



import argparse
import os.path as osp
import re

import torch
from safetensors.torch import load_file, save_file


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

__lowerCamelCase     :							List[Any]  							=				[
    # (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     :							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"),
]

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

__lowerCamelCase     :							Dict  							=				"mid_block.attentions.0."
__lowerCamelCase     :							Dict  							=				"middle_block.1."
unet_conversion_map_layer.append((sd_mid_atn_prefix, hf_mid_atn_prefix))

for j in range(2):
		__lowerCamelCase     :							Dict  							=				F'''mid_block.resnets.{j}.'''
		__lowerCamelCase     :							Optional[int]  							=				F'''middle_block.{2*j}.'''
		unet_conversion_map_layer.append((sd_mid_res_prefix, hf_mid_res_prefix))

def    lowerCamelCase_(lowerCamelCase_    ) ->       Tuple:
						# buyer beware: this is a *brittle* function,
						# and correct output requires that all of these pieces interact in
						# the exact order in which I have arranged them.
						UpperCAmelCase         =     {k: k for k in unet_state_dict.keys()}
						for sd_name, hf_name in unet_conversion_map:
												UpperCAmelCase         =     sd_name
						for k, v in mapping.items():
												if "resnets" in k:
																		for sd_part, hf_part in unet_conversion_map_resnet:
																								UpperCAmelCase         =     v.replace(lowerCamelCase_    ,   lowerCamelCase_    )
																		UpperCAmelCase         =     v
						for k, v in mapping.items():
												for sd_part, hf_part in unet_conversion_map_layer:
																		UpperCAmelCase         =     v.replace(lowerCamelCase_    ,   lowerCamelCase_    )
												UpperCAmelCase         =     v
						UpperCAmelCase         =     {v: unet_state_dict[k] for k, v in mapping.items()}
						return new_state_dict


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

__lowerCamelCase     :							Dict  							=				[
    # (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     :							Optional[int]  							=				F'''encoder.down_blocks.{i}.resnets.{j}.'''
				__lowerCamelCase     :							int  							=				F'''encoder.down.{i}.block.{j}.'''
				vae_conversion_map.append((sd_down_prefix, hf_down_prefix))

		if i < 3:
				__lowerCamelCase     :							Optional[int]  							=				F'''down_blocks.{i}.downsamplers.0.'''
				__lowerCamelCase     :							List[Any]  							=				F'''down.{i}.downsample.'''
				vae_conversion_map.append((sd_downsample_prefix, hf_downsample_prefix))

				__lowerCamelCase     :							Union[str, Any]  							=				F'''up_blocks.{i}.upsamplers.0.'''
				__lowerCamelCase     :							int  							=				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     :							int  							=				F'''decoder.up_blocks.{i}.resnets.{j}.'''
				__lowerCamelCase     :							List[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):
		__lowerCamelCase     :							Any  							=				F'''mid_block.resnets.{i}.'''
		__lowerCamelCase     :							List[str]  							=				F'''mid.block_{i+1}.'''
		vae_conversion_map.append((sd_mid_res_prefix, hf_mid_res_prefix))


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

def    lowerCamelCase_(lowerCamelCase_    ) ->       Dict:
						# convert HF linear weights to SD conv2d weights
						return w.reshape(*w.shape    ,   1    ,   1    )

def    lowerCamelCase_(lowerCamelCase_    ) ->       str:
						UpperCAmelCase         =     {k: k for k in vae_state_dict.keys()}
						for k, v in mapping.items():
												for sd_part, hf_part in vae_conversion_map:
																		UpperCAmelCase         =     v.replace(lowerCamelCase_    ,   lowerCamelCase_    )
												UpperCAmelCase         =     v
						for k, v in mapping.items():
												if "attentions" in k:
																		for sd_part, hf_part in vae_conversion_map_attn:
																								UpperCAmelCase         =     v.replace(lowerCamelCase_    ,   lowerCamelCase_    )
																		UpperCAmelCase         =     v
						UpperCAmelCase         =     {v: vae_state_dict[k] for k, v in mapping.items()}
						UpperCAmelCase         =     ["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'    )
																								UpperCAmelCase         =     reshape_weight_for_sd(lowerCamelCase_    )
						return new_state_dict


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


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

# Ordering is from https://github.com/pytorch/pytorch/blob/master/test/cpp/api/modules.cpp
__lowerCamelCase     :							Union[str, Any]  							=				{"q": 0, "k": 1, "v": 2}

def    lowerCamelCase_(lowerCamelCase_    ) ->       List[Any]:
						UpperCAmelCase         =     {}
						UpperCAmelCase         =     {}
						UpperCAmelCase         =     {}
						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"    )
												):
																		UpperCAmelCase         =     k[: -len(".q_proj.weight"    )]
																		UpperCAmelCase         =     k[-len("q_proj.weight"    )]
																		if k_pre not in capture_qkv_weight:
																								UpperCAmelCase         =     [None, None, None]
																		UpperCAmelCase         =     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"    )
												):
																		UpperCAmelCase         =     k[: -len(".q_proj.bias"    )]
																		UpperCAmelCase         =     k[-len("q_proj.bias"    )]
																		if k_pre not in capture_qkv_bias:
																								UpperCAmelCase         =     [None, None, None]
																		UpperCAmelCase         =     v
																		continue

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

						return new_state_dict

def    lowerCamelCase_(lowerCamelCase_    ) ->       List[str]:
						return text_enc_dict


if __name__ == "__main__":
		__lowerCamelCase     :							Union[str, Any]  							=				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     :							Optional[Any]  							=				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     :							List[str]  							=				osp.join(args.model_path, "unet", "diffusion_pytorch_model.safetensors")
		__lowerCamelCase     :							Optional[Any]  							=				osp.join(args.model_path, "vae", "diffusion_pytorch_model.safetensors")
		__lowerCamelCase     :							Optional[Any]  							=				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     :							Union[str, Any]  							=				load_file(unet_path, device="cpu")
		else:
				__lowerCamelCase     :							Any  							=				osp.join(args.model_path, "unet", "diffusion_pytorch_model.bin")
				__lowerCamelCase     :							Tuple  							=				torch.load(unet_path, map_location="cpu")

		if osp.exists(vae_path):
				__lowerCamelCase     :							Optional[Any]  							=				load_file(vae_path, device="cpu")
		else:
				__lowerCamelCase     :							Union[str, Any]  							=				osp.join(args.model_path, "vae", "diffusion_pytorch_model.bin")
				__lowerCamelCase     :							List[str]  							=				torch.load(vae_path, map_location="cpu")

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

		# Convert the UNet model
		__lowerCamelCase     :							Tuple  							=				convert_unet_state_dict(unet_state_dict)
		__lowerCamelCase     :							int  							=				{"model.diffusion_model." + k: v for k, v in unet_state_dict.items()}

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

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

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








def    lowerCamelCase_(lowerCamelCase_ = 600_851_475_143    ) ->       int:

						try:
												UpperCAmelCase         =     int(lowerCamelCase_    )
						except (TypeError, ValueError):
												raise TypeError("Parameter n must be int or castable to int."    )
						if n <= 0:
												raise ValueError("Parameter n must be greater than or equal to one."    )
						UpperCAmelCase         =     1
						UpperCAmelCase         =     2
						while i * i <= n:
												while n % i == 0:
																		UpperCAmelCase         =     i
																		n //= i
												i += 1
						if n > 1:
												UpperCAmelCase         =     n
						return int(lowerCamelCase_    )


if __name__ == "__main__":
		print(F'''{solution() = }''')



