#!/usr/bin/env python
# -*- coding: utf-8 -*-
#
# Copyright (c) 2022 Intel Corporation
#
# 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.

import json
import tensorflow as tf
from tensorflow.keras import activations
from tensorflow.keras import backend
from tensorflow.keras import constraints
from tensorflow.keras import initializers
from tensorflow.keras import regularizers
from tensorflow.keras.layers import Dense
from tensorflow import quantization

class QDense(Dense):
    def __init__(self,
                 units,
                 activation=None,
                 use_bias=True,
                 kernel_initializer='glorot_uniform',
                 bias_initializer='zeros',
                 kernel_regularizer=None,
                 bias_regularizer=None,
                 activity_regularizer=None,
                 kernel_constraint=None,
                 bias_constraint=None,
                 min_value=-10000,
                 max_value=10000,
                 **kwargs):
      super(QDense, self).__init__(
                          units=units,
                          activation=activation,
                          use_bias=use_bias,
                          kernel_initializer=kernel_initializer,
                          bias_initializer=bias_initializer,
                          kernel_regularizer=kernel_regularizer,
                          bias_regularizer=bias_regularizer,
                          activity_regularizer=activity_regularizer,
                          kernel_constraint=kernel_constraint,
                          bias_constraint=bias_constraint,
                          **kwargs)
      self.min_value = json.loads(min_value)
      self.max_value = json.loads(max_value)

    def call(self, inputs):
      # add the Q/DQ here
      kernel, _, _ = quantization.quantize(self.kernel, self.min_value,
                                      self.max_value, tf.qint8,
                                      axis=1, mode='SCALED',)
      kernel = quantization.dequantize(kernel, self.min_value,
                                      self.max_value, axis=1, mode='SCALED',)
      outputs = tf.keras.backend.dot(inputs, kernel)

      if self.use_bias:
        outputs = tf.keras.backend.bias_add(outputs, self.bias)
      if self.activation is not None:
        outputs = self.activation(outputs)
      return outputs

