"""Generic, type-driven fuzz-input generator for OpenSleuth Level 2.

Given a Python callable annotated with ``typing`` hints, ``auto_fuzz`` produces
``n`` argument tuples that respect the signature so the verifier can score
unannotated *arbitrary* targets without requiring a hand-written fuzzer the
way the 9 builtin BLACK_BOX_FUNCTIONS do.

Each per-type generator mixes a small set of "edge" values (``0``, ``-1``,
``""``, ``None`` for ``Optional``, ...) with random values, weighted ~30/70.
This biases the fuzz batch toward the boundaries that actually distinguish
implementations while still covering the boring middle.

A caller-supplied ``fuzz_spec: dict`` overrides the type-based generation on
a per-parameter basis, e.g.::

    auto_fuzz(my_fn, n=20, fuzz_spec={"n": {"type": "int", "min": 1, "max": 90}})

Returned shape: ``List[tuple]`` -- one tuple per fuzz input, with one element
per (positional) parameter of ``fn``. Even for unary ``fn`` we return tuples
so the catalog wrapper has a single, uniform calling convention.
"""

from __future__ import annotations

import inspect
import random
import string
import typing
from typing import Any, Callable, Dict, List, Optional, Tuple, Union, get_args, get_origin


# Probability that a per-type generator emits an "edge" value (0, "", None,
# ...) instead of a random sample. Kept small enough that the boring middle
# still gets coverage but high enough that the edge cases reliably appear.
EDGE_PROB = 0.30


# Per-type edge pools. These are used by the ``_g_*`` helpers below.
_INT_EDGES = (0, 1, -1, 2, -2, 10, -10, 100, -100)
_FLOAT_EDGES = (0.0, 1.0, -1.0, 0.5, -0.5, 1e-9, -1e-9, 100.0)
_STR_EDGES = ("", "a", "ab", "Hello", "  ", "0", "abc def")
_BYTES_EDGES = (b"", b"a", b"ab", b"\x00", b"abc")


# ---------------------------------------------------------------------------
# Per-type generators (do not assume any param-name dispatch).
# ---------------------------------------------------------------------------


def _maybe_edge(rng: random.Random, edges: tuple, random_fn: Callable[[], Any]) -> Any:
    if edges and rng.random() < EDGE_PROB:
        return rng.choice(edges)
    return random_fn()


def _g_int(rng: random.Random, *, lo: int = -100, hi: int = 100) -> int:
    # Filter the edge pool by [lo, hi] so a caller-supplied fuzz_spec
    # ``{"type": "int", "min": 1, "max": 5}`` never emits ``-100``.
    edges = tuple(v for v in _INT_EDGES if lo <= v <= hi) or (lo,)
    return _maybe_edge(rng, edges, lambda: rng.randint(lo, hi))


def _g_float(rng: random.Random, *, lo: float = -100.0, hi: float = 100.0) -> float:
    edges = tuple(v for v in _FLOAT_EDGES if lo <= v <= hi) or (lo,)
    return _maybe_edge(rng, edges, lambda: rng.uniform(lo, hi))


def _g_bool(rng: random.Random) -> bool:
    return bool(rng.getrandbits(1))


def _g_str(rng: random.Random, *, max_len: int = 12, alphabet: Optional[str] = None) -> str:
    alpha = alphabet or (string.ascii_letters + string.digits)

    def _rand():
        return "".join(rng.choices(alpha, k=rng.randint(0, max_len)))

    if alphabet is not None:
        # When the caller restricts the alphabet, our generic edge pool
        # ("Hello", "  ", ...) would violate it. Build a deterministic
        # alphabet-respecting edge set instead.
        custom_edges = ("",)
        if alphabet:
            custom_edges = ("", alphabet[0], alphabet[0] * min(max_len, 2))
        return _maybe_edge(rng, custom_edges, _rand)
    return _maybe_edge(rng, _STR_EDGES, _rand)


def _g_bytes(rng: random.Random, *, max_len: int = 8) -> bytes:
    def _rand():
        return bytes(rng.randint(0, 255) for _ in range(rng.randint(0, max_len)))

    return _maybe_edge(rng, _BYTES_EDGES, _rand)


def _g_list(rng: random.Random, elem_gen: Callable[[], Any], *, max_len: int = 6) -> list:
    if rng.random() < EDGE_PROB / 2:
        return []
    return [elem_gen() for _ in range(rng.randint(0, max_len))]


def _g_tuple_homogeneous(
    rng: random.Random, elem_gen: Callable[[], Any], *, max_len: int = 6
) -> tuple:
    return tuple(_g_list(rng, elem_gen, max_len=max_len))


def _g_tuple_heterogeneous(rng: random.Random, elem_gens: List[Callable[[], Any]]) -> tuple:
    return tuple(g() for g in elem_gens)


def _g_set(rng: random.Random, elem_gen: Callable[[], Any], *, max_len: int = 6) -> set:
    if rng.random() < EDGE_PROB / 2:
        return set()
    return {elem_gen() for _ in range(rng.randint(0, max_len))}


def _g_dict(
    rng: random.Random,
    key_gen: Callable[[], Any],
    val_gen: Callable[[], Any],
    *,
    max_len: int = 5,
) -> dict:
    if rng.random() < EDGE_PROB / 2:
        return {}
    return {key_gen(): val_gen() for _ in range(rng.randint(0, max_len))}


