



import math

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





def 		a__			(		_UpperCamelCase			: int = 3							):
   if isinstance(_UpperCamelCase ,_UpperCamelCase							):
      raise TypeError('''number of qubits must be a integer.'''							)
   if number_of_qubits <= 0:
      raise ValueError('''number of qubits must be > 0.'''							)
   if math.floor(_UpperCamelCase							) != number_of_qubits:
      raise ValueError('''number of qubits must be exact integer.'''							)
   if number_of_qubits > 10:
      raise ValueError('''number of qubits too large to simulate(>10).'''							)

   __lowerCamelCase													=    QuantumRegister(_UpperCamelCase ,'''qr'''							)
   __lowerCamelCase													=    ClassicalRegister(_UpperCamelCase ,'''cr'''							)

   __lowerCamelCase													=    QuantumCircuit(_UpperCamelCase ,_UpperCamelCase							)

   __lowerCamelCase													=    number_of_qubits

   for i in range(_UpperCamelCase							):
      quantum_circuit.h(number_of_qubits - i - 1							)
      counter -= 1
      for j in range(_UpperCamelCase							):
         quantum_circuit.cp(np.pi / 2 ** (counter - j) ,_UpperCamelCase ,_UpperCamelCase							)

   for k in range(number_of_qubits // 2							):
      quantum_circuit.swap(_UpperCamelCase ,number_of_qubits - k - 1							)

   # measure all the qubits
   quantum_circuit.measure(_UpperCamelCase ,_UpperCamelCase							)
   # simulate with 10000 shots
   __lowerCamelCase													=    Aer.get_backend('''qasm_simulator'''							)
   __lowerCamelCase													=    execute(_UpperCamelCase ,_UpperCamelCase ,shots=1_00_00							)

   return job.result().get_counts(_UpperCamelCase							)


if __name__ == "__main__":
  print(
      f"Total count for quantum fourier transform state is: \
    {quantum_fourier_transform(3)}"
  )











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


a_			=					logging.get_logger(__name__)

a_			=					{
    """sail/poolformer_s12""": """https://huggingface.co/sail/poolformer_s12/resolve/main/config.json""",
    # See all PoolFormer models at https://huggingface.co/models?filter=poolformer
}



class  __lowerCAmelCase     (	lowerCAmelCase__   ):
 lowerCAmelCase__       					=  """poolformer"""




 def __init__(						self	,				__UpperCAmelCase=3	,				__UpperCAmelCase=16	,				__UpperCAmelCase=16	,				__UpperCAmelCase=3	,				__UpperCAmelCase=4.0	,				__UpperCAmelCase=[2, 2, 6, 2]	,				__UpperCAmelCase=[64, 128, 320, 512]	,				__UpperCAmelCase=[7, 3, 3, 3]	,				__UpperCAmelCase=[4, 2, 2, 2]	,				__UpperCAmelCase=[2, 1, 1, 1]	,				__UpperCAmelCase=4	,				__UpperCAmelCase=0.0	,				__UpperCAmelCase="gelu"	,				__UpperCAmelCase=True	,				__UpperCAmelCase=1E-5	,				__UpperCAmelCase=0.02	,				**__UpperCAmelCase	,				):



    '''simple docstring'''
    __lowerCamelCase													=    num_channels
    __lowerCamelCase													=    patch_size
    __lowerCamelCase													=    stride
    __lowerCamelCase													=    padding
    __lowerCamelCase													=    pool_size
    __lowerCamelCase													=    hidden_sizes
    __lowerCamelCase													=    mlp_ratio
    __lowerCamelCase													=    depths
    __lowerCamelCase													=    patch_sizes
    __lowerCamelCase													=    strides
    __lowerCamelCase													=    num_encoder_blocks
    __lowerCamelCase													=    drop_path_rate
    __lowerCamelCase													=    hidden_act
    __lowerCamelCase													=    use_layer_scale
    __lowerCamelCase													=    layer_scale_init_value
    __lowerCamelCase													=    initializer_range
    super().__init__(**__UpperCAmelCase				)



class  __lowerCAmelCase     (	lowerCAmelCase__   ):
 lowerCAmelCase__       					=  version.parse("""1.11"""   )




 @property
 def        lowerCamelCase      (						self				):



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




 @property
 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    return 2E-3






