


'''simple docstring'''
from dataclasses import dataclass
from typing import Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin






@dataclass
class        lowerCAmelCase_  (     UpperCAmelCase	):
       __UpperCAmelCase					=42
       __UpperCAmelCase					=42
       __UpperCAmelCase					=None






class        lowerCAmelCase_  (     UpperCAmelCase      ,							UpperCAmelCase	):
       __UpperCAmelCase					=2






       @register_to_config
       def __init__( self			,     _UpperCamelCase = 0.02			,     _UpperCamelCase = 100			,     _UpperCamelCase = 1.007			,     _UpperCamelCase = 80			,     _UpperCamelCase = 0.05			,     _UpperCamelCase = 50			,     )->      List[str]:
            # standard deviation of the initial noise distribution
            _A				=							sigma_max

            # setable values
            _A				=							None
            _A				=							None
            _A				=							None  # sigma(t_i)






       def   UpperCamelCase						( self			,     _UpperCamelCase			,     _UpperCamelCase = None   )->      torch.FloatTensor:
            return sample






       def   UpperCamelCase						( self			,     _UpperCamelCase			,     _UpperCamelCase = None   )->      int:
            _A				=							num_inference_steps
            _A				=							np.arange(0			,     self.num_inference_steps   )[::-1].copy()
            _A				=							torch.from_numpy(_UpperCamelCase   ).to(_UpperCamelCase   )
            _A				=							[
                (
                    self.config.sigma_max**2
                    * (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
                )
                for i in self.timesteps
            ]
            _A				=							torch.tensor(_UpperCamelCase			,     dtype=torch.floataa			,     device=_UpperCamelCase   )






       def   UpperCamelCase						( self			,     _UpperCamelCase			,     _UpperCamelCase			,     _UpperCamelCase = None   )->      Tuple[torch.FloatTensor, float]:
            if self.config.s_min <= sigma <= self.config.s_max:
                 _A				=							min(self.config.s_churn / self.num_inference_steps			,     2**0.5 - 1   )
            else:
                 _A				=							0

            # sample eps ~ N(0, S_noise^2 * I)
            _A				=							self.config.s_noise * randn_tensor(sample.shape			,     generator=_UpperCamelCase   ).to(sample.device   )
            _A				=							sigma + gamma * sigma
            _A				=							sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

            return sample_hat, sigma_hat






       def   UpperCamelCase						( self			,     _UpperCamelCase			,     _UpperCamelCase			,     _UpperCamelCase			,     _UpperCamelCase			,     _UpperCamelCase = True			,     )->      Union[KarrasVeOutput, Tuple]:
            _A				=							sample_hat + sigma_hat * model_output
            _A				=							(sample_hat - pred_original_sample) / sigma_hat
            _A				=							sample_hat + (sigma_prev - sigma_hat) * derivative

            if not return_dict:
                 return (sample_prev, derivative)

            return KarrasVeOutput(
                prev_sample=_UpperCamelCase			,     derivative=_UpperCamelCase			,     pred_original_sample=_UpperCamelCase   )






       def   UpperCamelCase						( self			,     _UpperCamelCase			,     _UpperCamelCase			,     _UpperCamelCase			,     _UpperCamelCase			,     _UpperCamelCase			,     _UpperCamelCase			,     _UpperCamelCase = True			,     )->      Union[KarrasVeOutput, Tuple]:
            _A				=							sample_prev + sigma_prev * model_output
            _A				=							(sample_prev - pred_original_sample) / sigma_prev
            _A				=							sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)

            if not return_dict:
                 return (sample_prev, derivative)

            return KarrasVeOutput(
                prev_sample=_UpperCamelCase			,     derivative=_UpperCamelCase			,     pred_original_sample=_UpperCamelCase   )






       def   UpperCamelCase						( self			,     _UpperCamelCase			,     _UpperCamelCase			,     _UpperCamelCase   )->      List[Any]:
            raise NotImplementedError()









'''simple docstring'''
import math

import qiskit

def 					lowerCamelCase_ ( __UpperCamelCase					:       int = 1							,    __UpperCamelCase					:       int = 1							,    __UpperCamelCase					:       int = 1		)					->	qiskit.result.counts.Counts:







     """simple docstring"""
     if (
         isinstance(__UpperCamelCase							,    __UpperCamelCase		)
         or isinstance(__UpperCamelCase							,    __UpperCamelCase		)
         or isinstance(__UpperCamelCase							,    __UpperCamelCase		)
     ):
          raise TypeError('inputs must be integers.'		)

     if (input_a < 0) or (input_a < 0) or (carry_in < 0):
          raise ValueError('inputs must be positive.'		)

     if (
         (math.floor(__UpperCamelCase		) != input_a)
         or (math.floor(__UpperCamelCase		) != input_a)
         or (math.floor(__UpperCamelCase		) != carry_in)
     ):
          raise ValueError('inputs must be exact integers.'		)

     if (input_a > 2) or (input_a > 2) or (carry_in > 2):
          raise ValueError('inputs must be less or equal to 2.'		)

     # build registers
     _A				=							qiskit.QuantumRegister(4							,    'qr'		)
     _A				=							qiskit.ClassicalRegister(2							,    'cr'		)
     # list the entries
     _A				=							[input_a, input_a, carry_in]

     _A				=							qiskit.QuantumCircuit(__UpperCamelCase							,    __UpperCamelCase		)

     for i in range(0							,    3		):
          if entry[i] == 2:
               quantum_circuit.h(__UpperCamelCase		)  # for hadamard entries
          elif entry[i] == 1:
               quantum_circuit.x(__UpperCamelCase		)  # for 1 entries
          elif entry[i] == 0:
               quantum_circuit.i(__UpperCamelCase		)  # for 0 entries

    # build the circuit
     quantum_circuit.ccx(0							,    1							,    3		)  # ccx = toffoli gate
     quantum_circuit.cx(0							,    1		)
     quantum_circuit.ccx(1							,    2							,    3		)
     quantum_circuit.cx(1							,    2		)
     quantum_circuit.cx(0							,    1		)

     quantum_circuit.measure([2, 3]							,    __UpperCamelCase		)  # measure the last two qbits

     _A				=							qiskit.Aer.get_backend('aer_simulator'		)
     _A				=							qiskit.execute(__UpperCamelCase							,    __UpperCamelCase							,    shots=1_0_0_0		)

     return job.result().get_counts(__UpperCamelCase		)


if __name__ == "__main__":
    print(F'Total sum count for state is: {quantum_full_adder(1, 1, 1)}')





