





import math
import sys

class      A_	:
	def __init__(   self		):






				'''simple docstring'''







				UpperCAmelCase       		=    0
				UpperCAmelCase       		=    []
				UpperCAmelCase       		=    {}  # To store the pos of node in array
	def 					_lowercase    (   self		):






				'''simple docstring'''







				return self.cur_size == 0
	def 					_lowercase    (   self							, _A		):






				'''simple docstring'''







				UpperCAmelCase       		=    self.left(_A		)
				UpperCAmelCase       		=    self.right(_A		)
				if lc < self.cur_size and self.array(_A		)[0] < self.array(_A		)[0]:
							UpperCAmelCase       		=    lc
				else:
							UpperCAmelCase       		=    idx
				if rc < self.cur_size and self.array(_A		)[0] < self.array(_A		)[0]:
							UpperCAmelCase       		=    rc
				if smallest != idx:
							self.swap(_A							, _A		)
							self.min_heapify(_A		)
	def 					_lowercase    (   self							, _A		):






				'''simple docstring'''







				UpperCAmelCase       		=    self.cur_size
				self.cur_size += 1
				self.array.append((sys.maxsize, tup[1])		)
				self.decrease_key((sys.maxsize, tup[1])							, tup[0]		)
	def 					_lowercase    (   self		):






				'''simple docstring'''







				UpperCAmelCase       		=    self.array[0][1]
				UpperCAmelCase       		=    self.array[self.cur_size - 1]
				self.cur_size -= 1
				self.min_heapify(1		)
				del self.pos[min_node]
				return min_node
	def 					_lowercase    (   self							, _A		):






				'''simple docstring'''







				return 2 * i + 1
	def 					_lowercase    (   self							, _A		):






				'''simple docstring'''







				return 2 * i + 2
	def 					_lowercase    (   self							, _A		):






				'''simple docstring'''







				return math.floor(i / 2		)
	def 					_lowercase    (   self							, _A							, _A		):






				'''simple docstring'''







				UpperCAmelCase       		=    j
				UpperCAmelCase       		=    i
				UpperCAmelCase       		=    self.array[i]
				UpperCAmelCase       		=    self.array[j]
				UpperCAmelCase       		=    temp







	def 					_lowercase    (   self							, _A							, _A		):






				'''simple docstring'''







				UpperCAmelCase       		=    self.pos[tup[1]]
				# assuming the new_d is atmost old_d
				UpperCAmelCase       		=    (new_d, tup[1])
				while idx > 0 and self.array[self.par(_A		)][0] > self.array[idx][0]:
							self.swap(_A							, self.par(_A		)		)
							UpperCAmelCase       		=    self.par(_A		)

class      A_	:
	def __init__(   self							, _A		):






				'''simple docstring'''







				UpperCAmelCase       		=    {}  # To store graph: u -> (v,w)
				UpperCAmelCase       		=    num  # Number of nodes in graph
				# To store the distance from source vertex
				UpperCAmelCase       		=    [0] * self.num_nodes
				UpperCAmelCase       		=    [-1] * self.num_nodes  # To store the path
	def 					_lowercase    (   self							, _A							, _A							, _A		):






				'''simple docstring'''







				if u in self.adjList:
							self.adjList[u].append((v, w)		)
				else:
							UpperCAmelCase       		=    [(v, w)]

				# Assuming undirected graph
				if v in self.adjList:
							self.adjList[v].append((u, w)		)
				else:
							UpperCAmelCase       		=    [(u, w)]
	def 					_lowercase    (   self		):






				'''simple docstring'''







				for u in self.adjList:
							print(_A							, '''->'''							, ''' -> '''.join(str(F"""{v}({w})"""		) for v, w in self.adjList[u]		)		)
	def 					_lowercase    (   self							, _A		):






				'''simple docstring'''







