





"""simple docstring"""



import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput







def 		__lowercase     (   snake_case_  :  Optional[int]				,snake_case_  :  str=0.999				,snake_case_  :  str="cosine"				,)   ->List[str]:



    '''simple docstring'''







    if alpha_transform_type == "cosine":

        def alpha_bar_fn(snake_case_  :  Union[str, Any]						):
            return math.cos((t + 0.008) / 1.008 * math.pi / 2						) ** 2

    elif alpha_transform_type == "exp":

        def alpha_bar_fn(snake_case_  :  List[str]						):
            return math.exp(t * -12.0						)

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

    __A    :						List[Any]						 =     []
    for i in range(snake_case_						):
        __A    :						List[Any]						 =     i / num_diffusion_timesteps
        __A    :						Any						 =     (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 		__snake_case					(		SCREAMING_SNAKE_CASE__							,							SCREAMING_SNAKE_CASE__     ):





 """simple docstring"""


 _lowerCamelCase        =     [e.name for e in KarrasDiffusionSchedulers]
 _lowerCamelCase        =     2

 @register_to_config
 def __init__(    self					,						__lowerCamelCase = 1000					,						__lowerCamelCase = 0.0_0_0_8_5					,						__lowerCamelCase = 0.0_1_2					,						__lowerCamelCase = "linear"					,						__lowerCamelCase = None					,						__lowerCamelCase = "epsilon"					,						__lowerCamelCase = False					,						__lowerCamelCase = False					,						__lowerCamelCase = 1.0					,						__lowerCamelCase = "linspace"					,						__lowerCamelCase = 0					,						):



     '''simple docstring'''




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

     __A    :						List[Any]						 =     1.0 - self.betas
     __A    :						Any						 =     torch.cumprod(self.alphas					,						dim=0  )

     #  set all values
     self.set_timesteps(__lowerCamelCase					,						__lowerCamelCase					,						__lowerCamelCase  )
     __A    :						Optional[Any]						 =     use_karras_sigmas

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



     '''simple docstring'''




     if schedule_timesteps is None:
         __A    :						Union[str, Any]						 =     self.timesteps

     __A    :						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:
         __A    :						Optional[Any]						 =     1 if len(__lowerCamelCase  ) > 1 else 0
     else:
         __A    :						str						 =     timestep.cpu().item() if torch.is_tensor(__lowerCamelCase  ) else timestep
         __A    :						Union[str, Any]						 =     self._index_counter[timestep_int]

     return indices[pos].item()

 @property
 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




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

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

 def 					UpperCamelCase__(    self					,						__lowerCamelCase					,						__lowerCamelCase					,						):



     '''simple docstring'''




     __A    :						Optional[int]						 =     self.index_for_timestep(__lowerCamelCase  )

     __A    :						Optional[Any]						 =     self.sigmas[step_index]
     __A    :						int						 =     sample / ((sigma**2 + 1) ** 0.5)
     return sample

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



     '''simple docstring'''




     __A    :						Any						 =     num_inference_steps

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

     __A    :						Union[str, Any]						 =     np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5  )
     __A    :						List[Any]						 =     np.log(__lowerCamelCase  )
     __A    :						Dict						 =     np.interp(__lowerCamelCase					,						np.arange(0					,						len(__lowerCamelCase  )  )					,						__lowerCamelCase  )

     if self.config.use_karras_sigmas:
         __A    :						int						 =     self._convert_to_karras(in_sigmas=__lowerCamelCase					,						num_inference_steps=self.num_inference_steps  )
         __A    :						Optional[int]						 =     np.array([self._sigma_to_t(__lowerCamelCase					,						__lowerCamelCase  ) for sigma in sigmas]  )

     __A    :						Any						 =     np.concatenate([sigmas, [0.0]]  ).astype(np.floataa  )
     __A    :						Optional[Any]						 =     torch.from_numpy(__lowerCamelCase  ).to(device=__lowerCamelCase  )
     __A    :						List[str]						 =     torch.cat([sigmas[:1], sigmas[1:-1].repeat_interleave(2  ), sigmas[-1:]]  )

     __A    :						Any						 =     torch.from_numpy(__lowerCamelCase  )
     __A    :						Dict						 =     torch.cat([timesteps[:1], timesteps[1:].repeat_interleave(2  )]  )

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

     # empty dt and derivative
     __A    :						str						 =     None
     __A    :						List[str]						 =     None

     # for exp beta schedules, such as the one for `pipeline_shap_e.py`
     # we need an index counter
     __A    :						List[Any]						 =     defaultdict(__lowerCamelCase  )

 def 					UpperCamelCase__(    self					,						__lowerCamelCase					,						__lowerCamelCase  ):



