




import enum
import inspect
from typing import Iterable, List, Optional, Tuple, Union
class 							lowerCamelCase		( enum.Enum				):
   lowercase       :  Dict      =				'''timm'''
   lowercase       :  str      =				'''transformers'''







def 					A_			(		snake_case_     :       Any						,snake_case_     :       Any						,snake_case_     :       Optional[Any]    ):







    '''simple docstring'''






    if stage_names is None:
        raise ValueError("""Stage_names must be set for transformers backbones"""    )

    if out_features is not None:
        if not isinstance(UpperCamelCase__						,(list,)    ):
            raise ValueError(f'out_features must be a list {type(UpperCamelCase__    )}'    )
        if any(feat not in stage_names for feat in out_features    ):
            raise ValueError(f'out_features must be a subset of stage_names: {stage_names} got {out_features}'    )

    if out_indices is not None:
        if not isinstance(UpperCamelCase__						,(list, tuple)    ):
            raise ValueError(f'out_indices must be a list or tuple, got {type(UpperCamelCase__    )}'    )
        if any(idx >= len(UpperCamelCase__    ) for idx in out_indices    ):
            raise ValueError("""out_indices must be valid indices for stage_names {stage_names}, got {out_indices}"""    )

    if out_features is not None and out_indices is not None:
        if len(UpperCamelCase__    ) != len(UpperCamelCase__    ):
            raise ValueError("""out_features and out_indices should have the same length if both are set"""    )
        if out_features != [stage_names[idx] for idx in out_indices]:
            raise ValueError("""out_features and out_indices should correspond to the same stages if both are set"""    )
def 					A_			(		snake_case_     :       Any						,snake_case_     :       Optional[Any]						,snake_case_     :       int						,):







    '''simple docstring'''






    if out_indices is None and out_features is None:
        UpperCamelCase				:   Dict          =							[len(UpperCamelCase__    ) - 1]
        UpperCamelCase				:   List[str]          =							[stage_names[-1]]
    elif out_indices is None and out_features is not None:
        UpperCamelCase				:   Union[str, Any]          =							[stage_names.index(UpperCamelCase__    ) for layer in out_features]
    elif out_features is None and out_indices is not None:
        UpperCamelCase				:   int          =							[stage_names[idx] for idx in out_indices]
    return out_features, out_indices
def 					A_			(		snake_case_     :       Union[str, Any]						,snake_case_     :       Union[str, Any]						,snake_case_     :       Optional[Any]						,):







    '''simple docstring'''
    verify_out_features_out_indices(out_features=UpperCamelCase__						,out_indices=UpperCamelCase__						,stage_names=UpperCamelCase__    )
    UpperCamelCase       ,							UpperCamelCase				:   int          =							_align_output_features_output_indices(
        out_features=UpperCamelCase__						,out_indices=UpperCamelCase__						,stage_names=UpperCamelCase__    )
    # Verify that the aligned out_features and out_indices are valid
    verify_out_features_out_indices(out_features=UpperCamelCase__						,out_indices=UpperCamelCase__						,stage_names=UpperCamelCase__    )
    return output_features, output_indices
class 							lowerCamelCase		:
   lowercase       :  Optional[BackboneType]      =				None







   def       a_							(							self    ,      SCREAMING_SNAKE_CASE_ ):
       if getattr(self    ,      """_backbone"""    ,      a_ ) is None:
           raise ValueError("""self._backbone must be set before calling _init_timm_backbone""" )

       # These will diagree with the defaults for the transformers models e.g. for resnet50
       # the transformer model has out_features = ['stem', 'stage1', 'stage2', 'stage3', 'stage4']
       # the timm model has out_features = ['act', 'layer1', 'layer2', 'layer3', 'layer4']
       UpperCamelCase				:   Any          =							[stage["""module"""] for stage in self._backbone.feature_info.info]
       UpperCamelCase				:   Union[str, Any]          =							[stage["""num_chs"""] for stage in self._backbone.feature_info.info]
       UpperCamelCase				:   Optional[Any]          =							self._backbone.feature_info.out_indices
       UpperCamelCase				:   List[Any]          =							self._backbone.feature_info.module_name()

