




'''simple docstring'''


import inspect
import unittest

import torch
import torch.nn as nn

from accelerate.hooks import (
    AlignDevicesHook,
    ModelHook,
    SequentialHook,
    add_hook_to_module,
    attach_align_device_hook,
    remove_hook_from_module,
    remove_hook_from_submodules,
)
from accelerate.test_utils import require_multi_gpu





class  __lowercase    (	nn.Module			):







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







   def 		UpperCamelCase__      (						self   ,				UpperCamelCase   )					->     Tuple:
     return self.lineara(self.batchnorm(self.lineara(UpperCamelCase   )   )   )





class  __lowercase    (	__magic_name__			):







   def 		UpperCamelCase__      (						self   ,				UpperCamelCase   ,				*UpperCamelCase   ,				**UpperCamelCase   )					->     str:
     return (args[0] + 1,) + args[1:], kwargs





class  __lowercase    (	__magic_name__			):







   def 		UpperCamelCase__      (						self   ,				UpperCamelCase   ,				UpperCamelCase   )					->     Optional[Any]:
     return output + 1





class  __lowercase    (	unittest.TestCase			):







   def 		UpperCamelCase__      (						self   )					->     Any:
     __a								=       ModelForTest()
     __a								=       ModelHook()

     add_hook_to_module(UpperCamelCase   ,				UpperCamelCase   )
     self.assertEqual(test_model._hf_hook   ,				UpperCamelCase   )
     self.assertTrue(hasattr(UpperCamelCase   ,				'_old_forward'   )   )

     # Check adding the hook did not change the name or the signature
     self.assertEqual(test_model.forward.__name__   ,				'forward'   )
     self.assertListEqual(list(inspect.signature(test_model.forward   ).parameters   )   ,				['x']   )

     remove_hook_from_module(UpperCamelCase   )
     self.assertFalse(hasattr(UpperCamelCase   ,				'_hf_hook'   )   )
     self.assertFalse(hasattr(UpperCamelCase   ,				'_old_forward'   )   )







   def 		UpperCamelCase__      (						self   )					->     Any:
     __a								=       ModelForTest()
     __a								=       ModelHook()

     add_hook_to_module(UpperCamelCase   ,				UpperCamelCase   )
     add_hook_to_module(UpperCamelCase   ,				UpperCamelCase   ,				append=UpperCamelCase   )

     self.assertEqual(isinstance(test_model._hf_hook   ,				UpperCamelCase   )   ,				UpperCamelCase   )
     self.assertEqual(len(test_model._hf_hook.hooks   )   ,				2   )
     self.assertTrue(hasattr(UpperCamelCase   ,				'_old_forward'   )   )

     # Check adding the hook did not change the name or the signature
     self.assertEqual(test_model.forward.__name__   ,				'forward'   )
     self.assertListEqual(list(inspect.signature(test_model.forward   ).parameters   )   ,				['x']   )

     remove_hook_from_module(UpperCamelCase   )
     self.assertFalse(hasattr(UpperCamelCase   ,				'_hf_hook'   )   )
     self.assertFalse(hasattr(UpperCamelCase   ,				'_old_forward'   )   )







   def 		UpperCamelCase__      (						self   )					->     Dict:
     __a								=       ModelForTest()
     __a								=       torch.randn(2   ,				3   )
     __a								=       test_model(x + 1   )
     __a								=       test_model(x + 2   )

     __a								=       PreForwardHook()
     add_hook_to_module(UpperCamelCase   ,				UpperCamelCase   )
     __a								=       test_model(UpperCamelCase   )
     self.assertTrue(torch.allclose(UpperCamelCase   ,				UpperCamelCase   ,				atol=1e-5   )   )

     # Attaching a hook to a model when it already has one replaces, does not chain
     __a								=       PreForwardHook()
     add_hook_to_module(UpperCamelCase   ,				UpperCamelCase   )
     __a								=       test_model(UpperCamelCase   )
     self.assertTrue(torch.allclose(UpperCamelCase   ,				UpperCamelCase   ,				atol=1e-5   )   )

     # You need to use the sequential hook to chain two or more hooks
     __a								=       SequentialHook(PreForwardHook()   ,				PreForwardHook()   )
     add_hook_to_module(UpperCamelCase   ,				UpperCamelCase   )

     __a								=       test_model(UpperCamelCase   )
     assert torch.allclose(UpperCamelCase   ,				UpperCamelCase   ,				atol=1e-5   )







   def 		UpperCamelCase__      (						self   )					->     Tuple:
     __a								=       ModelForTest()
     __a								=       torch.randn(2   ,				3   )
     __a								=       test_model(UpperCamelCase   )

