


'''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'''






							lowercase__			:   CommonSchedulerState

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

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







							@classmethod
							def   __SCREAMING_SNAKE_CASE							(		cls					,    lowerCamelCase_					,    lowerCamelCase_					,    lowerCamelCase_					,    lowerCamelCase_   )       ->							Optional[Any]:
														return cls(common=lowerCamelCase_					,    init_noise_sigma=lowerCamelCase_					,    timesteps=lowerCamelCase_					,    sigmas=lowerCamelCase_   )




@dataclass
class 		a__				(							a__  ):





							'''simple docstring'''






							lowercase__			:   LMSDiscreteSchedulerState




class 		a__				(							a__				,				a__  ):





							'''simple docstring'''






							lowercase__			:   List[str]							=      [e.name for e in FlaxKarrasDiffusionSchedulers]

							lowercase__			:   jnp.dtype







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







							@register_to_config
							def __init__(		self					,    lowerCamelCase_ = 10_00					,    lowerCamelCase_ = 0.0_001					,    lowerCamelCase_ = 0.02					,    lowerCamelCase_ = "linear"					,    lowerCamelCase_ = None					,    lowerCamelCase_ = "epsilon"					,    lowerCamelCase_ = jnp.floataa					,    )       ->							List[str]:
														lowerCAmelCase__          =	dtype







							def   __SCREAMING_SNAKE_CASE							(		self					,    lowerCamelCase_ = None   )       ->							LMSDiscreteSchedulerState:
														if common is None:
																					lowerCAmelCase__          =	CommonSchedulerState.create(self   )

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

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

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







							def   __SCREAMING_SNAKE_CASE							(		self					,    lowerCamelCase_					,    lowerCamelCase_					,    lowerCamelCase_   )       ->							jnp.ndarray:
														((lowerCAmelCase__)   ,					)          =	jnp.where(state.timesteps == timestep					,    size=1   )
														lowerCAmelCase__          =	step_index[0]

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







							def   __SCREAMING_SNAKE_CASE							(		self					,    lowerCamelCase_					,    lowerCamelCase_					,    lowerCamelCase_					,    lowerCamelCase_   )       ->							Tuple:

														def lms_derivative(lowerCamelCase_   ):
																					lowerCAmelCase__          =	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

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

														return integrated_coeff







							def   __SCREAMING_SNAKE_CASE							(		self					,    lowerCamelCase_					,    lowerCamelCase_					,    lowerCamelCase_ = ()   )       ->							LMSDiscreteSchedulerState:
														lowerCAmelCase__          =	jnp.linspace(self.config.num_train_timesteps - 1					,    0					,    lowerCamelCase_					,    dtype=self.dtype   )

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

														lowerCAmelCase__          =	jnp.mod(lowerCamelCase_					,    1.0   )

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

														lowerCAmelCase__          =	timesteps.astype(jnp.intaa   )

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

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







							def   __SCREAMING_SNAKE_CASE							(		self					,    lowerCamelCase_					,    lowerCamelCase_					,    lowerCamelCase_					,    lowerCamelCase_					,    lowerCamelCase_ = 4					,    lowerCamelCase_ = 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'''   )

														lowerCAmelCase__          =	state.sigmas[timestep]

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

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

														# 4. Compute previous sample based on the derivatives path
														lowerCAmelCase__          =	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   __SCREAMING_SNAKE_CASE							(		self					,    lowerCamelCase_					,    lowerCamelCase_					,    lowerCamelCase_					,    lowerCamelCase_					,    )       ->							jnp.ndarray:
														lowerCAmelCase__          =	state.sigmas[timesteps].flatten()
														lowerCAmelCase__          =	broadcast_to_shape_from_left(lowerCamelCase_					,    noise.shape   )

														lowerCAmelCase__          =	original_samples + noise * sigma

														return noisy_samples







							def __len__(		self   )       ->							List[str]:
														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()