





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




def 			lowerCAmelCase_			(				UpperCamelCase_	)  ->		Any:
       UpperCamelCase_ 							=	download_model(pretrained_models[args.image_size]	)

       UpperCamelCase_ 							=	state_dict["x_embedder.proj.weight"]
       UpperCamelCase_ 							=	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_ 							=	state_dict[
                  "t_embedder.mlp.0.weight"
              ]
              UpperCamelCase_ 							=	state_dict[
                  "t_embedder.mlp.0.bias"
              ]
              UpperCamelCase_ 							=	state_dict[
                  "t_embedder.mlp.2.weight"
              ]
              UpperCamelCase_ 							=	state_dict[
                  "t_embedder.mlp.2.bias"
              ]
              UpperCamelCase_ 							=	state_dict[
                  "y_embedder.embedding_table.weight"
              ]

              UpperCamelCase_ 							=	state_dict[
                  F'''blocks.{depth}.adaLN_modulation.1.weight'''
              ]
              UpperCamelCase_ 							=	state_dict[
                  F'''blocks.{depth}.adaLN_modulation.1.bias'''
              ]

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

              UpperCamelCase_ 							=	q
              UpperCamelCase_ 							=	q_bias
              UpperCamelCase_ 							=	k
              UpperCamelCase_ 							=	k_bias
              UpperCamelCase_ 							=	v
              UpperCamelCase_ 							=	v_bias

              UpperCamelCase_ 							=	state_dict[
                  F'''blocks.{depth}.attn.proj.weight'''
              ]
              UpperCamelCase_ 							=	state_dict[F'''blocks.{depth}.attn.proj.bias''']

              UpperCamelCase_ 							=	state_dict[F'''blocks.{depth}.mlp.fc1.weight''']
              UpperCamelCase_ 							=	state_dict[F'''blocks.{depth}.mlp.fc1.bias''']
              UpperCamelCase_ 							=	state_dict[F'''blocks.{depth}.mlp.fc2.weight''']
              UpperCamelCase_ 							=	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_ 							=	state_dict["final_layer.adaLN_modulation.1.weight"]
       UpperCamelCase_ 							=	state_dict["final_layer.adaLN_modulation.1.bias"]
       UpperCamelCase_ 							=	state_dict["final_layer.linear.weight"]
       UpperCamelCase_ 							=	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_ 							=	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=1000       ,		norm_type="ada_norm_zero"       ,		norm_elementwise_affine=UpperCamelCase_       ,		)
       transformer.load_state_dict(UpperCamelCase_       ,		strict=UpperCamelCase_	)

       UpperCamelCase_ 							=	DDIMScheduler(
           num_train_timesteps=1000       ,		beta_schedule="linear"       ,		prediction_type="epsilon"       ,		clip_sample=UpperCamelCase_       ,		)

       UpperCamelCase_ 							=	AutoencoderKL.from_pretrained(args.vae_model	)

       UpperCamelCase_ 							=	DiTPipeline(transformer=UpperCamelCase_       ,		vae=UpperCamelCase_       ,		scheduler=UpperCamelCase_	)

       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)







"""simple docstring"""

import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig
from ..clip.configuration_clip import CLIPVisionConfig


UpperCamelCase__			:     Optional[Any]			       =				logging.get_logger(__name__)
class      A__   (   lowercase_  ):
	A_				:					List[str]      =   """vision-text-dual-encoder"""
	A_				:					Tuple      =   True




	def __init__( self	:						Tuple   ,     lowercase	:						List[Any]=512   ,     lowercase	:						Any=2.65_92   ,     **lowercase	:						int						)       ->			int:
						super().__init__(**lowercase						)

						if "vision_config" not in kwargs:
											raise ValueError("""`vision_config` can not be `None`."""						)

						if "text_config" not in kwargs:
											raise ValueError("""`text_config` can not be `None`."""						)

						__snake_case        =						kwargs.pop("""vision_config"""						)
						__snake_case        =						kwargs.pop("""text_config"""						)

						__snake_case        =						vision_config.pop("""model_type"""						)
						__snake_case        =						text_config.pop("""model_type"""						)

						if vision_model_type == "clip":
											__snake_case        =						AutoConfig.for_model(lowercase   ,     **lowercase						).vision_config
						elif vision_model_type == "clip_vision_model":
											__snake_case        =						CLIPVisionConfig(**lowercase						)
						else:
											__snake_case        =						AutoConfig.for_model(lowercase   ,     **lowercase						)

						__snake_case        =						AutoConfig.for_model(lowercase   ,     **lowercase						)

						__snake_case        =						projection_dim
						__snake_case        =						logit_scale_init_value




	@classmethod
	def 				a_		( cls	:						Union[str, Any]   ,     lowercase	:						PretrainedConfig   ,     lowercase	:						PretrainedConfig   ,     **lowercase	:						Optional[int]						)       ->			Dict:
						return cls(vision_config=vision_config.to_dict()   ,     text_config=text_config.to_dict()   ,     **lowercase						)




	def 				a_		( self	:						Dict						)       ->			Dict:
						__snake_case        =						copy.deepcopy(self.__dict__						)
						__snake_case        =						self.vision_config.to_dict()
						__snake_case        =						self.text_config.to_dict()
						__snake_case        =						self.__class__.model_type
						return output


