"""The tensor level: what to compute, on arrays that live on the card.

Level 5. An op is a call and a result is a tensor; the only sub-level things
here are the three device controls. Every op is one kernel from
:mod:`kohakutpu.ops`, or a fused one from :mod:`kohakutpu.kernels` where the
work is worth fusing.
"""

import os

import numpy as np
from kohakuaccel.lang import dims as _dims
from kohakutpu.hw.vector import LANES as _LANES
from kohakutpu.lang import VLMAX as _VLMAX
from kohakutpu.rt import Device, Tensor

from kohakutpu import kernels as _k
from kohakutpu import ops as _o

LOG2E = _k.LOG2E

#: Re-exported so a kernel written beside an application needs one import.
dims = _dims

#: Set to `card` to reach the hardware without saying so at every call site.
DEVICE_ENV = "KOHAKUTPU_DEVICE"
TARGETS = ("card", "sim")

#: What opens when nothing asks. NOT the card: reaching a shared card unasked
#: corrupts whoever is measuring, and neither side can tell it happened.
DEFAULT_TARGET = "sim"

_device: Device | None = None


class Array(Tensor):
    """A device tensor and everything you can compute with it.

    Level 5, on level 2's storage: :class:`kohakutpu.rt.Tensor` knows where the
    bytes are and this knows what they MEAN. Every operator is one kernel from
    :mod:`kohakutpu.ops` -- there is no second implementation path.
    """

    #: So numpy defers `array * tensor` here instead of broadcasting over it.
    __array_ufunc__ = None

    def _operand(self, other) -> "Array":
        """`other` as a tensor of this shape, uploading a scalar as a whole one.

        An elementwise pass walks two operands of ONE length, so a scalar has
        to be materialised rather than folded. Raises :class:`TypeError` for
        anything that is neither a tensor nor a number.
        """
        if isinstance(other, Tensor):
            return other
        if isinstance(other, (int, float, np.floating, np.integer)):
            return self.dev.tensor(np.full(self.shape, other, np.float16))
        raise TypeError(
            f"a tensor operates on another tensor or on a number, not on "
            f"{type(other).__name__}; put an array on the device with "
            f"dev.tensor(...) first"
        )

    def __matmul__(self, other: "Array") -> "Array":
        """``self @ other.T``: the last axis of both operands is contracted."""
        return _o.matmul(self, other)

    def __add__(self, other) -> "Array":
        return _o.residual(self, self._operand(other))

    __radd__ = __add__

    def __mul__(self, other) -> "Array":
        return _o.mul(self, self._operand(other))

    __rmul__ = __mul__

    def __sub__(self, other) -> "Array":
        return _o.sub(self, self._operand(other))

    def __rsub__(self, other) -> "Array":
        return _o.sub(self._operand(other), self)

    def __truediv__(self, other) -> "Array":
        return _o.div(self, self._operand(other))

    def __rtruediv__(self, other) -> "Array":
        return _o.div(self._operand(other), self)

    def __neg__(self) -> "Array":
        return _o.neg(self)

    def silu(self) -> "Array":
        return _o.silu(self)

    def gelu(self) -> "Array":
        return _o.gelu(self)

    def relu(self) -> "Array":
        return _o.relu(self)

    # Through the module functions, not the kernels: the width dispatch lives
    # there, and two entry points would let one of them pick the wrong path.
    def softmax(self) -> "Array":
        return softmax(self)

    def rmsnorm(self, weight: "Array") -> "Array":
        return rmsnorm(self, weight)

    def layernorm(self, weight: "Array", bias: "Array") -> "Array":
        return layernorm(self, weight, bias)


def device(card=None, kind: str | None = None) -> Device:
    """The device tensors land on, opened the first time it is asked.

    `kind` is ``card`` or ``sim``, defaulting to ``$KOHAKUTPU_DEVICE`` and then
    to :data:`DEFAULT_TARGET`, which is the simulator. Passing a `card` object
    is itself a request for hardware. `sim` is `kohakutpu.model.SimDevice`: the
    same kernels through the same artifact, run by the unit models.

    Raises :class:`ValueError` for an unknown `kind`, and propagates whatever
    went wrong opening a card -- the models are never substituted for one.
    """
    global _device
    if _device is None or card is not None or kind is not None:
        _device = _open(kind, card)
    return _device


def add_device_flag(parser) -> None:
    """Give `parser` the `--device` every demo and example takes."""
    parser.add_argument(
        "--device",
        choices=TARGETS,
        default=None,
        help=f"where to run; defaults to {DEFAULT_TARGET}, or ${DEVICE_ENV}",
    )


def script_device(kind: str | None = None) -> Device:
    """The device a demo or example runs on, from its `--device` or None.

