import contextlib
from typing import List, Optional, Tuple, Type

import torch

try:
    import vllm._C
except ImportError as e:
    from vllm.logger import init_logger
    logger = init_logger(__name__)
    logger.warning("Failed to import from vllm._C with %r", e)

with contextlib.suppress(ImportError):
    import vllm._moe_C

with contextlib.suppress(ImportError):
    # ruff: noqa: F401
    import vllm._punica_C


def is_custom_op_supported(op_name: str) -> bool:
    op, overloads = torch._C._jit_get_operation(op_name)
    return op is not None


# activation ops
def silu_and_mul(out: torch.Tensor, x: torch.Tensor) -> None:
    torch.ops._C.silu_and_mul(out, x)


def gelu_and_mul(out: torch.Tensor, x: torch.Tensor) -> None:
    torch.ops._C.gelu_and_mul(out, x)


def gelu_tanh_and_mul(out: torch.Tensor, x: torch.Tensor) -> None:
    torch.ops._C.gelu_tanh_and_mul(out, x)


def gelu_fast(out: torch.Tensor, x: torch.Tensor) -> None:
    torch.ops._C.gelu_fast(out, x)


def gelu_new(out: torch.Tensor, x: torch.Tensor) -> None:
    torch.ops._C.gelu_new(out, x)


# page attention ops
def paged_attention_v1(
    out: torch.Tensor,
    query: torch.Tensor,
    key_cache: torch.Tensor,
    value_cache: torch.Tensor,
    num_kv_heads: int,
    scale: float,
    block_tables: torch.Tensor,
    seq_lens: torch.Tensor,
    block_size: int,
    max_seq_len: int,
    alibi_slopes: Optional[torch.Tensor],
    kv_cache_dtype: str,
    kv_scale: float,
    tp_rank: int = 0,
    blocksparse_local_blocks: int = 0,
    blocksparse_vert_stride: int = 0,
    blocksparse_block_size: int = 64,
    blocksparse_head_sliding_step: int = 0,
) -> None:
    torch.ops._C.paged_attention_v1(
        out, query, key_cache, value_cache, num_kv_heads, scale, block_tables,
        seq_lens, block_size, max_seq_len, alibi_slopes, kv_cache_dtype,
        kv_scale, tp_rank, blocksparse_local_blocks, blocksparse_vert_stride,
        blocksparse_block_size, blocksparse_head_sliding_step)


def paged_attention_v2(
    out: torch.Tensor,
    exp_sum: torch.Tensor,
    max_logits: torch.Tensor,
    tmp_out: torch.Tensor,
    query: torch.Tensor,
    key_cache: torch.Tensor,
    value_cache: torch.Tensor,
    num_kv_heads: int,
    scale: float,
    block_tables: torch.Tensor,
    seq_lens: torch.Tensor,
    block_size: int,
    max_seq_len: int,
    alibi_slopes: Optional[torch.Tensor],
    kv_cache_dtype: str,
    kv_scale: float,
    tp_rank: int = 0,
    blocksparse_local_blocks: int = 0,
    blocksparse_vert_stride: int = 0,
    blocksparse_block_size: int = 64,
    blocksparse_head_sliding_step: int = 0,
) -> None:
    torch.ops._C.paged_attention_v2(
        out, exp_sum, max_logits, tmp_out, query, key_cache, value_cache,
        num_kv_heads, scale, block_tables, seq_lens, block_size, max_seq_len,
        alibi_slopes, kv_cache_dtype, kv_scale, tp_rank,
        blocksparse_local_blocks, blocksparse_vert_stride,
        blocksparse_block_size, blocksparse_head_sliding_step)


# pos encoding ops
def rotary_embedding(
    positions: torch.Tensor,
    query: torch.Tensor,
    key: torch.Tensor,
    head_size: int,
    cos_sin_cache: torch.Tensor,
    is_neox: bool,
) -> None:
    torch.ops._C.rotary_embedding(positions, query, key, head_size,
                                  cos_sin_cache, is_neox)


def batched_rotary_embedding(positions: torch.Tensor, query: torch.Tensor,
                             key: torch.Tensor, head_size: int,
                             cos_sin_cache: torch.Tensor, is_neox: bool,
                             rot_dim: int,
                             cos_sin_cache_offsets: torch.Tensor) -> None:
    torch.ops._C.batched_rotary_embedding(positions, query, key, head_size,
                                          cos_sin_cache, is_neox, rot_dim,
                                          cos_sin_cache_offsets)


