


# 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 							a       :
     # setable values
     UpperCAmelCase__			:	Optional[jnp.ndarray]       =						None
     UpperCAmelCase__			:	Optional[jnp.ndarray]       =						None
     UpperCAmelCase__			:	Optional[jnp.ndarray]       =						None





     @classmethod
     def 			SCREAMING_SNAKE_CASE__							(     cls       :					Optional[int]				):
        return cls()



@dataclass
class 							a       (      _UpperCAmelCase ):
     UpperCAmelCase__			:	ScoreSdeVeSchedulerState
     UpperCAmelCase__			:	jnp.ndarray
     UpperCAmelCase__			:	Optional[jnp.ndarray]       =						None



class 							a       (      _UpperCAmelCase  ,_UpperCAmelCase ):





     @property
     def 			SCREAMING_SNAKE_CASE__							(     self       :					Optional[Any]				):
        return True





     @register_to_config
     def __init__(     self       :					int     ,						SCREAMING_SNAKE_CASE_       :					int = 2000     ,						SCREAMING_SNAKE_CASE_       :					float = 0.15     ,						SCREAMING_SNAKE_CASE_       :					float = 0.01     ,						SCREAMING_SNAKE_CASE_       :					float = 1348.0     ,						SCREAMING_SNAKE_CASE_       :					float = 1E-5     ,						SCREAMING_SNAKE_CASE_       :					int = 1     ,						):
        pass





     def 			SCREAMING_SNAKE_CASE__							(     self       :					str				):
        __lowerCamelCase:					str	 =						ScoreSdeVeSchedulerState.create()
        return self.set_sigmas(
            SCREAMING_SNAKE_CASE_     ,						self.config.num_train_timesteps     ,						self.config.sigma_min     ,						self.config.sigma_max     ,						self.config.sampling_eps     ,						)





     def 			SCREAMING_SNAKE_CASE__							(     self       :					List[str]     ,						SCREAMING_SNAKE_CASE_       :					ScoreSdeVeSchedulerState     ,						SCREAMING_SNAKE_CASE_       :					int     ,						SCREAMING_SNAKE_CASE_       :					Tuple = ()     ,						SCREAMING_SNAKE_CASE_       :					float = None				):
        __lowerCamelCase:					str	 =						sampling_eps if sampling_eps is not None else self.config.sampling_eps

        __lowerCamelCase:					Union[str, Any]	 =						jnp.linspace(1     ,						SCREAMING_SNAKE_CASE_     ,						SCREAMING_SNAKE_CASE_				)
        return state.replace(timesteps=SCREAMING_SNAKE_CASE_				)





     def 			SCREAMING_SNAKE_CASE__							(     self       :					List[str]     ,						SCREAMING_SNAKE_CASE_       :					ScoreSdeVeSchedulerState     ,						SCREAMING_SNAKE_CASE_       :					int     ,						SCREAMING_SNAKE_CASE_       :					float = None     ,						SCREAMING_SNAKE_CASE_       :					float = None     ,						SCREAMING_SNAKE_CASE_       :					float = None     ,						):
        __lowerCamelCase:					Union[str, Any]	 =						sigma_min if sigma_min is not None else self.config.sigma_min
        __lowerCamelCase:					List[Any]	 =						sigma_max if sigma_max is not None else self.config.sigma_max
        __lowerCamelCase:					str	 =						sampling_eps if sampling_eps is not None else self.config.sampling_eps
        if state.timesteps is None:
           __lowerCamelCase:					Union[str, Any]	 =						self.set_timesteps(SCREAMING_SNAKE_CASE_     ,						SCREAMING_SNAKE_CASE_     ,						SCREAMING_SNAKE_CASE_				)

        __lowerCamelCase:					Dict	 =						jnp.exp(jnp.linspace(jnp.log(SCREAMING_SNAKE_CASE_				)     ,						jnp.log(SCREAMING_SNAKE_CASE_				)     ,						SCREAMING_SNAKE_CASE_				)				)
        __lowerCamelCase:					Union[str, Any]	 =						jnp.array([sigma_min * (sigma_max / sigma_min) ** t for t in state.timesteps]				)

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





     def 			SCREAMING_SNAKE_CASE__							(     self       :					Optional[int]     ,						SCREAMING_SNAKE_CASE_       :					str     ,						SCREAMING_SNAKE_CASE_       :					int     ,						SCREAMING_SNAKE_CASE_       :					Tuple				):
        return jnp.where(timesteps == 0     ,						jnp.zeros_like(SCREAMING_SNAKE_CASE_				)     ,						state.discrete_sigmas[timesteps - 1]				)





     def 			SCREAMING_SNAKE_CASE__							(     self       :					List[Any]     ,						SCREAMING_SNAKE_CASE_       :					ScoreSdeVeSchedulerState     ,						SCREAMING_SNAKE_CASE_       :					jnp.ndarray     ,						SCREAMING_SNAKE_CASE_       :					int     ,						SCREAMING_SNAKE_CASE_       :					jnp.ndarray     ,						SCREAMING_SNAKE_CASE_       :					random.KeyArray     ,						SCREAMING_SNAKE_CASE_       :					bool = True     ,						):
        if state.timesteps is None:
           raise ValueError(
               """`state.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"""				)

        __lowerCamelCase:					Optional[Any]	 =						timestep * jnp.ones(
            sample.shape[0]     ,						)
        __lowerCamelCase:					str	 =						(timestep * (len(state.timesteps				) - 1)).long()

