






'''simple docstring'''







import argparse
import json
import re
import string
import sys

import numpy as np


_A      :     Optional[Any]       	=0.5





def 					SCREAMING_SNAKE_CASE_			(UpperCamelCase							,     UpperCamelCase ) ->			Tuple:
							lowerCamelCase__   :      Optional[Any]			    =			[""".""", """,""", """;""", """:"""]

							for token in remove_tokens:
														lowerCamelCase__   :      str			    =			ground_truth.replace(UpperCamelCase							,     """""" )
														lowerCamelCase__   :      Union[str, Any]			    =			prediction.replace(UpperCamelCase							,     """""" )

							lowerCamelCase__    ,						lowerCamelCase__   :      List[str]			    =			ground_truth.lower(), prediction.lower()
							lowerCamelCase__    ,						lowerCamelCase__   :      int			    =			ground_truth.replace("""/"""							,     """ """ ), prediction.replace("""/"""							,     """ """ )
							lowerCamelCase__    ,						lowerCamelCase__   :      Optional[int]			    =			set(ground_truth.split(""" """ ) ), set(prediction.split(""" """ ) )

							lowerCamelCase__   :      List[str]			    =			ground_truth.intersection(UpperCamelCase )
							lowerCamelCase__   :      Optional[Any]			    =			ground_truth.union(UpperCamelCase )
							lowerCamelCase__   :      Tuple			    =			len(UpperCamelCase ) / len(UpperCamelCase )
							return jaccard





def 					SCREAMING_SNAKE_CASE_			(UpperCamelCase ) ->			Optional[Any]:

							def remove_articles(UpperCamelCase ):
														return re.sub(r"""\b(a|an|the)\b"""							,     """ """							,     UpperCamelCase )

							def white_space_fix(UpperCamelCase ):
														return " ".join(text.split() )

							def remove_punc(UpperCamelCase ):
														lowerCamelCase__   :      str			    =			set(string.punctuation )
														return "".join(ch for ch in text if ch not in exclude )

							def lower(UpperCamelCase ):
														return text.lower()

							return white_space_fix(remove_articles(remove_punc(lower(UpperCamelCase ) ) ) )





def 					SCREAMING_SNAKE_CASE_			(UpperCamelCase							,     UpperCamelCase							,     UpperCamelCase ) ->			int:
							lowerCamelCase__    ,						lowerCamelCase__    ,						lowerCamelCase__   :      int			    =			0, 0, 0

							lowerCamelCase__   :      Any			    =			"""Parties""" in qa_id

							# first check if ground truth is empty
							if len(UpperCamelCase ) == 0:
														if len(UpperCamelCase ) > 0:
																					fp += len(UpperCamelCase )  # false positive for each one
							else:
														for ground_truth in ground_truths:
																					assert len(UpperCamelCase ) > 0
																					# check if there is a match
																					lowerCamelCase__   :      Optional[Any]			    =			False
																					for pred in predictions:
																												if substr_ok:
																																			lowerCamelCase__   :      List[Any]			    =			get_jaccard(UpperCamelCase							,     UpperCamelCase ) >= IOU_THRESH or ground_truth in pred
																												else:
																																			lowerCamelCase__   :      int			    =			get_jaccard(UpperCamelCase							,     UpperCamelCase ) >= IOU_THRESH
																												if is_match:
																																			lowerCamelCase__   :      List[Any]			    =			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__   :      List[Any]			    =			False
																					for ground_truth in ground_truths:
																												assert len(UpperCamelCase ) > 0
																												if substr_ok:
																																			lowerCamelCase__   :      Union[str, Any]			    =			get_jaccard(UpperCamelCase							,     UpperCamelCase ) >= IOU_THRESH or ground_truth in pred
																												else:
																																			lowerCamelCase__   :      str			    =			get_jaccard(UpperCamelCase							,     UpperCamelCase ) >= IOU_THRESH
																												if is_match:
																																			lowerCamelCase__   :      Dict			    =			True

																					if not match_found:
																												fp += 1

							lowerCamelCase__   :      List[str]			    =			tp / (tp + fp) if tp + fp > 0 else np.nan
							lowerCamelCase__   :      Any			    =			tp / (tp + fn) if tp + fn > 0 else np.nan

							return precision, recall





def 					SCREAMING_SNAKE_CASE_			(UpperCamelCase ) ->			int:
							lowerCamelCase__   :      Tuple			    =			precisions[::-1]
							for i in range(1							,     len(UpperCamelCase ) ):
														lowerCamelCase__   :      List[str]			    =			max(precision_best[i - 1]							,     precision_best[i] )
							lowerCamelCase__   :      int			    =			precision_best[::-1]
							return precisions





def 					SCREAMING_SNAKE_CASE_			(UpperCamelCase							,     UpperCamelCase ) ->			int:
							lowerCamelCase__   :      Optional[Any]			    =			process_precisions(UpperCamelCase )
							lowerCamelCase__   :      int			    =			np.trapz(UpperCamelCase							,     UpperCamelCase )
							if np.isnan(UpperCamelCase ):
														return 0
							return aupr





