

'''simple docstring'''






import glob
import os
from typing import Dict, List, Union

import torch

from diffusers.utils import is_safetensors_available


if is_safetensors_available():
      import safetensors.torch

from huggingface_hub import snapshot_download

from diffusers import DiffusionPipeline, __version__
from diffusers.schedulers.scheduling_utils import SCHEDULER_CONFIG_NAME
from diffusers.utils import CONFIG_NAME, DIFFUSERS_CACHE, ONNX_WEIGHTS_NAME, WEIGHTS_NAME
class 	SCREAMING_SNAKE_CASE__			(				_UpperCAmelCase      ):




    def __init__(					self			):






          """simple docstring"""






          self.register_to_config()
          super().__init__()




    def   __lowerCamelCase      (					self				,     lowercase__				,     lowercase__			):






          """simple docstring"""






          if dicta == dicta:
                return True
          else:
                SCREAMING_SNAKE_CASE_,						SCREAMING_SNAKE_CASE_      :			Dict     =       self._remove_meta_keys(lowercase__			)
                SCREAMING_SNAKE_CASE_,						SCREAMING_SNAKE_CASE_      :			Dict     =       self._remove_meta_keys(lowercase__			)
                if configa == configa:
                      print(F"Warning !: Mismatch in keys {meta_keysa} and {meta_keysa}."			)
                      return True
          return False




    def   __lowerCamelCase      (					self				,     lowercase__			):






          """simple docstring"""






          SCREAMING_SNAKE_CASE_      :			Union[str, Any]     =       []
          SCREAMING_SNAKE_CASE_      :			Optional[Any]     =       config_dict.copy()
          for key in config_dict.keys():
                if key.startswith("_"			):
                      temp_dict.pop(lowercase__			)
                      meta_keys.append(lowercase__			)
          return (temp_dict, meta_keys)




    @torch.no_grad()
    def   __lowerCamelCase      (					self				,     lowercase__				,     **lowercase__			):






          """simple docstring"""






          SCREAMING_SNAKE_CASE_      :			Any     =       kwargs.pop("cache_dir"				,     lowercase__			)
          SCREAMING_SNAKE_CASE_      :			Optional[int]     =       kwargs.pop("resume_download"				,     lowercase__			)
          SCREAMING_SNAKE_CASE_      :			str     =       kwargs.pop("force_download"				,     lowercase__			)
          SCREAMING_SNAKE_CASE_      :			Optional[Any]     =       kwargs.pop("proxies"				,     lowercase__			)
          SCREAMING_SNAKE_CASE_      :			Tuple     =       kwargs.pop("local_files_only"				,     lowercase__			)
          SCREAMING_SNAKE_CASE_      :			List[str]     =       kwargs.pop("use_auth_token"				,     lowercase__			)
          SCREAMING_SNAKE_CASE_      :			int     =       kwargs.pop("revision"				,     lowercase__			)
          SCREAMING_SNAKE_CASE_      :			Optional[Any]     =       kwargs.pop("torch_dtype"				,     lowercase__			)
          SCREAMING_SNAKE_CASE_      :			Optional[Any]     =       kwargs.pop("device_map"				,     lowercase__			)

          SCREAMING_SNAKE_CASE_      :			str     =       kwargs.pop("alpha"				,     0.5			)
          SCREAMING_SNAKE_CASE_      :			Any     =       kwargs.pop("interp"				,     lowercase__			)

          print("Received list"				,     lowercase__			)
          print(F"Combining with alpha={alpha}, interpolation mode={interp}"			)

          SCREAMING_SNAKE_CASE_      :			List[Any]     =       len(lowercase__			)
          # Ignore result from model_index_json comparision of the two checkpoints
          SCREAMING_SNAKE_CASE_      :			int     =       kwargs.pop("force"				,     lowercase__			)

