





"""simple docstring"""



import argparse
import json
import re
import string
import sys

import numpy as np


a_				=    0.5







def 	__lowercase  (     snake_case_						:						int       ,snake_case_						:						str		)     ->Optional[int]:



    '''simple docstring'''







    __A					:		Optional[Any]						=  ['''.''', ''',''', ''';''', ''':''']

    for token in remove_tokens:
        __A					:		List[Any]						=  ground_truth.replace(snake_case_       ,''''''		)
        __A					:		Optional[int]						=  prediction.replace(snake_case_       ,''''''		)

    __A				, __A					:		Optional[int]						=  ground_truth.lower(), prediction.lower()
    __A				, __A					:		Any						=  ground_truth.replace('''/'''       ,''' '''		), prediction.replace('''/'''       ,''' '''		)
    __A				, __A					:		str						=  set(ground_truth.split(''' '''		)		), set(prediction.split(''' '''		)		)

    __A					:		str						=  ground_truth.intersection(snake_case_		)
    __A					:		Union[str, Any]						=  ground_truth.union(snake_case_		)
    __A					:		List[str]						=  len(snake_case_		) / len(snake_case_		)
    return jaccard







def 	__lowercase  (     snake_case_						:						Any		)     ->Dict:



    '''simple docstring'''







    def remove_articles(snake_case_						:						List[Any]		):
        return re.sub(r'''\b(a|an|the)\b'''       ,''' '''       ,snake_case_		)

    def white_space_fix(snake_case_						:						Optional[Any]		):
        return " ".join(text.split()		)

    def remove_punc(snake_case_						:						int		):
        __A					:		int						=  set(string.punctuation		)
        return "".join(ch for ch in text if ch not in exclude		)

    def lower(snake_case_						:						Tuple		):
        return text.lower()

    return white_space_fix(remove_articles(remove_punc(lower(snake_case_		)		)		)		)







def 	__lowercase  (     snake_case_						:						Dict       ,snake_case_						:						Union[str, Any]       ,snake_case_						:						Union[str, Any]		)     ->List[str]:



    '''simple docstring'''







    __A				, __A				, __A					:		List[str]						=  0, 0, 0

    __A					:		str						=  '''Parties''' in qa_id

    # first check if ground truth is empty
    if len(snake_case_		) == 0:
        if len(snake_case_		) > 0:
            fp += len(snake_case_		)  # false positive for each one
    else:
        for ground_truth in ground_truths:
            assert len(snake_case_		) > 0
            # check if there is a match
            __A					:		Any						=  False
            for pred in predictions:
                if substr_ok:
                    __A					:		Optional[int]						=  get_jaccard(snake_case_       ,snake_case_		) >= IOU_THRESH or ground_truth in pred
                else:
                    __A					:		str						=  get_jaccard(snake_case_       ,snake_case_		) >= IOU_THRESH
                if is_match:
                    __A					:		List[Any]						=  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					:		int						=  False
            for ground_truth in ground_truths:
                assert len(snake_case_		) > 0
                if substr_ok:
                    __A					:		Tuple						=  get_jaccard(snake_case_       ,snake_case_		) >= IOU_THRESH or ground_truth in pred
                else:
                    __A					:		Optional[int]						=  get_jaccard(snake_case_       ,snake_case_		) >= IOU_THRESH
                if is_match:
                    __A					:		Dict						=  True

            if not match_found:
                fp += 1

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

    return precision, recall







def 	__lowercase  (     snake_case_						:						Union[str, Any]		)     ->Optional[Any]:



    '''simple docstring'''







    __A					:		List[Any]						=  precisions[::-1]
    for i in range(1       ,len(snake_case_		)		):
        __A					:		Tuple						=  max(precision_best[i - 1]       ,precision_best[i]		)
    __A					:		List[str]						=  precision_best[::-1]
    return precisions







def 	__lowercase  (     snake_case_						:						Union[str, Any]       ,snake_case_						:						List[Any]		)     ->Any:



    '''simple docstring'''







    __A					:		Tuple						=  process_precisions(snake_case_		)
    __A					:		str						=  np.trapz(snake_case_       ,snake_case_		)
    if np.isnan(snake_case_		):
        return 0
    return aupr







def 	__lowercase  (     snake_case_						:						int       ,snake_case_						:						Any       ,snake_case_						:						Optional[Any]		)     ->Dict:



    '''simple docstring'''







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







def 	__lowercase  (     snake_case_						:						Optional[Any]       ,snake_case_						:						List[Any]		)     ->int:



    '''simple docstring'''







    return normalize_answer(snake_case_		) == normalize_answer(snake_case_		)







def 	__lowercase  (     snake_case_						:						Optional[Any]       ,snake_case_						:						str       ,snake_case_						:						Optional[Any]		)     ->List[Any]:



    '''simple docstring'''







