






"""simple docstring"""





import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPImageProcessor, CLIPVisionConfig

from diffusers import AutoencoderKL, PaintByExamplePipeline, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.paint_by_example import PaintByExampleImageEncoder
from diffusers.utils import floats_tensor, load_image, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import IMAGE_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, IMAGE_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()







class 		_a   (						__a				,     unittest.TestCase    ):





   '''simple docstring'''






   UpperCamelCase__			     =				PaintByExamplePipeline
   UpperCamelCase__			     =				IMAGE_GUIDED_IMAGE_INPAINTING_PARAMS
   UpperCamelCase__			     =				IMAGE_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
   UpperCamelCase__			     =				frozenset([]    )  # TO_DO: update the image_prams once refactored VaeImageProcessor.preprocess





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




          '''simple docstring'''




          torch.manual_seed(0)
          lowercase__:				Tuple			  =			UNetaDConditionModel(
              block_out_channels=(32, 64) ,      layers_per_block=2 ,      sample_size=32 ,      in_channels=9 ,      out_channels=4 ,      down_block_types=("DownBlock2D", "CrossAttnDownBlock2D") ,      up_block_types=("CrossAttnUpBlock2D", "UpBlock2D") ,      cross_attention_dim=32 ,      )
          lowercase__:				Union[str, Any]			  =			PNDMScheduler(skip_prk_steps=lowerCAmelCase__)
          torch.manual_seed(0)
          lowercase__:				int			  =			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 ,      )
          torch.manual_seed(0)
          lowercase__:				Tuple			  =			CLIPVisionConfig(
              hidden_size=32 ,      projection_dim=32 ,      intermediate_size=37 ,      layer_norm_eps=1E-0_5 ,      num_attention_heads=4 ,      num_hidden_layers=5 ,      image_size=32 ,      patch_size=4 ,      )
          lowercase__:				Optional[Any]			  =			PaintByExampleImageEncoder(lowerCAmelCase__ ,      proj_size=32)
          lowercase__:				int			  =			CLIPImageProcessor(crop_size=32 ,      size=32)

          lowercase__:				Tuple			  =			{
              """unet""": unet,
              """scheduler""": scheduler,
              """vae""": vae,
              """image_encoder""": image_encoder,
              """safety_checker""": None,
              """feature_extractor""": feature_extractor,
          }
          return components





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




          '''simple docstring'''




          lowercase__:				int			  =			np.array(image.convert("RGB"))
          lowercase__:				Any			  =			image[None].transpose(0 ,      3 ,      1 ,      2)
          lowercase__:				str			  =			torch.from_numpy(lowerCAmelCase__).to(dtype=torch.floataa) / 127.5 - 1.0
          return image





   def __lowercase				(    self ,      UpperCAmelCase_="cpu" ,      UpperCAmelCase_=0)       ->					List[str]:




          '''simple docstring'''




          lowercase__:				Optional[Any]			  =			floats_tensor((1, 3, 32, 32) ,      rng=random.Random(lowerCAmelCase__)).to(lowerCAmelCase__)
          lowercase__:				Tuple			  =			image.cpu().permute(0 ,      2 ,      3 ,      1)[0]
          lowercase__:				Union[str, Any]			  =			Image.fromarray(np.uinta(lowerCAmelCase__)).convert("RGB").resize((64, 64))
          lowercase__:				List[str]			  =			Image.fromarray(np.uinta(image + 4)).convert("RGB").resize((64, 64))
          lowercase__:				Optional[int]			  =			Image.fromarray(np.uinta(lowerCAmelCase__)).convert("RGB").resize((32, 32))

          if str(lowerCAmelCase__).startswith("mps"):
                 lowercase__:				List[str]			  =			torch.manual_seed(lowerCAmelCase__)
          else:
                 lowercase__:				Optional[int]			  =			torch.Generator(device=lowerCAmelCase__).manual_seed(lowerCAmelCase__)
          lowercase__:				Optional[Any]			  =			{
              """example_image""": example_image,
              """image""": init_image,
              """mask_image""": mask_image,
              """generator""": generator,
              """num_inference_steps""": 2,
              """guidance_scale""": 6.0,
              """output_type""": """numpy""",
          }
          return inputs





   def __lowercase				(    self)       ->					List[str]:




          '''simple docstring'''




          lowercase__:				int			  =			self.get_dummy_components()

