






'''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()




'''simple docstring'''







from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


__lowerCamelCase									=     logging.get_logger(__name__)

__lowerCamelCase									=     {
    '''microsoft/resnet-50''': '''https://huggingface.co/microsoft/resnet-50/blob/main/config.json''',
}


class        A__   (   _snake_case    ,    _snake_case      ):
    lowercase						       =		"resnet"
    lowercase						       =		["basic", "bottleneck"]



    def __init__(       self		,					UpperCamelCase__=3		,					UpperCamelCase__=64		,					UpperCamelCase__=[256, 512, 1024, 2048]		,					UpperCamelCase__=[3, 4, 6, 3]		,					UpperCamelCase__="bottleneck"		,					UpperCamelCase__="relu"		,					UpperCamelCase__=False		,					UpperCamelCase__=None		,					UpperCamelCase__=None		,					**UpperCamelCase__		,					)    ->   List[str]:





           '''simple docstring'''
           super().__init__(**UpperCamelCase__							)
           if layer_type not in self.layer_types:
                  raise ValueError(f'''layer_type={layer_type} is not one of {",".join(self.layer_types							)}'''							)
           A_						       =   num_channels
           A_						       =   embedding_size
           A_						       =   hidden_sizes
           A_						       =   depths
           A_						       =   layer_type
           A_						       =   hidden_act
           A_						       =   downsample_in_first_stage
           A_						       =   ["""stem"""] + [f'''stage{idx}''' for idx in range(1		,					len(UpperCamelCase__							) + 1							)]
           A_       ,      A_						       =   get_aligned_output_features_output_indices(
               out_features=UpperCamelCase__		,					out_indices=UpperCamelCase__		,					stage_names=self.stage_names							)


class        A__   (   _snake_case      ):
    lowercase						       =		version.parse("1.11"      )



    @property
    def        snake_case_      (       self							)    ->   Mapping[str, Mapping[int, str]]:





           '''simple docstring'''
           return OrderedDict(
               [
                   ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
               ]							)







    @property
    def        snake_case_      (       self							)    ->   float:





           '''simple docstring'''
           return 1e-3
