code: 
  
  
  '''simple docstring'''
  
  
  
  import argparse
  import os
  
  import torch
  from torchvision.datasets.utils import download_url
  
  from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, TransformeraDModel
  
  
  A__	:      Union[str, Any]				=						{512: """DiT-XL-2-512x512.pt""", 256: """DiT-XL-2-256x256.pt"""}
  
  
  
  def     UpperCAmelCase__      (			UpperCAmelCase_					:       Dict							)  ->	Dict:
       __lowerCamelCase     :			int              =	F'pretrained_models/{model_name}'
       if not os.path.isfile(lowercase__							):
            os.makedirs('pretrained_models'   ,					exist_ok=lowercase__							)
            __lowerCamelCase     :			Optional[int]              =	F'https://dl.fbaipublicfiles.com/DiT/models/{model_name}'
            download_url(lowercase__   ,					'pretrained_models'							)
       __lowerCamelCase     :			Union[str, Any]              =	torch.load(lowercase__   ,					map_location=lambda UpperCAmelCase_   ,					UpperCAmelCase_							: storage							)
       return model
  
  
  
  def     UpperCAmelCase__      (			UpperCAmelCase_					:       str							)  ->	int:
       __lowerCamelCase     :			Optional[Any]              =	download_model(pretrained_models[args.image_size]							)
  
       __lowerCamelCase     :			str              =	state_dict['x_embedder.proj.weight']
       __lowerCamelCase     :			Union[str, Any]              =	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     :			Dict              =	state_dict[
                't_embedder.mlp.0.weight'
            ]
            __lowerCamelCase     :			Tuple              =	state_dict[
                't_embedder.mlp.0.bias'
            ]
            __lowerCamelCase     :			Tuple              =	state_dict[
                't_embedder.mlp.2.weight'
            ]
            __lowerCamelCase     :			Optional[Any]              =	state_dict[
                't_embedder.mlp.2.bias'
            ]
            __lowerCamelCase     :			List[Any]              =	state_dict[
                'y_embedder.embedding_table.weight'
            ]
  
            __lowerCamelCase     :			Union[str, Any]              =	state_dict[
                F'blocks.{depth}.adaLN_modulation.1.weight'
            ]
            __lowerCamelCase     :			List[str]              =	state_dict[
                F'blocks.{depth}.adaLN_modulation.1.bias'
            ]
  
            __lowerCamelCase       , __lowerCamelCase       , __lowerCamelCase     :			Tuple              =	torch.chunk(state_dict[F'blocks.{depth}.attn.qkv.weight']   ,					3   ,					dim=0							)
            __lowerCamelCase       , __lowerCamelCase       , __lowerCamelCase     :			Union[str, Any]              =	torch.chunk(state_dict[F'blocks.{depth}.attn.qkv.bias']   ,					3   ,					dim=0							)
  
            __lowerCamelCase     :			str              =	q
            __lowerCamelCase     :			Optional[int]              =	q_bias
            __lowerCamelCase     :			List[str]              =	k
            __lowerCamelCase     :			Optional[int]              =	k_bias
            __lowerCamelCase     :			str              =	v
            __lowerCamelCase     :			Tuple              =	v_bias
  
            __lowerCamelCase     :			str              =	state_dict[
                F'blocks.{depth}.attn.proj.weight'
            ]
            __lowerCamelCase     :			Optional[int]              =	state_dict[F'blocks.{depth}.attn.proj.bias']
  
            __lowerCamelCase     :			List[str]              =	state_dict[F'blocks.{depth}.mlp.fc1.weight']
            __lowerCamelCase     :			Tuple              =	state_dict[F'blocks.{depth}.mlp.fc1.bias']
            __lowerCamelCase     :			List[Any]              =	state_dict[F'blocks.{depth}.mlp.fc2.weight']
            __lowerCamelCase     :			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'							)
  
