


import argparse
import os

import torch
from torchvision.datasets.utils import download_url

from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, TransformeraDModel


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

def  lowercase_						(						_A     :       Optional[Any]		):

      """simple docstring"""




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

def  lowercase_						(						_A     :       str		):

      """simple docstring"""




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

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

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

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

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

            lowerCamelCase__						:							Optional[int]       =    state_dict[
                F"blocks.{depth}.attn.proj.weight"
            ]
            lowerCamelCase__						:							Optional[Any]       =    state_dict[F"blocks.{depth}.attn.proj.bias"]

            lowerCamelCase__						:							int       =    state_dict[F"blocks.{depth}.mlp.fc1.weight"]
            lowerCamelCase__						:							int       =    state_dict[F"blocks.{depth}.mlp.fc1.bias"]
            lowerCamelCase__						:							Any       =    state_dict[F"blocks.{depth}.mlp.fc2.weight"]
            lowerCamelCase__						:							List[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__						:							List[Any]       =    state_dict["final_layer.adaLN_modulation.1.weight"]
      lowerCamelCase__						:							str       =    state_dict["final_layer.adaLN_modulation.1.bias"]
      lowerCamelCase__						:							Optional[int]       =    state_dict["final_layer.linear.weight"]
      lowerCamelCase__						:							str       =    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__						:							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		)

      lowerCamelCase__						:							int       =    DDIMScheduler(
          num_train_timesteps=1000       ,   beta_schedule="linear"       ,   prediction_type="epsilon"       ,   clip_sample=_A       ,   )

      lowerCamelCase__						:							int       =    AutoencoderKL.from_pretrained(args.vae_model		)

      lowerCamelCase__						:							Tuple       =    DiTPipeline(transformer=_A       ,   vae=_A       ,   scheduler=_A		)

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


if __name__ == "__main__":
    A :       Union[str, 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."
    )

    A :       Union[str, Any]						=   parser.parse_args()
    main(args)






def  lowercase_						(						_A     :       Dict		):

      """simple docstring"""




      lowerCamelCase__						:							Optional[Any]       =    len(_A		)
      for _ in range(
          0       ,   int(((no_of_elements - 1) / 2) + 1		)		):  # we don't need to traverse to end of list as
            for j in range(0       ,   no_of_elements - 1		):
                  if (
                      lst[j + 1] < lst[j]
                  ):  # applying bubble sort algorithm from left to right (or forwards)
                        lowerCamelCase__						:							Tuple       =    lst[j + 1]
                        lowerCamelCase__						:							Optional[Any]       =    lst[j]
                        lowerCamelCase__						:							Optional[Any]       =    temp
                  if (
                      lst[no_of_elements - 1 - j] < lst[no_of_elements - 2 - j]
                  ):  # applying bubble sort algorithm from right to left (or backwards)
                        lowerCamelCase__						:							Any       =    lst[no_of_elements - 1 - j]
                        lowerCamelCase__						:							List[str]       =    lst[no_of_elements - 2 - j]
                        lowerCamelCase__						:							str       =    temp
      return lst


if __name__ == "__main__":
    print("enter the list to be sorted")
    A :       Optional[int]						=   [int(x) for x in input().split()]  # inputing elements of the list in one line
    A :       Optional[Any]						=   double_sort(lst)
    print("the sorted list is")
    print(sorted_lst)


