


import argparse
import os

import torch
from torchvision.datasets.utils import download_url

from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, TransformeraDModel


__UpperCamelCase      						=    {5_1_2: """DiT-XL-2-512x512.pt""", 2_5_6: """DiT-XL-2-256x256.pt"""}
def     UpperCamelCase_(			_A				:Optional[int]					)->						Any:
			UpperCamelCase__   =    F'''pretrained_models/{model_name}'''
			if not os.path.isfile(_lowerCAmelCase					):
						os.makedirs("pretrained_models"			,       exist_ok=_lowerCAmelCase					)
						UpperCamelCase__   =    F'''https://dl.fbaipublicfiles.com/DiT/models/{model_name}'''
						download_url(_lowerCAmelCase			,       "pretrained_models"					)
			UpperCamelCase__   =    torch.load(_lowerCAmelCase			,       map_location=lambda _A			,       _A					: storage					)
			return model
def     UpperCamelCase_(			_A				:Union[str, Any]					)->						Any:
			UpperCamelCase__   =    download_model(pretrained_models[args.image_size]					)

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

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

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

						UpperCamelCase__   =    q
						UpperCamelCase__   =    q_bias
						UpperCamelCase__   =    k
						UpperCamelCase__   =    k_bias
						UpperCamelCase__   =    v
						UpperCamelCase__   =    v_bias

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

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

			UpperCamelCase__   =    state_dict["final_layer.adaLN_modulation.1.weight"]
			UpperCamelCase__   =    state_dict["final_layer.adaLN_modulation.1.bias"]
			UpperCamelCase__   =    state_dict["final_layer.linear.weight"]
			UpperCamelCase__   =    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
			UpperCamelCase__   =    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=_lowerCAmelCase			,       num_attention_heads=16			,       activation_fn="gelu-approximate"			,       num_embeds_ada_norm=10_00			,       norm_type="ada_norm_zero"			,       norm_elementwise_affine=_lowerCAmelCase			,       )
			transformer.load_state_dict(_lowerCAmelCase			,       strict=_lowerCAmelCase					)

			UpperCamelCase__   =    DDIMScheduler(
			    num_train_timesteps=10_00			,       beta_schedule="linear"			,       prediction_type="epsilon"			,       clip_sample=_lowerCAmelCase			,       )

			UpperCamelCase__   =    AutoencoderKL.from_pretrained(args.vae_model					)

			UpperCamelCase__   =    DiTPipeline(transformer=_lowerCAmelCase			,       vae=_lowerCAmelCase			,       scheduler=_lowerCAmelCase					)

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


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

						parser.add_argument(
						    '--image_size',
						    default=2_5_6,
						    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)






from __future__ import annotations
def     UpperCamelCase_(			_A				:list					)->						int | float:
			UpperCamelCase__   =    sorted(_A					)
			UpperCamelCase__   =    len(_A					)
			UpperCamelCase__   =    length >> 1
			return (
			    (sorted_list[mid_index] + sorted_list[mid_index - 1]) / 2
			    if length % 2 == 0
			    else sorted_list[mid_index]
			)
def     UpperCamelCase_(			)->						List[Any]:
			import doctest

			doctest.testmod()


if __name__ == "__main__":
						main()


