


import gc
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    EulerAncestralDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionModelEditingPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps

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


enable_full_determinism()



@skip_mps
class  UpperCAmelCase_						(				__lowerCAmelCase,  __lowerCAmelCase,  __lowerCAmelCase,  unittest.TestCase):
       '''simple docstring'''
       __UpperCamelCase   :  Any				=       StableDiffusionModelEditingPipeline
       __UpperCamelCase   :  List[str]				=       TEXT_TO_IMAGE_PARAMS
       __UpperCamelCase   :  Any				=       TEXT_TO_IMAGE_BATCH_PARAMS
       __UpperCamelCase   :  str				=       TEXT_TO_IMAGE_IMAGE_PARAMS
       __UpperCamelCase   :  List[str]				=       TEXT_TO_IMAGE_IMAGE_PARAMS






       def 			_lowercase (  self    ):




              """simple docstring"""




              torch.manual_seed(0    )
              UpperCamelCase     :						List[str] 						=		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			,       )
              UpperCamelCase     :						List[Any] 						=		DDIMScheduler()
              torch.manual_seed(0    )
              UpperCamelCase     :						List[str] 						=		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    )
              UpperCamelCase     :						List[Any] 						=		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=1_000			,       )
              UpperCamelCase     :						Dict 						=		CLIPTextModel(_UpperCamelCase    )
              UpperCamelCase     :						Optional[Any] 						=		CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''    )

              UpperCamelCase     :						List[Any] 						=		{
                  """unet""": unet,
                  """scheduler""": scheduler,
                  """vae""": vae,
                  """text_encoder""": text_encoder,
                  """tokenizer""": tokenizer,
                  """safety_checker""": None,
                  """feature_extractor""": None,
              }
              return components






       def 			_lowercase (  self			,       __SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE=0    ):




              """simple docstring"""




              UpperCamelCase     :						List[Any] 						=		torch.manual_seed(_UpperCamelCase    )
              UpperCamelCase     :						List[Any] 						=		{
                  """prompt""": """A field of roses""",
                  """generator""": generator,
                  # Setting height and width to None to prevent OOMs on CPU.
                  """height""": None,
                  """width""": None,
                  """num_inference_steps""": 2,
                  """guidance_scale""": 6.0,
                  """output_type""": """numpy""",
              }
              return inputs






       def 			_lowercase (  self    ):




              """simple docstring"""




              UpperCamelCase     :						Optional[int] 						=		"""cpu"""  # ensure determinism for the device-dependent torch.Generator
              UpperCamelCase     :						Any 						=		self.get_dummy_components()
              UpperCamelCase     :						List[str] 						=		StableDiffusionModelEditingPipeline(**_UpperCamelCase    )
              UpperCamelCase     :						Dict 						=		sd_pipe.to(_UpperCamelCase    )
              sd_pipe.set_progress_bar_config(disable=_UpperCamelCase    )

              UpperCamelCase     :						List[str] 						=		self.get_dummy_inputs(_UpperCamelCase    )
              UpperCamelCase     :						List[Any] 						=		sd_pipe(**_UpperCamelCase    ).images
              UpperCamelCase     :						Optional[int] 						=		image[0, -3:, -3:, -1]
              assert image.shape == (1, 64, 64, 3)

              UpperCamelCase     :						List[Any] 						=		np.array([0.4_755, 0.5_132, 0.4_976, 0.3_904, 0.3_554, 0.4_765, 0.5_139, 0.5_158, 0.4_889]    )

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






       def 			_lowercase (  self    ):




              """simple docstring"""




              UpperCamelCase     :						Optional[int] 						=		"""cpu"""  # ensure determinism for the device-dependent torch.Generator
              UpperCamelCase     :						List[str] 						=		self.get_dummy_components()
              UpperCamelCase     :						int 						=		StableDiffusionModelEditingPipeline(**_UpperCamelCase    )
              UpperCamelCase     :						List[str] 						=		sd_pipe.to(_UpperCamelCase    )
              sd_pipe.set_progress_bar_config(disable=_UpperCamelCase    )

