






import os
from typing import Dict, List, Tuple, TypeVar, Union


SCREAMING_SNAKE_CASE__		:							Any 				=				TypeVar("""T""")

SCREAMING_SNAKE_CASE__		:							List[Any] 				=				Union[List[T], Tuple[T, ...]]
SCREAMING_SNAKE_CASE__		:							int 				=				Union[T, List[T], Dict[str, T]]
SCREAMING_SNAKE_CASE__		:							Optional[int] 				=				Union[str, bytes, os.PathLike]










import argparse
import os.path as osp
import re

import torch
from safetensors.torch import load_file, save_file


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

UpperCAmelCase   :    Tuple     =		[
    # (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"),
]

UpperCAmelCase   :    Any     =		[
    # (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"),
]

UpperCAmelCase   :    int     =		[]
# 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
				UpperCAmelCase   :    Any     =		f"""down_blocks.{i}.resnets.{j}."""
				UpperCAmelCase   :    Dict     =		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
						UpperCAmelCase   :    List[Any]     =		f"""down_blocks.{i}.attentions.{j}."""
						UpperCAmelCase   :    Optional[int]     =		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
				UpperCAmelCase   :    List[Any]     =		f"""up_blocks.{i}.resnets.{j}."""
				UpperCAmelCase   :    int     =		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
						UpperCAmelCase   :    Optional[Any]     =		f"""up_blocks.{i}.attentions.{j}."""
						UpperCAmelCase   :    Tuple     =		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
				UpperCAmelCase   :    int     =		f"""down_blocks.{i}.downsamplers.0.conv."""
				UpperCAmelCase   :    Union[str, Any]     =		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
				UpperCAmelCase   :    Optional[Any]     =		f"""up_blocks.{i}.upsamplers.0."""
				UpperCAmelCase   :    str     =		f"""output_blocks.{3*i + 2}.{1 if i == 0 else 2}."""
				unet_conversion_map_layer.append((sd_upsample_prefix, hf_upsample_prefix))

UpperCAmelCase   :    str     =		"mid_block.attentions.0."
UpperCAmelCase   :    int     =		"middle_block.1."
unet_conversion_map_layer.append((sd_mid_atn_prefix, hf_mid_atn_prefix))

for j in range(2):
		UpperCAmelCase   :    int     =		f"""mid_block.resnets.{j}."""
		UpperCAmelCase   :    List[str]     =		f"""middle_block.{2*j}."""
		unet_conversion_map_layer.append((sd_mid_res_prefix, hf_mid_res_prefix))






def 	__lowerCamelCase						(							lowerCamelCase__		:						Any      ):





			'''simple docstring'''




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


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

UpperCAmelCase   :    int     =		[
    # (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):
				UpperCAmelCase   :    List[Any]     =		f"""encoder.down_blocks.{i}.resnets.{j}."""
				UpperCAmelCase   :    int     =		f"""encoder.down.{i}.block.{j}."""
				vae_conversion_map.append((sd_down_prefix, hf_down_prefix))

		if i < 3:
				UpperCAmelCase   :    Any     =		f"""down_blocks.{i}.downsamplers.0."""
				UpperCAmelCase   :    Tuple     =		f"""down.{i}.downsample."""
				vae_conversion_map.append((sd_downsample_prefix, hf_downsample_prefix))

				UpperCAmelCase   :    List[Any]     =		f"""up_blocks.{i}.upsamplers.0."""
				UpperCAmelCase   :    List[str]     =		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):
				UpperCAmelCase   :    Any     =		f"""decoder.up_blocks.{i}.resnets.{j}."""
				UpperCAmelCase   :    str     =		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):
		UpperCAmelCase   :    Dict     =		f"""mid_block.resnets.{i}."""
		UpperCAmelCase   :    Union[str, Any]     =		f"""mid.block_{i+1}."""
		vae_conversion_map.append((sd_mid_res_prefix, hf_mid_res_prefix))


UpperCAmelCase   :    Tuple     =		[
    # (stable-diffusion, HF Diffusers)
    ("norm.", "group_norm."),
    ("q.", "query."),
    ("k.", "key."),
    ("v.", "value."),
    ("proj_out.", "proj_attn."),
]






def 	__lowerCamelCase						(							lowerCamelCase__		:						Optional[int]      ):





			'''simple docstring'''




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






def 	__lowerCamelCase						(							lowerCamelCase__		:						int      ):





			'''simple docstring'''




			lowerCamelCase    		=  {k: k for k in vae_state_dict.keys()}
			for k, v in mapping.items():
						for sd_part, hf_part in vae_conversion_map:
									lowerCamelCase    		=  v.replace(lowerCamelCase__  ,							lowerCamelCase__      )
						lowerCamelCase    		=  v
			for k, v in mapping.items():
						if "attentions" in k:
									for sd_part, hf_part in vae_conversion_map_attn:
												lowerCamelCase    		=  v.replace(lowerCamelCase__  ,							lowerCamelCase__      )
									lowerCamelCase    		=  v
			lowerCamelCase    		=  {v: vae_state_dict[k] for k, v in mapping.items()}
			lowerCamelCase    		=  ["""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'      )
												lowerCamelCase    		=  reshape_weight_for_sd(lowerCamelCase__      )
			return new_state_dict


