






class 				_A			:
    """simple docstring"""

    def __init__(				self      :     int ,     __SCREAMING_SNAKE_CASE      :     str      )      ->							List[str]:
           __UpperCAmelCase     				=n
           __UpperCAmelCase     				=[None] * self.n
           __UpperCAmelCase     				=0  # index of the first element
           __UpperCAmelCase     				=0
           __UpperCAmelCase     				=0



    def __len__(				self      :     Dict      )      ->							int:
           return self.size



    def _a							(				self      :     Any      )      ->							bool:
           return self.size == 0



    def _a							(				self      :     Union[str, Any]      )      ->							Tuple:
           return False if self.is_empty() else self.array[self.front]



    def _a							(				self      :     List[Any] ,     __SCREAMING_SNAKE_CASE      :     Dict      )      ->							Optional[int]:
           if self.size >= self.n:
                  raise Exception("""QUEUE IS FULL"""      )

           __UpperCAmelCase     				=data
           __UpperCAmelCase     				=(self.rear + 1) % self.n
           self.size += 1
           return self



    def _a							(				self      :     str      )      ->							List[str]:
           if self.size == 0:
                  raise Exception("""UNDERFLOW"""      )

           __UpperCAmelCase     				=self.array[self.front]
           __UpperCAmelCase     				=None
           __UpperCAmelCase     				=(self.front + 1) % self.n
           self.size -= 1
           return temp








'''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    _snake_case  (	)		->       Union[str, Any]:
							raise RuntimeError('''CUDA out of memory.'''			)




class 						a__							(				nn.Module    ):





							'''simple docstring'''






							def __init__(						self  )			->				int:
														super().__init__()
														lowerCAmelCase__			  =      nn.Linear(3   ,       4  )
														lowerCAmelCase__			  =      nn.BatchNormad(4  )
														lowerCAmelCase__			  =      nn.Linear(4   ,       5  )







							def     __SCREAMING_SNAKE_CASE				(						self   ,       lowerCamelCase_  )			->				Optional[Any]:
														return self.lineara(self.batchnorm(self.lineara(lowerCamelCase_  )  )  )




class 						a__							(				unittest.TestCase    ):





							'''simple docstring'''






							def     __SCREAMING_SNAKE_CASE				(						self  )			->				str:
														lowerCAmelCase__			  =      []

														@find_executable_batch_size(starting_batch_size=1_28  )
														def mock_training_loop_function(lowerCamelCase_  ):
																					nonlocal batch_sizes
																					batch_sizes.append(lowerCamelCase_  )
																					if batch_size != 8:
																												raise_fake_out_of_memory()

														mock_training_loop_function()
														self.assertListEqual(lowerCamelCase_   ,       [1_28, 64, 32, 16, 8]  )







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Optional[int]:
														lowerCAmelCase__			  =      []

														@find_executable_batch_size(starting_batch_size=1_28  )
														def mock_training_loop_function(lowerCamelCase_   ,       lowerCamelCase_  ):
																					nonlocal batch_sizes
																					batch_sizes.append(lowerCamelCase_  )
																					if batch_size != 8:
																												raise_fake_out_of_memory()
																					return batch_size, arga

														lowerCAmelCase__  ,   lowerCAmelCase__			  =      mock_training_loop_function('''hello'''  )
														self.assertListEqual(lowerCamelCase_   ,       [1_28, 64, 32, 16, 8]  )
														self.assertListEqual([bs, arga]   ,       [8, '''hello''']  )







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Tuple:
														@find_executable_batch_size(starting_batch_size=0  )
														def mock_training_loop_function(lowerCamelCase_  ):
																					pass

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







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Dict:
														@find_executable_batch_size(starting_batch_size=16  )
														def mock_training_loop_function(lowerCamelCase_  ):
																					if batch_size > 0:
																												raise_fake_out_of_memory()
																					pass

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







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Optional[int]:
														@find_executable_batch_size(starting_batch_size=1_28  )
														def mock_training_loop_function(lowerCamelCase_   ,       lowerCamelCase_   ,       lowerCamelCase_  ):
																					if batch_size != 8:
																												raise raise_fake_out_of_memory()

														with self.assertRaises(lowerCamelCase_  ) as cm:
																					mock_training_loop_function(1_28   ,       '''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     __SCREAMING_SNAKE_CASE				(						self  )			->				Union[str, Any]:
														@find_executable_batch_size(starting_batch_size=16  )
														def mock_training_loop_function(lowerCamelCase_  ):
																					raise ValueError('''Oops, we had an error!'''  )

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







							@require_cuda
							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Optional[Any]:
														lowerCAmelCase__			  =      torch.cuda.memory_allocated()
														lowerCAmelCase__			  =      ModelForTest()
														model.cuda()
														self.assertGreater(torch.cuda.memory_allocated()   ,       lowerCamelCase_  )
														lowerCAmelCase__			  =      release_memory(lowerCamelCase_  )
														self.assertEqual(torch.cuda.memory_allocated()   ,       lowerCamelCase_  )