






'''simple docstring'''







from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCamelCase_					=				logging.get_logger(__name__)

UpperCamelCase_					=				{
    'dandelin/vilt-b32-mlm': 'https://huggingface.co/dandelin/vilt-b32-mlm/blob/main/config.json'
}




class 					__UpperCAmelCase (		_SCREAMING_SNAKE_CASE	):

      '''simple docstring'''





      SCREAMING_SNAKE_CASE				:    List[str]				       =					"vilt"




      def __init__(					self  ,		_UpperCAmelCase=3_0522  ,		_UpperCAmelCase=2  ,		_UpperCAmelCase=2  ,		_UpperCAmelCase=40  ,		_UpperCAmelCase=768  ,		_UpperCAmelCase=12  ,		_UpperCAmelCase=12  ,		_UpperCAmelCase=3072  ,		_UpperCAmelCase="gelu"  ,		_UpperCAmelCase=0.0  ,		_UpperCAmelCase=0.0  ,		_UpperCAmelCase=0.02  ,		_UpperCAmelCase=1E-12  ,		_UpperCAmelCase=384  ,		_UpperCAmelCase=32  ,		_UpperCAmelCase=3  ,		_UpperCAmelCase=True  ,		_UpperCAmelCase=-1  ,		_UpperCAmelCase=False  ,		_UpperCAmelCase=-1  ,		**_UpperCAmelCase  ,		):
             super().__init__(tie_word_embeddings=_UpperCAmelCase  ,		**_UpperCAmelCase						)

             UpperCAmelCase__	:   List[Any]												=	vocab_size
             UpperCAmelCase__	:   Tuple												=	type_vocab_size
             UpperCAmelCase__	:   Optional[int]												=	modality_type_vocab_size
             UpperCAmelCase__	:   int												=	max_position_embeddings

             UpperCAmelCase__	:   Tuple												=	hidden_size
             UpperCAmelCase__	:   Dict												=	num_hidden_layers
             UpperCAmelCase__	:   Optional[int]												=	num_attention_heads
             UpperCAmelCase__	:   List[str]												=	intermediate_size
             UpperCAmelCase__	:   Optional[int]												=	hidden_act
             UpperCAmelCase__	:   Dict												=	hidden_dropout_prob
             UpperCAmelCase__	:   int												=	attention_probs_dropout_prob
             UpperCAmelCase__	:   List[Any]												=	initializer_range
             UpperCAmelCase__	:   Tuple												=	layer_norm_eps

             UpperCAmelCase__	:   Dict												=	image_size
             UpperCAmelCase__	:   Optional[int]												=	patch_size
             UpperCAmelCase__	:   List[Any]												=	num_channels
             UpperCAmelCase__	:   Union[str, Any]												=	qkv_bias
             UpperCAmelCase__	:   Union[str, Any]												=	max_image_length
             UpperCAmelCase__	:   str												=	num_images

'''simple docstring'''
import argparse
import os

import torch
from torchvision.datasets.utils import download_url

from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, TransformeraDModel


A__ :		Tuple					={512: 'DiT-XL-2-512x512.pt', 256: 'DiT-XL-2-256x256.pt'}







def 							A_  (    __SCREAMING_SNAKE_CASE    :	int			)					->							Tuple:





  """simple docstring"""

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







def 							A_  (    __SCREAMING_SNAKE_CASE    :	Optional[int]			)					->							Dict:





  """simple docstring"""

  __A		:  Optional[Any]       				=		download_model(pretrained_models[args.image_size]			)

  __A		:  Tuple       				=		state_dict["""x_embedder.proj.weight"""]
  __A		:  Dict       				=		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			):
    __A		:  Union[str, Any]       				=		state_dict[
        """t_embedder.mlp.0.weight"""
    ]
    __A		:  int       				=		state_dict[
        """t_embedder.mlp.0.bias"""
    ]
    __A		:  List[Any]       				=		state_dict[
        """t_embedder.mlp.2.weight"""
    ]
    __A		:  str       				=		state_dict[
        """t_embedder.mlp.2.bias"""
    ]
    __A		:  Optional[int]       				=		state_dict[
        """y_embedder.embedding_table.weight"""
    ]

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

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

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

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

    __A		:  str       				=		state_dict[F"blocks.{depth}.mlp.fc1.weight"]
    __A		:  Union[str, Any]       				=		state_dict[F"blocks.{depth}.mlp.fc1.bias"]
    __A		:  int       				=		state_dict[F"blocks.{depth}.mlp.fc2.weight"]
    __A		:  str       				=		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"""			)

  __A		:  List[str]       				=		state_dict["""final_layer.adaLN_modulation.1.weight"""]
  __A		:  Optional[int]       				=		state_dict["""final_layer.adaLN_modulation.1.bias"""]
  __A		:  List[str]       				=		state_dict["""final_layer.linear.weight"""]
  __A		:  Optional[int]       				=		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
  __A		:  Union[str, 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=__SCREAMING_SNAKE_CASE      ,  num_attention_heads=16      ,  activation_fn="""gelu-approximate"""      ,  num_embeds_ada_norm=1000      ,  norm_type="""ada_norm_zero"""      ,  norm_elementwise_affine=__SCREAMING_SNAKE_CASE      ,  )
  transformer.load_state_dict(__SCREAMING_SNAKE_CASE      ,  strict=__SCREAMING_SNAKE_CASE			)

  __A		:  str       				=		DDIMScheduler(
      num_train_timesteps=1000      ,  beta_schedule="""linear"""      ,  prediction_type="""epsilon"""      ,  clip_sample=__SCREAMING_SNAKE_CASE      ,  )

  __A		:  List[Any]       				=		AutoencoderKL.from_pretrained(args.vae_model			)

  __A		:  Optional[int]       				=		DiTPipeline(transformer=__SCREAMING_SNAKE_CASE      ,  vae=__SCREAMING_SNAKE_CASE      ,  scheduler=__SCREAMING_SNAKE_CASE			)

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


if __name__ == "__main__":
     A__ :		List[str]					=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.'
     )

     A__ :		Dict					=parser.parse_args()
     main(args)



