


import argparse
import os

import torch
from torchvision.datasets.utils import download_url

from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, TransformeraDModel


lowerCamelCase		      = {5_12: 'DiT-XL-2-512x512.pt', 2_56: 'DiT-XL-2-256x256.pt'}




def 			a_   (					SCREAMING_SNAKE_CASE__      :   Dict  ):
       '''simple docstring'''





       _lowerCamelCase :	str						=F'''pretrained_models/{model_name}'''
       if not os.path.isfile(SCREAMING_SNAKE_CASE__  ):
              os.makedirs('pretrained_models'	,    exist_ok=SCREAMING_SNAKE_CASE__  )
              _lowerCamelCase :	List[Any]						=F'''https://dl.fbaipublicfiles.com/DiT/models/{model_name}'''
              download_url(SCREAMING_SNAKE_CASE__	,    'pretrained_models'  )
       _lowerCamelCase :	Optional[int]						=torch.load(SCREAMING_SNAKE_CASE__	,    map_location=lambda SCREAMING_SNAKE_CASE__	,    SCREAMING_SNAKE_CASE__  : storage  )
       return model




def 			a_   (					SCREAMING_SNAKE_CASE__      :   str  ):
       '''simple docstring'''





       _lowerCamelCase :	List[str]						=download_model(pretrained_models[args.image_size]  )

       _lowerCamelCase :	Union[str, Any]						=state_dict['x_embedder.proj.weight']
       _lowerCamelCase :	Dict						=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[Any]						=state_dict[
                  't_embedder.mlp.0.weight'
              ]
              _lowerCamelCase :	int						=state_dict[
                  't_embedder.mlp.0.bias'
              ]
              _lowerCamelCase :	Dict						=state_dict[
                  't_embedder.mlp.2.weight'
              ]
              _lowerCamelCase :	Optional[Any]						=state_dict[
                  't_embedder.mlp.2.bias'
              ]
              _lowerCamelCase :	Optional[Any]						=state_dict[
                  'y_embedder.embedding_table.weight'
              ]

              _lowerCamelCase :	Union[str, Any]						=state_dict[
                  F'''blocks.{depth}.adaLN_modulation.1.weight'''
              ]
              _lowerCamelCase :	List[str]						=state_dict[
                  F'''blocks.{depth}.adaLN_modulation.1.bias'''
              ]

              _lowerCamelCase :	Optional[int]						=torch.chunk(state_dict[F'''blocks.{depth}.attn.qkv.weight''']	,    3	,    dim=0  )
              _lowerCamelCase :	Dict						=torch.chunk(state_dict[F'''blocks.{depth}.attn.qkv.bias''']	,    3	,    dim=0  )

              _lowerCamelCase :	Any						=q
              _lowerCamelCase :	int						=q_bias
              _lowerCamelCase :	str						=k
              _lowerCamelCase :	List[str]						=k_bias
              _lowerCamelCase :	Union[str, Any]						=v
              _lowerCamelCase :	Union[str, Any]						=v_bias

              _lowerCamelCase :	str						=state_dict[
                  F'''blocks.{depth}.attn.proj.weight'''
              ]
              _lowerCamelCase :	Any						=state_dict[F'''blocks.{depth}.attn.proj.bias''']

              _lowerCamelCase :	int						=state_dict[F'''blocks.{depth}.mlp.fc1.weight''']
              _lowerCamelCase :	List[str]						=state_dict[F'''blocks.{depth}.mlp.fc1.bias''']
              _lowerCamelCase :	List[Any]						=state_dict[F'''blocks.{depth}.mlp.fc2.weight''']
              _lowerCamelCase :	List[Any]						=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 :	Union[str, Any]						=state_dict['final_layer.adaLN_modulation.1.weight']
       _lowerCamelCase :	Tuple						=state_dict['final_layer.adaLN_modulation.1.bias']
       _lowerCamelCase :	Dict						=state_dict['final_layer.linear.weight']
       _lowerCamelCase :	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
       _lowerCamelCase :	Union[str, 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=SCREAMING_SNAKE_CASE__	,    num_attention_heads=16	,    activation_fn='gelu-approximate'	,    num_embeds_ada_norm=1_000	,    norm_type='ada_norm_zero'	,    norm_elementwise_affine=SCREAMING_SNAKE_CASE__	,    )
       transformer.load_state_dict(SCREAMING_SNAKE_CASE__	,    strict=SCREAMING_SNAKE_CASE__  )

       _lowerCamelCase :	Any						=DDIMScheduler(
           num_train_timesteps=1_000	,    beta_schedule='linear'	,    prediction_type='epsilon'	,    clip_sample=SCREAMING_SNAKE_CASE__	,    )

       _lowerCamelCase :	Dict						=AutoencoderKL.from_pretrained(args.vae_model  )

       _lowerCamelCase :	Dict						=DiTPipeline(transformer=SCREAMING_SNAKE_CASE__	,    vae=SCREAMING_SNAKE_CASE__	,    scheduler=SCREAMING_SNAKE_CASE__  )

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


if __name__ == "__main__":
 lowerCamelCase		      = argparse.ArgumentParser()

 parser.add_argument(
     '--image_size',
     default=2_56,
     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.'
 )

 lowerCamelCase		      = parser.parse_args()
 main(args)






from __future__ import annotations

from collections.abc import Generator




def 			a_   (					SCREAMING_SNAKE_CASE__      :   int  ):
       '''simple docstring'''





       if not isinstance(SCREAMING_SNAKE_CASE__	,    SCREAMING_SNAKE_CASE__  ) or n < 1:
              raise Exception('Sequence only defined for positive integers'  )

       yield n
       while n != 1:
              if n % 2 == 0:
                     n //= 2
              else:
                     _lowerCamelCase :	Optional[int]						=3 * n + 1
              yield n




def 			a_   (					):
       '''simple docstring'''





       _lowerCamelCase :	Dict						=43
       _lowerCamelCase :	Dict						=tuple(collatz_sequence(SCREAMING_SNAKE_CASE__  )  )
       print(SCREAMING_SNAKE_CASE__  )
       print(F'''Collatz sequence from {n} took {len(SCREAMING_SNAKE_CASE__  )} steps.'''  )


if __name__ == "__main__":
 main()


