



'''simple docstring'''






import argparse
import os

import torch
from torchvision.datasets.utils import download_url

from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, TransformeraDModel


lowerCamelCase__	  =		{5_12: 'DiT-XL-2-512x512.pt', 2_56: 'DiT-XL-2-256x256.pt'}





def 				_SCREAMING_SNAKE_CASE(					snake_case_    :     List[str]  )		->Optional[int]:






							'''simple docstring'''
							_lowercase							:		Any				=   F"pretrained_models/{model_name}"
							if not os.path.isfile(snake_case_  ):
														os.makedirs('''pretrained_models''' ,       exist_ok=snake_case_  )
														_lowercase							:		Optional[Any]				=   F"https://dl.fbaipublicfiles.com/DiT/models/{model_name}"
														download_url(snake_case_ ,       '''pretrained_models'''  )
							_lowercase							:		List[str]				=   torch.load(snake_case_ ,       map_location=lambda snake_case_ ,       snake_case_  : storage  )
							return model





def 				_SCREAMING_SNAKE_CASE(					snake_case_    :     Any  )		->str:






							'''simple docstring'''
							_lowercase							:		str				=   download_model(pretrained_models[args.image_size]  )

							_lowercase							:		str				=   state_dict['x_embedder.proj.weight']
							_lowercase							:		List[Any]				=   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  ):
														_lowercase							:		List[Any]				=   state_dict[
														    't_embedder.mlp.0.weight'
														]
														_lowercase							:		Tuple				=   state_dict[
														    't_embedder.mlp.0.bias'
														]
														_lowercase							:		str				=   state_dict[
														    't_embedder.mlp.2.weight'
														]
														_lowercase							:		List[Any]				=   state_dict[
														    't_embedder.mlp.2.bias'
														]
														_lowercase							:		Optional[Any]				=   state_dict[
														    'y_embedder.embedding_table.weight'
														]

														_lowercase							:		str				=   state_dict[
														    F"blocks.{depth}.adaLN_modulation.1.weight"
														]
														_lowercase							:		Union[str, Any]				=   state_dict[
														    F"blocks.{depth}.adaLN_modulation.1.bias"
														]

														_lowercase							:		List[str]				=   torch.chunk(state_dict[F"blocks.{depth}.attn.qkv.weight"] ,       3 ,       dim=0  )
														_lowercase							:		List[str]				=   torch.chunk(state_dict[F"blocks.{depth}.attn.qkv.bias"] ,       3 ,       dim=0  )

														_lowercase							:		List[str]				=   q
														_lowercase							:		str				=   q_bias
														_lowercase							:		List[str]				=   k
														_lowercase							:		Optional[Any]				=   k_bias
														_lowercase							:		int				=   v
														_lowercase							:		int				=   v_bias

														_lowercase							:		Tuple				=   state_dict[
														    F"blocks.{depth}.attn.proj.weight"
														]
														_lowercase							:		List[Any]				=   state_dict[F"blocks.{depth}.attn.proj.bias"]

														_lowercase							:		Optional[Any]				=   state_dict[F"blocks.{depth}.mlp.fc1.weight"]
														_lowercase							:		Optional[int]				=   state_dict[F"blocks.{depth}.mlp.fc1.bias"]
														_lowercase							:		Dict				=   state_dict[F"blocks.{depth}.mlp.fc2.weight"]
														_lowercase							:		Optional[int]				=   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'''  )

							_lowercase							:		Optional[int]				=   state_dict['final_layer.adaLN_modulation.1.weight']
							_lowercase							:		Union[str, Any]				=   state_dict['final_layer.adaLN_modulation.1.bias']
							_lowercase							:		str				=   state_dict['final_layer.linear.weight']
							_lowercase							:		Union[str, Any]				=   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
							_lowercase							:		Dict				=   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=snake_case_ ,       num_attention_heads=16 ,       activation_fn='''gelu-approximate''' ,       num_embeds_ada_norm=10_00 ,       norm_type='''ada_norm_zero''' ,       norm_elementwise_affine=snake_case_ ,       )
							transformer.load_state_dict(snake_case_ ,       strict=snake_case_  )

							_lowercase							:		int				=   DDIMScheduler(
							    num_train_timesteps=10_00 ,       beta_schedule='''linear''' ,       prediction_type='''epsilon''' ,       clip_sample=snake_case_ ,       )

							_lowercase							:		List[str]				=   AutoencoderKL.from_pretrained(args.vae_model  )

							_lowercase							:		Any				=   DiTPipeline(transformer=snake_case_ ,       vae=snake_case_ ,       scheduler=snake_case_  )

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


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

						parser.add_argument(
						    '--image_size',
						    default=2_56,
						    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.'
						)

						lowerCamelCase__	  =		parser.parse_args()
						main(args)






def  _snake_case		(snake_case_					:     int = 100    ,       snake_case_					:     int = 100  )							->		int:
  return max(
      sum(int(snake_case_  ) for x in str(base**power  )  )
      for base in range(snake_case_  )
      for power in range(snake_case_  )  )


# Tests
if __name__ == "__main__":
      import doctest

      doctest.testmod()





