code: 
  
  
  """simple docstring"""
  import numpy as np
  from cva import destroyAllWindows, imread, imshow, waitKey
  
  
  
  
  class   lowercase__							:
  
  
  
  
  
     def __init__(       self			,      SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE)     ->     Tuple:
        _lowerCamelCase  :				str      =		0
        # max greyscale value for #FFFFFF
        _lowerCamelCase  :				Optional[int]      =		int(self.get_greyscale(255			,      255			,      255))
  
        if not self.min_threshold < threshold < self.max_threshold:
           _lowerCamelCase  :				int      =		F'Factor value should be from 0 to {self.max_threshold}'
           raise ValueError(SCREAMING_SNAKE_CASE)
  
        _lowerCamelCase  :				Optional[Any]      =		input_img
        _lowerCamelCase  :				Any      =		threshold
        _lowerCamelCase      ,						_lowerCamelCase  :				Optional[int]      =		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
        _lowerCamelCase  :				Optional[Any]      =		[
            [0 for _ in range(self.height + 4)] for __ in range(self.width + 1)
        ]
        _lowerCamelCase  :				int      =		np.ones((self.width, self.height, 3)			,      np.uinta) * 255
  
  
  
  
  
     @classmethod
     def 		UpperCamelCase_     (       cls			,      SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE)     ->     float:
        return 0.1_14 * blue + 0.5_87 * green + 0.21_26 * red
  
  
  
  
  
     def 		UpperCamelCase_     (       self)     ->     None:
        for y in range(self.height):
           for x in range(self.width):
              _lowerCamelCase  :				Union[str, Any]      =		int(self.get_greyscale(*self.input_img[y][x]))
              if self.threshold > greyscale + self.error_table[y][x]:
                 _lowerCamelCase  :				List[str]      =		(0, 0, 0)
                 _lowerCamelCase  :				str      =		greyscale + self.error_table[x][y]
              else:
                 _lowerCamelCase  :				Union[str, Any]      =		(255, 255, 255)
                 _lowerCamelCase  :				int      =		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
   UpperCAmelCase   =	[
       Burkes(imread("""image_data/lena.jpg""", 1), threshold)
       for threshold in (1, 126, 130, 140)
   ]
  
   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()
  
  
style_context: 
  
  
  """simple docstring"""
  import itertools
  from dataclasses import dataclass
  from io import StringIO
  from typing import Optional
  
  import pyarrow as pa
  
  import datasets
  from datasets.features.features import require_storage_cast
  from datasets.table import table_cast
  
  
  UpperCAmelCase   =	datasets.utils.logging.get_logger(__name__)
  
  
  
  
  @dataclass
  class   lowercase__							(				datasets.BuilderConfig							):
     __UpperCAmelCase       					=				None
     __UpperCAmelCase       					=				"utf-8"
     __UpperCAmelCase       					=				None
     __UpperCAmelCase       					=				10 << 20  # 10MB
     __UpperCAmelCase       					=				False
     __UpperCAmelCase       					=				"line"
  
  
  
  
  
  
  
  class   lowercase__							(				datasets.ArrowBasedBuilder							):
     __UpperCAmelCase       					=				TextConfig
  
  
  
  
  
     def 		UpperCamelCase_     (       self)     ->     int:
        return datasets.DatasetInfo(features=self.config.features)
  
  
  
  
  
     def 		UpperCamelCase_     (       self			,      SCREAMING_SNAKE_CASE)     ->     Optional[int]:
        if not self.config.data_files:
           raise ValueError(F'At least one data file must be specified, but got data_files={self.config.data_files}')
        _lowerCamelCase  :				Dict      =		dl_manager.download_and_extract(self.config.data_files)
        if isinstance(SCREAMING_SNAKE_CASE			,      (str, list, tuple)):
           _lowerCamelCase  :				Optional[Any]      =		data_files
           if isinstance(SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE):
              _lowerCamelCase  :				List[Any]      =		[files]
           _lowerCamelCase  :				List[str]      =		[dl_manager.iter_files(SCREAMING_SNAKE_CASE) for file in files]
           return [datasets.SplitGenerator(name=datasets.Split.TRAIN			,      gen_kwargs={"""files""": files})]
        _lowerCamelCase  :				int      =		[]
        for split_name, files in data_files.items():
           if isinstance(SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE):
              _lowerCamelCase  :				Any      =		[files]
           _lowerCamelCase  :				Any      =		[dl_manager.iter_files(SCREAMING_SNAKE_CASE) for file in files]
           splits.append(datasets.SplitGenerator(name=SCREAMING_SNAKE_CASE			,      gen_kwargs={"""files""": files}))
        return splits
  
