
"""simple docstring"""




import torch
from torch import nn
from transformers import CLIPPreTrainedModel, CLIPVisionModel

from ...models.attention import BasicTransformerBlock
from ...utils import logging


A__ :       Any				= logging.get_logger(__name__)  # pylint: disable=invalid-name


class       __magic_name__							(							SCREAMING_SNAKE_CASE__			):
       def __init__(  self			,					A_			,					A_=768							)  ->						int:





         """simple docstring"""




         super().__init__(A_							)
         _lowercase:      List[Any]   			=  proj_size

         _lowercase:      List[str]   			=  CLIPVisionModel(A_							)
         _lowercase:      List[Any]   			=  PaintByExampleMapper(A_							)
         _lowercase:      str   			=  nn.LayerNorm(config.hidden_size							)
         _lowercase:      Union[str, Any]   			=  nn.Linear(config.hidden_size			,					self.proj_size							)

         # uncondition for scaling
         _lowercase:      str   			=  nn.Parameter(torch.randn((1, 1, self.proj_size)							)							)



       def 				lowercase_				(  self			,					A_			,					A_=False							)  ->						Optional[Any]:





         """simple docstring"""




         _lowercase:      List[str]   			=  self.model(pixel_values=A_							)
         _lowercase:      Optional[Any]   			=  clip_output.pooler_output
         _lowercase:      Dict   			=  self.mapper(latent_states[:, None]							)
         _lowercase:      int   			=  self.final_layer_norm(A_							)
         _lowercase:      Union[str, Any]   			=  self.proj_out(A_							)
         if return_uncond_vector:
           return latent_states, self.uncond_vector

         return latent_states


class       __magic_name__							(							nn.Module			):
       def __init__(  self			,					A_							)  ->						List[str]:





         """simple docstring"""




         super().__init__()
         _lowercase:      int   			=  (config.num_hidden_layers + 1) // 5
         _lowercase:      Optional[int]   			=  config.hidden_size
         _lowercase:      Optional[int]   			=  1
         _lowercase:      Optional[int]   			=  nn.ModuleList(
             [
                 BasicTransformerBlock(A_			,					A_			,					A_			,					activation_fn='''gelu'''			,					attention_bias=A_							)
                 for _ in range(A_							)
             ]							)



       def 				lowercase_				(  self			,					A_							)  ->						Optional[Any]:





         """simple docstring"""




         for block in self.blocks:
           _lowercase:      Tuple   			=  block(A_							)

         return hidden_states


"""simple docstring"""




from cva import destroyAllWindows, imread, imshow, waitKey




def 			_lowerCAmelCase		(		_UpperCamelCase    ):






  """simple docstring"""



  _lowercase			,					_lowercase:      Union[str, Any]   			=  img.shape[0], img.shape[1]

  # converting each pixel's color to its negative
  for i in range(_UpperCamelCase    ):
    for j in range(_UpperCamelCase    ):
      _lowercase:      Dict   			=  [255, 255, 255] - img[i][j]

  return img


if __name__ == "__main__":
  # read original image
  A__ :       Optional[Any]				= imread('image_data/lena.jpg', 1)

  # convert to its negative
  A__ :       Dict				= convert_to_negative(img)

  # show result image
  imshow('negative of original image', img)
  waitKey(0)
  destroyAllWindows()
