



'''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 							lowerCAmelCase_		:
    __UpperCAmelCase							=	42
    __UpperCAmelCase							=	42

    # setable values
    __UpperCAmelCase							=	42
    __UpperCAmelCase							=	42
    __UpperCAmelCase							=	None





    @classmethod
    def 					__snake_case       (    cls    :    Any,    _snake_case    :    CommonSchedulerState,    _snake_case    :    jnp.ndarray,    _snake_case    :    jnp.ndarray,    _snake_case    :    jnp.ndarray,    ):



       '''simple docstring'''







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





@dataclass
class 							lowerCAmelCase_		(	a_						):
    __UpperCAmelCase							=	42





class 							lowerCAmelCase_		(	a_  ,      a_						):
    __UpperCAmelCase							=	[e.name for e in FlaxKarrasDiffusionSchedulers]

    __UpperCAmelCase							=	42





    @property
    def 					__snake_case       (    self    :    Optional[Any]     ):



       '''simple docstring'''







       return True





    @register_to_config
    def __init__(    self    :    Dict,    _snake_case    :    int = 1_000,    _snake_case    :    float = 0.0001,    _snake_case    :    float = 0.02,    _snake_case    :    str = "linear",    _snake_case    :    Optional[jnp.ndarray] = None,    _snake_case    :    bool = True,    _snake_case    :    int = 0,    _snake_case    :    str = "epsilon",    _snake_case    :    jnp.dtype = jnp.floataa,    ):



       '''simple docstring'''







       snake_case   :							List[str]						  =dtype





    def 					__snake_case       (    self    :    Optional[Any],    _snake_case    :    Optional[CommonSchedulerState] = None     ):



       '''simple docstring'''







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

       snake_case   :							List[Any]						  =jnp.arange(0,    self.config.num_train_timesteps     ).round()[::-1]

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





    def 					__snake_case       (    self    :    str,    _snake_case    :    DDIMSchedulerState,    _snake_case    :    jnp.ndarray,    _snake_case    :    Optional[int] = None     ):



       '''simple docstring'''







       return sample





    def 					__snake_case       (    self    :    Any,    _snake_case    :    DDIMSchedulerState,    _snake_case    :    int,    _snake_case    :    Tuple = ()     ):



       '''simple docstring'''







       snake_case   :							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
       snake_case   :							Union[str, Any]						  =(jnp.arange(0,    _snake_case     ) * step_ratio).round()[::-1] + self.config.steps_offset

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





    def 					__snake_case       (    self    :    Dict,    _snake_case    :    DDIMSchedulerState,    _snake_case    :    List[Any],    _snake_case    :    Optional[Any]     ):



       '''simple docstring'''







       snake_case   :							Tuple						  =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   :							Dict						  =1 - alpha_prod_t_prev

       snake_case   :							List[Any]						  =(beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)

       return variance





    def 					__snake_case       (    self    :    Optional[Any],    _snake_case    :    DDIMSchedulerState,    _snake_case    :    jnp.ndarray,    _snake_case    :    int,    _snake_case    :    jnp.ndarray,    _snake_case    :    float = 0.0,    _snake_case    :    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)
       snake_case   :							Any						  =timestep - self.config.num_train_timesteps // state.num_inference_steps

       snake_case   :							Dict						  =state.common.alphas_cumprod
       snake_case   :							int						  =state.final_alpha_cumprod

       # 2. compute alphas, betas
       snake_case   :							Optional[int]						  =alphas_cumprod[timestep]
       snake_case   :							List[Any]						  =jnp.where(prev_timestep >= 0,    alphas_cumprod[prev_timestep],    _snake_case     )

       snake_case   :							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":
          snake_case   :							List[str]						  =(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
          snake_case   :							Optional[Any]						  =model_output
       elif self.config.prediction_type == "sample":
          snake_case   :							Optional[Any]						  =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   :							Optional[Any]						  =(alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
          snake_case   :							Dict						  =(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   :							List[Any]						  =self._get_variance(_snake_case,    _snake_case,    _snake_case     )
       snake_case   :							Any						  =eta * variance ** 0.5

