




"""simple docstring"""

# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

from dataclasses import dataclass
from typing import Optional, Tuple, Union

import flax
import jax.numpy as jnp
from jax import random

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import FlaxSchedulerMixin, FlaxSchedulerOutput, broadcast_to_shape_from_left
@flax.struct.dataclass
class     A__      :
	# setable values
	A_							:				Optional[jnp.ndarray]						     =      None
	A_							:				Optional[jnp.ndarray]						     =      None
	A_							:				Optional[jnp.ndarray]						     =      None




	@classmethod
	def 							a_		(   cls  :	str	)					-> Tuple:
						return cls()

@dataclass
class     A__      (      lowercase_     ):
	A_							:				ScoreSdeVeSchedulerState
	A_							:				jnp.ndarray
	A_							:				Optional[jnp.ndarray]						     =      None

class     A__      (      lowercase_     ,	lowercase_     ):




	@property
	def 							a_		(   self  :	Any	)					-> List[str]:
						return True




	@register_to_config
	def __init__(   self  :	Dict      ,    lowercase  :	int = 2_000      ,    lowercase  :	float = 0.15      ,    lowercase  :	float = 0.01      ,    lowercase  :	float = 13_48.0      ,    lowercase  :	float = 1e-5      ,    lowercase  :	int = 1      ,    )					-> List[Any]:
						pass




	def 							a_		(   self  :	Union[str, Any]	)					-> Any:
						__snake_case          =	ScoreSdeVeSchedulerState.create()
						return self.set_sigmas(
						    lowercase      ,    self.config.num_train_timesteps      ,    self.config.sigma_min      ,    self.config.sigma_max      ,    self.config.sampling_eps      ,    )




	def 							a_		(   self  :	List[str]      ,    lowercase  :	ScoreSdeVeSchedulerState      ,    lowercase  :	int      ,    lowercase  :	Tuple = ()      ,    lowercase  :	float = None	)					-> ScoreSdeVeSchedulerState:
						__snake_case          =	sampling_eps if sampling_eps is not None else self.config.sampling_eps

						__snake_case          =	jnp.linspace(1      ,    lowercase      ,    lowercase	)
						return state.replace(timesteps=lowercase	)




	def 							a_		(   self  :	List[Any]      ,    lowercase  :	ScoreSdeVeSchedulerState      ,    lowercase  :	int      ,    lowercase  :	float = None      ,    lowercase  :	float = None      ,    lowercase  :	float = None      ,    )					-> ScoreSdeVeSchedulerState:
						__snake_case          =	sigma_min if sigma_min is not None else self.config.sigma_min
						__snake_case          =	sigma_max if sigma_max is not None else self.config.sigma_max
						__snake_case          =	sampling_eps if sampling_eps is not None else self.config.sampling_eps
						if state.timesteps is None:
											__snake_case          =	self.set_timesteps(lowercase      ,    lowercase      ,    lowercase	)

						__snake_case          =	jnp.exp(jnp.linspace(jnp.log(lowercase	)      ,    jnp.log(lowercase	)      ,    lowercase	)	)
						__snake_case          =	jnp.array([sigma_min * (sigma_max / sigma_min) ** t for t in state.timesteps]	)

						return state.replace(discrete_sigmas=lowercase      ,    sigmas=lowercase	)




	def 							a_		(   self  :	Union[str, Any]      ,    lowercase  :	int      ,    lowercase  :	List[Any]      ,    lowercase  :	List[Any]	)					-> List[Any]:
						return jnp.where(timesteps == 0      ,    jnp.zeros_like(lowercase	)      ,    state.discrete_sigmas[timesteps - 1]	)




	def 							a_		(   self  :	Optional[Any]      ,    lowercase  :	ScoreSdeVeSchedulerState      ,    lowercase  :	jnp.ndarray      ,    lowercase  :	int      ,    lowercase  :	jnp.ndarray      ,    lowercase  :	random.KeyArray      ,    lowercase  :	bool = True      ,    )					-> Union[FlaxSdeVeOutput, Tuple]:
						if state.timesteps is None:
											raise ValueError(
											    """`state.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"""	)

						__snake_case          =	timestep * jnp.ones(
						    sample.shape[0]      ,    )
						__snake_case          =	(timestep * (len(state.timesteps	) - 1)).long()

