
import argparse
import json
import re
import string
import sys

import numpy as np


__lowerCamelCase								=					0.5




def 		lowerCAmelCase_     (     _SCREAMING_SNAKE_CASE      ,	_SCREAMING_SNAKE_CASE  )   ->      Union[str, Any]:

							"""simple docstring"""






							_UpperCAmelCase  :			str	       =		[".", ",", ";", ":"]

							for token in remove_tokens:
														_UpperCAmelCase  :			List[Any]	       =		ground_truth.replace(_UpperCamelCase      ,	""  )
														_UpperCAmelCase  :			List[str]	       =		prediction.replace(_UpperCamelCase      ,	""  )

							_UpperCAmelCase       ,_UpperCAmelCase  :			str	       =		ground_truth.lower(), prediction.lower()
							_UpperCAmelCase       ,_UpperCAmelCase  :			List[str]	       =		ground_truth.replace("/"      ,	" "  ), prediction.replace("/"      ,	" "  )
							_UpperCAmelCase       ,_UpperCAmelCase  :			List[Any]	       =		set(ground_truth.split(" "  )  ), set(prediction.split(" "  )  )

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




def 		lowerCAmelCase_     (     _SCREAMING_SNAKE_CASE  )   ->      Dict:

							"""simple docstring"""






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

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

							def remove_punc(_SCREAMING_SNAKE_CASE  ):
														_UpperCAmelCase  :			Optional[int]	       =		set(string.punctuation  )
														return "".join(ch for ch in text if ch not in exclude  )

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

							return white_space_fix(remove_articles(remove_punc(lower(_UpperCamelCase  )  )  )  )




def 		lowerCAmelCase_     (     _SCREAMING_SNAKE_CASE      ,	_SCREAMING_SNAKE_CASE      ,	_SCREAMING_SNAKE_CASE  )   ->      Union[str, Any]:

							"""simple docstring"""






							_UpperCAmelCase       ,_UpperCAmelCase       ,_UpperCAmelCase  :			Union[str, Any]	       =		0, 0, 0

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

																					if not match_found:
																												fp += 1

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

							return precision, recall




def 		lowerCAmelCase_     (     _SCREAMING_SNAKE_CASE  )   ->      Optional[Any]:

							"""simple docstring"""






							_UpperCAmelCase  :			List[str]	       =		precisions[::-1]
							for i in range(1      ,	len(_UpperCamelCase  )  ):
														_UpperCAmelCase  :			Union[str, Any]	       =		max(precision_best[i - 1]      ,	precision_best[i]  )
							_UpperCAmelCase  :			str	       =		precision_best[::-1]
							return precisions




def 		lowerCAmelCase_     (     _SCREAMING_SNAKE_CASE      ,	_SCREAMING_SNAKE_CASE  )   ->      Dict:

							"""simple docstring"""






							_UpperCAmelCase  :			List[Any]	       =		process_precisions(_UpperCamelCase  )
							_UpperCAmelCase  :			List[Any]	       =		np.trapz(_UpperCamelCase      ,	_UpperCamelCase  )
							if np.isnan(_UpperCamelCase  ):
														return 0
							return aupr




def 		lowerCAmelCase_     (     _SCREAMING_SNAKE_CASE      ,	_SCREAMING_SNAKE_CASE      ,	_SCREAMING_SNAKE_CASE  )   ->      Tuple:

							"""simple docstring"""






							_UpperCAmelCase  :			str	       =		process_precisions(_UpperCamelCase  )
							_UpperCAmelCase  :			List[Any]	       =		0
							for prec, recall in zip(_UpperCamelCase      ,	_UpperCamelCase  ):
														if recall >= recall_thresh:
																					_UpperCAmelCase  :			str	       =		prec
																					break
							return prec_at_recall




def 		lowerCAmelCase_     (     _SCREAMING_SNAKE_CASE      ,	_SCREAMING_SNAKE_CASE  )   ->      Tuple:

							"""simple docstring"""






							return normalize_answer(_UpperCamelCase  ) == normalize_answer(_UpperCamelCase  )




def 		lowerCAmelCase_     (     _SCREAMING_SNAKE_CASE      ,	_SCREAMING_SNAKE_CASE      ,	_SCREAMING_SNAKE_CASE  )   ->      Union[str, Any]:

							"""simple docstring"""






							_UpperCAmelCase  :			Optional[Any]	       =		0
							for pred in predictions:
														for ground_truth in ground_truths:
																					_UpperCAmelCase  :			Dict	       =		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 		lowerCAmelCase_     (     _SCREAMING_SNAKE_CASE      ,	_SCREAMING_SNAKE_CASE  )   ->      Any:

							"""simple docstring"""






							_UpperCAmelCase  :			Tuple	       =		0
							_UpperCAmelCase  :			Optional[int]	       =		[]
							_UpperCAmelCase  :			Union[str, Any]	       =		[]
							for article in dataset:
														for paragraph in article["paragraphs"]:
																					for qa in paragraph["qas"]:
																												total += 1
																												if qa["id"] not in predictions:
																																			_UpperCAmelCase  :			Union[str, Any]	       =		"Unanswered question " + qa["id"] + " will receive score 0."
																																			print(_UpperCamelCase      ,	file=sys.stderr  )
																																			continue
																												_UpperCAmelCase  :			str	       =		[x["text"] for x in qa["answers"]]
																												_UpperCAmelCase  :			Any	       =		predictions[qa["id"]]
																												_UpperCAmelCase       ,_UpperCAmelCase  :			int	       =		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  :			Dict	       =		[x for _, x in sorted(zip(_UpperCamelCase      ,	_UpperCamelCase  )  )]
							recalls.sort()

