



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





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


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

		# get values with two different mask size
		__UpperCAmelCase						  =     median_filter(gray, 3)
		__UpperCAmelCase						  =     median_filter(gray, 5)

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










import argparse
import json

import torch

from diffusers import AutoencoderKL, DDPMPipeline, DDPMScheduler, UNetaDModel, VQModel





def 						__lowerCamelCase       (      __magic_name__	:  int     , __magic_name__	:  Optional[Any]=1						):
							if n_shave_prefix_segments >= 0:
														return ".".join(path.split("."						)[n_shave_prefix_segments:]						)
							else:
														return ".".join(path.split("."						)[:n_shave_prefix_segments]						)





def 						__lowerCamelCase       (      __magic_name__	:  Optional[Any]     , __magic_name__	:  Tuple=0						):
							a__:       Dict											=[]
							for old_item in old_list:
														a__:       Tuple											=old_item
														a__:       Any											=new_item.replace("block."     , "resnets."						)
														a__:       Optional[int]											=new_item.replace("conv_shorcut"     , "conv1"						)
														a__:       Dict											=new_item.replace("in_shortcut"     , "conv_shortcut"						)
														a__:       Optional[int]											=new_item.replace("temb_proj"     , "time_emb_proj"						)

														a__:       Optional[int]											=shave_segments(__magic_name__     , n_shave_prefix_segments=__magic_name__						)

														mapping.append({"old": old_item, "new": new_item}						)

							return mapping





def 						__lowerCamelCase       (      __magic_name__	:  Union[str, Any]     , __magic_name__	:  Optional[int]=0     , __magic_name__	:  Dict=False						):
							a__:       Optional[int]											=[]
							for old_item in old_list:
														a__:       Dict											=old_item

														# In `model.mid`, the layer is called `attn`.
														if not in_mid:
																					a__:       Optional[int]											=new_item.replace("attn"     , "attentions"						)
														a__:       Dict											=new_item.replace(".k."     , ".key."						)
														a__:       Dict											=new_item.replace(".v."     , ".value."						)
														a__:       str											=new_item.replace(".q."     , ".query."						)

														a__:       Union[str, Any]											=new_item.replace("proj_out"     , "proj_attn"						)
														a__:       Tuple											=new_item.replace("norm"     , "group_norm"						)

														a__:       Any											=shave_segments(__magic_name__     , n_shave_prefix_segments=__magic_name__						)
														mapping.append({"old": old_item, "new": new_item}						)

							return mapping





def 						__lowerCamelCase       (      __magic_name__	:  Tuple     , __magic_name__	:  Tuple     , __magic_name__	:  int     , __magic_name__	:  Union[str, Any]=None     , __magic_name__	:  Tuple=None     , __magic_name__	:  Union[str, Any]=None						):
							assert isinstance(__magic_name__     , __magic_name__						), "Paths should be a list of dicts containing 'old' and 'new' keys."

							if attention_paths_to_split is not None:
														if config is None:
																					raise ValueError("Please specify the config if setting 'attention_paths_to_split' to 'True'."						)

														for path, path_map in attention_paths_to_split.items():
																					a__:       Any											=old_checkpoint[path]
																					a__:       List[Any]											=old_tensor.shape[0] // 3

																					a__:       List[Any]											=(-1, channels) if len(old_tensor.shape						) == 3 else (-1)

																					a__:       Optional[int]											=old_tensor.shape[0] // config.get("num_head_channels"     , 1						) // 3

																					a__:       List[str]											=old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:]						)
																					a__							,			a__							,			a__:       Any											=old_tensor.split(channels // num_heads     , dim=1						)

																					a__:       Any											=query.reshape(__magic_name__						).squeeze()
																					a__:       Optional[Any]											=key.reshape(__magic_name__						).squeeze()
																					a__:       List[Any]											=value.reshape(__magic_name__						).squeeze()

							for path in paths:
														a__:       Optional[Any]											=path["new"]

														if attention_paths_to_split is not None and new_path in attention_paths_to_split:
																					continue

														a__:       Any											=new_path.replace("down."     , "down_blocks."						)
														a__:       Union[str, Any]											=new_path.replace("up."     , "up_blocks."						)

