
"""simple docstring"""

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


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

class __lowerCAmelCase	(   __SCREAMING_SNAKE_CASE    ,						__SCREAMING_SNAKE_CASE    ):
     lowercase		     =		"maskformer-swin"

     lowercase		     =		{
         "num_attention_heads": "num_heads",
         "num_hidden_layers": "num_layers",
     }





     def __init__(			self			,  __UpperCAmelCase=224			,  __UpperCAmelCase=4			,  __UpperCAmelCase=3			,  __UpperCAmelCase=96			,  __UpperCAmelCase=[2, 2, 6, 2]			,  __UpperCAmelCase=[3, 6, 12, 24]			,  __UpperCAmelCase=7			,  __UpperCAmelCase=4.0			,  __UpperCAmelCase=True			,  __UpperCAmelCase=0.0			,  __UpperCAmelCase=0.0			,  __UpperCAmelCase=0.1			,  __UpperCAmelCase="gelu"			,  __UpperCAmelCase=False			,  __UpperCAmelCase=0.0_2			,  __UpperCAmelCase=1E-5			,  __UpperCAmelCase=None			,  __UpperCAmelCase=None			,  **__UpperCAmelCase			,  ):

      '''simple docstring'''




      super().__init__(**__UpperCAmelCase   )

      __UpperCamelCase       						= image_size
      __UpperCamelCase       						= patch_size
      __UpperCamelCase       						= num_channels
      __UpperCamelCase       						= embed_dim
      __UpperCamelCase       						= depths
      __UpperCamelCase       						= len(__UpperCAmelCase   )
      __UpperCamelCase       						= num_heads
      __UpperCamelCase       						= window_size
      __UpperCamelCase       						= mlp_ratio
      __UpperCamelCase       						= qkv_bias
      __UpperCamelCase       						= hidden_dropout_prob
      __UpperCamelCase       						= attention_probs_dropout_prob
      __UpperCamelCase       						= drop_path_rate
      __UpperCamelCase       						= hidden_act
      __UpperCamelCase       						= use_absolute_embeddings
      __UpperCamelCase       						= layer_norm_eps
      __UpperCamelCase       						= 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
      __UpperCamelCase       						= int(embed_dim * 2 ** (len(__UpperCAmelCase   ) - 1)   )
      __UpperCamelCase       						= ['stem'] + [F'stage{idx}' for idx in range(1			,  len(__UpperCAmelCase   ) + 1   )]
      __UpperCamelCase						,			__UpperCamelCase       						= get_aligned_output_features_output_indices(
          out_features=__UpperCAmelCase			,  out_indices=__UpperCAmelCase			,  stage_names=self.stage_names   )





"""simple docstring"""

import numpy as np
import qiskit






def 							A		(       snake_case   :int = 8       ,      snake_case   :int | None = None	)   ->			str:
 __UpperCamelCase       						= np.random.default_rng(seed=snake_case	)

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

 # Quantum Circuit to simulate BB84
 __UpperCamelCase       						= qiskit.QuantumCircuit(snake_case       ,      name='BB84'	)

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

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

 bbaa_circ.barrier()
 bbaa_circ.measure_all()

 # Simulate the quantum circuit.
 __UpperCamelCase       						= 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       						= qiskit.execute(snake_case       ,      snake_case       ,      shots=1       ,      seed_simulator=snake_case	)
 # Returns the result of measurement.
 __UpperCamelCase       						= job.result().get_counts(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       						= ''.join(
     [
         result_bit
         for alice_basis_bit, bob_basis_bit, result_bit in zip(
             snake_case       ,      snake_case       ,      snake_case	)
         if alice_basis_bit == bob_basis_bit
     ]	)

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


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

      testmod()



