#!/usr/bin/env python3
# prepend underscores so these don't leak to __all__.
from hashlib import sha256 as _sha256
from itertools import chain as _chain
from os import environ as _environ
from pathlib import Path as _Path
from traceback import print_exception as _print_exception
import ast
import inspect
import numpy as np

# NOTE: the environment variable "BITTEN_CACHE" takes precedence here.
default_cache = "bitten.cache"
optimizer_defaults = dict(tol="weak", depth=1, attempts=1)


def pack(*args):
    """Return positional arguments as a tuple."""
    return args


def _is_in(needle, *haystack):
    # like the "in" keyword, but uses "is" instead of "==".
    for thing in haystack:
        if thing is needle:
            return True
    return False


def _hashable(a):  # currently only used for caching adjustments
    if isinstance(a, np.ndarray):
        if a.ndim > 0:
            # key = tuple(a)  # probably won't work for ndim >= 2
            key = a.shape + tuple(a.ravel())
        else:
            key = a.item()
    else:
        key = a  # just hope for the best
    return key


def _promote_all(*dtypes):
    # new_dtypes = [np.promote_types(t, _promote_all(*dtypes)) for t in dtypes]
    a, dtypes = dtypes[0], dtypes[1:]  # TODO: use next(iter(dtypes)) pattern instead?
    new_dtypes = [np.promote_types(a, b) for b in dtypes]
    while len(new_dtypes) > 1:
        a, dtypes = new_dtypes[0], new_dtypes[1:]
        new_dtypes = [np.promote_types(a, b) for b in dtypes]
    return new_dtypes[0]


def _promote_equal(a, b, *etc):
    a = np.asanyarray(a)
    b = np.asanyarray(b)

    if etc:
        etc_arrays = (np.asanyarray(e) for e in etc)
        etc_dtypes = (e.dtype for e in etc_arrays)
        all_arrays = (a, b, *etc_arrays)
        all_dtypes = (a.dtype, b.dtype, *etc_dtypes)
        promo = _promote_all(*all_dtypes)
        return tuple(a.astype(promo) for a in all_arrays)

    if a.dtype != b.dtype:
        promo = np.promote_types(a.dtype, b.dtype)
        a = a.astype(promo)
        b = b.astype(promo)
        assert a.dtype == b.dtype
    return a, b


def _remove_docstring(node):
    # via: https://stackoverflow.com/a/49998190
    # remove all the doc strings in a FunctionDef or ClassDef as node.

    if not (isinstance(node, ast.FunctionDef) or isinstance(node, ast.ClassDef)):
        return

    if len(node.body) != 0:
        docstr = node.body[0]
        if isinstance(docstr, ast.Expr) and isinstance(docstr.value, ast.Str):
            node.body.pop(0)


def _prepare_function(func):
    # TODO: describe what this does and why.
    # adapted from: https://stackoverflow.com/a/49998190
    # TODO: (optionally) strip the initial decorator from the AST as well.

    func_str = inspect.getsource(func)

    # Remove leading indentation, if any.
    indent = len(func_str[: len(func_str) - len(func_str.lstrip())])
    if indent:
        func_str = "\n".join(line[indent:] for line in func_str.splitlines())

    module = ast.parse(func_str)

    assert len(module.body) == 1 and isinstance(module.body[0], ast.FunctionDef)

    # Clear function name so it doesn't affect the hash.
    func_node = module.body[0]
    func_node.name = ""

    # Clear all the doc strings.
    for node in ast.walk(module):
        _remove_docstring(node)

    # TODO: clear pure constant expressions as well? like a simple string or number on its own line.

    # Convert the ast to a string for hashing.
    ast_str = ast.dump(module, annotate_fields=False)
    return ast_str.encode()


def _flatten(it):
    # TODO: make this more robust (and more picky? and faster?).
    res = []
    for value in it:
        if hasattr(value, "__len__"):
            res += _flatten(value)
        else:
            res.append(value)
    return res


def _penalize(constraints, *x, tol=1e-5, scale=1e10, growth=4.0):
    # NOTE: maybe growth shouldn't be configurable unless
    #       it also affects the order of the penalty equation (currently 3).
    # NOTE: different ordering of constraints can result in different solutions.
    # NOTE: this function doesn't use numpy, so you can copypaste it elsewhere.
    # growth = growth ** (1.0 / len(constraints))
    # penalties = [cons(*x) for cons in constraints]
    penalties = _flatten(cons(*x) for cons in constraints)
    growth = growth ** (1.0 / len(penalties))
    unsat = sum(p > tol for p in penalties)
    # cubic = [p + p * p + p * p * p for p in penalties]
    penalsum = 0.0
    for p in penalties:
        penalsum *= growth  # always happens so each penalty gets its own stratum
        if p > tol:
            penalsum += p + p * p + p * p * p
    return scale * (unsat + penalsum)