														if additional_replacements is not None:
																					for replacement in additional_replacements:
																												a__:       Union[str, Any]											=new_path.replace(replacement["old"]     , replacement["new"]						)

														if "attentions" in new_path:
																					a__:       int											=old_checkpoint[path["old"]].squeeze()
														else:
																					a__:       int											=old_checkpoint[path["old"]]





def 						__lowerCamelCase       (      __magic_name__	:  Any     , __magic_name__	:  List[str]						):
							a__:       Tuple											={}

							a__:       Optional[Any]											=checkpoint["temb.dense.0.weight"]
							a__:       List[str]											=checkpoint["temb.dense.0.bias"]
							a__:       Dict											=checkpoint["temb.dense.1.weight"]
							a__:       int											=checkpoint["temb.dense.1.bias"]

							a__:       Union[str, Any]											=checkpoint["norm_out.weight"]
							a__:       Tuple											=checkpoint["norm_out.bias"]

							a__:       Dict											=checkpoint["conv_in.weight"]
							a__:       Any											=checkpoint["conv_in.bias"]
							a__:       List[Any]											=checkpoint["conv_out.weight"]
							a__:       Tuple											=checkpoint["conv_out.bias"]

							a__:       Optional[int]											=len({".".join(layer.split("."						)[:2]						) for layer in checkpoint if "down" in layer}						)
							a__:       int											={
							    layer_id: [key for key in checkpoint if F"down.{layer_id}" in key] for layer_id in range(__magic_name__						)
							}

							a__:       Any											=len({".".join(layer.split("."						)[:2]						) for layer in checkpoint if "up" in layer}						)
							a__:       List[str]											={layer_id: [key for key in checkpoint if F"up.{layer_id}" in key] for layer_id in range(__magic_name__						)}

							for i in range(__magic_name__						):
														a__:       Dict											=(i - 1) // (config["layers_per_block"] + 1)

														if any("downsample" in layer for layer in down_blocks[i]						):
																					a__:       List[str]											=checkpoint[
																					    F"down.{i}.downsample.op.weight"
																					]
																					a__:       int											=checkpoint[F"down.{i}.downsample.op.bias"]
														#            new_checkpoint[f'down_blocks.{i}.downsamplers.0.op.weight'] = checkpoint[f'down.{i}.downsample.conv.weight']
														#            new_checkpoint[f'down_blocks.{i}.downsamplers.0.op.bias'] = checkpoint[f'down.{i}.downsample.conv.bias']

														if any("block" in layer for layer in down_blocks[i]						):
																					a__:       List[Any]											=len(
																					    {".".join(shave_segments(__magic_name__     , 2						).split("."						)[:2]						) for layer in down_blocks[i] if "block" in layer}						)
																					a__:       Optional[int]											={
																					    layer_id: [key for key in down_blocks[i] if F"block.{layer_id}" in key]
																					    for layer_id in range(__magic_name__						)
																					}

																					if num_blocks > 0:
																												for j in range(config["layers_per_block"]						):
																																			a__:       List[Any]											=renew_resnet_paths(blocks[j]						)
																																			assign_to_checkpoint(__magic_name__     , __magic_name__     , __magic_name__						)

														if any("attn" in layer for layer in down_blocks[i]						):
																					a__:       str											=len(
																					    {".".join(shave_segments(__magic_name__     , 2						).split("."						)[:2]						) for layer in down_blocks[i] if "attn" in layer}						)
																					a__:       str											={
																					    layer_id: [key for key in down_blocks[i] if F"attn.{layer_id}" in key]
																					    for layer_id in range(__magic_name__						)
																					}

																					if num_attn > 0:
																												for j in range(config["layers_per_block"]						):
																																			a__:       Tuple											=renew_attention_paths(attns[j]						)
																																			assign_to_checkpoint(__magic_name__     , __magic_name__     , __magic_name__     , config=__magic_name__						)

							a__:       Union[str, Any]											=[key for key in checkpoint if "mid.block_1" in key]
							a__:       Dict											=[key for key in checkpoint if "mid.block_2" in key]
							a__:       List[str]											=[key for key in checkpoint if "mid.attn_1" in key]

