






import argparse
import json
import re
import string
import sys

import numpy as np


A__     :      Optional[Any]             =			0.5






def   a      (						lowerCamelCase_		,       lowerCamelCase_    ):






				'''simple docstring'''
				lowercase__       			=      ['''.''', ''',''', ''';''', ''':''']

				for token in remove_tokens:
								lowercase__       			=      ground_truth.replace(lowerCamelCase_		,       ''''''    )
								lowercase__       			=      prediction.replace(lowerCamelCase_		,       ''''''    )

				lowercase__			,						lowercase__       			=      ground_truth.lower(), prediction.lower()
				lowercase__			,						lowercase__       			=      ground_truth.replace('''/'''		,       ''' '''    ), prediction.replace('''/'''		,       ''' '''    )
				lowercase__			,						lowercase__       			=      set(ground_truth.split(''' '''    )    ), set(prediction.split(''' '''    )    )

				lowercase__       			=      ground_truth.intersection(lowerCamelCase_    )
				lowercase__       			=      ground_truth.union(lowerCamelCase_    )
				lowercase__       			=      len(lowerCamelCase_    ) / len(lowerCamelCase_    )
				return jaccard






def   a      (						lowerCamelCase_    ):






				'''simple docstring'''

				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_    ):
								lowercase__       			=      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   a      (						lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_    ):






				'''simple docstring'''
				lowercase__			,						lowercase__			,						lowercase__       			=      0, 0, 0

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

												if not match_found:
																fp += 1

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

				return precision, recall






def   a      (						lowerCamelCase_    ):






				'''simple docstring'''
				lowercase__       			=      precisions[::-1]
				for i in range(1		,       len(lowerCamelCase_    )    ):
								lowercase__       			=      max(precision_best[i - 1]		,       precision_best[i]    )
				lowercase__       			=      precision_best[::-1]
				return precisions






def   a      (						lowerCamelCase_		,       lowerCamelCase_    ):






				'''simple docstring'''
				lowercase__       			=      process_precisions(lowerCamelCase_    )
				lowercase__       			=      np.trapz(lowerCamelCase_		,       lowerCamelCase_    )
				if np.isnan(lowerCamelCase_    ):
								return 0
				return aupr






def   a      (						lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_    ):






				'''simple docstring'''
				lowercase__       			=      process_precisions(lowerCamelCase_    )
				lowercase__       			=      0
				for prec, recall in zip(lowerCamelCase_		,       lowerCamelCase_    ):
								if recall >= recall_thresh:
												lowercase__       			=      prec
												break
				return prec_at_recall






def   a      (						lowerCamelCase_		,       lowerCamelCase_    ):






				'''simple docstring'''
				return normalize_answer(lowerCamelCase_    ) == normalize_answer(lowerCamelCase_    )






def   a      (						lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_    ):






				'''simple docstring'''
				lowercase__       			=      0
				for pred in predictions:
								for ground_truth in ground_truths:
												lowercase__       			=      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   a      (						lowerCamelCase_		,       lowerCamelCase_    ):






				'''simple docstring'''
				lowercase__       			=      lowercase__       			=      lowercase__       			=      0
				lowercase__       			=      []
				lowercase__       			=      []
				for article in dataset:
								for paragraph in article["paragraphs"]:
												for qa in paragraph["qas"]:
																total += 1
																if qa["id"] not in predictions:
																				lowercase__       			=      '''Unanswered question ''' + qa['''id'''] + ''' will receive score 0.'''
																				print(lowerCamelCase_		,       file=sys.stderr    )
																				continue
																lowercase__       			=      [x['''text'''] for x in qa['''answers''']]
																lowercase__       			=      predictions[qa['''id''']]
																lowercase__			,						lowercase__       			=      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_    )

				lowercase__       			=      [x for _, x in sorted(zip(lowerCamelCase_		,       lowerCamelCase_    )    )]
				recalls.sort()

				lowercase__       			=      1_00.0 * fa / total
				lowercase__       			=      1_00.0 * exact_match / total
				lowercase__       			=      get_aupr(lowerCamelCase_		,       lowerCamelCase_    )

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












import dataclasses
import enum
import functools
import math
import re

# The following script is adapted from the script of TaPas.
# Original: https://github.com/google-research/tapas/master/wikisql_utils.py
from typing import Any, List, Text