def _penalize2(constraints, *x, tol=1e-5, scale=1e10, growth=3.0):
    # updated from upstream (v2022.11)
    penalties = _flatten(cons(*x) for cons in constraints)
    growth = growth ** (1.0 / len(penalties))
    unsat = sum(p > tol for p in penalties)
    penalsum = 0.0
    for p in penalties:
        penalsum *= growth  # always happens so each penalty gets its own stratum
        if p > tol:
            penalsum += p + p * p * p
    return scale * (unsat + penalsum)


def _penalize3(constraints, *x, tol=1e-5, scale=1e10, growth=3.0):
    # updated from upstream (v2022.19)
    penalties = _flatten(cons(*x) for cons in constraints)
    n_con = len(penalties)
    growth = growth ** (1.0 / n_con)  # "ps"
    increment = 1.0 / np.sqrt(n_con)  # "pnsi"
    penalty, nominal = 0.0, 0.0  # "pns", "pnsm"
    for p in penalties:
        p = max(p - tol, 0.0)
        penalty = penalty * growth + increment + p + p * p * p
        nominal = nominal * growth + increment
    return scale * (penalty - nominal + 1.0)


def penalize(x, constraints, tol=1e-5, *, scale=1e10, growth=4.0):
    # DEPRECATED
    return _penalize(constraints, x, tol=tol, scale=scale, growth=growth)


def count_unsat(x, constraints, tol=1e-5):
    # DEPRECATED
    penalties = [cons(*x) for cons in constraints]
    return sum(p > tol for p in penalties)


class Impure:  # TODO: rename? volatile? aaa the word is on the tip of my tongue
    def needs_hashing(self):
        return ()


class Objective(Impure):
    pass


class Hyper(Impure):
    pass


class Minimize(Objective):
    def __init__(self, *args):
        self.args = args

    # TODO: rename to "realize" to amalgamate with the non-Bounds Hypers?
    def compute_with(self, approx, **kw_hypers):
        return approx(*self.args)

    def needs_hashing(self):
        return tuple(self.args)


class Maximize(Objective):
    def __init__(self, *args):
        self.args = args

    def compute_with(self, approx, **kw_hypers):
        return -approx(*self.args)

    def needs_hashing(self):
        return tuple(self.args)


class AbstractError(Objective):
    def __init__(self, inputs, outputs, iwrap=pack, owrap=np.asanyarray):
        assert callable(iwrap), type(iwrap)
        assert callable(owrap), type(owrap)
        self.inputs = np.asanyarray(inputs)
        self.outputs = np.asanyarray(outputs)
        self.iwrap = iwrap
        self.owrap = owrap

    def compute_with(self, fun, **kw_hypers):
        args = self.iwrap(self.inputs)
        predictions = fun(*args)
        predictions = self.owrap(predictions)
        assert predictions.shape == self.outputs.shape
        return self._compute(predictions)

    def needs_hashing(self):
        return (self.inputs, self.outputs)


class MeanSquaredError(AbstractError):
    def _compute(self, predictions):
        error = self.outputs - predictions
        return np.mean(np.square(error))


class MeanAbsoluteError(AbstractError):
    def _compute(self, predictions):
        error = self.outputs - predictions
        return np.mean(np.abs(error))


class PeakError(AbstractError):
    def _compute(self, predictions):
        error = self.outputs - predictions
        return np.max(np.abs(error))


class HuberError(AbstractError):
    def _compute(self, predictions, delta=1.0):
        error = self.outputs - predictions
        return np.where(
            error <= delta,
            0.5 * np.square(error),
            delta * (np.abs(error) - 0.5 * delta),
        )


class Accuracy(AbstractError):
    def _compute(self, predictions):
        p, t = predictions, self.outputs
        correct = np.argmax(p, axis=-1) == np.argmax(t, axis=-1)
        return np.mean(correct)


class Crossentropy(AbstractError):
    def crossentropy(self, predictions, eps=1e-6):
        p, t = np.clip(predictions, eps, 1 - eps), self.outputs
        f = np.sum(-t * np.log(p) - (1 - t) * np.log(1 - p), axis=-1)
        return np.mean(f)


class Constrain(Objective):
    def __init__(self, *constraints, tol=1e-5, version=1):
        for cons in constraints:
            assert callable(cons)
        self.constraints = constraints
        self.tol = float(tol)
        self.version = version

