



'''simple docstring'''





import math

import tensorflow as tf
from packaging import version





def   a_   (		_UpperCAmelCase    :      Dict				)    ->  Optional[int]:
      __snake_case :   Dict													=    tf.convert_to_tensor(_UpperCAmelCase				)
      __snake_case :   Optional[int]													=    0.5 * (1.0 + tf.math.erf(x / tf.cast(tf.sqrt(2.0				) ,x.dtype				)				))

      return x * cdf





def   a_   (		_UpperCAmelCase    :      Any				)    ->  List[Any]:
      __snake_case :   Optional[Any]													=    tf.convert_to_tensor(_UpperCAmelCase				)
      __snake_case :   Optional[int]													=    tf.cast(math.pi ,x.dtype				)
      __snake_case :   Union[str, Any]													=    tf.cast(0.0_4_4_7_1_5 ,x.dtype				)
      __snake_case :   List[Any]													=    0.5 * (1.0 + tf.tanh(tf.sqrt(2.0 / pi				) * (x + coeff * tf.pow(_UpperCAmelCase ,3				))				))

      return x * cdf





def   a_   (		_UpperCAmelCase    :      List[str]				)    ->  List[Any]:
      __snake_case :   List[Any]													=    tf.convert_to_tensor(_UpperCAmelCase				)

      return x * tf.tanh(tf.math.softplus(_UpperCAmelCase				)				)





def   a_   (		_UpperCAmelCase    :      Optional[Any]				)    ->  int:
      __snake_case :   List[Any]													=    tf.convert_to_tensor(_UpperCAmelCase				)
      __snake_case :   int													=    tf.cast(0.0_4_4_7_1_5 ,x.dtype				)
      __snake_case :   str													=    tf.cast(0.7_9_7_8_8_4_5_6_0_8 ,x.dtype				)

      return 0.5 * x * (1.0 + tf.tanh(x * coeffa * (1.0 + coeffa * x * x)				))





def   a_   (		_UpperCAmelCase    :      Tuple				)    ->  Optional[int]:
      __snake_case :   int													=    tf.convert_to_tensor(_UpperCAmelCase				)
      __snake_case :   Dict													=    tf.cast(1.7_0_2 ,x.dtype				)
      return x * tf.math.sigmoid(coeff * x				)





def   a_   (		_UpperCAmelCase    :      Dict				)    ->  Optional[int]:
      return tf.clip_by_value(_gelu(_UpperCAmelCase				) ,-10 ,10				)





def   a_   (		_UpperCAmelCase    :      Union[str, Any] ,_UpperCAmelCase    :      Dict=-1				)    ->  Union[str, Any]:
      __snake_case							,     __snake_case :   Tuple													=    tf.split(_UpperCAmelCase ,2 ,axis=_UpperCAmelCase				)
      return a * tf.math.sigmoid(_UpperCAmelCase				)


if version.parse(tf.version.VERSION) >= version.parse('''2.4'''):





    def   a_   (		_UpperCAmelCase    :      Optional[int]				)    ->  Tuple:
          return tf.keras.activations.gelu(_UpperCAmelCase ,approximate=_UpperCAmelCase				)

    A__    :   Optional[int]           =					tf.keras.activations.gelu





    A__    :   int           =					approximate_gelu_wrap
else:
    A__    :   List[str]           =					_gelu
    A__    :   Dict           =					_gelu_new


A__    :   Union[str, Any]           =					{
    '''gelu''': gelu,
    '''gelu_10''': gelu_aa,
    '''gelu_fast''': gelu_fast,
    '''gelu_new''': gelu_new,
    '''glu''': glu,
    '''mish''': mish,
    '''quick_gelu''': quick_gelu,
    '''relu''': tf.keras.activations.relu,
    '''sigmoid''': tf.keras.activations.sigmoid,
    '''silu''': tf.keras.activations.swish,
    '''swish''': tf.keras.activations.swish,
    '''tanh''': tf.keras.activations.tanh,
}





def   a_   (		_UpperCAmelCase    :      str				)    ->  str:
      if activation_string in ACTaFN:
            return ACTaFN[activation_string]
      else:
            raise KeyError(f'''function {activation_string} not found in ACT2FN mapping {list(ACTaFN.keys()				)}'''				)








'''simple docstring'''





import qiskit





def   a_   (		_UpperCAmelCase    :      int = 2				)    ->  qiskit.result.counts.Counts:
      __snake_case :   Union[str, Any]													=    qubits

      # Using Aer's simulator
      __snake_case :   List[Any]													=    qiskit.Aer.get_backend('aer_simulator'				)

      # Creating a Quantum Circuit acting on the q register
      __snake_case :   Dict													=    qiskit.QuantumCircuit(_UpperCAmelCase ,_UpperCAmelCase				)

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

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

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

      # 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
      __snake_case :   Optional[Any]													=    qiskit.execute(_UpperCAmelCase ,_UpperCAmelCase ,shots=10_00				)

      return job.result().get_counts(_UpperCAmelCase				)


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



