import importlib

import torch
import yaml
from omegaconf import OmegaConf
from taming.models.vqgan import VQModel




def   lowerCAmelCase_					(	__A,    __A=False				)	->							Union[str, Any]:







      '''simple docstring'''


      UpperCAmelCase__								=    OmegaConf.load(a__				)
      if display:
            print(yaml.dump(OmegaConf.to_container(a__				)				)				)
      return config




def   lowerCAmelCase_					(	__A,    __A=None,    __A=None				)	->							Optional[int]:







      '''simple docstring'''


      if conf_path is None:
            UpperCAmelCase__								=    "./model_checkpoints/vqgan_only.yaml"
      UpperCAmelCase__								=    load_config(a__,    display=a__				)
      UpperCAmelCase__								=    VQModel(**config.model.params				)
      if ckpt_path is None:
            UpperCAmelCase__								=    "./model_checkpoints/vqgan_only.pt"
      UpperCAmelCase__								=    torch.load(a__,    map_location=a__				)
      if ".ckpt" in ckpt_path:
            UpperCAmelCase__								=    sd["state_dict"]
      model.load_state_dict(a__,    strict=a__				)
      model.to(a__				)
      del sd
      return model




def   lowerCAmelCase_					(	__A,    __A				)	->							Optional[int]:







      '''simple docstring'''


      UpperCAmelCase__						, UpperCAmelCase__						, UpperCAmelCase__								=    model.encode(a__				)
      print(f"""VQGAN --- {model.__class__.__name__}: latent shape: {z.shape[2:]}"""				)
      UpperCAmelCase__								=    model.decode(a__				)
      return xrec




def   lowerCAmelCase_					(	__A,    __A=False				)	->							int:







      '''simple docstring'''


      UpperCAmelCase__						, UpperCAmelCase__								=    string.rsplit(".",    1				)
      if reload:
            UpperCAmelCase__								=    importlib.import_module(a__				)
            importlib.reload(a__				)
      return getattr(importlib.import_module(a__,    package=a__				),    cls				)




def   lowerCAmelCase_					(	__A				)	->							Optional[Any]:







      '''simple docstring'''


      if "target" not in config:
            raise KeyError("Expected key `target` to instantiate."				)
      return get_obj_from_str(config["target"]				)(**config.get("params",    {}				)				)




def   lowerCAmelCase_					(	__A,    __A,    __A=True,    __A=True				)	->							Tuple:







      '''simple docstring'''


      UpperCAmelCase__								=    instantiate_from_config(a__				)
      if sd is not None:
            model.load_state_dict(a__				)
      if gpu:
            model.cuda()
      if eval_mode:
            model.eval()
      return {"model": model}







def   lowerCAmelCase_					(	__A,    __A,    __A,    __A				)	->							Union[str, Any]:







      '''simple docstring'''


      if ckpt:
            UpperCAmelCase__								=    torch.load(a__,    map_location="cpu"				)
            UpperCAmelCase__								=    pl_sd["global_step"]
            print(f"""loaded model from global step {global_step}."""				)
      else:
            UpperCAmelCase__								=    {"state_dict": None}
            UpperCAmelCase__								=    None
      UpperCAmelCase__								=    load_model_from_config(config.model,    pl_sd["state_dict"],    gpu=a__,    eval_mode=a__				)["model"]
      return model, global_step






'''simple docstring'''



import math
import sys

import cva
import numpy as np







def   a__			(    a__ ,       a__		):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE      = math.sqrt(a__		)
     __SCREAMING_SNAKE_CASE      = 1 / (sigma * math.sqrt(2 * math.pi		))
     return cons * np.exp(-((img / sigma) ** 2) * 0.5		)







def   a__			(    a__ ,       a__ ,       a__ ,       a__		):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE      = kernel_size // 2
     return img[x - half : x + half + 1, y - half : y + half + 1]







def   a__			(    a__ ,       a__		):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE      = np.zeros((kernel_size, kernel_size)		)
     for i in range(0 ,       a__		):
          for j in range(0 ,       a__		):
               __SCREAMING_SNAKE_CASE      = math.sqrt(
                   abs(i - kernel_size // 2		) ** 2 + abs(j - kernel_size // 2		) ** 2		)
     return vec_gaussian(a__ ,       a__		)







def   a__			(    a__ ,       a__ ,       a__ ,       a__ ,       ):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE      = np.zeros(img.shape		)
     __SCREAMING_SNAKE_CASE      = get_gauss_kernel(a__ ,       a__		)
     __SCREAMING_SNAKE_CASE  ,     __SCREAMING_SNAKE_CASE      = img.shape
     for i in range(kernel_size // 2 ,       size_x - kernel_size // 2		):
          for j in range(kernel_size // 2 ,       size_y - kernel_size // 2		):
               __SCREAMING_SNAKE_CASE      = get_slice(a__ ,       a__ ,       a__ ,       a__		)
               __SCREAMING_SNAKE_CASE      = img_s - img_s[kernel_size // 2, kernel_size // 2]
               __SCREAMING_SNAKE_CASE      = vec_gaussian(a__ ,       a__		)
               __SCREAMING_SNAKE_CASE      = np.multiply(a__ ,       a__		)
               __SCREAMING_SNAKE_CASE      = np.multiply(a__ ,       a__		)
               __SCREAMING_SNAKE_CASE      = np.sum(a__		) / np.sum(a__		)
               __SCREAMING_SNAKE_CASE      = val
     return imga







def   a__			(    a__		):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE      = args[1] if args[1:] else """../image_data/lena.jpg"""
     __SCREAMING_SNAKE_CASE      = float(args[2]		) if args[2:] else 1.0
     __SCREAMING_SNAKE_CASE      = float(args[3]		) if args[3:] else 1.0
     if args[4:]:
          __SCREAMING_SNAKE_CASE      = int(args[4]		)
          __SCREAMING_SNAKE_CASE      = kernel_size + abs(kernel_size % 2 - 1		)
     else:
          __SCREAMING_SNAKE_CASE      = 5
     return filename, spatial_variance, intensity_variance, kernel_size


if __name__ == "__main__":
  UpperCAmelCase  ,	UpperCAmelCase  ,	UpperCAmelCase  ,	UpperCAmelCase					:			Any       =	parse_args(sys.argv)
  UpperCAmelCase					:			Union[str, Any]       =	cva.imread(filename, 0)
  cva.imshow('input image', img)

  UpperCAmelCase					:			Optional[int]       =	img / 2_5_5
  UpperCAmelCase					:			Optional[Any]       =	out.astype('float32')
  UpperCAmelCase					:			Tuple       =	bilateral_filter(out, spatial_variance, intensity_variance, kernel_size)
  UpperCAmelCase					:			List[Any]       =	out * 2_5_5
  UpperCAmelCase					:			Any       =	np.uinta(out)
  cva.imshow('output image', out)
  cva.waitKey(0)
  cva.destroyAllWindows()






