# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
# SPDX-FileCopyrightText: Songlin Yang, Yu Zhang
#
# This file contains code copied from the flash-linear-attention project.
# The original source code was licensed under the MIT license and included
# the following copyright notice:
# Copyright (c) 2023-2025, Songlin Yang, Yu Zhang
# ruff: noqa: E501
import contextlib
import functools
from collections.abc import Callable

import torch
from vllm.triton_utils import tl, triton


def prepare_lens(cu_seqlens: torch.LongTensor) -> torch.LongTensor:
    return cu_seqlens[1:] - cu_seqlens[:-1]


def prepare_chunk_indices(cu_seqlens: torch.LongTensor, chunk_size: int) -> torch.LongTensor:
    indices = torch.cat([torch.arange(n) for n in triton.cdiv(prepare_lens(cu_seqlens), chunk_size).tolist()])
    return torch.stack([indices.eq(0).cumsum(0) - 1, indices], 1).to(cu_seqlens)


def prepare_final_chunk_indices(cu_seqlens: torch.LongTensor, chunk_size: int) -> torch.LongTensor:
    indices = triton.cdiv(prepare_lens(cu_seqlens), chunk_size) + 1
    return torch.cumsum(indices, 0) - 1


def prepare_chunk_offsets(cu_seqlens: torch.LongTensor, chunk_size: int) -> torch.LongTensor:
    return torch.cat([cu_seqlens.new_tensor([0]), triton.cdiv(prepare_lens(cu_seqlens), chunk_size)]).cumsum(-1)


def prepare_update_chunk_offsets(cu_seqlens: torch.LongTensor, chunk_size: int) -> torch.LongTensor:
    return torch.cat([cu_seqlens.new_tensor([0]), triton.cdiv(prepare_lens(cu_seqlens), chunk_size) + 1]).cumsum(-1)


def input_guard(fn: Callable[..., torch.Tensor]) -> Callable[..., torch.Tensor]:
    """
    A decorator to make sure all input tensors are contiguous and set the device based on input tensors.
    """

    @functools.wraps(fn)
    def wrapper(*args, **kwargs):
        contiguous_args = (i if not isinstance(i, torch.Tensor) else i.contiguous() for i in args)
        contiguous_kwargs = {k: (v if not isinstance(v, torch.Tensor) else v.contiguous()) for k, v in kwargs.items()}

        tensor = None
        for arg in args:
            if isinstance(arg, torch.Tensor):
                tensor = arg
                break
        if tensor is None:
            for value in kwargs.values():
                if isinstance(value, torch.Tensor):
                    tensor = value
                    break

        if tensor is not None:
            ctx = torch.npu.device(tensor.device.index)
        else:
            ctx = contextlib.nullcontext()

        with ctx:
            return fn(*contiguous_args, **contiguous_kwargs)

    return wrapper


@triton.jit
def safe_exp(x):
    return tl.exp(tl.where(x <= 0, x, float("-inf")))


@triton.jit(do_not_specialize=["inner_size", "row_stride"])
def _clear_ssm_states_kernel(
    states_ptr,
    has_initial_state_ptr,
    inner_size,
    row_stride,
    BLOCK_SIZE: tl.constexpr,
):
    row_idx = tl.program_id(axis=0)
    col_block_idx = tl.program_id(axis=1)

    has_state = tl.load(has_initial_state_ptr + row_idx).to(tl.int1)
    if has_state:
        return

    cols = col_block_idx * BLOCK_SIZE + tl.arange(0, BLOCK_SIZE)
    mask = cols < inner_size
    row_ptr = states_ptr + row_idx * row_stride + cols
    tl.store(row_ptr, tl.zeros((BLOCK_SIZE,), dtype=states_ptr.dtype.element_ty), mask=mask)


def clear_ssm_states(ssm_states: torch.Tensor, has_initial_state: torch.Tensor) -> None:
    """Zero out specific rows for the SSM states

    Args:
        ssm_states (torch.Tensor): input SSM states
        has_initial_state (torch.Tensor): indicates whether the row has initial states already
    """
    if ssm_states.numel() == 0:
        return

    if has_initial_state.device != ssm_states.device:
        has_initial_state = has_initial_state.to(ssm_states.device, non_blocking=True)
    if has_initial_state.dtype != torch.bool:
        has_initial_state = has_initial_state.to(torch.bool)

    has_initial_state = has_initial_state.reshape(-1).contiguous()
    num_rows = ssm_states.shape[0]
    if num_rows == 0:
        return
    if has_initial_state.numel() != num_rows:
        raise ValueError(
            f"clear_ssm_states: has_initial_state size mismatch: expected {num_rows}, got {has_initial_state.numel()}"
        )
    inner_size = ssm_states.numel() // num_rows
    if inner_size == 0:
        return

    block_size = 4096
    grid = (num_rows, triton.cdiv(inner_size, block_size))
    _clear_ssm_states_kernel[grid](
        ssm_states,
        has_initial_state,
        inner_size,
        ssm_states.stride(0),
        BLOCK_SIZE=block_size,
    )