





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 .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput






def 					lowerCamelCase_						(    _UpperCamelCase			,    _UpperCamelCase=0.999			,    _UpperCamelCase="cosine"			,    )		->						int:






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





class  __lowerCAmelCase	(      lowerCamelCase__,					lowerCamelCase__					):
			lowerCamelCase_					:					List[Any]	      =				[e.name for e in KarrasDiffusionSchedulers]
			lowerCamelCase_					:					str	      =				2






			@register_to_config
			def __init__(self ,							__magic_name__ = 1000 ,							__magic_name__ = 0.00_085 ,							__magic_name__ = 0.012 ,							__magic_name__ = "linear" ,							__magic_name__ = None ,							__magic_name__ = "epsilon" ,							__magic_name__ = False ,							__magic_name__ = False ,							__magic_name__ = 1.0 ,							__magic_name__ = "linspace" ,							__magic_name__ = 0 ,							)					->       int:


							'''simple docstring'''



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

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

							#  set all values
							self.set_timesteps(__magic_name__ ,							__magic_name__ ,							__magic_name__   )
							snake_case_						:   List[str]           =  use_karras_sigmas






			def       lowerCamelCase  (self ,							__magic_name__ ,							__magic_name__=None   )					->       Optional[int]:


							'''simple docstring'''



							if schedule_timesteps is None:
											snake_case_						:   Optional[Any]           =  self.timesteps

							snake_case_						:   Optional[Any]           =  (schedule_timesteps == timestep).nonzero()

							# The sigma index that is taken for the **very** first `step`
							# is always the second index (or the last index if there is only 1)
							# This way we can ensure we don't accidentally skip a sigma in
							# case we start in the middle of the denoising schedule (e.g. for image-to-image)
							if len(self._index_counter   ) == 0:
											snake_case_						:   int           =  1 if len(__magic_name__   ) > 1 else 0
							else:
											snake_case_						:   int           =  timestep.cpu().item() if torch.is_tensor(__magic_name__   ) else timestep
											snake_case_						:   Dict           =  self._index_counter[timestep_int]

							return indices[pos].item()






			@property
			def       lowerCamelCase  (self   )					->       Optional[Any]:


							'''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 ,							__magic_name__ ,							__magic_name__ ,							)					->       List[str]:


							'''simple docstring'''



							snake_case_						:   str           =  self.index_for_timestep(__magic_name__   )

							snake_case_						:   Tuple           =  self.sigmas[step_index]
							snake_case_						:   List[Any]           =  sample / ((sigma**2 + 1) ** 0.5)
							return sample






			def       lowerCamelCase  (self ,							__magic_name__ ,							__magic_name__ = None ,							__magic_name__ = None ,							)					->       Union[str, Any]:


							'''simple docstring'''



							snake_case_						:   List[str]           =  num_inference_steps

							snake_case_						:   Any           =  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":
											snake_case_						:   Optional[Any]           =  np.linspace(0 ,							num_train_timesteps - 1 ,							__magic_name__ ,							dtype=__magic_name__   )[::-1].copy()
							elif self.config.timestep_spacing == "leading":
											snake_case_						:   Union[str, Any]           =  num_train_timesteps // self.num_inference_steps
											# creates integer timesteps by multiplying by ratio
											# casting to int to avoid issues when num_inference_step is power of 3
											snake_case_						:   List[Any]           =  (np.arange(0 ,							__magic_name__   ) * step_ratio).round()[::-1].copy().astype(__magic_name__   )
											timesteps += self.config.steps_offset
							elif self.config.timestep_spacing == "trailing":
											snake_case_						:   List[str]           =  num_train_timesteps / self.num_inference_steps
											# creates integer timesteps by multiplying by ratio
											# casting to int to avoid issues when num_inference_step is power of 3
											snake_case_						:   str           =  (np.arange(__magic_name__ ,							0 ,							-step_ratio   )).round().copy().astype(__magic_name__   )
											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_						:   Optional[Any]           =  np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5   )
							snake_case_						:   Tuple           =  np.log(__magic_name__   )
							snake_case_						:   List[str]           =  np.interp(__magic_name__ ,							np.arange(0 ,							len(__magic_name__   )   ) ,							__magic_name__   )

							if self.config.use_karras_sigmas:
											snake_case_						:   Optional[Any]           =  self._convert_to_karras(in_sigmas=__magic_name__ ,							num_inference_steps=self.num_inference_steps   )
											snake_case_						:   Any           =  np.array([self._sigma_to_t(__magic_name__ ,							__magic_name__   ) for sigma in sigmas]   )

							snake_case_						:   int           =  np.concatenate([sigmas, [0.0]]   ).astype(np.floataa   )
							snake_case_						:   List[str]           =  torch.from_numpy(__magic_name__   ).to(device=__magic_name__   )
							snake_case_						:   Tuple           =  torch.cat([sigmas[:1], sigmas[1:-1].repeat_interleave(2   ), sigmas[-1:]]   )

							snake_case_						:   Any           =  torch.from_numpy(__magic_name__   )
							snake_case_						:   Optional[int]           =  torch.cat([timesteps[:1], timesteps[1:].repeat_interleave(2   )]   )

							if str(__magic_name__   ).startswith('''mps'''   ):
											# mps does not support float64
											snake_case_						:   int           =  timesteps.to(__magic_name__ ,							dtype=torch.floataa   )
							else:
											snake_case_						:   List[str]           =  timesteps.to(device=__magic_name__   )