        __lowerCamelCase:					Dict	 =						state.discrete_sigmas[timesteps]
        __lowerCamelCase:					Optional[Any]	 =						self.get_adjacent_sigma(SCREAMING_SNAKE_CASE_     ,						SCREAMING_SNAKE_CASE_     ,						SCREAMING_SNAKE_CASE_				)
        __lowerCamelCase:					List[Any]	 =						jnp.zeros_like(SCREAMING_SNAKE_CASE_				)
        __lowerCamelCase:					List[Any]	 =						(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
        __lowerCamelCase:					Optional[int]	 =						diffusion.flatten()
        __lowerCamelCase:					Dict	 =						broadcast_to_shape_from_left(SCREAMING_SNAKE_CASE_     ,						sample.shape				)
        __lowerCamelCase:					List[str]	 =						drift - diffusion**2 * model_output

        #  equation 6: sample noise for the diffusion term of
        __lowerCamelCase:					List[Any]	 =						random.split(SCREAMING_SNAKE_CASE_     ,						num=1				)
        __lowerCamelCase:					int	 =						random.normal(key=SCREAMING_SNAKE_CASE_     ,						shape=sample.shape				)
        __lowerCamelCase:					List[str]	 =						sample - drift  # subtract because `dt` is a small negative timestep
        # TODO is the variable diffusion the correct scaling term for the noise?
        __lowerCamelCase:					int	 =						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=SCREAMING_SNAKE_CASE_     ,						prev_sample_mean=SCREAMING_SNAKE_CASE_     ,						state=SCREAMING_SNAKE_CASE_				)





     def 			SCREAMING_SNAKE_CASE__							(     self       :					Optional[int]     ,						SCREAMING_SNAKE_CASE_       :					ScoreSdeVeSchedulerState     ,						SCREAMING_SNAKE_CASE_       :					jnp.ndarray     ,						SCREAMING_SNAKE_CASE_       :					jnp.ndarray     ,						SCREAMING_SNAKE_CASE_       :					random.KeyArray     ,						SCREAMING_SNAKE_CASE_       :					bool = True     ,						):
        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
        __lowerCamelCase:					str	 =						random.split(SCREAMING_SNAKE_CASE_     ,						num=1				)
        __lowerCamelCase:					Union[str, Any]	 =						random.normal(key=SCREAMING_SNAKE_CASE_     ,						shape=sample.shape				)

        # compute step size from the model_output, the noise, and the snr
        __lowerCamelCase:					Optional[Any]	 =						jnp.linalg.norm(SCREAMING_SNAKE_CASE_				)
        __lowerCamelCase:					Tuple	 =						jnp.linalg.norm(SCREAMING_SNAKE_CASE_				)
        __lowerCamelCase:					Tuple	 =						(self.config.snr * noise_norm / grad_norm) ** 2 * 2
        __lowerCamelCase:					int	 =						step_size * jnp.ones(sample.shape[0]				)

        # compute corrected sample: model_output term and noise term
        __lowerCamelCase:					Any	 =						step_size.flatten()
        __lowerCamelCase:					Dict	 =						broadcast_to_shape_from_left(SCREAMING_SNAKE_CASE_     ,						sample.shape				)
        __lowerCamelCase:					int	 =						sample + step_size * model_output
        __lowerCamelCase:					str	 =						prev_sample_mean + ((step_size * 2) ** 0.5) * noise

        if not return_dict:
           return (prev_sample, state)

        return FlaxSdeVeOutput(prev_sample=SCREAMING_SNAKE_CASE_     ,						state=SCREAMING_SNAKE_CASE_				)







     def __len__(     self       :					Tuple				):
        return self.config.num_train_timesteps






from string import ascii_uppercase



def      __lowerCAmelCase    (				snake_case						:     str    ,   snake_case						:     str    ,   snake_case						:     bool = False    ,   snake_case						:     str = ascii_uppercase			)							->				str:
   __lowerCamelCase:					List[str]	 =						keyword.upper()
   __lowerCamelCase:					Tuple	 =						plaintext.upper()
   __lowerCamelCase:					Optional[Any]	 =						set(snake_case			)

   # create a list of unique characters in the keyword - their order matters
   # it determines how we will map plaintext characters to the ciphertext
   __lowerCamelCase:					Tuple	 =						[]
   for char in keyword:
      if char in alphabet_set and char not in unique_chars:
         unique_chars.append(snake_case			)
    # the number of those unique characters will determine the number of rows
   __lowerCamelCase:					Tuple	 =						len(snake_case			)

   # create a shifted version of the alphabet
   __lowerCamelCase:					List[str]	 =						unique_chars + [
       char for char in alphabet if char not in unique_chars
   ]

   # create a modified alphabet by splitting the shifted alphabet into rows
   __lowerCamelCase:					List[str]	 =						[
       shifted_alphabet[k : k + num_unique_chars_in_keyword]
       for k in range(0    ,   26    ,   snake_case			)
   ]

   # map the alphabet characters to the modified alphabet characters
   # going 'vertically' through the modified alphabet - consider columns first
   __lowerCamelCase:					Union[str, Any]	 =						{}
   __lowerCamelCase:					str	 =						0
   for column in range(snake_case			):
      for row in modified_alphabet:
         # if current row (the last one) is too short, break out of loop
         if len(snake_case			) <= column:
            break

         # map current letter to letter in modified alphabet
         __lowerCamelCase:					Union[str, Any]	 =						row[column]
         letter_index += 1

   if verbose:
      print(snake_case			)
   # create the encrypted text by mapping the plaintext to the modified alphabet
   return "".join(mapping[char] if char in mapping else char for char in plaintext			)


if __name__ == "__main__":
   # example use
   print(mixed_keyword('''college''', '''UNIVERSITY'''))


