import pytest
import torch

import vllm_omni.utils.mm_outputs as mm_outputs
from vllm_omni.utils.mm_outputs import partition_flat_payload, partition_payload_list

pytestmark = [pytest.mark.core_model, pytest.mark.cpu]


def test_partition_thinker_latent_payload():
    payload = {
        "hidden_states.layer_0": torch.zeros(2, 4),
        "hidden_states.layer_24": torch.zeros(2, 8),
        "embed.tts_bos": [torch.zeros(1, 1, 4)],
    }
    inter, client = partition_flat_payload(payload)
    assert inter == payload
    assert client == {}


def test_partition_talker_intermediate_codes():
    payload = {
        "codes.audio": torch.zeros(3, 2),
        "hidden": torch.zeros(3, 16),
    }
    inter, client = partition_flat_payload(payload)
    assert inter == payload
    assert client == {}


def test_partition_code2wav_client_audio():
    payload = {
        "model_outputs": torch.zeros(1, 2400),
        "sr": torch.tensor(24000, dtype=torch.int32),
    }
    inter, client = partition_flat_payload(payload)
    assert inter == {}
    assert client == payload


def test_partition_payload_list_preserves_request_alignment():
    payloads = [
        {"hidden_states.layer_0": torch.zeros(1, 2)},
        {"model_outputs": torch.zeros(1, 10)},
    ]
    inter_list, client_list = partition_payload_list(payloads)
    assert inter_list == [payloads[0], None]
    assert client_list == [None, payloads[1]]


def test_partition_duplex_audio_transcript_metadata_to_client_mm():
    payload = {
        "model_outputs": torch.zeros(1, 2400),
        "meta.duplex_epoch": torch.tensor([3], dtype=torch.int32),
        "meta.duplex_turn_id": torch.tensor([2], dtype=torch.int32),
        "meta.llm_output_text_utf8": torch.tensor([104, 105], dtype=torch.uint8),
        "meta.audio_text_total_chars": torch.tensor([2], dtype=torch.int32),
        "meta.tts_is_last_chunk": torch.tensor([1], dtype=torch.int32),
        "meta.turn_end": torch.tensor([1], dtype=torch.int32),
        "meta.native_duplex_segment_text": "hi",
    }

    inter, client = partition_flat_payload(payload)

    assert "meta.llm_output_text_utf8" in client
    assert "meta.audio_text_total_chars" in client
    assert "meta.duplex_epoch" in client
    assert "meta.duplex_turn_id" in client
    assert "meta.tts_is_last_chunk" in client
    assert "meta.turn_end" in client
    assert "meta.native_duplex_segment_text" not in client
    assert "meta.native_duplex_segment_text" in inter


def test_snapshot_mm_payload_coalesces_compatible_tensor_list(monkeypatch):
    chunks = [
        torch.tensor([[7]], dtype=torch.long),
        torch.empty(0, 1, dtype=torch.long),
        torch.tensor([[9], [10]], dtype=torch.long),
    ]
    real_cat = torch.cat
    cat_shapes = []

    def recording_cat(tensors, *args, **kwargs):
        cat_shapes.append([tuple(tensor.shape) for tensor in tensors])
        return real_cat(tensors, *args, **kwargs)

    monkeypatch.setattr(torch, "cat", recording_cat)

    result = mm_outputs.snapshot_mm_payload({"codes.mel": chunks})

    assert cat_shapes == [[(1, 1), (0, 1), (2, 1)]]
    output_chunks = result["codes.mel"]
    assert [tuple(chunk.shape) for chunk in output_chunks] == [
        (1, 1),
        (0, 1),
        (2, 1),
    ]
    torch.testing.assert_close(output_chunks[0], chunks[0])
    torch.testing.assert_close(output_chunks[1], chunks[1])
    torch.testing.assert_close(output_chunks[2], chunks[2])
    assert output_chunks[0].untyped_storage().data_ptr() == output_chunks[2].untyped_storage().data_ptr()