


'''simple docstring'''
import importlib

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





def    a__			(   lowerCAmelCase__    ,   lowerCAmelCase__=False		)       ->      List[str]:
    UpperCAmelCase__     :						str						=							OmegaConf.load(lowerCAmelCase__		)
    if display:
        print(yaml.dump(OmegaConf.to_container(lowerCAmelCase__		)		)		)
    return config





def    a__			(   lowerCAmelCase__    ,   lowerCAmelCase__=None    ,   lowerCAmelCase__=None		)       ->      Optional[Any]:
    if conf_path is None:
        UpperCAmelCase__     :						Dict						=							'''./model_checkpoints/vqgan_only.yaml'''
    UpperCAmelCase__     :						Optional[Any]						=							load_config(lowerCAmelCase__    ,   display=lowerCAmelCase__		)
    UpperCAmelCase__     :						int						=							VQModel(**config.model.params		)
    if ckpt_path is None:
        UpperCAmelCase__     :						List[str]						=							'''./model_checkpoints/vqgan_only.pt'''
    UpperCAmelCase__     :						Tuple						=							torch.load(lowerCAmelCase__    ,   map_location=lowerCAmelCase__		)
    if ".ckpt" in ckpt_path:
        UpperCAmelCase__     :						Tuple						=							sd['''state_dict''']
    model.load_state_dict(lowerCAmelCase__    ,   strict=lowerCAmelCase__		)
    model.to(lowerCAmelCase__		)
    del sd
    return model





def    a__			(   lowerCAmelCase__    ,   lowerCAmelCase__		)       ->      int:
    UpperCAmelCase__		,		UpperCAmelCase__		,		UpperCAmelCase__     :						Dict						=							model.encode(lowerCAmelCase__		)
    print(F"""VQGAN --- {model.__class__.__name__}: latent shape: {z.shape[2:]}"""		)
    UpperCAmelCase__     :						str						=							model.decode(lowerCAmelCase__		)
    return xrec





def    a__			(   lowerCAmelCase__    ,   lowerCAmelCase__=False		)       ->      Optional[int]:
    UpperCAmelCase__		,		UpperCAmelCase__     :						int						=							string.rsplit('''.'''    ,   1		)
    if reload:
        UpperCAmelCase__     :						Tuple						=							importlib.import_module(lowerCAmelCase__		)
        importlib.reload(lowerCAmelCase__		)
    return getattr(importlib.import_module(lowerCAmelCase__    ,   package=lowerCAmelCase__		)    ,   cls		)





def    a__			(   lowerCAmelCase__		)       ->      List[str]:
    if "target" not in config:
        raise KeyError('''Expected key `target` to instantiate.'''		)
    return get_obj_from_str(config['''target''']		)(**config.get('''params'''    ,   {}		)		)





def    a__			(   lowerCAmelCase__    ,   lowerCAmelCase__    ,   lowerCAmelCase__=True    ,   lowerCAmelCase__=True		)       ->      Optional[int]:
    UpperCAmelCase__     :						Tuple						=							instantiate_from_config(lowerCAmelCase__		)
    if sd is not None:
        model.load_state_dict(lowerCAmelCase__		)
    if gpu:
        model.cuda()
    if eval_mode:
        model.eval()
    return {"model": model}







def    a__			(   lowerCAmelCase__    ,   lowerCAmelCase__    ,   lowerCAmelCase__    ,   lowerCAmelCase__		)       ->      Optional[Any]:
    # load the specified checkpoint
    if ckpt:
        UpperCAmelCase__     :						Tuple						=							torch.load(lowerCAmelCase__    ,   map_location='''cpu'''		)
        UpperCAmelCase__     :						Dict						=							pl_sd['''global_step''']
        print(F"""loaded model from global step {global_step}."""		)
    else:
        UpperCAmelCase__     :						Optional[int]						=							{'''state_dict''': None}
        UpperCAmelCase__     :						str						=							None
    UpperCAmelCase__     :						Tuple						=							load_model_from_config(config.model    ,   pl_sd['''state_dict''']    ,   gpu=lowerCAmelCase__    ,   eval_mode=lowerCAmelCase__		)['''model''']
    return model, global_step





from itertools import product

from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
from numpy import dot, exp, mgrid, pi, ravel, square, uinta, zeros







def     UpperCAmelCase_		(					snake_case__    ,			snake_case__					)	->     Dict:



				"""simple docstring"""





				lowerCAmelCase__		  =  k_size // 2
				lowerCAmelCase__  ,   lowerCAmelCase__		  =  mgrid[0 - center : k_size - center, 0 - center : k_size - center]
				lowerCAmelCase__		  =  1 / (2 * pi * sigma) * exp(-(square(snake_case__					) + square(snake_case__					)) / (2 * square(snake_case__					))					)
				return g







def     UpperCAmelCase_		(					snake_case__    ,			snake_case__    ,			snake_case__					)	->     str:



				"""simple docstring"""





				lowerCAmelCase__  ,   lowerCAmelCase__		  =  image.shape[0], image.shape[1]
				# dst image height and width
				lowerCAmelCase__		  =  height - k_size + 1
				lowerCAmelCase__		  =  width - k_size + 1

				# im2col, turn the k_size*k_size pixels into a row and np.vstack all rows
				lowerCAmelCase__		  =  zeros((dst_height * dst_width, k_size * k_size)					)
				lowerCAmelCase__		  =  0
				for i, j in product(range(snake_case__					)    ,			range(snake_case__					)					):
								lowerCAmelCase__		  =  ravel(image[i : i + k_size, j : j + k_size]					)
								lowerCAmelCase__		  =  window
								row += 1

				#  turn the kernel into shape(k*k, 1)
				lowerCAmelCase__		  =  gen_gaussian_kernel(snake_case__    ,			snake_case__					)
				lowerCAmelCase__		  =  ravel(snake_case__					)

				# reshape and get the dst image
				lowerCAmelCase__		  =  dot(snake_case__    ,			snake_case__					).reshape(snake_case__    ,			snake_case__					).astype(snake_case__					)

				return dst


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

	# get values with two different mask size
	_lowerCAmelCase	:   Optional[int]    =   gaussian_filter(gray, 3, sigma=1)
	_lowerCAmelCase	:   Tuple    =   gaussian_filter(gray, 5, sigma=0.8)

	# show result images
	imshow("gaussian filter with 3x3 mask", gaussianaxa)
	imshow("gaussian filter with 5x5 mask", gaussianaxa)
	waitKey()
