





import gc
import random
import tempfile
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMInverseScheduler,
    DDIMScheduler,
    DPMSolverMultistepInverseScheduler,
    DPMSolverMultistepScheduler,
    StableDiffusionDiffEditPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import load_image, slow
from diffusers.utils.testing_utils import enable_full_determinism, floats_tensor, require_torch_gpu, torch_device

from ..pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()




class     SCREAMING_SNAKE_CASE__ (						UpperCamelCase__				,					UpperCamelCase__				,					unittest.TestCase      ):
 __SCREAMING_SNAKE_CASE							       =	StableDiffusionDiffEditPipeline
 __SCREAMING_SNAKE_CASE							       =	TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'''height''', '''width''', '''image'''} | {'''image_latents'''}
 __SCREAMING_SNAKE_CASE							       =	TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS - {'''image'''} | {'''image_latents'''}
 __SCREAMING_SNAKE_CASE							       =	frozenset(
     []      )  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
 __SCREAMING_SNAKE_CASE							       =	frozenset([]      )



 def 				UpperCamelCase			(							self ):
        torch.manual_seed(0 )
        A__											=						UNetaDConditionModel(
            block_out_channels=(32, 64),layers_per_block=2,sample_size=32,in_channels=4,out_channels=4,down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D'''),up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D'''),cross_attention_dim=32,attention_head_dim=(2, 4),use_linear_projection=__lowerCamelCase,)
        A__											=						DDIMScheduler(
            beta_start=0.00085,beta_end=0.012,beta_schedule='''scaled_linear''',clip_sample=__lowerCamelCase,set_alpha_to_one=__lowerCamelCase,)
        A__											=						DDIMInverseScheduler(
            beta_start=0.00085,beta_end=0.012,beta_schedule='''scaled_linear''',clip_sample=__lowerCamelCase,set_alpha_to_zero=__lowerCamelCase,)
        torch.manual_seed(0 )
        A__											=						AutoencoderKL(
            block_out_channels=[32, 64],in_channels=3,out_channels=3,down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D'''],up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D'''],latent_channels=4,sample_size=128,)
        torch.manual_seed(0 )
        A__											=						CLIPTextConfig(
            bos_token_id=0,eos_token_id=2,hidden_size=32,intermediate_size=37,layer_norm_eps=1E-05,num_attention_heads=4,num_hidden_layers=5,pad_token_id=1,vocab_size=1000,hidden_act='''gelu''',projection_dim=512,)
        A__											=						CLIPTextModel(__lowerCamelCase )
        A__											=						CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''' )

        A__											=						{
            '''unet''': unet,
            '''scheduler''': scheduler,
            '''inverse_scheduler''': inverse_scheduler,
            '''vae''': vae,
            '''text_encoder''': text_encoder,
            '''tokenizer''': tokenizer,
            '''safety_checker''': None,
            '''feature_extractor''': None,
        }

        return components



 def 				UpperCamelCase			(							self,__lowerCamelCase,__lowerCamelCase=0 ):
        A__											=						floats_tensor((1, 16, 16),rng=random.Random(__lowerCamelCase ) ).to(__lowerCamelCase )
        A__											=						floats_tensor((1, 2, 4, 16, 16),rng=random.Random(__lowerCamelCase ) ).to(__lowerCamelCase )
        if str(__lowerCamelCase ).startswith('''mps''' ):
               A__											=						torch.manual_seed(__lowerCamelCase )
        else:
               A__											=						torch.Generator(device=__lowerCamelCase ).manual_seed(__lowerCamelCase )
        A__											=						{
            '''prompt''': '''a dog and a newt''',
            '''mask_image''': mask,
            '''image_latents''': latents,
            '''generator''': generator,
            '''num_inference_steps''': 2,
            '''inpaint_strength''': 1.0,
            '''guidance_scale''': 6.0,
            '''output_type''': '''numpy''',
        }

