




import argparse
import os

import torch
from torchvision.datasets.utils import download_url

from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, TransformeraDModel


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

def 					A				(				_SCREAMING_SNAKE_CASE   )     ->			Optional[Any]:
       lowerCamelCase       :				List[str]      			= f'''pretrained_models/{model_name}'''
       if not os.path.isfile(_lowerCAmelCase   ):
              os.makedirs("pretrained_models"			,exist_ok=_lowerCAmelCase   )
              lowerCamelCase       :				Optional[Any]      			= f'''https://dl.fbaipublicfiles.com/DiT/models/{model_name}'''
              download_url(_lowerCAmelCase			,"pretrained_models"   )
       lowerCamelCase       :				List[str]      			= torch.load(_lowerCAmelCase			,map_location=lambda _SCREAMING_SNAKE_CASE			,_SCREAMING_SNAKE_CASE   : storage   )
       return model

def 					A				(				_SCREAMING_SNAKE_CASE   )     ->			Union[str, Any]:
       lowerCamelCase       :				List[Any]      			= download_model(pretrained_models[args.image_size]   )

       lowerCamelCase       :				List[str]      			= state_dict["x_embedder.proj.weight"]
       lowerCamelCase       :				Dict      			= 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   ):
              lowerCamelCase       :				Optional[int]      			= state_dict[
                  "t_embedder.mlp.0.weight"
              ]
              lowerCamelCase       :				Union[str, Any]      			= state_dict[
                  "t_embedder.mlp.0.bias"
              ]
              lowerCamelCase       :				Optional[int]      			= state_dict[
                  "t_embedder.mlp.2.weight"
              ]
              lowerCamelCase       :				int      			= state_dict[
                  "t_embedder.mlp.2.bias"
              ]
              lowerCamelCase       :				List[str]      			= state_dict[
                  "y_embedder.embedding_table.weight"
              ]

              lowerCamelCase       :				int      			= state_dict[
                  f'''blocks.{depth}.adaLN_modulation.1.weight'''
              ]
              lowerCamelCase       :				Any      			= state_dict[
                  f'''blocks.{depth}.adaLN_modulation.1.bias'''
              ]

              lowerCamelCase			,				lowerCamelCase			,				lowerCamelCase       :				int      			= torch.chunk(state_dict[f'''blocks.{depth}.attn.qkv.weight''']			,3			,dim=0   )
              lowerCamelCase			,				lowerCamelCase			,				lowerCamelCase       :				List[str]      			= torch.chunk(state_dict[f'''blocks.{depth}.attn.qkv.bias''']			,3			,dim=0   )

              lowerCamelCase       :				List[Any]      			= q
              lowerCamelCase       :				str      			= q_bias
              lowerCamelCase       :				Optional[int]      			= k
              lowerCamelCase       :				List[str]      			= k_bias
              lowerCamelCase       :				Any      			= v
              lowerCamelCase       :				str      			= v_bias

              lowerCamelCase       :				Union[str, Any]      			= state_dict[
                  f'''blocks.{depth}.attn.proj.weight'''
              ]
              lowerCamelCase       :				Optional[Any]      			= state_dict[f'''blocks.{depth}.attn.proj.bias''']

              lowerCamelCase       :				Optional[int]      			= state_dict[f'''blocks.{depth}.mlp.fc1.weight''']
              lowerCamelCase       :				Optional[Any]      			= state_dict[f'''blocks.{depth}.mlp.fc1.bias''']
              lowerCamelCase       :				Tuple      			= state_dict[f'''blocks.{depth}.mlp.fc2.weight''']
              lowerCamelCase       :				int      			= 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"   )