  
  
  
  
     def 		UpperCamelCase_     (       self			,      SCREAMING_SNAKE_CASE)     ->     pa.Table:
        if self.config.features is not None:
           _lowerCamelCase  :				Union[str, Any]      =		self.config.features.arrow_schema
           if all(not require_storage_cast(SCREAMING_SNAKE_CASE) for feature in self.config.features.values()):
              # cheaper cast
              _lowerCamelCase  :				Any      =		pa_table.cast(SCREAMING_SNAKE_CASE)
           else:
              # more expensive cast; allows str <-> int/float or str to Audio for example
              _lowerCamelCase  :				Any      =		table_cast(SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE)
           return pa_table
        else:
           return pa_table.cast(pa.schema({"""text""": pa.string()}))
  
  
  
  
  
     def 		UpperCamelCase_     (       self			,      SCREAMING_SNAKE_CASE)     ->     Any:
        _lowerCamelCase  :				Optional[Any]      =		list(self.config.features) if self.config.features is not None else ["""text"""]
        for file_idx, file in enumerate(itertools.chain.from_iterable(SCREAMING_SNAKE_CASE)):
           # open in text mode, by default translates universal newlines ("\n", "\r\n" and "\r") into "\n"
           with open(SCREAMING_SNAKE_CASE			,      encoding=self.config.encoding			,      errors=self.config.errors) as f:
              if self.config.sample_by == "line":
                 _lowerCamelCase  :				Union[str, Any]      =		0
                 while True:
                    _lowerCamelCase  :				int      =		f.read(self.config.chunksize)
                    if not batch:
                       break
                    batch += f.readline()  # finish current line
                    # StringIO.readlines, by default splits only on "\n" (and keeps line breaks)
                    _lowerCamelCase  :				List[str]      =		StringIO(SCREAMING_SNAKE_CASE).readlines()
                    if not self.config.keep_linebreaks:
                       _lowerCamelCase  :				Optional[Any]      =		[line.rstrip("""\n""") for line in batch]
                    _lowerCamelCase  :				Any      =		pa.Table.from_arrays([pa.array(SCREAMING_SNAKE_CASE)]			,      names=SCREAMING_SNAKE_CASE)
                    # Uncomment for debugging (will print the Arrow table size and elements)
                    # logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
                    # logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
                    yield (file_idx, batch_idx), self._cast_table(SCREAMING_SNAKE_CASE)
                    batch_idx += 1
              elif self.config.sample_by == "paragraph":
                 _lowerCamelCase  :				Optional[Any]      =		0
                 _lowerCamelCase  :				Dict      =		""""""
                 while True:
                    _lowerCamelCase  :				Union[str, Any]      =		f.read(self.config.chunksize)
                    if not new_batch:
                       break
                    batch += new_batch
                    batch += f.readline()  # finish current line
                    _lowerCamelCase  :				Any      =		batch.split("""\n\n""")
                    _lowerCamelCase  :				Dict      =		pa.Table.from_arrays(
                        [pa.array([example for example in batch[:-1] if example])]			,      names=SCREAMING_SNAKE_CASE)
                    # Uncomment for debugging (will print the Arrow table size and elements)
                    # logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
                    # logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
                    yield (file_idx, batch_idx), self._cast_table(SCREAMING_SNAKE_CASE)
                    batch_idx += 1
                    _lowerCamelCase  :				Union[str, Any]      =		batch[-1]
                 if batch:
                    _lowerCamelCase  :				Optional[int]      =		pa.Table.from_arrays([pa.array([batch])]			,      names=SCREAMING_SNAKE_CASE)
                    yield (file_idx, batch_idx), self._cast_table(SCREAMING_SNAKE_CASE)
              elif self.config.sample_by == "document":
                 _lowerCamelCase  :				Tuple      =		f.read()
                 _lowerCamelCase  :				List[Any]      =		pa.Table.from_arrays([pa.array([text])]			,      names=SCREAMING_SNAKE_CASE)
                 yield file_idx, self._cast_table(SCREAMING_SNAKE_CASE)
  
  