




'''simple docstring'''





import argparse
import os

import torch
from torchvision.datasets.utils import download_url

from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, TransformeraDModel


_UpperCamelCase							       =		{512: 'DiT-XL-2-512x512.pt', 256: 'DiT-XL-2-256x256.pt'}

def  a_  (							_lowerCAmelCase )   ->   Union[str, Any]:
  __lowerCamelCase : Tuple					 =				F'pretrained_models/{model_name}'
  if not os.path.isfile(_lowerCAmelCase ):
    os.makedirs('pretrained_models' ,exist_ok=_lowerCAmelCase )
    __lowerCamelCase : Dict					 =				F'https://dl.fbaipublicfiles.com/DiT/models/{model_name}'
    download_url(_lowerCAmelCase ,'pretrained_models' )
  __lowerCamelCase : str					 =				torch.load(_lowerCAmelCase ,map_location=lambda _lowerCAmelCase ,_lowerCAmelCase : storage )
  return model

def  a_  (							_lowerCAmelCase )   ->   Dict:
  __lowerCamelCase : Tuple					 =				download_model(pretrained_models[args.image_size] )

  __lowerCamelCase : List[str]					 =				state_dict['x_embedder.proj.weight']
  __lowerCamelCase : Union[str, 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(28 ):
    __lowerCamelCase : Optional[int]					 =				state_dict[
        't_embedder.mlp.0.weight'
    ]
    __lowerCamelCase : List[Any]					 =				state_dict[
        't_embedder.mlp.0.bias'
    ]
    __lowerCamelCase : int					 =				state_dict[
        't_embedder.mlp.2.weight'
    ]
    __lowerCamelCase : int					 =				state_dict[
        't_embedder.mlp.2.bias'
    ]
    __lowerCamelCase : List[str]					 =				state_dict[
        'y_embedder.embedding_table.weight'
    ]

    __lowerCamelCase : Optional[int]					 =				state_dict[
        F'blocks.{depth}.adaLN_modulation.1.weight'
    ]
    __lowerCamelCase : str					 =				state_dict[
        F'blocks.{depth}.adaLN_modulation.1.bias'
    ]

    __lowerCamelCase : str					 =				torch.chunk(state_dict[F'blocks.{depth}.attn.qkv.weight'] ,3 ,dim=0 )
    __lowerCamelCase : Union[str, Any]					 =				torch.chunk(state_dict[F'blocks.{depth}.attn.qkv.bias'] ,3 ,dim=0 )

    __lowerCamelCase : Dict					 =				q
    __lowerCamelCase : Union[str, Any]					 =				q_bias
    __lowerCamelCase : Dict					 =				k
    __lowerCamelCase : Any					 =				k_bias
    __lowerCamelCase : List[str]					 =				v
    __lowerCamelCase : str					 =				v_bias

    __lowerCamelCase : List[str]					 =				state_dict[
        F'blocks.{depth}.attn.proj.weight'
    ]
    __lowerCamelCase : Optional[int]					 =				state_dict[F'blocks.{depth}.attn.proj.bias']

    __lowerCamelCase : List[Any]					 =				state_dict[F'blocks.{depth}.mlp.fc1.weight']
    __lowerCamelCase : Optional[Any]					 =				state_dict[F'blocks.{depth}.mlp.fc1.bias']
    __lowerCamelCase : Union[str, Any]					 =				state_dict[F'blocks.{depth}.mlp.fc2.weight']
    __lowerCamelCase : str					 =				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' )

  __lowerCamelCase : List[Any]					 =				state_dict['final_layer.adaLN_modulation.1.weight']
  __lowerCamelCase : int					 =				state_dict['final_layer.adaLN_modulation.1.bias']
  __lowerCamelCase : Dict					 =				state_dict['final_layer.linear.weight']
  __lowerCamelCase : Optional[int]					 =				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
  __lowerCamelCase : List[Any]					 =				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=_lowerCAmelCase ,num_attention_heads=16 ,activation_fn='gelu-approximate' ,num_embeds_ada_norm=1000 ,norm_type='ada_norm_zero' ,norm_elementwise_affine=_lowerCAmelCase ,)
  transformer.load_state_dict(_lowerCAmelCase ,strict=_lowerCAmelCase )