    @property
    def version(self):
        return self._version + 1

    @version.setter
    def version(self, version):
        assert 1 <= version <= 3, version
        self._version = version - 1
        self._penalize = [_penalize, _penalize2, _penalize3][self._version]

    def penalize(self, *args):
        return self._penalize(self.constraints, *args, tol=self.tol)

    def compute_with(self, fun, **kw_hypers):
        return self.penalize(*kw_hypers.values())

    def needs_hashing(self):
        return (self.tol,)


class L1(Objective):
    def __init__(self, scale):
        self.scale = float(scale)

    def compute_with(self, fun, **kw_hypers):
        a = np.array(list(kw_hypers.values()))
        return self.scale * np.sum(np.abs(a))

    def needs_hashing(self):
        return (self.scale,)


class L2(Objective):
    def __init__(self, scale):
        self.scale = float(scale)

    def compute_with(self, fun, **kw_hypers):
        a = np.array(list(kw_hypers.values()))
        return self.scale * np.sum(np.square(a))

    def needs_hashing(self):
        return (self.scale,)


class Const(Hyper):
    def __init__(self, value):
        self.value = value

    # def compute(self, a):
    #     return a

    def realize(self, kw_hypers, *, name, approx=None):
        kw_hypers[name] = self.value
        return True  # realized

    def needs_hashing(self):
        return (self.value,)


class Bound(Hyper):
    def __init__(self, lower, upper):
        self.lower, self.upper = _promote_equal(lower, upper)

    @property
    def bounds(self):  # allowed to be overridden for log-scale, etc.
        return self.lower, self.upper

    def compute(self, a):  # allowed to be overridden for log-scale, etc.
        return a

    def needs_hashing(self):
        return (self.lower, self.upper)


class BoundArray(Hyper):  # TODO: write and use a Mixin for Array types?
    def __init__(self, lower, upper, dims, dtype=np.float64):
        self.lower, self.upper = _promote_equal(lower, upper)
        self.dims = int(dims)
        self.dtype = dtype

    @property
    def bounds(self):  # allowed to be overridden for log-scale, etc.
        return self.lower, self.upper

    def vectorize(self, a):  # the Array equivalent of self.compute(a)
        return np.array(a, self.dtype, copy=True)

    def needs_hashing(self):
        return (self.lower, self.upper, self.dims)


class Round(Bound):
    def __init__(self, lower, upper, decimals=0):
        self.decimals = int(decimals)
        super().__init__(lower, upper)

    def compute(self, a):
        return np.round(a, self.decimals)

    def needs_hashing(self):
        return (self.lower, self.upper, self.decimals)


class Adjustment(Hyper):
    def __init__(self, x, y, default=0):
        self._x, self._y, self._default = _promote_equal(x, y, default)

    @property
    def x(self):  # allowed to be overridden for log-scale, etc.
        return self._x

    @property
    def y(self):  # allowed to be overridden for log-scale, etc.
        return self._y

    @property
    def default(self):  # allowed to be overridden for log-scale, etc.
        return self._default

    def perform(self, result):
        return self.y - result

    def realize(self, kw_hypers, *, name, approx=None):
        if approx is None:
            kw_hypers[name] = self.default
            return False  # not realized
        else:
            result = approx(self.x)  # TODO: abstract better (especially multiple args)
            kw_hypers[name] = self.perform(result)
            return True  # realized

    def needs_hashing(self):
        return (self._x, self._y, self._default)


class Storage:  # TODO: use dict-like interface?
    def __init__(self, filepath):
        open(filepath, "ab").close()  # ensure it exists and is readable/writable
        self.filepath = filepath

    def get(self, key=None, default=None):
        tokens = None
        with open(self.filepath, "rb") as f:
            for line in f:
                stored, _, rest = line.partition(b",")
                if key == stored:
                    tokens = rest.rstrip(b"\r\n").split(b",")

        cached = default
        if tokens is not None:
            cached = np.array([float(x) for x in tokens], float)

        return cached

    def set(self, key, values):
        formatted = ",".join(repr(x) for x in values).encode()
        # FIXME: doesn't overwrite old value, if any.
        with open(self.filepath, "ab") as f:
            f.write(key + b"," + formatted + b"\n")


class Cache:  # TODO: rename to something a little more specific?
    def __init__(self, storage):
        assert isinstance(storage, Storage), type(storage)
        self.storage = storage
        self.hashing = _sha256()
        self._hashed = None
        self._hashed_n = 0
        self._n = 0

    def hash(self, *hashme):
        for h in hashme:
            if self._n == 0 and callable(h):
                data = _prepare_function(h)
            elif callable(h):
                raise Exception(
                    "functions other than the one being decorated cannot be hashed (for now?)"
                )
            else:
                data = np.asanyarray(h).tobytes()

            self.hashing.update(data)
            self._n += 1
            del data

    def finish(self):
        self._hashed = self.hashing.hexdigest().encode()
        self._hashed_n = self._n
        return self._hashed

