




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:



	"""simple docstring"""






	a     : CommonSchedulerState

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

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

	@classmethod
	def    __a     (					cls	,				lowerCamelCase	,				lowerCamelCase	,				lowerCamelCase	,				lowerCamelCase    ) ->      List[Any]:





								"""simple docstring"""


								return cls(common=lowerCamelCase	,				init_noise_sigma=lowerCamelCase	,				timesteps=lowerCamelCase	,				sigmas=lowerCamelCase    )







@dataclass
class 				UpperCAmelCase(					snake_case_					):



	"""simple docstring"""






	a     : LMSDiscreteSchedulerState







class 				UpperCAmelCase(					snake_case_   ,     snake_case_					):



	"""simple docstring"""






	a     : List[Any]	  =					[e.name for e in FlaxKarrasDiffusionSchedulers]

	a     : jnp.dtype

	@property
	def    __a     (					self    ) ->      List[Any]:





								"""simple docstring"""


								return True

	@register_to_config
	def __init__(					self	,				lowerCamelCase = 1000	,				lowerCamelCase = 0.00_01	,				lowerCamelCase = 0.02	,				lowerCamelCase = "linear"	,				lowerCamelCase = None	,				lowerCamelCase = "epsilon"	,				lowerCamelCase = jnp.floataa	,				) ->      Optional[Any]:





								"""simple docstring"""


								lowercase__  :	Union[str, Any]        =							dtype

	def    __a     (					self	,				lowerCamelCase = None    ) ->      LMSDiscreteSchedulerState:





								"""simple docstring"""


								if common is None:
															lowercase__  :	Union[str, Any]        =							CommonSchedulerState.create(self    )

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

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

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

	def    __a     (					self	,				lowerCamelCase	,				lowerCamelCase	,				lowerCamelCase    ) ->      jnp.ndarray:





								"""simple docstring"""


								((lowercase__) ,       )  :	Tuple        =							jnp.where(state.timesteps == timestep	,				size=1    )
								lowercase__  :	Union[str, Any]        =							step_index[0]

								lowercase__  :	List[str]        =							state.sigmas[step_index]
								lowercase__  :	Any        =							sample / ((sigma**2 + 1) ** 0.5)
								return sample

	def    __a     (					self	,				lowerCamelCase	,				lowerCamelCase	,				lowerCamelCase	,				lowerCamelCase    ) ->      List[str]:





								"""simple docstring"""


								def lms_derivative(lowerCamelCase    ):
															lowercase__  :	Union[str, Any]        =							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

								lowercase__  :	List[str]        =							integrate.quad(lowerCamelCase	,				state.sigmas[t]	,				state.sigmas[t + 1]	,				epsrel=1E-4    )[0]

								return integrated_coeff

	def    __a     (					self	,				lowerCamelCase	,				lowerCamelCase	,				lowerCamelCase = ()    ) ->      LMSDiscreteSchedulerState:





								"""simple docstring"""


								lowercase__  :	Union[str, Any]        =							jnp.linspace(self.config.num_train_timesteps - 1	,				0	,				lowerCamelCase	,				dtype=self.dtype    )

								lowercase__  :	int        =							jnp.floor(lowerCamelCase    ).astype(jnp.intaa    )
								lowercase__  :	int        =							jnp.ceil(lowerCamelCase    ).astype(jnp.intaa    )

								lowercase__  :	Dict        =							jnp.mod(lowerCamelCase	,				1.0    )

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

								lowercase__  :	int        =							timesteps.astype(jnp.intaa    )

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

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

	def    __a     (					self	,				lowerCamelCase	,				lowerCamelCase	,				lowerCamelCase	,				lowerCamelCase	,				lowerCamelCase = 4	,				lowerCamelCase = True	,				) ->      Union[FlaxLMSSchedulerOutput, Tuple]:





								"""simple docstring"""


								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__  :	Any        =							state.sigmas[timestep]

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

								# 3. Compute linear multistep coefficients
								lowercase__  :	int        =							min(timestep + 1	,				lowerCamelCase    )
								lowercase__  :	Any        =							[self.get_lms_coefficient(lowerCamelCase	,				lowerCamelCase	,				lowerCamelCase	,				lowerCamelCase    ) for curr_order in range(lowerCamelCase    )]

