


from typing import Any, Callable, Dict, List, Optional, Union

import PIL.Image
import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionImgaImgPipeline,
    StableDiffusionInpaintPipelineLegacy,
    StableDiffusionPipeline,
    UNetaDConditionModel,
)
from diffusers.configuration_utils import FrozenDict
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.utils import deprecate, logging


SCREAMING_SNAKE_CASE		:List[Any]											=       logging.get_logger(__name__)  # pylint: disable=invalid-name


class       __lowerCAmelCase       (	a   ):






  """simple docstring"""





  _SCREAMING_SNAKE_CASE						      =    ['safety_checker', 'feature_extractor']



  def __init__(       self    :    Dict      ,			_lowerCAmelCase    :    AutoencoderKL      ,			_lowerCAmelCase    :    CLIPTextModel      ,			_lowerCAmelCase    :    CLIPTokenizer      ,			_lowerCAmelCase    :    UNetaDConditionModel      ,			_lowerCAmelCase    :    Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler]      ,			_lowerCAmelCase    :    StableDiffusionSafetyChecker      ,			_lowerCAmelCase    :    CLIPImageProcessor      ,			_lowerCAmelCase    :    bool = True      ,			)				->	Dict:




     """simple docstring"""
     super().__init__()
     if hasattr(scheduler.config      ,			"steps_offset"					) and scheduler.config.steps_offset != 1:
        snake_case_         =   (
            F'''The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`'''
            F''' should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure '''
            "to update the config accordingly as leaving `steps_offset` might led to incorrect results"
            " in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,"
            " it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`"
            " file"
        )
        deprecate("steps_offset!=1"      ,			"1.0.0"      ,			_lowerCAmelCase      ,			standard_warn=_lowerCAmelCase					)
        snake_case_         =   dict(scheduler.config					)
        snake_case_         =   1
        snake_case_         =   FrozenDict(_lowerCAmelCase					)

     self.register_modules(
         vae=_lowerCAmelCase      ,			text_encoder=_lowerCAmelCase      ,			tokenizer=_lowerCAmelCase      ,			unet=_lowerCAmelCase      ,			scheduler=_lowerCAmelCase      ,			safety_checker=_lowerCAmelCase      ,			feature_extractor=_lowerCAmelCase      ,			)
     self.register_to_config(requires_safety_checker=_lowerCAmelCase					)



  @property
  def    lowerCAmelCase__					(       self    :    Tuple					)				->	Dict[str, Any]:




     """simple docstring"""
     return {k: getattr(self      ,			_lowerCAmelCase					) for k in self.config.keys() if not k.startswith("_"					)}



  def    lowerCAmelCase__					(       self    :    List[str]      ,			_lowerCAmelCase    :    Optional[Union[str, int]] = "auto"					)				->	List[str]:




     """simple docstring"""
     if slice_size == "auto":
        # half the attention head size is usually a good trade-off between
        # speed and memory
        snake_case_         =   self.unet.config.attention_head_dim // 2
     self.unet.set_attention_slice(_lowerCAmelCase					)



  def    lowerCAmelCase__					(       self    :    Dict					)				->	Union[str, Any]:




     """simple docstring"""
     self.enable_attention_slicing(_lowerCAmelCase					)



  @torch.no_grad()
  def    lowerCAmelCase__					(       self    :    List[str]      ,			_lowerCAmelCase    :    Union[str, List[str]]      ,			_lowerCAmelCase    :    Union[torch.FloatTensor, PIL.Image.Image]      ,			_lowerCAmelCase    :    Union[torch.FloatTensor, PIL.Image.Image]      ,			_lowerCAmelCase    :    float = 0.8      ,			_lowerCAmelCase    :    Optional[int] = 5_0      ,			_lowerCAmelCase    :    Optional[float] = 7.5      ,			_lowerCAmelCase    :    Optional[Union[str, List[str]]] = None      ,			_lowerCAmelCase    :    Optional[int] = 1      ,			_lowerCAmelCase    :    Optional[float] = 0.0      ,			_lowerCAmelCase    :    Optional[torch.Generator] = None      ,			_lowerCAmelCase    :    Optional[str] = "pil"      ,			_lowerCAmelCase    :    bool = True      ,			_lowerCAmelCase    :    Optional[Callable[[int, int, torch.FloatTensor], None]] = None      ,			_lowerCAmelCase    :    int = 1      ,			)				->	Optional[int]:




     """simple docstring"""
     # For more information on how this function works, please see: https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion#diffusers.StableDiffusionImg2ImgPipeline
     return StableDiffusionInpaintPipelineLegacy(**self.components					)(
         prompt=_lowerCAmelCase      ,			image=_lowerCAmelCase      ,			mask_image=_lowerCAmelCase      ,			strength=_lowerCAmelCase      ,			num_inference_steps=_lowerCAmelCase      ,			guidance_scale=_lowerCAmelCase      ,			negative_prompt=_lowerCAmelCase      ,			num_images_per_prompt=_lowerCAmelCase      ,			eta=_lowerCAmelCase      ,			generator=_lowerCAmelCase      ,			output_type=_lowerCAmelCase      ,			return_dict=_lowerCAmelCase      ,			callback=_lowerCAmelCase      ,			)