    __A					:		Optional[Any]						=  0
    for pred in predictions:
        for ground_truth in ground_truths:
            __A					:		Tuple						=  metric_fn(snake_case_       ,snake_case_		)
            if score == 1:  # break the loop when one prediction matches the ground truth
                break
        if score == 1:
            break
    return score







def 	__lowercase  (     snake_case_						:						int       ,snake_case_						:						Union[str, Any]		)     ->List[Any]:



    '''simple docstring'''







    __A					:		Optional[int]						=  0
    __A					:		Optional[Any]						=  []
    __A					:		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:
                    __A					:		Optional[Any]						=  '''Unanswered question ''' + qa['''id'''] + ''' will receive score 0.'''
                    print(snake_case_       ,file=sys.stderr		)
                    continue
                __A					:		int						=  [x['''text'''] for x in qa['''answers''']]
                __A					:		Dict						=  predictions[qa['''id''']]
                __A				, __A					:		Dict						=  compute_precision_recall(snake_case_       ,snake_case_       ,qa['''id''']		)

                precisions.append(snake_case_		)
                recalls.append(snake_case_		)

                if precision == 0 and recall == 0:
                    fa += 0
                else:
                    fa += 2 * (precision * recall) / (precision + recall)

                exact_match += metric_max_over_ground_truths(snake_case_       ,snake_case_       ,snake_case_		)

    __A					:		Union[str, Any]						=  [x for _, x in sorted(zip(snake_case_       ,snake_case_		)		)]
    recalls.sort()

    __A					:		Dict						=  100.0 * fa / total
    __A					:		str						=  100.0 * exact_match / total
    __A					:		Union[str, Any]						=  get_aupr(snake_case_       ,snake_case_		)

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











"""simple docstring"""



import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_video_inputs


if is_torch_available():
       import torch

if is_vision_available():
       from PIL import Image

       from transformers import VideoMAEImageProcessor






class 	__snake_case      (	unittest.TestCase  ):





 """simple docstring"""


 def __init__(					self      ,		__lowerCamelCase      ,		__lowerCamelCase=7      ,		__lowerCamelCase=3      ,		__lowerCamelCase=10      ,		__lowerCamelCase=18      ,		__lowerCamelCase=30      ,		__lowerCamelCase=400      ,		__lowerCamelCase=True      ,		__lowerCamelCase=None      ,		__lowerCamelCase=True      ,		__lowerCamelCase=[0.5, 0.5, 0.5]      ,		__lowerCamelCase=[0.5, 0.5, 0.5]      ,		__lowerCamelCase=None      ,		):



     '''simple docstring'''




     __A					:		Optional[Any]						=  size if size is not None else {'''shortest_edge''': 18}
     __A					:		Optional[int]						=  crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}

     __A					:		Any						=  parent
     __A					:		Union[str, Any]						=  batch_size
     __A					:		Optional[int]						=  num_channels
     __A					:		int						=  num_frames
     __A					:		Optional[int]						=  image_size
     __A					:		Union[str, Any]						=  min_resolution
     __A					:		int						=  max_resolution
     __A					:		Tuple						=  do_resize
     __A					:		str						=  size
     __A					:		Any						=  do_normalize
     __A					:		Union[str, Any]						=  image_mean
     __A					:		List[str]						=  image_std
     __A					:		Union[str, Any]						=  crop_size







 def       UpperCamelCase__(					self						):



     '''simple docstring'''




     return {
         "image_mean": self.image_mean,
         "image_std": self.image_std,
         "do_normalize": self.do_normalize,
         "do_resize": self.do_resize,
         "size": self.size,
         "crop_size": self.crop_size,
     }






@require_torch
@require_vision
class 	__snake_case      (	SCREAMING_SNAKE_CASE__							,							unittest.TestCase  ):





 """simple docstring"""


 _lowerCamelCase				 =  VideoMAEImageProcessor if is_vision_available() else None

 def       UpperCamelCase__(					self						):



     '''simple docstring'''




     __A					:		List[str]						=  VideoMAEImageProcessingTester(self						)

 @property
 def       UpperCamelCase__(					self						):



     '''simple docstring'''




     return self.image_processor_tester.prepare_image_processor_dict()

 def       UpperCamelCase__(					self						):



     '''simple docstring'''




     __A					:		Dict						=  self.image_processing_class(**self.image_processor_dict						)
     self.assertTrue(hasattr(__lowerCamelCase      ,		'''image_mean'''						)						)
     self.assertTrue(hasattr(__lowerCamelCase      ,		'''image_std'''						)						)
     self.assertTrue(hasattr(__lowerCamelCase      ,		'''do_normalize'''						)						)
     self.assertTrue(hasattr(__lowerCamelCase      ,		'''do_resize'''						)						)
     self.assertTrue(hasattr(__lowerCamelCase      ,		'''do_center_crop'''						)						)
     self.assertTrue(hasattr(__lowerCamelCase      ,		'''size'''						)						)

 def       UpperCamelCase__(					self						):



     '''simple docstring'''




