code: 
  
  
  
  
  from typing import Mapping
  
  from ...configuration_utils import PretrainedConfig
  from ...onnx import OnnxSeqaSeqConfigWithPast
  from ...utils import logging
  
  
  __A :		int      							=    logging.get_logger(__name__)
  
  __A :		int      							=    {
      '''google/long-t5-local-base''': '''https://huggingface.co/google/long-t5-local-base/blob/main/config.json''',
      '''google/long-t5-local-large''': '''https://huggingface.co/google/long-t5-local-large/blob/main/config.json''',
      '''google/long-t5-tglobal-base''': '''https://huggingface.co/google/long-t5-tglobal-base/blob/main/config.json''',
      '''google/long-t5-tglobal-large''': '''https://huggingface.co/google/long-t5-tglobal-large/blob/main/config.json''',
  }
  class 							lowerCamelCase		( _UpperCAmelCase				):
     lowercase       :  List[Any]      =				"longt5"
     lowercase       :  int      =				["past_key_values"]
     lowercase       :  Optional[int]      =				{"hidden_size": "d_model", "num_attention_heads": "num_heads", "num_hidden_layers": "num_layers"}
  
  
  
  
  
  
  
     def __init__(							self    ,      SCREAMING_SNAKE_CASE_=3_2128    ,      SCREAMING_SNAKE_CASE_=512    ,      SCREAMING_SNAKE_CASE_=64    ,      SCREAMING_SNAKE_CASE_=2048    ,      SCREAMING_SNAKE_CASE_=6    ,      SCREAMING_SNAKE_CASE_=None    ,      SCREAMING_SNAKE_CASE_=8    ,      SCREAMING_SNAKE_CASE_=127    ,      SCREAMING_SNAKE_CASE_=16    ,      SCREAMING_SNAKE_CASE_=32    ,      SCREAMING_SNAKE_CASE_=128    ,      SCREAMING_SNAKE_CASE_=0.1    ,      SCREAMING_SNAKE_CASE_=1e-6    ,      SCREAMING_SNAKE_CASE_=1.0    ,      SCREAMING_SNAKE_CASE_="relu"    ,      SCREAMING_SNAKE_CASE_=True    ,      SCREAMING_SNAKE_CASE_="local"    ,      SCREAMING_SNAKE_CASE_=True    ,      SCREAMING_SNAKE_CASE_=0    ,      SCREAMING_SNAKE_CASE_=1    ,      **SCREAMING_SNAKE_CASE_    ,      ):
         UpperCamelCase				:   Union[str, Any]          =							vocab_size
         UpperCamelCase				:   List[str]          =							d_model
         UpperCamelCase				:   Tuple          =							d_kv
         UpperCamelCase				:   List[Any]          =							d_ff
         UpperCamelCase				:   List[Any]          =							num_layers
         # default = symmetry
         UpperCamelCase				:   Dict          =							num_decoder_layers if num_decoder_layers is not None else self.num_layers
         UpperCamelCase				:   str          =							num_heads
         UpperCamelCase				:   Union[str, Any]          =							local_radius
         UpperCamelCase				:   Optional[int]          =							global_block_size
         UpperCamelCase				:   Any          =							relative_attention_num_buckets
         UpperCamelCase				:   Optional[int]          =							relative_attention_max_distance
         UpperCamelCase				:   int          =							dropout_rate
         UpperCamelCase				:   List[Any]          =							layer_norm_epsilon
         UpperCamelCase				:   List[Any]          =							initializer_factor
         UpperCamelCase				:   List[str]          =							feed_forward_proj
         UpperCamelCase				:   List[Any]          =							encoder_attention_type
         UpperCamelCase				:   int          =							use_cache
  
         UpperCamelCase				:   Any          =							self.feed_forward_proj.split("""-""" )
         UpperCamelCase				:   Union[str, Any]          =							act_info[-1]
         UpperCamelCase				:   Optional[int]          =							act_info[0] == 'gated'
  
         if len(UpperCAmelCase_ ) > 1 and act_info[0] != "gated" or len(UpperCAmelCase_ ) > 2:
             raise ValueError(
                 f'`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer.'
                 """Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. """
                 """\'gated-gelu\' or \'relu\'""" )
  
         # for backwards compatibility
         if feed_forward_proj == "gated-gelu":
             UpperCamelCase				:   Optional[Any]          =							'gelu_new'
  
         super().__init__(
             pad_token_id=UpperCAmelCase_    ,      eos_token_id=UpperCAmelCase_    ,      is_encoder_decoder=UpperCAmelCase_    ,      **UpperCAmelCase_    ,      )
  
  
  
  
  
  
  
  class 							lowerCamelCase		( _UpperCAmelCase				):
  
  
  
  
  
  
  
     @property
     def       a_							(							self ):
         UpperCamelCase				:   int          =							{
             'input_ids': {0: 'batch', 1: 'encoder_sequence'},
             'attention_mask': {0: 'batch', 1: 'encoder_sequence'},
         }
         if self.use_past:
             UpperCamelCase				:   Dict          =							'past_encoder_sequence + sequence'
             UpperCamelCase				:   Any          =							{0: 'batch'}
             UpperCamelCase				:   List[str]          =							{0: 'batch', 1: 'past_decoder_sequence + sequence'}
         else:
             UpperCamelCase				:   Optional[Any]          =							{0: 'batch', 1: 'decoder_sequence'}
             UpperCamelCase				:   Optional[Any]          =							{0: 'batch', 1: 'decoder_sequence'}
  
         if self.use_past:
             self.fill_with_past_key_values_(UpperCAmelCase_    ,      direction="""inputs""" )
  
         return common_inputs
  
  
  
  
  
  
  
     @property
     def       a_							(							self ):
         return 13
  
  
  
  
  
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)
  
  
  
  
  