




'''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_   :







  '''simple docstring'''







  lowerCAmelCase_				:					CommonSchedulerState
  lowerCAmelCase_				:					jnp.ndarray

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


  @classmethod
  def 							SCREAMING_SNAKE_CASE__			(						cls    :	str						,		_UpperCAmelCase    :	CommonSchedulerState						,		_UpperCAmelCase    :	jnp.ndarray						,		_UpperCAmelCase    :	jnp.ndarray						,		_UpperCAmelCase    :	jnp.ndarray						,		):






   """simple docstring"""






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

@dataclass
class 						lowerCAmelCase_   ( lowerCamelCase_   ):







  '''simple docstring'''







  lowerCAmelCase_				:					DDIMSchedulerState

class 						lowerCAmelCase_   ( lowerCamelCase_			,  lowerCamelCase_   ):







  '''simple docstring'''







  lowerCAmelCase_				:					List[str]				     =						[e.name for e in FlaxKarrasDiffusionSchedulers]

  lowerCAmelCase_				:					jnp.dtype


  @property
  def 							SCREAMING_SNAKE_CASE__			(						self    :	Union[str, Any] ):






   """simple docstring"""






   return True


  @register_to_config
  def __init__(						self    :	List[str]						,		_UpperCAmelCase    :	int = 10_00						,		_UpperCAmelCase    :	float = 0.0001						,		_UpperCAmelCase    :	float = 0.02						,		_UpperCAmelCase    :	str = "linear"						,		_UpperCAmelCase    :	Optional[jnp.ndarray] = None						,		_UpperCAmelCase    :	bool = True						,		_UpperCAmelCase    :	int = 0						,		_UpperCAmelCase    :	str = "epsilon"						,		_UpperCAmelCase    :	jnp.dtype = jnp.floataa						,		):






   """simple docstring"""






   UpperCAmelCase__										=    dtype


  def 							SCREAMING_SNAKE_CASE__			(						self    :	Union[str, Any]						,		_UpperCAmelCase    :	Optional[CommonSchedulerState] = None ):






   """simple docstring"""






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

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

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


  def 							SCREAMING_SNAKE_CASE__			(						self    :	Union[str, Any]						,		_UpperCAmelCase    :	DDIMSchedulerState						,		_UpperCAmelCase    :	jnp.ndarray						,		_UpperCAmelCase    :	Optional[int] = None ):






   """simple docstring"""






   return sample


  def 							SCREAMING_SNAKE_CASE__			(						self    :	List[str]						,		_UpperCAmelCase    :	DDIMSchedulerState						,		_UpperCAmelCase    :	int						,		_UpperCAmelCase    :	Tuple = () ):






   """simple docstring"""






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

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


  def 							SCREAMING_SNAKE_CASE__			(						self    :	int						,		_UpperCAmelCase    :	DDIMSchedulerState						,		_UpperCAmelCase    :	Any						,		_UpperCAmelCase    :	str ):






   """simple docstring"""






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

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

   return variance


  def 							SCREAMING_SNAKE_CASE__			(						self    :	int						,		_UpperCAmelCase    :	DDIMSchedulerState						,		_UpperCAmelCase    :	jnp.ndarray						,		_UpperCAmelCase    :	int						,		_UpperCAmelCase    :	jnp.ndarray						,		_UpperCAmelCase    :	float = 0.0						,		_UpperCAmelCase    :	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)
   UpperCAmelCase__										=    timestep - self.config.num_train_timesteps // state.num_inference_steps

   UpperCAmelCase__										=    state.common.alphas_cumprod
   UpperCAmelCase__										=    state.final_alpha_cumprod

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

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

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

   if not return_dict:
    return (prev_sample, state)

   return FlaxDDIMSchedulerOutput(prev_sample=_UpperCAmelCase						,		state=_UpperCAmelCase )


  def 							SCREAMING_SNAKE_CASE__			(						self    :	Dict						,		_UpperCAmelCase    :	DDIMSchedulerState						,		_UpperCAmelCase    :	jnp.ndarray						,		_UpperCAmelCase    :	jnp.ndarray						,		_UpperCAmelCase    :	jnp.ndarray						,		):






   """simple docstring"""






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


  def 							SCREAMING_SNAKE_CASE__			(						self    :	Optional[Any]						,		_UpperCAmelCase    :	DDIMSchedulerState						,		_UpperCAmelCase    :	jnp.ndarray						,		_UpperCAmelCase    :	jnp.ndarray						,		_UpperCAmelCase    :	jnp.ndarray						,		):






   """simple docstring"""






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



  def __len__(						self    :	List[str] ):






   """simple docstring"""






   return self.config.num_train_timesteps






'''simple docstring'''




from __future__ import annotations

import random
import string

class 						lowerCAmelCase_   :







  '''simple docstring'''







  def __init__(						self    :	int						,		_UpperCAmelCase    :	str | None = None ):






   """simple docstring"""






   UpperCAmelCase__										=    passcode or self.__passcode_creator()
   UpperCAmelCase__										=    self.__make_key_list()
   UpperCAmelCase__										=    self.__make_shift_key()


  def __str__(						self    :	int ):






   """simple docstring"""






   return "".join(self.__passcode )


  def 							SCREAMING_SNAKE_CASE__			(						self    :	List[str]						,		_UpperCAmelCase    :	list[int] ):






   """simple docstring"""






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


  def 							SCREAMING_SNAKE_CASE__			(						self    :	int ):






   """simple docstring"""






   UpperCAmelCase__										=    string.ascii_letters + string.digits
   UpperCAmelCase__										=    [random.choice(_UpperCAmelCase ) for _ in range(random.randint(10						,		20 ) )]
   return password


  def 							SCREAMING_SNAKE_CASE__			(						self    :	Tuple ):






   """simple docstring"""






   UpperCAmelCase__										=    (
       string.ascii_letters + string.digits + string.punctuation + """ \t\n"""
   )

   UpperCAmelCase__										=    []

   # creates points known as breakpoints to break the key_list_options at those
   # points and pivot each substring
   UpperCAmelCase__										=    sorted(set(self.__passcode ) )
   UpperCAmelCase__										=    []

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

    # 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 							SCREAMING_SNAKE_CASE__			(						self    :	Tuple ):






   """simple docstring"""






   UpperCAmelCase__										=    sum(self.__neg_pos([ord(_UpperCAmelCase ) for x in self.__passcode] ) )
   return num if num > 0 else len(self.__passcode )


  def 							SCREAMING_SNAKE_CASE__			(						self    :	Optional[Any]						,		_UpperCAmelCase    :	str ):






   """simple docstring"""






   UpperCAmelCase__										=    """"""

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

   return decoded_message



  def 							SCREAMING_SNAKE_CASE__			(						self    :	List[str]						,		_UpperCAmelCase    :	str ):






   """simple docstring"""






   UpperCAmelCase__										=    """"""

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

   return encoded_message

def    _UpperCamelCase      (				SCREAMING_SNAKE_CASE__			:      str = "Hello, this is a modified Caesar cipher"       ):
 '''simple docstring'''





 UpperCAmelCase__										=    ShuffledShiftCipher()
 return cipa.decrypt(cipa.encrypt(SCREAMING_SNAKE_CASE__       )       )


if __name__ == "__main__":
    import doctest

    doctest.testmod()
