import argparse
import json
import re
import string
import sys

import numpy as np


_SCREAMING_SNAKE_CASE:    Union[str, Any]     						=							0.5






def  UpperCAmelCase__(    lowerCamelCase_				,						lowerCamelCase_	):



			"""simple docstring"""





			_snake_case														=   [".", ",", ";", ":"]

			for token in remove_tokens:
						_snake_case														=   ground_truth.replace(lowerCamelCase_				,						""	)
						_snake_case														=   prediction.replace(lowerCamelCase_				,						""	)

			_snake_case							,			_snake_case														=   ground_truth.lower(), prediction.lower()
			_snake_case							,			_snake_case														=   ground_truth.replace("/"				,						" "	), prediction.replace("/"				,						" "	)
			_snake_case							,			_snake_case														=   set(ground_truth.split(" "	)	), set(prediction.split(" "	)	)

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






def  UpperCAmelCase__(    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_	):
						_snake_case														=   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_	):



			"""simple docstring"""





			_snake_case							,			_snake_case							,			_snake_case														=   0, 0, 0

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

									if not match_found:
												fp += 1

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

			return precision, recall






def  UpperCAmelCase__(    lowerCamelCase_	):



			"""simple docstring"""





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






def  UpperCAmelCase__(    lowerCamelCase_				,						lowerCamelCase_	):



			"""simple docstring"""





			_snake_case														=   process_precisions(lowerCamelCase_	)
			_snake_case														=   np.trapz(lowerCamelCase_				,						lowerCamelCase_	)
			if np.isnan(lowerCamelCase_	):
						return 0
			return aupr






def  UpperCAmelCase__(    lowerCamelCase_				,						lowerCamelCase_				,						lowerCamelCase_	):



			"""simple docstring"""





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






def  UpperCAmelCase__(    lowerCamelCase_				,						lowerCamelCase_	):



			"""simple docstring"""





			return normalize_answer(lowerCamelCase_	) == normalize_answer(lowerCamelCase_	)






def  UpperCAmelCase__(    lowerCamelCase_				,						lowerCamelCase_				,						lowerCamelCase_	):



			"""simple docstring"""





			_snake_case														=   0
			for pred in predictions:
						for ground_truth in ground_truths:
									_snake_case														=   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_	):



			"""simple docstring"""





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

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

			_snake_case														=   1_0_0.0 * fa / total
			_snake_case														=   1_0_0.0 * exact_match / total
			_snake_case														=   get_aupr(lowerCamelCase_				,						lowerCamelCase_	)

			_snake_case														=   get_prec_at_recall(lowerCamelCase_				,						lowerCamelCase_				,						recall_thresh=0.9	)
			_snake_case														=   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:    Any     						=							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:    Optional[Any]     						=							parser.parse_args()
			with open(args.dataset_file) as dataset_file:
						_SCREAMING_SNAKE_CASE:    List[Any]     						=							json.load(dataset_file)
						_SCREAMING_SNAKE_CASE:    str     						=							dataset_json['''data''']
			with open(args.prediction_file) as prediction_file:
						_SCREAMING_SNAKE_CASE:    Union[str, Any]     						=							json.load(prediction_file)
			print(json.dumps(evaluate(dataset, predictions)))


from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


_SCREAMING_SNAKE_CASE:    List[Any]     						=							logging.get_logger(__name__)

class 						_SCREAMING_SNAKE_CASE				(  _lowercase				):






		"""simple docstring"""







		A_    			=   ['''pixel_values''']



		def __init__(      self     :					Tuple	,       __SCREAMING_SNAKE_CASE     :					bool = True	,       __SCREAMING_SNAKE_CASE     :					Optional[Dict[str, int]] = None	,       __SCREAMING_SNAKE_CASE     :					PILImageResampling = PILImageResampling.BILINEAR	,       __SCREAMING_SNAKE_CASE     :					bool = True	,       __SCREAMING_SNAKE_CASE     :					Union[int, float] = 1 / 255	,       __SCREAMING_SNAKE_CASE     :					bool = True	,       __SCREAMING_SNAKE_CASE     :					Optional[Union[float, List[float]]] = None	,       __SCREAMING_SNAKE_CASE     :					Optional[Union[float, List[float]]] = None	,       **__SCREAMING_SNAKE_CASE     :					str	,       ):






					"""simple docstring"""
					super().__init__(**__SCREAMING_SNAKE_CASE )
					_snake_case														=   size if size is not None else {"height": 224, "width": 224}
					_snake_case														=   get_size_dict(__SCREAMING_SNAKE_CASE )
					_snake_case														=   do_resize
					_snake_case														=   do_rescale
					_snake_case														=   do_normalize
					_snake_case														=   size
					_snake_case														=   resample
					_snake_case														=   rescale_factor
					_snake_case														=   image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
					_snake_case														=   image_std if image_std is not None else IMAGENET_STANDARD_STD



		def 					_a					(      self     :					int	,       __SCREAMING_SNAKE_CASE     :					np.ndarray	,       __SCREAMING_SNAKE_CASE     :					Dict[str, int]	,       __SCREAMING_SNAKE_CASE     :					PILImageResampling = PILImageResampling.BILINEAR	,       __SCREAMING_SNAKE_CASE     :					Optional[Union[str, ChannelDimension]] = None	,       **__SCREAMING_SNAKE_CASE     :					Tuple	,       ):