							# Mid new 2
							a__:       Any											=renew_resnet_paths(__magic_name__						)
							assign_to_checkpoint(
							    __magic_name__     , __magic_name__     , __magic_name__     , additional_replacements=[{"old": "mid.", "new": "mid_new_2."}, {"old": "block_1", "new": "resnets.0"}]     , )

							a__:       Optional[Any]											=renew_resnet_paths(__magic_name__						)
							assign_to_checkpoint(
							    __magic_name__     , __magic_name__     , __magic_name__     , additional_replacements=[{"old": "mid.", "new": "mid_new_2."}, {"old": "block_2", "new": "resnets.1"}]     , )

							a__:       Tuple											=renew_attention_paths(__magic_name__     , in_mid=__magic_name__						)
							assign_to_checkpoint(
							    __magic_name__     , __magic_name__     , __magic_name__     , additional_replacements=[{"old": "mid.", "new": "mid_new_2."}, {"old": "attn_1", "new": "attentions.0"}]     , )

							for i in range(__magic_name__						):
														a__:       Union[str, Any]											=num_up_blocks - 1 - i

														if any("upsample" in layer for layer in up_blocks[i]						):
																					a__:       Tuple											=checkpoint[
																					    F"up.{i}.upsample.conv.weight"
																					]
																					a__:       Any											=checkpoint[F"up.{i}.upsample.conv.bias"]

														if any("block" in layer for layer in up_blocks[i]						):
																					a__:       Dict											=len(
																					    {".".join(shave_segments(__magic_name__     , 2						).split("."						)[:2]						) for layer in up_blocks[i] if "block" in layer}						)
																					a__:       Dict											={
																					    layer_id: [key for key in up_blocks[i] if F"block.{layer_id}" in key] for layer_id in range(__magic_name__						)
																					}

																					if num_blocks > 0:
																												for j in range(config["layers_per_block"] + 1						):
																																			a__:       Optional[int]											={"old": F"up_blocks.{i}", "new": F"up_blocks.{block_id}"}
																																			a__:       Union[str, Any]											=renew_resnet_paths(blocks[j]						)
																																			assign_to_checkpoint(__magic_name__     , __magic_name__     , __magic_name__     , additional_replacements=[replace_indices]						)

														if any("attn" in layer for layer in up_blocks[i]						):
																					a__:       Union[str, Any]											=len(
																					    {".".join(shave_segments(__magic_name__     , 2						).split("."						)[:2]						) for layer in up_blocks[i] if "attn" in layer}						)
																					a__:       List[Any]											={
																					    layer_id: [key for key in up_blocks[i] if F"attn.{layer_id}" in key] for layer_id in range(__magic_name__						)
																					}

																					if num_attn > 0:
																												for j in range(config["layers_per_block"] + 1						):
																																			a__:       List[str]											={"old": F"up_blocks.{i}", "new": F"up_blocks.{block_id}"}
																																			a__:       List[str]											=renew_attention_paths(attns[j]						)
																																			assign_to_checkpoint(__magic_name__     , __magic_name__     , __magic_name__     , additional_replacements=[replace_indices]						)

							a__:       Optional[int]											={k.replace("mid_new_2"     , "mid_block"						): v for k, v in new_checkpoint.items()}
							return new_checkpoint





def 						__lowerCamelCase       (      __magic_name__	:  str     , __magic_name__	:  Optional[Any]						):
							a__:       List[str]											={}

							a__:       Optional[Any]											=checkpoint["encoder.norm_out.weight"]
							a__:       Any											=checkpoint["encoder.norm_out.bias"]

							a__:       List[str]											=checkpoint["encoder.conv_in.weight"]
							a__:       List[str]											=checkpoint["encoder.conv_in.bias"]
							a__:       Union[str, Any]											=checkpoint["encoder.conv_out.weight"]
							a__:       Optional[Any]											=checkpoint["encoder.conv_out.bias"]

							a__:       List[Any]											=checkpoint["decoder.norm_out.weight"]
							a__:       Tuple											=checkpoint["decoder.norm_out.bias"]

							a__:       Optional[int]											=checkpoint["decoder.conv_in.weight"]
							a__:       str											=checkpoint["decoder.conv_in.bias"]
							a__:       Tuple											=checkpoint["decoder.conv_out.weight"]
							a__:       Dict											=checkpoint["decoder.conv_out.bias"]

