






"""simple docstring"""



import argparse
import os

import torch
from torchvision.datasets.utils import download_url

from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, TransformeraDModel


_snake_case       :      Union[str, Any]       =					{512: "DiT-XL-2-512x512.pt", 256: "DiT-XL-2-256x256.pt"}




def 						lowerCamelCase_				(  __A  :		int			):
					lowerCamelCase__				=f"pretrained_models/{model_name}"
					if not os.path.isfile(__A			):
										os.makedirs('pretrained_models'			,	exist_ok=__A			)
										lowerCamelCase__				=f"https://dl.fbaipublicfiles.com/DiT/models/{model_name}"
										download_url(__A			,	'pretrained_models'			)
					lowerCamelCase__				=torch.load(__A			,	map_location=lambda __A			,	__A			: storage			)
					return model




def 						lowerCamelCase_				(  __A  :		Any			):
					lowerCamelCase__				=download_model(pretrained_models[args.image_size]			)

					lowerCamelCase__				=state_dict['x_embedder.proj.weight']
					lowerCamelCase__				=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			):
										lowerCamelCase__				=state_dict[
										    't_embedder.mlp.0.weight'
										]
										lowerCamelCase__				=state_dict[
										    't_embedder.mlp.0.bias'
										]
										lowerCamelCase__				=state_dict[
										    't_embedder.mlp.2.weight'
										]
										lowerCamelCase__				=state_dict[
										    't_embedder.mlp.2.bias'
										]
										lowerCamelCase__				=state_dict[
										    'y_embedder.embedding_table.weight'
										]

										lowerCamelCase__				=state_dict[
										    f"blocks.{depth}.adaLN_modulation.1.weight"
										]
										lowerCamelCase__				=state_dict[
										    f"blocks.{depth}.adaLN_modulation.1.bias"
										]

										lowerCamelCase__				,     lowerCamelCase__				,     lowerCamelCase__				=torch.chunk(state_dict[f"blocks.{depth}.attn.qkv.weight"]			,	3			,	dim=0			)
										lowerCamelCase__				,     lowerCamelCase__				,     lowerCamelCase__				=torch.chunk(state_dict[f"blocks.{depth}.attn.qkv.bias"]			,	3			,	dim=0			)

										lowerCamelCase__				=q
										lowerCamelCase__				=q_bias
										lowerCamelCase__				=k
										lowerCamelCase__				=k_bias
										lowerCamelCase__				=v
										lowerCamelCase__				=v_bias

										lowerCamelCase__				=state_dict[
										    f"blocks.{depth}.attn.proj.weight"
										]
										lowerCamelCase__				=state_dict[f"blocks.{depth}.attn.proj.bias"]

										lowerCamelCase__				=state_dict[f"blocks.{depth}.mlp.fc1.weight"]
										lowerCamelCase__				=state_dict[f"blocks.{depth}.mlp.fc1.bias"]
										lowerCamelCase__				=state_dict[f"blocks.{depth}.mlp.fc2.weight"]
										lowerCamelCase__				=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'			)

					lowerCamelCase__				=state_dict['final_layer.adaLN_modulation.1.weight']
					lowerCamelCase__				=state_dict['final_layer.adaLN_modulation.1.bias']
					lowerCamelCase__				=state_dict['final_layer.linear.weight']
					lowerCamelCase__				=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
					lowerCamelCase__				=TransformeraDModel(
					    sample_size=args.image_size // 8			,	num_layers=28			,	attention_head_dim=72			,	in_channels=4			,	out_channels=8			,	patch_size=2			,	attention_bias=__A			,	num_attention_heads=16			,	activation_fn='gelu-approximate'			,	num_embeds_ada_norm=1000			,	norm_type='ada_norm_zero'			,	norm_elementwise_affine=__A			,	)
					transformer.load_state_dict(__A			,	strict=__A			)

					lowerCamelCase__				=DDIMScheduler(
					    num_train_timesteps=1000			,	beta_schedule='linear'			,	prediction_type='epsilon'			,	clip_sample=__A			,	)

					lowerCamelCase__				=AutoencoderKL.from_pretrained(args.vae_model			)

					lowerCamelCase__				=DiTPipeline(transformer=__A			,	vae=__A			,	scheduler=__A			)

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


if __name__ == "__main__":
						_snake_case       :      List[Any]       =					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."
						)

						_snake_case       :      int       =					parser.parse_args()
						main(args)







"""simple docstring"""



_snake_case       :      Dict       =					[0.485, 0.456, 0.406]
_snake_case       :      int       =					[0.229, 0.224, 0.225]
_snake_case       :      Optional[int]       =					[0.5, 0.5, 0.5]
_snake_case       :      str       =					[0.5, 0.5, 0.5]
_snake_case       :      str       =					[0.4814_5466, 0.457_8275, 0.4082_1073]
_snake_case       :      str       =					[0.2686_2954, 0.2613_0258, 0.2757_7711]