# 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.
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"""Base model abstract base class that handles quantization."""


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

import abc

import tensorflow as tf


class QuantizableBase(object):
    """Base model abstract base class that handles quantization."""

    __metaclass__ = abc.ABCMeta

    def __init__(self, quantize, representative_dataset):
        """Constructs a QuantizableBase instance.

        Args:
          quantize: whether the model weights should be quantized.
          representative_dataset: generator that yields representative data for full
            integer quantization. If None, hybrid quantization is performed.

        Raises:
          ValueError: when an unsupported combination of arguments is used.
        """
        if representative_dataset and not quantize:
            raise ValueError(
                "representative_dataset cannot be specified when quantize is False."
            )
        self._quantize = quantize
        self._representative_dataset = representative_dataset

    @abc.abstractmethod
    def prepare_converter(self):
        """Prepares an initial configuration of a TFLiteConverter.

        Quantization parameters are possibly added to this configuration
        when the model is generated.

        Returns:
          TFLiteConverter instance.
        """

    def tflite_model(self):
        converter = self.prepare_converter()
        if self._quantize and self._representative_dataset:
            converter.optimizations = [tf.lite.Optimize.DEFAULT]
            converter.representative_dataset = self._representative_dataset
        elif self._quantize:
            converter.optimizations = [tf.lite.Optimize.OPTIMIZE_FOR_SIZE]

        return converter.convert()
