






import argparse
import json
import os

import torch
from transformers.file_utils import has_file

from diffusers import UNetaDConditionModel, UNetaDModel


__UpperCamelCase      :      Optional[int]     						=						False
__UpperCamelCase      :      Optional[Any]     						=						True
__UpperCamelCase      :      List[str]     						=						False


if __name__ == "__main__":
						__UpperCamelCase      :      List[str]     						=						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      :      str     						=						parser.parse_args()

						__UpperCamelCase      :      Dict     						=						{
						    """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      :      List[str]     						=						{
						    """time_steps""": """time_proj""",
						    """mid""": """mid_block""",
						    """downsample_blocks""": """down_blocks""",
						    """upsample_blocks""": """up_blocks""",
						}

						__UpperCamelCase      :      Optional[int]     						=						"""""" 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      :      List[str]     						=						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      :      Tuple     						=						UNetaDModel(**config)
						else:
												__UpperCamelCase      :      Union[str, Any]     						=						UNetaDConditionModel if """ldm-text2im-large-256""" in args.repo_path else UNetaDModel
												__UpperCamelCase      :      Any     						=						class_name(**config)

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

						__UpperCamelCase      :      Dict     						=						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      :      Optional[int]     						=						[k.replace("""UNetRes""", """""") for k in config["""down_block_types"""]]
												__UpperCamelCase      :      Any     						=						[k.replace("""UNetRes""", """""") for k in config["""up_block_types"""]]

						if do_only_weights:
												__UpperCamelCase      :      Optional[int]     						=						torch.load(os.path.join(args.repo_path, subfolder, """diffusion_pytorch_model.bin"""))

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




	'''simple docstring'''







	if (ksize % 2) == 0:
		__lowercase             =  ksize + 1
	__lowercase             =  np.zeros((ksize, ksize)				,  dtype=np.floataa					)

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

			# degree to radiant
			__lowercase             =  theta / 180 * 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
						__UpperCamelCase      :      List[Any]     						=						imread("""../image_data/lena.jpg""")
						# turn image in gray scale value
						__UpperCamelCase      :      Union[str, Any]     						=						cvtColor(img, COLOR_BGR2GRAY)

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

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

						waitKey(0)

