





'''simple docstring'''




import math
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
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->UnCLIP
class UpperCAmelCase							(		_a							):
    _A :			str						   =       42
    _A :			Tuple						   =       None







def _lowerCAmelCase     (      _lowerCAmelCase       ,    _lowerCAmelCase=0.999       ,    _lowerCAmelCase="cosine"       ,    )->     int:

 '''simple docstring'''





 if alpha_transform_type == "cosine":

  def alpha_bar_fn(_lowerCAmelCase    ):
   return math.cos((t + 0.008) / 1.008 * math.pi / 2    ) ** 2

 elif alpha_transform_type == "exp":

  def alpha_bar_fn(_lowerCAmelCase    ):
   return math.exp(t * -12.0    )

 else:
  raise ValueError(F'Unsupported alpha_tranform_type: {alpha_transform_type}'    )

 __UpperCAmelCase					=   []
 for i in range(UpperCAmelCase__    ):
  __UpperCAmelCase					=   i / num_diffusion_timesteps
  __UpperCAmelCase					=   (i + 1) / num_diffusion_timesteps
  betas.append(min(1 - alpha_bar_fn(UpperCAmelCase__    ) / alpha_bar_fn(UpperCAmelCase__    )       ,    UpperCAmelCase__    )    )
 return torch.tensor(UpperCAmelCase__       ,    dtype=torch.floataa    )






class UpperCAmelCase							(		_a					,			_a							):



    @register_to_config
    def __init__(  self     ,			__A = 1_000     ,			__A = "fixed_small_log"     ,			__A = True     ,			__A = 1.0     ,			__A = "epsilon"     ,			__A = "squaredcos_cap_v2"     ,			):
     if beta_schedule != "squaredcos_cap_v2":
      raise ValueError('UnCLIPScheduler only supports `beta_schedule`: \'squaredcos_cap_v2\''					)

     __UpperCAmelCase					=   betas_for_alpha_bar(_A					)

     __UpperCAmelCase					=   1.0 - self.betas
     __UpperCAmelCase					=   torch.cumprod(self.alphas     ,			dim=0					)
     __UpperCAmelCase					=   torch.tensor(1.0					)

     # standard deviation of the initial noise distribution
     __UpperCAmelCase					=   1.0

     # setable values
     __UpperCAmelCase					=   None
     __UpperCAmelCase					=   torch.from_numpy(np.arange(0     ,			_A					)[::-1].copy()					)

     __UpperCAmelCase					=   variance_type



    def 					__lowerCamelCase						(  self     ,			__A     ,			__A = None					):
     return sample



    def 					__lowerCamelCase						(  self     ,			__A     ,			__A = None					):
     __UpperCAmelCase					=   num_inference_steps
     __UpperCAmelCase					=   (self.config.num_train_timesteps - 1) / (self.num_inference_steps - 1)
     __UpperCAmelCase					=   (np.arange(0     ,			_A					) * step_ratio).round()[::-1].copy().astype(np.intaa					)
     __UpperCAmelCase					=   torch.from_numpy(_A					).to(_A					)



    def 					__lowerCamelCase						(  self     ,			__A     ,			__A=None     ,			__A=None     ,			__A=None					):
     if prev_timestep is None:
      __UpperCAmelCase					=   t - 1

     __UpperCAmelCase					=   self.alphas_cumprod[t]
     __UpperCAmelCase					=   self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
     __UpperCAmelCase					=   1 - alpha_prod_t
     __UpperCAmelCase					=   1 - alpha_prod_t_prev

     if prev_timestep == t - 1:
      __UpperCAmelCase					=   self.betas[t]
     else:
      __UpperCAmelCase					=   1 - alpha_prod_t / alpha_prod_t_prev

     # For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf)
     # and sample from it to get previous sample
     # x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample
     __UpperCAmelCase					=   beta_prod_t_prev / beta_prod_t * beta

     if variance_type is None:
      __UpperCAmelCase					=   self.config.variance_type

     # hacks - were probably added for training stability
     if variance_type == "fixed_small_log":
      __UpperCAmelCase					=   torch.log(torch.clamp(_A     ,			min=1E-20					)					)
      __UpperCAmelCase					=   torch.exp(0.5 * variance					)
     elif variance_type == "learned_range":
      # NOTE difference with DDPM scheduler
      __UpperCAmelCase					=   variance.log()
      __UpperCAmelCase					=   beta.log()

      __UpperCAmelCase					=   (predicted_variance + 1) / 2
      __UpperCAmelCase					=   frac * max_log + (1 - frac) * min_log

     return variance



    def 					__lowerCamelCase						(  self     ,			__A     ,			__A     ,			__A     ,			__A = None     ,			__A=None     ,			__A = True     ,			):
     __UpperCAmelCase					=   timestep

     if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type == "learned_range":
      __UpperCAmelCase					,    __UpperCAmelCase					=   torch.split(_A     ,			sample.shape[1]     ,			dim=1					)
     else:
      __UpperCAmelCase					=   None

     # 1. compute alphas, betas
     if prev_timestep is None:
      __UpperCAmelCase					=   t - 1

     __UpperCAmelCase					=   self.alphas_cumprod[t]
     __UpperCAmelCase					=   self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
     __UpperCAmelCase					=   1 - alpha_prod_t
     __UpperCAmelCase					=   1 - alpha_prod_t_prev

     if prev_timestep == t - 1:
      __UpperCAmelCase					=   self.betas[t]
      __UpperCAmelCase					=   self.alphas[t]
     else:
      __UpperCAmelCase					=   1 - alpha_prod_t / alpha_prod_t_prev
      __UpperCAmelCase					=   1 - beta

