
import numpy as np
import qiskit
from qiskit import Aer, ClassicalRegister, QuantumCircuit, QuantumRegister, execute






def 		SCREAMING_SNAKE_CASE__      ( __lowerCAmelCase = np.pi / 2      ,						__lowerCAmelCase = np.pi / 2      ,						__lowerCAmelCase = np.pi / 2							):
    snake_case__  =			QuantumRegister(3      ,						"qr"							)  # Define the number of quantum bits
    snake_case__  =			ClassicalRegister(1      ,						"cr"							)  # Define the number of classical bits

    snake_case__  =			QuantumCircuit(__lowerCAmelCase      ,						__lowerCAmelCase							)  # Define the quantum circuit.

    # Build the circuit
    quantum_circuit.u(__lowerCAmelCase      ,						__lowerCAmelCase      ,						__lowerCAmelCase      ,						0							)  # Quantum State to teleport
    quantum_circuit.h(1							)  # add hadamard gate
    quantum_circuit.cx(
        1      ,						2							)  # add control gate with qubit 1 as control and 2 as target.
    quantum_circuit.cx(0      ,						1							)
    quantum_circuit.h(0							)
    quantum_circuit.cz(0      ,						2							)  # add control z gate.
    quantum_circuit.cx(1      ,						2							)

    quantum_circuit.measure([2]      ,						[0]							)  # measure the qubit.

    # Simulate the circuit using qasm simulator
    snake_case__  =			Aer.get_backend("aer_simulator"							)
    snake_case__  =			execute(__lowerCAmelCase      ,						__lowerCAmelCase      ,						shots=1_000							)

    return job.result().get_counts(__lowerCAmelCase							)


if __name__ == "__main__":
     print(
         '''Total count for teleported state is: '''
         F'''{quantum_teleportation(np.pi/2, np.pi/2, np.pi/2)}'''
     )





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


__magic_name__           =					logging.get_logger(__name__)

__magic_name__           =					{
    '''MBZUAI/swiftformer-xs''': '''https://huggingface.co/MBZUAI/swiftformer-xs/resolve/main/config.json''',
}




class    _SCREAMING_SNAKE_CASE					( __UpperCamelCase	):
      _A  :						Any      		=       'swiftformer'
      def __init__(     self						,		lowerCamelCase=3						,		lowerCamelCase=[3, 3, 6, 4]						,		lowerCamelCase=[48, 56, 1_12, 2_20]						,		lowerCamelCase=4						,		lowerCamelCase=[True, True, True, True]						,		lowerCamelCase="gelu"						,		lowerCamelCase=3						,		lowerCamelCase=2						,		lowerCamelCase=1						,		lowerCamelCase=0.0						,		lowerCamelCase=True						,		lowerCamelCase=1e-5						,		lowerCamelCase=1e-5						,		**lowerCamelCase						,		):
          super().__init__(**lowerCamelCase	)
          snake_case__  =			num_channels
          snake_case__  =			depths
          snake_case__  =			embed_dims
          snake_case__  =			mlp_ratio
          snake_case__  =			downsamples
          snake_case__  =			hidden_act
          snake_case__  =			down_patch_size
          snake_case__  =			down_stride
          snake_case__  =			down_pad
          snake_case__  =			drop_path_rate
          snake_case__  =			use_layer_scale
          snake_case__  =			layer_scale_init_value
          snake_case__  =			batch_norm_eps





class    _SCREAMING_SNAKE_CASE					( __UpperCamelCase	):
      _A  :						int      		=       version.parse('1.11'	)
      @property
      def A_     (     self	):
          return OrderedDict(
              [
                  ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
              ]	)



      @property
      def A_     (     self	):
          return 1e-4