				UpperCAmelCase       		=    [-1] * self.num_nodes
				# src is the source node
				UpperCAmelCase       		=    0
				UpperCAmelCase       		=    PriorityQueue()
				q.insert((0, src)		)  # (dist from src, node)
				for u in self.adjList:
							if u != src:
										UpperCAmelCase       		=    sys.maxsize  # Infinity
										UpperCAmelCase       		=    -1

				while not q.is_empty():
							UpperCAmelCase       		=    q.extract_min()  # Returns node with the min dist from source
							# Update the distance of all the neighbours of u and
							# if their prev dist was INFINITY then push them in Q
							for v, w in self.adjList[u]:
										UpperCAmelCase       		=    self.dist[u] + w
										if self.dist[v] > new_dist:
													if self.dist[v] == sys.maxsize:
																q.insert((new_dist, v)		)
													else:
																q.decrease_key((self.dist[v], v)							, _A		)
													UpperCAmelCase       		=    new_dist
													UpperCAmelCase       		=    u

        # Show the shortest distances from src
				self.show_distances(_A		)
	def 					_lowercase    (   self							, _A		):






				'''simple docstring'''







				print(F"""Distance from node: {src}"""		)
				for u in range(self.num_nodes		):
							print(F"""Node {u} has distance: {self.dist[u]}"""		)







	def 					_lowercase    (   self							, _A							, _A		):






				'''simple docstring'''







				UpperCAmelCase       		=    []
				UpperCAmelCase       		=    0
				UpperCAmelCase       		=    dest
				# Backtracking from dest to src
				while self.par[temp] != -1:
							path.append(_A		)
							if temp != src:
										for v, w in self.adjList[temp]:
													if v == self.par[temp]:
																cost += w
																break
							UpperCAmelCase       		=    self.par[temp]
				path.append(_A		)
				path.reverse()

				print(F"""----Path to reach {dest} from {src}----"""		)
				for u in path:
							print(F"""{u}"""							, end=''' '''		)
							if u != dest:
										print('''-> '''							, end=''''''		)

				print('''\nTotal cost of path: '''							, _A		)


if __name__ == "__main__":
				__A    :   Optional[Any]          =       Graph(9)
				graph.add_edge(0, 1, 4)
				graph.add_edge(0, 7, 8)
				graph.add_edge(1, 2, 8)
				graph.add_edge(1, 7, 11)
				graph.add_edge(2, 3, 7)
				graph.add_edge(2, 8, 2)
				graph.add_edge(2, 5, 4)
				graph.add_edge(3, 4, 9)
				graph.add_edge(3, 5, 14)
				graph.add_edge(4, 5, 10)
				graph.add_edge(5, 6, 2)
				graph.add_edge(6, 7, 1)
				graph.add_edge(6, 8, 6)
				graph.add_edge(7, 8, 7)
				graph.show_graph()
				graph.dijkstra(0)
				graph.show_path(0, 4)

# OUTPUT
# 0 -> 1(4) -> 7(8)
# 1 -> 0(4) -> 2(8) -> 7(11)
# 7 -> 0(8) -> 1(11) -> 6(1) -> 8(7)
# 2 -> 1(8) -> 3(7) -> 8(2) -> 5(4)
# 3 -> 2(7) -> 4(9) -> 5(14)
# 8 -> 2(2) -> 6(6) -> 7(7)
# 5 -> 2(4) -> 3(14) -> 4(10) -> 6(2)
# 4 -> 3(9) -> 5(10)
# 6 -> 5(2) -> 7(1) -> 8(6)
# Distance from node: 0
# Node 0 has distance: 0
# Node 1 has distance: 4
# Node 2 has distance: 12
# Node 3 has distance: 19
# Node 4 has distance: 21
# Node 5 has distance: 11
# Node 6 has distance: 9
# Node 7 has distance: 8
# Node 8 has distance: 14
# ----Path to reach 4 from 0----
# 0 -> 7 -> 6 -> 5 -> 4
# Total cost of path:  21








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__			)  ->    List[Any]:







