



import argparse
import json
import re
import string
import sys

import numpy as np


__SCREAMING_SNAKE_CASE     =	0.5




def 						UpperCAmelCase (    _lowerCamelCase			,     _lowerCamelCase     ):
			A				:       str														=    [".", ",", ";", ":"]

			for token in remove_tokens:
						A				:       Tuple														=    ground_truth.replace(_lowerCamelCase			,     ""     )
						A				:       str														=    prediction.replace(_lowerCamelCase			,     ""     )

			A							,			A				:       Optional[Any]														=    ground_truth.lower(), prediction.lower()
			A							,			A				:       Union[str, Any]														=    ground_truth.replace("/"			,     " "     ), prediction.replace("/"			,     " "     )
			A							,			A				:       str														=    set(ground_truth.split(" "     )     ), set(prediction.split(" "     )     )

			A				:       List[str]														=    ground_truth.intersection(_lowerCamelCase     )
			A				:       List[str]														=    ground_truth.union(_lowerCamelCase     )
			A				:       Optional[Any]														=    len(_lowerCamelCase     ) / len(_lowerCamelCase     )
			return jaccard




def 						UpperCAmelCase (    _lowerCamelCase     ):

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

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

			def remove_punc(_lowerCamelCase     ):
						A				:       Union[str, Any]														=    set(string.punctuation     )
						return "".join(ch for ch in text if ch not in exclude     )

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

			return white_space_fix(remove_articles(remove_punc(lower(_lowerCamelCase     )     )     )     )




def 						UpperCAmelCase (    _lowerCamelCase			,     _lowerCamelCase			,     _lowerCamelCase     ):
			A							,			A							,			A				:       List[Any]														=    0, 0, 0

			A				:       int														=    "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
									A				:       Any														=    False
									for pred in predictions:
												if substr_ok:
															A				:       Any														=    get_jaccard(_lowerCamelCase			,     _lowerCamelCase     ) >= IOU_THRESH or ground_truth in pred
												else:
															A				:       str														=    get_jaccard(_lowerCamelCase			,     _lowerCamelCase     ) >= IOU_THRESH
												if is_match:
															A				:       str														=    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				:       List[Any]														=    False
									for ground_truth in ground_truths:
												assert len(_lowerCamelCase     ) > 0
												if substr_ok:
															A				:       int														=    get_jaccard(_lowerCamelCase			,     _lowerCamelCase     ) >= IOU_THRESH or ground_truth in pred
												else:
															A				:       Tuple														=    get_jaccard(_lowerCamelCase			,     _lowerCamelCase     ) >= IOU_THRESH
												if is_match:
															A				:       List[str]														=    True

									if not match_found:
												fp += 1

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

			return precision, recall




def 						UpperCAmelCase (    _lowerCamelCase     ):
			A				:       str														=    precisions[::-1]
			for i in range(1			,     len(_lowerCamelCase     )     ):
						A				:       Any														=    max(precision_best[i - 1]			,     precision_best[i]     )
			A				:       List[Any]														=    precision_best[::-1]
			return precisions




def 						UpperCAmelCase (    _lowerCamelCase			,     _lowerCamelCase     ):
			A				:       int														=    process_precisions(_lowerCamelCase     )
			A				:       List[str]														=    np.trapz(_lowerCamelCase			,     _lowerCamelCase     )
			if np.isnan(_lowerCamelCase     ):
						return 0
			return aupr




def 						UpperCAmelCase (    _lowerCamelCase			,     _lowerCamelCase			,     _lowerCamelCase     ):
			A				:       Optional[Any]														=    process_precisions(_lowerCamelCase     )
			A				:       str														=    0
			for prec, recall in zip(_lowerCamelCase			,     _lowerCamelCase     ):
						if recall >= recall_thresh:
									A				:       Optional[int]														=    prec
									break
			return prec_at_recall




def 						UpperCAmelCase (    _lowerCamelCase			,     _lowerCamelCase     ):
			return normalize_answer(_lowerCamelCase     ) == normalize_answer(_lowerCamelCase     )




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

			A				:       List[str]														=    [x for _, x in sorted(zip(_lowerCamelCase			,     _lowerCamelCase     )     )]
			recalls.sort()

			A				:       int														=    1_00.0 * fa / total
			A				:       int														=    1_00.0 * exact_match / total
			A				:       Optional[int]														=    get_aupr(_lowerCamelCase			,     _lowerCamelCase     )

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




class 						lowerCamelCase_     :






