




'''simple docstring'''
from cva import destroyAllWindows, imread, imshow, waitKey
def 				snake_case_  (   a__		:	Tuple    ,a__		:	int			):



    """simple docstring"""




    __lowercase				,__lowercase							=						img.shape[0], img.shape[1]

    def to_grayscale(a__		:	int    ,a__		:	Any    ,a__		:	List[Any]			):
        return 0.2_1_2_6 * red + 0.5_8_7 * green + 0.1_1_4 * blue

    def normalize(a__		:	List[Any]			):
        return min(a__    ,2_55			)

    for i in range(a__			):
        for j in range(a__			):
            __lowercase							=						int(to_grayscale(*img[i][j]			)			)
            __lowercase							=						[
                normalize(a__			),
                normalize(greyscale + factor			),
                normalize(greyscale + 2 * factor			),
            ]

    return img


if __name__ == "__main__":
  # read original image
  A							:      List[str] 	=       {
      percentage: imread("""image_data/lena.jpg""", 1) for percentage in (10, 20, 30, 40)
  }

  for percentage, img in images.items():
    make_sepia(img, percentage)

  for percentage, img in images.items():
    imshow(F"""Original image with sepia (factor: {percentage})""", 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        SCREAMING_SNAKE_CASE(				__A   ,      __A   ,      __A     ):

       @register_to_config
       def __init__(    self					,		lowerCamelCase__					,		lowerCamelCase__					,		lowerCamelCase__					,		lowerCamelCase__					,		lowerCamelCase__					,		lowerCamelCase__					,		lowerCamelCase__					,		lowerCamelCase__					,		lowerCamelCase__					,		lowerCamelCase__ = False					,		)    ->					Optional[int]:

           """simple docstring"""



           super().__init__()

           __lowercase							=						nn.Linear(lowerCamelCase__					,		lowerCamelCase__					,		bias=lowerCamelCase__     )

           __lowercase							=						nn.Embedding(lowerCamelCase__					,		lowerCamelCase__     )
           __lowercase							=						False

           __lowercase							=						nn.Dropout(p=lowerCamelCase__     )

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

           __lowercase							=						TaLayerNorm(lowerCamelCase__     )
           __lowercase							=						nn.Dropout(p=lowerCamelCase__     )




       def     snake_case__			(    self					,		lowerCamelCase__					,		lowerCamelCase__     )    ->					str:

           """simple docstring"""



           __lowercase							=						self.input_proj(lowerCamelCase__     )

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

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

           __lowercase							=						self.dropout_pre(lowerCamelCase__     )

           # inverted the attention mask
           __lowercase							=						encoder_inputs.size()
           __lowercase							=						self.get_extended_attention_mask(lowerCamelCase__					,		lowerCamelCase__     )

           for lyr in self.encoders:
               __lowercase							=						lyr(lowerCamelCase__					,		lowerCamelCase__     )[0]
           __lowercase							=						self.layer_norm(lowerCamelCase__     )

           return self.dropout_post(lowerCamelCase__     ), encoder_inputs_mask