       __lowerCamelCase     :			List[str]              =	state_dict['final_layer.adaLN_modulation.1.weight']
       __lowerCamelCase     :			Dict              =	state_dict['final_layer.adaLN_modulation.1.bias']
       __lowerCamelCase     :			Union[str, Any]              =	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     :			Optional[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=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     :			str              =	DDIMScheduler(
           num_train_timesteps=10_00   ,					beta_schedule='linear'   ,					prediction_type='epsilon'   ,					clip_sample=lowercase__   ,					)
  
       __lowerCamelCase     :			List[str]              =	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__":
        A__	:      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__	:      List[Any]				=						parser.parse_args()
        main(args)
style_context: 
  
  
  '''simple docstring'''
  
  
  
  import torch
  
  from diffusers import KDPMaAncestralDiscreteScheduler
  from diffusers.utils import torch_device
  
  from .test_schedulers import SchedulerCommonTest
  
  
  
  
  
  class      UpperCAmelCase_						(_UpperCAmelCase						):
  
  
  
  
  
      """simple docstring"""
  
  
  
  
  
  
      lowerCamelCase						:      Optional[Any]							  =    (KDPMaAncestralDiscreteScheduler,)
      lowerCamelCase						:      Tuple							  =    1_0
  
  
  
  
  
  
  
      def    lowercase_       (							self    ,  **SCREAMING_SNAKE_CASE_			)       ->  Tuple:
           __lowerCamelCase     :			Union[str, Any]              =	{
               'num_train_timesteps': 11_00,
               'beta_start': 0.0_0_0_1,
               'beta_end': 0.0_2,
               'beta_schedule': 'linear',
           }
  
           config.update(**SCREAMING_SNAKE_CASE_			)
           return config
  
  
  
  
  
  
  
      def    lowercase_       (							self			)       ->  Union[str, Any]:
           for timesteps in [10, 50, 1_00, 10_00]:
                self.check_over_configs(num_train_timesteps=SCREAMING_SNAKE_CASE_			)
  
  
  
  
  
  
  
      def    lowercase_       (							self			)       ->  Dict:
           for beta_start, beta_end in zip([0.0_0_0_0_1, 0.0_0_0_1, 0.0_0_1]    ,  [0.0_0_0_2, 0.0_0_2, 0.0_2]			):
                self.check_over_configs(beta_start=SCREAMING_SNAKE_CASE_    ,  beta_end=SCREAMING_SNAKE_CASE_			)
  
  
  
  
  
  
  
      def    lowercase_       (							self			)       ->  List[str]:
           for schedule in ["linear", "scaled_linear"]:
                self.check_over_configs(beta_schedule=SCREAMING_SNAKE_CASE_			)
  
  
  
  
  
  
  
      def    lowercase_       (							self			)       ->  int:
           if torch_device == "mps":
                return
           __lowerCamelCase     :			Union[str, Any]              =	self.scheduler_classes[0]
           __lowerCamelCase     :			Optional[Any]              =	self.get_scheduler_config()
           __lowerCamelCase     :			Optional[int]              =	scheduler_class(**SCREAMING_SNAKE_CASE_			)
  
           scheduler.set_timesteps(self.num_inference_steps			)
  
           __lowerCamelCase     :			int              =	torch.manual_seed(0			)
  
           __lowerCamelCase     :			int              =	self.dummy_model()
           __lowerCamelCase     :			List[Any]              =	self.dummy_sample_deter * scheduler.init_noise_sigma
           __lowerCamelCase     :			Any              =	sample.to(SCREAMING_SNAKE_CASE_			)
  
           for i, t in enumerate(scheduler.timesteps			):
                __lowerCamelCase     :			Dict              =	scheduler.scale_model_input(SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_			)
  
                __lowerCamelCase     :			Dict              =	model(SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_			)
  
                __lowerCamelCase     :			Union[str, Any]              =	scheduler.step(SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_    ,  generator=SCREAMING_SNAKE_CASE_			)
                __lowerCamelCase     :			Any              =	output.prev_sample
  
           __lowerCamelCase     :			Tuple              =	torch.sum(torch.abs(SCREAMING_SNAKE_CASE_			)			)
           __lowerCamelCase     :			Optional[int]              =	torch.mean(torch.abs(SCREAMING_SNAKE_CASE_			)			)
  
           assert abs(result_sum.item() - 1_3_8_4_9.3_8_7_7			) < 1E-2
           assert abs(result_mean.item() - 1_8.0_3_3_1			) < 5E-3
  
