






import argparse
import json
import re
import string
import sys

import numpy as np


__UpperCAmelCase      =				0.5





def 						A_					(						lowercase_	,  lowercase_			)				->Tuple:







 """simple docstring"""

 SCREAMING_SNAKE_CASE				    =		['.', ',', ';', ':']

 for token in remove_tokens:
  SCREAMING_SNAKE_CASE				    =		ground_truth.replace(lowercase_	,  ''			)
  SCREAMING_SNAKE_CASE				    =		prediction.replace(lowercase_	,  ''			)

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

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





def 						A_					(						lowercase_			)				->List[Any]:







 """simple docstring"""

 def remove_articles(lowercase_			):
  return re.sub(r'\b(a|an|the)\b'	,  ' '	,  lowercase_			)

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

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

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

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





def 						A_					(						lowercase_	,  lowercase_	,  lowercase_			)				->Optional[Any]:







 """simple docstring"""

 SCREAMING_SNAKE_CASE    ,   SCREAMING_SNAKE_CASE    ,   SCREAMING_SNAKE_CASE				    =		0, 0, 0

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

   if not match_found:
    fp += 1

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

 return precision, recall





def 						A_					(						lowercase_			)				->Dict:







 """simple docstring"""

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





def 						A_					(						lowercase_	,  lowercase_			)				->int:







 """simple docstring"""

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





def 						A_					(						lowercase_	,  lowercase_	,  lowercase_			)				->List[str]:







 """simple docstring"""

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





def 						A_					(						lowercase_	,  lowercase_			)				->Optional[int]:







 """simple docstring"""

 return normalize_answer(lowercase_			) == normalize_answer(lowercase_			)





def 						A_					(						lowercase_	,  lowercase_	,  lowercase_			)				->List[str]:







 """simple docstring"""

 SCREAMING_SNAKE_CASE				    =		0
 for pred in predictions:
  for ground_truth in ground_truths:
   SCREAMING_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 						A_					(						lowercase_	,  lowercase_			)				->Dict:







 """simple docstring"""

 SCREAMING_SNAKE_CASE				    =		SCREAMING_SNAKE_CASE				    =		SCREAMING_SNAKE_CASE				    =		0
 SCREAMING_SNAKE_CASE				    =		[]
 SCREAMING_SNAKE_CASE				    =		[]
 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				    =		'Unanswered question ' + qa['id'] + ' will receive score 0.'
     print(lowercase_	,  file=sys.stderr			)
     continue
    SCREAMING_SNAKE_CASE				    =		[x['text'] for x in qa['answers']]
    SCREAMING_SNAKE_CASE				    =		predictions[qa['id']]
    SCREAMING_SNAKE_CASE    ,   SCREAMING_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_			)

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

 SCREAMING_SNAKE_CASE				    =		1_0_0.0 * fa / total
 SCREAMING_SNAKE_CASE				    =		1_0_0.0 * exact_match / total
 SCREAMING_SNAKE_CASE				    =		get_aupr(lowercase_	,  lowercase_			)

 SCREAMING_SNAKE_CASE				    =		get_prec_at_recall(lowercase_	,  lowercase_	,  recall_thresh=0.9			)
 SCREAMING_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__":
      __UpperCAmelCase      =				argparse.ArgumentParser(description="Evaluation for CUAD")
      parser.add_argument("dataset_file", help="Dataset file")
      parser.add_argument("prediction_file", help="Prediction File")
      __UpperCAmelCase      =				parser.parse_args()
      with open(args.dataset_file) as dataset_file:
            __UpperCAmelCase      =				json.load(dataset_file)
            __UpperCAmelCase      =				dataset_json["data"]
      with open(args.prediction_file) as prediction_file:
            __UpperCAmelCase      =				json.load(prediction_file)
      print(json.dumps(evaluate(dataset, predictions)))












from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


A										=      {'configuration_detr': ['DETR_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DetrConfig', 'DetrOnnxConfig']}

try:
				if not is_vision_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				A										=      ['DetrFeatureExtractor']
				A										=      ['DetrImageProcessor']

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				A										=      [
				    'DETR_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'DetrForObjectDetection',
				    'DetrForSegmentation',
				    'DetrModel',
				    'DetrPreTrainedModel',
				]


if TYPE_CHECKING:
				from .configuration_detr import DETR_PRETRAINED_CONFIG_ARCHIVE_MAP, DetrConfig, DetrOnnxConfig

				try:
								if not is_vision_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .feature_extraction_detr import DetrFeatureExtractor
								from .image_processing_detr import DetrImageProcessor

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_detr import (
								    DETR_PRETRAINED_MODEL_ARCHIVE_LIST,
								    DetrForObjectDetection,
								    DetrForSegmentation,
								    DetrModel,
								    DetrPreTrainedModel,
								)

else:
				import sys

				A										=      _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)