from future import standard_library

import functools
import inspect
import logging
import os
import sys
import time
from timeit import default_timer as timer

import numpy as np

from hyperopt import tpe, exceptions
from hyperopt.base import validate_timeout, validate_loss_threshold
from . import pyll
from .utils import coarse_utcnow
from . import base
from . import progress

standard_library.install_aliases()
logger = logging.getLogger(__name__)


try:
    import cloudpickle as pickler
except Exception as e:
    logger.info(
        'Failed to load cloudpickle, try installing cloudpickle via "pip install '
        'cloudpickle" for enhanced pickling support.'
    )
    import pickle as pickler


def generate_trial(tid, space):
    variables = space.keys()
    idxs = {v: [tid] for v in variables}
    vals = {k: [v] for k, v in space.items()}
    return {
        "state": base.JOB_STATE_NEW,
        "tid": tid,
        "spec": None,
        "result": {"status": "new"},
        "misc": {
            "tid": tid,
            "cmd": ("domain_attachment", "FMinIter_Domain"),
            "workdir": None,
            "idxs": idxs,
            "vals": vals,
        },
        "exp_key": None,
        "owner": None,
        "version": 0,
        "book_time": None,
        "refresh_time": None,
    }


def generate_trials_to_calculate(points):
    """
    Function that generates trials to be evaluated from list of points

    :param points: List of points to be inserted in trials object in form of
        dictionary with variable names as keys and variable values as dict
        values. Example value:
        [{'x': 0.0, 'y': 0.0}, {'x': 1.0, 'y': 1.0}]

    :return: object of class base.Trials() with points which will be calculated
        before optimisation start if passed to fmin().
    """
    trials = base.Trials()
    new_trials = [generate_trial(tid, x) for tid, x in enumerate(points)]
    trials.insert_trial_docs(new_trials)
    return trials


def fmin_pass_expr_memo_ctrl(f):
    """
    Mark a function as expecting kwargs 'expr', 'memo' and 'ctrl' from
    hyperopt.fmin.

    expr - the pyll expression of the search space
    memo - a partially-filled memo dictionary such that
           `rec_eval(expr, memo=memo)` will build the proposed trial point.
    ctrl - the Experiment control object (see base.Ctrl)

    """
    f.fmin_pass_expr_memo_ctrl = True
    return f


def partial(fn, **kwargs):
    """functools.partial work-alike for functions decorated with
    fmin_pass_expr_memo_ctrl
    """
    rval = functools.partial(fn, **kwargs)
    if hasattr(fn, "fmin_pass_expr_memo_ctrl"):
        rval.fmin_pass_expr_memo_ctrl = fn.fmin_pass_expr_memo_ctrl
    return rval


def __objective_fmin_wrapper(func):
    """
    Wrap the objective function on a dict to kwargs
    """

    def _objective(kwargs):
        return func(**kwargs)

    return _objective


class FMinIter:
    """Object for conducting search experiments."""

    catch_eval_exceptions = False
    pickle_protocol = -1

    def __init__(
        self,
        algo,
        domain,
        trials,
        rstate,
        asynchronous=None,
        max_queue_len=1,
        poll_interval_secs=1.0,
        max_evals=sys.maxsize,
        timeout=None,
        loss_threshold=None,
        verbose=False,
        show_progressbar=True,
        early_stop_fn=None,
        trials_save_file="",
    ):
        self.algo = algo
        self.domain = domain
        self.trials = trials
        if not show_progressbar or not verbose:
            self.progress_callback = progress.no_progress_callback
        elif show_progressbar is True:
            self.progress_callback = progress.default_callback
        else:
            self.progress_callback = show_progressbar
        if asynchronous is None:
            self.asynchronous = trials.asynchronous
        else:
            self.asynchronous = asynchronous
        self.poll_interval_secs = poll_interval_secs
        self.max_queue_len = max_queue_len
        self.max_evals = max_evals
        self.early_stop_fn = early_stop_fn
        self.early_stop_args = []
        self.trials_save_file = trials_save_file
        self.timeout = timeout
        self.loss_threshold = loss_threshold
        self.start_time = timer()
        self.rstate = rstate
        self.verbose = verbose

