


"""simple docstring"""




import argparse
import json

import torch

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





def 			a_   (  lowercase__   :Optional[Any], lowercase__   :Optional[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 			a_   (  lowercase__   :Optional[int], lowercase__   :Optional[Any]=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(lowercase__, n_shave_prefix_segments=lowercase__    )

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

			return mapping





def 			a_   (  lowercase__   :List[str], lowercase__   :Optional[Any]=0, lowercase__   :Dict=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(lowercase__, n_shave_prefix_segments=lowercase__    )
						mapping.append({"""old""": old_item, """new""": new_item}    )

			return mapping





def 			a_   (  lowercase__   :str, lowercase__   :Optional[Any], lowercase__   :int, lowercase__   :Dict=None, lowercase__   :Union[str, Any]=None, lowercase__   :Optional[Any]=None    ):
			assert isinstance(lowercase__, lowercase__    ), "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(lowercase__    ).squeeze()
									__lowerCamelCase        =			key.reshape(lowercase__    ).squeeze()
									__lowerCamelCase        =			value.reshape(lowercase__    ).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 			a_   (  lowercase__   :Union[str, Any], lowercase__   :Tuple    ):
			__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(lowercase__    )
			}

			__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(lowercase__    )}

			for i in range(lowercase__    ):
						__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(lowercase__, 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(lowercase__    )
									}

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

						if any("""attn""" in layer for layer in down_blocks[i]    ):
									__lowerCamelCase        =			len(
									    {""".""".join(shave_segments(lowercase__, 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(lowercase__    )
									}

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

			__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(lowercase__    )
			assign_to_checkpoint(
			    lowercase__, lowercase__, lowercase__, additional_replacements=[{"""old""": """mid.""", """new""": """mid_new_2."""}, {"""old""": """block_1""", """new""": """resnets.0"""}], )

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

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

			for i in range(lowercase__    ):
						__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(lowercase__, 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(lowercase__    )
									}

									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(lowercase__, lowercase__, lowercase__, additional_replacements=[replace_indices]    )

						if any("""attn""" in layer for layer in up_blocks[i]    ):
									__lowerCamelCase        =			len(
									    {""".""".join(shave_segments(lowercase__, 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(lowercase__    )
									}

									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(lowercase__, lowercase__, lowercase__, additional_replacements=[replace_indices]    )

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





def 			a_   (  lowercase__   :List[Any], lowercase__   :Optional[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(lowercase__    )
			}

			__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(lowercase__    )}

			for i in range(lowercase__    ):
						__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(lowercase__, 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(lowercase__    )
									}

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

						if any("""attn""" in layer for layer in down_blocks[i]    ):
									__lowerCamelCase        =			len(
									    {""".""".join(shave_segments(lowercase__, 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(lowercase__    )
									}

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

			__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(lowercase__    )
			assign_to_checkpoint(
			    lowercase__, lowercase__, lowercase__, additional_replacements=[{"""old""": """mid.""", """new""": """mid_new_2."""}, {"""old""": """block_1""", """new""": """resnets.0"""}], )

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

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

			for i in range(lowercase__    ):
						__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(lowercase__, 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(lowercase__    )
									}

									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(lowercase__, lowercase__, lowercase__, additional_replacements=[replace_indices]    )

						if any("""attn""" in layer for layer in up_blocks[i]    ):
									__lowerCamelCase        =			len(
									    {""".""".join(shave_segments(lowercase__, 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(lowercase__    )
									}

									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(lowercase__, lowercase__, lowercase__, 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__":
					__magic_name__	:   Dict          =							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.')

					__magic_name__	:   int          =							parser.parse_args()
					__magic_name__	:   List[str]          =							torch.load(args.checkpoint_path)

					with open(args.config_file) as f:
										__magic_name__	:   Union[str, Any]          =							json.loads(f.read())

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

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

					if config["_class_name"] == "VQModel":
										__magic_name__	:   Union[str, Any]          =							VQModel(**config)
										model.load_state_dict(converted_checkpoint)
										model.save_pretrained(args.dump_path)
					elif config["_class_name"] == "AutoencoderKL":
										__magic_name__	:   List[Any]          =							AutoencoderKL(**config)
										model.load_state_dict(converted_checkpoint)
										model.save_pretrained(args.dump_path)
					else:
										__magic_name__	:   List[Any]          =							UNetaDModel(**config)
										model.load_state_dict(converted_checkpoint)

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

										__magic_name__	:   str          =							DDPMPipeline(unet=model, scheduler=scheduler)
										pipe.save_pretrained(args.dump_path)








"""simple docstring"""




from __future__ import annotations

import re





def 			a_   (  lowercase__   :list[str]    ):

			def alphanum_key(lowercase__   :int    ):
						return [int(lowercase__    ) if s.isdigit() else s.lower() for s in re.split("""([0-9]+)""", lowercase__    )]

			return sorted(lowercase__, key=lowercase__    )


if __name__ == "__main__":
					import doctest

					doctest.testmod()




