



import argparse
import os

import torch
from torchvision.datasets.utils import download_url

from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, TransformeraDModel


UpperCAmelCase				:							Union[str, Any]								={512: """DiT-XL-2-512x512.pt""", 256: """DiT-XL-2-256x256.pt"""}



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



def 				_lowerCAmelCase      (_lowerCAmelCase):
					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=_lowerCAmelCase				,		num_attention_heads=16				,		activation_fn="gelu-approximate"				,		num_embeds_ada_norm=10_00				,		norm_type="ada_norm_zero"				,		norm_elementwise_affine=_lowerCAmelCase				,		)
					transformer.load_state_dict(_lowerCAmelCase				,		strict=_lowerCAmelCase)

					UpperCamelCase_											=				DDIMScheduler(
					    num_train_timesteps=10_00				,		beta_schedule="linear"				,		prediction_type="epsilon"				,		clip_sample=_lowerCAmelCase				,		)

					UpperCamelCase_											=				AutoencoderKL.from_pretrained(args.vae_model)

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

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


if __name__ == "__main__":
	UpperCAmelCase				:							List[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."""
	)

	UpperCAmelCase				:							Optional[Any]								=parser.parse_args()
	main(args)





from transformers import HfArgumentParser, PyTorchBenchmark, PyTorchBenchmarkArguments



def 				_lowerCAmelCase      ():
					UpperCamelCase_											=				HfArgumentParser(_lowerCAmelCase)
					try:
										UpperCamelCase_											=				parser.parse_args_into_dataclasses()[0]
					except ValueError as e:
										UpperCamelCase_											=				"Arg --no_{0} is no longer used, please use --no-{0} instead."
										UpperCamelCase_											=				" ".join(str(_lowerCAmelCase).split(" ")[:-1])
										UpperCamelCase_											=				""
										UpperCamelCase_											=				eval(str(_lowerCAmelCase).split(" ")[-1])
										UpperCamelCase_											=				[]
										for arg in depreciated_args:
															# arg[2:] removes '--'
															if arg[2:] in PyTorchBenchmarkArguments.deprecated_args:
																				# arg[5:] removes '--no_'
																				full_error_msg += arg_error_msg.format(arg[5:])
															else:
																				wrong_args.append(_lowerCAmelCase)
										if len(_lowerCAmelCase) > 0:
															UpperCamelCase_											=				full_error_msg + begin_error_msg + str(_lowerCAmelCase)
										raise ValueError(_lowerCAmelCase)

					UpperCamelCase_											=				PyTorchBenchmark(args=_lowerCAmelCase)
					benchmark.run()


if __name__ == "__main__":
	main()
