



'''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 				lowercase			:


 """simple docstring"""


 UpperCAmelCase		      =    42

 # setable values
 UpperCAmelCase		      =    42
 UpperCAmelCase		      =    42
 UpperCAmelCase		      =    42
 UpperCAmelCase		      =    None

 # running values
 UpperCAmelCase		      =    None







 @classmethod
 def _snake_case					(				cls    ,a_    ,a_    ,a_    ,a_				)						->  str:
        return cls(common=a_    ,init_noise_sigma=a_    ,timesteps=a_    ,sigmas=a_				)







@dataclass
class 				lowercase			(    _lowerCamelCase							):


 """simple docstring"""


 UpperCAmelCase		      =    42







class 				lowercase			(    _lowerCamelCase   ,   _lowerCamelCase							):


 """simple docstring"""


 UpperCAmelCase		      =    [e.name for e in FlaxKarrasDiffusionSchedulers]

 UpperCAmelCase		      =    42







 @property
 def _snake_case					(				self				)						->  Dict:
        return True







 @register_to_config
 def __init__(				self    ,a_ = 1_000    ,a_ = 0.0001    ,a_ = 0.02    ,a_ = "linear"    ,a_ = None    ,a_ = "epsilon"    ,a_ = jnp.floataa    ,)						->  Union[str, Any]:
        _UpperCAmelCase    :		Optional[int]     	=					dtype







 def _snake_case					(				self    ,a_ = None				)						->  LMSDiscreteSchedulerState:
        if common is None:
               _UpperCAmelCase    :		str     	=					CommonSchedulerState.create(self				)

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

        # standard deviation of the initial noise distribution
        _UpperCAmelCase    :		Optional[Any]     	=					sigmas.max()

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







 def _snake_case					(				self    ,a_    ,a_    ,a_				)						->  jnp.ndarray:
        ((_UpperCAmelCase)	,)    :		str     	=					jnp.where(state.timesteps == timestep    ,size=1				)
        _UpperCAmelCase    :		Union[str, Any]     	=					step_index[0]

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







 def _snake_case					(				self    ,a_    ,a_    ,a_    ,a_				)						->  Tuple:

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

        _UpperCAmelCase    :		Dict     	=					integrate.quad(a_    ,state.sigmas[t]    ,state.sigmas[t + 1]    ,epsrel=1E-4				)[0]

        return integrated_coeff







 def _snake_case					(				self    ,a_    ,a_    ,a_ = ()				)						->  LMSDiscreteSchedulerState:
        _UpperCAmelCase    :		Optional[int]     	=					jnp.linspace(self.config.num_train_timesteps - 1    ,0    ,a_    ,dtype=self.dtype				)

        _UpperCAmelCase    :		str     	=					jnp.floor(a_				).astype(jnp.intaa				)
        _UpperCAmelCase    :		int     	=					jnp.ceil(a_				).astype(jnp.intaa				)

        _UpperCAmelCase    :		Optional[Any]     	=					jnp.mod(a_    ,1.0				)

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

        _UpperCAmelCase    :		Union[str, Any]     	=					timesteps.astype(jnp.intaa				)

        # initial running values
        _UpperCAmelCase    :		List[Any]     	=					jnp.zeros((0,) + shape    ,dtype=self.dtype				)

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







 def _snake_case					(				self    ,a_    ,a_    ,a_    ,a_    ,a_ = 4    ,a_ = True    ,)						->  Union[FlaxLMSSchedulerOutput, Tuple]:
        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    :		Dict     	=					state.sigmas[timestep]

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

        # 3. Compute linear multistep coefficients
        _UpperCAmelCase    :		Tuple     	=					min(timestep + 1    ,a_				)
        _UpperCAmelCase    :		Union[str, Any]     	=					[self.get_lms_coefficient(a_    ,a_    ,a_    ,a_				) for curr_order in range(a_				)]

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

        if not return_dict:
               return (prev_sample, state)

        return FlaxLMSSchedulerOutput(prev_sample=a_    ,state=a_				)







 def _snake_case					(				self    ,a_    ,a_    ,a_    ,a_    ,)						->  jnp.ndarray:
        _UpperCAmelCase    :		Any     	=					state.sigmas[timesteps].flatten()
        _UpperCAmelCase    :		Dict     	=					broadcast_to_shape_from_left(a_    ,noise.shape				)

        _UpperCAmelCase    :		Optional[int]     	=					original_samples + noise * sigma

        return noisy_samples







 def __len__(				self				)						->  List[str]:
        return self.config.num_train_timesteps








'''simple docstring'''






def        snake_case_   (	lowerCAmelCase_			,  lowerCAmelCase_						)->						Union[str, Any]:





       '''simple docstring'''


       if n < 0:
              return 0
       # table[i] represents the number of ways to get to amount i
       _UpperCAmelCase    :		Union[str, Any]     	=					[0] * (n + 1)

       # There is exactly 1 way to get to zero(You pick no coins).
       _UpperCAmelCase    :		Optional[int]     	=					1

       # Pick all coins one by one and update table[] values
       # after the index greater than or equal to the value of the
       # picked coin
       for coin_val in s:
              for j in range(lowerCAmelCase_			,  n + 1						):
                     table[j] += table[j - coin_val]

       return table[n]


if __name__ == "__main__":
   import doctest

   doctest.testmod()



