




'''simple docstring'''

import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DiffusionPipeline,
    EulerDiscreteScheduler,
    StableDiffusionXLPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()





class __magic_name__(						__lowerCAmelCase    ,		__lowerCAmelCase    ,		unittest.TestCase      ):
						UpperCAmelCase_		:					List[str]    =  StableDiffusionXLPipeline
						UpperCAmelCase_		:					List[str]    =  TEXT_TO_IMAGE_PARAMS
						UpperCAmelCase_		:					Dict    =  TEXT_TO_IMAGE_BATCH_PARAMS
						UpperCAmelCase_		:					Dict    =  TEXT_TO_IMAGE_IMAGE_PARAMS
						UpperCAmelCase_		:					Optional[Any]    =  TEXT_TO_IMAGE_IMAGE_PARAMS






						def       __lowerCAmelCase(   self						:						List[Any]       ):



									'''simple docstring'''






									torch.manual_seed(0       )
									snake_case__          =						UNetaDConditionModel(
									    block_out_channels=(3_2, 6_4)					,   layers_per_block=2					,   sample_size=3_2					,   in_channels=4					,   out_channels=4					,   down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")					,   up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""")					,   attention_head_dim=(2, 4)					,   use_linear_projection=__UpperCamelCase					,   addition_embed_type="""text_time"""					,   addition_time_embed_dim=8					,   transformer_layers_per_block=(1, 2)					,   projection_class_embeddings_input_dim=8_0					,   cross_attention_dim=6_4					,   )
									snake_case__          =						EulerDiscreteScheduler(
									    beta_start=0.0_00_85					,   beta_end=0.0_12					,   steps_offset=1					,   beta_schedule="""scaled_linear"""					,   timestep_spacing="""leading"""					,   )
									torch.manual_seed(0       )
									snake_case__          =						AutoencoderKL(
									    block_out_channels=[3_2, 6_4]					,   in_channels=3					,   out_channels=3					,   down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""]					,   up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""]					,   latent_channels=4					,   sample_size=1_2_8					,   )
									torch.manual_seed(0       )
									snake_case__          =						CLIPTextConfig(
									    bos_token_id=0					,   eos_token_id=2					,   hidden_size=3_2					,   intermediate_size=3_7					,   layer_norm_eps=1E-05					,   num_attention_heads=4					,   num_hidden_layers=5					,   pad_token_id=1					,   vocab_size=1_0_0_0					,   hidden_act="""gelu"""					,   projection_dim=3_2					,   )
									snake_case__          =						CLIPTextModel(__UpperCamelCase       )
									snake_case__          =						CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""					,   local_files_only=__UpperCamelCase       )

									snake_case__          =						CLIPTextModelWithProjection(__UpperCamelCase       )
									snake_case__          =						CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""					,   local_files_only=__UpperCamelCase       )

									snake_case__          =						{
									    """unet""": unet,
									    """scheduler""": scheduler,
									    """vae""": vae,
									    """text_encoder""": text_encoder,
									    """tokenizer""": tokenizer,
									    """text_encoder_2""": text_encoder_a,
									    """tokenizer_2""": tokenizer_a,
									    # "safety_checker": None,
									    # "feature_extractor": None,
									}
									return components






						def       __lowerCAmelCase(   self						:						str					,   __UpperCamelCase						:						Dict					,   __UpperCamelCase						:						Tuple=0       ):



									'''simple docstring'''






									if str(__UpperCamelCase       ).startswith("""mps"""       ):
												snake_case__          =						torch.manual_seed(__UpperCamelCase       )
									else:
												snake_case__          =						torch.Generator(device=__UpperCamelCase       ).manual_seed(__UpperCamelCase       )
									snake_case__          =						{
									    """prompt""": """A painting of a squirrel eating a burger""",
									    """generator""": generator,
									    """num_inference_steps""": 2,
									    """guidance_scale""": 5.0,
									    """output_type""": """numpy""",
									}
									return inputs






						def       __lowerCAmelCase(   self						:						List[Any]       ):



