




import qiskit

def     _SCREAMING_SNAKE_CASE    (							lowercase			: int = 2  ):


      '''simple docstring'''



      lowerCamelCase_	  =					qubits

      # Using Aer's simulator
      lowerCamelCase_	  =					qiskit.Aer.get_backend('aer_simulator'  )

      # Creating a Quantum Circuit acting on the q register
      lowerCamelCase_	  =					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_	  =					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)}""")









from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCamelCase       :	str									=     logging.get_logger(__name__)

lowerCamelCase       :	List[str]									=     {
    "abeja/gpt-neox-japanese-2.7b": "https://huggingface.co/abeja/gpt-neox-japanese-2.7b/resolve/main/config.json",
}







class 						A(		UpperCamelCase     ):



 '''simple docstring'''


 UpperCamelCase		     =		'''gpt_neox_japanese'''

 def __init__(       self      :						int		,							A_      :						Dict=32000		,							A_      :						List[Any]=2560		,							A_      :						Dict=32		,							A_      :						Union[str, Any]=32		,							A_      :						List[Any]=4		,							A_      :						List[str]="gelu"		,							A_      :						Dict=1.00		,							A_      :						int=10000		,							A_      :						Dict=2048		,							A_      :						Dict=0.02		,							A_      :						Any=1E-5		,							A_      :						Union[str, Any]=True		,							A_      :						int=31996		,							A_      :						List[str]=31999		,							A_      :						List[Any]=0.1		,							A_      :						List[Any]=0.0		,							**A_      :						Tuple		,							)     ->			Dict:

       """simple docstring"""






       super().__init__(bos_token_id=A_		,							eos_token_id=A_		,							**A_						)
       lowerCamelCase_	  =					vocab_size
       lowerCamelCase_	  =					max_position_embeddings
       lowerCamelCase_	  =					hidden_size
       lowerCamelCase_	  =					num_hidden_layers
       lowerCamelCase_	  =					num_attention_heads
       lowerCamelCase_	  =					intermediate_multiple_size
       lowerCamelCase_	  =					hidden_act
       lowerCamelCase_	  =					rotary_pct
       lowerCamelCase_	  =					rotary_emb_base
       lowerCamelCase_	  =					initializer_range
       lowerCamelCase_	  =					layer_norm_eps
       lowerCamelCase_	  =					use_cache
       lowerCamelCase_	  =					attention_dropout
       lowerCamelCase_	  =					hidden_dropout