# layer norm ops
def rms_norm(out: torch.Tensor, input: torch.Tensor, weight: torch.Tensor,
             epsilon: float) -> None:
    torch.ops._C.rms_norm(out, input, weight, epsilon)


def fused_add_rms_norm(input: torch.Tensor, residual: torch.Tensor,
                       weight: torch.Tensor, epsilon: float) -> None:
    torch.ops._C.fused_add_rms_norm(input, residual, weight, epsilon)


# quantization ops
# awq
def awq_dequantize(qweight: torch.Tensor, scales: torch.Tensor,
                   zeros: torch.Tensor, split_k_iters: int, thx: int,
                   thy: int) -> torch.Tensor:
    return torch.ops._C.awq_dequantize(qweight, scales, zeros, split_k_iters,
                                       thx, thy)


def awq_gemm(input: torch.Tensor, qweight: torch.Tensor, qzeros: torch.Tensor,
             scales: torch.Tensor, split_k_iters: int) -> torch.Tensor:
    return torch.ops._C.awq_gemm(input, qweight, qzeros, scales, split_k_iters)


# gptq
def gptq_gemm(a: torch.Tensor, b_q_weight: torch.Tensor,
              b_gptq_qzeros: torch.Tensor, b_gptq_scales: torch.Tensor,
              b_g_idx: torch.Tensor, use_exllama: bool,
              bit: int) -> torch.Tensor:
    return torch.ops._C.gptq_gemm(a, b_q_weight, b_gptq_qzeros, b_gptq_scales,
                                  b_g_idx, use_exllama, bit)


def gptq_shuffle(q_weight: torch.Tensor, q_perm: torch.Tensor,
                 bit: int) -> None:
    torch.ops._C.gptq_shuffle(q_weight, q_perm, bit)


# squeezellm
def squeezellm_gemm(vec: torch.Tensor, mat: torch.Tensor, mul: torch.Tensor,
                    lookup_table: torch.Tensor) -> None:
    torch.ops._C.squeezellm_gemm(vec, mat, mul, lookup_table)


# marlin
def marlin_gemm(a: torch.Tensor, b_q_weight: torch.Tensor,
                b_scales: torch.Tensor, workspace: torch.Tensor, size_m: int,
                size_n: int, size_k: int) -> torch.Tensor:
    return torch.ops._C.marlin_gemm(a, b_q_weight, b_scales, workspace, size_m,
                                    size_n, size_k)


# marlin_24
def gptq_marlin_24_gemm(a: torch.Tensor, b_q_weight: torch.Tensor,
                        b_meta: torch.Tensor, b_scales: torch.Tensor,
                        workspace: torch.Tensor, num_bits: int, size_m: int,
                        size_n: int, size_k: int) -> torch.Tensor:
    return torch.ops._C.gptq_marlin_24_gemm(a, b_q_weight, b_meta, b_scales,
                                            workspace, num_bits, size_m,
                                            size_n, size_k)


# cutlass
def cutlass_scaled_mm_dq(a: torch.Tensor, b: torch.Tensor,
                         scale_a: torch.Tensor, scale_b: torch.Tensor,
                         out_dtype: Type[torch.dtype]) -> torch.Tensor:
    assert (b.shape[0] % 16 == 0 and b.shape[1] % 16 == 0)
    assert (out_dtype is torch.bfloat16 or out_dtype is torch.float16)

    m = a.shape[0]
    n = b.shape[1]
    out = torch.empty((m, n), dtype=out_dtype, device=a.device)

    torch.ops._C.cutlass_scaled_mm_dq(out, a, b, scale_a, scale_b)

    return out


# aqlm
def aqlm_gemm(input: torch.Tensor, codes: torch.Tensor,
              codebooks: torch.Tensor, scales: torch.Tensor,
              codebook_partition_sizes: torch.Tensor,
              bias: Optional[torch.Tensor]) -> torch.Tensor:
    return torch.ops._C.aqlm_gemm(input, codes, codebooks, scales,
                                  codebook_partition_sizes, bias)


def aqlm_dequant(codes: torch.Tensor, codebooks: torch.Tensor,
                 codebook_partition_sizes: torch.Tensor) -> torch.Tensor:
    return torch.ops._C.aqlm_dequant(codes, codebooks,
                                     codebook_partition_sizes)


