'''simple docstring'''




import argparse
import json
import re
import string
import sys

import numpy as np


_lowercase		:					str													=		0.5




def 				lowerCamelCase      ( UpperCAmelCase__			:   Dict , UpperCAmelCase__			:   List[str]					)     -> Dict:
							lowercase_   :     Optional[int]									=			[""".""", """,""", """;""", """:"""]

							for token in remove_tokens:
														lowercase_   :     Optional[Any]									=			ground_truth.replace(UpperCAmelCase__ , """"""					)
														lowercase_   :     Optional[int]									=			prediction.replace(UpperCAmelCase__ , """"""					)

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

							lowercase_   :     Any									=			ground_truth.intersection(UpperCAmelCase__					)
							lowercase_   :     Any									=			ground_truth.union(UpperCAmelCase__					)
							lowercase_   :     Union[str, Any]									=			len(UpperCAmelCase__					) / len(UpperCAmelCase__					)
							return jaccard




def 				lowerCamelCase      ( UpperCAmelCase__			:   str					)     -> str:

							def remove_articles(UpperCAmelCase__			:   Union[str, Any]					):
														return re.sub(R"""\b(a|an|the)\b""" , """ """ , UpperCAmelCase__					)

							def white_space_fix(UpperCAmelCase__			:   List[Any]					):
														return " ".join(text.split()					)

							def remove_punc(UpperCAmelCase__			:   Dict					):
														lowercase_   :     List[str]									=			set(string.punctuation					)
														return "".join(ch for ch in text if ch not in exclude					)

							def lower(UpperCAmelCase__			:   Optional[int]					):
														return text.lower()

							return white_space_fix(remove_articles(remove_punc(lower(UpperCAmelCase__					)					)					)					)




def 				lowerCamelCase      ( UpperCAmelCase__			:   Union[str, Any] , UpperCAmelCase__			:   List[str] , UpperCAmelCase__			:   Dict					)     -> Optional[Any]:
							lowercase_		,		lowercase_		,		lowercase_   :     Optional[int]									=			0, 0, 0

							lowercase_   :     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
																					lowercase_   :     Dict									=			False
																					for pred in predictions:
																												if substr_ok:
																																			lowercase_   :     List[Any]									=			get_jaccard(UpperCAmelCase__ , UpperCAmelCase__					) >= IOU_THRESH or ground_truth in pred
																												else:
																																			lowercase_   :     Union[str, Any]									=			get_jaccard(UpperCAmelCase__ , UpperCAmelCase__					) >= IOU_THRESH
																												if is_match:
																																			lowercase_   :     Tuple									=			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.)
																					lowercase_   :     List[Any]									=			False
																					for ground_truth in ground_truths:
																												assert len(UpperCAmelCase__					) > 0
																												if substr_ok:
																																			lowercase_   :     str									=			get_jaccard(UpperCAmelCase__ , UpperCAmelCase__					) >= IOU_THRESH or ground_truth in pred
																												else:
																																			lowercase_   :     List[Any]									=			get_jaccard(UpperCAmelCase__ , UpperCAmelCase__					) >= IOU_THRESH
																												if is_match:
																																			lowercase_   :     Tuple									=			True

																					if not match_found:
																												fp += 1

							lowercase_   :     Tuple									=			tp / (tp + fp) if tp + fp > 0 else np.nan
							lowercase_   :     Tuple									=			tp / (tp + fn) if tp + fn > 0 else np.nan

							return precision, recall




def 				lowerCamelCase      ( UpperCAmelCase__			:   Tuple					)     -> str:
							lowercase_   :     Dict									=			precisions[::-1]
							for i in range(1 , len(UpperCAmelCase__					)					):
														lowercase_   :     Dict									=			max(precision_best[i - 1] , precision_best[i]					)
							lowercase_   :     Union[str, Any]									=			precision_best[::-1]
							return precisions




def 				lowerCamelCase      ( UpperCAmelCase__			:   str , UpperCAmelCase__			:   Optional[Any]					)     -> Tuple:
							lowercase_   :     Optional[Any]									=			process_precisions(UpperCAmelCase__					)
							lowercase_   :     Optional[Any]									=			np.trapz(UpperCAmelCase__ , UpperCAmelCase__					)
							if np.isnan(UpperCAmelCase__					):
														return 0
							return aupr




