code: 
  
  
  
  
  import enum
  import inspect
  from typing import Iterable, List, Optional, Tuple, Union
  class 							lowerCamelCase		( enum.Enum				):
     lowercase       :  Dict      =				'''timm'''
     lowercase       :  str      =				'''transformers'''
  
  
  
  
  
  
  
  def 					A_			(		snake_case_     :       Any						,snake_case_     :       Any						,snake_case_     :       Optional[Any]    ):
  
  
  
  
  
  
  
      '''simple docstring'''
  
  
  
  
  
  
      if stage_names is None:
          raise ValueError("""Stage_names must be set for transformers backbones"""    )
  
      if out_features is not None:
          if not isinstance(UpperCamelCase__						,(list,)    ):
              raise ValueError(f'out_features must be a list {type(UpperCamelCase__    )}'    )
          if any(feat not in stage_names for feat in out_features    ):
              raise ValueError(f'out_features must be a subset of stage_names: {stage_names} got {out_features}'    )
  
      if out_indices is not None:
          if not isinstance(UpperCamelCase__						,(list, tuple)    ):
              raise ValueError(f'out_indices must be a list or tuple, got {type(UpperCamelCase__    )}'    )
          if any(idx >= len(UpperCamelCase__    ) for idx in out_indices    ):
              raise ValueError("""out_indices must be valid indices for stage_names {stage_names}, got {out_indices}"""    )
  
      if out_features is not None and out_indices is not None:
          if len(UpperCamelCase__    ) != len(UpperCamelCase__    ):
              raise ValueError("""out_features and out_indices should have the same length if both are set"""    )
          if out_features != [stage_names[idx] for idx in out_indices]:
              raise ValueError("""out_features and out_indices should correspond to the same stages if both are set"""    )
  def 					A_			(		snake_case_     :       Any						,snake_case_     :       Optional[Any]						,snake_case_     :       int						,):
  
  
  
  
  
  
  
      '''simple docstring'''
  
  
  
  
  
  
      if out_indices is None and out_features is None:
          UpperCamelCase				:   Dict          =							[len(UpperCamelCase__    ) - 1]
          UpperCamelCase				:   List[str]          =							[stage_names[-1]]
      elif out_indices is None and out_features is not None:
          UpperCamelCase				:   Union[str, Any]          =							[stage_names.index(UpperCamelCase__    ) for layer in out_features]
      elif out_features is None and out_indices is not None:
          UpperCamelCase				:   int          =							[stage_names[idx] for idx in out_indices]
      return out_features, out_indices
  def 					A_			(		snake_case_     :       Union[str, Any]						,snake_case_     :       Union[str, Any]						,snake_case_     :       Optional[Any]						,):
  
  
  
  
  
  
  
      '''simple docstring'''
      verify_out_features_out_indices(out_features=UpperCamelCase__						,out_indices=UpperCamelCase__						,stage_names=UpperCamelCase__    )
      UpperCamelCase       ,							UpperCamelCase				:   int          =							_align_output_features_output_indices(
          out_features=UpperCamelCase__						,out_indices=UpperCamelCase__						,stage_names=UpperCamelCase__    )
      # Verify that the aligned out_features and out_indices are valid
      verify_out_features_out_indices(out_features=UpperCamelCase__						,out_indices=UpperCamelCase__						,stage_names=UpperCamelCase__    )
      return output_features, output_indices
  class 							lowerCamelCase		:
     lowercase       :  Optional[BackboneType]      =				None
  
  
  
  
  
  
  
     def       a_							(							self    ,      SCREAMING_SNAKE_CASE_ ):
         if getattr(self    ,      """_backbone"""    ,      a_ ) is None:
             raise ValueError("""self._backbone must be set before calling _init_timm_backbone""" )
  
         # These will diagree with the defaults for the transformers models e.g. for resnet50
         # the transformer model has out_features = ['stem', 'stage1', 'stage2', 'stage3', 'stage4']
         # the timm model has out_features = ['act', 'layer1', 'layer2', 'layer3', 'layer4']
         UpperCamelCase				:   Any          =							[stage["""module"""] for stage in self._backbone.feature_info.info]
         UpperCamelCase				:   Union[str, Any]          =							[stage["""num_chs"""] for stage in self._backbone.feature_info.info]
         UpperCamelCase				:   Optional[Any]          =							self._backbone.feature_info.out_indices
         UpperCamelCase				:   List[Any]          =							self._backbone.feature_info.module_name()
  
         # We verify the out indices and out features are valid
         verify_out_features_out_indices(
             out_features=a_    ,      out_indices=a_    ,      stage_names=self.stage_names )
         UpperCamelCase       ,							UpperCamelCase				:   List[str]          =							out_features, out_indices
  
  
  
  
  
  
  
     def       a_							(							self    ,      SCREAMING_SNAKE_CASE_ ):
         UpperCamelCase				:   Any          =							getattr(a_    ,      """stage_names""" )
         UpperCamelCase				:   Any          =							getattr(a_    ,      """out_features"""    ,      a_ )
         UpperCamelCase				:   str          =							getattr(a_    ,      """out_indices"""    ,      a_ )
  
