import argparse
import json
import re
import string
import sys

import numpy as np


lowercase__    :       int						=			0.5

def      SCREAMING_SNAKE_CASE_  (		snake_case__     ,						snake_case__							)  ->			Dict:
							lowerCAmelCase				=	['''.''', ''',''', ''';''', ''':''']

							for token in remove_tokens:
														lowerCAmelCase				=	ground_truth.replace(snake_case__     ,						''''''							)
														lowerCAmelCase				=	prediction.replace(snake_case__     ,						''''''							)

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

							lowerCAmelCase				=	ground_truth.intersection(snake_case__							)
							lowerCAmelCase				=	ground_truth.union(snake_case__							)
							lowerCAmelCase				=	len(snake_case__							) / len(snake_case__							)
							return jaccard

def      SCREAMING_SNAKE_CASE_  (		snake_case__							)  ->			Optional[Any]:

							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__							):
														lowerCAmelCase				=	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      SCREAMING_SNAKE_CASE_  (		snake_case__     ,						snake_case__     ,						snake_case__							)  ->			List[str]:
							lowerCAmelCase	,   lowerCAmelCase	,   lowerCAmelCase				=	0, 0, 0

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

																					if not match_found:
																												fp += 1

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

							return precision, recall

def      SCREAMING_SNAKE_CASE_  (		snake_case__							)  ->			Optional[Any]:
							lowerCAmelCase				=	precisions[::-1]
							for i in range(1     ,						len(snake_case__							)							):
														lowerCAmelCase				=	max(precision_best[i - 1]     ,						precision_best[i]							)
							lowerCAmelCase				=	precision_best[::-1]
							return precisions

def      SCREAMING_SNAKE_CASE_  (		snake_case__     ,						snake_case__							)  ->			Tuple:
							lowerCAmelCase				=	process_precisions(snake_case__							)
							lowerCAmelCase				=	np.trapz(snake_case__     ,						snake_case__							)
							if np.isnan(snake_case__							):
														return 0
							return aupr

def      SCREAMING_SNAKE_CASE_  (		snake_case__     ,						snake_case__     ,						snake_case__							)  ->			Any:
							lowerCAmelCase				=	process_precisions(snake_case__							)
							lowerCAmelCase				=	0
							for prec, recall in zip(snake_case__     ,						snake_case__							):
														if recall >= recall_thresh:
																					lowerCAmelCase				=	prec
																					break
							return prec_at_recall

def      SCREAMING_SNAKE_CASE_  (		snake_case__     ,						snake_case__							)  ->			Optional[int]:
							return normalize_answer(snake_case__							) == normalize_answer(snake_case__							)

def      SCREAMING_SNAKE_CASE_  (		snake_case__     ,						snake_case__     ,						snake_case__							)  ->			List[str]:
							lowerCAmelCase				=	0
							for pred in predictions:
														for ground_truth in ground_truths:
																					lowerCAmelCase				=	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      SCREAMING_SNAKE_CASE_  (		snake_case__     ,						snake_case__							)  ->			List[Any]:
							lowerCAmelCase				=	lowerCAmelCase				=	lowerCAmelCase				=	0
							lowerCAmelCase				=	[]
							lowerCAmelCase				=	[]
							for article in dataset:
														for paragraph in article["paragraphs"]:
																					for qa in paragraph["qas"]:
																												total += 1
																												if qa["id"] not in predictions:
																																			lowerCAmelCase				=	'''Unanswered question ''' + qa['''id'''] + ''' will receive score 0.'''
																																			print(snake_case__     ,						file=sys.stderr							)
																																			continue
																												lowerCAmelCase				=	[x['''text'''] for x in qa['''answers''']]
																												lowerCAmelCase				=	predictions[qa['''id''']]
																												lowerCAmelCase	,   lowerCAmelCase				=	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__							)

							lowerCAmelCase				=	[x for _, x in sorted(zip(snake_case__     ,						snake_case__							)							)]
							recalls.sort()

							lowerCAmelCase				=	1_0_0.0 * fa / total
							lowerCAmelCase				=	1_0_0.0 * exact_match / total
							lowerCAmelCase				=	get_aupr(snake_case__     ,						snake_case__							)

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




import glob
import os

from get_test_info import get_tester_classes


if __name__ == "__main__":
	lowercase__    :       List[Any]						=			[]

	lowercase__    :       Optional[Any]						=			os.path.join('''tests''', '''models''', '''**''', '''test_modeling_*.py''')
	lowercase__    :       Optional[int]						=			glob.glob(pattern)
	# TODO: deal with TF/Flax too
	lowercase__    :       Dict						=			[
	    x for x in test_files if not (x.startswith('''test_modeling_tf_''') or x.startswith('''test_modeling_flax_'''))
	]

	for test_file in test_files:
		lowercase__    :       Tuple						=			get_tester_classes(test_file)
		for tester_class in tester_classes:
			# A few tester classes don't have `parent` parameter in `__init__`.
			# TODO: deal this better
			try:
				lowercase__    :       Union[str, Any]						=			tester_class(parent=None)
			except Exception:
				continue
			if hasattr(tester, '''get_config'''):
				lowercase__    :       Union[str, Any]						=			tester.get_config()
				for k, v in config.to_dict().items():
					if isinstance(v, int):
						lowercase__    :       int						=			None
						if k in ["vocab_size"]:
							lowercase__    :       Optional[int]						=			1_0_0
						elif k in ["max_position_embeddings"]:
							lowercase__    :       Union[str, Any]						=			1_2_8
						elif k in ["hidden_size", "d_model"]:
							lowercase__    :       Optional[Any]						=			4_0
						elif k == ["num_layers", "num_hidden_layers", "num_encoder_layers", "num_decoder_layers"]:
							lowercase__    :       Dict						=			5
						if target is not None and v > target:
							failures.append(
							    f'{tester_class.__name__} will produce a `config` of type `{config.__class__.__name__}`'
							    f' with config["{k}"] = {v} which is too large for testing! Set its value to be smaller'
							    f' than {target}.'
							)

	if len(failures) > 0:
		raise Exception(f'There were {len(failures)} failures:\n' + '''\n'''.join(failures))



