


'''simple docstring'''




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


_a      :      Union[str, Any]       				=      logging.get_logger(__name__)  # pylint: disable=invalid-name




class  _lowercase   (  __lowercase       ):
   _SCREAMING_SNAKE_CASE     :     Optional[Any]									=  ["safety_checker", "feature_extractor"]
   def __init__(	self					:			List[Any]					,      SCREAMING_SNAKE_CASE_					:			AutoencoderKL					,      SCREAMING_SNAKE_CASE_					:			CLIPTextModel					,      SCREAMING_SNAKE_CASE_					:			CLIPTokenizer					,      SCREAMING_SNAKE_CASE_					:			UNetaDConditionModel					,      SCREAMING_SNAKE_CASE_					:			Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler]					,      SCREAMING_SNAKE_CASE_					:			StableDiffusionSafetyChecker					,      SCREAMING_SNAKE_CASE_					:			CLIPImageProcessor					,      SCREAMING_SNAKE_CASE_					:			bool = True					,      )   ->       List[str]:
        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                 f'The configuration file of this scheduler: {scheduler} is outdated.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'					,      SCREAMING_SNAKE_CASE_					,      standard_warn=SCREAMING_SNAKE_CASE_			)
             __snake_case    			=	dict(scheduler.config			)
             __snake_case    			=	1
             __snake_case    			=	FrozenDict(SCREAMING_SNAKE_CASE_			)

        self.register_modules(
            vae=SCREAMING_SNAKE_CASE_					,      text_encoder=SCREAMING_SNAKE_CASE_					,      tokenizer=SCREAMING_SNAKE_CASE_					,      unet=SCREAMING_SNAKE_CASE_					,      scheduler=SCREAMING_SNAKE_CASE_					,      safety_checker=SCREAMING_SNAKE_CASE_					,      feature_extractor=SCREAMING_SNAKE_CASE_					,      )
        self.register_to_config(requires_safety_checker=SCREAMING_SNAKE_CASE_			)
   @property
   def a     (	self					:			List[str]			)   ->       Dict[str, Any]:
        return {k: getattr(self					,      SCREAMING_SNAKE_CASE_			) for k in self.config.keys() if not k.startswith('_'			)}
   def a     (	self					:			Union[str, Any]					,      SCREAMING_SNAKE_CASE_					:			Optional[Union[str, int]] = "auto"			)   ->       Union[str, Any]:
        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(SCREAMING_SNAKE_CASE_			)
   def a     (	self					:			Dict			)   ->       List[str]:
        self.enable_attention_slicing(SCREAMING_SNAKE_CASE_			)
   @torch.no_grad()
   def a     (	self					:			List[str]					,      SCREAMING_SNAKE_CASE_					:			Union[str, List[str]]					,      SCREAMING_SNAKE_CASE_					:			Union[torch.FloatTensor, PIL.Image.Image]					,      SCREAMING_SNAKE_CASE_					:			Union[torch.FloatTensor, PIL.Image.Image]					,      SCREAMING_SNAKE_CASE_					:			float = 0.8					,      SCREAMING_SNAKE_CASE_					:			Optional[int] = 50					,      SCREAMING_SNAKE_CASE_					:			Optional[float] = 7.5					,      SCREAMING_SNAKE_CASE_					:			Optional[Union[str, List[str]]] = None					,      SCREAMING_SNAKE_CASE_					:			Optional[int] = 1					,      SCREAMING_SNAKE_CASE_					:			Optional[float] = 0.0					,      SCREAMING_SNAKE_CASE_					:			Optional[torch.Generator] = None					,      SCREAMING_SNAKE_CASE_					:			Optional[str] = "pil"					,      SCREAMING_SNAKE_CASE_					:			bool = True					,      SCREAMING_SNAKE_CASE_					:			Optional[Callable[[int, int, torch.FloatTensor], None]] = None					,      SCREAMING_SNAKE_CASE_					:			int = 1					,      )   ->       Optional[int]:
        # 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=SCREAMING_SNAKE_CASE_					,      image=SCREAMING_SNAKE_CASE_					,      mask_image=SCREAMING_SNAKE_CASE_					,      strength=SCREAMING_SNAKE_CASE_					,      num_inference_steps=SCREAMING_SNAKE_CASE_					,      guidance_scale=SCREAMING_SNAKE_CASE_					,      negative_prompt=SCREAMING_SNAKE_CASE_					,      num_images_per_prompt=SCREAMING_SNAKE_CASE_					,      eta=SCREAMING_SNAKE_CASE_					,      generator=SCREAMING_SNAKE_CASE_					,      output_type=SCREAMING_SNAKE_CASE_					,      return_dict=SCREAMING_SNAKE_CASE_					,      callback=SCREAMING_SNAKE_CASE_					,      )
   @torch.no_grad()
   def a     (	self					:			Union[str, Any]					,      SCREAMING_SNAKE_CASE_					:			Union[str, List[str]]					,      SCREAMING_SNAKE_CASE_					:			Union[torch.FloatTensor, PIL.Image.Image]					,      SCREAMING_SNAKE_CASE_					:			float = 0.8					,      SCREAMING_SNAKE_CASE_					:			Optional[int] = 50					,      SCREAMING_SNAKE_CASE_					:			Optional[float] = 7.5					,      SCREAMING_SNAKE_CASE_					:			Optional[Union[str, List[str]]] = None					,      SCREAMING_SNAKE_CASE_					:			Optional[int] = 1					,      SCREAMING_SNAKE_CASE_					:			Optional[float] = 0.0					,      SCREAMING_SNAKE_CASE_					:			Optional[torch.Generator] = None					,      SCREAMING_SNAKE_CASE_					:			Optional[str] = "pil"					,      SCREAMING_SNAKE_CASE_					:			bool = True					,      SCREAMING_SNAKE_CASE_					:			Optional[Callable[[int, int, torch.FloatTensor], None]] = None					,      SCREAMING_SNAKE_CASE_					:			int = 1					,      **SCREAMING_SNAKE_CASE_					:			Union[str, Any]					,      )   ->       str:
        # 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=SCREAMING_SNAKE_CASE_					,      image=SCREAMING_SNAKE_CASE_					,      strength=SCREAMING_SNAKE_CASE_					,      num_inference_steps=SCREAMING_SNAKE_CASE_					,      guidance_scale=SCREAMING_SNAKE_CASE_					,      negative_prompt=SCREAMING_SNAKE_CASE_					,      num_images_per_prompt=SCREAMING_SNAKE_CASE_					,      eta=SCREAMING_SNAKE_CASE_					,      generator=SCREAMING_SNAKE_CASE_					,      output_type=SCREAMING_SNAKE_CASE_					,      return_dict=SCREAMING_SNAKE_CASE_					,      callback=SCREAMING_SNAKE_CASE_					,      callback_steps=SCREAMING_SNAKE_CASE_					,      )