        return inputs



 def 				UpperCamelCase			(							self,__lowerCamelCase,__lowerCamelCase=0 ):
        A__											=						floats_tensor((1, 3, 32, 32),rng=random.Random(__lowerCamelCase ) ).to(__lowerCamelCase )
        A__											=						image.cpu().permute(0,2,3,1 )[0]
        A__											=						Image.fromarray(np.uinta(__lowerCamelCase ) ).convert('''RGB''' )
        if str(__lowerCamelCase ).startswith('''mps''' ):
               A__											=						torch.manual_seed(__lowerCamelCase )
        else:
               A__											=						torch.Generator(device=__lowerCamelCase ).manual_seed(__lowerCamelCase )
        A__											=						{
            '''image''': image,
            '''source_prompt''': '''a cat and a frog''',
            '''target_prompt''': '''a dog and a newt''',
            '''generator''': generator,
            '''num_inference_steps''': 2,
            '''num_maps_per_mask''': 2,
            '''mask_encode_strength''': 1.0,
            '''guidance_scale''': 6.0,
            '''output_type''': '''numpy''',
        }

        return inputs



 def 				UpperCamelCase			(							self,__lowerCamelCase,__lowerCamelCase=0 ):
        A__											=						floats_tensor((1, 3, 32, 32),rng=random.Random(__lowerCamelCase ) ).to(__lowerCamelCase )
        A__											=						image.cpu().permute(0,2,3,1 )[0]
        A__											=						Image.fromarray(np.uinta(__lowerCamelCase ) ).convert('''RGB''' )
        if str(__lowerCamelCase ).startswith('''mps''' ):
               A__											=						torch.manual_seed(__lowerCamelCase )
        else:
               A__											=						torch.Generator(device=__lowerCamelCase ).manual_seed(__lowerCamelCase )
        A__											=						{
            '''image''': image,
            '''prompt''': '''a cat and a frog''',
            '''generator''': generator,
            '''num_inference_steps''': 2,
            '''inpaint_strength''': 1.0,
            '''guidance_scale''': 6.0,
            '''decode_latents''': True,
            '''output_type''': '''numpy''',
        }
        return inputs



 def 				UpperCamelCase			(							self ):
        if not hasattr(self.pipeline_class,'''_optional_components''' ):
               return

        A__											=						self.get_dummy_components()
        A__											=						self.pipeline_class(**__lowerCamelCase )
        pipe.to(__lowerCamelCase )
        pipe.set_progress_bar_config(disable=__lowerCamelCase )

        # set all optional components to None and update pipeline config accordingly
        for optional_component in pipe._optional_components:
               setattr(__lowerCamelCase,__lowerCamelCase,__lowerCamelCase )
        pipe.register_modules(**{optional_component: None for optional_component in pipe._optional_components} )

        A__											=						self.get_dummy_inputs(__lowerCamelCase )
        A__											=						pipe(**__lowerCamelCase )[0]

        with tempfile.TemporaryDirectory() as tmpdir:
               pipe.save_pretrained(__lowerCamelCase )
               A__											=						self.pipeline_class.from_pretrained(__lowerCamelCase )
               pipe_loaded.to(__lowerCamelCase )
               pipe_loaded.set_progress_bar_config(disable=__lowerCamelCase )

        for optional_component in pipe._optional_components:
               self.assertTrue(
                   getattr(__lowerCamelCase,__lowerCamelCase ) is None,f"`{optional_component}` did not stay set to None after loading.",)

        A__											=						self.get_dummy_inputs(__lowerCamelCase )
        A__											=						pipe_loaded(**__lowerCamelCase )[0]

        A__											=						np.abs(output - output_loaded ).max()
        self.assertLess(__lowerCamelCase,1E-4 )



 def 				UpperCamelCase			(							self ):
        A__											=						'''cpu'''

        A__											=						self.get_dummy_components()
        A__											=						self.pipeline_class(**__lowerCamelCase )
        pipe.to(__lowerCamelCase )
        pipe.set_progress_bar_config(disable=__lowerCamelCase )

        A__											=						self.get_dummy_mask_inputs(__lowerCamelCase )
        A__											=						pipe.generate_mask(**__lowerCamelCase )
        A__											=						mask[0, -3:, -3:]

        self.assertEqual(mask.shape,(1, 16, 16) )
        A__											=						np.array([0] * 9 )
        A__											=						np.abs(mask_slice.flatten() - expected_slice ).max()
        self.assertLessEqual(__lowerCamelCase,1E-3 )
        self.assertEqual(mask[0, -3, -4],0 )



 def 				UpperCamelCase			(							self ):
        A__											=						'''cpu'''

        A__											=						self.get_dummy_components()
        A__											=						self.pipeline_class(**__lowerCamelCase )
        pipe.to(__lowerCamelCase )
        pipe.set_progress_bar_config(disable=__lowerCamelCase )