  
  
  
  
  
  
      def    lowercase_       (							self			)       ->  List[str]:
           for prediction_type in ["epsilon", "v_prediction"]:
                self.check_over_configs(prediction_type=SCREAMING_SNAKE_CASE_			)
  
  
  
  
  
  
  
      def    lowercase_       (							self			)       ->  List[str]:
           if torch_device == "mps":
                return
           __lowerCamelCase     :			Union[str, Any]              =	self.scheduler_classes[0]
           __lowerCamelCase     :			Optional[Any]              =	self.get_scheduler_config(prediction_type='v_prediction'			)
           __lowerCamelCase     :			str              =	scheduler_class(**SCREAMING_SNAKE_CASE_			)
  
           scheduler.set_timesteps(self.num_inference_steps			)
  
           __lowerCamelCase     :			int              =	self.dummy_model()
           __lowerCamelCase     :			int              =	self.dummy_sample_deter * scheduler.init_noise_sigma
           __lowerCamelCase     :			Dict              =	sample.to(SCREAMING_SNAKE_CASE_			)
  
           __lowerCamelCase     :			int              =	torch.manual_seed(0			)
  
           for i, t in enumerate(scheduler.timesteps			):
                __lowerCamelCase     :			Any              =	scheduler.scale_model_input(SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_			)
  
                __lowerCamelCase     :			List[str]              =	model(SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_			)
  
                __lowerCamelCase     :			str              =	scheduler.step(SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_    ,  generator=SCREAMING_SNAKE_CASE_			)
                __lowerCamelCase     :			Tuple              =	output.prev_sample
  
           __lowerCamelCase     :			int              =	torch.sum(torch.abs(SCREAMING_SNAKE_CASE_			)			)
           __lowerCamelCase     :			int              =	torch.mean(torch.abs(SCREAMING_SNAKE_CASE_			)			)
  
           assert abs(result_sum.item() - 3_2_8.9_9_7_0			) < 1E-2
           assert abs(result_mean.item() - 0.4_2_8_4			) < 1E-3
  
  
  
  
  
  
  
      def    lowercase_       (							self			)       ->  Dict:
           if torch_device == "mps":
                return
           __lowerCamelCase     :			Optional[int]              =	self.scheduler_classes[0]
           __lowerCamelCase     :			str              =	self.get_scheduler_config()
           __lowerCamelCase     :			Union[str, Any]              =	scheduler_class(**SCREAMING_SNAKE_CASE_			)
  
           scheduler.set_timesteps(self.num_inference_steps    ,  device=SCREAMING_SNAKE_CASE_			)
           __lowerCamelCase     :			List[Any]              =	torch.manual_seed(0			)
  
           __lowerCamelCase     :			Optional[Any]              =	self.dummy_model()
           __lowerCamelCase     :			Dict              =	self.dummy_sample_deter.to(SCREAMING_SNAKE_CASE_			) * scheduler.init_noise_sigma
  
           for t in scheduler.timesteps:
                __lowerCamelCase     :			Any              =	scheduler.scale_model_input(SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_			)
  
                __lowerCamelCase     :			Optional[Any]              =	model(SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_			)
  
                __lowerCamelCase     :			Dict              =	scheduler.step(SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_    ,  generator=SCREAMING_SNAKE_CASE_			)
                __lowerCamelCase     :			Union[str, Any]              =	output.prev_sample
  
           __lowerCamelCase     :			int              =	torch.sum(torch.abs(SCREAMING_SNAKE_CASE_			)			)
           __lowerCamelCase     :			Dict              =	torch.mean(torch.abs(SCREAMING_SNAKE_CASE_			)			)
  
           assert abs(result_sum.item() - 1_3_8_4_9.3_8_1_8			) < 1E-1
           assert abs(result_mean.item() - 1_8.0_3_3_1			) < 1E-3
  
  
  
  
  
  
  