



import argparse
import json
import re
import string
import sys

import numpy as np


snake_case     :    List[Any]						=    0.5
def 						snake_case__							(       __lowercase				,  __lowercase							)		->		str:







						"""simple docstring"""

						A__						:				Optional[int]         =			[".", ",", ";", ":"]

						for token in remove_tokens:
												A__						:				Optional[int]         =			ground_truth.replace(__lowercase				,  ""							)
												A__						:				List[str]         =			prediction.replace(__lowercase				,  ""							)

						A__     ,							A__						:				Optional[int]         =			ground_truth.lower(), prediction.lower()
						A__     ,							A__						:				Optional[int]         =			ground_truth.replace("/"				,  " "							), prediction.replace("/"				,  " "							)
						A__     ,							A__						:				Any         =			set(ground_truth.split(" "							)							), set(prediction.split(" "							)							)

						A__						:				Optional[int]         =			ground_truth.intersection(__lowercase							)
						A__						:				int         =			ground_truth.union(__lowercase							)
						A__						:				Optional[Any]         =			len(__lowercase							) / len(__lowercase							)
						return jaccard
def 						snake_case__							(       __lowercase							)		->		Tuple:







						"""simple docstring"""

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

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

						def remove_punc(__lowercase							):
												A__						:				List[Any]         =			set(string.punctuation							)
												return "".join(ch for ch in text if ch not in exclude							)

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

						return white_space_fix(remove_articles(remove_punc(lower(__lowercase							)							)							)							)
def 						snake_case__							(       __lowercase				,  __lowercase				,  __lowercase							)		->		Dict:







						"""simple docstring"""

						A__     ,							A__     ,							A__						:				Optional[Any]         =			0, 0, 0

						A__						:				int         =			"Parties" in qa_id

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

																		if not match_found:
																								fp += 1

						A__						:				Tuple         =			tp / (tp + fp) if tp + fp > 0 else np.nan
						A__						:				Union[str, Any]         =			tp / (tp + fn) if tp + fn > 0 else np.nan

						return precision, recall
def 						snake_case__							(       __lowercase							)		->		Union[str, Any]:







						"""simple docstring"""

						A__						:				List[Any]         =			precisions[::-1]
						for i in range(1				,  len(__lowercase							)							):
												A__						:				Any         =			max(precision_best[i - 1]				,  precision_best[i]							)
						A__						:				Tuple         =			precision_best[::-1]
						return precisions
def 						snake_case__							(       __lowercase				,  __lowercase							)		->		Optional[int]:







						"""simple docstring"""

						A__						:				List[str]         =			process_precisions(__lowercase							)
						A__						:				Tuple         =			np.trapz(__lowercase				,  __lowercase							)
						if np.isnan(__lowercase							):
												return 0
						return aupr
def 						snake_case__							(       __lowercase				,  __lowercase				,  __lowercase							)		->		Dict:







						"""simple docstring"""

						A__						:				List[Any]         =			process_precisions(__lowercase							)
						A__						:				str         =			0
						for prec, recall in zip(__lowercase				,  __lowercase							):
												if recall >= recall_thresh:
																		A__						:				List[str]         =			prec
																		break
						return prec_at_recall
def 						snake_case__							(       __lowercase				,  __lowercase							)		->		Optional[Any]:







						"""simple docstring"""

						return normalize_answer(__lowercase							) == normalize_answer(__lowercase							)
def 						snake_case__							(       __lowercase				,  __lowercase				,  __lowercase							)		->		Dict:







						"""simple docstring"""

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







						"""simple docstring"""

						A__						:				int         =			0
						A__						:				Any         =			[]
						A__						:				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[str]         =			"Unanswered question " + qa["id"] + " will receive score 0."
																														print(__lowercase				,  file=sys.stderr							)
																														continue
																								A__						:				List[str]         =			[x["text"] for x in qa["answers"]]
																								A__						:				int         =			predictions[qa["id"]]
																								A__     ,							A__						:				List[str]         =			compute_precision_recall(__lowercase				,  __lowercase				,  qa["id"]							)

																								precisions.append(__lowercase							)
																								recalls.append(__lowercase							)

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

																								exact_match += metric_max_over_ground_truths(__lowercase				,  __lowercase				,  __lowercase							)

						A__						:				Dict         =			[x for _, x in sorted(zip(__lowercase				,  __lowercase							)							)]
						recalls.sort()

						A__						:				Optional[Any]         =			100.0 * fa / total
						A__						:				Tuple         =			100.0 * exact_match / total
						A__						:				int         =			get_aupr(__lowercase				,  __lowercase							)

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




import argparse

from . import (
    BertConfig,
    BertGenerationConfig,
    BertGenerationDecoder,
    BertGenerationEncoder,
    load_tf_weights_in_bert_generation,
    logging,
)


logging.set_verbosity_info()
def 						snake_case__							(       __lowercase				,  __lowercase				,  __lowercase				,  __lowercase				,  __lowercase							)		->		Dict:







						"""simple docstring"""

						A__						:				str         =			BertConfig.from_pretrained(
						    "bert-large-cased"				,  vocab_size=__lowercase				,  max_position_embeddings=5_1_2				,  is_decoder=__lowercase				,  add_cross_attention=__lowercase				,  )
						A__						:				List[str]         =			bert_config.to_dict()
						del bert_config_dict["type_vocab_size"]
						A__						:				Union[str, Any]         =			BertGenerationConfig(**__lowercase							)
						if is_encoder:
												A__						:				Optional[int]         =			BertGenerationEncoder(__lowercase							)
						else:
												A__						:				List[Any]         =			BertGenerationDecoder(__lowercase							)
						print(F'Building PyTorch model from configuration: {config}'							)

						# Load weights from tf checkpoint
						load_tf_weights_in_bert_generation(
						    __lowercase				,  __lowercase				,  model_class="bert"				,  is_encoder_named_decoder=__lowercase				,  is_encoder=__lowercase				,  )

						# Save pytorch-model
						print(F'Save PyTorch model and config to {pytorch_dump_path}'							)
						model.save_pretrained(__lowercase							)


if __name__ == "__main__":
		snake_case     :    List[Any]						=    argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '--tf_hub_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
		)
		parser.add_argument(
		    '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
		)
		parser.add_argument(
		    '--is_encoder_named_decoder',
		    action='store_true',
		    help='If decoder has to be renamed to encoder in PyTorch model.',
		)
		parser.add_argument('--is_encoder', action='store_true', help='If model is an encoder.')
		parser.add_argument('--vocab_size', default=5_0_3_5_8, type=int, help='Vocab size of model')
		snake_case     :    List[Any]						=    parser.parse_args()
		convert_tf_checkpoint_to_pytorch(
		    args.tf_hub_path,
		    args.pytorch_dump_path,
		    args.is_encoder_named_decoder,
		    args.vocab_size,
		    is_encoder=args.is_encoder,
		)