

"""simple docstring"""

import argparse
import os

import torch
from torchvision.datasets.utils import download_url

from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, TransformeraDModel


a__ :	str						= {512: """DiT-XL-2-512x512.pt""", 256: """DiT-XL-2-256x256.pt"""}





def 				__UpperCamelCase   (    a__: List[str]			)				->			List[Any]:




					"""simple docstring"""



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





def 				__UpperCamelCase   (    a__: Tuple			)				->			List[Any]:




					"""simple docstring"""



					__lowercase												=			download_model(pretrained_models[args.image_size]			)

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

										__lowercase												=			state_dict[
										    f'''blocks.{depth}.adaLN_modulation.1.weight'''
										]
										__lowercase												=			state_dict[
										    f'''blocks.{depth}.adaLN_modulation.1.bias'''
										]

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

										__lowercase												=			q
										__lowercase												=			q_bias
										__lowercase												=			k
										__lowercase												=			k_bias
										__lowercase												=			v
										__lowercase												=			v_bias

										__lowercase												=			state_dict[
										    f'''blocks.{depth}.attn.proj.weight'''
										]
										__lowercase												=			state_dict[f'''blocks.{depth}.attn.proj.bias''']

										__lowercase												=			state_dict[f'''blocks.{depth}.mlp.fc1.weight''']
										__lowercase												=			state_dict[f'''blocks.{depth}.mlp.fc1.bias''']
										__lowercase												=			state_dict[f'''blocks.{depth}.mlp.fc2.weight''']
										__lowercase												=			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'''			)

					__lowercase												=			state_dict['''final_layer.adaLN_modulation.1.weight''']
					__lowercase												=			state_dict['''final_layer.adaLN_modulation.1.bias''']
					__lowercase												=			state_dict['''final_layer.linear.weight''']
					__lowercase												=			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
					__lowercase												=			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=a__ ,	num_attention_heads=16 ,	activation_fn='''gelu-approximate''' ,	num_embeds_ada_norm=10_00 ,	norm_type='''ada_norm_zero''' ,	norm_elementwise_affine=a__ ,	)
					transformer.load_state_dict(a__ ,	strict=a__			)

					__lowercase												=			DDIMScheduler(
					    num_train_timesteps=10_00 ,	beta_schedule='''linear''' ,	prediction_type='''epsilon''' ,	clip_sample=a__ ,	)

					__lowercase												=			AutoencoderKL.from_pretrained(args.vae_model			)

					__lowercase												=			DiTPipeline(transformer=a__ ,	vae=a__ ,	scheduler=a__			)

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


if __name__ == "__main__":
							a__ :	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."""
							)

							a__ :	str						= parser.parse_args()
							main(args)





"""simple docstring"""

import json
import logging
import os
import subprocess
from argparse import ArgumentParser


a__ :	List[str]						= logging.getLogger(__name__)





def 				__UpperCamelCase   (    )				->			Optional[Any]:




					"""simple docstring"""



					__lowercase												=			ArgumentParser()
					__lowercase						, __lowercase												=			parser.parse_known_args()
					for arg in unknown:
										if arg.startswith(('''-''', '''--''')			):
															parser.add_argument(arg.split('''='''			)[0]			)

					return parser.parse_args()





def 				__UpperCamelCase   (    )				->			int:




					"""simple docstring"""



					__lowercase												=			parse_args()
					__lowercase												=			88_88
					__lowercase												=			int(os.environ['''SM_NUM_GPUS''']			)
					__lowercase												=			json.loads(os.environ['''SM_HOSTS''']			)
					__lowercase												=			len(a__			)
					__lowercase												=			os.environ['''SM_CURRENT_HOST''']
					__lowercase												=			hosts.index(a__			)
					__lowercase												=			'''INFO'''

					if num_nodes > 1:
										__lowercase												=			f'''python -m torch.distributed.launch \
                --nnodes={num_nodes}  \
                --node_rank={rank}  \
                --nproc_per_node={num_gpus}  \
                --master_addr={hosts[0]}  \
                --master_port={port} \
                ./run_glue.py \
                {"".join(["nested fstring" for parameter,value in args.__dict__.items()]			)}'''
					else:
										__lowercase												=			f'''python -m torch.distributed.launch \
            --nproc_per_node={num_gpus}  \
            ./run_glue.py \
            {"".join(["nested fstring" for parameter,value in args.__dict__.items()]			)}'''
					try:
										subprocess.run(a__ ,	shell=a__			)
					except Exception as e:
										logger.info(a__			)


if __name__ == "__main__":
							main()


