




import argparse
import json

import torch

from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel


def  __lowerCamelCase (       lowerCamelCase__					,			lowerCamelCase__=1						):

				"""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  __lowerCamelCase (       lowerCamelCase__					,			lowerCamelCase__=0						):

				"""simple docstring"""


				lowercase__					:   List[str]													=					[]
				for old_item in old_list:
								lowercase__					:   Optional[Any]													=					old_item.replace("in_layers.0"					,			"norm1"						)
								lowercase__					:   Union[str, Any]													=					new_item.replace("in_layers.2"					,			"conv1"						)

								lowercase__					:   Optional[Any]													=					new_item.replace("out_layers.0"					,			"norm2"						)
								lowercase__					:   Union[str, Any]													=					new_item.replace("out_layers.3"					,			"conv2"						)

								lowercase__					:   Dict													=					new_item.replace("emb_layers.1"					,			"time_emb_proj"						)
								lowercase__					:   int													=					new_item.replace("skip_connection"					,			"conv_shortcut"						)

								lowercase__					:   Tuple													=					shave_segments(lowerCamelCase__					,			n_shave_prefix_segments=lowerCamelCase__						)

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

				return mapping


def  __lowerCamelCase (       lowerCamelCase__					,			lowerCamelCase__=0						):

				"""simple docstring"""


				lowercase__					:   str													=					[]
				for old_item in old_list:
								lowercase__					:   Optional[int]													=					old_item

								lowercase__					:   Dict													=					new_item.replace("norm.weight"					,			"group_norm.weight"						)
								lowercase__					:   Optional[int]													=					new_item.replace("norm.bias"					,			"group_norm.bias"						)

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

								lowercase__					:   Optional[Any]													=					shave_segments(lowerCamelCase__					,			n_shave_prefix_segments=lowerCamelCase__						)

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

				return mapping


def  __lowerCamelCase (       lowerCamelCase__					,			lowerCamelCase__					,			lowerCamelCase__					,			lowerCamelCase__=None					,			lowerCamelCase__=None					,			lowerCamelCase__=None						):

				"""simple docstring"""


				assert isinstance(lowerCamelCase__					,			lowerCamelCase__						), "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():
												lowercase__					:   List[str]													=					old_checkpoint[path]
												lowercase__					:   str													=					old_tensor.shape[0] // 3

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

												lowercase__					:   Union[str, Any]													=					old_tensor.shape[0] // config["num_head_channels"] // 3

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

												lowercase__					:   Dict													=					query.reshape(lowerCamelCase__						)
												lowercase__					:   Dict													=					key.reshape(lowerCamelCase__						)
												lowercase__					:   int													=					value.reshape(lowerCamelCase__						)

				for path in paths:
								lowercase__					:   Union[str, 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
								lowercase__					:   List[Any]													=					new_path.replace("middle_block.0"					,			"mid_block.resnets.0"						)
								lowercase__					:   Optional[Any]													=					new_path.replace("middle_block.1"					,			"mid_block.attentions.0"						)
								lowercase__					:   List[str]													=					new_path.replace("middle_block.2"					,			"mid_block.resnets.1"						)

								if additional_replacements is not None:
												for replacement in additional_replacements:
																lowercase__					:   Tuple													=					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:
												lowercase__					:   List[Any]													=					old_checkpoint[path["old"]][:, :, 0]
								else:
												lowercase__					:   List[Any]													=					old_checkpoint[path["old"]]


def  __lowerCamelCase (       lowerCamelCase__					,			lowerCamelCase__						):

				"""simple docstring"""


				lowercase__					:   Union[str, Any]													=					{}

				lowercase__					:   Optional[Any]													=					checkpoint["time_embed.0.weight"]
				lowercase__					:   Tuple													=					checkpoint["time_embed.0.bias"]
				lowercase__					:   Dict													=					checkpoint["time_embed.2.weight"]
				lowercase__					:   Optional[Any]													=					checkpoint["time_embed.2.bias"]

				lowercase__					:   Optional[int]													=					checkpoint["input_blocks.0.0.weight"]
				lowercase__					:   List[Any]													=					checkpoint["input_blocks.0.0.bias"]

				lowercase__					:   Tuple													=					checkpoint["out.0.weight"]
				lowercase__					:   List[Any]													=					checkpoint["out.0.bias"]
				lowercase__					:   Tuple													=					checkpoint["out.2.weight"]
				lowercase__					:   Optional[Any]													=					checkpoint["out.2.bias"]

