# Path: blackjax/adaptation/step_size.py
# def find_reasonable_step_size(
#     rng_key: PRNGKey,
#     kernel_generator: Callable[[float], Callable],
#     reference_state: HMCState,
#     initial_step_size: float,
#     target_accept: float = 0.65,
# ) -> float:
#     """Find a reasonable initial step size during warmup.
# 
#     While the dual averaging scheme is guaranteed to converge to a reasonable
#     value for the step size starting from any value, choosing a good first
#     value can speed up the convergence. This heuristics doubles and halves the
#     step size until the acceptance probability of the HMC proposal crosses the
#     target value [Hoffman2014b]_.
# 
#     Parameters
#     ----------
#     rng_key
#        Key used by JAX's random number generator.
#     kernel_generator
#         A function that takes a step size as an input and returns the corresponding
#         sampling kernel.
#     reference_hmc_state
#         The location (HMC state) where this first step size must be found. This function
#         never advances the chain.
#     inverse_mass_matrix
#         The inverse mass matrix relative to which the step size must be found.
#     initial_step_size
#         The first step size used to start the search.
#     target_accept
#         Once that value of the metropolis acceptance probability is reached we
#         estimate that we have found a "reasonable" first step size.
# 
#     Returns
#     -------
#     float
#         A reasonable first value for the step size.
# 
#     References
#     ----------
#     .. [Hoffman2014b] Hoffman, Matthew D., and Andrew Gelman. "The No-U-Turn sampler:
#            adaptively setting path lengths in Hamiltonian Monte Carlo." Journal
#            of Machine Learning Research 15.1 (2014): 1593-1623.
# 
#     """
#     fp_limit = jnp.finfo(jax.lax.dtype(initial_step_size))
# 
#     def do_continue(rss_state: ReasonableStepSizeState) -> bool:
#         """Decides whether the search should continue.
# 
#         The search stops when it crosses the `target_accept` threshold, i.e.
#         when the current direction is opposite to the previous direction.
# 
#         Note
#         ----
#         Per JAX's documentation [1]_ the `jnp.finfo` object is cached so we do not
#         occur any performance penalty when calling it repeatedly inside this
#         function.
# 
#         References
#         ----------
#         .. [1] jax.numpy.finfo documentation. https://jax.readthedocs.io/en/latest/_autosummary/jax.numpy.finfo.html
# 
#         """
#         _, direction, previous_direction, step_size = rss_state
# 
#         not_too_large = (step_size < fp_limit.max) | (direction <= 0)
#         not_too_small = (step_size > fp_limit.tiny) | (direction >= 0)
#         is_step_size_not_extreme = not_too_large & not_too_small
#         has_acceptance_rate_not_crossed_threshold = (previous_direction == 0) | (
#             direction == previous_direction
#         )
#         return is_step_size_not_extreme & has_acceptance_rate_not_crossed_threshold
# 
#     def update(rss_state: ReasonableStepSizeState) -> Tuple:
#         """Perform one step of the step size search."""
#         rng_key, direction, _, step_size = rss_state
#         _, rng_key = jax.random.split(rng_key)
# 
#         step_size = (2.0 ** direction) * step_size
#         kernel = kernel_generator(step_size)
#         _, info = kernel(rng_key, reference_state)
# 
#         new_direction = jnp.where(target_accept < info.acceptance_probability, 1, -1)
#         return ReasonableStepSizeState(rng_key, new_direction, direction, step_size)
# 
#     rss_state = ReasonableStepSizeState(rng_key, 0, 0, initial_step_size)
#     rss_state = jax.lax.while_loop(do_continue, update, rss_state)
# 
#     return rss_state.step_size


import functools
import chex
import jax
import jax.numpy as jnp
import numpy as np
import blackjax.mcmc.hmc as hmc
from absl.testing import absltest
from blackjax.adaptation.step_size import find_reasonable_step_size


class StepSizeTest(chex.TestCase):
    @chex.all_variants(with_pmap=False)
    def test_reasonable_step_size(self):
        def logprob_fn(x):
            return -jnp.sum(0.5 * x)

        rng_key = jax.random.PRNGKey(0)
        run_key0, run_key1 = jax.random.split(rng_key, 2)

        init_position = jnp.array([3.0])
        reference_state = hmc.init(init_position, logprob_fn)

        inv_mass_matrix = jnp.array([1.0])

        kernel = hmc.kernel()

        def kernel_generator(step_size: float):
            return functools.partial(
                kernel,
                logprob_fn=logprob_fn,
                step_size=step_size,
                inverse_mass_matrix=inv_mass_matrix,
                num_integration_steps=10,
            )

