

def  _snake_case		(snake_case_					:     float  )							->		int:
  if num < 0:
    return -1
  return 1 if num else 0





def  _snake_case		()							->		None:
  assert signum(5  ) == 1
  assert signum(-5  ) == -1
  assert signum(0  ) == 0


if __name__ == "__main__":
      print(signum(1_2))
      print(signum(-1_2))
      print(signum(0))












import argparse
import os

import torch
from torchvision.datasets.utils import download_url

from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, TransformeraDModel


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


def        __SCREAMING_SNAKE_CASE	(				__lowercase:		Any):



  '''simple docstring'''






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


def        __SCREAMING_SNAKE_CASE	(				__lowercase:		Tuple):



  '''simple docstring'''






  __lowerCAmelCase								=					download_model(pretrained_models[args.image_size])

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

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

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

    __lowerCAmelCase								=					q
    __lowerCAmelCase								=					q_bias
    __lowerCAmelCase								=					k
    __lowerCAmelCase								=					k_bias
    __lowerCAmelCase								=					v
    __lowerCAmelCase								=					v_bias

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

    __lowerCAmelCase								=					state_dict[F'''blocks.{depth}.mlp.fc1.weight''']
    __lowerCAmelCase								=					state_dict[F'''blocks.{depth}.mlp.fc1.bias''']
    __lowerCAmelCase								=					state_dict[F'''blocks.{depth}.mlp.fc2.weight''']
    __lowerCAmelCase								=					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								=					state_dict['''final_layer.adaLN_modulation.1.weight''']
  __lowerCAmelCase								=					state_dict['''final_layer.adaLN_modulation.1.bias''']
  __lowerCAmelCase								=					state_dict['''final_layer.linear.weight''']
  __lowerCAmelCase								=					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								=					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=__lowercase  ,				num_attention_heads=16  ,				activation_fn='''gelu-approximate'''  ,				num_embeds_ada_norm=10_00  ,				norm_type='''ada_norm_zero'''  ,				norm_elementwise_affine=__lowercase  ,				)
  transformer.load_state_dict(__lowercase  ,				strict=__lowercase)

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

  __lowerCAmelCase								=					AutoencoderKL.from_pretrained(args.vae_model)

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

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


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

      __lowercase				    =      parser.parse_args()
      main(args)
