

import datasets

from .evaluate import (
    apply_no_ans_threshold,
    find_all_best_thresh,
    get_raw_scores,
    make_eval_dict,
    make_qid_to_has_ans,
    merge_eval,
)


lowerCAmelCase_	     =     '''\
@inproceedings{Rajpurkar2016SQuAD10,
  title={SQuAD: 100, 000+ Questions for Machine Comprehension of Text},
  author={Pranav Rajpurkar and Jian Zhang and Konstantin Lopyrev and Percy Liang},
  booktitle={EMNLP},
  year={2016}
}
'''

lowerCAmelCase_	     =     '''
This metric wrap the official scoring script for version 2 of the Stanford Question
Answering Dataset (SQuAD).

Stanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by
crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span,
from the corresponding reading passage, or the question might be unanswerable.

SQuAD2.0 combines the 100,000 questions in SQuAD1.1 with over 50,000 unanswerable questions
written adversarially by crowdworkers to look similar to answerable ones.
To do well on SQuAD2.0, systems must not only answer questions when possible, but also
determine when no answer is supported by the paragraph and abstain from answering.
'''

lowerCAmelCase_	     =     '''
Computes SQuAD v2 scores (F1 and EM).
Args:
    predictions: List of triple for question-answers to score with the following elements:
        - the question-answer \'id\' field as given in the references (see below)
        - the text of the answer
        - the probability that the question has no answer
    references: List of question-answers dictionaries with the following key-values:
            - \'id\': id of the question-answer pair (see above),
            - \'answers\': a list of Dict {\'text\': text of the answer as a string}
    no_answer_threshold: float
        Probability threshold to decide that a question has no answer.
Returns:
    \'exact\': Exact match (the normalized answer exactly match the gold answer)
    \'f1\': The F-score of predicted tokens versus the gold answer
    \'total\': Number of score considered
    \'HasAns_exact\': Exact match (the normalized answer exactly match the gold answer)
    \'HasAns_f1\': The F-score of predicted tokens versus the gold answer
    \'HasAns_total\': Number of score considered
    \'NoAns_exact\': Exact match (the normalized answer exactly match the gold answer)
    \'NoAns_f1\': The F-score of predicted tokens versus the gold answer
    \'NoAns_total\': Number of score considered
    \'best_exact\': Best exact match (with varying threshold)
    \'best_exact_thresh\': No-answer probability threshold associated to the best exact match
    \'best_f1\': Best F1 (with varying threshold)
    \'best_f1_thresh\': No-answer probability threshold associated to the best F1
Examples:

    >>> predictions = [{\'prediction_text\': \'1976\', \'id\': \'56e10a3be3433e1400422b22\', \'no_answer_probability\': 0.}]
    >>> references = [{\'answers\': {\'answer_start\': [97], \'text\': [\'1976\']}, \'id\': \'56e10a3be3433e1400422b22\'}]
    >>> squad_v2_metric = datasets.load_metric("squad_v2")
    >>> results = squad_v2_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {\'exact\': 100.0, \'f1\': 100.0, \'total\': 1, \'HasAns_exact\': 100.0, \'HasAns_f1\': 100.0, \'HasAns_total\': 1, \'best_exact\': 100.0, \'best_exact_thresh\': 0.0, \'best_f1\': 100.0, \'best_f1_thresh\': 0.0}
'''





@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION     ,	_KWARGS_DESCRIPTION )
class      snake_case_       (		datasets.Metric ):







			'''simple docstring'''

			def     snake_case__(					self			:    int							)				->List[str]:
									return datasets.MetricInfo(
									    description=_DESCRIPTION							,       citation=_CITATION							,       inputs_description=_KWARGS_DESCRIPTION							,       features=datasets.Features(
									        {
									            '''predictions''': {
									                '''id''': datasets.Value('''string'''							),
									                '''prediction_text''': datasets.Value('''string'''							),
									                '''no_answer_probability''': datasets.Value('''float32'''							),
									            },
									            '''references''': {
									                '''id''': datasets.Value('''string'''							),
									                '''answers''': datasets.features.Sequence(
									                    {'''text''': datasets.Value('''string'''							), '''answer_start''': datasets.Value('''int32'''							)}							),
									            },
									        }							)							,       codebase_urls=['''https://rajpurkar.github.io/SQuAD-explorer/''']							,       reference_urls=['''https://rajpurkar.github.io/SQuAD-explorer/''']							,       )







