


import argparse
import json
import re
import string
import sys
from collections import Counter







def   A   (		A__ ):

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

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

					def remove_punc(A__ ):
										UpperCAmelCase						:				Dict		  =			set(string.punctuation )
										return "".join(ch for ch in text if ch not in exclude )

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

					return white_space_fix(remove_articles(remove_punc(lower(A__ ) ) ) )







def   A   (		A__		,		A__ ):
					UpperCAmelCase						:				int		  =			normalize_answer(A__ ).split()
					UpperCAmelCase						:				List[str]		  =			normalize_answer(A__ ).split()
					UpperCAmelCase						:				List[Any]		  =			Counter(A__ ) & Counter(A__ )
					UpperCAmelCase						:				str		  =			sum(common.values() )
					if num_same == 0:
										return 0
					UpperCAmelCase						:				Union[str, Any]		  =			1.0 * num_same / len(A__ )
					UpperCAmelCase						:				Union[str, Any]		  =			1.0 * num_same / len(A__ )
					UpperCAmelCase						:				Union[str, Any]		  =			(2 * precision * recall) / (precision + recall)
					return fa







def   A   (		A__		,		A__ ):
					return normalize_answer(A__ ) == normalize_answer(A__ )







def   A   (		A__		,		A__		,		A__ ):
					UpperCAmelCase						:				Union[str, Any]		  =			[]
					for ground_truth in ground_truths:
										UpperCAmelCase						:				str		  =			metric_fn(A__		,		A__ )
										scores_for_ground_truths.append(A__ )
					return max(A__ )







def   A   (		A__		,		A__ ):
					UpperCAmelCase						:				List[Any]		  =			0
					UpperCAmelCase						:				Optional[Any]		  =			[]
					for passage in dataset:
										for qa in passage["qas"]:
															total += 1
															if qa["id"] not in predictions:
																				UpperCAmelCase						:				List[str]		  =			f'Unanswered question {qa["id"]} will receive score 0.'
																				print(A__		,		file=sys.stderr )
																				continue

															UpperCAmelCase						:				List[str]		  =			[x["text"] for x in qa["answers"]]
															UpperCAmelCase						:				Optional[int]		  =			predictions[qa["id"]]

															UpperCAmelCase						:				Optional[int]		  =			metric_max_over_ground_truths(A__		,		A__		,		A__ )
															if int(_exact_match ) == 1:
																				correct_ids.append(qa["id"] )
															exact_match += _exact_match

															fa += metric_max_over_ground_truths(A__		,		A__		,		A__ )

					UpperCAmelCase						:				Optional[int]		  =			exact_match / total
					UpperCAmelCase						:				Optional[Any]		  =			fa / total

					return {"exact_match": exact_match, "f1": fa}, correct_ids


if __name__ == "__main__":
						_snake_case   = "1.0"
						_snake_case   = argparse.ArgumentParser("Official evaluation script for ReCoRD v1.0.")
						parser.add_argument("data_file", help="The dataset file in JSON format.")
						parser.add_argument("pred_file", help="The model prediction file in JSON format.")
						parser.add_argument("--output_correct_ids", action="store_true", help="Output the correctly answered query IDs.")
						_snake_case   = parser.parse_args()

						with open(args.data_file) as data_file:
												_snake_case   = json.load(data_file)
												if dataset_json["version"] != expected_version:
																		print(
																		    f'''Evaluation expects v-{expected_version}, but got dataset with v-{dataset_json['version']}''',
																		    file=sys.stderr,
																		)
												_snake_case   = dataset_json["data"]

						with open(args.pred_file) as pred_file:
												_snake_case   = json.load(pred_file)

						_snake_case ,						_snake_case   = evaluate(dataset, predictions)

						if args.output_correct_ids:
												print(f'''Output {len(correct_ids)} correctly answered question IDs.''')
												with open("correct_ids.json", "w") as f:
																		json.dump(correct_ids, f)







from cva import destroyAllWindows, imread, imshow, waitKey







def   A   (		A__		,		A__ ):
     UpperCAmelCase  , UpperCAmelCase						:				Optional[Any]		  =			img.shape[0], img.shape[1]

     def to_grayscale(A__		,		A__		,		A__ ):
          return 0.2126 * red + 0.587 * green + 0.114 * blue

     def normalize(A__ ):
          return min(A__		,		2_5_5 )

     for i in range(A__ ):
          for j in range(A__ ):
               UpperCAmelCase						:				Optional[Any]		  =			int(to_grayscale(*img[i][j] ) )
               UpperCAmelCase						:				List[str]		  =			[
                   normalize(A__ ),
                   normalize(greyscale + factor ),
                   normalize(greyscale + 2 * factor ),
               ]

     return img


if __name__ == "__main__":
      # read original image
      _snake_case   = {
          percentage: imread("image_data/lena.jpg", 1) for percentage in (1_0, 2_0, 3_0, 4_0)
      }

      for percentage, img in images.items():
            make_sepia(img, percentage)

      for percentage, img in images.items():
            imshow(f'''Original image with sepia (factor: {percentage})''', img)

      waitKey(0)
      destroyAllWindows()



