


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_					:
							_lowerCamelCase				: CommonSchedulerState

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

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





							@classmethod
							def   __magic_name__      (							cls			,       _SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE    ):
								return cls(common=UpperCamelCase_			,       init_noise_sigma=UpperCamelCase_			,       timesteps=UpperCamelCase_			,       sigmas=UpperCamelCase_    )







@dataclass
class 			lowerCamelCase_					(    __lowerCamelCase    ):
							_lowerCamelCase				: LMSDiscreteSchedulerState







class 			lowerCamelCase_					(    __lowerCamelCase   ,		__lowerCamelCase    ):
							_lowerCamelCase				: str       =					[e.name for e in FlaxKarrasDiffusionSchedulers]

							_lowerCamelCase				: jnp.dtype





							@property
							def   __magic_name__      (							self    ):
								return True





							@register_to_config
							def __init__(							self			,       _SCREAMING_SNAKE_CASE = 1000			,       _SCREAMING_SNAKE_CASE = 0.0_0_0_1			,       _SCREAMING_SNAKE_CASE = 0.0_2			,       _SCREAMING_SNAKE_CASE = "linear"			,       _SCREAMING_SNAKE_CASE = None			,       _SCREAMING_SNAKE_CASE = "epsilon"			,       _SCREAMING_SNAKE_CASE = jnp.floataa			,       ):
								a_     			=   dtype





							def   __magic_name__      (							self			,       _SCREAMING_SNAKE_CASE = None    ):
								if common is None:
									a_     			=   CommonSchedulerState.create(self    )

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

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

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





							def   __magic_name__      (							self			,       _SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE    ):
								((a_  )			,   )     			=   jnp.where(state.timesteps == timestep			,       size=1    )
								a_     			=   step_index[0]

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





							def   __magic_name__      (							self			,       _SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE    ):

								def lms_derivative(_SCREAMING_SNAKE_CASE    ):
									a_     			=   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

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

								return integrated_coeff





							def   __magic_name__      (							self			,       _SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE = ()    ):
								a_     			=   jnp.linspace(self.config.num_train_timesteps - 1			,       0			,       UpperCamelCase_			,       dtype=self.dtype    )

								a_     			=   jnp.floor(UpperCamelCase_    ).astype(jnp.intaa    )
								a_     			=   jnp.ceil(UpperCamelCase_    ).astype(jnp.intaa    )

								a_     			=   jnp.mod(UpperCamelCase_			,       1.0    )

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

								a_     			=   timesteps.astype(jnp.intaa    )

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

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





							def   __magic_name__      (							self			,       _SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE = 4			,       _SCREAMING_SNAKE_CASE = 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"""    )

								a_     			=   state.sigmas[timestep]

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

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

								# 4. Compute previous sample based on the derivatives path
								a_     			=   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   __magic_name__      (							self			,       _SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE			,       ):
								a_     			=   state.sigmas[timesteps].flatten()
								a_     			=   broadcast_to_shape_from_left(UpperCamelCase_			,       noise.shape    )

								a_     			=   original_samples + noise * sigma

								return noisy_samples





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





from typing import Callable




def    lowerCamelCase__			(		A__			:   Callable      ):





       '''simple docstring'''



       def wrapper(A__			:   int      ):
              __lowerCamelCase  =		function_with_docstring.__doc__
              return target_function

       return wrapper