          # If less than 2 checkpoints, nothing to merge. If more than 3, not supported for now.
          if checkpoint_count > 3 or checkpoint_count < 2:
                raise ValueError(
                    "Received incorrect number of checkpoints to merge. Ensure that either 2 or 3 checkpoints are being"
                    " passed."			)

          print("Received the right number of checkpoints"			)
          # chkpt0, chkpt1 = pretrained_model_name_or_path_list[0:2]
          # chkpt2 = pretrained_model_name_or_path_list[2] if checkpoint_count == 3 else None

          # Validate that the checkpoints can be merged
          # Step 1: Load the model config and compare the checkpoints. We'll compare the model_index.json first while ignoring the keys starting with '_'
          SCREAMING_SNAKE_CASE_      :			List[Any]     =       []
          for pretrained_model_name_or_path in pretrained_model_name_or_path_list:
                SCREAMING_SNAKE_CASE_      :			int     =       DiffusionPipeline.load_config(
                    lowercase__				,     cache_dir=lowercase__				,     resume_download=lowercase__				,     force_download=lowercase__				,     proxies=lowercase__				,     local_files_only=lowercase__				,     use_auth_token=lowercase__				,     revision=lowercase__				,     )
                config_dicts.append(lowercase__			)

          SCREAMING_SNAKE_CASE_      :			Optional[Any]     =       True
          for idx in range(1				,     len(lowercase__			)			):
                comparison_result &= self._compare_model_configs(config_dicts[idx - 1]				,     config_dicts[idx]			)
                if not force and comparison_result is False:
                      raise ValueError("Incompatible checkpoints. Please check model_index.json for the models."			)
                      print(config_dicts[0]				,     config_dicts[1]			)
          print("Compatible model_index.json files found"			)
          # Step 2: Basic Validation has succeeded. Let's download the models and save them into our local files.
          SCREAMING_SNAKE_CASE_      :			Tuple     =       []
          for pretrained_model_name_or_path, config_dict in zip(lowercase__				,     lowercase__			):
                SCREAMING_SNAKE_CASE_      :			Union[str, Any]     =       [k for k in config_dict.keys() if not k.startswith("_"			)]
                SCREAMING_SNAKE_CASE_      :			int     =       [os.path.join(lowercase__				,     "*"			) for k in folder_names]
                allow_patterns += [
                    WEIGHTS_NAME,
                    SCHEDULER_CONFIG_NAME,
                    CONFIG_NAME,
                    ONNX_WEIGHTS_NAME,
                    DiffusionPipeline.config_name,
                ]
                SCREAMING_SNAKE_CASE_      :			Union[str, Any]     =       config_dict.get("_class_name"			)
                SCREAMING_SNAKE_CASE_      :			List[Any]     =       {"diffusers": __version__, "pipeline_class": requested_pipeline_class}

                SCREAMING_SNAKE_CASE_      :			Dict     =       (
                    pretrained_model_name_or_path
                    if os.path.isdir(lowercase__			)
                    else snapshot_download(
                        lowercase__				,     cache_dir=lowercase__				,     resume_download=lowercase__				,     proxies=lowercase__				,     local_files_only=lowercase__				,     use_auth_token=lowercase__				,     revision=lowercase__				,     allow_patterns=lowercase__				,     user_agent=lowercase__				,     )
                )
                print("Cached Folder"				,     lowercase__			)
                cached_folders.append(lowercase__			)

          # Step 3:-
          # Load the first checkpoint as a diffusion pipeline and modify its module state_dict in place
          SCREAMING_SNAKE_CASE_      :			Any     =       DiffusionPipeline.from_pretrained(
              cached_folders[0]				,     torch_dtype=lowercase__				,     device_map=lowercase__			)
          final_pipe.to(self.device			)

