
from dataclasses import dataclass
from typing import Optional, Tuple, Union

import flax
import jax.numpy as jnp
from scipy import integrate

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import (
    CommonSchedulerState,
    FlaxKarrasDiffusionSchedulers,
    FlaxSchedulerMixin,
    FlaxSchedulerOutput,
    broadcast_to_shape_from_left,
)




@flax.struct.dataclass
class     _SCREAMING_SNAKE_CASE     :
      _A  :						CommonSchedulerState

      # setable values
      _A  :						jnp.ndarray
      _A  :						jnp.ndarray
      _A  :						jnp.ndarray
      _A  :						Optional[int]					=   None

      # running values
      _A  :						Optional[jnp.ndarray]					=   None
      @classmethod
      def A_	(	cls						,		lowerCamelCase						,		lowerCamelCase						,		lowerCamelCase						,		lowerCamelCase       ):
          return cls(common=lowerCamelCase_						,		init_noise_sigma=lowerCamelCase_						,		timesteps=lowerCamelCase_						,		sigmas=lowerCamelCase_       )





@dataclass
class     _SCREAMING_SNAKE_CASE     (					a__       ):
      _A  :						LMSDiscreteSchedulerState





class     _SCREAMING_SNAKE_CASE     (					a__				,    a__       ):
      _A  :						List[str]					=   [e.name for e in FlaxKarrasDiffusionSchedulers]

      _A  :						jnp.dtype
      @property
      def A_	(	self       ):
          return True
      @register_to_config
      def __init__(	self						,		lowerCamelCase = 10_00						,		lowerCamelCase = 0.0_0_0_1						,		lowerCamelCase = 0.0_2						,		lowerCamelCase = "linear"						,		lowerCamelCase = None						,		lowerCamelCase = "epsilon"						,		lowerCamelCase = jnp.floataa						,		):
          snake_case__  = dtype
      def A_	(	self						,		lowerCamelCase = None       ):
          if common is None:
              snake_case__  = CommonSchedulerState.create(self       )

          snake_case__  = jnp.arange(0						,		self.config.num_train_timesteps       ).round()[::-1]
          snake_case__  = ((1 - common.alphas_cumprod) / common.alphas_cumprod) ** 0.5

          # standard deviation of the initial noise distribution
          snake_case__  = sigmas.max()

          return LMSDiscreteSchedulerState.create(
              common=lowerCamelCase_						,		init_noise_sigma=lowerCamelCase_						,		timesteps=lowerCamelCase_						,		sigmas=lowerCamelCase_						,		)
      def A_	(	self						,		lowerCamelCase						,		lowerCamelCase						,		lowerCamelCase       ):
          ((snake_case__					)  ,    					)  = jnp.where(state.timesteps == timestep						,		size=1       )
          snake_case__  = step_index[0]

          snake_case__  = state.sigmas[step_index]
          snake_case__  = sample / ((sigma**2 + 1) ** 0.5)
          return sample
      def A_	(	self						,		lowerCamelCase						,		lowerCamelCase						,		lowerCamelCase						,		lowerCamelCase       ):

          def lms_derivative(lowerCamelCase       ):
              snake_case__  = 1.0
              for k in range(lowerCamelCase_       ):
                  if current_order == k:
                      continue
                  prod *= (tau - state.sigmas[t - k]) / (state.sigmas[t - current_order] - state.sigmas[t - k])
              return prod

          snake_case__  = integrate.quad(lowerCamelCase_						,		state.sigmas[t]						,		state.sigmas[t + 1]						,		epsrel=1e-4       )[0]

          return integrated_coeff
      def A_	(	self						,		lowerCamelCase						,		lowerCamelCase						,		lowerCamelCase = ()       ):
          snake_case__  = jnp.linspace(self.config.num_train_timesteps - 1						,		0						,		lowerCamelCase_						,		dtype=self.dtype       )

          snake_case__  = jnp.floor(lowerCamelCase_       ).astype(jnp.intaa       )
          snake_case__  = jnp.ceil(lowerCamelCase_       ).astype(jnp.intaa       )

          snake_case__  = jnp.mod(lowerCamelCase_						,		1.0       )

          snake_case__  = ((1 - state.common.alphas_cumprod) / state.common.alphas_cumprod) ** 0.5
          snake_case__  = (1 - frac) * sigmas[low_idx] + frac * sigmas[high_idx]
          snake_case__  = jnp.concatenate([sigmas, jnp.array([0.0]						,		dtype=self.dtype       )]       )

          snake_case__  = timesteps.astype(jnp.intaa       )

          # initial running values
          snake_case__  = jnp.zeros((0,) + shape						,		dtype=self.dtype       )

          return state.replace(
              timesteps=lowerCamelCase_						,		sigmas=lowerCamelCase_						,		num_inference_steps=lowerCamelCase_						,		derivatives=lowerCamelCase_						,		)
      def A_	(	self						,		lowerCamelCase						,		lowerCamelCase						,		lowerCamelCase						,		lowerCamelCase						,		lowerCamelCase = 4						,		lowerCamelCase = True						,		):
          if state.num_inference_steps is None:
              raise ValueError(
                  "Number of inference steps is \'None\', you need to run \'set_timesteps\' after creating the scheduler"       )

          snake_case__  = state.sigmas[timestep]

          # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
          if self.config.prediction_type == "epsilon":
              snake_case__  = sample - sigma * model_output
          elif self.config.prediction_type == "v_prediction":
              # * c_out + input * c_skip
              snake_case__  = model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
          else:
              raise ValueError(
                  F"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`"""       )

          # 2. Convert to an ODE derivative
          snake_case__  = (sample - pred_original_sample) / sigma
          snake_case__  = state.replace(derivatives=jnp.append(state.derivatives						,		lowerCamelCase_       )       )
          if len(state.derivatives       ) > order:
              snake_case__  = state.replace(derivatives=jnp.delete(state.derivatives						,		0       )       )

          # 3. Compute linear multistep coefficients
          snake_case__  = min(timestep + 1						,		lowerCamelCase_       )
          snake_case__  = [self.get_lms_coefficient(lowerCamelCase_						,		lowerCamelCase_						,		lowerCamelCase_						,		lowerCamelCase_       ) for curr_order in range(lowerCamelCase_       )]

          # 4. Compute previous sample based on the derivatives path
          snake_case__  = sample + sum(
              coeff * derivative for coeff, derivative in zip(lowerCamelCase_						,		reversed(state.derivatives       )       )       )

          if not return_dict:
              return (prev_sample, state)

          return FlaxLMSSchedulerOutput(prev_sample=lowerCamelCase_						,		state=lowerCamelCase_       )
      def A_	(	self						,		lowerCamelCase						,		lowerCamelCase						,		lowerCamelCase						,		lowerCamelCase						,		):
          snake_case__  = state.sigmas[timesteps].flatten()
          snake_case__  = broadcast_to_shape_from_left(lowerCamelCase_						,		noise.shape       )

          snake_case__  = original_samples + noise * sigma

          return noisy_samples



      def __len__(	self       ):
          return self.config.num_train_timesteps







'''simple docstring'''






def 					_snake_case   (     A       )						->    str:
							return " ".join(sorted(set(sentence.split()       )       )       )


if __name__ == "__main__":
		import doctest

		doctest.testmod()