     __A					:		Optional[int]						=  self.image_processing_class.from_dict(self.image_processor_dict						)
     self.assertEqual(image_processor.size      ,		{'''shortest_edge''': 18}						)
     self.assertEqual(image_processor.crop_size      ,		{'''height''': 18, '''width''': 18}						)

     __A					:		Dict						=  self.image_processing_class.from_dict(self.image_processor_dict      ,		size=42      ,		crop_size=84						)
     self.assertEqual(image_processor.size      ,		{'''shortest_edge''': 42}						)
     self.assertEqual(image_processor.crop_size      ,		{'''height''': 84, '''width''': 84}						)

 def       UpperCamelCase__(					self						):



     '''simple docstring'''




     pass

 def       UpperCamelCase__(					self						):



     '''simple docstring'''




     __A					:		List[Any]						=  self.image_processing_class(**self.image_processor_dict						)
     # create random PIL videos
     __A					:		Optional[int]						=  prepare_video_inputs(self.image_processor_tester      ,		equal_resolution=__lowerCamelCase						)
     for video in video_inputs:
         self.assertIsInstance(__lowerCamelCase      ,		__lowerCamelCase						)
         self.assertIsInstance(video[0]      ,		Image.Image						)

     # Test not batched input
     __A					:		Any						=  image_processing(video_inputs[0]      ,		return_tensors='''pt'''						).pixel_values
     self.assertEqual(
         encoded_videos.shape      ,		(
             1,
             self.image_processor_tester.num_frames,
             self.image_processor_tester.num_channels,
             self.image_processor_tester.crop_size['''height'''],
             self.image_processor_tester.crop_size['''width'''],
         )      ,		)

     # Test batched
     __A					:		int						=  image_processing(__lowerCamelCase      ,		return_tensors='''pt'''						).pixel_values
     self.assertEqual(
         encoded_videos.shape      ,		(
             self.image_processor_tester.batch_size,
             self.image_processor_tester.num_frames,
             self.image_processor_tester.num_channels,
             self.image_processor_tester.crop_size['''height'''],
             self.image_processor_tester.crop_size['''width'''],
         )      ,		)

 def       UpperCamelCase__(					self						):



     '''simple docstring'''




     __A					:		List[Any]						=  self.image_processing_class(**self.image_processor_dict						)
     # create random numpy tensors
     __A					:		List[str]						=  prepare_video_inputs(self.image_processor_tester      ,		equal_resolution=__lowerCamelCase      ,		numpify=__lowerCamelCase						)
     for video in video_inputs:
         self.assertIsInstance(__lowerCamelCase      ,		__lowerCamelCase						)
         self.assertIsInstance(video[0]      ,		np.ndarray						)

     # Test not batched input
     __A					:		List[str]						=  image_processing(video_inputs[0]      ,		return_tensors='''pt'''						).pixel_values
     self.assertEqual(
         encoded_videos.shape      ,		(
             1,
             self.image_processor_tester.num_frames,
             self.image_processor_tester.num_channels,
             self.image_processor_tester.crop_size['''height'''],
             self.image_processor_tester.crop_size['''width'''],
         )      ,		)

     # Test batched
     __A					:		int						=  image_processing(__lowerCamelCase      ,		return_tensors='''pt'''						).pixel_values
     self.assertEqual(
         encoded_videos.shape      ,		(
             self.image_processor_tester.batch_size,
             self.image_processor_tester.num_frames,
             self.image_processor_tester.num_channels,
             self.image_processor_tester.crop_size['''height'''],
             self.image_processor_tester.crop_size['''width'''],
         )      ,		)







 def       UpperCamelCase__(					self						):



     '''simple docstring'''




     __A					:		Optional[int]						=  self.image_processing_class(**self.image_processor_dict						)
     # create random PyTorch tensors
     __A					:		int						=  prepare_video_inputs(self.image_processor_tester      ,		equal_resolution=__lowerCamelCase      ,		torchify=__lowerCamelCase						)
     for video in video_inputs:
         self.assertIsInstance(__lowerCamelCase      ,		__lowerCamelCase						)
         self.assertIsInstance(video[0]      ,		torch.Tensor						)

     # Test not batched input
     __A					:		Optional[int]						=  image_processing(video_inputs[0]      ,		return_tensors='''pt'''						).pixel_values
     self.assertEqual(
         encoded_videos.shape      ,		(
             1,
             self.image_processor_tester.num_frames,
             self.image_processor_tester.num_channels,
             self.image_processor_tester.crop_size['''height'''],
             self.image_processor_tester.crop_size['''width'''],
         )      ,		)

     # Test batched
     __A					:		str						=  image_processing(__lowerCamelCase      ,		return_tensors='''pt'''						).pixel_values
     self.assertEqual(
         encoded_videos.shape      ,		(
             self.image_processor_tester.batch_size,
             self.image_processor_tester.num_frames,
             self.image_processor_tester.num_channels,
             self.image_processor_tester.crop_size['''height'''],
             self.image_processor_tester.crop_size['''width'''],
         )      ,		)




