code: 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_00.0 * fa / total
  							lowerCAmelCase				=	1_00.0 * 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)))
  
  
  
  
style_context: from typing import Callable, Optional, Union
  
  from ...configuration_utils import PretrainedConfig
  from ...utils import logging
  
  
  lowercase__    :       Optional[Any]						=			logging.get_logger(__name__)
  
  lowercase__    :       Union[str, Any]						=			{
      '''microsoft/prophetnet-large-uncased''': (
          '''https://huggingface.co/microsoft/prophetnet-large-uncased/resolve/main/config.json'''
      ),
  }
  
  
  
  
  class      lowercase_						(						UpperCamelCase_		):
  
  
  
  	"""simple docstring"""
  
  
  
  
  
  	UpperCAmelCase_						:      Optional[int]     =       """prophetnet"""
  	UpperCAmelCase_						:      Optional[int]     =       ["""past_key_values"""]
  	UpperCAmelCase_						:      Any     =       {
  	    """num_attention_heads""": """num_encoder_attention_heads""",
  	}
  
  
  	def __init__(							self	,    __SCREAMING_SNAKE_CASE = 0.1	,    __SCREAMING_SNAKE_CASE = "gelu"	,    __SCREAMING_SNAKE_CASE = 30522	,    __SCREAMING_SNAKE_CASE = 1024	,    __SCREAMING_SNAKE_CASE = 4096	,    __SCREAMING_SNAKE_CASE = 12	,    __SCREAMING_SNAKE_CASE = 16	,    __SCREAMING_SNAKE_CASE = 4096	,    __SCREAMING_SNAKE_CASE = 12	,    __SCREAMING_SNAKE_CASE = 16	,    __SCREAMING_SNAKE_CASE = 0.1	,    __SCREAMING_SNAKE_CASE = 0.1	,    __SCREAMING_SNAKE_CASE = 512	,    __SCREAMING_SNAKE_CASE = 0.0_2	,    __SCREAMING_SNAKE_CASE = True	,    __SCREAMING_SNAKE_CASE = True	,    __SCREAMING_SNAKE_CASE = 0	,    __SCREAMING_SNAKE_CASE = 2	,    __SCREAMING_SNAKE_CASE = 32	,    __SCREAMING_SNAKE_CASE = 128	,    __SCREAMING_SNAKE_CASE = False	,    __SCREAMING_SNAKE_CASE = 0.0	,    __SCREAMING_SNAKE_CASE = True	,    __SCREAMING_SNAKE_CASE = 0	,    __SCREAMING_SNAKE_CASE = 1	,    __SCREAMING_SNAKE_CASE = 2	,    **__SCREAMING_SNAKE_CASE	,    )		->int:
  								lowerCAmelCase				=	vocab_size
  								lowerCAmelCase				=	hidden_size
  								lowerCAmelCase				=	encoder_ffn_dim
  								lowerCAmelCase				=	num_encoder_layers
  								lowerCAmelCase				=	num_encoder_attention_heads
  								lowerCAmelCase				=	decoder_ffn_dim
  								lowerCAmelCase				=	num_decoder_layers
  								lowerCAmelCase				=	num_decoder_attention_heads
  								lowerCAmelCase				=	max_position_embeddings
  								lowerCAmelCase				=	init_std  # Normal(0, this parameter)
  								lowerCAmelCase				=	activation_function
  
  								# parameters for prophetnet
  								lowerCAmelCase				=	ngram
  								lowerCAmelCase				=	num_buckets
  								lowerCAmelCase				=	relative_max_distance
  								lowerCAmelCase				=	disable_ngram_loss
  								lowerCAmelCase				=	eps
  
  								# 3 Types of Dropout
  								lowerCAmelCase				=	attention_dropout
  								lowerCAmelCase				=	activation_dropout
  								lowerCAmelCase				=	dropout
  
  								lowerCAmelCase				=	use_cache
  
  								super().__init__(
  								    pad_token_id=__SCREAMING_SNAKE_CASE	,    bos_token_id=__SCREAMING_SNAKE_CASE	,    eos_token_id=__SCREAMING_SNAKE_CASE	,    is_encoder_decoder=__SCREAMING_SNAKE_CASE	,    add_cross_attention=__SCREAMING_SNAKE_CASE	,    decoder_start_token_id=__SCREAMING_SNAKE_CASE	,    **__SCREAMING_SNAKE_CASE	,    )
  
  
  	@property
  	def     SCREAMING_SNAKE_CASE_       (							self       )		->int:
  								return self.num_encoder_layers + self.num_decoder_layers
  
  
  
  
  	@num_hidden_layers.setter
  	def     SCREAMING_SNAKE_CASE_       (							self	,    __SCREAMING_SNAKE_CASE       )		->Union[str, Any]:
  								raise NotImplementedError(
  								    '''This model does not support the setting of `num_hidden_layers`. Please set `num_encoder_layers` and'''
  								    ''' `num_decoder_layers`.'''       )
  
  
  
  