
import argparse
import os

import torch
from torchvision.datasets.utils import download_url

from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, TransformeraDModel


SCREAMING_SNAKE_CASE							:Dict											=       {512: 'DiT-XL-2-512x512.pt', 256: 'DiT-XL-2-256x256.pt'}


def 		UpperCAmelCase    (			a_		)	->			str:







				"""simple docstring"""



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


def 		UpperCAmelCase    (			a_		)	->			List[str]:







				"""simple docstring"""



				__A         =				download_model(pretrained_models[args.image_size]		)

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

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

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

								__A         =				q
								__A         =				q_bias
								__A         =				k
								__A         =				k_bias
								__A         =				v
								__A         =				v_bias

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

								__A         =				state_dict[F'''blocks.{depth}.mlp.fc1.weight''']
								__A         =				state_dict[F'''blocks.{depth}.mlp.fc1.bias''']
								__A         =				state_dict[F'''blocks.{depth}.mlp.fc2.weight''']
								__A         =				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"		)

				__A         =				state_dict["final_layer.adaLN_modulation.1.weight"]
				__A         =				state_dict["final_layer.adaLN_modulation.1.bias"]
				__A         =				state_dict["final_layer.linear.weight"]
				__A         =				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
				__A         =				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=a_	,     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=a_	,     )
				transformer.load_state_dict(a_	,     strict=a_		)

				__A         =				DDIMScheduler(
				    num_train_timesteps=1_0_0_0	,     beta_schedule="linear"	,     prediction_type="epsilon"	,     clip_sample=a_	,     )

				__A         =				AutoencoderKL.from_pretrained(args.vae_model		)

				__A         =				DiTPipeline(transformer=a_	,     vae=a_	,     scheduler=a_		)

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


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE							:Union[str, 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.'
					)

					SCREAMING_SNAKE_CASE							:List[Any]											=       parser.parse_args()
					main(args)


import torch

from transformers import PreTrainedModel

from .custom_configuration import CustomConfig, NoSuperInitConfig





class 							UpperCAmelCase   (						__SCREAMING_SNAKE_CASE						):




	'''simple docstring'''





	snake_case_      					=  CustomConfig






	def __init__( self      :				Optional[int]							,A      :				List[str]				):
					super().__init__(A				)
					__A         =				torch.nn.Linear(config.hidden_size							,config.hidden_size				)






	def        UpperCamelCase_						( self      :				Union[str, Any]							,A      :				Optional[int]				):
					return self.linear(A				)







	def        UpperCamelCase_						( self      :				Optional[int]							,A      :				int				):
					pass





class 							UpperCAmelCase   (						__SCREAMING_SNAKE_CASE						):




	'''simple docstring'''





	snake_case_      					=  NoSuperInitConfig






	def __init__( self      :				Optional[int]							,A      :				Union[str, Any]				):
					super().__init__(A				)
					__A         =				torch.nn.Linear(config.attribute							,config.attribute				)






	def        UpperCamelCase_						( self      :				str							,A      :				Any				):
					return self.linear(A				)







	def        UpperCamelCase_						( self      :				Tuple							,A      :				int				):
					pass
