# Path: blackjax/types.py



from typing import Callable, NamedTuple, Tuple
from blackjax.types import Array, PRNGKey, PyTree
import jax
import jax.numpy as jnp
import numpy as np
import blackjax.mcmc.hmc as hmc
import blackjax.mcmc.integrators as integrators
import blackjax.mcmc.metrics as metrics
import blackjax.mcmc.proposal as proposal
import blackjax.mcmc.termination as termination
import blackjax.mcmc.trajectory as trajectory

    The implementation can seem unusual for those familiar with similar
    algorithms. Indeed, we do not conceptualize the trajectory construction as
    building a tree. We feel that the tree lingo, inherited from the recursive
    version, is unnecessarily complicated and hides the more general concepts
    on which the NUTS algorithm is built.

    NUTS, in essence, consists in sampling a trajectory by iteratively choosing
    a direction at random and integrating in this direction a number of times
    that doubles at every step. From this trajectory we continuously sample a
    proposal. When the trajectory turns on itself or when we have reached the
    maximum trajectory length we return the current proposal.

    Parameters
    ----------
    logprob_fb
        Log probability function we wish to sample from.
    parameters
        A NamedTuple that contains the parameters of the kernel to be built.

    References
    ----------
    .. [Hoffman2014] Hoffman, Matthew D., and Andrew Gelman. "The No-U-Turn sampler: adaptively setting path lengths in Hamiltonian Monte Carlo." J. Mach. Learn. Res. 15.1 (2014): 1593-1623.
    .. [Betancourt2017] Betancourt, Michael. "A conceptual introduction to Hamiltonian Monte Carlo." arXiv preprint arXiv:1701.02434 (2017).
    .. [Phan2019] Phan, Du, Neeraj Pradhan, and Martin Jankowiak. "Composable effects for flexible and accelerated probabilistic programming in NumPyro." arXiv preprint arXiv:1912.11554 (2019).
    .. [Lao2020] Lao, Junpeng, et al. "tfp. mcmc: Modern markov chain monte carlo tools built for modern hardware." arXiv preprint arXiv:2002.01184 (2020).

    """

    def one_step(

# Path: blackjax/types.py



# Path: blackjax/mcmc/nuts.py
from typing import Callable, NamedTuple, Tuple
from blackjax.types import Array, PRNGKey, PyTree
import jax
import jax.numpy as jnp
import numpy as np
import blackjax.mcmc.hmc as hmc
import blackjax.mcmc.integrators as integrators
import blackjax.mcmc.metrics as metrics
import blackjax.mcmc.proposal as proposal
import blackjax.mcmc.termination as termination
import blackjax.mcmc.trajectory as trajectory


    The implementation can seem unusual for those familiar with similar
    algorithms. Indeed, we do not conceptualize the trajectory construction as
    building a tree. We feel that the tree lingo, inherited from the recursive
    version, is unnecessarily complicated and hides the more general concepts
    on which the NUTS algorithm is built.

    NUTS, in essence, consists in sampling a trajectory by iteratively choosing
    a direction at random and integrating in this direction a number of times
    that doubles at every step. From this trajectory we continuously sample a
    proposal. When the trajectory turns on itself or when we have reached the
    maximum trajectory length we return the current proposal.

    Parameters
    ----------
    logprob_fb
        Log probability function we wish to sample from.
    parameters
        A NamedTuple that contains the parameters of the kernel to be built.

    References
    ----------
    .. [Hoffman2014] Hoffman, Matthew D., and Andrew Gelman. "The No-U-Turn sampler: adaptively setting path lengths in Hamiltonian Monte Carlo." J. Mach. Learn. Res. 15.1 (2014): 1593-1623.
    .. [Betancourt2017] Betancourt, Michael. "A conceptual introduction to Hamiltonian Monte Carlo." arXiv preprint arXiv:1701.02434 (2017).
    .. [Phan2019] Phan, Du, Neeraj Pradhan, and Martin Jankowiak. "Composable effects for flexible and accelerated probabilistic programming in NumPyro." arXiv preprint arXiv:1912.11554 (2019).
    .. [Lao2020] Lao, Junpeng, et al. "tfp. mcmc: Modern markov chain monte carlo tools built for modern hardware." arXiv preprint arXiv:2002.01184 (2020).

    """

    def one_step(

        rng_key: PRNGKey,