


'''simple docstring'''
# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch

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






@dataclass
class        lowerCAmelCase_  (     UpperCAmelCase	):
       __UpperCAmelCase					=42
       __UpperCAmelCase					=42






class        lowerCAmelCase_  (     UpperCAmelCase      ,							UpperCAmelCase	):
       __UpperCAmelCase					=1






       @register_to_config
       def __init__( self			,     _UpperCamelCase = 2000			,     _UpperCamelCase = 0.15			,     _UpperCamelCase = 0.01			,     _UpperCamelCase = 1348.0			,     _UpperCamelCase = 1e-5			,     _UpperCamelCase = 1			,     )->      Optional[Any]:
            # standard deviation of the initial noise distribution
            _A				=							sigma_max

            # setable values
            _A				=							None

            self.set_sigmas(_UpperCamelCase			,     _UpperCamelCase			,     _UpperCamelCase			,     _UpperCamelCase   )






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






       def   UpperCamelCase						( self			,     _UpperCamelCase			,     _UpperCamelCase = None			,     _UpperCamelCase = None   )->      Any:
            _A				=							sampling_eps if sampling_eps is not None else self.config.sampling_eps

            _A				=							torch.linspace(1			,     _UpperCamelCase			,     _UpperCamelCase			,     device=_UpperCamelCase   )






       def   UpperCamelCase						( self			,     _UpperCamelCase			,     _UpperCamelCase = None			,     _UpperCamelCase = None			,     _UpperCamelCase = None   )->      Any:
            _A				=							sigma_min if sigma_min is not None else self.config.sigma_min
            _A				=							sigma_max if sigma_max is not None else self.config.sigma_max
            _A				=							sampling_eps if sampling_eps is not None else self.config.sampling_eps
            if self.timesteps is None:
                 self.set_timesteps(_UpperCamelCase			,     _UpperCamelCase   )

            _A				=							sigma_min * (sigma_max / sigma_min) ** (self.timesteps / sampling_eps)
            _A				=							torch.exp(torch.linspace(math.log(_UpperCamelCase   )			,     math.log(_UpperCamelCase   )			,     _UpperCamelCase   )   )
            _A				=							torch.tensor([sigma_min * (sigma_max / sigma_min) ** t for t in self.timesteps]   )






       def   UpperCamelCase						( self			,     _UpperCamelCase			,     _UpperCamelCase   )->      Dict:
            return torch.where(
                timesteps == 0			,     torch.zeros_like(t.to(timesteps.device   )   )			,     self.discrete_sigmas[timesteps - 1].to(timesteps.device   )			,     )






       def   UpperCamelCase						( self			,     _UpperCamelCase			,     _UpperCamelCase			,     _UpperCamelCase			,     _UpperCamelCase = None			,     _UpperCamelCase = True			,     )->      Union[SdeVeOutput, Tuple]:
            if self.timesteps is None:
                 raise ValueError(
                     '`self.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler'   )

            _A				=							timestep * torch.ones(
                sample.shape[0]			,     device=sample.device   )  # torch.repeat_interleave(timestep, sample.shape[0])
            _A				=							(timestep * (len(self.timesteps   ) - 1)).long()

            # mps requires indices to be in the same device, so we use cpu as is the default with cuda
            _A				=							timesteps.to(self.discrete_sigmas.device   )

            _A				=							self.discrete_sigmas[timesteps].to(sample.device   )
            _A				=							self.get_adjacent_sigma(_UpperCamelCase			,     _UpperCamelCase   ).to(sample.device   )
            _A				=							torch.zeros_like(_UpperCamelCase   )
            _A				=							(sigma**2 - adjacent_sigma**2) ** 0.5

            # equation 6 in the paper: the model_output modeled by the network is grad_x log pt(x)
            # also equation 47 shows the analog from SDE models to ancestral sampling methods
            _A				=							diffusion.flatten()
            while len(diffusion.shape   ) < len(sample.shape   ):
                 _A				=							diffusion.unsqueeze(-1   )
            _A				=							drift - diffusion**2 * model_output

            #  equation 6: sample noise for the diffusion term of
            _A				=							randn_tensor(
                sample.shape			,     layout=sample.layout			,     generator=_UpperCamelCase			,     device=sample.device			,     dtype=sample.dtype   )
            _A				=							sample - drift  # subtract because `dt` is a small negative timestep
            # TODO is the variable diffusion the correct scaling term for the noise?
            _A				=							prev_sample_mean + diffusion * noise  # add impact of diffusion field g

            if not return_dict:
                 return (prev_sample, prev_sample_mean)

            return SdeVeOutput(prev_sample=_UpperCamelCase			,     prev_sample_mean=_UpperCamelCase   )






       def   UpperCamelCase						( self			,     _UpperCamelCase			,     _UpperCamelCase			,     _UpperCamelCase = None			,     _UpperCamelCase = True			,     )->      Union[SchedulerOutput, Tuple]:
            if self.timesteps is None:
                 raise ValueError(
                     '`self.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler'   )

            # For small batch sizes, the paper "suggest replacing norm(z) with sqrt(d), where d is the dim. of z"
            # sample noise for correction
            _A				=							randn_tensor(sample.shape			,     layout=sample.layout			,     generator=_UpperCamelCase   ).to(sample.device   )

            # compute step size from the model_output, the noise, and the snr
            _A				=							torch.norm(model_output.reshape(model_output.shape[0]			,     -1   )			,     dim=-1   ).mean()
            _A				=							torch.norm(noise.reshape(noise.shape[0]			,     -1   )			,     dim=-1   ).mean()
            _A				=							(self.config.snr * noise_norm / grad_norm) ** 2 * 2
            _A				=							step_size * torch.ones(sample.shape[0]   ).to(sample.device   )
            # self.repeat_scalar(step_size, sample.shape[0])

            # compute corrected sample: model_output term and noise term
            _A				=							step_size.flatten()
            while len(step_size.shape   ) < len(sample.shape   ):
                 _A				=							step_size.unsqueeze(-1   )
            _A				=							sample + step_size * model_output
            _A				=							prev_sample_mean + ((step_size * 2) ** 0.5) * noise

            if not return_dict:
                 return (prev_sample,)

            return SchedulerOutput(prev_sample=_UpperCamelCase   )






       def   UpperCamelCase						( self			,     _UpperCamelCase			,     _UpperCamelCase			,     _UpperCamelCase			,     )->      torch.FloatTensor:
            # Make sure sigmas and timesteps have the same device and dtype as original_samples
            _A				=							timesteps.to(original_samples.device   )
            _A				=							self.discrete_sigmas.to(original_samples.device   )[timesteps]
            _A				=							(
                noise * sigmas[:, None, None, None]
                if noise is not None
                else torch.randn_like(_UpperCamelCase   ) * sigmas[:, None, None, None]
            )
            _A				=							noise + original_samples
            return noisy_samples






       def __len__( self   )->      Any:
            return self.config.num_train_timesteps









'''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)}')





