




from typing import Union

import torch
from PIL import Image
from torchvision import transforms as tfms
from tqdm.auto import tqdm
from transformers import CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)

class 	_lowerCamelCase  (     _SCREAMING_SNAKE_CASE				):

      """simple docstring"""

      def __init__(       self   :	Tuple	,    snake_case   :	AutoencoderKL	,    snake_case   :	CLIPTextModel	,    snake_case   :	CLIPTokenizer	,    snake_case   :	UNetaDConditionModel	,    snake_case   :	Union[PNDMScheduler, LMSDiscreteScheduler, DDIMScheduler]	,    ):
          super().__init__()

          self.register_modules(vae=snake_case	,    text_encoder=snake_case	,    tokenizer=snake_case	,    unet=snake_case	,    scheduler=snake_case			)







      def  snake_case		(       self   :	Any	,    snake_case   :	Optional[int]			):
          with torch.no_grad():
              __UpperCamelCase											=  self.vae.encode(tfms.ToTensor()(snake_case			).unsqueeze(0			).to(self.device			) * 2 - 1			)
              __UpperCamelCase											=  0.1_82_15 * latent.latent_dist.sample()
          return latent







      def  snake_case		(       self   :	int	,    snake_case   :	Any			):
          __UpperCamelCase											=  (1 / 0.1_82_15) * latent
          with torch.no_grad():
              __UpperCamelCase											=  self.vae.decode(snake_case			).sample
          __UpperCamelCase											=  (img / 2 + 0.5).clamp(0	,    1			)
          __UpperCamelCase											=  img.detach().cpu().permute(0	,    2	,    3	,    1			).numpy()
          __UpperCamelCase											=  (img * 255).round().astype('''uint8'''			)
          return Image.fromarray(img[0]			)







      def  snake_case		(       self   :	Any	,    snake_case   :	str			):
          __UpperCamelCase											=  self.tokenizer(
              snake_case	,    padding='''max_length'''	,    max_length=self.tokenizer.model_max_length	,    truncation=snake_case	,    return_tensors='''pt'''	,    )

          __UpperCamelCase											=  self.text_encoder(text_input.input_ids.to(self.device			)			)[0]

          __UpperCamelCase											=  self.tokenizer(
              ''''''	,    padding='''max_length'''	,    max_length=self.tokenizer.model_max_length	,    truncation=snake_case	,    return_tensors='''pt'''	,    )

          __UpperCamelCase											=  self.text_encoder(uncond_input.input_ids.to(self.device			)			)[0]

          return torch.cat([uncond_embedding, text_embedding]			)







      def __call__(       self   :	int	,    snake_case   :	Image.Image	,    snake_case   :	str	,    snake_case   :	float = 0.3	,    snake_case   :	float = 0.6	,    snake_case   :	float = 0.5	,    snake_case   :	int = 42	,    snake_case   :	int = 50	,    snake_case   :	float = 7.5	,    ):
          __UpperCamelCase											=  steps - int(kmin * steps			)
          __UpperCamelCase											=  steps - int(kmax * steps			)

          __UpperCamelCase											=  self.prep_text(snake_case			)

          self.scheduler.set_timesteps(snake_case			)

          __UpperCamelCase						, __UpperCamelCase											=  img.size
          __UpperCamelCase											=  self.encode(snake_case			)

          torch.manual_seed(snake_case			)
          __UpperCamelCase											=  torch.randn(
              (1, self.unet.config.in_channels, height // 8, width // 8)	,    ).to(self.device			)

          __UpperCamelCase											=  self.scheduler.add_noise(
              snake_case	,    snake_case	,    timesteps=self.scheduler.timesteps[tmax]	,    )

          __UpperCamelCase											=  torch.cat([latents] * 2			)

          __UpperCamelCase											=  self.scheduler.scale_model_input(snake_case	,    self.scheduler.timesteps[tmax]			)

          with torch.no_grad():
              __UpperCamelCase											=  self.unet(
                  snake_case	,    self.scheduler.timesteps[tmax]	,    encoder_hidden_states=snake_case	,    ).sample

          __UpperCamelCase						, __UpperCamelCase											=  pred.chunk(2			)
          __UpperCamelCase											=  pred_uncond + guidance_scale * (pred_text - pred_uncond)