     __a								=       PostForwardHook()
     add_hook_to_module(UpperCamelCase   ,				UpperCamelCase   )
     __a								=       test_model(UpperCamelCase   )
     self.assertTrue(torch.allclose(UpperCamelCase   ,				output + 1   ,				atol=1e-5   )   )

     # Attaching a hook to a model when it already has one replaces, does not chain
     __a								=       PostForwardHook()
     add_hook_to_module(UpperCamelCase   ,				UpperCamelCase   )
     __a								=       test_model(UpperCamelCase   )
     self.assertTrue(torch.allclose(UpperCamelCase   ,				output + 1   ,				atol=1e-5   )   )

     # You need to use the sequential hook to chain two or more hooks
     __a								=       SequentialHook(PostForwardHook()   ,				PostForwardHook()   )
     add_hook_to_module(UpperCamelCase   ,				UpperCamelCase   )

     __a								=       test_model(UpperCamelCase   )
     assert torch.allclose(UpperCamelCase   ,				output + 2   ,				atol=1e-5   )







   def 		UpperCamelCase__      (						self   )					->     Optional[Any]:
     __a								=       ModelForTest()
     __a								=       torch.randn(2   ,				3   )
     __a								=       test_model(UpperCamelCase   )

     __a								=       PostForwardHook()
     add_hook_to_module(UpperCamelCase   ,				UpperCamelCase   )
     __a								=       test_model(UpperCamelCase   )
     self.assertTrue(torch.allclose(UpperCamelCase   ,				output + 1   )   )
     self.assertTrue(outputa.requires_grad   )

     __a								=       True
     __a								=       test_model(UpperCamelCase   )
     self.assertFalse(outputa.requires_grad   )







   @require_multi_gpu
   def 		UpperCamelCase__      (						self   )					->     str:
     __a								=       ModelForTest()
     # Everything is on CPU
     self.assertEqual(model.lineara.weight.device   ,				torch.device('cpu'   )   )
     self.assertEqual(model.batchnorm.weight.device   ,				torch.device('cpu'   )   )
     self.assertEqual(model.lineara.weight.device   ,				torch.device('cpu'   )   )

     # This will move each submodule on different devices
     add_hook_to_module(model.lineara   ,				AlignDevicesHook(execution_device=0   )   )
     add_hook_to_module(model.batchnorm   ,				AlignDevicesHook(execution_device=0   )   )
     add_hook_to_module(model.lineara   ,				AlignDevicesHook(execution_device=1   )   )

     self.assertEqual(model.lineara.weight.device   ,				torch.device(0   )   )
     self.assertEqual(model.batchnorm.weight.device   ,				torch.device(0   )   )
     self.assertEqual(model.batchnorm.running_mean.device   ,				torch.device(0   )   )
     self.assertEqual(model.lineara.weight.device   ,				torch.device(1   )   )

     # We can still make a forward pass. The input does not need to be on any particular device
     __a								=       torch.randn(2   ,				3   )
     __a								=       model(UpperCamelCase   )
     self.assertEqual(output.device   ,				torch.device(1   )   )

     # We can add a general hook to put back output on same device as input.
     add_hook_to_module(UpperCamelCase   ,				AlignDevicesHook(io_same_device=UpperCamelCase   )   )
     __a								=       torch.randn(2   ,				3   ).to(0   )
     __a								=       model(UpperCamelCase   )
     self.assertEqual(output.device   ,				torch.device(0   )   )







   def 		UpperCamelCase__      (						self   )					->     int:
     __a								=       ModelForTest()

     # Everything is on CPU
     self.assertEqual(model.lineara.weight.device   ,				torch.device('cpu'   )   )
     self.assertEqual(model.batchnorm.weight.device   ,				torch.device('cpu'   )   )
     self.assertEqual(model.lineara.weight.device   ,				torch.device('cpu'   )   )

     # This will move each submodule on different devices
     __a								=       {'execution_device': 0 if torch.cuda.is_available() else 'cpu', 'offload': True}

     add_hook_to_module(model.lineara   ,				AlignDevicesHook(**UpperCamelCase   )   )
     add_hook_to_module(model.batchnorm   ,				AlignDevicesHook(**UpperCamelCase   )   )
     add_hook_to_module(model.lineara   ,				AlignDevicesHook(**UpperCamelCase   )   )