# ---------------------------------------------------------------------------
# Type -> generator dispatch.
# ---------------------------------------------------------------------------


def _is_optional(tp: Any) -> bool:
    """``Optional[X]`` is ``Union[X, None]`` under the hood."""
    if get_origin(tp) is Union:
        return type(None) in get_args(tp)
    return False


def _strip_optional(tp: Any) -> Any:
    """Return ``X`` for ``Optional[X]``; for unions with None + multiple, pick
    the first non-None member (we can't satisfy a union in a single call)."""
    if get_origin(tp) is Union:
        non_none = [a for a in get_args(tp) if a is not type(None)]
        if len(non_none) == 1:
            return non_none[0]
        if non_none:
            return non_none[0]
    return tp


def _make_generator(tp: Any, rng: random.Random) -> Callable[[], Any]:
    """Return a 0-arg callable that produces one random value of type ``tp``.

    The recursion handles container element types (``list[int]``,
    ``dict[str, list[int]]``, etc).
    """

    if tp is None or tp is type(None):
        return lambda: None

    if _is_optional(tp):
        inner = _strip_optional(tp)
        inner_gen = _make_generator(inner, rng)

        def _gen_opt():
            if rng.random() < EDGE_PROB:
                return None
            return inner_gen()

        return _gen_opt

    origin = get_origin(tp)

    if origin is typing.Literal:
        choices = list(get_args(tp))
        return lambda: rng.choice(choices)

    if origin is None:
        if tp is int:
            return lambda: _g_int(rng)
        if tp is float:
            return lambda: _g_float(rng)
        if tp is bool:
            return lambda: _g_bool(rng)
        if tp is str:
            return lambda: _g_str(rng)
        if tp is bytes:
            return lambda: _g_bytes(rng)
        if tp is list:
            return lambda: _g_list(rng, lambda: _g_int(rng))
        if tp is tuple:
            return lambda: _g_tuple_homogeneous(rng, lambda: _g_int(rng))
        if tp is set:
            return lambda: _g_set(rng, lambda: _g_int(rng))
        if tp is dict:
            return lambda: _g_dict(rng, lambda: _g_str(rng, max_len=4), lambda: _g_int(rng))
        if tp is type(None):
            return lambda: None
        if tp is typing.Any:
            return lambda: _g_int(rng)
        # Unknown bare type -> fall back to int.
        return lambda: _g_int(rng)

    args = get_args(tp)

    if origin in (list, List):
        elem_t = args[0] if args else int
        elem_gen = _make_generator(elem_t, rng)
        return lambda: _g_list(rng, elem_gen)

    if origin in (set, frozenset):
        elem_t = args[0] if args else int
        elem_gen = _make_generator(elem_t, rng)
        return lambda: _g_set(rng, elem_gen)

    if origin in (tuple, Tuple):
        if not args:
            return lambda: _g_tuple_homogeneous(rng, lambda: _g_int(rng))
        if len(args) == 2 and args[1] is Ellipsis:
            elem_gen = _make_generator(args[0], rng)
            return lambda: _g_tuple_homogeneous(rng, elem_gen)
        elem_gens = [_make_generator(a, rng) for a in args]
        return lambda: _g_tuple_heterogeneous(rng, elem_gens)

    if origin in (dict, Dict):
        key_t = args[0] if args else str
        val_t = args[1] if len(args) > 1 else int
        key_gen = _make_generator(key_t, rng)
        val_gen = _make_generator(val_t, rng)
        return lambda: _g_dict(rng, key_gen, val_gen)

    if origin is Union:
        # Already handled Optional above. For pure unions, pick first member.
        return _make_generator(args[0], rng)

    return lambda: _g_int(rng)


# ---------------------------------------------------------------------------
# fuzz_spec overrides
# ---------------------------------------------------------------------------


def _generator_from_spec(entry: Dict[str, Any], rng: random.Random) -> Callable[[], Any]:
    """Build a generator from a ``fuzz_spec`` entry dict.

