


'''simple docstring'''




import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest




class 			_lowercase (	__lowercase							):
   _SCREAMING_SNAKE_CASE  :  Tuple          =	(DDPMScheduler,)
   def a  (						self       :      Any       ,   **SCREAMING_SNAKE_CASE_       :      Optional[Any]      ) ->							Tuple:
        __snake_case           =						{
            'num_train_timesteps': 1000,
            'beta_start': 0.0_0_0_1,
            'beta_end': 0.0_2,
            'beta_schedule': 'linear',
            'variance_type': 'fixed_small',
            'clip_sample': True,
        }

        config.update(**SCREAMING_SNAKE_CASE_      )
        return config
   def a  (						self       :      List[Any]      ) ->							Dict:
        for timesteps in [1, 5, 100, 1000]:
             self.check_over_configs(num_train_timesteps=SCREAMING_SNAKE_CASE_      )
   def a  (						self       :      Tuple      ) ->							List[Any]:
        for beta_start, beta_end in zip([0.0_0_0_1, 0.0_0_1, 0.0_1, 0.1]       ,   [0.0_0_2, 0.0_2, 0.2, 2]      ):
             self.check_over_configs(beta_start=SCREAMING_SNAKE_CASE_       ,   beta_end=SCREAMING_SNAKE_CASE_      )
   def a  (						self       :      Union[str, Any]      ) ->							Dict:
        for schedule in ["linear", "squaredcos_cap_v2"]:
             self.check_over_configs(beta_schedule=SCREAMING_SNAKE_CASE_      )
   def a  (						self       :      int      ) ->							str:
        for variance in ["fixed_small", "fixed_large", "other"]:
             self.check_over_configs(variance_type=SCREAMING_SNAKE_CASE_      )
   def a  (						self       :      Tuple      ) ->							int:
        for clip_sample in [True, False]:
             self.check_over_configs(clip_sample=SCREAMING_SNAKE_CASE_      )
   def a  (						self       :      int      ) ->							Any:
        self.check_over_configs(thresholding=SCREAMING_SNAKE_CASE_      )
        for threshold in [0.5, 1.0, 2.0]:
             for prediction_type in ["epsilon", "sample", "v_prediction"]:
                  self.check_over_configs(
                      thresholding=SCREAMING_SNAKE_CASE_       ,   prediction_type=SCREAMING_SNAKE_CASE_       ,   sample_max_value=SCREAMING_SNAKE_CASE_       ,   )
   def a  (						self       :      List[Any]      ) ->							int:
        for prediction_type in ["epsilon", "sample", "v_prediction"]:
             self.check_over_configs(prediction_type=SCREAMING_SNAKE_CASE_      )
   def a  (						self       :      List[Any]      ) ->							Optional[Any]:
        for t in [0, 500, 999]:
             self.check_over_forward(time_step=SCREAMING_SNAKE_CASE_      )
   def a  (						self       :      str      ) ->							Optional[int]:
        __snake_case           =						self.scheduler_classes[0]
        __snake_case           =						self.get_scheduler_config()
        __snake_case           =						scheduler_class(**SCREAMING_SNAKE_CASE_      )

        assert torch.sum(torch.abs(scheduler._get_variance(0      ) - 0.0      )      ) < 1e-5
        assert torch.sum(torch.abs(scheduler._get_variance(487      ) - 0.0_0_9_7_9      )      ) < 1e-5
        assert torch.sum(torch.abs(scheduler._get_variance(999      ) - 0.0_2      )      ) < 1e-5
   def a  (						self       :      Union[str, Any]      ) ->							int:
        __snake_case           =						self.scheduler_classes[0]
        __snake_case           =						self.get_scheduler_config()
        __snake_case           =						scheduler_class(**SCREAMING_SNAKE_CASE_      )

        __snake_case           =						len(SCREAMING_SNAKE_CASE_      )

        __snake_case           =						self.dummy_model()
        __snake_case           =						self.dummy_sample_deter
        __snake_case           =						torch.manual_seed(0      )

        for t in reversed(range(SCREAMING_SNAKE_CASE_      )      ):
             # 1. predict noise residual
             __snake_case           =						model(SCREAMING_SNAKE_CASE_       ,   SCREAMING_SNAKE_CASE_      )

             # 2. predict previous mean of sample x_t-1
             __snake_case           =						scheduler.step(SCREAMING_SNAKE_CASE_       ,   SCREAMING_SNAKE_CASE_       ,   SCREAMING_SNAKE_CASE_       ,   generator=SCREAMING_SNAKE_CASE_      ).prev_sample

             # if t > 0:
             #     noise = self.dummy_sample_deter
             #     variance = scheduler.get_variance(t) ** (0.5) * noise
             #
             # sample = pred_prev_sample + variance
             __snake_case           =						pred_prev_sample

        __snake_case           =						torch.sum(torch.abs(SCREAMING_SNAKE_CASE_      )      )
        __snake_case           =						torch.mean(torch.abs(SCREAMING_SNAKE_CASE_      )      )

        assert abs(result_sum.item() - 2_5_8.9_6_0_6      ) < 1e-2
        assert abs(result_mean.item() - 0.3_3_7_2      ) < 1e-3
   def a  (						self       :      Any      ) ->							int:
        __snake_case           =						self.scheduler_classes[0]
        __snake_case           =						self.get_scheduler_config(prediction_type='v_prediction'      )
        __snake_case           =						scheduler_class(**SCREAMING_SNAKE_CASE_      )