				# Retrieves the keys for the input blocks only
				lowercase__					:   Dict													=					len({".".join(layer.split("."						)[:2]						) for layer in checkpoint if "input_blocks" in layer}						)
				lowercase__					:   str													=					{
				    layer_id: [key for key in checkpoint if F"""input_blocks.{layer_id}""" in key]
				    for layer_id in range(lowerCamelCase__						)
				}

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

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

				for i in range(1					,			lowerCamelCase__						):
								lowercase__					:   Tuple													=					(i - 1) // (config["num_res_blocks"] + 1)
								lowercase__					:   Optional[int]													=					(i - 1) % (config["num_res_blocks"] + 1)

								lowercase__					:   List[Any]													=					[key for key in input_blocks[i] if F"""input_blocks.{i}.0""" in key]
								lowercase__					:   Dict													=					[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:
												lowercase__					:   int													=					checkpoint[
												    F"""input_blocks.{i}.0.op.weight"""
												]
												lowercase__					:   List[str]													=					checkpoint[
												    F"""input_blocks.{i}.0.op.bias"""
												]
												continue

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

								if len(lowerCamelCase__						):
												lowercase__					:   Tuple													=					renew_attention_paths(lowerCamelCase__						)
												lowercase__					:   str													=					{
												    "old": F"""input_blocks.{i}.1""",
												    "new": F"""down_blocks.{block_id}.attentions.{layer_in_block_id}""",
												}
												lowercase__					:   List[str]													=					{
												    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(
												    lowerCamelCase__					,			lowerCamelCase__					,			lowerCamelCase__					,			additional_replacements=[meta_path]					,			attention_paths_to_split=lowerCamelCase__					,			config=lowerCamelCase__					,			)

				lowercase__					:   int													=					middle_blocks[0]
				lowercase__					:   Dict													=					middle_blocks[1]
				lowercase__					:   Dict													=					middle_blocks[2]

				lowercase__					:   Any													=					renew_resnet_paths(lowerCamelCase__						)
				assign_to_checkpoint(lowerCamelCase__					,			lowerCamelCase__					,			lowerCamelCase__					,			config=lowerCamelCase__						)

				lowercase__					:   List[Any]													=					renew_resnet_paths(lowerCamelCase__						)
				assign_to_checkpoint(lowerCamelCase__					,			lowerCamelCase__					,			lowerCamelCase__					,			config=lowerCamelCase__						)

				lowercase__					:   Optional[int]													=					renew_attention_paths(lowerCamelCase__						)
				lowercase__					:   Optional[int]													=					{
				    "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(
				    lowerCamelCase__					,			lowerCamelCase__					,			lowerCamelCase__					,			attention_paths_to_split=lowerCamelCase__					,			config=lowerCamelCase__						)

				for i in range(lowerCamelCase__						):
								lowercase__					:   List[Any]													=					i // (config["num_res_blocks"] + 1)
								lowercase__					:   Optional[int]													=					i % (config["num_res_blocks"] + 1)
								lowercase__					:   List[Any]													=					[shave_segments(lowerCamelCase__					,			2						) for name in output_blocks[i]]
								lowercase__					:   Optional[Any]													=					{}

								for layer in output_block_layers:
												lowercase__					:   str													=					layer.split("."						)[0], shave_segments(lowerCamelCase__					,			1						)
												if layer_id in output_block_list:
																output_block_list[layer_id].append(lowerCamelCase__						)
												else:
																lowercase__					:   Tuple													=					[layer_name]

								if len(lowerCamelCase__						) > 1:
												lowercase__					:   Dict													=					[key for key in output_blocks[i] if F"""output_blocks.{i}.0""" in key]
												lowercase__					:   Dict													=					[key for key in output_blocks[i] if F"""output_blocks.{i}.1""" in key]

												lowercase__					:   Optional[Any]													=					renew_resnet_paths(lowerCamelCase__						)
												lowercase__					:   Optional[Any]													=					renew_resnet_paths(lowerCamelCase__						)

												lowercase__					:   Tuple													=					{"old": F"""output_blocks.{i}.0""", "new": F"""up_blocks.{block_id}.resnets.{layer_in_block_id}"""}
												assign_to_checkpoint(lowerCamelCase__					,			lowerCamelCase__					,			lowerCamelCase__					,			additional_replacements=[meta_path]					,			config=lowerCamelCase__						)