# gptq_marlin
def gptq_marlin_repack(b_q_weight: torch.Tensor, perm: torch.Tensor,
                       size_k: int, size_n: int,
                       num_bits: int) -> torch.Tensor:
    return torch.ops._C.gptq_marlin_repack(b_q_weight, perm, size_k, size_n,
                                           num_bits)


def gptq_marlin_gemm(a: torch.Tensor, b_q_weight: torch.Tensor,
                     b_scales: torch.Tensor, g_idx: torch.Tensor,
                     perm: torch.Tensor, workspace: torch.Tensor,
                     num_bits: int, size_m: int, size_n: int, size_k: int,
                     is_k_full: bool) -> torch.Tensor:
    return torch.ops._C.gptq_marlin_gemm(a, b_q_weight, b_scales, g_idx, perm,
                                         workspace, num_bits, size_m, size_n,
                                         size_k, is_k_full)


# fp8
# def scaled_fp8_quant(
#     input: torch.Tensor,
#     scale: Optional[torch.Tensor] = None,
#     batch_dim_padding: Optional[int] = None,
# ) -> Tuple[torch.Tensor, torch.Tensor]:
#     """
#     Quantize input tensor to FP8 and return quantized tensor and scale.

#     This function supports both static and dynamic quantization: If you
#     provide the scale, it will use static scaling and if you omit it,
#     the scale will be determined dynamically. The function also allows
#     optional padding of the output tensor for downstream kernels that
#     will benefit from padding.

#     Args:
#         input: The input tensor to be quantized to FP8
#         scale: Optional scaling factor for the FP8 quantization
#         batch_dim_padding: If specified, pad the first dimension
#             of the output to at least this value.

#     Returns:
#         Tuple[torch.Tensor, torch.Tensor]: The output tensor in FP8 and
#             scaling factor.
#     """
#     if batch_dim_padding:
#         shape = (max(batch_dim_padding, input.shape[0]), *input.shape[1:])
#         output = torch.empty(shape,
#                              device=input.device,
#                              dtype=torch.float8_e4m3fn)
#     else:
#         output = torch.empty_like(input, dtype=torch.float8_e4m3fn)
#     if scale is None:
#         scale = torch.zeros(1, device=input.device, dtype=torch.float32)
#         torch.ops._C.dynamic_scaled_fp8_quant(output, input, scale)
#     else:
#         torch.ops._C.static_scaled_fp8_quant(output, input, scale)
#     return output, scale


# int8
def scaled_int8_quant(
        input: torch.Tensor,
        scale: Optional[torch.Tensor] = None
) -> Tuple[torch.Tensor, torch.Tensor]:
    """
    Quantize the input tensor to int8 and return the quantized tensor and scale.

    Args:
        input: The input tensor to be quantized to int8.
        scale: Optional scaling factor for the int8 quantization.
            When not provided, we invoke dynamic-per-token quantization.

    Returns:
      Tuple[Torch.Tensor, Torch.Tensor] : Output int8 tensor and scales.
    """
    output = torch.empty_like(input, dtype=torch.int8)
    if scale is not None:
        # static-per-tensor quantization.
        torch.ops._C.static_scaled_int8_quant(output, input, scale)
        return output, scale

    # dynamic-per-token quantization.
    input_scales = torch.empty((input.numel() // input.shape[-1], 1),
                               device=input.device,
                               dtype=torch.float32)
    torch.ops._C.dynamic_scaled_int8_quant(output, input, input_scales)
    return output, input_scales


# moe
def moe_align_block_size(topk_ids: torch.Tensor, num_experts: int,
                         block_size: int, sorted_token_ids: torch.Tensor,
                         experts_ids: torch.Tensor,
                         num_tokens_post_pad: torch.Tensor) -> None:
    torch.ops._C.moe_align_block_size(topk_ids, num_experts, block_size,
                                      sorted_token_ids, experts_ids,
                                      num_tokens_post_pad)


def topk_softmax(topk_weights: torch.Tensor, topk_ids: torch.Tensor,
                 token_expert_indicies: torch.Tensor,
                 gating_output: float) -> None:
    torch.ops._moe_C.topk_softmax(topk_weights, topk_ids,
                                  token_expert_indicies, gating_output)


