





"""simple docstring"""




import argparse
import json

import torch

from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel






def  _SCREAMING_SNAKE_CASE						(      _lowercase		:       int       ,			_lowercase		:       Dict=1   )					->List[str]:






	'''simple docstring'''






	if n_shave_prefix_segments >= 0:
		return ".".join(path.split("."   )[n_shave_prefix_segments:]   )
	else:
		return ".".join(path.split("."   )[:n_shave_prefix_segments]   )






def  _SCREAMING_SNAKE_CASE						(      _lowercase		:       Union[str, Any]       ,			_lowercase		:       int=0   )					->List[str]:






	'''simple docstring'''






	a :						Tuple      =							[]
	for old_item in old_list:
		a :						Tuple      =							old_item.replace("in_layers.0"       ,			"norm1"   )
		a :						List[str]      =							new_item.replace("in_layers.2"       ,			"conv1"   )

		a :						Tuple      =							new_item.replace("out_layers.0"       ,			"norm2"   )
		a :						int      =							new_item.replace("out_layers.3"       ,			"conv2"   )

		a :						Union[str, Any]      =							new_item.replace("emb_layers.1"       ,			"time_emb_proj"   )
		a :						Any      =							new_item.replace("skip_connection"       ,			"conv_shortcut"   )

		a :						Tuple      =							shave_segments(_lowercase       ,			n_shave_prefix_segments=_lowercase   )

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

	return mapping






def  _SCREAMING_SNAKE_CASE						(      _lowercase		:       str       ,			_lowercase		:       int=0   )					->Tuple:






	'''simple docstring'''






	a :						Optional[int]      =							[]
	for old_item in old_list:
		a :						Optional[int]      =							old_item

		a :						int      =							new_item.replace("norm.weight"       ,			"group_norm.weight"   )
		a :						str      =							new_item.replace("norm.bias"       ,			"group_norm.bias"   )

		a :						List[Any]      =							new_item.replace("proj_out.weight"       ,			"proj_attn.weight"   )
		a :						Optional[Any]      =							new_item.replace("proj_out.bias"       ,			"proj_attn.bias"   )

		a :						Optional[Any]      =							shave_segments(_lowercase       ,			n_shave_prefix_segments=_lowercase   )

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

	return mapping






def  _SCREAMING_SNAKE_CASE						(      _lowercase		:       Tuple       ,			_lowercase		:       str       ,			_lowercase		:       List[str]       ,			_lowercase		:       str=None       ,			_lowercase		:       List[Any]=None       ,			_lowercase		:       Tuple=None   )					->Optional[int]:






	'''simple docstring'''






	assert isinstance(_lowercase       ,			_lowercase   ), "Paths should be a list of dicts containing 'old' and 'new' keys."

	# Splits the attention layers into three variables.
	if attention_paths_to_split is not None:
		for path, path_map in attention_paths_to_split.items():
			a :						Optional[int]      =							old_checkpoint[path]
			a :						Optional[int]      =							old_tensor.shape[0] // 3

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

			a :						List[Any]      =							old_tensor.shape[0] // config["num_head_channels"] // 3

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

			a :						Dict      =							query.reshape(_lowercase   )
			a :						Any      =							key.reshape(_lowercase   )
			a :						Dict      =							value.reshape(_lowercase   )

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

		# These have already been assigned
		if attention_paths_to_split is not None and new_path in attention_paths_to_split:
			continue

		# Global renaming happens here
		a :						List[Any]      =							new_path.replace("middle_block.0"       ,			"mid_block.resnets.0"   )
		a :						int      =							new_path.replace("middle_block.1"       ,			"mid_block.attentions.0"   )
		a :						List[Any]      =							new_path.replace("middle_block.2"       ,			"mid_block.resnets.1"   )

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

        # proj_attn.weight has to be converted from conv 1D to linear
		if "proj_attn.weight" in new_path:
			a :						Union[str, Any]      =							old_checkpoint[path["old"]][:, :, 0]
		else:
			a :						Any      =							old_checkpoint[path["old"]]






def  _SCREAMING_SNAKE_CASE						(      _lowercase		:       Optional[int]       ,			_lowercase		:       List[str]   )					->Optional[int]:






	'''simple docstring'''






	a :						Optional[int]      =							{}

	a :						Any      =							checkpoint["time_embed.0.weight"]
	a :						List[Any]      =							checkpoint["time_embed.0.bias"]
	a :						str      =							checkpoint["time_embed.2.weight"]
	a :						Tuple      =							checkpoint["time_embed.2.bias"]

	a :						Union[str, Any]      =							checkpoint["input_blocks.0.0.weight"]
	a :						Union[str, Any]      =							checkpoint["input_blocks.0.0.bias"]

	a :						Optional[Any]      =							checkpoint["out.0.weight"]
	a :						Any      =							checkpoint["out.0.bias"]
	a :						Dict      =							checkpoint["out.2.weight"]
	a :						Union[str, Any]      =							checkpoint["out.2.bias"]

