



'''simple docstring'''
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   lowercase_	(   lowerCAmelCase__	:  Union[str, Any] ):

			"""simple docstring"""





			__UpperCAmelCase   :      Tuple										=     f'pretrained_models/{model_name}'
			if not os.path.isfile(lowerCAmelCase__ ):
						os.makedirs("""pretrained_models"""			,							exist_ok=lowerCAmelCase__ )
						__UpperCAmelCase   :      Optional[Any]										=     f'https://dl.fbaipublicfiles.com/DiT/models/{model_name}'
						download_url(lowerCAmelCase__			,							"""pretrained_models""" )
			__UpperCAmelCase   :      str										=     torch.load(lowerCAmelCase__			,							map_location=lambda lowerCAmelCase__			,							lowerCAmelCase__ : storage )
			return model


def   lowercase_	(   lowerCAmelCase__	:  str ):

			"""simple docstring"""





			__UpperCAmelCase   :      List[str]										=     download_model(pretrained_models[args.image_size] )

			__UpperCAmelCase   :      Optional[int]										=     state_dict["""x_embedder.proj.weight"""]
			__UpperCAmelCase   :      Optional[int]										=     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   :      Optional[Any]										=     state_dict[
						    """t_embedder.mlp.0.weight"""
						]
						__UpperCAmelCase   :      List[Any]										=     state_dict[
						    """t_embedder.mlp.0.bias"""
						]
						__UpperCAmelCase   :      int										=     state_dict[
						    """t_embedder.mlp.2.weight"""
						]
						__UpperCAmelCase   :      List[Any]										=     state_dict[
						    """t_embedder.mlp.2.bias"""
						]
						__UpperCAmelCase   :      int										=     state_dict[
						    """y_embedder.embedding_table.weight"""
						]

						__UpperCAmelCase   :      int										=     state_dict[
						    f'blocks.{depth}.adaLN_modulation.1.weight'
						]
						__UpperCAmelCase   :      Tuple										=     state_dict[
						    f'blocks.{depth}.adaLN_modulation.1.bias'
						]

						__UpperCAmelCase				,  __UpperCAmelCase				,  __UpperCAmelCase   :      Dict										=     torch.chunk(state_dict[f'blocks.{depth}.attn.qkv.weight']			,							3			,							dim=0 )
						__UpperCAmelCase				,  __UpperCAmelCase				,  __UpperCAmelCase   :      Optional[Any]										=     torch.chunk(state_dict[f'blocks.{depth}.attn.qkv.bias']			,							3			,							dim=0 )

						__UpperCAmelCase   :      int										=     q
						__UpperCAmelCase   :      List[str]										=     q_bias
						__UpperCAmelCase   :      List[Any]										=     k
						__UpperCAmelCase   :      Union[str, Any]										=     k_bias
						__UpperCAmelCase   :      Optional[Any]										=     v
						__UpperCAmelCase   :      Optional[int]										=     v_bias

						__UpperCAmelCase   :      Dict										=     state_dict[
						    f'blocks.{depth}.attn.proj.weight'
						]
						__UpperCAmelCase   :      Optional[int]										=     state_dict[f'blocks.{depth}.attn.proj.bias']

						__UpperCAmelCase   :      Union[str, Any]										=     state_dict[f'blocks.{depth}.mlp.fc1.weight']
						__UpperCAmelCase   :      Optional[Any]										=     state_dict[f'blocks.{depth}.mlp.fc1.bias']
						__UpperCAmelCase   :      int										=     state_dict[f'blocks.{depth}.mlp.fc2.weight']
						__UpperCAmelCase   :      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""" )

			__UpperCAmelCase   :      Optional[int]										=     state_dict["""final_layer.adaLN_modulation.1.weight"""]
			__UpperCAmelCase   :      Any										=     state_dict["""final_layer.adaLN_modulation.1.bias"""]
			__UpperCAmelCase   :      Optional[Any]										=     state_dict["""final_layer.linear.weight"""]
			__UpperCAmelCase   :      str										=     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   :      str										=     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=1000			,							norm_type="""ada_norm_zero"""			,							norm_elementwise_affine=lowerCAmelCase__			,							)
			transformer.load_state_dict(lowerCAmelCase__			,							strict=lowerCAmelCase__ )

			__UpperCAmelCase   :      Tuple										=     DDIMScheduler(
			    num_train_timesteps=1000			,							beta_schedule="""linear"""			,							prediction_type="""epsilon"""			,							clip_sample=lowerCAmelCase__			,							)

			__UpperCAmelCase   :      Tuple										=     AutoencoderKL.from_pretrained(args.vae_model )

			__UpperCAmelCase   :      Optional[int]										=     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=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)









'''simple docstring'''
from .chunk_utils import chunk_layer
from .data_transforms import make_atomaa_masks
from .feats import atomaa_to_atomaa, frames_and_literature_positions_to_atomaa_pos, torsion_angles_to_frames
from .loss import compute_predicted_aligned_error, compute_tm
from .protein import Protein as OFProtein
from .protein import to_pdb
from .rigid_utils import Rigid, Rotation
from .tensor_utils import dict_multimap, flatten_final_dims, permute_final_dims