									'''simple docstring'''






									snake_case__          =						"""cpu"""  # ensure determinism for the device-dependent torch.Generator
									snake_case__          =						self.get_dummy_components()
									snake_case__          =						StableDiffusionXLPipeline(**__UpperCamelCase       )
									snake_case__          =						sd_pipe.to(__UpperCamelCase       )
									sd_pipe.set_progress_bar_config(disable=__UpperCamelCase       )

									snake_case__          =						self.get_dummy_inputs(__UpperCamelCase       )
									snake_case__          =						sd_pipe(**__UpperCamelCase       ).images
									snake_case__          =						image[0, -3:, -3:, -1]

									assert image.shape == (1, 6_4, 6_4, 3)
									snake_case__          =						np.array([0.58_73, 0.61_28, 0.47_97, 0.51_22, 0.56_74, 0.46_39, 0.52_27, 0.51_49, 0.47_47]       )

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






						def       __lowerCAmelCase(   self						:						str       ):



									'''simple docstring'''






									snake_case__          =						self.get_dummy_components()
									snake_case__          =						StableDiffusionXLPipeline(**__UpperCamelCase       )
									snake_case__          =						sd_pipe.to(__UpperCamelCase       )
									snake_case__          =						sd_pipe.to(__UpperCamelCase       )
									sd_pipe.set_progress_bar_config(disable=__UpperCamelCase       )

									# forward without prompt embeds
									snake_case__          =						self.get_dummy_inputs(__UpperCamelCase       )
									snake_case__          =						3 * ["""this is a negative prompt"""]
									snake_case__          =						negative_prompt
									snake_case__          =						3 * [inputs["""prompt"""]]

									snake_case__          =						sd_pipe(**__UpperCamelCase       )
									snake_case__          =						output.images[0, -3:, -3:, -1]

									# forward with prompt embeds
									snake_case__          =						self.get_dummy_inputs(__UpperCamelCase       )
									snake_case__          =						3 * ["""this is a negative prompt"""]
									snake_case__          =						3 * [inputs.pop("""prompt"""       )]

									(
									    (
									    snake_case__
									)    ,			(
									    snake_case__
									)    ,			(
									    snake_case__
									)    ,			(
									    snake_case__
									)    ,			
									)          =						sd_pipe.encode_prompt(__UpperCamelCase					,   negative_prompt=__UpperCamelCase       )

									snake_case__          =						sd_pipe(
									    **__UpperCamelCase					,   prompt_embeds=__UpperCamelCase					,   negative_prompt_embeds=__UpperCamelCase					,   pooled_prompt_embeds=__UpperCamelCase					,   negative_pooled_prompt_embeds=__UpperCamelCase					,   )
									snake_case__          =						output.images[0, -3:, -3:, -1]

									# make sure that it's equal
									assert np.abs(image_slice_a.flatten() - image_slice_a.flatten()       ).max() < 1E-4






						def       __lowerCAmelCase(   self						:						Optional[Any]       ):



									'''simple docstring'''






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







						def       __lowerCAmelCase(   self						:						str       ):



									'''simple docstring'''






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







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






						def       __lowerCAmelCase(   self						:						Optional[int]       ):



									'''simple docstring'''






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






						def       __lowerCAmelCase(   self						:						int					,   __UpperCamelCase						:						str					,   __UpperCamelCase						:						Optional[int]="cpu"					,   __UpperCamelCase						:						List[Any]=torch.floataa					,   __UpperCamelCase						:						Tuple=0       ):



									'''simple docstring'''






									snake_case__          =						torch.Generator(device=__UpperCamelCase       ).manual_seed(__UpperCamelCase       )
									snake_case__          =						np.random.RandomState(__UpperCamelCase       ).standard_normal((1, 4, 6_4, 6_4)       )
									snake_case__          =						torch.from_numpy(__UpperCamelCase       ).to(device=__UpperCamelCase					,   dtype=__UpperCamelCase       )
									snake_case__          =						{
									    """prompt""": """a photograph of an astronaut riding a horse""",
									    """latents""": latents,
									    """generator""": generator,
									    """num_inference_steps""": 3,
									    """guidance_scale""": 7.5,
									    """output_type""": """numpy""",
									}
									return inputs







						def       __lowerCAmelCase(   self						:						Tuple       ):



