

import argparse
import os.path as osp
import re

import torch
from safetensors.torch import load_file, save_file


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

A__						=							[
    # (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"),
]

A__						=							[
    # (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"),
]

A__						=							[]
# 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
												A__						=							f'''down_blocks.{i}.resnets.{j}.'''
												A__						=							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
																		A__						=							f'''down_blocks.{i}.attentions.{j}.'''
																		A__						=							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
												A__						=							f'''up_blocks.{i}.resnets.{j}.'''
												A__						=							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
																		A__						=							f'''up_blocks.{i}.attentions.{j}.'''
																		A__						=							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
												A__						=							f'''down_blocks.{i}.downsamplers.0.conv.'''
												A__						=							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
												A__						=							f'''up_blocks.{i}.upsamplers.0.'''
												A__						=							f'''output_blocks.{3*i + 2}.{1 if i == 0 else 2}.'''
												unet_conversion_map_layer.append((sd_upsample_prefix, hf_upsample_prefix))

A__						=							"mid_block.attentions.0."
A__						=							"middle_block.1."
unet_conversion_map_layer.append((sd_mid_atn_prefix, hf_mid_atn_prefix))

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




def 					_lowercase     (				a_				:					List[str]				)				->	int:




					'''simple docstring'''







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


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

A__						=							[
    # (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):
												A__						=							f'''encoder.down_blocks.{i}.resnets.{j}.'''
												A__						=							f'''encoder.down.{i}.block.{j}.'''
												vae_conversion_map.append((sd_down_prefix, hf_down_prefix))

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

												A__						=							f'''up_blocks.{i}.upsamplers.0.'''
												A__						=							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):
												A__						=							f'''decoder.up_blocks.{i}.resnets.{j}.'''
												A__						=							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):
						A__						=							f'''mid_block.resnets.{i}.'''
						A__						=							f'''mid.block_{i+1}.'''
						vae_conversion_map.append((sd_mid_res_prefix, hf_mid_res_prefix))


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




def 					_lowercase     (				a_				:					List[str]				)				->	List[Any]:




					'''simple docstring'''







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




def 					_lowercase     (				a_				:					List[str]				)				->	Tuple:




					'''simple docstring'''







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


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


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

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




def 					_lowercase     (				a_				:					Optional[int]				)				->	Tuple:




					'''simple docstring'''







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

										__magic_name__							=    textenc_pattern.sub(lambda a_				: protected[re.escape(m.group(0				)				)]					,snake_case__				)
										__magic_name__							=    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'				)
										__magic_name__							=    textenc_pattern.sub(lambda a_				: protected[re.escape(m.group(0				)				)]					,snake_case__				)
										__magic_name__							=    torch.cat(snake_case__				)

					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'				)
										__magic_name__							=    textenc_pattern.sub(lambda a_				: protected[re.escape(m.group(0				)				)]					,snake_case__				)
										__magic_name__							=    torch.cat(snake_case__				)

					return new_state_dict




def 					_lowercase     (				a_				:					Optional[Any]				)				->	List[str]:




					'''simple docstring'''







					return text_enc_dict


if __name__ == "__main__":
						A__						=							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."
						)

						A__						=							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
						A__						=							osp.join(args.model_path, "unet", "diffusion_pytorch_model.safetensors")
						A__						=							osp.join(args.model_path, "vae", "diffusion_pytorch_model.safetensors")
						A__						=							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):
												A__						=							load_file(unet_path, device="cpu")
						else:
												A__						=							osp.join(args.model_path, "unet", "diffusion_pytorch_model.bin")
												A__						=							torch.load(unet_path, map_location="cpu")

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

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

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

						# Convert the VAE model
						A__						=							convert_vae_state_dict(vae_state_dict)
						A__						=							{"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
						A__						=							"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
												A__						=							{"transformer." + k: v for k, v in text_enc_dict.items()}
												A__						=							convert_text_enc_state_dict_vaa(text_enc_dict)
												A__						=							{"cond_stage_model.model." + k: v for k, v in text_enc_dict.items()}
						else:
												A__						=							convert_text_enc_state_dict(text_enc_dict)
												A__						=							{"cond_stage_model.transformer." + k: v for k, v in text_enc_dict.items()}

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

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



from __future__ import annotations




def 					_lowercase     (				a_				:					list[int]				)				->	int:




					'''simple docstring'''







					if not nums:
										return 0
					__magic_name__							=    nums[0]
					__magic_name__							=    0
					for num in nums[1:]:
										__magic_name__,	__magic_name__							=    (
										    max_excluding + num,
										    max(a_					,a_				),
										)
					return max(a_					,a_				)


if __name__ == "__main__":
						import doctest

						doctest.testmod()




