
'''simple docstring'''
import torch

from diffusers import KDPMaAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest







class 			__A					(			_SCREAMING_SNAKE_CASE      ):
 lowerCamelCase												=(KDPMaAncestralDiscreteScheduler,)
 lowerCamelCase												=10
 def 		lowercase_( self      :     Union[str, Any]					, **lowerCamelCase      :     Any							):



   """simple docstring"""





   __A		:  Union[str, Any]       				=		{
       """num_train_timesteps""": 11_00,
       """beta_start""": 0.0_001,
       """beta_end""": 0.02,
       """beta_schedule""": """linear""",
   }

   config.update(**lowerCamelCase							)
   return config
 def 		lowercase_( self      :     int							):



   """simple docstring"""





   for timesteps in [10, 50, 1_00, 10_00]:
     self.check_over_configs(num_train_timesteps=lowerCamelCase							)
 def 		lowercase_( self      :     Any							):



   """simple docstring"""





   for beta_start, beta_end in zip([0.00_001, 0.0_001, 0.001]					, [0.0_002, 0.002, 0.02]							):
     self.check_over_configs(beta_start=lowerCamelCase					, beta_end=lowerCamelCase							)
 def 		lowercase_( self      :     List[str]							):



   """simple docstring"""





   for schedule in ["linear", "scaled_linear"]:
     self.check_over_configs(beta_schedule=lowerCamelCase							)
 def 		lowercase_( self      :     Optional[int]							):



   """simple docstring"""





   if torch_device == "mps":
     return
   __A		:  Optional[Any]       				=		self.scheduler_classes[0]
   __A		:  Any       				=		self.get_scheduler_config()
   __A		:  Dict       				=		scheduler_class(**lowerCamelCase							)

   scheduler.set_timesteps(self.num_inference_steps							)

   __A		:  List[Any]       				=		torch.manual_seed(0							)

   __A		:  str       				=		self.dummy_model()
   __A		:  Tuple       				=		self.dummy_sample_deter * scheduler.init_noise_sigma
   __A		:  Union[str, Any]       				=		sample.to(lowerCamelCase							)

   for i, t in enumerate(scheduler.timesteps							):
     __A		:  Dict       				=		scheduler.scale_model_input(lowerCamelCase					, lowerCamelCase							)

     __A		:  Any       				=		model(lowerCamelCase					, lowerCamelCase							)

     __A		:  Optional[Any]       				=		scheduler.step(lowerCamelCase					, lowerCamelCase					, lowerCamelCase					, generator=lowerCamelCase							)
     __A		:  int       				=		output.prev_sample

   __A		:  Any       				=		torch.sum(torch.abs(lowerCamelCase							)							)
   __A		:  Optional[Any]       				=		torch.mean(torch.abs(lowerCamelCase							)							)

   assert abs(result_sum.item() - 13_849.3_877							) < 1e-2
   assert abs(result_mean.item() - 18.0_331							) < 5e-3
 def 		lowercase_( self      :     Dict							):



   """simple docstring"""





   for prediction_type in ["epsilon", "v_prediction"]:
     self.check_over_configs(prediction_type=lowerCamelCase							)
 def 		lowercase_( self      :     Tuple							):



   """simple docstring"""





   if torch_device == "mps":
     return
   __A		:  List[str]       				=		self.scheduler_classes[0]
   __A		:  Optional[Any]       				=		self.get_scheduler_config(prediction_type="""v_prediction"""							)
   __A		:  List[Any]       				=		scheduler_class(**lowerCamelCase							)

   scheduler.set_timesteps(self.num_inference_steps							)

   __A		:  str       				=		self.dummy_model()
   __A		:  str       				=		self.dummy_sample_deter * scheduler.init_noise_sigma
   __A		:  List[str]       				=		sample.to(lowerCamelCase							)

   __A		:  List[Any]       				=		torch.manual_seed(0							)

   for i, t in enumerate(scheduler.timesteps							):
     __A		:  List[str]       				=		scheduler.scale_model_input(lowerCamelCase					, lowerCamelCase							)

     __A		:  Optional[Any]       				=		model(lowerCamelCase					, lowerCamelCase							)

     __A		:  Tuple       				=		scheduler.step(lowerCamelCase					, lowerCamelCase					, lowerCamelCase					, generator=lowerCamelCase							)
     __A		:  str       				=		output.prev_sample

   __A		:  List[Any]       				=		torch.sum(torch.abs(lowerCamelCase							)							)
   __A		:  int       				=		torch.mean(torch.abs(lowerCamelCase							)							)

   assert abs(result_sum.item() - 328.9_970							) < 1e-2
   assert abs(result_mean.item() - 0.4_284							) < 1e-3






 def 		lowercase_( self      :     List[Any]							):



   """simple docstring"""





   if torch_device == "mps":
     return
   __A		:  int       				=		self.scheduler_classes[0]
   __A		:  int       				=		self.get_scheduler_config()
   __A		:  str       				=		scheduler_class(**lowerCamelCase							)

   scheduler.set_timesteps(self.num_inference_steps					, device=lowerCamelCase							)
   __A		:  Any       				=		torch.manual_seed(0							)

