


'''simple docstring'''


import numpy as np


def 		_lowercase   (SCREAMING_SNAKE_CASE					,				SCREAMING_SNAKE_CASE					,				SCREAMING_SNAKE_CASE       ):




     '''simple docstring'''





     __A  :					str						     =       n_draws * class_counts / class_counts.sum()
     # floored means we don't overshoot n_samples, but probably undershoot
     __A  :					List[Any]						     =       np.floor(SCREAMING_SNAKE_CASE       )
     # we add samples according to how much "left over" probability
     # they had, until we arrive at n_samples
     __A  :					Union[str, Any]						     =       int(n_draws - floored.sum()       )
     if need_to_add > 0:
          __A  :					Union[str, Any]						     =       continuous - floored
          __A  :					Any						     =       np.sort(np.unique(SCREAMING_SNAKE_CASE       )       )[::-1]
          # add according to remainder, but break ties
          # randomly to avoid biases
          for value in values:
               ((__A)     ,)  :					List[str]						     =       np.where(remainder == value       )
               # if we need_to_add less than what's in inds
               # we draw randomly from them.
               # if we need to add more, we add them all and
               # go to the next value
               __A  :					Optional[Any]						     =       min(len(SCREAMING_SNAKE_CASE       )					,				SCREAMING_SNAKE_CASE       )
               __A  :					Dict						     =       rng.choice(SCREAMING_SNAKE_CASE					,				size=SCREAMING_SNAKE_CASE					,				replace=SCREAMING_SNAKE_CASE       )
               floored[inds] += 1
               need_to_add -= add_now
               if need_to_add == 0:
                    break
     return floored.astype(np.intaa       )






def 		_lowercase   (SCREAMING_SNAKE_CASE					,				SCREAMING_SNAKE_CASE					,				SCREAMING_SNAKE_CASE					,				SCREAMING_SNAKE_CASE					,				SCREAMING_SNAKE_CASE=10       ):




     '''simple docstring'''





     __A     ,__A  :					str						     =       np.unique(SCREAMING_SNAKE_CASE					,				return_inverse=SCREAMING_SNAKE_CASE       )
     __A  :					Any						     =       classes.shape[0]
     __A  :					Union[str, Any]						     =       np.bincount(SCREAMING_SNAKE_CASE       )
     if np.min(SCREAMING_SNAKE_CASE       ) < 2:
          raise ValueError("Minimum class count error"       )
     if n_train < n_classes:
          raise ValueError(
              "The train_size = %d should be greater or " "equal to the number of classes = %d" % (n_train, n_classes)       )
     if n_test < n_classes:
          raise ValueError(
              "The test_size = %d should be greater or " "equal to the number of classes = %d" % (n_test, n_classes)       )
     __A  :					Optional[int]						     =       np.split(np.argsort(SCREAMING_SNAKE_CASE					,				kind="mergesort"       )					,				np.cumsum(SCREAMING_SNAKE_CASE       )[:-1]       )
     for _ in range(SCREAMING_SNAKE_CASE       ):
          __A  :					Optional[Any]						     =       approximate_mode(SCREAMING_SNAKE_CASE					,				SCREAMING_SNAKE_CASE					,				SCREAMING_SNAKE_CASE       )
          __A  :					List[Any]						     =       class_counts - n_i
          __A  :					List[Any]						     =       approximate_mode(SCREAMING_SNAKE_CASE					,				SCREAMING_SNAKE_CASE					,				SCREAMING_SNAKE_CASE       )

          __A  :					Optional[int]						     =       []
          __A  :					Optional[Any]						     =       []

          for i in range(SCREAMING_SNAKE_CASE       ):
               __A  :					Optional[int]						     =       rng.permutation(class_counts[i]       )
               __A  :					int						     =       class_indices[i].take(SCREAMING_SNAKE_CASE					,				mode="clip"       )
               train.extend(perm_indices_class_i[: n_i[i]]       )
               test.extend(perm_indices_class_i[n_i[i] : n_i[i] + t_i[i]]       )
          __A  :					Optional[int]						     =       rng.permutation(SCREAMING_SNAKE_CASE       )
          __A  :					List[str]						     =       rng.permutation(SCREAMING_SNAKE_CASE       )

          yield train, test





'''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        __magic_name__  :

