'''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 _lowerCAmelCase			(	snake_case					):
      UpperCAmelCase_	:						Optional[Any]	 =       ["safety_checker", "feature_extractor"]







      def __init__(  self  ,    lowercase  ,    lowercase  ,    lowercase  ,    lowercase  ,    lowercase  ,    lowercase  ,    lowercase  ,    lowercase = True  ,    ):
        super().__init__()
        if hasattr(scheduler.config  ,    '''steps_offset'''     ) and scheduler.config.steps_offset != 1:
          lowercase				:							List[str]									=   (
              F"""The configuration file of this scheduler: {scheduler} is outdated.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'''  ,    lowercase  ,    standard_warn=lowercase     )
          lowercase				:							List[str]									=   dict(scheduler.config     )
          lowercase				:							Dict									=   1
          lowercase				:							List[Any]									=   FrozenDict(lowercase     )

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







      @property
      def      lowerCamelCase__      (  self     ):
        return {k: getattr(self  ,    lowercase     ) for k in self.config.keys() if not k.startswith('''_'''     )}







      def      lowerCamelCase__      (  self  ,    lowercase = "auto"     ):
        if slice_size == "auto":
          # half the attention head size is usually a good trade-off between
          # speed and memory
          lowercase				:							Optional[Any]									=   self.unet.config.attention_head_dim // 2
        self.unet.set_attention_slice(lowercase     )







      def      lowerCamelCase__      (  self     ):
        self.enable_attention_slicing(lowercase     )







      @torch.no_grad()
      def      lowerCamelCase__      (  self  ,    lowercase  ,    lowercase  ,    lowercase  ,    lowercase = 0.8  ,    lowercase = 50  ,    lowercase = 7.5  ,    lowercase = None  ,    lowercase = 1  ,    lowercase = 0.0  ,    lowercase = None  ,    lowercase = "pil"  ,    lowercase = True  ,    lowercase = None  ,    lowercase = 1  ,    ):
        # 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=lowercase  ,    image=lowercase  ,    mask_image=lowercase  ,    strength=lowercase  ,    num_inference_steps=lowercase  ,    guidance_scale=lowercase  ,    negative_prompt=lowercase  ,    num_images_per_prompt=lowercase  ,    eta=lowercase  ,    generator=lowercase  ,    output_type=lowercase  ,    return_dict=lowercase  ,    callback=lowercase  ,    )







      @torch.no_grad()
      def      lowerCamelCase__      (  self  ,    lowercase  ,    lowercase  ,    lowercase = 0.8  ,    lowercase = 50  ,    lowercase = 7.5  ,    lowercase = None  ,    lowercase = 1  ,    lowercase = 0.0  ,    lowercase = None  ,    lowercase = "pil"  ,    lowercase = True  ,    lowercase = None  ,    lowercase = 1  ,    **lowercase  ,    ):
        # 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=lowercase  ,    image=lowercase  ,    strength=lowercase  ,    num_inference_steps=lowercase  ,    guidance_scale=lowercase  ,    negative_prompt=lowercase  ,    num_images_per_prompt=lowercase  ,    eta=lowercase  ,    generator=lowercase  ,    output_type=lowercase  ,    return_dict=lowercase  ,    callback=lowercase  ,    callback_steps=lowercase  ,    )







      @torch.no_grad()
      def      lowerCamelCase__      (  self  ,    lowercase  ,    lowercase = 512  ,    lowercase = 512  ,    lowercase = 50  ,    lowercase = 7.5  ,    lowercase = None  ,    lowercase = 1  ,    lowercase = 0.0  ,    lowercase = None  ,    lowercase = None  ,    lowercase = "pil"  ,    lowercase = True  ,    lowercase = None  ,    lowercase = 1  ,    ):
        # For more information on how this function https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion#diffusers.StableDiffusionPipeline
        return StableDiffusionPipeline(**self.components     )(
            prompt=lowercase  ,    height=lowercase  ,    width=lowercase  ,    num_inference_steps=lowercase  ,    guidance_scale=lowercase  ,    negative_prompt=lowercase  ,    num_images_per_prompt=lowercase  ,    eta=lowercase  ,    generator=lowercase  ,    latents=lowercase  ,    output_type=lowercase  ,    return_dict=lowercase  ,    callback=lowercase  ,    callback_steps=lowercase  ,    )
'''simple docstring'''
import numpy as np
from PIL import Image







def 				__UpperCamelCase (					__UpperCAmelCase  :					np.array			)							->	np.array:
  lowercase				,      lowercase				,      lowercase				:							Dict									=   rgb[:, :, 0], rgb[:, :, 1], rgb[:, :, 2]
  return 0.2989 * r + 0.5870 * g + 0.1140 * b







def 				__UpperCamelCase (					__UpperCAmelCase  :					np.array			)							->	np.array:
  return (gray > 127) & (gray <= 255)







def 				__UpperCamelCase (					__UpperCAmelCase  :					np.array ,   __UpperCAmelCase  :					np.array			)							->	np.array:
  lowercase				:							str									=   np.zeros_like(__UpperCAmelCase			)
  lowercase				:							Tuple									=   np.zeros(
      (image.shape[0] + kernel.shape[0] - 1, image.shape[1] + kernel.shape[1] - 1)			)

  # Copy image to padded image
  lowercase				:							int									=   image

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


# kernel to be applied
_A		:      Union[str, Any]						   =np.array([[0, 1, 0], [1, 1, 1], [0, 1, 0]])

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