import sys
import warnings

import torch

_onnx_opset_version_11 = 11
_onnx_opset_version_16 = 16
base_onnx_opset_version = _onnx_opset_version_11


def _register_custom_op():
    from torch.onnx.symbolic_helper import parse_args
    from torch.onnx.symbolic_opset11 import select, squeeze, unsqueeze

    @parse_args("v", "v", "f")
    def symbolic_multi_label_nms(g, boxes, scores, iou_threshold):
        boxes = unsqueeze(g, boxes, 0)
        scores = unsqueeze(g, unsqueeze(g, scores, 0), 0)
        max_output_per_class = g.op("Constant", value_t=torch.tensor([sys.maxsize], dtype=torch.long))
        iou_threshold = g.op("Constant", value_t=torch.tensor([iou_threshold], dtype=torch.float))
        nms_out = g.op(
            "NonMaxSuppression",
            g.op("Cast", boxes, to_i=torch.onnx.TensorProtoDataType.FLOAT),
            g.op("Cast", scores, to_i=torch.onnx.TensorProtoDataType.FLOAT),
            max_output_per_class,
            iou_threshold,
        )
        return squeeze(g, select(g, nms_out, 1, g.op("Constant", value_t=torch.tensor([2], dtype=torch.long))), 1)

    def _process_batch_indices_for_roi_align(g, rois):
        indices = squeeze(g, select(g, rois, 1, g.op("Constant", value_t=torch.tensor([0], dtype=torch.long))), 1)
        return g.op("Cast", indices, to_i=torch.onnx.TensorProtoDataType.INT64)

    def _process_rois_for_roi_align(g, rois):
        return select(g, rois, 1, g.op("Constant", value_t=torch.tensor([1, 2, 3, 4], dtype=torch.long)))

    def _process_sampling_ratio_for_roi_align(g, sampling_ratio: int):
        if sampling_ratio < 0:
            warnings.warn(
                "ONNX export for RoIAlign with a non-zero sampling_ratio is not supported. "
                "The model will be exported with a sampling_ratio of 0."
            )
            sampling_ratio = 0
        return sampling_ratio

    @parse_args("v", "v", "f", "i", "i", "i", "i")
    def roi_align_opset11(g, input, rois, spatial_scale, pooled_height, pooled_width, sampling_ratio, aligned):
        batch_indices = _process_batch_indices_for_roi_align(g, rois)
        rois = _process_rois_for_roi_align(g, rois)
        if aligned:
            warnings.warn(
                "ROIAlign with aligned=True is only supported in opset >= 16. "
                "Please export with opset 16 or higher, or use aligned=False."
            )
        sampling_ratio = _process_sampling_ratio_for_roi_align(g, sampling_ratio)
        return g.op(
            "RoiAlign",
            input,
            rois,
            batch_indices,
            spatial_scale_f=spatial_scale,
            output_height_i=pooled_height,
            output_width_i=pooled_width,
            sampling_ratio_i=sampling_ratio,
        )

    @parse_args("v", "v", "f", "i", "i", "i", "i")
    def roi_align_opset16(g, input, rois, spatial_scale, pooled_height, pooled_width, sampling_ratio, aligned):
        batch_indices = _process_batch_indices_for_roi_align(g, rois)
        rois = _process_rois_for_roi_align(g, rois)
        coordinate_transformation_mode = "half_pixel" if aligned else "output_half_pixel"
        sampling_ratio = _process_sampling_ratio_for_roi_align(g, sampling_ratio)
        return g.op(
            "RoiAlign",
            input,
            rois,
            batch_indices,
            coordinate_transformation_mode_s=coordinate_transformation_mode,
            spatial_scale_f=spatial_scale,
            output_height_i=pooled_height,
            output_width_i=pooled_width,
            sampling_ratio_i=sampling_ratio,
        )

    @parse_args("v", "v", "f", "i", "i")
    def roi_pool(g, input, rois, spatial_scale, pooled_height, pooled_width):
        roi_pool = g.op(
            "MaxRoiPool", input, rois, pooled_shape_i=(pooled_height, pooled_width), spatial_scale_f=spatial_scale
        )
        return roi_pool, None

    from torch.onnx import register_custom_op_symbolic

    register_custom_op_symbolic("torchvision::nms", symbolic_multi_label_nms, _onnx_opset_version_11)
    register_custom_op_symbolic("torchvision::roi_align", roi_align_opset11, _onnx_opset_version_11)
    register_custom_op_symbolic("torchvision::roi_align", roi_align_opset16, _onnx_opset_version_16)
    register_custom_op_symbolic("torchvision::roi_pool", roi_pool, _onnx_opset_version_11)

import sys
import warnings

import torch

_onnx_opset_version_11 = 11
_onnx_opset_version_16 = 16
base_onnx_opset_version = _onnx_opset_version_11


def _register_custom_op():
    from torch.onnx.symbolic_helper import parse_args
    from torch.onnx.symbolic_opset11 import select, squeeze, unsqueeze
    from torch.onnx.symbolic_opset9 import _cast_Long

