






'''simple docstring'''





from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowerCamelCase__							=     logging.get_logger(__name__)

lowerCamelCase__							=     {
    'facebook/levit-128S': 'https://huggingface.co/facebook/levit-128S/resolve/main/config.json',
    # See all LeViT models at https://huggingface.co/models?filter=levit
}




class 			snake_case__   (							UpperCamelCase_):

       '''simple docstring'''
       lowerCamelCase							:				int 				= "levit"



       def __init__(							self			,						a__=2_24			,						a__=3			,						a__=3			,						a__=2			,						a__=1			,						a__=16			,						a__=[1_28, 2_56, 3_84]			,						a__=[4, 8, 12]			,						a__=[4, 4, 4]			,						a__=[16, 16, 16]			,						a__=0			,						a__=[2, 2, 2]			,						a__=[2, 2, 2]			,						a__=0.02			,						**a__			,						) ->      Dict:






            '''simple docstring'''





            super().__init__(**a__    )
            __snake_case			:List[Any]          =						image_size
            __snake_case			:Optional[Any]          =						num_channels
            __snake_case			:int          =						kernel_size
            __snake_case			:int          =						stride
            __snake_case			:List[str]          =						padding
            __snake_case			:Union[str, Any]          =						hidden_sizes
            __snake_case			:Any          =						num_attention_heads
            __snake_case			:Union[str, Any]          =						depths
            __snake_case			:Dict          =						key_dim
            __snake_case			:List[Any]          =						drop_path_rate
            __snake_case			:Union[str, Any]          =						patch_size
            __snake_case			:Any          =						attention_ratio
            __snake_case			:Optional[int]          =						mlp_ratio
            __snake_case			:str          =						initializer_range
            __snake_case			:List[str]          =						[
                ['''Subsample''', key_dim[0], hidden_sizes[0] // key_dim[0], 4, 2, 2],
                ['''Subsample''', key_dim[0], hidden_sizes[1] // key_dim[0], 4, 2, 2],
            ]




class 			snake_case__   (							UpperCamelCase_):

       '''simple docstring'''
       lowerCamelCase							:				Dict 				= version.parse("1.11")



       @property
       def 					__lowercase						(							self    ) ->      Mapping[str, Mapping[int, str]]:






            '''simple docstring'''





            return OrderedDict(
                [
                    ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
                ]    )



       @property
       def 					__lowercase						(							self    ) ->      float:






            '''simple docstring'''





            return 1e-4










import numpy as np
import qiskit






def   UpperCamelCase  (       snake_case__     :   int = 8	,snake_case__     :   int | None = None  ):




     '''simple docstring'''






     __snake_case			:Tuple          =						np.random.default_rng(seed=snake_case__  )

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

     # Quantum Circuit to simulate BB84
     __snake_case			:Optional[int]          =						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.
     __snake_case			: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.
     __snake_case			:List[Any]          =						qiskit.execute(snake_case__	,snake_case__	,shots=1	,seed_simulator=snake_case__  )
     # Returns the result of measurement.
     __snake_case			:Tuple          =						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.
     __snake_case			:Any          =						"""""".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.
     __snake_case			:Dict          =						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()


