code: 
  
  
  '''simple docstring'''
  
  
  
  
  
  import argparse
  import json
  import re
  import string
  import sys
  
  import numpy as np
  
  
  __lowerCamelCase       :       List[Any]				   =			0.5
  
  
  
  def   SCREAMING_SNAKE_CASE__						(							snake_case_,	snake_case_	)			->							List[str]:
  				A__     :							List[Any]   				=['''.''', ''',''', ''';''', ''':''']
  
  				for token in remove_tokens:
  								A__     :							List[str]   				=ground_truth.replace(__a,	''''''	)
  								A__     :							Optional[int]   				=prediction.replace(__a,	''''''	)
  
  				A__     :							str   				=ground_truth.lower(), prediction.lower()
  				A__     :							List[Any]   				=ground_truth.replace('''/''',	''' '''	), prediction.replace('''/''',	''' '''	)
  				A__     :							Union[str, Any]   				=set(ground_truth.split(''' '''	)	), set(prediction.split(''' '''	)	)
  
  				A__     :							Optional[int]   				=ground_truth.intersection(__a	)
  				A__     :							List[str]   				=ground_truth.union(__a	)
  				A__     :							Tuple   				=len(__a	) / len(__a	)
  				return jaccard
  
  
  
  def   SCREAMING_SNAKE_CASE__						(							snake_case_	)			->							List[str]:
  
  
  
  
  
  				def remove_articles(snake_case_	):
  								return re.sub(R'''\b(a|an|the)\b''',	''' ''',	__a	)
  
  				def white_space_fix(snake_case_	):
  								return " ".join(text.split()	)
  
  				def remove_punc(snake_case_	):
  								A__     :							Optional[int]   				=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(__a	)	)	)	)
  
  
  
  def   SCREAMING_SNAKE_CASE__						(							snake_case_,	snake_case_,	snake_case_	)			->							List[str]:
  				A__     :							Optional[Any]   				=0, 0, 0
  
  				A__     :							Union[str, Any]   				='''Parties''' in qa_id
  
  				# first check if ground truth is empty
  				if len(__a	) == 0:
  								if len(__a	) > 0:
  												fp += len(__a	)  # false positive for each one
  				else:
  								for ground_truth in ground_truths:
  												assert len(__a	) > 0
  												# check if there is a match
  												A__     :							Dict   				=False
  												for pred in predictions:
  																if substr_ok:
  																				A__     :							Tuple   				=get_jaccard(__a,	__a	) >= IOU_THRESH or ground_truth in pred
  																else:
  																				A__     :							Dict   				=get_jaccard(__a,	__a	) >= IOU_THRESH
  																if is_match:
  																				A__     :							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.)
  												A__     :							Any   				=False
  												for ground_truth in ground_truths:
  																assert len(__a	) > 0
  																if substr_ok:
  																				A__     :							int   				=get_jaccard(__a,	__a	) >= IOU_THRESH or ground_truth in pred
  																else:
  																				A__     :							Optional[Any]   				=get_jaccard(__a,	__a	) >= IOU_THRESH
  																if is_match:
  																				A__     :							Any   				=True
  
  												if not match_found:
  																fp += 1
  
  				A__     :							int   				=tp / (tp + fp) if tp + fp > 0 else np.nan
  				A__     :							List[Any]   				=tp / (tp + fn) if tp + fn > 0 else np.nan
  
  				return precision, recall
  
  
  
  def   SCREAMING_SNAKE_CASE__						(							snake_case_	)			->							List[str]:
  				A__     :							str   				=precisions[::-1]
  				for i in range(1,	len(__a	)	):
  								A__     :							int   				=max(precision_best[i - 1],	precision_best[i]	)
  				A__     :							List[Any]   				=precision_best[::-1]
  				return precisions
  
  
  
  def   SCREAMING_SNAKE_CASE__						(							snake_case_,	snake_case_	)			->							Optional[Any]:
  				A__     :							List[Any]   				=process_precisions(__a	)
  				A__     :							Optional[int]   				=np.trapz(__a,	__a	)
  				if np.isnan(__a	):
  								return 0
  				return aupr
  
  
  
  def   SCREAMING_SNAKE_CASE__						(							snake_case_,	snake_case_,	snake_case_	)			->							Dict:
  				A__     :							List[str]   				=process_precisions(__a	)
  				A__     :							List[Any]   				=0
  				for prec, recall in zip(__a,	__a	):
  								if recall >= recall_thresh:
  												A__     :							Optional[Any]   				=prec
  												break
  				return prec_at_recall
  
  
  
  def   SCREAMING_SNAKE_CASE__						(							snake_case_,	snake_case_	)			->							Union[str, Any]:
  				return normalize_answer(__a	) == normalize_answer(__a	)
  
