



'''simple docstring'''



# 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 			_SCREAMING_SNAKE_CASE				:
			# setable values
			A__								=  None
			A__								=  None
			A__								=  None



			@classmethod
			def 				lowerCAmelCase (     cls		:						Union[str, Any]			)      ->      List[Any]:
							"""simple docstring"""





							return cls()






@dataclass
class 			_SCREAMING_SNAKE_CASE				(lowercase__      ):
			A__								=  42
			A__								=  42
			A__								=  None






class 			_SCREAMING_SNAKE_CASE				(lowercase__,	lowercase__      ):



			@property
			def 				lowerCAmelCase (     self		:						Dict			)      ->      str:
							"""simple docstring"""





							return True



			@register_to_config
			def __init__(     self		:						List[str]    ,			__UpperCamelCase		:						int = 2000    ,			__UpperCamelCase		:						float = 0.15    ,			__UpperCamelCase		:						float = 0.01    ,			__UpperCamelCase		:						float = 1348.0    ,			__UpperCamelCase		:						float = 1e-5    ,			__UpperCamelCase		:						int = 1    ,			)      ->      Optional[Any]:
							"""simple docstring"""





							pass



			def 				lowerCAmelCase (     self		:						Union[str, Any]			)      ->      List[Any]:
							"""simple docstring"""





							snake_case__      :   str      =    ScoreSdeVeSchedulerState.create()
							return self.set_sigmas(
							    __UpperCamelCase    ,			self.config.num_train_timesteps    ,			self.config.sigma_min    ,			self.config.sigma_max    ,			self.config.sampling_eps    ,			)



			def 				lowerCAmelCase (     self		:						Union[str, Any]    ,			__UpperCamelCase		:						ScoreSdeVeSchedulerState    ,			__UpperCamelCase		:						int    ,			__UpperCamelCase		:						Tuple = ()    ,			__UpperCamelCase		:						float = None			)      ->      ScoreSdeVeSchedulerState:
							"""simple docstring"""





							snake_case__      :   str      =    sampling_eps if sampling_eps is not None else self.config.sampling_eps

							snake_case__      :   Dict      =    jnp.linspace(1    ,			__UpperCamelCase    ,			__UpperCamelCase			)
							return state.replace(timesteps=__UpperCamelCase			)



			def 				lowerCAmelCase (     self		:						Optional[int]    ,			__UpperCamelCase		:						ScoreSdeVeSchedulerState    ,			__UpperCamelCase		:						int    ,			__UpperCamelCase		:						float = None    ,			__UpperCamelCase		:						float = None    ,			__UpperCamelCase		:						float = None    ,			)      ->      ScoreSdeVeSchedulerState:
							"""simple docstring"""





							snake_case__      :   List[str]      =    sigma_min if sigma_min is not None else self.config.sigma_min
							snake_case__      :   int      =    sigma_max if sigma_max is not None else self.config.sigma_max
							snake_case__      :   Union[str, Any]      =    sampling_eps if sampling_eps is not None else self.config.sampling_eps
							if state.timesteps is None:
											snake_case__      :   int      =    self.set_timesteps(__UpperCamelCase    ,			__UpperCamelCase    ,			__UpperCamelCase			)

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

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



			def 				lowerCAmelCase (     self		:						Optional[int]    ,			__UpperCamelCase		:						Any    ,			__UpperCamelCase		:						Optional[int]    ,			__UpperCamelCase		:						List[Any]			)      ->      Optional[int]:
							"""simple docstring"""





							return jnp.where(timesteps == 0    ,			jnp.zeros_like(__UpperCamelCase			)    ,			state.discrete_sigmas[timesteps - 1]			)



			def 				lowerCAmelCase (     self		:						Tuple    ,			__UpperCamelCase		:						ScoreSdeVeSchedulerState    ,			__UpperCamelCase		:						jnp.ndarray    ,			__UpperCamelCase		:						int    ,			__UpperCamelCase		:						jnp.ndarray    ,			__UpperCamelCase		:						random.KeyArray    ,			__UpperCamelCase		:						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__      :   Optional[int]      =    timestep * jnp.ones(
							    sample.shape[0]    ,			)
							snake_case__      :   List[str]      =    (timestep * (len(state.timesteps			) - 1)).long()

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

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



			def 				lowerCAmelCase (     self		:						Union[str, Any]    ,			__UpperCamelCase		:						ScoreSdeVeSchedulerState    ,			__UpperCamelCase		:						jnp.ndarray    ,			__UpperCamelCase		:						jnp.ndarray    ,			__UpperCamelCase		:						random.KeyArray    ,			__UpperCamelCase		:						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__      :   Union[str, Any]      =    random.split(__UpperCamelCase    ,			num=1			)
							snake_case__      :   int      =    random.normal(key=__UpperCamelCase    ,			shape=sample.shape			)

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

							# compute corrected sample: model_output term and noise term
							snake_case__      :   Any      =    step_size.flatten()
							snake_case__      :   Optional[Any]      =    broadcast_to_shape_from_left(__UpperCamelCase    ,			sample.shape			)
							snake_case__      :   int      =    sample + step_size * model_output
							snake_case__      :   List[str]      =    prev_sample_mean + ((step_size * 2) ** 0.5) * noise

							if not return_dict:
											return (prev_sample, state)

							return FlaxSdeVeOutput(prev_sample=__UpperCamelCase    ,			state=__UpperCamelCase			)







			def __len__(     self		:						Optional[int]			)      ->      Tuple:
							"""simple docstring"""





							return self.config.num_train_timesteps








from .hash_table import HashTable
from .number_theory.prime_numbers import is_prime, next_prime





class     __a     (       __UpperCamelCase ):
 def __init__(  self							,					*lowerCAmelCase__							,					**lowerCAmelCase__					)   ->							Any:




        '''simple docstring'''
        super().__init__(*lowerCAmelCase__							,					**lowerCAmelCase__					)
 def 						SCREAMING_SNAKE_CASE__  (  self							,					lowerCAmelCase__							,					lowerCAmelCase__					)   ->							Union[str, Any]:




        '''simple docstring'''
        lowercase__:  str         =  (
            next_prime(value % self.size_table					)
            if not is_prime(value % self.size_table					)
            else value % self.size_table
        )  # gt = bigger than
        return next_prime_gt - (data % next_prime_gt)
 def 						SCREAMING_SNAKE_CASE__  (  self							,					lowerCAmelCase__							,					lowerCAmelCase__							,					lowerCAmelCase__					)   ->							Union[str, Any]:




        '''simple docstring'''
        return (increment * self.__hash_function_a(lowerCAmelCase__							,					lowerCAmelCase__					)) % self.size_table
 def 						SCREAMING_SNAKE_CASE__  (  self							,					lowerCAmelCase__							,					lowerCAmelCase__=None					)   ->							str:




        '''simple docstring'''
        lowercase__:  Dict         =  1
        lowercase__:  str         =  self.hash_function(lowerCAmelCase__					)

        while self.values[new_key] is not None and self.values[new_key] != key:
               lowercase__:  List[Any]         =  (
                   self.__hash_double_function(lowerCAmelCase__							,					lowerCAmelCase__							,					lowerCAmelCase__					)
                   if self.balanced_factor() >= self.lim_charge
                   else None
               )
               if new_key is None:
                      break
               else:
                      i += 1

        return new_key