							# empty dt and derivative
							snake_case_						:   Optional[Any]           =  None
							snake_case_						:   Union[str, Any]           =  None

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






			def       lowerCamelCase  (self ,							__magic_name__ ,							__magic_name__   )					->       Optional[Any]:


							'''simple docstring'''



							snake_case_						:   int           =  np.log(__magic_name__   )

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

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

							snake_case_						:   str           =  log_sigmas[low_idx]
							snake_case_						:   Optional[Any]           =  log_sigmas[high_idx]

							# interpolate sigmas
							snake_case_						:   List[str]           =  (low - log_sigma) / (low - high)
							snake_case_						:   List[Any]           =  np.clip(__magic_name__ ,							0 ,							1   )

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






			def       lowerCamelCase  (self ,							__magic_name__ ,							__magic_name__   )					->       Any:


							'''simple docstring'''



							snake_case_						:   str           =  in_sigmas[-1].item()
							snake_case_						:   List[str]           =  in_sigmas[0].item()

							snake_case_						:   int           =  7.0  # 7.0 is the value used in the paper
							snake_case_						:   Union[str, Any]           =  np.linspace(0 ,							1 ,							__magic_name__   )
							snake_case_						:   List[Any]           =  sigma_min ** (1 / rho)
							snake_case_						:   Any           =  sigma_max ** (1 / rho)
							snake_case_						:   Tuple           =  (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
							return sigmas






			@property
			def       lowerCamelCase  (self   )					->       str:


							'''simple docstring'''



							return self.dt is None






			def       lowerCamelCase  (self ,							__magic_name__ ,							__magic_name__ ,							__magic_name__ ,							__magic_name__ = True ,							)					->       int:


							'''simple docstring'''



							snake_case_						:   Optional[int]           =  self.index_for_timestep(__magic_name__   )

							# advance index counter by 1
							snake_case_						:   Tuple           =  timestep.cpu().item() if torch.is_tensor(__magic_name__   ) else timestep
							self._index_counter[timestep_int] += 1

							if self.state_in_first_order:
											snake_case_						:   List[str]           =  self.sigmas[step_index]
											snake_case_						:   str           =  self.sigmas[step_index + 1]
							else:
											# 2nd order / Heun's method
											snake_case_						:   str           =  self.sigmas[step_index - 1]
											snake_case_						:   Dict           =  self.sigmas[step_index]

							# 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
							snake_case_						:   Tuple           =  0
							snake_case_						:   Union[str, Any]           =  sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

							# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
							if self.config.prediction_type == "epsilon":
											snake_case_						:   Optional[int]           =  sigma_hat if self.state_in_first_order else sigma_next
											snake_case_						:   int           =  sample - sigma_input * model_output
							elif self.config.prediction_type == "v_prediction":
											snake_case_						:   Tuple           =  sigma_hat if self.state_in_first_order else sigma_next
											snake_case_						:   Any           =  model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
											    sample / (sigma_input**2 + 1)
											)
							elif self.config.prediction_type == "sample":
											snake_case_						:   int           =  model_output
							else:
											raise ValueError(
											    F'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`'''   )

							if self.config.clip_sample:
											snake_case_						:   Optional[int]           =  pred_original_sample.clamp(
											    -self.config.clip_sample_range ,							self.config.clip_sample_range   )

							if self.state_in_first_order:
											# 2. Convert to an ODE derivative for 1st order
											snake_case_						:   int           =  (sample - pred_original_sample) / sigma_hat
											# 3. delta timestep
											snake_case_						:   List[Any]           =  sigma_next - sigma_hat

											# store for 2nd order step
											snake_case_						:   Any           =  derivative
											snake_case_						:   Tuple           =  dt
											snake_case_						:   Optional[Any]           =  sample
							else:
											# 2. 2nd order / Heun's method
											snake_case_						:   Union[str, Any]           =  (sample - pred_original_sample) / sigma_next
											snake_case_						:   List[Any]           =  (self.prev_derivative + derivative) / 2

											# 3. take prev timestep & sample
											snake_case_						:   str           =  self.dt
											snake_case_						:   Optional[int]           =  self.sample

