# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project

from unittest.mock import MagicMock, patch

import numpy as np
import pytest
import torch
import torch.nn as nn

from vllm.model_executor.models.bert import (
    BertMLMHead,
    SPLADESparsePooler,
)
from vllm.platforms import current_platform
from vllm.pooling_params import PoolingParams
from vllm.utils.torch_utils import PIN_MEMORY
from vllm.v1.pool.metadata import PoolingMetadata, PoolingStates
from vllm.v1.worker.gpu.input_batch import InputBatch
from vllm.v1.worker.gpu.pool.pooling_runner import PoolingRunner
from vllm.v1.worker.gpu.states import RequestState

# ---------------------------------------------------------------------
# Functional test: SPLADE formula correctness (no HF download needed)
# ---------------------------------------------------------------------


@pytest.mark.parametrize("B,T,H,V", [(2, 3, 5, 7)])
@torch.inference_mode
def test_splade_pooler_matches_reference_formula(B, T, H, V):
    """Ensure SPLADESparsePooler forward() matches the mathematical formula:
    log1p(relu(logits)) -> max over sequence length (after masking)."""
    torch.manual_seed(0)

    # Prepare [B] sequences of shape [T, H]
    hs_list = [torch.randn(T, H) for _ in range(B)]
    hs_tenser = torch.cat(hs_list)

    # Simulate PoolingMetadata (only required fields)
    prompt_lens = [T, T - 1]
    prompt_lens_tenser = torch.tensor(prompt_lens, dtype=torch.int32)
    token_ids = torch.tensor(
        [
            [101, 5, 102],  # Batch 0: [CLS], token, [SEP]
            [101, 6, 6],  # Batch 1: [CLS], token, token (last token ignored)
        ],
        dtype=torch.long,
    )
    meta = PoolingMetadata(
        prompt_lens=prompt_lens_tenser,
        prompt_token_ids=token_ids,
        prompt_token_ids_cpu=token_ids,
        pooling_params=[PoolingParams(task="embed")] * B,
        pooling_states=[PoolingStates() for _ in range(B)],
    )

    # MLM head (prefer BertMLMHead, fallback to Linear if unavailable)
    try:
        mlm_head = BertMLMHead(hidden_size=H, vocab_size=V, layer_norm_eps=1e-12)
    except Exception:
        mlm_head = nn.Linear(H, V, bias=True)

    # Forward pass through SPLADE pooler
    pooler = SPLADESparsePooler(mlm_head=mlm_head, pooling="max", remove_cls_sep=True)
    pooled = pooler(hidden_states=hs_tenser, pooling_metadata=meta)  # list of [V]

    # Basic output checks
    assert isinstance(pooled, torch.Tensor) and len(pooled) == B
    for vec in pooled:
        assert vec.shape == (V,)
        assert torch.isfinite(vec).all()
        assert (vec >= 0).all(), "SPLADE outputs must be non-negative."

    # Reference implementation for comparison
    def ref_one(hs: torch.Tensor, L: int, tid_row: torch.Tensor) -> torch.Tensor:
        keep = torch.ones(L, dtype=torch.bool)
        if L > 0 and tid_row[0].item() == 101:  # remove CLS
            keep[0] = False
        if L > 0 and tid_row[L - 1].item() == 102:  # remove SEP
            keep[L - 1] = False

        valid = hs[:L][keep[:L]]
        if valid.numel() == 0:
            return torch.zeros(V, dtype=torch.float32)

        logits = mlm_head(valid)  # [L', V]
        scores = torch.log1p(torch.relu(logits))  # [L', V]
        return scores.max(dim=0).values.to(torch.float32)

    torch.testing.assert_close(
        pooled[0],
        ref_one(hs_list[0], prompt_lens[0], token_ids[0]),
        rtol=1e-4,
        atol=1e-4,
    )
    torch.testing.assert_close(
        pooled[1],
        ref_one(hs_list[1], prompt_lens[1], token_ids[1]),
        rtol=1e-4,
        atol=1e-4,
    )


def test_pooling_runner_gathers_required_token_ids() -> None:
    runner = PoolingRunner.__new__(PoolingRunner)
    pooling_params = PoolingParams(task="embed", requires_token_ids=True)
    runner.pooling_params = {1: pooling_params, 3: pooling_params}
    runner.pooling_states = {1: PoolingStates(), 3: PoolingStates()}
    runner.prompt_token_ids = {
        1: torch.tensor([101, 102]),
        3: torch.tensor([101, 11, 102]),
    }

    input_batch = MagicMock(spec=InputBatch)
    input_batch.idx_mapping_np = np.array([3, 1], dtype=np.int32)
    input_batch.num_reqs = 2
    req_states = MagicMock(spec=RequestState)
    req_states.prompt_len = MagicMock(np=np.array([0, 2, 0, 3], dtype=np.int32))
    metadata = runner._get_pooling_metadata(
        input_batch, req_states, torch.device(current_platform.device_type)
    )

    expected = torch.tensor([[101, 11, 102], [101, 102, 0]])
    assert metadata.prompt_token_ids_cpu is not None
    assert metadata.prompt_token_ids is not None
    assert metadata.prompt_token_ids_cpu.is_pinned() == PIN_MEMORY
    torch.testing.assert_close(
        metadata.prompt_lens, torch.tensor([3, 2], dtype=torch.int32)
    )
    torch.testing.assert_close(metadata.prompt_token_ids_cpu, expected)
    torch.testing.assert_close(metadata.prompt_token_ids.cpu(), expected)


def test_pooling_runner_stores_only_required_token_ids() -> None:
    runner = PoolingRunner.__new__(PoolingRunner)
    runner.model = MagicMock()
    runner.supported_tasks = frozenset({"embed"})
    runner.pooling_params = {}
    runner.pooling_states = {}
    runner.prompt_token_ids = {}

    runner.add_request(1, PoolingParams(task="embed"), [101, 102])
    runner.add_request(
        2,
        PoolingParams(task="embed", requires_token_ids=True),
        [101, 11, 102],
    )

    assert 1 not in runner.prompt_token_ids
    torch.testing.assert_close(runner.prompt_token_ids[2], torch.tensor([101, 11, 102]))


def test_pooling_runner_rejects_unsupported_selected_task() -> None:
    model = MagicMock()
    model.pooler.get_supported_tasks.return_value = {
        "embed",
        "embed&token_classify",
        "token_classify",
    }
    vllm_config = MagicMock()
    vllm_config.scheduler_config.max_num_seqs = 2
    vllm_config.model_config.get_pooling_task.return_value = "embed&token_classify"

    with (
        patch.object(PoolingRunner, "get_supported_tasks", return_value=["embed"]),
        pytest.raises(ValueError, match="selects 'embed&token_classify'"),
    ):
        PoolingRunner(model, vllm_config)