# This file is part of Hypothesis, which may be found at
# https://github.com/HypothesisWorks/hypothesis/
#
# Copyright the Hypothesis Authors.
# Individual contributors are listed in AUTHORS.rst and the git log.
#
# This Source Code Form is subject to the terms of the Mozilla Public License,
# v. 2.0. If a copy of the MPL was not distributed with this file, You can
# obtain one at https://mozilla.org/MPL/2.0/.

import math
import sys

from hypothesis.internal.conjecture.floats import float_to_lex
from hypothesis.internal.conjecture.shrinking.common import Shrinker
from hypothesis.internal.conjecture.shrinking.integer import Integer

MAX_PRECISE_INTEGER = 2**53


class Float(Shrinker):
    def setup(self):
        self.NAN = math.nan
        self.debugging_enabled = True

    def make_immutable(self, f):
        f = float(f)
        if math.isnan(f):
            # Always use the same NAN so it works properly in self.seen
            f = self.NAN
        return f

    def check_invariants(self, value):
        # We only handle positive floats because we encode the sign separately
        # anyway.
        assert not (value < 0)

    def left_is_better(self, left, right):
        lex1 = float_to_lex(left)
        lex2 = float_to_lex(right)
        return lex1 < lex2

    def short_circuit(self):
        # We check for a bunch of standard "large" floats. If we're currently
        # worse than them and the shrink downwards doesn't help, abort early
        # because there's not much useful we can do here.

        for g in [sys.float_info.max, math.inf, math.nan]:
            self.consider(g)

        # If we're stuck at a nasty float don't try to shrink it further.
        if not math.isfinite(self.current):
            return True

        # If its too large to represent as an integer, bail out here. It's
        # better to try shrinking it in the main representation.
        return self.current >= MAX_PRECISE_INTEGER

    def run_step(self):
        # Finally we get to the important bit: Each of these is a small change
        # to the floating point number that corresponds to a large change in
        # the lexical representation. Trying these ensures that our floating
        # point shrink can always move past these obstacles. In particular it
        # ensures we can always move to integer boundaries and shrink past a
        # change that would require shifting the exponent while not changing
        # the float value much.

        # First, try dropping precision bits by rounding the scaled value. We
        # try values ordered from least-precise (integer) to more precise, ie.
        # approximate lexicographical order. Once we find an acceptable shrink,
        # self.consider discards the remaining attempts early and skips test
        # invocation. The loop count sets max fractional bits to keep, and is a
        # compromise between completeness and performance.

        for p in range(10):
            scaled = self.current * 2**p  # note: self.current may change in loop
            for truncate in [math.floor, math.ceil]:
                self.consider(truncate(scaled) / 2**p)

        if self.consider(int(self.current)):
            self.debug("Just an integer now")
            self.delegate(Integer, convert_to=int, convert_from=float)
            return

        # Now try to minimize the top part of the fraction as an integer. This
        # basically splits the float as k + x with 0 <= x < 1 and minimizes
        # k as an integer, but without the precision issues that would have.
        m, n = self.current.as_integer_ratio()
        i, r = divmod(m, n)
        self.call_shrinker(Integer, i, lambda k: self.consider((k * n + r) / n))