code: 
  
  
  
  
  """simple docstring"""
  
  import argparse
  import json
  import re
  import string
  import sys
  
  import numpy as np
  
  
  __A 		=			0.5
  
  
  
  def   UpperCamelCase__     (		lowercase__  :     int       ,     lowercase__  :     Dict   ):
  		snake_case		:						int								=				[".", ",", ";", ":"]
  
  		for token in remove_tokens:
  				snake_case		:						Dict								=				ground_truth.replace(lowercase__       ,     ""   )
  				snake_case		:						Dict								=				prediction.replace(lowercase__       ,     ""   )
  
  		snake_case		:						Any								=				ground_truth.lower(), prediction.lower()
  		snake_case		:						Dict								=				ground_truth.replace("/"       ,     " "   ), prediction.replace("/"       ,     " "   )
  		snake_case		:						Tuple								=				set(ground_truth.split(" "   )   ), set(prediction.split(" "   )   )
  
  		snake_case		:						Union[str, Any]								=				ground_truth.intersection(lowercase__   )
  		snake_case		:						Optional[Any]								=				ground_truth.union(lowercase__   )
  		snake_case		:						Dict								=				len(lowercase__   ) / len(lowercase__   )
  		return jaccard
  
  
  
  def   UpperCamelCase__     (		lowercase__  :     Union[str, Any]   ):
  
  		def remove_articles(lowercase__  :     List[str]   ):
  				return re.sub(R"\b(a|an|the)\b"       ,     " "       ,     lowercase__   )
  
  		def white_space_fix(lowercase__  :     List[str]   ):
  				return " ".join(text.split()   )
  
  		def remove_punc(lowercase__  :     Any   ):
  				snake_case		:						Dict								=				set(string.punctuation   )
  				return "".join(ch for ch in text if ch not in exclude   )
  
  		def lower(lowercase__  :     Dict   ):
  				return text.lower()
  
  		return white_space_fix(remove_articles(remove_punc(lower(lowercase__   )   )   )   )
  
  
  
  def   UpperCamelCase__     (		lowercase__  :     Any       ,     lowercase__  :     Tuple       ,     lowercase__  :     Union[str, Any]   ):
  		snake_case		:						List[str]								=				0, 0, 0
  
  		snake_case		:						Union[str, Any]								=				"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
  						snake_case		:						str								=				False
  						for pred in predictions:
  								if substr_ok:
  										snake_case		:						List[Any]								=				get_jaccard(lowercase__       ,     lowercase__   ) >= IOU_THRESH or ground_truth in pred
  								else:
  										snake_case		:						List[str]								=				get_jaccard(lowercase__       ,     lowercase__   ) >= IOU_THRESH
  								if is_match:
  										snake_case		:						List[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.)
  						snake_case		:						Union[str, Any]								=				False
  						for ground_truth in ground_truths:
  								assert len(lowercase__   ) > 0
  								if substr_ok:
  										snake_case		:						Any								=				get_jaccard(lowercase__       ,     lowercase__   ) >= IOU_THRESH or ground_truth in pred
  								else:
  										snake_case		:						str								=				get_jaccard(lowercase__       ,     lowercase__   ) >= IOU_THRESH
  								if is_match:
  										snake_case		:						Union[str, Any]								=				True
  
  						if not match_found:
  								fp += 1
  
  		snake_case		:						Tuple								=				tp / (tp + fp) if tp + fp > 0 else np.nan
  		snake_case		:						Union[str, Any]								=				tp / (tp + fn) if tp + fn > 0 else np.nan
  
  		return precision, recall
  
  
  
  def   UpperCamelCase__     (		lowercase__  :     List[Any]   ):
  		snake_case		:						Optional[Any]								=				precisions[::-1]
  		for i in range(1       ,     len(lowercase__   )   ):
  				snake_case		:						List[str]								=				max(precision_best[i - 1]       ,     precision_best[i]   )
  		snake_case		:						Optional[Any]								=				precision_best[::-1]
  		return precisions
  
  
  
  def   UpperCamelCase__     (		lowercase__  :     Tuple       ,     lowercase__  :     Any   ):
  		snake_case		:						int								=				process_precisions(lowercase__   )
  		snake_case		:						Optional[int]								=				np.trapz(lowercase__       ,     lowercase__   )
  		if np.isnan(lowercase__   ):
  				return 0
  		return aupr
  
  
  
  def   UpperCamelCase__     (		lowercase__  :     List[str]       ,     lowercase__  :     str       ,     lowercase__  :     Union[str, Any]   ):
  		snake_case		:						List[str]								=				process_precisions(lowercase__   )
  		snake_case		:						List[Any]								=				0
  		for prec, recall in zip(lowercase__       ,     lowercase__   ):
  				if recall >= recall_thresh:
  						snake_case		:						Dict								=				prec
  						break
  		return prec_at_recall
  
  
  
  def   UpperCamelCase__     (		lowercase__  :     Union[str, Any]       ,     lowercase__  :     Optional[Any]   ):
  		return normalize_answer(lowercase__   ) == normalize_answer(lowercase__   )
  