							a__:       Optional[int]											=len({".".join(layer.split("."						)[:3]						) for layer in checkpoint if "down" in layer}						)
							a__:       Optional[Any]											={
							    layer_id: [key for key in checkpoint if F"down.{layer_id}" in key] for layer_id in range(__magic_name__						)
							}

							a__:       int											=len({".".join(layer.split("."						)[:3]						) for layer in checkpoint if "up" in layer}						)
							a__:       Optional[int]											={layer_id: [key for key in checkpoint if F"up.{layer_id}" in key] for layer_id in range(__magic_name__						)}

							for i in range(__magic_name__						):
														a__:       Union[str, Any]											=(i - 1) // (config["layers_per_block"] + 1)

														if any("downsample" in layer for layer in down_blocks[i]						):
																					a__:       List[str]											=checkpoint[
																					    F"encoder.down.{i}.downsample.conv.weight"
																					]
																					a__:       Union[str, Any]											=checkpoint[
																					    F"encoder.down.{i}.downsample.conv.bias"
																					]

														if any("block" in layer for layer in down_blocks[i]						):
																					a__:       Union[str, Any]											=len(
																					    {".".join(shave_segments(__magic_name__     , 3						).split("."						)[:3]						) for layer in down_blocks[i] if "block" in layer}						)
																					a__:       List[str]											={
																					    layer_id: [key for key in down_blocks[i] if F"block.{layer_id}" in key]
																					    for layer_id in range(__magic_name__						)
																					}

																					if num_blocks > 0:
																												for j in range(config["layers_per_block"]						):
																																			a__:       Tuple											=renew_resnet_paths(blocks[j]						)
																																			assign_to_checkpoint(__magic_name__     , __magic_name__     , __magic_name__						)

														if any("attn" in layer for layer in down_blocks[i]						):
																					a__:       int											=len(
																					    {".".join(shave_segments(__magic_name__     , 3						).split("."						)[:3]						) for layer in down_blocks[i] if "attn" in layer}						)
																					a__:       int											={
																					    layer_id: [key for key in down_blocks[i] if F"attn.{layer_id}" in key]
																					    for layer_id in range(__magic_name__						)
																					}

																					if num_attn > 0:
																												for j in range(config["layers_per_block"]						):
																																			a__:       str											=renew_attention_paths(attns[j]						)
																																			assign_to_checkpoint(__magic_name__     , __magic_name__     , __magic_name__     , config=__magic_name__						)

							a__:       List[Any]											=[key for key in checkpoint if "mid.block_1" in key]
							a__:       int											=[key for key in checkpoint if "mid.block_2" in key]
							a__:       List[str]											=[key for key in checkpoint if "mid.attn_1" in key]

							# Mid new 2
							a__:       Optional[Any]											=renew_resnet_paths(__magic_name__						)
							assign_to_checkpoint(
							    __magic_name__     , __magic_name__     , __magic_name__     , additional_replacements=[{"old": "mid.", "new": "mid_new_2."}, {"old": "block_1", "new": "resnets.0"}]     , )

							a__:       int											=renew_resnet_paths(__magic_name__						)
							assign_to_checkpoint(
							    __magic_name__     , __magic_name__     , __magic_name__     , additional_replacements=[{"old": "mid.", "new": "mid_new_2."}, {"old": "block_2", "new": "resnets.1"}]     , )

							a__:       Any											=renew_attention_paths(__magic_name__     , in_mid=__magic_name__						)
							assign_to_checkpoint(
							    __magic_name__     , __magic_name__     , __magic_name__     , additional_replacements=[{"old": "mid.", "new": "mid_new_2."}, {"old": "attn_1", "new": "attentions.0"}]     , )

							for i in range(__magic_name__						):
														a__:       List[Any]											=num_up_blocks - 1 - i

														if any("upsample" in layer for layer in up_blocks[i]						):
																					a__:       Optional[Any]											=checkpoint[
																					    F"decoder.up.{i}.upsample.conv.weight"
																					]
																					a__:       Any											=checkpoint[
																					    F"decoder.up.{i}.upsample.conv.bias"
																					]