              UpperCamelCase     :						int 						=		self.get_dummy_inputs(_UpperCamelCase    )
              UpperCamelCase     :						Union[str, Any] 						=		"""french fries"""
              UpperCamelCase     :						Dict 						=		sd_pipe(**_UpperCamelCase			,       negative_prompt=_UpperCamelCase    )
              UpperCamelCase     :						List[Any] 						=		output.images
              UpperCamelCase     :						List[Any] 						=		image[0, -3:, -3:, -1]

              assert image.shape == (1, 64, 64, 3)

              UpperCamelCase     :						Optional[Any] 						=		np.array([0.4_992, 0.5_101, 0.5_004, 0.3_949, 0.3_604, 0.4_735, 0.5_216, 0.5_204, 0.4_913]    )

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






       def 			_lowercase (  self    ):




              """simple docstring"""




              UpperCamelCase     :						List[str] 						=		"""cpu"""  # ensure determinism for the device-dependent torch.Generator
              UpperCamelCase     :						Tuple 						=		self.get_dummy_components()
              UpperCamelCase     :						Dict 						=		EulerAncestralDiscreteScheduler(
                  beta_start=0.00_085			,       beta_end=0.012			,       beta_schedule='''scaled_linear'''    )
              UpperCamelCase     :						int 						=		StableDiffusionModelEditingPipeline(**_UpperCamelCase    )
              UpperCamelCase     :						str 						=		sd_pipe.to(_UpperCamelCase    )
              sd_pipe.set_progress_bar_config(disable=_UpperCamelCase    )

              UpperCamelCase     :						Optional[int] 						=		self.get_dummy_inputs(_UpperCamelCase    )
              UpperCamelCase     :						Dict 						=		sd_pipe(**_UpperCamelCase    ).images
              UpperCamelCase     :						Optional[Any] 						=		image[0, -3:, -3:, -1]

              assert image.shape == (1, 64, 64, 3)

              UpperCamelCase     :						List[str] 						=		np.array([0.4_747, 0.5_372, 0.4_779, 0.4_982, 0.5_543, 0.4_816, 0.5_238, 0.4_904, 0.5_027]    )

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






       def 			_lowercase (  self    ):




              """simple docstring"""




              UpperCamelCase     :						List[Any] 						=		"""cpu"""  # ensure determinism for the device-dependent torch.Generator
              UpperCamelCase     :						Union[str, Any] 						=		self.get_dummy_components()
              UpperCamelCase     :						Tuple 						=		PNDMScheduler()
              UpperCamelCase     :						Union[str, Any] 						=		StableDiffusionModelEditingPipeline(**_UpperCamelCase    )
              UpperCamelCase     :						Tuple 						=		sd_pipe.to(_UpperCamelCase    )
              sd_pipe.set_progress_bar_config(disable=_UpperCamelCase    )

              UpperCamelCase     :						List[str] 						=		self.get_dummy_inputs(_UpperCamelCase    )
              # the pipeline does not expect pndm so test if it raises error.
              with self.assertRaises(_UpperCamelCase    ):
                     UpperCamelCase     :						List[str] 						=		sd_pipe(**_UpperCamelCase    ).images






       def 			_lowercase (  self    ):




              """simple docstring"""




              super().test_inference_batch_single_identical(expected_max_diff=5e-3    )






       def 			_lowercase (  self    ):




              """simple docstring"""




              super().test_attention_slicing_forward_pass(expected_max_diff=5e-3    )




@slow
@require_torch_gpu
class  UpperCAmelCase_						(				unittest.TestCase):
       '''simple docstring'''






       def 			_lowercase (  self    ):




              """simple docstring"""




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






       def 			_lowercase (  self			,       __SCREAMING_SNAKE_CASE=0    ):




              """simple docstring"""




              UpperCamelCase     :						Any 						=		torch.manual_seed(_UpperCamelCase    )
              UpperCamelCase     :						Dict 						=		{
                  """prompt""": """A field of roses""",
                  """generator""": generator,
                  """num_inference_steps""": 3,
                  """guidance_scale""": 7.5,
                  """output_type""": """numpy""",
              }
              return inputs






       def 			_lowercase (  self    ):




              """simple docstring"""




              UpperCamelCase     :						Dict 						=		"""CompVis/stable-diffusion-v1-4"""
              UpperCamelCase     :						List[str] 						=		StableDiffusionModelEditingPipeline.from_pretrained(_UpperCamelCase			,       safety_checker=_UpperCamelCase    )
              pipe.to(_UpperCamelCase    )
              pipe.set_progress_bar_config(disable=_UpperCamelCase    )
              pipe.enable_attention_slicing()

