


import importlib
from typing import List, Optional, Union

from seqeval.metrics import accuracy_score, classification_report

import datasets


SCREAMING_SNAKE_CASE		:int											=       '\\n@inproceedings{ramshaw-marcus-1995-text,\n    title = "Text Chunking using Transformation-Based Learning",\n    author = "Ramshaw, Lance  and\n      Marcus, Mitch",\n    booktitle = "Third Workshop on Very Large Corpora",\n    year = "1995",\n    url = "https://www.aclweb.org/anthology/W95-0107",\n}\n@misc{seqeval,\n  title={{seqeval}: A Python framework for sequence labeling evaluation},\n  url={https://github.com/chakki-works/seqeval},\n  note={Software available from https://github.com/chakki-works/seqeval},\n  author={Hiroki Nakayama},\n  year={2018},\n}\n'

SCREAMING_SNAKE_CASE		:Any											=       '\\nseqeval is a Python framework for sequence labeling evaluation.\nseqeval can evaluate the performance of chunking tasks such as named-entity recognition, part-of-speech tagging, semantic role labeling and so on.\n\nThis is well-tested by using the Perl script conlleval, which can be used for\nmeasuring the performance of a system that has processed the CoNLL-2000 shared task data.\n\nseqeval supports following formats:\nIOB1\nIOB2\nIOE1\nIOE2\nIOBES\n\nSee the [README.md] file at https://github.com/chakki-works/seqeval for more information.\n'

SCREAMING_SNAKE_CASE		:str											=       '\nProduces labelling scores along with its sufficient statistics\nfrom a source against one or more references.\n\nArgs:\n    predictions: List of List of predicted labels (Estimated targets as returned by a tagger)\n    references: List of List of reference labels (Ground truth (correct) target values)\n    suffix: True if the IOB prefix is after type, False otherwise. default: False\n    scheme: Specify target tagging scheme. Should be one of ["IOB1", "IOB2", "IOE1", "IOE2", "IOBES", "BILOU"].\n        default: None\n    mode: Whether to count correct entity labels with incorrect I/B tags as true positives or not.\n        If you want to only count exact matches, pass mode="strict". default: None.\n    sample_weight: Array-like of shape (n_samples,), weights for individual samples. default: None\n    zero_division: Which value to substitute as a metric value when encountering zero division. Should be on of 0, 1,\n        "warn". "warn" acts as 0, but the warning is raised.\n\nReturns:\n    \'scores\': dict. Summary of the scores for overall and per type\n        Overall:\n            \'accuracy\': accuracy,\n            \'precision\': precision,\n            \'recall\': recall,\n            \'f1\': F1 score, also known as balanced F-score or F-measure,\n        Per type:\n            \'precision\': precision,\n            \'recall\': recall,\n            \'f1\': F1 score, also known as balanced F-score or F-measure\nExamples:\n\n    >>> predictions = [[\'O\', \'O\', \'B-MISC\', \'I-MISC\', \'I-MISC\', \'I-MISC\', \'O\'], [\'B-PER\', \'I-PER\', \'O\']]\n    >>> references = [[\'O\', \'O\', \'O\', \'B-MISC\', \'I-MISC\', \'I-MISC\', \'O\'], [\'B-PER\', \'I-PER\', \'O\']]\n    >>> seqeval = datasets.load_metric("seqeval")\n    >>> results = seqeval.compute(predictions=predictions, references=references)\n    >>> print(list(results.keys()))\n    [\'MISC\', \'PER\', \'overall_precision\', \'overall_recall\', \'overall_f1\', \'overall_accuracy\']\n    >>> print(results["overall_f1"])\n    0.5\n    >>> print(results["PER"]["f1"])\n    1.0\n'


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






  """simple docstring"""





  def    lowerCAmelCase__					(       self    :    Any					)				->	int:




     """simple docstring"""
     return datasets.MetricInfo(
         description=_DESCRIPTION      ,			citation=_CITATION      ,			homepage="https://github.com/chakki-works/seqeval"      ,			inputs_description=_KWARGS_DESCRIPTION      ,			features=datasets.Features(
             {
                 "predictions": datasets.Sequence(datasets.Value("string"      ,			id="label"					)      ,			id="sequence"					),
                 "references": datasets.Sequence(datasets.Value("string"      ,			id="label"					)      ,			id="sequence"					),
             }					)      ,			codebase_urls=["https://github.com/chakki-works/seqeval"]      ,			reference_urls=["https://github.com/chakki-works/seqeval"]      ,			)



  def    lowerCAmelCase__					(       self    :    Optional[int]      ,			_lowerCAmelCase    :    Any      ,			_lowerCAmelCase    :    Dict      ,			_lowerCAmelCase    :    Optional[int] = False      ,			_lowerCAmelCase    :    Union[str, Any] = None      ,			_lowerCAmelCase    :    str = None      ,			_lowerCAmelCase    :    Union[str, Any] = None      ,			_lowerCAmelCase    :    Optional[int] = "warn"      ,			)				->	Any:




     """simple docstring"""
     if scheme is not None:
        try:
           snake_case_         =   importlib.import_module("seqeval.scheme"					)
           snake_case_         =   getattr(__UpperCamelCase      ,			__UpperCamelCase					)
        except AttributeError:
           raise ValueError(F'''Scheme should be one of [IOB1, IOB2, IOE1, IOE2, IOBES, BILOU], got {scheme}'''					)
     snake_case_         =   classification_report(
         y_true=__UpperCamelCase      ,			y_pred=__UpperCamelCase      ,			suffix=__UpperCamelCase      ,			output_dict=__UpperCamelCase      ,			scheme=__UpperCamelCase      ,			mode=__UpperCamelCase      ,			sample_weight=__UpperCamelCase      ,			zero_division=__UpperCamelCase      ,			)
     report.pop("macro avg"					)
     report.pop("weighted avg"					)
     snake_case_         =   report.pop("micro avg"					)

     snake_case_         =   {
         type_name: {
             "precision": score["precision"],
             "recall": score["recall"],
             "f1": score["f1-score"],
             "number": score["support"],
         }
         for type_name, score in report.items()
     }
     snake_case_         =   overall_score["precision"]
     snake_case_         =   overall_score["recall"]
     snake_case_         =   overall_score["f1-score"]
     snake_case_         =   accuracy_score(y_true=__UpperCamelCase      ,			y_pred=__UpperCamelCase					)

     return scores







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


class       __lowerCAmelCase       :






  """simple docstring"""





  def __init__(       self    :    Optional[Any]      ,			_lowerCAmelCase    :    Union[str, Any]      ,			_lowerCAmelCase    :    int					)				->	Tuple:




     """simple docstring"""
     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(_lowerCAmelCase					)

     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    lowerCAmelCase__					(       cls    :    Optional[Any]      ,			_lowerCAmelCase    :    int      ,			_lowerCAmelCase    :    int      ,			_lowerCAmelCase    :    int					)				->	float:




     """simple docstring"""
     return 0.114 * blue + 0.587 * green + 0.2_126 * red



  def    lowerCAmelCase__					(       self    :    Union[str, Any]					)				->	None:




     """simple docstring"""
     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
   SCREAMING_SNAKE_CASE		:Dict											=       [
       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()




