import pytest
import torch
from packaging import version

from colossalai.kernel.triton import copy_kv_to_blocked_cache
from colossalai.utils import get_current_device
from tests.test_infer.test_ops.triton.kernel_utils import generate_caches_and_block_tables_v2, mock_alloc_single_token

try:
    import triton  # noqa

    HAS_TRITON = True
except ImportError:
    HAS_TRITON = False
    print("please install triton from https://github.com/openai/triton")

TRITON_CUDA_SUPPORT = version.parse(torch.version.cuda) > version.parse("11.4")

HEAD_DIM = 128


def prepare_data(
    bsz,
    num_kv_heads,
    head_dim,
    block_size,
    max_num_blocks_per_seq,
    same_context_len,
    max_seq_len,
    device,
    dtype=torch.float16,
):
    # past_kv_seq_lengths in this test records the previous kv seq len
    # (not incorporating the current input whose seq len is 1)
    past_kv_seq_lengths = (
        torch.tensor([max_seq_len - 1 for _ in range(bsz)], dtype=torch.int32, device=device)
        if same_context_len
        else torch.randint(low=1, high=max_seq_len - 1, size=(bsz,), dtype=torch.int32, device=device)
    )
    num_tokens = torch.sum(past_kv_seq_lengths).item()

    kv_size = (num_tokens, 2 * num_kv_heads, head_dim)
    kv_unpad = torch.empty(size=kv_size, dtype=dtype, device=device).normal_(mean=0.0, std=0.5)
    k_unpad, v_unpad = torch.split(kv_unpad, [num_kv_heads, num_kv_heads], dim=-2)

    k_cache, v_cache, block_tables = generate_caches_and_block_tables_v2(
        k_unpad, v_unpad, past_kv_seq_lengths, bsz, max_num_blocks_per_seq, block_size, dtype=dtype, device=device
    )
    block_tables = block_tables.to(device=device)

    new_k = torch.randn((bsz, 1, num_kv_heads, head_dim), dtype=dtype, device=device)
    new_v = torch.randn((bsz, 1, num_kv_heads, head_dim), dtype=dtype, device=device)
    # mock allocating blocks for the new k/v and update block tables
    mock_alloc_single_token(block_tables, past_kv_seq_lengths, block_size)
    # kv seq len = past kv seq len + seq len (1 during decoding stage)
    kv_seq_lengths = past_kv_seq_lengths + 1

    return new_k, new_v, k_cache, v_cache, kv_seq_lengths, block_tables


@pytest.mark.skipif(not (HAS_TRITON and TRITON_CUDA_SUPPORT), reason="requires triton")
@pytest.mark.parametrize("bsz", [4, 7, 32])
@pytest.mark.parametrize("block_size", [16, 32, 64])
@pytest.mark.parametrize("max_num_blocks_per_seq", [8, 32])
@pytest.mark.parametrize("num_kv_heads", [16])
@pytest.mark.parametrize("same_context_len", [True, False])
def test_copy_kv_to_caches(
    bsz: int,
    block_size: int,
    max_num_blocks_per_seq: int,
    num_kv_heads: int,
    same_context_len: bool,
):
    torch.manual_seed(123)
    torch.cuda.empty_cache()
    torch.cuda.synchronize()
    torch.cuda.reset_peak_memory_stats()

    max_seq_len = block_size * max_num_blocks_per_seq
    dtype = torch.float16
    device = get_current_device()

    new_k, new_v, k_cache, v_cache, kv_seq_lengths, block_tables = prepare_data(
        bsz,
        num_kv_heads,
        HEAD_DIM,
        block_size,
        max_num_blocks_per_seq,
        same_context_len,
        max_seq_len,
        device=device,
        dtype=dtype,
    )
    # k_cache_torch = k_cache.clone().detach()
    # copy_to_cache(new_k, k_cache_torch, lengths=kv_seq_lengths, block_tables=block_tables, type="decoding")
    copy_kv_to_blocked_cache(new_k, new_v, k_cache, v_cache, kv_seq_lengths, block_tables)

    past_kv_seq_len = kv_seq_lengths - 1
    target_block_ids = block_tables[range(0, block_tables.size(0)), past_kv_seq_len // block_size]
    offsets_in_block = past_kv_seq_len % block_size
    k_target = k_cache[target_block_ids, :, offsets_in_block, :]
    k_source = new_k.squeeze()
    v_target = v_cache[target_block_ids, :, offsets_in_block, :]
    v_source = new_v.squeeze()

    assert k_target.shape == k_source.shape
    assert torch.equal(k_target, k_source)
    assert v_target.shape == v_source.shape
    assert torch.equal(v_target, v_source)


if __name__ == "__main__":
    test_copy_kv_to_caches(4, 32, 8, 16, True)