



'''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_  (      )   ->     Optional[int]:
							raise RuntimeError('''CUDA out of memory.'''						)

class        SCREAMING_SNAKE_CASE(							nn.Module    ):







	"""simple docstring"""






	def __init__(		self    :  Union[str, Any]	)    ->	List[str]:
								super().__init__()
								UpperCAmelCase				: Dict											=	nn.Linear(3	,    4	)
								UpperCAmelCase				: Union[str, Any]											=	nn.BatchNormad(4	)
								UpperCAmelCase				: Tuple											=	nn.Linear(4	,    5	)







	def   A			(		self    :  str	,    __snake_case    :  Union[str, Any]	)    ->	Any:
								return self.lineara(self.batchnorm(self.lineara(__snake_case	)	)	)

class        SCREAMING_SNAKE_CASE(							unittest.TestCase    ):







	"""simple docstring"""






	def   A			(		self    :  Optional[Any]	)    ->	str:
								UpperCAmelCase				: Optional[int]											=	[]

								@find_executable_batch_size(starting_batch_size=128	)
								def mock_training_loop_function(__snake_case    :  int	):
															nonlocal batch_sizes
															batch_sizes.append(__snake_case	)
															if batch_size != 8:
																						raise_fake_out_of_memory()

								mock_training_loop_function()
								self.assertListEqual(__snake_case	,    [128, 64, 32, 16, 8]	)







	def   A			(		self    :  int	)    ->	str:
								UpperCAmelCase				: Any											=	[]

								@find_executable_batch_size(starting_batch_size=128	)
								def mock_training_loop_function(__snake_case    :  Union[str, Any]	,    __snake_case    :  Any	):
															nonlocal batch_sizes
															batch_sizes.append(__snake_case	)
															if batch_size != 8:
																						raise_fake_out_of_memory()
															return batch_size, arga

								UpperCAmelCase							,	UpperCAmelCase				: int											=	mock_training_loop_function('''hello'''	)
								self.assertListEqual(__snake_case	,    [128, 64, 32, 16, 8]	)
								self.assertListEqual([bs, arga]	,    [8, '''hello''']	)







	def   A			(		self    :  Optional[int]	)    ->	List[str]:
								@find_executable_batch_size(starting_batch_size=0	)
								def mock_training_loop_function(__snake_case    :  Optional[Any]	):
															pass

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







	def   A			(		self    :  Tuple	)    ->	List[Any]:
								@find_executable_batch_size(starting_batch_size=16	)
								def mock_training_loop_function(__snake_case    :  Any	):
															if batch_size > 0:
																						raise_fake_out_of_memory()
															pass

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







	def   A			(		self    :  Optional[Any]	)    ->	List[Any]:
								@find_executable_batch_size(starting_batch_size=128	)
								def mock_training_loop_function(__snake_case    :  List[Any]	,    __snake_case    :  Union[str, Any]	,    __snake_case    :  Optional[Any]	):
															if batch_size != 8:
																						raise raise_fake_out_of_memory()

								with self.assertRaises(__snake_case	) 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    :  Dict	)    ->	Optional[int]:
								@find_executable_batch_size(starting_batch_size=16	)
								def mock_training_loop_function(__snake_case    :  Dict	):
															raise ValueError('''Oops, we had an error!'''	)

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







	@require_cuda
	def   A			(		self    :  List[str]	)    ->	Optional[Any]:
								UpperCAmelCase				: Union[str, Any]											=	torch.cuda.memory_allocated()
								UpperCAmelCase				: Union[str, Any]											=	ModelForTest()
								model.cuda()
								self.assertGreater(torch.cuda.memory_allocated()	,    __snake_case	)
								UpperCAmelCase				: int											=	release_memory(__snake_case	)
								self.assertEqual(torch.cuda.memory_allocated()	,    __snake_case	)










'''simple docstring'''






# Lint as: python3
import dataclasses
import re
from dataclasses import dataclass
from functools import total_ordering
from typing import Optional, Union


UpperCamelCase__:     List[str]										=    re.compile(r"^(?P<major>\d+)" r"\.(?P<minor>\d+)" r"\.(?P<patch>\d+)$")

@total_ordering
@dataclass
class        SCREAMING_SNAKE_CASE:







	"""simple docstring"""






	lowerCamelCase__    				= 42
	lowerCamelCase__    				= None
	lowerCamelCase__    				= None
	lowerCamelCase__    				= None
	lowerCamelCase__    				= None







	def   A			(		self    :  Optional[Any]	)    ->	Dict:
								UpperCAmelCase							,	UpperCAmelCase							,	UpperCAmelCase				: Any											=	_str_to_version_tuple(self.version_str	)







	def __repr__(		self    :  str	)    ->	Optional[Any]:
								return F"""{self.tuple[0]}.{self.tuple[1]}.{self.tuple[2]}"""







	@property
	def   A			(		self    :  Tuple	)    ->	Dict:
								return self.major, self.minor, self.patch







	def   A			(		self    :  str	,    __snake_case    :  Any	)    ->	Tuple:
								if isinstance(__snake_case	,    __snake_case	):
															return Version(__snake_case	)
								elif isinstance(__snake_case	,    __snake_case	):
															return other
								raise TypeError(F"""{other} (type {type(__snake_case	)}) cannot be compared to version."""	)







	def __eq__(		self    :  str	,    __snake_case    :  Union[str, Any]	)    ->	List[Any]:
								try:
															UpperCAmelCase				: Dict											=	self._validate_operand(__snake_case	)
								except (TypeError, ValueError):
															return False
								else:
															return self.tuple == other.tuple







	def __lt__(		self    :  Any	,    __snake_case    :  int	)    ->	Optional[int]:
								UpperCAmelCase				: int											=	self._validate_operand(__snake_case	)
								return self.tuple < other.tuple







	def __hash__(		self    :  List[str]	)    ->	str:
								return hash(_version_tuple_to_str(self.tuple	)	)







	@classmethod
	def   A			(		cls    :  Any	,    __snake_case    :  Tuple	)    ->	Union[str, Any]:
								UpperCAmelCase				: Optional[Any]											=	{f.name for f in dataclasses.fields(cls	)}
								return cls(**{k: v for k, v in dic.items() if k in field_names}	)







	def   A			(		self    :  Dict	)    ->	str:
								return self.version_str


def 				snake_case_  (      _lowerCAmelCase						:		Optional[Any]						)   ->     List[Any]:
							UpperCAmelCase				: Optional[Any]											=	_VERSION_REG.match(_lowerCAmelCase						)
							if not res:
														raise ValueError(f"""Invalid version '{version_str}'. Format should be x.y.z with {{x,y,z}} being digits."""						)
							return tuple(int(_lowerCAmelCase						) for v in [res.group('''major'''						), res.group('''minor'''						), res.group('''patch'''						)]						)




def 				snake_case_  (      _lowerCAmelCase						:		List[str]						)   ->     List[Any]:
							return ".".join(str(_lowerCAmelCase						) for v in version_tuple						)





