# 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
        max_num_doublings: int = 10,
        divergence_threshold: int = 1000,
        integrator: Callable = mcmc.integrators.velocity_verlet,
    ) -> SamplingAlgorithm:

        step = cls.kernel(integrator, divergence_threshold, max_num_doublings)

        def init_fn(position: PyTree):
            return cls.init(position, logprob_fn)

        def step_fn(rng_key: PRNGKey, state):
            return step(
                rng_key,
                state,
                logprob_fn,
                step_size,
                inverse_mass_matrix,
            )

        return SamplingAlgorithm(init_fn, step_fn)


def window_adaptation(
    algorithm: Union[hmc, nuts],
    logprob_fn: Callable,
    num_steps: int = 1000,
    is_mass_matrix_diagonal: bool = True,
    initial_step_size: float = 1.0,
    target_acceptance_rate: float = 0.65,
    **parameters,

# 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

        max_num_doublings: int = 10,
        divergence_threshold: int = 1000,
        integrator: Callable = mcmc.integrators.velocity_verlet,
    ) -> SamplingAlgorithm:

        step = cls.kernel(integrator, divergence_threshold, max_num_doublings)

        def init_fn(position: PyTree):
            return cls.init(position, logprob_fn)

        def step_fn(rng_key: PRNGKey, state):
            return step(
                rng_key,
                state,
                logprob_fn,
                step_size,
                inverse_mass_matrix,
            )

        return SamplingAlgorithm(init_fn, step_fn)


def window_adaptation(
    algorithm: Union[hmc, nuts],
    logprob_fn: Callable,
    num_steps: int = 1000,
    is_mass_matrix_diagonal: bool = True,
    initial_step_size: float = 1.0,
    target_acceptance_rate: float = 0.65,
    **parameters,

) -> AdaptationAlgorithm: