






"""simple docstring"""



import argparse
import json

import torch

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




def 						lowerCamelCase_				(  __A  :		Dict			,	__A  :		int=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_				(  __A  :		Optional[int]			,	__A  :		Tuple=0			):
					lowerCamelCase__				=[]
					for old_item in old_list:
										lowerCamelCase__				=old_item
										lowerCamelCase__				=new_item.replace('block.'			,	'resnets.'			)
										lowerCamelCase__				=new_item.replace('conv_shorcut'			,	'conv1'			)
										lowerCamelCase__				=new_item.replace('in_shortcut'			,	'conv_shortcut'			)
										lowerCamelCase__				=new_item.replace('temb_proj'			,	'time_emb_proj'			)

										lowerCamelCase__				=shave_segments(__A			,	n_shave_prefix_segments=__A			)

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

					return mapping




def 						lowerCamelCase_				(  __A  :		int			,	__A  :		Tuple=0			,	__A  :		List[Any]=False			):
					lowerCamelCase__				=[]
					for old_item in old_list:
										lowerCamelCase__				=old_item

										# In `model.mid`, the layer is called `attn`.
										if not in_mid:
															lowerCamelCase__				=new_item.replace('attn'			,	'attentions'			)
										lowerCamelCase__				=new_item.replace('.k.'			,	'.key.'			)
										lowerCamelCase__				=new_item.replace('.v.'			,	'.value.'			)
										lowerCamelCase__				=new_item.replace('.q.'			,	'.query.'			)

										lowerCamelCase__				=new_item.replace('proj_out'			,	'proj_attn'			)
										lowerCamelCase__				=new_item.replace('norm'			,	'group_norm'			)

										lowerCamelCase__				=shave_segments(__A			,	n_shave_prefix_segments=__A			)
										mapping.append({'old': old_item, 'new': new_item}			)

					return mapping




def 						lowerCamelCase_				(  __A  :		Any			,	__A  :		str			,	__A  :		Optional[Any]			,	__A  :		Optional[int]=None			,	__A  :		Dict=None			,	__A  :		Optional[int]=None			):
					assert isinstance(__A			,	__A			), "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():
															lowerCamelCase__				=old_checkpoint[path]
															lowerCamelCase__				=old_tensor.shape[0] // 3

															lowerCamelCase__				=(-1, channels) if len(old_tensor.shape			) == 3 else (-1)

															lowerCamelCase__				=old_tensor.shape[0] // config.get('num_head_channels'			,	1			) // 3

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

															lowerCamelCase__				=query.reshape(__A			).squeeze()
															lowerCamelCase__				=key.reshape(__A			).squeeze()
															lowerCamelCase__				=value.reshape(__A			).squeeze()

					for path in paths:
										lowerCamelCase__				=path['new']

										if attention_paths_to_split is not None and new_path in attention_paths_to_split:
															continue

										lowerCamelCase__				=new_path.replace('down.'			,	'down_blocks.'			)
										lowerCamelCase__				=new_path.replace('up.'			,	'up_blocks.'			)

										if additional_replacements is not None:
															for replacement in additional_replacements:
																				lowerCamelCase__				=new_path.replace(replacement['old']			,	replacement['new']			)

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




def 						lowerCamelCase_				(  __A  :		int			,	__A  :		Dict			):
					lowerCamelCase__				={}

					lowerCamelCase__				=checkpoint['temb.dense.0.weight']
					lowerCamelCase__				=checkpoint['temb.dense.0.bias']
					lowerCamelCase__				=checkpoint['temb.dense.1.weight']
					lowerCamelCase__				=checkpoint['temb.dense.1.bias']

					lowerCamelCase__				=checkpoint['norm_out.weight']
					lowerCamelCase__				=checkpoint['norm_out.bias']

					lowerCamelCase__				=checkpoint['conv_in.weight']
					lowerCamelCase__				=checkpoint['conv_in.bias']
					lowerCamelCase__				=checkpoint['conv_out.weight']
					lowerCamelCase__				=checkpoint['conv_out.bias']

					lowerCamelCase__				=len({'.'.join(layer.split('.'			)[:2]			) for layer in checkpoint if 'down' in layer}			)
					lowerCamelCase__				={
					    layer_id: [key for key in checkpoint if f"down.{layer_id}" in key] for layer_id in range(__A			)
					}

					lowerCamelCase__				=len({'.'.join(layer.split('.'			)[:2]			) for layer in checkpoint if 'up' in layer}			)
					lowerCamelCase__				={layer_id: [key for key in checkpoint if f"up.{layer_id}" in key] for layer_id in range(__A			)}

					for i in range(__A			):
										lowerCamelCase__				=(i - 1) // (config['layers_per_block'] + 1)

