"""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 							__snake_case:


    '''simple docstring'''





    _UpperCAmelCase       =			42

    # setable values
    _UpperCAmelCase       =			42
    _UpperCAmelCase       =			42
    _UpperCAmelCase       =			42
    _UpperCAmelCase       =			None

    # running values
    _UpperCAmelCase       =			None
    @classmethod
    def 						_a							(  cls					,       __lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase	):




         '''simple docstring'''



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







@dataclass
class 							__snake_case(					A_	):


    '''simple docstring'''





    _UpperCAmelCase       =			42







class 							__snake_case(					A_				,	A_	):


    '''simple docstring'''





    _UpperCAmelCase       =			[e.name for e in FlaxKarrasDiffusionSchedulers]

    _UpperCAmelCase       =			42
    @property
    def 						_a							(  self	):




         '''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					,       ):




         '''simple docstring'''



         __A					:					int								=		dtype
    def 						_a							(  self					,       __lowerCamelCase = None	):




         '''simple docstring'''



         if common is None:
              __A					:					Optional[int]								=		CommonSchedulerState.create(self	)

         __A					:					List[str]								=		jnp.arange(0					,       self.config.num_train_timesteps	).round()[::-1]
         __A					:					Union[str, Any]								=		((1 - common.alphas_cumprod) / common.alphas_cumprod) ** 0.5

         # standard deviation of the initial noise distribution
         __A					:					Any								=		sigmas.max()

         return LMSDiscreteSchedulerState.create(
             common=__lowerCamelCase					,       init_noise_sigma=__lowerCamelCase					,       timesteps=__lowerCamelCase					,       sigmas=__lowerCamelCase					,       )
    def 						_a							(  self					,       __lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase	):




         '''simple docstring'''



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

         __A					:					Optional[Any]								=		state.sigmas[step_index]
         __A					:					Dict								=		sample / ((sigma**2 + 1) ** 0.5)
         return sample
    def 						_a							(  self					,       __lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase	):




         '''simple docstring'''



         def lms_derivative(__lowerCamelCase	):
              __A					:					List[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

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

         return integrated_coeff
    def 						_a							(  self					,       __lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase = ()	):




         '''simple docstring'''



         __A					:					Any								=		jnp.linspace(self.config.num_train_timesteps - 1					,       0					,       __lowerCamelCase					,       dtype=self.dtype	)

         __A					:					Any								=		jnp.floor(__lowerCamelCase	).astype(jnp.intaa	)
         __A					:					str								=		jnp.ceil(__lowerCamelCase	).astype(jnp.intaa	)

         __A					:					str								=		jnp.mod(__lowerCamelCase					,       1.0	)

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

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

         # initial running values
         __A					:					Union[str, 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					,       ):




         '''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'	)

         __A					:					Optional[int]								=		state.sigmas[timestep]

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

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

         # 4. Compute previous sample based on the derivatives path
         __A					:					Tuple								=		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					,       ):




         '''simple docstring'''



         __A					:					Dict								=		state.sigmas[timesteps].flatten()
         __A					:					Dict								=		broadcast_to_shape_from_left(__lowerCamelCase					,       noise.shape	)

         __A					:					Tuple								=		original_samples + noise * sigma

         return noisy_samples

    def __len__(  self	):




         '''simple docstring'''



         return self.config.num_train_timesteps




"""simple docstring"""







from __future__ import annotations

import random
from collections.abc import Iterable
from typing import Any, Generic, TypeVar

lowerCamelCase      : List[str]       =TypeVar('''T''', bound=bool)






class 							__snake_case(					Generic[T]	):


    '''simple docstring'''





    def __init__(  self					,       __lowerCamelCase	):




         '''simple docstring'''



         __A					:					T								=		value
         __A					:					RandomizedHeapNode[T] | None								=		None
         __A					:					RandomizedHeapNode[T] | None								=		None
    @property
    def 						_a							(  self	):




         '''simple docstring'''



         return self._value

    @staticmethod
    def 						_a							(  __lowerCamelCase					,       __lowerCamelCase	):




         '''simple docstring'''



         if not roota:
              return roota

         if not roota:
              return roota

         if roota.value > roota.value:
              __A						,		__A					:					List[str]								=		roota, roota

         if random.choice([True, False]	):
              __A						,		__A					:					Dict								=		roota.right, roota.left

         __A					:					Dict								=		RandomizedHeapNode.merge(roota.left					,       __lowerCamelCase	)

         return roota







class 							__snake_case(					Generic[T]	):


    '''simple docstring'''





    def __init__(  self					,       __lowerCamelCase = ()	):




         '''simple docstring'''



         __A					:					RandomizedHeapNode[T] | None								=		None

         if data:
              for item in data:
                   self.insert(__lowerCamelCase	)
    def 						_a							(  self					,       __lowerCamelCase	):




         '''simple docstring'''



         __A					:					List[str]								=		RandomizedHeapNode.merge(self._root					,       RandomizedHeapNode(__lowerCamelCase	)	)
    def 						_a							(  self	):




         '''simple docstring'''



         __A					:					int								=		self.top()

         if self._root is None:
              return None

         __A					:					Dict								=		RandomizedHeapNode.merge(self._root.left					,       self._root.right	)

         return result
    def 						_a							(  self	):




         '''simple docstring'''



         if not self._root:
              raise IndexError('Can\'t get top element for the empty heap.'	)
         return self._root.value
    def 						_a							(  self	):




         '''simple docstring'''



         __A					:					Dict								=		None
    def 						_a							(  self	):




         '''simple docstring'''



         __A					:					Tuple								=		[]
         while self:
              result.append(self.pop()	)

         return result

    def __bool__(  self	):




         '''simple docstring'''



         return self._root is not None


if __name__ == "__main__":
       import doctest

       doctest.testmod()



