


'''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     a   (    )						->			Tuple:



					'''simple docstring'''






					raise RuntimeError('''CUDA out of memory.'''     )
class      lowercase			(							nn.Module      ):

		"""simple docstring"""

		def __init__(	self							):

							'''simple docstring'''


							super().__init__()
							UpperCamelCase__     :Optional[Any]							      =				nn.Linear(3					,     4							)
							UpperCamelCase__     :Optional[int]							      =				nn.BatchNormad(4							)
							UpperCamelCase__     :List[str]							      =				nn.Linear(4					,     5							)




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_							):

							'''simple docstring'''


							return self.lineara(self.batchnorm(self.lineara(_a							)							)							)



class      lowercase			(							unittest.TestCase      ):

		"""simple docstring"""

		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							UpperCamelCase__     :List[str]							      =				[]

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

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




		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							UpperCamelCase__     :int							      =				[]

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

							UpperCamelCase__      ,       UpperCamelCase__     :str							      =				mock_training_loop_function('''hello'''							)
							self.assertListEqual(_a					,     [128, 64, 32, 16, 8]							)
							self.assertListEqual([bs, arga]					,     [8, '''hello''']							)




		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							@find_executable_batch_size(starting_batch_size=0							)
							def mock_training_loop_function(UpperCamelCase_							):
												pass

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




		def 					lowerCAmelCase__       (	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(_a							) as cm:
												mock_training_loop_function()
												self.assertIn('''No executable batch size found, reached zero.'''					,     cm.exception.args[0]							)




		def 					lowerCAmelCase__       (	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(_a							) 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 					lowerCAmelCase__       (	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(_a							) as cm:
												mock_training_loop_function()
												self.assertIn('''Oops, we had an error!'''					,     cm.exception.args[0]							)




		@require_cuda
		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							UpperCamelCase__     :Tuple							      =				torch.cuda.memory_allocated()
							UpperCamelCase__     :Optional[int]							      =				ModelForTest()
							model.cuda()
							self.assertGreater(torch.cuda.memory_allocated()					,     _a							)
							UpperCamelCase__     :int							      =				release_memory(_a							)
							self.assertEqual(torch.cuda.memory_allocated()					,     _a							)




'''simple docstring'''
# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

import math
from typing import Union

import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import randn_tensor
from .scheduling_utils import SchedulerMixin
class      lowercase			(							A__			,	A__      ):

		"""simple docstring"""

		_a									=       1




		@register_to_config
		def __init__(	self					,     UpperCamelCase_=2000					,     UpperCamelCase_=0.1					,     UpperCamelCase_=20					,     UpperCamelCase_=1e-3							):

							'''simple docstring'''


							UpperCamelCase__     :Dict							      =				None
							UpperCamelCase__     :Any							      =				None
							UpperCamelCase__     :List[str]							      =				None




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_					,     UpperCamelCase_ = None							):

							'''simple docstring'''


							UpperCamelCase__     :Union[str, Any]							      =				torch.linspace(1					,     self.config.sampling_eps					,     UpperCamelCase_					,     device=UpperCamelCase_							)




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_=None							):

							'''simple docstring'''


							if self.timesteps is None:
												raise ValueError(
												    '''`self.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler'''							)

							# TODO(Patrick) better comments + non-PyTorch
							# postprocess model score
							UpperCamelCase__     :Optional[int]							      =				(
							    -0.25 * t**2 * (self.config.beta_max - self.config.beta_min) - 0.5 * t * self.config.beta_min
							)
							UpperCamelCase__     :Tuple							      =				torch.sqrt(1.0 - torch.exp(2.0 * log_mean_coeff							)							)
							UpperCamelCase__     :Tuple							      =				std.flatten()
							while len(std.shape							) < len(score.shape							):
												UpperCamelCase__     :int							      =				std.unsqueeze(-1							)
							UpperCamelCase__     :List[Any]							      =				-score / std

							# compute
							UpperCamelCase__     :List[str]							      =				-1.0 / len(self.timesteps							)

							UpperCamelCase__     :Optional[int]							      =				self.config.beta_min + t * (self.config.beta_max - self.config.beta_min)
							UpperCamelCase__     :Any							      =				beta_t.flatten()
							while len(beta_t.shape							) < len(x.shape							):
												UpperCamelCase__     :Any							      =				beta_t.unsqueeze(-1							)
							UpperCamelCase__     :Optional[int]							      =				-0.5 * beta_t * x

							UpperCamelCase__     :int							      =				torch.sqrt(UpperCamelCase_							)
							UpperCamelCase__     :List[str]							      =				drift - diffusion**2 * score
							UpperCamelCase__     :Dict							      =				x + drift * dt

							# add noise
							UpperCamelCase__     :List[Any]							      =				randn_tensor(x.shape					,     layout=x.layout					,     generator=UpperCamelCase_					,     device=x.device					,     dtype=x.dtype							)
							UpperCamelCase__     :int							      =				x_mean + diffusion * math.sqrt(-dt							) * noise

							return x, x_mean




		def __len__(	self							):

							'''simple docstring'''


							return self.config.num_train_timesteps