
"""simple docstring"""





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 A_      :




  '''simple docstring'''
  SCREAMING_SNAKE_CASE__     :		CommonSchedulerState

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

  # running values
  SCREAMING_SNAKE_CASE__     :		Optional[jnp.ndarray]    	=						None







  @classmethod
  def 			UpperCamelCase__   (						cls							,  lowercase_							,  lowercase_							,  lowercase_							,  lowercase_    ):

     """simple docstring"""
     return cls(common=lowercase_							,  init_noise_sigma=lowercase_							,  timesteps=lowercase_							,  sigmas=lowercase_    )




@dataclass
class A_      (lowercase__ ):




  '''simple docstring'''
  SCREAMING_SNAKE_CASE__     :		LMSDiscreteSchedulerState




class A_      (lowercase__							,lowercase__ ):




  '''simple docstring'''
  SCREAMING_SNAKE_CASE__     :		Dict    	=						[e.name for e in FlaxKarrasDiffusionSchedulers]

  SCREAMING_SNAKE_CASE__     :		jnp.dtype







  @property
  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     return True







  @register_to_config
  def __init__(						self							,  lowercase_ = 1000							,  lowercase_ = 0.00_01							,  lowercase_ = 0.02							,  lowercase_ = "linear"							,  lowercase_ = None							,  lowercase_ = "epsilon"							,  lowercase_ = jnp.floataa							,  ):

     """simple docstring"""
     UpperCAmelCase_   :   Dict     	=       dtype







  def 			UpperCamelCase__   (						self							,  lowercase_ = None    ):

     """simple docstring"""
     if common is None:
        UpperCAmelCase_   :   Optional[Any]     	=       CommonSchedulerState.create(self    )

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

     # standard deviation of the initial noise distribution
     UpperCAmelCase_   :   List[str]     	=       sigmas.max()

     return LMSDiscreteSchedulerState.create(
         common=lowercase_							,  init_noise_sigma=lowercase_							,  timesteps=lowercase_							,  sigmas=lowercase_							,  )







  def 			UpperCamelCase__   (						self							,  lowercase_							,  lowercase_							,  lowercase_    ):

     """simple docstring"""
     ((UpperCAmelCase_)	,  )   :   Union[str, Any]     	=       jnp.where(state.timesteps == timestep							,  size=1    )
     UpperCAmelCase_   :   str     	=       step_index[0]

     UpperCAmelCase_   :   str     	=       state.sigmas[step_index]
     UpperCAmelCase_   :   Optional[int]     	=       sample / ((sigma**2 + 1) ** 0.5)
     return sample







  def 			UpperCamelCase__   (						self							,  lowercase_							,  lowercase_							,  lowercase_							,  lowercase_    ):

     """simple docstring"""

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

     UpperCAmelCase_   :   Optional[int]     	=       integrate.quad(lowercase_							,  state.sigmas[t]							,  state.sigmas[t + 1]							,  epsrel=1E-4    )[0]

     return integrated_coeff







  def 			UpperCamelCase__   (						self							,  lowercase_							,  lowercase_							,  lowercase_ = ()    ):

     """simple docstring"""
     UpperCAmelCase_   :   Tuple     	=       jnp.linspace(self.config.num_train_timesteps - 1							,  0							,  lowercase_							,  dtype=self.dtype    )

     UpperCAmelCase_   :   Optional[int]     	=       jnp.floor(lowercase_    ).astype(jnp.intaa    )
     UpperCAmelCase_   :   Any     	=       jnp.ceil(lowercase_    ).astype(jnp.intaa    )

     UpperCAmelCase_   :   Optional[int]     	=       jnp.mod(lowercase_							,  1.0    )

     UpperCAmelCase_   :   List[Any]     	=       ((1 - state.common.alphas_cumprod) / state.common.alphas_cumprod) ** 0.5
     UpperCAmelCase_   :   Optional[int]     	=       (1 - frac) * sigmas[low_idx] + frac * sigmas[high_idx]
     UpperCAmelCase_   :   List[Any]     	=       jnp.concatenate([sigmas, jnp.array([0.0]							,  dtype=self.dtype    )]    )

     UpperCAmelCase_   :   Dict     	=       timesteps.astype(jnp.intaa    )

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

     return state.replace(
         timesteps=lowercase_							,  sigmas=lowercase_							,  num_inference_steps=lowercase_							,  derivatives=lowercase_							,  )







  def 			UpperCamelCase__   (						self							,  lowercase_							,  lowercase_							,  lowercase_							,  lowercase_							,  lowercase_ = 4							,  lowercase_ = True							,  ):

     """simple docstring"""
     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"    )

     UpperCAmelCase_   :   Optional[Any]     	=       state.sigmas[timestep]

     # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
     if self.config.prediction_type == "epsilon":
        UpperCAmelCase_   :   str     	=       sample - sigma * model_output
     elif self.config.prediction_type == "v_prediction":
        # * c_out + input * c_skip
        UpperCAmelCase_   :   Union[str, Any]     	=       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
     UpperCAmelCase_   :   List[str]     	=       (sample - pred_original_sample) / sigma
     UpperCAmelCase_   :   Dict     	=       state.replace(derivatives=jnp.append(state.derivatives							,  lowercase_    )    )
     if len(state.derivatives    ) > order:
        UpperCAmelCase_   :   List[str]     	=       state.replace(derivatives=jnp.delete(state.derivatives							,  0    )    )

     # 3. Compute linear multistep coefficients
     UpperCAmelCase_   :   List[str]     	=       min(timestep + 1							,  lowercase_    )
     UpperCAmelCase_   :   Tuple     	=       [self.get_lms_coefficient(lowercase_							,  lowercase_							,  lowercase_							,  lowercase_    ) for curr_order in range(lowercase_    )]

     # 4. Compute previous sample based on the derivatives path
     UpperCAmelCase_   :   Union[str, Any]     	=       sample + sum(
         coeff * derivative for coeff, derivative in zip(lowercase_							,  reversed(state.derivatives    )    )    )

     if not return_dict:
        return (prev_sample, state)

     return FlaxLMSSchedulerOutput(prev_sample=lowercase_							,  state=lowercase_    )







  def 			UpperCamelCase__   (						self							,  lowercase_							,  lowercase_							,  lowercase_							,  lowercase_							,  ):

     """simple docstring"""
     UpperCAmelCase_   :   List[str]     	=       state.sigmas[timesteps].flatten()
     UpperCAmelCase_   :   List[str]     	=       broadcast_to_shape_from_left(lowercase_							,  noise.shape    )

     UpperCAmelCase_   :   List[str]     	=       original_samples + noise * sigma

     return noisy_samples







  def __len__(						self    ):

     """simple docstring"""
     return self.config.num_train_timesteps



def  UpperCAmelCase__(    lowerCamelCase_ = 1000	):



			"""simple docstring"""





			return sum(i for i in range(lowerCamelCase_	) if i % 3 == 0 or i % 5 == 0	)


if __name__ == "__main__":
			print(F"{solution() = }")