              UpperCamelCase     :						List[str] 						=		self.get_inputs()
              UpperCamelCase     :						str 						=		pipe(**_UpperCamelCase    ).images
              UpperCamelCase     :						List[str] 						=		image[0, -3:, -3:, -1].flatten()

              assert image.shape == (1, 512, 512, 3)

              UpperCamelCase     :						Tuple 						=		np.array(
                  [0.6_749_496, 0.6_386_453, 0.51_443_267, 0.66_094_905, 0.61_921_215, 0.5_491_332, 0.5_744_417, 0.58_075_106, 0.5_174_658]    )

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

              # make sure image changes after editing
              pipe.edit_model('''A pack of roses'''			,       '''A pack of blue roses'''    )

              UpperCamelCase     :						Optional[Any] 						=		pipe(**_UpperCamelCase    ).images
              UpperCamelCase     :						Tuple 						=		image[0, -3:, -3:, -1].flatten()

              assert image.shape == (1, 512, 512, 3)

              assert np.abs(expected_slice - image_slice    ).max() > 1e-1






       def 			_lowercase (  self    ):




              """simple docstring"""




              torch.cuda.empty_cache()
              torch.cuda.reset_max_memory_allocated()
              torch.cuda.reset_peak_memory_stats()

              UpperCamelCase     :						Optional[int] 						=		"""CompVis/stable-diffusion-v1-4"""
              UpperCamelCase     :						Dict 						=		DDIMScheduler.from_pretrained(_UpperCamelCase			,       subfolder='''scheduler'''    )
              UpperCamelCase     :						str 						=		StableDiffusionModelEditingPipeline.from_pretrained(
                  _UpperCamelCase			,       scheduler=_UpperCamelCase			,       safety_checker=_UpperCamelCase    )
              UpperCamelCase     :						int 						=		pipe.to(_UpperCamelCase    )
              pipe.set_progress_bar_config(disable=_UpperCamelCase    )
              pipe.enable_attention_slicing(1    )
              pipe.enable_sequential_cpu_offload()

              UpperCamelCase     :						List[Any] 						=		self.get_inputs()
              UpperCamelCase     :						int 						=		pipe(**_UpperCamelCase    )

              UpperCamelCase     :						Any 						=		torch.cuda.max_memory_allocated()
              # make sure that less than 4.4 GB is allocated
              assert mem_bytes < 4.4 * 10**9


import math
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, logging, randn_tensor
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin


lowerCAmelCase      :				Dict     			=logging.get_logger(__name__)  # pylint: disable=invalid-name




@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->EulerAncestralDiscrete
class 						__snake_case						(			__lowerCAmelCase		):





      '''simple docstring'''






      _snake_case     		=							42
      _snake_case     		=							None







def     A__	(       __A ,			__A=0.999 ,			__A="cosine" ,			):







    '''simple docstring'''
    if alpha_transform_type == "cosine":

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

    elif alpha_transform_type == "exp":

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

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

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




class 						__snake_case						(			__lowerCAmelCase			,	__lowerCAmelCase		):





      '''simple docstring'''






      _snake_case     		=							[e.name for e in KarrasDiffusionSchedulers]
      _snake_case     		=							1



      @register_to_config
      def __init__(       self  :					Tuple     ,	_UpperCamelCase  :					int = 1000     ,	_UpperCamelCase  :					float = 0.0_0_0_1     ,	_UpperCamelCase  :					float = 0.0_2     ,	_UpperCamelCase  :					str = "linear"     ,	_UpperCamelCase  :					Optional[Union[np.ndarray, List[float]]] = None     ,	_UpperCamelCase  :					str = "epsilon"     ,	_UpperCamelCase  :					str = "linspace"     ,	_UpperCamelCase  :					int = 0     ,	)					->Optional[int]:


          """simple docstring"""


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

          _lowerCamelCase  :		Tuple			    =    1.0 - self.betas
          _lowerCamelCase  :		int			    =    torch.cumprod(self.alphas     ,	dim=0)

