

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      A__					(			)					->List[Any]:
      raise RuntimeError('''CUDA out of memory.'''			)





class        lowerCAmelCase__  (					nn.Module			):



       '''simple docstring'''







       def __init__(							self     ):







             '''simple docstring'''



             super().__init__()
             __A    =nn.Linear(3 ,   4     )
             __A    =nn.BatchNormad(4     )
             __A    =nn.Linear(4 ,   5     )


       def     __UpperCamelCase     (							self ,   lowercase__     ):







             '''simple docstring'''



             return self.lineara(self.batchnorm(self.lineara(__a     )     )     )





class        lowerCAmelCase__  (					unittest.TestCase			):



       '''simple docstring'''







       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             __A    =[]

             @find_executable_batch_size(starting_batch_size=1_2_8     )
             def mock_training_loop_function(lowercase__     ):
                   nonlocal batch_sizes
                   batch_sizes.append(__a     )
                   if batch_size != 8:
                         raise_fake_out_of_memory()

             mock_training_loop_function()
             self.assertListEqual(__a ,   [1_2_8, 6_4, 3_2, 1_6, 8]     )

       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             __A    =[]

             @find_executable_batch_size(starting_batch_size=1_2_8     )
             def mock_training_loop_function(lowercase__ ,   lowercase__     ):
                   nonlocal batch_sizes
                   batch_sizes.append(__a     )
                   if batch_size != 8:
                         raise_fake_out_of_memory()
                   return batch_size, arga

             __A    =mock_training_loop_function('''hello'''     )
             self.assertListEqual(__a ,   [1_2_8, 6_4, 3_2, 1_6, 8]     )
             self.assertListEqual([bs, arga] ,   [8, '''hello''']     )

       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             @find_executable_batch_size(starting_batch_size=0     )
             def mock_training_loop_function(lowercase__     ):
                   pass

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

       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             @find_executable_batch_size(starting_batch_size=1_6     )
             def mock_training_loop_function(lowercase__     ):
                   if batch_size > 0:
                         raise_fake_out_of_memory()
                   pass

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

       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             @find_executable_batch_size(starting_batch_size=1_2_8     )
             def mock_training_loop_function(lowercase__ ,   lowercase__ ,   lowercase__     ):
                   if batch_size != 8:
                         raise raise_fake_out_of_memory()

             with self.assertRaises(__a     ) as cm:
                   mock_training_loop_function(1_2_8 ,   '''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     ):







             '''simple docstring'''



             @find_executable_batch_size(starting_batch_size=1_6     )
             def mock_training_loop_function(lowercase__     ):
                   raise ValueError('''Oops, we had an error!'''     )

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


       @require_cuda
       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             __A    =torch.cuda.memory_allocated()
             __A    =ModelForTest()
             model.cuda()
             self.assertGreater(torch.cuda.memory_allocated() ,   __a     )
             __A    =release_memory(__a     )
             self.assertEqual(torch.cuda.memory_allocated() ,   __a     )







'''simple docstring'''





from ..utils import DummyObject, requires_backends



class  snake_case__						(    metaclass=SCREAMING_SNAKE_CASE_				):
 A__				= ['''note_seq''']
 def __init__(   self						:							Tuple      ,     *__a						:							int      ,     **__a						:							List[str]  )      ->   Any:







       '''simple docstring'''
       requires_backends(self      ,     ['note_seq']  )
 @classmethod
 def A_   (   cls						:							Optional[Any]      ,     *__a						:							Optional[Any]      ,     **__a						:							List[str]  )      ->   Union[str, Any]:







       '''simple docstring'''
       requires_backends(cls      ,     ['note_seq']  )





 @classmethod
 def A_   (   cls						:							List[Any]      ,     *__a						:							Union[str, Any]      ,     **__a						:							List[str]  )      ->   Any:







       '''simple docstring'''
       requires_backends(cls      ,     ['note_seq']  )