  
  
  def   UpperCamelCase__     (		lowercase__  :     List[Any]       ,     lowercase__  :     int       ,     lowercase__  :     Optional[Any]   ):
  		snake_case		:						Optional[Any]								=				0
  		for pred in predictions:
  				for ground_truth in ground_truths:
  						snake_case		:						Optional[Any]								=				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   UpperCamelCase__     (		lowercase__  :     Tuple       ,     lowercase__  :     List[str]   ):
  		snake_case		:						Optional[int]								=				0
  		snake_case		:						Any								=				[]
  		snake_case		:						Dict								=				[]
  		for article in dataset:
  				for paragraph in article["paragraphs"]:
  						for qa in paragraph["qas"]:
  								total += 1
  								if qa["id"] not in predictions:
  										snake_case		:						Union[str, Any]								=				"Unanswered question " + qa["id"] + " will receive score 0."
  										print(lowercase__       ,     file=sys.stderr   )
  										continue
  								snake_case		:						Optional[int]								=				[x["text"] for x in qa["answers"]]
  								snake_case		:						Dict								=				predictions[qa["id"]]
  								snake_case		:						Union[str, Any]								=				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__   )
  
  		snake_case		:						List[str]								=				[x for _, x in sorted(zip(lowercase__       ,     lowercase__   )   )]
  		recalls.sort()
  
  		snake_case		:						List[str]								=				100.0 * fa / total
  		snake_case		:						List[Any]								=				100.0 * exact_match / total
  		snake_case		:						Dict								=				get_aupr(lowercase__       ,     lowercase__   )
  
  		snake_case		:						List[Any]								=				get_prec_at_recall(lowercase__       ,     lowercase__       ,     recall_thresh=0.9   )
  		snake_case		:						Optional[int]								=				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__":
  							__A 		=			argparse.ArgumentParser(description="Evaluation for CUAD")
  							parser.add_argument("dataset_file", help="Dataset file")
  							parser.add_argument("prediction_file", help="Prediction File")
  							__A 		=			parser.parse_args()
  							with open(args.dataset_file) as dataset_file:
  														__A 		=			json.load(dataset_file)
  														__A 		=			dataset_json["data"]
  							with open(args.prediction_file) as prediction_file:
  														__A 		=			json.load(prediction_file)
  							print(json.dumps(evaluate(dataset, predictions)))
  
  
  
  
  
style_context: 
  
  
  
  
  """simple docstring"""
  
  from ...processing_utils import ProcessorMixin
  from ...tokenization_utils_base import BatchEncoding
  class 			lowerCamelCase__    (  lowerCamelCase_			):
  			a__ :      Tuple								=     ["""image_processor""", """tokenizer"""]
  			a__ :      List[Any]								=     """EfficientNetImageProcessor"""
  			a__ :      str								=     ("""BertTokenizer""", """BertTokenizerFast""")
  
  
  			def __init__(     self				, SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					super().__init__(SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE							)
  
  
  			def __call__(     self				, SCREAMING_SNAKE_CASE=None				, SCREAMING_SNAKE_CASE=None				, SCREAMING_SNAKE_CASE="max_length"				, SCREAMING_SNAKE_CASE=64				, SCREAMING_SNAKE_CASE=None				, **SCREAMING_SNAKE_CASE							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					if text is None and images is None:
  							raise ValueError("You have to specify either text or images. Both cannot be none."							)
  
  					if text is not None:
  							snake_case		:						Dict								=				self.tokenizer(
  							    SCREAMING_SNAKE_CASE				, padding=SCREAMING_SNAKE_CASE				, max_length=SCREAMING_SNAKE_CASE				, return_tensors=SCREAMING_SNAKE_CASE				, **SCREAMING_SNAKE_CASE							)
  
  					if images is not None:
  							snake_case		:						Tuple								=				self.image_processor(SCREAMING_SNAKE_CASE				, return_tensors=SCREAMING_SNAKE_CASE				, **SCREAMING_SNAKE_CASE							)
  
  					if text is not None and images is not None:
  							snake_case		:						Dict								=				image_features.pixel_values
  							return encoding
  					elif text is not None:
  							return encoding
  					else:
  							return BatchEncoding(data=dict(**SCREAMING_SNAKE_CASE							)				, tensor_type=SCREAMING_SNAKE_CASE							)
  
  
  			def 							lowerCamelCase_				(     self				, *SCREAMING_SNAKE_CASE				, **SCREAMING_SNAKE_CASE							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					return self.tokenizer.batch_decode(*SCREAMING_SNAKE_CASE				, **SCREAMING_SNAKE_CASE							)
  
  
  			def 							lowerCamelCase_				(     self				, *SCREAMING_SNAKE_CASE				, **SCREAMING_SNAKE_CASE							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					return self.tokenizer.decode(*SCREAMING_SNAKE_CASE				, **SCREAMING_SNAKE_CASE							)
  
  
  
  			@property
  			def 							lowerCamelCase_				(     self							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					snake_case		:						Optional[Any]								=				self.tokenizer.model_input_names
  					snake_case		:						Any								=				self.image_processor.model_input_names
  					return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names							)							)
  
  