          __UpperCamelCase											=  self.scheduler.step(snake_case	,    self.scheduler.timesteps[tmax]	,    snake_case			).prev_sample

          for i, t in enumerate(tqdm(self.scheduler.timesteps			)			):
              if i > tmax:
                  if i < tmin:  # layout generation phase
                      __UpperCamelCase											=  self.scheduler.add_noise(
                          snake_case	,    snake_case	,    timesteps=snake_case	,    )

                      __UpperCamelCase											=  (mix_factor * latents) + (
                          1 - mix_factor
                      ) * orig_latents  # interpolating between layout noise and conditionally generated noise to preserve layout sematics
                      __UpperCamelCase											=  torch.cat([input] * 2			)

                  else:  # content generation phase
                      __UpperCamelCase											=  torch.cat([latents] * 2			)

                  __UpperCamelCase											=  self.scheduler.scale_model_input(snake_case	,    snake_case			)

                  with torch.no_grad():
                      __UpperCamelCase											=  self.unet(
                          snake_case	,    snake_case	,    encoder_hidden_states=snake_case	,    ).sample

                  __UpperCamelCase						, __UpperCamelCase											=  pred.chunk(2			)
                  __UpperCamelCase											=  pred_uncond + guidance_scale * (pred_text - pred_uncond)

                  __UpperCamelCase											=  self.scheduler.step(snake_case	,    snake_case	,    snake_case			).prev_sample

          return self.decode(snake_case			)












import cva
import numpy as np




def    __SCREAMING_SNAKE_CASE				(					lowercase_   ,       lowercase_   ,       lowercase_   ,       lowercase_ )    ->		int:

    """simple docstring"""


    try:
        return int(image[x_coordinate][y_coordinate] >= center )
    except (IndexError, TypeError):
        return 0




def    __SCREAMING_SNAKE_CASE				(					lowercase_   ,       lowercase_   ,       lowercase_ )    ->		int:

    """simple docstring"""


    __UpperCamelCase											=  image[x_coordinate][y_coordinate]
    __UpperCamelCase											=  [1, 2, 4, 8, 16, 32, 64, 1_28]

    # skip get_neighbors_pixel if center is null
    if center is None:
        return 0

    # Starting from the top right, assigning value to pixels clockwise
    __UpperCamelCase											=  [
        get_neighbors_pixel(lowercase_   ,       x_coordinate - 1   ,       y_coordinate + 1   ,       lowercase_ ),
        get_neighbors_pixel(lowercase_   ,       lowercase_   ,       y_coordinate + 1   ,       lowercase_ ),
        get_neighbors_pixel(lowercase_   ,       x_coordinate - 1   ,       lowercase_   ,       lowercase_ ),
        get_neighbors_pixel(lowercase_   ,       x_coordinate + 1   ,       y_coordinate + 1   ,       lowercase_ ),
        get_neighbors_pixel(lowercase_   ,       x_coordinate + 1   ,       lowercase_   ,       lowercase_ ),
        get_neighbors_pixel(lowercase_   ,       x_coordinate + 1   ,       y_coordinate - 1   ,       lowercase_ ),
        get_neighbors_pixel(lowercase_   ,       lowercase_   ,       y_coordinate - 1   ,       lowercase_ ),
        get_neighbors_pixel(lowercase_   ,       x_coordinate - 1   ,       y_coordinate - 1   ,       lowercase_ ),
    ]

    # Converting the binary value to decimal.
    return sum(
        binary_value * power for binary_value, power in zip(lowercase_   ,       lowercase_ ) )


if __name__ == "__main__":
 # Reading the image and converting it to grayscale.
 a_										=       cva.imread(
     "digital_image_processing/image_data/lena.jpg", cva.IMREAD_GRAYSCALE
 )

 # Create a numpy array as the same height and width of read image
 a_										=       np.zeros((image.shape[0], image.shape[1]))

 # Iterating through the image and calculating the
 # local binary pattern value for each pixel.
 for i in range(0, image.shape[0]):
  for j in range(0, image.shape[1]):
   a_										=       local_binary_value(image, i, j)

 cva.imshow("local binary pattern", lbp_image)
 cva.waitKey(0)
 cva.destroyAllWindows()






