




"""simple docstring"""






import inspect
import logging
import os
import random
import shutil
import tempfile
import unittest

import pytest
import torch
from torch import nn
from torch.utils.data import DataLoader, TensorDataset

from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_async, require_cuda
from accelerate.utils import ProjectConfiguration, set_seed


UpperCamelCase__						=			logging.getLogger(__name__)





def 							UpperCAmelCase		(		snake_case			:					List[Any]=2      ,       snake_case			:					int=3      ,       snake_case			:					Optional[int]=16      ,       snake_case			:					int = 10      ,       snake_case			:					int = 2    ):

					def get_dataset(snake_case			:					Any    ):
										_lowerCAmelCase:Union[str, Any]									=  torch.randn(batch_size * n_batches      ,       1    )
										return TensorDataset(snake_case      ,       a * x + b + 0.1 * torch.randn(batch_size * n_batches      ,       1    )    )

					_lowerCAmelCase:Tuple									=  get_dataset(snake_case    )
					_lowerCAmelCase:List[Any]									=  get_dataset(snake_case    )
					_lowerCAmelCase:Dict									=  DataLoader(snake_case      ,       shuffle=snake_case      ,       batch_size=snake_case      ,       num_workers=4    )
					_lowerCAmelCase:Any									=  DataLoader(snake_case      ,       shuffle=snake_case      ,       batch_size=snake_case      ,       num_workers=4    )
					return (train_dataloader, valid_dataloader)





def 							UpperCAmelCase		(		snake_case			:					List[Any]      ,       snake_case			:					int      ,       snake_case			:					Dict      ,       snake_case			:					Dict      ,       snake_case			:					int      ,       snake_case			:					Tuple=None    ):
					_lowerCAmelCase:Optional[Any]									=  []
					for epoch in range(snake_case    ):
										# Train quickly
										model.train()
										for batch in dataloader:
															_lowerCAmelCase							,   _lowerCAmelCase:List[Any]									=  batch
															_lowerCAmelCase:int									=  model(snake_case    )
															_lowerCAmelCase:Optional[int]									=  torch.nn.functional.mse_loss(snake_case      ,       snake_case    )
															accelerator.backward(snake_case    )
															optimizer.step()
															optimizer.zero_grad()
										rands.append(random.random()    )  # Introduce some randomness
										if scheduler is not None:
															scheduler.step()
					return rands




class   a__			(       nn.Module  ):





						def __init__(							self      :	List[Any])						->       Any:




											"""simple docstring"""







											super().__init__()
											_lowerCAmelCase:str									=  nn.Parameter(torch.randn(1))
											_lowerCAmelCase:Dict									=  nn.Parameter(torch.randn(1))







						def       __UpperCamelCase     (							self      :	Tuple     ,a__      :	List[Any])						->       Tuple:




											"""simple docstring"""







											return x * self.a + self.b




class   a__			(       unittest.TestCase  ):





						def       __UpperCamelCase     (							self      :	Any)						->       Optional[Any]:




											"""simple docstring"""







											with tempfile.TemporaryDirectory() as tmpdir:
																set_seed(42)
																_lowerCAmelCase:List[str]									=  DummyModel()
																_lowerCAmelCase:Any									=  torch.optim.Adam(params=model.parameters()     ,lr=1E-3)
																_lowerCAmelCase							,   _lowerCAmelCase:Optional[Any]									=  dummy_dataloaders()
																_lowerCAmelCase:List[str]									=  ProjectConfiguration(total_limit=1     ,project_dir=a__     ,automatic_checkpoint_naming=a__)
																# Train baseline
																_lowerCAmelCase:List[Any]									=  Accelerator(project_config=a__)
																_lowerCAmelCase							,   _lowerCAmelCase							,   _lowerCAmelCase							,   _lowerCAmelCase:int									=  accelerator.prepare(
																    a__     ,a__     ,a__     ,a__)
																# Save initial
																accelerator.save_state()

																# Save second state
																accelerator.save_state()
																self.assertEqual(len(os.listdir(accelerator.project_dir))     ,1)





						def       __UpperCamelCase     (							self      :	Any)						->       Union[str, Any]:




