






import argparse
import json
import re
import string
import sys

import numpy as np


lowerCAmelCase__							= 0.5





def 							lowercase__		(				lowerCamelCase,	lowerCamelCase					):
				_SCREAMING_SNAKE_CASE      :		List[Any]							=						['''.''', ''',''', ''';''', ''':''']

				for token in remove_tokens:
								_SCREAMING_SNAKE_CASE      :		List[str]							=						ground_truth.replace(__a,	''					)
								_SCREAMING_SNAKE_CASE      :		Optional[int]							=						prediction.replace(__a,	''					)

				_SCREAMING_SNAKE_CASE      :		str							=						ground_truth.lower(), prediction.lower()
				_SCREAMING_SNAKE_CASE      :		List[Any]							=						ground_truth.replace('/',	' '					), prediction.replace('/',	' '					)
				_SCREAMING_SNAKE_CASE      :		Union[str, Any]							=						set(ground_truth.split(' '					)					), set(prediction.split(' '					)					)

				_SCREAMING_SNAKE_CASE      :		Optional[int]							=						ground_truth.intersection(__a					)
				_SCREAMING_SNAKE_CASE      :		List[str]							=						ground_truth.union(__a					)
				_SCREAMING_SNAKE_CASE      :		Tuple							=						len(__a					) / len(__a					)
				return jaccard





def 							lowercase__		(				lowerCamelCase					):





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

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

				def remove_punc(lowerCamelCase					):
								_SCREAMING_SNAKE_CASE      :		Optional[int]							=						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(__a					)					)					)					)





def 							lowercase__		(				lowerCamelCase,	lowerCamelCase,	lowerCamelCase					):
				_SCREAMING_SNAKE_CASE      :		Optional[Any]							=						0, 0, 0

				_SCREAMING_SNAKE_CASE      :		Union[str, Any]							=						'''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
												_SCREAMING_SNAKE_CASE      :		Dict							=						False
												for pred in predictions:
																if substr_ok:
																				_SCREAMING_SNAKE_CASE      :		Tuple							=						get_jaccard(__a,	__a					) >= IOU_THRESH or ground_truth in pred
																else:
																				_SCREAMING_SNAKE_CASE      :		Dict							=						get_jaccard(__a,	__a					) >= IOU_THRESH
																if is_match:
																				_SCREAMING_SNAKE_CASE      :		str							=						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.)
												_SCREAMING_SNAKE_CASE      :		Any							=						False
												for ground_truth in ground_truths:
																assert len(__a					) > 0
																if substr_ok:
																				_SCREAMING_SNAKE_CASE      :		int							=						get_jaccard(__a,	__a					) >= IOU_THRESH or ground_truth in pred
																else:
																				_SCREAMING_SNAKE_CASE      :		Optional[Any]							=						get_jaccard(__a,	__a					) >= IOU_THRESH
																if is_match:
																				_SCREAMING_SNAKE_CASE      :		Any							=						True

												if not match_found:
																fp += 1

				_SCREAMING_SNAKE_CASE      :		int							=						tp / (tp + fp) if tp + fp > 0 else np.nan
				_SCREAMING_SNAKE_CASE      :		List[Any]							=						tp / (tp + fn) if tp + fn > 0 else np.nan

				return precision, recall





def 							lowercase__		(				lowerCamelCase					):
				_SCREAMING_SNAKE_CASE      :		str							=						precisions[::-1]
				for i in range(1,	len(__a					)					):
								_SCREAMING_SNAKE_CASE      :		int							=						max(precision_best[i - 1],	precision_best[i]					)
				_SCREAMING_SNAKE_CASE      :		List[Any]							=						precision_best[::-1]
				return precisions





def 							lowercase__		(				lowerCamelCase,	lowerCamelCase					):
				_SCREAMING_SNAKE_CASE      :		List[Any]							=						process_precisions(__a					)
				_SCREAMING_SNAKE_CASE      :		Optional[int]							=						np.trapz(__a,	__a					)
				if np.isnan(__a					):
								return 0
				return aupr





def 							lowercase__		(				lowerCamelCase,	lowerCamelCase,	lowerCamelCase					):
				_SCREAMING_SNAKE_CASE      :		List[str]							=						process_precisions(__a					)
				_SCREAMING_SNAKE_CASE      :		List[Any]							=						0
				for prec, recall in zip(__a,	__a					):
								if recall >= recall_thresh:
												_SCREAMING_SNAKE_CASE      :		Optional[Any]							=						prec
												break
				return prec_at_recall





def 							lowercase__		(				lowerCamelCase,	lowerCamelCase					):
				return normalize_answer(__a					) == normalize_answer(__a					)





def 							lowercase__		(				lowerCamelCase,	lowerCamelCase,	lowerCamelCase					):
				_SCREAMING_SNAKE_CASE      :		Tuple							=						0
				for pred in predictions:
								for ground_truth in ground_truths:
												_SCREAMING_SNAKE_CASE      :		int							=						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 							lowercase__		(				lowerCamelCase,	lowerCamelCase					):
				_SCREAMING_SNAKE_CASE      :		Any							=						0
				_SCREAMING_SNAKE_CASE      :		Union[str, Any]							=						[]
				_SCREAMING_SNAKE_CASE      :		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:
																				_SCREAMING_SNAKE_CASE      :		List[Any]							=						'''Unanswered question ''' + qa['''id'''] + ''' will receive score 0.'''
																				print(__a,	file=sys.stderr					)
																				continue
																_SCREAMING_SNAKE_CASE      :		Any							=						[x['''text'''] for x in qa['''answers''']]
																_SCREAMING_SNAKE_CASE      :		Optional[int]							=						predictions[qa['''id''']]
																_SCREAMING_SNAKE_CASE      :		Optional[int]							=						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					)

				_SCREAMING_SNAKE_CASE      :		Tuple							=						[x for _, x in sorted(zip(__a,	__a					)					)]
				recalls.sort()

				_SCREAMING_SNAKE_CASE      :		int							=						100.0 * fa / total
				_SCREAMING_SNAKE_CASE      :		List[str]							=						100.0 * exact_match / total
				_SCREAMING_SNAKE_CASE      :		List[Any]							=						get_aupr(__a,	__a					)

				_SCREAMING_SNAKE_CASE      :		Optional[Any]							=						get_prec_at_recall(__a,	__a,	recall_thresh=0.9					)
				_SCREAMING_SNAKE_CASE      :		int							=						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)))









"""simple docstring"""
from .chunk_utils import chunk_layer
from .data_transforms import make_atomaa_masks
from .feats import atomaa_to_atomaa, frames_and_literature_positions_to_atomaa_pos, torsion_angles_to_frames
from .loss import compute_predicted_aligned_error, compute_tm
from .protein import Protein as OFProtein
from .protein import to_pdb
from .rigid_utils import Rigid, Rotation
from .tensor_utils import dict_multimap, flatten_final_dims, permute_final_dims