						'''simple docstring'''







						def __init__(						self   :      Optional[int]						,						__lowerCamelCase   :      List[Any]			)   ->					Tuple:
									A				:       Dict														=    val
									# Number of nodes in left subtree
									A				:       List[Any]														=    0
									A				:       List[str]														=    None
									A				:       Union[str, Any]														=    None
									A				:       Dict														=    None







						def       SCREAMING_SNAKE_CASE__		(						self   :      Union[str, Any]						,						__lowerCamelCase   :      Union[str, Any]			)   ->					Optional[Any]:
									assert self.left_tree_size == other.left_tree_size, "Unequal Sizes of Blocks"

									if self.val < other.val:
												A				:       List[str]														=    self.right
												A				:       Any														=    None
												if self.right:
															A				:       Dict														=    other
												A				:       Optional[int]														=    other
												A				:       int														=    self.left_tree_size * 2 + 1
												return self
									else:
												A				:       Optional[Any]														=    other.right
												A				:       Dict														=    None
												if other.right:
															A				:       str														=    self
												A				:       Any														=    self
												A				:       int														=    other.left_tree_size * 2 + 1
												return other







class 						lowerCamelCase_     :






						'''simple docstring'''







						def __init__(						self   :      Optional[int]						,						__lowerCamelCase   :      Optional[Any]=None						,						__lowerCamelCase   :      Union[str, Any]=None						,						__lowerCamelCase   :      Union[str, Any]=0			)   ->					Optional[Any]:
									A				:       List[str]														=    heap_size
									A				:       Optional[int]														=    bottom_root
									A				:       str														=    min_node







						def       SCREAMING_SNAKE_CASE__		(						self   :      Optional[int]						,						__lowerCamelCase   :      Optional[int]			)   ->					Optional[int]:

									# Empty heaps corner cases
									if other.size == 0:
												return None
									if self.size == 0:
												A				:       int														=    other.size
												A				:       Dict														=    other.bottom_root
												A				:       Tuple														=    other.min_node
												return None
									# Update size
									A				:       Optional[Any]														=    self.size + other.size

									# Update min.node
									if self.min_node.val > other.min_node.val:
												A				:       Tuple														=    other.min_node
									# Merge

									# Order roots by left_subtree_size
									A				:       Tuple														=    []
									A							,			A				:       List[str]														=    self.bottom_root, other.bottom_root
									while i or j:
												if i and ((not j) or i.left_tree_size < j.left_tree_size):
															combined_roots_list.append((i, True)			)
															A				:       List[Any]														=    i.parent
												else:
															combined_roots_list.append((j, False)			)
															A				:       Optional[Any]														=    j.parent
        # Insert links between them
									for i in range(len(__lowerCamelCase			) - 1			):
												if combined_roots_list[i][1] != combined_roots_list[i + 1][1]:
															A				:       str														=    combined_roots_list[i + 1][0]
															A				:       Any														=    combined_roots_list[i][0]
        # Consecutively merge roots with same left_tree_size
									A				:       Dict														=    combined_roots_list[0][0]
									while i.parent:
												if (
												    (i.left_tree_size == i.parent.left_tree_size) and (not i.parent.parent)
												) or (
												    i.left_tree_size == i.parent.left_tree_size
												    and i.left_tree_size != i.parent.parent.left_tree_size
												):
															# Neighbouring Nodes
															A				:       Union[str, Any]														=    i.left
															A				:       Tuple														=    i.parent.parent

															# Merging trees
															A				:       Optional[int]														=    i.merge_trees(i.parent			)

															# Updating links
															A				:       List[str]														=    previous_node
															A				:       int														=    next_node
															if previous_node:
																		A				:       int														=    i
															if next_node:
																		A				:       Optional[int]														=    i
												else:
															A				:       str														=    i.parent
        # Updating self.bottom_root
									while i.left:
												A				:       List[str]														=    i.left
									A				:       List[str]														=    i

									# Update other
									A				:       Optional[Any]														=    self.size
									A				:       Any														=    self.bottom_root
									A				:       Optional[Any]														=    self.min_node

									# Return the merged heap
									return self







						def       SCREAMING_SNAKE_CASE__		(						self   :      Union[str, Any]						,						__lowerCamelCase   :      int			)   ->					Optional[Any]:
									if self.size == 0:
												A				:       Dict														=    Node(__lowerCamelCase			)
												A				:       List[Any]														=    1
												A				:       Optional[Any]														=    self.bottom_root
									else:
												# Create new node
												A				:       Tuple														=    Node(__lowerCamelCase			)

												# Update size
												self.size += 1

												# update min_node
												if val < self.min_node.val:
															A				:       List[Any]														=    new_node
												# Put new_node as a bottom_root in heap
												A				:       int														=    new_node
												A				:       Tuple														=    self.bottom_root
												A				:       Any														=    new_node

												# Consecutively merge roots with same left_tree_size
												while (
												    self.bottom_root.parent
												    and self.bottom_root.left_tree_size
												    == self.bottom_root.parent.left_tree_size
												):
															# Next node
															A				:       List[Any]														=    self.bottom_root.parent.parent

															# Merge
															A				:       Optional[Any]														=    self.bottom_root.merge_trees(self.bottom_root.parent			)

