





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




def 			__snake_case						(      __A    )		->				Union[str, Any]:
   lowercase    :			str  =       F'''pretrained_models/{model_name}'''
   if not os.path.isfile(__A    ):
      os.makedirs("""pretrained_models""" ,exist_ok=__A    )
      lowercase    :			Dict  =       F'''https://dl.fbaipublicfiles.com/DiT/models/{model_name}'''
      download_url(__A ,"""pretrained_models"""    )
   lowercase    :			Optional[Any]  =       torch.load(__A ,map_location=lambda __A ,__A    : storage    )
   return model




def 			__snake_case						(      __A    )		->				Optional[int]:
   lowercase    :			Optional[int]  =       download_model(pretrained_models[args.image_size]    )

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

      lowercase    :			Any  =       state_dict[
          F'''blocks.{depth}.adaLN_modulation.1.weight'''
      ]
      lowercase    :			Union[str, Any]  =       state_dict[
          F'''blocks.{depth}.adaLN_modulation.1.bias'''
      ]

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

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

      lowercase    :			Optional[int]  =       state_dict[
          F'''blocks.{depth}.attn.proj.weight'''
      ]
      lowercase    :			List[Any]  =       state_dict[F'''blocks.{depth}.attn.proj.bias''']

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

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

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

   lowercase    :			Union[str, Any]  =       AutoencoderKL.from_pretrained(args.vae_model    )

   lowercase    :			int  =       DiTPipeline(transformer=__A ,vae=__A ,scheduler=__A    )

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


if __name__ == "__main__":
    lowerCAmelCase:	Optional[Any]							      =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."
    )

    lowerCAmelCase:	List[Any]							      =parser.parse_args()
    main(args)









"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCAmelCase:	int							      =logging.get_logger(__name__)

lowerCAmelCase:	int							      ={
    "bert-base-uncased": "https://huggingface.co/bert-base-uncased/resolve/main/config.json",
    # QDQBERT models can be loaded from any BERT checkpoint, available at https://huggingface.co/models?filter=bert
}
class lowerCamelCase__  (    __UpperCamelCase				):
   __UpperCAmelCase        =					"""qdqbert"""



   def __init__(       self      ,   snake_case=3_0_5_2_2      ,   snake_case=7_6_8      ,   snake_case=1_2      ,   snake_case=1_2      ,   snake_case=3_0_7_2      ,   snake_case="gelu"      ,   snake_case=0.1      ,   snake_case=0.1      ,   snake_case=5_1_2      ,   snake_case=2      ,   snake_case=0.02      ,   snake_case=1E-12      ,   snake_case=True      ,   snake_case=1      ,   snake_case=0      ,   snake_case=2      ,   **snake_case      ,   ) ->    List[str]:







      """simple docstring"""
      super().__init__(pad_token_id=snake_case      ,   bos_token_id=snake_case      ,   eos_token_id=snake_case      ,   **snake_case	)

      lowercase    :			Optional[Any]  =       vocab_size
      lowercase    :			int  =       max_position_embeddings
      lowercase    :			Dict  =       hidden_size
      lowercase    :			Union[str, Any]  =       num_hidden_layers
      lowercase    :			str  =       num_attention_heads
      lowercase    :			List[str]  =       intermediate_size
      lowercase    :			List[str]  =       hidden_act
      lowercase    :			List[str]  =       hidden_dropout_prob
      lowercase    :			Optional[Any]  =       attention_probs_dropout_prob
      lowercase    :			Optional[Any]  =       initializer_range
      lowercase    :			str  =       type_vocab_size
      lowercase    :			Optional[int]  =       layer_norm_eps
      lowercase    :			Optional[int]  =       use_cache


