
"""simple docstring"""

import torch

from diffusers import DPMSolverSDEScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import require_torchsde

from .test_schedulers import SchedulerCommonTest

@require_torchsde
class __lowerCAmelCase	(   __lowercase    ):
     lowercase		     =		(DPMSolverSDEScheduler,)
     lowercase		     =		10





     def 		UpperCAmelCase      (			self			,  **__UpperCAmelCase   ):

      '''simple docstring'''




      __UpperCamelCase       						= {
          'num_train_timesteps': 1100,
          'beta_start': 0.0_0_0_1,
          'beta_end': 0.0_2,
          'beta_schedule': 'linear',
          'noise_sampler_seed': 0,
      }

      config.update(**_A   )
      return config





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      for timesteps in [10, 50, 100, 1000]:
       self.check_over_configs(num_train_timesteps=_A   )





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      for beta_start, beta_end in zip([0.0_0_0_0_1, 0.0_0_0_1, 0.0_0_1]			,  [0.0_0_0_2, 0.0_0_2, 0.0_2]   ):
       self.check_over_configs(beta_start=_A			,  beta_end=_A   )





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      for schedule in ["linear", "scaled_linear"]:
       self.check_over_configs(beta_schedule=_A   )





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      for prediction_type in ["epsilon", "v_prediction"]:
       self.check_over_configs(prediction_type=_A   )





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      __UpperCamelCase       						= self.scheduler_classes[0]
      __UpperCamelCase       						= self.get_scheduler_config()
      __UpperCamelCase       						= scheduler_class(**_A   )

      scheduler.set_timesteps(self.num_inference_steps   )

      __UpperCamelCase       						= self.dummy_model()
      __UpperCamelCase       						= self.dummy_sample_deter * scheduler.init_noise_sigma
      __UpperCamelCase       						= sample.to(_A   )

      for i, t in enumerate(scheduler.timesteps   ):
       __UpperCamelCase       						= scheduler.scale_model_input(_A			,  _A   )

       __UpperCamelCase       						= model(_A			,  _A   )

       __UpperCamelCase       						= scheduler.step(_A			,  _A			,  _A   )
       __UpperCamelCase       						= output.prev_sample

      __UpperCamelCase       						= torch.sum(torch.abs(_A   )   )
      __UpperCamelCase       						= torch.mean(torch.abs(_A   )   )

      if torch_device in ["mps"]:
       assert abs(result_sum.item() - 1_6_7.4_7_8_2_1_0_4_4_9_2_1_8_7_5   ) < 1E-2
       assert abs(result_mean.item() - 0.2_1_7_8_7_0_5_9_6_4_5_6_5_2_7_7   ) < 1E-3
      elif torch_device in ["cuda"]:
       assert abs(result_sum.item() - 1_7_1.5_9_3_5_2_1_1_1_8_1_6_4_0_6   ) < 1E-2
       assert abs(result_mean.item() - 0.2_2_3_4_2_9_0_6_8_9_2_2_9_9_6_5_2   ) < 1E-3
      else:
       assert abs(result_sum.item() - 1_6_2.5_2_3_8_3_4_2_2_8_5_1_5_6_2   ) < 1E-2
       assert abs(result_mean.item() - 0.2_1_1_6_1_9_5_7_0_8_5_1_3_2_6   ) < 1E-3





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      __UpperCamelCase       						= self.scheduler_classes[0]
      __UpperCamelCase       						= self.get_scheduler_config(prediction_type='v_prediction'   )
      __UpperCamelCase       						= scheduler_class(**_A   )

      scheduler.set_timesteps(self.num_inference_steps   )

      __UpperCamelCase       						= self.dummy_model()
      __UpperCamelCase       						= self.dummy_sample_deter * scheduler.init_noise_sigma
      __UpperCamelCase       						= sample.to(_A   )

      for i, t in enumerate(scheduler.timesteps   ):
       __UpperCamelCase       						= scheduler.scale_model_input(_A			,  _A   )

