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# pylint: disable=invalid-name, no-member, too-many-locals, too-many-arguments, too-many-statements, too-many-function-args
"""SSD multibox operators"""
from __future__ import absolute_import as _abs
import math
import tvm

from tvm import api
from tvm.intrin import if_then_else, exp

import topi

from topi.vision.ssd import multibox_prior
from topi.vision.ssd import multibox_detection
from topi.vision.ssd import multibox_transform_loc
from ..nms import non_max_suppression


def multibox_prior_ir(data, out, sizes, ratios, steps, offsets):
    """Low level IR routing for multibox_prior operator.

    Parameters
    ----------
    data : Buffer
        Input data buffer.

    out : Buffer
        Output buffer.

    sizes : tuple of float
        Tuple of sizes for anchor boxes.

    ratios : tuple of float
        Tuple of ratios for anchor boxes.

    steps : Tuple of float
        Priorbox step across y and x, -1 for auto calculation.

    offsets : tuple of int
        Priorbox center offsets, y and x respectively.

    Returns
    -------
    stmt : Stmt
        The result IR statement.
    """
    max_threads = int(math.sqrt(
        tvm.target.current_target(allow_none=False).max_num_threads))
    tx = tvm.thread_axis("threadIdx.x")
    ty = tvm.thread_axis("threadIdx.y")
    bx = tvm.thread_axis("blockIdx.x")
    by = tvm.thread_axis("blockIdx.y")
    ib = tvm.ir_builder.create()
    p_out = ib.buffer_ptr(out)
    in_height = data.shape[2]
    in_width = data.shape[3]
    nthread_tx = max_threads
    nthread_bx = in_height // max_threads + 1
    nthread_ty = max_threads
    nthread_by = in_width // max_threads + 1
    ib.scope_attr(tx, "thread_extent", nthread_tx)
    ib.scope_attr(ty, "thread_extent", nthread_ty)
    ib.scope_attr(bx, "thread_extent", nthread_bx)
    ib.scope_attr(by, "thread_extent", nthread_by)

    num_sizes = len(sizes)
    num_ratios = len(ratios)
    size_ratio_concat = sizes + ratios
    steps_h = steps[0] if steps[0] > 0 else 1.0 / in_height
    steps_w = steps[1] if steps[1] > 0 else 1.0 / in_width
    offset_h = offsets[0]
    offset_w = offsets[1]

    i = bx * max_threads + tx
    j = by * max_threads + ty
    with ib.if_scope((i < in_height)):
        with ib.if_scope((j < in_width)):
            center_h = (i + offset_h) * steps_h
            center_w = (j + offset_w) * steps_w

            for k in range(num_sizes + num_ratios - 1):
                w = if_then_else(k < num_sizes,
                                 size_ratio_concat[k] * in_height / in_width / 2.0,
                                 size_ratio_concat[0] * in_height / in_width *
                                 math.sqrt(size_ratio_concat[k + 1]) / 2.0)
                h = if_then_else(
                    k < num_sizes, size_ratio_concat[k] / 2.0,
                    size_ratio_concat[0] / math.sqrt(size_ratio_concat[k + 1]) / 2.0)
                count = (i * in_width * (num_sizes + num_ratios - 1) +
                         j * (num_sizes + num_ratios - 1) + k) * 4
                p_out[count] = center_w - w
                p_out[count + 1] = center_h - h
                p_out[count + 2] = center_w + w
                p_out[count + 3] = center_h + h

    body = ib.get()
    return body


@multibox_prior.register(["cuda", "gpu"])
def multibox_prior_gpu(data, sizes=(1,), ratios=(1,), steps=(-1, -1),
                       offsets=(0.5, 0.5), clip=False):
    """Generate prior(anchor) boxes from data, sizes and ratios.

    Parameters
    ----------
    data : tvm.Tensor
        4-D with shape [batch, c_in, h_in, w_in]]

    sizes : tuple of float
        Tuple of sizes for anchor boxes.

    ratios : tuple of float
        Tuple of ratios for anchor boxes.

    steps : Tuple of float
        Priorbox step across y and x, -1 for auto calculation.

    offsets : tuple of int
        Priorbox center offsets, y and x respectively.

    clip : boolean
        Whether to clip out-of-boundary boxes.

