





'''simple docstring'''

# DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion
# and https://github.com/hojonathanho/diffusion

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

import flax
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    :
  _lowercase	:						CommonSchedulerState
  _lowercase	:						jnp.ndarray

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







  @classmethod
  def 					UpperCAmelCase_					(		cls  ,  lowercase__  ,  lowercase__  ,  lowercase__  ,  lowercase__  ,  )		->    Optional[int]:






        """simple docstring"""





        return cls(
            common=lowercase__  ,  final_alpha_cumprod=lowercase__  ,  init_noise_sigma=lowercase__  ,  timesteps=lowercase__  ,  )





@dataclass
class lowerCamelCase    (a__       ):
  _lowercase	:						DDIMSchedulerState





class lowerCamelCase    (a__    ,   a__       ):
  _lowercase	:						Union[str, Any]							       =					[e.name for e in FlaxKarrasDiffusionSchedulers]

  _lowercase	:						jnp.dtype







  @property
  def 					UpperCAmelCase_					(		self		)		->    Tuple:






        """simple docstring"""





        return True







  @register_to_config
  def __init__(		self  ,  lowercase__ = 1_000  ,  lowercase__ = 0.0_001  ,  lowercase__ = 0.02  ,  lowercase__ = "linear"  ,  lowercase__ = None  ,  lowercase__ = True  ,  lowercase__ = 0  ,  lowercase__ = "epsilon"  ,  lowercase__ = jnp.floataa  ,  )		->    str:






        """simple docstring"""





        _snake_case		:						str         =  dtype







  def 					UpperCAmelCase_					(		self  ,  lowercase__ = None		)		->    DDIMSchedulerState:






        """simple docstring"""





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

        # At every step in ddim, we are looking into the previous alphas_cumprod
        # For the final step, there is no previous alphas_cumprod because we are already at 0
        # `set_alpha_to_one` decides whether we set this parameter simply to one or
        # whether we use the final alpha of the "non-previous" one.
        _snake_case		:						Dict         =  (
            jnp.array(1.0  ,  dtype=self.dtype		) if self.config.set_alpha_to_one else common.alphas_cumprod[0]
        )

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

        _snake_case		:						Dict         =  jnp.arange(0  ,  self.config.num_train_timesteps		).round()[::-1]

        return DDIMSchedulerState.create(
            common=lowercase__  ,  final_alpha_cumprod=lowercase__  ,  init_noise_sigma=lowercase__  ,  timesteps=lowercase__  ,  )







  def 					UpperCAmelCase_					(		self  ,  lowercase__  ,  lowercase__  ,  lowercase__ = None		)		->    jnp.ndarray:






        """simple docstring"""





        return sample







  def 					UpperCAmelCase_					(		self  ,  lowercase__  ,  lowercase__  ,  lowercase__ = ()		)		->    DDIMSchedulerState:






        """simple docstring"""





        _snake_case		:						int         =  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		:						Optional[Any]         =  (jnp.arange(0  ,  lowercase__		) * step_ratio).round()[::-1] + self.config.steps_offset

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







  def 					UpperCAmelCase_					(		self  ,  lowercase__  ,  lowercase__  ,  lowercase__		)		->    Dict:






        """simple docstring"""





        _snake_case		:						List[str]         =  state.common.alphas_cumprod[timestep]
        _snake_case		:						Dict         =  jnp.where(
            prev_timestep >= 0  ,  state.common.alphas_cumprod[prev_timestep]  ,  state.final_alpha_cumprod		)
        _snake_case		:						Tuple         =  1 - alpha_prod_t
        _snake_case		:						List[str]         =  1 - alpha_prod_t_prev

        _snake_case		:						int         =  (beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)

        return variance







  def 					UpperCAmelCase_					(		self  ,  lowercase__  ,  lowercase__  ,  lowercase__  ,  lowercase__  ,  lowercase__ = 0.0  ,  lowercase__ = True  ,  )		->    Union[FlaxDDIMSchedulerOutput, Tuple]:






        """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'''		)

