"""Pure-Python reward tuning schema shared by the service and trainer."""

from __future__ import annotations

import math
from collections.abc import Mapping
from copy import deepcopy
from dataclasses import dataclass
from typing import Any

MAX_PROPOSAL_CHANGES = 4
MIN_CHANGE_RATIO = 0.5
MAX_CHANGE_RATIO = 2.0


class RewardConfigError(ValueError):
    """A reward configuration or proposal violated the allowlist."""


@dataclass(frozen=True)
class NumericSpec:
    minimum: float
    maximum: float
    default: float
    allow_zero: bool = True


WEIGHT_SPECS: dict[str, NumericSpec] = {
    "track_linear_velocity": NumericSpec(0.5, 3.0, 1.0, False),
    "track_angular_velocity": NumericSpec(0.25, 2.0, 1.0, False),
    "body_orientation_l2": NumericSpec(-3.0, -0.1, -1.0, False),
    "pose": NumericSpec(0.0, 2.5, 1.0),
    "body_ang_vel": NumericSpec(-0.2, 0.0, -0.05),
    "angular_momentum": NumericSpec(-0.1, 0.0, -0.025),
    "is_terminated": NumericSpec(-400.0, -50.0, -200.0, False),
    "joint_acc_l2": NumericSpec(-2.0e-6, 0.0, -2.5e-7),
    "joint_pos_limits": NumericSpec(-30.0, -2.0, -10.0, False),
    "action_rate_l2": NumericSpec(-0.2, -0.005, -0.05, False),
    "foot_gait": NumericSpec(0.0, 1.5, 0.5),
    "foot_clearance": NumericSpec(-3.0, 0.0, -1.0),
    "foot_slip": NumericSpec(-1.0, 0.0, -0.25),
    "soft_landing": NumericSpec(-5.0e-3, 0.0, -1.0e-3),
    "stand_still": NumericSpec(-3.0, 0.0, -1.0),
    "electrical_power": NumericSpec(-5.0e-3, 0.0, 0.0),
}

PARAMETER_SPECS: dict[str, NumericSpec] = {
    "track_linear_velocity.std": NumericSpec(0.25, 1.0, math.sqrt(0.25), False),
    "track_angular_velocity.std": NumericSpec(0.35, 1.2, math.sqrt(0.5), False),
    "pose.std_standing_scale": NumericSpec(0.5, 2.0, 1.0, False),
    "pose.std_walking_scale": NumericSpec(0.5, 2.0, 1.0, False),
    "pose.std_running_scale": NumericSpec(0.5, 2.0, 1.0, False),
    "pose.walking_threshold": NumericSpec(0.05, 0.5, 0.1, False),
    "pose.running_threshold": NumericSpec(1.0, 2.5, 1.5, False),
    "foot_gait.period": NumericSpec(0.4, 0.8, 0.6, False),
    "foot_gait.threshold": NumericSpec(0.45, 0.65, 0.56, False),
    "foot_gait.command_threshold": NumericSpec(0.02, 0.3, 0.1, False),
    "foot_clearance.target_height": NumericSpec(0.06, 0.16, 0.1, False),
    "foot_clearance.command_threshold": NumericSpec(0.02, 0.3, 0.1, False),
    "foot_slip.command_threshold": NumericSpec(0.02, 0.3, 0.1, False),
    "soft_landing.command_threshold": NumericSpec(0.02, 0.3, 0.1, False),
    "stand_still.command_threshold": NumericSpec(0.02, 0.3, 0.1, False),
}

BASE_REWARD_CONFIGURATION: dict[str, dict[str, float]] = {
    "weights": {name: spec.default for name, spec in WEIGHT_SPECS.items()},
    "params": {name: spec.default for name, spec in PARAMETER_SPECS.items()},
}


def _number(name: str, value: Any, spec: NumericSpec) -> float:
    if isinstance(value, bool) or not isinstance(value, (int, float)):
        raise RewardConfigError(f"{name} 必须是数值")
    result = float(value)
    if not math.isfinite(result):
        raise RewardConfigError(f"{name} 必须是有限数值")
    if result == 0.0 and not spec.allow_zero:
        raise RewardConfigError(f"{name} 不允许关闭")
    if result < spec.minimum or result > spec.maximum:
        raise RewardConfigError(f"{name} 必须在 {spec.minimum}–{spec.maximum} 之间")
    return result


def _mapping(value: Any, name: str) -> Mapping[str, Any]:
    if not isinstance(value, Mapping):
        raise RewardConfigError(f"{name} 必须是对象")
    return value


def _cross_validate(config: Mapping[str, Mapping[str, float]]) -> None:
    params = config["params"]
    if params["pose.walking_threshold"] >= params["pose.running_threshold"]:
        raise RewardConfigError("pose.walking_threshold 必须小于 pose.running_threshold")