											"""simple docstring"""







											with tempfile.TemporaryDirectory() as tmpdir:
																set_seed(42)
																_lowerCAmelCase:int									=  DummyModel()
																_lowerCAmelCase:Dict									=  torch.optim.Adam(params=model.parameters()     ,lr=1E-3)
																_lowerCAmelCase							,   _lowerCAmelCase:Dict									=  dummy_dataloaders()
																# Train baseline
																_lowerCAmelCase:List[str]									=  Accelerator()
																_lowerCAmelCase							,   _lowerCAmelCase							,   _lowerCAmelCase							,   _lowerCAmelCase:Optional[Any]									=  accelerator.prepare(
																    a__     ,a__     ,a__     ,a__)
																# Save initial
																_lowerCAmelCase:Union[str, Any]									=  os.path.join(a__     ,'''initial''')
																accelerator.save_state(a__)
																((_lowerCAmelCase)							,   (_lowerCAmelCase)):Any									=  model.a.item(), model.b.item()
																_lowerCAmelCase:Optional[int]									=  optimizer.state_dict()
																_lowerCAmelCase:Tuple									=  train(3     ,a__     ,a__     ,a__     ,a__)
																((_lowerCAmelCase)							,   (_lowerCAmelCase)):Optional[int]									=  model.a.item(), model.b.item()
																_lowerCAmelCase:Any									=  optimizer.state_dict()

																# Train partially
																set_seed(42)
																_lowerCAmelCase:str									=  DummyModel()
																_lowerCAmelCase:Union[str, Any]									=  torch.optim.Adam(params=model.parameters()     ,lr=1E-3)
																_lowerCAmelCase							,   _lowerCAmelCase:Any									=  dummy_dataloaders()
																_lowerCAmelCase:Union[str, Any]									=  Accelerator()
																_lowerCAmelCase							,   _lowerCAmelCase							,   _lowerCAmelCase							,   _lowerCAmelCase:Tuple									=  accelerator.prepare(
																    a__     ,a__     ,a__     ,a__)
																accelerator.load_state(a__)
																((_lowerCAmelCase)							,   (_lowerCAmelCase)):str									=  model.a.item(), model.b.item()
																_lowerCAmelCase:Any									=  optimizer.state_dict()
																self.assertEqual(a__     ,a__)
																self.assertEqual(a__     ,a__)
																self.assertEqual(a__     ,a__)

																_lowerCAmelCase:str									=  train(2     ,a__     ,a__     ,a__     ,a__)
																# Save everything
																_lowerCAmelCase:Optional[Any]									=  os.path.join(a__     ,'''checkpoint''')
																accelerator.save_state(a__)

																# Load everything back in and make sure all states work
																accelerator.load_state(a__)
																test_rands += train(1     ,a__     ,a__     ,a__     ,a__)
																((_lowerCAmelCase)							,   (_lowerCAmelCase)):List[Any]									=  model.a.item(), model.b.item()
																_lowerCAmelCase:List[Any]									=  optimizer.state_dict()
																self.assertEqual(a__     ,a__)
																self.assertEqual(a__     ,a__)
																self.assertEqual(a__     ,a__)
																self.assertEqual(a__     ,a__)





						def       __UpperCamelCase     (							self      :	Optional[Any])						->       Optional[Any]:




											"""simple docstring"""







											with tempfile.TemporaryDirectory() as tmpdir:
																set_seed(42)
																_lowerCAmelCase:List[str]									=  DummyModel()
																_lowerCAmelCase:int									=  torch.optim.Adam(params=model.parameters()     ,lr=1E-3)
																_lowerCAmelCase							,   _lowerCAmelCase:Tuple									=  dummy_dataloaders()
																_lowerCAmelCase:Any									=  ProjectConfiguration(automatic_checkpoint_naming=a__)

																# Train baseline
																_lowerCAmelCase:str									=  Accelerator(project_dir=a__     ,project_config=a__)
																_lowerCAmelCase							,   _lowerCAmelCase							,   _lowerCAmelCase							,   _lowerCAmelCase:List[Any]									=  accelerator.prepare(
																    a__     ,a__     ,a__     ,a__)
																# Save initial
																accelerator.save_state()
																((_lowerCAmelCase)							,   (_lowerCAmelCase)):List[str]									=  model.a.item(), model.b.item()
																_lowerCAmelCase:str									=  optimizer.state_dict()
																_lowerCAmelCase:Optional[int]									=  train(3     ,a__     ,a__     ,a__     ,a__)
																((_lowerCAmelCase)							,   (_lowerCAmelCase)):int									=  model.a.item(), model.b.item()
																_lowerCAmelCase:int									=  optimizer.state_dict()