       # We verify the out indices and out features are valid
       verify_out_features_out_indices(
           out_features=a_    ,      out_indices=a_    ,      stage_names=self.stage_names )
       UpperCamelCase       ,							UpperCamelCase				:   List[str]          =							out_features, out_indices







   def       a_							(							self    ,      SCREAMING_SNAKE_CASE_ ):
       UpperCamelCase				:   Any          =							getattr(a_    ,      """stage_names""" )
       UpperCamelCase				:   Any          =							getattr(a_    ,      """out_features"""    ,      a_ )
       UpperCamelCase				:   str          =							getattr(a_    ,      """out_indices"""    ,      a_ )

       UpperCamelCase				:   List[Any]          =							stage_names
       UpperCamelCase       ,							UpperCamelCase				:   int          =							get_aligned_output_features_output_indices(
           out_features=a_    ,      out_indices=a_    ,      stage_names=a_ )
       # Number of channels for each stage. This is set in the transformer backbone model init
       UpperCamelCase				:   Union[str, Any]          =							None







   def       a_							(							self    ,      SCREAMING_SNAKE_CASE_ ):
       UpperCamelCase				:   str          =							config

       UpperCamelCase				:   Union[str, Any]          =							getattr(a_    ,      """use_timm_backbone"""    ,      a_ )
       UpperCamelCase				:   Tuple          =							BackboneType.TIMM if self.use_timm_backbone else BackboneType.TRANSFORMERS

       if self.backbone_type == BackboneType.TIMM:
           self._init_timm_backbone(a_ )
       elif self.backbone_type == BackboneType.TRANSFORMERS:
           self._init_transformers_backbone(a_ )
       else:
           raise ValueError(f'backbone_type {self.backbone_type} not supported.' )







   @property
   def       a_							(							self ):
       return self._out_features







   @out_features.setter
   def       a_							(							self    ,      SCREAMING_SNAKE_CASE_ ):
       UpperCamelCase       ,							UpperCamelCase				:   Optional[Any]          =							get_aligned_output_features_output_indices(
           out_features=a_    ,      out_indices=a_    ,      stage_names=self.stage_names )







   @property
   def       a_							(							self ):
       return self._out_indices







   @out_indices.setter
   def       a_							(							self    ,      SCREAMING_SNAKE_CASE_ ):
       UpperCamelCase       ,							UpperCamelCase				:   Dict          =							get_aligned_output_features_output_indices(
           out_features=a_    ,      out_indices=a_    ,      stage_names=self.stage_names )







   @property
   def       a_							(							self ):
       # the current backbones will output the number of channels for each stage
       # even if that stage is not in the out_features list.
       return {stage: self.num_features[i] for i, stage in enumerate(self.stage_names )}







   @property
   def       a_							(							self ):
       return [self.out_feature_channels[name] for name in self.out_features]







   def       a_							(							self    ,      *SCREAMING_SNAKE_CASE_    ,      **SCREAMING_SNAKE_CASE_ ):
       UpperCamelCase				:   Union[str, Any]          =							dict(inspect.signature(self.forward ).parameters )
       UpperCamelCase				:   Tuple          =							{k: v for k, v in kwargs.items() if k in signature}
       return self(*a_    ,      **a_ )







   def       a_							(							self    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_ = None    ,      SCREAMING_SNAKE_CASE_ = None    ,      SCREAMING_SNAKE_CASE_ = None    ,      ):
       raise NotImplementedError("""This method should be implemented by the derived class.""" )







   def       a_							(							self ):
       UpperCamelCase				:   Optional[int]          =							super().to_dict()
       UpperCamelCase				:   Any          =							output.pop("""_out_features""" )
       UpperCamelCase				:   Any          =							output.pop("""_out_indices""" )
       return output







class 							lowerCamelCase		:







   @property
   def       a_							(							self ):
       return self._out_features







   @out_features.setter
   def       a_							(							self    ,      SCREAMING_SNAKE_CASE_ ):
       UpperCamelCase       ,							UpperCamelCase				:   List[Any]          =							get_aligned_output_features_output_indices(
           out_features=a_    ,      out_indices=a_    ,      stage_names=self.stage_names )







