




'''simple docstring'''
import argparse
import os

import torch
from torchvision.datasets.utils import download_url

from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, TransformeraDModel


A				: Tuple					      =			{512: """DiT-XL-2-512x512.pt""", 256: """DiT-XL-2-256x256.pt"""}
def 							snake_case_    (  a__						:      Dict     ):



    """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 							snake_case_    (  a__						:      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				: 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."""
  )

  A				: Union[str, Any]					      =			parser.parse_args()
  main(args)










'''simple docstring'''
import os
def 							snake_case_    (  ):



    """simple docstring"""




    __lowercase     							=		os.path.dirname(os.path.realpath(a__     )     )
    __lowercase     							=		os.path.join(a__       ,"""triangle.txt"""     )

    with open(a__     ) as f:
        __lowercase     							=		f.readlines()

    __lowercase     							=		[[int(a__     ) for y in x.rstrip("""\r\n"""     ).split(""" """     )] for x in triangle]

    for i in range(1       ,len(a__     )     ):
        for j in range(len(a[i]     )     ):
            __lowercase     							=		a[i - 1][j] if j != len(a[i - 1]     ) else 0
            __lowercase     							=		a[i - 1][j - 1] if j > 0 else 0
            a[i][j] += max(a__       ,a__     )
    return max(a[-1]     )


if __name__ == "__main__":
  print(solution())




