





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




def 			lowerCAmelCase_			(				UpperCamelCase_	)  ->		str:

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

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

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

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

       return white_space_fix(remove_articles(remove_punc(lower(UpperCamelCase_	)	)	)	)




def 			lowerCAmelCase_			(				UpperCamelCase_       ,		UpperCamelCase_	)  ->		Any:
       UpperCamelCase_ 							=	normalize_answer(UpperCamelCase_	).split()
       UpperCamelCase_ 							=	normalize_answer(UpperCamelCase_	).split()
       UpperCamelCase_ 							=	Counter(UpperCamelCase_	) & Counter(UpperCamelCase_	)
       UpperCamelCase_ 							=	sum(common.values()	)
       if num_same == 0:
              return 0
       UpperCamelCase_ 							=	1.0 * num_same / len(UpperCamelCase_	)
       UpperCamelCase_ 							=	1.0 * num_same / len(UpperCamelCase_	)
       UpperCamelCase_ 							=	(2 * precision * recall) / (precision + recall)
       return fa




def 			lowerCAmelCase_			(				UpperCamelCase_       ,		UpperCamelCase_	)  ->		Optional[Any]:
       return normalize_answer(UpperCamelCase_	) == normalize_answer(UpperCamelCase_	)




def 			lowerCAmelCase_			(				UpperCamelCase_       ,		UpperCamelCase_       ,		UpperCamelCase_	)  ->		Tuple:
       UpperCamelCase_ 							=	[]
       for ground_truth in ground_truths:
              UpperCamelCase_ 							=	metric_fn(UpperCamelCase_       ,		UpperCamelCase_	)
              scores_for_ground_truths.append(UpperCamelCase_	)
       return max(UpperCamelCase_	)




def 			lowerCAmelCase_			(				UpperCamelCase_       ,		UpperCamelCase_	)  ->		str:
       UpperCamelCase_ 							=	UpperCamelCase_ 							=	UpperCamelCase_ 							=	0
       for article in dataset:
              for paragraph in article["paragraphs"]:
                     for qa in paragraph["qas"]:
                            total += 1
                            if qa["id"] not in predictions:
                                   UpperCamelCase_ 							=	"Unanswered question " + qa["id"] + " will receive score 0."
                                   print(UpperCamelCase_       ,		file=sys.stderr	)
                                   continue
                            UpperCamelCase_ 							=	[x["text"] for x in qa["answers"]]
                            UpperCamelCase_ 							=	predictions[qa["id"]]
                            exact_match += metric_max_over_ground_truths(UpperCamelCase_       ,		UpperCamelCase_       ,		UpperCamelCase_	)
                            fa += metric_max_over_ground_truths(UpperCamelCase_       ,		UpperCamelCase_       ,		UpperCamelCase_	)

       UpperCamelCase_ 							=	1_00.0 * exact_match / total
       UpperCamelCase_ 							=	1_00.0 * fa / total

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


if __name__ == "__main__":
     _UpperCAmelCase										=					'1.1'
     _UpperCAmelCase										=					argparse.ArgumentParser(description='Evaluation for SQuAD ' + expected_version)
     parser.add_argument('dataset_file', help='Dataset file')
     parser.add_argument('prediction_file', help='Prediction File')
     _UpperCAmelCase										=					parser.parse_args()
     with open(args.dataset_file) as dataset_file:
          _UpperCAmelCase										=					json.load(dataset_file)
          if dataset_json["version"] != expected_version:
               print(
                   'Evaluation expects v-' + expected_version + ', but got dataset with v-' + dataset_json['version'],
                   file=sys.stderr,
               )
          _UpperCAmelCase										=					dataset_json['data']
     with open(args.prediction_file) as prediction_file:
          _UpperCAmelCase										=					json.load(prediction_file)
     print(json.dumps(evaluate(dataset, predictions)))





import numpy as np
from cva import destroyAllWindows, imread, imshow, waitKey







class     __lowerCamelCase     :


							"""simple docstring"""

							def __init__(  self						,       UpperCAmelCase						,       UpperCAmelCase  )     ->       Tuple:






								'''simple docstring'''






								lowercase_			 = 0
								# max greyscale value for #FFFFFF
								lowercase_			 = int(self.get_greyscale(255						,       255						,       255  )  )

								if not self.min_threshold < threshold < self.max_threshold:
									lowercase_			 = F'Factor value should be from 0 to {self.max_threshold}'
									raise ValueError(UpperCAmelCase  )

								lowercase_			 = input_img
								lowercase_			 = threshold
								lowercase_ ,	lowercase_			 = self.input_img.shape[1], self.input_img.shape[0]

								# error table size (+4 columns and +1 row) greater than input image because of
								# lack of if statements
								lowercase_			 = [
								    [0 for _ in range(self.height + 4  )] for __ in range(self.width + 1  )
								]
								lowercase_			 = np.ones((self.width, self.height, 3)						,       np.uinta  ) * 255




							@classmethod
							def 				A__      (  cls						,       UpperCAmelCase						,       UpperCAmelCase						,       UpperCAmelCase  )     ->       float:






								'''simple docstring'''






								return 0.114 * blue + 0.587 * green + 0.2126 * red




							def 				A__      (  self  )     ->       None:






								'''simple docstring'''






								for y in range(self.height  ):
									for x in range(self.width  ):
										lowercase_			 = int(self.get_greyscale(*self.input_img[y][x]  )  )
										if self.threshold > greyscale + self.error_table[y][x]:
											lowercase_			 = (0, 0, 0)
											lowercase_			 = greyscale + self.error_table[x][y]
										else:
											lowercase_			 = (255, 255, 255)
											lowercase_			 = greyscale + self.error_table[x][y] - 255
										self.error_table[y][x + 1] += int(8 / 32 * current_error  )
										self.error_table[y][x + 2] += int(4 / 32 * current_error  )
										self.error_table[y + 1][x] += int(8 / 32 * current_error  )
										self.error_table[y + 1][x + 1] += int(4 / 32 * current_error  )
										self.error_table[y + 1][x + 2] += int(2 / 32 * current_error  )
										self.error_table[y + 1][x - 1] += int(4 / 32 * current_error  )
										self.error_table[y + 1][x - 2] += int(2 / 32 * current_error  )


if __name__ == "__main__":
							# create Burke's instances with original images in greyscale
							SCREAMING_SNAKE_CASE__       			=				[
							    Burkes(imread("""image_data/lena.jpg""", 1), threshold)
							    for threshold in (1, 1_2_6, 1_3_0, 1_4_0)
							]

							for burkes in burkes_instances:
														burkes.process()

							for burkes in burkes_instances:
														imshow(
														    f"""Original image with dithering threshold: {burkes.threshold}""",
														    burkes.output_img,
														)

							waitKey(0)
							destroyAllWindows()

