
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__							(							):




			"""simple docstring"""


			raise RuntimeError("CUDA out of memory."							)





class       _lowerCAmelCase   (      nn.Module			):




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




				def 	__a		(   self							,							_UpperCamelCase	)							-> str:
							return self.lineara(self.batchnorm(self.lineara(_UpperCamelCase	)	)	)





class       _lowerCAmelCase   (      unittest.TestCase			):




				def 	__a		(   self	)							-> int:
							lowerCAmelCase_         =      []

							@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 	__a		(   self	)							-> int:
							lowerCAmelCase_         =      []

							@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

							lowerCAmelCase_      ,    lowerCAmelCase_         =      mock_training_loop_function("hello"	)
							self.assertListEqual(_UpperCamelCase							,							[128, 64, 32, 16, 8]	)
							self.assertListEqual([bs, arga]							,							[8, "hello"]	)




				def 	__a		(   self	)							-> Any:
							@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 	__a		(   self	)							-> Tuple:
							@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 	__a		(   self	)							-> List[Any]:
							@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 	__a		(   self	)							-> List[str]:
							@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 	__a		(   self	)							-> List[str]:
							lowerCAmelCase_         =      torch.cuda.memory_allocated()
							lowerCAmelCase_         =      ModelForTest()
							model.cuda()
							self.assertGreater(torch.cuda.memory_allocated()							,							_UpperCamelCase	)
							lowerCAmelCase_         =      release_memory(_UpperCamelCase	)
							self.assertEqual(torch.cuda.memory_allocated()							,							_UpperCamelCase	)





from typing import List

import datasets
from datasets.tasks import AudioClassification

from ..folder_based_builder import folder_based_builder


_A  =	datasets.utils.logging.get_logger(__name__)





class       _lowerCAmelCase   (      folder_based_builder.FolderBasedBuilderConfig			):
				_lowercase     						=None
				_lowercase     						=None





class       _lowerCAmelCase   (      folder_based_builder.FolderBasedBuilder			):
				_lowercase     						=datasets.Audio()
				_lowercase     						='''audio'''
				_lowercase     						=AudioFolderConfig
				_lowercase     						=42  # definition at the bottom of the script
				_lowercase     						=AudioClassification(audio_column='''audio'''   , label_column='''label'''			)


_A  =	[
    ".aiff",
    ".au",
    ".avr",
    ".caf",
    ".flac",
    ".htk",
    ".svx",
    ".mat4",
    ".mat5",
    ".mpc2k",
    ".ogg",
    ".paf",
    ".pvf",
    ".raw",
    ".rf64",
    ".sd2",
    ".sds",
    ".ircam",
    ".voc",
    ".w64",
    ".wav",
    ".nist",
    ".wavex",
    ".wve",
    ".xi",
    ".mp3",
    ".opus",
]
_A  =	AUDIO_EXTENSIONS



