



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 	UpperCAmelCase   (						):
			raise RuntimeError("CUDA out of memory."    )

class 	lowerCamelCase_    ( nn.Module						):






						'''simple docstring'''







						def __init__(	self      :			Tuple )      ->				Tuple:
									super().__init__()
									A							:					Dict              =						nn.Linear(3	,	4 )
									A							:					Dict              =						nn.BatchNormad(4 )
									A							:					Optional[Any]              =						nn.Linear(4	,	5 )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Optional[int]	,	__lowerCamelCase      :			Optional[Any] )      ->				str:
									return self.lineara(self.batchnorm(self.lineara(__lowerCamelCase ) ) )







class 	lowerCamelCase_    ( unittest.TestCase						):






						'''simple docstring'''







						def 			SCREAMING_SNAKE_CASE__		(	self      :			int )      ->				Optional[Any]:
									A							:					Union[str, Any]              =						[]

									@find_executable_batch_size(starting_batch_size=1_28 )
									def mock_training_loop_function(__lowerCamelCase      :			Optional[Any] ):
												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      :			Dict )      ->				Optional[Any]:
									A							:					Optional[int]              =						[]

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

									A							:					str              =						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      :			str )      ->				str:
									@find_executable_batch_size(starting_batch_size=0 )
									def mock_training_loop_function(__lowerCamelCase      :			Union[str, Any] ):
												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] )      ->				Dict:
									@find_executable_batch_size(starting_batch_size=16 )
									def mock_training_loop_function(__lowerCamelCase      :			Optional[Any] ):
												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      :			Tuple )      ->				Optional[Any]:
									@find_executable_batch_size(starting_batch_size=1_28 )
									def mock_training_loop_function(__lowerCamelCase      :			int	,	__lowerCamelCase      :			Optional[int]	,	__lowerCamelCase      :			Dict ):
												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      :			Dict )      ->				Any:
									@find_executable_batch_size(starting_batch_size=16 )
									def mock_training_loop_function(__lowerCamelCase      :			Optional[int] ):
												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] )      ->				Optional[int]:
									A							:					Optional[int]              =						torch.cuda.memory_allocated()
									A							:					Optional[int]              =						ModelForTest()
									model.cuda()
									self.assertGreater(torch.cuda.memory_allocated()	,	__lowerCamelCase )
									A							:					Optional[Any]              =						release_memory(__lowerCamelCase )
									self.assertEqual(torch.cuda.memory_allocated()	,	__lowerCamelCase )




from __future__ import annotations

from math import pi

# Define the Reduced Planck Constant ℏ (H bar), speed of light C, value of
# Pi and the function
__SCREAMING_SNAKE_CASE           =  1.0_5_4_5_7_1_8_1_7e-3_4  # unit of ℏ : J * s

__SCREAMING_SNAKE_CASE           =  3e8  # unit of c : m * s^-1




def 	UpperCAmelCase   (						_lowerCamelCase ,    _lowerCamelCase ,    _lowerCamelCase    ):

			if (force, area, distance).count(0    ) != 1:
						raise ValueError("One and only one argument must be 0"    )
			if force < 0:
						raise ValueError("Magnitude of force can not be negative"    )
			if distance < 0:
						raise ValueError("Distance can not be negative"    )
			if area < 0:
						raise ValueError("Area can not be negative"    )
			if force == 0:
						A							:					int              =						(REDUCED_PLANCK_CONSTANT * SPEED_OF_LIGHT * pi**2 * area) / (
						    240 * (distance) ** 4
						)
						return {"force": force}
			elif area == 0:
						A							:					Tuple              =						(240 * force * (distance) ** 4) / (
						    REDUCED_PLANCK_CONSTANT * SPEED_OF_LIGHT * pi**2
						)
						return {"area": area}
			elif distance == 0:
						A							:					Dict              =						(
						    (REDUCED_PLANCK_CONSTANT * SPEED_OF_LIGHT * pi**2 * area) / (240 * force)
						) ** (1 / 4)
						return {"distance": distance}
			raise ValueError("One and only one argument must be 0"    )


# Run doctest
if __name__ == "__main__":
							import doctest

							doctest.testmod()