          # make sure here that pndm scheduler skips prk
          lowercase__:				int			  =			PaintByExamplePipeline(**lowerCAmelCase__)
          lowercase__:				Union[str, Any]			  =			pipe.to("cpu")
          pipe.set_progress_bar_config(disable=lowerCAmelCase__)

          lowercase__:				Optional[Any]			  =			self.get_dummy_inputs()
          lowercase__:				int			  =			pipe(**lowerCAmelCase__)
          lowercase__:				Any			  =			output.images

          lowercase__:				Optional[Any]			  =			image[0, -3:, -3:, -1]

          assert image.shape == (1, 64, 64, 3)
          lowercase__:				Optional[Any]			  =			np.array([0.46_86, 0.56_87, 0.40_07, 0.52_18, 0.57_41, 0.44_82, 0.49_40, 0.46_29, 0.45_03])

          assert np.abs(image_slice.flatten() - expected_slice).max() < 1E-2





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




          '''simple docstring'''




          lowercase__:				int			  =			"""cpu"""
          lowercase__:				str			  =			self.get_dummy_inputs()
          inputs.pop("mask_image")
          lowercase__:				Dict			  =			self.convert_to_pt(inputs.pop("image"))
          lowercase__:				Optional[int]			  =			image.clamp(0 ,      1) / 2

          # make sure here that pndm scheduler skips prk
          lowercase__:				str			  =			PaintByExamplePipeline(**self.get_dummy_components())
          lowercase__:				Optional[int]			  =			pipe.to(lowerCAmelCase__)
          pipe.set_progress_bar_config(disable=lowerCAmelCase__)

          lowercase__:				str			  =			pipe(image=lowerCAmelCase__ ,      mask_image=mask_image[:, 0] ,      **lowerCAmelCase__)
          lowercase__:				List[Any]			  =			output.images

          lowercase__:				Optional[int]			  =			image.cpu().permute(0 ,      2 ,      3 ,      1)[0]
          lowercase__:				int			  =			mask_image.cpu().permute(0 ,      2 ,      3 ,      1)[0]

          lowercase__:				Optional[Any]			  =			Image.fromarray(np.uinta(lowerCAmelCase__)).convert("RGB")
          lowercase__:				Tuple			  =			Image.fromarray(np.uinta(lowerCAmelCase__)).convert("RGB")

          lowercase__:				Any			  =			pipe(**self.get_dummy_inputs())
          lowercase__:				List[Any]			  =			output.images

          assert out_a.shape == (1, 64, 64, 3)
          assert np.abs(out_a.flatten() - out_a.flatten()).max() < 5E-2





   def __lowercase				(    self)       ->					Tuple:




          '''simple docstring'''




          super().test_inference_batch_single_identical(expected_max_diff=3E-3)







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





   '''simple docstring'''






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




          '''simple docstring'''




          super().tearDown()
          gc.collect()
          torch.cuda.empty_cache()





   def __lowercase				(    self)       ->					Dict:




          '''simple docstring'''




          lowercase__:				Optional[int]			  =			load_image(
              "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
              "/paint_by_example/dog_in_bucket.png")
          lowercase__:				Dict			  =			load_image(
              "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
              "/paint_by_example/mask.png")
          lowercase__:				List[str]			  =			load_image(
              "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
              "/paint_by_example/panda.jpg")

          lowercase__:				List[Any]			  =			PaintByExamplePipeline.from_pretrained("Fantasy-Studio/Paint-by-Example")
          lowercase__:				Optional[Any]			  =			pipe.to(lowerCAmelCase__)
          pipe.set_progress_bar_config(disable=lowerCAmelCase__)

          lowercase__:				str			  =			torch.manual_seed(321)
          lowercase__:				Any			  =			pipe(
              image=lowerCAmelCase__ ,      mask_image=lowerCAmelCase__ ,      example_image=lowerCAmelCase__ ,      generator=lowerCAmelCase__ ,      guidance_scale=5.0 ,      num_inference_steps=50 ,      output_type="np" ,      )

          lowercase__:				str			  =			output.images

          lowercase__:				Any			  =			image[0, -3:, -3:, -1]

          assert image.shape == (1, 512, 512, 3)
          lowercase__:				Optional[int]			  =			np.array([0.48_34, 0.48_11, 0.48_74, 0.51_22, 0.50_81, 0.51_44, 0.52_91, 0.52_90, 0.53_74])

          assert np.abs(image_slice.flatten() - expected_slice).max() < 1E-2








'''simple docstring'''

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 ..utils import randn_tensor
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput







def __A  (    lowerCAmelCase_ ,       lowerCAmelCase_=0.999 ,       lowerCAmelCase_="cosine" ,       ):
     if alpha_transform_type == "cosine":

          def alpha_bar_fn(lowerCAmelCase_       ):
               return math.cos((t + 0.008) / 1.008 * math.pi / 2       ) ** 2

     elif alpha_transform_type == "exp":

