

import unittest
from dataclasses import dataclass

import pytest

from accelerate.commands.config.config_args import SageMakerConfig
from accelerate.utils import ComputeEnvironment
from accelerate.utils.launch import _convert_nargs_to_dict







@dataclass
class 						__magic_name__			(							lowerCamelCase__							):

   '''simple docstring'''




   lowerCamelCase__ :	Optional[int]    =   ComputeEnvironment.AMAZON_SAGEMAKER
   lowerCamelCase__ :	Dict    =   True
   lowerCamelCase__ :	str    =   'ml.p3.2xlarge'
   lowerCamelCase__ :	List[Any]    =   'accelerate_sagemaker_execution_role'
   lowerCamelCase__ :	List[Any]    =   'hf-sm'
   lowerCamelCase__ :	List[str]    =   'us-east-1'
   lowerCamelCase__ :	str    =   1
   lowerCamelCase__ :	Tuple    =   'accelerate-sagemaker-1'
   lowerCamelCase__ :	Optional[Any]    =   '1.6'
   lowerCamelCase__ :	Any    =   '4.4'
   lowerCamelCase__ :	str    =   'train.py'
   lowerCamelCase__ :	List[Any]    =   [
       '--model_name_or_path',
       'bert',
       '--do_train',
       'False',
       '--epochs',
       '3',
       '--learning_rate',
       '5e-5',
       '--max_steps',
       '50.5',
   ]
   lowerCamelCase__ :	Optional[Any]    =   [
       '--model_name_or_path',
       'bert',
       '--do_train',
       '--do_test',
       'False',
       '--do_predict',
       '--epochs',
       '3',
       '--learning_rate',
       '5e-5',
       '--max_steps',
       '50.5',
   ]







class 						__magic_name__			(							unittest.TestCase							):

   '''simple docstring'''




   def  _UpperCAmelCase (				self       )							->    int:






     """simple docstring"""






     a__  =_convert_nargs_to_dict(MockLaunchConfig.success_training_script_args       )
     assert isinstance(converted_args['''model_name_or_path'''],    lowercase_       )
     assert isinstance(converted_args['''do_train'''],    lowercase_       )
     assert isinstance(converted_args['''epochs'''],    lowercase_       )
     assert isinstance(converted_args['''learning_rate'''],    lowercase_       )
     assert isinstance(converted_args['''max_steps'''],    lowercase_       )

     with pytest.raises(lowercase_       ):
       _convert_nargs_to_dict(MockLaunchConfig.fail_training_script_args       )



import math

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




def 			UpperCAmelCase__				(			_A      : int = 3							):





  '''simple docstring'''







  if isinstance(_A	,      _A							):
    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(_A							) != 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).'''							)

  a__  =QuantumRegister(_A	,      '''qr'''							)
  a__  =ClassicalRegister(_A	,      '''cr'''							)

  a__  =QuantumCircuit(_A	,      _A							)

  a__  =number_of_qubits

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

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

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

  return job.result().get_counts(_A							)


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