										if any('downsample' in layer for layer in down_blocks[i]			):
															lowerCamelCase__				=checkpoint[
															    f"down.{i}.downsample.op.weight"
															]
															lowerCamelCase__				=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]			):
															lowerCamelCase__				=len(
															    {'.'.join(shave_segments(__A			,	2			).split('.'			)[:2]			) for layer in down_blocks[i] if 'block' in layer}			)
															lowerCamelCase__				={
															    layer_id: [key for key in down_blocks[i] if f"block.{layer_id}" in key]
															    for layer_id in range(__A			)
															}

															if num_blocks > 0:
																				for j in range(config['layers_per_block']			):
																									lowerCamelCase__				=renew_resnet_paths(blocks[j]			)
																									assign_to_checkpoint(__A			,	__A			,	__A			)

										if any('attn' in layer for layer in down_blocks[i]			):
															lowerCamelCase__				=len(
															    {'.'.join(shave_segments(__A			,	2			).split('.'			)[:2]			) for layer in down_blocks[i] if 'attn' in layer}			)
															lowerCamelCase__				={
															    layer_id: [key for key in down_blocks[i] if f"attn.{layer_id}" in key]
															    for layer_id in range(__A			)
															}

															if num_attn > 0:
																				for j in range(config['layers_per_block']			):
																									lowerCamelCase__				=renew_attention_paths(attns[j]			)
																									assign_to_checkpoint(__A			,	__A			,	__A			,	config=__A			)

					lowerCamelCase__				=[key for key in checkpoint if 'mid.block_1' in key]
					lowerCamelCase__				=[key for key in checkpoint if 'mid.block_2' in key]
					lowerCamelCase__				=[key for key in checkpoint if 'mid.attn_1' in key]

					# Mid new 2
					lowerCamelCase__				=renew_resnet_paths(__A			)
					assign_to_checkpoint(
					    __A			,	__A			,	__A			,	additional_replacements=[{'old': 'mid.', 'new': 'mid_new_2.'}, {'old': 'block_1', 'new': 'resnets.0'}]			,	)

					lowerCamelCase__				=renew_resnet_paths(__A			)
					assign_to_checkpoint(
					    __A			,	__A			,	__A			,	additional_replacements=[{'old': 'mid.', 'new': 'mid_new_2.'}, {'old': 'block_2', 'new': 'resnets.1'}]			,	)

					lowerCamelCase__				=renew_attention_paths(__A			,	in_mid=__A			)
					assign_to_checkpoint(
					    __A			,	__A			,	__A			,	additional_replacements=[{'old': 'mid.', 'new': 'mid_new_2.'}, {'old': 'attn_1', 'new': 'attentions.0'}]			,	)

					for i in range(__A			):
										lowerCamelCase__				=num_up_blocks - 1 - i

										if any('upsample' in layer for layer in up_blocks[i]			):
															lowerCamelCase__				=checkpoint[
															    f"up.{i}.upsample.conv.weight"
															]
															lowerCamelCase__				=checkpoint[f"up.{i}.upsample.conv.bias"]

										if any('block' in layer for layer in up_blocks[i]			):
															lowerCamelCase__				=len(
															    {'.'.join(shave_segments(__A			,	2			).split('.'			)[:2]			) for layer in up_blocks[i] if 'block' in layer}			)
															lowerCamelCase__				={
															    layer_id: [key for key in up_blocks[i] if f"block.{layer_id}" in key] for layer_id in range(__A			)
															}

															if num_blocks > 0:
																				for j in range(config['layers_per_block'] + 1			):
																									lowerCamelCase__				={'old': f"up_blocks.{i}", 'new': f"up_blocks.{block_id}"}
																									lowerCamelCase__				=renew_resnet_paths(blocks[j]			)
																									assign_to_checkpoint(__A			,	__A			,	__A			,	additional_replacements=[replace_indices]			)

										if any('attn' in layer for layer in up_blocks[i]			):
															lowerCamelCase__				=len(
															    {'.'.join(shave_segments(__A			,	2			).split('.'			)[:2]			) for layer in up_blocks[i] if 'attn' in layer}			)
															lowerCamelCase__				={
															    layer_id: [key for key in up_blocks[i] if f"attn.{layer_id}" in key] for layer_id in range(__A			)
															}

															if num_attn > 0:
																				for j in range(config['layers_per_block'] + 1			):
																									lowerCamelCase__				={'old': f"up_blocks.{i}", 'new': f"up_blocks.{block_id}"}
																									lowerCamelCase__				=renew_attention_paths(attns[j]			)
																									assign_to_checkpoint(__A			,	__A			,	__A			,	additional_replacements=[replace_indices]			)