     '''simple docstring'''




     __A    :						int						 =     np.log(__lowerCamelCase  )

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

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

     __A    :						Any						 =     log_sigmas[low_idx]
     __A    :						str						 =     log_sigmas[high_idx]

     # interpolate sigmas
     __A    :						Optional[int]						 =     (low - log_sigma) / (low - high)
     __A    :						Union[str, Any]						 =     np.clip(__lowerCamelCase					,						0					,						1  )

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

 def 					UpperCamelCase__(    self					,						__lowerCamelCase					,						__lowerCamelCase  ):



     '''simple docstring'''




     __A    :						float						 =     in_sigmas[-1].item()
     __A    :						float						 =     in_sigmas[0].item()

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

 @property
 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     return self.dt is None

 def 					UpperCamelCase__(    self					,						__lowerCamelCase					,						__lowerCamelCase					,						__lowerCamelCase					,						__lowerCamelCase = True					,						):



     '''simple docstring'''




     __A    :						List[Any]						 =     self.index_for_timestep(__lowerCamelCase  )

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

     if self.state_in_first_order:
         __A    :						Union[str, Any]						 =     self.sigmas[step_index]
         __A    :						Dict						 =     self.sigmas[step_index + 1]
     else:
         # 2nd order / Heun's method
         __A    :						str						 =     self.sigmas[step_index - 1]
         __A    :						Any						 =     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
     __A    :						List[str]						 =     0
     __A    :						Tuple						 =     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":
         __A    :						List[str]						 =     sigma_hat if self.state_in_first_order else sigma_next
         __A    :						int						 =     sample - sigma_input * model_output
     elif self.config.prediction_type == "v_prediction":
         __A    :						Tuple						 =     sigma_hat if self.state_in_first_order else sigma_next
         __A    :						Optional[int]						 =     model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
             sample / (sigma_input**2 + 1)
         )
     elif self.config.prediction_type == "sample":
         __A    :						str						 =     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:
         __A    :						List[str]						 =     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
         __A    :						List[Any]						 =     (sample - pred_original_sample) / sigma_hat
         # 3. delta timestep
         __A    :						Union[str, Any]						 =     sigma_next - sigma_hat

         # store for 2nd order step
         __A    :						Dict						 =     derivative
         __A    :						Optional[int]						 =     dt
         __A    :						int						 =     sample
     else:
         # 2. 2nd order / Heun's method
         __A    :						Tuple						 =     (sample - pred_original_sample) / sigma_next
         __A    :						Tuple						 =     (self.prev_derivative + derivative) / 2

         # 3. take prev timestep & sample
         __A    :						int						 =     self.dt
         __A    :						int						 =     self.sample

         # free dt and derivative
         # Note, this puts the scheduler in "first order mode"
         __A    :						List[str]						 =     None
         __A    :						Union[str, Any]						 =     None
         __A    :						Any						 =     None

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

     if not return_dict:
         return (prev_sample,)

     return SchedulerOutput(prev_sample=__lowerCamelCase  )

 def 					UpperCamelCase__(    self					,						__lowerCamelCase					,						__lowerCamelCase					,						__lowerCamelCase					,						):



     '''simple docstring'''




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

     __A    :						Union[str, Any]						 =     [self.index_for_timestep(__lowerCamelCase					,						__lowerCamelCase  ) for t in timesteps]

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

     __A    :						Optional[Any]						 =     original_samples + noise * sigma
     return noisy_samples







 def __len__(    self  ):



     '''simple docstring'''




     return self.config.num_train_timesteps











"""simple docstring"""



# 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 		__snake_case					:





 """simple docstring"""


 _lowerCamelCase        =     42

 # setable values
 _lowerCamelCase        =     42
 _lowerCamelCase        =     42
 _lowerCamelCase        =     None

 @classmethod
 def 					UpperCamelCase__(    cls					,						__lowerCamelCase					,						__lowerCamelCase					,						__lowerCamelCase  ):



     '''simple docstring'''




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






@dataclass
class 		__snake_case					(		SCREAMING_SNAKE_CASE__     ):





 """simple docstring"""


 _lowerCamelCase        =     42






class 		__snake_case					(		SCREAMING_SNAKE_CASE__							,							SCREAMING_SNAKE_CASE__     ):





 """simple docstring"""


 _lowerCamelCase        =     [e.name for e in FlaxKarrasDiffusionSchedulers]

