#
#  -*- coding: utf-8 -*-
#
#  Copyright (c) 2021 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 os
import numpy as np
from onnx import onnx_pb as onnx_proto         

def collate_preds(results):
    batch = results[0]
    if isinstance(batch, list):
        results = zip(*results)
        collate_results = []
        for output in results:
           collate_results.append(np.concatenate(output))
    elif isinstance(batch, np.ndarray):
        collate_results = np.concatenate(results)
    return collate_results

def quantize_data_with_scale_zo(data, qType, scale, zero_point):
    '''
        :parameter data: data to quantize
        :parameter qType: data type to quantize to. Supported types UINT8 and INT8
        :parameter scale: computed scale of quantized data
        :parameter zero_point: computed zero point of quantized data
        :return: quantized weights
        To pack weights, we compute a linear transformation
            - when data type == uint8 mode, from [rmin, rmax] -> [0, 2^{b-1}] and
            - when data type == int8, from [-m , m] -> [-(2^{b-1}-1), 2^{b-1}-1] where
                m = max(abs(rmin), abs(rmax))
    '''
    if qType == onnx_proto.TensorProto.INT8:
        # signed byte type
        quantized_data = (np.asarray(data) / scale).round().astype('b')
    elif qType == onnx_proto.TensorProto.UINT8:
        quantized_data = ((np.asarray(data) / scale).round() + zero_point).astype('B')
    else:
        raise ValueError("Unexpected data type {} requested. Only INT8 and UINT8 \
                                                    are supported.".format(qType))
    return quantized_data

def quantize_data(data, quantize_range, qType):
    '''
        :parameter data: data to quantize
        :parameter quantize_range: list of data to weight pack.
        :parameter qType: data type to quantize to. Supported types UINT8 and INT8
        :return: minimum, maximum, zero point, scale, and quantized weights
        To pack weights, we compute a linear transformation
            - when data type == uint8 mode, from [rmin, rmax] -> [0, 2^{b-1}] and
            - when data type == int8, from [-m , m] -> [-(2^{b-1}-1), 2^{b-1}-1] where
                m = max(abs(rmin), abs(rmax))
        and add necessary intermediate nodes to trasnform quantized weight to full weight
        using the equation r = S(q-z), where
            r: real original value
            q: quantized value
            S: scale
            z: zero point
    '''
    rmin = min(min(data), 0)
    rmax = max(max(data), 0)

    if qType == onnx_proto.TensorProto.INT8:
        max_range = max(abs(rmin), abs(rmax))
        scale = (float(max_range) * 2) / quantize_range if max_range > 0 else 1
        zero_point = 0
    elif qType == onnx_proto.TensorProto.UINT8:
        scale = (float(rmax) - rmin) / quantize_range if rmin != rmax else 1
        zero_point = round((0 - rmin) / scale)  # round to nearest integer
    else:
        raise ValueError("Unexpected data type {} requested. Only INT8 and UINT8 \
                                                    are supported.".format(qType))

    quantized_data = quantize_data_with_scale_zo(data, qType, scale, zero_point)
    return rmin, rmax, zero_point, scale, quantized_data

def quantize_data_per_channel(tensor_value, qType, scale_value, zo_value):
    channel_count = tensor_value.shape[0] # TBD, default from axis 0
    new_per_channel_tensor_values = []
    for i in range(channel_count):
        per_channel_tensor_value = tensor_value.take(i, 0)
        per_channel_scale_value = scale_value.take(i)
        per_channel_zo_value = zo_value.take(i)
        new_per_channel_tensor_values.append(quantize_data_with_scale_zo(\
                                                       per_channel_tensor_value,
                                                       qType,
                                                       per_channel_scale_value,
                                                       per_channel_zo_value))
    # combine per_channel_data into one
    reshape_dims = list(tensor_value.shape)  # deep copy
    reshape_dims[0] = 1  # only one per channel for reshape
    new_tensor_value = new_per_channel_tensor_values[0].reshape(reshape_dims)
    for i in range(1, channel_count):
        new_per_channel_tensor_value = new_per_channel_tensor_values[i].\
                                                       reshape(reshape_dims)
        new_tensor_value = np.concatenate((new_tensor_value, \
                                           new_per_channel_tensor_value), 0)
    return new_tensor_value

def dequantize_data_with_scale_zo(tensor_value, scale_value, zo_value):
    return (tensor_value.astype(np.float32) - zo_value.astype(np.float32)) * scale_value

def dequantize_data(tensor_value, scale_value, zo_value, axis=0):
    if scale_value.size == 1:
        return dequantize_data_with_scale_zo(tensor_value, scale_value, zo_value)
    else:
        channel_count = tensor_value.shape[axis] # TBD, default from axis 0
        new_per_channel_tensor_values = []
        for i in range(channel_count):
            per_channel_tensor_value = tensor_value.take(i, 0)
            per_channel_scale_value = scale_value.take(i)
            per_channel_zo_value = zo_value.take(i)
            new_per_channel_tensor_values.append(dequantize_data_with_scale_zo(\
                                                           per_channel_tensor_value,
                                                           per_channel_scale_value,
                                                           per_channel_zo_value))
        # combine per_channel_data into one
        reshape_dims = list(tensor_value.shape)  # deep copy
        reshape_dims[0] = 1  # only one per channel for reshape
        new_tensor_value = new_per_channel_tensor_values[0].reshape(reshape_dims)
        for i in range(1, channel_count):
            new_per_channel_tensor_value = new_per_channel_tensor_values[i].\
                                                           reshape(reshape_dims)
            new_tensor_value = np.concatenate((new_tensor_value, \
                                               new_per_channel_tensor_value), 0)
        return new_tensor_value
