'''simple docstring'''

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




class    lowerCamelCase__  :



   def __init__(    self					,      lowerCAmelCase__					,      lowerCAmelCase__     )				->     Optional[Any]:
        """simple docstring"""





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

        if not self.min_threshold < threshold < self.max_threshold:
             _UpperCamelCase  :Union[str, Any]       =f'''Factor value should be from 0 to {self.max_threshold}'''
             raise ValueError(lowerCAmelCase__     )

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



   @classmethod
   def  _UpperCamelCase		(    cls					,      lowerCAmelCase__					,      lowerCAmelCase__					,      lowerCAmelCase__     )				->     float:
        """simple docstring"""





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




   def  _UpperCamelCase		(    self     )				->     None:
        """simple docstring"""





        for y in range(self.height     ):
             for x in range(self.width     ):
                  _UpperCamelCase  :str       =int(self.get_greyscale(*self.input_img[y][x]     )     )
                  if self.threshold > greyscale + self.error_table[y][x]:
                       _UpperCamelCase  :Any       =(0, 0, 0)
                       _UpperCamelCase  :Tuple       =greyscale + self.error_table[x][y]
                  else:
                       _UpperCamelCase  :Optional[Any]       =(255, 255, 255)
                       _UpperCamelCase  :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
      _lowerCamelCase :				Optional[Any]														= [
          Burkes(imread("""image_data/lena.jpg""", 1), threshold)
          for threshold in (1, 1_26, 1_30, 1_40)
      ]

      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 torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import (
    TaBlock,
    TaConfig,
    TaLayerNorm,
)

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin





class   __A  (snake_case__    ,		snake_case__    ,		snake_case__):





							'''simple docstring'''

							@register_to_config
							def __init__(					self				:   List[str]		,							UpperCAmelCase_				:   int		,							UpperCAmelCase_				:   int		,							UpperCAmelCase_				:   int		,							UpperCAmelCase_				:   float		,							UpperCAmelCase_				:   int		,							UpperCAmelCase_				:   int		,							UpperCAmelCase_				:   int		,							UpperCAmelCase_				:   int		,							UpperCAmelCase_				:   str		,							UpperCAmelCase_				:   bool = False		,							)       ->Dict:



														"""simple docstring"""



														super().__init__()

														snake_case_     							=		nn.Linear(UpperCAmelCase_		,							UpperCAmelCase_		,							bias=UpperCAmelCase_							)

														snake_case_     							=		nn.Embedding(UpperCAmelCase_		,							UpperCAmelCase_							)
														snake_case_     							=		False

														snake_case_     							=		nn.Dropout(p=UpperCAmelCase_							)

														snake_case_     							=		TaConfig(
														    d_model=UpperCAmelCase_		,							num_heads=UpperCAmelCase_		,							d_kv=UpperCAmelCase_		,							d_ff=UpperCAmelCase_		,							feed_forward_proj=UpperCAmelCase_		,							dropout_rate=UpperCAmelCase_		,							is_decoder=UpperCAmelCase_		,							is_encoder_decoder=UpperCAmelCase_		,							)
														snake_case_     							=		nn.ModuleList()
														for lyr_num in range(UpperCAmelCase_							):
																					snake_case_     							=		TaBlock(UpperCAmelCase_							)
																					self.encoders.append(UpperCAmelCase_							)

														snake_case_     							=		TaLayerNorm(UpperCAmelCase_							)
														snake_case_     							=		nn.Dropout(p=UpperCAmelCase_							)



							def 		lowerCAmelCase							(					self				:   Optional[Any]		,							UpperCAmelCase_				:   Any		,							UpperCAmelCase_				:   List[str]							)       ->List[Any]:



														"""simple docstring"""



														snake_case_     							=		self.input_proj(UpperCAmelCase_							)

														# terminal relative positional encodings
														snake_case_     							=		encoder_inputs.shape[1]
														snake_case_     							=		torch.arange(UpperCAmelCase_		,							device=encoder_inputs.device							)

														snake_case_     							=		encoder_inputs_mask.sum(-1							)
														snake_case_     							=		torch.roll(input_positions.unsqueeze(0							)		,							tuple(seq_lens.tolist()							)		,							dims=0							)
														x += self.position_encoding(UpperCAmelCase_							)

														snake_case_     							=		self.dropout_pre(UpperCAmelCase_							)

														# inverted the attention mask
														snake_case_     							=		encoder_inputs.size()
														snake_case_     							=		self.get_extended_attention_mask(UpperCAmelCase_		,							UpperCAmelCase_							)

														for lyr in self.encoders:
																					snake_case_     							=		lyr(UpperCAmelCase_		,							UpperCAmelCase_							)[0]
														snake_case_     							=		self.layer_norm(UpperCAmelCase_							)

														return self.dropout_post(UpperCAmelCase_							), encoder_inputs_mask