   @torch.no_grad()
   def a     (	self					:			List[Any]					,      SCREAMING_SNAKE_CASE_					:			Union[str, List[str]]					,      SCREAMING_SNAKE_CASE_					:			int = 512					,      SCREAMING_SNAKE_CASE_					:			int = 512					,      SCREAMING_SNAKE_CASE_					:			int = 50					,      SCREAMING_SNAKE_CASE_					:			float = 7.5					,      SCREAMING_SNAKE_CASE_					:			Optional[Union[str, List[str]]] = None					,      SCREAMING_SNAKE_CASE_					:			Optional[int] = 1					,      SCREAMING_SNAKE_CASE_					:			float = 0.0					,      SCREAMING_SNAKE_CASE_					:			Optional[torch.Generator] = None					,      SCREAMING_SNAKE_CASE_					:			Optional[torch.FloatTensor] = None					,      SCREAMING_SNAKE_CASE_					:			Optional[str] = "pil"					,      SCREAMING_SNAKE_CASE_					:			bool = True					,      SCREAMING_SNAKE_CASE_					:			Optional[Callable[[int, int, torch.FloatTensor], None]] = None					,      SCREAMING_SNAKE_CASE_					:			int = 1					,      )   ->       str:
        # For more information on how this function https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion#diffusers.StableDiffusionPipeline
        return StableDiffusionPipeline(**self.components			)(
            prompt=SCREAMING_SNAKE_CASE_					,      height=SCREAMING_SNAKE_CASE_					,      width=SCREAMING_SNAKE_CASE_					,      num_inference_steps=SCREAMING_SNAKE_CASE_					,      guidance_scale=SCREAMING_SNAKE_CASE_					,      negative_prompt=SCREAMING_SNAKE_CASE_					,      num_images_per_prompt=SCREAMING_SNAKE_CASE_					,      eta=SCREAMING_SNAKE_CASE_					,      generator=SCREAMING_SNAKE_CASE_					,      latents=SCREAMING_SNAKE_CASE_					,      output_type=SCREAMING_SNAKE_CASE_					,      return_dict=SCREAMING_SNAKE_CASE_					,      callback=SCREAMING_SNAKE_CASE_					,      callback_steps=SCREAMING_SNAKE_CASE_					,      )








'''simple docstring'''




import numpy as np
from PIL import Image





def      _a		(lowercase__ :      np.array	) -> np.array:






     """simple docstring"""
     __snake_case			,					__snake_case			,					__snake_case    			=	rgb[:, :, 0], rgb[:, :, 1], rgb[:, :, 2]
     return 0.29_89 * r + 0.58_70 * g + 0.11_40 * b





def      _a		(lowercase__ :      np.array	) -> np.array:






     """simple docstring"""
     return (gray > 1_2_7) & (gray <= 2_5_5)





def      _a		(lowercase__ :      np.array       ,      lowercase__ :      np.array	) -> np.array:






     """simple docstring"""
     __snake_case    			=	np.zeros_like(lowercase__	)
     __snake_case    			=	np.zeros(
         (image.shape[0] + kernel.shape[0] - 1, image.shape[1] + kernel.shape[1] - 1)	)

     # Copy image to padded image
     __snake_case    			=	image

     # Iterate over image & apply kernel
     for x in range(image.shape[1]	):
          for y in range(image.shape[0]	):
               __snake_case    			=	(
                   kernel * image_padded[y : y + kernel.shape[0], x : x + kernel.shape[1]]
               ).sum()
               __snake_case    			=	int(summation == 5	)
     return output


# kernel to be applied
_a      :      Tuple       				=      np.array([[0, 1, 0], [1, 1, 1], [0, 1, 0]])

if __name__ == "__main__":
       # read original image
       _a      :      Any       				=      np.array(Image.open(R"..\image_data\lena.jpg"))
       # Apply erosion operation to a binary image
       _a      :      List[Any]       				=      erosion(grayabinary(rgbagray(image)), structuring_element)
       # Save the output image
       _a      :      int       				=      Image.fromarray(output).convert("RGB")
       pil_img.save("result_erosion.png")




