






import argparse
import os

import torch
from torchvision.datasets.utils import download_url

from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, TransformeraDModel


__UpperCAmelCase      				=							{5_1_2: "DiT-XL-2-512x512.pt", 2_5_6: "DiT-XL-2-256x256.pt"}





def 		A_			(		lowercase_	)							->str:







 """simple docstring"""

 SCREAMING_SNAKE_CASE							  =						f'''pretrained_models/{model_name}'''
 if not os.path.isfile(lowercase_	):
  os.makedirs('pretrained_models'					,       exist_ok=lowercase_	)
  SCREAMING_SNAKE_CASE							  =						f'''https://dl.fbaipublicfiles.com/DiT/models/{model_name}'''
  download_url(lowercase_					,       'pretrained_models'	)
 SCREAMING_SNAKE_CASE							  =						torch.load(lowercase_					,       map_location=lambda lowercase_					,       lowercase_	: storage	)
 return model





def 		A_			(		lowercase_	)							->List[str]:







 """simple docstring"""

 SCREAMING_SNAKE_CASE							  =						download_model(pretrained_models[args.image_size]	)

 SCREAMING_SNAKE_CASE							  =						state_dict['x_embedder.proj.weight']
 SCREAMING_SNAKE_CASE							  =						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	):
  SCREAMING_SNAKE_CASE							  =						state_dict[
      't_embedder.mlp.0.weight'
  ]
  SCREAMING_SNAKE_CASE							  =						state_dict[
      't_embedder.mlp.0.bias'
  ]
  SCREAMING_SNAKE_CASE							  =						state_dict[
      't_embedder.mlp.2.weight'
  ]
  SCREAMING_SNAKE_CASE							  =						state_dict[
      't_embedder.mlp.2.bias'
  ]
  SCREAMING_SNAKE_CASE							  =						state_dict[
      'y_embedder.embedding_table.weight'
  ]

  SCREAMING_SNAKE_CASE							  =						state_dict[
      f'''blocks.{depth}.adaLN_modulation.1.weight'''
  ]
  SCREAMING_SNAKE_CASE							  =						state_dict[
      f'''blocks.{depth}.adaLN_modulation.1.bias'''
  ]

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

  SCREAMING_SNAKE_CASE							  =						q
  SCREAMING_SNAKE_CASE							  =						q_bias
  SCREAMING_SNAKE_CASE							  =						k
  SCREAMING_SNAKE_CASE							  =						k_bias
  SCREAMING_SNAKE_CASE							  =						v
  SCREAMING_SNAKE_CASE							  =						v_bias

  SCREAMING_SNAKE_CASE							  =						state_dict[
      f'''blocks.{depth}.attn.proj.weight'''
  ]
  SCREAMING_SNAKE_CASE							  =						state_dict[f'''blocks.{depth}.attn.proj.bias''']

  SCREAMING_SNAKE_CASE							  =						state_dict[f'''blocks.{depth}.mlp.fc1.weight''']
  SCREAMING_SNAKE_CASE							  =						state_dict[f'''blocks.{depth}.mlp.fc1.bias''']
  SCREAMING_SNAKE_CASE							  =						state_dict[f'''blocks.{depth}.mlp.fc2.weight''']
  SCREAMING_SNAKE_CASE							  =						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'	)

 SCREAMING_SNAKE_CASE							  =						state_dict['final_layer.adaLN_modulation.1.weight']
 SCREAMING_SNAKE_CASE							  =						state_dict['final_layer.adaLN_modulation.1.bias']
 SCREAMING_SNAKE_CASE							  =						state_dict['final_layer.linear.weight']
 SCREAMING_SNAKE_CASE							  =						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
 SCREAMING_SNAKE_CASE							  =						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=lowercase_					,       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=lowercase_					,       )
 transformer.load_state_dict(lowercase_					,       strict=lowercase_	)

 SCREAMING_SNAKE_CASE							  =						DDIMScheduler(
     num_train_timesteps=1_0_0_0					,       beta_schedule='linear'					,       prediction_type='epsilon'					,       clip_sample=lowercase_					,       )

 SCREAMING_SNAKE_CASE							  =						AutoencoderKL.from_pretrained(args.vae_model	)

 SCREAMING_SNAKE_CASE							  =						DiTPipeline(transformer=lowercase_					,       vae=lowercase_					,       scheduler=lowercase_	)

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


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

      parser.add_argument(
          "--image_size",
          default=2_5_6,
          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)












from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__UpperCAmelCase      				=							{"configuration_visual_bert": ["VISUAL_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "VisualBertConfig"]}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __UpperCAmelCase      				=							[
          "VISUAL_BERT_PRETRAINED_MODEL_ARCHIVE_LIST",
          "VisualBertForMultipleChoice",
          "VisualBertForPreTraining",
          "VisualBertForQuestionAnswering",
          "VisualBertForRegionToPhraseAlignment",
          "VisualBertForVisualReasoning",
          "VisualBertLayer",
          "VisualBertModel",
          "VisualBertPreTrainedModel",
      ]


if TYPE_CHECKING:
      from .configuration_visual_bert import VISUAL_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, VisualBertConfig

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_visual_bert import (
                VISUAL_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                VisualBertForMultipleChoice,
                VisualBertForPreTraining,
                VisualBertForQuestionAnswering,
                VisualBertForRegionToPhraseAlignment,
                VisualBertForVisualReasoning,
                VisualBertLayer,
                VisualBertModel,
                VisualBertPreTrainedModel,
            )


else:
      import sys

      __UpperCAmelCase      				=							_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




