import argparse
import json
import re
import string
import sys

import numpy as np


snake_case__						      =			0.5
def    __magic_name__(	__UpperCAmelCase			,    __UpperCAmelCase		)    ->     Any:



      '''simple docstring'''

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

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

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

      _lowerCamelCase           =					ground_truth.intersection(lowercase__		)
      _lowerCamelCase           =					ground_truth.union(lowercase__		)
      _lowerCamelCase           =					len(lowercase__		) / len(lowercase__		)
      return jaccard
def    __magic_name__(	__UpperCAmelCase		)    ->     str:



      '''simple docstring'''

      def remove_articles(__UpperCAmelCase		):
            return re.sub(r'''\b(a|an|the)\b'''			,    ''' '''			,    lowercase__		)

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

      def remove_punc(__UpperCAmelCase		):
            _lowerCamelCase           =					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(lowercase__		)		)		)		)
def    __magic_name__(	__UpperCAmelCase			,    __UpperCAmelCase			,    __UpperCAmelCase		)    ->     List[str]:



      '''simple docstring'''

      _lowerCamelCase     ,       _lowerCamelCase     ,       _lowerCamelCase           =					0, 0, 0

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

                  if not match_found:
                        fp += 1

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

      return precision, recall
def    __magic_name__(	__UpperCAmelCase		)    ->     List[str]:



      '''simple docstring'''

      _lowerCamelCase           =					precisions[::-1]
      for i in range(1			,    len(lowercase__		)		):
            _lowerCamelCase           =					max(precision_best[i - 1]			,    precision_best[i]		)
      _lowerCamelCase           =					precision_best[::-1]
      return precisions
def    __magic_name__(	__UpperCAmelCase			,    __UpperCAmelCase		)    ->     Union[str, Any]:



      '''simple docstring'''

      _lowerCamelCase           =					process_precisions(lowercase__		)
      _lowerCamelCase           =					np.trapz(lowercase__			,    lowercase__		)
      if np.isnan(lowercase__		):
            return 0
      return aupr
def    __magic_name__(	__UpperCAmelCase			,    __UpperCAmelCase			,    __UpperCAmelCase		)    ->     Tuple:



      '''simple docstring'''

      _lowerCamelCase           =					process_precisions(lowercase__		)
      _lowerCamelCase           =					0
      for prec, recall in zip(lowercase__			,    lowercase__		):
            if recall >= recall_thresh:
                  _lowerCamelCase           =					prec
                  break
      return prec_at_recall
def    __magic_name__(	__UpperCAmelCase			,    __UpperCAmelCase		)    ->     List[Any]:



      '''simple docstring'''

      return normalize_answer(lowercase__		) == normalize_answer(lowercase__		)
def    __magic_name__(	__UpperCAmelCase			,    __UpperCAmelCase			,    __UpperCAmelCase		)    ->     Tuple:



      '''simple docstring'''

      _lowerCamelCase           =					0
      for pred in predictions:
            for ground_truth in ground_truths:
                  _lowerCamelCase           =					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    __magic_name__(	__UpperCAmelCase			,    __UpperCAmelCase		)    ->     List[str]:



      '''simple docstring'''

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

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

      _lowerCamelCase           =					100.0 * fa / total
      _lowerCamelCase           =					100.0 * exact_match / total
      _lowerCamelCase           =					get_aupr(lowercase__			,    lowercase__		)

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



from functools import partial
from typing import Any, Callable, Dict, List, Type, TypeVar, Union, overload

import torch
import torch.nn as nn
import torch.types

def 		snake_case_   (				lowercase__     :    torch.Tensor       ,     lowercase__     :    torch.Tensor       ,     lowercase__     :    bool     ):






				'''simple docstring'''






				if not inplace:
								_lowerCAmelCase  						=ma + ma
				else:
								ma += ma

				return ma

def 		snake_case_   (				lowercase__     :    torch.Tensor       ,     lowercase__     :    List[int]     ):






				'''simple docstring'''






				_lowerCAmelCase  						=-1 * len(lowercase__     )
				_lowerCAmelCase  						=list(range(len(tensor.shape[:zero_index]     )     )     )
				return tensor.permute(first_inds + [zero_index + i for i in inds]     )

def 		snake_case_   (				lowercase__     :    torch.Tensor       ,     lowercase__     :    int     ):






				'''simple docstring'''






				return t.reshape(t.shape[:-no_dims] + (-1,)     )

def 		snake_case_   (				lowercase__     :    torch.Tensor       ,     lowercase__     :    torch.Tensor       ,     lowercase__     :    int       ,     lowercase__     :    float = 1e-4     ):






				'''simple docstring'''






				_lowerCAmelCase  						=mask.expand(*value.shape     )
				return torch.sum(mask * value       ,     dim=lowercase__     ) / (eps + torch.sum(lowercase__       ,     dim=lowercase__     ))

def 		snake_case_   (				lowercase__     :    torch.Tensor       ,     lowercase__     :    torch.types.Number = 2.3_1_2_5       ,     lowercase__     :    torch.types.Number = 2_1.6_8_7_5       ,     lowercase__     :    int = 64     ):