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

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

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







class 	__lowerCAmelCase    (  __a       ,					__a				):
     snake_case   :    Any							      =      [e.name for e in KarrasDiffusionSchedulers]
     snake_case   :    List[Any]							      =      2



     @register_to_config
     def __init__(self						,	lowerCAmelCase__ = 1_0_0_0						,	lowerCAmelCase__ = 0.0_0_0_8_5						,	lowerCAmelCase__ = 0.0_1_2						,	lowerCAmelCase__ = "linear"						,	lowerCAmelCase__ = None						,	lowerCAmelCase__ = "epsilon"						,	lowerCAmelCase__ = "linspace"						,	lowerCAmelCase__ = 0						,	):
          if trained_betas is not None:
               _UpperCAmelCase					:  Optional[int]         =    torch.tensor(lowerCAmelCase__						,	dtype=torch.floataa	)
          elif beta_schedule == "linear":
               _UpperCAmelCase					:  Optional[int]         =    torch.linspace(lowerCAmelCase__						,	lowerCAmelCase__						,	lowerCAmelCase__						,	dtype=torch.floataa	)
          elif beta_schedule == "scaled_linear":
               # this schedule is very specific to the latent diffusion model.
               _UpperCAmelCase					:  Tuple         =    (
                   torch.linspace(beta_start**0.5						,	beta_end**0.5						,	lowerCAmelCase__						,	dtype=torch.floataa	) ** 2
               )
          elif beta_schedule == "squaredcos_cap_v2":
               # Glide cosine schedule
               _UpperCAmelCase					:  Union[str, Any]         =    betas_for_alpha_bar(lowerCAmelCase__	)
          else:
               raise NotImplementedError(F"{beta_schedule} does is not implemented for {self.__class__}"	)

          _UpperCAmelCase					:  Union[str, Any]         =    1.0 - self.betas
          _UpperCAmelCase					:  List[Any]         =    torch.cumprod(self.alphas						,	dim=0	)

          #  set all values
          self.set_timesteps(lowerCAmelCase__						,	lowerCAmelCase__						,	lowerCAmelCase__	)



     def     snake_case_ (self						,	lowerCAmelCase__						,	lowerCAmelCase__=None	):
          if schedule_timesteps is None:
               _UpperCAmelCase					:  Optional[Any]         =    self.timesteps

          _UpperCAmelCase					:  Optional[Any]         =    (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:
               _UpperCAmelCase					:  Dict         =    1 if len(lowerCAmelCase__	) > 1 else 0
          else:
               _UpperCAmelCase					:  str         =    timestep.cpu().item() if torch.is_tensor(lowerCAmelCase__	) else timestep
               _UpperCAmelCase					:  Any         =    self._index_counter[timestep_int]

          return indices[pos].item()



     @property
     def     snake_case_ (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     snake_case_ (self						,	lowerCAmelCase__						,	lowerCAmelCase__						,	):
          _UpperCAmelCase					:  Dict         =    self.index_for_timestep(lowerCAmelCase__	)

          if self.state_in_first_order:
               _UpperCAmelCase					:  Any         =    self.sigmas[step_index]
          else:
               _UpperCAmelCase					:  Tuple         =    self.sigmas_interpol[step_index - 1]

          _UpperCAmelCase					:  Dict         =    sample / ((sigma**2 + 1) ** 0.5)
          return sample



     def     snake_case_ (self						,	lowerCAmelCase__						,	lowerCAmelCase__ = None						,	lowerCAmelCase__ = None						,	):
          _UpperCAmelCase					:  Dict         =    num_inference_steps

          _UpperCAmelCase					:  int         =    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":
               _UpperCAmelCase					:  Dict         =    np.linspace(0						,	num_train_timesteps - 1						,	lowerCAmelCase__						,	dtype=lowerCAmelCase__	)[::-1].copy()
          elif self.config.timestep_spacing == "leading":
               _UpperCAmelCase					:  List[str]         =    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
               _UpperCAmelCase					:  Optional[Any]         =    (np.arange(0						,	lowerCAmelCase__	) * step_ratio).round()[::-1].copy().astype(lowerCAmelCase__	)
               timesteps += self.config.steps_offset
          elif self.config.timestep_spacing == "trailing":
               _UpperCAmelCase					:  Any         =    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
               _UpperCAmelCase					:  List[Any]         =    (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'."	)