          SCREAMING_SNAKE_CASE_      :			Union[str, Any]     =       None
          if len(lowercase__			) > 2:
                SCREAMING_SNAKE_CASE_      :			Tuple     =       os.path.join(cached_folders[2]			)

          if interp == "sigmoid":
                SCREAMING_SNAKE_CASE_      :			Any     =       CheckpointMergerPipeline.sigmoid
          elif interp == "inv_sigmoid":
                SCREAMING_SNAKE_CASE_      :			Union[str, Any]     =       CheckpointMergerPipeline.inv_sigmoid
          elif interp == "add_diff":
                SCREAMING_SNAKE_CASE_      :			List[Any]     =       CheckpointMergerPipeline.add_difference
          else:
                SCREAMING_SNAKE_CASE_      :			Optional[int]     =       CheckpointMergerPipeline.weighted_sum

          # Find each module's state dict.
          for attr in final_pipe.config.keys():
                if not attr.startswith("_"			):
                      SCREAMING_SNAKE_CASE_      :			str     =       os.path.join(cached_folders[1]				,     lowercase__			)
                      if os.path.exists(lowercase__			):
                            SCREAMING_SNAKE_CASE_      :			List[Any]     =       [
                                *glob.glob(os.path.join(lowercase__				,     "*.safetensors"			)			),
                                *glob.glob(os.path.join(lowercase__				,     "*.bin"			)			),
                            ]
                            SCREAMING_SNAKE_CASE_      :			Tuple     =       files[0] if len(lowercase__			) > 0 else None
                      if len(lowercase__			) < 3:
                            SCREAMING_SNAKE_CASE_      :			Union[str, Any]     =       None
                      else:
                            SCREAMING_SNAKE_CASE_      :			Optional[int]     =       os.path.join(cached_folders[2]				,     lowercase__			)
                            if os.path.exists(lowercase__			):
                                  SCREAMING_SNAKE_CASE_      :			Dict     =       [
                                      *glob.glob(os.path.join(lowercase__				,     "*.safetensors"			)			),
                                      *glob.glob(os.path.join(lowercase__				,     "*.bin"			)			),
                                  ]
                                  SCREAMING_SNAKE_CASE_      :			List[Any]     =       files[0] if len(lowercase__			) > 0 else None
                # For an attr if both checkpoint_path_1 and 2 are None, ignore.
                # If atleast one is present, deal with it according to interp method, of course only if the state_dict keys match.
                      if checkpoint_path_a is None and checkpoint_path_a is None:
                            print(F"Skipping {attr}: not present in 2nd or 3d model"			)
                            continue
                      try:
                            SCREAMING_SNAKE_CASE_      :			Optional[int]     =       getattr(lowercase__				,     lowercase__			)
                            if isinstance(lowercase__				,     lowercase__			):  # ignore requires_safety_checker boolean
                                  continue
                            SCREAMING_SNAKE_CASE_      :			List[str]     =       getattr(lowercase__				,     "state_dict"			)
                            SCREAMING_SNAKE_CASE_      :			str     =       theta_a()

                            SCREAMING_SNAKE_CASE_      :			Dict     =       getattr(lowercase__				,     "load_state_dict"			)
                            SCREAMING_SNAKE_CASE_      :			Any     =       (
                                safetensors.torch.load_file(lowercase__			)
                                if (is_safetensors_available() and checkpoint_path_a.endswith(".safetensors"			))
                                else torch.load(lowercase__				,     map_location="cpu"			)
                            )
                            SCREAMING_SNAKE_CASE_      :			Dict     =       None
                            if checkpoint_path_a:
                                  SCREAMING_SNAKE_CASE_      :			Tuple     =       (
                                      safetensors.torch.load_file(lowercase__			)
                                      if (is_safetensors_available() and checkpoint_path_a.endswith(".safetensors"			))
                                      else torch.load(lowercase__				,     map_location="cpu"			)
                                  )

