'''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


if is_torch_available():
			import torch

if is_vision_available():
			from PIL import Image

			from transformers import TvltImageProcessor





def    _SCREAMING_SNAKE_CASE		(			__snake_case       : List[str]      ,		__snake_case       : Union[str, Any]=1_0      ,		__snake_case       : Optional[Any]=1_0      ,		__snake_case       : str=False      ,		__snake_case       : Any=False							):
					_A								=     []
					for i in range(image_processor_tester.num_frames							):
										video.append(np.random.randint(2_5_5      ,		size=(image_processor_tester.num_channels, width, height)      ,		dtype=np.uinta							)							)

					if not numpify and not torchify:
										# PIL expects the channel dimension as last dimension
										_A								=     [Image.fromarray(np.moveaxis(lowercase__      ,		0      ,		-1							)							) for frame in video]

					if torchify:
										_A								=     [torch.from_numpy(lowercase__							) for frame in video]

					return video





def    _SCREAMING_SNAKE_CASE		(			__snake_case       : List[Any]      ,		__snake_case       : Tuple=False      ,		__snake_case       : Dict=False      ,		__snake_case       : str=False							):
					assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

					_A								=     []
					for i in range(image_processor_tester.batch_size							):
										if equal_resolution:
															_A								=     image_processor_tester.max_resolution
										else:
															_A								=     np.random.choice(
															    np.arange(image_processor_tester.min_resolution      ,		image_processor_tester.max_resolution							)      ,		2							)
															_A								=     prepare_video(
															    image_processor_tester=lowercase__      ,		width=lowercase__      ,		height=lowercase__      ,		numpify=lowercase__      ,		torchify=lowercase__      ,		)
										video_inputs.append(lowercase__							)

					return video_inputs







class lowercase_					(		unittest.TestCase ):







			"""simple docstring"""

			def __init__(			self			: Tuple,   UpperCamelCase__			: Any,   UpperCamelCase__			: int=7,   UpperCamelCase__			: Union[str, Any]=3,   UpperCamelCase__			: Tuple=4,   UpperCamelCase__			: Optional[Any]=18,   UpperCamelCase__			: Optional[Any]=30,   UpperCamelCase__			: int=4_00,   UpperCamelCase__			: str=True,   UpperCamelCase__			: Any=None,   UpperCamelCase__			: Dict=True,   UpperCamelCase__			: List[str]=[0.5, 0.5, 0.5],   UpperCamelCase__			: List[str]=[0.5, 0.5, 0.5],   UpperCamelCase__			: List[Any]=True,   UpperCamelCase__			: List[Any]=None,   )     -> Union[str, Any]:
								_A								=     size if size is not None else {"shortest_edge": 18}
								_A								=     crop_size if crop_size is not None else {"height": 18, "width": 18}

								_A								=     parent
								_A								=     batch_size
								_A								=     num_channels
								_A								=     num_frames
								_A								=     image_size
								_A								=     min_resolution
								_A								=     max_resolution
								_A								=     do_resize
								_A								=     size
								_A								=     do_normalize
								_A								=     image_mean
								_A								=     image_std
								_A								=     do_center_crop
								_A								=     crop_size

			def   __UpperCAmelCase      (			self			: Dict    )     -> Optional[int]:
								return {
								    "image_mean": self.image_mean,
								    "image_std": self.image_std,
								    "do_normalize": self.do_normalize,
								    "do_resize": self.do_resize,
								    "size": self.size,
								    "do_center_crop": self.do_center_crop,
								    "crop_size": self.crop_size,
								}







@require_torch
@require_vision
class lowercase_					(		lowerCamelCase_						,						unittest.TestCase ):







			"""simple docstring"""

			__lowerCAmelCase    						=      TvltImageProcessor if is_vision_available() else None
			def   __UpperCAmelCase      (			self			: Optional[int]    )     -> Union[str, Any]:
								_A								=     TvltImageProcessorTester(self    )
			@property
			def   __UpperCAmelCase      (			self			: Union[str, Any]    )     -> str:
								return self.image_processor_tester.prepare_image_processor_dict()
			def   __UpperCAmelCase      (			self			: int    )     -> str:
								_A								=     self.image_processing_class(**self.image_processor_dict    )
								self.assertTrue(hasattr(UpperCamelCase__,   'image_mean'    )    )
								self.assertTrue(hasattr(UpperCamelCase__,   'image_std'    )    )
								self.assertTrue(hasattr(UpperCamelCase__,   'do_normalize'    )    )
								self.assertTrue(hasattr(UpperCamelCase__,   'do_resize'    )    )
								self.assertTrue(hasattr(UpperCamelCase__,   'do_center_crop'    )    )
								self.assertTrue(hasattr(UpperCamelCase__,   'size'    )    )
			def   __UpperCAmelCase      (			self			: Dict    )     -> List[str]:
								_A								=     self.image_processing_class(**self.image_processor_dict    )
								# create random PIL videos
								_A								=     prepare_video_inputs(self.image_processor_tester,   equal_resolution=UpperCamelCase__    )
								for video in video_inputs:
													self.assertIsInstance(UpperCamelCase__,   UpperCamelCase__    )
													self.assertIsInstance(video[0],   Image.Image    )