         UpperCamelCase				:   List[Any]          =							stage_names
         UpperCamelCase       ,							UpperCamelCase				:   int          =							get_aligned_output_features_output_indices(
             out_features=a_    ,      out_indices=a_    ,      stage_names=a_ )
         # Number of channels for each stage. This is set in the transformer backbone model init
         UpperCamelCase				:   Union[str, Any]          =							None
  
  
  
  
  
  
  
     def       a_							(							self    ,      SCREAMING_SNAKE_CASE_ ):
         UpperCamelCase				:   str          =							config
  
         UpperCamelCase				:   Union[str, Any]          =							getattr(a_    ,      """use_timm_backbone"""    ,      a_ )
         UpperCamelCase				:   Tuple          =							BackboneType.TIMM if self.use_timm_backbone else BackboneType.TRANSFORMERS
  
         if self.backbone_type == BackboneType.TIMM:
             self._init_timm_backbone(a_ )
         elif self.backbone_type == BackboneType.TRANSFORMERS:
             self._init_transformers_backbone(a_ )
         else:
             raise ValueError(f'backbone_type {self.backbone_type} not supported.' )
  
  
  
  
  
  
  
     @property
     def       a_							(							self ):
         return self._out_features
  
  
  
  
  
  
  
     @out_features.setter
     def       a_							(							self    ,      SCREAMING_SNAKE_CASE_ ):
         UpperCamelCase       ,							UpperCamelCase				:   Optional[Any]          =							get_aligned_output_features_output_indices(
             out_features=a_    ,      out_indices=a_    ,      stage_names=self.stage_names )
  
  
  
  
  
  
  
     @property
     def       a_							(							self ):
         return self._out_indices
  
  
  
  
  
  
  
     @out_indices.setter
     def       a_							(							self    ,      SCREAMING_SNAKE_CASE_ ):
         UpperCamelCase       ,							UpperCamelCase				:   Dict          =							get_aligned_output_features_output_indices(
             out_features=a_    ,      out_indices=a_    ,      stage_names=self.stage_names )
  
  
  
  
  
  
  
     @property
     def       a_							(							self ):
         # the current backbones will output the number of channels for each stage
         # even if that stage is not in the out_features list.
         return {stage: self.num_features[i] for i, stage in enumerate(self.stage_names )}
  
  
  
  
  
  
  
     @property
     def       a_							(							self ):
         return [self.out_feature_channels[name] for name in self.out_features]
  
  
  
  
  
  
  
     def       a_							(							self    ,      *SCREAMING_SNAKE_CASE_    ,      **SCREAMING_SNAKE_CASE_ ):
         UpperCamelCase				:   Union[str, Any]          =							dict(inspect.signature(self.forward ).parameters )
         UpperCamelCase				:   Tuple          =							{k: v for k, v in kwargs.items() if k in signature}
         return self(*a_    ,      **a_ )
  
  
  
  
  
  
  
     def       a_							(							self    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_ = None    ,      SCREAMING_SNAKE_CASE_ = None    ,      SCREAMING_SNAKE_CASE_ = None    ,      ):
         raise NotImplementedError("""This method should be implemented by the derived class.""" )
  
  
  
  
  
  
  
     def       a_							(							self ):
         UpperCamelCase				:   Optional[int]          =							super().to_dict()
         UpperCamelCase				:   Any          =							output.pop("""_out_features""" )
         UpperCamelCase				:   Any          =							output.pop("""_out_indices""" )
         return output
  
  
  
  
  
  
  
  class 							lowerCamelCase		:
  
  
  
  
  
  
  
     @property
     def       a_							(							self ):
         return self._out_features
  
  
  
  
  
  
  
     @out_features.setter
     def       a_							(							self    ,      SCREAMING_SNAKE_CASE_ ):
         UpperCamelCase       ,							UpperCamelCase				:   List[Any]          =							get_aligned_output_features_output_indices(
             out_features=a_    ,      out_indices=a_    ,      stage_names=self.stage_names )
  
  
  
  
  
  
  
     @property
     def       a_							(							self ):
         return self._out_indices
  
  
  
  
  
  
  
     @out_indices.setter
     def       a_							(							self    ,      SCREAMING_SNAKE_CASE_ ):
         UpperCamelCase       ,							UpperCamelCase				:   Union[str, Any]          =							get_aligned_output_features_output_indices(
             out_features=a_    ,      out_indices=a_    ,      stage_names=self.stage_names )
  
  
  
  
  
  
  
     def       a_							(							self ):
         UpperCamelCase				:   Optional[int]          =							super().to_dict()
         UpperCamelCase				:   Optional[int]          =							output.pop("""_out_features""" )
         UpperCamelCase				:   str          =							output.pop("""_out_indices""" )
         return output
  
  
  
  
  
style_context: 
  
  
  
  
  """simple docstring"""
  
  
  
  
  import argparse
  import os
  
  import torch
  from torchvision.datasets.utils import download_url
  
  from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, TransformeraDModel
  
  
  __A :		int      							=    {512: '''DiT-XL-2-512x512.pt''', 256: '''DiT-XL-2-256x256.pt'''}
  def 					A_			(		snake_case_     :       Dict    ):
  
