			_snake_case							,			_snake_case														=   set(ground_truth.split(" "	)	), set(prediction.split(" "	)	)

			_snake_case														=   ground_truth.intersection(lowercase_	)
			_snake_case														=   ground_truth.union(lowercase_	)
			_snake_case														=   len(lowercase_	) / len(lowercase_	)
			return jaccard






def  UpperCAmelCase__(    lowerCamelCase_	):



			"""simple docstring"""





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

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

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

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

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






def  UpperCAmelCase__(    lowerCamelCase_				,						lowerCamelCase_				,						lowerCamelCase_	):



			"""simple docstring"""





			_snake_case							,			_snake_case							,			_snake_case														=   0, 0, 0

			_snake_case														=   "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
									_snake_case														=   False
									for pred in predictions:
												if substr_ok:
															_snake_case														=   get_jaccard(lowercase_				,						lowercase_	) >= IOU_THRESH or ground_truth in pred
												else:
															_snake_case														=   get_jaccard(lowercase_				,						lowercase_	) >= IOU_THRESH
												if is_match:
															_snake_case														=   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.)
									_snake_case														=   False
									for ground_truth in ground_truths:
												assert len(lowercase_	) > 0
												if substr_ok:
															_snake_case														=   get_jaccard(lowercase_				,						lowercase_	) >= IOU_THRESH or ground_truth in pred
												else:
															_snake_case														=   get_jaccard(lowercase_				,						lowercase_	) >= IOU_THRESH
												if is_match:
															_snake_case														=   True

									if not match_found:
												fp += 1

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

			return precision, recall






def  UpperCAmelCase__(    lowerCamelCase_	):



			"""simple docstring"""





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






def  UpperCAmelCase__(    lowerCamelCase_				,						lowerCamelCase_	):



			"""simple docstring"""





			_snake_case														=   process_precisions(lowercase_	)
			_snake_case														=   np.trapz(lowercase_				,						lowercase_	)
			if np.isnan(lowercase_	):
						return 0
			return aupr






def  UpperCAmelCase__(    lowerCamelCase_				,						lowerCamelCase_				,						lowerCamelCase_	):



			"""simple docstring"""





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






def  UpperCAmelCase__(    lowerCamelCase_				,						lowerCamelCase_	):



			"""simple docstring"""





			return normalize_answer(lowercase_	) == normalize_answer(lowercase_	)






def  UpperCAmelCase__(    lowerCamelCase_				,						lowerCamelCase_				,						lowerCamelCase_	):



			"""simple docstring"""





			_snake_case														=   0
			for pred in predictions:
						for ground_truth in ground_truths:
									_snake_case														=   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  UpperCAmelCase__(    lowerCamelCase_				,						lowerCamelCase_	):



			"""simple docstring"""





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

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

			_snake_case														=   100.0 * fa / total
			_snake_case														=   100.0 * exact_match / total
			_snake_case														=   get_aupr(lowercase_				,						lowercase_	)

			_snake_case														=   get_prec_at_recall(lowercase_				,						lowercase_				,						recall_thresh=0.9	)
			_snake_case														=   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:    int     						=							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:    str     						=							parser.parse_args()
			with open(args.dataset_file) as dataset_file:
						_SCREAMING_SNAKE_CASE:    Dict     						=							json.load(dataset_file)
						_SCREAMING_SNAKE_CASE:    Any     						=							dataset_json['''data''']
			with open(args.prediction_file) as prediction_file:
						_SCREAMING_SNAKE_CASE:    Optional[int]     						=							json.load(prediction_file)
			print(json.dumps(evaluate(dataset, predictions)))









import json
import os
import tempfile
import unittest

from transformers.modelcard import ModelCard
class 	a_(		unittest.TestCase	):



     """simple docstring"""



     def     __UpperCamelCase						(		self					:	Optional[int])							->	List[str]:

      """simple docstring"""
      SCREAMING_SNAKE_CASE				    =		{
          'model_details': {
              'Organization': 'testing',
              'Model date': 'today',
              'Model version': 'v2.1, Developed by Test Corp in 2019.',
              'Architecture': 'Convolutional Neural Network.',
          },
          'metrics': 'BLEU and ROUGE-1',
          'evaluation_data': {
              'Datasets': {'BLEU': 'My-great-dataset-v1', 'ROUGE-1': 'My-short-dataset-v2.1'},
              'Preprocessing': 'See details on https://arxiv.org/pdf/1810.03993.pdf',
          },
          'training_data': {
              'Dataset': 'English Wikipedia dump dated 2018-12-01',
              'Preprocessing': (
                  'Using SentencePiece vocabulary of size 52k tokens. See details on'
                  ' https://arxiv.org/pdf/1810.03993.pdf'
              ),
          },
          'quantitative_analyses': {'BLEU': 55.1, 'ROUGE-1': 7_6},
      }





     def     __UpperCamelCase						(		self					:	Optional[Any])							->	Any:

      """simple docstring"""
      SCREAMING_SNAKE_CASE				    =		ModelCard.from_dict(self.inputs_dict)
      self.assertTrue(hasattr(lowerCAmelCase__  ,  'model_details'))
      self.assertTrue(hasattr(lowerCAmelCase__  ,  'intended_use'))
      self.assertTrue(hasattr(lowerCAmelCase__  ,  'factors'))
      self.assertTrue(hasattr(lowerCAmelCase__  ,  'metrics'))
      self.assertTrue(hasattr(lowerCAmelCase__  ,  'evaluation_data'))
      self.assertTrue(hasattr(lowerCAmelCase__  ,  'training_data'))
      self.assertTrue(hasattr(lowerCAmelCase__  ,  'quantitative_analyses'))
      self.assertTrue(hasattr(lowerCAmelCase__  ,  'ethical_considerations'))
      self.assertTrue(hasattr(lowerCAmelCase__  ,  'caveats_and_recommendations'))





     def     __UpperCamelCase						(		self					:	Any)							->	Any:

      """simple docstring"""
      SCREAMING_SNAKE_CASE				    =		ModelCard.from_dict(self.inputs_dict)
      SCREAMING_SNAKE_CASE				    =		json.loads(modelcard.to_json_string())
      for key, value in self.inputs_dict.items():
       self.assertEqual(obj[key]  ,  lowerCAmelCase__)





     def     __UpperCamelCase						(		self					:	int)							->	Optional[Any]:

      """simple docstring"""
      SCREAMING_SNAKE_CASE				    =		ModelCard.from_dict(self.inputs_dict)

      with tempfile.TemporaryDirectory() as tmpdirname:
       SCREAMING_SNAKE_CASE				    =		os.path.join(lowerCAmelCase__  ,  'modelcard.json')
       model_card_first.to_json_file(lowerCAmelCase__)
       SCREAMING_SNAKE_CASE				    =		ModelCard.from_json_file(lowerCAmelCase__)

      self.assertEqual(model_card_second.to_dict()  ,  model_card_first.to_dict())





     def     __UpperCamelCase						(		self					:	Optional[int])							->	Any:

      """simple docstring"""
      SCREAMING_SNAKE_CASE				    =		ModelCard.from_dict(self.inputs_dict)

      with tempfile.TemporaryDirectory() as tmpdirname:
       model_card_first.save_pretrained(lowerCAmelCase__)
       SCREAMING_SNAKE_CASE				    =		ModelCard.from_pretrained(lowerCAmelCase__)

      self.assertEqual(model_card_second.to_dict()  ,  model_card_first.to_dict())