	# Retrieves the keys for the input blocks only
	a :						Union[str, Any]      =							len({".".join(layer.split("."   )[:2]   ) for layer in checkpoint if "input_blocks" in layer}   )
	a :						Tuple      =							{
	    layer_id: [key for key in checkpoint if F"""input_blocks.{layer_id}""" in key]
	    for layer_id in range(_lowercase   )
	}

	# Retrieves the keys for the middle blocks only
	a :						List[str]      =							len({".".join(layer.split("."   )[:2]   ) for layer in checkpoint if "middle_block" in layer}   )
	a :						List[str]      =							{
	    layer_id: [key for key in checkpoint if F"""middle_block.{layer_id}""" in key]
	    for layer_id in range(_lowercase   )
	}

	# Retrieves the keys for the output blocks only
	a :						int      =							len({".".join(layer.split("."   )[:2]   ) for layer in checkpoint if "output_blocks" in layer}   )
	a :						Tuple      =							{
	    layer_id: [key for key in checkpoint if F"""output_blocks.{layer_id}""" in key]
	    for layer_id in range(_lowercase   )
	}

	for i in range(1       ,			_lowercase   ):
		a :						Optional[Any]      =							(i - 1) // (config["num_res_blocks"] + 1)
		a :						List[Any]      =							(i - 1) % (config["num_res_blocks"] + 1)

		a :						Tuple      =							[key for key in input_blocks[i] if F"""input_blocks.{i}.0""" in key]
		a :						int      =							[key for key in input_blocks[i] if F"""input_blocks.{i}.1""" in key]

		if F"""input_blocks.{i}.0.op.weight""" in checkpoint:
			a :						Tuple      =							checkpoint[
			    F"""input_blocks.{i}.0.op.weight"""
			]
			a :						Union[str, Any]      =							checkpoint[
			    F"""input_blocks.{i}.0.op.bias"""
			]
			continue

		a :						Union[str, Any]      =							renew_resnet_paths(_lowercase   )
		a :						Dict      =							{"old": F"""input_blocks.{i}.0""", "new": F"""down_blocks.{block_id}.resnets.{layer_in_block_id}"""}
		a :						str      =							{"old": "resnets.2.op", "new": "downsamplers.0.op"}
		assign_to_checkpoint(
		    _lowercase       ,			_lowercase       ,			_lowercase       ,			additional_replacements=[meta_path, resnet_op]       ,			config=_lowercase   )

		if len(_lowercase   ):
			a :						int      =							renew_attention_paths(_lowercase   )
			a :						str      =							{
			    "old": F"""input_blocks.{i}.1""",
			    "new": F"""down_blocks.{block_id}.attentions.{layer_in_block_id}""",
			}
			a :						Any      =							{
			    F"""input_blocks.{i}.1.qkv.bias""": {
			        "key": F"""down_blocks.{block_id}.attentions.{layer_in_block_id}.key.bias""",
			        "query": F"""down_blocks.{block_id}.attentions.{layer_in_block_id}.query.bias""",
			        "value": F"""down_blocks.{block_id}.attentions.{layer_in_block_id}.value.bias""",
			    },
			    F"""input_blocks.{i}.1.qkv.weight""": {
			        "key": F"""down_blocks.{block_id}.attentions.{layer_in_block_id}.key.weight""",
			        "query": F"""down_blocks.{block_id}.attentions.{layer_in_block_id}.query.weight""",
			        "value": F"""down_blocks.{block_id}.attentions.{layer_in_block_id}.value.weight""",
			    },
			}
			assign_to_checkpoint(
			    _lowercase       ,			_lowercase       ,			_lowercase       ,			additional_replacements=[meta_path]       ,			attention_paths_to_split=_lowercase       ,			config=_lowercase       ,			)

	a :						Optional[int]      =							middle_blocks[0]
	a :						Any      =							middle_blocks[1]
	a :						Dict      =							middle_blocks[2]

	a :						Any      =							renew_resnet_paths(_lowercase   )
	assign_to_checkpoint(_lowercase       ,			_lowercase       ,			_lowercase       ,			config=_lowercase   )

	a :						List[Any]      =							renew_resnet_paths(_lowercase   )
	assign_to_checkpoint(_lowercase       ,			_lowercase       ,			_lowercase       ,			config=_lowercase   )

	a :						Union[str, Any]      =							renew_attention_paths(_lowercase   )
	a :						Tuple      =							{
	    "middle_block.1.qkv.bias": {
	        "key": "mid_block.attentions.0.key.bias",
	        "query": "mid_block.attentions.0.query.bias",
	        "value": "mid_block.attentions.0.value.bias",
	    },
	    "middle_block.1.qkv.weight": {
	        "key": "mid_block.attentions.0.key.weight",
	        "query": "mid_block.attentions.0.query.weight",
	        "value": "mid_block.attentions.0.value.weight",
	    },
	}
	assign_to_checkpoint(
	    _lowercase       ,			_lowercase       ,			_lowercase       ,			attention_paths_to_split=_lowercase       ,			config=_lowercase   )

	for i in range(_lowercase   ):
		a :						List[str]      =							i // (config["num_res_blocks"] + 1)
		a :						Tuple      =							i % (config["num_res_blocks"] + 1)
		a :						Any      =							[shave_segments(_lowercase       ,			2   ) for name in output_blocks[i]]
		a :						Tuple      =							{}

