'''simple docstring'''

import torch

from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest







class 				_lowercase     (				UpperCAmelCase__			):


 '''simple docstring'''






 _SCREAMING_SNAKE_CASE     :       int      	=					(EulerDiscreteScheduler,)
 _SCREAMING_SNAKE_CASE     :       Any      	=					10
 def a					(      self   :	Dict      ,       **SCREAMING_SNAKE_CASE__   :	Optional[Any]							)  ->    Dict:
  __lowerCAmelCase											=						{
      """num_train_timesteps""": 11_00,
      """beta_start""": 0.0_0_0_1,
      """beta_end""": 0.0_2,
      """beta_schedule""": """linear""",
  }

  config.update(**SCREAMING_SNAKE_CASE__							)
  return config
 def a					(      self   :	List[str]							)  ->    List[str]:
  for timesteps in [10, 50, 1_00, 10_00]:
   self.check_over_configs(num_train_timesteps=SCREAMING_SNAKE_CASE__							)
 def a					(      self   :	str							)  ->    Optional[Any]:
  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=SCREAMING_SNAKE_CASE__      ,       beta_end=SCREAMING_SNAKE_CASE__							)
 def a					(      self   :	Optional[Any]							)  ->    int:
  for schedule in ["linear", "scaled_linear"]:
   self.check_over_configs(beta_schedule=SCREAMING_SNAKE_CASE__							)
 def a					(      self   :	int							)  ->    Optional[Any]:
  for prediction_type in ["epsilon", "v_prediction"]:
   self.check_over_configs(prediction_type=SCREAMING_SNAKE_CASE__							)
 def a					(      self   :	str							)  ->    str:
  __lowerCAmelCase											=						self.scheduler_classes[0]
  __lowerCAmelCase											=						self.get_scheduler_config()
  __lowerCAmelCase											=						scheduler_class(**SCREAMING_SNAKE_CASE__							)

  scheduler.set_timesteps(self.num_inference_steps							)

  __lowerCAmelCase											=						torch.manual_seed(0							)

  __lowerCAmelCase											=						self.dummy_model()
  __lowerCAmelCase											=						self.dummy_sample_deter * scheduler.init_noise_sigma
  __lowerCAmelCase											=						sample.to(SCREAMING_SNAKE_CASE__							)

  for i, t in enumerate(scheduler.timesteps							):
   __lowerCAmelCase											=						scheduler.scale_model_input(SCREAMING_SNAKE_CASE__      ,       SCREAMING_SNAKE_CASE__							)

   __lowerCAmelCase											=						model(SCREAMING_SNAKE_CASE__      ,       SCREAMING_SNAKE_CASE__							)

   __lowerCAmelCase											=						scheduler.step(SCREAMING_SNAKE_CASE__      ,       SCREAMING_SNAKE_CASE__      ,       SCREAMING_SNAKE_CASE__      ,       generator=SCREAMING_SNAKE_CASE__							)
   __lowerCAmelCase											=						output.prev_sample

  __lowerCAmelCase											=						torch.sum(torch.abs(SCREAMING_SNAKE_CASE__							)							)
  __lowerCAmelCase											=						torch.mean(torch.abs(SCREAMING_SNAKE_CASE__							)							)

  assert abs(result_sum.item() - 1_0.0_8_0_7							) < 1e-2
  assert abs(result_mean.item() - 0.0_1_3_1							) < 1e-3
 def a					(      self   :	Union[str, Any]							)  ->    int:
  __lowerCAmelCase											=						self.scheduler_classes[0]
  __lowerCAmelCase											=						self.get_scheduler_config(prediction_type="""v_prediction"""							)
  __lowerCAmelCase											=						scheduler_class(**SCREAMING_SNAKE_CASE__							)

  scheduler.set_timesteps(self.num_inference_steps							)

  __lowerCAmelCase											=						torch.manual_seed(0							)

  __lowerCAmelCase											=						self.dummy_model()
  __lowerCAmelCase											=						self.dummy_sample_deter * scheduler.init_noise_sigma
  __lowerCAmelCase											=						sample.to(SCREAMING_SNAKE_CASE__							)

  for i, t in enumerate(scheduler.timesteps							):
   __lowerCAmelCase											=						scheduler.scale_model_input(SCREAMING_SNAKE_CASE__      ,       SCREAMING_SNAKE_CASE__							)

   __lowerCAmelCase											=						model(SCREAMING_SNAKE_CASE__      ,       SCREAMING_SNAKE_CASE__							)

   __lowerCAmelCase											=						scheduler.step(SCREAMING_SNAKE_CASE__      ,       SCREAMING_SNAKE_CASE__      ,       SCREAMING_SNAKE_CASE__      ,       generator=SCREAMING_SNAKE_CASE__							)
   __lowerCAmelCase											=						output.prev_sample

