from __future__ import annotations

from collections import OrderedDict
from .state import DEFAULT_TIMEFRAME_LABELS

def _zone_cache_ttl(tf_name: str) -> int:
	"""根据时间周期返回缓存过期时间（秒）"""
	minutes = timeframe_to_minutes(tf_name) or 5
	if minutes <= 5:
		return 120      # 5m及以下: 2分钟
	elif minutes <= 15:
		return 300      # 15m: 5分钟
	elif minutes <= 60:
		return 600      # 1h: 10分钟
	else:
		return 1800     # 4h+: 30分钟


def timeframe_to_minutes(tf: str):
	"""将时间周期转换为分钟数，用于排序。"""
	if not tf:
		return None
	unit = tf[-1]
	try:
		value = int(tf[:-1])
	except (ValueError, TypeError):
		return None
	multiplier = {
		'm': 1,
		'h': 60,
		'd': 1440,
		'w': 10080,
		'M': 43200,  # 30天近似
	}.get(unit)
	if multiplier is None:
		return None
	return value * multiplier


def format_timeframe_label(tf: str) -> str:
	"""将时间周期转换为可读标签。"""
	if not tf:
		return tf
	unit = tf[-1]
	try:
		value = int(tf[:-1])
	except (ValueError, TypeError):
		return tf
	if unit == 'm':
		return f"{value}分钟"
	if unit == 'h':
		return f"{value}小时"
	if unit == 'd':
		return "日线" if value == 1 else f"{value}日线"
	if unit == 'w':
		return "周线" if value == 1 else f"{value}周线"
	if unit == 'M':
		return "月线" if value == 1 else f"{value}月线"
	return tf


def build_timeframe_labels(timeframes):
	ordered = sorted(
		timeframes,
		key=lambda tf: timeframe_to_minutes(tf) if timeframe_to_minutes(tf) is not None else float('inf'),
	)
	labels = OrderedDict()
	for tf in ordered:
		labels[tf] = format_timeframe_label(tf)
	return labels


def _adjacent_smaller(timeframe_keys, ceiling_tf):
	"""取排序列表中严格小于 ceiling 的相邻周期。"""
	if not timeframe_keys:
		return ceiling_tf
	try:
		idx = timeframe_keys.index(ceiling_tf)
		return timeframe_keys[idx - 1] if idx > 0 else timeframe_keys[0]
	except ValueError:
		return timeframe_keys[0]


def _prefer_smaller(candidates, labels_ordered, ceiling_tf, timeframe_keys):
	"""从候选中选第一个存在且严格小于 ceiling 的周期，否则回退相邻更小。"""
	ceil_m = timeframe_to_minutes(ceiling_tf)
	for tf in candidates:
		m = timeframe_to_minutes(tf)
		if tf in labels_ordered and m is not None and ceil_m is not None and m < ceil_m:
			return tf
	return _adjacent_smaller(timeframe_keys, ceiling_tf)


def compute_timeframe_defaults(labels_ordered):
	"""
	根据已排序的「周期 → 中文标签」映射，计算主 / 次 / 次次周期默认值。
	默认偏好：主 4h、次 1h、次次 15m。
	labels_ordered: OrderedDict 或按插入顺序排列的 dict。
	"""
	if not labels_ordered:
		labels_ordered = DEFAULT_TIMEFRAME_LABELS.copy()
	timeframe_keys = list(labels_ordered.keys())
	preferred_main = next((tf for tf in ['4h', '1h', '15m'] if tf in labels_ordered), None)
	default_main = preferred_main or (timeframe_keys[0] if timeframe_keys else '1m')
	if default_main not in labels_ordered and timeframe_keys:
		default_main = timeframe_keys[0]

	default_element = _prefer_smaller(['1h', '15m'], labels_ordered, default_main, timeframe_keys)
	default_sub_sub = _prefer_smaller(['15m', '5m'], labels_ordered, default_element, timeframe_keys)

	return default_main, default_element, default_sub_sub, timeframe_keys

def is_smaller_timeframe(tf1, tf2):
	"""判断时间周期tf1是否小于tf2"""
	tf1_value = timeframe_to_minutes(tf1)
	tf2_value = timeframe_to_minutes(tf2)
	if tf1_value is None or tf2_value is None:
		return False
	return tf1_value < tf2_value

def is_smaller_or_equal_timeframe(tf1, tf2):
	"""判断时间周期tf1是否小于等于tf2"""
	tf1_value = timeframe_to_minutes(tf1)
	tf2_value = timeframe_to_minutes(tf2)
	if tf1_value is None or tf2_value is None:
		return False
	return tf1_value <= tf2_value