       __UpperCamelCase       						= model(_A			,  _A   )

       __UpperCamelCase       						= scheduler.step(_A			,  _A			,  _A   )
       __UpperCamelCase       						= output.prev_sample

      __UpperCamelCase       						= torch.sum(torch.abs(_A   )   )
      __UpperCamelCase       						= torch.mean(torch.abs(_A   )   )

      if torch_device in ["mps"]:
       assert abs(result_sum.item() - 1_2_4.7_7_1_4_9_2_0_0_4_3_9_4_5_3   ) < 1E-2
       assert abs(result_mean.item() - 0.1_6_2_2_6_2_8_9_0_1_4_8_1_6_2_8_4   ) < 1E-3
      elif torch_device in ["cuda"]:
       assert abs(result_sum.item() - 1_2_8.1_6_6_3_3_6_0_5_9_5_7_0_3   ) < 1E-2
       assert abs(result_mean.item() - 0.1_6_6_8_8_3_2_6_0_0_1_1_6_7_2_9_7   ) < 1E-3
      else:
       assert abs(result_sum.item() - 1_1_9.8_4_8_7_5_4_8_8_2_8_1_2_5   ) < 1E-2
       assert abs(result_mean.item() - 0.1_5_6_0_5_3_0_6_6_2_5_3_6_6_2_1   ) < 1E-3





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      __UpperCamelCase       						= self.scheduler_classes[0]
      __UpperCamelCase       						= self.get_scheduler_config()
      __UpperCamelCase       						= scheduler_class(**_A   )

      scheduler.set_timesteps(self.num_inference_steps			,  device=_A   )

      __UpperCamelCase       						= self.dummy_model()
      __UpperCamelCase       						= self.dummy_sample_deter.to(_A   ) * scheduler.init_noise_sigma

      for t in scheduler.timesteps:
       __UpperCamelCase       						= scheduler.scale_model_input(_A			,  _A   )

       __UpperCamelCase       						= model(_A			,  _A   )

       __UpperCamelCase       						= scheduler.step(_A			,  _A			,  _A   )
       __UpperCamelCase       						= output.prev_sample

      __UpperCamelCase       						= torch.sum(torch.abs(_A   )   )
      __UpperCamelCase       						= torch.mean(torch.abs(_A   )   )

      if torch_device in ["mps"]:
       assert abs(result_sum.item() - 1_6_7.4_6_9_5_7_3_9_7_4_6_0_9_3_8   ) < 1E-2
       assert abs(result_mean.item() - 0.2_1_8_0_5_9_3_4_6_0_7_9_8_2_6_3_5   ) < 1E-3
      elif torch_device in ["cuda"]:
       assert abs(result_sum.item() - 1_7_1.5_9_3_5_3_6_3_7_6_9_5_3_1_2   ) < 1E-2
       assert abs(result_mean.item() - 0.2_2_3_4_2_9_0_8_3_8_2_4_1_5_7_7_1   ) < 1E-3
      else:
       assert abs(result_sum.item() - 1_6_2.5_2_3_8_3_4_2_2_8_5_1_5_6_2   ) < 1E-2
       assert abs(result_mean.item() - 0.2_1_1_6_1_9_5_7_0_8_5_1_3_2_6   ) < 1E-3







     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      __UpperCamelCase       						= self.scheduler_classes[0]
      __UpperCamelCase       						= self.get_scheduler_config()
      __UpperCamelCase       						= scheduler_class(**_A			,  use_karras_sigmas=_A   )

      scheduler.set_timesteps(self.num_inference_steps			,  device=_A   )

      __UpperCamelCase       						= self.dummy_model()
      __UpperCamelCase       						= self.dummy_sample_deter.to(_A   ) * scheduler.init_noise_sigma
      __UpperCamelCase       						= sample.to(_A   )

      for t in scheduler.timesteps:
       __UpperCamelCase       						= scheduler.scale_model_input(_A			,  _A   )