     # 2. compute predicted original sample from predicted noise also called
     # "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
     if self.config.prediction_type == "epsilon":
      __UpperCAmelCase					=   (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
     elif self.config.prediction_type == "sample":
      __UpperCAmelCase					=   model_output
     else:
      raise ValueError(
          f'prediction_type given as {self.config.prediction_type} must be one of `epsilon` or `sample`'
          ' for the UnCLIPScheduler.'					)

     # 3. Clip "predicted x_0"
     if self.config.clip_sample:
      __UpperCAmelCase					=   torch.clamp(
          _A     ,			-self.config.clip_sample_range     ,			self.config.clip_sample_range					)

     # 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
     # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
     __UpperCAmelCase					=   (alpha_prod_t_prev ** 0.5 * beta) / beta_prod_t
     __UpperCAmelCase					=   alpha ** 0.5 * beta_prod_t_prev / beta_prod_t

     # 5. Compute predicted previous sample µ_t
     # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
     __UpperCAmelCase					=   pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

     # 6. Add noise
     __UpperCAmelCase					=   0
     if t > 0:
      __UpperCAmelCase					=   randn_tensor(
          model_output.shape     ,			dtype=model_output.dtype     ,			generator=_A     ,			device=model_output.device					)

      __UpperCAmelCase					=   self._get_variance(
          _A     ,			predicted_variance=_A     ,			prev_timestep=_A     ,			)

      if self.variance_type == "fixed_small_log":
       __UpperCAmelCase					=   variance
      elif self.variance_type == "learned_range":
       __UpperCAmelCase					=   (0.5 * variance).exp()
      else:
       raise ValueError(
           f'variance_type given as {self.variance_type} must be one of `fixed_small_log` or `learned_range`'
           ' for the UnCLIPScheduler.'					)

      __UpperCAmelCase					=   variance * variance_noise

     __UpperCAmelCase					=   pred_prev_sample + variance

     if not return_dict:
      return (pred_prev_sample,)

     return UnCLIPSchedulerOutput(prev_sample=_A     ,			pred_original_sample=_A					)




    def 					__lowerCamelCase						(  self     ,			__A     ,			__A     ,			__A     ,			):
     # Make sure alphas_cumprod and timestep have same device and dtype as original_samples
     __UpperCAmelCase					=   self.alphas_cumprod.to(device=original_samples.device     ,			dtype=original_samples.dtype					)
     __UpperCAmelCase					=   timesteps.to(original_samples.device					)

     __UpperCAmelCase					=   alphas_cumprod[timesteps] ** 0.5
     __UpperCAmelCase					=   sqrt_alpha_prod.flatten()
     while len(sqrt_alpha_prod.shape					) < len(original_samples.shape					):
      __UpperCAmelCase					=   sqrt_alpha_prod.unsqueeze(-1					)

     __UpperCAmelCase					=   (1 - alphas_cumprod[timesteps]) ** 0.5
     __UpperCAmelCase					=   sqrt_one_minus_alpha_prod.flatten()
     while len(sqrt_one_minus_alpha_prod.shape					) < len(original_samples.shape					):
      __UpperCAmelCase					=   sqrt_one_minus_alpha_prod.unsqueeze(-1					)

     __UpperCAmelCase					=   sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
     return noisy_samples












'''simple docstring'''




import math

import numpy as np
import qiskit
from qiskit import Aer, ClassicalRegister, QuantumCircuit, QuantumRegister, execute







def _lowerCAmelCase     (      _lowerCAmelCase = 3    )->     qiskit.result.counts.Counts:
 if isinstance(_lowerCAmelCase       ,    _lowerCAmelCase    ):
  raise TypeError('number of qubits must be a integer.'    )
 if number_of_qubits <= 0:
  raise ValueError('number of qubits must be > 0.'    )
 if math.floor(_lowerCAmelCase    ) != number_of_qubits:
  raise ValueError('number of qubits must be exact integer.'    )
 if number_of_qubits > 10:
  raise ValueError('number of qubits too large to simulate(>10).'    )

 __UpperCAmelCase					=   QuantumRegister(_lowerCAmelCase       ,    'qr'    )
 __UpperCAmelCase					=   ClassicalRegister(_lowerCAmelCase       ,    'cr'    )

 __UpperCAmelCase					=   QuantumCircuit(_lowerCAmelCase       ,    _lowerCAmelCase    )

 __UpperCAmelCase					=   number_of_qubits

 for i in range(_lowerCAmelCase    ):
  quantum_circuit.h(number_of_qubits - i - 1    )
  counter -= 1
  for j in range(_lowerCAmelCase    ):
   quantum_circuit.cp(np.pi / 2 ** (counter - j)       ,    _lowerCAmelCase       ,    _lowerCAmelCase    )

 for k in range(number_of_qubits // 2    ):
  quantum_circuit.swap(_lowerCAmelCase       ,    number_of_qubits - k - 1    )

 # measure all the qubits
 quantum_circuit.measure(_lowerCAmelCase       ,    _lowerCAmelCase    )
 # simulate with 10000 shots
 __UpperCAmelCase					=   Aer.get_backend('qasm_simulator'    )
 __UpperCAmelCase					=   execute(_lowerCAmelCase       ,    _lowerCAmelCase       ,    shots=1_00_00    )

 return job.result().get_counts(_lowerCAmelCase    )


if __name__ == "__main__":
    print(
        F"""Total count for quantum fourier transform state is: \
    {quantum_fourier_transform(3)}"""
    )





