'''simple docstring'''

import argparse
import os

import torch
from torchvision.datasets.utils import download_url

from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, TransformeraDModel


_lowerCamelCase :				Optional[int]														= {5_12: """DiT-XL-2-512x512.pt""", 2_56: """DiT-XL-2-256x256.pt"""}



def  _lowerCAmelCase				(				__a      )  ->      Union[str, Any]:
     '''simple docstring'''







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



def  _lowerCAmelCase				(				__a      )  ->      Dict:
     '''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  :str       =state_dict[
              """t_embedder.mlp.0.weight"""
          ]
          _UpperCamelCase  :List[str]       =state_dict[
              """t_embedder.mlp.0.bias"""
          ]
          _UpperCamelCase  :int       =state_dict[
              """t_embedder.mlp.2.weight"""
          ]
          _UpperCamelCase  :Any       =state_dict[
              """t_embedder.mlp.2.bias"""
          ]
          _UpperCamelCase  :Any       =state_dict[
              """y_embedder.embedding_table.weight"""
          ]

          _UpperCamelCase  :List[str]       =state_dict[
              F'''blocks.{depth}.adaLN_modulation.1.weight'''
          ]
          _UpperCamelCase  :Optional[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  :Optional[Any]       =torch.chunk(state_dict[F'''blocks.{depth}.attn.qkv.bias''']			,      3			,      dim=0      )

          _UpperCamelCase  :Tuple       =q
          _UpperCamelCase  :Union[str, Any]       =q_bias
          _UpperCamelCase  :Optional[Any]       =k
          _UpperCamelCase  :Optional[int]       =k_bias
          _UpperCamelCase  :List[Any]       =v
          _UpperCamelCase  :Dict       =v_bias

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

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

     _UpperCamelCase  :Union[str, Any]       =DDIMScheduler(
         num_train_timesteps=10_00			,      beta_schedule="""linear"""			,      prediction_type="""epsilon"""			,      clip_sample=__a			,      )

     _UpperCamelCase  :Tuple       =AutoencoderKL.from_pretrained(args.vae_model      )

     _UpperCamelCase  :Any       =DiTPipeline(transformer=__a			,      vae=__a			,      scheduler=__a      )

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


if __name__ == "__main__":
      _lowerCamelCase :				Any														= 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 :				List[str]														= parser.parse_args()
      main(args)

"""simple docstring"""







import warnings

from ...utils import logging
from .image_processing_mobilenet_va import MobileNetVaImageProcessor


SCREAMING_SNAKE_CASE_  					= logging.get_logger(__name__)






class      __a      (       lowerCAmelCase_      ):







       def __init__(       self				,*lowerCamelCase_				,**lowerCamelCase_		):



          '''simple docstring'''

          warnings.warn(
              "The class MobileNetV1FeatureExtractor is deprecated and will be removed in version 5 of Transformers."
              " Please use MobileNetV1ImageProcessor instead."				,lowerCamelCase_				,)
          super().__init__(*lowerCamelCase_				,**lowerCamelCase_		)

