

"""simple docstring"""






import argparse
import os

import torch
from torchvision.datasets.utils import download_url

from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, TransformeraDModel


lowerCamelCase__				:  int													=  {5_12: '''DiT-XL-2-512x512.pt''', 2_56: '''DiT-XL-2-256x256.pt'''}
def       UpperCamelCase					(			_lowerCAmelCase     :						Union[str, Any] )				->			Union[str, Any]:
 _UpperCAmelCase    :							Tuple									=					f'''pretrained_models/{model_name}'''
 if not os.path.isfile(lowerCamelCase__ ):
  os.makedirs("""pretrained_models""",  exist_ok=lowerCamelCase__ )
  _UpperCAmelCase    :							Any									=					f'''https://dl.fbaipublicfiles.com/DiT/models/{model_name}'''
  download_url(lowerCamelCase__,  """pretrained_models""" )
 _UpperCAmelCase    :							Optional[Any]									=					torch.load(lowerCamelCase__,  map_location=lambda _lowerCAmelCase,  _lowerCAmelCase : storage )
 return model
def       UpperCamelCase					(			_lowerCAmelCase     :						str )				->			str:
 _UpperCAmelCase    :							List[Any]									=					download_model(pretrained_models[args.image_size] )

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

  _UpperCAmelCase    :							Tuple									=					state_dict[
      f'''blocks.{depth}.adaLN_modulation.1.weight'''
  ]
  _UpperCAmelCase    :							Optional[Any]									=					state_dict[
      f'''blocks.{depth}.adaLN_modulation.1.bias'''
  ]

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

  _UpperCAmelCase    :							Any									=					q
  _UpperCAmelCase    :							List[Any]									=					q_bias
  _UpperCAmelCase    :							Optional[int]									=					k
  _UpperCAmelCase    :							Optional[Any]									=					k_bias
  _UpperCAmelCase    :							Dict									=					v
  _UpperCAmelCase    :							Any									=					v_bias

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

  _UpperCAmelCase    :							Tuple									=					state_dict[f'''blocks.{depth}.mlp.fc1.weight''']
  _UpperCAmelCase    :							List[str]									=					state_dict[f'''blocks.{depth}.mlp.fc1.bias''']
  _UpperCAmelCase    :							str									=					state_dict[f'''blocks.{depth}.mlp.fc2.weight''']
  _UpperCAmelCase    :							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    :							List[Any]									=					state_dict["""final_layer.adaLN_modulation.1.weight"""]
 _UpperCAmelCase    :							Any									=					state_dict["""final_layer.adaLN_modulation.1.bias"""]
 _UpperCAmelCase    :							Optional[int]									=					state_dict["""final_layer.linear.weight"""]
 _UpperCAmelCase    :							Tuple									=					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    :							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=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    :							int									=					DDIMScheduler(
     num_train_timesteps=1000,  beta_schedule="""linear""",  prediction_type="""epsilon""",  clip_sample=lowerCamelCase__,  )

 _UpperCAmelCase    :							Optional[int]									=					AutoencoderKL.from_pretrained(args.vae_model )

 _UpperCAmelCase    :							List[str]									=					DiTPipeline(transformer=lowerCamelCase__,  vae=lowerCamelCase__,  scheduler=lowerCamelCase__ )

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


if __name__ == "__main__":
      lowerCamelCase__				:  Dict													=  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__				:  Any													=  parser.parse_args()
      main(args)








"""simple docstring"""






import safs






class     _UpperCAmelCase			(		safs.SaFileSystem):
			pass