          _UpperCAmelCase					:  List[str]         =    np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5	)
          _UpperCAmelCase					:  Dict         =    torch.from_numpy(np.log(lowerCAmelCase__	)	).to(lowerCAmelCase__	)

          _UpperCAmelCase					:  Optional[Any]         =    np.interp(lowerCAmelCase__						,	np.arange(0						,	len(lowerCAmelCase__	)	)						,	lowerCAmelCase__	)
          _UpperCAmelCase					:  Dict         =    np.concatenate([sigmas, [0.0]]	).astype(np.floataa	)
          _UpperCAmelCase					:  Any         =    torch.from_numpy(lowerCAmelCase__	).to(device=lowerCAmelCase__	)

          # compute up and down sigmas
          _UpperCAmelCase					:  str         =    sigmas.roll(-1	)
          _UpperCAmelCase					:  List[Any]         =    0.0
          _UpperCAmelCase					:  Optional[int]         =    (sigmas_next**2 * (sigmas**2 - sigmas_next**2) / sigmas**2) ** 0.5
          _UpperCAmelCase					:  Any         =    (sigmas_next**2 - sigmas_up**2) ** 0.5
          _UpperCAmelCase					:  Tuple         =    0.0

          # compute interpolated sigmas
          _UpperCAmelCase					:  Union[str, Any]         =    sigmas.log().lerp(sigmas_down.log()						,	0.5	).exp()
          _UpperCAmelCase					:  int         =    0.0

          # set sigmas
          _UpperCAmelCase					:  List[Any]         =    torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2	), sigmas[-1:]]	)
          _UpperCAmelCase					:  str         =    torch.cat(
              [sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2	), sigmas_interpol[-1:]]	)
          _UpperCAmelCase					:  Optional[int]         =    torch.cat([sigmas_up[:1], sigmas_up[1:].repeat_interleave(2	), sigmas_up[-1:]]	)
          _UpperCAmelCase					:  List[Any]         =    torch.cat([sigmas_down[:1], sigmas_down[1:].repeat_interleave(2	), sigmas_down[-1:]]	)

          if str(lowerCAmelCase__	).startswith("""mps"""	):
               # mps does not support float64
               _UpperCAmelCase					:  List[str]         =    torch.from_numpy(lowerCAmelCase__	).to(lowerCAmelCase__						,	dtype=torch.floataa	)
          else:
               _UpperCAmelCase					:  int         =    torch.from_numpy(lowerCAmelCase__	).to(lowerCAmelCase__	)

          _UpperCAmelCase					:  int         =    self.sigma_to_t(lowerCAmelCase__	).to(lowerCAmelCase__						,	dtype=timesteps.dtype	)
          _UpperCAmelCase					:  Optional[int]         =    torch.stack((timesteps_interpol[:-2, None], timesteps[1:, None])						,	dim=-1	).flatten()

          _UpperCAmelCase					:  Any         =    torch.cat([timesteps[:1], interleaved_timesteps]	)

          _UpperCAmelCase					:  Dict         =    None

          # for exp beta schedules, such as the one for `pipeline_shap_e.py`
          # we need an index counter
          _UpperCAmelCase					:  Dict         =    defaultdict(lowerCAmelCase__	)



     def     snake_case_ (self						,	lowerCAmelCase__	):
          # get log sigma
          _UpperCAmelCase					:  Union[str, Any]         =    sigma.log()

          # get distribution
          _UpperCAmelCase					:  str         =    log_sigma - self.log_sigmas[:, None]

          # get sigmas range
          _UpperCAmelCase					:  str         =    dists.ge(0	).cumsum(dim=0	).argmax(dim=0	).clamp(max=self.log_sigmas.shape[0] - 2	)
          _UpperCAmelCase					:  List[Any]         =    low_idx + 1

          _UpperCAmelCase					:  Optional[int]         =    self.log_sigmas[low_idx]
          _UpperCAmelCase					:  Dict         =    self.log_sigmas[high_idx]

          # interpolate sigmas
          _UpperCAmelCase					:  Union[str, Any]         =    (low - log_sigma) / (low - high)
          _UpperCAmelCase					:  Union[str, Any]         =    w.clamp(0						,	1	)

          # transform interpolation to time range
          _UpperCAmelCase					:  Union[str, Any]         =    (1 - w) * low_idx + w * high_idx
          _UpperCAmelCase					:  str         =    t.view(sigma.shape	)
          return t



     @property
     def     snake_case_ (self	):
          return self.sample is None



     def     snake_case_ (self						,	lowerCAmelCase__						,	lowerCAmelCase__						,	lowerCAmelCase__						,	lowerCAmelCase__ = None						,	lowerCAmelCase__ = True						,	):
          _UpperCAmelCase					:  Union[str, Any]         =    self.index_for_timestep(lowerCAmelCase__	)

