from __future__ import annotations

import random
from typing import Any

from .hypothesis import HypothesisAgentGenome

FEATURE_POOL: tuple[str, ...] = ("open", "high", "low", "close", "volume")

COGNITIVE_STYLES: tuple[str, ...] = (
    "physicist",
    "biologist",
    "historian",
    "meteorologist",
    "ecologist",
    "engineer",
)


def _clone_with(g: HypothesisAgentGenome, **overrides: Any) -> HypothesisAgentGenome:
    payload: dict[str, Any] = g.to_dict()
    payload.update(overrides)
    payload.pop("id", None)
    payload["parent_ids"] = [*g.parent_ids, g.id]
    payload["generation"] = g.generation + 1
    return HypothesisAgentGenome.from_dict(payload)


def mutate_temperature(g: HypothesisAgentGenome, rng: random.Random) -> HypothesisAgentGenome:
    delta = rng.choice([-0.1, 0.1])
    new_t = max(0.6, min(1.3, g.temperature + delta))
    return _clone_with(g, temperature=round(new_t, 4))


def mutate_lookback(g: HypothesisAgentGenome, rng: random.Random) -> HypothesisAgentGenome:
    delta = rng.choice([-50, 50])
    new_lb = max(50, min(500, g.lookback_window + delta))
    return _clone_with(g, lookback_window=new_lb)


def mutate_feature_access(g: HypothesisAgentGenome, rng: random.Random) -> HypothesisAgentGenome:
    current = set(g.feature_access)
    if len(current) == len(FEATURE_POOL):
        op = "remove"
    elif len(current) <= 1:
        op = "add"
    else:
        op = rng.choice(["add", "remove"])

    if op == "add":
        candidates = [f for f in FEATURE_POOL if f not in current]
        choice = rng.choice(candidates)
        new_set = current | {choice}
    else:
        choice = rng.choice(sorted(current))
        new_set = current - {choice}

    return _clone_with(g, feature_access=sorted(new_set))


def mutate_cognitive_style(g: HypothesisAgentGenome, rng: random.Random) -> HypothesisAgentGenome:
    candidates = [s for s in COGNITIVE_STYLES if s != g.cognitive_style]
    new_style = rng.choice(candidates)
    return _clone_with(g, cognitive_style=new_style)


MUTATION_OPS = (
    mutate_temperature,
    mutate_lookback,
    mutate_feature_access,
    mutate_cognitive_style,
)


def random_mutate(g: HypothesisAgentGenome, rng: random.Random) -> HypothesisAgentGenome:
    op = rng.choice(MUTATION_OPS)
    return op(g, rng)