

'''simple docstring'''

import numpy as np
import qiskit




def 							_UpperCamelCase				(   lowerCAmelCase__:					int = 8		,lowerCAmelCase__:					int | None = None   )      -> str:
							SCREAMING_SNAKE_CASE_				=				np.random.default_rng(seed=lowerCAmelCase__   )

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

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

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

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

							bbaa_circ.barrier()
							bbaa_circ.measure_all()

							# Simulate the quantum circuit.
							SCREAMING_SNAKE_CASE_				=				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.
							SCREAMING_SNAKE_CASE_				=				qiskit.execute(lowerCAmelCase__		,lowerCAmelCase__		,shots=1		,seed_simulator=lowerCAmelCase__   )
							# Returns the result of measurement.
							SCREAMING_SNAKE_CASE_				=				job.result().get_counts(lowerCAmelCase__   ).most_frequent()

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

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


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

							testmod()








'''simple docstring'''

import inspect
from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel, VQModel
from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput






class    snake_case     (      lowercase_		):






				"""simple docstring"""



				def __init__(    self,     _lowercase,     _lowercase,     _lowercase       )     ->							List[Any]:
											super().__init__()
											self.register_modules(vqvae=_lowercase,     unet=_lowercase,     scheduler=_lowercase       )





				@torch.no_grad()
				def __call__(    self,     _lowercase = 1,     _lowercase = None,     _lowercase = 0.0,     _lowercase = 50,     _lowercase = "pil",     _lowercase = True,     **_lowercase,     )     ->							Union[Tuple, ImagePipelineOutput]:
											SCREAMING_SNAKE_CASE_				=				randn_tensor(
											    (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size),     generator=_lowercase,     )
											SCREAMING_SNAKE_CASE_				=				latents.to(self.device       )

											# scale the initial noise by the standard deviation required by the scheduler
											SCREAMING_SNAKE_CASE_				=				latents * self.scheduler.init_noise_sigma

											self.scheduler.set_timesteps(_lowercase       )

											# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
											SCREAMING_SNAKE_CASE_				=				'eta' in set(inspect.signature(self.scheduler.step       ).parameters.keys()       )

											SCREAMING_SNAKE_CASE_				=				{}
											if accepts_eta:
																		SCREAMING_SNAKE_CASE_				=				eta

											for t in self.progress_bar(self.scheduler.timesteps       ):
																		SCREAMING_SNAKE_CASE_				=				self.scheduler.scale_model_input(_lowercase,     _lowercase       )
																		# predict the noise residual
																		SCREAMING_SNAKE_CASE_				=				self.unet(_lowercase,     _lowercase       ).sample
																		# compute the previous noisy sample x_t -> x_t-1
																		SCREAMING_SNAKE_CASE_				=				self.scheduler.step(_lowercase,     _lowercase,     _lowercase,     **_lowercase       ).prev_sample

											# decode the image latents with the VAE
											SCREAMING_SNAKE_CASE_				=				self.vqvae.decode(_lowercase       ).sample

											SCREAMING_SNAKE_CASE_				=				(image / 2 + 0.5).clamp(0,     1       )
											SCREAMING_SNAKE_CASE_				=				image.cpu().permute(0,     2,     3,     1       ).numpy()
											if output_type == "pil":
																		SCREAMING_SNAKE_CASE_				=				self.numpy_to_pil(_lowercase       )

											if not return_dict:
																		return (image,)

											return ImagePipelineOutput(images=_lowercase       )





