




'''simple docstring'''



import argparse
import os

import torch
from torchvision.datasets.utils import download_url

from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, TransformeraDModel


lowerCamelCase_	 =		{5_12: 'DiT-XL-2-512x512.pt', 2_56: 'DiT-XL-2-256x256.pt'}
def 				SCREAMING_SNAKE_CASE_				(							__A       :				Optional[Any]						)				->				Optional[int]:
   _SCREAMING_SNAKE_CASE =   f"""pretrained_models/{model_name}"""
   if not os.path.isfile(__A						):
      os.makedirs("pretrained_models" ,  exist_ok=__A						)
      _SCREAMING_SNAKE_CASE =   f"""https://dl.fbaipublicfiles.com/DiT/models/{model_name}"""
      download_url(__A ,  "pretrained_models"						)
   _SCREAMING_SNAKE_CASE =   torch.load(__A ,  map_location=lambda __A ,  __A						: storage						)
   return model
def 				SCREAMING_SNAKE_CASE_				(							__A       :				List[str]						)				->				int:
   _SCREAMING_SNAKE_CASE =   download_model(pretrained_models[args.image_size]						)

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

      _SCREAMING_SNAKE_CASE =   state_dict[
          f"""blocks.{depth}.adaLN_modulation.1.weight"""
      ]
      _SCREAMING_SNAKE_CASE =   state_dict[
          f"""blocks.{depth}.adaLN_modulation.1.bias"""
      ]

      _SCREAMING_SNAKE_CASE, _SCREAMING_SNAKE_CASE, _SCREAMING_SNAKE_CASE =   torch.chunk(state_dict[f"""blocks.{depth}.attn.qkv.weight"""] ,  3 ,  dim=0						)
      _SCREAMING_SNAKE_CASE, _SCREAMING_SNAKE_CASE, _SCREAMING_SNAKE_CASE =   torch.chunk(state_dict[f"""blocks.{depth}.attn.qkv.bias"""] ,  3 ,  dim=0						)

      _SCREAMING_SNAKE_CASE =   q
      _SCREAMING_SNAKE_CASE =   q_bias
      _SCREAMING_SNAKE_CASE =   k
      _SCREAMING_SNAKE_CASE =   k_bias
      _SCREAMING_SNAKE_CASE =   v
      _SCREAMING_SNAKE_CASE =   v_bias

      _SCREAMING_SNAKE_CASE =   state_dict[
          f"""blocks.{depth}.attn.proj.weight"""
      ]
      _SCREAMING_SNAKE_CASE =   state_dict[f"""blocks.{depth}.attn.proj.bias"""]

      _SCREAMING_SNAKE_CASE =   state_dict[f"""blocks.{depth}.mlp.fc1.weight"""]
      _SCREAMING_SNAKE_CASE =   state_dict[f"""blocks.{depth}.mlp.fc1.bias"""]
      _SCREAMING_SNAKE_CASE =   state_dict[f"""blocks.{depth}.mlp.fc2.weight"""]
      _SCREAMING_SNAKE_CASE =   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"						)

   _SCREAMING_SNAKE_CASE =   state_dict["final_layer.adaLN_modulation.1.weight"]
   _SCREAMING_SNAKE_CASE =   state_dict["final_layer.adaLN_modulation.1.bias"]
   _SCREAMING_SNAKE_CASE =   state_dict["final_layer.linear.weight"]
   _SCREAMING_SNAKE_CASE =   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
   _SCREAMING_SNAKE_CASE =   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						)

   _SCREAMING_SNAKE_CASE =   DDIMScheduler(
       num_train_timesteps=10_00 ,  beta_schedule="linear" ,  prediction_type="epsilon" ,  clip_sample=__A ,  )

   _SCREAMING_SNAKE_CASE =   AutoencoderKL.from_pretrained(args.vae_model						)

   _SCREAMING_SNAKE_CASE =   DiTPipeline(transformer=__A ,  vae=__A ,  scheduler=__A						)

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


if __name__ == "__main__":
 lowerCamelCase_	 =		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_	 =		parser.parse_args()
 main(args)







'''simple docstring'''



import unittest
from pathlib import Path
from tempfile import TemporaryDirectory
from unittest.mock import Mock, patch

import diffusers.utils.hub_utils







class 	lowercase_					(				unittest.TestCase     ):


   """simple docstring"""

   @patch("diffusers.utils.hub_utils.get_full_repo_name"					)
   def 							lowerCAmelCase_    (					self      :			List[Any]						,   __lowerCamelCase      :			Mock					):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   "full_repo_name"
      with TemporaryDirectory() as tmpdir:
         # Dummy args values
         _SCREAMING_SNAKE_CASE =   Mock()
         _SCREAMING_SNAKE_CASE =   tmpdir
         _SCREAMING_SNAKE_CASE =   0
         _SCREAMING_SNAKE_CASE =   "hub_token"
         _SCREAMING_SNAKE_CASE =   "dataset_name"
         _SCREAMING_SNAKE_CASE =   0.0_1
         _SCREAMING_SNAKE_CASE =   1_0_0_0_0_0
         _SCREAMING_SNAKE_CASE =   1_0_0_0_0
         _SCREAMING_SNAKE_CASE =   0.0_1
         _SCREAMING_SNAKE_CASE =   0.0_2
         _SCREAMING_SNAKE_CASE =   0.0_3
         _SCREAMING_SNAKE_CASE =   0.0_0_0_5
         _SCREAMING_SNAKE_CASE =   0.0_0_0_0_0_1
         _SCREAMING_SNAKE_CASE =   1
         _SCREAMING_SNAKE_CASE =   1_0
         _SCREAMING_SNAKE_CASE =   0.0_0_1
         _SCREAMING_SNAKE_CASE =   0.1
         _SCREAMING_SNAKE_CASE =   0.2
         _SCREAMING_SNAKE_CASE =   True

         # Model card mush be rendered and saved
         diffusers.utils.hub_utils.create_model_card(__lowerCamelCase						,   model_name="model_name"					)
         self.assertTrue((Path(__lowerCamelCase					) / "README.md").is_file()					)