    @parse_args("v", "v", "f")
    def symbolic_multi_label_nms(g, boxes, scores, iou_threshold):
        boxes = unsqueeze(g, boxes, 0)
        scores = unsqueeze(g, unsqueeze(g, scores, 0), 0)
        max_output_per_class = g.op("Constant", value_t=torch.tensor([sys.maxsize], dtype=torch.long))
        iou_threshold = g.op("Constant", value_t=torch.tensor([iou_threshold], dtype=torch.float))
        nms_out = g.op("NonMaxSuppression", boxes, scores, max_output_per_class, iou_threshold)
        return squeeze(g, select(g, nms_out, 1, g.op("Constant", value_t=torch.tensor([2], dtype=torch.long))), 1)

    def _process_batch_indices_for_roi_align(g, rois):
        return _cast_Long(
            g, squeeze(g, select(g, rois, 1, g.op("Constant", value_t=torch.tensor([0], dtype=torch.long))), 1), False
        )

    def _process_rois_for_roi_align(g, rois):
        return select(g, rois, 1, g.op("Constant", value_t=torch.tensor([1, 2, 3, 4], dtype=torch.long)))

    def _process_sampling_ratio_for_roi_align(g, sampling_ratio: int):
        if sampling_ratio < 0:
            warnings.warn(
                "ONNX export for RoIAlign with a non-zero sampling_ratio is not supported. "
                "The model will be exported with a sampling_ratio of 0."
            )
            sampling_ratio = 0
        return sampling_ratio

    @parse_args("v", "v", "f", "i", "i", "i", "i")
    def roi_align_opset11(g, input, rois, spatial_scale, pooled_height, pooled_width, sampling_ratio, aligned):
        batch_indices = _process_batch_indices_for_roi_align(g, rois)
        rois = _process_rois_for_roi_align(g, rois)
        if aligned:
            warnings.warn(
                "ROIAlign with aligned=True is only supported in opset >= 16. "
                "Please export with opset 16 or higher, or use aligned=False."
            )
        sampling_ratio = _process_sampling_ratio_for_roi_align(g, sampling_ratio)
        return g.op(
            "RoiAlign",
            input,
            rois,
            batch_indices,
            spatial_scale_f=spatial_scale,
            output_height_i=pooled_height,
            output_width_i=pooled_width,
            sampling_ratio_i=sampling_ratio,
        )

    @parse_args("v", "v", "f", "i", "i", "i", "i")
    def roi_align_opset16(g, input, rois, spatial_scale, pooled_height, pooled_width, sampling_ratio, aligned):
        batch_indices = _process_batch_indices_for_roi_align(g, rois)
        rois = _process_rois_for_roi_align(g, rois)
        coordinate_transformation_mode = "half_pixel" if aligned else "output_half_pixel"
        sampling_ratio = _process_sampling_ratio_for_roi_align(g, sampling_ratio)
        return g.op(
            "RoiAlign",
            input,
            rois,
            batch_indices,
            coordinate_transformation_mode_s=coordinate_transformation_mode,
            spatial_scale_f=spatial_scale,
            output_height_i=pooled_height,
            output_width_i=pooled_width,
            sampling_ratio_i=sampling_ratio,
        )

    @parse_args("v", "v", "f", "i", "i")
    def roi_pool(g, input, rois, spatial_scale, pooled_height, pooled_width):
        roi_pool = g.op(
            "MaxRoiPool", input, rois, pooled_shape_i=(pooled_height, pooled_width), spatial_scale_f=spatial_scale
        )
        return roi_pool, None

    from torch.onnx import register_custom_op_symbolic

    register_custom_op_symbolic("torchvision::nms", symbolic_multi_label_nms, _onnx_opset_version_11)
    register_custom_op_symbolic("torchvision::roi_align", roi_align_opset11, _onnx_opset_version_11)
    register_custom_op_symbolic("torchvision::roi_align", roi_align_opset16, _onnx_opset_version_16)
    register_custom_op_symbolic("torchvision::roi_pool", roi_pool, _onnx_opset_version_11)