        # See formulas (12) and (16) of DDIM paper https://arxiv.org/pdf/2010.02502.pdf
        # Ideally, read DDIM paper in-detail understanding

        # Notation (<variable name> -> <name in paper>
        # - pred_noise_t -> e_theta(x_t, t)
        # - pred_original_sample -> f_theta(x_t, t) or x_0
        # - std_dev_t -> sigma_t
        # - eta -> η
        # - pred_sample_direction -> "direction pointing to x_t"
        # - pred_prev_sample -> "x_t-1"

        # 1. get previous step value (=t-1)
        _snake_case		:						List[str]         =  timestep - self.config.num_train_timesteps // state.num_inference_steps

        _snake_case		:						Optional[Any]         =  state.common.alphas_cumprod
        _snake_case		:						Tuple         =  state.final_alpha_cumprod

        # 2. compute alphas, betas
        _snake_case		:						List[str]         =  alphas_cumprod[timestep]
        _snake_case		:						str         =  jnp.where(prev_timestep >= 0  ,  alphas_cumprod[prev_timestep]  ,  lowercase__		)

        _snake_case		:						Tuple         =  1 - alpha_prod_t

        # 3. compute predicted original sample from predicted noise also called
        # "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
        if self.config.prediction_type == "epsilon":
              _snake_case		:						Optional[Any]         =  (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
              _snake_case		:						int         =  model_output
        elif self.config.prediction_type == "sample":
              _snake_case		:						str         =  model_output
              _snake_case		:						Dict         =  (sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
        elif self.config.prediction_type == "v_prediction":
              _snake_case		:						str         =  (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
              _snake_case		:						Tuple         =  (alpha_prod_t**0.5) * model_output + (beta_prod_t**0.5) * sample
        else:
              raise ValueError(
                  F'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or'''
                  ''' `v_prediction`'''		)

        # 4. compute variance: "sigma_t(η)" -> see formula (16)
        # σ_t = sqrt((1 − α_t−1)/(1 − α_t)) * sqrt(1 − α_t/α_t−1)
        _snake_case		:						Tuple         =  self._get_variance(lowercase__  ,  lowercase__  ,  lowercase__		)
        _snake_case		:						Dict         =  eta * variance ** 0.5

        # 5. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
        _snake_case		:						int         =  (1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * pred_epsilon

        # 6. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
        _snake_case		:						Optional[int]         =  alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction

        if not return_dict:
              return (prev_sample, state)

        return FlaxDDIMSchedulerOutput(prev_sample=lowercase__  ,  state=lowercase__		)







  def 					UpperCAmelCase_					(		self  ,  lowercase__  ,  lowercase__  ,  lowercase__  ,  lowercase__  ,  )		->    jnp.ndarray:






        """simple docstring"""





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







  def 					UpperCAmelCase_					(		self  ,  lowercase__  ,  lowercase__  ,  lowercase__  ,  lowercase__  ,  )		->    jnp.ndarray:






        """simple docstring"""





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







  def __len__(		self		)		->    Optional[Any]:






        """simple docstring"""





        return self.config.num_train_timesteps









'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


UpperCAmelCase       :		Dict		=				{'configuration_hubert': ['HUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'HubertConfig']}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCAmelCase       :		Union[str, Any]		=				[
       'HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
       'HubertForCTC',
       'HubertForSequenceClassification',
       'HubertModel',
       'HubertPreTrainedModel',
   ]


try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCAmelCase       :		Optional[int]		=				[
       'TF_HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
       'TFHubertForCTC',
       'TFHubertModel',
       'TFHubertPreTrainedModel',
   ]

if TYPE_CHECKING:
   from .configuration_hubert import HUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, HubertConfig

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_hubert import (
          HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
          HubertForCTC,
          HubertForSequenceClassification,
          HubertModel,
          HubertPreTrainedModel,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_hubert import (
          TF_HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFHubertForCTC,
          TFHubertModel,
          TFHubertPreTrainedModel,
      )


else:
   import sys

   UpperCAmelCase       :		Optional[int]		=				_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


