





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


_UpperCamelCase    =	{'''configuration_hubert''': ['''HUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''HubertConfig''']}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _UpperCamelCase    =	[
         '''HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
         '''HubertForCTC''',
         '''HubertForSequenceClassification''',
         '''HubertModel''',
         '''HubertPreTrainedModel''',
     ]


try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _UpperCamelCase    =	[
         '''TF_HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
         '''TFHubertForCTC''',
         '''TFHubertModel''',
         '''TFHubertPreTrainedModel''',
     ]

if TYPE_CHECKING:
     from .configuration_hubert import HUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, HubertConfig

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_hubert import (
              HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
              HubertForCTC,
              HubertForSequenceClassification,
              HubertModel,
              HubertPreTrainedModel,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_hubert import (
              TF_HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
              TFHubertForCTC,
              TFHubertModel,
              TFHubertPreTrainedModel,
          )


else:
     import sys

     _UpperCamelCase    =	_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)








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




class 			_lowerCamelCase	:



      """simple docstring"""






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






        '''simple docstring'''






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

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

        __snake_case				:			List[Any]            =    input_img
        __snake_case				:			Any            =    threshold
        __snake_case      ,				__snake_case				:			str            =    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				:			Dict            =    [
            [0 for _ in range(self.height + 4				)] for __ in range(self.width + 1				)
        ]
        __snake_case				:			str            =    np.ones((self.width, self.height, 3)			,  np.uinta				) * 255







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






        '''simple docstring'''






        return 0.114 * blue + 0.587 * green + 0.2_126 * red







      def   UpperCAmelCase						(	self				)    ->				None:






        '''simple docstring'''






        for y in range(self.height				):
          for x in range(self.width				):
            __snake_case				:			Optional[int]            =    int(self.get_greyscale(*self.input_img[y][x]				)				)
            if self.threshold > greyscale + self.error_table[y][x]:
              __snake_case				:			Any            =    (0, 0, 0)
              __snake_case				:			Optional[int]            =    greyscale + self.error_table[x][y]
            else:
              __snake_case				:			str            =    (255, 255, 255)
              __snake_case				:			Dict            =    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()