     # Parameters have been offloaded, so on the meta device
     self.assertEqual(model.lineara.weight.device   ,				torch.device('meta'   )   )
     self.assertEqual(model.batchnorm.weight.device   ,				torch.device('meta'   )   )
     self.assertEqual(model.lineara.weight.device   ,				torch.device('meta'   )   )
     # Buffers are not included in the offload by default, so are on the execution device
     __a								=       torch.device(hook_kwargs['execution_device']   )
     self.assertEqual(model.batchnorm.running_mean.device   ,				UpperCamelCase   )

     __a								=       torch.randn(2   ,				3   )
     __a								=       model(UpperCamelCase   )
     self.assertEqual(output.device   ,				UpperCamelCase   )

     # Removing hooks loads back the weights in the model.
     remove_hook_from_module(model.lineara   )
     remove_hook_from_module(model.batchnorm   )
     remove_hook_from_module(model.lineara   )
     self.assertEqual(model.lineara.weight.device   ,				torch.device('cpu'   )   )
     self.assertEqual(model.batchnorm.weight.device   ,				torch.device('cpu'   )   )
     self.assertEqual(model.lineara.weight.device   ,				torch.device('cpu'   )   )

     # Now test with buffers included in the offload
     __a								=       {
         'execution_device': 0 if torch.cuda.is_available() else 'cpu',
         'offload': True,
         'offload_buffers': True,
     }

     add_hook_to_module(model.lineara   ,				AlignDevicesHook(**UpperCamelCase   )   )
     add_hook_to_module(model.batchnorm   ,				AlignDevicesHook(**UpperCamelCase   )   )
     add_hook_to_module(model.lineara   ,				AlignDevicesHook(**UpperCamelCase   )   )

     # Parameters have been offloaded, so on the meta device, buffers included
     self.assertEqual(model.lineara.weight.device   ,				torch.device('meta'   )   )
     self.assertEqual(model.batchnorm.weight.device   ,				torch.device('meta'   )   )
     self.assertEqual(model.lineara.weight.device   ,				torch.device('meta'   )   )
     self.assertEqual(model.batchnorm.running_mean.device   ,				torch.device('meta'   )   )

     __a								=       torch.randn(2   ,				3   )
     __a								=       model(UpperCamelCase   )
     self.assertEqual(output.device   ,				UpperCamelCase   )

     # Removing hooks loads back the weights in the model.
     remove_hook_from_module(model.lineara   )
     remove_hook_from_module(model.batchnorm   )
     remove_hook_from_module(model.lineara   )
     self.assertEqual(model.lineara.weight.device   ,				torch.device('cpu'   )   )
     self.assertEqual(model.batchnorm.weight.device   ,				torch.device('cpu'   )   )
     self.assertEqual(model.lineara.weight.device   ,				torch.device('cpu'   )   )







   def 		UpperCamelCase__      (						self   )					->     Union[str, Any]:
     __a								=       ModelForTest()

     # Everything is on CPU
     self.assertEqual(model.lineara.weight.device   ,				torch.device('cpu'   )   )
     self.assertEqual(model.batchnorm.weight.device   ,				torch.device('cpu'   )   )
     self.assertEqual(model.lineara.weight.device   ,				torch.device('cpu'   )   )

     # This will move each submodule on different devices
     __a								=       0 if torch.cuda.is_available() else 'cpu'
     attach_align_device_hook(UpperCamelCase   ,				execution_device=UpperCamelCase   ,				offload=UpperCamelCase   )

     # Parameters have been offloaded, so on the meta device
     self.assertEqual(model.lineara.weight.device   ,				torch.device('meta'   )   )
     self.assertEqual(model.batchnorm.weight.device   ,				torch.device('meta'   )   )
     self.assertEqual(model.lineara.weight.device   ,				torch.device('meta'   )   )
     # Buffers are not included in the offload by default, so are on the execution device
     __a								=       torch.device(UpperCamelCase   )
     self.assertEqual(model.batchnorm.running_mean.device   ,				UpperCamelCase   )

     __a								=       torch.randn(2   ,				3   )
     __a								=       model(UpperCamelCase   )
     self.assertEqual(output.device   ,				UpperCamelCase   )

     # Removing hooks loads back the weights in the model.
     remove_hook_from_submodules(UpperCamelCase   )
     self.assertEqual(model.lineara.weight.device   ,				torch.device('cpu'   )   )
     self.assertEqual(model.batchnorm.weight.device   ,				torch.device('cpu'   )   )
     self.assertEqual(model.lineara.weight.device   ,				torch.device('cpu'   )   )

