






"""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 	__UpperCamelCase    :
					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 					UpperCAmelCase__   (       cls       :  Optional[int]    ,   UpperCAmelCase       :  CommonSchedulerState    ,   UpperCAmelCase       :  jnp.ndarray    ,   UpperCAmelCase       :  jnp.ndarray    ,   UpperCAmelCase       :  jnp.ndarray      )  ->					Optional[Any]:
											return cls(common=UpperCAmelCase    ,   init_noise_sigma=UpperCAmelCase    ,   timesteps=UpperCAmelCase    ,   sigmas=UpperCAmelCase      )




@dataclass
class 	__UpperCamelCase    (    UpperCamelCase	):
					lowercase_						:							LMSDiscreteSchedulerState




class 	__UpperCamelCase    (    UpperCamelCase      ,    UpperCamelCase	):
					lowercase_						:							Tuple			=     [e.name for e in FlaxKarrasDiffusionSchedulers]

					lowercase_						:							jnp.dtype
					@property
					def 					UpperCAmelCase__   (       self       :  Any      )  ->					str:
											return True
					@register_to_config
					def __init__(       self       :  Tuple    ,   UpperCAmelCase       :  int = 1000    ,   UpperCAmelCase       :  float = 0.0_0_0_1    ,   UpperCAmelCase       :  float = 0.0_2    ,   UpperCAmelCase       :  str = "linear"    ,   UpperCAmelCase       :  Optional[jnp.ndarray] = None    ,   UpperCAmelCase       :  str = "epsilon"    ,   UpperCAmelCase       :  jnp.dtype = jnp.floataa    ,   )  ->					List[Any]:
											lowerCAmelCase      :Optional[Any]				  = dtype
					def 					UpperCAmelCase__   (       self       :  Optional[Any]    ,   UpperCAmelCase       :  Optional[CommonSchedulerState] = None      )  ->					LMSDiscreteSchedulerState:
											if common is None:
																	lowerCAmelCase      :Tuple				  = CommonSchedulerState.create(self      )

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

											# standard deviation of the initial noise distribution
											lowerCAmelCase      :Tuple				  = sigmas.max()

											return LMSDiscreteSchedulerState.create(
											    common=UpperCAmelCase    ,   init_noise_sigma=UpperCAmelCase    ,   timesteps=UpperCAmelCase    ,   sigmas=UpperCAmelCase    ,   )
					def 					UpperCAmelCase__   (       self       :  Any    ,   UpperCAmelCase       :  LMSDiscreteSchedulerState    ,   UpperCAmelCase       :  jnp.ndarray    ,   UpperCAmelCase       :  int      )  ->					jnp.ndarray:
											((lowerCAmelCase)  ,   )      :str				  = jnp.where(state.timesteps == timestep    ,   size=1      )
											lowerCAmelCase      :List[str]				  = step_index[0]

											lowerCAmelCase      :Any				  = state.sigmas[step_index]
											lowerCAmelCase      :str				  = sample / ((sigma**2 + 1) ** 0.5)
											return sample
					def 					UpperCAmelCase__   (       self       :  List[Any]    ,   UpperCAmelCase       :  LMSDiscreteSchedulerState    ,   UpperCAmelCase       :  Dict    ,   UpperCAmelCase       :  Tuple    ,   UpperCAmelCase       :  Tuple      )  ->					int:

											def lms_derivative(UpperCAmelCase       :  Tuple      ):
																	lowerCAmelCase      :Optional[int]				  = 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

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

											return integrated_coeff
					def 					UpperCAmelCase__   (       self       :  Tuple    ,   UpperCAmelCase       :  LMSDiscreteSchedulerState    ,   UpperCAmelCase       :  int    ,   UpperCAmelCase       :  Tuple = ()      )  ->					LMSDiscreteSchedulerState:
											lowerCAmelCase      :Any				  = jnp.linspace(self.config.num_train_timesteps - 1    ,   0    ,   UpperCAmelCase    ,   dtype=self.dtype      )

											lowerCAmelCase      :Union[str, Any]				  = jnp.floor(UpperCAmelCase      ).astype(jnp.intaa      )
											lowerCAmelCase      :Optional[int]				  = jnp.ceil(UpperCAmelCase      ).astype(jnp.intaa      )

											lowerCAmelCase      :Any				  = jnp.mod(UpperCAmelCase    ,   1.0      )

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

											lowerCAmelCase      :Any				  = timesteps.astype(jnp.intaa      )

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

											return state.replace(
											    timesteps=UpperCAmelCase    ,   sigmas=UpperCAmelCase    ,   num_inference_steps=UpperCAmelCase    ,   derivatives=UpperCAmelCase    ,   )
					def 					UpperCAmelCase__   (       self       :  Tuple    ,   UpperCAmelCase       :  LMSDiscreteSchedulerState    ,   UpperCAmelCase       :  jnp.ndarray    ,   UpperCAmelCase       :  int    ,   UpperCAmelCase       :  jnp.ndarray    ,   UpperCAmelCase       :  int = 4    ,   UpperCAmelCase       :  bool = 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      :int				  = state.sigmas[timestep]

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

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

											# 4. Compute previous sample based on the derivatives path
											lowerCAmelCase      :Union[str, Any]				  = 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 					UpperCAmelCase__   (       self       :  int    ,   UpperCAmelCase       :  LMSDiscreteSchedulerState    ,   UpperCAmelCase       :  jnp.ndarray    ,   UpperCAmelCase       :  jnp.ndarray    ,   UpperCAmelCase       :  jnp.ndarray    ,   )  ->					jnp.ndarray:
											lowerCAmelCase      :Optional[Any]				  = state.sigmas[timesteps].flatten()
											lowerCAmelCase      :Optional[Any]				  = broadcast_to_shape_from_left(UpperCAmelCase    ,   noise.shape      )

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

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







"""simple docstring"""


import safs




class 	__UpperCamelCase    (    safs.SaFileSystem	):
					pass