								# Test not batched input
								_A								=     image_processor(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								=     image_processor(UpperCamelCase__,   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			: List[Any]    )     -> str:
								_A								=     self.image_processing_class(**self.image_processor_dict    )
								# create random numpy tensors
								_A								=     prepare_video_inputs(self.image_processor_tester,   equal_resolution=UpperCamelCase__,   numpify=UpperCamelCase__    )
								for video in video_inputs:
													self.assertIsInstance(UpperCamelCase__,   UpperCamelCase__    )
													self.assertIsInstance(video[0],   np.ndarray    )

								# Test not batched input
								_A								=     image_processor(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								=     image_processor(UpperCamelCase__,   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			: Optional[int]    )     -> Dict:
								_A								=     self.image_processing_class(**self.image_processor_dict    )
								# create random PyTorch tensors
								_A								=     prepare_video_inputs(self.image_processor_tester,   equal_resolution=UpperCamelCase__,   torchify=UpperCamelCase__    )
								for video in video_inputs:
													self.assertIsInstance(UpperCamelCase__,   UpperCamelCase__    )
													self.assertIsInstance(video[0],   torch.Tensor    )

								# Test not batched input
								_A								=     image_processor(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								=     image_processor(UpperCamelCase__,   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'],
								    ),   )




'''simple docstring'''






import argparse
import json
import re
import string
import sys

import numpy as np


_UpperCAmelCase						:    Dict     					=					0.5





def    _SCREAMING_SNAKE_CASE		(			__snake_case       : Tuple      ,		__snake_case       : List[Any]							):
     _A								=     ['.', ',', ';', ':']

     for token in remove_tokens:
          _A								=     ground_truth.replace(__snake_case      ,		''							)
          _A								=     prediction.replace(__snake_case      ,		''							)

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

     _A								=     ground_truth.intersection(__snake_case							)
     _A								=     ground_truth.union(__snake_case							)
     _A								=     len(__snake_case							) / len(__snake_case							)
     return jaccard





def    _SCREAMING_SNAKE_CASE		(			__snake_case       : Any							):

     def remove_articles(__snake_case       : Dict							):
          return re.sub(R'\b(a|an|the)\b'      ,		' '      ,		__snake_case							)

     def white_space_fix(__snake_case       : int							):
          return " ".join(text.split()							)

     def remove_punc(__snake_case       : List[str]							):
          _A								=     set(string.punctuation							)
          return "".join(ch for ch in text if ch not in exclude							)

     def lower(__snake_case       : Optional[int]							):
          return text.lower()

     return white_space_fix(remove_articles(remove_punc(lower(__snake_case							)							)							)							)





def    _SCREAMING_SNAKE_CASE		(			__snake_case       : Optional[int]      ,		__snake_case       : Tuple      ,		__snake_case       : List[str]							):
     _A		,					_A		,					_A								=     0, 0, 0

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

               if not match_found:
                    fp += 1

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

     return precision, recall





def    _SCREAMING_SNAKE_CASE		(			__snake_case       : List[Any]							):
     _A								=     precisions[::-1]
     for i in range(1      ,		len(__snake_case							)							):
          _A								=     max(precision_best[i - 1]      ,		precision_best[i]							)
     _A								=     precision_best[::-1]
     return precisions





def    _SCREAMING_SNAKE_CASE		(			__snake_case       : Any      ,		__snake_case       : Any							):
     _A								=     process_precisions(__snake_case							)
     _A								=     np.trapz(__snake_case      ,		__snake_case							)
     if np.isnan(__snake_case							):
          return 0
     return aupr





def    _SCREAMING_SNAKE_CASE		(			__snake_case       : Tuple      ,		__snake_case       : Union[str, Any]      ,		__snake_case       : List[str]							):
     _A								=     process_precisions(__snake_case							)
     _A								=     0
     for prec, recall in zip(__snake_case      ,		__snake_case							):
          if recall >= recall_thresh:
               _A								=     prec
               break
     return prec_at_recall





def    _SCREAMING_SNAKE_CASE		(			__snake_case       : Optional[Any]      ,		__snake_case       : Dict							):
     return normalize_answer(__snake_case							) == normalize_answer(__snake_case							)





def    _SCREAMING_SNAKE_CASE		(			__snake_case       : int      ,		__snake_case       : Tuple      ,		__snake_case       : List[Any]							):
     _A								=     0
     for pred in predictions:
          for ground_truth in ground_truths:
               _A								=     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    _SCREAMING_SNAKE_CASE		(			__snake_case       : Tuple      ,		__snake_case       : Tuple							):
     _A								=     _A								=     _A								=     0
     _A								=     []
     _A								=     []
     for article in dataset:
          for paragraph in article["paragraphs"]:
               for qa in paragraph["qas"]:
                    total += 1
                    if qa["id"] not in predictions:
                         _A								=     'Unanswered question ' + qa['id'] + ' will receive score 0.'
                         print(__snake_case      ,		file=sys.stderr							)
                         continue
                    _A								=     [x['text'] for x in qa['answers']]
                    _A								=     predictions[qa['id']]
                    _A		,					_A								=     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								=     [x for _, x in sorted(zip(__snake_case      ,		__snake_case							)							)]
     recalls.sort()

     _A								=     1_00.0 * fa / total
     _A								=     1_00.0 * exact_match / total
     _A								=     get_aupr(__snake_case      ,		__snake_case							)

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