A__     :      Dict             =			'none'
A__     :      Optional[Any]             =			'none'






def   a      (						lowerCamelCase_		,       lowerCamelCase_    ):






				'''simple docstring'''
				lowercase__       			=      value.split(lowerCamelCase_    )
				return len(lowerCamelCase_    ) > 1 and any((len(lowerCamelCase_    ) == 3 for x in split)    )






def   a      (						lowerCamelCase_    ):






				'''simple docstring'''
				if isinstance(lowerCamelCase_		,       lowerCamelCase_    ):
								return value
				if isinstance(lowerCamelCase_		,       lowerCamelCase_    ):
								return float(lowerCamelCase_    )
				if not isinstance(lowerCamelCase_		,       lowerCamelCase_    ):
								raise ValueError('''Argument value is not a string. Can\'t parse it as float'''    )
				lowercase__       			=      value

				try:
								# Example: 1,000.7
								if "." in sanitized and "," in sanitized:
												return float(sanitized.replace(''','''		,       ''''''    )    )
								# 1,000
								if "," in sanitized and _split_thousands(''','''		,       lowerCamelCase_    ):
												return float(sanitized.replace(''','''		,       ''''''    )    )
								# 5,5556
								if "," in sanitized and sanitized.count(''','''    ) == 1 and not _split_thousands(''','''		,       lowerCamelCase_    ):
												return float(sanitized.replace(''','''		,       '''.'''    )    )
								# 0.0.0.1
								if sanitized.count('''.'''    ) > 1:
												return float(sanitized.replace('''.'''		,       ''''''    )    )
								# 0,0,0,1
								if sanitized.count(''','''    ) > 1:
												return float(sanitized.replace(''','''		,       ''''''    )    )
								return float(lowerCamelCase_    )
				except ValueError:
								# Avoid adding the sanitized value in the error message.
								raise ValueError('''Unable to convert value to float'''    )






def   a      (						lowerCamelCase_    ):






				'''simple docstring'''
				if answer is None:
								return None
				try:
								lowercase__       			=      convert_to_float(lowerCamelCase_    )
								if isinstance(lowerCamelCase_		,       lowerCamelCase_    ) and math.isnan(lowerCamelCase_    ):
												return None
								return value
				except ValueError:
								return answer.lower()


A__     :      Tuple             =			{
    'text': lambda x: x,
    'real': convert_to_float,
}
class 				_UpperCAmelCase (	enum.Enum     ):

				"""simple docstring"""




				lowercase__    							=	0
				lowercase__    							=	1
				lowercase__    							=	2
				lowercase__    							=	3
				lowercase__    							=	4
				lowercase__    							=	5



class 				_UpperCAmelCase (	enum.Enum     ):

				"""simple docstring"""




				lowercase__    							=	0
				lowercase__    							=	1
				lowercase__    							=	2



@dataclasses.dataclass
class 				_UpperCAmelCase :

				"""simple docstring"""




				lowercase__    							=	42
				lowercase__    							=	42
				lowercase__    							=	42



A__     :      str             =			re.compile(r'\w+|[^\w\s]+', re.UNICODE | re.MULTILINE | re.DOTALL)






def   a      (						lowerCamelCase_    ):






				'''simple docstring'''
				return list(_TOKENIZER.findall(x.lower()    )    )






def   a      (						lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_    ):






				'''simple docstring'''
				if operator == _Operator.EQUALS:
								return src == tgt
				elif operator == _Operator.GREATER:
								return src > tgt
				elif operator == _Operator.LESSER:
								return src < tgt
				raise ValueError(F"""Unknown operator: {operator}"""    )






def   a      (						lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_    ):






				'''simple docstring'''
				lowercase__       			=      table['''types''']
				return _TYPE_CONVERTER[types[column]](lowerCamelCase_    )






def   a      (						lowerCamelCase_    ):






				'''simple docstring'''
				return isinstance(lowerCamelCase_		,       lowerCamelCase_    )






def   a      (						lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_    ):






