






import numpy as np
import qiskit





def  _lowerCamelCase						(						__lowerCamelCase = 8 ,							__lowerCamelCase = None       ) ->			str:

     '''simple docstring'''
     UpperCAmelCase__      :  Dict				      =			np.random.default_rng(seed=__lowerCamelCase       )

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

     # Quantum Circuit to simulate BB84
     UpperCAmelCase__      :  Any				      =			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.
     UpperCAmelCase__      :  Optional[int]				      =			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__      :  Optional[int]				      =			qiskit.execute(__lowerCamelCase ,							__lowerCamelCase ,							shots=1 ,							seed_simulator=__lowerCamelCase       )
     # Returns the result of measurement.
     UpperCAmelCase__      :  Optional[Any]				      =			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.
     UpperCAmelCase__      :  Any				      =			"""""".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.
     UpperCAmelCase__      :  List[Any]				      =			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 torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import TaBlock, TaConfig, TaLayerNorm

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin







class     lowercase(_lowercase    ,						_lowercase    ,						_lowercase       ):



     @register_to_config
     def __init__(							self     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE = False     ,				)		->     Union[str, Any]:







         """simple docstring"""



         super().__init__()

         a__		=			nn.Embedding(__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE   )

         a__		=			nn.Embedding(__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE   )
         a__		=			False

         a__		=			nn.Dropout(p=__SCREAMING_SNAKE_CASE   )

         a__		=			TaConfig(
             vocab_size=__SCREAMING_SNAKE_CASE     ,				d_model=__SCREAMING_SNAKE_CASE     ,				num_heads=__SCREAMING_SNAKE_CASE     ,				d_kv=__SCREAMING_SNAKE_CASE     ,				d_ff=__SCREAMING_SNAKE_CASE     ,				dropout_rate=__SCREAMING_SNAKE_CASE     ,				feed_forward_proj=__SCREAMING_SNAKE_CASE     ,				is_decoder=__SCREAMING_SNAKE_CASE     ,				is_encoder_decoder=__SCREAMING_SNAKE_CASE     ,				)

         a__		=			nn.ModuleList()
         for lyr_num in range(__SCREAMING_SNAKE_CASE   ):
             a__		=			TaBlock(__SCREAMING_SNAKE_CASE   )
             self.encoders.append(__SCREAMING_SNAKE_CASE   )

         a__		=			TaLayerNorm(__SCREAMING_SNAKE_CASE   )
         a__		=			nn.Dropout(p=__SCREAMING_SNAKE_CASE   )




     def 			lowercase__  (							self     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE   )		->     List[Any]:







         """simple docstring"""



         a__		=			self.token_embedder(__SCREAMING_SNAKE_CASE   )

         a__		=			encoder_input_tokens.shape[1]
         a__		=			torch.arange(__SCREAMING_SNAKE_CASE     ,				device=encoder_input_tokens.device   )
         x += self.position_encoding(__SCREAMING_SNAKE_CASE   )

         a__		=			self.dropout_pre(__SCREAMING_SNAKE_CASE   )

         # inverted the attention mask
         a__		=			encoder_input_tokens.size()
         a__		=			self.get_extended_attention_mask(__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE   )

         for lyr in self.encoders:
             a__		=			lyr(__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE   )[0]
         a__		=			self.layer_norm(__SCREAMING_SNAKE_CASE   )

         return self.dropout_post(__SCREAMING_SNAKE_CASE   ), encoder_inputs_mask