          # advance index counter by 1
          _UpperCAmelCase					:  Optional[Any]         =    timestep.cpu().item() if torch.is_tensor(lowerCAmelCase__	) else timestep
          self._index_counter[timestep_int] += 1

          if self.state_in_first_order:
               _UpperCAmelCase					:  Tuple         =    self.sigmas[step_index]
               _UpperCAmelCase					:  List[str]         =    self.sigmas_interpol[step_index]
               _UpperCAmelCase					:  Tuple         =    self.sigmas_up[step_index]
               _UpperCAmelCase					:  Optional[int]         =    self.sigmas_down[step_index - 1]
          else:
               # 2nd order / KPDM2's method
               _UpperCAmelCase					:  List[Any]         =    self.sigmas[step_index - 1]
               _UpperCAmelCase					:  Tuple         =    self.sigmas_interpol[step_index - 1]
               _UpperCAmelCase					:  List[Any]         =    self.sigmas_up[step_index - 1]
               _UpperCAmelCase					:  Tuple         =    self.sigmas_down[step_index - 1]

          # 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
          _UpperCAmelCase					:  int         =    0
          _UpperCAmelCase					:  str         =    sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

          _UpperCAmelCase					:  Optional[int]         =    model_output.device
          _UpperCAmelCase					:  Tuple         =    randn_tensor(model_output.shape						,	dtype=model_output.dtype						,	device=lowerCAmelCase__						,	generator=lowerCAmelCase__	)

          # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
          if self.config.prediction_type == "epsilon":
               _UpperCAmelCase					:  int         =    sigma_hat if self.state_in_first_order else sigma_interpol
               _UpperCAmelCase					:  List[Any]         =    sample - sigma_input * model_output
          elif self.config.prediction_type == "v_prediction":
               _UpperCAmelCase					:  Tuple         =    sigma_hat if self.state_in_first_order else sigma_interpol
               _UpperCAmelCase					:  Dict         =    model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
                   sample / (sigma_input**2 + 1)
               )
          elif self.config.prediction_type == "sample":
               raise NotImplementedError("""prediction_type not implemented yet: sample"""	)
          else:
               raise ValueError(
                   F"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`"	)

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

               # store for 2nd order step
               _UpperCAmelCase					:  List[str]         =    sample
               _UpperCAmelCase					:  Optional[Any]         =    dt
               _UpperCAmelCase					:  Optional[int]         =    sample + derivative * dt
          else:
               # DPM-Solver-2
               # 2. Convert to an ODE derivative for 2nd order
               _UpperCAmelCase					:  Optional[int]         =    (sample - pred_original_sample) / sigma_interpol
               # 3. delta timestep
               _UpperCAmelCase					:  List[Any]         =    sigma_down - sigma_hat

               _UpperCAmelCase					:  int         =    self.sample
               _UpperCAmelCase					:  Union[str, Any]         =    None

               _UpperCAmelCase					:  str         =    sample + derivative * dt
               _UpperCAmelCase					:  List[str]         =    prev_sample + noise * sigma_up

          if not return_dict:
               return (prev_sample,)

          return SchedulerOutput(prev_sample=lowerCAmelCase__	)



     def     snake_case_ (self						,	lowerCAmelCase__						,	lowerCAmelCase__						,	lowerCAmelCase__						,	):
          # Make sure sigmas and timesteps have the same device and dtype as original_samples
          _UpperCAmelCase					:  List[Any]         =    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
               _UpperCAmelCase					:  Any         =    self.timesteps.to(original_samples.device						,	dtype=torch.floataa	)
               _UpperCAmelCase					:  Dict         =    timesteps.to(original_samples.device						,	dtype=torch.floataa	)
          else:
               _UpperCAmelCase					:  Dict         =    self.timesteps.to(original_samples.device	)
               _UpperCAmelCase					:  Tuple         =    timesteps.to(original_samples.device	)

          _UpperCAmelCase					:  Any         =    [self.index_for_timestep(lowerCAmelCase__						,	lowerCAmelCase__	) for t in timesteps]

          _UpperCAmelCase					:  List[Any]         =    sigmas[step_indices].flatten()
          while len(sigma.shape	) < len(original_samples.shape	):
               _UpperCAmelCase					:  Dict         =    sigma.unsqueeze(-1	)

          _UpperCAmelCase					:  str         =    original_samples + noise * sigma
          return noisy_samples



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


