code: 
  
  
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
  
  
  SCREAMING_SNAKE_CASE__    :    Dict								=					{
      "configuration_donut_swin": ["DONUT_SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP", "DonutSwinConfig"],
      "processing_donut": ["DonutProcessor"],
  }
  
  try:
  				if not is_torch_available():
  								raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  				pass
  else:
  				SCREAMING_SNAKE_CASE__    :    Any								=					[
  				    "DONUT_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST",
  				    "DonutSwinModel",
  				    "DonutSwinPreTrainedModel",
  				]
  
  try:
  				if not is_vision_available():
  								raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  				pass
  else:
  				SCREAMING_SNAKE_CASE__    :    Any								=					["DonutFeatureExtractor"]
  				SCREAMING_SNAKE_CASE__    :    List[str]								=					["DonutImageProcessor"]
  
  
  if TYPE_CHECKING:
  				from .configuration_donut_swin import DONUT_SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP, DonutSwinConfig
  				from .processing_donut import DonutProcessor
  
  				try:
  								if not is_torch_available():
  												raise OptionalDependencyNotAvailable()
  				except OptionalDependencyNotAvailable:
  								pass
  				else:
  								from .modeling_donut_swin import (
  								    DONUT_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
  								    DonutSwinModel,
  								    DonutSwinPreTrainedModel,
  								)
  
  				try:
  								if not is_vision_available():
  												raise OptionalDependencyNotAvailable()
  				except OptionalDependencyNotAvailable:
  								pass
  				else:
  								from .feature_extraction_donut import DonutFeatureExtractor
  								from .image_processing_donut import DonutImageProcessor
  
  else:
  				import sys
  
  				SCREAMING_SNAKE_CASE__    :    Optional[Any]								=					_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
  
style_context: 
  
  
  
  
  import argparse
  import json
  import re
  import string
  import sys
  
  import numpy as np
  
  
  SCREAMING_SNAKE_CASE__    :    List[Any]								=					0.5
  
  def lowercase						(     SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE   ) ->       Optional[Any]:
  
  
  			'''simple docstring'''
  
  
  
  
  
  
  
  			SCREAMING_SNAKE_CASE_			=	['.', ',', ';', ':']
  
  			for token in remove_tokens:
  						SCREAMING_SNAKE_CASE_			=	ground_truth.replace(SCREAMING_SNAKE_CASE					,						''   )
  						SCREAMING_SNAKE_CASE_			=	prediction.replace(SCREAMING_SNAKE_CASE					,						''   )
  
  			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(SCREAMING_SNAKE_CASE   )
  			SCREAMING_SNAKE_CASE_			=	ground_truth.union(SCREAMING_SNAKE_CASE   )
  			SCREAMING_SNAKE_CASE_			=	len(SCREAMING_SNAKE_CASE   ) / len(SCREAMING_SNAKE_CASE   )
  			return jaccard
  
  def lowercase						(     SCREAMING_SNAKE_CASE   ) ->       List[str]:
  
  
  			'''simple docstring'''
  
  
  
  
  
  
  
  			def remove_articles(SCREAMING_SNAKE_CASE   ):
  						return re.sub(r'\b(a|an|the)\b'					,						' '					,						SCREAMING_SNAKE_CASE   )
  
  			def white_space_fix(SCREAMING_SNAKE_CASE   ):
  						return " ".join(text.split()   )
  
  			def remove_punc(SCREAMING_SNAKE_CASE   ):
  						SCREAMING_SNAKE_CASE_			=	set(string.punctuation   )
  						return "".join(ch for ch in text if ch not in exclude   )
  
  			def lower(SCREAMING_SNAKE_CASE   ):
  						return text.lower()
  
  			return white_space_fix(remove_articles(remove_punc(lower(SCREAMING_SNAKE_CASE   )   )   )   )
  
  def lowercase						(     SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE   ) ->       str:
  
  
  			'''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(SCREAMING_SNAKE_CASE   ) == 0:
  						if len(SCREAMING_SNAKE_CASE   ) > 0:
  									fp += len(SCREAMING_SNAKE_CASE   )  # false positive for each one
  			else:
  						for ground_truth in ground_truths:
  									assert len(SCREAMING_SNAKE_CASE   ) > 0
  									# check if there is a match
  									SCREAMING_SNAKE_CASE_			=	False
  									for pred in predictions:
  												if substr_ok:
  															SCREAMING_SNAKE_CASE_			=	get_jaccard(SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE   ) >= IOU_THRESH or ground_truth in pred
  												else:
  															SCREAMING_SNAKE_CASE_			=	get_jaccard(SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE   ) >= 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(SCREAMING_SNAKE_CASE   ) > 0
  												if substr_ok:
  															SCREAMING_SNAKE_CASE_			=	get_jaccard(SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE   ) >= IOU_THRESH or ground_truth in pred
  												else:
  															SCREAMING_SNAKE_CASE_			=	get_jaccard(SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE   ) >= 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 lowercase						(     SCREAMING_SNAKE_CASE   ) ->       Tuple:
  
