'''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 _lowerCAmelCase			:
      # setable values
      UpperCAmelCase_	:						Optional[jnp.ndarray]	 =       None
      UpperCAmelCase_	:						Optional[jnp.ndarray]	 =       None
      UpperCAmelCase_	:						Optional[jnp.ndarray]	 =       None







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





@dataclass
class _lowerCAmelCase			(	snake_case					):
      UpperCAmelCase_	:						ScoreSdeVeSchedulerState
      UpperCAmelCase_	:						jnp.ndarray
      UpperCAmelCase_	:						Optional[jnp.ndarray]	 =       None





class _lowerCAmelCase			(	snake_case							,   snake_case					):







      @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				:							Union[str, Any]									=   ScoreSdeVeSchedulerState.create()
        return self.set_sigmas(
            lowercase  ,    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				:							int									=   jnp.linspace(1  ,    lowercase  ,    lowercase     )
        return state.replace(timesteps=lowercase     )







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

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

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







      def      lowerCamelCase__      (  self  ,    lowercase  ,    lowercase  ,    lowercase     ):
        return jnp.where(timesteps == 0  ,    jnp.zeros_like(lowercase     )  ,    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				:							Optional[Any]									=   timestep * jnp.ones(
            sample.shape[0]  ,    )
        lowercase				:							str									=   (timestep * (len(state.timesteps     ) - 1)).long()

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

        #  equation 6: sample noise for the diffusion term of
        lowercase				:							int									=   random.split(lowercase  ,    num=1     )
        lowercase				:							List[Any]									=   random.normal(key=lowercase  ,    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				:							List[str]									=   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=lowercase  ,    prev_sample_mean=lowercase  ,    state=lowercase     )







      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				:							Union[str, Any]									=   random.split(lowercase  ,    num=1     )
        lowercase				:							List[Any]									=   random.normal(key=lowercase  ,    shape=sample.shape     )

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

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

        if not return_dict:
          return (prev_sample, state)

        return FlaxSdeVeOutput(prev_sample=lowercase  ,    state=lowercase     )







      def __len__(  self     ):
        return self.config.num_train_timesteps
'''simple docstring'''
def 				__UpperCamelCase (					__UpperCAmelCase  :					float ,   __UpperCAmelCase  :					float ,   __UpperCAmelCase  :					float			)							->	list[int]:
  if hue < 0 or hue > 360:
    raise Exception('''hue should be between 0 and 360'''			)

  if saturation < 0 or saturation > 1:
    raise Exception('''saturation should be between 0 and 1'''			)

  if value < 0 or value > 1:
    raise Exception('''value should be between 0 and 1'''			)

  lowercase				:							List[str]									=   value * saturation
  lowercase				:							Optional[Any]									=   hue / 60
  lowercase				:							Dict									=   chroma * (1 - abs(hue_section % 2 - 1			))
  lowercase				:							List[Any]									=   value - chroma

  if hue_section >= 0 and hue_section <= 1:
    lowercase				:							Optional[int]									=   round(255 * (chroma + match_value)			)
    lowercase				:							List[Any]									=   round(255 * (second_largest_component + match_value)			)
    lowercase				:							int									=   round(255 * (match_value)			)
  elif hue_section > 1 and hue_section <= 2:
    lowercase				:							Optional[Any]									=   round(255 * (second_largest_component + match_value)			)
    lowercase				:							Any									=   round(255 * (chroma + match_value)			)
    lowercase				:							Any									=   round(255 * (match_value)			)
  elif hue_section > 2 and hue_section <= 3:
    lowercase				:							Dict									=   round(255 * (match_value)			)
    lowercase				:							Optional[int]									=   round(255 * (chroma + match_value)			)
    lowercase				:							List[str]									=   round(255 * (second_largest_component + match_value)			)
  elif hue_section > 3 and hue_section <= 4:
    lowercase				:							List[str]									=   round(255 * (match_value)			)
    lowercase				:							List[str]									=   round(255 * (second_largest_component + match_value)			)
    lowercase				:							Tuple									=   round(255 * (chroma + match_value)			)
  elif hue_section > 4 and hue_section <= 5:
    lowercase				:							Optional[int]									=   round(255 * (second_largest_component + match_value)			)
    lowercase				:							Tuple									=   round(255 * (match_value)			)
    lowercase				:							Optional[int]									=   round(255 * (chroma + match_value)			)
  else:
    lowercase				:							int									=   round(255 * (chroma + match_value)			)
    lowercase				:							Optional[Any]									=   round(255 * (match_value)			)
    lowercase				:							Optional[int]									=   round(255 * (second_largest_component + match_value)			)

  return [red, green, blue]







def 				__UpperCamelCase (					__UpperCAmelCase  :					int ,   __UpperCAmelCase  :					int ,   __UpperCAmelCase  :					int			)							->	list[float]:
  if red < 0 or red > 255:
    raise Exception('''red should be between 0 and 255'''			)

  if green < 0 or green > 255:
    raise Exception('''green should be between 0 and 255'''			)

  if blue < 0 or blue > 255:
    raise Exception('''blue should be between 0 and 255'''			)

  lowercase				:							str									=   red / 255
  lowercase				:							Union[str, Any]									=   green / 255
  lowercase				:							Union[str, Any]									=   blue / 255
  lowercase				:							str									=   max(__UpperCAmelCase ,   __UpperCAmelCase ,   __UpperCAmelCase			)
  lowercase				:							int									=   value - min(__UpperCAmelCase ,   __UpperCAmelCase ,   __UpperCAmelCase			)
  lowercase				:							Optional[int]									=   0 if value == 0 else chroma / value

  if chroma == 0:
    lowercase				:							Dict									=   0.0
  elif value == float_red:
    lowercase				:							Tuple									=   60 * (0 + (float_green - float_blue) / chroma)
  elif value == float_green:
    lowercase				:							Optional[int]									=   60 * (2 + (float_blue - float_red) / chroma)
  else:
    lowercase				:							Dict									=   60 * (4 + (float_red - float_green) / chroma)

  lowercase				:							List[str]									=   (hue + 360) % 360

  return [hue, saturation, value]







def 				__UpperCamelCase (					__UpperCAmelCase  :					list[float] ,   __UpperCAmelCase  :					list[float]			)							->	bool:
  lowercase				:							int									=   abs(hsv_a[0] - hsv_a[0]			) < 0.2
  lowercase				:							Optional[int]									=   abs(hsv_a[1] - hsv_a[1]			) < 0.002
  lowercase				:							Dict									=   abs(hsv_a[2] - hsv_a[2]			) < 0.002

  return check_hue and check_saturation and check_value