       __UpperCamelCase       						= model(_A			,  _A   )

       __UpperCamelCase       						= scheduler.step(_A			,  _A			,  _A   )
       __UpperCamelCase       						= output.prev_sample

      __UpperCamelCase       						= torch.sum(torch.abs(_A   )   )
      __UpperCamelCase       						= torch.mean(torch.abs(_A   )   )

      if torch_device in ["mps"]:
       assert abs(result_sum.item() - 1_7_6.6_6_9_7_4_1_3_5_7_4_2_1_8_8   ) < 1E-2
       assert abs(result_mean.item() - 0.2_3_0_0_3_8_7_2_7_3_0_9_8_1_8_1_1   ) < 1E-2
      elif torch_device in ["cuda"]:
       assert abs(result_sum.item() - 1_7_7.6_3_6_5_3_5_6_4_4_5_3_1_2_5   ) < 1E-2
       assert abs(result_mean.item() - 0.2_3_0_0_3_8_7_2_7_3_0_9_8_1_8_1_1   ) < 1E-2
      else:
       assert abs(result_sum.item() - 1_7_0.3_1_3_5_2_2_3_3_8_8_6_7_2   ) < 1E-2
       assert abs(result_mean.item() - 0.2_3_0_0_3_8_7_2_7_3_0_9_8_1_8_1_1   ) < 1E-2





"""simple docstring"""

import numpy as np
import qiskit






def 							A		(       snake_case   :int = 8       ,      snake_case   :int | None = None	)   ->			str:
 __UpperCamelCase       						= np.random.default_rng(seed=snake_case	)

 # Roughly 25% of the qubits will contribute to the key.
 # So we take more than we need.
 __UpperCamelCase       						= 6 * key_len
 # Measurement basis for Alice's qubits.
 __UpperCamelCase       						= rng.integers(2       ,      size=snake_case	)
 # The set of states Alice will prepare.
 __UpperCamelCase       						= rng.integers(2       ,      size=snake_case	)
 # Measurement basis for Bob's qubits.
 __UpperCamelCase       						= rng.integers(2       ,      size=snake_case	)

 # Quantum Circuit to simulate BB84
 __UpperCamelCase       						= qiskit.QuantumCircuit(snake_case       ,      name='BB84'	)

 # Alice prepares her qubits according to rules above.
 for index, _ in enumerate(snake_case	):
  if alice_state[index] == 1:
   bbaa_circ.x(snake_case	)
  if alice_basis[index] == 1:
   bbaa_circ.h(snake_case	)
 bbaa_circ.barrier()

 # Bob measures the received qubits according to rules above.
 for index, _ in enumerate(snake_case	):
  if bob_basis[index] == 1:
   bbaa_circ.h(snake_case	)

 bbaa_circ.barrier()
 bbaa_circ.measure_all()

 # Simulate the quantum circuit.
 __UpperCamelCase       						= qiskit.Aer.get_backend('aer_simulator'	)
 # We only need to run one shot because the key is unique.
 # Multiple shots will produce the same key.
 __UpperCamelCase       						= qiskit.execute(snake_case       ,      snake_case       ,      shots=1       ,      seed_simulator=snake_case	)
 # Returns the result of measurement.
 __UpperCamelCase       						= job.result().get_counts(snake_case	).most_frequent()

 # Extracting the generated key from the simulation results.
 # Only keep measurement results where Alice and Bob chose the same basis.
 __UpperCamelCase       						= ''.join(
     [
         result_bit
         for alice_basis_bit, bob_basis_bit, result_bit in zip(
             snake_case       ,      snake_case       ,      snake_case	)
         if alice_basis_bit == bob_basis_bit
     ]	)

 # Get final key. Pad with 0 if too short, otherwise truncate.
 __UpperCamelCase       						= gen_key[:key_len] if len(snake_case	) >= key_len else gen_key.ljust(snake_case       ,      '0'	)
 return key


if __name__ == "__main__":
      print(f'''The generated key is : {bbaa(8, seed=0)}''')
      from doctest import testmod

      testmod()



