





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)








from ...configuration_utils import PretrainedConfig
from ...utils import logging


_UpperCAmelCase										=					logging.get_logger(__name__)

_UpperCAmelCase										=					{
    'bert-base-uncased': 'https://huggingface.co/bert-base-uncased/resolve/main/config.json',
    # QDQBERT models can be loaded from any BERT checkpoint, available at https://huggingface.co/models?filter=bert
}



class 				_UpperCamelCase       (						lowerCAmelCase_  ):
       _UpperCamelCase					:       Union[str, Any]       = '''qdqbert'''

       def __init__(		self:			Union[str, Any]       , _SCREAMING_SNAKE_CASE:			Any=30522       , _SCREAMING_SNAKE_CASE:			List[str]=768       , _SCREAMING_SNAKE_CASE:			int=12       , _SCREAMING_SNAKE_CASE:			List[Any]=12       , _SCREAMING_SNAKE_CASE:			Union[str, Any]=3072       , _SCREAMING_SNAKE_CASE:			List[str]="gelu"       , _SCREAMING_SNAKE_CASE:			List[Any]=0.1       , _SCREAMING_SNAKE_CASE:			List[Any]=0.1       , _SCREAMING_SNAKE_CASE:			Dict=512       , _SCREAMING_SNAKE_CASE:			Union[str, Any]=2       , _SCREAMING_SNAKE_CASE:			int=0.02       , _SCREAMING_SNAKE_CASE:			Union[str, Any]=1e-12       , _SCREAMING_SNAKE_CASE:			str=True       , _SCREAMING_SNAKE_CASE:			Any=1       , _SCREAMING_SNAKE_CASE:			int=0       , _SCREAMING_SNAKE_CASE:			Tuple=2       , **_SCREAMING_SNAKE_CASE:			List[str]       , )							->			Tuple:




              """simple docstring"""


              super().__init__(pad_token_id=_SCREAMING_SNAKE_CASE       , bos_token_id=_SCREAMING_SNAKE_CASE       , eos_token_id=_SCREAMING_SNAKE_CASE       , **_SCREAMING_SNAKE_CASE		)

              UpperCamelCase_ 							=	vocab_size
              UpperCamelCase_ 							=	max_position_embeddings
              UpperCamelCase_ 							=	hidden_size
              UpperCamelCase_ 							=	num_hidden_layers
              UpperCamelCase_ 							=	num_attention_heads
              UpperCamelCase_ 							=	intermediate_size
              UpperCamelCase_ 							=	hidden_act
              UpperCamelCase_ 							=	hidden_dropout_prob
              UpperCamelCase_ 							=	attention_probs_dropout_prob
              UpperCamelCase_ 							=	initializer_range
              UpperCamelCase_ 							=	type_vocab_size
              UpperCamelCase_ 							=	layer_norm_eps
              UpperCamelCase_ 							=	use_cache