					lowerCamelCase__				={k.replace('mid_new_2'			,	'mid_block'			): v for k, v in new_checkpoint.items()}
					return new_checkpoint




def 						lowerCamelCase_				(  __A  :		Dict			,	__A  :		int			):
					lowerCamelCase__				={}

					lowerCamelCase__				=checkpoint['encoder.norm_out.weight']
					lowerCamelCase__				=checkpoint['encoder.norm_out.bias']

					lowerCamelCase__				=checkpoint['encoder.conv_in.weight']
					lowerCamelCase__				=checkpoint['encoder.conv_in.bias']
					lowerCamelCase__				=checkpoint['encoder.conv_out.weight']
					lowerCamelCase__				=checkpoint['encoder.conv_out.bias']

					lowerCamelCase__				=checkpoint['decoder.norm_out.weight']
					lowerCamelCase__				=checkpoint['decoder.norm_out.bias']

					lowerCamelCase__				=checkpoint['decoder.conv_in.weight']
					lowerCamelCase__				=checkpoint['decoder.conv_in.bias']
					lowerCamelCase__				=checkpoint['decoder.conv_out.weight']
					lowerCamelCase__				=checkpoint['decoder.conv_out.bias']

					lowerCamelCase__				=len({'.'.join(layer.split('.'			)[:3]			) for layer in checkpoint if 'down' in layer}			)
					lowerCamelCase__				={
					    layer_id: [key for key in checkpoint if f"down.{layer_id}" in key] for layer_id in range(__A			)
					}

					lowerCamelCase__				=len({'.'.join(layer.split('.'			)[:3]			) for layer in checkpoint if 'up' in layer}			)
					lowerCamelCase__				={layer_id: [key for key in checkpoint if f"up.{layer_id}" in key] for layer_id in range(__A			)}

					for i in range(__A			):
										lowerCamelCase__				=(i - 1) // (config['layers_per_block'] + 1)

										if any('downsample' in layer for layer in down_blocks[i]			):
															lowerCamelCase__				=checkpoint[
															    f"encoder.down.{i}.downsample.conv.weight"
															]
															lowerCamelCase__				=checkpoint[
															    f"encoder.down.{i}.downsample.conv.bias"
															]

										if any('block' in layer for layer in down_blocks[i]			):
															lowerCamelCase__				=len(
															    {'.'.join(shave_segments(__A			,	3			).split('.'			)[:3]			) for layer in down_blocks[i] if 'block' in layer}			)
															lowerCamelCase__				={
															    layer_id: [key for key in down_blocks[i] if f"block.{layer_id}" in key]
															    for layer_id in range(__A			)
															}

															if num_blocks > 0:
																				for j in range(config['layers_per_block']			):
																									lowerCamelCase__				=renew_resnet_paths(blocks[j]			)
																									assign_to_checkpoint(__A			,	__A			,	__A			)

										if any('attn' in layer for layer in down_blocks[i]			):
															lowerCamelCase__				=len(
															    {'.'.join(shave_segments(__A			,	3			).split('.'			)[:3]			) for layer in down_blocks[i] if 'attn' in layer}			)
															lowerCamelCase__				={
															    layer_id: [key for key in down_blocks[i] if f"attn.{layer_id}" in key]
															    for layer_id in range(__A			)
															}

															if num_attn > 0:
																				for j in range(config['layers_per_block']			):
																									lowerCamelCase__				=renew_attention_paths(attns[j]			)
																									assign_to_checkpoint(__A			,	__A			,	__A			,	config=__A			)

					lowerCamelCase__				=[key for key in checkpoint if 'mid.block_1' in key]
					lowerCamelCase__				=[key for key in checkpoint if 'mid.block_2' in key]
					lowerCamelCase__				=[key for key in checkpoint if 'mid.attn_1' in key]

					# Mid new 2
					lowerCamelCase__				=renew_resnet_paths(__A			)
					assign_to_checkpoint(
					    __A			,	__A			,	__A			,	additional_replacements=[{'old': 'mid.', 'new': 'mid_new_2.'}, {'old': 'block_1', 'new': 'resnets.0'}]			,	)

					lowerCamelCase__				=renew_resnet_paths(__A			)
					assign_to_checkpoint(
					    __A			,	__A			,	__A			,	additional_replacements=[{'old': 'mid.', 'new': 'mid_new_2.'}, {'old': 'block_2', 'new': 'resnets.1'}]			,	)

					lowerCamelCase__				=renew_attention_paths(__A			,	in_mid=__A			)
					assign_to_checkpoint(
					    __A			,	__A			,	__A			,	additional_replacements=[{'old': 'mid.', 'new': 'mid_new_2.'}, {'old': 'attn_1', 'new': 'attentions.0'}]			,	)