def 					SCREAMING_SNAKE_CASE_			(UpperCamelCase							,     UpperCamelCase							,     UpperCamelCase ) ->			List[str]:
							lowerCamelCase__   :      Tuple			    =			process_precisions(UpperCamelCase )
							lowerCamelCase__   :      Optional[Any]			    =			0
							for prec, recall in zip(UpperCamelCase							,     UpperCamelCase ):
														if recall >= recall_thresh:
																					lowerCamelCase__   :      List[Any]			    =			prec
																					break
							return prec_at_recall





def 					SCREAMING_SNAKE_CASE_			(UpperCamelCase							,     UpperCamelCase ) ->			List[str]:
							return normalize_answer(UpperCamelCase ) == normalize_answer(UpperCamelCase )





def 					SCREAMING_SNAKE_CASE_			(UpperCamelCase							,     UpperCamelCase							,     UpperCamelCase ) ->			Any:
							lowerCamelCase__   :      List[str]			    =			0
							for pred in predictions:
														for ground_truth in ground_truths:
																					lowerCamelCase__   :      List[str]			    =			metric_fn(UpperCamelCase							,     UpperCamelCase )
																					if score == 1:  # break the loop when one prediction matches the ground truth
																												break
														if score == 1:
																					break
							return score





def 					SCREAMING_SNAKE_CASE_			(UpperCamelCase							,     UpperCamelCase ) ->			int:
							lowerCamelCase__   :      Optional[Any]			    =			0
							lowerCamelCase__   :      Tuple			    =			[]
							lowerCamelCase__   :      List[Any]			    =			[]
							for article in dataset:
														for paragraph in article["paragraphs"]:
																					for qa in paragraph["qas"]:
																												total += 1
																												if qa["id"] not in predictions:
																																			lowerCamelCase__   :      Optional[int]			    =			"""Unanswered question """ + qa["""id"""] + """ will receive score 0."""
																																			print(UpperCamelCase							,     file=sys.stderr )
																																			continue
																												lowerCamelCase__   :      Optional[Any]			    =			[x["""text"""] for x in qa["""answers"""]]
																												lowerCamelCase__   :      str			    =			predictions[qa["""id"""]]
																												lowerCamelCase__    ,						lowerCamelCase__   :      List[Any]			    =			compute_precision_recall(UpperCamelCase							,     UpperCamelCase							,     qa["""id"""] )

																												precisions.append(UpperCamelCase )
																												recalls.append(UpperCamelCase )

																												if precision == 0 and recall == 0:
																																			fa += 0
																												else:
																																			fa += 2 * (precision * recall) / (precision + recall)

																												exact_match += metric_max_over_ground_truths(UpperCamelCase							,     UpperCamelCase							,     UpperCamelCase )

							lowerCamelCase__   :      List[Any]			    =			[x for _, x in sorted(zip(UpperCamelCase							,     UpperCamelCase ) )]
							recalls.sort()

							lowerCamelCase__   :      List[str]			    =			100.0 * fa / total
							lowerCamelCase__   :      Optional[Any]			    =			100.0 * exact_match / total
							lowerCamelCase__   :      Dict			    =			get_aupr(UpperCamelCase							,     UpperCamelCase )

							lowerCamelCase__   :      Tuple			    =			get_prec_at_recall(UpperCamelCase							,     UpperCamelCase							,     recall_thresh=0.9 )
							lowerCamelCase__   :      Dict			    =			get_prec_at_recall(UpperCamelCase							,     UpperCamelCase							,     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      :     int       	=argparse.ArgumentParser(description='''Evaluation for CUAD''')
			parser.add_argument('''dataset_file''', help='''Dataset file''')
			parser.add_argument('''prediction_file''', help='''Prediction File''')
			_A      :     int       	=parser.parse_args()
			with open(args.dataset_file) as dataset_file:
						_A      :     List[Any]       	=json.load(dataset_file)
						_A      :     Any       	=dataset_json['''data''']
			with open(args.prediction_file) as prediction_file:
						_A      :     Union[str, Any]       	=json.load(prediction_file)
			print(json.dumps(evaluate(dataset, predictions)))









'''simple docstring'''







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


_A      :     Optional[Any]       	={
    '''configuration_realm''': ['''REALM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''RealmConfig'''],
    '''tokenization_realm''': ['''RealmTokenizer'''],
}

try:
			if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_A      :     str       	=['''RealmTokenizerFast''']

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_A      :     List[Any]       	=[
			    '''REALM_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''RealmEmbedder''',
			    '''RealmForOpenQA''',
			    '''RealmKnowledgeAugEncoder''',
			    '''RealmPreTrainedModel''',
			    '''RealmReader''',
			    '''RealmScorer''',
			    '''load_tf_weights_in_realm''',
			]
			_A      :     Union[str, Any]       	=['''RealmRetriever''']


if TYPE_CHECKING:
			from .configuration_realm import REALM_PRETRAINED_CONFIG_ARCHIVE_MAP, RealmConfig
			from .tokenization_realm import RealmTokenizer

			try:
						if not is_tokenizers_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_realm import RealmTokenizerFast

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_realm import (
						    REALM_PRETRAINED_MODEL_ARCHIVE_LIST,
						    RealmEmbedder,
						    RealmForOpenQA,
						    RealmKnowledgeAugEncoder,
						    RealmPreTrainedModel,
						    RealmReader,
						    RealmScorer,
						    load_tf_weights_in_realm,
						)
						from .retrieval_realm import RealmRetriever


else:
			import sys

			_A      :     int       	=_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)