    Returns
    -------
    out : tvm.Tensor
        3-D tensor with shape [1, h_in * w_in * (num_sizes + num_ratios - 1), 4]
    """
    num_sizes = len(sizes)
    num_ratios = len(ratios)
    oshape = (
        1, data.shape[2] * data.shape[3] * (num_sizes + num_ratios - 1), 4)
    out = tvm.extern(oshape, [data], lambda ins, outs:
                     multibox_prior_ir(
                         ins[0], outs[0], sizes, ratios, steps, offsets),
                     tag="multibox_prior")
    if clip:
        out = topi.clip(out, 0, 1)
    return out

def transform_loc_pre(cls_prob, valid_count, temp_valid_count, temp_cls_id, temp_score, threshold):
    """Low level IR routing for transform location data preparation.

    Parameters
    ----------
    cls_prob : Buffer
        Buffer of class probabilities.

    valid_count : Buffer
        Buffer of number of valid output boxes.

    temp_valid_count : Buffer
        Output intermediate result buffer

    temp_cls_id : Buffer
        Output intermediate result buffer

    temp_score : Buffer
        Output buffer

    threshold : float
        Threshold to be a positive prediction.

    Returns
    -------
    stmt : Stmt
        The result IR statement.
    """
    batch_size = cls_prob.shape[0]
    num_classes = cls_prob.shape[1]
    num_anchors = cls_prob.shape[2]

    ib = tvm.ir_builder.create()

    cls_prob = ib.buffer_ptr(cls_prob)
    cls_id = ib.buffer_ptr(temp_cls_id)
    valid_count = ib.buffer_ptr(valid_count)
    temp_valid_count = ib.buffer_ptr(temp_valid_count)
    score = ib.buffer_ptr(temp_score)

    threshold = tvm.make.node("FloatImm", dtype="float32", value=threshold)

    max_threads = int(tvm.target.current_target(allow_none=False).max_num_threads)
    nthread_tx = max_threads
    nthread_bx = (batch_size *  num_anchors) // max_threads + 1
    tx = tvm.thread_axis("threadIdx.x")
    bx = tvm.thread_axis("blockIdx.x")
    ib.scope_attr(tx, "thread_extent", nthread_tx)
    ib.scope_attr(bx, "thread_extent", nthread_bx)
    tid = bx * max_threads + tx

    with ib.if_scope(tid < batch_size * num_anchors):
        i = tid / num_anchors
        j = tid % num_anchors
        valid_count[i] = 0
        score[tid] = -1.0
        cls_id[tid] = 0
        with ib.for_range(0, num_classes - 1) as k:
            temp = cls_prob[i * num_classes * num_anchors + (k + 1) * num_anchors + j]
            cls_id[tid] = if_then_else(temp > score[tid], k + 1, cls_id[tid])
            score[tid] = tvm.max(temp, score[tid])
        with ib.if_scope(tvm.all(cls_id[tid] > 0, score[tid] < threshold)):
            cls_id[tid] = 0
        with ib.if_scope(cls_id[tid] > 0):
            temp_valid_count[tid] = 1
        with ib.else_scope():
            temp_valid_count[tid] = 0

        with ib.if_scope(tid < batch_size):
            with ib.for_range(0, num_anchors) as k:
                with ib.if_scope(k > 0):
                    temp_valid_count[tid * num_anchors + k] += \
                    temp_valid_count[tid * num_anchors + k - 1]
            valid_count[i] = temp_valid_count[tid * num_anchors + num_anchors - 1]

    return ib.get()

def transform_loc_ir(loc_pred, anchor, temp_valid_count, temp_cls_id, temp_score, out, \
                     clip, variances, batch_size, num_anchors):
    """Low level IR routing for transform location in multibox_detection operator.

