




"""simple docstring"""
import argparse
import json
import re
import string
import sys

import numpy as np


_SCREAMING_SNAKE_CASE						 =     0.5






def 				__lowerCAmelCase					(					__lowerCAmelCase			:		Optional[Any]  ,	__lowerCAmelCase			:		str						)    ->       Dict:
 _UpperCamelCase      :	Optional[int]					      =  [".", ",", ";", ":"]

 for token in remove_tokens:
  _UpperCamelCase      :	List[str]					      =  ground_truth.replace(lowerCamelCase_  ,	""						)
  _UpperCamelCase      :	Optional[int]					      =  prediction.replace(lowerCamelCase_  ,	""						)

 _UpperCamelCase      ,	_UpperCamelCase      :	Union[str, Any]					      =  ground_truth.lower(), prediction.lower()
 _UpperCamelCase      ,	_UpperCamelCase      :	Any					      =  ground_truth.replace("/"  ,	" "						), prediction.replace("/"  ,	" "						)
 _UpperCamelCase      ,	_UpperCamelCase      :	str					      =  set(ground_truth.split(" "						)						), set(prediction.split(" "						)						)

 _UpperCamelCase      :	str					      =  ground_truth.intersection(lowerCamelCase_						)
 _UpperCamelCase      :	List[Any]					      =  ground_truth.union(lowerCamelCase_						)
 _UpperCamelCase      :	Tuple					      =  len(lowerCamelCase_						) / len(lowerCamelCase_						)
 return jaccard






def 				__lowerCAmelCase					(					__lowerCAmelCase			:		Optional[int]						)    ->       Tuple:





 def remove_articles(__lowerCAmelCase			:		Optional[int]						):
  return re.sub(R"\b(a|an|the)\b"  ,	" "  ,	lowerCamelCase_						)

 def white_space_fix(__lowerCAmelCase			:		List[Any]						):
  return " ".join(text.split()						)

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

 def lower(__lowerCAmelCase			:		Any						):
  return text.lower()

 return white_space_fix(remove_articles(remove_punc(lower(lowerCamelCase_						)						)						)						)






def 				__lowerCAmelCase					(					__lowerCAmelCase			:		Any  ,	__lowerCAmelCase			:		Optional[Any]  ,	__lowerCAmelCase			:		Optional[Any]						)    ->       Optional[int]:
 _UpperCamelCase      ,	_UpperCamelCase      ,	_UpperCamelCase      :	Optional[Any]					      =  0, 0, 0

 _UpperCamelCase      :	Optional[Any]					      =  "Parties" in qa_id

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

   if not match_found:
    fp += 1

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

 return precision, recall






def 				__lowerCAmelCase					(					__lowerCAmelCase			:		Union[str, Any]						)    ->       int:
 _UpperCamelCase      :	List[str]					      =  precisions[::-1]
 for i in range(1  ,	len(lowerCamelCase_						)						):
  _UpperCamelCase      :	Optional[int]					      =  max(precision_best[i - 1]  ,	precision_best[i]						)
 _UpperCamelCase      :	Union[str, Any]					      =  precision_best[::-1]
 return precisions






def 				__lowerCAmelCase					(					__lowerCAmelCase			:		List[Any]  ,	__lowerCAmelCase			:		str						)    ->       str:
 _UpperCamelCase      :	str					      =  process_precisions(lowerCamelCase_						)
 _UpperCamelCase      :	List[Any]					      =  np.trapz(lowerCamelCase_  ,	lowerCamelCase_						)
 if np.isnan(lowerCamelCase_						):
  return 0
 return aupr






def 				__lowerCAmelCase					(					__lowerCAmelCase			:		Dict  ,	__lowerCAmelCase			:		Any  ,	__lowerCAmelCase			:		Optional[int]						)    ->       List[str]:
 _UpperCamelCase      :	Union[str, Any]					      =  process_precisions(lowerCamelCase_						)
 _UpperCamelCase      :	Any					      =  0
 for prec, recall in zip(lowerCamelCase_  ,	lowerCamelCase_						):
  if recall >= recall_thresh:
   _UpperCamelCase      :	Optional[int]					      =  prec
   break
 return prec_at_recall






def 				__lowerCAmelCase					(					__lowerCAmelCase			:		Dict  ,	__lowerCAmelCase			:		int						)    ->       Union[str, Any]:
 return normalize_answer(lowerCamelCase_						) == normalize_answer(lowerCamelCase_						)






def 				__lowerCAmelCase					(					__lowerCAmelCase			:		Any  ,	__lowerCAmelCase			:		Optional[int]  ,	__lowerCAmelCase			:		List[str]						)    ->       str:
 _UpperCamelCase      :	List[Any]					      =  0
 for pred in predictions:
  for ground_truth in ground_truths:
   _UpperCamelCase      :	Optional[Any]					      =  metric_fn(lowerCamelCase_  ,	lowerCamelCase_						)
   if score == 1:  # break the loop when one prediction matches the ground truth
    break
  if score == 1:
   break
 return score






def 				__lowerCAmelCase					(					__lowerCAmelCase			:		int  ,	__lowerCAmelCase			:		List[str]						)    ->       List[Any]:
 _UpperCamelCase      :	Optional[int]					      =  0
 _UpperCamelCase      :	int					      =  []
 _UpperCamelCase      :	List[str]					      =  []
 for article in dataset:
  for paragraph in article["paragraphs"]:
   for qa in paragraph["qas"]:
    total += 1
    if qa["id"] not in predictions:
     _UpperCamelCase      :	int					      =  "Unanswered question " + qa["id"] + " will receive score 0."
     print(lowerCamelCase_  ,	file=sys.stderr						)
     continue
    _UpperCamelCase      :	Optional[Any]					      =  [x["text"] for x in qa["answers"]]
    _UpperCamelCase      :	Any					      =  predictions[qa["id"]]
    _UpperCamelCase      ,	_UpperCamelCase      :	Optional[Any]					      =  compute_precision_recall(lowerCamelCase_  ,	lowerCamelCase_  ,	qa["id"]						)

    precisions.append(lowerCamelCase_						)
    recalls.append(lowerCamelCase_						)

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

    exact_match += metric_max_over_ground_truths(lowerCamelCase_  ,	lowerCamelCase_  ,	lowerCamelCase_						)

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

 _UpperCamelCase      :	str					      =  100.0 * fa / total
 _UpperCamelCase      :	List[Any]					      =  100.0 * exact_match / total
 _UpperCamelCase      :	Dict					      =  get_aupr(lowerCamelCase_  ,	lowerCamelCase_						)

 _UpperCamelCase      :	Dict					      =  get_prec_at_recall(lowerCamelCase_  ,	lowerCamelCase_  ,	recall_thresh=0.9						)
 _UpperCamelCase      :	Dict					      =  get_prec_at_recall(lowerCamelCase_  ,	lowerCamelCase_  ,	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						 =     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						 =     parser.parse_args()
    with open(args.dataset_file) as dataset_file:
        _SCREAMING_SNAKE_CASE						 =     json.load(dataset_file)
        _SCREAMING_SNAKE_CASE						 =     dataset_json['''data''']
    with open(args.prediction_file) as prediction_file:
        _SCREAMING_SNAKE_CASE						 =     json.load(prediction_file)
    print(json.dumps(evaluate(dataset, predictions)))




import sys

from transformers import AutoTokenizer


_SCREAMING_SNAKE_CASE:    Optional[int]     						=							sys.argv[1]
_SCREAMING_SNAKE_CASE:    int     						=							sys.argv[2]
_SCREAMING_SNAKE_CASE:    str     						=							int(sys.argv[3])

_SCREAMING_SNAKE_CASE:    Union[str, Any]     						=							0

_SCREAMING_SNAKE_CASE:    Any     						=							AutoTokenizer.from_pretrained(model_name_or_path)
max_len -= tokenizer.num_special_tokens_to_add()

with open(dataset, '''rt''') as f_p:
			for line in f_p:
						_SCREAMING_SNAKE_CASE:    Any     						=							line.rstrip()

						if not line:
									print(line)
									_SCREAMING_SNAKE_CASE:    str     						=							0
									continue

						_SCREAMING_SNAKE_CASE:    Optional[int]     						=							line.split()[0]

						_SCREAMING_SNAKE_CASE:    Tuple     						=							len(tokenizer.tokenize(token))

						# Token contains strange control characters like \x96 or \x95
						# Just filter out the complete line
						if current_subwords_len == 0:
									continue

						if (subword_len_counter + current_subwords_len) > max_len:
									print('''''')
									print(line)
									_SCREAMING_SNAKE_CASE:    Any     						=							current_subwords_len
									continue

						subword_len_counter += current_subwords_len

						print(line)