     # Now test with buffers included in the offload
     attach_align_device_hook(UpperCamelCase   ,				execution_device=UpperCamelCase   ,				offload=UpperCamelCase   ,				offload_buffers=UpperCamelCase   )

     # Parameters have been offloaded, so on the meta device, buffers included
     self.assertEqual(model.lineara.weight.device   ,				torch.device('meta'   )   )
     self.assertEqual(model.batchnorm.weight.device   ,				torch.device('meta'   )   )
     self.assertEqual(model.lineara.weight.device   ,				torch.device('meta'   )   )
     self.assertEqual(model.batchnorm.running_mean.device   ,				torch.device('meta'   )   )

     __a								=       torch.randn(2   ,				3   )
     __a								=       model(UpperCamelCase   )
     self.assertEqual(output.device   ,				UpperCamelCase   )

     # Removing hooks loads back the weights in the model.
     remove_hook_from_submodules(UpperCamelCase   )
     self.assertEqual(model.lineara.weight.device   ,				torch.device('cpu'   )   )
     self.assertEqual(model.batchnorm.weight.device   ,				torch.device('cpu'   )   )
     self.assertEqual(model.lineara.weight.device   ,				torch.device('cpu'   )   )







   def 		UpperCamelCase__      (						self   )					->     List[str]:
     __a								=       ModelForTest()

     # Everything is on CPU
     self.assertEqual(model.lineara.weight.device   ,				torch.device('cpu'   )   )
     self.assertEqual(model.batchnorm.weight.device   ,				torch.device('cpu'   )   )
     self.assertEqual(model.lineara.weight.device   ,				torch.device('cpu'   )   )

     # This will move each submodule on different devices
     __a								=       0 if torch.cuda.is_available() else 'cpu'
     attach_align_device_hook(
         UpperCamelCase   ,				execution_device=UpperCamelCase   ,				offload=UpperCamelCase   ,				weights_map=model.state_dict()   )

     # Parameters have been offloaded, so on the meta device
     self.assertEqual(model.lineara.weight.device   ,				torch.device('meta'   )   )
     self.assertEqual(model.batchnorm.weight.device   ,				torch.device('meta'   )   )
     self.assertEqual(model.lineara.weight.device   ,				torch.device('meta'   )   )
     # Buffers are not included in the offload by default, so are on the execution device
     __a								=       torch.device(UpperCamelCase   )
     self.assertEqual(model.batchnorm.running_mean.device   ,				UpperCamelCase   )

     __a								=       torch.randn(2   ,				3   )
     __a								=       model(UpperCamelCase   )
     self.assertEqual(output.device   ,				UpperCamelCase   )

     # Removing hooks loads back the weights in the model.
     remove_hook_from_submodules(UpperCamelCase   )
     self.assertEqual(model.lineara.weight.device   ,				torch.device('cpu'   )   )
     self.assertEqual(model.batchnorm.weight.device   ,				torch.device('cpu'   )   )
     self.assertEqual(model.lineara.weight.device   ,				torch.device('cpu'   )   )

     # Now test with buffers included in the offload
     attach_align_device_hook(
         UpperCamelCase   ,				execution_device=UpperCamelCase   ,				offload=UpperCamelCase   ,				weights_map=model.state_dict()   ,				offload_buffers=UpperCamelCase   ,				)

     # Parameters have been offloaded, so on the meta device, buffers included
     self.assertEqual(model.lineara.weight.device   ,				torch.device('meta'   )   )
     self.assertEqual(model.batchnorm.weight.device   ,				torch.device('meta'   )   )
     self.assertEqual(model.lineara.weight.device   ,				torch.device('meta'   )   )
     self.assertEqual(model.batchnorm.running_mean.device   ,				torch.device('meta'   )   )

     __a								=       torch.randn(2   ,				3   )
     __a								=       model(UpperCamelCase   )
     self.assertEqual(output.device   ,				UpperCamelCase   )

     # Removing hooks loads back the weights in the model.
     remove_hook_from_submodules(UpperCamelCase   )
     self.assertEqual(model.lineara.weight.device   ,				torch.device('cpu'   )   )
     self.assertEqual(model.batchnorm.weight.device   ,				torch.device('cpu'   )   )
     self.assertEqual(model.lineara.weight.device   ,				torch.device('cpu'   )   )









"""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 lowerCAmelCase__      (	lowercase		):






