


# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

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

import flax
import jax.numpy as jnp
from jax import random

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import FlaxSchedulerMixin, FlaxSchedulerOutput, broadcast_to_shape_from_left


@flax.struct.dataclass
class       __lowerCAmelCase       :






  """simple docstring"""





  _SCREAMING_SNAKE_CASE						      =    None
  _SCREAMING_SNAKE_CASE						      =    None
  _SCREAMING_SNAKE_CASE						      =    None



  @classmethod
  def    lowerCAmelCase__					(       cls    :    int					)				->	Dict:




     """simple docstring"""
     return cls()






@dataclass
class       __lowerCAmelCase       (	a   ):






  """simple docstring"""





  _SCREAMING_SNAKE_CASE						      =    42
  _SCREAMING_SNAKE_CASE						      =    42
  _SCREAMING_SNAKE_CASE						      =    None






class       __lowerCAmelCase       (	a	,					a   ):






  """simple docstring"""





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




     """simple docstring"""
     return True



  @register_to_config
  def __init__(       self    :    List[str]      ,			_lowerCAmelCase    :    int = 2_0_0_0      ,			_lowerCAmelCase    :    float = 0.15      ,			_lowerCAmelCase    :    float = 0.01      ,			_lowerCAmelCase    :    float = 1_3_4_8.0      ,			_lowerCAmelCase    :    float = 1e-5      ,			_lowerCAmelCase    :    int = 1      ,			)				->	List[str]:




     """simple docstring"""
     pass



  def    lowerCAmelCase__					(       self    :    Tuple					)				->	str:




     """simple docstring"""
     snake_case_         =   ScoreSdeVeSchedulerState.create()
     return self.set_sigmas(
         _lowerCAmelCase      ,			self.config.num_train_timesteps      ,			self.config.sigma_min      ,			self.config.sigma_max      ,			self.config.sampling_eps      ,			)



  def    lowerCAmelCase__					(       self    :    Dict      ,			_lowerCAmelCase    :    ScoreSdeVeSchedulerState      ,			_lowerCAmelCase    :    int      ,			_lowerCAmelCase    :    Tuple = ()      ,			_lowerCAmelCase    :    float = None					)				->	ScoreSdeVeSchedulerState:




     """simple docstring"""
     snake_case_         =   sampling_eps if sampling_eps is not None else self.config.sampling_eps

     snake_case_         =   jnp.linspace(1      ,			_lowerCAmelCase      ,			_lowerCAmelCase					)
     return state.replace(timesteps=_lowerCAmelCase					)



  def    lowerCAmelCase__					(       self    :    Any      ,			_lowerCAmelCase    :    ScoreSdeVeSchedulerState      ,			_lowerCAmelCase    :    int      ,			_lowerCAmelCase    :    float = None      ,			_lowerCAmelCase    :    float = None      ,			_lowerCAmelCase    :    float = None      ,			)				->	ScoreSdeVeSchedulerState:




     """simple docstring"""
     snake_case_         =   sigma_min if sigma_min is not None else self.config.sigma_min
     snake_case_         =   sigma_max if sigma_max is not None else self.config.sigma_max
     snake_case_         =   sampling_eps if sampling_eps is not None else self.config.sampling_eps
     if state.timesteps is None:
        snake_case_         =   self.set_timesteps(_lowerCAmelCase      ,			_lowerCAmelCase      ,			_lowerCAmelCase					)

     snake_case_         =   jnp.exp(jnp.linspace(jnp.log(_lowerCAmelCase					)      ,			jnp.log(_lowerCAmelCase					)      ,			_lowerCAmelCase					)					)
     snake_case_         =   jnp.array([sigma_min * (sigma_max / sigma_min) ** t for t in state.timesteps]					)

     return state.replace(discrete_sigmas=_lowerCAmelCase      ,			sigmas=_lowerCAmelCase					)



  def    lowerCAmelCase__					(       self    :    str      ,			_lowerCAmelCase    :    Optional[Any]      ,			_lowerCAmelCase    :    Any      ,			_lowerCAmelCase    :    Any					)				->	int:




     """simple docstring"""
     return jnp.where(timesteps == 0      ,			jnp.zeros_like(_lowerCAmelCase					)      ,			state.discrete_sigmas[timesteps - 1]					)



  def    lowerCAmelCase__					(       self    :    Any      ,			_lowerCAmelCase    :    ScoreSdeVeSchedulerState      ,			_lowerCAmelCase    :    jnp.ndarray      ,			_lowerCAmelCase    :    int      ,			_lowerCAmelCase    :    jnp.ndarray      ,			_lowerCAmelCase    :    random.KeyArray      ,			_lowerCAmelCase    :    bool = True      ,			)				->	Union[FlaxSdeVeOutput, Tuple]:




     """simple docstring"""
     if state.timesteps is None:
        raise ValueError(
            "`state.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"					)

     snake_case_         =   timestep * jnp.ones(
         sample.shape[0]      ,			)
     snake_case_         =   (timestep * (len(state.timesteps					) - 1)).long()

     snake_case_         =   state.discrete_sigmas[timesteps]
     snake_case_         =   self.get_adjacent_sigma(_lowerCAmelCase      ,			_lowerCAmelCase      ,			_lowerCAmelCase					)
     snake_case_         =   jnp.zeros_like(_lowerCAmelCase					)
     snake_case_         =   (sigma**2 - adjacent_sigma**2) ** 0.5

     # equation 6 in the paper: the model_output modeled by the network is grad_x log pt(x)
     # also equation 47 shows the analog from SDE models to ancestral sampling methods
     snake_case_         =   diffusion.flatten()
     snake_case_         =   broadcast_to_shape_from_left(_lowerCAmelCase      ,			sample.shape					)
     snake_case_         =   drift - diffusion**2 * model_output

     #  equation 6: sample noise for the diffusion term of
     snake_case_         =   random.split(_lowerCAmelCase      ,			num=1					)
     snake_case_         =   random.normal(key=_lowerCAmelCase      ,			shape=sample.shape					)
     snake_case_         =   sample - drift  # subtract because `dt` is a small negative timestep
     # TODO is the variable diffusion the correct scaling term for the noise?
     snake_case_         =   prev_sample_mean + diffusion * noise  # add impact of diffusion field g

     if not return_dict:
        return (prev_sample, prev_sample_mean, state)

     return FlaxSdeVeOutput(prev_sample=_lowerCAmelCase      ,			prev_sample_mean=_lowerCAmelCase      ,			state=_lowerCAmelCase					)



  def    lowerCAmelCase__					(       self    :    List[str]      ,			_lowerCAmelCase    :    ScoreSdeVeSchedulerState      ,			_lowerCAmelCase    :    jnp.ndarray      ,			_lowerCAmelCase    :    jnp.ndarray      ,			_lowerCAmelCase    :    random.KeyArray      ,			_lowerCAmelCase    :    bool = True      ,			)				->	Union[FlaxSdeVeOutput, Tuple]:




     """simple docstring"""
     if state.timesteps is None:
        raise ValueError(
            "`state.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"					)

     # For small batch sizes, the paper "suggest replacing norm(z) with sqrt(d), where d is the dim. of z"
     # sample noise for correction
     snake_case_         =   random.split(_lowerCAmelCase      ,			num=1					)
     snake_case_         =   random.normal(key=_lowerCAmelCase      ,			shape=sample.shape					)

     # compute step size from the model_output, the noise, and the snr
     snake_case_         =   jnp.linalg.norm(_lowerCAmelCase					)
     snake_case_         =   jnp.linalg.norm(_lowerCAmelCase					)
     snake_case_         =   (self.config.snr * noise_norm / grad_norm) ** 2 * 2
     snake_case_         =   step_size * jnp.ones(sample.shape[0]					)

     # compute corrected sample: model_output term and noise term
     snake_case_         =   step_size.flatten()
     snake_case_         =   broadcast_to_shape_from_left(_lowerCAmelCase      ,			sample.shape					)
     snake_case_         =   sample + step_size * model_output
     snake_case_         =   prev_sample_mean + ((step_size * 2) ** 0.5) * noise

     if not return_dict:
        return (prev_sample, state)

     return FlaxSdeVeOutput(prev_sample=_lowerCAmelCase      ,			state=_lowerCAmelCase					)



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




     """simple docstring"""
     return self.config.num_train_timesteps








def      _lowerCAmelCase  (							lowerCAmelCase_							:Dict						,  lowerCAmelCase_							:Optional[int]						,  lowerCAmelCase_							:Tuple							)->Optional[Any]:
   '''simple docstring'''






   assert b != 0, "This cannot accept modulo that is == 0"
   if n == 0:
      return 1

   if n % 2 == 1:
      return (bin_exp_mod(lowerCAmelCase_						,  n - 1						,  lowerCAmelCase_							) * a) % b

   snake_case_         =   bin_exp_mod(lowerCAmelCase_						,  n / 2						,  lowerCAmelCase_							)
   return (r * r) % b


if __name__ == "__main__":
   try:
      SCREAMING_SNAKE_CASE		:str											=       int(input('''Enter Base : ''').strip())
      SCREAMING_SNAKE_CASE		:List[Any]											=       int(input('''Enter Power : ''').strip())
      SCREAMING_SNAKE_CASE		:Dict											=       int(input('''Enter Modulo : ''').strip())
   except ValueError:
      print('''Invalid literal for integer''')

   print(bin_exp_mod(BASE, POWER, MODULO))




