from types import SimpleNamespace

import torch
from torch import nn

from math_jlens.fitting import jacobian_for_tokens, valid_position_mask
from math_jlens.corpus import response_window_start


class Block(nn.Module):
    def __init__(self, width: int) -> None:
        super().__init__()
        self.linear = nn.Linear(width, width, bias=False)
        with torch.no_grad():
            self.linear.weight.mul_(0.1)

    def forward(self, hidden):
        return hidden + self.linear(hidden)


class TinyModel(nn.Module):
    def __init__(self, layers=4, width=8, vocab=32) -> None:
        super().__init__()
        torch.manual_seed(0)
        self.n_layers = layers
        self.d_model = width
        self.embedding = nn.Embedding(vocab, width)
        self.layers = nn.ModuleList(Block(width) for _ in range(layers))
        for parameter in self.parameters():
            parameter.requires_grad_(False)

    def forward(self, input_ids):
        hidden = self.embedding(input_ids)
        for block in self.layers:
            hidden = block(hidden)
        return SimpleNamespace(last_hidden_state=hidden)


def test_position_mask():
    mask = valid_position_mask(12, skip_first=3)
    assert mask.sum() == 8
    assert not mask[:3].any()
    assert not mask[-1]


def test_four_stratified_response_windows():
    starts = [response_window_start(4096, 1024, stage) for stage in range(4)]
    assert starts == [0, 1024, 2048, 3072]


def test_jacobian_shape_orientation_and_layers():
    model = TinyModel()
    tokens = torch.arange(20).remainder(32).unsqueeze(0)
    matrices, valid = jacobian_for_tokens(
        model, tokens, source_layers=[0, 1, 2], target_layer=3,
        dim_batch=4, skip_first=2,
    )
    assert valid == 17
    assert set(matrices) == {0, 1, 2}
    assert all(value.shape == (8, 8) for value in matrices.values())
    expected = torch.eye(8) + model.layers[3].linear.weight.detach()
    torch.testing.assert_close(matrices[2], expected, rtol=0, atol=1e-5)