 _lowerCamelCase        =     42

 @property
 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     return True

 @register_to_config
 def __init__(    self					,						__lowerCamelCase = 1000					,						__lowerCamelCase = 0.0_0_0_1					,						__lowerCamelCase = 0.0_2					,						__lowerCamelCase = "linear"					,						__lowerCamelCase = None					,						__lowerCamelCase = "fixed_small"					,						__lowerCamelCase = True					,						__lowerCamelCase = "epsilon"					,						__lowerCamelCase = jnp.floataa					,						):



     '''simple docstring'''




     __A    :						Tuple						 =     dtype

 def 					UpperCamelCase__(    self					,						__lowerCamelCase = None  ):



     '''simple docstring'''




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

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

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

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

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



     '''simple docstring'''




     return sample

 def 					UpperCamelCase__(    self					,						__lowerCamelCase					,						__lowerCamelCase					,						__lowerCamelCase = ()  ):



     '''simple docstring'''




     __A    :						Optional[Any]						 =     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
     __A    :						Optional[Any]						 =     (jnp.arange(0					,						__lowerCamelCase  ) * step_ratio).round()[::-1]

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

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



     '''simple docstring'''




     __A    :						int						 =     state.common.alphas_cumprod[t]
     __A    :						List[str]						 =     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
     __A    :						str						 =     (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * state.common.betas[t]

     if variance_type is None:
         __A    :						Dict						 =     self.config.variance_type

     # hacks - were probably added for training stability
     if variance_type == "fixed_small":
         __A    :						List[Any]						 =     jnp.clip(__lowerCamelCase					,						a_min=1e-2_0  )
     # for rl-diffuser https://arxiv.org/abs/2205.09991
     elif variance_type == "fixed_small_log":
         __A    :						Optional[Any]						 =     jnp.log(jnp.clip(__lowerCamelCase					,						a_min=1e-2_0  )  )
     elif variance_type == "fixed_large":
         __A    :						Tuple						 =     state.common.betas[t]
     elif variance_type == "fixed_large_log":
         # Glide max_log
         __A    :						Union[str, Any]						 =     jnp.log(state.common.betas[t]  )
     elif variance_type == "learned":
         return predicted_variance
     elif variance_type == "learned_range":
         __A    :						Optional[Any]						 =     variance
         __A    :						Optional[Any]						 =     state.common.betas[t]
         __A    :						Any						 =     (predicted_variance + 1) / 2
         __A    :						Union[str, Any]						 =     frac * max_log + (1 - frac) * min_log

     return variance

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



     '''simple docstring'''




     __A    :						Optional[int]						 =     timestep

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

     if model_output.shape[1] == sample.shape[1] * 2 and self.config.variance_type in ["learned", "learned_range"]:
         __A ,       __A    :						Tuple						 =     jnp.split(__lowerCamelCase					,						sample.shape[1]					,						axis=1  )
     else:
         __A    :						List[str]						 =     None

     # 1. compute alphas, betas
     __A    :						Dict						 =     state.common.alphas_cumprod[t]
     __A    :						int						 =     jnp.where(t > 0					,						state.common.alphas_cumprod[t - 1]					,						jnp.array(1.0					,						dtype=self.dtype  )  )
     __A    :						Tuple						 =     1 - alpha_prod_t
     __A    :						Optional[int]						 =     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":
         __A    :						Optional[Any]						 =     (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
     elif self.config.prediction_type == "sample":
         __A    :						Any						 =     model_output
     elif self.config.prediction_type == "v_prediction":
         __A    :						str						 =     (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:
         __A    :						str						 =     jnp.clip(__lowerCamelCase					,						-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
     __A    :						Optional[Any]						 =     (alpha_prod_t_prev ** 0.5 * state.common.betas[t]) / beta_prod_t
     __A    :						Union[str, Any]						 =     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
     __A    :						Optional[Any]						 =     pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

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

     __A    :						Optional[Any]						 =     jnp.where(t > 0					,						random_variance()					,						jnp.zeros(model_output.shape					,						dtype=self.dtype  )  )

     __A    :						Any						 =     pred_prev_sample + variance

     if not return_dict:
         return (pred_prev_sample, state)

     return FlaxDDPMSchedulerOutput(prev_sample=__lowerCamelCase					,						state=__lowerCamelCase  )

 def 					UpperCamelCase__(    self					,						__lowerCamelCase					,						__lowerCamelCase					,						__lowerCamelCase					,						__lowerCamelCase					,						):



     '''simple docstring'''




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

 def 					UpperCamelCase__(    self					,						__lowerCamelCase					,						__lowerCamelCase					,						__lowerCamelCase					,						__lowerCamelCase					,						):



     '''simple docstring'''




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







 def __len__(    self  ):



     '''simple docstring'''




     return self.config.num_train_timesteps