  
  
  def   SCREAMING_SNAKE_CASE__						(							snake_case_,	snake_case_,	snake_case_	)			->							Any:
  				A__     :							Tuple   				=0
  				for pred in predictions:
  								for ground_truth in ground_truths:
  												A__     :							int   				=metric_fn(__a,	__a	)
  												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_	)			->							str:
  				A__     :							Any   				=0
  				A__     :							Union[str, Any]   				=[]
  				A__     :							Union[str, Any]   				=[]
  				for article in dataset:
  								for paragraph in article["paragraphs"]:
  												for qa in paragraph["qas"]:
  																total += 1
  																if qa["id"] not in predictions:
  																				A__     :							List[Any]   				='''Unanswered question ''' + qa['''id'''] + ''' will receive score 0.'''
  																				print(__a,	file=sys.stderr	)
  																				continue
  																A__     :							Any   				=[x['''text'''] for x in qa['''answers''']]
  																A__     :							Optional[int]   				=predictions[qa['''id''']]
  																A__     :							Optional[int]   				=compute_precision_recall(__a,	__a,	qa['''id''']	)
  
  																precisions.append(__a	)
  																recalls.append(__a	)
  
  																if precision == 0 and recall == 0:
  																				fa += 0
  																else:
  																				fa += 2 * (precision * recall) / (precision + recall)
  
  																exact_match += metric_max_over_ground_truths(__a,	__a,	__a	)
  
  				A__     :							Tuple   				=[x for _, x in sorted(zip(__a,	__a	)	)]
  				recalls.sort()
  
  				A__     :							int   				=1_0_0.0 * fa / total
  				A__     :							List[str]   				=1_0_0.0 * exact_match / total
  				A__     :							List[Any]   				=get_aupr(__a,	__a	)
  
  				A__     :							Optional[Any]   				=get_prec_at_recall(__a,	__a,	recall_thresh=0.9	)
  				A__     :							int   				=get_prec_at_recall(__a,	__a,	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__":
  						__lowerCamelCase       :       List[Any]				   =			argparse.ArgumentParser(description="Evaluation for CUAD")
  						parser.add_argument("dataset_file", help="Dataset file")
  						parser.add_argument("prediction_file", help="Prediction File")
  						__lowerCamelCase       :       Tuple				   =			parser.parse_args()
  						with open(args.dataset_file) as dataset_file:
  												__lowerCamelCase       :       Any				   =			json.load(dataset_file)
  												__lowerCamelCase       :       List[Any]				   =			dataset_json["data"]
  						with open(args.prediction_file) as prediction_file:
  												__lowerCamelCase       :       List[str]				   =			json.load(prediction_file)
  						print(json.dumps(evaluate(dataset, predictions)))
  
  
  
  
style_context: 
  
  
  from dataclasses import dataclass
  from enum import Enum
  from typing import List, Optional, Union
  
  import numpy as np
  import PIL
  from PIL import Image
  
  from ...utils import BaseOutput, is_torch_available, is_transformers_available
  
  
  
  @dataclass
  class 			a      (      UpperCamelCase_   ):
  		__lowercase													=		{
  		    """sld_warmup_steps""": 15,
  		    """sld_guidance_scale""": 20,
  		    """sld_threshold""": 0.0,
  		    """sld_momentum_scale""": 0.0,
  		    """sld_mom_beta""": 0.0,
  		}
  		__lowercase													=		{
  		    """sld_warmup_steps""": 10,
  		    """sld_guidance_scale""": 1_000,
  		    """sld_threshold""": 0.01,
  		    """sld_momentum_scale""": 0.3,
  		    """sld_mom_beta""": 0.4,
  		}
  		__lowercase													=		{
  		    """sld_warmup_steps""": 7,
  		    """sld_guidance_scale""": 2_000,
  		    """sld_threshold""": 0.0_25,
  		    """sld_momentum_scale""": 0.5,
  		    """sld_mom_beta""": 0.7,
  		}
  		__lowercase													=		{
  		    """sld_warmup_steps""": 0,
  		    """sld_guidance_scale""": 5_000,
  		    """sld_threshold""": 1.0,
  		    """sld_momentum_scale""": 0.5,
  		    """sld_mom_beta""": 0.7,
  		}
  
  
  
  
  
  
  
  @dataclass
  class 			a      (      UpperCamelCase_   ):
  		__lowercase													=		42
  		__lowercase													=		42
  		__lowercase													=		42
  		__lowercase													=		42
  
  
  if is_transformers_available() and is_torch_available():
  						from .pipeline_stable_diffusion_safe import StableDiffusionPipelineSafe
  						from .safety_checker import SafeStableDiffusionSafetyChecker
  
  
  
  