        A__											=						self.get_dummy_inversion_inputs(__lowerCamelCase )
        A__											=						pipe.invert(**__lowerCamelCase ).images
        A__											=						image[0, -1, -3:, -3:]

        self.assertEqual(image.shape,(2, 32, 32, 3) )
        A__											=						np.array(
            [0.5150, 0.5134, 0.5043, 0.5376, 0.4694, 0.51050, 0.5015, 0.4407, 0.4799],)
        A__											=						np.abs(image_slice.flatten() - expected_slice ).max()
        self.assertLessEqual(__lowerCamelCase,1E-3 )



 def 				UpperCamelCase			(							self ):
        super().test_inference_batch_single_identical(expected_max_diff=5E-3 )




 def 				UpperCamelCase			(							self ):
        A__											=						'''cpu'''

        A__											=						self.get_dummy_components()

        A__											=						{'''beta_start''': 0.00085, '''beta_end''': 0.012, '''beta_schedule''': '''scaled_linear'''}
        A__											=						DPMSolverMultistepScheduler(**__lowerCamelCase )
        A__											=						DPMSolverMultistepInverseScheduler(**__lowerCamelCase )

        A__											=						self.pipeline_class(**__lowerCamelCase )
        pipe.to(__lowerCamelCase )
        pipe.set_progress_bar_config(disable=__lowerCamelCase )

        A__											=						self.get_dummy_inversion_inputs(__lowerCamelCase )
        A__											=						pipe.invert(**__lowerCamelCase ).images
        A__											=						image[0, -1, -3:, -3:]

        self.assertEqual(image.shape,(2, 32, 32, 3) )
        A__											=						np.array(
            [0.5150, 0.5134, 0.5043, 0.5376, 0.4694, 0.51050, 0.5015, 0.4407, 0.4799],)
        A__											=						np.abs(image_slice.flatten() - expected_slice ).max()
        self.assertLessEqual(__lowerCamelCase,1E-3 )




@require_torch_gpu
@slow
class     SCREAMING_SNAKE_CASE__ (						unittest.TestCase      ):



 def 				UpperCamelCase			(							self ):
        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()



 @classmethod
 def 				UpperCamelCase			(							cls ):
        A__											=						load_image(
            '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/diffedit/fruit.png''' )

        A__											=						raw_image.convert('''RGB''' ).resize((768, 768) )

        A__											=						raw_image



 def 				UpperCamelCase			(							self ):
        A__											=						torch.manual_seed(0 )

        A__											=						StableDiffusionDiffEditPipeline.from_pretrained(
            '''stabilityai/stable-diffusion-2-1''',safety_checker=__lowerCamelCase,torch_dtype=torch.floataa )
        A__											=						DDIMScheduler.from_config(pipe.scheduler.config )
        A__											=						DDIMInverseScheduler.from_config(pipe.scheduler.config )
        pipe.enable_model_cpu_offload()
        pipe.set_progress_bar_config(disable=__lowerCamelCase )

        A__											=						'''a bowl of fruit'''
        A__											=						'''a bowl of pears'''

        A__											=						pipe.generate_mask(
            image=self.raw_image,source_prompt=__lowerCamelCase,target_prompt=__lowerCamelCase,generator=__lowerCamelCase,)

        A__											=						pipe.invert(
            prompt=__lowerCamelCase,image=self.raw_image,inpaint_strength=0.7,generator=__lowerCamelCase ).latents

        A__											=						pipe(
            prompt=__lowerCamelCase,mask_image=__lowerCamelCase,image_latents=__lowerCamelCase,generator=__lowerCamelCase,negative_prompt=__lowerCamelCase,inpaint_strength=0.7,output_type='''numpy''',).images[0]

        A__											=						(
            np.array(
                load_image(
                    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
                    '''/diffedit/pears.png''' ).resize((768, 768) ) )
            / 255
        )
        assert np.abs((expected_image - image).max() ) < 5E-1




 def 				UpperCamelCase			(							self ):
        A__											=						torch.manual_seed(0 )