									'''simple docstring'''






									snake_case__          =						DiffusionPipeline.from_pretrained("""stabilityai/stable-diffusion-2-base"""       )
									pipe.to(__UpperCamelCase       )
									pipe.set_progress_bar_config(disable=__UpperCamelCase       )

									snake_case__          =						self.get_inputs(__UpperCamelCase       )
									snake_case__          =						pipe(**__UpperCamelCase       ).images
									snake_case__          =						image[0, -3:, -3:, -1].flatten()

									assert image.shape == (1, 5_1_2, 5_1_2, 3)
									snake_case__          =						np.array([0.4_94_93, 0.4_78_96, 0.4_07_98, 0.5_42_14, 0.5_32_12, 0.4_82_02, 0.4_76_56, 0.4_63_29, 0.4_85_06]       )
									assert np.abs(image_slice - expected_slice       ).max() < 7E-3
"""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__    (	lowercase_     :	Any      , lowercase_     :	int=0.9_9_9      , lowercase_     :	Dict="cosine"      , )  ->							Optional[int]:
   if alpha_transform_type == "cosine":

      def alpha_bar_fn(lowercase_     :	List[str]						):
         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_     :	Union[str, Any]						):
         return math.exp(t * -1_2.0						)

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

   __lowerCamelCase    		=					[]
   for i in range(lowercase_						):
      __lowerCamelCase    		=					i / num_diffusion_timesteps
      __lowerCamelCase    		=					(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      _SCREAMING_SNAKE_CASE							(			UpperCAmelCase    ,				UpperCAmelCase				):



   '''simple docstring'''



   __UpperCAmelCase	:							int    =    [e.name for e in KarrasDiffusionSchedulers]
   __UpperCAmelCase	:							Tuple    =    2







   @register_to_config
   def __init__(	self ,				_SCREAMING_SNAKE_CASE = 1000 ,				_SCREAMING_SNAKE_CASE = 0.0_0085 ,				_SCREAMING_SNAKE_CASE = 0.012 ,				_SCREAMING_SNAKE_CASE = "linear" ,				_SCREAMING_SNAKE_CASE = None ,				_SCREAMING_SNAKE_CASE = "epsilon" ,				_SCREAMING_SNAKE_CASE = "linspace" ,				_SCREAMING_SNAKE_CASE = 0 ,				):


      '''simple docstring'''

      if trained_betas is not None:
         __lowerCamelCase    		=					torch.tensor(_SCREAMING_SNAKE_CASE ,				dtype=torch.floataa	)
      elif beta_schedule == "linear":
         __lowerCamelCase    		=					torch.linspace(_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				dtype=torch.floataa	)
      elif beta_schedule == "scaled_linear":
         # this schedule is very specific to the latent diffusion model.
         __lowerCamelCase    		=					(
             torch.linspace(beta_start**0.5 ,				beta_end**0.5 ,				_SCREAMING_SNAKE_CASE ,				dtype=torch.floataa	) ** 2
         )
      elif beta_schedule == "squaredcos_cap_v2":
         # Glide cosine schedule
         __lowerCamelCase    		=					betas_for_alpha_bar(_SCREAMING_SNAKE_CASE	)
      else:
         raise NotImplementedError(F'''{beta_schedule} does is not implemented for {self.__class__}'''	)

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

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







   def    _A				(	self ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE=None	):


      '''simple docstring'''

      if schedule_timesteps is None:
         __lowerCamelCase    		=					self.timesteps

      __lowerCamelCase    		=					(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:
         __lowerCamelCase    		=					1 if len(_SCREAMING_SNAKE_CASE	) > 1 else 0
      else:
         __lowerCamelCase    		=					timestep.cpu().item() if torch.is_tensor(_SCREAMING_SNAKE_CASE	) else timestep
         __lowerCamelCase    		=					self._index_counter[timestep_int]

      return indices[pos].item()







   @property
   def    _A				(	self	):


      '''simple docstring'''

      if self.config.timestep_spacing in ["linspace", "trailing"]:
         return self.sigmas.max()

