






'''simple docstring'''







import numpy as np
import qiskit
def     A__      (					__lowerCAmelCase		:      int = 8 ,     __lowerCAmelCase		:      int | None = None    ):
						lowerCamelCase__								=	np.random.default_rng(seed=__lowerCAmelCase    )

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

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




import logging
from dataclasses import dataclass, field
from pathlib import Path
from typing import Optional, Union

from .generation.configuration_utils import GenerationConfig
from .training_args import TrainingArguments
from .utils import add_start_docstrings


__A			:					Optional[Any]						=			logging.getLogger(__name__)





@dataclass
@add_start_docstrings(TrainingArguments.__doc__		)
class 			_SCREAMING_SNAKE_CASE (   __snake_case		):







   '''simple docstring'''




   lowerCamelCase__				=      field(default=__snake_case					,       metadata={"help": "Whether to use SortishSampler or not."}		)
   lowerCamelCase__				=      field(
       default=__snake_case					,       metadata={"help": "Whether to use generate to calculate generative metrics (ROUGE, BLEU)."}		)
   lowerCamelCase__				=      field(
       default=__snake_case					,       metadata={
           "help": (
               "The `max_length` to use on each evaluation loop when `predict_with_generate=True`. Will default "
               "to the `max_length` value of the model configuration."
           )
       }					,       )
   lowerCamelCase__				=      field(
       default=__snake_case					,       metadata={
           "help": (
               "The `num_beams` to use on each evaluation loop when `predict_with_generate=True`. Will default "
               "to the `num_beams` value of the model configuration."
           )
       }					,       )
   lowerCamelCase__				=      field(
       default=__snake_case					,       metadata={
           "help": "Model id, file path or url pointing to a GenerationConfig json file, to use during prediction."
       }					,       )





   def      _snake_case		(       self					:			Union[str, Any]			):
       SCREAMING_SNAKE_CASE										=   super().to_dict()
       for k, v in d.items():
           if isinstance(__lowerCamelCase     , __lowerCamelCase			):
               SCREAMING_SNAKE_CASE										=   v.to_dict()
       return d