





import argparse
import os

import torch
from torchvision.datasets.utils import download_url

from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, TransformeraDModel


__UpperCAmelCase						=      {5_12: 'DiT-XL-2-512x512.pt', 2_56: 'DiT-XL-2-256x256.pt'}
def 			__UpperCamelCase				(			lowercase__	:       Tuple		)	->      List[Any]:
    '''simple docstring'''





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





    lowerCAmelCase_					:	Dict						=   download_model(pretrained_models[args.image_size]		)

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

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

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

        lowerCAmelCase_					:	Dict						=   q
        lowerCAmelCase_					:	str						=   q_bias
        lowerCAmelCase_					:	Tuple						=   k
        lowerCAmelCase_					:	str						=   k_bias
        lowerCAmelCase_					:	Optional[Any]						=   v
        lowerCAmelCase_					:	Any						=   v_bias

        lowerCAmelCase_					:	List[str]						=   state_dict[
            f'blocks.{depth}.attn.proj.weight'
        ]
        lowerCAmelCase_					:	List[Any]						=   state_dict[f'blocks.{depth}.attn.proj.bias']

        lowerCAmelCase_					:	Tuple						=   state_dict[f'blocks.{depth}.mlp.fc1.weight']
        lowerCAmelCase_					:	Tuple						=   state_dict[f'blocks.{depth}.mlp.fc1.bias']
        lowerCAmelCase_					:	Union[str, Any]						=   state_dict[f'blocks.{depth}.mlp.fc2.weight']
        lowerCAmelCase_					:	Tuple						=   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_					:	List[Any]						=   state_dict["""final_layer.adaLN_modulation.1.weight"""]
    lowerCAmelCase_					:	Dict						=   state_dict["""final_layer.adaLN_modulation.1.bias"""]
    lowerCAmelCase_					:	Tuple						=   state_dict["""final_layer.linear.weight"""]
    lowerCAmelCase_					:	List[str]						=   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_					:	str						=   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=1000		,    norm_type="""ada_norm_zero"""		,    norm_elementwise_affine=lowercase__		,    )
    transformer.load_state_dict(lowercase__		,    strict=lowercase__		)

    lowerCAmelCase_					:	str						=   DDIMScheduler(
        num_train_timesteps=1000		,    beta_schedule="""linear"""		,    prediction_type="""epsilon"""		,    clip_sample=lowercase__		,    )

    lowerCAmelCase_					:	Union[str, Any]						=   AutoencoderKL.from_pretrained(args.vae_model		)

    lowerCAmelCase_					:	str						=   DiTPipeline(transformer=lowercase__		,    vae=lowercase__		,    scheduler=lowercase__		)

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


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

  __UpperCAmelCase						=      parser.parse_args()
  main(args)









import math
def 			__UpperCamelCase				(			lowercase__	:       int		)	->      list:
    '''simple docstring'''





    if n <= 0:
        raise ValueError("""Only positive integers have prime factors"""		)
    lowerCAmelCase_					:	str						=   []
    while n % 2 == 0:
        pf.append(2		)
        lowerCAmelCase_					:	str						=   int(n / 2		)
    for i in range(3		,    int(math.sqrt(lowercase__		)		) + 1		,    2		):
        while n % i == 0:
            pf.append(lowercase__		)
            lowerCAmelCase_					:	List[Any]						=   int(n / i		)
    if n > 2:
        pf.append(lowercase__		)
    return pf
def 			__UpperCamelCase				(			lowercase__	:       int		)	->      int:
    '''simple docstring'''





    if n <= 0:
        raise ValueError("""Only positive numbers are accepted"""		)
    lowerCAmelCase_					:	Union[str, Any]						=   1
    lowerCAmelCase_					:	str						=   1
    while n % 2 == 0:
        temp += 1
        lowerCAmelCase_					:	Optional[Any]						=   int(n / 2		)
    div *= temp
    for i in range(3		,    int(math.sqrt(lowercase__		)		) + 1		,    2		):
        lowerCAmelCase_					:	Tuple						=   1
        while n % i == 0:
            temp += 1
            lowerCAmelCase_					:	Any						=   int(n / i		)
        div *= temp
    if n > 1:
        div *= 2
    return div
def 			__UpperCamelCase				(			lowercase__	:       int		)	->      int:
    '''simple docstring'''





    if n <= 0:
        raise ValueError("""Only positive numbers are accepted"""		)
    lowerCAmelCase_					:	Tuple						=   1
    lowerCAmelCase_					:	str						=   1
    while n % 2 == 0:
        temp += 1
        lowerCAmelCase_					:	Optional[int]						=   int(n / 2		)
    if temp > 1:
        s *= (2**temp - 1) / (2 - 1)
    for i in range(3		,    int(math.sqrt(lowercase__		)		) + 1		,    2		):
        lowerCAmelCase_					:	int						=   1
        while n % i == 0:
            temp += 1
            lowerCAmelCase_					:	Any						=   int(n / i		)
        if temp > 1:
            s *= (i**temp - 1) / (i - 1)
    return int(lowercase__		)
def 			__UpperCamelCase				(			lowercase__	:       int		)	->      int:
    '''simple docstring'''





    lowerCAmelCase_					:	str						=   n
    for x in set(prime_factors(lowercase__		)		):
        s *= (x - 1) / x
    return int(lowercase__		)


if __name__ == "__main__":
  import doctest

  doctest.testmod()


