


'''simple docstring'''






import torch

from diffusers import UnCLIPScheduler

from .test_schedulers import SchedulerCommonTest




class 	a__       (							a__						):





							'''simple docstring'''






							lowercase__    :     Any					=   (UnCLIPScheduler,)







							def 						__SCREAMING_SNAKE_CASE							(	self      ,					**lowerCamelCase_     ) ->							Optional[Any]:
														lowerCAmelCase__      =			{
														    '''num_train_timesteps''': 10_00,
														    '''variance_type''': '''fixed_small_log''',
														    '''clip_sample''': True,
														    '''clip_sample_range''': 1.0,
														    '''prediction_type''': '''epsilon''',
														}

														config.update(**lowerCamelCase_     )
														return config







							def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							Tuple:
														for timesteps in [1, 5, 1_00, 10_00]:
																					self.check_over_configs(num_train_timesteps=lowerCamelCase_     )







							def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							Any:
														for variance in ["fixed_small_log", "learned_range"]:
																					self.check_over_configs(variance_type=lowerCamelCase_     )







							def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							Union[str, Any]:
														for clip_sample in [True, False]:
																					self.check_over_configs(clip_sample=lowerCamelCase_     )







							def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							List[str]:
														for clip_sample_range in [1, 5, 10, 20]:
																					self.check_over_configs(clip_sample_range=lowerCamelCase_     )







							def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							Tuple:
														for prediction_type in ["epsilon", "sample"]:
																					self.check_over_configs(prediction_type=lowerCamelCase_     )







							def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							List[Any]:
														for time_step in [0, 5_00, 9_99]:
																					for prev_timestep in [None, 5, 1_00, 2_50, 5_00, 7_50]:
																												if prev_timestep is not None and prev_timestep >= time_step:
																																			continue

																												self.check_over_forward(time_step=lowerCamelCase_      ,					prev_timestep=lowerCamelCase_     )







							def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							str:
														lowerCAmelCase__      =			self.scheduler_classes[0]
														lowerCAmelCase__      =			self.get_scheduler_config(variance_type='''fixed_small_log'''     )
														lowerCAmelCase__      =			scheduler_class(**lowerCamelCase_     )

														assert torch.sum(torch.abs(scheduler._get_variance(0     ) - 1.00_00e-10     )     ) < 1e-5
														assert torch.sum(torch.abs(scheduler._get_variance(4_87     ) - 0.0_549_625     )     ) < 1e-5
														assert torch.sum(torch.abs(scheduler._get_variance(9_99     ) - 0.9_994_987     )     ) < 1e-5







							def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							List[Any]:
														lowerCAmelCase__      =			self.scheduler_classes[0]
														lowerCAmelCase__      =			self.get_scheduler_config(variance_type='''learned_range'''     )
														lowerCAmelCase__      =			scheduler_class(**lowerCamelCase_     )

														lowerCAmelCase__      =			0.5

														assert scheduler._get_variance(1      ,					predicted_variance=lowerCamelCase_     ) - -10.1_712_790 < 1e-5
														assert scheduler._get_variance(4_87      ,					predicted_variance=lowerCamelCase_     ) - -5.7_998_052 < 1e-5
														assert scheduler._get_variance(9_99      ,					predicted_variance=lowerCamelCase_     ) - -0.0_010_011 < 1e-5







							def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							Dict:
														lowerCAmelCase__      =			self.scheduler_classes[0]
														lowerCAmelCase__      =			self.get_scheduler_config()
														lowerCAmelCase__      =			scheduler_class(**lowerCamelCase_     )

														lowerCAmelCase__      =			scheduler.timesteps

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

														for i, t in enumerate(lowerCamelCase_     ):
																					# 1. predict noise residual
																					lowerCAmelCase__      =			model(lowerCamelCase_      ,					lowerCamelCase_     )

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

																					lowerCAmelCase__      =			pred_prev_sample

														lowerCAmelCase__      =			torch.sum(torch.abs(lowerCamelCase_     )     )
														lowerCAmelCase__      =			torch.mean(torch.abs(lowerCamelCase_     )     )

														assert abs(result_sum.item() - 252.2_682_495     ) < 1e-2
														assert abs(result_mean.item() - 0.3_284_743     ) < 1e-3







							def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							Optional[Any]:
														lowerCAmelCase__      =			self.scheduler_classes[0]
														lowerCAmelCase__      =			self.get_scheduler_config()
														lowerCAmelCase__      =			scheduler_class(**lowerCamelCase_     )

														scheduler.set_timesteps(25     )

														lowerCAmelCase__      =			scheduler.timesteps

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

														for i, t in enumerate(lowerCamelCase_     ):
																					# 1. predict noise residual
																					lowerCAmelCase__      =			model(lowerCamelCase_      ,					lowerCamelCase_     )

																					if i + 1 == timesteps.shape[0]:
																												lowerCAmelCase__      =			None
																					else:
																												lowerCAmelCase__      =			timesteps[i + 1]

																					# 2. predict previous mean of sample x_t-1
																					lowerCAmelCase__      =			scheduler.step(
																					    lowerCamelCase_      ,					lowerCamelCase_      ,					lowerCamelCase_      ,					prev_timestep=lowerCamelCase_      ,					generator=lowerCamelCase_     ).prev_sample

																					lowerCAmelCase__      =			pred_prev_sample

														lowerCAmelCase__      =			torch.sum(torch.abs(lowerCamelCase_     )     )
														lowerCAmelCase__      =			torch.mean(torch.abs(lowerCamelCase_     )     )

														assert abs(result_sum.item() - 258.2_044_983     ) < 1e-2
														assert abs(result_mean.item() - 0.3_362_038     ) < 1e-3







							def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							List[Any]:
														pass







							def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							Optional[Any]:
														pass



'''simple docstring'''






import numpy as np
import qiskit


def       _snake_case     (  A = 8				,					A = None )		->					str:
							lowerCAmelCase__      =			np.random.default_rng(seed=A )

							# 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=A )
							# The set of states Alice will prepare.
							lowerCAmelCase__      =			rng.integers(2				,					size=A )
							# Measurement basis for Bob's qubits.
							lowerCAmelCase__      =			rng.integers(2				,					size=A )

							# Quantum Circuit to simulate BB84
							lowerCAmelCase__      =			qiskit.QuantumCircuit(A				,					name='''BB84''' )

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

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

							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(A				,					A				,					shots=1				,					seed_simulator=A )
							# Returns the result of measurement.
							lowerCAmelCase__      =			job.result().get_counts(A ).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(
							            A				,					A				,					A )
							        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(A ) >= key_len else gen_key.ljust(A				,					'''0''' )
							return key


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

		testmod()