															# Update Links
															A				:       List[Any]														=    next_node
															A				:       List[str]														=    None
															if next_node:
																		A				:       List[Any]														=    self.bottom_root







						def       SCREAMING_SNAKE_CASE__		(						self   :      Tuple			)   ->					int:
									return self.min_node.val







						def       SCREAMING_SNAKE_CASE__		(						self   :      List[str]			)   ->					Tuple:
									return self.size == 0







						def       SCREAMING_SNAKE_CASE__		(						self   :      Optional[int]			)   ->					Optional[int]:
									A				:       List[str]														=    self.min_node.val

									# Last element in heap corner case
									if self.size == 1:
												# Update size
												A				:       Union[str, Any]														=    0

												# Update bottom root
												A				:       int														=    None

												# Update min_node
												A				:       Optional[int]														=    None

												return min_value
									# No right subtree corner case
									# The structure of the tree implies that this should be the bottom root
									# and there is at least one other root
									if self.min_node.right is None:
												# Update size
												self.size -= 1

												# Update bottom root
												A				:       List[str]														=    self.bottom_root.parent
												A				:       Dict														=    None

												# Update min_node
												A				:       List[str]														=    self.bottom_root
												A				:       Dict														=    self.bottom_root.parent
												while i:
															if i.val < self.min_node.val:
																		A				:       Optional[int]														=    i
															A				:       List[Any]														=    i.parent
												return min_value
									# General case
									# Find the BinomialHeap of the right subtree of min_node
									A				:       List[str]														=    self.min_node.right
									A				:       List[Any]														=    None
									A				:       Tuple														=    bottom_of_new
									A				:       Optional[Any]														=    1

									# Size, min_node and bottom_root
									while bottom_of_new.left:
												A				:       List[str]														=    size_of_new * 2 + 1
												A				:       int														=    bottom_of_new.left
												if bottom_of_new.val < min_of_new.val:
															A				:       int														=    bottom_of_new
        # Corner case of single root on top left path
									if (not self.min_node.left) and (not self.min_node.parent):
												A				:       Optional[Any]														=    size_of_new
												A				:       List[str]														=    bottom_of_new
												A				:       Optional[int]														=    min_of_new
												# print("Single root, multiple nodes case")
												return min_value
									# Remaining cases
									# Construct heap of right subtree
									A				:       List[str]														=    BinomialHeap(
									    bottom_root=__lowerCamelCase						,						min_node=__lowerCamelCase						,						heap_size=__lowerCamelCase			)

									# Update size
									A				:       Tuple														=    self.size - 1 - size_of_new

									# Neighbour nodes
									A				:       List[str]														=    self.min_node.left
									A				:       List[str]														=    self.min_node.parent

									# Initialize new bottom_root and min_node
									A				:       List[Any]														=    previous_node or next_node
									A				:       int														=    next_node

									# Update links of previous_node and search below for new min_node and
									# bottom_root
									if previous_node:
												A				:       Optional[int]														=    next_node

												# Update bottom_root and search for min_node below
												A				:       Optional[Any]														=    previous_node
												A				:       Dict														=    previous_node
												while self.bottom_root.left:
															A				:       List[str]														=    self.bottom_root.left
															if self.bottom_root.val < self.min_node.val:
																		A				:       Optional[Any]														=    self.bottom_root
									if next_node:
												A				:       Any														=    previous_node

												# Search for new min_node above min_node
												A				:       int														=    next_node
												while i:
															if i.val < self.min_node.val:
																		A				:       Optional[int]														=    i
															A				:       Any														=    i.parent
        # Merge heaps
									self.merge_heaps(__lowerCamelCase			)

									return min_value







						def       SCREAMING_SNAKE_CASE__		(						self   :      Union[str, Any]			)   ->					Optional[Any]:
									A				:       Tuple														=    self.bottom_root
									while top_root.parent:
												A				:       Optional[int]														=    top_root.parent
									# preorder
									A				:       Union[str, Any]														=    []
									self.__traversal(__lowerCamelCase						,						__lowerCamelCase			)
									return heap_pre_order







						def       SCREAMING_SNAKE_CASE__		(						self   :      Optional[int]						,						__lowerCamelCase   :      List[Any]						,						__lowerCamelCase   :      Optional[int]						,						__lowerCamelCase   :      Any=0			)   ->					List[Any]:
									if curr_node:
												preorder.append((curr_node.val, level)			)
												self.__traversal(curr_node.left						,						__lowerCamelCase						,						level + 1			)
												self.__traversal(curr_node.right						,						__lowerCamelCase						,						level + 1			)
									else:
												preorder.append(("#", level)			)







						def __str__(						self   :      List[str]			)   ->					Optional[Any]:
									if self.is_empty():
												return ""
									A				:       Optional[int]														=    self.pre_order()

									return "\n".join(("-" * level + str(__lowerCamelCase			)) for value, level in preorder_heap			)


# Unit Tests
if __name__ == "__main__":
							import doctest

							doctest.testmod()