  
  			'''simple docstring'''
  
  
  
  
  
  
  
  			SCREAMING_SNAKE_CASE_			=	precisions[::-1]
  			for i in range(1					,						len(SCREAMING_SNAKE_CASE   )   ):
  						SCREAMING_SNAKE_CASE_			=	max(precision_best[i - 1]					,						precision_best[i]   )
  			SCREAMING_SNAKE_CASE_			=	precision_best[::-1]
  			return precisions
  
  def lowercase						(     SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE   ) ->       List[str]:
  
  
  			'''simple docstring'''
  
  
  
  
  
  
  
  			SCREAMING_SNAKE_CASE_			=	process_precisions(SCREAMING_SNAKE_CASE   )
  			SCREAMING_SNAKE_CASE_			=	np.trapz(SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE   )
  			if np.isnan(SCREAMING_SNAKE_CASE   ):
  						return 0
  			return aupr
  
  def lowercase						(     SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE   ) ->       Dict:
  
  
  			'''simple docstring'''
  
  
  
  
  
  
  
  			SCREAMING_SNAKE_CASE_			=	process_precisions(SCREAMING_SNAKE_CASE   )
  			SCREAMING_SNAKE_CASE_			=	0
  			for prec, recall in zip(SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE   ):
  						if recall >= recall_thresh:
  									SCREAMING_SNAKE_CASE_			=	prec
  									break
  			return prec_at_recall
  
  def lowercase						(     SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE   ) ->       str:
  
  
  			'''simple docstring'''
  
  
  
  
  
  
  
  			return normalize_answer(SCREAMING_SNAKE_CASE   ) == normalize_answer(SCREAMING_SNAKE_CASE   )
  
  def lowercase						(     SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE   ) ->       List[str]:
  
  
  			'''simple docstring'''
  
  
  
  
  
  
  
  			SCREAMING_SNAKE_CASE_			=	0
  			for pred in predictions:
  						for ground_truth in ground_truths:
  									SCREAMING_SNAKE_CASE_			=	metric_fn(SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE   )
  									if score == 1:  # break the loop when one prediction matches the ground truth
  												break
  						if score == 1:
  									break
  			return score
  
  def lowercase						(     SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE   ) ->       Any:
  
  
  			'''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(SCREAMING_SNAKE_CASE					,						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(SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE					,						qa['id']   )
  
  												precisions.append(SCREAMING_SNAKE_CASE   )
  												recalls.append(SCREAMING_SNAKE_CASE   )
  
  												if precision == 0 and recall == 0:
  															fa += 0
  												else:
  															fa += 2 * (precision * recall) / (precision + recall)
  
  												exact_match += metric_max_over_ground_truths(SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE   )
  
  			SCREAMING_SNAKE_CASE_			=	[x for _, x in sorted(zip(SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE   )   )]
  			recalls.sort()
  
  			SCREAMING_SNAKE_CASE_			=	100.0 * fa / total
  			SCREAMING_SNAKE_CASE_			=	100.0 * exact_match / total
  			SCREAMING_SNAKE_CASE_			=	get_aupr(SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE   )
  
  			SCREAMING_SNAKE_CASE_			=	get_prec_at_recall(SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE					,						recall_thresh=0.9   )
  			SCREAMING_SNAKE_CASE_			=	get_prec_at_recall(SCREAMING_SNAKE_CASE					,						SCREAMING_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__":
  				SCREAMING_SNAKE_CASE__    :    Optional[Any]								=					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__    :    Any								=					parser.parse_args()
  				with open(args.dataset_file) as dataset_file:
  								SCREAMING_SNAKE_CASE__    :    Any								=					json.load(dataset_file)
  								SCREAMING_SNAKE_CASE__    :    str								=					dataset_json["data"]
  				with open(args.prediction_file) as prediction_file:
  								SCREAMING_SNAKE_CASE__    :    Union[str, Any]								=					json.load(prediction_file)
  				print(json.dumps(evaluate(dataset, predictions)))
  