        # Test that the algorithm actually does something
        _find_step_size = self.variant(
            functools.partial(

# Path: blackjax/adaptation/step_size.py
# def find_reasonable_step_size(
#     rng_key: PRNGKey,
#     kernel_generator: Callable[[float], Callable],
#     reference_state: HMCState,
#     initial_step_size: float,
#     target_accept: float = 0.65,
# ) -> float:
#     """Find a reasonable initial step size during warmup.
# 
#     While the dual averaging scheme is guaranteed to converge to a reasonable
#     value for the step size starting from any value, choosing a good first
#     value can speed up the convergence. This heuristics doubles and halves the
#     step size until the acceptance probability of the HMC proposal crosses the
#     target value [Hoffman2014b]_.
# 
#     Parameters
#     ----------
#     rng_key
#        Key used by JAX's random number generator.
#     kernel_generator
#         A function that takes a step size as an input and returns the corresponding
#         sampling kernel.
#     reference_hmc_state
#         The location (HMC state) where this first step size must be found. This function
#         never advances the chain.
#     inverse_mass_matrix
#         The inverse mass matrix relative to which the step size must be found.
#     initial_step_size
#         The first step size used to start the search.
#     target_accept
#         Once that value of the metropolis acceptance probability is reached we
#         estimate that we have found a "reasonable" first step size.
# 
#     Returns
#     -------
#     float
#         A reasonable first value for the step size.
# 
#     References
#     ----------
#     .. [Hoffman2014b] Hoffman, Matthew D., and Andrew Gelman. "The No-U-Turn sampler:
#            adaptively setting path lengths in Hamiltonian Monte Carlo." Journal
#            of Machine Learning Research 15.1 (2014): 1593-1623.
# 
#     """
#     fp_limit = jnp.finfo(jax.lax.dtype(initial_step_size))
# 
#     def do_continue(rss_state: ReasonableStepSizeState) -> bool:
#         """Decides whether the search should continue.
# 
#         The search stops when it crosses the `target_accept` threshold, i.e.
#         when the current direction is opposite to the previous direction.
# 
#         Note
#         ----
#         Per JAX's documentation [1]_ the `jnp.finfo` object is cached so we do not
#         occur any performance penalty when calling it repeatedly inside this
#         function.
# 
#         References
#         ----------
#         .. [1] jax.numpy.finfo documentation. https://jax.readthedocs.io/en/latest/_autosummary/jax.numpy.finfo.html
# 
#         """
#         _, direction, previous_direction, step_size = rss_state
# 
#         not_too_large = (step_size < fp_limit.max) | (direction <= 0)
#         not_too_small = (step_size > fp_limit.tiny) | (direction >= 0)
#         is_step_size_not_extreme = not_too_large & not_too_small
#         has_acceptance_rate_not_crossed_threshold = (previous_direction == 0) | (
#             direction == previous_direction
#         )
#         return is_step_size_not_extreme & has_acceptance_rate_not_crossed_threshold
# 
#     def update(rss_state: ReasonableStepSizeState) -> Tuple:
#         """Perform one step of the step size search."""
#         rng_key, direction, _, step_size = rss_state
#         _, rng_key = jax.random.split(rng_key)
# 
#         step_size = (2.0 ** direction) * step_size
#         kernel = kernel_generator(step_size)
#         _, info = kernel(rng_key, reference_state)
# 
#         new_direction = jnp.where(target_accept < info.acceptance_probability, 1, -1)
#         return ReasonableStepSizeState(rng_key, new_direction, direction, step_size)
# 
#     rss_state = ReasonableStepSizeState(rng_key, 0, 0, initial_step_size)
#     rss_state = jax.lax.while_loop(do_continue, update, rss_state)
# 
#     return rss_state.step_size


# Path: tests/test_step_size.py
import functools
import chex
import jax
import jax.numpy as jnp
import numpy as np
import blackjax.mcmc.hmc as hmc
from absl.testing import absltest
from blackjax.adaptation.step_size import find_reasonable_step_size



class StepSizeTest(chex.TestCase):
    @chex.all_variants(with_pmap=False)
    def test_reasonable_step_size(self):
        def logprob_fn(x):
            return -jnp.sum(0.5 * x)

        rng_key = jax.random.PRNGKey(0)
        run_key0, run_key1 = jax.random.split(rng_key, 2)

        init_position = jnp.array([3.0])
        reference_state = hmc.init(init_position, logprob_fn)

        inv_mass_matrix = jnp.array([1.0])

        kernel = hmc.kernel()

        def kernel_generator(step_size: float):
            return functools.partial(
                kernel,
                logprob_fn=logprob_fn,
                step_size=step_size,
                inverse_mass_matrix=inv_mass_matrix,
                num_integration_steps=10,
            )

        # Test that the algorithm actually does something
        _find_step_size = self.variant(
            functools.partial(

                find_reasonable_step_size, kernel_generator=kernel_generator