  __lowerCAmelCase											=						torch.sum(torch.abs(SCREAMING_SNAKE_CASE__							)							)
  __lowerCAmelCase											=						torch.mean(torch.abs(SCREAMING_SNAKE_CASE__							)							)

  assert abs(result_sum.item() - 0.0_0_0_2							) < 1e-2
  assert abs(result_mean.item() - 2.2_6_7_6e-0_6							) < 1e-3
 def a					(      self   :	Any							)  ->    List[str]:
  __lowerCAmelCase											=						self.scheduler_classes[0]
  __lowerCAmelCase											=						self.get_scheduler_config()
  __lowerCAmelCase											=						scheduler_class(**SCREAMING_SNAKE_CASE__							)

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

  __lowerCAmelCase											=						torch.manual_seed(0							)

  __lowerCAmelCase											=						self.dummy_model()
  __lowerCAmelCase											=						self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
  __lowerCAmelCase											=						sample.to(SCREAMING_SNAKE_CASE__							)

  for t in scheduler.timesteps:
   __lowerCAmelCase											=						scheduler.scale_model_input(SCREAMING_SNAKE_CASE__      ,       SCREAMING_SNAKE_CASE__							)

   __lowerCAmelCase											=						model(SCREAMING_SNAKE_CASE__      ,       SCREAMING_SNAKE_CASE__							)

   __lowerCAmelCase											=						scheduler.step(SCREAMING_SNAKE_CASE__      ,       SCREAMING_SNAKE_CASE__      ,       SCREAMING_SNAKE_CASE__      ,       generator=SCREAMING_SNAKE_CASE__							)
   __lowerCAmelCase											=						output.prev_sample

  __lowerCAmelCase											=						torch.sum(torch.abs(SCREAMING_SNAKE_CASE__							)							)
  __lowerCAmelCase											=						torch.mean(torch.abs(SCREAMING_SNAKE_CASE__							)							)

  assert abs(result_sum.item() - 1_0.0_8_0_7							) < 1e-2
  assert abs(result_mean.item() - 0.0_1_3_1							) < 1e-3





 def a					(      self   :	int							)  ->    Optional[Any]:
  __lowerCAmelCase											=						self.scheduler_classes[0]
  __lowerCAmelCase											=						self.get_scheduler_config()
  __lowerCAmelCase											=						scheduler_class(**SCREAMING_SNAKE_CASE__      ,       use_karras_sigmas=SCREAMING_SNAKE_CASE__							)

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

  __lowerCAmelCase											=						torch.manual_seed(0							)

  __lowerCAmelCase											=						self.dummy_model()
  __lowerCAmelCase											=						self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
  __lowerCAmelCase											=						sample.to(SCREAMING_SNAKE_CASE__							)

  for t in scheduler.timesteps:
   __lowerCAmelCase											=						scheduler.scale_model_input(SCREAMING_SNAKE_CASE__      ,       SCREAMING_SNAKE_CASE__							)

   __lowerCAmelCase											=						model(SCREAMING_SNAKE_CASE__      ,       SCREAMING_SNAKE_CASE__							)

   __lowerCAmelCase											=						scheduler.step(SCREAMING_SNAKE_CASE__      ,       SCREAMING_SNAKE_CASE__      ,       SCREAMING_SNAKE_CASE__      ,       generator=SCREAMING_SNAKE_CASE__							)
   __lowerCAmelCase											=						output.prev_sample

  __lowerCAmelCase											=						torch.sum(torch.abs(SCREAMING_SNAKE_CASE__							)							)
  __lowerCAmelCase											=						torch.mean(torch.abs(SCREAMING_SNAKE_CASE__							)							)

  assert abs(result_sum.item() - 1_2_4.5_2_2_9_9_4_9_9_5_1_1_7_1_9							) < 1e-2
  assert abs(result_mean.item() - 0.1_6_2_1_3_9_3_2_6_3_3_3_9_9_9_6_3							) < 1e-3







'''simple docstring'''

import numpy as np
import qiskit







def      UpperCamelCase_						(					snake_case_       :       int = 8						,		snake_case_       :       int | None = None      )					->     str:






 '''simple docstring'''

 __lowerCAmelCase											=						np.random.default_rng(seed=snake_case_      )

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

 # Quantum Circuit to simulate BB84
 __lowerCAmelCase											=						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.
 __lowerCAmelCase											=						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.
 __lowerCAmelCase											=						qiskit.execute(snake_case_						,		snake_case_						,		shots=1						,		seed_simulator=snake_case_      )
 # Returns the result of measurement.
 __lowerCAmelCase											=						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.
 __lowerCAmelCase											=						"""""".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.
 __lowerCAmelCase											=						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()






