


# 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  UpperCamelCase__ :
      # setable values
      a__   :     Optional[jnp.ndarray]								=						None
      a__   :     Optional[jnp.ndarray]								=						None
      a__   :     Optional[jnp.ndarray]								=						None






      @classmethod
      def 	__lowercase(     cls					:   int   )  -> Optional[Any]:
           return cls()

@dataclass
class  UpperCamelCase__ ( __lowerCamelCase			):
      a__   :     ScoreSdeVeSchedulerState
      a__   :     jnp.ndarray
      a__   :     Optional[jnp.ndarray]								=						None

class  UpperCamelCase__ ( __lowerCamelCase							,						__lowerCamelCase			):






      @property
      def 	__lowercase(     self					:   int   )  -> Optional[Any]:
           return True






      @register_to_config
      def __init__(     self					:   Optional[Any],		__lowerCamelCase					:   int = 20_00,		__lowerCamelCase					:   float = 0.15,		__lowerCamelCase					:   float = 0.01,		__lowerCamelCase					:   float = 1348.0,		__lowerCamelCase					:   float = 1e-5,		__lowerCamelCase					:   int = 1,		)  -> Dict:
           pass






      def 	__lowercase(     self					:   Tuple   )  -> Union[str, Any]:
           UpperCamelCase__							:  Union[str, Any]								=     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 	__lowercase(     self					:   Dict,		__lowerCamelCase					:   ScoreSdeVeSchedulerState,		__lowerCamelCase					:   int,		__lowerCamelCase					:   Tuple = (),		__lowerCamelCase					:   float = None   )  -> ScoreSdeVeSchedulerState:
           UpperCamelCase__							:  Any								=     sampling_eps if sampling_eps is not None else self.config.sampling_eps

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






      def 	__lowercase(     self					:   Optional[Any],		__lowerCamelCase					:   ScoreSdeVeSchedulerState,		__lowerCamelCase					:   int,		__lowerCamelCase					:   float = None,		__lowerCamelCase					:   float = None,		__lowerCamelCase					:   float = None,		)  -> ScoreSdeVeSchedulerState:
           UpperCamelCase__							:  Tuple								=     sigma_min if sigma_min is not None else self.config.sigma_min
           UpperCamelCase__							:  Union[str, Any]								=     sigma_max if sigma_max is not None else self.config.sigma_max
           UpperCamelCase__							:  int								=     sampling_eps if sampling_eps is not None else self.config.sampling_eps
           if state.timesteps is None:
                UpperCamelCase__							:  Optional[Any]								=     self.set_timesteps(__lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase   )

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

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






      def 	__lowercase(     self					:   int,		__lowerCamelCase					:   Any,		__lowerCamelCase					:   str,		__lowerCamelCase					:   str   )  -> Optional[Any]:
           return jnp.where(timesteps == 0,		jnp.zeros_like(__lowerCamelCase   ),		state.discrete_sigmas[timesteps - 1]   )






      def 	__lowercase(     self					:   Dict,		__lowerCamelCase					:   ScoreSdeVeSchedulerState,		__lowerCamelCase					:   jnp.ndarray,		__lowerCamelCase					:   int,		__lowerCamelCase					:   jnp.ndarray,		__lowerCamelCase					:   random.KeyArray,		__lowerCamelCase					:   bool = True,		)  -> Union[FlaxSdeVeOutput, Tuple]:
           if state.timesteps is None:
                raise ValueError(
                    '''`state.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler'''   )

           UpperCamelCase__							:  Optional[int]								=     timestep * jnp.ones(
               sample.shape[0],		)
           UpperCamelCase__							:  Dict								=     (timestep * (len(state.timesteps   ) - 1)).long()

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

           #  equation 6: sample noise for the diffusion term of
           UpperCamelCase__							:  Tuple								=     random.split(__lowerCamelCase,		num=1   )
           UpperCamelCase__							:  Union[str, Any]								=     random.normal(key=__lowerCamelCase,		shape=sample.shape   )
           UpperCamelCase__							:  Optional[int]								=     sample - drift  # subtract because `dt` is a small negative timestep
           # TODO is the variable diffusion the correct scaling term for the noise?
           UpperCamelCase__							:  Dict								=     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 	__lowercase(     self					:   int,		__lowerCamelCase					:   ScoreSdeVeSchedulerState,		__lowerCamelCase					:   jnp.ndarray,		__lowerCamelCase					:   jnp.ndarray,		__lowerCamelCase					:   random.KeyArray,		__lowerCamelCase					:   bool = True,		)  -> Union[FlaxSdeVeOutput, Tuple]:
           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
           UpperCamelCase__							:  Union[str, Any]								=     random.split(__lowerCamelCase,		num=1   )
           UpperCamelCase__							:  str								=     random.normal(key=__lowerCamelCase,		shape=sample.shape   )

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

           # compute corrected sample: model_output term and noise term
           UpperCamelCase__							:  Any								=     step_size.flatten()
           UpperCamelCase__							:  List[str]								=     broadcast_to_shape_from_left(__lowerCamelCase,		sample.shape   )
           UpperCamelCase__							:  Dict								=     sample + step_size * model_output
           UpperCamelCase__							:  List[str]								=     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					:   Tuple   )  -> Optional[int]:
           return self.config.num_train_timesteps








import math







def 						_lowercase       (     __lowerCamelCase			:					int			)  ->   str:





