





import argparse
import json
import re
import string
import sys

import numpy as np


UpperCamelCase__ :			str       	=			0.5




def  A_(      A					,   A ):
	UpperCAmelCase_		    =  [""".""", """,""", """;""", """:"""]

	for token in remove_tokens:
		UpperCAmelCase_		    =  ground_truth.replace(A					,   """""" )
		UpperCAmelCase_		    =  prediction.replace(A					,   """""" )

	UpperCAmelCase_    ,						UpperCAmelCase_		    =  ground_truth.lower(), prediction.lower()
	UpperCAmelCase_    ,						UpperCAmelCase_		    =  ground_truth.replace("""/"""					,   """ """ ), prediction.replace("""/"""					,   """ """ )
	UpperCAmelCase_    ,						UpperCAmelCase_		    =  set(ground_truth.split(""" """ ) ), set(prediction.split(""" """ ) )

	UpperCAmelCase_		    =  ground_truth.intersection(A )
	UpperCAmelCase_		    =  ground_truth.union(A )
	UpperCAmelCase_		    =  len(A ) / len(A )
	return jaccard




def  A_(      A ):

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

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

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

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

	return white_space_fix(remove_articles(remove_punc(lower(A ) ) ) )




def  A_(      A					,   A					,   A ):
	UpperCAmelCase_    ,						UpperCAmelCase_    ,						UpperCAmelCase_		    =  0, 0, 0

	UpperCAmelCase_		    =  """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
			UpperCAmelCase_		    =  False
			for pred in predictions:
				if substr_ok:
					UpperCAmelCase_		    =  get_jaccard(A					,   A ) >= IOU_THRESH or ground_truth in pred
				else:
					UpperCAmelCase_		    =  get_jaccard(A					,   A ) >= IOU_THRESH
				if is_match:
					UpperCAmelCase_		    =  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.)
			UpperCAmelCase_		    =  False
			for ground_truth in ground_truths:
				assert len(A ) > 0
				if substr_ok:
					UpperCAmelCase_		    =  get_jaccard(A					,   A ) >= IOU_THRESH or ground_truth in pred
				else:
					UpperCAmelCase_		    =  get_jaccard(A					,   A ) >= IOU_THRESH
				if is_match:
					UpperCAmelCase_		    =  True

			if not match_found:
				fp += 1

	UpperCAmelCase_		    =  tp / (tp + fp) if tp + fp > 0 else np.nan
	UpperCAmelCase_		    =  tp / (tp + fn) if tp + fn > 0 else np.nan

	return precision, recall




def  A_(      A ):
	UpperCAmelCase_		    =  precisions[::-1]
	for i in range(1					,   len(A ) ):
		UpperCAmelCase_		    =  max(precision_best[i - 1]					,   precision_best[i] )
	UpperCAmelCase_		    =  precision_best[::-1]
	return precisions




def  A_(      A					,   A ):
	UpperCAmelCase_		    =  process_precisions(A )
	UpperCAmelCase_		    =  np.trapz(A					,   A )
	if np.isnan(A ):
		return 0
	return aupr




def  A_(      A					,   A					,   A ):
	UpperCAmelCase_		    =  process_precisions(A )
	UpperCAmelCase_		    =  0
	for prec, recall in zip(A					,   A ):
		if recall >= recall_thresh:
			UpperCAmelCase_		    =  prec
			break
	return prec_at_recall




def  A_(      A					,   A ):
	return normalize_answer(A ) == normalize_answer(A )




def  A_(      A					,   A					,   A ):
	UpperCAmelCase_		    =  0
	for pred in predictions:
		for ground_truth in ground_truths:
			UpperCAmelCase_		    =  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  A_(      A					,   A ):
	UpperCAmelCase_		    =  UpperCAmelCase_		    =  UpperCAmelCase_		    =  0
	UpperCAmelCase_		    =  []
	UpperCAmelCase_		    =  []
	for article in dataset:
		for paragraph in article["paragraphs"]:
			for qa in paragraph["qas"]:
				total += 1
				if qa["id"] not in predictions:
					UpperCAmelCase_		    =  """Unanswered question """ + qa["""id"""] + """ will receive score 0."""
					print(A					,   file=sys.stderr )
					continue
				UpperCAmelCase_		    =  [x["""text"""] for x in qa["""answers"""]]
				UpperCAmelCase_		    =  predictions[qa["""id"""]]
				UpperCAmelCase_    ,						UpperCAmelCase_		    =  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 )

