





"""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


a__					:     str							=				logging.get_logger(__name__)  # pylint: disable=invalid-name



@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->EulerDiscrete
class      lowercase_				(     SCREAMING_SNAKE_CASE__							):
       snake_case__				:							Union[str, Any]											=     42
       snake_case__				:							Optional[int]											=     None





def 							a__			(  _UpperCAmelCase	,     _UpperCAmelCase=0.999	,     _UpperCAmelCase="cosine"	,     )    ->  Dict:

 '''simple docstring'''






 if alpha_transform_type == "cosine":

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

 elif alpha_transform_type == "exp":

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

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

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



class      lowercase_				(     SCREAMING_SNAKE_CASE__				,SCREAMING_SNAKE_CASE__							):
       snake_case__				:							Optional[Any]											=     [e.name for e in KarrasDiffusionSchedulers]
       snake_case__				:							Any											=     1

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






        '''simple docstring'''

        if trained_betas is not None:
         snake_case__    							=				torch.tensor(__SCREAMING_SNAKE_CASE   ,					dtype=torch.floataa     )
        elif beta_schedule == "linear":
         snake_case__    							=				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.
         snake_case__    							=				(
             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
         snake_case__    							=				betas_for_alpha_bar(__SCREAMING_SNAKE_CASE     )
        else:
         raise NotImplementedError(F"{beta_schedule} does is not implemented for {self.__class__}"     )

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

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

        # setable values
        snake_case__    							=				None
        snake_case__    							=				np.linspace(0   ,					num_train_timesteps - 1   ,					__SCREAMING_SNAKE_CASE   ,					dtype=__SCREAMING_SNAKE_CASE     )[::-1].copy()
        snake_case__    							=				torch.from_numpy(__SCREAMING_SNAKE_CASE     )
        snake_case__    							=				False
        snake_case__    							=				use_karras_sigmas

       @property
       def 		_SCREAMING_SNAKE_CASE(			self     ):






        '''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   ,					__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE     ):






        '''simple docstring'''

        if isinstance(__SCREAMING_SNAKE_CASE   ,					torch.Tensor     ):
         snake_case__    							=				timestep.to(self.timesteps.device     )
        snake_case__    							=				(self.timesteps == timestep).nonzero().item()
        snake_case__    							=				self.sigmas[step_index]

        snake_case__    							=				sample / ((sigma**2 + 1) ** 0.5)

        snake_case__    							=				True
        return sample

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






        '''simple docstring'''

        snake_case__    							=				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":
         snake_case__    							=				np.linspace(0   ,					self.config.num_train_timesteps - 1   ,					__SCREAMING_SNAKE_CASE   ,					dtype=__SCREAMING_SNAKE_CASE     )[
             ::-1
         ].copy()
        elif self.config.timestep_spacing == "leading":
         snake_case__    							=				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
         snake_case__    							=				(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":
         snake_case__    							=				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
         snake_case__    							=				(np.arange(self.config.num_train_timesteps   ,					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\'."     )

        snake_case__    							=				np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5     )
        snake_case__    							=				np.log(__SCREAMING_SNAKE_CASE     )

        if self.config.interpolation_type == "linear":
         snake_case__    							=				np.interp(__SCREAMING_SNAKE_CASE   ,					np.arange(0   ,					len(__SCREAMING_SNAKE_CASE     )     )   ,					__SCREAMING_SNAKE_CASE     )
        elif self.config.interpolation_type == "log_linear":
         snake_case__    							=				torch.linspace(np.log(sigmas[-1]     )   ,					np.log(sigmas[0]     )   ,					num_inference_steps + 1     ).exp()
        else:
         raise ValueError(
             F"{self.config.interpolation_type} is not implemented. Please specify interpolation_type to either"
             " \'linear\' or \'log_linear\'"     )

