# Copyright 2021 Sony Semiconductors Israel, Inc. 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
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# ==============================================================================


import numpy as np

from model_compression_toolkit.common.constants import MIN_THRESHOLD, THRESHOLD

from model_compression_toolkit.common.quantization.quantization_params_generation.qparams_search import qparams_selection_tensor_search, qparams_selection_histogram_search
from model_compression_toolkit.common.similarity_analyzer import compute_mae





def mae_selection_tensor(tensor_data: np.ndarray,
                         p: int,
                         n_bits: int,
                         per_channel: bool = False,
                         channel_axis: int = 1,
                         n_iter: int = 10,
                         min_threshold: float = MIN_THRESHOLD) -> dict:
    """
    Compute the optimal threshold based on mean absolute error (MAE) to quantize the tensor.
    The threshold is constrained and the search is done iteratively over n_iter thresholds.

    Args:
        tensor_data: Tensor content as Numpy array.
        p: p-norm to use for the Lp-norm distance (not used for this method).
        n_bits: Number of bits to quantize the tensor.
        per_channel: Whether the quantization should be per-channel or not.
        channel_axis: Output channel index.
        n_iter: Number of iterations to search for the optimal threshold.
        min_threshold: Minimal threshold to chose when the computed one is smaller.

    Returns:
        Optimal threshold to quantize the tensor based on MAE.
    """
    return {THRESHOLD: qparams_selection_tensor_search(lambda x, y, threshold: compute_mae(x, y),
                                                       tensor_data,
                                                       n_bits,
                                                       per_channel=per_channel,
                                                       channel_axis=channel_axis,
                                                       n_iter=n_iter,
                                                       min_threshold=min_threshold)}


def mae_selection_histogram(bins: np.ndarray,
                            counts: np.ndarray,
                            p: int,
                            n_bits: int,
                            min_value: float,
                            max_value: float,
                            constrained: bool = True,
                            n_iter: int = 10,
                            min_threshold: float = MIN_THRESHOLD) -> dict:
    """
    Compute the optimal threshold based on the mean absolute error (MAE) to quantize a histogram.
    The threshold is either constrained or unconstrained and the search is done iteratively over
    n_iter thresholds. Minimal threshold to use if threshold is too small can be passed.

    Args:
        bins: Bins values of the histogram.
        counts: Bins counts of the histogram.
        p: p-norm to use for the Lp-norm distance (not used for this method).
        n_bits: Number of bits to quantize the tensor.
        min_value: Min value (not used for this method).
        max_value: Max value (not used for this method).
        constrained: Whether the threshold should be constrained or not.
        n_iter: Number of iteration ot search for the threshold.
        min_threshold: Minimal threshold to use if threshold is too small.

    Returns:
        Optimal threshold to quantize the histogram based on the MAE.
    """

    return {THRESHOLD: qparams_selection_histogram_search(_mae_error_histogram,
                                                          bins,
                                                          counts,
                                                          n_bits,
                                                          constrained=constrained,
                                                          n_iter=n_iter,
                                                          min_threshold=min_threshold)}


def _mae_error_histogram(q_bins: np.ndarray,
                         q_count: np.ndarray,
                         bins: np.ndarray,
                         counts: np.ndarray) -> np.ndarray:
    """
    Compute the error function between a histogram to its quantized version.
    The error is computed using the mean absolute error between the two histograms.

    Args:
        q_bins: Bins values of the quantized histogram.
        q_count: Bins counts of the quantized histogram.
        bins: Bins values of the original histogram.
        counts: Bins counts of the original histogram.

    Returns:
        Mean absolute error of the two histograms.
    """

    return np.sum((np.abs((q_bins - bins)[:-1]) * counts)) / np.sum(counts)