     """simple docstring"""






     lowerCamelCase__						=							None
     lowerCamelCase__						=							None
     lowerCamelCase__						=							None


     @classmethod
     def lowerCAmelCase__     (      cls		):


          '''simple docstring'''





          return cls()







@dataclass
class        __magic_name__  (					lowerCAmelCase   ):

     """simple docstring"""






     lowerCamelCase__						=							42
     lowerCamelCase__						=							42
     lowerCamelCase__						=							None







class        __magic_name__  (					lowerCAmelCase						,		lowerCAmelCase   ):

     """simple docstring"""






     @property
     def lowerCAmelCase__     (      self		):


          '''simple docstring'''





          return True


     @register_to_config
     def __init__(      self ,    lowerCamelCase = 2000 ,    lowerCamelCase = 0.15 ,    lowerCamelCase = 0.01 ,    lowerCamelCase = 1348.0 ,    lowerCamelCase = 1E-5 ,    lowerCamelCase = 1 ,    ):


          '''simple docstring'''





          pass


     def lowerCAmelCase__     (      self		):


          '''simple docstring'''





          __A  :					List[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 lowerCAmelCase__     (      self ,    lowerCamelCase ,    lowerCamelCase ,    lowerCamelCase = () ,    lowerCamelCase = None		):


          '''simple docstring'''





          __A  :					Tuple						     =       sampling_eps if sampling_eps is not None else self.config.sampling_eps

          __A  :					Optional[int]						     =       jnp.linspace(1 ,    lowerCamelCase ,    lowerCamelCase		)
          return state.replace(timesteps=lowerCamelCase		)


     def lowerCAmelCase__     (      self ,    lowerCamelCase ,    lowerCamelCase ,    lowerCamelCase = None ,    lowerCamelCase = None ,    lowerCamelCase = None ,    ):


          '''simple docstring'''





          __A  :					str						     =       sigma_min if sigma_min is not None else self.config.sigma_min
          __A  :					Union[str, Any]						     =       sigma_max if sigma_max is not None else self.config.sigma_max
          __A  :					Dict						     =       sampling_eps if sampling_eps is not None else self.config.sampling_eps
          if state.timesteps is None:
               __A  :					Union[str, Any]						     =       self.set_timesteps(lowerCamelCase ,    lowerCamelCase ,    lowerCamelCase		)

          __A  :					Dict						     =       jnp.exp(jnp.linspace(jnp.log(lowerCamelCase		) ,    jnp.log(lowerCamelCase		) ,    lowerCamelCase		)		)
          __A  :					int						     =       jnp.array([sigma_min * (sigma_max / sigma_min) ** t for t in state.timesteps]		)

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


     def lowerCAmelCase__     (      self ,    lowerCamelCase ,    lowerCamelCase ,    lowerCamelCase		):


          '''simple docstring'''





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


     def lowerCAmelCase__     (      self ,    lowerCamelCase ,    lowerCamelCase ,    lowerCamelCase ,    lowerCamelCase ,    lowerCamelCase ,    lowerCamelCase = True ,    ):


          '''simple docstring'''





          if state.timesteps is None:
               raise ValueError(
                   "`state.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"		)

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

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

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


     def lowerCAmelCase__     (      self ,    lowerCamelCase ,    lowerCamelCase ,    lowerCamelCase ,    lowerCamelCase ,    lowerCamelCase = True ,    ):


          '''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
          __A  :					int						     =       random.split(lowerCamelCase ,    num=1		)
          __A  :					int						     =       random.normal(key=lowerCamelCase ,    shape=sample.shape		)

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

          # compute corrected sample: model_output term and noise term
          __A  :					Optional[Any]						     =       step_size.flatten()
          __A  :					Optional[int]						     =       broadcast_to_shape_from_left(lowerCamelCase ,    sample.shape		)
          __A  :					Dict						     =       sample + step_size * model_output
          __A  :					List[Any]						     =       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		):


          '''simple docstring'''





          return self.config.num_train_timesteps