          _lowerCamelCase  :		Tuple			    =    np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5)
          _lowerCamelCase  :		Dict			    =    np.concatenate([sigmas[::-1], [0.0]]).astype(np.floataa)
          _lowerCamelCase  :		Tuple			    =    torch.from_numpy(_UpperCamelCase)

          # setable values
          _lowerCamelCase  :		List[str]			    =    None
          _lowerCamelCase  :		int			    =    np.linspace(0     ,	num_train_timesteps - 1     ,	_UpperCamelCase     ,	dtype=_UpperCamelCase)[::-1].copy()
          _lowerCamelCase  :		Optional[Any]			    =    torch.from_numpy(_UpperCamelCase)
          _lowerCamelCase  :		Tuple			    =    False



      @property
      def 						_SCREAMING_SNAKE_CASE     (       self  :					List[str])					->Dict:


          """simple docstring"""


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

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



      def 						_SCREAMING_SNAKE_CASE     (       self  :					int     ,	_UpperCamelCase  :					torch.FloatTensor     ,	_UpperCamelCase  :					Union[float, torch.FloatTensor])					->torch.FloatTensor:


          """simple docstring"""


          if isinstance(_UpperCamelCase     ,	torch.Tensor):
              _lowerCamelCase  :		Optional[int]			    =    timestep.to(self.timesteps.device)
          _lowerCamelCase  :		Tuple			    =    (self.timesteps == timestep).nonzero().item()
          _lowerCamelCase  :		str			    =    self.sigmas[step_index]
          _lowerCamelCase  :		List[str]			    =    sample / ((sigma**2 + 1) ** 0.5)
          _lowerCamelCase  :		str			    =    True
          return sample



      def 						_SCREAMING_SNAKE_CASE     (       self  :					Tuple     ,	_UpperCamelCase  :					int     ,	_UpperCamelCase  :					Union[str, torch.device] = None)					->int:


          """simple docstring"""


          _lowerCamelCase  :		Union[str, Any]			    =    num_inference_steps

          # "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
          if self.config.timestep_spacing == "linspace":
              _lowerCamelCase  :		Any			    =    np.linspace(0     ,	self.config.num_train_timesteps - 1     ,	_UpperCamelCase     ,	dtype=_UpperCamelCase)[
                  ::-1
              ].copy()
          elif self.config.timestep_spacing == "leading":
              _lowerCamelCase  :		Tuple			    =    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  :		List[Any]			    =    (np.arange(0     ,	_UpperCamelCase) * step_ratio).round()[::-1].copy().astype(_UpperCamelCase)
              timesteps += self.config.steps_offset
          elif self.config.timestep_spacing == "trailing":
              _lowerCamelCase  :		List[str]			    =    self.config.num_train_timesteps / self.num_inference_steps
              # creates integer timesteps by multiplying by ratio
              # casting to int to avoid issues when num_inference_step is power of 3
              _lowerCamelCase  :		str			    =    (np.arange(self.config.num_train_timesteps     ,	0     ,	-step_ratio)).round().copy().astype(_UpperCamelCase)
              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  :		Tuple			    =    np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5)
          _lowerCamelCase  :		Union[str, Any]			    =    np.interp(_UpperCamelCase     ,	np.arange(0     ,	len(_UpperCamelCase))     ,	_UpperCamelCase)
          _lowerCamelCase  :		Optional[int]			    =    np.concatenate([sigmas, [0.0]]).astype(np.floataa)
          _lowerCamelCase  :		Dict			    =    torch.from_numpy(_UpperCamelCase).to(device=_UpperCamelCase)
          if str(_UpperCamelCase).startswith("""mps"""):
              # mps does not support float64
              _lowerCamelCase  :		List[str]			    =    torch.from_numpy(_UpperCamelCase).to(_UpperCamelCase     ,	dtype=torch.floataa)
          else:
              _lowerCamelCase  :		Any			    =    torch.from_numpy(_UpperCamelCase).to(device=_UpperCamelCase)



      def 						_SCREAMING_SNAKE_CASE     (       self  :					Union[str, Any]     ,	_UpperCamelCase  :					torch.FloatTensor     ,	_UpperCamelCase  :					Union[float, torch.FloatTensor]     ,	_UpperCamelCase  :					torch.FloatTensor     ,	_UpperCamelCase  :					Optional[torch.Generator] = None     ,	_UpperCamelCase  :					bool = True     ,	)					->Union[EulerAncestralDiscreteSchedulerOutput, Tuple]:


          """simple docstring"""


          if (
              isinstance(_UpperCamelCase     ,	_UpperCamelCase)
              or isinstance(_UpperCamelCase     ,	torch.IntTensor)
              or isinstance(_UpperCamelCase     ,	torch.LongTensor)
          ):
              raise ValueError(
                  (
                      """Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to"""
                      """ `EulerDiscreteScheduler.step()` is not supported. Make sure to pass"""
                      """ one of the `scheduler.timesteps` as a timestep."""
                  )     ,	)

          if not self.is_scale_input_called:
              logger.warning(
                  """The `scale_model_input` function should be called before `step` to ensure correct denoising. """
                  """See `StableDiffusionPipeline` for a usage example.""")

          if isinstance(_UpperCamelCase     ,	torch.Tensor):
              _lowerCamelCase  :		Optional[Any]			    =    timestep.to(self.timesteps.device)

          _lowerCamelCase  :		Optional[int]			    =    (self.timesteps == timestep).nonzero().item()
          _lowerCamelCase  :		Union[str, Any]			    =    self.sigmas[step_index]

          # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
          if self.config.prediction_type == "epsilon":
              _lowerCamelCase  :		int			    =    sample - sigma * model_output
          elif self.config.prediction_type == "v_prediction":
              # * c_out + input * c_skip
              _lowerCamelCase  :		Optional[Any]			    =    model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**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`""")

          _lowerCamelCase  :		Tuple			    =    self.sigmas[step_index]
          _lowerCamelCase  :		int			    =    self.sigmas[step_index + 1]
          _lowerCamelCase  :		Tuple			    =    (sigma_to**2 * (sigma_from**2 - sigma_to**2) / sigma_from**2) ** 0.5
          _lowerCamelCase  :		List[Any]			    =    (sigma_to**2 - sigma_up**2) ** 0.5

          # 2. Convert to an ODE derivative
          _lowerCamelCase  :		List[Any]			    =    (sample - pred_original_sample) / sigma

          _lowerCamelCase  :		Tuple			    =    sigma_down - sigma

          _lowerCamelCase  :		Optional[int]			    =    sample + derivative * dt

          _lowerCamelCase  :		int			    =    model_output.device
          _lowerCamelCase  :		List[Any]			    =    randn_tensor(model_output.shape     ,	dtype=model_output.dtype     ,	device=_UpperCamelCase     ,	generator=_UpperCamelCase)

          _lowerCamelCase  :		List[Any]			    =    prev_sample + noise * sigma_up

          if not return_dict:
              return (prev_sample,)

          return EulerAncestralDiscreteSchedulerOutput(
              prev_sample=_UpperCamelCase     ,	pred_original_sample=_UpperCamelCase)



      def 						_SCREAMING_SNAKE_CASE     (       self  :					Optional[Any]     ,	_UpperCamelCase  :					torch.FloatTensor     ,	_UpperCamelCase  :					torch.FloatTensor     ,	_UpperCamelCase  :					torch.FloatTensor     ,	)					->torch.FloatTensor:


          """simple docstring"""


          _lowerCamelCase  :		Any			    =    self.sigmas.to(device=original_samples.device     ,	dtype=original_samples.dtype)
          if original_samples.device.type == "mps" and torch.is_floating_point(_UpperCamelCase):
              # mps does not support float64
              _lowerCamelCase  :		str			    =    self.timesteps.to(original_samples.device     ,	dtype=torch.floataa)
              _lowerCamelCase  :		Union[str, Any]			    =    timesteps.to(original_samples.device     ,	dtype=torch.floataa)
          else:
              _lowerCamelCase  :		Union[str, Any]			    =    self.timesteps.to(original_samples.device)
              _lowerCamelCase  :		List[str]			    =    timesteps.to(original_samples.device)

          _lowerCamelCase  :		Optional[int]			    =    [(schedule_timesteps == t).nonzero().item() for t in timesteps]

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

          _lowerCamelCase  :		List[str]			    =    original_samples + noise * sigma
          return noisy_samples




      def __len__(       self  :					Dict)					->str:


          """simple docstring"""


          return self.config.num_train_timesteps