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

        snake_case__    							=				np.concatenate([sigmas, [0.0]]     ).astype(np.floataa     )
        snake_case__    							=				torch.from_numpy(__SCREAMING_SNAKE_CASE     ).to(device=__SCREAMING_SNAKE_CASE     )
        if str(__SCREAMING_SNAKE_CASE     ).startswith("mps"     ):
         # mps does not support float64
         snake_case__    							=				torch.from_numpy(__SCREAMING_SNAKE_CASE     ).to(__SCREAMING_SNAKE_CASE   ,					dtype=torch.floataa     )
        else:
         snake_case__    							=				torch.from_numpy(__SCREAMING_SNAKE_CASE     ).to(device=__SCREAMING_SNAKE_CASE     )

       def 		_SCREAMING_SNAKE_CASE(			self   ,					__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE     ):






        '''simple docstring'''

        snake_case__    							=				np.log(__SCREAMING_SNAKE_CASE     )

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

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

        snake_case__    							=				log_sigmas[low_idx]
        snake_case__    							=				log_sigmas[high_idx]

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

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

       def 		_SCREAMING_SNAKE_CASE(			self   ,					__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE     ):






        '''simple docstring'''

        snake_case__    							=				in_sigmas[-1].item()
        snake_case__    							=				in_sigmas[0].item()

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

       def 		_SCREAMING_SNAKE_CASE(			self   ,					__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE = 0.0   ,					__SCREAMING_SNAKE_CASE = 0.0   ,					__SCREAMING_SNAKE_CASE = float("inf"     )   ,					__SCREAMING_SNAKE_CASE = 1.0   ,					__SCREAMING_SNAKE_CASE = None   ,					__SCREAMING_SNAKE_CASE = True   ,					):






        '''simple docstring'''

        if (
            isinstance(__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE     )
            or isinstance(__SCREAMING_SNAKE_CASE   ,					torch.IntTensor     )
            or isinstance(__SCREAMING_SNAKE_CASE   ,					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(__SCREAMING_SNAKE_CASE   ,					torch.Tensor     ):
         snake_case__    							=				timestep.to(self.timesteps.device     )

        snake_case__    							=				(self.timesteps == timestep).nonzero().item()
        snake_case__    							=				self.sigmas[step_index]

        snake_case__    							=				min(s_churn / (len(self.sigmas     ) - 1)   ,					2**0.5 - 1     ) if s_tmin <= sigma <= s_tmax else 0.0

        snake_case__    							=				randn_tensor(
            model_output.shape   ,					dtype=model_output.dtype   ,					device=model_output.device   ,					generator=__SCREAMING_SNAKE_CASE     )

        snake_case__    							=				noise * s_noise
        snake_case__    							=				sigma * (gamma + 1)

        if gamma > 0:
         snake_case__    							=				sample + eps * (sigma_hat**2 - sigma**2) ** 0.5

        # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
        # NOTE: "original_sample" should not be an expected prediction_type but is left in for
        # backwards compatibility
        if self.config.prediction_type == "original_sample" or self.config.prediction_type == "sample":
         snake_case__    							=				model_output
        elif self.config.prediction_type == "epsilon":
         snake_case__    							=				sample - sigma_hat * model_output
        elif self.config.prediction_type == "v_prediction":
         # * c_out + input * c_skip
         snake_case__    							=				model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
        else:
         raise ValueError(
             F"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`"     )

        # 2. Convert to an ODE derivative
        snake_case__    							=				(sample - pred_original_sample) / sigma_hat

        snake_case__    							=				self.sigmas[step_index + 1] - sigma_hat

        snake_case__    							=				sample + derivative * dt

        if not return_dict:
         return (prev_sample,)

        return EulerDiscreteSchedulerOutput(prev_sample=__SCREAMING_SNAKE_CASE   ,					pred_original_sample=__SCREAMING_SNAKE_CASE     )

       def 		_SCREAMING_SNAKE_CASE(			self   ,					__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE   ,					):






