


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





def     lowercase__	(  lowerCamelCase     :  Dict				)					->     List[str]:
  lowerCAmelCase__    :    List[str]								=					download_model(pretrained_models[args.image_size]				)

  lowerCAmelCase__    :    int								=					state_dict["x_embedder.proj.weight"]
  lowerCAmelCase__    :    Tuple								=					state_dict["x_embedder.proj.bias"]
  state_dict.pop("x_embedder.proj.weight"				)
  state_dict.pop("x_embedder.proj.bias"				)

  for depth in range(2_8				):
    lowerCAmelCase__    :    Tuple								=					state_dict[
        "t_embedder.mlp.0.weight"
    ]
    lowerCAmelCase__    :    List[Any]								=					state_dict[
        "t_embedder.mlp.0.bias"
    ]
    lowerCAmelCase__    :    Union[str, Any]								=					state_dict[
        "t_embedder.mlp.2.weight"
    ]
    lowerCAmelCase__    :    Tuple								=					state_dict[
        "t_embedder.mlp.2.bias"
    ]
    lowerCAmelCase__    :    Optional[Any]								=					state_dict[
        "y_embedder.embedding_table.weight"
    ]

    lowerCAmelCase__    :    Tuple								=					state_dict[
        F"blocks.{depth}.adaLN_modulation.1.weight"
    ]
    lowerCAmelCase__    :    int								=					state_dict[
        F"blocks.{depth}.adaLN_modulation.1.bias"
    ]

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

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

    lowerCAmelCase__    :    Dict								=					state_dict[
        F"blocks.{depth}.attn.proj.weight"
    ]
    lowerCAmelCase__    :    int								=					state_dict[F"blocks.{depth}.attn.proj.bias"]

    lowerCAmelCase__    :    Tuple								=					state_dict[F"blocks.{depth}.mlp.fc1.weight"]
    lowerCAmelCase__    :    int								=					state_dict[F"blocks.{depth}.mlp.fc1.bias"]
    lowerCAmelCase__    :    Optional[int]								=					state_dict[F"blocks.{depth}.mlp.fc2.weight"]
    lowerCAmelCase__    :    Optional[Any]								=					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__    :    Any								=					state_dict["final_layer.adaLN_modulation.1.weight"]
  lowerCAmelCase__    :    int								=					state_dict["final_layer.adaLN_modulation.1.bias"]
  lowerCAmelCase__    :    Dict								=					state_dict["final_layer.linear.weight"]
  lowerCAmelCase__    :    List[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
  lowerCAmelCase__    :    Optional[Any]								=					TransformeraDModel(
      sample_size=args.image_size // 8				,	num_layers=2_8				,	attention_head_dim=7_2				,	in_channels=4				,	out_channels=8				,	patch_size=2				,	attention_bias=lowerCamelCase				,	num_attention_heads=1_6				,	activation_fn="gelu-approximate"				,	num_embeds_ada_norm=1_0_0_0				,	norm_type="ada_norm_zero"				,	norm_elementwise_affine=lowerCamelCase				,	)
  transformer.load_state_dict(lowerCamelCase				,	strict=lowerCamelCase				)

  lowerCAmelCase__    :    Tuple								=					DDIMScheduler(
      num_train_timesteps=1_0_0_0				,	beta_schedule="linear"				,	prediction_type="epsilon"				,	clip_sample=lowerCamelCase				,	)

  lowerCAmelCase__    :    Any								=					AutoencoderKL.from_pretrained(args.vae_model				)

  lowerCAmelCase__    :    Dict								=					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 ...configuration_utils import PretrainedConfig
from ...utils import logging


__UpperCAmelCase    				=      logging.get_logger(__name__)

__UpperCAmelCase    				=      {
    "MCG-NJU/videomae-base": "https://huggingface.co/MCG-NJU/videomae-base/resolve/main/config.json",
}

class  lowercase_  ( a_						):
      __magic_name__     :							Union[str, Any]          =				"""videomae"""







      def __init__(      self :							Dict		,  _lowercase :							int=2_2_4		,  _lowercase :							Optional[Any]=1_6		,  _lowercase :							List[Any]=3		,  _lowercase :							int=1_6		,  _lowercase :							Optional[Any]=2		,  _lowercase :							Union[str, Any]=7_6_8		,  _lowercase :							int=1_2		,  _lowercase :							Dict=1_2		,  _lowercase :							Optional[Any]=3_0_7_2		,  _lowercase :							List[str]="gelu"		,  _lowercase :							int=0.0		,  _lowercase :							int=0.0		,  _lowercase :							Optional[int]=0.02		,  _lowercase :							List[str]=1e-1_2		,  _lowercase :							Union[str, Any]=True		,  _lowercase :							Union[str, Any]=True		,  _lowercase :							Any=6		,  _lowercase :							Any=3_8_4		,  _lowercase :							int=4		,  _lowercase :							Optional[int]=1_5_3_6		,  _lowercase :							Tuple=True		,  **_lowercase :							List[str]		,  ):
        super().__init__(**_lowercase    )

        lowerCAmelCase__    :    List[Any]								=					image_size
        lowerCAmelCase__    :    Tuple								=					patch_size
        lowerCAmelCase__    :    Union[str, Any]								=					num_channels
        lowerCAmelCase__    :    Optional[Any]								=					num_frames
        lowerCAmelCase__    :    Union[str, Any]								=					tubelet_size

        lowerCAmelCase__    :    Union[str, Any]								=					hidden_size
        lowerCAmelCase__    :    Dict								=					num_hidden_layers
        lowerCAmelCase__    :    Any								=					num_attention_heads
        lowerCAmelCase__    :    List[Any]								=					intermediate_size
        lowerCAmelCase__    :    Optional[Any]								=					hidden_act
        lowerCAmelCase__    :    int								=					hidden_dropout_prob
        lowerCAmelCase__    :    Optional[Any]								=					attention_probs_dropout_prob
        lowerCAmelCase__    :    Any								=					initializer_range
        lowerCAmelCase__    :    Any								=					layer_norm_eps
        lowerCAmelCase__    :    Union[str, Any]								=					qkv_bias
        lowerCAmelCase__    :    int								=					use_mean_pooling

        lowerCAmelCase__    :    List[Any]								=					decoder_num_attention_heads
        lowerCAmelCase__    :    List[str]								=					decoder_hidden_size
        lowerCAmelCase__    :    int								=					decoder_num_hidden_layers
        lowerCAmelCase__    :    Optional[int]								=					decoder_intermediate_size
        lowerCAmelCase__    :    Optional[Any]								=					norm_pix_loss
