import argparse
import os

import torch
from torchvision.datasets.utils import download_url

from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, Transformer2DModel


pretrained_models = {512: "DiT-XL-2-512x512.pt", 256: "DiT-XL-2-256x256.pt"}


def download_model(model_name):
 """
 Downloads a pre-trained DiT model from the web.
 """
 local_path = f"pretrained_models/{model_name}"
 if not os.path.isfile(local_path):
 os.makedirs("pretrained_models", exist_ok=True)
 web_path = f"https://dl.fbaipublicfiles.com/DiT/models/{model_name}"
 download_url(web_path, "pretrained_models")
 model = torch.load(local_path, map_location=lambda storage, loc: storage)
 return model


def main(args):
 state_dict = download_model(pretrained_models[args.image_size])

 state_dict["pos_embed.proj.weight"] = state_dict["x_embedder.proj.weight"]
 state_dict["pos_embed.proj.bias"] = state_dict["x_embedder.proj.bias"]
 state_dict.pop("x_embedder.proj.weight")
 state_dict.pop("x_embedder.proj.bias")

 for depth in range(28):
 state_dict[f"transformer_blocks.{depth}.norm1.emb.timestep_embedder.linear_1.weight"] = state_dict[
 "t_embedder.mlp.0.weight"
 ]
 state_dict[f"transformer_blocks.{depth}.norm1.emb.timestep_embedder.linear_1.bias"] = state_dict[
 "t_embedder.mlp.0.bias"
 ]
 state_dict[f"transformer_blocks.{depth}.norm1.emb.timestep_embedder.linear_2.weight"] = state_dict[
 "t_embedder.mlp.2.weight"
 ]
 state_dict[f"transformer_blocks.{depth}.norm1.emb.timestep_embedder.linear_2.bias"] = state_dict[
 "t_embedder.mlp.2.bias"
 ]
 state_dict[f"transformer_blocks.{depth}.norm1.emb.class_embedder.embedding_table.weight"] = state_dict[
 "y_embedder.embedding_table.weight"
 ]

 state_dict[f"transformer_blocks.{depth}.norm1.linear.weight"] = state_dict[
 f"blocks.{depth}.adaLN_modulation.1.weight"
 ]
 state_dict[f"transformer_blocks.{depth}.norm1.linear.bias"] = state_dict[
 f"blocks.{depth}.adaLN_modulation.1.bias"
 ]

 q, k, v = torch.chunk(state_dict[f"blocks.{depth}.attn.qkv.weight"], 3, dim=0)
 q_bias, k_bias, v_bias = torch.chunk(state_dict[f"blocks.{depth}.attn.qkv.bias"], 3, dim=0)

 state_dict[f"transformer_blocks.{depth}.attn1.to_q.weight"] = q
 state_dict[f"transformer_blocks.{depth}.attn1.to_q.bias"] = q_bias
 state_dict[f"transformer_blocks.{depth}.attn1.to_k.weight"] = k
 state_dict[f"transformer_blocks.{depth}.attn1.to_k.bias"] = k_bias
 state_dict[f"transformer_blocks.{depth}.attn1.to_v.weight"] = v
 state_dict[f"transformer_blocks.{depth}.attn1.to_v.bias"] = v_bias

 state_dict[f"transformer_blocks.{depth}.attn1.to_out.0.weight"] = state_dict[
 f"blocks.{depth}.attn.proj.weight"
 ]
 state_dict[f"transformer_blocks.{depth}.attn1.to_out.0.bias"] = state_dict[f"blocks.{depth}.attn.proj.bias"]

 state_dict[f"transformer_blocks.{depth}.ff.net.0.proj.weight"] = state_dict[f"blocks.{depth}.mlp.fc1.weight"]
 state_dict[f"transformer_blocks.{depth}.ff.net.0.proj.bias"] = state_dict[f"blocks.{depth}.mlp.fc1.bias"]
 state_dict[f"transformer_blocks.{depth}.ff.net.2.weight"] = state_dict[f"blocks.{depth}.mlp.fc2.weight"]
 state_dict[f"transformer_blocks.{depth}.ff.net.2.bias"] = state_dict[f"blocks.{depth}.mlp.fc2.bias"]

 state_dict.pop(f"blocks.{depth}.attn.qkv.weight")
 state_dict.pop(f"blocks.{depth}.attn.qkv.bias")
 state_dict.pop(f"blocks.{depth}.attn.proj.weight")
 state_dict.pop(f"blocks.{depth}.attn.proj.bias")
 state_dict.pop(f"blocks.{depth}.mlp.fc1.weight")
 state_dict.pop(f"blocks.{depth}.mlp.fc1.bias")
 state_dict.pop(f"blocks.{depth}.mlp.fc2.weight")
 state_dict.pop(f"blocks.{depth}.mlp.fc2.bias")
 state_dict.pop(f"blocks.{depth}.adaLN_modulation.1.weight")
 state_dict.pop(f"blocks.{depth}.adaLN_modulation.1.bias")

 state_dict.pop("t_embedder.mlp.0.weight")
 state_dict.pop("t_embedder.mlp.0.bias")
 state_dict.pop("t_embedder.mlp.2.weight")
 state_dict.pop("t_embedder.mlp.2.bias")
 state_dict.pop("y_embedder.embedding_table.weight")

 state_dict["proj_out_1.weight"] = state_dict["final_layer.adaLN_modulation.1.weight"]
 state_dict["proj_out_1.bias"] = state_dict["final_layer.adaLN_modulation.1.bias"]
 state_dict["proj_out_2.weight"] = state_dict["final_layer.linear.weight"]
 state_dict["proj_out_2.bias"] = state_dict["final_layer.linear.bias"]

 state_dict.pop("final_layer.linear.weight")
 state_dict.pop("final_layer.linear.bias")
 state_dict.pop("final_layer.adaLN_modulation.1.weight")
 state_dict.pop("final_layer.adaLN_modulation.1.bias")

 # DiT XL/2
 transformer = Transformer2DModel(
 sample_size=args.image_size // 8,
 num_layers=28,
 attention_head_dim=72,
 in_channels=4,
 out_channels=8,
 patch_size=2,
 attention_bias=True,
 num_attention_heads=16,
 activation_fn="gelu-approximate",
 num_embeds_ada_norm=1000,
 norm_type="ada_norm_zero",
 norm_elementwise_affine=False,
 )
 transformer.load_state_dict(state_dict, strict=True)

 scheduler = DDIMScheduler(
 num_train_timesteps=1000,
 beta_schedule="linear",
 prediction_type="epsilon",
 clip_sample=False,
 )

 vae = AutoencoderKL.from_pretrained(args.vae_model)

 pipeline = DiTPipeline(transformer=transformer, vae=vae, scheduler=scheduler)

 if args.save:
 pipeline.save_pretrained(args.checkpoint_path)


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

 parser.add_argument(
 "--image_size",
 default=256,
 type=int,
 required=False,
 help="Image size of pretrained model, either 256 or 512.",
 )
 parser.add_argument(
 "--vae_model",
 default="stabilityai/sd-vae-ft-ema",
 type=str,
 required=False,
 help="Path to pretrained VAE model, either stabilityai/sd-vae-ft-mse or stabilityai/sd-vae-ft-ema.",
 )
 parser.add_argument(
 "--save", default=True, type=bool, required=False, help="Whether to save the converted pipeline or not."
 )
 parser.add_argument(
 "--checkpoint_path", default=None, type=str, required=True, help="Path to the output pipeline."
 )

 args = parser.parse_args()
 main(args)