def reshape_and_cache(
    key: torch.Tensor,
    value: torch.Tensor,
    key_cache: torch.Tensor,
    value_cache: torch.Tensor,
    slot_mapping: torch.Tensor,
    kv_cache_dtype: str,
    kv_scale: float,
) -> None:
    torch.ops._C_cache_ops.reshape_and_cache(key, value, key_cache,
                                             value_cache, slot_mapping,
                                             kv_cache_dtype, kv_scale)


def reshape_and_cache_flash(
    key: torch.Tensor,
    value: torch.Tensor,
    key_cache: torch.Tensor,
    value_cache: torch.Tensor,
    slot_mapping: torch.Tensor,
    kv_cache_dtype: str,
) -> None:
    torch.ops._C_cache_ops.reshape_and_cache_flash(key, value, key_cache,
                                                   value_cache, slot_mapping,
                                                   kv_cache_dtype)


def copy_blocks(key_caches: torch.Tensor, value_caches: torch.Tensor,
                block_mapping: torch.Tensor) -> None:
    torch.ops._C_cache_ops.copy_blocks(key_caches, value_caches, block_mapping)


def swap_blocks(src: torch.Tensor, dst: torch.Tensor,
                block_mapping: torch.Tensor) -> None:
    torch.ops._C_cache_ops.swap_blocks(src, dst, block_mapping)


def convert_fp8(output: torch.Tensor,
                input: torch.Tensor,
                scale: float = 1.0,
                kv_dtype: str = "fp8") -> None:
    torch.ops._C_cache_ops.convert_fp8(output, input, scale, kv_dtype)


def get_device_attribute(attribute: int, device: int) -> int:
    return torch.ops._C_cuda_utils.get_device_attribute(attribute, device)


def get_max_shared_memory_per_block_device_attribute(device: int) -> int:
    # ruff: noqa: E501
    return torch.ops._C_cuda_utils.get_max_shared_memory_per_block_device_attribute(
        device)


# custom ar
def init_custom_ar(meta: torch.Tensor, rank_data: torch.Tensor,
                   handles: List[str], offsets: List[int], rank: int,
                   full_nvlink: bool) -> int:
    return torch.ops._C_custom_ar.init_custom_ar(meta, rank_data, handles,
                                                 offsets, rank, full_nvlink)


def should_custom_ar(inp: torch.Tensor, max_size: int, world_size: int,
                     full_nvlink: bool) -> bool:
    return torch.ops._C_custom_ar.should_custom_ar(inp, max_size, world_size,
                                                   full_nvlink)


def all_reduce_reg(fa: int, inp: torch.Tensor, out: torch.Tensor) -> None:
    torch.ops._C_custom_ar.all_reduce_reg(fa, inp, out)


def all_reduce_unreg(fa: int, inp: torch.Tensor, reg_buffer: torch.Tensor,
                     out: torch.Tensor) -> None:
    torch.ops._C_custom_ar.all_reduce_unreg(fa, inp, reg_buffer, out)


def dispose(fa: int) -> None:
    torch.ops._C_custom_ar.dispose(fa)


def meta_size() -> int:
    return torch.ops._C_custom_ar.meta_size()


def register_buffer(fa: int, t: torch.Tensor, handles: List[str],
                    offsets: List[int]) -> None:
    return torch.ops._C_custom_ar.register_buffer(fa, t, handles, offsets)


def get_graph_buffer_ipc_meta(fa: int) -> Tuple[List[str], List[int]]:
    return torch.ops._C_custom_ar.get_graph_buffer_ipc_meta(fa)


def register_graph_buffers(fa: int, handles: List[str],
                           offsets: List[List[int]]) -> None:
    torch.ops._C_custom_ar.register_graph_buffers(fa, handles, offsets)


# punica
def dispatch_bgmv(
    y: torch.Tensor,
    x: torch.Tensor,
    w_t_all: torch.Tensor,
    indicies: torch.Tensor,
    layer_idx: int,
    scale: float,
) -> None:
    torch.ops._punica_C.dispatch_bgmv(y, x, w_t_all, indicies, layer_idx,
                                      scale)


def dispatch_bgmv_low_level(
    y: torch.Tensor,
    x: torch.Tensor,
    w_t_all: torch.Tensor,
    indicies: torch.Tensor,
    layer_idx: int,
    scale: float,
    h_in: int,
    h_out: int,
    y_offset: int,
) -> None:
    torch.ops._punica_C.dispatch_bgmv_low_level(
        y,
        x,
        w_t_all,
        indicies,
        layer_idx,
        scale,
        h_in,
        h_out,
        y_offset,
    )
