


import argparse
import json
import re
import string
import sys

import numpy as np


UpperCAmelCase      :						List[str]    =		0.5






def   __lowerCamelCase	(       lowerCamelCase__		:	Dict     ,       lowerCamelCase__		:	Optional[Any]			):





			'''simple docstring'''




			lowerCamelCase								=     [""".""", """,""", """;""", """:"""]

			for token in remove_tokens:
						lowerCamelCase								=     ground_truth.replace(lowerCamelCase__     ,       """"""			)
						lowerCamelCase								=     prediction.replace(lowerCamelCase__     ,       """"""			)

			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(lowerCamelCase__			)
			lowerCamelCase								=     ground_truth.union(lowerCamelCase__			)
			lowerCamelCase								=     len(lowerCamelCase__			) / len(lowerCamelCase__			)
			return jaccard






def   __lowerCamelCase	(       lowerCamelCase__		:	str			):





			'''simple docstring'''




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

			def white_space_fix(lowerCamelCase__		:	Dict			):
						return " ".join(text.split()			)

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

			def lower(lowerCamelCase__		:	Union[str, Any]			):
						return text.lower()

			return white_space_fix(remove_articles(remove_punc(lower(lowerCamelCase__			)			)			)			)






def   __lowerCamelCase	(       lowerCamelCase__		:	int     ,       lowerCamelCase__		:	Optional[int]     ,       lowerCamelCase__		:	int			):





			'''simple docstring'''




			lowerCamelCase		,		lowerCamelCase		,		lowerCamelCase								=     0, 0, 0

			lowerCamelCase								=     """Parties""" in qa_id

			# first check if ground truth is empty
			if len(lowerCamelCase__			) == 0:
						if len(lowerCamelCase__			) > 0:
									fp += len(lowerCamelCase__			)  # false positive for each one
			else:
						for ground_truth in ground_truths:
									assert len(lowerCamelCase__			) > 0
									# check if there is a match
									lowerCamelCase								=     False
									for pred in predictions:
												if substr_ok:
															lowerCamelCase								=     get_jaccard(lowerCamelCase__     ,       lowerCamelCase__			) >= IOU_THRESH or ground_truth in pred
												else:
															lowerCamelCase								=     get_jaccard(lowerCamelCase__     ,       lowerCamelCase__			) >= 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(lowerCamelCase__			) > 0
												if substr_ok:
															lowerCamelCase								=     get_jaccard(lowerCamelCase__     ,       lowerCamelCase__			) >= IOU_THRESH or ground_truth in pred
												else:
															lowerCamelCase								=     get_jaccard(lowerCamelCase__     ,       lowerCamelCase__			) >= 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   __lowerCamelCase	(       lowerCamelCase__		:	List[Any]			):





			'''simple docstring'''




			lowerCamelCase								=     precisions[::-1]
			for i in range(1     ,       len(lowerCamelCase__			)			):
						lowerCamelCase								=     max(precision_best[i - 1]     ,       precision_best[i]			)
			lowerCamelCase								=     precision_best[::-1]
			return precisions






def   __lowerCamelCase	(       lowerCamelCase__		:	Union[str, Any]     ,       lowerCamelCase__		:	Any			):





			'''simple docstring'''




			lowerCamelCase								=     process_precisions(lowerCamelCase__			)
			lowerCamelCase								=     np.trapz(lowerCamelCase__     ,       lowerCamelCase__			)
			if np.isnan(lowerCamelCase__			):
						return 0
			return aupr






def   __lowerCamelCase	(       lowerCamelCase__		:	str     ,       lowerCamelCase__		:	str     ,       lowerCamelCase__		:	Union[str, Any]			):





			'''simple docstring'''




			lowerCamelCase								=     process_precisions(lowerCamelCase__			)
			lowerCamelCase								=     0
			for prec, recall in zip(lowerCamelCase__     ,       lowerCamelCase__			):
						if recall >= recall_thresh:
									lowerCamelCase								=     prec
									break
			return prec_at_recall






def   __lowerCamelCase	(       lowerCamelCase__		:	str     ,       lowerCamelCase__		:	List[Any]			):





			'''simple docstring'''




			return normalize_answer(lowerCamelCase__			) == normalize_answer(lowerCamelCase__			)






def   __lowerCamelCase	(       lowerCamelCase__		:	Optional[Any]     ,       lowerCamelCase__		:	Dict     ,       lowerCamelCase__		:	Optional[Any]			):