       lowerCamelCase       :				str      			= state_dict["final_layer.adaLN_modulation.1.weight"]
       lowerCamelCase       :				List[str]      			= state_dict["final_layer.adaLN_modulation.1.bias"]
       lowerCamelCase       :				List[str]      			= state_dict["final_layer.linear.weight"]
       lowerCamelCase       :				Union[str, Any]      			= 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
       lowerCamelCase       :				Any      			= 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=1000			,norm_type="ada_norm_zero"			,norm_elementwise_affine=_lowerCAmelCase			,)
       transformer.load_state_dict(_lowerCAmelCase			,strict=_lowerCAmelCase   )

       lowerCamelCase       :				str      			= DDIMScheduler(
           num_train_timesteps=1000			,beta_schedule="linear"			,prediction_type="epsilon"			,clip_sample=_lowerCAmelCase			,)

       lowerCamelCase       :				List[Any]      			= AutoencoderKL.from_pretrained(args.vae_model   )

       lowerCamelCase       :				Optional[int]      			= DiTPipeline(transformer=_lowerCAmelCase			,vae=_lowerCAmelCase			,scheduler=_lowerCAmelCase   )

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


if __name__ == "__main__":
       SCREAMING_SNAKE_CASE__	:				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.'
       )

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












import argparse

import runhouse as rh
import torch
from nlp_example import training_function

from accelerate.utils import PrepareForLaunch, patch_environment

def 					A				(				*_SCREAMING_SNAKE_CASE   )     ->			Union[str, Any]:
       lowerCamelCase       :				List[str]      			= torch.cuda.device_count()
       print(f'''Device count: {num_processes}'''   )
       with patch_environment(
           world_size=_SCREAMING_SNAKE_CASE			,master_addr="127.0.01"			,master_port="29500"			,mixed_precision=args[1].mixed_precision   ):
              lowerCamelCase       :				Union[str, Any]      			= PrepareForLaunch(_SCREAMING_SNAKE_CASE			,distributed_type="MULTI_GPU"   )
              torch.multiprocessing.start_processes(_SCREAMING_SNAKE_CASE			,args=_SCREAMING_SNAKE_CASE			,nprocs=_SCREAMING_SNAKE_CASE			,start_method="spawn"   )


if __name__ == "__main__":
       # Refer to https://runhouse-docs.readthedocs-hosted.com/en/main/rh_primitives/cluster.html#hardware-setup
       # for cloud access setup instructions (if using on-demand hardware), and for API specifications.

       # on-demand GPU
       # gpu = rh.cluster(name='rh-cluster', instance_type='V100:1', provider='cheapest', use_spot=False)  # single GPU
       SCREAMING_SNAKE_CASE__	:				Tuple  =       rh.cluster(name='rh-cluster', instance_type='V100:4', provider='cheapest', use_spot=False)  # multi GPU
       gpu.up_if_not()

       # on-prem GPU
       # gpu = rh.cluster(
       #           ips=["ip_addr"], ssh_creds={ssh_user:"<username>", ssh_private_key:"<key_path>"}, name="rh-cluster"
       #       )

       # Set up remote function
       SCREAMING_SNAKE_CASE__	:				int  =       [
           'pip:./',
           'transformers',
           'datasets',
           'evaluate',
           'tqdm',
           'scipy',
           'scikit-learn',
           'tensorboard',
           'torch --upgrade --extra-index-url https://download.pytorch.org/whl/cu117',
       ]
       SCREAMING_SNAKE_CASE__	:				Dict  =       rh.function(fn=launch_train, system=gpu, reqs=reqs, name='train_bert_glue')

       # Define train args/config, run train function
       SCREAMING_SNAKE_CASE__	:				int  =       argparse.Namespace(cpu=False, mixed_precision='fp16')
       SCREAMING_SNAKE_CASE__	:				int  =       {'lr': 2E-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
       launch_train_gpu(config, train_args, stream_logs=True)

       # Alternatively, we can just run as instructed in the README (but only because there's already a wrapper CLI):
       # gpu.install_packages(reqs)
       # gpu.run(['accelerate launch --multi_gpu accelerate/examples/nlp_example.py'])






