



"""simple docstring"""




import numpy as np
import qiskit

def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		int = 8		,    SCREAMING_SNAKE_CASE__      :		int | None = None							)    ->  str:





     '''simple docstring'''

     _UpperCAmelCase     :      Optional[Any]														=       np.random.default_rng(seed=SCREAMING_SNAKE_CASE__							)

     # Roughly 25% of the qubits will contribute to the key.
     # So we take more than we need.
     _UpperCAmelCase     :      Tuple														=       6 * key_len
     # Measurement basis for Alice's qubits.
     _UpperCAmelCase     :      Optional[int]														=       rng.integers(2		,    size=SCREAMING_SNAKE_CASE__							)
     # The set of states Alice will prepare.
     _UpperCAmelCase     :      Any														=       rng.integers(2		,    size=SCREAMING_SNAKE_CASE__							)
     # Measurement basis for Bob's qubits.
     _UpperCAmelCase     :      Union[str, Any]														=       rng.integers(2		,    size=SCREAMING_SNAKE_CASE__							)

     # Quantum Circuit to simulate BB84
     _UpperCAmelCase     :      List[Any]														=       qiskit.QuantumCircuit(SCREAMING_SNAKE_CASE__		,    name="BB84"							)

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

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

     bbaa_circ.barrier()
     bbaa_circ.measure_all()

     # Simulate the quantum circuit.
     _UpperCAmelCase     :      str														=       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.
     _UpperCAmelCase     :      int														=       qiskit.execute(SCREAMING_SNAKE_CASE__		,    SCREAMING_SNAKE_CASE__		,    shots=1		,    seed_simulator=SCREAMING_SNAKE_CASE__							)
     # Returns the result of measurement.
     _UpperCAmelCase     :      Optional[int]														=       job.result().get_counts(SCREAMING_SNAKE_CASE__							).most_frequent()

     # Extracting the generated key from the simulation results.
     # Only keep measurement results where Alice and Bob chose the same basis.
     _UpperCAmelCase     :      List[str]														=       "".join(
         [
             result_bit
             for alice_basis_bit, bob_basis_bit, result_bit in zip(
                 SCREAMING_SNAKE_CASE__		,    SCREAMING_SNAKE_CASE__		,    SCREAMING_SNAKE_CASE__							)
             if alice_basis_bit == bob_basis_bit
         ]							)

     # Get final key. Pad with 0 if too short, otherwise truncate.
     _UpperCAmelCase     :      Tuple														=       gen_key[:key_len] if len(SCREAMING_SNAKE_CASE__							) >= key_len else gen_key.ljust(SCREAMING_SNAKE_CASE__		,    "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 ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


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

_lowerCAmelCase							:   Any				=				{
    "roberta-base": "https://huggingface.co/roberta-base/resolve/main/config.json",
    "roberta-large": "https://huggingface.co/roberta-large/resolve/main/config.json",
    "roberta-large-mnli": "https://huggingface.co/roberta-large-mnli/resolve/main/config.json",
    "distilroberta-base": "https://huggingface.co/distilroberta-base/resolve/main/config.json",
    "roberta-base-openai-detector": "https://huggingface.co/roberta-base-openai-detector/resolve/main/config.json",
    "roberta-large-openai-detector": "https://huggingface.co/roberta-large-openai-detector/resolve/main/config.json",
}






class       UpperCAmelCase_    (							_UpperCamelCase		):
      __SCREAMING_SNAKE_CASE		:  Optional[int]						=					'roberta'



      def __init__(	self	:      str	,      A	:      Optional[int]=5_0_2_6_5	,      A	:      Optional[int]=7_6_8	,      A	:      Tuple=1_2	,      A	:      Optional[Any]=1_2	,      A	:      Optional[int]=3_0_7_2	,      A	:      str="gelu"	,      A	:      List[Any]=0.1	,      A	:      Optional[Any]=0.1	,      A	:      Any=5_1_2	,      A	:      Dict=2	,      A	:      Union[str, Any]=0.02	,      A	:      str=1e-12	,      A	:      Optional[int]=1	,      A	:      List[str]=0	,      A	:      int=2	,      A	:      Any="absolute"	,      A	:      Optional[int]=True	,      A	:      int=None	,      **A	:      str	,      ):
           super().__init__(pad_token_id=A	,      bos_token_id=A	,      eos_token_id=A	,      **A		)

           _UpperCAmelCase     :      str														=       vocab_size
           _UpperCAmelCase     :      Dict														=       hidden_size
           _UpperCAmelCase     :      List[str]														=       num_hidden_layers
           _UpperCAmelCase     :      Any														=       num_attention_heads
           _UpperCAmelCase     :      List[str]														=       hidden_act
           _UpperCAmelCase     :      Any														=       intermediate_size
           _UpperCAmelCase     :      Union[str, Any]														=       hidden_dropout_prob
           _UpperCAmelCase     :      str														=       attention_probs_dropout_prob
           _UpperCAmelCase     :      Tuple														=       max_position_embeddings
           _UpperCAmelCase     :      Union[str, Any]														=       type_vocab_size
           _UpperCAmelCase     :      str														=       initializer_range
           _UpperCAmelCase     :      int														=       layer_norm_eps
           _UpperCAmelCase     :      Tuple														=       position_embedding_type
           _UpperCAmelCase     :      int														=       use_cache
           _UpperCAmelCase     :      Dict														=       classifier_dropout







class       UpperCAmelCase_    (							_UpperCamelCase		):



      @property
      def 		snake_case_   (	self	:      Tuple		):
           if self.task == "multiple-choice":
                _UpperCAmelCase     :      Optional[Any]														=       {0: "batch", 1: "choice", 2: "sequence"}
           else:
                _UpperCAmelCase     :      List[Any]														=       {0: "batch", 1: "sequence"}
           return OrderedDict(
               [
                   ("input_ids", dynamic_axis),
                   ("attention_mask", dynamic_axis),
               ]		)