    Parameters
    ----------
    loc_pred : Buffer
        Buffer of location regression predictions.

    anchor : Buffer
        Buffer of prior anchor boxes.

    temp_valid_count : Buffer
        Intermediate result buffer.

    temp_cls_id : Buffer
        Intermediate result buffer.

    temp_score : Buffer
        Input buffer which stores intermediate results.

    out : Buffer
        Output buffer.

    clip : boolean
        Whether to clip out-of-boundary boxes.

    variances : tuple of float
        Variances to be decoded from box regression output.

    batch_size : int
        Batch size

    num_anchors : int
        Number of anchors

    Returns
    -------
    stmt : Stmt
        The result IR statement.
    """
    def transform_loc(loc, loc_base_idx, anchor, anchor_base_idx, clip, vx, vy, vw, vh):
        """Transform prior anchor box to output box through location predictions.
        """
        al = anchor[anchor_base_idx]
        at = anchor[anchor_base_idx + 1]
        ar = anchor[anchor_base_idx + 2]
        ab = anchor[anchor_base_idx + 3]
        aw = ar - al
        ah = ab - at
        ax = (al + ar) / 2.0
        ay = (at + ab) / 2.0
        px = loc[loc_base_idx]
        py = loc[loc_base_idx + 1]
        pw = loc[loc_base_idx + 2]
        ph = loc[loc_base_idx + 3]
        ox = px * vx * aw + ax
        oy = py * vy * ah + ay
        ow = exp(pw * vw) * aw / 2.0
        oh = exp(ph * vh) * ah / 2.0
        return tvm.if_then_else(clip, tvm.max(0.0, tvm.min(1.0, ox - ow)), ox - ow), \
            tvm.if_then_else(clip, tvm.max(0.0, tvm.min(1.0, oy - oh)), oy - oh), \
            tvm.if_then_else(clip, tvm.max(0.0, tvm.min(1.0, ox + ow)), ox + ow), \
            tvm.if_then_else(clip, tvm.max(0.0, tvm.min(1.0, oy + oh)), oy + oh)

    ib = tvm.ir_builder.create()

    loc_pred = ib.buffer_ptr(loc_pred)
    anchor = ib.buffer_ptr(anchor)
    temp_valid_count = ib.buffer_ptr(temp_valid_count)
    cls_id = ib.buffer_ptr(temp_cls_id)
    score = ib.buffer_ptr(temp_score)
    out_loc = ib.buffer_ptr(out)

    max_threads = int(tvm.target.current_target(allow_none=False).max_num_threads)
    nthread_tx = max_threads
    nthread_bx = (batch_size * num_anchors) // max_threads + 1
    tx = tvm.thread_axis("threadIdx.x")
    bx = tvm.thread_axis("blockIdx.x")
    ib.scope_attr(tx, "thread_extent", nthread_tx)
    ib.scope_attr(bx, "thread_extent", nthread_bx)
    tid = bx * max_threads + tx

    with ib.if_scope(tid < batch_size * num_anchors):
        i = tid / num_anchors
        j = tid % num_anchors
        with ib.if_scope(cls_id[tid] > 0):
            with ib.if_scope(tid == 0):
                out_base_idx = i * num_anchors * 6
                out_loc[out_base_idx] = cls_id[tid] - 1.0
                out_loc[out_base_idx + 1] = score[tid]
                out_loc[out_base_idx + 2], out_loc[out_base_idx + 3], out_loc[out_base_idx + 4], \
                    out_loc[out_base_idx + 5] = transform_loc(loc_pred, tid * 4,
                                                              anchor, j * 4, clip, variances[0],
                                                              variances[1], variances[2],
                                                              variances[3])
            with ib.else_scope():
                out_base_idx = i * num_anchors * 6 + temp_valid_count[tid - 1] * 6
                out_loc[out_base_idx] = cls_id[tid] - 1.0
                out_loc[out_base_idx + 1] = score[tid]
                out_loc[out_base_idx + 2], out_loc[out_base_idx + 3], out_loc[out_base_idx + 4], \
                    out_loc[out_base_idx + 5] = transform_loc(loc_pred, tid * 4,
                                                              anchor, j * 4, clip, variances[0],
                                                              variances[1], variances[2],
                                                              variances[3])

    return ib.get()


@multibox_transform_loc.register(["cuda", "gpu"])
def multibox_transform_loc_gpu(cls_prob, loc_pred, anchor, clip=True, \
                               threshold=0.01, variances=(0.1, 0.1, 0.2, 0.2)):
    """Location transformation for multibox detection

    Parameters
    ----------
    cls_prob : tvm.Tensor
        Class probabilities.

    loc_pred : tvm.Tensor
        Location regression predictions.

    anchor : tvm.Tensor
        Prior anchor boxes.

    clip : boolean
        Whether to clip out-of-boundary boxes.

    threshold : float
        Threshold to be a positive prediction.

    variances : tuple of float
        Variances to be decoded from box regression output.

