code: 
  
  
  '''simple docstring'''
  
  import argparse
  import os
  
  import torch
  from torchvision.datasets.utils import download_url
  
  from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, TransformeraDModel
  
  
  __lowercase:		int							=     {512: "DiT-XL-2-512x512.pt", 256: "DiT-XL-2-256x256.pt"}
  
  
  
  def        SCREAMING_SNAKE_CASE__(		_UpperCamelCase      :				Optional[int]   )		->					int:
  
  
  
  
  
     '''simple docstring'''
  
  
  
  
  
  
  
     UpperCamelCase__ 						=						F'pretrained_models/{model_name}'
     if not os.path.isfile(_UpperCamelCase   ):
        os.makedirs("pretrained_models"    ,		exist_ok=_UpperCamelCase   )
        UpperCamelCase__ 						=						F'https://dl.fbaipublicfiles.com/DiT/models/{model_name}'
        download_url(_UpperCamelCase    ,		"pretrained_models"   )
     UpperCamelCase__ 						=						torch.load(_UpperCamelCase    ,		map_location=lambda _UpperCamelCase    ,		_UpperCamelCase   : storage   )
     return model
  
  
  
  def        SCREAMING_SNAKE_CASE__(		_UpperCamelCase      :				Tuple   )		->					Union[str, Any]:
  
  
  
  
  
     '''simple docstring'''
  
  
  
  
  
  
  
     UpperCamelCase__ 						=						download_model(pretrained_models[args.image_size]   )
  
     UpperCamelCase__ 						=						state_dict["x_embedder.proj.weight"]
     UpperCamelCase__ 						=						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   ):
        UpperCamelCase__ 						=						state_dict[
            "t_embedder.mlp.0.weight"
        ]
        UpperCamelCase__ 						=						state_dict[
            "t_embedder.mlp.0.bias"
        ]
        UpperCamelCase__ 						=						state_dict[
            "t_embedder.mlp.2.weight"
        ]
        UpperCamelCase__ 						=						state_dict[
            "t_embedder.mlp.2.bias"
        ]
        UpperCamelCase__ 						=						state_dict[
            "y_embedder.embedding_table.weight"
        ]
  
        UpperCamelCase__ 						=						state_dict[
            F'blocks.{depth}.adaLN_modulation.1.weight'
        ]
        UpperCamelCase__ 						=						state_dict[
            F'blocks.{depth}.adaLN_modulation.1.bias'
        ]
  
        UpperCamelCase__						,		UpperCamelCase__						,		UpperCamelCase__ 						=						torch.chunk(state_dict[F'blocks.{depth}.attn.qkv.weight']    ,		3    ,		dim=0   )
        UpperCamelCase__						,		UpperCamelCase__						,		UpperCamelCase__ 						=						torch.chunk(state_dict[F'blocks.{depth}.attn.qkv.bias']    ,		3    ,		dim=0   )
  
        UpperCamelCase__ 						=						q
        UpperCamelCase__ 						=						q_bias
        UpperCamelCase__ 						=						k
        UpperCamelCase__ 						=						k_bias
        UpperCamelCase__ 						=						v
        UpperCamelCase__ 						=						v_bias
  
        UpperCamelCase__ 						=						state_dict[
            F'blocks.{depth}.attn.proj.weight'
        ]
        UpperCamelCase__ 						=						state_dict[F'blocks.{depth}.attn.proj.bias']
  
        UpperCamelCase__ 						=						state_dict[F'blocks.{depth}.mlp.fc1.weight']
        UpperCamelCase__ 						=						state_dict[F'blocks.{depth}.mlp.fc1.bias']
        UpperCamelCase__ 						=						state_dict[F'blocks.{depth}.mlp.fc2.weight']
        UpperCamelCase__ 						=						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"   )
  
     UpperCamelCase__ 						=						state_dict["final_layer.adaLN_modulation.1.weight"]
     UpperCamelCase__ 						=						state_dict["final_layer.adaLN_modulation.1.bias"]
     UpperCamelCase__ 						=						state_dict["final_layer.linear.weight"]
     UpperCamelCase__ 						=						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
     UpperCamelCase__ 						=						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=_UpperCamelCase    ,		num_attention_heads=16    ,		activation_fn="gelu-approximate"    ,		num_embeds_ada_norm=10_00    ,		norm_type="ada_norm_zero"    ,		norm_elementwise_affine=_UpperCamelCase    ,		)
     transformer.load_state_dict(_UpperCamelCase    ,		strict=_UpperCamelCase   )
  
     UpperCamelCase__ 						=						DDIMScheduler(
         num_train_timesteps=10_00    ,		beta_schedule="linear"    ,		prediction_type="epsilon"    ,		clip_sample=_UpperCamelCase    ,		)
  
     UpperCamelCase__ 						=						AutoencoderKL.from_pretrained(args.vae_model   )
  
     UpperCamelCase__ 						=						DiTPipeline(transformer=_UpperCamelCase    ,		vae=_UpperCamelCase    ,		scheduler=_UpperCamelCase   )
  
     if args.save:
        pipeline.save_pretrained(args.checkpoint_path   )
  
  
  if __name__ == "__main__":
         __lowercase:		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."
         )
  
         __lowercase:		List[str]							=     parser.parse_args()
         main(args)
style_context: 
  
  
  '''simple docstring'''
  
  from transformers import SqueezeBertTokenizer, SqueezeBertTokenizerFast
  from transformers.testing_utils import require_tokenizers, slow
  
  from ..bert.test_tokenization_bert import BertTokenizationTest
  
  
  
  
  
  
  
  @require_tokenizers
  class  UpperCAmelCase (       SCREAMING_SNAKE_CASE__):
   _lowerCamelCase :					Optional[int]    					=					SqueezeBertTokenizer
   _lowerCamelCase :					List[Any]    					=					SqueezeBertTokenizerFast
   _lowerCamelCase :					int    					=					True
  
   def 		lowercase_						( self :	List[str],					**a_ :	str					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      return SqueezeBertTokenizerFast.from_pretrained(self.tmpdirname,					**a_					)
  
   @slow
   def 		lowercase_						( self :	int					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      UpperCamelCase__ 						=						SqueezeBertTokenizer.from_pretrained("squeezebert/squeezebert-mnli-headless"					)
  
      UpperCamelCase__ 						=						tokenizer.encode("sequence builders",					add_special_tokens=a_					)
      UpperCamelCase__ 						=						tokenizer.encode("multi-sequence build",					add_special_tokens=a_					)
  
      UpperCamelCase__ 						=						tokenizer.build_inputs_with_special_tokens(a_					)
      UpperCamelCase__ 						=						tokenizer.build_inputs_with_special_tokens(a_,					a_					)
  
      assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]
      assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + text_a + [
          tokenizer.sep_token_id
      ]