        A__											=						StableDiffusionDiffEditPipeline.from_pretrained(
            '''stabilityai/stable-diffusion-2-1''',safety_checker=__lowerCamelCase,torch_dtype=torch.floataa )
        A__											=						DPMSolverMultistepScheduler.from_config(pipe.scheduler.config )
        A__											=						DPMSolverMultistepInverseScheduler.from_config(pipe.scheduler.config )
        pipe.enable_model_cpu_offload()
        pipe.set_progress_bar_config(disable=__lowerCamelCase )

        A__											=						'''a bowl of fruit'''
        A__											=						'''a bowl of pears'''

        A__											=						pipe.generate_mask(
            image=self.raw_image,source_prompt=__lowerCamelCase,target_prompt=__lowerCamelCase,generator=__lowerCamelCase,)

        A__											=						pipe.invert(
            prompt=__lowerCamelCase,image=self.raw_image,inpaint_strength=0.7,generator=__lowerCamelCase,num_inference_steps=25,).latents

        A__											=						pipe(
            prompt=__lowerCamelCase,mask_image=__lowerCamelCase,image_latents=__lowerCamelCase,generator=__lowerCamelCase,negative_prompt=__lowerCamelCase,inpaint_strength=0.7,num_inference_steps=25,output_type='''numpy''',).images[0]

        A__											=						(
            np.array(
                load_image(
                    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
                    '''/diffedit/pears.png''' ).resize((768, 768) ) )
            / 255
        )
        assert np.abs((expected_image - image).max() ) < 5E-1












import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput


def 	UpperCamelCase__(       UpperCamelCase__			:       List[Any] ,     UpperCamelCase__			:       Tuple=0.999 ,     UpperCamelCase__			:       Any="cosine" ,     )->List[str]:
       if alpha_transform_type == "cosine":

              def alpha_bar_fn(UpperCamelCase__			:       Optional[int]  ):
                     return math.cos((t + 0.008) / 1.008 * math.pi / 2  ) ** 2

       elif alpha_transform_type == "exp":

              def alpha_bar_fn(UpperCamelCase__			:       str  ):
                     return math.exp(t * -12.0  )

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

       A__											=						[]
       for i in range(UpperCamelCase__  ):
              A__											=						i / num_diffusion_timesteps
              A__											=						(i + 1) / num_diffusion_timesteps
              betas.append(min(1 - alpha_bar_fn(UpperCamelCase__  ) / alpha_bar_fn(UpperCamelCase__  ) ,     UpperCamelCase__  )  )
       return torch.tensor(UpperCamelCase__ ,     dtype=torch.floataa  )




class     SCREAMING_SNAKE_CASE__ (						UpperCamelCase__				,					UpperCamelCase__      ):
 __SCREAMING_SNAKE_CASE							       =	[e.name for e in KarrasDiffusionSchedulers]
 __SCREAMING_SNAKE_CASE							       =	2



 @register_to_config
 def __init__(							self,__lowerCamelCase = 1000,__lowerCamelCase = 0.00085,__lowerCamelCase = 0.012,__lowerCamelCase = "linear",__lowerCamelCase = None,__lowerCamelCase = "epsilon",__lowerCamelCase = False,__lowerCamelCase = False,__lowerCamelCase = 1.0,__lowerCamelCase = "linspace",__lowerCamelCase = 0,):
        if trained_betas is not None:
               A__											=						torch.tensor(__lowerCamelCase,dtype=torch.floataa )
        elif beta_schedule == "linear":
               A__											=						torch.linspace(__lowerCamelCase,__lowerCamelCase,__lowerCamelCase,dtype=torch.floataa )
        elif beta_schedule == "scaled_linear":
               # this schedule is very specific to the latent diffusion model.
               A__											=						(
                   torch.linspace(beta_start**0.5,beta_end**0.5,__lowerCamelCase,dtype=torch.floataa ) ** 2
               )
        elif beta_schedule == "squaredcos_cap_v2":
               # Glide cosine schedule
               A__											=						betas_for_alpha_bar(__lowerCamelCase,alpha_transform_type='''cosine''' )
        elif beta_schedule == "exp":
               A__											=						betas_for_alpha_bar(__lowerCamelCase,alpha_transform_type='''exp''' )
        else:
               raise NotImplementedError(f"{beta_schedule} does is not implemented for {self.__class__}" )

        A__											=						1.0 - self.betas
        A__											=						torch.cumprod(self.alphas,dim=0 )