																# Train partially
																set_seed(42)
																_lowerCAmelCase:Optional[int]									=  DummyModel()
																_lowerCAmelCase:Union[str, Any]									=  torch.optim.Adam(params=model.parameters()     ,lr=1E-3)
																_lowerCAmelCase							,   _lowerCAmelCase:Optional[Any]									=  dummy_dataloaders()
																_lowerCAmelCase:Union[str, Any]									=  ProjectConfiguration(iteration=1     ,automatic_checkpoint_naming=a__)
																_lowerCAmelCase:Dict									=  Accelerator(project_dir=a__     ,project_config=a__)
																_lowerCAmelCase							,   _lowerCAmelCase							,   _lowerCAmelCase							,   _lowerCAmelCase:Optional[int]									=  accelerator.prepare(
																    a__     ,a__     ,a__     ,a__)
																accelerator.load_state(os.path.join(a__     ,'''checkpoints'''     ,'''checkpoint_0'''))
																((_lowerCAmelCase)							,   (_lowerCAmelCase)):Dict									=  model.a.item(), model.b.item()
																_lowerCAmelCase:int									=  optimizer.state_dict()
																self.assertEqual(a__     ,a__)
																self.assertEqual(a__     ,a__)
																self.assertEqual(a__     ,a__)

																_lowerCAmelCase:Dict									=  train(2     ,a__     ,a__     ,a__     ,a__)
																# Save everything
																accelerator.save_state()

																# Load everything back in and make sure all states work
																accelerator.load_state(os.path.join(a__     ,'''checkpoints'''     ,'''checkpoint_1'''))
																test_rands += train(1     ,a__     ,a__     ,a__     ,a__)
																((_lowerCAmelCase)							,   (_lowerCAmelCase)):Dict									=  model.a.item(), model.b.item()
																_lowerCAmelCase:Union[str, Any]									=  optimizer.state_dict()
																self.assertEqual(a__     ,a__)
																self.assertEqual(a__     ,a__)
																self.assertEqual(a__     ,a__)
																self.assertEqual(a__     ,a__)





						def       __UpperCamelCase     (							self      :	Dict)						->       int:




											"""simple docstring"""







											_lowerCAmelCase:str									=  torch.tensor([1, 2, 3])
											_lowerCAmelCase:Tuple									=  torch.tensor([2, 3, 4])
											_lowerCAmelCase:Tuple									=  DummyModel()
											_lowerCAmelCase:Union[str, Any]									=  torch.optim.Adam(net.parameters())
											_lowerCAmelCase:Union[str, Any]									=  Accelerator()
											with self.assertRaises(a__) as ve:
																accelerator.register_for_checkpointing(a__     ,a__     ,a__     ,a__)
											_lowerCAmelCase:List[Any]									=  str(ve.exception)
											self.assertTrue('''Item at index 0''' in message)
											self.assertTrue('''Item at index 1''' in message)
											self.assertFalse('''Item at index 2''' in message)
											self.assertFalse('''Item at index 3''' in message)





						def       __UpperCamelCase     (							self      :	str)						->       Optional[Any]:




											"""simple docstring"""







											with tempfile.TemporaryDirectory() as tmpdir:
																set_seed(42)
																_lowerCAmelCase:int									=  DummyModel()
																_lowerCAmelCase:str									=  torch.optim.Adam(params=model.parameters()     ,lr=1E-3)
																_lowerCAmelCase:Union[str, Any]									=  torch.optim.lr_scheduler.StepLR(a__     ,step_size=1     ,gamma=0.99)
																_lowerCAmelCase							,   _lowerCAmelCase:List[str]									=  dummy_dataloaders()
																_lowerCAmelCase:Any									=  ProjectConfiguration(automatic_checkpoint_naming=a__)
																# Train baseline
																_lowerCAmelCase:Optional[int]									=  Accelerator(project_dir=a__     ,project_config=a__)
																_lowerCAmelCase							,   _lowerCAmelCase							,   _lowerCAmelCase							,   _lowerCAmelCase							,   _lowerCAmelCase:int									=  accelerator.prepare(
																    a__     ,a__     ,a__     ,a__     ,a__)
																# Save initial
																accelerator.save_state()
																_lowerCAmelCase:Dict									=  scheduler.state_dict()
																train(3     ,a__     ,a__     ,a__     ,a__     ,a__)
																self.assertNotEqual(a__     ,scheduler.state_dict())