def 				lowerCamelCase      ( UpperCAmelCase__			:   Optional[Any] , UpperCAmelCase__			:   List[Any] , UpperCAmelCase__			:   Dict					)     -> Union[str, Any]:
							lowercase_   :     Union[str, Any]									=			process_precisions(UpperCAmelCase__					)
							lowercase_   :     Union[str, Any]									=			0
							for prec, recall in zip(UpperCAmelCase__ , UpperCAmelCase__					):
														if recall >= recall_thresh:
																					lowercase_   :     Optional[Any]									=			prec
																					break
							return prec_at_recall




def 				lowerCamelCase      ( UpperCAmelCase__			:   Optional[Any] , UpperCAmelCase__			:   Dict					)     -> Tuple:
							return normalize_answer(UpperCAmelCase__					) == normalize_answer(UpperCAmelCase__					)




def 				lowerCamelCase      ( UpperCAmelCase__			:   Dict , UpperCAmelCase__			:   List[Any] , UpperCAmelCase__			:   List[str]					)     -> int:
							lowercase_   :     Union[str, Any]									=			0
							for pred in predictions:
														for ground_truth in ground_truths:
																					lowercase_   :     Union[str, Any]									=			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 				lowerCamelCase      ( UpperCAmelCase__			:   Dict , UpperCAmelCase__			:   str					)     -> Dict:
							lowercase_   :     Optional[int]									=			0
							lowercase_   :     Optional[Any]									=			[]
							lowercase_   :     Tuple									=			[]
							for article in dataset:
														for paragraph in article["paragraphs"]:
																					for qa in paragraph["qas"]:
																												total += 1
																												if qa["id"] not in predictions:
																																			lowercase_   :     Tuple									=			"""Unanswered question """ + qa["""id"""] + """ will receive score 0."""
																																			print(UpperCAmelCase__ , file=sys.stderr					)
																																			continue
																												lowercase_   :     List[str]									=			[x["""text"""] for x in qa["""answers"""]]
																												lowercase_   :     Tuple									=			predictions[qa["""id"""]]
																												lowercase_		,		lowercase_   :     int									=			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__					)

							lowercase_   :     Tuple									=			[x for _, x in sorted(zip(UpperCAmelCase__ , UpperCAmelCase__					)					)]
							recalls.sort()

							lowercase_   :     Union[str, Any]									=			100.0 * fa / total
							lowercase_   :     List[Any]									=			100.0 * exact_match / total
							lowercase_   :     Any									=			get_aupr(UpperCAmelCase__ , UpperCAmelCase__					)

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












from typing import TYPE_CHECKING

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


UpperCamelCase__ :			Any       	=			{
    """configuration_splinter""": ["""SPLINTER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """SplinterConfig"""],
    """tokenization_splinter""": ["""SplinterTokenizer"""],
}

try:
				if not is_tokenizers_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				UpperCamelCase__ :			Any       	=			["""SplinterTokenizerFast"""]

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				UpperCamelCase__ :			Union[str, Any]       	=			[
				    """SPLINTER_PRETRAINED_MODEL_ARCHIVE_LIST""",
				    """SplinterForQuestionAnswering""",
				    """SplinterForPreTraining""",
				    """SplinterLayer""",
				    """SplinterModel""",
				    """SplinterPreTrainedModel""",
				]


if TYPE_CHECKING:
				from .configuration_splinter import SPLINTER_PRETRAINED_CONFIG_ARCHIVE_MAP, SplinterConfig
				from .tokenization_splinter import SplinterTokenizer

				try:
								if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_splinter_fast import SplinterTokenizerFast

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_splinter import (
								    SPLINTER_PRETRAINED_MODEL_ARCHIVE_LIST,
								    SplinterForPreTraining,
								    SplinterForQuestionAnswering,
								    SplinterLayer,
								    SplinterModel,
								    SplinterPreTrainedModel,
								)


else:
				import sys

				UpperCamelCase__ :			List[str]       	=			_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


