




import argparse
import json
import os

import torch
from transformers.file_utils import has_file

from diffusers import UNetaDConditionModel, UNetaDModel


UpperCamelCase__ :   str						=							False
UpperCamelCase__ :   List[str]						=							True
UpperCamelCase__ :   Optional[Any]						=							False


if __name__ == "__main__":
						UpperCamelCase__ :   Union[str, Any]						=							argparse.ArgumentParser()

						parser.add_argument(
						    '''--repo_path''',
						    default=None,
						    type=str,
						    required=True,
						    help='''The config json file corresponding to the architecture.''',
						)

						parser.add_argument('''--dump_path''', default=None, type=str, required=True, help='''Path to the output model.''')

						UpperCamelCase__ :   Optional[Any]						=							parser.parse_args()

						UpperCamelCase__ :   Tuple						=							{
						    '''image_size''': '''sample_size''',
						    '''num_res_blocks''': '''layers_per_block''',
						    '''block_channels''': '''block_out_channels''',
						    '''down_blocks''': '''down_block_types''',
						    '''up_blocks''': '''up_block_types''',
						    '''downscale_freq_shift''': '''freq_shift''',
						    '''resnet_num_groups''': '''norm_num_groups''',
						    '''resnet_act_fn''': '''act_fn''',
						    '''resnet_eps''': '''norm_eps''',
						    '''num_head_channels''': '''attention_head_dim''',
						}

						UpperCamelCase__ :   Optional[int]						=							{
						    '''time_steps''': '''time_proj''',
						    '''mid''': '''mid_block''',
						    '''downsample_blocks''': '''down_blocks''',
						    '''upsample_blocks''': '''up_blocks''',
						}

						UpperCamelCase__ :   Tuple						=							'''''' if has_file(args.repo_path, '''config.json''') else '''unet'''

						with open(os.path.join(args.repo_path, subfolder, '''config.json'''), '''r''', encoding='''utf-8''') as reader:
												UpperCamelCase__ :   Optional[int]						=							reader.read()
												UpperCamelCase__ :   Union[str, Any]						=							json.loads(text)

						if do_only_config:
												for key in config_parameters_to_change.keys():
																		config.pop(key, None)

						if has_file(args.repo_path, '''config.json'''):
												UpperCamelCase__ :   Any						=							UNetaDModel(**config)
						else:
												UpperCamelCase__ :   Tuple						=							UNetaDConditionModel if '''ldm-text2im-large-256''' in args.repo_path else UNetaDModel
												UpperCamelCase__ :   List[str]						=							class_name(**config)

						if do_only_config:
												model.save_config(os.path.join(args.repo_path, subfolder))

						UpperCamelCase__ :   Union[str, Any]						=							dict(model.config)

						if do_only_renaming:
												for key, value in config_parameters_to_change.items():
																		if key in config:
																								UpperCamelCase__ :   List[str]						=							config[key]
																								del config[key]

												UpperCamelCase__ :   Dict						=							[k.replace('''UNetRes''', '''''') for k in config['''down_block_types''']]
												UpperCamelCase__ :   Dict						=							[k.replace('''UNetRes''', '''''') for k in config['''up_block_types''']]

						if do_only_weights:
												UpperCamelCase__ :   int						=							torch.load(os.path.join(args.repo_path, subfolder, '''diffusion_pytorch_model.bin'''))

												UpperCamelCase__ :   List[Any]						=							{}
												for param_key, param_value in state_dict.items():
																		if param_key.endswith('''.op.bias''') or param_key.endswith('''.op.weight'''):
																								continue
																		UpperCamelCase__ :   str						=							False
																		for key, new_key in key_parameters_to_change.items():
																								if not has_changed and param_key.split('''.''')[0] == key:
																														UpperCamelCase__ :   Tuple						=							param_value
																														UpperCamelCase__ :   Dict						=							True
																		if not has_changed:
																								UpperCamelCase__ :   str						=							param_value

												model.load_state_dict(new_state_dict)
												model.save_pretrained(os.path.join(args.repo_path, subfolder))





import numpy as np
from cva import COLOR_BGR2GRAY, CV_8UC3, cvtColor, filteraD, imread, imshow, waitKey




def 	UpperCamelCase__  (     SCREAMING_SNAKE_CASE_	:	int		,   SCREAMING_SNAKE_CASE_	:	int		,   SCREAMING_SNAKE_CASE_	:	int		,   SCREAMING_SNAKE_CASE_	:	int		,   SCREAMING_SNAKE_CASE_	:	int		,   SCREAMING_SNAKE_CASE_	:	int       )  ->     np.ndarray:

						# prepare kernel
						# the kernel size have to be odd
						if (ksize % 2) == 0:
												_lowercase					       =					ksize + 1
						_lowercase					       =					np.zeros((ksize, ksize)		,   dtype=np.floataa       )

						# each value
						for y in range(SCREAMING_SNAKE_CASE_       ):
												for x in range(SCREAMING_SNAKE_CASE_       ):
																		# distance from center
																		_lowercase					       =					x - ksize // 2
																		_lowercase					       =					y - ksize // 2

																		# degree to radiant
																		_lowercase					       =					theta / 1_80 * np.pi
																		_lowercase					       =					np.cos(_theta       )
																		_lowercase					       =					np.sin(_theta       )

																		# get kernel x
																		_lowercase					       =					cos_theta * px + sin_theta * py

																		# get kernel y
																		_lowercase					       =					-sin_theta * px + cos_theta * py

																		# fill kernel
																		_lowercase					       =					np.exp(
																		    -(_x**2 + gamma**2 * _y**2) / (2 * sigma**2)       ) * np.cos(2 * np.pi * _x / lambd + psi       )

						return gabor


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		# read original image
		A		: Optional[Any]								=     imread('''../image_data/lena.jpg''')
		# turn image in gray scale value
		A		: Optional[int]								=     cvtColor(img, COLOR_BGR2GRAY)

		# Apply multiple Kernel to detect edges
		A		: Optional[int]								=     np.zeros(gray.shape[:2])
		for theta in [0, 30, 60, 90, 120, 150]:
				A		: Optional[int]								=     gabor_filter_kernel(10, 8, theta, 10, 0, 0)
				out += filteraD(gray, CV_8UC3, kernel_aa)
		A		: int								=     out / out.max() * 255
		A		: str								=     out.astype(np.uinta)

		imshow('''Original''', gray)
		imshow('''Gabor filter with 20x20 mask and 6 directions''', out)

		waitKey(0)