				'''simple docstring'''
				for cond in conditions:
								lowercase__       			=      row[cond.column]

								lowercase__       			=      _parse_value(lowerCamelCase_		,       cond.column		,       cond.cmp_value    )

								if _is_string(lowerCamelCase_    ) and _is_string(lowerCamelCase_    ):
												lowercase__       			=      _normalize_for_match(lowerCamelCase_    )
												lowercase__       			=      _normalize_for_match(lowerCamelCase_    )

								if not isinstance(lowerCamelCase_		,       type(lowerCamelCase_    )    ):
												raise ValueError('''Type difference {} != {}'''.format(type(lowerCamelCase_    )		,       type(lowerCamelCase_    )    )    )

								if not _compare(cond.operator		,       lowerCamelCase_		,       lowerCamelCase_    ):
												return False
				return True






def   a      (						lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_    ):






				'''simple docstring'''
				if not answer_coordinates:
								if aggregation_op == _Aggregation.COUNT:
												return 0.0
								else:
												return EMPTY_ANSWER_AGG

    # Count can support non numeric answers.
				if aggregation_op == _Aggregation.COUNT:
								return float(len(lowerCamelCase_    )    )

				# If we have just one answer, if float returns it or try a conversion.
				lowercase__       			=      [table['''rows'''][i][j] for (i, j) in answer_coordinates]
				if len(lowerCamelCase_    ) == 1:
								try:
												return convert_to_float(values[0]    )
								except ValueError as e:
												if aggregation_op != _Aggregation.NONE:
																raise e

				if aggregation_op == _Aggregation.NONE:
								return None

				# Other aggregation only support numeric values. Bail out if we have strings.
				if not all((isinstance(lowerCamelCase_		,       (int, float)    ) for v in values)    ):
								return None

				if aggregation_op == _Aggregation.SUM:
								return float(sum(lowerCamelCase_    )    )
				elif aggregation_op == _Aggregation.AVERAGE:
								return sum(lowerCamelCase_    ) / len(lowerCamelCase_    )
				else:
								raise ValueError(F"""Unknown aggregation: {aggregation_op}"""    )






def   a      (						lowerCamelCase_		,       lowerCamelCase_    ):






				'''simple docstring'''
				lowercase__       			=      sql_query['''agg''']
				if aggregation_op_index >= 3:
								lowercase__       			=      _Aggregation(lowerCamelCase_    )
				else:
								lowercase__       			=      _Aggregation.NONE

				lowercase__       			=      sql_query['''sel''']
				lowercase__       			=      [
				    _Condition(lowerCamelCase_		,       _Operator(lowerCamelCase_    )		,       lowerCamelCase_    )
				    for column, operator, cmp_value in zip(
				        sql_query['''conds''']['''column_index''']		,       sql_query['''conds''']['''operator_index''']		,       sql_query['''conds''']['''condition''']    )
				]

				lowercase__       			=      []
				for row in range(len(table['''rows''']    )    ):
								if _respect_conditions(lowerCamelCase_		,       table['''rows'''][row]		,       lowerCamelCase_    ):
												indices.append((row, target_column)    )

				if not indices:
								return [], aggregation_op

				if len(lowerCamelCase_    ) == 1:
								return indices, aggregation_op

				# Parsing of MIN/MAX.
				if aggregation_op_index in (1, 2):
								lowercase__       			=      {2: min, 1: max}
								lowercase__       			=      [(table['''rows'''][i][j], index) for index, (i, j) in enumerate(lowerCamelCase_    )]
								lowercase__       			=      functools.reduce(operators[sql_query['''agg''']]		,       lowerCamelCase_    )

								lowercase__       			=      [indices[reduced[1]]]
								return ret, _Aggregation.NONE

				return indices, aggregation_op






def   a      (						lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_    ):






				'''simple docstring'''
				if float_answer is not None:
								return [str(lowerCamelCase_    )]
				return [str(table['''real_rows'''][r][c]    ) for r, c in answer_coordinates]






def   a      (						lowerCamelCase_		,       lowerCamelCase_    ):






				'''simple docstring'''
				lowercase__			,						lowercase__       			=      _get_answer_coordinates(lowerCamelCase_		,       lowerCamelCase_    )
				lowercase__       			=      _get_float_answer(lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_    )
				lowercase__       			=      _get_answer_text(lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_    )
				# keep the original data the same with TaPas
				if len(lowerCamelCase_    ) == 0:
								lowercase__       			=      [EMPTY_ANSWER]
				return answer_text