    Returns the opened device and makes it the one every later call lands on.
    Raises :class:`ValueError` for an unknown `kind`.
    """
    return device(kind=kind)


def target(kind: str | None = None) -> str:
    """Which of :data:`TARGETS` a request means: `kind`, the environment, sim.

    Raises :class:`ValueError` naming the choices for anything else.
    """
    want = kind or os.environ.get(DEVICE_ENV) or DEFAULT_TARGET
    if want not in TARGETS:
        raise ValueError(f"device kind must be one of {TARGETS}, not {want!r}")
    return want


def _open(kind: str | None, card) -> Device:
    """Open one target, never substituting the other for it.

    A card that will not open raises: silently handing back the models instead
    would report numbers from a simulation as if they came off the silicon.
    """
    # An actual card object is a request for hardware however `kind` was left.
    if kind is None and card is not None:
        kind = "card"
    if target(kind) == "card":
        return Device(card)
    # Imported here: a script that never asks for the models should not pay for
    # importing them, and the card path should not depend on them at all.
    from kohakutpu.model import SimDevice

    return SimDevice()


def tensor(array) -> Tensor:
    """A device tensor holding `array`. Nothing crosses the link until it must."""
    return device().tensor(array)


def zeros(shape) -> Tensor:
    """A device tensor of `shape`, all zero."""
    return tensor(np.zeros(shape, np.float16))


def full(shape, value: float) -> Tensor:
    """A device tensor of `shape`, every element `value`."""
    return tensor(np.full(shape, value, np.float16))


# ------------------------------------------------------------------ device control
def sync() -> None:
    """Wait for outstanding work."""
    device().sync()


def empty_cache() -> None:
    """Return memory nothing is using. Live tensors keep theirs."""
    device().empty_cache()


def stats() -> dict:
    """What is resident and what is free."""
    return device().stats()


# ------------------------------------------------------------------------ compute
def matmul(a: Tensor, b: Tensor, **tiling) -> Tensor:
    """``a @ b.T``. `b` is ``[N][K]``, as a torch Linear weight already is."""
    return _o.matmul(a, b, **tiling)


def bmm(a: Tensor, b: Tensor, **tiling) -> Tensor:
    """``a @ b.T`` with `a` of any leading rank and `b` shared across it.

    The SAME kernel as :func:`matmul`, whose `a` is declared `(..., M, K)` and
    so already takes a batch. Kept as a name people reach for.
    """
    return _o.matmul(a, b, **tiling)


def linear_silu(x: Tensor, w: Tensor, **tiling) -> Tensor:
    """``silu(x @ w.T)`` as ONE kernel: the activation never leaves the card."""
    return _k.linear_silu(x, w, **tiling)


def silu(x: Tensor, **tiling) -> Tensor:
    """``x * sigmoid(x)``, at any rank."""
    return _o.silu(x, **tiling)


def gelu(x: Tensor, **tiling) -> Tensor:
    """``x * sigmoid(1.702 x)``, at any rank."""
    return _o.gelu(x, **tiling)


def relu(x: Tensor, **tiling) -> Tensor:
    """``max(x, 0)``, at any rank."""
    return _o.relu(x, **tiling)


def _fold(x: Tensor, width: int = _VLMAX, **tiling):
    """`{}` when this row fits ONE reduction pass, else the `rows`/`part` for it.

    The dispatch a kernel cannot make: while tracing, an extent is a symbol and
    `x.cols > width` raises `TypeError`. Here the shape is a concrete tuple.

    `part` rides along because it is the same dispatch -- a group must divide
    it, and 1280 does not divide the kernel's own 8192. A caller who names one
    keeps it.
    """
    cols = int(x.shape[-1])
    if cols <= width:
        return {}
    rows = _k.split(cols, width)
    part = tiling.get("part") or _k.part_for(rows * width)
    return {"rows": rows, "part": part}


def softmax(x: Tensor, keys: int | None = None, **tiling) -> Tensor:
    """Row-wise softmax, at ANY row width; over the first `keys` columns of it.

    `VRED` folds at most VLMAX lanes, so a wider row goes to the hierarchical
    kernel. Which one is not the caller's problem -- a DiT's 1024-wide row and
    a 64-wide one are the same call.