       # 5. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
       snake_case   :							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
       snake_case   :							List[str]						  =alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction

       if not return_dict:
          return (prev_sample, state)

       return FlaxDDIMSchedulerOutput(prev_sample=_snake_case,    state=_snake_case     )





    def 					__snake_case       (    self    :    Optional[int],    _snake_case    :    DDIMSchedulerState,    _snake_case    :    jnp.ndarray,    _snake_case    :    jnp.ndarray,    _snake_case    :    jnp.ndarray,    ):



       '''simple docstring'''







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





    def 					__snake_case       (    self    :    List[str],    _snake_case    :    DDIMSchedulerState,    _snake_case    :    jnp.ndarray,    _snake_case    :    jnp.ndarray,    _snake_case    :    jnp.ndarray,    ):



       '''simple docstring'''







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






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



       '''simple docstring'''







       return self.config.num_train_timesteps










'''simple docstring'''





from __future__ import annotations

import random
import string





class 							lowerCAmelCase_		:





    def __init__(    self    :    Union[str, Any],    _snake_case    :    str | None = None     ):



       '''simple docstring'''







       snake_case   :							Optional[int]						  =passcode or self.__passcode_creator()
       snake_case   :							List[str]						  =self.__make_key_list()
       snake_case   :							Dict						  =self.__make_shift_key()





    def __str__(    self    :    List[Any]     ):



       '''simple docstring'''







       return "".join(self.__passcode     )





    def 					__snake_case       (    self    :    int,    _snake_case    :    list[int]     ):



       '''simple docstring'''







       for i in range(1,    len(_snake_case     ),    2     ):
          iterlist[i] *= -1
       return iterlist





    def 					__snake_case       (    self    :    Optional[int]     ):



       '''simple docstring'''







       snake_case   :							int						  =string.ascii_letters + string.digits
       snake_case   :							Union[str, Any]						  =[random.choice(_snake_case     ) for _ in range(random.randint(10,    20     )     )]
       return password





    def 					__snake_case       (    self    :    Optional[int]     ):



       '''simple docstring'''







       snake_case   :							Dict						  =(
           string.ascii_letters + string.digits + string.punctuation + ''' \t\n'''
       )

       snake_case   :							Any						  =[]

       # creates points known as breakpoints to break the key_list_options at those
       # points and pivot each substring
       snake_case   :							Optional[int]						  =sorted(set(self.__passcode     )     )
       snake_case   :							list[str]						  =[]

       # algorithm for creating a new shuffled list, keys_l, out of key_list_options
       for i in key_list_options:
          temp_list.extend(_snake_case     )

          # checking breakpoints at which to pivot temporary sublist and add it into
          # keys_l
          if i in breakpoints or i == key_list_options[-1]:
             keys_l.extend(temp_list[::-1]     )
             temp_list.clear()

        # returning a shuffled keys_l to prevent brute force guessing of shift key
       return keys_l





    def 					__snake_case       (    self    :    Union[str, Any]     ):



       '''simple docstring'''







       snake_case   :							str						  =sum(self.__neg_pos([ord(_snake_case     ) for x in self.__passcode]     )     )
       return num if num > 0 else len(self.__passcode     )





    def 					__snake_case       (    self    :    Optional[int],    _snake_case    :    str     ):



       '''simple docstring'''







       snake_case   :							List[Any]						  =''''''

       # decoding shift like Caesar cipher algorithm implementing negative shift or
       # reverse shift or left shift
       for i in encoded_message:
          snake_case   :							Optional[int]						  =self.__key_list.index(_snake_case     )
          decoded_message += self.__key_list[
              (position - self.__shift_key) % -len(self.__key_list     )
          ]

       return decoded_message






    def 					__snake_case       (    self    :    Union[str, Any],    _snake_case    :    str     ):



       '''simple docstring'''







       snake_case   :							int						  =''''''

       # encoding shift like Caesar cipher algorithm implementing positive shift or
       # forward shift or right shift
       for i in plaintext:
          snake_case   :							int						  =self.__key_list.index(_snake_case     )
          encoded_message += self.__key_list[
              (position + self.__shift_key) % len(self.__key_list     )
          ]

       return encoded_message




def 		_a   (  lowerCamelCase_ = "Hello, this is a modified Caesar cipher"							):
   snake_case   :							List[Any]						  =ShuffledShiftCipher()
   return cipa.decrypt(cipa.encrypt(lowerCamelCase_							)							)


if __name__ == "__main__":
      import doctest

      doctest.testmod()





