'''simple docstring'''





import unittest

import torch
from torch import nn

from accelerate.test_utils import require_cuda
from accelerate.utils.memory import find_executable_batch_size, release_memory


def      __lowerCAmelCase    (							)   ->						Optional[int]:





     '''simple docstring'''





     raise RuntimeError('CUDA out of memory.'		)






class 					UpperCAmelCase			(							nn.Module			):




    '''simple docstring'''







    def __init__(							self			)				->			Dict:
         super().__init__()
         SCREAMING_SNAKE_CASE       :      List[Any]     						=							nn.Linear(3     ,				4			)
         SCREAMING_SNAKE_CASE       :      Any     						=							nn.BatchNormad(4			)
         SCREAMING_SNAKE_CASE       :      Tuple     						=							nn.Linear(4     ,				5			)






    def    _UpperCamelCase     (							self     ,				lowercase__			)				->			Any:
         return self.lineara(self.batchnorm(self.lineara(_UpperCamelCase			)			)			)






class 					UpperCAmelCase			(							unittest.TestCase			):




    '''simple docstring'''







    def    _UpperCamelCase     (							self			)				->			str:
         SCREAMING_SNAKE_CASE       :      Optional[int]     						=							[]

         @find_executable_batch_size(starting_batch_size=128			)
         def mock_training_loop_function(lowercase__			):
              nonlocal batch_sizes
              batch_sizes.append(_UpperCamelCase			)
              if batch_size != 8:
                   raise_fake_out_of_memory()

         mock_training_loop_function()
         self.assertListEqual(_UpperCamelCase     ,				[128, 64, 32, 16, 8]			)






    def    _UpperCamelCase     (							self			)				->			List[Any]:
         SCREAMING_SNAKE_CASE       :      Optional[Any]     						=							[]

         @find_executable_batch_size(starting_batch_size=128			)
         def mock_training_loop_function(lowercase__     ,				lowercase__			):
              nonlocal batch_sizes
              batch_sizes.append(_UpperCamelCase			)
              if batch_size != 8:
                   raise_fake_out_of_memory()
              return batch_size, arga

         SCREAMING_SNAKE_CASE						, SCREAMING_SNAKE_CASE       :      int     						=							mock_training_loop_function('hello'			)
         self.assertListEqual(_UpperCamelCase     ,				[128, 64, 32, 16, 8]			)
         self.assertListEqual([bs, arga]     ,				[8, 'hello']			)






    def    _UpperCamelCase     (							self			)				->			int:
         @find_executable_batch_size(starting_batch_size=0			)
         def mock_training_loop_function(lowercase__			):
              pass

         with self.assertRaises(_UpperCamelCase			) as cm:
              mock_training_loop_function()
              self.assertIn('No executable batch size found, reached zero.'     ,				cm.exception.args[0]			)






    def    _UpperCamelCase     (							self			)				->			Optional[int]:
         @find_executable_batch_size(starting_batch_size=16			)
         def mock_training_loop_function(lowercase__			):
              if batch_size > 0:
                   raise_fake_out_of_memory()
              pass

         with self.assertRaises(_UpperCamelCase			) as cm:
              mock_training_loop_function()
              self.assertIn('No executable batch size found, reached zero.'     ,				cm.exception.args[0]			)






    def    _UpperCamelCase     (							self			)				->			int:
         @find_executable_batch_size(starting_batch_size=128			)
         def mock_training_loop_function(lowercase__     ,				lowercase__     ,				lowercase__			):
              if batch_size != 8:
                   raise raise_fake_out_of_memory()

         with self.assertRaises(_UpperCamelCase			) as cm:
              mock_training_loop_function(128     ,				'hello'     ,				'world'			)
              self.assertIn('Batch size was passed into `f`'     ,				cm.exception.args[0]			)
              self.assertIn('`f(arg1=\'hello\', arg2=\'world\')'     ,				cm.exception.args[0]			)






    def    _UpperCamelCase     (							self			)				->			Union[str, Any]:
         @find_executable_batch_size(starting_batch_size=16			)
         def mock_training_loop_function(lowercase__			):
              raise ValueError('Oops, we had an error!'			)

         with self.assertRaises(_UpperCamelCase			) as cm:
              mock_training_loop_function()
              self.assertIn('Oops, we had an error!'     ,				cm.exception.args[0]			)






    @require_cuda
    def    _UpperCamelCase     (							self			)				->			Tuple:
         SCREAMING_SNAKE_CASE       :      Any     						=							torch.cuda.memory_allocated()
         SCREAMING_SNAKE_CASE       :      str     						=							ModelForTest()
         model.cuda()
         self.assertGreater(torch.cuda.memory_allocated()     ,				_UpperCamelCase			)
         SCREAMING_SNAKE_CASE       :      int     						=							release_memory(_UpperCamelCase			)
         self.assertEqual(torch.cuda.memory_allocated()     ,				_UpperCamelCase			)







'''simple docstring'''
import math

import qiskit

def 					lowerCamelCase_ ( __UpperCamelCase					:       int = 1							,    __UpperCamelCase					:       int = 1							,    __UpperCamelCase					:       int = 1		)					->	qiskit.result.counts.Counts:







     """simple docstring"""
     if (
         isinstance(__UpperCamelCase							,    __UpperCamelCase		)
         or isinstance(__UpperCamelCase							,    __UpperCamelCase		)
         or isinstance(__UpperCamelCase							,    __UpperCamelCase		)
     ):
          raise TypeError('inputs must be integers.'		)

     if (input_a < 0) or (input_a < 0) or (carry_in < 0):
          raise ValueError('inputs must be positive.'		)

     if (
         (math.floor(__UpperCamelCase		) != input_a)
         or (math.floor(__UpperCamelCase		) != input_a)
         or (math.floor(__UpperCamelCase		) != carry_in)
     ):
          raise ValueError('inputs must be exact integers.'		)

     if (input_a > 2) or (input_a > 2) or (carry_in > 2):
          raise ValueError('inputs must be less or equal to 2.'		)

     # build registers
     _A				=							qiskit.QuantumRegister(4							,    'qr'		)
     _A				=							qiskit.ClassicalRegister(2							,    'cr'		)
     # list the entries
     _A				=							[input_a, input_a, carry_in]

     _A				=							qiskit.QuantumCircuit(__UpperCamelCase							,    __UpperCamelCase		)

     for i in range(0							,    3		):
          if entry[i] == 2:
               quantum_circuit.h(__UpperCamelCase		)  # for hadamard entries
          elif entry[i] == 1:
               quantum_circuit.x(__UpperCamelCase		)  # for 1 entries
          elif entry[i] == 0:
               quantum_circuit.i(__UpperCamelCase		)  # for 0 entries

    # build the circuit
     quantum_circuit.ccx(0							,    1							,    3		)  # ccx = toffoli gate
     quantum_circuit.cx(0							,    1		)
     quantum_circuit.ccx(1							,    2							,    3		)
     quantum_circuit.cx(1							,    2		)
     quantum_circuit.cx(0							,    1		)

     quantum_circuit.measure([2, 3]							,    __UpperCamelCase		)  # measure the last two qbits

     _A				=							qiskit.Aer.get_backend('aer_simulator'		)
     _A				=							qiskit.execute(__UpperCamelCase							,    __UpperCamelCase							,    shots=1_0_0_0		)

     return job.result().get_counts(__UpperCamelCase		)


if __name__ == "__main__":
    print(F'Total sum count for state is: {quantum_full_adder(1, 1, 1)}')





