from __future__ import annotations

from typing import TYPE_CHECKING

from sglang.kernel_api_logging import debug_kernel_api
from sglang.kernels.jit.utils import cache_once, load_jit

if TYPE_CHECKING:
    import torch
    from tvm_ffi.module import Module


@cache_once
def _jit_ngram_embedding_module() -> Module:
    return load_jit(
        "ngram_embedding",
        cuda_files=["ngram_embedding.cuh"],
        cuda_wrappers=[
            ("compute_n_gram_ids", "&NgramEmbeddingKernel::compute_n_gram_ids"),
            (
                "compute_n_gram_ids_decode",
                "&NgramEmbeddingKernel::compute_n_gram_ids_decode",
            ),
            ("update_token_table", "&NgramEmbeddingKernel::update_token_table"),
            (
                "update_token_table_decode",
                "&NgramEmbeddingKernel::update_token_table_decode",
            ),
        ],
    )


@debug_kernel_api
def compute_n_gram_ids(
    ne_n: int,
    ne_k: int,
    ne_weights: torch.Tensor,
    ne_mods: torch.Tensor,
    exclusive_ne_embedder_size_sums: torch.Tensor,
    tokens: torch.Tensor,
    exclusive_req_len_sums: torch.Tensor,
    ne_token_table: torch.Tensor,
    row_indices: torch.Tensor,
    column_starts: torch.Tensor,
    n_gram_ids: torch.Tensor,
    eos_token_id: int,
) -> None:
    """
    Compute n-gram IDs for embedding.

    Args:
        ne_n: n value for n-gram
        ne_k: k value for n-gram configurations
        ne_weights: weights tensor with shape [ne_n-1, ne_k, ne_n]
        ne_mods: mods tensor with shape [ne_n-1, ne_k]
        exclusive_ne_embedder_size_sums: exclusive sum of embedder sizes
        tokens: input token ids
        exclusive_req_len_sums: exclusive sum of request lengths
        ne_token_table: token table for all requests
        row_indices: row indices for each request
        column_starts: column start positions for each request
        n_gram_ids: output tensor for n-gram ids
        eos_token_id: tokens before an eos are excluded from the n-gram context
    """
    module = _jit_ngram_embedding_module()
    module.compute_n_gram_ids(
        ne_n,
        ne_k,
        ne_weights,
        ne_mods,
        exclusive_ne_embedder_size_sums,
        tokens,
        exclusive_req_len_sums,
        ne_token_table,
        row_indices,
        column_starts,
        n_gram_ids,
        eos_token_id,
    )


@debug_kernel_api
def compute_n_gram_ids_decode(
    ne_n: int,
    ne_k: int,
    ne_weights: torch.Tensor,
    ne_mods: torch.Tensor,
    exclusive_ne_embedder_size_sums: torch.Tensor,
    ne_token_table: torch.Tensor,
    row_indices: torch.Tensor,
    column_starts: torch.Tensor,
    n_gram_ids: torch.Tensor,
    eos_token_id: int,
) -> None:
    """
    Compute n-gram IDs for decode, where each request contributes one token.
    """
    module = _jit_ngram_embedding_module()
    module.compute_n_gram_ids_decode(
        ne_n,
        ne_k,
        ne_weights,
        ne_mods,
        exclusive_ne_embedder_size_sums,
        ne_token_table,
        row_indices,
        column_starts,
        n_gram_ids,
        eos_token_id,
    )


@debug_kernel_api
def update_token_table(
    tokens: torch.Tensor,
    ne_token_table: torch.Tensor,
    row_indices: torch.Tensor,
    column_starts: torch.Tensor,
    req_lens: torch.Tensor,
    ignore_tokens: torch.Tensor | None = None,
) -> None:
    """
    Update the token table with new tokens.

    Args:
        tokens: input token ids
        ne_token_table: token table for all requests
        row_indices: row indices for each request
        column_starts: column start positions for each request
        req_lens: request lengths
        ignore_tokens: tokens to be ignored (marked as negative in table)
    """
    module = _jit_ngram_embedding_module()
    if ignore_tokens is None:
        # Create an empty tensor for ignore_tokens
        ignore_tokens = tokens.new_empty(0, dtype=tokens.dtype)
    module.update_token_table(
        tokens,
        ne_token_table,
        row_indices,
        column_starts,
        req_lens,
        ignore_tokens,
    )


@debug_kernel_api
def update_token_table_decode(
    tokens: torch.Tensor,
    ne_token_table: torch.Tensor,
    row_indices: torch.Tensor,
    column_starts: torch.Tensor,
) -> None:
    """
    Update one decoded token per request in the ngram embedding token table.

    This is the decode-only fast path for req_lens == 1 and no ignored tokens.
    """
    module = _jit_ngram_embedding_module()
    module.update_token_table_decode(
        tokens,
        ne_token_table,
        row_indices,
        column_starts,
    )