  __lowerCamelCase : List[str]					 =				DDIMScheduler(
      num_train_timesteps=1000 ,beta_schedule='linear' ,prediction_type='epsilon' ,clip_sample=_lowerCAmelCase ,)

  __lowerCamelCase : Union[str, Any]					 =				AutoencoderKL.from_pretrained(args.vae_model )

  __lowerCamelCase : Any					 =				DiTPipeline(transformer=_lowerCAmelCase ,vae=_lowerCAmelCase ,scheduler=_lowerCAmelCase )

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


if __name__ == "__main__":
   _UpperCamelCase							       =		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.'
   )

   _UpperCamelCase							       =		parser.parse_args()
   main(args)











'''simple docstring'''





from typing import List, Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


_UpperCamelCase							       =		logging.get_logger(__name__)

_UpperCamelCase							       =		{
    'allenai/led-base-16384': 'https://huggingface.co/allenai/led-base-16384/resolve/main/config.json',
    # See all LED models at https://huggingface.co/models?filter=led
}






class       lowerCamelCase_					(       SCREAMING_SNAKE_CASE_  ):
  """simple docstring"""




  a_							      ="""led"""
  a_							      ={
      """num_attention_heads""": """encoder_attention_heads""",
      """hidden_size""": """d_model""",
      """attention_probs_dropout_prob""": """attention_dropout""",
      """initializer_range""": """init_std""",
  }




  def __init__(				self   :			str    ,			_a   :			Union[str, Any]=5_0265    ,			_a   :			Any=1_6384    ,			_a   :			List[Any]=1024    ,			_a   :			List[Any]=12    ,			_a   :			Any=4096    ,			_a   :			int=16    ,			_a   :			int=12    ,			_a   :			Optional[int]=4096    ,			_a   :			Tuple=16    ,			_a   :			Optional[Any]=0.0    ,			_a   :			Tuple=0.0    ,			_a   :			List[Any]=True    ,			_a   :			Optional[Any]=True    ,			_a   :			Any="gelu"    ,			_a   :			List[Any]=1024    ,			_a   :			List[str]=0.1    ,			_a   :			Dict=0.0    ,			_a   :			Tuple=0.0    ,			_a   :			Any=0.02    ,			_a   :			Optional[int]=2    ,			_a   :			Optional[int]=0.0    ,			_a   :			int=1    ,			_a   :			List[Any]=0    ,			_a   :			str=2    ,			_a   :			Union[List[int], int] = 512    ,			**_a   :			Optional[int]    ,			)      ->      Tuple:
    __lowerCamelCase : Optional[int]					 =				vocab_size
    __lowerCamelCase : List[str]					 =				max_encoder_position_embeddings
    __lowerCamelCase : Any					 =				max_decoder_position_embeddings
    __lowerCamelCase : Optional[Any]					 =				d_model
    __lowerCamelCase : str					 =				encoder_ffn_dim
    __lowerCamelCase : List[Any]					 =				encoder_layers
    __lowerCamelCase : Union[str, Any]					 =				encoder_attention_heads
    __lowerCamelCase : Optional[Any]					 =				decoder_ffn_dim
    __lowerCamelCase : Union[str, Any]					 =				decoder_layers
    __lowerCamelCase : Optional[Any]					 =				decoder_attention_heads
    __lowerCamelCase : Optional[Any]					 =				dropout
    __lowerCamelCase : List[Any]					 =				attention_dropout
    __lowerCamelCase : Optional[int]					 =				activation_dropout
    __lowerCamelCase : Optional[int]					 =				activation_function
    __lowerCamelCase : Union[str, Any]					 =				init_std
    __lowerCamelCase : str					 =				encoder_layerdrop
    __lowerCamelCase : Tuple					 =				decoder_layerdrop
    __lowerCamelCase : Dict					 =				classifier_dropout
    __lowerCamelCase : Any					 =				use_cache
    __lowerCamelCase : Union[str, Any]					 =				encoder_layers
    __lowerCamelCase : Any					 =				attention_window

    super().__init__(
        pad_token_id=_a    ,			bos_token_id=_a    ,			eos_token_id=_a    ,			is_encoder_decoder=_a    ,			decoder_start_token_id=_a    ,			**_a    ,			)






