
"""simple docstring"""




from typing import Optional, Tuple, Union

import torch

from diffusers import DiffusionPipeline, ImagePipelineOutput


class 			__magic_name__       (       _UpperCAmelCase ):
       def __init__(     self ,				A_ ,				A_       )     ->	Any:





         """simple docstring"""




         super().__init__()
         self.register_modules(unet=A_ ,				scheduler=A_       )



       @torch.no_grad()
       def __call__(     self ,				A_ = 1 ,				A_ = None ,				A_ = 50 ,				A_ = "pil" ,				A_ = True ,				**A_ ,				)     ->	Union[ImagePipelineOutput, Tuple]:





         """simple docstring"""




         _lowercase:			Optional[int]       =     torch.randn(
             (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size) ,				generator=A_ ,				)
         _lowercase:			int       =     image.to(self.device       )

         # set step values
         self.scheduler.set_timesteps(A_       )

         for t in self.progress_bar(self.scheduler.timesteps       ):
           # 1. predict noise model_output
           _lowercase:			Tuple       =     self.unet(A_ ,				A_       ).sample

           # 2. predict previous mean of image x_t-1 and add variance depending on eta
           # eta corresponds to η in paper and should be between [0, 1]
           # do x_t -> x_t-1
           _lowercase:			int       =     self.scheduler.step(A_ ,				A_ ,				A_       ).prev_sample

         _lowercase:			Any       =     (image / 2 + 0.5).clamp(0 ,				1       )
         _lowercase:			Optional[Any]       =     image.cpu().permute(0 ,				2 ,				3 ,				1       ).numpy()
         if output_type == "pil":
           _lowercase:			str       =     self.numpy_to_pil(A_       )

         if not return_dict:
           return (image,), "This is a local test"

         return ImagePipelineOutput(images=A_       ), "This is a local test"


"""simple docstring"""




import qiskit




def  _lowerCAmelCase		(		_UpperCamelCase ,				_UpperCamelCase						):






  """simple docstring"""



  _lowercase:			Dict       =     qiskit.Aer.get_backend('''aer_simulator'''						)

  # Create a Quantum Circuit acting on the q register
  _lowercase:			str       =     qiskit.QuantumCircuit(_UpperCamelCase ,				_UpperCamelCase						)

  # Apply X (NOT) Gate to Qubits 0 & 1
  circuit.x(0						)
  circuit.x(1						)

  # Map the quantum measurement to the classical bits
  circuit.measure([0, 1] ,				[0, 1]						)

  # Execute the circuit on the qasm simulator
  _lowercase:			Tuple       =     qiskit.execute(_UpperCamelCase ,				_UpperCamelCase ,				shots=1_000						)

  # Return the histogram data of the results of the experiment.
  return job.result().get_counts(_UpperCamelCase						)


if __name__ == "__main__":
  A__   :					Optional[int]							=   single_qubit_measure(2, 2)
  print(f"""Total count for various states are: {counts}""")