        if self.asynchronous and not hasattr(self.trials, "_spark"):
            if "FMinIter_Domain" in trials.attachments:
                logger.warning("over-writing old domain trials attachment")
            msg = pickler.dumps(domain)
            # -- sanity check for unpickling
            pickler.loads(msg)
            trials.attachments["FMinIter_Domain"] = msg

    def serial_evaluate(self, N=-1):
        for trial in self.trials._dynamic_trials:
            if trial["state"] == base.JOB_STATE_NEW:
                trial["state"] = base.JOB_STATE_RUNNING
                now = coarse_utcnow()
                trial["book_time"] = now
                trial["refresh_time"] = now
                spec = base.spec_from_misc(trial["misc"])
                ctrl = base.Ctrl(self.trials, current_trial=trial)
                try:
                    result = self.domain.evaluate(spec, ctrl)
                except Exception as e:
                    logger.error("job exception: %s" % str(e))
                    trial["state"] = base.JOB_STATE_ERROR
                    trial["misc"]["error"] = (str(type(e)), str(e))
                    trial["refresh_time"] = coarse_utcnow()
                    if not self.catch_eval_exceptions:
                        # -- JOB_STATE_ERROR means this trial
                        #    will be removed from self.trials.trials
                        #    by this refresh call.
                        self.trials.refresh()
                        raise
                else:
                    trial["state"] = base.JOB_STATE_DONE
                    trial["result"] = result
                    trial["refresh_time"] = coarse_utcnow()
                N -= 1
                if N == 0:
                    break
        self.trials.refresh()

    @property
    def is_cancelled(self):
        """
        Indicates whether this fmin run has been cancelled.  SparkTrials supports cancellation.
        """
        if hasattr(self.trials, "_fmin_cancelled"):
            if self.trials._fmin_cancelled:
                return True
        return False

    def block_until_done(self):
        already_printed = False
        if self.asynchronous:
            unfinished_states = [base.JOB_STATE_NEW, base.JOB_STATE_RUNNING]

            def get_queue_len():
                return self.trials.count_by_state_unsynced(unfinished_states)

            qlen = get_queue_len()
            while qlen > 0:
                if not already_printed and self.verbose:
                    logger.info("Waiting for %d jobs to finish ..." % qlen)
                    already_printed = True
                time.sleep(self.poll_interval_secs)
                qlen = get_queue_len()
            self.trials.refresh()
        else:
            self.serial_evaluate()

    def run(self, N, block_until_done=True):
        """
        Run `self.algo` iteratively (use existing `self.trials` to produce the new
        ones), update, and repeat
        block_until_done  means that the process blocks until ALL jobs in
        trials are not in running or new state

        """
        trials = self.trials
        algo = self.algo
        n_queued = 0

        def get_queue_len():
            return self.trials.count_by_state_unsynced(base.JOB_STATE_NEW)

        def get_n_done():
            return self.trials.count_by_state_unsynced(base.JOB_STATE_DONE)

        def get_n_unfinished():
            unfinished_states = [base.JOB_STATE_NEW, base.JOB_STATE_RUNNING]
            return self.trials.count_by_state_unsynced(unfinished_states)

