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import numpy as np
import mici

def get_rwm_sampler_and_adapters(
    neg_log_posterior_density, rng, target_accept_stat=0.234
):
    """Get a Mici sampler and adapters for adaptive Gaussian random-walk Metropolis proposals."""
    system = mici.systems.EuclideanMetricSystem(
        neg_log_dens=neg_log_posterior_density,
        grad_neg_log_dens=lambda q: q * 0,

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from collections.abc import Iterator
from dataclasses import dataclass

from cola import PSD
from cola.ops import BlockDiag, LinearOperator
from gpjax.base import static_field
from gpjax.kernels import AbstractKernel
from gpjax.kernels.computations.base import AbstractKernelComputation, Kernel
from gpjax.kernels.computations import DenseKernelComputation
from gpjax.typing import Array, ScalarFloat

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"""Comparison of different approaches to summing dict values with 'sparse' keys.

Sparse here means the keys of all dicts are drawn from a common set but only a few
keys (compared to the total number) are present in each.
"""

import pandas as pd
import numpy as np
import timeit
from string import ascii_lowercase

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"""Print formatted Markdown list of all NAG subroutines in a source tree"""

import re
import argparse
import pathlib
from itertools import groupby

parser = argparse.ArgumentParser(
    description="Generate Markdown list of NAG subroutines used in source tree."
)

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import cPickle
import gzip
import numpy as np
import os

DEFAULT_SEED = 1234

class DataProvider(object):
    """Generic data provider."""

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