																# Load everything back in and make sure all states work
																accelerator.load_state(os.path.join(a__     ,'''checkpoints'''     ,'''checkpoint_0'''))
																self.assertEqual(a__     ,scheduler.state_dict())





						def       __UpperCamelCase     (							self      :	Union[str, Any])						->       Tuple:




											"""simple docstring"""







											with tempfile.TemporaryDirectory() as tmpdir:
																set_seed(42)
																_lowerCAmelCase:str									=  DummyModel()
																_lowerCAmelCase:Optional[Any]									=  ProjectConfiguration(automatic_checkpoint_naming=a__     ,total_limit=2)
																# Train baseline
																_lowerCAmelCase:List[str]									=  Accelerator(project_dir=a__     ,project_config=a__)
																_lowerCAmelCase:List[str]									=  accelerator.prepare(a__)
																# Save 3 states:
																for _ in range(11):
																					accelerator.save_state()
																self.assertTrue(not os.path.exists(os.path.join(a__     ,'''checkpoints'''     ,'''checkpoint_0''')))
																self.assertTrue(os.path.exists(os.path.join(a__     ,'''checkpoints'''     ,'''checkpoint_9''')))
																self.assertTrue(os.path.exists(os.path.join(a__     ,'''checkpoints'''     ,'''checkpoint_10''')))







						@require_cuda
						def       __UpperCamelCase     (							self      :	int)						->       str:




											"""simple docstring"""







											_lowerCAmelCase:Union[str, Any]									=  ['''torchrun''', F'--nproc_per_node={torch.cuda.device_count()}', inspect.getfile(self.__class__)]
											execute_subprocess_async(a__     ,env=os.environ.copy())


if __name__ == "__main__":
				UpperCamelCase__						=			'''/tmp/accelerate/state_checkpointing'''
				UpperCamelCase__						=			DummyModel()
				UpperCamelCase__						=			torch.optim.Adam(params=model.parameters(), lr=1e-3)
				UpperCamelCase__						=			torch.optim.lr_scheduler.StepLR(optimizer, step_size=1, gamma=0.99)
				UpperCamelCase__						,							UpperCamelCase__						=			dummy_dataloaders()
				UpperCamelCase__						=			ProjectConfiguration(automatic_checkpoint_naming=True)
				# Train baseline
				UpperCamelCase__						=			Accelerator(project_dir=savedir, project_config=project_config, mixed_precision='''no''')
				if accelerator.process_index == 0:
								if os.path.exists(savedir):
												shutil.rmtree(savedir)
								os.makedirs(savedir)
				UpperCamelCase__						,							UpperCamelCase__						,							UpperCamelCase__						,							UpperCamelCase__						,							UpperCamelCase__						=			accelerator.prepare(
				    model, optimizer, train_dataloader, valid_dataloader, scheduler
				)
				UpperCamelCase__						,							UpperCamelCase__						=			accelerator.prepare(model, optimizer)
				train(3, model, train_dataloader, optimizer, accelerator, scheduler)
				# Check that the intial optimizer is loaded on the GPU
				for group in optimizer.param_groups:
								UpperCamelCase__						=			group['''params'''][0].device
								break
				assert param_device.type == accelerator.device.type
				UpperCamelCase__						=			model.cpu()
				accelerator.wait_for_everyone()
				accelerator.save_state()
				accelerator.wait_for_everyone()

				# Check CPU state
				accelerator.load_state(os.path.join(savedir, '''checkpoints''', '''checkpoint_0'''), map_location='''cpu''')
				for group in optimizer.param_groups:
								UpperCamelCase__						=			group['''params'''][0].device
								break
				assert (
				    param_device.type == torch.device('''cpu''').type
				), F"Loaded optimizer states did not match, expected to be loaded on the CPU but got {param_device}"

				# Check device state
				model.to(accelerator.device)
				accelerator.load_state(os.path.join(savedir, '''checkpoints''', '''checkpoint_0'''), map_location='''on_device''')
				for group in optimizer.param_groups:
								UpperCamelCase__						=			group['''params'''][0].device
								break
				assert (
				    param_device.type == accelerator.device.type
				), F"Loaded optimizer states did not match, expected to be loaded on {accelerator.device} but got {param_device}"