                            if not theta_a.keys() == theta_a.keys():
                                  print(F"Skipping {attr}: key mismatch"			)
                                  continue
                            if theta_a and not theta_a.keys() == theta_a.keys():
                                  print(F"Skipping {attr}:y mismatch"			)
                      except Exception as e:
                            print(F"Skipping {attr} do to an unexpected error: {str(lowercase__			)}"			)
                            continue
                      print(F"MERGING {attr}"			)

                      for key in theta_a.keys():
                            if theta_a:
                                  SCREAMING_SNAKE_CASE_      :			Tuple     =       theta_func(theta_a[key]				,     theta_a[key]				,     theta_a[key]				,     lowercase__			)
                            else:
                                  SCREAMING_SNAKE_CASE_      :			Tuple     =       theta_func(theta_a[key]				,     theta_a[key]				,     lowercase__				,     lowercase__			)

                      del theta_a
                      del theta_a
                      update_theta_a(lowercase__			)

                      del theta_a
          return final_pipe




    @staticmethod
    def   __lowerCamelCase      (					lowercase__				,     lowercase__				,     lowercase__				,     lowercase__			):






          """simple docstring"""






          return ((1 - alpha) * thetaa) + (alpha * thetaa)




    @staticmethod
    def   __lowerCamelCase      (					lowercase__				,     lowercase__				,     lowercase__				,     lowercase__			):






          """simple docstring"""






          SCREAMING_SNAKE_CASE_      :			Tuple     =       alpha * alpha * (3 - (2 * alpha))
          return thetaa + ((thetaa - thetaa) * alpha)




    @staticmethod
    def   __lowerCamelCase      (					lowercase__				,     lowercase__				,     lowercase__				,     lowercase__			):






          """simple docstring"""






          import math

          SCREAMING_SNAKE_CASE_      :			Optional[Any]     =       0.5 - math.sin(math.asin(1.0 - 2.0 * alpha			) / 3.0			)
          return thetaa + ((thetaa - thetaa) * alpha)







    @staticmethod
    def   __lowerCamelCase      (					lowercase__				,     lowercase__				,     lowercase__				,     lowercase__			):






          """simple docstring"""






          return thetaa + (thetaa - thetaa) * (1.0 - alpha)




'''simple docstring'''






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


snake_case_       =       logging.get_logger(__name__)  # pylint: disable=invalid-name
@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->EulerAncestralDiscrete
class 	SCREAMING_SNAKE_CASE__			(				_UpperCAmelCase      ):
    _A			    =							42
    _A			    =							None






def 					__lowerCamelCase			(      SCREAMING_SNAKE_CASE_     :	Any  ,  SCREAMING_SNAKE_CASE_     :	int=0.999  ,  SCREAMING_SNAKE_CASE_     :	Optional[Any]="cosine"  ,  )							->   Optional[int]:
      """simple docstring"""

      if alpha_transform_type == "cosine":

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

      elif alpha_transform_type == "exp":

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

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

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





class 	SCREAMING_SNAKE_CASE__			(				_UpperCAmelCase,_UpperCAmelCase      ):
    _A			    =							[e.name for e in KarrasDiffusionSchedulers]
    _A			    =							1




    @register_to_config
    def __init__(					self				,     lowercase__ = 1000				,     lowercase__ = 0.0001				,     lowercase__ = 0.02				,     lowercase__ = "linear"				,     lowercase__ = None				,     lowercase__ = "epsilon"				,     lowercase__ = "linspace"				,     lowercase__ = 0				,     ):






          """simple docstring"""






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

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

          SCREAMING_SNAKE_CASE_      :			Dict     =       np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5			)
          SCREAMING_SNAKE_CASE_      :			List[str]     =       np.concatenate([sigmas[::-1], [0.0]]			).astype(np.floataa			)
          SCREAMING_SNAKE_CASE_      :			List[str]     =       torch.from_numpy(lowercase__			)

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




    @property
    def   __lowerCamelCase      (					self			):






          """simple docstring"""






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

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




    def   __lowerCamelCase      (					self				,     lowercase__				,     lowercase__			):