  @torch.no_grad()
  def    lowerCAmelCase__					(       self    :    Union[str, Any]      ,			_lowerCAmelCase    :    Union[str, List[str]]      ,			_lowerCAmelCase    :    Union[torch.FloatTensor, PIL.Image.Image]      ,			_lowerCAmelCase    :    float = 0.8      ,			_lowerCAmelCase    :    Optional[int] = 5_0      ,			_lowerCAmelCase    :    Optional[float] = 7.5      ,			_lowerCAmelCase    :    Optional[Union[str, List[str]]] = None      ,			_lowerCAmelCase    :    Optional[int] = 1      ,			_lowerCAmelCase    :    Optional[float] = 0.0      ,			_lowerCAmelCase    :    Optional[torch.Generator] = None      ,			_lowerCAmelCase    :    Optional[str] = "pil"      ,			_lowerCAmelCase    :    bool = True      ,			_lowerCAmelCase    :    Optional[Callable[[int, int, torch.FloatTensor], None]] = None      ,			_lowerCAmelCase    :    int = 1      ,			**_lowerCAmelCase    :    Optional[int]      ,			)				->	Any:




     """simple docstring"""
     # For more information on how this function works, please see: https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion#diffusers.StableDiffusionImg2ImgPipeline
     return StableDiffusionImgaImgPipeline(**self.components					)(
         prompt=_lowerCAmelCase      ,			image=_lowerCAmelCase      ,			strength=_lowerCAmelCase      ,			num_inference_steps=_lowerCAmelCase      ,			guidance_scale=_lowerCAmelCase      ,			negative_prompt=_lowerCAmelCase      ,			num_images_per_prompt=_lowerCAmelCase      ,			eta=_lowerCAmelCase      ,			generator=_lowerCAmelCase      ,			output_type=_lowerCAmelCase      ,			return_dict=_lowerCAmelCase      ,			callback=_lowerCAmelCase      ,			callback_steps=_lowerCAmelCase      ,			)



  @torch.no_grad()
  def    lowerCAmelCase__					(       self    :    Tuple      ,			_lowerCAmelCase    :    Union[str, List[str]]      ,			_lowerCAmelCase    :    int = 5_1_2      ,			_lowerCAmelCase    :    int = 5_1_2      ,			_lowerCAmelCase    :    int = 5_0      ,			_lowerCAmelCase    :    float = 7.5      ,			_lowerCAmelCase    :    Optional[Union[str, List[str]]] = None      ,			_lowerCAmelCase    :    Optional[int] = 1      ,			_lowerCAmelCase    :    float = 0.0      ,			_lowerCAmelCase    :    Optional[torch.Generator] = None      ,			_lowerCAmelCase    :    Optional[torch.FloatTensor] = None      ,			_lowerCAmelCase    :    Optional[str] = "pil"      ,			_lowerCAmelCase    :    bool = True      ,			_lowerCAmelCase    :    Optional[Callable[[int, int, torch.FloatTensor], None]] = None      ,			_lowerCAmelCase    :    int = 1      ,			)				->	Tuple:




     """simple docstring"""
     # For more information on how this function https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion#diffusers.StableDiffusionPipeline
     return StableDiffusionPipeline(**self.components					)(
         prompt=_lowerCAmelCase      ,			height=_lowerCAmelCase      ,			width=_lowerCAmelCase      ,			num_inference_steps=_lowerCAmelCase      ,			guidance_scale=_lowerCAmelCase      ,			negative_prompt=_lowerCAmelCase      ,			num_images_per_prompt=_lowerCAmelCase      ,			eta=_lowerCAmelCase      ,			generator=_lowerCAmelCase      ,			latents=_lowerCAmelCase      ,			output_type=_lowerCAmelCase      ,			return_dict=_lowerCAmelCase      ,			callback=_lowerCAmelCase      ,			callback_steps=_lowerCAmelCase      ,			)








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


class       __lowerCAmelCase       :






  """simple docstring"""





  def __init__(       self    :    Optional[Any]      ,			_lowerCAmelCase    :    Union[str, Any]      ,			_lowerCAmelCase    :    int					)				->	Tuple:




     """simple docstring"""
     snake_case_         =   0
     # max greyscale value for #FFFFFF
     snake_case_         =   int(self.get_greyscale(2_5_5      ,			2_5_5      ,			2_5_5					)					)

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

     snake_case_         =   input_img
     snake_case_         =   threshold
     snake_case_      ,      snake_case_         =   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
     snake_case_         =   [
         [0 for _ in range(self.height + 4					)] for __ in range(self.width + 1					)
     ]
     snake_case_         =   np.ones((self.width, self.height, 3)      ,			np.uinta					) * 2_5_5



  @classmethod
  def    lowerCAmelCase__					(       cls    :    Optional[Any]      ,			_lowerCAmelCase    :    int      ,			_lowerCAmelCase    :    int      ,			_lowerCAmelCase    :    int					)				->	float:




     """simple docstring"""
     return 0.114 * blue + 0.587 * green + 0.2_126 * red



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




     """simple docstring"""
     for y in range(self.height					):
        for x in range(self.width					):
           snake_case_         =   int(self.get_greyscale(*self.input_img[y][x]					)					)
           if self.threshold > greyscale + self.error_table[y][x]:
              snake_case_         =   (0, 0, 0)
              snake_case_         =   greyscale + self.error_table[x][y]
           else:
              snake_case_         =   (2_5_5, 2_5_5, 2_5_5)
              snake_case_         =   greyscale + self.error_table[x][y] - 2_5_5
           self.error_table[y][x + 1] += int(8 / 3_2 * current_error					)
           self.error_table[y][x + 2] += int(4 / 3_2 * current_error					)
           self.error_table[y + 1][x] += int(8 / 3_2 * current_error					)
           self.error_table[y + 1][x + 1] += int(4 / 3_2 * current_error					)
           self.error_table[y + 1][x + 2] += int(2 / 3_2 * current_error					)
           self.error_table[y + 1][x - 1] += int(4 / 3_2 * current_error					)
           self.error_table[y + 1][x - 2] += int(2 / 3_2 * current_error					)


if __name__ == "__main__":
   # create Burke's instances with original images in greyscale
   SCREAMING_SNAKE_CASE		:Dict											=       [
       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()