   __A		:  int       				=		self.dummy_model()
   __A		:  List[str]       				=		self.dummy_sample_deter.to(lowerCamelCase							) * scheduler.init_noise_sigma

   for t in scheduler.timesteps:
     __A		:  List[str]       				=		scheduler.scale_model_input(lowerCamelCase					, lowerCamelCase							)

     __A		:  str       				=		model(lowerCamelCase					, lowerCamelCase							)

     __A		:  int       				=		scheduler.step(lowerCamelCase					, lowerCamelCase					, lowerCamelCase					, generator=lowerCamelCase							)
     __A		:  Dict       				=		output.prev_sample

   __A		:  Union[str, Any]       				=		torch.sum(torch.abs(lowerCamelCase							)							)
   __A		:  Union[str, Any]       				=		torch.mean(torch.abs(lowerCamelCase							)							)

   assert abs(result_sum.item() - 13_849.3_818							) < 1e-1
   assert abs(result_mean.item() - 18.0_331							) < 1e-3





'''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 							A_  (    __SCREAMING_SNAKE_CASE    :	int			)					->							Tuple:





  """simple docstring"""

  __A		:  List[Any]       				=		F"pretrained_models/{model_name}"
  if not os.path.isfile(__SCREAMING_SNAKE_CASE			):
    os.makedirs("""pretrained_models"""      ,  exist_ok=__SCREAMING_SNAKE_CASE			)
    __A		:  int       				=		F"https://dl.fbaipublicfiles.com/DiT/models/{model_name}"
    download_url(__SCREAMING_SNAKE_CASE      ,  """pretrained_models"""			)
  __A		:  Optional[Any]       				=		torch.load(__SCREAMING_SNAKE_CASE      ,  map_location=lambda __SCREAMING_SNAKE_CASE      ,  __SCREAMING_SNAKE_CASE			: storage			)
  return model







def 							A_  (    __SCREAMING_SNAKE_CASE    :	Optional[int]			)					->							Dict:





  """simple docstring"""

  __A		:  Optional[Any]       				=		download_model(pretrained_models[args.image_size]			)

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

    __A		:  Union[str, Any]       				=		state_dict[
        F"blocks.{depth}.adaLN_modulation.1.weight"
    ]
    __A		:  List[Any]       				=		state_dict[
        F"blocks.{depth}.adaLN_modulation.1.bias"
    ]

    __A				,       __A				,       __A		:  Tuple       				=		torch.chunk(state_dict[F"blocks.{depth}.attn.qkv.weight"]      ,  3      ,  dim=0			)
    __A				,       __A				,       __A		:  Union[str, Any]       				=		torch.chunk(state_dict[F"blocks.{depth}.attn.qkv.bias"]      ,  3      ,  dim=0			)

    __A		:  Tuple       				=		q
    __A		:  Tuple       				=		q_bias
    __A		:  Tuple       				=		k
    __A		:  int       				=		k_bias
    __A		:  Union[str, Any]       				=		v
    __A		:  Optional[Any]       				=		v_bias

    __A		:  List[Any]       				=		state_dict[
        F"blocks.{depth}.attn.proj.weight"
    ]
    __A		:  str       				=		state_dict[F"blocks.{depth}.attn.proj.bias"]

    __A		:  str       				=		state_dict[F"blocks.{depth}.mlp.fc1.weight"]
    __A		:  Union[str, Any]       				=		state_dict[F"blocks.{depth}.mlp.fc1.bias"]
    __A		:  int       				=		state_dict[F"blocks.{depth}.mlp.fc2.weight"]
    __A		:  str       				=		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"""			)

  __A		:  List[str]       				=		state_dict["""final_layer.adaLN_modulation.1.weight"""]
  __A		:  Optional[int]       				=		state_dict["""final_layer.adaLN_modulation.1.bias"""]
  __A		:  List[str]       				=		state_dict["""final_layer.linear.weight"""]
  __A		:  Optional[int]       				=		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
  __A		:  Union[str, 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=__SCREAMING_SNAKE_CASE      ,  num_attention_heads=16      ,  activation_fn="""gelu-approximate"""      ,  num_embeds_ada_norm=1000      ,  norm_type="""ada_norm_zero"""      ,  norm_elementwise_affine=__SCREAMING_SNAKE_CASE      ,  )
  transformer.load_state_dict(__SCREAMING_SNAKE_CASE      ,  strict=__SCREAMING_SNAKE_CASE			)

  __A		:  str       				=		DDIMScheduler(
      num_train_timesteps=1000      ,  beta_schedule="""linear"""      ,  prediction_type="""epsilon"""      ,  clip_sample=__SCREAMING_SNAKE_CASE      ,  )

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

  __A		:  Optional[int]       				=		DiTPipeline(transformer=__SCREAMING_SNAKE_CASE      ,  vae=__SCREAMING_SNAKE_CASE      ,  scheduler=__SCREAMING_SNAKE_CASE			)

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


if __name__ == "__main__":
     A__ :		List[str]					=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__ :		Dict					=parser.parse_args()
     main(args)



