



'''simple docstring'''





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


lowercase_	 =						logging.get_logger(__name__)






class __A    (						A     ,     A		):



 '''simple docstring'''







 __lowerCamelCase					: int    = 'maskformer-swin'

 __lowerCamelCase					: Dict    = {
     'num_attention_heads': 'num_heads',
     'num_hidden_layers': 'num_layers',
 }



 def __init__(self				,     A=224				,     A=4				,     A=3				,     A=96				,     A=[2, 2, 6, 2]				,     A=[3, 6, 12, 24]				,     A=7				,     A=4.0				,     A=True				,     A=0.0				,     A=0.0				,     A=0.1				,     A="gelu"				,     A=False				,     A=0.02				,     A=1E-5				,     A=None				,     A=None				,     **A				,     )					->  Optional[Any]:


      """simple docstring"""



      super().__init__(**A )

      _a											=						image_size
      _a											=						patch_size
      _a											=						num_channels
      _a											=						embed_dim
      _a											=						depths
      _a											=						len(A )
      _a											=						num_heads
      _a											=						window_size
      _a											=						mlp_ratio
      _a											=						qkv_bias
      _a											=						hidden_dropout_prob
      _a											=						attention_probs_dropout_prob
      _a											=						drop_path_rate
      _a											=						hidden_act
      _a											=						use_absolute_embeddings
      _a											=						layer_norm_eps
      _a											=						initializer_range
      # we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
      # this indicates the channel dimension after the last stage of the model
      _a											=						int(embed_dim * 2 ** (len(A ) - 1) )
      _a											=						['''stem'''] + [f'''stage{idx}''' for idx in range(1				,     len(A ) + 1 )]
      _a						,					_a											=						get_aligned_output_features_output_indices(
          out_features=A				,     out_indices=A				,     stage_names=self.stage_names )








'''simple docstring'''





import numpy as np
import qiskit




def lowerCAmelCase						(__A = 8						,			__A = None):



     """simple docstring"""



     _a											=						np.random.default_rng(seed=__A)

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

     # Quantum Circuit to simulate BB84
     _a											=						qiskit.QuantumCircuit(__A						,			name='''BB84''')

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

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

     bbaa_circ.barrier()
     bbaa_circ.measure_all()

     # Simulate the quantum circuit.
     _a											=						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.
     _a											=						qiskit.execute(__A						,			__A						,			shots=1						,			seed_simulator=__A)
     # Returns the result of measurement.
     _a											=						job.result().get_counts(__A).most_frequent()

     # Extracting the generated key from the simulation results.
     # Only keep measurement results where Alice and Bob chose the same basis.
     _a											=						''''''.join(
         [
             result_bit
             for alice_basis_bit, bob_basis_bit, result_bit in zip(
                 __A						,			__A						,			__A)
             if alice_basis_bit == bob_basis_bit
         ])

     # Get final key. Pad with 0 if too short, otherwise truncate.
     _a											=						gen_key[:key_len] if len(__A) >= key_len else gen_key.ljust(__A						,			'''0''')
     return key


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

  testmod()



