
import argparse
import os

import torch
from torchvision.datasets.utils import download_url

from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, TransformeraDModel


_A       =					{5_12: "DiT-XL-2-512x512.pt", 2_56: "DiT-XL-2-256x256.pt"}



def 		lowercase_     (  A__						)				->				Tuple:
    """simple docstring"""

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



def 		lowercase_     (  A__						)				->				Dict:
    """simple docstring"""

    snake_case						=   download_model(pretrained_models[args.image_size]						)

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

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

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

        snake_case						=   q
        snake_case						=   q_bias
        snake_case						=   k
        snake_case						=   k_bias
        snake_case						=   v
        snake_case						=   v_bias

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

        snake_case						=   state_dict[F'blocks.{depth}.mlp.fc1.weight']
        snake_case						=   state_dict[F'blocks.{depth}.mlp.fc1.bias']
        snake_case						=   state_dict[F'blocks.{depth}.mlp.fc2.weight']
        snake_case						=   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"						)

    snake_case						=   state_dict["final_layer.adaLN_modulation.1.weight"]
    snake_case						=   state_dict["final_layer.adaLN_modulation.1.bias"]
    snake_case						=   state_dict["final_layer.linear.weight"]
    snake_case						=   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
    snake_case						=   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=1000					,						norm_type="ada_norm_zero"					,						norm_elementwise_affine=A__					,						)
    transformer.load_state_dict(A__					,						strict=A__						)

    snake_case						=   DDIMScheduler(
        num_train_timesteps=1000					,						beta_schedule="linear"					,						prediction_type="epsilon"					,						clip_sample=A__					,						)

    snake_case						=   AutoencoderKL.from_pretrained(args.vae_model						)

    snake_case						=   DiTPipeline(transformer=A__					,						vae=A__					,						scheduler=A__						)

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


if __name__ == "__main__":
   _A       =					argparse.ArgumentParser()

   parser.add_argument(
       "--image_size",
       default=2_56,
       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       =					parser.parse_args()
   main(args)



_A       =					{
    "0": 1,
    "1": 1,
    "2": 2,
    "3": 6,
    "4": 24,
    "5": 1_20,
    "6": 7_20,
    "7": 50_40,
    "8": 4_03_20,
    "9": 36_28_80,
}

_A       =					{1_45: 1_45}

_A       =					{
    1_45: 0,
    1_69: 3,
    3_63_01: 3,
    14_54: 3,
    8_71: 2,
    4_53_61: 2,
    8_72: 2,
}



def 		lowercase_     (  A__						)				->				int:
    """simple docstring"""

    if n in CACHE_SUM_DIGIT_FACTORIALS:
        return CACHE_SUM_DIGIT_FACTORIALS[n]
    snake_case						=   sum(DIGIT_FACTORIALS[let] for let in str(A__						)						)
    snake_case						=   ret
    return ret



def 		lowercase_     (  A__					,						A__ = None						)				->				int:
    """simple docstring"""

    snake_case						=   previous or set()
    if n in CHAIN_LENGTH_CACHE:
        return CHAIN_LENGTH_CACHE[n]
    snake_case						=   sum_digit_factorials(A__						)
    if next_number in previous:
        snake_case						=   0
        return 0
    else:
        previous.add(A__						)
        snake_case						=   1 + chain_length(A__					,						A__						)
        snake_case						=   ret
        return ret



def 		lowercase_     (  A__ = 60					,						A__ = 100_0000						)				->				int:
    """simple docstring"""

    return sum(1 for i in range(1					,						A__						) if chain_length(A__						) == num_terms						)


if __name__ == "__main__":
   print(f"{solution() = }")