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

																# Clear attentions as they have been attributed above.
																if len(lowerCamelCase__						) == 2:
																				lowercase__					:   int													=					[]

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

																lowercase__					:   List[Any]													=					checkpoint[old_path]

				return new_checkpoint


if __name__ == "__main__":
				lowerCAmelCase__					  =  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.''')

				lowerCAmelCase__					  =  parser.parse_args()

				lowerCAmelCase__					  =  torch.load(args.checkpoint_path)

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

				lowerCAmelCase__					  =  convert_ldm_checkpoint(checkpoint, config)

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

				lowerCAmelCase__					  =  UNetaDModel(**config)
				model.load_state_dict(converted_checkpoint)

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

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









import argparse
import os.path as osp
import re

import torch
from safetensors.torch import load_file, save_file


# =================#
# UNet Conversion #
# =================#

lowerCAmelCase__					  =  [
    # (stable-diffusion, HF Diffusers)
    ('''time_embed.0.weight''', '''time_embedding.linear_1.weight'''),
    ('''time_embed.0.bias''', '''time_embedding.linear_1.bias'''),
    ('''time_embed.2.weight''', '''time_embedding.linear_2.weight'''),
    ('''time_embed.2.bias''', '''time_embedding.linear_2.bias'''),
    ('''input_blocks.0.0.weight''', '''conv_in.weight'''),
    ('''input_blocks.0.0.bias''', '''conv_in.bias'''),
    ('''out.0.weight''', '''conv_norm_out.weight'''),
    ('''out.0.bias''', '''conv_norm_out.bias'''),
    ('''out.2.weight''', '''conv_out.weight'''),
    ('''out.2.bias''', '''conv_out.bias'''),
]

lowerCAmelCase__					  =  [
    # (stable-diffusion, HF Diffusers)
    ('''in_layers.0''', '''norm1'''),
    ('''in_layers.2''', '''conv1'''),
    ('''out_layers.0''', '''norm2'''),
    ('''out_layers.3''', '''conv2'''),
    ('''emb_layers.1''', '''time_emb_proj'''),
    ('''skip_connection''', '''conv_shortcut'''),
]

lowerCAmelCase__					  =  []
# hardcoded number of downblocks and resnets/attentions...
# would need smarter logic for other networks.
for i in range(4):
				# loop over downblocks/upblocks

				for j in range(2):
								# loop over resnets/attentions for downblocks
								lowerCAmelCase__					  =  f'''down_blocks.{i}.resnets.{j}.'''
								lowerCAmelCase__					  =  f'''input_blocks.{3*i + j + 1}.0.'''
								unet_conversion_map_layer.append((sd_down_res_prefix, hf_down_res_prefix))

								if i < 3:
												# no attention layers in down_blocks.3
												lowerCAmelCase__					  =  f'''down_blocks.{i}.attentions.{j}.'''
												lowerCAmelCase__					  =  f'''input_blocks.{3*i + j + 1}.1.'''
												unet_conversion_map_layer.append((sd_down_atn_prefix, hf_down_atn_prefix))

				for j in range(3):
								# loop over resnets/attentions for upblocks
								lowerCAmelCase__					  =  f'''up_blocks.{i}.resnets.{j}.'''
								lowerCAmelCase__					  =  f'''output_blocks.{3*i + j}.0.'''
								unet_conversion_map_layer.append((sd_up_res_prefix, hf_up_res_prefix))

								if i > 0:
												# no attention layers in up_blocks.0
												lowerCAmelCase__					  =  f'''up_blocks.{i}.attentions.{j}.'''
												lowerCAmelCase__					  =  f'''output_blocks.{3*i + j}.1.'''
												unet_conversion_map_layer.append((sd_up_atn_prefix, hf_up_atn_prefix))

				if i < 3:
								# no downsample in down_blocks.3
								lowerCAmelCase__					  =  f'''down_blocks.{i}.downsamplers.0.conv.'''
								lowerCAmelCase__					  =  f'''input_blocks.{3*(i+1)}.0.op.'''
								unet_conversion_map_layer.append((sd_downsample_prefix, hf_downsample_prefix))

