#!/usr/bin/env python
# ******************************************************************************
# Copyright 2023 Brainchip Holdings Ltd.
#
# 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.
# ******************************************************************************
__all__ = ["DequantizerOnnxConverter"]

import numpy as np
import akida

from .base_converter import OnnxConverter
from .weights import broadcast_and_set_variable
from .register import register_onnx_converter_target


@register_onnx_converter_target("DequantizeLinear")
class DequantizerOnnxConverter(OnnxConverter):
    """Convert DequantizeLinear node into an akida.DequantizeLinear.

    Args:
        node (NodeProto): the node to convert.
        model (ModelProto): the model that the node is.
    """
    @property
    def func(self):
        return None

    def _additional_checks(self):
        if not issubclass(self.input_dtype.type, np.integer):
            raise ValueError(f"Expected an integer input on {self.name} dequantizer node.")
        if len(self.weights) != 1:
            raise ValueError(f"{self.name} dequantizer only supports one weight: the scale.")

    def _parse_akida_layer(self):
        return akida.Dequantizer(name=self.name)

    def _set_akida_variables(self, ak_layer):
        scales = list(self.weights.values())[0]
        broadcast_and_set_variable(ak_layer.variables, "scales", scales)