        #  set all values
        self.set_timesteps(__lowerCamelCase,__lowerCamelCase,__lowerCamelCase )
        A__											=						use_karras_sigmas



 def 				UpperCamelCase			(							self,__lowerCamelCase,__lowerCamelCase=None ):
        if schedule_timesteps is None:
               A__											=						self.timesteps

        A__											=						(schedule_timesteps == timestep).nonzero()

        # The sigma index that is taken for the **very** first `step`
        # is always the second index (or the last index if there is only 1)
        # This way we can ensure we don't accidentally skip a sigma in
        # case we start in the middle of the denoising schedule (e.g. for image-to-image)
        if len(self._index_counter ) == 0:
               A__											=						1 if len(__lowerCamelCase ) > 1 else 0
        else:
               A__											=						timestep.cpu().item() if torch.is_tensor(__lowerCamelCase ) else timestep
               A__											=						self._index_counter[timestep_int]

        return indices[pos].item()



 @property
 def 				UpperCamelCase			(							self ):
        # standard deviation of the initial noise distribution
        if self.config.timestep_spacing in ["linspace", "trailing"]:
               return self.sigmas.max()

        return (self.sigmas.max() ** 2 + 1) ** 0.5



 def 				UpperCamelCase			(							self,__lowerCamelCase,__lowerCamelCase,):
        A__											=						self.index_for_timestep(__lowerCamelCase )

        A__											=						self.sigmas[step_index]
        A__											=						sample / ((sigma**2 + 1) ** 0.5)
        return sample



 def 				UpperCamelCase			(							self,__lowerCamelCase,__lowerCamelCase = None,__lowerCamelCase = None,):
        A__											=						num_inference_steps

        A__											=						num_train_timesteps or self.config.num_train_timesteps

        # "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
        if self.config.timestep_spacing == "linspace":
               A__											=						np.linspace(0,num_train_timesteps - 1,__lowerCamelCase,dtype=__lowerCamelCase )[::-1].copy()
        elif self.config.timestep_spacing == "leading":
               A__											=						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
               A__											=						(np.arange(0,__lowerCamelCase ) * step_ratio).round()[::-1].copy().astype(__lowerCamelCase )
               timesteps += self.config.steps_offset
        elif self.config.timestep_spacing == "trailing":
               A__											=						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
               A__											=						(np.arange(__lowerCamelCase,0,-step_ratio )).round().copy().astype(__lowerCamelCase )
               timesteps -= 1
        else:
               raise ValueError(
                   f"{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'." )

        A__											=						np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5 )
        A__											=						np.log(__lowerCamelCase )
        A__											=						np.interp(__lowerCamelCase,np.arange(0,len(__lowerCamelCase ) ),__lowerCamelCase )

        if self.config.use_karras_sigmas:
               A__											=						self._convert_to_karras(in_sigmas=__lowerCamelCase,num_inference_steps=self.num_inference_steps )
               A__											=						np.array([self._sigma_to_t(__lowerCamelCase,__lowerCamelCase ) for sigma in sigmas] )

        A__											=						np.concatenate([sigmas, [0.0]] ).astype(np.floataa )
        A__											=						torch.from_numpy(__lowerCamelCase ).to(device=__lowerCamelCase )
        A__											=						torch.cat([sigmas[:1], sigmas[1:-1].repeat_interleave(2 ), sigmas[-1:]] )

        A__											=						torch.from_numpy(__lowerCamelCase )
        A__											=						torch.cat([timesteps[:1], timesteps[1:].repeat_interleave(2 )] )

        if str(__lowerCamelCase ).startswith('''mps''' ):
               # mps does not support float64
               A__											=						timesteps.to(__lowerCamelCase,dtype=torch.floataa )
        else:
               A__											=						timesteps.to(device=__lowerCamelCase )

        # empty dt and derivative
        A__											=						None
        A__											=						None

        # for exp beta schedules, such as the one for `pipeline_shap_e.py`
        # we need an index counter
        A__											=						defaultdict(__lowerCamelCase )



 def 				UpperCamelCase			(							self,__lowerCamelCase,__lowerCamelCase ):
        # get log sigma
        A__											=						np.log(__lowerCamelCase )

        # get distribution
        A__											=						log_sigma - log_sigmas[:, np.newaxis]

        # get sigmas range
        A__											=						np.cumsum((dists >= 0),axis=0 ).argmax(axis=0 ).clip(max=log_sigmas.shape[0] - 2 )
        A__											=						low_idx + 1