								# no upsample in up_blocks.3
								lowerCAmelCase__					  =  f'''up_blocks.{i}.upsamplers.0.'''
								lowerCAmelCase__					  =  f'''output_blocks.{3*i + 2}.{1 if i == 0 else 2}.'''
								unet_conversion_map_layer.append((sd_upsample_prefix, hf_upsample_prefix))

lowerCAmelCase__					  =  '''mid_block.attentions.0.'''
lowerCAmelCase__					  =  '''middle_block.1.'''
unet_conversion_map_layer.append((sd_mid_atn_prefix, hf_mid_atn_prefix))

for j in range(2):
				lowerCAmelCase__					  =  f'''mid_block.resnets.{j}.'''
				lowerCAmelCase__					  =  f'''middle_block.{2*j}.'''
				unet_conversion_map_layer.append((sd_mid_res_prefix, hf_mid_res_prefix))


def  __lowerCamelCase (       lowerCamelCase__						):

				"""simple docstring"""


				lowercase__					:   List[str]													=					{k: k for k in unet_state_dict.keys()}
				for sd_name, hf_name in unet_conversion_map:
								lowercase__					:   str													=					sd_name
				for k, v in mapping.items():
								if "resnets" in k:
												for sd_part, hf_part in unet_conversion_map_resnet:
																lowercase__					:   List[str]													=					v.replace(lowerCamelCase__					,			lowerCamelCase__						)
												lowercase__					:   str													=					v
				for k, v in mapping.items():
								for sd_part, hf_part in unet_conversion_map_layer:
												lowercase__					:   int													=					v.replace(lowerCamelCase__					,			lowerCamelCase__						)
								lowercase__					:   Optional[Any]													=					v
				lowercase__					:   Union[str, Any]													=					{v: unet_state_dict[k] for k, v in mapping.items()}
				return new_state_dict


# ================#
# VAE Conversion #
# ================#

lowerCAmelCase__					  =  [
    # (stable-diffusion, HF Diffusers)
    ('''nin_shortcut''', '''conv_shortcut'''),
    ('''norm_out''', '''conv_norm_out'''),
    ('''mid.attn_1.''', '''mid_block.attentions.0.'''),
]

for i in range(4):
				# down_blocks have two resnets
				for j in range(2):
								lowerCAmelCase__					  =  f'''encoder.down_blocks.{i}.resnets.{j}.'''
								lowerCAmelCase__					  =  f'''encoder.down.{i}.block.{j}.'''
								vae_conversion_map.append((sd_down_prefix, hf_down_prefix))

				if i < 3:
								lowerCAmelCase__					  =  f'''down_blocks.{i}.downsamplers.0.'''
								lowerCAmelCase__					  =  f'''down.{i}.downsample.'''
								vae_conversion_map.append((sd_downsample_prefix, hf_downsample_prefix))

								lowerCAmelCase__					  =  f'''up_blocks.{i}.upsamplers.0.'''
								lowerCAmelCase__					  =  f'''up.{3-i}.upsample.'''
								vae_conversion_map.append((sd_upsample_prefix, hf_upsample_prefix))

				# up_blocks have three resnets
				# also, up blocks in hf are numbered in reverse from sd
				for j in range(3):
								lowerCAmelCase__					  =  f'''decoder.up_blocks.{i}.resnets.{j}.'''
								lowerCAmelCase__					  =  f'''decoder.up.{3-i}.block.{j}.'''
								vae_conversion_map.append((sd_up_prefix, hf_up_prefix))

# this part accounts for mid blocks in both the encoder and the decoder
for i in range(2):
				lowerCAmelCase__					  =  f'''mid_block.resnets.{i}.'''
				lowerCAmelCase__					  =  f'''mid.block_{i+1}.'''
				vae_conversion_map.append((sd_mid_res_prefix, hf_mid_res_prefix))


lowerCAmelCase__					  =  [
    # (stable-diffusion, HF Diffusers)
    ('''norm.''', '''group_norm.'''),
    ('''q.''', '''query.'''),
    ('''k.''', '''key.'''),
    ('''v.''', '''value.'''),
    ('''proj_out.''', '''proj_attn.'''),
]


def  __lowerCamelCase (       lowerCamelCase__						):

				"""simple docstring"""


				return w.reshape(*w.shape					,			1					,			1						)


def  __lowerCamelCase (       lowerCamelCase__						):

