"""similarity.py 单测——docs/04 §5.1:手构字符串钉死 φ 与 k_sem(式3)。

全部本地 CPU、纯标准库,不依赖 torch/transformers。
关键手算用例在各测试的注释里逐步展开,方便对着验算。
"""

import math

import pytest

from ars_opd.similarity import aggregate_similarity, edit_similarity, phi, rouge1

# ---------------------------------------------------------------- rouge1


def test_rouge1_identical_is_exact_one():
    # 全同串必须精确 = 1.0(参考实现因分母 +1e-8 只能得 ≈0.99999998)
    s = "so x = 5 and y = 12"
    assert rouge1(s, s) == 1.0


def test_rouge1_disjoint_is_zero():
    assert rouge1("a b c", "x y z") == 0.0


def test_rouge1_partial_overlap_hand_computed():
    # hyp = {a, b, c}, ref = {a, b, d}:overlap = 2
    # precision = 2/3, recall = 2/3, F1 = 2·(2/3)(2/3) / (4/3) = 2/3
    assert math.isclose(rouge1("a b c", "a b d"), 2 / 3)


def test_rouge1_multiset_counts_repeats():
    # 多重集语义:hyp = [x,x,x,x], ref = [x] → overlap = min(4,1) = 1
    # precision = 1/4, recall = 1/1, F1 = 2·(1/4)/(5/4) = 0.4
    # (参考实现的 set 版会给满分 1.0——数学文本重复词多,这是关键失真点)
    assert math.isclose(rouge1("x x x x", "x"), 0.4)


def test_rouge1_is_bag_of_words_order_blind():
    # 词袋:只看用了哪些词,不看顺序
    assert rouge1("a b", "b a") == 1.0


def test_rouge1_empty_sides():
    assert rouge1("", "a b") == 0.0
    assert rouge1("a b", "") == 0.0
    assert rouge1("", "") == 0.0
    assert rouge1("   ", "a") == 0.0  # 纯空白 split 后无词


# ---------------------------------------------------------- edit_similarity


def test_edit_identical_is_one():
    s = "so x = 5 and y = 12"
    assert edit_similarity(s, s) == 1.0


def test_edit_totally_different_is_zero():
    # ["a","b"] vs ["c","d"]:2 次替换,dist=2, max(m,n)=2 → 1 − 1 = 0
    assert edit_similarity("a b", "c d") == 0.0


def test_edit_single_substitution_hand_computed():
    # ["a","b","c"] vs ["a","x","c"]:1 次替换,dist=1, max=3 → 2/3
    assert math.isclose(edit_similarity("a b c", "a x c"), 2 / 3)


def test_edit_insertion_hand_computed():
    # ["a","b"] vs ["a","x","b"]:1 次插入,dist=1, max=3 → 2/3
    assert math.isclose(edit_similarity("a b", "a x b"), 2 / 3)


def test_edit_is_order_sensitive():
    # ["a","b"] vs ["b","a"]:两次替换 dist=2 → 0.0;与 rouge1 的 1.0 互补
    assert edit_similarity("a b", "b a") == 0.0
    assert rouge1("a b", "b a") == 1.0


def test_edit_empty_sides():
    assert edit_similarity("", "") == 1.0  # 零距离
    assert edit_similarity("a b", "") == 0.0  # 全删
    assert edit_similarity("", "a b") == 0.0  # 全插


def test_edit_asymmetric_lengths():
    # ["a"] vs ["a","b","c","d"]:3 次插入,dist=3, max=4 → 1/4
    assert math.isclose(edit_similarity("a", "a b c d"), 1 / 4)


# --------------------------------------------------------------------- phi


def test_phi_default_is_edit_distance():
    # 论文 §5.1 默认;"a b" vs "b a" 恰能区分两度量(edit=0, rouge1=1)
    assert phi("a b", "b a") == edit_similarity("a b", "b a") == 0.0


def test_phi_dispatch():
    h, r = "a b c", "a b d"
    assert phi(h, r, metric="rouge1") == rouge1(h, r)
    assert phi(h, r, metric="edit_distance") == edit_similarity(h, r)


def test_phi_unknown_metric_raises():
    with pytest.raises(ValueError, match="bleu"):
        phi("a", "a", metric="bleu")


# ----------------------------------------------------- aggregate_similarity


def test_aggregate_is_sum_of_phi():
    # 式(3) 手算:rollouts 与 "a b c" 的 edit 相似度分别为 1.0, 2/3, 0.0
    chunk = "a b c"
    rollouts = ["a b c", "a x c", "x y z"]
    expected = 1.0 + 2 / 3 + 0.0
    assert math.isclose(aggregate_similarity(chunk, rollouts), expected)


def test_aggregate_bounds():
    # k_sem ∈ [0, N]:全同 → N,全不同 → 0
    n = 5
    assert aggregate_similarity("a b", ["a b"] * n) == float(n)
    assert aggregate_similarity("a b", ["x y"] * n) == 0.0


def test_aggregate_is_continuous_soft_count():
    # φ 连续 ⇒ k_sem 非整数是常态(区别于 token 精确匹配的硬计数)
    k = aggregate_similarity("a b c", ["a b c", "a x c"])
    assert 1.0 < k < 2.0


def test_aggregate_empty_rollouts():
    assert aggregate_similarity("a b", []) == 0.0


def test_aggregate_metric_passthrough():
    # "a b" vs "b a":edit 全零,rouge1 全满——验证 metric 真的传下去了
    chunk, rollouts = "a b", ["b a", "b a"]
    assert aggregate_similarity(chunk, rollouts, metric="edit_distance") == 0.0
    assert aggregate_similarity(chunk, rollouts, metric="rouge1") == 2.0