

import numpy as np

_lowerCAmelCase					:			Optional[Any]  				=			[
    ["a", "b", "c", "d", "e"],
    ["f", "g", "h", "i", "k"],
    ["l", "m", "n", "o", "p"],
    ["q", "r", "s", "t", "u"],
    ["v", "w", "x", "y", "z"],
]






class 	__magic_name__    :





			def __init__(		self					)   -> Optional[Any]:




						'''simple docstring'''
						__a        =np.array(__snake_case					)





			def 		__magic_name__    (		self					,  __snake_case					)   -> Any:




						'''simple docstring'''
						__a      ,    __a        =np.where(letter == self.SQUARE					)
						__a        =np.concatenate([indexa + 1, indexa + 1]					)
						return indexes





			def 		__magic_name__    (		self					,  __snake_case					,  __snake_case					)   -> List[Any]:




						'''simple docstring'''
						__a        =self.SQUARE[indexa - 1, indexa - 1]
						return letter





			def 		__magic_name__    (		self					,  __snake_case					)   -> Optional[int]:




						'''simple docstring'''
						__a        =message.lower()
						__a        =message.replace(' '					,  ''					)
						__a        =message.replace('j'					,  'i'					)

						__a        =np.empty((2, len(__snake_case					))					)
						for letter_index in range(len(__snake_case					)					):
									__a        =self.letter_to_numbers(message[letter_index]					)

									__a        =numbers[0]
									__a        =numbers[1]

						__a        =first_step.reshape(2 * len(__snake_case					)					)
						__a        =''
						for numbers_index in range(len(__snake_case					)					):
									__a        =int(second_step[numbers_index * 2]					)
									__a        =int(second_step[(numbers_index * 2) + 1]					)
									__a        =self.numbers_to_letter(__snake_case					,  __snake_case					)
									__a        =encoded_message + letter

						return encoded_message





			def 		__magic_name__    (		self					,  __snake_case					)   -> Tuple:




						'''simple docstring'''
						__a        =message.lower()
						message.replace(' '					,  ''					)
						__a        =np.empty(2 * len(__snake_case					)					)
						for letter_index in range(len(__snake_case					)					):
									__a        =self.letter_to_numbers(message[letter_index]					)
									__a        =numbers[0]
									__a        =numbers[1]

						__a        =first_step.reshape((2, len(__snake_case					))					)
						__a        =''
						for numbers_index in range(len(__snake_case					)					):
									__a        =int(second_step[0, numbers_index]					)
									__a        =int(second_step[1, numbers_index]					)
									__a        =self.numbers_to_letter(__snake_case					,  __snake_case					)
									__a        =decoded_message + letter

						return decoded_message














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    (      )->  Tuple:





						"""simple docstring"""



						raise RuntimeError('CUDA out of memory.'	)





class __lowerCamelCase (      nn.Module):





	"""simple docstring"""






	def __init__(							self    ):
							"""simple docstring"""







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

	def        UpperCamelCase    (							self       ,    UpperCAmelCase    ):
							"""simple docstring"""







							return self.lineara(self.batchnorm(self.lineara(UpperCAmelCase    )    )    )





class __lowerCamelCase (      unittest.TestCase):





	"""simple docstring"""






	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase									=			[]

							@find_executable_batch_size(starting_batch_size=128    )
							def mock_training_loop_function(UpperCAmelCase    ):
													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    ):
							"""simple docstring"""







							_UpperCAmelCase									=			[]

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

							_UpperCAmelCase					, _UpperCAmelCase									=			mock_training_loop_function('hello'    )
							self.assertListEqual(UpperCAmelCase       ,    [128, 64, 32, 16, 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(UpperCAmelCase    ):
													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    ):
							"""simple docstring"""







							@find_executable_batch_size(starting_batch_size=16    )
							def mock_training_loop_function(UpperCAmelCase    ):
													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    ):
							"""simple docstring"""







							@find_executable_batch_size(starting_batch_size=128    )
							def mock_training_loop_function(UpperCAmelCase       ,    UpperCAmelCase       ,    UpperCAmelCase    ):
													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    ):
							"""simple docstring"""







							@find_executable_batch_size(starting_batch_size=16    )
							def mock_training_loop_function(UpperCAmelCase    ):
													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    ):
							"""simple docstring"""







							_UpperCAmelCase									=			torch.cuda.memory_allocated()
							_UpperCAmelCase									=			ModelForTest()
							model.cuda()
							self.assertGreater(torch.cuda.memory_allocated()       ,    UpperCAmelCase    )
							_UpperCAmelCase									=			release_memory(UpperCAmelCase    )
							self.assertEqual(torch.cuda.memory_allocated()       ,    UpperCAmelCase    )
