





from ... import PretrainedConfig


_lowerCamelCase			       =      {
    'sijunhe/nezha-cn-base': 'https://huggingface.co/sijunhe/nezha-cn-base/resolve/main/config.json',
}







class 			__A       (      lowerCamelCase__  ):




      """simple docstring"""







      UpperCAmelCase__						=      NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP
      UpperCAmelCase__						=      """nezha"""


      def __init__(		self      ,							a__=2_1128      ,							a__=768      ,							a__=12      ,							a__=12      ,							a__=3072      ,							a__="gelu"      ,							a__=0.1      ,							a__=0.1      ,							a__=512      ,							a__=64      ,							a__=2      ,							a__=0.02      ,							a__=1e-12      ,							a__=0.1      ,							a__=0      ,							a__=2      ,							a__=3      ,							a__=True      ,							**a__      ,							):



             """simple docstring"""





             super().__init__(pad_token_id=a__      ,							bos_token_id=a__      ,							eos_token_id=a__      ,							**a__)

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








import qiskit




def    __UpperCAmelCase(      lowercase_ = 2      ):
       _lowerCamelCase							:     Optional[Any]	=		qubits

       # Using Aer's simulator
       _lowerCamelCase							:     Optional[int]	=		qiskit.Aer.get_backend('''aer_simulator'''      )

       # Creating a Quantum Circuit acting on the q register
       _lowerCamelCase							:     List[str]	=		qiskit.QuantumCircuit(lowercase_ ,    lowercase_      )

       # Adding a H gate on qubit 0 (now q0 in superposition)
       circuit.h(0      )

       for i in range(1 ,    lowercase_      ):
              # Adding CX (CNOT) gate
              circuit.cx(i - 1 ,    lowercase_      )

       # Mapping the quantum measurement to the classical bits
       circuit.measure(list(range(lowercase_      )      ) ,    list(range(lowercase_      )      )      )

       # Now measuring any one qubit would affect other qubits to collapse
       # their super position and have same state as the measured one.

       # Executing the circuit on the simulator
       _lowerCamelCase							:     str	=		qiskit.execute(lowercase_ ,    lowercase_ ,    shots=10_00      )

       return job.result().get_counts(lowercase_      )


if __name__ == "__main__":
       print(F'''Total count for various states are: {quantum_entanglement(3)}''')