        stopped = False
        initial_n_done = get_n_done()
        with self.progress_callback(
            initial=initial_n_done, total=self.max_evals
        ) as progress_ctx:
            all_trials_complete = False
            best_loss = float("inf")
            while (
                # more run to Q     || ( block_flag & trials not done )
                (n_queued < N or (block_until_done and not all_trials_complete))
                # no timeout        || < current last time
                and (self.timeout is None or (timer() - self.start_time) < self.timeout)
                # no loss_threshold || < current best_loss
                and (self.loss_threshold is None or best_loss >= self.loss_threshold)
            ):
                qlen = get_queue_len()
                while (
                    qlen < self.max_queue_len and n_queued < N and not self.is_cancelled
                ):
                    n_to_enqueue = min(self.max_queue_len - qlen, N - n_queued)
                    # get ids for next trials to enqueue
                    new_ids = trials.new_trial_ids(n_to_enqueue)
                    self.trials.refresh()
                    # Based on existing trials and the domain, use `algo` to probe in
                    # new hp points. Save the results of those inspections into
                    # `new_trials`. This is the core of `run`, all the rest is just
                    # processes orchestration
                    new_trials = algo(
                        new_ids, self.domain, trials, self.rstate.integers(2**31 - 1)
                    )
                    assert len(new_ids) >= len(new_trials)

                    if len(new_trials):
                        self.trials.insert_trial_docs(new_trials)
                        self.trials.refresh()
                        n_queued += len(new_trials)
                        qlen = get_queue_len()
                    else:
                        stopped = True
                        break

                if self.is_cancelled:
                    break

                if self.asynchronous:
                    # -- wait for workers to fill in the trials
                    time.sleep(self.poll_interval_secs)
                else:
                    # -- loop over trials and do the jobs directly
                    self.serial_evaluate()

                self.trials.refresh()
                if self.trials_save_file != "":
                    pickler.dump(self.trials, open(self.trials_save_file, "wb"))
                if self.early_stop_fn is not None:
                    stop, kwargs = self.early_stop_fn(
                        self.trials, *self.early_stop_args
                    )
                    self.early_stop_args = kwargs
                    if stop:
                        logger.info(
                            "Early stop triggered. Stopping iterations as condition is reach."
                        )
                        stopped = True
                # update progress bar with the min loss among trials with status ok
                losses = [
                    loss
                    for loss in self.trials.losses()
                    if loss is not None and not np.isnan(loss)
                ]
                if losses:
                    best_loss = min(losses)
                    progress_ctx.postfix = "best loss: " + str(best_loss)

                n_unfinished = get_n_unfinished()
                if n_unfinished == 0:
                    all_trials_complete = True

                n_done = get_n_done()
                n_done_this_iteration = n_done - initial_n_done
                if n_done_this_iteration > 0:
                    progress_ctx.update(n_done_this_iteration)
                initial_n_done = n_done

                if stopped:
                    break

        if block_until_done:
            self.block_until_done()
            self.trials.refresh()
            logger.info("Queue empty, exiting run.")
        else:
            qlen = get_queue_len()
            if qlen:
                msg = "Exiting run, not waiting for %d jobs." % qlen
                logger.info(msg)

    def __iter__(self):
        return self

    def __next__(self):
        self.run(1, block_until_done=self.asynchronous)
        if self.early_stop_fn is not None:
            stop, kwargs = self.early_stop_fn(self.trials, *self.early_stop_args)
            self.early_stop_args = kwargs
            if stop:
                raise StopIteration()
        if len(self.trials) >= self.max_evals:
            raise StopIteration()
        return self.trials

    def exhaust(self):
        n_done = len(self.trials)
        self.run(self.max_evals - n_done, block_until_done=self.asynchronous)
        self.trials.refresh()
        return self


def fmin(
    fn,
    space,
    algo=None,
    max_evals=None,
    timeout=None,
    loss_threshold=None,
    trials=None,
    rstate=None,
    allow_trials_fmin=True,
    pass_expr_memo_ctrl=None,
    catch_eval_exceptions=False,
    verbose=True,
    return_argmin=True,
    points_to_evaluate=None,
    max_queue_len=1,
    show_progressbar=True,
    early_stop_fn=None,
    trials_save_file="",
):
    """Minimize a function over a hyperparameter space.

