'''simple docstring'''

import argparse
import os

import torch
from torchvision.datasets.utils import download_url

from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, TransformeraDModel


_lowerCamelCase :				Optional[int]														= {5_12: """DiT-XL-2-512x512.pt""", 2_56: """DiT-XL-2-256x256.pt"""}



def  _lowerCAmelCase				(				__a      )  ->      Union[str, Any]:
     '''simple docstring'''







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



def  _lowerCAmelCase				(				__a      )  ->      Dict:
     '''simple docstring'''







     _UpperCamelCase  :Union[str, Any]       =download_model(pretrained_models[args.image_size]      )

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

          _UpperCamelCase  :List[str]       =state_dict[
              F'''blocks.{depth}.adaLN_modulation.1.weight'''
          ]
          _UpperCamelCase  :Optional[Any]       =state_dict[
              F'''blocks.{depth}.adaLN_modulation.1.bias'''
          ]

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

          _UpperCamelCase  :List[str]       =state_dict[
              F'''blocks.{depth}.attn.proj.weight'''
          ]
          _UpperCamelCase  :Union[str, Any]       =state_dict[F'''blocks.{depth}.attn.proj.bias''']

          _UpperCamelCase  :Union[str, Any]       =state_dict[F'''blocks.{depth}.mlp.fc1.weight''']
          _UpperCamelCase  :str       =state_dict[F'''blocks.{depth}.mlp.fc1.bias''']
          _UpperCamelCase  :List[Any]       =state_dict[F'''blocks.{depth}.mlp.fc2.weight''']
          _UpperCamelCase  :Tuple       =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  :Any       =state_dict["""final_layer.adaLN_modulation.1.weight"""]
     _UpperCamelCase  :Optional[Any]       =state_dict["""final_layer.adaLN_modulation.1.bias"""]
     _UpperCamelCase  :Dict       =state_dict["""final_layer.linear.weight"""]
     _UpperCamelCase  :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
     _UpperCamelCase  :List[Any]       =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=__a			,      num_attention_heads=16			,      activation_fn="""gelu-approximate"""			,      num_embeds_ada_norm=10_00			,      norm_type="""ada_norm_zero"""			,      norm_elementwise_affine=__a			,      )
     transformer.load_state_dict(__a			,      strict=__a      )

     _UpperCamelCase  :Union[str, Any]       =DDIMScheduler(
         num_train_timesteps=10_00			,      beta_schedule="""linear"""			,      prediction_type="""epsilon"""			,      clip_sample=__a			,      )

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

     _UpperCamelCase  :Any       =DiTPipeline(transformer=__a			,      vae=__a			,      scheduler=__a      )

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


if __name__ == "__main__":
      _lowerCamelCase :				Any														= 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 :				List[str]														= parser.parse_args()
      main(args)






import argparse
import os

import torch
from torchvision.datasets.utils import download_url

from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, TransformeraDModel


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


def        __SCREAMING_SNAKE_CASE	(				__lowercase:		Any):



  '''simple docstring'''






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


def        __SCREAMING_SNAKE_CASE	(				__lowercase:		Tuple):



  '''simple docstring'''






  __lowerCAmelCase								=					download_model(pretrained_models[args.image_size])

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

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

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

    __lowerCAmelCase								=					q
    __lowerCAmelCase								=					q_bias
    __lowerCAmelCase								=					k
    __lowerCAmelCase								=					k_bias
    __lowerCAmelCase								=					v
    __lowerCAmelCase								=					v_bias

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

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

  __lowerCAmelCase								=					state_dict['''final_layer.adaLN_modulation.1.weight''']
  __lowerCAmelCase								=					state_dict['''final_layer.adaLN_modulation.1.bias''']
  __lowerCAmelCase								=					state_dict['''final_layer.linear.weight''']
  __lowerCAmelCase								=					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
  __lowerCAmelCase								=					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=10_00  ,				norm_type='''ada_norm_zero'''  ,				norm_elementwise_affine=__lowercase  ,				)
  transformer.load_state_dict(__lowercase  ,				strict=__lowercase)

  __lowerCAmelCase								=					DDIMScheduler(
      num_train_timesteps=10_00  ,				beta_schedule='''linear'''  ,				prediction_type='''epsilon'''  ,				clip_sample=__lowercase  ,				)

  __lowerCAmelCase								=					AutoencoderKL.from_pretrained(args.vae_model)

  __lowerCAmelCase								=					DiTPipeline(transformer=__lowercase  ,				vae=__lowercase  ,				scheduler=__lowercase)

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


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

      __lowercase				    =      parser.parse_args()
      main(args)
