"""Valuation Uplift — the estimated market-value increase a retrofit produces.

Percentage-primary (ADR-0018): the uplift is computed purely from the EPC Band
jump (current -> target) and is always returned as a percentage; the absolute £
forms appear only when a Property Valuation (current market value) is supplied,
and are capped so the £ uplift never exceeds twice the retrofit cost.

The band-transition percentages are ported verbatim from the legacy
`backend/ml_models/Valuation.py` — four published broker sources, a provenance
snapshot rather than a live feed. MoneySupermarket / Lloyds give per-band-step
figures we compound across the jump; Knight Frank / Rightmove give whole-jump
spot figures. The uplift takes the min / max / mean across the sources that
cover the jump. See CONTEXT.md (Property Valuation, Valuation Uplift).
"""

from __future__ import annotations

from dataclasses import dataclass
from math import prod
from typing import Optional

# Ascending energy efficiency, worst -> best (RdSAP band letters).
_EPC_BANDS: tuple[str, ...] = ("G", "F", "E", "D", "C", "B", "A")

# Per-band-step uplift %, compounded across the jump.
_MSM_STEP: dict[tuple[str, str], float] = {
    ("G", "F"): 0.06,
    ("F", "E"): 0.01,
    ("E", "D"): 0.01,
    ("D", "C"): 0.02,
    ("C", "B"): 0.04,
    ("B", "A"): 0.0,
}
_LLOYDS_STEP: dict[tuple[str, str], float] = {
    ("G", "F"): 0.038,
    ("F", "E"): 0.029,
    ("E", "D"): 0.024,
    ("D", "C"): 0.02,
    ("C", "B"): 0.02,
    ("B", "A"): 0.018,
}

# Whole-jump spot uplift %, looked up by (current, target); absent jumps don't
# contribute a source.
_KNIGHT_FRANK_JUMP: dict[tuple[str, str], float] = {
    ("D", "C"): 0.03,
    ("D", "B"): 0.088,
    ("D", "A"): 0.088,
}
_RIGHTMOVE_JUMP: dict[tuple[str, str], float] = {
    ("G", "C"): 0.15,
    ("G", "B"): 0.15,
    ("G", "A"): 0.15,
    ("F", "C"): 0.15,
    ("F", "B"): 0.15,
    ("F", "A"): 0.15,
    ("E", "C"): 0.07,
    ("E", "B"): 0.07,
    ("E", "A"): 0.07,
    ("D", "C"): 0.03,
    ("D", "B"): 0.03,
    ("D", "A"): 0.03,
}

_ROI_CAP = 2.0  # the £ uplift is capped at this multiple of the retrofit cost


@dataclass(frozen=True)
class ValuationUplift:
    """A retrofit's estimated market-value uplift. The percentages are always
    present (from the Band jump); the £ forms are populated only when a current
    market value was supplied. `lower_value` / `upper_value` / `average_value`
    are the £ *increase* at the min / max / mean source; `post_retrofit_value`
    is the resulting market value (current + average increase)."""

    lower_pct: float
    upper_pct: float
    average_pct: float
    lower_value: Optional[float] = None
    upper_value: Optional[float] = None
    average_value: Optional[float] = None
    post_retrofit_value: Optional[float] = None


def _require_band(band: str) -> int:
    if band not in _EPC_BANDS:
        raise ValueError(f"unknown EPC band {band!r}")
    return _EPC_BANDS.index(band)


def _band_uplift_percentages(current_band: str, target_band: str) -> tuple[float, float, float]:
    """The (min, max, mean) uplift percentages across the sources covering the
    jump. A non-improving jump (target no better than current) compounds over no
    steps and matches no spot source, so MoneySupermarket / Lloyds both yield
    0 and the result is a no-op 0%."""
    current_index = _require_band(current_band)
    target_index = _require_band(target_band)
    steps = [
        (_EPC_BANDS[i], _EPC_BANDS[i + 1]) for i in range(current_index, target_index)
    ]
    msm: float = prod(1 + _MSM_STEP[step] for step in steps) - 1
    lloyds: float = prod(1 + _LLOYDS_STEP[step] for step in steps) - 1
    increases: list[float] = [msm, lloyds]
    knight_frank: Optional[float] = _KNIGHT_FRANK_JUMP.get((current_band, target_band))
    rightmove: Optional[float] = _RIGHTMOVE_JUMP.get((current_band, target_band))
    if knight_frank is not None:
        increases.append(knight_frank)
    if rightmove is not None:
        increases.append(rightmove)
    return min(increases), max(increases), sum(increases) / len(increases)


def estimate_valuation_uplift(
    current_band: str,
    target_band: str,
    current_value: Optional[float] = None,
    total_cost: Optional[float] = None,
) -> ValuationUplift:
    """Estimate the Valuation Uplift of moving a Property from `current_band` to
    `target_band`. Returns percentages always; absolute £ forms only when
    `current_value` is given. When both `current_value` and `total_cost` are
    given, the percentages are rescaled so the average £ uplift does not exceed
    `_ROI_CAP` times the cost (the cap can only bite once a value is known)."""
    lower_pct, upper_pct, average_pct = _band_uplift_percentages(
        current_band, target_band
    )

    if current_value is not None and total_cost is not None and total_cost > 0:
        average_value = current_value * average_pct
        if average_value > _ROI_CAP * total_cost:
            capped_average_pct = _ROI_CAP * total_cost / current_value
            scalar = capped_average_pct / average_pct
            lower_pct *= scalar
            upper_pct *= scalar
            average_pct = capped_average_pct

    if current_value is None:
        return ValuationUplift(
            lower_pct=lower_pct, upper_pct=upper_pct, average_pct=average_pct
        )

    average_increase: float = current_value * average_pct
    return ValuationUplift(
        lower_pct=lower_pct,
        upper_pct=upper_pct,
        average_pct=average_pct,
        lower_value=current_value * lower_pct,
        upper_value=current_value * upper_pct,
        average_value=average_increase,
        post_retrofit_value=current_value + average_increase,
    )