    More realistically: *explore* a function over a hyperparameter space
    according to a given algorithm, allowing up to a certain number of
    function evaluations.  As points are explored, they are accumulated in
    `trials`


    Parameters
    ----------

    fn : callable (trial point -> loss)
        This function will be called with a value generated from `space`
        as the first and possibly only argument.  It can return either
        a scalar-valued loss, or a dictionary.  A returned dictionary must
        contain a 'status' key with a value from `STATUS_STRINGS`, must
        contain a 'loss' key if the status is `STATUS_OK`. Particular
        optimization algorithms may look for other keys as well.  An
        optional sub-dictionary associated with an 'attachments' key will
        be removed by fmin its contents will be available via
        `trials.trial_attachments`. The rest (usually all) of the returned
        dictionary will be stored and available later as some 'result'
        sub-dictionary within `trials.trials`.

    space : hyperopt.pyll.Apply node or "annotated"
        The set of possible arguments to `fn` is the set of objects
        that could be created with non-zero probability by drawing randomly
        from this stochastic program involving involving hp_<xxx> nodes
        (see `hyperopt.hp` and `hyperopt.pyll_utils`).
        If set to "annotated", will read space using type hint in fn. Ex:
        (`def fn(x: hp.uniform("x", -1, 1)): return x`)

    algo : search algorithm
        This object, such as `hyperopt.rand.suggest` and
        `hyperopt.tpe.suggest` provides logic for sequential search of the
        hyperparameter space.

    max_evals : int
        Allow up to this many function evaluations before returning.

    timeout : None or int, default None
        Limits search time by parametrized number of seconds.
        If None, then the search process has no time constraint.

    loss_threshold : None or double, default None
        Limits search time when minimal loss reduced to certain amount.
        If None, then the search process has no constraint on the loss,
        and will stop based on other parameters, e.g. `max_evals`, `timeout`

    trials : None or base.Trials (or subclass)
        Storage for completed, ongoing, and scheduled evaluation points.  If
        None, then a temporary `base.Trials` instance will be created.  If
        a trials object, then that trials object will be affected by
        side-effect of this call.

    rstate : numpy.random.Generator, default numpy.random or `$HYPEROPT_FMIN_SEED`
        Each call to `algo` requires a seed value, which should be different
        on each call. This object is used to draw these seeds via `randint`.
        The default rstate is
        `numpy.random.default_rng(int(env['HYPEROPT_FMIN_SEED']))`
        if the `HYPEROPT_FMIN_SEED` environment variable is set to a non-empty
        string, otherwise np.random is used in whatever state it is in.

    verbose : bool
        Print out some information to stdout during search. If False, disable
            progress bar irrespectively of show_progressbar argument

    allow_trials_fmin : bool, default True
        If the `trials` argument

    pass_expr_memo_ctrl : bool, default False
        If set to True, `fn` will be called in a different more low-level
        way: it will receive raw hyperparameters, a partially-populated
        `memo`, and a Ctrl object for communication with this Trials
        object.

    return_argmin : bool, default True
        If set to False, this function returns nothing, which can be useful
        for example if it is expected that `len(trials)` may be zero after
        fmin, and therefore `trials.argmin` would be undefined.

    points_to_evaluate : list, default None
        Only works if trials=None. If points_to_evaluate equals None then the
        trials are evaluated normally. If list of dicts is passed then
        given points are evaluated before optimisation starts, so the overall
        number of optimisation steps is len(points_to_evaluate) + max_evals.
        Elements of this list must be in a form of a dictionary with variable
        names as keys and variable values as dict values. Example
        points_to_evaluate value is [{'x': 0.0, 'y': 0.0}, {'x': 1.0, 'y': 2.0}]

    max_queue_len : integer, default 1
        Sets the queue length generated in the dictionary or trials. Increasing this
        value helps to slightly speed up parallel simulatulations which sometimes lag
        on suggesting a new trial.

    show_progressbar : bool or context manager, default True (or False if verbose is False).
        Show a progressbar. See `hyperopt.progress` for customizing progress reporting.

    early_stop_fn: callable ((result, *args) -> (Boolean, *args)).
        Called after every run with the result of the run and the values returned by the function previously.
        Stop the search if the function return true.
        Default None.

    trials_save_file: str, default ""
        Optional file name to save the trials object to every iteration.
        If specified and the file already exists, will load from this file when
        trials=None instead of creating a new base.Trials object