    Supported keys (all optional except ``type``):
      - ``type``: one of ``"int" | "float" | "bool" | "str" | "bytes" |
        "list" | "tuple" | "set" | "dict" | "literal" | "any"``
      - ``min``, ``max``: int/float bounds
      - ``max_len``: container/string length cap
      - ``alphabet``: str-only character pool
      - ``elem``: nested ``fuzz_spec`` entry for container elements
      - ``key``, ``value``: nested entries for dict
      - ``elems``: list of nested entries for fixed-arity tuple
      - ``choices``: list of literals to sample from
      - ``optional``: bool; if True, occasionally yields ``None``
    """
    t = entry.get("type", "any")

    def _maybe_optional(gen: Callable[[], Any]) -> Callable[[], Any]:
        if not entry.get("optional"):
            return gen

        def _g():
            if rng.random() < EDGE_PROB:
                return None
            return gen()

        return _g

    if t == "int":
        lo = int(entry.get("min", -100))
        hi = int(entry.get("max", 100))
        return _maybe_optional(lambda: _g_int(rng, lo=lo, hi=hi))
    if t == "float":
        lo = float(entry.get("min", -100.0))
        hi = float(entry.get("max", 100.0))
        return _maybe_optional(lambda: _g_float(rng, lo=lo, hi=hi))
    if t == "bool":
        return _maybe_optional(lambda: _g_bool(rng))
    if t == "str":
        max_len = int(entry.get("max_len", 12))
        alphabet = entry.get("alphabet")
        return _maybe_optional(lambda: _g_str(rng, max_len=max_len, alphabet=alphabet))
    if t == "bytes":
        max_len = int(entry.get("max_len", 8))
        return _maybe_optional(lambda: _g_bytes(rng, max_len=max_len))
    if t == "literal":
        choices = list(entry.get("choices", []))
        if not choices:
            return _maybe_optional(lambda: None)
        return _maybe_optional(lambda: rng.choice(choices))
    if t == "list":
        elem = entry.get("elem", {"type": "int"})
        elem_gen = _generator_from_spec(elem, rng)
        max_len = int(entry.get("max_len", 6))
        return _maybe_optional(lambda: _g_list(rng, elem_gen, max_len=max_len))
    if t == "tuple":
        if "elems" in entry:
            elem_gens = [_generator_from_spec(e, rng) for e in entry["elems"]]
            return _maybe_optional(lambda: _g_tuple_heterogeneous(rng, elem_gens))
        elem = entry.get("elem", {"type": "int"})
        elem_gen = _generator_from_spec(elem, rng)
        max_len = int(entry.get("max_len", 6))
        return _maybe_optional(lambda: _g_tuple_homogeneous(rng, elem_gen, max_len=max_len))
    if t == "set":
        elem = entry.get("elem", {"type": "int"})
        elem_gen = _generator_from_spec(elem, rng)
        max_len = int(entry.get("max_len", 6))
        return _maybe_optional(lambda: _g_set(rng, elem_gen, max_len=max_len))
    if t == "dict":
        key = entry.get("key", {"type": "str", "max_len": 4})
        value = entry.get("value", {"type": "int"})
        key_gen = _generator_from_spec(key, rng)
        val_gen = _generator_from_spec(value, rng)
        max_len = int(entry.get("max_len", 5))
        return _maybe_optional(lambda: _g_dict(rng, key_gen, val_gen, max_len=max_len))
    return _maybe_optional(lambda: _g_int(rng))


# ---------------------------------------------------------------------------
# Public API
# ---------------------------------------------------------------------------


def auto_fuzz(
    fn: Callable[..., Any],
    n: int,
    rng: Optional[random.Random] = None,
    *,
    fuzz_spec: Optional[Dict[str, Dict[str, Any]]] = None,
) -> List[tuple]:
    """Produce ``n`` argument tuples for calling ``fn``.

    Each returned element is an ``args`` tuple, intended to be applied as
    ``fn(*args)``. ``fuzz_spec`` is keyed by parameter name and overrides
    the type-based generation per-parameter.
    """
    rng = rng or random.Random()
    fuzz_spec = fuzz_spec or {}

    sig = inspect.signature(fn)
    try:
        hints = typing.get_type_hints(fn)
    except Exception:  # noqa: BLE001 -- bad annotations shouldn't crash fuzzing
        hints = {}

    param_gens: List[Callable[[], Any]] = []
    for pname, param in sig.parameters.items():
        if param.kind in (
            inspect.Parameter.VAR_POSITIONAL,
            inspect.Parameter.VAR_KEYWORD,
            inspect.Parameter.KEYWORD_ONLY,
        ):
            # We only fuzz positional / positional-or-keyword params.
            continue
        if pname in fuzz_spec:
            param_gens.append(_generator_from_spec(fuzz_spec[pname], rng))
            continue
        annot = hints.get(pname, param.annotation)
        if annot is inspect.Parameter.empty:
            param_gens.append(lambda r=rng: _g_int(r))
        else:
            param_gens.append(_make_generator(annot, rng))

    return [tuple(g() for g in param_gens) for _ in range(n)]


def make_fuzzer(
    fn: Callable[..., Any],
    fuzz_spec: Optional[Dict[str, Dict[str, Any]]] = None,
) -> Callable[[random.Random, int], List[tuple]]:
    """Adapt ``auto_fuzz`` to the ``FunctionSpec.fuzzer`` signature
    (``(rng, n) -> list``)."""

    def _fuzzer(rng: random.Random, n: int) -> List[tuple]:
        return auto_fuzz(fn, n, rng, fuzz_spec=fuzz_spec)

    return _fuzzer