code: 
  
  
  
  
  """simple docstring"""
  
  
  
  
  import argparse
  import os
  
  import torch
  from torchvision.datasets.utils import download_url
  
  from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, TransformeraDModel
  
  
  __A :		Optional[int]      							=    {512: '''DiT-XL-2-512x512.pt''', 256: '''DiT-XL-2-256x256.pt'''}
  def 					A_			(		snake_case_     :       Optional[Any]    ):
  
  
  
  
  
  
  
      '''simple docstring'''
      UpperCamelCase				:   Optional[Any]          =							f'pretrained_models/{model_name}'
      if not os.path.isfile(lowercase__    ):
          os.makedirs("""pretrained_models"""						,exist_ok=lowercase__    )
          UpperCamelCase				:   int          =							f'https://dl.fbaipublicfiles.com/DiT/models/{model_name}'
          download_url(lowercase__						,"""pretrained_models"""    )
      UpperCamelCase				:   List[Any]          =							torch.load(lowercase__						,map_location=lambda snake_case_						,snake_case_    : storage    )
      return model
  def 					A_			(		snake_case_     :       str    ):
  
  
  
  
  
  
  
      '''simple docstring'''
      UpperCamelCase				:   int          =							download_model(pretrained_models[args.image_size]    )
  
      UpperCamelCase				:   Any          =							state_dict["""x_embedder.proj.weight"""]
      UpperCamelCase				:   List[str]          =							state_dict["""x_embedder.proj.bias"""]
      state_dict.pop("""x_embedder.proj.weight"""    )
      state_dict.pop("""x_embedder.proj.bias"""    )
  
      for depth in range(2_8    ):
          UpperCamelCase				:   Optional[int]          =							state_dict[
              """t_embedder.mlp.0.weight"""
          ]
          UpperCamelCase				:   Optional[int]          =							state_dict[
              """t_embedder.mlp.0.bias"""
          ]
          UpperCamelCase				:   Optional[Any]          =							state_dict[
              """t_embedder.mlp.2.weight"""
          ]
          UpperCamelCase				:   int          =							state_dict[
              """t_embedder.mlp.2.bias"""
          ]
          UpperCamelCase				:   List[Any]          =							state_dict[
              """y_embedder.embedding_table.weight"""
          ]
  
          UpperCamelCase				:   Dict          =							state_dict[
              f'blocks.{depth}.adaLN_modulation.1.weight'
          ]
          UpperCamelCase				:   List[str]          =							state_dict[
              f'blocks.{depth}.adaLN_modulation.1.bias'
          ]
  
          UpperCamelCase       ,							UpperCamelCase       ,							UpperCamelCase				:   Optional[Any]          =							torch.chunk(state_dict[f'blocks.{depth}.attn.qkv.weight']						,3						,dim=0    )
          UpperCamelCase       ,							UpperCamelCase       ,							UpperCamelCase				:   Any          =							torch.chunk(state_dict[f'blocks.{depth}.attn.qkv.bias']						,3						,dim=0    )
  
          UpperCamelCase				:   Optional[Any]          =							q
          UpperCamelCase				:   int          =							q_bias
          UpperCamelCase				:   Optional[Any]          =							k
          UpperCamelCase				:   int          =							k_bias
          UpperCamelCase				:   List[str]          =							v
          UpperCamelCase				:   Any          =							v_bias
  
          UpperCamelCase				:   Optional[int]          =							state_dict[
              f'blocks.{depth}.attn.proj.weight'
          ]
          UpperCamelCase				:   List[Any]          =							state_dict[f'blocks.{depth}.attn.proj.bias']
  
          UpperCamelCase				:   List[Any]          =							state_dict[f'blocks.{depth}.mlp.fc1.weight']
          UpperCamelCase				:   Optional[int]          =							state_dict[f'blocks.{depth}.mlp.fc1.bias']
          UpperCamelCase				:   Union[str, Any]          =							state_dict[f'blocks.{depth}.mlp.fc2.weight']
          UpperCamelCase				:   Optional[int]          =							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				:   Optional[Any]          =							state_dict["""final_layer.adaLN_modulation.1.weight"""]
      UpperCamelCase				:   int          =							state_dict["""final_layer.adaLN_modulation.1.bias"""]
      UpperCamelCase				:   int          =							state_dict["""final_layer.linear.weight"""]
      UpperCamelCase				:   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
      UpperCamelCase				:   List[Any]          =							TransformeraDModel(
          sample_size=args.image_size // 8						,num_layers=2_8						,attention_head_dim=7_2						,in_channels=4						,out_channels=8						,patch_size=2						,attention_bias=lowercase__						,num_attention_heads=1_6						,activation_fn="""gelu-approximate"""						,num_embeds_ada_norm=1_0_0_0						,norm_type="""ada_norm_zero"""						,norm_elementwise_affine=lowercase__						,)
      transformer.load_state_dict(lowercase__						,strict=lowercase__    )
  
      UpperCamelCase				:   Tuple          =							DDIMScheduler(
          num_train_timesteps=1_0_0_0						,beta_schedule="""linear"""						,prediction_type="""epsilon"""						,clip_sample=lowercase__						,)
  
      UpperCamelCase				:   Any          =							AutoencoderKL.from_pretrained(args.vae_model    )
  
      UpperCamelCase				:   Any          =							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 :		Union[str, Any]      							=    parser.parse_args()
         main(args)
  
  
  
  
  
style_context: 
  
  
  
  
  """simple docstring"""
  
  
  
  
  import numpy as np
  import qiskit
  from qiskit import Aer, ClassicalRegister, QuantumCircuit, QuantumRegister, execute
  def 					A_			(		snake_case_     :       float = np.pi / 2						,snake_case_     :       float = np.pi / 2						,snake_case_     :       float = np.pi / 2    ):
  
  
  
  
  
  
  
      '''simple docstring'''
      UpperCamelCase				:   Union[str, Any]          =							QuantumRegister(3						,"""qr"""    )  # Define the number of quantum bits
      UpperCamelCase				:   Any          =							ClassicalRegister(1						,"""cr"""    )  # Define the number of classical bits
  
      UpperCamelCase				:   Any          =							QuantumCircuit(snake_case_						,snake_case_    )  # Define the quantum circuit.
  
      # Build the circuit
      quantum_circuit.u(snake_case_						,snake_case_						,snake_case_						,0    )  # Quantum State to teleport
      quantum_circuit.h(1    )  # add hadamard gate
      quantum_circuit.cx(
          1						,2    )  # add control gate with qubit 1 as control and 2 as target.
      quantum_circuit.cx(0						,1    )
      quantum_circuit.h(0    )
      quantum_circuit.cz(0						,2    )  # add control z gate.
      quantum_circuit.cx(1						,2    )
  
      quantum_circuit.measure([2]						,[0]    )  # measure the qubit.
  
      # Simulate the circuit using qasm simulator
      UpperCamelCase				:   Tuple          =							Aer.get_backend("""aer_simulator"""    )
      UpperCamelCase				:   Any          =							execute(snake_case_						,snake_case_						,shots=1_0_0_0    )
  
      return job.result().get_counts(snake_case_    )
  
  
  if __name__ == "__main__":
    print(
        '''Total count for teleported state is: '''
        F'''{quantum_teleportation(np.pi/2, np.pi/2, np.pi/2)}'''
    )
  
  
  
  
  