        A__											=						log_sigmas[low_idx]
        A__											=						log_sigmas[high_idx]

        # interpolate sigmas
        A__											=						(low - log_sigma) / (low - high)
        A__											=						np.clip(__lowerCamelCase,0,1 )

        # transform interpolation to time range
        A__											=						(1 - w) * low_idx + w * high_idx
        A__											=						t.reshape(sigma.shape )
        return t



 def 				UpperCamelCase			(							self,__lowerCamelCase,__lowerCamelCase ):
        A__											=						in_sigmas[-1].item()
        A__											=						in_sigmas[0].item()

        A__											=						7.0  # 7.0 is the value used in the paper
        A__											=						np.linspace(0,1,__lowerCamelCase )
        A__											=						sigma_min ** (1 / rho)
        A__											=						sigma_max ** (1 / rho)
        A__											=						(max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
        return sigmas



 @property
 def 				UpperCamelCase			(							self ):
        return self.dt is None



 def 				UpperCamelCase			(							self,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase = True,):
        A__											=						self.index_for_timestep(__lowerCamelCase )

        # advance index counter by 1
        A__											=						timestep.cpu().item() if torch.is_tensor(__lowerCamelCase ) else timestep
        self._index_counter[timestep_int] += 1

        if self.state_in_first_order:
               A__											=						self.sigmas[step_index]
               A__											=						self.sigmas[step_index + 1]
        else:
               # 2nd order / Heun's method
               A__											=						self.sigmas[step_index - 1]
               A__											=						self.sigmas[step_index]

        # currently only gamma=0 is supported. This usually works best anyways.
        # We can support gamma in the future but then need to scale the timestep before
        # passing it to the model which requires a change in API
        A__											=						0
        A__											=						sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

        # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
        if self.config.prediction_type == "epsilon":
               A__											=						sigma_hat if self.state_in_first_order else sigma_next
               A__											=						sample - sigma_input * model_output
        elif self.config.prediction_type == "v_prediction":
               A__											=						sigma_hat if self.state_in_first_order else sigma_next
               A__											=						model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
                   sample / (sigma_input**2 + 1)
               )
        elif self.config.prediction_type == "sample":
               A__											=						model_output
        else:
               raise ValueError(
                   f"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`" )

        if self.config.clip_sample:
               A__											=						pred_original_sample.clamp(
                   -self.config.clip_sample_range,self.config.clip_sample_range )

        if self.state_in_first_order:
               # 2. Convert to an ODE derivative for 1st order
               A__											=						(sample - pred_original_sample) / sigma_hat
               # 3. delta timestep
               A__											=						sigma_next - sigma_hat

               # store for 2nd order step
               A__											=						derivative
               A__											=						dt
               A__											=						sample
        else:
               # 2. 2nd order / Heun's method
               A__											=						(sample - pred_original_sample) / sigma_next
               A__											=						(self.prev_derivative + derivative) / 2

               # 3. take prev timestep & sample
               A__											=						self.dt
               A__											=						self.sample

               # free dt and derivative
               # Note, this puts the scheduler in "first order mode"
               A__											=						None
               A__											=						None
               A__											=						None

        A__											=						sample + derivative * dt

        if not return_dict:
               return (prev_sample,)

        return SchedulerOutput(prev_sample=__lowerCamelCase )



 def 				UpperCamelCase			(							self,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase,):
        # Make sure sigmas and timesteps have the same device and dtype as original_samples
        A__											=						self.sigmas.to(device=original_samples.device,dtype=original_samples.dtype )
        if original_samples.device.type == "mps" and torch.is_floating_point(__lowerCamelCase ):
               # mps does not support float64
               A__											=						self.timesteps.to(original_samples.device,dtype=torch.floataa )
               A__											=						timesteps.to(original_samples.device,dtype=torch.floataa )
        else:
               A__											=						self.timesteps.to(original_samples.device )
               A__											=						timesteps.to(original_samples.device )

        A__											=						[self.index_for_timestep(__lowerCamelCase,__lowerCamelCase ) for t in timesteps]

        A__											=						sigmas[step_indices].flatten()
        while len(sigma.shape ) < len(original_samples.shape ):
               A__											=						sigma.unsqueeze(-1 )

        A__											=						original_samples + noise * sigma
        return noisy_samples




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