			'''simple docstring'''



			lowerCamelCase__ =     42
			lowerCamelCase__ =     None







def 	_snake_case     (							lowercase__				,       lowercase__=0.9_9_9				,       lowercase__="cosine"				,       ):
				if alpha_transform_type == "cosine":

								def alpha_bar_fn(lowercase__						):
												return math.cos((t + 0.0_0_8) / 1.0_0_8 * math.pi / 2						) ** 2

				elif alpha_transform_type == "exp":

								def alpha_bar_fn(lowercase__						):
												return math.exp(t * -1_2.0						)

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

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






class lowerCAmelCase__      (	lowercase,      lowercase		):






			'''simple docstring'''



			lowerCamelCase__ =     1
			@register_to_config
			def __init__(    self    , lowercase = 1000    , lowercase = 0.00_01    , lowercase = 0.02    , lowercase = "linear"    , lowercase = None    , lowercase = True    , lowercase = True    , lowercase = 0    , lowercase = "epsilon"    , lowercase = 1.0    , **lowercase    , ):
							if kwargs.get('set_alpha_to_one'    , lowercase    ) is not None:
											_lowerCamelCase	:     List[str]       =       (
											    'The `set_alpha_to_one` argument is deprecated. Please use `set_alpha_to_zero` instead.'
											)
											deprecate('set_alpha_to_one'    , '1.0.0'    , lowercase    , standard_warn=lowercase    )
											_lowerCamelCase	:     Union[str, Any]       =       kwargs['set_alpha_to_one']
							if trained_betas is not None:
											_lowerCamelCase	:     Tuple       =       torch.tensor(lowercase    , dtype=torch.floataa    )
							elif beta_schedule == "linear":
											_lowerCamelCase	:     List[str]       =       torch.linspace(lowercase    , lowercase    , lowercase    , dtype=torch.floataa    )
							elif beta_schedule == "scaled_linear":
											# this schedule is very specific to the latent diffusion model.
											_lowerCamelCase	:     Any       =       (
											    torch.linspace(beta_start**0.5    , beta_end**0.5    , lowercase    , dtype=torch.floataa    ) ** 2
											)
							elif beta_schedule == "squaredcos_cap_v2":
											# Glide cosine schedule
											_lowerCamelCase	:     Tuple       =       betas_for_alpha_bar(lowercase    )
							else:
											raise NotImplementedError(F'''{beta_schedule} does is not implemented for {self.__class__}'''    )

							_lowerCamelCase	:     str       =       1.0 - self.betas
							_lowerCamelCase	:     str       =       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	:     int       =       torch.tensor(0.0    ) if set_alpha_to_zero else self.alphas_cumprod[-1]

							# standard deviation of the initial noise distribution
							_lowerCamelCase	:     Optional[Any]       =       1.0

							# setable values
							_lowerCamelCase	:     Dict       =       None
							_lowerCamelCase	:     int       =       torch.from_numpy(np.arange(0    , lowercase    ).copy().astype(np.intaa    )    )
			def    A_       (    self    , lowercase    , lowercase = None    ):
							return sample
			def    A_       (    self    , lowercase    , lowercase = None    ):

							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	:     int       =       num_inference_steps
							_lowerCamelCase	:     Optional[int]       =       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	:     Any       =       (np.arange(0    , lowercase    ) * step_ratio).round().copy().astype(np.intaa    )
							_lowerCamelCase	:     Any       =       torch.from_numpy(lowercase    ).to(lowercase    )
							self.timesteps += self.config.steps_offset
			def    A_       (    self    , lowercase    , lowercase    , lowercase    , lowercase = 0.0    , lowercase = False    , lowercase = None    , lowercase = True    , ):
							# 1. get previous step value (=t+1)
							_lowerCamelCase	:     int       =       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	:     Dict       =       self.alphas_cumprod[timestep]
							_lowerCamelCase	:     str       =       (
							    self.alphas_cumprod[prev_timestep]
							    if prev_timestep < self.config.num_train_timesteps
							    else self.final_alpha_cumprod
							)

							_lowerCamelCase	:     Any       =       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	:     Optional[int]       =       (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
											_lowerCamelCase	:     Any       =       model_output
							elif self.config.prediction_type == "sample":
											_lowerCamelCase	:     Optional[Any]       =       model_output
											_lowerCamelCase	:     Tuple       =       (sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
							elif self.config.prediction_type == "v_prediction":
											_lowerCamelCase	:     Any       =       (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
											_lowerCamelCase	:     Tuple       =       (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	:     Tuple       =       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	:     Optional[int]       =       (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	:     Dict       =       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=lowercase    , pred_original_sample=lowercase    )






			def __len__(    self    ):
							return self.config.num_train_timesteps