

"""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


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







class        SCREAMING_SNAKE_CASE      (	a_ ):






					"""simple docstring"""



					lowercase__       				= ["safety_checker", "feature_extractor"]







					def __init__( self    :       str     ,lowercase_    :       AutoencoderKL     ,lowercase_    :       CLIPTextModel     ,lowercase_    :       CLIPTokenizer     ,lowercase_    :       UNetaDConditionModel     ,lowercase_    :       Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler]     ,lowercase_    :       StableDiffusionSafetyChecker     ,lowercase_    :       CLIPImageProcessor     ,lowercase_    :       bool = True     ,):
						super().__init__()
						if hasattr(scheduler.config     ,'''steps_offset'''	) and scheduler.config.steps_offset != 1:
							lowerCAmelCase__			:		int						       =			(
							    F'The configuration file of this scheduler: {scheduler} is outdated.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_	)
							lowerCAmelCase__			:		Dict						       =			dict(scheduler.config	)
							lowerCAmelCase__			:		Optional[Any]						       =			1
							lowerCAmelCase__			:		int						       =			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    :       Optional[Any]	):
						return {k: getattr(self     ,lowercase_	) for k in self.config.keys() if not k.startswith('''_'''	)}







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







					def  __lowerCAmelCase				( self    :       List[str]	):
						self.enable_attention_slicing(lowercase_	)







					@torch.no_grad()
					def  __lowerCAmelCase				( self    :       Tuple     ,lowercase_    :       Union[str, List[str]]     ,lowercase_    :       Union[torch.FloatTensor, PIL.Image.Image]     ,lowercase_    :       Union[torch.FloatTensor, PIL.Image.Image]     ,lowercase_    :       float = 0.8     ,lowercase_    :       Optional[int] = 5_0     ,lowercase_    :       Optional[float] = 7.5     ,lowercase_    :       Optional[Union[str, List[str]]] = None     ,lowercase_    :       Optional[int] = 1     ,lowercase_    :       Optional[float] = 0.0     ,lowercase_    :       Optional[torch.Generator] = None     ,lowercase_    :       Optional[str] = "pil"     ,lowercase_    :       bool = True     ,lowercase_    :       Optional[Callable[[int, int, torch.FloatTensor], None]] = None     ,lowercase_    :       int = 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    :       Any     ,lowercase_    :       Union[str, List[str]]     ,lowercase_    :       Union[torch.FloatTensor, PIL.Image.Image]     ,lowercase_    :       float = 0.8     ,lowercase_    :       Optional[int] = 5_0     ,lowercase_    :       Optional[float] = 7.5     ,lowercase_    :       Optional[Union[str, List[str]]] = None     ,lowercase_    :       Optional[int] = 1     ,lowercase_    :       Optional[float] = 0.0     ,lowercase_    :       Optional[torch.Generator] = None     ,lowercase_    :       Optional[str] = "pil"     ,lowercase_    :       bool = True     ,lowercase_    :       Optional[Callable[[int, int, torch.FloatTensor], None]] = None     ,lowercase_    :       int = 1     ,**lowercase_    :       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=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    :       List[str]     ,lowercase_    :       Union[str, List[str]]     ,lowercase_    :       int = 5_1_2     ,lowercase_    :       int = 5_1_2     ,lowercase_    :       int = 5_0     ,lowercase_    :       float = 7.5     ,lowercase_    :       Optional[Union[str, List[str]]] = None     ,lowercase_    :       Optional[int] = 1     ,lowercase_    :       float = 0.0     ,lowercase_    :       Optional[torch.Generator] = None     ,lowercase_    :       Optional[torch.FloatTensor] = None     ,lowercase_    :       Optional[str] = "pil"     ,lowercase_    :       bool = True     ,lowercase_    :       Optional[Callable[[int, int, torch.FloatTensor], None]] = None     ,lowercase_    :       int = 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"""





from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
from numpy import divide, inta, multiply, ravel, sort, zeros_like

def 						__SCREAMING_SNAKE_CASE						(   A_		,  A_=3				):
	lowerCAmelCase__			:		Union[str, Any]						       =			int(mask / 2				)
	# copy image size
	lowerCAmelCase__			:		Dict						       =			zeros_like(A_				)
	for i in range(A_		,  gray_img.shape[0] - bd				):
		for j in range(A_		,  gray_img.shape[1] - bd				):
			# get mask according with mask
			lowerCAmelCase__			:		str						       =			ravel(gray_img[i - bd : i + bd + 1, j - bd : j + bd + 1]				)
			# calculate mask median
			lowerCAmelCase__			:		str						       =			sort(A_				)[inta(divide((multiply(A_		,  A_				))		,  2				) + 1				)]
			lowerCAmelCase__			:		List[str]						       =			median
	return median_img


if __name__ == "__main__":
							# read original image
							__UpperCamelCase						:   Dict							   =	imread('''../image_data/lena.jpg''')
							# turn image in gray scale value
							__UpperCamelCase						:   Optional[Any]							   =	cvtColor(img, COLOR_BGR2GRAY)

							# get values with two different mask size
							__UpperCamelCase						:   int							   =	median_filter(gray, 3)
							__UpperCamelCase						:   Tuple							   =	median_filter(gray, 5)

							# show result images
							imshow('''median filter with 3x3 mask''', medianaxa)
							imshow('''median filter with 5x5 mask''', medianaxa)
							waitKey(0)