				'''simple docstring'''






				_lowerCAmelCase  						=torch.linspace(lowercase__       ,     lowercase__       ,     no_bins - 1       ,     device=pts.device     )
				_lowerCAmelCase  						=torch.sqrt(torch.sum((pts.unsqueeze(-2     ) - pts.unsqueeze(-3     )) ** 2       ,     dim=-1     )     )
				return torch.bucketize(lowercase__       ,     lowercase__     )

def 		snake_case_   (				lowercase__     :    Callable[[list], Any]       ,     lowercase__     :    List[dict]     ):






				'''simple docstring'''






				_lowerCAmelCase  						=dicts[0]
				_lowerCAmelCase  						={}
				for k, v in first.items():
								_lowerCAmelCase  						=[d[k] for d in dicts]
								if isinstance(lowercase__       ,     lowercase__     ):
												_lowerCAmelCase  						=dict_multimap(lowercase__       ,     lowercase__     )
								else:
												_lowerCAmelCase  						=fn(lowercase__     )

				return new_dict

def 		snake_case_   (				lowercase__     :    torch.Tensor       ,     lowercase__     :    torch.Tensor     ):






				'''simple docstring'''






				_lowerCAmelCase  						=v_bins.view(((1,) * len(x.shape     )) + (len(lowercase__     ),)     )
				_lowerCAmelCase  						=x[..., None] - reshaped_bins
				_lowerCAmelCase  						=torch.argmin(torch.abs(lowercase__     )       ,     dim=-1     )
				return nn.functional.one_hot(lowercase__       ,     num_classes=len(lowercase__     )     ).float()

def 		snake_case_   (				lowercase__     :    torch.Tensor       ,     lowercase__     :    torch.Tensor       ,     lowercase__     :    int = 0       ,     lowercase__     :    int = 0     ):






				'''simple docstring'''






				_lowerCAmelCase  						=[]
				for i, s in enumerate(data.shape[:no_batch_dims]     ):
								_lowerCAmelCase  						=torch.arange(lowercase__     )
								_lowerCAmelCase  						=r.view(*(*((1,) * i), -1, *((1,) * (len(inds.shape     ) - i - 1)))     )
								ranges.append(lowercase__     )

				_lowerCAmelCase  						=[slice(lowercase__     ) for _ in range(len(data.shape     ) - no_batch_dims     )]
				_lowerCAmelCase  						=inds
				ranges.extend(lowercase__     )
				# Matt note: Editing this to get around the behaviour of using a list as an array index changing
				# in recent Numpy versions
				return data[tuple(lowercase__     )]


__SCREAMING_SNAKE_CASE	:							str           =  TypeVar('''T''')

def 		snake_case_   (				lowercase__     :    Callable[[T], Any]       ,     lowercase__     :    Dict[Any, Union[dict, list, tuple, T]]       ,     lowercase__     :    Type[T]     ):






				'''simple docstring'''






				_lowerCAmelCase  						={}
				for k, v in dic.items():
								if isinstance(lowercase__       ,     lowercase__     ):
												_lowerCAmelCase  						=dict_map(lowercase__       ,     lowercase__       ,     lowercase__     )
								else:
												_lowerCAmelCase  						=tree_map(lowercase__       ,     lowercase__       ,     lowercase__     )

				return new_dict

@overload
def 		snake_case_   (				lowercase__     :    Callable[[T], Any]       ,     lowercase__     :    T       ,     lowercase__     :    Type[T]     ):






				'''simple docstring'''






				...

@overload
def 		snake_case_   (				lowercase__     :    Callable[[T], Any]       ,     lowercase__     :    dict       ,     lowercase__     :    Type[T]     ):






				'''simple docstring'''






				...

@overload
def 		snake_case_   (				lowercase__     :    Callable[[T], Any]       ,     lowercase__     :    list       ,     lowercase__     :    Type[T]     ):






				'''simple docstring'''






				...

@overload
def 		snake_case_   (				lowercase__     :    Callable[[T], Any]       ,     lowercase__     :    tuple       ,     lowercase__     :    Type[T]     ):






				'''simple docstring'''






				...

def 		snake_case_   (				lowercase__     :    Optional[Any]       ,     lowercase__     :    str       ,     lowercase__     :    Optional[int]     ):






				'''simple docstring'''






				if isinstance(lowercase__       ,     lowercase__     ):
								return dict_map(lowercase__       ,     lowercase__       ,     lowercase__     )
				elif isinstance(lowercase__       ,     lowercase__     ):
								return [tree_map(lowercase__       ,     lowercase__       ,     lowercase__     ) for x in tree]
				elif isinstance(lowercase__       ,     lowercase__     ):
								return tuple(tree_map(lowercase__       ,     lowercase__       ,     lowercase__     ) for x in tree     )
				elif isinstance(lowercase__       ,     lowercase__     ):
								return fn(lowercase__     )
				else:
								print(type(lowercase__     )     )
								raise ValueError("""Not supported"""     )





__SCREAMING_SNAKE_CASE	:							Any           =  partial(tree_map, leaf_type=torch.Tensor)

