



"""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_(   )	->       str:
				raise RuntimeError('''CUDA out of memory.'''							)


class 							lowerCamelCase_(   nn.Module    ):





					'''simple docstring'''



					def __init__(    self   ):
									super().__init__()
									_lowerCamelCase        =				nn.Linear(3				,       4   )
									_lowerCamelCase        =				nn.BatchNormad(4   )
									_lowerCamelCase        =				nn.Linear(4				,       5   )





					def 			snake_case__      (    self				,       lowerCamelCase__   ):
									return self.lineara(self.batchnorm(self.lineara(lowerCamelCase__   )   )   )


class 							lowerCamelCase_(   unittest.TestCase    ):





					'''simple docstring'''



					def 			snake_case__      (    self   ):
									_lowerCamelCase        =				[]

									@find_executable_batch_size(starting_batch_size=1_2_8   )
									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_2_8, 6_4, 3_2, 1_6, 8]   )

					def 			snake_case__      (    self   ):
									_lowerCamelCase        =				[]

									@find_executable_batch_size(starting_batch_size=1_2_8   )
									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_2_8, 6_4, 3_2, 1_6, 8]   )
									self.assertListEqual([bs, arga]				,       [8, '''hello''']   )

					def 			snake_case__      (    self   ):
									@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 			snake_case__      (    self   ):
									@find_executable_batch_size(starting_batch_size=1_6   )
									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 			snake_case__      (    self   ):
									@find_executable_batch_size(starting_batch_size=1_2_8   )
									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_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 			snake_case__      (    self   ):
									@find_executable_batch_size(starting_batch_size=1_6   )
									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 			snake_case__      (    self   ):
									_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__   )





"""simple docstring"""
import warnings

from .generation import TFGenerationMixin


class 							lowerCamelCase_(   A__    ):





					'''simple docstring'''



					warnings.warn(
					    'Importing `TFGenerationMixin` from `src/transformers/generation_tf_utils.py` is deprecated and will '
					    'be removed in Transformers v5. Import as `from transformers import TFGenerationMixin` instead.',				A__,				)
