code: 
  
  
  
  
  """simple docstring"""
  
  
  
  
  # Copyright 2022 The HuggingFace Team. All rights reserved.
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #     http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  import os
  from typing import List
  def 					A_			(		snake_case_     :       List[str]						,snake_case_     :       str    ):
  
  
  
  
  
  
  
      '''simple docstring'''
      if name != "training_function" and name != "main":
          raise ValueError(f'Incorrect function name passed: {name}, choose either \'main\' or \'training_function\''    )
      UpperCamelCase       ,							UpperCamelCase				:   Optional[int]          =							[], False
      for line in lines:
          if not found_start and f'def {name}' in line:
              UpperCamelCase				:   List[str]          =							True
              good_lines.append(snake_case_    )
              continue
          if found_start:
              if name == "training_function" and "def main" in line:
                  return good_lines
              if name == "main" and "if __name__" in line:
                  return good_lines
              good_lines.append(snake_case_    )
  def 					A_			(		snake_case_     :       List[str]    ):
  
  
  
  
  
  
  
      '''simple docstring'''
      return [line for line in lines if not line.lstrip().startswith("""#"""    ) and line != "\n"]
  
  
  def 					A_			(		snake_case_     :       str						,snake_case_     :       str						,snake_case_     :       bool						,snake_case_     :       str = None    ):
  
  
  
  
  
  
  
      '''simple docstring'''
      with open(snake_case_						,"""r"""    ) as f:
          UpperCamelCase				:   Tuple          =							f.readlines()
      with open(os.path.abspath(os.path.join("""examples"""						,"""nlp_example.py"""    )    )						,"""r"""    ) as f:
          UpperCamelCase				:   Optional[Any]          =							f.readlines()
      with open(snake_case_						,"""r"""    ) as f:
          UpperCamelCase				:   Optional[Any]          =							f.readlines()
      if secondary_filename is not None:
          with open(snake_case_						,"""r"""    ) as f:
              UpperCamelCase				:   List[Any]          =							f.readlines()
  
      # This is our base, we remove all the code from here in our `full_filename` and `feature_filename` to find the new content
      if parser_only:
          UpperCamelCase				:   List[Any]          =							clean_lines(get_function_contents_by_name(snake_case_						,"""main"""    )    )
          UpperCamelCase				:   Optional[int]          =							clean_lines(get_function_contents_by_name(snake_case_						,"""main"""    )    )
          UpperCamelCase				:   Optional[Any]          =							clean_lines(get_function_contents_by_name(snake_case_						,"""main"""    )    )
          if secondary_filename is not None:
              UpperCamelCase				:   Tuple          =							clean_lines(get_function_contents_by_name(snake_case_						,"""main"""    )    )
      else:
          UpperCamelCase				:   List[str]          =							clean_lines(get_function_contents_by_name(snake_case_						,"""training_function"""    )    )
          UpperCamelCase				:   int          =							clean_lines(get_function_contents_by_name(snake_case_						,"""training_function"""    )    )
          UpperCamelCase				:   int          =							clean_lines(get_function_contents_by_name(snake_case_						,"""training_function"""    )    )
          if secondary_filename is not None:
              UpperCamelCase				:   Dict          =							clean_lines(
                  get_function_contents_by_name(snake_case_						,"""training_function"""    )    )
  
      UpperCamelCase				:   Optional[Any]          =							"""train_dataloader, eval_dataloader = get_dataloaders(accelerator, batch_size)\n"""
  
      # Specific code in our script that differs from the full version, aka what is new
      UpperCamelCase				:   Any          =							[]
      UpperCamelCase				:   int          =							[]  # We keep track of the idxs just in case it's a repeated statement
      UpperCamelCase				:   Optional[Any]          =							iter(snake_case_    )
      for i in range(len(snake_case_    ) - 1    ):
          if i not in passed_idxs:
              UpperCamelCase				:   Any          =							next(snake_case_    )
              if (line not in full_file_func) and (line.lstrip() != _dl_line):
                  if "TESTING_MOCKED_DATALOADERS" not in line:
                      new_feature_code.append(snake_case_    )
                      passed_idxs.append(snake_case_    )
                  else:
                      # Skip over the `config['num_epochs'] = 2` statement
                      UpperCamelCase				:   List[str]          =							next(snake_case_    )
  
      # Extract out just the new parts from the full_file_training_func
      UpperCamelCase				:   Any          =							[]
      UpperCamelCase				:   List[Any]          =							[]  # We keep track of the idxs just in case it's a repeated statement
      for i, line in enumerate(snake_case_    ):
          if i not in passed_idxs:
              if (line not in full_file_func) and (line.lstrip() != _dl_line):
                  if "TESTING_MOCKED_DATALOADERS" not in line:
                      new_full_example_parts.append(snake_case_    )
                      passed_idxs.append(snake_case_    )
  
      # Finally, get the overall diff
      UpperCamelCase				:   Any          =							[line for line in new_feature_code if line not in new_full_example_parts]
      if secondary_filename is not None:
          UpperCamelCase				:   Tuple          =							[line for line in full_file_contents if line not in secondary_file_func]
          UpperCamelCase				:   Optional[Any]          =							[line for line in diff_from_example if line not in diff_from_two]
  
      return diff_from_example
  
  
  
  
  
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)
  
  
  
  
  