				"""simple docstring"""


				lowercase__					:   str													=					{k: k for k in vae_state_dict.keys()}
				for k, v in mapping.items():
								for sd_part, hf_part in vae_conversion_map:
												lowercase__					:   Optional[int]													=					v.replace(lowerCamelCase__					,			lowerCamelCase__						)
								lowercase__					:   Dict													=					v
				for k, v in mapping.items():
								if "attentions" in k:
												for sd_part, hf_part in vae_conversion_map_attn:
																lowercase__					:   List[str]													=					v.replace(lowerCamelCase__					,			lowerCamelCase__						)
												lowercase__					:   int													=					v
				lowercase__					:   Union[str, Any]													=					{v: vae_state_dict[k] for k, v in mapping.items()}
				lowercase__					:   Optional[int]													=					["q", "k", "v", "proj_out"]
				for k, v in new_state_dict.items():
								for weight_name in weights_to_convert:
												if F"""mid.attn_1.{weight_name}.weight""" in k:
																print(F"""Reshaping {k} for SD format"""						)
																lowercase__					:   Dict													=					reshape_weight_for_sd(lowerCamelCase__						)
				return new_state_dict


# =========================#
# Text Encoder Conversion #
# =========================#


lowerCAmelCase__					  =  [
    # (stable-diffusion, HF Diffusers)
    ('''resblocks.''', '''text_model.encoder.layers.'''),
    ('''ln_1''', '''layer_norm1'''),
    ('''ln_2''', '''layer_norm2'''),
    ('''.c_fc.''', '''.fc1.'''),
    ('''.c_proj.''', '''.fc2.'''),
    ('''.attn''', '''.self_attn'''),
    ('''ln_final.''', '''transformer.text_model.final_layer_norm.'''),
    ('''token_embedding.weight''', '''transformer.text_model.embeddings.token_embedding.weight'''),
    ('''positional_embedding''', '''transformer.text_model.embeddings.position_embedding.weight'''),
]
lowerCAmelCase__					  =  {re.escape(x[1]): x[0] for x in textenc_conversion_lst}
lowerCAmelCase__					  =  re.compile('''|'''.join(protected.keys()))

# Ordering is from https://github.com/pytorch/pytorch/blob/master/test/cpp/api/modules.cpp
lowerCAmelCase__					  =  {'''q''': 0, '''k''': 1, '''v''': 2}


def  __lowerCamelCase (       lowerCamelCase__						):

				"""simple docstring"""


				lowercase__					:   Union[str, Any]													=					{}
				lowercase__					:   List[Any]													=					{}
				lowercase__					:   List[Any]													=					{}
				for k, v in text_enc_dict.items():
								if (
								    k.endswith(".self_attn.q_proj.weight"						)
								    or k.endswith(".self_attn.k_proj.weight"						)
								    or k.endswith(".self_attn.v_proj.weight"						)
								):
												lowercase__					:   int													=					k[: -len(".q_proj.weight"						)]
												lowercase__					:   Optional[Any]													=					k[-len("q_proj.weight"						)]
												if k_pre not in capture_qkv_weight:
																lowercase__					:   Dict													=					[None, None, None]
												lowercase__					:   Any													=					v
												continue

								if (
								    k.endswith(".self_attn.q_proj.bias"						)
								    or k.endswith(".self_attn.k_proj.bias"						)
								    or k.endswith(".self_attn.v_proj.bias"						)
								):
												lowercase__					:   Optional[int]													=					k[: -len(".q_proj.bias"						)]
												lowercase__					:   Any													=					k[-len("q_proj.bias"						)]
												if k_pre not in capture_qkv_bias:
																lowercase__					:   str													=					[None, None, None]
												lowercase__					:   str													=					v
												continue

								lowercase__					:   Union[str, Any]													=					textenc_pattern.sub(lambda lowerCamelCase__						: protected[re.escape(m.group(0						)						)]					,			lowerCamelCase__						)
								lowercase__					:   List[Any]													=					v

				for k_pre, tensors in capture_qkv_weight.items():
								if None in tensors:
												raise Exception("CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing"						)
								lowercase__					:   str													=					textenc_pattern.sub(lambda lowerCamelCase__						: protected[re.escape(m.group(0						)						)]					,			lowerCamelCase__						)
								lowercase__					:   Any													=					torch.cat(lowerCamelCase__						)

				for k_pre, tensors in capture_qkv_bias.items():
								if None in tensors:
												raise Exception("CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing"						)
								lowercase__					:   List[str]													=					textenc_pattern.sub(lambda lowerCamelCase__						: protected[re.escape(m.group(0						)						)]					,			lowerCamelCase__						)
								lowercase__					:   Tuple													=					torch.cat(lowerCamelCase__						)

				return new_state_dict


def  __lowerCamelCase (       lowerCamelCase__						):