     '''simple docstring'''





     if number == 0:
          UpperCamelCase__							:  List[str]								=     '''Zero'''
          return words
     else:
          UpperCamelCase__							:  str								=     math.logaa(__lowerCamelCase			)
          UpperCamelCase__							:  Tuple								=     digits + 1
          UpperCamelCase__							:  List[str]								=     {}
          UpperCamelCase__							:  Optional[int]								=     ''''''
          UpperCamelCase__							:  str								=     '''One'''
          UpperCamelCase__							:  int								=     '''Two'''
          UpperCamelCase__							:  str								=     '''Three'''
          UpperCamelCase__							:  Dict								=     '''Four'''
          UpperCamelCase__							:  List[str]								=     '''Five'''
          UpperCamelCase__							:  int								=     '''Six'''
          UpperCamelCase__							:  str								=     '''Seven'''
          UpperCamelCase__							:  List[str]								=     '''Eight'''
          UpperCamelCase__							:  Tuple								=     '''Nine'''

          UpperCamelCase__							:  int								=     {}
          UpperCamelCase__							:  Any								=     ''''''
          UpperCamelCase__							:  Union[str, Any]								=     '''Twenty'''
          UpperCamelCase__							:  Optional[int]								=     '''Thirty'''
          UpperCamelCase__							:  Optional[int]								=     '''Forty'''
          UpperCamelCase__							:  Any								=     '''Fifty'''
          UpperCamelCase__							:  Optional[Any]								=     '''Sixty'''
          UpperCamelCase__							:  Union[str, Any]								=     '''Seventy'''
          UpperCamelCase__							:  Tuple								=     '''Eighty'''
          UpperCamelCase__							:  Dict								=     '''Ninety'''

          UpperCamelCase__							:  Any								=     {}
          UpperCamelCase__							:  str								=     '''Ten'''
          UpperCamelCase__							:  List[Any]								=     '''Eleven'''
          UpperCamelCase__							:  List[str]								=     '''Twelve'''
          UpperCamelCase__							:  List[str]								=     '''Thirteen'''
          UpperCamelCase__							:  List[Any]								=     '''Fourteen'''
          UpperCamelCase__							:  Union[str, Any]								=     '''Fifteen'''
          UpperCamelCase__							:  str								=     '''Sixteen'''
          UpperCamelCase__							:  int								=     '''Seventeen'''
          UpperCamelCase__							:  Tuple								=     '''Eighteen'''
          UpperCamelCase__							:  int								=     '''Nineteen'''

          UpperCamelCase__							:  Union[str, Any]								=     {}
          UpperCamelCase__							:  Dict								=     '''Hundred,'''
          UpperCamelCase__							:  Union[str, Any]								=     '''Thousand,'''
          UpperCamelCase__							:  List[Any]								=     '''Lakh,'''
          UpperCamelCase__							:  str								=     '''Crore,'''

          UpperCamelCase__							:  int								=     number
          UpperCamelCase__							:  int								=     ''''''
          UpperCamelCase__							:  int								=     0
          UpperCamelCase__							:  int								=     int(__lowerCamelCase			)
          while counter < digits:
               UpperCamelCase__							:  str								=     temp_num % 10
               if counter % 2 == 0:
                    UpperCamelCase__							:  List[Any]								=     ''''''
                    if counter in placevalue and current != 0:
                         UpperCamelCase__							:  Optional[Any]								=     placevalue[counter]
                    if counter == 2:
                         UpperCamelCase__							:  Tuple								=     singles[current] + addition + words
                    elif counter == 0:
                         if ((temp_num % 100) // 10) == 1:
                              UpperCamelCase__							:  Optional[Any]								=     teens[current] + addition + words
                              UpperCamelCase__							:  int								=     temp_num // 10
                              counter += 1
                         else:
                              UpperCamelCase__							:  Optional[Any]								=     singles[current] + addition + words

                    else:
                         UpperCamelCase__							:  Tuple								=     doubles[current] + addition + words

               else:
                    if counter == 1:
                         if current == 1:
                              UpperCamelCase__							:  Optional[Any]								=     teens[number % 10] + words
                         else:
                              UpperCamelCase__							:  Union[str, Any]								=     ''''''
                              if counter in placevalue:
                                   UpperCamelCase__							:  Optional[int]								=     placevalue[counter]
                              UpperCamelCase__							:  Optional[Any]								=     doubles[current] + addition + words
                    else:
                         UpperCamelCase__							:  List[Any]								=     ''''''
                         if counter in placevalue:
                              if current == 0 and ((temp_num % 100) // 10) == 0:
                                   UpperCamelCase__							:  Dict								=     ''''''
                              else:
                                   UpperCamelCase__							:  List[Any]								=     placevalue[counter]
                         if ((temp_num % 100) // 10) == 1:
                              UpperCamelCase__							:  Optional[int]								=     teens[current] + addition + words
                              UpperCamelCase__							:  Dict								=     temp_num // 10
                              counter += 1
                         else:
                              UpperCamelCase__							:  Optional[int]								=     singles[current] + addition + words
               counter += 1
               UpperCamelCase__							:  List[str]								=     temp_num // 10
     return words


if __name__ == "__main__":
   import doctest

   doctest.testmod()




