'''simple docstring'''
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 _lowerCAmelCase			:
      UpperCAmelCase_	:						Union[str, Any]	 =       None
      UpperCAmelCase_	:						Tuple	 =       None
      UpperCAmelCase_	:						Optional[Any]	 =       None







      @classmethod
      def      lowerCamelCase__      (  cls     ):
        return cls()





@dataclass
class _lowerCAmelCase			(	_lowerCamelCase					):
      UpperCAmelCase_	:						Any	 =       42
      UpperCAmelCase_	:						List[str]	 =       42
      UpperCAmelCase_	:						List[str]	 =       None





class _lowerCAmelCase			(	_lowerCamelCase							,   _lowerCamelCase					):







      @property
      def      lowerCamelCase__      (  self     ):
        return True







      @register_to_config
      def __init__(  self  ,    lowercase = 2000  ,    lowercase = 0.15  ,    lowercase = 0.01  ,    lowercase = 1348.0  ,    lowercase = 1e-5  ,    lowercase = 1  ,    ):
        pass







      def      lowerCamelCase__      (  self     ):
        lowercase				:							int									=   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  ,    lowercase  ,    lowercase  ,    lowercase = ()  ,    lowercase = None     ):
        lowercase				:							List[str]									=   sampling_eps if sampling_eps is not None else self.config.sampling_eps

        lowercase				:							Any									=   jnp.linspace(1  ,    _lowerCamelCase  ,    _lowerCamelCase     )
        return state.replace(timesteps=_lowerCamelCase     )







      def      lowerCamelCase__      (  self  ,    lowercase  ,    lowercase  ,    lowercase = None  ,    lowercase = None  ,    lowercase = None  ,    ):
        lowercase				:							Optional[int]									=   sigma_min if sigma_min is not None else self.config.sigma_min
        lowercase				:							Optional[Any]									=   sigma_max if sigma_max is not None else self.config.sigma_max
        lowercase				:							int									=   sampling_eps if sampling_eps is not None else self.config.sampling_eps
        if state.timesteps is None:
          lowercase				:							Union[str, Any]									=   self.set_timesteps(_lowerCamelCase  ,    _lowerCamelCase  ,    _lowerCamelCase     )

        lowercase				:							int									=   jnp.exp(jnp.linspace(jnp.log(_lowerCamelCase     )  ,    jnp.log(_lowerCamelCase     )  ,    _lowerCamelCase     )     )
        lowercase				:							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  ,    lowercase  ,    lowercase  ,    lowercase     ):
        return jnp.where(timesteps == 0  ,    jnp.zeros_like(_lowerCamelCase     )  ,    state.discrete_sigmas[timesteps - 1]     )







      def      lowerCamelCase__      (  self  ,    lowercase  ,    lowercase  ,    lowercase  ,    lowercase  ,    lowercase  ,    lowercase = True  ,    ):
        if state.timesteps is None:
          raise ValueError(
              '''`state.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler'''     )

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

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

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

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

        # compute corrected sample: model_output term and noise term
        lowercase				:							Optional[Any]									=   step_size.flatten()
        lowercase				:							Tuple									=   broadcast_to_shape_from_left(_lowerCamelCase  ,    sample.shape     )
        lowercase				:							List[Any]									=   sample + step_size * model_output
        lowercase				:							Tuple									=   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     ):
        return self.config.num_train_timesteps
'''simple docstring'''
import numpy as np







def 			_UpperCamelCase			(  _a						:						Union[str, Any]					,  _a						:						Any					,  _a						:						Dict      ):




    """simple docstring"""



    __UpperCamelCase						:						Tuple		     =	n_draws * class_counts / class_counts.sum()
    # floored means we don't overshoot n_samples, but probably undershoot
    __UpperCamelCase						:						Optional[Any]		     =	np.floor(_a      )
    # we add samples according to how much "left over" probability
    # they had, until we arrive at n_samples
    __UpperCamelCase						:						Tuple		     =	int(n_draws - floored.sum()      )
    if need_to_add > 0:
        __UpperCamelCase						:						Any		     =	continuous - floored
        __UpperCamelCase						:						Any		     =	np.sort(np.unique(_a      )      )[::-1]
        # add according to remainder, but break ties
        # randomly to avoid biases
        for value in values:
            ((__UpperCamelCase)     ,       )						:						Any		     =	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
            __UpperCamelCase						:						List[Any]		     =	min(len(_a      )					,  _a      )
            __UpperCamelCase						:						Tuple		     =	rng.choice(_a					,  size=_a					,  replace=_a      )
            floored[inds] += 1
            need_to_add -= add_now
            if need_to_add == 0:
                break
    return floored.astype(np.intaa      )







def 			_UpperCamelCase			(  _a						:						Optional[int]					,  _a						:						Union[str, Any]					,  _a						:						int					,  _a						:						Union[str, Any]					,  _a						:						str=1_0      ):




    """simple docstring"""



    __UpperCamelCase     ,       __UpperCamelCase						:						int		     =	np.unique(_a					,  return_inverse=_a      )
    __UpperCamelCase						:						str		     =	classes.shape[0]
    __UpperCamelCase						:						str		     =	np.bincount(_a      )
    if np.min(_a      ) < 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)      )
    __UpperCamelCase						:						Optional[int]		     =	np.split(np.argsort(_a					,  kind='mergesort'      )					,  np.cumsum(_a      )[:-1]      )
    for _ in range(_a      ):
        __UpperCamelCase						:						List[str]		     =	approximate_mode(_a					,  _a					,  _a      )
        __UpperCamelCase						:						Tuple		     =	class_counts - n_i
        __UpperCamelCase						:						int		     =	approximate_mode(_a					,  _a					,  _a      )

        __UpperCamelCase						:						List[Any]		     =	[]
        __UpperCamelCase						:						str		     =	[]

        for i in range(_a      ):
            __UpperCamelCase						:						Optional[Any]		     =	rng.permutation(class_counts[i]      )
            __UpperCamelCase						:						List[Any]		     =	class_indices[i].take(_a					,  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]]      )
        __UpperCamelCase						:						int		     =	rng.permutation(_a      )
        __UpperCamelCase						:						int		     =	rng.permutation(_a      )

        yield train, test