				# Check error
				with pytest.raises(TypeError, match='''Unsupported optimizer map location passed'''):
								accelerator.load_state(os.path.join(savedir, '''checkpoints''', '''checkpoint_0'''), map_location='''invalid''')
				accelerator.wait_for_everyone()
				if accelerator.process_index == 0:
								shutil.rmtree(savedir)
				accelerator.wait_for_everyone()












"""simple docstring"""






# DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion
# and https://github.com/hojonathanho/diffusion
import math
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from diffusers.configuration_utils import ConfigMixin, register_to_config
from diffusers.schedulers.scheduling_utils import SchedulerMixin
from diffusers.utils import BaseOutput, deprecate




@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->DDIM
class   a__			(       UpperCamelCase_  ):
						snake_case__             = 42
						snake_case__             = None





def 							UpperCAmelCase		(		snake_case			:					int      ,       snake_case			:					int=0.9_99      ,       snake_case			:					Union[str, Any]="cosine"      ,       ):
					if alpha_transform_type == "cosine":

										def alpha_bar_fn(snake_case			:					Dict    ):
															return math.cos((t + 0.0_08) / 1.0_08 * math.pi / 2    ) ** 2

					elif alpha_transform_type == "exp":

										def alpha_bar_fn(snake_case			:					Optional[Any]    ):
															return math.exp(t * -12.0    )

					else:
										raise ValueError(F'Unsupported alpha_tranform_type: {alpha_transform_type}'    )

					_lowerCAmelCase:List[str]									=  []
					for i in range(snake_case    ):
										_lowerCAmelCase:Union[str, Any]									=  i / num_diffusion_timesteps
										_lowerCAmelCase:Dict									=  (i + 1) / num_diffusion_timesteps
										betas.append(min(1 - alpha_bar_fn(snake_case    ) / alpha_bar_fn(snake_case    )      ,       snake_case    )    )
					return torch.tensor(snake_case      ,       dtype=torch.floataa    )





class   a__			(       UpperCamelCase_					,	UpperCamelCase_  ):
						snake_case__             = 1





						@register_to_config
						def __init__(							self      :	Optional[Any]     ,a__      :	int = 1000     ,a__      :	float = 0.0001     ,a__      :	float = 0.02     ,a__      :	str = "linear"     ,a__      :	Optional[Union[np.ndarray, List[float]]] = None     ,a__      :	bool = True     ,a__      :	bool = True     ,a__      :	int = 0     ,a__      :	str = "epsilon"     ,a__      :	float = 1.0     ,**a__      :	Any     ,)						->       str:




											"""simple docstring"""







											if kwargs.get('''set_alpha_to_one'''     ,a__) is not None:
																_lowerCAmelCase:Tuple									=  (
																    '''The `set_alpha_to_one` argument is deprecated. Please use `set_alpha_to_zero` instead.'''
																)
																deprecate('''set_alpha_to_one'''     ,'''1.0.0'''     ,a__     ,standard_warn=a__)
																_lowerCAmelCase:Optional[Any]									=  kwargs['''set_alpha_to_one''']
											if trained_betas is not None:
																_lowerCAmelCase:Any									=  torch.tensor(a__     ,dtype=torch.floataa)
											elif beta_schedule == "linear":
																_lowerCAmelCase:str									=  torch.linspace(a__     ,a__     ,a__     ,dtype=torch.floataa)
											elif beta_schedule == "scaled_linear":
																# this schedule is very specific to the latent diffusion model.
																_lowerCAmelCase:int									=  (
																    torch.linspace(beta_start**0.5     ,beta_end**0.5     ,a__     ,dtype=torch.floataa) ** 2
																)
											elif beta_schedule == "squaredcos_cap_v2":
																# Glide cosine schedule
																_lowerCAmelCase:Optional[int]									=  betas_for_alpha_bar(a__)
											else:
																raise NotImplementedError(F'{beta_schedule} does is not implemented for {self.__class__}')

											_lowerCAmelCase:Any									=  1.0 - self.betas
											_lowerCAmelCase:Optional[Any]									=  torch.cumprod(self.alphas     ,dim=0)

											# At every step in inverted ddim, we are looking into the next alphas_cumprod
											# For the final step, there is no next alphas_cumprod, and the index is out of bounds
											# `set_alpha_to_zero` decides whether we set this parameter simply to zero
											# in this case, self.step() just output the predicted noise
											# or whether we use the final alpha of the "non-previous" one.
											_lowerCAmelCase:Optional[Any]									=  torch.tensor(0.0) if set_alpha_to_zero else self.alphas_cumprod[-1]