    @property
    def hashed(self):
        if self._n != self._hashed_n or self._hashed is None:
            return self.finish()
        return self._hashed

    def get(self):
        return self.storage.get(self.hashed)

    def set(self, values):
        return self.storage.set(self.hashed, values)


class HashMe(Impure):
    def __init__(self, *objects):
        self.objects = objects

    def needs_hashing(self):
        return self.objects


def _categorize_objectives(objectives):
    yes, no = [], []
    for subjective in objectives:  # forgive me
        # if isinstance(objective, Constrain): do something?
        if isinstance(subjective, Objective):
            yes.append(subjective)
        else:
            no.append(subjective)
    return yes, no


def _categorize_parameters(params):  # or "organize"?
    positional_kinds = (
        inspect.Parameter.POSITIONAL_ONLY,
        inspect.Parameter.POSITIONAL_OR_KEYWORD,
    )

    inputs, hypers, positional, ind = {}, {}, {}, 0
    for param in params:
        nodefault = param.default != inspect.Parameter.empty
        ishyper = isinstance(param.default, Hyper)
        if nodefault and ishyper:
            if param.kind == inspect.Parameter.POSITIONAL_ONLY:
                raise Exception("hyperparameters must be accessible through keywords")
            hypers[param.name] = param.default
            if param.kind == inspect.Parameter.POSITIONAL_OR_KEYWORD:
                positional[param.name] = ind
            ind += 1
        elif nodefault:
            ind += 1
        elif param.kind in positional_kinds:
            inputs[param.name] = param.default

    return inputs, hypers, positional


def _evaluator(objective, budget=1):
    # most optimizers don't fare well when there's
    # literally nothing provided for them to optimize.
    # this evaluator is a substitute for an optimizer in the case
    # that there are no hyperparameters, but possibly still parameters.
    from scipybiteopt import OptimizeResult

    x, fun, nfev = (), np.inf, 0
    for i in range(budget):
        new_fun = objective(x)
        if new_fun < fun:
            fun = new_fun
        nfev += 1
    return OptimizeResult(x=x, fun=fun, nfev=nfev)


def _set_new_defaults(fun, positional, kw_hypers):
    # instead of creating a new function masquerading as the old,
    # or attempting to change the function's signature,
    # simply change the function's default values
    # from dummy values to their optimized values.
    unfun = fun
    while hasattr(unfun, "__wrapped__"):
        unfun = unfun.__wrapped__

    defaults = unfun.__defaults__
    kwdefaults = unfun.__kwdefaults__
    if defaults:
        new_defaults = list(defaults)

    for k, v in kw_hypers.items():
        if k in positional:
            ind = positional[k]
            new_defaults[ind] = v
        else:
            kwdefaults[k] = v

    if defaults:
        unfun.__defaults__ = tuple(new_defaults)
    if kwdefaults:
        unfun.__kwdefaults__ = kwdefaults

    # return unfun


def _bite(
    fun,
    *objectives,
    budget=1_000_000,  # maximum allowed evaluations of "fun"
    cache=False,  # True for default filepath, or a path
    multimethod="sum",  # how to handle multiple objectives
    deterministic="unknown",
    optimizer_kwargs=None,
    _debug=False,
    _optim="biteopt",  # ignored for now
):
    # NOTE: cache only properly supports floats for now.
    from scipybiteopt import biteopt as _biteopt

    if cache is True:  # intentionally not using ==
        env_cache = _environ.get("BITTEN_CACHE", None)
        storage = Storage(env_cache if env_cache else default_cache)
        cache = Cache(storage)
    elif cache is False or cache is None:  # intentionally not using ==
        cache = None
    else:
        assert isinstance(cache, Cache), type(cache)

