


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  lowerCamelCase__  :
     SCREAMING_SNAKE_CASE__      =     42

     # setable values
     SCREAMING_SNAKE_CASE__      =     42
     SCREAMING_SNAKE_CASE__      =     42
     SCREAMING_SNAKE_CASE__      =     42
     SCREAMING_SNAKE_CASE__      =     None

     # running values
     SCREAMING_SNAKE_CASE__      =     None





     @classmethod
     def 	__A   (cls						,	UpperCAmelCase						,	UpperCAmelCase						,	UpperCAmelCase						,	UpperCAmelCase			)	->  Union[str, Any]:
      return cls(common=UpperCAmelCase						,	init_noise_sigma=UpperCAmelCase						,	timesteps=UpperCAmelCase						,	sigmas=UpperCAmelCase			)







@dataclass
class  lowerCamelCase__  (			__snake_case):
     SCREAMING_SNAKE_CASE__      =     42







class  lowerCamelCase__  (			__snake_case  ,				__snake_case):
     SCREAMING_SNAKE_CASE__      =     [e.name for e in FlaxKarrasDiffusionSchedulers]

     SCREAMING_SNAKE_CASE__      =     42





     @property
     def 	__A   (self			)	->  List[str]:
      return True





     @register_to_config
     def __init__(self						,	UpperCAmelCase = 1_0_0_0						,	UpperCAmelCase = 0.0001						,	UpperCAmelCase = 0.02						,	UpperCAmelCase = "linear"						,	UpperCAmelCase = None						,	UpperCAmelCase = "epsilon"						,	UpperCAmelCase = jnp.floataa						,	)	->  Optional[Any]:
      _lowercase				 =dtype





     def 	__A   (self						,	UpperCAmelCase = None			)	->  LMSDiscreteSchedulerState:
      if common is None:
       _lowercase				 =CommonSchedulerState.create(self			)

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

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

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





     def 	__A   (self						,	UpperCAmelCase						,	UpperCAmelCase						,	UpperCAmelCase			)	->  jnp.ndarray:
      ((_lowercase							) ,       							)				 =jnp.where(state.timesteps == timestep						,	size=1			)
      _lowercase				 =step_index[0]

      _lowercase				 =state.sigmas[step_index]
      _lowercase				 =sample / ((sigma**2 + 1) ** 0.5)
      return sample





     def 	__A   (self						,	UpperCAmelCase						,	UpperCAmelCase						,	UpperCAmelCase						,	UpperCAmelCase			)	->  Any:

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

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

      return integrated_coeff





     def 	__A   (self						,	UpperCAmelCase						,	UpperCAmelCase						,	UpperCAmelCase = ()			)	->  LMSDiscreteSchedulerState:
      _lowercase				 =jnp.linspace(self.config.num_train_timesteps - 1						,	0						,	UpperCAmelCase						,	dtype=self.dtype			)

      _lowercase				 =jnp.floor(UpperCAmelCase			).astype(jnp.intaa			)
      _lowercase				 =jnp.ceil(UpperCAmelCase			).astype(jnp.intaa			)

      _lowercase				 =jnp.mod(UpperCAmelCase						,	1.0			)

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

      _lowercase				 =timesteps.astype(jnp.intaa			)

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

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





     def 	__A   (self						,	UpperCAmelCase						,	UpperCAmelCase						,	UpperCAmelCase						,	UpperCAmelCase						,	UpperCAmelCase = 4						,	UpperCAmelCase = 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'''			)

      _lowercase				 =state.sigmas[timestep]

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

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

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

      if not return_dict:
       return (prev_sample, state)

      return FlaxLMSSchedulerOutput(prev_sample=UpperCAmelCase						,	state=UpperCAmelCase			)





     def 	__A   (self						,	UpperCAmelCase						,	UpperCAmelCase						,	UpperCAmelCase						,	UpperCAmelCase						,	)	->  jnp.ndarray:
      _lowercase				 =state.sigmas[timesteps].flatten()
      _lowercase				 =broadcast_to_shape_from_left(UpperCAmelCase						,	noise.shape			)

      _lowercase				 =original_samples + noise * sigma

      return noisy_samples





     def __len__(self			)	->  Optional[Any]:
      return self.config.num_train_timesteps




def   UpperCamelCase(   lowercase_				)      ->							str:





  '''simple docstring'''





  return "".join(chr(ord(lowercase_				) + 32				) if """A""" <= char <= """Z""" else char for char in word				)


if __name__ == "__main__":
    from doctest import testmod

    testmod()