'''simple docstring'''

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







class     _lowercase					:


 '''simple docstring'''






 def __init__(				self       :     Optional[Any]				,	SCREAMING_SNAKE_CASE__       :     Any				,	SCREAMING_SNAKE_CASE__       :     int							)  ->   Any:
  __lowerCAmelCase    							=						0
  # max greyscale value for #FFFFFF
  __lowerCAmelCase    							=						int(self.get_greyscale(2_55				,	2_55				,	2_55							)							)

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

  __lowerCAmelCase    							=						input_img
  __lowerCAmelCase    							=						threshold
  __lowerCAmelCase							,	__lowerCAmelCase    							=						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    							=						[
      [0 for _ in range(self.height + 4							)] for __ in range(self.width + 1							)
  ]
  __lowerCAmelCase    							=						np.ones((self.width, self.height, 3)				,	np.uinta							) * 2_55
 @classmethod
 def a (				cls       :     Optional[int]				,	SCREAMING_SNAKE_CASE__       :     int				,	SCREAMING_SNAKE_CASE__       :     int				,	SCREAMING_SNAKE_CASE__       :     int							)  ->   float:
  return 0.1_1_4 * blue + 0.5_8_7 * green + 0.2_1_2_6 * red





 def a (				self       :     List[Any]							)  ->   None:
  for y in range(self.height							):
   for x in range(self.width							):
    __lowerCAmelCase    							=						int(self.get_greyscale(*self.input_img[y][x]							)							)
    if self.threshold > greyscale + self.error_table[y][x]:
     __lowerCAmelCase    							=						(0, 0, 0)
     __lowerCAmelCase    							=						greyscale + self.error_table[x][y]
    else:
     __lowerCAmelCase    							=						(2_55, 2_55, 2_55)
     __lowerCAmelCase    							=						greyscale + self.error_table[x][y] - 2_55
    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
 _A      :				Optional[Any] 				=    [
     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 math

import torch
from torch import nn
from torch.nn import init

from .binarizer import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer







class     _lowercase					(    nn.Linear      ):


 '''simple docstring'''






 def __init__(				self       :     List[str]				,	SCREAMING_SNAKE_CASE__       :     int				,	SCREAMING_SNAKE_CASE__       :     int				,	SCREAMING_SNAKE_CASE__       :     bool = True				,	SCREAMING_SNAKE_CASE__       :     str = "constant"				,	SCREAMING_SNAKE_CASE__       :     float = 0.0				,	SCREAMING_SNAKE_CASE__       :     str = "topK"				,	)  ->   Any:
  super(SCREAMING_SNAKE_CASE__				,	self							).__init__(in_features=SCREAMING_SNAKE_CASE__				,	out_features=SCREAMING_SNAKE_CASE__				,	bias=SCREAMING_SNAKE_CASE__							)
  assert pruning_method in ["topK", "threshold", "sigmoied_threshold", "magnitude", "l0"]
  __lowerCAmelCase    							=						pruning_method

  if self.pruning_method in ["topK", "threshold", "sigmoied_threshold", "l0"]:
   __lowerCAmelCase    							=						mask_scale
   __lowerCAmelCase    							=						mask_init
   __lowerCAmelCase    							=						nn.Parameter(torch.empty(self.weight.size()							)							)
   self.init_mask()
 def a (				self       :     Union[str, Any]							)  ->   int:
  if self.mask_init == "constant":
   init.constant_(self.mask_scores				,	val=self.mask_scale							)
  elif self.mask_init == "uniform":
   init.uniform_(self.mask_scores				,	a=-self.mask_scale				,	b=self.mask_scale							)
  elif self.mask_init == "kaiming":
   init.kaiming_uniform_(self.mask_scores				,	a=math.sqrt(5							)							)





 def a (				self       :     Any				,	SCREAMING_SNAKE_CASE__       :     torch.tensor				,	SCREAMING_SNAKE_CASE__       :     float							)  ->   Tuple:
  # Get the mask
  if self.pruning_method == "topK":
   __lowerCAmelCase    							=						TopKBinarizer.apply(self.mask_scores				,	SCREAMING_SNAKE_CASE__							)
  elif self.pruning_method in ["threshold", "sigmoied_threshold"]:
   __lowerCAmelCase    							=						"""sigmoied""" in self.pruning_method
   __lowerCAmelCase    							=						ThresholdBinarizer.apply(self.mask_scores				,	SCREAMING_SNAKE_CASE__				,	SCREAMING_SNAKE_CASE__							)
  elif self.pruning_method == "magnitude":
   __lowerCAmelCase    							=						MagnitudeBinarizer.apply(self.weight				,	SCREAMING_SNAKE_CASE__							)
  elif self.pruning_method == "l0":
   __lowerCAmelCase							,	__lowerCAmelCase							,	__lowerCAmelCase    							=						-0.1, 1.1, 2 / 3
   if self.training:
    __lowerCAmelCase    							=						torch.zeros_like(self.mask_scores							).uniform_().clamp(0.0_0_0_1				,	0.9_9_9_9							)
    __lowerCAmelCase    							=						torch.sigmoid((u.log() - (1 - u).log() + self.mask_scores) / b							)
   else:
    __lowerCAmelCase    							=						torch.sigmoid(self.mask_scores							)
   __lowerCAmelCase    							=						s * (r - l) + l
   __lowerCAmelCase    							=						s_bar.clamp(min=0.0				,	max=1.0							)
  # Mask weights with computed mask
  __lowerCAmelCase    							=						mask * self.weight
  # Compute output (linear layer) with masked weights
  return nn.functional.linear(SCREAMING_SNAKE_CASE__				,	SCREAMING_SNAKE_CASE__				,	self.bias							)