	UpperCAmelCase_		    =  [x for _, x in sorted(zip(A					,   A ) )]
	recalls.sort()

	UpperCAmelCase_		    =  100.0 * fa / total
	UpperCAmelCase_		    =  100.0 * exact_match / total
	UpperCAmelCase_		    =  get_aupr(A					,   A )

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









from typing import Any, Dict, List, Mapping

import numpy as np
import torch

from ...utils import is_cython_available, requires_backends


if is_cython_available():
				import pyximport

				pyximport.install(setup_args={"""include_dirs""": np.get_include()})
				from . import algos_graphormer  # noqa E402




def  A_(      A					,   A = 512 ):
	UpperCAmelCase_		    =  x.shape[1] if len(x.shape ) > 1 else 1
	UpperCAmelCase_		    =  1 + np.arange(0					,   feature_num * offset					,   A					,   dtype=np.intaa )
	UpperCAmelCase_		    =  x + feature_offset
	return x




def  A_(      A					,   A=True ):
	requires_backends(A					,   ["""cython"""] )

	if keep_features and "edge_attr" in item.keys():  # edge_attr
		UpperCAmelCase_		    =  np.asarray(item["""edge_attr"""]					,   dtype=np.intaa )
	else:
		UpperCAmelCase_		    =  np.ones((len(item["""edge_index"""][0] ), 1)					,   dtype=np.intaa )  # same embedding for all

	if keep_features and "node_feat" in item.keys():  # input_nodes
		UpperCAmelCase_		    =  np.asarray(item["""node_feat"""]					,   dtype=np.intaa )
	else:
		UpperCAmelCase_		    =  np.ones((item["""num_nodes"""], 1)					,   dtype=np.intaa )  # same embedding for all

	UpperCAmelCase_		    =  np.asarray(item["""edge_index"""]					,   dtype=np.intaa )

	UpperCAmelCase_		    =  convert_to_single_emb(A ) + 1
	UpperCAmelCase_		    =  item["""num_nodes"""]

	if len(edge_attr.shape ) == 1:
		UpperCAmelCase_		    =  edge_attr[:, None]
	UpperCAmelCase_		    =  np.zeros([num_nodes, num_nodes, edge_attr.shape[-1]]					,   dtype=np.intaa )
	UpperCAmelCase_		    =  convert_to_single_emb(A ) + 1

	# node adj matrix [num_nodes, num_nodes] bool
	UpperCAmelCase_		    =  np.zeros([num_nodes, num_nodes]					,   dtype=A )
	UpperCAmelCase_		    =  True

	UpperCAmelCase_    ,						UpperCAmelCase_		    =  algos_graphormer.floyd_warshall(A )
	UpperCAmelCase_		    =  np.amax(A )

	UpperCAmelCase_		    =  algos_graphormer.gen_edge_input(A					,   A					,   A )
	UpperCAmelCase_		    =  np.zeros([num_nodes + 1, num_nodes + 1]					,   dtype=np.single )  # with graph token

	# combine
	UpperCAmelCase_		    =  input_nodes + 1  # we shift all indices by one for padding
	UpperCAmelCase_		    =  attn_bias
	UpperCAmelCase_		    =  attn_edge_type
	UpperCAmelCase_		    =  shortest_path_result.astype(np.intaa ) + 1  # we shift all indices by one for padding
	UpperCAmelCase_		    =  np.sum(A					,   axis=1 ).reshape(-1 ) + 1  # we shift all indices by one for padding
	UpperCAmelCase_		    =  item["""in_degree"""]  # for undirected graph
	UpperCAmelCase_		    =  input_edges + 1  # we shift all indices by one for padding
	if "labels" not in item:
		UpperCAmelCase_		    =  item["""y"""]

	return item







class 	_UpperCamelCase	:







					'''simple docstring'''

					def __init__(    self     :	str			,       __lowercase     :	int=20			,       __lowercase     :	Union[str, Any]=False		):






						'''simple docstring'''







						if not is_cython_available():
							raise ImportError("""Graphormer preprocessing needs Cython (pyximport)"""		)

						UpperCAmelCase_		    =  spatial_pos_max
						UpperCAmelCase_		    =  on_the_fly_processing





					def __call__(    self     :	List[str]			,       __lowercase     :	List[dict]		):






						'''simple docstring'''







						if self.on_the_fly_processing:
							UpperCAmelCase_		    =  [preprocess_item(__lowercase		) for i in features]

						if not isinstance(features[0]			,       __lowercase		):
							UpperCAmelCase_		    =  [vars(__lowercase		) for f in features]
						UpperCAmelCase_		    =  {}

						UpperCAmelCase_		    =  max(len(i["""input_nodes"""]		) for i in features		)
						UpperCAmelCase_		    =  len(features[0]["""input_nodes"""][0]		)
						UpperCAmelCase_		    =  len(features[0]["""attn_edge_type"""][0][0]		)
						UpperCAmelCase_		    =  max(len(i["""input_edges"""][0][0]		) for i in features		)
						UpperCAmelCase_		    =  len(features[0]["""input_edges"""][0][0][0]		)
						UpperCAmelCase_		    =  len(__lowercase		)

						UpperCAmelCase_		    =  torch.zeros(__lowercase			,       max_node_num + 1			,       max_node_num + 1			,       dtype=torch.float		)
						UpperCAmelCase_		    =  torch.zeros(__lowercase			,       __lowercase			,       __lowercase			,       __lowercase			,       dtype=torch.long		)
						UpperCAmelCase_		    =  torch.zeros(__lowercase			,       __lowercase			,       __lowercase			,       dtype=torch.long		)
						UpperCAmelCase_		    =  torch.zeros(__lowercase			,       __lowercase			,       dtype=torch.long		)
						UpperCAmelCase_		    =  torch.zeros(__lowercase			,       __lowercase			,       __lowercase			,       dtype=torch.long		)
						UpperCAmelCase_		    =  torch.zeros(
						    __lowercase			,       __lowercase			,       __lowercase			,       __lowercase			,       __lowercase			,       dtype=torch.long		)

						for ix, f in enumerate(__lowercase		):
							for k in ["attn_bias", "attn_edge_type", "spatial_pos", "in_degree", "input_nodes", "input_edges"]:
								UpperCAmelCase_		    =  torch.tensor(f[k]		)

							if len(f["""attn_bias"""][1:, 1:][f["""spatial_pos"""] >= self.spatial_pos_max]		) > 0:
								UpperCAmelCase_		    =  float("""-inf"""		)

							UpperCAmelCase_		    =  f["""attn_bias"""]
							UpperCAmelCase_		    =  f[
							    """attn_edge_type"""
							]
							UpperCAmelCase_		    =  f["""spatial_pos"""]
							UpperCAmelCase_		    =  f["""in_degree"""]
							UpperCAmelCase_		    =  f["""input_nodes"""]
							UpperCAmelCase_		    =  f["""input_edges"""]

						UpperCAmelCase_		    =  batch["""in_degree"""]

						UpperCAmelCase_		    =  features[0]["""labels"""]
						if len(__lowercase		) == 1:  # one task
							if isinstance(sample[0]			,       __lowercase		):  # regression
								UpperCAmelCase_		    =  torch.from_numpy(np.concatenate([i["""labels"""] for i in features]		)		)
							else:  # binary classification
								UpperCAmelCase_		    =  torch.from_numpy(np.concatenate([i["""labels"""] for i in features]		)		)
						else:  # multi task classification, left to float to keep the NaNs
							UpperCAmelCase_		    =  torch.from_numpy(np.stack([i["""labels"""] for i in features]			,       axis=0		)		)

						return batch


