


"""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()





"""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)