								# 4. Compute previous sample based on the derivatives path
								lowercase__  :	Any        =							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    __a     (					self	,				lowerCamelCase	,				lowerCamelCase	,				lowerCamelCase	,				lowerCamelCase	,				) ->      jnp.ndarray:





								"""simple docstring"""


								lowercase__  :	int        =							state.sigmas[timesteps].flatten()
								lowercase__  :	Dict        =							broadcast_to_shape_from_left(lowerCamelCase	,				noise.shape    )

								lowercase__  :	Dict        =							original_samples + noise * sigma

								return noisy_samples







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





								"""simple docstring"""


								return self.config.num_train_timesteps





import numpy as np
def  snake_case_					(		SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_    )      ->			int:
							lowercase__  :	str        =							n_draws * class_counts / class_counts.sum()
							# floored means we don't overshoot n_samples, but probably undershoot
							lowercase__  :	Union[str, Any]        =							np.floor(SCREAMING_SNAKE_CASE_    )
							# we add samples according to how much "left over" probability
							# they had, until we arrive at n_samples
							lowercase__  :	Tuple        =							int(n_draws - floored.sum()    )
							if need_to_add > 0:
														lowercase__  :	Union[str, Any]        =							continuous - floored
														lowercase__  :	Optional[Any]        =							np.sort(np.unique(SCREAMING_SNAKE_CASE_    )    )[::-1]
														# add according to remainder, but break ties
														# randomly to avoid biases
														for value in values:
																					((lowercase__) ,       )  :	Optional[Any]        =							np.where(remainder == value    )
																					# if we need_to_add less than what's in inds
																					# we draw randomly from them.
																					# if we need to add more, we add them all and
																					# go to the next value
																					lowercase__  :	Dict        =							min(len(SCREAMING_SNAKE_CASE_    )    ,SCREAMING_SNAKE_CASE_    )
																					lowercase__  :	Any        =							rng.choice(SCREAMING_SNAKE_CASE_    ,size=SCREAMING_SNAKE_CASE_    ,replace=SCREAMING_SNAKE_CASE_    )
																					floored[inds] += 1
																					need_to_add -= add_now
																					if need_to_add == 0:
																												break
							return floored.astype(np.intaa    )




def  snake_case_					(		SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_=10    )      ->			List[str]:
							lowercase__ ,       lowercase__  :	int        =							np.unique(SCREAMING_SNAKE_CASE_    ,return_inverse=SCREAMING_SNAKE_CASE_    )
							lowercase__  :	int        =							classes.shape[0]
							lowercase__  :	Union[str, Any]        =							np.bincount(SCREAMING_SNAKE_CASE_    )
							if np.min(SCREAMING_SNAKE_CASE_    ) < 2:
														raise ValueError("Minimum class count error"    )
							if n_train < n_classes:
														raise ValueError(
														    "The train_size = %d should be greater or " "equal to the number of classes = %d" % (n_train, n_classes)    )
							if n_test < n_classes:
														raise ValueError(
														    "The test_size = %d should be greater or " "equal to the number of classes = %d" % (n_test, n_classes)    )
							lowercase__  :	List[Any]        =							np.split(np.argsort(SCREAMING_SNAKE_CASE_    ,kind="mergesort"    )    ,np.cumsum(SCREAMING_SNAKE_CASE_    )[:-1]    )
							for _ in range(SCREAMING_SNAKE_CASE_    ):
														lowercase__  :	str        =							approximate_mode(SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_    )
														lowercase__  :	int        =							class_counts - n_i
														lowercase__  :	int        =							approximate_mode(SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_    )

														lowercase__  :	Tuple        =							[]
														lowercase__  :	int        =							[]

														for i in range(SCREAMING_SNAKE_CASE_    ):
																					lowercase__  :	Union[str, Any]        =							rng.permutation(class_counts[i]    )
																					lowercase__  :	List[Any]        =							class_indices[i].take(SCREAMING_SNAKE_CASE_    ,mode="clip"    )
																					train.extend(perm_indices_class_i[: n_i[i]]    )
																					test.extend(perm_indices_class_i[n_i[i] : n_i[i] + t_i[i]]    )
														lowercase__  :	Tuple        =							rng.permutation(SCREAMING_SNAKE_CASE_    )
														lowercase__  :	Dict        =							rng.permutation(SCREAMING_SNAKE_CASE_    )

														yield train, test