          """simple docstring"""






          if isinstance(lowercase__				,     torch.Tensor			):
                SCREAMING_SNAKE_CASE_      :			Any     =       timestep.to(self.timesteps.device			)
          SCREAMING_SNAKE_CASE_      :			Optional[Any]     =       (self.timesteps == timestep).nonzero().item()
          SCREAMING_SNAKE_CASE_      :			Optional[Any]     =       self.sigmas[step_index]
          SCREAMING_SNAKE_CASE_      :			Optional[Any]     =       sample / ((sigma**2 + 1) ** 0.5)
          SCREAMING_SNAKE_CASE_      :			Union[str, Any]     =       True
          return sample




    def   __lowerCamelCase      (					self				,     lowercase__				,     lowercase__ = None			):






          """simple docstring"""






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

          SCREAMING_SNAKE_CASE_      :			int     =       np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5			)
          SCREAMING_SNAKE_CASE_      :			Any     =       np.interp(lowercase__				,     np.arange(0				,     len(lowercase__			)			)				,     lowercase__			)
          SCREAMING_SNAKE_CASE_      :			List[str]     =       np.concatenate([sigmas, [0.0]]			).astype(np.floataa			)
          SCREAMING_SNAKE_CASE_      :			Any     =       torch.from_numpy(lowercase__			).to(device=lowercase__			)
          if str(lowercase__			).startswith("mps"			):
                # mps does not support float64
                SCREAMING_SNAKE_CASE_      :			List[Any]     =       torch.from_numpy(lowercase__			).to(lowercase__				,     dtype=torch.floataa			)
          else:
                SCREAMING_SNAKE_CASE_      :			Tuple     =       torch.from_numpy(lowercase__			).to(device=lowercase__			)




    def   __lowerCamelCase      (					self				,     lowercase__				,     lowercase__				,     lowercase__				,     lowercase__ = None				,     lowercase__ = True				,     ):






          """simple docstring"""






          if (
              isinstance(lowercase__				,     lowercase__			)
              or isinstance(lowercase__				,     torch.IntTensor			)
              or isinstance(lowercase__				,     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(lowercase__				,     torch.Tensor			):
                SCREAMING_SNAKE_CASE_      :			Tuple     =       timestep.to(self.timesteps.device			)

          SCREAMING_SNAKE_CASE_      :			Tuple     =       (self.timesteps == timestep).nonzero().item()
          SCREAMING_SNAKE_CASE_      :			str     =       self.sigmas[step_index]

          # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
          if self.config.prediction_type == "epsilon":
                SCREAMING_SNAKE_CASE_      :			Any     =       sample - sigma * model_output
          elif self.config.prediction_type == "v_prediction":
                # * c_out + input * c_skip
                SCREAMING_SNAKE_CASE_      :			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`"			)

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

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

          SCREAMING_SNAKE_CASE_      :			List[Any]     =       sigma_down - sigma

          SCREAMING_SNAKE_CASE_      :			Optional[Any]     =       sample + derivative * dt

          SCREAMING_SNAKE_CASE_      :			Dict     =       model_output.device
          SCREAMING_SNAKE_CASE_      :			int     =       randn_tensor(model_output.shape				,     dtype=model_output.dtype				,     device=lowercase__				,     generator=lowercase__			)

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

          if not return_dict:
                return (prev_sample,)

          return EulerAncestralDiscreteSchedulerOutput(
              prev_sample=lowercase__				,     pred_original_sample=lowercase__			)




    def   __lowerCamelCase      (					self				,     lowercase__				,     lowercase__				,     lowercase__				,     ):






          """simple docstring"""






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

          SCREAMING_SNAKE_CASE_      :			Union[str, Any]     =       [(schedule_timesteps == t).nonzero().item() for t in timesteps]

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

          SCREAMING_SNAKE_CASE_      :			Dict     =       original_samples + noise * sigma
          return noisy_samples







    def __len__(					self			):






          """simple docstring"""






          return self.config.num_train_timesteps