							_UpperCAmelCase  :			List[str]	       =		100.0 * fa / total
							_UpperCAmelCase  :			Union[str, Any]	       =		100.0 * exact_match / total
							_UpperCAmelCase  :			Dict	       =		get_aupr(_UpperCamelCase      ,	_UpperCamelCase  )

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







import sys
from collections.abc import Mapping

import numpy as np
import pyarrow as pa

from .. import config
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter



class     lowercase_       (    TensorFormatter[Mapping, np.ndarray, Mapping]	):



      def __init__(     self :							Union[str, Any]     ,   A__ :							Dict=None     ,   **A__ :							str						)       ->	Any:
        super().__init__(features=A__						)
        _snake_case		=				np_array_kwargs



      def 		UpperCamelCase_						(     self :							str     ,   A__ :							Dict						)       ->	Dict:
        if isinstance(A__     ,   A__						):
          if column and all(
              isinstance(A__     ,   np.ndarray						) and x.shape == column[0].shape and x.dtype == column[0].dtype for x in column						):
            return np.stack(A__						)
          else:
            # don't use np.array(column, dtype=object)
            # since it fails in certain cases
            # see https://stackoverflow.com/q/51005699
            _snake_case		=				np.empty(len(A__						)     ,   dtype=A__						)
            _snake_case		=				column
            return out
        return column



      def 		UpperCamelCase_						(     self :							Tuple     ,   A__ :							Optional[int]						)       ->	Optional[Any]:
        if isinstance(A__     ,   (str, bytes, type(A__						))						):
          return value
        elif isinstance(A__     ,   (np.character, np.ndarray)						) and np.issubdtype(value.dtype     ,   np.character						):
          return value
        elif isinstance(A__     ,   np.number						):
          return value

        _snake_case		=				{}

        if isinstance(A__     ,   np.ndarray						) and np.issubdtype(value.dtype     ,   np.integer						):
          _snake_case		=				{'''dtype''': np.intaa}
        elif isinstance(A__     ,   np.ndarray						) and np.issubdtype(value.dtype     ,   np.floating						):
          _snake_case		=				{'''dtype''': np.floataa}
        elif config.PIL_AVAILABLE and "PIL" in sys.modules:
          import PIL.Image

          if isinstance(A__     ,   PIL.Image.Image						):
            return np.asarray(A__     ,   **self.np_array_kwargs						)

        return np.asarray(A__     ,   **{**default_dtype, **self.np_array_kwargs}						)



      def 		UpperCamelCase_						(     self :							Union[str, Any]     ,   A__ :							Union[str, Any]						)       ->	int:
        # support for torch, tf, jax etc.
        if config.TORCH_AVAILABLE and "torch" in sys.modules:
          import torch

          if isinstance(A__     ,   torch.Tensor						):
            return self._tensorize(data_struct.detach().cpu().numpy()[()]						)
        if hasattr(A__     ,   '''__array__'''						) and not isinstance(A__     ,   (np.ndarray, np.character, np.number)						):
          _snake_case		=				data_struct.__array__()
        # support for nested types like struct of list of struct
        if isinstance(A__     ,   np.ndarray						):
          if data_struct.dtype == object:
            return self._consolidate([self.recursive_tensorize(A__						) for substruct in data_struct]						)
        if isinstance(A__     ,   (list, tuple)						):
          return self._consolidate([self.recursive_tensorize(A__						) for substruct in data_struct]						)
        return self._tensorize(A__						)



      def 		UpperCamelCase_						(     self :							List[Any]     ,   A__ :							dict						)       ->	Tuple:
        return map_nested(self._recursive_tensorize     ,   A__     ,   map_list=A__						)



      def 		UpperCamelCase_						(     self :							str     ,   A__ :							pa.Table						)       ->	Mapping:
        _snake_case		=				self.numpy_arrow_extractor().extract_row(A__						)
        _snake_case		=				self.python_features_decoder.decode_row(A__						)
        return self.recursive_tensorize(A__						)



      def 		UpperCamelCase_						(     self :							Optional[Any]     ,   A__ :							pa.Table						)       ->	np.ndarray:
        _snake_case		=				self.numpy_arrow_extractor().extract_column(A__						)
        _snake_case		=				self.python_features_decoder.decode_column(A__     ,   pa_table.column_names[0]						)
        _snake_case		=				self.recursive_tensorize(A__						)
        _snake_case		=				self._consolidate(A__						)
        return column







      def 		UpperCamelCase_						(     self :							Dict     ,   A__ :							pa.Table						)       ->	Mapping:
        _snake_case		=				self.numpy_arrow_extractor().extract_batch(A__						)
        _snake_case		=				self.python_features_decoder.decode_batch(A__						)
        _snake_case		=				self.recursive_tensorize(A__						)
        for column_name in batch:
          _snake_case		=				self._consolidate(batch[column_name]						)
        return batch