def validate_configuration(value: Any) -> dict[str, dict[str, float]]:
    """Validate a complete configuration and reject missing/unknown fields."""
    root = _mapping(value, "rewardConfig")
    if set(root) != {"weights", "params"}:
        raise RewardConfigError("rewardConfig 只能包含 weights 和 params")
    raw_weights = _mapping(root["weights"], "weights")
    raw_params = _mapping(root["params"], "params")
    if set(raw_weights) != set(WEIGHT_SPECS):
        raise RewardConfigError("weights 必须完整且不能包含未知奖励项")
    if set(raw_params) != set(PARAMETER_SPECS):
        raise RewardConfigError("params 必须完整且不能包含未知参数")
    config = {
        "weights": {
            name: _number(f"weights.{name}", raw_weights[name], spec)
            for name, spec in WEIGHT_SPECS.items()
        },
        "params": {
            name: _number(f"params.{name}", raw_params[name], spec)
            for name, spec in PARAMETER_SPECS.items()
        },
    }
    _cross_validate(config)
    return config


def validate_proposal(value: Any, previous: Any) -> dict[str, dict[str, float]]:
    """Validate a sparse Agent patch relative to a complete previous config."""
    current = validate_configuration(previous)
    root = _mapping(value, "proposal")
    if not set(root).issubset({"weights", "params"}):
        raise RewardConfigError("proposal 只能包含 weights 和 params")
    raw_weights = _mapping(root.get("weights", {}), "weights")
    raw_params = _mapping(root.get("params", {}), "params")
    if len(raw_weights) + len(raw_params) == 0:
        raise RewardConfigError("proposal 至少需要一项修改")
    if len(raw_weights) + len(raw_params) > MAX_PROPOSAL_CHANGES:
        raise RewardConfigError(f"proposal 每轮最多修改 {MAX_PROPOSAL_CHANGES} 项")
    unknown_weights = set(raw_weights) - set(WEIGHT_SPECS)
    unknown_params = set(raw_params) - set(PARAMETER_SPECS)
    if unknown_weights:
        raise RewardConfigError(f"未知奖励项：{', '.join(sorted(unknown_weights))}")
    if unknown_params:
        raise RewardConfigError(f"未知奖励参数：{', '.join(sorted(unknown_params))}")

    patch: dict[str, dict[str, float]] = {"weights": {}, "params": {}}
    for name, raw in raw_weights.items():
        value_number = _number(f"weights.{name}", raw, WEIGHT_SPECS[name])
        old = current["weights"][name]
        if old != 0.0 and value_number != 0.0:
            ratio = abs(value_number / old)
            if ratio < MIN_CHANGE_RATIO or ratio > MAX_CHANGE_RATIO:
                raise RewardConfigError(
                    f"weights.{name} 单轮变化必须在旧值幅度的 "
                    f"{MIN_CHANGE_RATIO}×–{MAX_CHANGE_RATIO}×"
                )
        if value_number == old:
            raise RewardConfigError(f"weights.{name} 没有发生变化")
        patch["weights"][name] = value_number
    for name, raw in raw_params.items():
        value_number = _number(f"params.{name}", raw, PARAMETER_SPECS[name])
        old = current["params"][name]
        ratio = abs(value_number / old)
        if ratio < MIN_CHANGE_RATIO or ratio > MAX_CHANGE_RATIO:
            raise RewardConfigError(
                f"params.{name} 单轮变化必须在旧值的 {MIN_CHANGE_RATIO}×–{MAX_CHANGE_RATIO}×"
            )
        if value_number == old:
            raise RewardConfigError(f"params.{name} 没有发生变化")
        patch["params"][name] = value_number

    candidate = deepcopy(current)
    candidate["weights"].update(patch["weights"])
    candidate["params"].update(patch["params"])
    _cross_validate(candidate)
    return patch


def merge_proposal(previous: Any, proposal: Any) -> dict[str, dict[str, float]]:
    current = validate_configuration(previous)
    patch = validate_proposal(proposal, current)
    merged = deepcopy(current)
    merged["weights"].update(patch["weights"])
    merged["params"].update(patch["params"])
    return validate_configuration(merged)


def apply_reward_configuration(env_cfg: Any, value: Any) -> None:
    """Apply a validated full config to a fresh mjlab environment config."""
    config = validate_configuration(value)
    for name, weight in config["weights"].items():
        if name not in env_cfg.rewards:
            raise RewardConfigError(f"环境缺少奖励项：{name}")
        env_cfg.rewards[name].weight = weight

    params = config["params"]
    direct = {
        "track_linear_velocity.std": ("track_linear_velocity", "std"),
        "track_angular_velocity.std": ("track_angular_velocity", "std"),
        "pose.walking_threshold": ("pose", "walking_threshold"),
        "pose.running_threshold": ("pose", "running_threshold"),
        "foot_gait.period": ("foot_gait", "period"),
        "foot_gait.threshold": ("foot_gait", "threshold"),
        "foot_gait.command_threshold": ("foot_gait", "command_threshold"),
        "foot_clearance.target_height": ("foot_clearance", "target_height"),
        "foot_clearance.command_threshold": ("foot_clearance", "command_threshold"),
        "foot_slip.command_threshold": ("foot_slip", "command_threshold"),
        "soft_landing.command_threshold": ("soft_landing", "command_threshold"),
        "stand_still.command_threshold": ("stand_still", "command_threshold"),
    }
    for path, (term, parameter) in direct.items():
        env_cfg.rewards[term].params[parameter] = params[path]
    for regime in ("standing", "walking", "running"):
        key = f"std_{regime}"
        scale = params[f"pose.{key}_scale"]
        baseline = env_cfg.rewards["pose"].params[key]
        env_cfg.rewards["pose"].params[key] = {
            pattern: float(std) * scale for pattern, std in baseline.items()
        }