code: 
  
  
  
  import argparse
  import os
  
  import torch
  from torchvision.datasets.utils import download_url
  
  from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, TransformeraDModel
  
  
  __A       					=		{512: '''DiT-XL-2-512x512.pt''', 256: '''DiT-XL-2-256x256.pt'''}
  
  
  
  def 		__a  (					lowerCAmelCase_     :      Optional[int]       )   ->				Dict:
  
  
  
  
  
  
  						'''simple docstring'''
  
  
  
  						UpperCAmelCase_=     F"""pretrained_models/{model_name}"""
  						if not os.path.isfile(lowercase__       ):
  												os.makedirs("""pretrained_models"""    ,exist_ok=lowercase__       )
  												UpperCAmelCase_=     F"""https://dl.fbaipublicfiles.com/DiT/models/{model_name}"""
  												download_url(lowercase__    ,"""pretrained_models"""       )
  						UpperCAmelCase_=     torch.load(lowercase__    ,map_location=lambda lowerCAmelCase_    ,lowerCAmelCase_       : storage       )
  						return model
  
  
  
  def 		__a  (					lowerCAmelCase_     :      Tuple       )   ->				int:
  
  
  
  
  
  
  						'''simple docstring'''
  
  
  
  						UpperCAmelCase_=     download_model(pretrained_models[args.image_size]       )
  
  						UpperCAmelCase_=     state_dict["""x_embedder.proj.weight"""]
  						UpperCAmelCase_=     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       ):
  												UpperCAmelCase_=     state_dict[
  												    """t_embedder.mlp.0.weight"""
  												]
  												UpperCAmelCase_=     state_dict[
  												    """t_embedder.mlp.0.bias"""
  												]
  												UpperCAmelCase_=     state_dict[
  												    """t_embedder.mlp.2.weight"""
  												]
  												UpperCAmelCase_=     state_dict[
  												    """t_embedder.mlp.2.bias"""
  												]
  												UpperCAmelCase_=     state_dict[
  												    """y_embedder.embedding_table.weight"""
  												]
  
  												UpperCAmelCase_=     state_dict[
  												    F"""blocks.{depth}.adaLN_modulation.1.weight"""
  												]
  												UpperCAmelCase_=     state_dict[
  												    F"""blocks.{depth}.adaLN_modulation.1.bias"""
  												]
  
  												UpperCAmelCase_,     UpperCAmelCase_,     UpperCAmelCase_=     torch.chunk(state_dict[F"""blocks.{depth}.attn.qkv.weight"""]    ,3    ,dim=0       )
  												UpperCAmelCase_,     UpperCAmelCase_,     UpperCAmelCase_=     torch.chunk(state_dict[F"""blocks.{depth}.attn.qkv.bias"""]    ,3    ,dim=0       )
  
  												UpperCAmelCase_=     q
  												UpperCAmelCase_=     q_bias
  												UpperCAmelCase_=     k
  												UpperCAmelCase_=     k_bias
  												UpperCAmelCase_=     v
  												UpperCAmelCase_=     v_bias
  
  												UpperCAmelCase_=     state_dict[
  												    F"""blocks.{depth}.attn.proj.weight"""
  												]
  												UpperCAmelCase_=     state_dict[F"""blocks.{depth}.attn.proj.bias"""]
  
  												UpperCAmelCase_=     state_dict[F"""blocks.{depth}.mlp.fc1.weight"""]
  												UpperCAmelCase_=     state_dict[F"""blocks.{depth}.mlp.fc1.bias"""]
  												UpperCAmelCase_=     state_dict[F"""blocks.{depth}.mlp.fc2.weight"""]
  												UpperCAmelCase_=     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"""       )
  
  						UpperCAmelCase_=     state_dict["""final_layer.adaLN_modulation.1.weight"""]
  						UpperCAmelCase_=     state_dict["""final_layer.adaLN_modulation.1.bias"""]
  						UpperCAmelCase_=     state_dict["""final_layer.linear.weight"""]
  						UpperCAmelCase_=     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
  						UpperCAmelCase_=     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__       )
  
  						UpperCAmelCase_=     DDIMScheduler(
  						    num_train_timesteps=10_00    ,beta_schedule="""linear"""    ,prediction_type="""epsilon"""    ,clip_sample=lowercase__    ,)
  
  						UpperCAmelCase_=     AutoencoderKL.from_pretrained(args.vae_model       )
  
  						UpperCAmelCase_=     DiTPipeline(transformer=lowercase__    ,vae=lowercase__    ,scheduler=lowercase__       )
  
  						if args.save:
  												pipeline.save_pretrained(args.checkpoint_path       )
  
  
  if __name__ == "__main__":
  	__A       					=		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       					=		parser.parse_args()
  	main(args)
  
  
  
  
  
style_context: 
  
  
  
  from __future__ import annotations
  
  
  
  def  __a  (					lowerCAmelCase_     :      int       )   ->				int | None:
  
  
  
  
  
  
  						'''simple docstring'''
  
  
  
  						for r in range(1    ,lowerCAmelCase_       ):
  												UpperCAmelCase_=     []
  												for x in range(n - 1       ):
  																		UpperCAmelCase_=     pow(lowerCAmelCase_    ,lowerCAmelCase_    ,lowerCAmelCase_       )
  																		if val in li:
  																								break
  																		li.append(lowerCAmelCase_       )
  												else:
  																		return r
  						return None
  
  
  if __name__ == "__main__":
  	__A       					=		int(input('''Enter a prime number q: '''))
  	__A       					=		find_primitive(q)
  	if a is None:
  		print(f'Cannot find the primitive for the value: {a!r}')
  	else:
  		__A       					=		int(input('''Enter private key of A: '''))
  		__A       					=		pow(a, a_private, q)
  		__A       					=		int(input('''Enter private key of B: '''))
  		__A       					=		pow(a, b_private, q)
  
  		__A       					=		pow(b_public, a_private, q)
  		__A       					=		pow(a_public, b_private, q)
  
  		print('''The key value generated by A is: ''', a_secret)
  		print('''The key value generated by B is: ''', b_secret)
  
  
  
  
  