    Returns
    -------
    ret : tuple of tvm.Tensor composed of

    out : tvm.Tensor
        3-D tensor with shape (batch_size, num_anchors, 6)

    valid_count : tvm.Tensor
        1-D tensor with shape (batch_size,), number of valid anchor boxes.
    """
    batch_size = cls_prob.shape[0]
    num_anchors = cls_prob.shape[2]
    oshape = (batch_size, num_anchors, 6)
    # Define data alignment for intermediate buffer
    valid_count_dtype = "int32"
    out_loc_dtype = loc_pred.dtype

    valid_count_buf = api.decl_buffer((batch_size,), valid_count_dtype,
                                      "valid_count_buf", data_alignment=4)
    loc_pred_buf = api.decl_buffer(loc_pred.shape, loc_pred.dtype,
                                   "loc_pred_buf", data_alignment=8)
    anchor_buf = api.decl_buffer(anchor.shape, anchor.dtype,
                                 "anchor_buf", data_alignment=8)

    temp_valid_count_buf = api.decl_buffer(
        (batch_size, num_anchors,), valid_count_dtype, "temp_valid_count", data_alignment=8)
    temp_cls_id_buf = api.decl_buffer(
        (batch_size, num_anchors,), valid_count_dtype, "temp_cls_id", data_alignment=8)
    temp_score_buf = api.decl_buffer(
        (batch_size, num_anchors,), cls_prob.dtype, "temp_score", data_alignment=8)

    valid_count, temp_valid_count, temp_cls_id, temp_score = \
        tvm.extern([(batch_size,), (batch_size, num_anchors,), (batch_size, num_anchors,), \
                    (batch_size, num_anchors,)], [cls_prob],
                   lambda ins, outs: transform_loc_pre(
                       ins[0], outs[0], outs[1], outs[2], outs[3], threshold),
                   dtype=[valid_count_dtype, valid_count_dtype, valid_count_dtype, cls_prob.dtype],
                   out_buffers=[valid_count_buf, temp_valid_count_buf, \
                                temp_cls_id_buf, temp_score_buf],
                   tag="multibox_transform_loc_phase_one")

    out_loc = \
        tvm.extern([oshape],
                   [loc_pred, anchor, temp_valid_count, temp_cls_id, temp_score],
                   lambda ins, outs: transform_loc_ir(
                       ins[0], ins[1], ins[2], ins[3], ins[4], outs[0], clip, variances, \
                       batch_size, num_anchors),
                   in_buffers=[loc_pred_buf, anchor_buf, temp_valid_count_buf, \
                               temp_cls_id_buf, temp_score_buf],
                   dtype=[out_loc_dtype],
                   tag="multibox_transform_loc")

    return [out_loc, valid_count]


@multibox_detection.register(["cuda", "gpu"])
def multibox_detection_gpu(cls_prob, loc_pred, anchor, clip=True, threshold=0.01, nms_threshold=0.5,
                           force_suppress=False, variances=(0.1, 0.1, 0.2, 0.2), nms_topk=-1):
    """Convert multibox detection predictions.

    Parameters
    ----------
    cls_prob : tvm.Tensor
        Class probabilities.

    loc_pred : tvm.Tensor
        Location regression predictions.

    anchor : tvm.Tensor
        Prior anchor boxes.

    clip : boolean
        Whether to clip out-of-boundary boxes.

    nms_threshold : float
        Non-maximum suppression threshold.

    force_suppress : boolean
        Whether to suppress all detections regardless of class_id.

    threshold : float
        Threshold to be a positive prediction.

    variances : tuple of float
        Variances to be decoded from box regression output.

    nms_topk : int
        Keep maximum top k detections before nms, -1 for no limit.

    Returns
    -------
    out : tvm.Tensor
        3-D tensor with shape (batch_size, num_anchors, 6)
    """
    inter_out = multibox_transform_loc(cls_prob, loc_pred, anchor,
                                       clip, threshold, variances)
    out = non_max_suppression(inter_out[0], inter_out[1], max_output_size=-1,