			'''simple docstring'''




			lowerCamelCase								=     0
			for pred in predictions:
						for ground_truth in ground_truths:
									lowerCamelCase								=     metric_fn(lowerCamelCase__     ,       lowerCamelCase__			)
									if score == 1:  # break the loop when one prediction matches the ground truth
												break
						if score == 1:
									break
			return score






def   __lowerCamelCase	(       lowerCamelCase__		:	Optional[int]     ,       lowerCamelCase__		:	Tuple			):





			'''simple docstring'''




			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(lowerCamelCase__     ,       file=sys.stderr			)
															continue
												lowerCamelCase								=     [x["""text"""] for x in qa["""answers"""]]
												lowerCamelCase								=     predictions[qa["""id"""]]
												lowerCamelCase		,		lowerCamelCase								=     compute_precision_recall(lowerCamelCase__     ,       lowerCamelCase__     ,       qa["""id"""]			)

												precisions.append(lowerCamelCase__			)
												recalls.append(lowerCamelCase__			)

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

												exact_match += metric_max_over_ground_truths(lowerCamelCase__     ,       lowerCamelCase__     ,       lowerCamelCase__			)

			lowerCamelCase								=     [x for _, x in sorted(zip(lowerCamelCase__     ,       lowerCamelCase__			)			)]
			recalls.sort()

			lowerCamelCase								=     100.0 * fa / total
			lowerCamelCase								=     100.0 * exact_match / total
			lowerCamelCase								=     get_aupr(lowerCamelCase__     ,       lowerCamelCase__			)

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





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCAmelCase      :						List[str]    =		{
    "configuration_big_bird": ["BIG_BIRD_PRETRAINED_CONFIG_ARCHIVE_MAP", "BigBirdConfig", "BigBirdOnnxConfig"],
}

try:
		if not is_sentencepiece_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		UpperCAmelCase      :						str    =		["BigBirdTokenizer"]

try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		UpperCAmelCase      :						str    =		["BigBirdTokenizerFast"]

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		UpperCAmelCase      :						Dict    =		[
		    "BIG_BIRD_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "BigBirdForCausalLM",
		    "BigBirdForMaskedLM",
		    "BigBirdForMultipleChoice",
		    "BigBirdForPreTraining",
		    "BigBirdForQuestionAnswering",
		    "BigBirdForSequenceClassification",
		    "BigBirdForTokenClassification",
		    "BigBirdLayer",
		    "BigBirdModel",
		    "BigBirdPreTrainedModel",
		    "load_tf_weights_in_big_bird",
		]

try:
		if not is_flax_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		UpperCAmelCase      :						Dict    =		[
		    "FlaxBigBirdForCausalLM",
		    "FlaxBigBirdForMaskedLM",
		    "FlaxBigBirdForMultipleChoice",
		    "FlaxBigBirdForPreTraining",
		    "FlaxBigBirdForQuestionAnswering",
		    "FlaxBigBirdForSequenceClassification",
		    "FlaxBigBirdForTokenClassification",
		    "FlaxBigBirdModel",
		    "FlaxBigBirdPreTrainedModel",
		]

if TYPE_CHECKING:
		from .configuration_big_bird import BIG_BIRD_PRETRAINED_CONFIG_ARCHIVE_MAP, BigBirdConfig, BigBirdOnnxConfig

		try:
				if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_big_bird import BigBirdTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_big_bird_fast import BigBirdTokenizerFast

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_big_bird import (
				    BIG_BIRD_PRETRAINED_MODEL_ARCHIVE_LIST,
				    BigBirdForCausalLM,
				    BigBirdForMaskedLM,
				    BigBirdForMultipleChoice,
				    BigBirdForPreTraining,
				    BigBirdForQuestionAnswering,
				    BigBirdForSequenceClassification,
				    BigBirdForTokenClassification,
				    BigBirdLayer,
				    BigBirdModel,
				    BigBirdPreTrainedModel,
				    load_tf_weights_in_big_bird,
				)

		try:
				if not is_flax_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_flax_big_bird import (
				    FlaxBigBirdForCausalLM,
				    FlaxBigBirdForMaskedLM,
				    FlaxBigBirdForMultipleChoice,
				    FlaxBigBirdForPreTraining,
				    FlaxBigBirdForQuestionAnswering,
				    FlaxBigBirdForSequenceClassification,
				    FlaxBigBirdForTokenClassification,
				    FlaxBigBirdModel,
				    FlaxBigBirdPreTrainedModel,
				)

else:
		import sys

		UpperCAmelCase      :						Dict    =		_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