    Returns
    -------

    argmin : dictionary
        If return_argmin is True returns `trials.argmin` which is a dictionary.  Otherwise
        this function  returns the result of `hyperopt.space_eval(space, trails.argmin)` if there
        were successfull trails. This object shares the same structure as the space passed.
        If there were no successfull trails, it returns None.
    """
    if algo is None:
        algo = tpe.suggest
        logger.warning("TPE is being used as the default algorithm.")

    if max_evals is None:
        max_evals = sys.maxsize

    if rstate is None:
        env_rseed = os.environ.get("HYPEROPT_FMIN_SEED", "")
        if env_rseed:
            rstate = np.random.default_rng(int(env_rseed))
        else:
            rstate = np.random.default_rng()

    validate_timeout(timeout)
    validate_loss_threshold(loss_threshold)

    if space == "annotated":
        # Read space from objective fn
        space = inspect.getfullargspec(fn).annotations

        # Validate space
        for param, hp_func in space.items():
            if not isinstance(hp_func, pyll.base.Apply):
                raise exceptions.InvalidAnnotatedParameter(
                    'When using `space="annotated"`, please annotate the '
                    "objective function arguments with a `pyll.base.Apply` "
                    "subclass. See example in `fmin` docstring"
                )

        # Change fn to accept a dict-like argument
        fn = __objective_fmin_wrapper(fn)

    if allow_trials_fmin and hasattr(trials, "fmin"):
        return trials.fmin(
            fn,
            space,
            algo=algo,
            max_evals=max_evals,
            timeout=timeout,
            loss_threshold=loss_threshold,
            max_queue_len=max_queue_len,
            rstate=rstate,
            pass_expr_memo_ctrl=pass_expr_memo_ctrl,
            verbose=verbose,
            catch_eval_exceptions=catch_eval_exceptions,
            return_argmin=return_argmin,
            show_progressbar=show_progressbar,
            early_stop_fn=early_stop_fn,
            trials_save_file=trials_save_file,
        )

    if trials is None:
        if os.path.exists(trials_save_file):
            trials = pickler.load(open(trials_save_file, "rb"))
        elif points_to_evaluate is None:
            trials = base.Trials()
        else:
            assert type(points_to_evaluate) == list
            trials = generate_trials_to_calculate(points_to_evaluate)

    domain = base.Domain(fn, space, pass_expr_memo_ctrl=pass_expr_memo_ctrl)

    rval = FMinIter(
        algo,
        domain,
        trials,
        max_evals=max_evals,
        timeout=timeout,
        loss_threshold=loss_threshold,
        rstate=rstate,
        verbose=verbose,
        max_queue_len=max_queue_len,
        show_progressbar=show_progressbar,
        early_stop_fn=early_stop_fn,
        trials_save_file=trials_save_file,
    )
    rval.catch_eval_exceptions = catch_eval_exceptions

    # next line is where the fmin is actually executed
    rval.exhaust()

    if return_argmin:
        if len(trials.trials) == 0:
            raise Exception(
                "There are no evaluation tasks, cannot return argmin of task losses."
            )
        return trials.argmin
    if len(trials) > 0:
        # Only if there are some successful trail runs, return the best point in
        # the evaluation space
        return space_eval(space, trials.argmin)
    return None


def space_eval(space, hp_assignment):
    """Compute a point in a search space from a hyperparameter assignment.

    Parameters:
    -----------
    space - a pyll graph involving hp nodes (see `pyll_utils`).

    hp_assignment - a dictionary mapping hp node labels to values.
    """
    space = pyll.as_apply(space)
    nodes = pyll.toposort(space)
    memo = {}
    for node in nodes:
        if node.name == "hyperopt_param":
            label = node.arg["label"].eval()
            if label in hp_assignment:
                memo[node] = hp_assignment[label]
    rval = pyll.rec_eval(space, memo=memo)
    return rval


# -- flake8 doesn't like blank last line
