# Copyright 2019 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#         http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Tensorflow implementation of non max suppression."""

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

# Standard Imports

import tensorflow as tf

from nvidia_tao_tf1.cv.mask_rcnn.ops import box_utils


NMS_TILE_SIZE = 512


def _self_suppression(iou, _, iou_sum):
    batch_size = tf.shape(iou)[0]
    can_suppress_others = tf.cast(
            tf.reshape(tf.reduce_max(iou, 1) <= 0.5, [batch_size, -1, 1]), iou.dtype)
    iou_suppressed = tf.reshape(
            tf.cast(tf.reduce_max(can_suppress_others * iou, 1) <= 0.5, iou.dtype),
            [batch_size, -1, 1]) * iou
    iou_sum_new = tf.reduce_sum(iou_suppressed, [1, 2])
    return [
            iou_suppressed,
            tf.reduce_any(iou_sum - iou_sum_new > 0.5), iou_sum_new
    ]


def _cross_suppression(boxes, box_slice, iou_threshold, inner_idx):
    batch_size = tf.shape(boxes)[0]
    new_slice = tf.slice(boxes, [0, inner_idx * NMS_TILE_SIZE, 0],
                         [batch_size, NMS_TILE_SIZE, 4])
    iou = box_utils.bbox_overlap(new_slice, box_slice)
    ret_slice = tf.expand_dims(
        tf.cast(tf.reduce_all(iou < iou_threshold, [1]), box_slice.dtype),
        2) * box_slice
    return boxes, ret_slice, iou_threshold, inner_idx + 1


def _suppression_loop_body(boxes, iou_threshold, output_size, idx):
    """Process boxes in the range [idx*NMS_TILE_SIZE, (idx+1)*NMS_TILE_SIZE).

    Args:
        boxes: a tensor with a shape of [batch_size, anchors, 4].
        iou_threshold: a float representing the threshold for deciding whether boxes
            overlap too much with respect to IOU.
        output_size: an int32 tensor of size [batch_size]. Representing the number
            of selected boxes for each batch.
        idx: an integer scalar representing induction variable.

    Returns:
        boxes: updated boxes.
        iou_threshold: pass down iou_threshold to the next iteration.
        output_size: the updated output_size.
        idx: the updated induction variable.
    """
    num_tiles = tf.shape(boxes)[1] // NMS_TILE_SIZE
    batch_size = tf.shape(boxes)[0]

    # Iterates over tiles that can possibly suppress the current tile.
    box_slice = tf.slice(boxes, [0, idx * NMS_TILE_SIZE, 0],
                         [batch_size, NMS_TILE_SIZE, 4])
    _, box_slice, _, _ = tf.while_loop(
        lambda _boxes, _box_slice, _threshold, inner_idx: inner_idx < idx,
        _cross_suppression, [boxes, box_slice, iou_threshold, tf.constant(0)])

    # Iterates over the current tile to compute self-suppression.
    iou = box_utils.bbox_overlap(box_slice, box_slice)
    mask = tf.expand_dims(
        tf.reshape(tf.range(NMS_TILE_SIZE), [1, -1]) > tf.reshape(
            tf.range(NMS_TILE_SIZE), [-1, 1]), 0)
    iou *= tf.cast(tf.logical_and(mask, iou >= iou_threshold), iou.dtype)
    suppressed_iou, _, _ = tf.while_loop(
        lambda _iou, loop_condition, _iou_sum: loop_condition, _self_suppression,
        [iou, tf.constant(True),
         tf.reduce_sum(iou, [1, 2])])
    suppressed_box = tf.reduce_sum(suppressed_iou, 1) > 0
    box_slice *= tf.expand_dims(1.0 - tf.cast(suppressed_box, box_slice.dtype), 2)

    # Uses box_slice to update the input boxes.
    mask = tf.reshape(
        tf.cast(tf.equal(tf.range(num_tiles), idx), boxes.dtype), [1, -1, 1, 1])
    boxes = tf.tile(tf.expand_dims(
        box_slice, [1]), [1, num_tiles, 1, 1]) * mask + tf.reshape(
            boxes, [batch_size, num_tiles, NMS_TILE_SIZE, 4]) * (1 - mask)
    boxes = tf.reshape(boxes, [batch_size, -1, 4])

    # Updates output_size.
    output_size += tf.reduce_sum(
        tf.cast(tf.reduce_any(box_slice > 0, [2]), tf.int32), [1])
    return boxes, iou_threshold, output_size, idx + 1


def sorted_non_max_suppression_padded(scores,
                                      boxes,
                                      max_output_size,
                                      iou_threshold):
    """A wrapper that handles non-maximum suppression.