		for layer in output_block_layers:
			a,			a :						Dict      =							layer.split("."   )[0], shave_segments(_lowercase       ,			1   )
			if layer_id in output_block_list:
				output_block_list[layer_id].append(_lowercase   )
			else:
				a :						Union[str, Any]      =							[layer_name]

		if len(_lowercase   ) > 1:
			a :						Union[str, Any]      =							[key for key in output_blocks[i] if F"""output_blocks.{i}.0""" in key]
			a :						int      =							[key for key in output_blocks[i] if F"""output_blocks.{i}.1""" in key]

			a :						Dict      =							renew_resnet_paths(_lowercase   )
			a :						Optional[Any]      =							renew_resnet_paths(_lowercase   )

			a :						Optional[int]      =							{"old": F"""output_blocks.{i}.0""", "new": F"""up_blocks.{block_id}.resnets.{layer_in_block_id}"""}
			assign_to_checkpoint(_lowercase       ,			_lowercase       ,			_lowercase       ,			additional_replacements=[meta_path]       ,			config=_lowercase   )

			if ["conv.weight", "conv.bias"] in output_block_list.values():
				a :						List[str]      =							list(output_block_list.values()   ).index(["conv.weight", "conv.bias"]   )
				a :						int      =							checkpoint[
				    F"""output_blocks.{i}.{index}.conv.weight"""
				]
				a :						Optional[int]      =							checkpoint[
				    F"""output_blocks.{i}.{index}.conv.bias"""
				]

				# Clear attentions as they have been attributed above.
				if len(_lowercase   ) == 2:
					a :						str      =							[]

			if len(_lowercase   ):
				a :						str      =							renew_attention_paths(_lowercase   )
				a :						Tuple      =							{
				    "old": F"""output_blocks.{i}.1""",
				    "new": F"""up_blocks.{block_id}.attentions.{layer_in_block_id}""",
				}
				a :						Union[str, Any]      =							{
				    F"""output_blocks.{i}.1.qkv.bias""": {
				        "key": F"""up_blocks.{block_id}.attentions.{layer_in_block_id}.key.bias""",
				        "query": F"""up_blocks.{block_id}.attentions.{layer_in_block_id}.query.bias""",
				        "value": F"""up_blocks.{block_id}.attentions.{layer_in_block_id}.value.bias""",
				    },
				    F"""output_blocks.{i}.1.qkv.weight""": {
				        "key": F"""up_blocks.{block_id}.attentions.{layer_in_block_id}.key.weight""",
				        "query": F"""up_blocks.{block_id}.attentions.{layer_in_block_id}.query.weight""",
				        "value": F"""up_blocks.{block_id}.attentions.{layer_in_block_id}.value.weight""",
				    },
				}
				assign_to_checkpoint(
				    _lowercase       ,			_lowercase       ,			_lowercase       ,			additional_replacements=[meta_path]       ,			attention_paths_to_split=to_split if any("qkv" in key for key in attentions   ) else None       ,			config=_lowercase       ,			)
		else:
			a :						List[Any]      =							renew_resnet_paths(_lowercase       ,			n_shave_prefix_segments=1   )
			for path in resnet_0_paths:
				a :						Tuple      =							".".join(["output_blocks", str(_lowercase   ), path["old"]]   )
				a :						Union[str, Any]      =							".".join(["up_blocks", str(_lowercase   ), "resnets", str(_lowercase   ), path["new"]]   )

				a :						Dict      =							checkpoint[old_path]

	return new_checkpoint


if __name__ == "__main__":
		a		:					Any	      =     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.''')

		a		:					Any	      =     parser.parse_args()

		a		:					int	      =     torch.load(args.checkpoint_path)

		with open(args.config_file) as f:
				a		:					Optional[Any]	      =     json.loads(f.read())

		a		:					List[str]	      =     convert_ldm_checkpoint(checkpoint, config)

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

		a		:					Union[str, Any]	      =     UNetaDModel(**config)
		model.load_state_dict(converted_checkpoint)

		try:
				a		:					int	      =     DDPMScheduler.from_config('''/'''.join(args.checkpoint_path.split('''/''')[:-1]))
				a		:					Optional[int]	      =     VQModel.from_pretrained('''/'''.join(args.checkpoint_path.split('''/''')[:-1]))

				a		:					Any	      =     LDMPipeline(unet=model, scheduler=scheduler, vae=vqvae)
				pipe.save_pretrained(args.dump_path)
		except:  # noqa: E722
				model.save_pretrained(args.dump_path)









"""simple docstring"""




def  _SCREAMING_SNAKE_CASE						(      _lowercase		:       list[int]   )					->float:






	'''simple docstring'''






	if not nums:  # Makes sure that the list is not empty
		raise ValueError("List is empty"   )

	a :						Dict      =							sum(_lowercase   ) / len(_lowercase   )  # Calculate the average
	return sum(abs(x - average   ) for x in nums   ) / len(_lowercase   )


if __name__ == "__main__":
		import doctest

		doctest.testmod()


