
"""simple docstring"""







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 		UpperCAmelCase_	:
 """simple docstring"""




 UpperCamelCase_						:     int				   =    42
 UpperCamelCase_						:     Optional[int]				   =    42

 # setable values
 UpperCamelCase_						:     Dict				   =    42
 UpperCamelCase_						:     Dict				   =    42
 UpperCamelCase_						:     Tuple				   =    None




 @classmethod
 def   a      ( cls       : List[str]     ,   a_       : str     ,   a_       : Optional[int]     ,   a_       : Optional[Any]     ,   a_       : Tuple     ,   )->					List[Any]:



      """simple docstring"""

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



@dataclass
class 		UpperCAmelCase_	(snake_case_						):
 """simple docstring"""




 UpperCamelCase_						:     Dict				   =    42



class 		UpperCAmelCase_	(snake_case_  , snake_case_						):
 """simple docstring"""




 UpperCamelCase_						:     int				   =    [e.name for e in FlaxKarrasDiffusionSchedulers]

 UpperCamelCase_						:     int				   =    42




 @property
 def   a      ( self       : int )->					List[Any]:



      """simple docstring"""

      return True




 @register_to_config
 def __init__( self       : Any     ,   a_       : Optional[int] = 10_00     ,   a_       : str = 0.0001     ,   a_       : str = 0.02     ,   a_       : Dict = "linear"     ,   a_       : Optional[Any] = None     ,   a_       : List[str] = True     ,   a_       : Optional[Any] = 0     ,   a_       : Tuple = "epsilon"     ,   a_       : Optional[int] = jnp.floataa     ,   )->					int:



      """simple docstring"""

      UpperCAmelCase_						:    Optional[Any]    					=  dtype




 def   a      ( self       : int     ,   a_       : Dict = None )->					DDIMSchedulerState:



      """simple docstring"""

      if common is None:
           UpperCAmelCase_						:    List[str]    					=  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.
      UpperCAmelCase_						:    Tuple    					=  (
          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
      UpperCAmelCase_						:    Optional[Any]    					=  jnp.array(1.0     ,   dtype=self.dtype )

      UpperCAmelCase_						:    Union[str, Any]    					=  jnp.arange(0     ,   self.config.num_train_timesteps ).round()[::-1]

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




 def   a      ( self       : List[str]     ,   a_       : Any     ,   a_       : Tuple     ,   a_       : Union[str, Any] = None )->					jnp.ndarray:



      """simple docstring"""

      return sample




 def   a      ( self       : Any     ,   a_       : Dict     ,   a_       : Dict     ,   a_       : Optional[Any] = () )->					DDIMSchedulerState:



      """simple docstring"""

      UpperCAmelCase_						:    Optional[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
      UpperCAmelCase_						:    List[str]    					=  (jnp.arange(0     ,   a_ ) * step_ratio).round()[::-1] + self.config.steps_offset

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




 def   a      ( self       : int     ,   a_       : str     ,   a_       : int     ,   a_       : Optional[Any] )->					int:



      """simple docstring"""

      UpperCAmelCase_						:    str    					=  state.common.alphas_cumprod[timestep]
      UpperCAmelCase_						:    Union[str, Any]    					=  jnp.where(
          prev_timestep >= 0     ,   state.common.alphas_cumprod[prev_timestep]     ,   state.final_alpha_cumprod )
      UpperCAmelCase_						:    Any    					=  1 - alpha_prod_t
      UpperCAmelCase_						:    Any    					=  1 - alpha_prod_t_prev

      UpperCAmelCase_						:    List[str]    					=  (beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)

      return variance




 def   a      ( self       : Any     ,   a_       : int     ,   a_       : Optional[Any]     ,   a_       : str     ,   a_       : Optional[Any]     ,   a_       : Optional[int] = 0.0     ,   a_       : List[str] = 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)
      UpperCAmelCase_						:    List[str]    					=  timestep - self.config.num_train_timesteps // state.num_inference_steps

      UpperCAmelCase_						:    Any    					=  state.common.alphas_cumprod
      UpperCAmelCase_						:    Tuple    					=  state.final_alpha_cumprod

      # 2. compute alphas, betas
      UpperCAmelCase_						:    int    					=  alphas_cumprod[timestep]
      UpperCAmelCase_						:    Tuple    					=  jnp.where(prev_timestep >= 0     ,   alphas_cumprod[prev_timestep]     ,   a_ )

      UpperCAmelCase_						:    List[str]    					=  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":
           UpperCAmelCase_						:    List[Any]    					=  (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
           UpperCAmelCase_						:    Any    					=  model_output
      elif self.config.prediction_type == "sample":
           UpperCAmelCase_						:    Any    					=  model_output
           UpperCAmelCase_						:    Union[str, Any]    					=  (sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
      elif self.config.prediction_type == "v_prediction":
           UpperCAmelCase_						:    str    					=  (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
           UpperCAmelCase_						:    Optional[int]    					=  (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)
      UpperCAmelCase_						:    List[Any]    					=  self._get_variance(a_     ,   a_     ,   a_ )
      UpperCAmelCase_						:    Optional[int]    					=  eta * variance ** 0.5

      # 5. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
      UpperCAmelCase_						:    Optional[Any]    					=  (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
      UpperCAmelCase_						:    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=a_     ,   state=a_ )




 def   a      ( self       : Tuple     ,   a_       : List[Any]     ,   a_       : Tuple     ,   a_       : Optional[Any]     ,   a_       : Optional[Any]     ,   )->					jnp.ndarray:



      """simple docstring"""

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




 def   a      ( self       : List[Any]     ,   a_       : Optional[int]     ,   a_       : Optional[Any]     ,   a_       : Optional[int]     ,   a_       : Union[str, Any]     ,   )->					jnp.ndarray:



      """simple docstring"""

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






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



      """simple docstring"""

      return self.config.num_train_timesteps



"""simple docstring"""







import warnings

from .generation import FlaxGenerationMixin



class 		UpperCAmelCase_	(lowerCamelCase_						):
 """simple docstring"""




 warnings.warn(
     """Importing `FlaxGenerationMixin` from `src/transformers/generation_flax_utils.py` is deprecated and will """
     """be removed in Transformers v5. Import as `from transformers import FlaxGenerationMixin` instead."""  , lowerCamelCase_  , )