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


UpperCAmelCase   :    Union[str, Any]     =		[
    # (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"),
]
UpperCAmelCase   :    str     =		{re.escape(x[1]): x[0] for x in textenc_conversion_lst}
UpperCAmelCase   :    List[Any]     =		re.compile("|".join(protected.keys()))

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






def 	__lowerCamelCase						(							lowerCamelCase__		:						int      ):





			'''simple docstring'''




			lowerCamelCase    		=  {}
			lowerCamelCase    		=  {}
			lowerCamelCase    		=  {}
			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"""      )
						):
									lowerCamelCase    		=  k[: -len(""".q_proj.weight"""      )]
									lowerCamelCase    		=  k[-len("""q_proj.weight"""      )]
									if k_pre not in capture_qkv_weight:
												lowerCamelCase    		=  [None, None, None]
									lowerCamelCase    		=  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"""      )
						):
									lowerCamelCase    		=  k[: -len(""".q_proj.bias"""      )]
									lowerCamelCase    		=  k[-len("""q_proj.bias"""      )]
									if k_pre not in capture_qkv_bias:
												lowerCamelCase    		=  [None, None, None]
									lowerCamelCase    		=  v
									continue

						lowerCamelCase    		=  textenc_pattern.sub(lambda lowerCamelCase__      : protected[re.escape(m.group(0      )      )]  ,							lowerCamelCase__      )
						lowerCamelCase    		=  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"""      )
						lowerCamelCase    		=  textenc_pattern.sub(lambda lowerCamelCase__      : protected[re.escape(m.group(0      )      )]  ,							lowerCamelCase__      )
						lowerCamelCase    		=  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"""      )
						lowerCamelCase    		=  textenc_pattern.sub(lambda lowerCamelCase__      : protected[re.escape(m.group(0      )      )]  ,							lowerCamelCase__      )
						lowerCamelCase    		=  torch.cat(lowerCamelCase__      )

			return new_state_dict






def 	__lowerCamelCase						(							lowerCamelCase__		:						int      ):





			'''simple docstring'''




			return text_enc_dict


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

		UpperCAmelCase   :    List[str]     =		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
		UpperCAmelCase   :    int     =		osp.join(args.model_path, "unet", "diffusion_pytorch_model.safetensors")
		UpperCAmelCase   :    Dict     =		osp.join(args.model_path, "vae", "diffusion_pytorch_model.safetensors")
		UpperCAmelCase   :    Dict     =		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):
				UpperCAmelCase   :    Tuple     =		load_file(unet_path, device="cpu")
		else:
				UpperCAmelCase   :    Tuple     =		osp.join(args.model_path, "unet", "diffusion_pytorch_model.bin")
				UpperCAmelCase   :    List[Any]     =		torch.load(unet_path, map_location="cpu")

		if osp.exists(vae_path):
				UpperCAmelCase   :    Any     =		load_file(vae_path, device="cpu")
		else:
				UpperCAmelCase   :    Dict     =		osp.join(args.model_path, "vae", "diffusion_pytorch_model.bin")
				UpperCAmelCase   :    Dict     =		torch.load(vae_path, map_location="cpu")

		if osp.exists(text_enc_path):
				UpperCAmelCase   :    str     =		load_file(text_enc_path, device="cpu")
		else:
				UpperCAmelCase   :    Optional[int]     =		osp.join(args.model_path, "text_encoder", "pytorch_model.bin")
				UpperCAmelCase   :    str     =		torch.load(text_enc_path, map_location="cpu")

		# Convert the UNet model
		UpperCAmelCase   :    List[str]     =		convert_unet_state_dict(unet_state_dict)
		UpperCAmelCase   :    List[str]     =		{"model.diffusion_model." + k: v for k, v in unet_state_dict.items()}

		# Convert the VAE model
		UpperCAmelCase   :    Optional[Any]     =		convert_vae_state_dict(vae_state_dict)
		UpperCAmelCase   :    Union[str, Any]     =		{"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
		UpperCAmelCase   :    str     =		"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
				UpperCAmelCase   :    Optional[Any]     =		{"transformer." + k: v for k, v in text_enc_dict.items()}
				UpperCAmelCase   :    List[str]     =		convert_text_enc_state_dict_vaa(text_enc_dict)
				UpperCAmelCase   :    Optional[Any]     =		{"cond_stage_model.model." + k: v for k, v in text_enc_dict.items()}
		else:
				UpperCAmelCase   :    int     =		convert_text_enc_state_dict(text_enc_dict)
				UpperCAmelCase   :    Optional[Any]     =		{"cond_stage_model.transformer." + k: v for k, v in text_enc_dict.items()}

		# Put together new checkpoint
		UpperCAmelCase   :    Dict     =		{**unet_state_dict, **vae_state_dict, **text_enc_dict}
		if args.half:
				UpperCAmelCase   :    List[Any]     =		{k: v.half() for k, v in state_dict.items()}

		if args.use_safetensors:
				save_file(state_dict, args.checkpoint_path)
		else:
				UpperCAmelCase   :    Union[str, Any]     =		{"state_dict": state_dict}
				torch.save(state_dict, args.checkpoint_path)