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







   def    _A				(	self ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				):


      '''simple docstring'''

      __lowerCamelCase    		=					self.index_for_timestep(_SCREAMING_SNAKE_CASE	)

      if self.state_in_first_order:
         __lowerCamelCase    		=					self.sigmas[step_index]
      else:
         __lowerCamelCase    		=					self.sigmas_interpol[step_index - 1]

      __lowerCamelCase    		=					sample / ((sigma**2 + 1) ** 0.5)
      return sample







   def    _A				(	self ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE = None ,				_SCREAMING_SNAKE_CASE = None ,				):


      '''simple docstring'''

      __lowerCamelCase    		=					num_inference_steps

      __lowerCamelCase    		=					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":
         __lowerCamelCase    		=					np.linspace(0 ,				num_train_timesteps - 1 ,				_SCREAMING_SNAKE_CASE ,				dtype=_SCREAMING_SNAKE_CASE	)[::-1].copy()
      elif self.config.timestep_spacing == "leading":
         __lowerCamelCase    		=					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    		=					(np.arange(0 ,				_SCREAMING_SNAKE_CASE	) * step_ratio).round()[::-1].copy().astype(_SCREAMING_SNAKE_CASE	)
         timesteps += self.config.steps_offset
      elif self.config.timestep_spacing == "trailing":
         __lowerCamelCase    		=					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    		=					(np.arange(_SCREAMING_SNAKE_CASE ,				0 ,				-step_ratio	)).round().copy().astype(_SCREAMING_SNAKE_CASE	)
         timesteps -= 1
      else:
         raise ValueError(
             F'''{self.config.timestep_spacing} is not supported. Please make sure to choose one of \'linspace\', \'leading\' or \'trailing\'.'''	)

      __lowerCamelCase    		=					np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5	)
      __lowerCamelCase    		=					torch.from_numpy(np.log(_SCREAMING_SNAKE_CASE	)	).to(_SCREAMING_SNAKE_CASE	)

      __lowerCamelCase    		=					np.interp(_SCREAMING_SNAKE_CASE ,				np.arange(0 ,				len(_SCREAMING_SNAKE_CASE	)	) ,				_SCREAMING_SNAKE_CASE	)
      __lowerCamelCase    		=					np.concatenate([sigmas, [0.0]]	).astype(np.floataa	)
      __lowerCamelCase    		=					torch.from_numpy(_SCREAMING_SNAKE_CASE	).to(device=_SCREAMING_SNAKE_CASE	)

      # compute up and down sigmas
      __lowerCamelCase    		=					sigmas.roll(-1	)
      __lowerCamelCase    		=					0.0
      __lowerCamelCase    		=					(sigmas_next**2 * (sigmas**2 - sigmas_next**2) / sigmas**2) ** 0.5
      __lowerCamelCase    		=					(sigmas_next**2 - sigmas_up**2) ** 0.5
      __lowerCamelCase    		=					0.0

      # compute interpolated sigmas
      __lowerCamelCase    		=					sigmas.log().lerp(sigmas_down.log() ,				0.5	).exp()
      __lowerCamelCase    		=					0.0

      # set sigmas
      __lowerCamelCase    		=					torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2	), sigmas[-1:]]	)
      __lowerCamelCase    		=					torch.cat(
          [sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2	), sigmas_interpol[-1:]]	)
      __lowerCamelCase    		=					torch.cat([sigmas_up[:1], sigmas_up[1:].repeat_interleave(2	), sigmas_up[-1:]]	)
      __lowerCamelCase    		=					torch.cat([sigmas_down[:1], sigmas_down[1:].repeat_interleave(2	), sigmas_down[-1:]]	)

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

      __lowerCamelCase    		=					self.sigma_to_t(_SCREAMING_SNAKE_CASE	).to(_SCREAMING_SNAKE_CASE ,				dtype=timesteps.dtype	)
      __lowerCamelCase    		=					torch.stack((timesteps_interpol[:-2, None], timesteps[1:, None]) ,				dim=-1	).flatten()

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

      __lowerCamelCase    		=					None

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







   def    _A				(	self ,				_SCREAMING_SNAKE_CASE	):