					for i in range(__A			):
										lowerCamelCase__				=num_up_blocks - 1 - i

										if any('upsample' in layer for layer in up_blocks[i]			):
															lowerCamelCase__				=checkpoint[
															    f"decoder.up.{i}.upsample.conv.weight"
															]
															lowerCamelCase__				=checkpoint[
															    f"decoder.up.{i}.upsample.conv.bias"
															]

										if any('block' in layer for layer in up_blocks[i]			):
															lowerCamelCase__				=len(
															    {'.'.join(shave_segments(__A			,	3			).split('.'			)[:3]			) for layer in up_blocks[i] if 'block' in layer}			)
															lowerCamelCase__				={
															    layer_id: [key for key in up_blocks[i] if f"block.{layer_id}" in key] for layer_id in range(__A			)
															}

															if num_blocks > 0:
																				for j in range(config['layers_per_block'] + 1			):
																									lowerCamelCase__				={'old': f"up_blocks.{i}", 'new': f"up_blocks.{block_id}"}
																									lowerCamelCase__				=renew_resnet_paths(blocks[j]			)
																									assign_to_checkpoint(__A			,	__A			,	__A			,	additional_replacements=[replace_indices]			)

										if any('attn' in layer for layer in up_blocks[i]			):
															lowerCamelCase__				=len(
															    {'.'.join(shave_segments(__A			,	3			).split('.'			)[:3]			) for layer in up_blocks[i] if 'attn' in layer}			)
															lowerCamelCase__				={
															    layer_id: [key for key in up_blocks[i] if f"attn.{layer_id}" in key] for layer_id in range(__A			)
															}

															if num_attn > 0:
																				for j in range(config['layers_per_block'] + 1			):
																									lowerCamelCase__				={'old': f"up_blocks.{i}", 'new': f"up_blocks.{block_id}"}
																									lowerCamelCase__				=renew_attention_paths(attns[j]			)
																									assign_to_checkpoint(__A			,	__A			,	__A			,	additional_replacements=[replace_indices]			)

					lowerCamelCase__				={k.replace('mid_new_2'			,	'mid_block'			): v for k, v in new_checkpoint.items()}
					lowerCamelCase__				=checkpoint['quant_conv.weight']
					lowerCamelCase__				=checkpoint['quant_conv.bias']
					if "quantize.embedding.weight" in checkpoint:
										lowerCamelCase__				=checkpoint['quantize.embedding.weight']
					lowerCamelCase__				=checkpoint['post_quant_conv.weight']
					lowerCamelCase__				=checkpoint['post_quant_conv.bias']

					return new_checkpoint


if __name__ == "__main__":
						_snake_case       :      List[str]       =					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.")

						_snake_case       :      List[Any]       =					parser.parse_args()
						_snake_case       :      Optional[Any]       =					torch.load(args.checkpoint_path)

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

						# unet case
						_snake_case       :      List[str]       =					{key.split(".")[0] for key in checkpoint.keys()}
						if "encoder" in key_prefix_set and "decoder" in key_prefix_set:
												_snake_case       :      Dict       =					convert_vq_autoenc_checkpoint(checkpoint, config)
						else:
												_snake_case       :      Optional[int]       =					convert_ddpm_checkpoint(checkpoint, config)

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

						if config["_class_name"] == "VQModel":
												_snake_case       :      List[str]       =					VQModel(**config)
												model.load_state_dict(converted_checkpoint)
												model.save_pretrained(args.dump_path)
						elif config["_class_name"] == "AutoencoderKL":
												_snake_case       :      Optional[int]       =					AutoencoderKL(**config)
												model.load_state_dict(converted_checkpoint)
												model.save_pretrained(args.dump_path)
						else:
												_snake_case       :      List[str]       =					UNetaDModel(**config)
												model.load_state_dict(converted_checkpoint)

												_snake_case       :      Optional[Any]       =					DDPMScheduler.from_config("/".join(args.checkpoint_path.split("/")[:-1]))

												_snake_case       :      List[Any]       =					DDPMPipeline(unet=model, scheduler=scheduler)
												pipe.save_pretrained(args.dump_path)








"""simple docstring"""



_snake_case       :      Dict       =					[0.485, 0.456, 0.406]
_snake_case       :      int       =					[0.229, 0.224, 0.225]
_snake_case       :      Optional[int]       =					[0.5, 0.5, 0.5]
_snake_case       :      str       =					[0.5, 0.5, 0.5]
_snake_case       :      str       =					[0.4814_5466, 0.457_8275, 0.4082_1073]
_snake_case       :      str       =					[0.2686_2954, 0.2613_0258, 0.2757_7711]