   @property
   def       a_							(							self ):
       return self._out_indices







   @out_indices.setter
   def       a_							(							self    ,      SCREAMING_SNAKE_CASE_ ):
       UpperCamelCase       ,							UpperCamelCase				:   Union[str, Any]          =							get_aligned_output_features_output_indices(
           out_features=a_    ,      out_indices=a_    ,      stage_names=self.stage_names )







   def       a_							(							self ):
       UpperCamelCase				:   Optional[int]          =							super().to_dict()
       UpperCamelCase				:   Optional[int]          =							output.pop("""_out_features""" )
       UpperCamelCase				:   str          =							output.pop("""_out_indices""" )
       return output










"""simple docstring"""




import argparse
import os

import torch
from torchvision.datasets.utils import download_url

from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, TransformeraDModel


__A :		int      							=    {512: '''DiT-XL-2-512x512.pt''', 256: '''DiT-XL-2-256x256.pt'''}
def 					A_			(		snake_case_     :       Dict    ):







    '''simple docstring'''
    UpperCamelCase				:   Any          =							f'pretrained_models/{model_name}'
    if not os.path.isfile(snake_case_    ):
        os.makedirs("""pretrained_models"""						,exist_ok=snake_case_    )
        UpperCamelCase				:   Dict          =							f'https://dl.fbaipublicfiles.com/DiT/models/{model_name}'
        download_url(snake_case_						,"""pretrained_models"""    )
    UpperCamelCase				:   List[Any]          =							torch.load(snake_case_						,map_location=lambda snake_case_						,snake_case_    : storage    )
    return model
def 					A_			(		snake_case_     :       List[str]    ):







    '''simple docstring'''
    UpperCamelCase				:   int          =							download_model(pretrained_models[args.image_size]    )

    UpperCamelCase				:   str          =							state_dict["""x_embedder.proj.weight"""]
    UpperCamelCase				:   Optional[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(2_8    ):
        UpperCamelCase				:   Optional[int]          =							state_dict[
            """t_embedder.mlp.0.weight"""
        ]
        UpperCamelCase				:   Optional[Any]          =							state_dict[
            """t_embedder.mlp.0.bias"""
        ]
        UpperCamelCase				:   str          =							state_dict[
            """t_embedder.mlp.2.weight"""
        ]
        UpperCamelCase				:   Optional[int]          =							state_dict[
            """t_embedder.mlp.2.bias"""
        ]
        UpperCamelCase				:   int          =							state_dict[
            """y_embedder.embedding_table.weight"""
        ]

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

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

        UpperCamelCase				:   int          =							q
        UpperCamelCase				:   Tuple          =							q_bias
        UpperCamelCase				:   List[Any]          =							k
        UpperCamelCase				:   int          =							k_bias
        UpperCamelCase				:   List[str]          =							v
        UpperCamelCase				:   str          =							v_bias

        UpperCamelCase				:   Any          =							state_dict[
            f'blocks.{depth}.attn.proj.weight'
        ]
        UpperCamelCase				:   Optional[Any]          =							state_dict[f'blocks.{depth}.attn.proj.bias']

        UpperCamelCase				:   str          =							state_dict[f'blocks.{depth}.mlp.fc1.weight']
        UpperCamelCase				:   Tuple          =							state_dict[f'blocks.{depth}.mlp.fc1.bias']
        UpperCamelCase				:   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				:   List[str]          =							state_dict["""final_layer.adaLN_modulation.1.weight"""]
    UpperCamelCase				:   str          =							state_dict["""final_layer.adaLN_modulation.1.bias"""]
    UpperCamelCase				:   Tuple          =							state_dict["""final_layer.linear.weight"""]
    UpperCamelCase				:   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
    UpperCamelCase				:   int          =							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=snake_case_						,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=snake_case_						,)
    transformer.load_state_dict(snake_case_						,strict=snake_case_    )

    UpperCamelCase				:   str          =							DDIMScheduler(
        num_train_timesteps=1_0_0_0						,beta_schedule="""linear"""						,prediction_type="""epsilon"""						,clip_sample=snake_case_						,)

    UpperCamelCase				:   Optional[Any]          =							AutoencoderKL.from_pretrained(args.vae_model    )

    UpperCamelCase				:   List[Any]          =							DiTPipeline(transformer=snake_case_						,vae=snake_case_						,scheduler=snake_case_    )

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


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

  __A :		Dict      							=    parser.parse_args()
  main(args)