    Assumption:
        * The boxes are sorted by scores unless the box is a dot (all coordinates
            are zero).
        * Boxes with higher scores can be used to suppress boxes with lower scores.

    The overal design of the algorithm is to handle boxes tile-by-tile:

    boxes = boxes.pad_to_multiply_of(tile_size)
    num_tiles = len(boxes) // tile_size
    output_boxes = []
    for i in range(num_tiles):
        box_tile = boxes[i*tile_size : (i+1)*tile_size]
        for j in range(i - 1):
            suppressing_tile = boxes[j*tile_size : (j+1)*tile_size]
            iou = bbox_overlap(box_tile, suppressing_tile)
            # if the box is suppressed in iou, clear it to a dot
            box_tile *= _update_boxes(iou)
        # Iteratively handle the diagnal tile.
        iou = _box_overlap(box_tile, box_tile)
        iou_changed = True
        while iou_changed:
            # boxes that are not suppressed by anything else
            suppressing_boxes = _get_suppressing_boxes(iou)
            # boxes that are suppressed by suppressing_boxes
            suppressed_boxes = _get_suppressed_boxes(iou, suppressing_boxes)
            # clear iou to 0 for boxes that are suppressed, as they cannot be used
            # to suppress other boxes any more
            new_iou = _clear_iou(iou, suppressed_boxes)
            iou_changed = (new_iou != iou)
            iou = new_iou
        # remaining boxes that can still suppress others, are selected boxes.
        output_boxes.append(_get_suppressing_boxes(iou))
        if len(output_boxes) >= max_output_size:
            break

    Args:
        scores: a tensor with a shape of [batch_size, anchors].
        boxes: a tensor with a shape of [batch_size, anchors, 4].
        max_output_size: a scalar integer `Tensor` representing the maximum number
            of boxes to be selected by non max suppression.
        iou_threshold: a float representing the threshold for deciding whether boxes
            overlap too much with respect to IOU.

    Returns:
        nms_scores: a tensor with a shape of [batch_size, anchors]. It has same
            dtype as input scores.
        nms_proposals: a tensor with a shape of [batch_size, anchors, 4]. It has
            same dtype as input boxes.
    """
    batch_size = tf.shape(boxes)[0]
    num_boxes = tf.shape(boxes)[1]
    pad = tf.cast(
        tf.ceil(tf.cast(num_boxes, tf.float32) / NMS_TILE_SIZE),
        tf.int32) * NMS_TILE_SIZE - num_boxes
    boxes = tf.pad(tf.cast(boxes, tf.float32), [[0, 0], [0, pad], [0, 0]])
    scores = tf.pad(tf.cast(scores, tf.float32), [[0, 0], [0, pad]])
    num_boxes += pad

    def _loop_cond(unused_boxes, unused_threshold, output_size, idx):
        return tf.logical_and(
            tf.reduce_min(output_size) < max_output_size,
            idx < num_boxes // NMS_TILE_SIZE)

    selected_boxes, _, output_size, _ = tf.while_loop(
        _loop_cond, _suppression_loop_body, [
            boxes, iou_threshold,
            tf.zeros([batch_size], tf.int32),
            tf.constant(0)])
    idx = num_boxes - tf.cast(
        tf.nn.top_k(
            tf.cast(tf.reduce_any(selected_boxes > 0, [2]), tf.int32) *
            tf.expand_dims(tf.range(num_boxes, 0, -1), 0), max_output_size)[0],
        tf.int32)
    idx = tf.minimum(idx, num_boxes - 1)
    idx = tf.reshape(
        idx + tf.reshape(tf.range(batch_size) * num_boxes, [-1, 1]), [-1])
    boxes = tf.reshape(
        tf.gather(tf.reshape(boxes, [-1, 4]), idx),
        [batch_size, max_output_size, 4])
    boxes = boxes * tf.cast(
        tf.reshape(tf.range(max_output_size), [1, -1, 1]) < tf.reshape(
            output_size, [-1, 1, 1]), boxes.dtype)
    scores = tf.reshape(
        tf.gather(tf.reshape(scores, [-1, 1]), idx),
        [batch_size, max_output_size])
    scores = scores * tf.cast(
        tf.reshape(tf.range(max_output_size), [1, -1]) < tf.reshape(
            output_size, [-1, 1]), scores.dtype)
    return scores, boxes