											# free dt and derivative
											# Note, this puts the scheduler in "first order mode"
											snake_case_						:   Tuple           =  None
											snake_case_						:   str           =  None
											snake_case_						:   Optional[Any]           =  None

							snake_case_						:   str           =  sample + derivative * dt

							if not return_dict:
											return (prev_sample,)

							return SchedulerOutput(prev_sample=__magic_name__   )






			def       lowerCamelCase  (self ,							__magic_name__ ,							__magic_name__ ,							__magic_name__ ,							)					->       Optional[Any]:


							'''simple docstring'''



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

							snake_case_						:   Optional[int]           =  [self.index_for_timestep(__magic_name__ ,							__magic_name__   ) for t in timesteps]

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

							snake_case_						:   Union[str, Any]           =  original_samples + noise * sigma
							return noisy_samples






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


							'''simple docstring'''



							return self.config.num_train_timesteps







# DISCLAIMER: This file is strongly influenced by https://github.com/ermongroup/ddim

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

import flax
import jax
import jax.numpy as jnp

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



@flax.struct.dataclass
class  __lowerCAmelCase	:
			lowerCamelCase_					:					CommonSchedulerState

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






			@classmethod
			def       lowerCamelCase  (cls ,							__magic_name__ ,							__magic_name__ ,							__magic_name__   )					->       Dict:


							'''simple docstring'''



							return cls(common=__magic_name__ ,							init_noise_sigma=__magic_name__ ,							timesteps=__magic_name__   )




@dataclass
class  __lowerCAmelCase	(      _a					):
			lowerCamelCase_					:					DDPMSchedulerState




class  __lowerCAmelCase	(      _a,					_a					):
			lowerCamelCase_					:					List[Any]	      =				[e.name for e in FlaxKarrasDiffusionSchedulers]

			lowerCamelCase_					:					jnp.dtype






			@property
			def       lowerCamelCase  (self   )					->       int:


							'''simple docstring'''



							return True






			@register_to_config
			def __init__(self ,							__magic_name__ = 1000 ,							__magic_name__ = 0.0_001 ,							__magic_name__ = 0.02 ,							__magic_name__ = "linear" ,							__magic_name__ = None ,							__magic_name__ = "fixed_small" ,							__magic_name__ = True ,							__magic_name__ = "epsilon" ,							__magic_name__ = jnp.floataa ,							)					->       List[str]:


							'''simple docstring'''



							snake_case_						:   Optional[Any]           =  dtype






			def       lowerCamelCase  (self ,							__magic_name__ = None   )					->       DDPMSchedulerState:


							'''simple docstring'''



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

							# standard deviation of the initial noise distribution
							snake_case_						:   Any           =  jnp.array(1.0 ,							dtype=self.dtype   )

							snake_case_						:   Optional[int]           =  jnp.arange(0 ,							self.config.num_train_timesteps   ).round()[::-1]

							return DDPMSchedulerState.create(
							    common=__magic_name__ ,							init_noise_sigma=__magic_name__ ,							timesteps=__magic_name__ ,							)






			def       lowerCamelCase  (self ,							__magic_name__ ,							__magic_name__ ,							__magic_name__ = None   )					->       jnp.ndarray:


							'''simple docstring'''



							return sample






			def       lowerCamelCase  (self ,							__magic_name__ ,							__magic_name__ ,							__magic_name__ = ()   )					->       DDPMSchedulerState:


							'''simple docstring'''



							snake_case_						:   str           =  self.config.num_train_timesteps // num_inference_steps
							# creates integer timesteps by multiplying by ratio
							# rounding to avoid issues when num_inference_step is power of 3
							snake_case_						:   List[str]           =  (jnp.arange(0 ,							__magic_name__   ) * step_ratio).round()[::-1]

							return state.replace(
							    num_inference_steps=__magic_name__ ,							timesteps=__magic_name__ ,							)






			def       lowerCamelCase  (self ,							__magic_name__ ,							__magic_name__ ,							__magic_name__=None ,							__magic_name__=None   )					->       int:


							'''simple docstring'''



							snake_case_						:   Any           =  state.common.alphas_cumprod[t]
							snake_case_						:   Dict           =  jnp.where(t > 0 ,							state.common.alphas_cumprod[t - 1] ,							jnp.array(1.0 ,							dtype=self.dtype   )   )

							# For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf)
							# and sample from it to get previous sample
							# x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample
							snake_case_						:   Tuple           =  (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * state.common.betas[t]

							if variance_type is None:
											snake_case_						:   Any           =  self.config.variance_type