			'''simple docstring'''

			UpperCAmelCase       		=    ['''.''', ''',''', ''';''', ''':''']

			for token in remove_tokens:
						UpperCAmelCase       		=    ground_truth.replace(UpperCamelCase__      ,      ''''''			)
						UpperCAmelCase       		=    prediction.replace(UpperCamelCase__      ,      ''''''			)

			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(UpperCamelCase__			)
			UpperCAmelCase       		=    ground_truth.union(UpperCamelCase__			)
			UpperCAmelCase       		=    len(UpperCamelCase__			) / len(UpperCamelCase__			)
			return jaccard





def 						__SCREAMING_SNAKE_CASE  (    UpperCamelCase__			)  ->    Union[str, Any]:







			'''simple docstring'''

			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__			):
						UpperCAmelCase       		=    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__			)  ->    Optional[int]:







			'''simple docstring'''

			UpperCAmelCase		,      UpperCAmelCase		,      UpperCAmelCase       		=    0, 0, 0

			UpperCAmelCase       		=    '''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
									UpperCAmelCase       		=    False
									for pred in predictions:
												if substr_ok:
															UpperCAmelCase       		=    get_jaccard(UpperCamelCase__      ,      UpperCamelCase__			) >= IOU_THRESH or ground_truth in pred
												else:
															UpperCAmelCase       		=    get_jaccard(UpperCamelCase__      ,      UpperCamelCase__			) >= 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(UpperCamelCase__			) > 0
												if substr_ok:
															UpperCAmelCase       		=    get_jaccard(UpperCamelCase__      ,      UpperCamelCase__			) >= IOU_THRESH or ground_truth in pred
												else:
															UpperCAmelCase       		=    get_jaccard(UpperCamelCase__      ,      UpperCamelCase__			) >= 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 						__SCREAMING_SNAKE_CASE  (    UpperCamelCase__			)  ->    List[str]:







			'''simple docstring'''

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





def 						__SCREAMING_SNAKE_CASE  (    UpperCamelCase__      ,      UpperCamelCase__			)  ->    str:







			'''simple docstring'''

			UpperCAmelCase       		=    process_precisions(UpperCamelCase__			)
			UpperCAmelCase       		=    np.trapz(UpperCamelCase__      ,      UpperCamelCase__			)
			if np.isnan(UpperCamelCase__			):
						return 0
			return aupr





def 						__SCREAMING_SNAKE_CASE  (    UpperCamelCase__      ,      UpperCamelCase__      ,      UpperCamelCase__			)  ->    Optional[int]:







			'''simple docstring'''

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





def 						__SCREAMING_SNAKE_CASE  (    UpperCamelCase__      ,      UpperCamelCase__			)  ->    str:







			'''simple docstring'''

			return normalize_answer(UpperCamelCase__			) == normalize_answer(UpperCamelCase__			)





def 						__SCREAMING_SNAKE_CASE  (    UpperCamelCase__      ,      UpperCamelCase__      ,      UpperCamelCase__			)  ->    str:







			'''simple docstring'''

			UpperCAmelCase       		=    0
			for pred in predictions:
						for ground_truth in ground_truths:
									UpperCAmelCase       		=    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:







			'''simple docstring'''

			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(UpperCamelCase__      ,      file=sys.stderr			)
															continue
												UpperCAmelCase       		=    [x['''text'''] for x in qa['''answers''']]
												UpperCAmelCase       		=    predictions[qa['''id''']]
												UpperCAmelCase		,      UpperCAmelCase       		=    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__			)

			UpperCAmelCase       		=    [x for _, x in sorted(zip(UpperCamelCase__      ,      UpperCamelCase__			)			)]
			recalls.sort()

			UpperCAmelCase       		=    100.0 * fa / total
			UpperCAmelCase       		=    100.0 * exact_match / total
			UpperCAmelCase       		=    get_aupr(UpperCamelCase__      ,      UpperCamelCase__			)

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



