

'''simple docstring'''
import numpy as np
import qiskit


def 			_lowerCAmelCase   (		_UpperCamelCase		:	int = 8				,					_UpperCamelCase		:	int | None = None							) ->     str:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE				       =np.random.default_rng(seed=__lowercase							)

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

     # Quantum Circuit to simulate BB84
     _SCREAMING_SNAKE_CASE				       =qiskit.QuantumCircuit(__lowercase				,					name='BB84'							)

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

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

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


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

     testmod()








from ...configuration_utils import PretrainedConfig
from ...utils import logging


_UpperCamelCase			:	Optional[int]								=logging.get_logger(__name__)

_UpperCamelCase			:	Tuple								={
    'google/canine-s': 'https://huggingface.co/google/canine-s/resolve/main/config.json',
    # See all CANINE models at https://huggingface.co/models?filter=canine
}






class 						UpperCAmelCase__ (							__snake_case							):
		__snake_case      :       Any     =   "canine"







		def __init__(					self       ,A__=768       ,A__=12       ,A__=12       ,A__=3072       ,A__="gelu"       ,A__=0.1       ,A__=0.1       ,A__=16384       ,A__=16       ,A__=0.02       ,A__=1E-12       ,A__=0       ,A__=0xE_000       ,A__=0xE_001       ,A__=4       ,A__=4       ,A__=8       ,A__=16384       ,A__=128       ,**A__       ,):
						super().__init__(pad_token_id=A__       ,bos_token_id=A__       ,eos_token_id=A__       ,**A__		)

						_A	:				int   =      max_position_embeddings
						_A	:				Dict   =      hidden_size
						_A	:				List[str]   =      num_hidden_layers
						_A	:				Optional[Any]   =      num_attention_heads
						_A	:				Optional[Any]   =      intermediate_size
						_A	:				Optional[Any]   =      hidden_act
						_A	:				str   =      hidden_dropout_prob
						_A	:				Optional[int]   =      attention_probs_dropout_prob
						_A	:				Union[str, Any]   =      initializer_range
						_A	:				Dict   =      type_vocab_size
						_A	:				Optional[Any]   =      layer_norm_eps

						# Character config:
						_A	:				Union[str, Any]   =      downsampling_rate
						_A	:				List[str]   =      upsampling_kernel_size
						_A	:				str   =      num_hash_functions
						_A	:				Optional[Any]   =      num_hash_buckets
						_A	:				Optional[Any]   =      local_transformer_stride