											# standard deviation of the initial noise distribution
											_lowerCAmelCase:Any									=  1.0

											# setable values
											_lowerCAmelCase:Dict									=  None
											_lowerCAmelCase:List[Any]									=  torch.from_numpy(np.arange(0     ,a__).copy().astype(np.intaa))





						def       __UpperCamelCase     (							self      :	Union[str, Any]     ,a__      :	torch.FloatTensor     ,a__      :	Optional[int] = None)						->       torch.FloatTensor:




											"""simple docstring"""







											return sample





						def       __UpperCamelCase     (							self      :	Optional[int]     ,a__      :	int     ,a__      :	Union[str, torch.device] = None)						->       List[Any]:




											"""simple docstring"""







											if num_inference_steps > self.config.num_train_timesteps:
																raise ValueError(
																    F'`num_inference_steps`: {num_inference_steps} cannot be larger than `self.config.train_timesteps`:'
																    F' {self.config.num_train_timesteps} as the unet model trained with this scheduler can only handle'
																    F' maximal {self.config.num_train_timesteps} timesteps.')

											_lowerCAmelCase:Union[str, Any]									=  num_inference_steps
											_lowerCAmelCase:Union[str, Any]									=  self.config.num_train_timesteps // self.num_inference_steps
											# creates integer timesteps by multiplying by ratio
											# casting to int to avoid issues when num_inference_step is power of 3
											_lowerCAmelCase:str									=  (np.arange(0     ,a__) * step_ratio).round().copy().astype(np.intaa)
											_lowerCAmelCase:str									=  torch.from_numpy(a__).to(a__)
											self.timesteps += self.config.steps_offset





						def       __UpperCamelCase     (							self      :	int     ,a__      :	torch.FloatTensor     ,a__      :	int     ,a__      :	torch.FloatTensor     ,a__      :	float = 0.0     ,a__      :	bool = False     ,a__      :	Optional[torch.FloatTensor] = None     ,a__      :	bool = True     ,)						->       Union[DDIMSchedulerOutput, Tuple]:




											"""simple docstring"""







											_lowerCAmelCase:Union[str, Any]									=  timestep + self.config.num_train_timesteps // self.num_inference_steps

											# 2. compute alphas, betas
											# change original implementation to exactly match noise levels for analogous forward process
											_lowerCAmelCase:Optional[Any]									=  self.alphas_cumprod[timestep]
											_lowerCAmelCase:List[Any]									=  (
											    self.alphas_cumprod[prev_timestep]
											    if prev_timestep < self.config.num_train_timesteps
											    else self.final_alpha_cumprod
											)

											_lowerCAmelCase:str									=  1 - alpha_prod_t

											# 3. compute predicted original sample from predicted noise also called
											# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
											if self.config.prediction_type == "epsilon":
																_lowerCAmelCase:int									=  (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
																_lowerCAmelCase:str									=  model_output
											elif self.config.prediction_type == "sample":
																_lowerCAmelCase:List[str]									=  model_output
																_lowerCAmelCase:Optional[int]									=  (sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
											elif self.config.prediction_type == "v_prediction":
																_lowerCAmelCase:str									=  (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
																_lowerCAmelCase:Dict									=  (alpha_prod_t**0.5) * model_output + (beta_prod_t**0.5) * sample
											else:
																raise ValueError(
																    F'prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or'
																    ''' `v_prediction`''')

											# 4. Clip or threshold "predicted x_0"
											if self.config.clip_sample:
																_lowerCAmelCase:List[Any]									=  pred_original_sample.clamp(
																    -self.config.clip_sample_range     ,self.config.clip_sample_range)

											# 5. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
											_lowerCAmelCase:str									=  (1 - alpha_prod_t_prev) ** 0.5 * pred_epsilon

											# 6. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
											_lowerCAmelCase:Optional[Any]									=  alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction

											if not return_dict:
																return (prev_sample, pred_original_sample)
											return DDIMSchedulerOutput(prev_sample=a__     ,pred_original_sample=a__)







						def __len__(							self      :	Dict)						->       Union[str, Any]:




											"""simple docstring"""







											return self.config.num_train_timesteps