      '''simple docstring'''

      __lowerCamelCase    		=					sigma.log()

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

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

      __lowerCamelCase    		=					self.log_sigmas[low_idx]
      __lowerCamelCase    		=					self.log_sigmas[high_idx]

      # interpolate sigmas
      __lowerCamelCase    		=					(low - log_sigma) / (low - high)
      __lowerCamelCase    		=					w.clamp(0 ,				1	)

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







   @property
   def    _A				(	self	):


      '''simple docstring'''

      return self.sample is None







   def    _A				(	self ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE = None ,				_SCREAMING_SNAKE_CASE = True ,				):


      '''simple docstring'''

      __lowerCamelCase    		=					self.index_for_timestep(_SCREAMING_SNAKE_CASE	)

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

      if self.state_in_first_order:
         __lowerCamelCase    		=					self.sigmas[step_index]
         __lowerCamelCase    		=					self.sigmas_interpol[step_index]
         __lowerCamelCase    		=					self.sigmas_up[step_index]
         __lowerCamelCase    		=					self.sigmas_down[step_index - 1]
      else:
         # 2nd order / KPDM2's method
         __lowerCamelCase    		=					self.sigmas[step_index - 1]
         __lowerCamelCase    		=					self.sigmas_interpol[step_index - 1]
         __lowerCamelCase    		=					self.sigmas_up[step_index - 1]
         __lowerCamelCase    		=					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
      __lowerCamelCase    		=					0
      __lowerCamelCase    		=					sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

      __lowerCamelCase    		=					model_output.device
      __lowerCamelCase    		=					randn_tensor(model_output.shape ,				dtype=model_output.dtype ,				device=_SCREAMING_SNAKE_CASE ,				generator=_SCREAMING_SNAKE_CASE	)

      # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
      if self.config.prediction_type == "epsilon":
         __lowerCamelCase    		=					sigma_hat if self.state_in_first_order else sigma_interpol
         __lowerCamelCase    		=					sample - sigma_input * model_output
      elif self.config.prediction_type == "v_prediction":
         __lowerCamelCase    		=					sigma_hat if self.state_in_first_order else sigma_interpol
         __lowerCamelCase    		=					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
         __lowerCamelCase    		=					(sample - pred_original_sample) / sigma_hat
         # 3. delta timestep
         __lowerCamelCase    		=					sigma_interpol - sigma_hat

         # store for 2nd order step
         __lowerCamelCase    		=					sample
         __lowerCamelCase    		=					dt
         __lowerCamelCase    		=					sample + derivative * dt
      else:
         # DPM-Solver-2
         # 2. Convert to an ODE derivative for 2nd order
         __lowerCamelCase    		=					(sample - pred_original_sample) / sigma_interpol
         # 3. delta timestep
         __lowerCamelCase    		=					sigma_down - sigma_hat

         __lowerCamelCase    		=					self.sample
         __lowerCamelCase    		=					None

         __lowerCamelCase    		=					sample + derivative * dt
         __lowerCamelCase    		=					prev_sample + noise * sigma_up

      if not return_dict:
         return (prev_sample,)

      return SchedulerOutput(prev_sample=_SCREAMING_SNAKE_CASE	)







   def    _A				(	self ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				):


      '''simple docstring'''

      __lowerCamelCase    		=					self.sigmas.to(device=original_samples.device ,				dtype=original_samples.dtype	)
      if original_samples.device.type == "mps" and torch.is_floating_point(_SCREAMING_SNAKE_CASE	):
         # mps does not support float64
         __lowerCamelCase    		=					self.timesteps.to(original_samples.device ,				dtype=torch.floataa	)
         __lowerCamelCase    		=					timesteps.to(original_samples.device ,				dtype=torch.floataa	)
      else:
         __lowerCamelCase    		=					self.timesteps.to(original_samples.device	)
         __lowerCamelCase    		=					timesteps.to(original_samples.device	)

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

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

      __lowerCamelCase    		=					original_samples + noise * sigma
      return noisy_samples







   def __len__(	self	):


      '''simple docstring'''

      return self.config.num_train_timesteps