  
  
  
  
  
  
      '''simple docstring'''
      UpperCamelCase				:   Any          =							f'pretrained_models/{model_name}'
      if not os.path.isfile(snake_case_    ):
          os.makedirs("""pretrained_models"""						,exist_ok=snake_case_    )
          UpperCamelCase				:   Dict          =							f'https://dl.fbaipublicfiles.com/DiT/models/{model_name}'
          download_url(snake_case_						,"""pretrained_models"""    )
      UpperCamelCase				:   List[Any]          =							torch.load(snake_case_						,map_location=lambda snake_case_						,snake_case_    : storage    )
      return model
  def 					A_			(		snake_case_     :       List[str]    ):
  
  
  
  
  
  
  
      '''simple docstring'''
      UpperCamelCase				:   int          =							download_model(pretrained_models[args.image_size]    )
  
      UpperCamelCase				:   str          =							state_dict["""x_embedder.proj.weight"""]
      UpperCamelCase				:   Optional[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(2_8    ):
          UpperCamelCase				:   Optional[int]          =							state_dict[
              """t_embedder.mlp.0.weight"""
          ]
          UpperCamelCase				:   Optional[Any]          =							state_dict[
              """t_embedder.mlp.0.bias"""
          ]
          UpperCamelCase				:   str          =							state_dict[
              """t_embedder.mlp.2.weight"""
          ]
          UpperCamelCase				:   Optional[int]          =							state_dict[
              """t_embedder.mlp.2.bias"""
          ]
          UpperCamelCase				:   int          =							state_dict[
              """y_embedder.embedding_table.weight"""
          ]
  
          UpperCamelCase				:   List[Any]          =							state_dict[
              f'blocks.{depth}.adaLN_modulation.1.weight'
          ]
          UpperCamelCase				:   Any          =							state_dict[
              f'blocks.{depth}.adaLN_modulation.1.bias'
          ]
  
          UpperCamelCase       ,							UpperCamelCase       ,							UpperCamelCase				:   Dict          =							torch.chunk(state_dict[f'blocks.{depth}.attn.qkv.weight']						,3						,dim=0    )
          UpperCamelCase       ,							UpperCamelCase       ,							UpperCamelCase				:   Dict          =							torch.chunk(state_dict[f'blocks.{depth}.attn.qkv.bias']						,3						,dim=0    )
  
          UpperCamelCase				:   int          =							q
          UpperCamelCase				:   Tuple          =							q_bias
          UpperCamelCase				:   List[Any]          =							k
          UpperCamelCase				:   int          =							k_bias
          UpperCamelCase				:   List[str]          =							v
          UpperCamelCase				:   str          =							v_bias
  
          UpperCamelCase				:   Any          =							state_dict[
              f'blocks.{depth}.attn.proj.weight'
          ]
          UpperCamelCase				:   Optional[Any]          =							state_dict[f'blocks.{depth}.attn.proj.bias']
  
          UpperCamelCase				:   str          =							state_dict[f'blocks.{depth}.mlp.fc1.weight']
          UpperCamelCase				:   Tuple          =							state_dict[f'blocks.{depth}.mlp.fc1.bias']
          UpperCamelCase				:   str          =							state_dict[f'blocks.{depth}.mlp.fc2.weight']
          UpperCamelCase				:   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"""    )
  
      UpperCamelCase				:   List[str]          =							state_dict["""final_layer.adaLN_modulation.1.weight"""]
      UpperCamelCase				:   str          =							state_dict["""final_layer.adaLN_modulation.1.bias"""]
      UpperCamelCase				:   Tuple          =							state_dict["""final_layer.linear.weight"""]
      UpperCamelCase				:   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
      UpperCamelCase				:   int          =							TransformeraDModel(
          sample_size=args.image_size // 8						,num_layers=2_8						,attention_head_dim=7_2						,in_channels=4						,out_channels=8						,patch_size=2						,attention_bias=snake_case_						,num_attention_heads=1_6						,activation_fn="""gelu-approximate"""						,num_embeds_ada_norm=1_0_0_0						,norm_type="""ada_norm_zero"""						,norm_elementwise_affine=snake_case_						,)
      transformer.load_state_dict(snake_case_						,strict=snake_case_    )
  
      UpperCamelCase				:   str          =							DDIMScheduler(
          num_train_timesteps=1_0_0_0						,beta_schedule="""linear"""						,prediction_type="""epsilon"""						,clip_sample=snake_case_						,)
  
      UpperCamelCase				:   Optional[Any]          =							AutoencoderKL.from_pretrained(args.vae_model    )
  
      UpperCamelCase				:   List[Any]          =							DiTPipeline(transformer=snake_case_						,vae=snake_case_						,scheduler=snake_case_    )
  
      if args.save:
          pipeline.save_pretrained(args.checkpoint_path    )
  
  
  if __name__ == "__main__":
    __A :		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 :		Dict      							=    parser.parse_args()
    main(args)
  
  
  
  
  