			def     snake_case__(					self			:    int							,       _UpperCamelCase			:    List[Any]							,       _UpperCamelCase			:    Tuple							,       _UpperCamelCase			:    Optional[int]=1.0							)				->List[str]:
									snake_case_       =     {p['''id''']: p['''no_answer_probability'''] for p in predictions}
									snake_case_       =     [{'''paragraphs''': [{'''qas''': references}]}]
									snake_case_       =     {p['''id''']: p['''prediction_text'''] for p in predictions}

									snake_case_       =     make_qid_to_has_ans(_UpperCamelCase							)  # maps qid to True/False
									snake_case_       =     [k for k, v in qid_to_has_ans.items() if v]
									snake_case_       =     [k for k, v in qid_to_has_ans.items() if not v]

									snake_case_,   snake_case_       =     get_raw_scores(_UpperCamelCase							,       _UpperCamelCase							)
									snake_case_       =     apply_no_ans_threshold(_UpperCamelCase							,       _UpperCamelCase							,       _UpperCamelCase							,       _UpperCamelCase							)
									snake_case_       =     apply_no_ans_threshold(_UpperCamelCase							,       _UpperCamelCase							,       _UpperCamelCase							,       _UpperCamelCase							)
									snake_case_       =     make_eval_dict(_UpperCamelCase							,       _UpperCamelCase							)

									if has_ans_qids:
															snake_case_       =     make_eval_dict(_UpperCamelCase							,       _UpperCamelCase							,       qid_list=_UpperCamelCase							)
															merge_eval(_UpperCamelCase							,       _UpperCamelCase							,       '''HasAns'''							)
									if no_ans_qids:
															snake_case_       =     make_eval_dict(_UpperCamelCase							,       _UpperCamelCase							,       qid_list=_UpperCamelCase							)
															merge_eval(_UpperCamelCase							,       _UpperCamelCase							,       '''NoAns'''							)
									find_all_best_thresh(_UpperCamelCase							,       _UpperCamelCase							,       _UpperCamelCase							,       _UpperCamelCase							,       _UpperCamelCase							,       _UpperCamelCase							)
									return dict(_UpperCamelCase							)


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





class      snake_case_       :







			'''simple docstring'''

			def __init__(					self			:    Tuple							,       _UpperCamelCase			:    Optional[Any]							,       _UpperCamelCase			:    int							)				->List[Any]:
									snake_case_       =     0
									# max greyscale value for #FFFFFF
									snake_case_       =     int(self.get_greyscale(2_5_5							,       2_5_5							,       2_5_5							)							)

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

									snake_case_       =     input_img
									snake_case_       =     threshold
									snake_case_,   snake_case_       =     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
									snake_case_       =     [
									    [0 for _ in range(self.height + 4							)] for __ in range(self.width + 1							)
									]
									snake_case_       =     np.ones((self.width, self.height, 3)							,       np.uinta							) * 2_5_5







			@classmethod
			def     snake_case__(					cls			:    Optional[Any]							,       _UpperCamelCase			:    int							,       _UpperCamelCase			:    int							,       _UpperCamelCase			:    int							)				->float:
									return 0.114 * blue + 0.587 * green + 0.2126 * red







			def     snake_case__(					self			:    Any							)				->None:
									for y in range(self.height							):
															for x in range(self.width							):
																					snake_case_       =     int(self.get_greyscale(*self.input_img[y][x]							)							)
																					if self.threshold > greyscale + self.error_table[y][x]:
																											snake_case_       =     (0, 0, 0)
																											snake_case_       =     greyscale + self.error_table[x][y]
																					else:
																											snake_case_       =     (2_5_5, 2_5_5, 2_5_5)
																											snake_case_       =     greyscale + self.error_table[x][y] - 2_5_5
																					self.error_table[y][x + 1] += int(8 / 3_2 * current_error							)
																					self.error_table[y][x + 2] += int(4 / 3_2 * current_error							)
																					self.error_table[y + 1][x] += int(8 / 3_2 * current_error							)
																					self.error_table[y + 1][x + 1] += int(4 / 3_2 * current_error							)
																					self.error_table[y + 1][x + 2] += int(2 / 3_2 * current_error							)
																					self.error_table[y + 1][x - 1] += int(4 / 3_2 * current_error							)
																					self.error_table[y + 1][x - 2] += int(2 / 3_2 * current_error							)


if __name__ == "__main__":
					# create Burke's instances with original images in greyscale
					lowerCAmelCase_	     =     [
					    Burkes(imread('''image_data/lena.jpg''', 1), threshold)
					    for threshold in (1, 1_26, 1_30, 1_40)
					]

					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()