						__snake_case          =	state.discrete_sigmas[timesteps]
						__snake_case          =	self.get_adjacent_sigma(lowercase      ,    lowercase      ,    lowercase	)
						__snake_case          =	jnp.zeros_like(lowercase	)
						__snake_case          =	(sigma**2 - adjacent_sigma**2) ** 0.5

						# equation 6 in the paper: the model_output modeled by the network is grad_x log pt(x)
						# also equation 47 shows the analog from SDE models to ancestral sampling methods
						__snake_case          =	diffusion.flatten()
						__snake_case          =	broadcast_to_shape_from_left(lowercase      ,    sample.shape	)
						__snake_case          =	drift - diffusion**2 * model_output

						#  equation 6: sample noise for the diffusion term of
						__snake_case          =	random.split(lowercase      ,    num=1	)
						__snake_case          =	random.normal(key=lowercase      ,    shape=sample.shape	)
						__snake_case          =	sample - drift  # subtract because `dt` is a small negative timestep
						# TODO is the variable diffusion the correct scaling term for the noise?
						__snake_case          =	prev_sample_mean + diffusion * noise  # add impact of diffusion field g

						if not return_dict:
											return (prev_sample, prev_sample_mean, state)

						return FlaxSdeVeOutput(prev_sample=lowercase      ,    prev_sample_mean=lowercase      ,    state=lowercase	)




	def 							a_		(   self  :	Dict      ,    lowercase  :	ScoreSdeVeSchedulerState      ,    lowercase  :	jnp.ndarray      ,    lowercase  :	jnp.ndarray      ,    lowercase  :	random.KeyArray      ,    lowercase  :	bool = True      ,    )					-> Union[FlaxSdeVeOutput, Tuple]:
						if state.timesteps is None:
											raise ValueError(
											    """`state.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"""	)

						# For small batch sizes, the paper "suggest replacing norm(z) with sqrt(d), where d is the dim. of z"
						# sample noise for correction
						__snake_case          =	random.split(lowercase      ,    num=1	)
						__snake_case          =	random.normal(key=lowercase      ,    shape=sample.shape	)

						# compute step size from the model_output, the noise, and the snr
						__snake_case          =	jnp.linalg.norm(lowercase	)
						__snake_case          =	jnp.linalg.norm(lowercase	)
						__snake_case          =	(self.config.snr * noise_norm / grad_norm) ** 2 * 2
						__snake_case          =	step_size * jnp.ones(sample.shape[0]	)

						# compute corrected sample: model_output term and noise term
						__snake_case          =	step_size.flatten()
						__snake_case          =	broadcast_to_shape_from_left(lowercase      ,    sample.shape	)
						__snake_case          =	sample + step_size * model_output
						__snake_case          =	prev_sample_mean + ((step_size * 2) ** 0.5) * noise

						if not return_dict:
											return (prev_sample, state)

						return FlaxSdeVeOutput(prev_sample=lowercase      ,    state=lowercase	)




	def __len__(   self  :	Optional[Any]	)					-> Optional[int]:
						return self.config.num_train_timesteps








"""simple docstring"""

from functools import lru_cache




@lru_cache
def       snake_case__	(       lowerCAmelCase   :		int		):
					if not isinstance(lowerCAmelCase				,					lowerCAmelCase		):
										raise TypeError("""k must be an integer."""		)
					if k < 0:
										raise ValueError("""k integer must be greater or equal to zero."""		)
					if k == 0:
										return 0
					elif k == 1:
										return 1
					else:
										return fibonacci(k - 1		) + fibonacci(k - 2		)




def       snake_case__	(       lowerCAmelCase   :		list				,					lowerCAmelCase   :		int		):
					__snake_case          =	len(lowerCAmelCase		)
					# Find m such that F_m >= n where F_i is the i_th fibonacci number.
					__snake_case          =	0
					while True:
										if fibonacci(lowerCAmelCase		) >= len_list:
															__snake_case          =	i
															break
										i += 1
					__snake_case          =	0
					while fibb_k > 0:
										__snake_case          =	min(
										    offset + fibonacci(fibb_k - 1		)				,					len_list - 1		)  # Prevent out of range
										__snake_case          =	arr[index_k]
										if item_k_1 == val:
															return index_k
										elif val < item_k_1:
															fibb_k -= 1
										elif val > item_k_1:
															offset += fibonacci(fibb_k - 1		)
															fibb_k -= 2
					else:
										return -1


if __name__ == "__main__":
		import doctest

		doctest.testmod()