        __snake_case           =						len(SCREAMING_SNAKE_CASE_      )

        __snake_case           =						self.dummy_model()
        __snake_case           =						self.dummy_sample_deter
        __snake_case           =						torch.manual_seed(0      )

        for t in reversed(range(SCREAMING_SNAKE_CASE_      )      ):
             # 1. predict noise residual
             __snake_case           =						model(SCREAMING_SNAKE_CASE_       ,   SCREAMING_SNAKE_CASE_      )

             # 2. predict previous mean of sample x_t-1
             __snake_case           =						scheduler.step(SCREAMING_SNAKE_CASE_       ,   SCREAMING_SNAKE_CASE_       ,   SCREAMING_SNAKE_CASE_       ,   generator=SCREAMING_SNAKE_CASE_      ).prev_sample

             # if t > 0:
             #     noise = self.dummy_sample_deter
             #     variance = scheduler.get_variance(t) ** (0.5) * noise
             #
             # sample = pred_prev_sample + variance
             __snake_case           =						pred_prev_sample

        __snake_case           =						torch.sum(torch.abs(SCREAMING_SNAKE_CASE_      )      )
        __snake_case           =						torch.mean(torch.abs(SCREAMING_SNAKE_CASE_      )      )

        assert abs(result_sum.item() - 2_0_2.0_2_9_6      ) < 1e-2
        assert abs(result_mean.item() - 0.2_6_3_1      ) < 1e-3
   def a  (						self       :      Union[str, Any]      ) ->							List[Any]:
        __snake_case           =						self.scheduler_classes[0]
        __snake_case           =						self.get_scheduler_config()
        __snake_case           =						scheduler_class(**SCREAMING_SNAKE_CASE_      )

        __snake_case           =						[100, 87, 50, 1, 0]

        scheduler.set_timesteps(timesteps=SCREAMING_SNAKE_CASE_      )

        __snake_case           =						scheduler.timesteps

        for i, timestep in enumerate(SCREAMING_SNAKE_CASE_      ):
             if i == len(SCREAMING_SNAKE_CASE_      ) - 1:
                  __snake_case           =						-1
             else:
                  __snake_case           =						timesteps[i + 1]

             __snake_case           =						scheduler.previous_timestep(SCREAMING_SNAKE_CASE_      )
             __snake_case           =						prev_t.item()

             self.assertEqual(SCREAMING_SNAKE_CASE_       ,   SCREAMING_SNAKE_CASE_      )
   def a  (						self       :      Tuple      ) ->							Tuple:
        __snake_case           =						self.scheduler_classes[0]
        __snake_case           =						self.get_scheduler_config()
        __snake_case           =						scheduler_class(**SCREAMING_SNAKE_CASE_      )

        __snake_case           =						[100, 87, 50, 51, 0]

        with self.assertRaises(SCREAMING_SNAKE_CASE_       ,   msg='`custom_timesteps` must be in descending order.'      ):
             scheduler.set_timesteps(timesteps=SCREAMING_SNAKE_CASE_      )
   def a  (						self       :      Dict      ) ->							Any:
        __snake_case           =						self.scheduler_classes[0]
        __snake_case           =						self.get_scheduler_config()
        __snake_case           =						scheduler_class(**SCREAMING_SNAKE_CASE_      )

        __snake_case           =						[100, 87, 50, 1, 0]
        __snake_case           =						len(SCREAMING_SNAKE_CASE_      )

        with self.assertRaises(SCREAMING_SNAKE_CASE_       ,   msg='Can only pass one of `num_inference_steps` or `custom_timesteps`.'      ):
             scheduler.set_timesteps(num_inference_steps=SCREAMING_SNAKE_CASE_       ,   timesteps=SCREAMING_SNAKE_CASE_      )





   def a  (						self       :      int      ) ->							str:
        __snake_case           =						self.scheduler_classes[0]
        __snake_case           =						self.get_scheduler_config()
        __snake_case           =						scheduler_class(**SCREAMING_SNAKE_CASE_      )

        __snake_case           =						[scheduler.config.num_train_timesteps]

        with self.assertRaises(
            SCREAMING_SNAKE_CASE_       ,   msg='`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}'       ,   ):
             scheduler.set_timesteps(timesteps=SCREAMING_SNAKE_CASE_      )






'''simple docstring'''





import math

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







def snake_case_     (					lowercase__ = 3		):
  if isinstance(lowercase__   ,       lowercase__		):
    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(lowercase__		) != number_of_qubits:
    raise ValueError("number of qubits must be exact integer."		)
  if number_of_qubits > 1_0:
    raise ValueError("number of qubits too large to simulate(>10)."		)

  UpperCAmelCase__      :					Optional[int]				       =							QuantumRegister(lowercase__   ,       "qr"		)
  UpperCAmelCase__      :					List[Any]				       =							ClassicalRegister(lowercase__   ,       "cr"		)

  UpperCAmelCase__      :					List[str]				       =							QuantumCircuit(lowercase__   ,       lowercase__		)

  UpperCAmelCase__      :					Union[str, Any]				       =							number_of_qubits

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

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

  # measure all the qubits
  quantum_circuit.measure(lowercase__   ,       lowercase__		)
  # simulate with 10000 shots
  UpperCAmelCase__      :					Union[str, Any]				       =							Aer.get_backend("qasm_simulator"		)
  UpperCAmelCase__      :					Optional[int]				       =							execute(lowercase__   ,       lowercase__   ,       shots=1_0_0_0_0		)

  return job.result().get_counts(lowercase__		)


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