        '''simple docstring'''

        snake_case__    							=				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
         snake_case__    							=				self.timesteps.to(original_samples.device   ,					dtype=torch.floataa     )
         snake_case__    							=				timesteps.to(original_samples.device   ,					dtype=torch.floataa     )
        else:
         snake_case__    							=				self.timesteps.to(original_samples.device     )
         snake_case__    							=				timesteps.to(original_samples.device     )

        snake_case__    							=				[(schedule_timesteps == t).nonzero().item() for t in timesteps]

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

        snake_case__    							=				original_samples + noise * sigma
        return noisy_samples

       def __len__(			self     ):






        '''simple docstring'''

        return self.config.num_train_timesteps








"""simple docstring"""


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

import flax
import jax.numpy as jnp
from scipy import integrate

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import (
    CommonSchedulerState,
    FlaxKarrasDiffusionSchedulers,
    FlaxSchedulerMixin,
    FlaxSchedulerOutput,
    broadcast_to_shape_from_left,
)



@flax.struct.dataclass
class      lowercase_				:
       snake_case__				:							CommonSchedulerState

       # setable values
       snake_case__				:							jnp.ndarray
       snake_case__				:							jnp.ndarray
       snake_case__				:							jnp.ndarray
       snake_case__				:							Optional[int]											=     None

       # running values
       snake_case__				:							Optional[jnp.ndarray]											=     None

       @classmethod
       def 		_SCREAMING_SNAKE_CASE(			cls   ,					__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE     ):






        '''simple docstring'''

        return cls(common=__SCREAMING_SNAKE_CASE   ,					init_noise_sigma=__SCREAMING_SNAKE_CASE   ,					timesteps=__SCREAMING_SNAKE_CASE   ,					sigmas=__SCREAMING_SNAKE_CASE     )



@dataclass
class      lowercase_				(     __lowerCAmelCase							):
       snake_case__				:							LMSDiscreteSchedulerState



class      lowercase_				(     __lowerCAmelCase				,__lowerCAmelCase							):
       snake_case__				:							Any											=     [e.name for e in FlaxKarrasDiffusionSchedulers]

       snake_case__				:							jnp.dtype

       @property
       def 		_SCREAMING_SNAKE_CASE(			self     ):






        '''simple docstring'''

        return True

       @register_to_config
       def __init__(			self   ,					__SCREAMING_SNAKE_CASE = 1000   ,					__SCREAMING_SNAKE_CASE = 0.0001   ,					__SCREAMING_SNAKE_CASE = 0.02   ,					__SCREAMING_SNAKE_CASE = "linear"   ,					__SCREAMING_SNAKE_CASE = None   ,					__SCREAMING_SNAKE_CASE = "epsilon"   ,					__SCREAMING_SNAKE_CASE = jnp.floataa   ,					):






        '''simple docstring'''

        snake_case__    							=				dtype

       def 		_SCREAMING_SNAKE_CASE(			self   ,					__SCREAMING_SNAKE_CASE = None     ):






        '''simple docstring'''

        if common is None:
         snake_case__    							=				CommonSchedulerState.create(self     )

        snake_case__    							=				jnp.arange(0   ,					self.config.num_train_timesteps     ).round()[::-1]
        snake_case__    							=				((1 - common.alphas_cumprod) / common.alphas_cumprod) ** 0.5

        # standard deviation of the initial noise distribution
        snake_case__    							=				sigmas.max()

        return LMSDiscreteSchedulerState.create(
            common=__SCREAMING_SNAKE_CASE   ,					init_noise_sigma=__SCREAMING_SNAKE_CASE   ,					timesteps=__SCREAMING_SNAKE_CASE   ,					sigmas=__SCREAMING_SNAKE_CASE   ,					)

       def 		_SCREAMING_SNAKE_CASE(			self   ,					__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE     ):






        '''simple docstring'''

        ((snake_case__)							,   )    							=				jnp.where(state.timesteps == timestep   ,					size=1     )
        snake_case__    							=				step_index[0]

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

       def 		_SCREAMING_SNAKE_CASE(			self   ,					__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE     ):