					"""simple docstring"""
					_snake_case														=   get_size_dict(__SCREAMING_SNAKE_CASE )
					if "height" not in size or "width" not in size:
								raise ValueError(F'The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}' )
					return resize(
					    __SCREAMING_SNAKE_CASE	,       size=(size["height"], size["width"])	,       resample=__SCREAMING_SNAKE_CASE	,       data_format=__SCREAMING_SNAKE_CASE	,       **__SCREAMING_SNAKE_CASE )



		def 					_a					(      self     :					Tuple	,       __SCREAMING_SNAKE_CASE     :					np.ndarray	,       __SCREAMING_SNAKE_CASE     :					float	,       __SCREAMING_SNAKE_CASE     :					Optional[Union[str, ChannelDimension]] = None	,       **__SCREAMING_SNAKE_CASE     :					List[str] ):






					"""simple docstring"""
					return rescale(__SCREAMING_SNAKE_CASE	,       scale=__SCREAMING_SNAKE_CASE	,       data_format=__SCREAMING_SNAKE_CASE	,       **__SCREAMING_SNAKE_CASE )



		def 					_a					(      self     :					Optional[Any]	,       __SCREAMING_SNAKE_CASE     :					np.ndarray	,       __SCREAMING_SNAKE_CASE     :					Union[float, List[float]]	,       __SCREAMING_SNAKE_CASE     :					Union[float, List[float]]	,       __SCREAMING_SNAKE_CASE     :					Optional[Union[str, ChannelDimension]] = None	,       **__SCREAMING_SNAKE_CASE     :					Optional[Any]	,       ):






					"""simple docstring"""
					return normalize(__SCREAMING_SNAKE_CASE	,       mean=__SCREAMING_SNAKE_CASE	,       std=__SCREAMING_SNAKE_CASE	,       data_format=__SCREAMING_SNAKE_CASE	,       **__SCREAMING_SNAKE_CASE )







		def 					_a					(      self     :					int	,       __SCREAMING_SNAKE_CASE     :					ImageInput	,       __SCREAMING_SNAKE_CASE     :					Optional[bool] = None	,       __SCREAMING_SNAKE_CASE     :					Dict[str, int] = None	,       __SCREAMING_SNAKE_CASE     :					PILImageResampling = None	,       __SCREAMING_SNAKE_CASE     :					Optional[bool] = None	,       __SCREAMING_SNAKE_CASE     :					Optional[float] = None	,       __SCREAMING_SNAKE_CASE     :					Optional[bool] = None	,       __SCREAMING_SNAKE_CASE     :					Optional[Union[float, List[float]]] = None	,       __SCREAMING_SNAKE_CASE     :					Optional[Union[float, List[float]]] = None	,       __SCREAMING_SNAKE_CASE     :					Optional[Union[str, TensorType]] = None	,       __SCREAMING_SNAKE_CASE     :					Union[str, ChannelDimension] = ChannelDimension.FIRST	,       **__SCREAMING_SNAKE_CASE     :					List[Any]	,       ):






					"""simple docstring"""
					_snake_case														=   do_resize if do_resize is not None else self.do_resize
					_snake_case														=   do_rescale if do_rescale is not None else self.do_rescale
					_snake_case														=   do_normalize if do_normalize is not None else self.do_normalize
					_snake_case														=   resample if resample is not None else self.resample
					_snake_case														=   rescale_factor if rescale_factor is not None else self.rescale_factor
					_snake_case														=   image_mean if image_mean is not None else self.image_mean
					_snake_case														=   image_std if image_std is not None else self.image_std

					_snake_case														=   size if size is not None else self.size
					_snake_case														=   get_size_dict(__SCREAMING_SNAKE_CASE )

					_snake_case														=   make_list_of_images(__SCREAMING_SNAKE_CASE )

					if not valid_images(__SCREAMING_SNAKE_CASE ):
								raise ValueError(
								    "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
								    "torch.Tensor, tf.Tensor or jax.ndarray." )

					if do_resize and size is None:
								raise ValueError("Size must be specified if do_resize is True." )

					if do_rescale and rescale_factor is None:
								raise ValueError("Rescale factor must be specified if do_rescale is True." )

					# All transformations expect numpy arrays.
					_snake_case														=   [to_numpy_array(__SCREAMING_SNAKE_CASE ) for image in images]

					if do_resize:
								_snake_case														=   [self.resize(image=__SCREAMING_SNAKE_CASE	,       size=__SCREAMING_SNAKE_CASE	,       resample=__SCREAMING_SNAKE_CASE ) for image in images]

					if do_rescale:
								_snake_case														=   [self.rescale(image=__SCREAMING_SNAKE_CASE	,       scale=__SCREAMING_SNAKE_CASE ) for image in images]

					if do_normalize:
								_snake_case														=   [self.normalize(image=__SCREAMING_SNAKE_CASE	,       mean=__SCREAMING_SNAKE_CASE	,       std=__SCREAMING_SNAKE_CASE ) for image in images]

					_snake_case														=   [to_channel_dimension_format(__SCREAMING_SNAKE_CASE	,       __SCREAMING_SNAKE_CASE ) for image in images]

					_snake_case														=   {"pixel_values": images}
					return BatchFeature(data=__SCREAMING_SNAKE_CASE	,       tensor_type=__SCREAMING_SNAKE_CASE )