    # apply defaults.
    if optimizer_kwargs is None:
        optimizer_kwargs = {}
    for k, v in optimizer_defaults.items():
        if k not in optimizer_kwargs:
            optimizer_kwargs[k] = v

    assert _is_in(multimethod, "sum"), f"unknown multiobjective method: {multimethod}"

    assert _is_in(deterministic, False, True, "unknown"), deterministic

    sig = inspect.signature(fun)
    params = sig.parameters.values()

    objectives, nonjectives = _categorize_objectives(objectives)
    inputs, hypers, positional = _categorize_parameters(params)

    linear_bounds = []
    for key, hyper in hypers.items():
        if isinstance(hyper, BoundArray):  # TODO: ArrayMixin or whatever
            linear_bounds += [hyper.bounds] * hyper.dims
        elif isinstance(hyper, Bound):
            linear_bounds.append(hyper.bounds)

    memo = {}
    memo_secret = object()  # dummy object for uniqueness

    def make_approx(kw_hypers):
        # print(kw_hypers)
        def approx(*args):
            return fun(*args, **kw_hypers)
            # try:
            #     return fun(*args, **kw_hypers)
            # except Exception as e:
            #     _print_exception(e)
            #     return -np.inf

        return approx

    def make_keywords(params):
        kw_hypers = {}
        other_hypers = []
        remaining = iter(params)
        all_realized = True
        for name, hyper in hypers.items():
            if isinstance(hyper, BoundArray):
                params = [next(remaining) for _ in range(hyper.dims)]
                kw_hypers[name] = hyper.vectorize(params)
            elif isinstance(hyper, Bound):
                param = next(remaining)
                kw_hypers[name] = hyper.compute(param)
            else:
                other_hypers.append((name, hyper))
                realized = hyper.realize(kw_hypers, name=name)
                if not realized:
                    all_realized = False

        while not all_realized:
            # TODO: is there a design pattern to accomplish this that makes the user
            #       conscious of the possibility of accidentally infinite-looping?
            approx = make_approx(kw_hypers)
            all_realized = True

            for (name, hyper) in other_hypers:
                realized = hyper.realize(kw_hypers, name=name, approx=approx)
                if not realized:
                    all_realized = False

        return kw_hypers

    def objective(params):
        kw_hypers = make_keywords(params)
        approx = make_approx(kw_hypers)

        objective_values = []
        for subjective in objectives:  # forgive me
            if deterministic is True:
                key = (subjective,) + tuple(float(x) for x in kw_hypers.values())
                if key in memo:
                    value = memo[key]
                    # print("MEMO:", key, value)
                else:
                    value = subjective.compute_with(approx, **kw_hypers)
                    memo[key] = value
            else:
                value = subjective.compute_with(approx, **kw_hypers)
            objective_values.append(value)

        if multimethod == "sum":
            return np.sum(objective_values)
        elif callable(multimethod):
            return multimethod(objective_values)
        else:
            # huh? just do this i guess.
            return objective_values[0]

    cached = None
    if cache:
        cache.hash(fun)
        for obj in _chain(objectives, nonjectives, hypers.values()):
            for h in obj.needs_hashing():
                cache.hash(h)
        cached = cache.get()
        if _debug:
            print("HASH:", cache.hashed, sep="\n")

    if cached is None:
        if _debug:  # dirty test for exceptions
            center = np.mean(linear_bounds, axis=-1) if hypers else ()
            print("FIRST VALUE:", objective(center), sep="\n")

        if hypers:
            res = _biteopt(objective, linear_bounds, iters=budget, **optimizer_kwargs)
        else:
            if deterministic is True:
                res = _evaluator(objective, 1)
            else:
                res = _evaluator(objective, budget)

        if _debug:
            print("RES:", res, sep="\n")

        optimized = res.x
    else:
        assert len(cached) == len(linear_bounds)
        optimized = cached

    if cache and cached is None:
        cache.set(optimized)

    kw_hypers = make_keywords(optimized)
    _set_new_defaults(fun, positional, kw_hypers)
    fun.__optimized__ = kw_hypers  # secret!
    if cached is None:
        fun.__result__ = res

    return fun


def bite(*objectives, **config):
    def decorator(fun):
        # this prevents an unnecessary level of indentation.
        return _bite(fun, *objectives, **config)

    return decorator


# this is similar to default behaviour of having no __all__ variable at all,
# but ours ignores modules as well. this allows for `import sys` and such
# without clobbering `from our_module import *`.
__all__ = [
    k for k, v in locals().items() if not inspect.ismodule(v) and not k.startswith("_")
]

if __name__ == "__main__":
    # non-associative example from:
    # https://numpy.org/doc/stable/reference/generated/numpy.promote_types.html
    print("S4 or S6?", _promote_all("S", "i1", "u1"))
    print(np.promote_types("S4", "S6"))