


'''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)))







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