    `keys` is for a row whose real length `VRED` has no fold for -- SDXL's
    77-token context -- padded with ZEROS to a width it does have. Raises
    :class:`ValueError` for a row that needs it and did not say so, and for
    `keys` on a row past one pass, where the mask would be a whole tensor.
    """
    cols = int(x.shape[-1])
    if keys is not None:
        if cols > _VLMAX:
            raise ValueError(
                f"keys={keys} on a {cols}-wide row: the mask is one row read at "
                f"stride 0, and a row past {_VLMAX} is not one VRED pass. Fold "
                f"the row first, or pass the key block to flash_attention"
            )
        part = tiling.pop("part", None) or _k.part_for(cols)
        return _o.softmax_keys(x, keys=keys, width=cols, part=part, **tiling)
    wide = _fold(x, **tiling)
    if not wide:
        _reducible(cols)
        return _o.softmax(x, **tiling)
    return _k.softmax_wide(x, **{**tiling, **wide})


def _reducible(cols: int) -> None:
    """Raise unless `VRED` folds a `cols`-wide row, naming the way round.

    The emitter refuses this too, three levels down and in its own terms. Here
    the caller's own number is in the message and so is `keys=`, which is what
    a 77-wide row actually needs.
    """
    if cols % _LANES:
        raise ValueError(
            f"a {cols}-wide row: VRED folds a multiple of {_LANES} at most "
            f"{_VLMAX}. Pad the row with ZEROS to a width it folds and pass "
            f"keys={cols}, which masks the padding after the exponential"
        )


def rmsnorm(x: Tensor, w: Tensor, **tiling) -> Tensor:
    """``x * rsqrt(mean(x^2) + eps) * w``, per row, at any row width."""
    wide = _fold(x, **tiling)
    if not wide:
        return _k.rmsnorm(x, w, **tiling)
    return _k.rmsnorm_wide(x, w, **{**tiling, **wide})


def layernorm(x: Tensor, w: Tensor, b: Tensor, **tiling) -> Tensor:
    """``(x - mean) * rsqrt(var + eps) * w + b``, per row, at any row width."""
    wide = _fold(x, **tiling)
    if not wide:
        return _k.layernorm(x, w, b, **tiling)
    return _k.layernorm_wide(x, w, b, **{**tiling, **wide})


def group_norm(x: Tensor, w: Tensor, b: Tensor, **tiling) -> Tensor:
    """GroupNorm, at any group size. A GROUP IS A ROW.

    `(N, C, H, W)` arrives reshaped to `(N*G, C/G * H * W)`, so this is
    :func:`layernorm` with the row spanning the group -- and SDXL's groups are
    40,960 to 163,840 elements, which is where the same fold decides it.
    """
    wide = _fold(x, **tiling)
    if not wide:
        return _k.group_norm(x, w, b, **tiling)
    return _k.group_norm_wide(x, w, b, **{**tiling, **wide})


def group_norm_silu(x: Tensor, w: Tensor, b: Tensor, **tiling) -> Tensor:
    """``silu(group_norm(x, w, b))``: the pair every UNet and VAE resnet issues.

    `norm -> act -> conv`, so the activation touches the NORM and not the
    convolution -- a fused `conv -> act` is fitting the wrong shape here.
    """
    wide = _fold(x, **tiling)
    if not wide:
        return _k.group_norm_silu(x, w, b, **tiling)
    return _k.group_norm_silu_wide(x, w, b, **{**tiling, **wide})


def neg(x: Tensor) -> Tensor:
    """``-x``, at any rank."""
    return _o.neg(x)


def abs(x: Tensor) -> Tensor:
    """``|x|``, at any rank."""
    return _o.absolute(x)


def exp2(x: Tensor) -> Tensor:
    """``2**x``, at any rank."""
    return _o.exp2(x)


def log2(x: Tensor) -> Tensor:
    """``log2(x)``, at any rank."""
    return _o.log2(x)


def recip(x: Tensor) -> Tensor:
    """``1 / x``, at any rank."""
    return _o.recip(x)


def rsqrt(x: Tensor) -> Tensor:
    """``1 / sqrt(x)``, at any rank."""
    return _o.rsqrt(x)


def add(x: Tensor, y: Tensor) -> Tensor:
    """``x + y``, at any rank."""
    return _o.residual(x, y)


def sub(x: Tensor, y: Tensor) -> Tensor:
    """``x - y``, at any rank."""
    return _o.sub(x, y)


def mul(x: Tensor, y: Tensor) -> Tensor:
    """``x * y``, at any rank."""
    return _o.mul(x, y)


def div(x: Tensor, y: Tensor) -> Tensor:
    """``x / y``, at any rank."""
    return _o.div(x, y)


def row_sum(x: Tensor) -> Tensor:
    """Sum each row, broadcast back across it."""
    return _o.row_sum(x)


def row_max(x: Tensor) -> Tensor:
    """Maximum of each row, broadcast back across it."""
    return _o.row_max(x)