        '''simple docstring'''

        def lms_derivative(__SCREAMING_SNAKE_CASE     ):
         snake_case__    							=				1.0
         for k in range(__SCREAMING_SNAKE_CASE     ):
          if current_order == k:
           continue
          prod *= (tau - state.sigmas[t - k]) / (state.sigmas[t - current_order] - state.sigmas[t - k])
         return prod

        snake_case__    							=				integrate.quad(__SCREAMING_SNAKE_CASE   ,					state.sigmas[t]   ,					state.sigmas[t + 1]   ,					epsrel=1e-4     )[0]

        return integrated_coeff

       def 		_SCREAMING_SNAKE_CASE(			self   ,					__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE = ()     ):






        '''simple docstring'''

        snake_case__    							=				jnp.linspace(self.config.num_train_timesteps - 1   ,					0   ,					__SCREAMING_SNAKE_CASE   ,					dtype=self.dtype     )

        snake_case__    							=				jnp.floor(__SCREAMING_SNAKE_CASE     ).astype(jnp.intaa     )
        snake_case__    							=				jnp.ceil(__SCREAMING_SNAKE_CASE     ).astype(jnp.intaa     )

        snake_case__    							=				jnp.mod(__SCREAMING_SNAKE_CASE   ,					1.0     )

        snake_case__    							=				((1 - state.common.alphas_cumprod) / state.common.alphas_cumprod) ** 0.5
        snake_case__    							=				(1 - frac) * sigmas[low_idx] + frac * sigmas[high_idx]
        snake_case__    							=				jnp.concatenate([sigmas, jnp.array([0.0]   ,					dtype=self.dtype     )]     )

        snake_case__    							=				timesteps.astype(jnp.intaa     )

        # initial running values
        snake_case__    							=				jnp.zeros((0,) + shape   ,					dtype=self.dtype     )

        return state.replace(
            timesteps=__SCREAMING_SNAKE_CASE   ,					sigmas=__SCREAMING_SNAKE_CASE   ,					num_inference_steps=__SCREAMING_SNAKE_CASE   ,					derivatives=__SCREAMING_SNAKE_CASE   ,					)

       def 		_SCREAMING_SNAKE_CASE(			self   ,					__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE = 4   ,					__SCREAMING_SNAKE_CASE = True   ,					):






        '''simple docstring'''

        if state.num_inference_steps is None:
         raise ValueError(
             "Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler"     )

        snake_case__    							=				state.sigmas[timestep]

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

        # 2. Convert to an ODE derivative
        snake_case__    							=				(sample - pred_original_sample) / sigma
        snake_case__    							=				state.replace(derivatives=jnp.append(state.derivatives   ,					__SCREAMING_SNAKE_CASE     )     )
        if len(state.derivatives     ) > order:
         snake_case__    							=				state.replace(derivatives=jnp.delete(state.derivatives   ,					0     )     )

        # 3. Compute linear multistep coefficients
        snake_case__    							=				min(timestep + 1   ,					__SCREAMING_SNAKE_CASE     )
        snake_case__    							=				[self.get_lms_coefficient(__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE     ) for curr_order in range(__SCREAMING_SNAKE_CASE     )]

        # 4. Compute previous sample based on the derivatives path
        snake_case__    							=				sample + sum(
            coeff * derivative for coeff, derivative in zip(__SCREAMING_SNAKE_CASE   ,					reversed(state.derivatives     )     )     )

        if not return_dict:
         return (prev_sample, state)

        return FlaxLMSSchedulerOutput(prev_sample=__SCREAMING_SNAKE_CASE   ,					state=__SCREAMING_SNAKE_CASE     )

       def 		_SCREAMING_SNAKE_CASE(			self   ,					__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE   ,					):






        '''simple docstring'''

        snake_case__    							=				state.sigmas[timesteps].flatten()
        snake_case__    							=				broadcast_to_shape_from_left(__SCREAMING_SNAKE_CASE   ,					noise.shape     )

        snake_case__    							=				original_samples + noise * sigma

        return noisy_samples

       def __len__(			self     ):






        '''simple docstring'''

        return self.config.num_train_timesteps

