# Path: blackjax/mcmc/metrics.py
# def gaussian_euclidean(
#     inverse_mass_matrix: Array,
# ) -> Tuple[Callable, EuclideanKineticEnergy, Callable]:
#     def momentum_generator(rng_key: PRNGKey, position: PyTree) -> PyTree:
#     def kinetic_energy(momentum: PyTree) -> float:
#     def is_turning(
#         momentum_left: PyTree, momentum_right: PyTree, momentum_sum: PyTree
#     ) -> bool:


import chex
import jax.numpy as jnp
import numpy as np
from absl.testing import absltest, parameterized
from jax import random
from blackjax.mcmc import metrics



class GaussianEuclideanMetricsTest(chex.TestCase):
    def setUp(self):
        super().setUp()
        self.key = random.PRNGKey(0)
        self.dtype = "float32"

    @parameterized.named_parameters(
        {"testcase_name": "0d", "shape": ()},
        {"testcase_name": "3d", "shape": (1, 2, 3)},
    )
    def test_gaussian_euclidean_ndim_invalid(self, shape):
        """Test Gaussian Euclidean Function returns correct function invalid ndim"""
        x = jnp.ones(shape=shape)
        with self.assertRaisesRegex(
            ValueError, "The mass matrix has the wrong number of dimensions"
        ):

# Path: blackjax/mcmc/metrics.py
# def gaussian_euclidean(
#     inverse_mass_matrix: Array,
# ) -> Tuple[Callable, EuclideanKineticEnergy, Callable]:
#     def momentum_generator(rng_key: PRNGKey, position: PyTree) -> PyTree:
#     def kinetic_energy(momentum: PyTree) -> float:
#     def is_turning(
#         momentum_left: PyTree, momentum_right: PyTree, momentum_sum: PyTree
#     ) -> bool:


# Path: tests/test_metrics.py
import chex
import jax.numpy as jnp
import numpy as np
from absl.testing import absltest, parameterized
from jax import random
from blackjax.mcmc import metrics




class GaussianEuclideanMetricsTest(chex.TestCase):
    def setUp(self):
        super().setUp()
        self.key = random.PRNGKey(0)
        self.dtype = "float32"

    @parameterized.named_parameters(
        {"testcase_name": "0d", "shape": ()},
        {"testcase_name": "3d", "shape": (1, 2, 3)},
    )
    def test_gaussian_euclidean_ndim_invalid(self, shape):
        """Test Gaussian Euclidean Function returns correct function invalid ndim"""
        x = jnp.ones(shape=shape)
        with self.assertRaisesRegex(
            ValueError, "The mass matrix has the wrong number of dimensions"
        ):

            _ = metrics.gaussian_euclidean(x)