import argparse
import os

import torch
from torchvision.datasets.utils import download_url

from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, TransformeraDModel


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





def   lowerCAmelCase_ (				__lowerCAmelCase						)->  int:






      '''simple docstring'''





      UpperCAmelCase    : List[Any]      =f'''pretrained_models/{model_name}'''
      if not os.path.isfile(_UpperCamelCase						):
            os.makedirs('''pretrained_models'''		,					exist_ok=_UpperCamelCase						)
            UpperCAmelCase    : Tuple      =f'''https://dl.fbaipublicfiles.com/DiT/models/{model_name}'''
            download_url(_UpperCamelCase		,					'''pretrained_models'''						)
      UpperCAmelCase    : int      =torch.load(_UpperCamelCase		,					map_location=lambda __lowerCAmelCase		,					__lowerCAmelCase						: storage						)
      return model





def   lowerCAmelCase_ (				__lowerCAmelCase						)->  Union[str, Any]:






      '''simple docstring'''





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

      UpperCAmelCase    : Tuple      =state_dict['''x_embedder.proj.weight''']
      UpperCAmelCase    : 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						):
            UpperCAmelCase    : str      =state_dict[
                '''t_embedder.mlp.0.weight'''
            ]
            UpperCAmelCase    : Tuple      =state_dict[
                '''t_embedder.mlp.0.bias'''
            ]
            UpperCAmelCase    : List[Any]      =state_dict[
                '''t_embedder.mlp.2.weight'''
            ]
            UpperCAmelCase    : Any      =state_dict[
                '''t_embedder.mlp.2.bias'''
            ]
            UpperCAmelCase    : Optional[int]      =state_dict[
                '''y_embedder.embedding_table.weight'''
            ]

            UpperCAmelCase    : List[Any]      =state_dict[
                f'''blocks.{depth}.adaLN_modulation.1.weight'''
            ]
            UpperCAmelCase    : Dict      =state_dict[
                f'''blocks.{depth}.adaLN_modulation.1.bias'''
            ]

            UpperCAmelCase  ,						UpperCAmelCase  ,						UpperCAmelCase    : int      =torch.chunk(state_dict[f'''blocks.{depth}.attn.qkv.weight''']		,					3		,					dim=0						)
            UpperCAmelCase  ,						UpperCAmelCase  ,						UpperCAmelCase    : Any      =torch.chunk(state_dict[f'''blocks.{depth}.attn.qkv.bias''']		,					3		,					dim=0						)

            UpperCAmelCase    : Optional[int]      =q
            UpperCAmelCase    : Dict      =q_bias
            UpperCAmelCase    : Tuple      =k
            UpperCAmelCase    : int      =k_bias
            UpperCAmelCase    : List[Any]      =v
            UpperCAmelCase    : Dict      =v_bias

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

            UpperCAmelCase    : Optional[Any]      =state_dict[f'''blocks.{depth}.mlp.fc1.weight''']
            UpperCAmelCase    : Any      =state_dict[f'''blocks.{depth}.mlp.fc1.bias''']
            UpperCAmelCase    : List[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    : Union[str, Any]      =state_dict['''final_layer.adaLN_modulation.1.weight''']
      UpperCAmelCase    : Dict      =state_dict['''final_layer.adaLN_modulation.1.bias''']
      UpperCAmelCase    : int      =state_dict['''final_layer.linear.weight''']
      UpperCAmelCase    : Tuple      =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    : 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=_UpperCamelCase		,					num_attention_heads=16		,					activation_fn='''gelu-approximate'''		,					num_embeds_ada_norm=10_00		,					norm_type='''ada_norm_zero'''		,					norm_elementwise_affine=_UpperCamelCase		,					)
      transformer.load_state_dict(_UpperCamelCase		,					strict=_UpperCamelCase						)

      UpperCAmelCase    : List[Any]      =DDIMScheduler(
          num_train_timesteps=10_00		,					beta_schedule='''linear'''		,					prediction_type='''epsilon'''		,					clip_sample=_UpperCamelCase		,					)

      UpperCAmelCase    : List[Any]      =AutoencoderKL.from_pretrained(args.vae_model						)

      UpperCAmelCase    : Union[str, Any]      =DiTPipeline(transformer=_UpperCamelCase		,					vae=_UpperCamelCase		,					scheduler=_UpperCamelCase						)

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


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

     __snake_case			      =    parser.parse_args()
     main(args)

import math





def   lowerCAmelCase_ (				__lowerCAmelCase						)->  list[int]:






      '''simple docstring'''





      if not isinstance(__lowerCAmelCase		,					__lowerCAmelCase						):
            UpperCAmelCase    : Union[str, Any]      =f'''Input value of [number={number}] must be an integer'''
            raise TypeError(__lowerCAmelCase						)
      if number < 1:
            UpperCAmelCase    : Dict      =f'''Input value of [number={number}] must be a positive integer'''
            raise ValueError(__lowerCAmelCase						)
      UpperCAmelCase    : Dict      =[number]
      while number != 1:
            if number % 2 == 0:
                  UpperCAmelCase    : Optional[Any]      =math.floor(math.sqrt(__lowerCAmelCase						)						)
            else:
                  UpperCAmelCase    : List[str]      =math.floor(
                      math.sqrt(__lowerCAmelCase						) * math.sqrt(__lowerCAmelCase						) * math.sqrt(__lowerCAmelCase						)						)
            sequence.append(__lowerCAmelCase						)
      return sequence


if __name__ == "__main__":
     import doctest

     doctest.testmod()
