# 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 __snake_case							:
   lowerCAmelCase_								=				42
   lowerCAmelCase_								=				42

   # setable values
   lowerCAmelCase_								=				42
   lowerCAmelCase_								=				42
   lowerCAmelCase_								=				None




   @classmethod
   def       __a		(       cls     :							Dict			,      _lowercase     :							CommonSchedulerState			,      _lowercase     :							jnp.ndarray			,      _lowercase     :							jnp.ndarray			,      _lowercase     :							jnp.ndarray			,      ):



     """simple docstring"""






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







@dataclass
class __snake_case							(		lowerCamelCase_						):
   lowerCAmelCase_								=				42







class __snake_case							(		lowerCamelCase_  ,						lowerCamelCase_						):
   lowerCAmelCase_								=				[e.name for e in FlaxKarrasDiffusionSchedulers]

   lowerCAmelCase_								=				42




   @property
   def       __a		(       self     :							Dict ):



     """simple docstring"""






     return True




   @register_to_config
   def __init__(       self     :							str			,      _lowercase     :							int = 10_00			,      _lowercase     :							float = 0.00_01			,      _lowercase     :							float = 0.02			,      _lowercase     :							str = "linear"			,      _lowercase     :							Optional[jnp.ndarray] = None			,      _lowercase     :							bool = True			,      _lowercase     :							int = 0			,      _lowercase     :							str = "epsilon"			,      _lowercase     :							jnp.dtype = jnp.floataa			,      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= dtype




   def       __a		(       self     :							List[Any]			,      _lowercase     :							Optional[CommonSchedulerState] = None ):



     """simple docstring"""






     if common is None:
       SCREAMING_SNAKE_CASE__     				= 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.
     SCREAMING_SNAKE_CASE__     				= (
         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
     SCREAMING_SNAKE_CASE__     				= jnp.array(1.0			,      dtype=self.dtype )

     SCREAMING_SNAKE_CASE__     				= 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       __a		(       self     :							List[Any]			,      _lowercase     :							DDIMSchedulerState			,      _lowercase     :							jnp.ndarray			,      _lowercase     :							Optional[int] = None ):



     """simple docstring"""






     return sample




   def       __a		(       self     :							Optional[Any]			,      _lowercase     :							DDIMSchedulerState			,      _lowercase     :							int			,      _lowercase     :							Tuple = () ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= 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
     SCREAMING_SNAKE_CASE__     				= (jnp.arange(0			,      _lowercase ) * step_ratio).round()[::-1] + self.config.steps_offset

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




   def       __a		(       self     :							List[str]			,      _lowercase     :							DDIMSchedulerState			,      _lowercase     :							Union[str, Any]			,      _lowercase     :							Optional[Any] ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= state.common.alphas_cumprod[timestep]
     SCREAMING_SNAKE_CASE__     				= jnp.where(
         prev_timestep >= 0			,      state.common.alphas_cumprod[prev_timestep]			,      state.final_alpha_cumprod )
     SCREAMING_SNAKE_CASE__     				= 1 - alpha_prod_t
     SCREAMING_SNAKE_CASE__     				= 1 - alpha_prod_t_prev

     SCREAMING_SNAKE_CASE__     				= (beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)

     return variance




   def       __a		(       self     :							Union[str, Any]			,      _lowercase     :							DDIMSchedulerState			,      _lowercase     :							jnp.ndarray			,      _lowercase     :							int			,      _lowercase     :							jnp.ndarray			,      _lowercase     :							float = 0.0			,      _lowercase     :							bool = 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""" )

     # 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)
     SCREAMING_SNAKE_CASE__     				= timestep - self.config.num_train_timesteps // state.num_inference_steps

     SCREAMING_SNAKE_CASE__     				= state.common.alphas_cumprod
     SCREAMING_SNAKE_CASE__     				= state.final_alpha_cumprod

     # 2. compute alphas, betas
     SCREAMING_SNAKE_CASE__     				= alphas_cumprod[timestep]
     SCREAMING_SNAKE_CASE__     				= jnp.where(prev_timestep >= 0			,      alphas_cumprod[prev_timestep]			,      _lowercase )

     SCREAMING_SNAKE_CASE__     				= 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":
       SCREAMING_SNAKE_CASE__     				= (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
       SCREAMING_SNAKE_CASE__     				= model_output
     elif self.config.prediction_type == "sample":
       SCREAMING_SNAKE_CASE__     				= model_output
       SCREAMING_SNAKE_CASE__     				= (sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
     elif self.config.prediction_type == "v_prediction":
       SCREAMING_SNAKE_CASE__     				= (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
       SCREAMING_SNAKE_CASE__     				= (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)
     SCREAMING_SNAKE_CASE__     				= self._get_variance(_lowercase			,      _lowercase			,      _lowercase )
     SCREAMING_SNAKE_CASE__     				= eta * variance ** 0.5

     # 5. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
     SCREAMING_SNAKE_CASE__     				= (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
     SCREAMING_SNAKE_CASE__     				= 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       __a		(       self     :							Tuple			,      _lowercase     :							DDIMSchedulerState			,      _lowercase     :							jnp.ndarray			,      _lowercase     :							jnp.ndarray			,      _lowercase     :							jnp.ndarray			,      ):



     """simple docstring"""






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




   def       __a		(       self     :							Union[str, Any]			,      _lowercase     :							DDIMSchedulerState			,      _lowercase     :							jnp.ndarray			,      _lowercase     :							jnp.ndarray			,      _lowercase     :							jnp.ndarray			,      ):



     """simple docstring"""






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




   def __len__(       self     :							Optional[int] ):



     """simple docstring"""






     return self.config.num_train_timesteps

from __future__ import annotations



def       __SCREAMING_SNAKE_CASE    (   __UpperCamelCase       :			str      )				->   bool:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__     				= 42
  SCREAMING_SNAKE_CASE__     				= list(__UpperCamelCase      )
  SCREAMING_SNAKE_CASE__				,	SCREAMING_SNAKE_CASE__     				= _vector[:-1], int(_vector[-1]      )
  SCREAMING_SNAKE_CASE__     				= [int(__UpperCamelCase      ) for digit in __vector]

  vector.reverse()
  for i, digit in enumerate(__UpperCamelCase      ):
    if i & 1 == 0:
      SCREAMING_SNAKE_CASE__     				= digit * 2
      if doubled > 9:
        doubled -= 9
      check_digit += doubled
    else:
      check_digit += digit

  return check_digit % 10 == 0


if __name__ == "__main__":
   import doctest

   doctest.testmod()
   assert is_luhn('''79927398713''')
   assert not is_luhn('''79927398714''')
