#   Copyright (c) 2018 PaddlePaddle 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.
# See the License for the specific language governing permissions and
# limitations under the License.

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

import paddle
from .comfunc import rerange_index

class TriHardLoss(paddle.nn.Layer):
    """
    TriHard Loss, based on triplet loss. USE P * K samples.
    the batch size is fixed. Batch_size = P * K;  but the K may vary between batches.
    same label gather together
    
            supported_metrics = [
            'euclidean',
            'sqeuclidean',
            'cityblock',
        ]
    only consider samples_each_class = 2
    """
    def __init__(self, batch_size = 120, samples_each_class=2,  margin=0.1):
        super(TriHardLoss, self).__init__()
        self.margin = margin
        self.samples_each_class = samples_each_class
        self.batch_size         = batch_size
        self.rerange_index      = rerange_index(batch_size, samples_each_class)

    def forward(self, input, target=None):
        features = input["features"]
        assert (self.batch_size == features.shape[0])
        
        #normalization 
        features = self._nomalize(features)
        samples_each_class = self.samples_each_class
        rerange_index      = paddle.to_tensor(self.rerange_index)

        #calc sm
        diffs = paddle.unsqueeze(features, axis=1) - paddle.unsqueeze(features, axis=0)
        similary_matrix =  paddle.sum(paddle.square(diffs), axis=-1)
        
        #rerange 
        tmp = paddle.reshape(similary_matrix, shape = [-1, 1]) 
        tmp = paddle.gather(tmp, index=rerange_index)   
        similary_matrix = paddle.reshape(tmp, shape=[-1, self.batch_size])  
        
        #split
        ignore, pos, neg = paddle.split(similary_matrix, num_or_sections= [1, 
            samples_each_class - 1, -1], axis = 1)
        
        ignore.stop_gradient = True    
        hard_pos = paddle.max(pos, axis=1) 
        hard_neg = paddle.min(neg, axis=1)

        loss = hard_pos + self.margin - hard_neg
        loss = paddle.nn.ReLU()(loss)    
        loss = paddle.mean(loss)
        return {"trihardloss": loss}

    def _nomalize(self, input):
        input_norm = paddle.sqrt(paddle.sum(paddle.square(input), axis=1, keepdim=True))
        return paddle.divide(input, input_norm)
    
if __name__ == "__main__":
    
    import numpy as np
    metric = TriHardLoss(48)

    #prepare data
    np.random.seed(1)
    features = np.random.randn(48, 32)
    #print(features)

    #do inference
    features  = paddle.to_tensor(features)
    loss = metric(features)
    print(loss)