



import argparse
import os

import torch
from torchvision.datasets.utils import download_url

from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, TransformeraDModel


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

def      A_ (		__a       :			List[Any]						):

				"""simple docstring"""
				a__			 =						F'''pretrained_models/{model_name}'''
				if not os.path.isfile(lowercase_						):
								os.makedirs("""pretrained_models"""	,						exist_ok=lowercase_						)
								a__			 =						F'''https://dl.fbaipublicfiles.com/DiT/models/{model_name}'''
								download_url(lowercase_	,						"""pretrained_models"""						)
				a__			 =						torch.load(lowercase_	,						map_location=lambda __a	,						__a						: storage						)
				return model

def      A_ (		__a       :			Optional[Any]						):

				"""simple docstring"""
				a__			 =						download_model(pretrained_models[args.image_size]						)

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

								a__			 =						state_dict[
								    F'''blocks.{depth}.adaLN_modulation.1.weight'''
								]
								a__			 =						state_dict[
								    F'''blocks.{depth}.adaLN_modulation.1.bias'''
								]

								a__ ,     a__ ,     a__			 =						torch.chunk(state_dict[F'''blocks.{depth}.attn.qkv.weight''']	,						3	,						dim=0						)
								a__ ,     a__ ,     a__			 =						torch.chunk(state_dict[F'''blocks.{depth}.attn.qkv.bias''']	,						3	,						dim=0						)

								a__			 =						q
								a__			 =						q_bias
								a__			 =						k
								a__			 =						k_bias
								a__			 =						v
								a__			 =						v_bias

								a__			 =						state_dict[
								    F'''blocks.{depth}.attn.proj.weight'''
								]
								a__			 =						state_dict[F'''blocks.{depth}.attn.proj.bias''']

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

				a__			 =						state_dict["""final_layer.adaLN_modulation.1.weight"""]
				a__			 =						state_dict["""final_layer.adaLN_modulation.1.bias"""]
				a__			 =						state_dict["""final_layer.linear.weight"""]
				a__			 =						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
				a__			 =						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=lowercase_	,						num_attention_heads=16	,						activation_fn="""gelu-approximate"""	,						num_embeds_ada_norm=1_000	,						norm_type="""ada_norm_zero"""	,						norm_elementwise_affine=lowercase_	,						)
				transformer.load_state_dict(lowercase_	,						strict=lowercase_						)

				a__			 =						DDIMScheduler(
				    num_train_timesteps=1_000	,						beta_schedule="""linear"""	,						prediction_type="""epsilon"""	,						clip_sample=lowercase_	,						)

				a__			 =						AutoencoderKL.from_pretrained(args.vae_model						)

				a__			 =						DiTPipeline(transformer=lowercase_	,						vae=lowercase_	,						scheduler=lowercase_						)

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


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

	UpperCAmelCase								=   parser.parse_args()
	main(args)






def      A_ (		__a       :			int = 1_000						):

				"""simple docstring"""
				return sum(i for i in range(__a						) if i % 3 == 0 or i % 5 == 0						)


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

