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




class 	UpperCAmelCase_      :


  '''simple docstring'''







  def __init__(			self      :			List[str]      ,							UpperCamelCase__      :			List[Any]      ,							UpperCamelCase__      :			int   )      ->     Union[str, Any]:







    """simple docstring"""






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

    if not self.min_threshold < threshold < self.max_threshold:
      __magic_name__    					=	F'''Factor value should be from 0 to {self.max_threshold}'''
      raise ValueError(UpperCamelCase__   )

    __magic_name__    					=	input_img
    __magic_name__    					=	threshold
    __magic_name__					,   __magic_name__    					=	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
    __magic_name__    					=	[
        [0 for _ in range(self.height + 4   )] for __ in range(self.width + 1   )
    ]
    __magic_name__    					=	np.ones((self.width, self.height, 3)      ,							np.uinta   ) * 255
  @classmethod
  def 							_lowercase							(			cls      :			Dict      ,							UpperCamelCase__      :			int      ,							UpperCamelCase__      :			int      ,							UpperCamelCase__      :			int   )      ->     float:







    """simple docstring"""






    return 0.114 * blue + 0.587 * green + 0.2126 * red





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







    """simple docstring"""






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



import torch.nn as nn

from .imports import is_fpa_available


if is_fpa_available():
    import transformer_engine.pytorch as te

def a__   (	A_,					A_=True,					A_=True,					A_=True ):






  '''simple docstring'''





  if not is_fpa_available():
    raise ImportError("""Using `convert_model` requires transformer_engine to be installed.""" )
  for name, module in model.named_children():
    if isinstance(A_,					nn.Linear ) and to_transformer_engine and _convert_linear:
      # Return early if the linear layer weights are not multiples of 16
      if any(p % 16 != 0 for p in module.weight.shape ):
        return
      __magic_name__    					=	module.bias is not None
      __magic_name__    					=	te.Linear(
          module.in_features,					module.out_features,					bias=A_,					params_dtype=module.weight.dtype )
      __magic_name__    					=	module.weight.data.clone()
      if has_bias:
        __magic_name__    					=	module.bias.data.clone()

      setattr(A_,					A_,					A_ )
    elif isinstance(A_,					nn.LayerNorm ) and to_transformer_engine and _convert_ln:
      __magic_name__    					=	te.LayerNorm(module.normalized_shape[0],					eps=module.eps,					params_dtype=module.weight.dtype )
      __magic_name__    					=	module.weight.data.clone()
      __magic_name__    					=	module.bias.data.clone()

      setattr(A_,					A_,					A_ )
    elif isinstance(A_,					te.Linear ) and not to_transformer_engine and _convert_linear:
      __magic_name__    					=	module.bias is not None
      __magic_name__    					=	nn.Linear(
          module.in_features,					module.out_features,					bias=A_,					params_dtype=module.weight.dtype )
      __magic_name__    					=	module.weight.data.clone()
      if has_bias:
        __magic_name__    					=	module.bias.data.clone()

      setattr(A_,					A_,					A_ )
    elif isinstance(A_,					te.LayerNorm ) and not to_transformer_engine and _convert_ln:
      __magic_name__    					=	nn.LayerNorm(module.normalized_shape[0],					eps=module.eps,					params_dtype=module.weight.dtype )
      __magic_name__    					=	module.weight.data.clone()
      __magic_name__    					=	module.bias.data.clone()

      setattr(A_,					A_,					A_ )
    else:
      convert_model(
          A_,					to_transformer_engine=A_,					_convert_linear=_convert_linear,					_convert_ln=_convert_ln,					)





def a__   (	A_ ):






  '''simple docstring'''





  if not is_fpa_available():
    raise ImportError("""Using `has_transformer_engine_layers` requires transformer_engine to be installed.""" )
  for m in model.modules():
    if isinstance(A_,					(te.LayerNorm, te.Linear) ):
      return True
  return False