							# hacks - were probably added for training stability
							if variance_type == "fixed_small":
											snake_case_						:   int           =  jnp.clip(__magic_name__ ,							a_min=1e-20   )
							# for rl-diffuser https://arxiv.org/abs/2205.09991
							elif variance_type == "fixed_small_log":
											snake_case_						:   List[str]           =  jnp.log(jnp.clip(__magic_name__ ,							a_min=1e-20   )   )
							elif variance_type == "fixed_large":
											snake_case_						:   str           =  state.common.betas[t]
							elif variance_type == "fixed_large_log":
											# Glide max_log
											snake_case_						:   int           =  jnp.log(state.common.betas[t]   )
							elif variance_type == "learned":
											return predicted_variance
							elif variance_type == "learned_range":
											snake_case_						:   str           =  variance
											snake_case_						:   Optional[Any]           =  state.common.betas[t]
											snake_case_						:   Optional[Any]           =  (predicted_variance + 1) / 2
											snake_case_						:   Optional[Any]           =  frac * max_log + (1 - frac) * min_log

							return variance






			def       lowerCamelCase  (self ,							__magic_name__ ,							__magic_name__ ,							__magic_name__ ,							__magic_name__ ,							__magic_name__ = None ,							__magic_name__ = True ,							)					->       Union[FlaxDDPMSchedulerOutput, Tuple]:


							'''simple docstring'''



							snake_case_						:   Tuple           =  timestep

							if key is None:
											snake_case_						:   List[Any]           =  jax.random.PRNGKey(0   )

							if model_output.shape[1] == sample.shape[1] * 2 and self.config.variance_type in ["learned", "learned_range"]:
											snake_case_      ,							snake_case_						:   Any           =  jnp.split(__magic_name__ ,							sample.shape[1] ,							axis=1   )
							else:
											snake_case_						:   Optional[Any]           =  None

							# 1. compute alphas, betas
							snake_case_						:   List[Any]           =  state.common.alphas_cumprod[t]
							snake_case_						:   Union[str, Any]           =  jnp.where(t > 0 ,							state.common.alphas_cumprod[t - 1] ,							jnp.array(1.0 ,							dtype=self.dtype   )   )
							snake_case_						:   List[Any]           =  1 - alpha_prod_t
							snake_case_						:   str           =  1 - alpha_prod_t_prev

							# 2. compute predicted original sample from predicted noise also called
							# "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
							if self.config.prediction_type == "epsilon":
											snake_case_						:   int           =  (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
							elif self.config.prediction_type == "sample":
											snake_case_						:   List[Any]           =  model_output
							elif self.config.prediction_type == "v_prediction":
											snake_case_						:   Optional[Any]           =  (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
							else:
											raise ValueError(
											    F'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample` '''
											    ''' for the FlaxDDPMScheduler.'''   )

							# 3. Clip "predicted x_0"
							if self.config.clip_sample:
											snake_case_						:   Optional[int]           =  jnp.clip(__magic_name__ ,							-1 ,							1   )

							# 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
							# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
							snake_case_						:   str           =  (alpha_prod_t_prev ** 0.5 * state.common.betas[t]) / beta_prod_t
							snake_case_						:   Dict           =  state.common.alphas[t] ** 0.5 * beta_prod_t_prev / beta_prod_t

							# 5. Compute predicted previous sample µ_t
							# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
							snake_case_						:   Optional[Any]           =  pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

							# 6. Add noise
							def random_variance():
											snake_case_						:   List[Any]           =  jax.random.split(__magic_name__ ,							num=1   )
											snake_case_						:   Tuple           =  jax.random.normal(__magic_name__ ,							shape=model_output.shape ,							dtype=self.dtype   )
											return (self._get_variance(__magic_name__ ,							__magic_name__ ,							predicted_variance=__magic_name__   ) ** 0.5) * noise

							snake_case_						:   List[str]           =  jnp.where(t > 0 ,							random_variance() ,							jnp.zeros(model_output.shape ,							dtype=self.dtype   )   )

							snake_case_						:   Any           =  pred_prev_sample + variance

							if not return_dict:
											return (pred_prev_sample, state)

							return FlaxDDPMSchedulerOutput(prev_sample=__magic_name__ ,							state=__magic_name__   )






			def       lowerCamelCase  (self ,							__magic_name__ ,							__magic_name__ ,							__magic_name__ ,							__magic_name__ ,							)					->       jnp.ndarray:


							'''simple docstring'''



							return add_noise_common(state.common ,							__magic_name__ ,							__magic_name__ ,							__magic_name__   )






			def       lowerCamelCase  (self ,							__magic_name__ ,							__magic_name__ ,							__magic_name__ ,							__magic_name__ ,							)					->       jnp.ndarray:


							'''simple docstring'''



							return get_velocity_common(state.common ,							__magic_name__ ,							__magic_name__ ,							__magic_name__   )






			def __len__(self   )					->       int:


							'''simple docstring'''



							return self.config.num_train_timesteps