														if any("block" in layer for layer in up_blocks[i]						):
																					a__:       List[str]											=len(
																					    {".".join(shave_segments(__magic_name__     , 3						).split("."						)[:3]						) for layer in up_blocks[i] if "block" in layer}						)
																					a__:       Union[str, Any]											={
																					    layer_id: [key for key in up_blocks[i] if F"block.{layer_id}" in key] for layer_id in range(__magic_name__						)
																					}

																					if num_blocks > 0:
																												for j in range(config["layers_per_block"] + 1						):
																																			a__:       Union[str, Any]											={"old": F"up_blocks.{i}", "new": F"up_blocks.{block_id}"}
																																			a__:       Optional[Any]											=renew_resnet_paths(blocks[j]						)
																																			assign_to_checkpoint(__magic_name__     , __magic_name__     , __magic_name__     , additional_replacements=[replace_indices]						)

														if any("attn" in layer for layer in up_blocks[i]						):
																					a__:       List[str]											=len(
																					    {".".join(shave_segments(__magic_name__     , 3						).split("."						)[:3]						) for layer in up_blocks[i] if "attn" in layer}						)
																					a__:       Optional[int]											={
																					    layer_id: [key for key in up_blocks[i] if F"attn.{layer_id}" in key] for layer_id in range(__magic_name__						)
																					}

																					if num_attn > 0:
																												for j in range(config["layers_per_block"] + 1						):
																																			a__:       Dict											={"old": F"up_blocks.{i}", "new": F"up_blocks.{block_id}"}
																																			a__:       int											=renew_attention_paths(attns[j]						)
																																			assign_to_checkpoint(__magic_name__     , __magic_name__     , __magic_name__     , additional_replacements=[replace_indices]						)

							a__:       List[Any]											={k.replace("mid_new_2"     , "mid_block"						): v for k, v in new_checkpoint.items()}
							a__:       List[Any]											=checkpoint["quant_conv.weight"]
							a__:       Tuple											=checkpoint["quant_conv.bias"]
							if "quantize.embedding.weight" in checkpoint:
														a__:       Dict											=checkpoint["quantize.embedding.weight"]
							a__:       Union[str, Any]											=checkpoint["post_quant_conv.weight"]
							a__:       str											=checkpoint["post_quant_conv.bias"]

							return new_checkpoint


if __name__ == "__main__":
		__UpperCAmelCase						  =     argparse.ArgumentParser()

		parser.add_argument(
		    '''--checkpoint_path''', default=None, type=str, required=True, help='''Path to the checkpoint to convert.'''
		)

		parser.add_argument(
		    '''--config_file''',
		    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						  =     parser.parse_args()
		__UpperCAmelCase						  =     torch.load(args.checkpoint_path)

		with open(args.config_file) as f:
				__UpperCAmelCase						  =     json.loads(f.read())

		# unet case
		__UpperCAmelCase						  =     {key.split('''.''')[0] for key in checkpoint.keys()}
		if "encoder" in key_prefix_set and "decoder" in key_prefix_set:
				__UpperCAmelCase						  =     convert_vq_autoenc_checkpoint(checkpoint, config)
		else:
				__UpperCAmelCase						  =     convert_ddpm_checkpoint(checkpoint, config)

		if "ddpm" in config:
				del config["ddpm"]

		if config["_class_name"] == "VQModel":
				__UpperCAmelCase						  =     VQModel(**config)
				model.load_state_dict(converted_checkpoint)
				model.save_pretrained(args.dump_path)
		elif config["_class_name"] == "AutoencoderKL":
				__UpperCAmelCase						  =     AutoencoderKL(**config)
				model.load_state_dict(converted_checkpoint)
				model.save_pretrained(args.dump_path)
		else:
				__UpperCAmelCase						  =     UNetaDModel(**config)
				model.load_state_dict(converted_checkpoint)

				__UpperCAmelCase						  =     DDPMScheduler.from_config('''/'''.join(args.checkpoint_path.split('''/''')[:-1]))

				__UpperCAmelCase						  =     DDPMPipeline(unet=model, scheduler=scheduler)
				pipe.save_pretrained(args.dump_path)





