'''simple docstring'''


import argparse
import os

import torch
from torchvision.datasets.utils import download_url

from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, TransformeraDModel


snake_case__     :			Union[str, Any]      			=  {512: '''DiT-XL-2-512x512.pt''', 256: '''DiT-XL-2-256x256.pt'''}


def 					_lowerCamelCase    (      lowerCamelCase_					:							int   ):



     """simple docstring"""





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


def 					_lowerCamelCase    (      lowerCamelCase_					:							Any   ):



     """simple docstring"""





     UpperCAmelCase_							:   Union[str, Any]   =      download_model(pretrained_models[args.image_size]   )

     UpperCAmelCase_							:   int   =      state_dict['x_embedder.proj.weight']
     UpperCAmelCase_							:   str   =      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_							:   Dict   =      state_dict[
              't_embedder.mlp.0.weight'
          ]
          UpperCAmelCase_							:   int   =      state_dict[
              't_embedder.mlp.0.bias'
          ]
          UpperCAmelCase_							:   List[Any]   =      state_dict[
              't_embedder.mlp.2.weight'
          ]
          UpperCAmelCase_							:   List[Any]   =      state_dict[
              't_embedder.mlp.2.bias'
          ]
          UpperCAmelCase_							:   List[str]   =      state_dict[
              'y_embedder.embedding_table.weight'
          ]

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

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

          UpperCAmelCase_							:   Optional[Any]   =      q
          UpperCAmelCase_							:   Any   =      q_bias
          UpperCAmelCase_							:   Dict   =      k
          UpperCAmelCase_							:   List[str]   =      k_bias
          UpperCAmelCase_							:   str   =      v
          UpperCAmelCase_							:   Optional[Any]   =      v_bias

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

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

     UpperCAmelCase_							:   Any   =      state_dict['final_layer.adaLN_modulation.1.weight']
     UpperCAmelCase_							:   int   =      state_dict['final_layer.adaLN_modulation.1.bias']
     UpperCAmelCase_							:   Optional[Any]   =      state_dict['final_layer.linear.weight']
     UpperCAmelCase_							:   List[str]   =      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_							:   Optional[int]   =      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_   )

     UpperCAmelCase_							:   Optional[int]   =      DDIMScheduler(
         num_train_timesteps=1000	,     beta_schedule='linear'	,     prediction_type='epsilon'	,     clip_sample=lowerCamelCase_	,     )

     UpperCAmelCase_							:   Any   =      AutoencoderKL.from_pretrained(args.vae_model   )

     UpperCAmelCase_							:   Dict   =      DiTPipeline(transformer=lowerCamelCase_	,     vae=lowerCamelCase_	,     scheduler=lowerCamelCase_   )

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


if __name__ == "__main__":
 snake_case__     :			Union[str, 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.'''
 )

 snake_case__     :			List[str]      			=  parser.parse_args()
 main(args)







'''simple docstring'''


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


snake_case__     :			Optional[Any]      			=  {'''configuration_nat''': ['''NAT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''NatConfig''']}


try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 snake_case__     :			Tuple      			=  [
     '''NAT_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''NatForImageClassification''',
     '''NatModel''',
     '''NatPreTrainedModel''',
     '''NatBackbone''',
 ]

if TYPE_CHECKING:
 from .configuration_nat import NAT_PRETRAINED_CONFIG_ARCHIVE_MAP, NatConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_nat import (
      NAT_PRETRAINED_MODEL_ARCHIVE_LIST,
      NatBackbone,
      NatForImageClassification,
      NatModel,
      NatPreTrainedModel,
  )

else:
 import sys

 snake_case__     :			int      			=  _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






