# Path: blackjax/base.py
# class AdaptationAlgorithm(NamedTuple):
#     """A function that implements an adaptation algorithm."""
# 
#     run: RunFn
# 
# class SamplingAlgorithm(NamedTuple):
#     """A pair of functions that implement a sampling algorithm.
# 
#     Blackjax sampling algorithms are implemented as a pair of pure functions: a
#     kernel, that takes a new samples starting from the current state, and an
#     initialization function that creates a kernel state from a chain position.
# 
#     As they represent Markov kernels, the kernel functions are pure functions
#     and do not have internal state. To save computation time they also operate
#     on states which contain the chain state and additional information that
#     needs to be carried over for the next step.
# 
#     Attributes
#     ---------
#     init:
#         A pure function which when called with the initial position and the
#         target density probability function will return the kernel's initial
#         state.
# 
#     step:
#         A pure function that takes a rng key, a state and possibly some
#         parameters and returns a new state and some information about the
#         transition.
# 
#     """
# 
#     init: InitFn
#     step: UpdateFn
# 
# Path: blackjax/types.py



from typing import Callable, Dict, Union
from blackjax.base import AdaptationAlgorithm, SamplingAlgorithm
from blackjax.types import Array, PRNGKey, PyTree
import jax
import blackjax.adaptation as adaptation
import blackjax.mcmc as mcmc
import blackjax.smc as smc
    logprob_fn
        The logprobability density function we wish to draw samples from. This
        is minus the potential function.
    step_size
        The value to use for the step size in the symplectic integrator.
    inverse_mass_matrix
        The value to use for the inverse mass matrix when drawing a value for
        the momentum and computing the kinetic energy.
    num_integration_steps
        The number of steps we take with the symplectic integrator at each
        sample step before returning a sample.
    divergence_threshold
        The absolute value of the difference in energy between two states above
        which we say that the transition is divergent. The default value is
        commonly found in other libraries, and yet is arbitrary.
    integrator
        (algorithm parameter) The symplectic integrator to use to integrate the trajectory.\

    Returns
    -------
    A ``SamplingAlgorithm``.
    """

    init = staticmethod(mcmc.hmc.init)
    kernel = staticmethod(mcmc.hmc.kernel)

    def __new__(  # type: ignore[misc]
        cls,
        logprob_fn: Callable,
        step_size: float,

# Path: blackjax/base.py
# class AdaptationAlgorithm(NamedTuple):
#     """A function that implements an adaptation algorithm."""
# 
#     run: RunFn
# 
# class SamplingAlgorithm(NamedTuple):
#     """A pair of functions that implement a sampling algorithm.
# 
#     Blackjax sampling algorithms are implemented as a pair of pure functions: a
#     kernel, that takes a new samples starting from the current state, and an
#     initialization function that creates a kernel state from a chain position.
# 
#     As they represent Markov kernels, the kernel functions are pure functions
#     and do not have internal state. To save computation time they also operate
#     on states which contain the chain state and additional information that
#     needs to be carried over for the next step.
# 
#     Attributes
#     ---------
#     init:
#         A pure function which when called with the initial position and the
#         target density probability function will return the kernel's initial
#         state.
# 
#     step:
#         A pure function that takes a rng key, a state and possibly some
#         parameters and returns a new state and some information about the
#         transition.
# 
#     """
# 
#     init: InitFn
#     step: UpdateFn
# 
# Path: blackjax/types.py



# Path: blackjax/kernels.py
from typing import Callable, Dict, Union
from blackjax.base import AdaptationAlgorithm, SamplingAlgorithm
from blackjax.types import Array, PRNGKey, PyTree
import jax
import blackjax.adaptation as adaptation
import blackjax.mcmc as mcmc
import blackjax.smc as smc

    logprob_fn
        The logprobability density function we wish to draw samples from. This
        is minus the potential function.
    step_size
        The value to use for the step size in the symplectic integrator.
    inverse_mass_matrix
        The value to use for the inverse mass matrix when drawing a value for
        the momentum and computing the kinetic energy.
    num_integration_steps
        The number of steps we take with the symplectic integrator at each
        sample step before returning a sample.
    divergence_threshold
        The absolute value of the difference in energy between two states above
        which we say that the transition is divergent. The default value is
        commonly found in other libraries, and yet is arbitrary.
    integrator
        (algorithm parameter) The symplectic integrator to use to integrate the trajectory.\

    Returns
    -------
    A ``SamplingAlgorithm``.
    """

    init = staticmethod(mcmc.hmc.init)
    kernel = staticmethod(mcmc.hmc.kernel)

    def __new__(  # type: ignore[misc]
        cls,
        logprob_fn: Callable,
        step_size: float,

        inverse_mass_matrix: Array,