				"""simple docstring"""


				return text_enc_dict


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

				parser.add_argument('''--model_path''', default=None, type=str, required=True, help='''Path to the model to convert.''')
				parser.add_argument('''--checkpoint_path''', default=None, type=str, required=True, help='''Path to the output model.''')
				parser.add_argument('''--half''', action='''store_true''', help='''Save weights in half precision.''')
				parser.add_argument(
				    '''--use_safetensors''', action='''store_true''', help='''Save weights use safetensors, default is ckpt.'''
				)

				lowerCAmelCase__					  =  parser.parse_args()

				assert args.model_path is not None, "Must provide a model path!"

				assert args.checkpoint_path is not None, "Must provide a checkpoint path!"

				# Path for safetensors
				lowerCAmelCase__					  =  osp.join(args.model_path, '''unet''', '''diffusion_pytorch_model.safetensors''')
				lowerCAmelCase__					  =  osp.join(args.model_path, '''vae''', '''diffusion_pytorch_model.safetensors''')
				lowerCAmelCase__					  =  osp.join(args.model_path, '''text_encoder''', '''model.safetensors''')

				# Load models from safetensors if it exists, if it doesn't pytorch
				if osp.exists(unet_path):
								lowerCAmelCase__					  =  load_file(unet_path, device='''cpu''')
				else:
								lowerCAmelCase__					  =  osp.join(args.model_path, '''unet''', '''diffusion_pytorch_model.bin''')
								lowerCAmelCase__					  =  torch.load(unet_path, map_location='''cpu''')

				if osp.exists(vae_path):
								lowerCAmelCase__					  =  load_file(vae_path, device='''cpu''')
				else:
								lowerCAmelCase__					  =  osp.join(args.model_path, '''vae''', '''diffusion_pytorch_model.bin''')
								lowerCAmelCase__					  =  torch.load(vae_path, map_location='''cpu''')

				if osp.exists(text_enc_path):
								lowerCAmelCase__					  =  load_file(text_enc_path, device='''cpu''')
				else:
								lowerCAmelCase__					  =  osp.join(args.model_path, '''text_encoder''', '''pytorch_model.bin''')
								lowerCAmelCase__					  =  torch.load(text_enc_path, map_location='''cpu''')

				# Convert the UNet model
				lowerCAmelCase__					  =  convert_unet_state_dict(unet_state_dict)
				lowerCAmelCase__					  =  {'''model.diffusion_model.''' + k: v for k, v in unet_state_dict.items()}

				# Convert the VAE model
				lowerCAmelCase__					  =  convert_vae_state_dict(vae_state_dict)
				lowerCAmelCase__					  =  {'''first_stage_model.''' + k: v for k, v in vae_state_dict.items()}

				# Easiest way to identify v2.0 model seems to be that the text encoder (OpenCLIP) is deeper
				lowerCAmelCase__					  =  '''text_model.encoder.layers.22.layer_norm2.bias''' in text_enc_dict

				if is_vaa_model:
								# Need to add the tag 'transformer' in advance so we can knock it out from the final layer-norm
								lowerCAmelCase__					  =  {'''transformer.''' + k: v for k, v in text_enc_dict.items()}
								lowerCAmelCase__					  =  convert_text_enc_state_dict_vaa(text_enc_dict)
								lowerCAmelCase__					  =  {'''cond_stage_model.model.''' + k: v for k, v in text_enc_dict.items()}
				else:
								lowerCAmelCase__					  =  convert_text_enc_state_dict(text_enc_dict)
								lowerCAmelCase__					  =  {'''cond_stage_model.transformer.''' + k: v for k, v in text_enc_dict.items()}

				# Put together new checkpoint
				lowerCAmelCase__					  =  {**unet_state_dict, **vae_state_dict, **text_enc_dict}
				if args.half:
								lowerCAmelCase__					  =  {k: v.half() for k, v in state_dict.items()}

				if args.use_safetensors:
								save_file(state_dict, args.checkpoint_path)
				else:
								lowerCAmelCase__					  =  {'''state_dict''': state_dict}
								torch.save(state_dict, args.checkpoint_path)




