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

import numpy as np

import torch
from torch.nn import functional as F


def compute_distance_matrix(input1, input2, metric='euclidean'):
    # check input
    assert isinstance(input1, torch.Tensor)
    assert isinstance(input2, torch.Tensor)
    assert input1.dim() == 2, 'Expected 2-D tensor, but got {}-D'.format(input1.dim())
    assert input2.dim() == 2, 'Expected 2-D tensor, but got {}-D'.format(input2.dim())
    assert input1.size(1) == input2.size(1)

    if metric == 'euclidean':
        distmat = euclidean_squared_distance(input1, input2)
    elif metric == 'cosine':
        distmat = cosine_distance(input1, input2)
    else:
        raise ValueError(
            'Unknown distance metric: {}. '
            'Please choose either "euclidean" or "cosine"'.format(metric)
        )
    
    return distmat


def euclidean_squared_distance(input1, input2):
    """
    Args:
        input1 (torch.Tensor): 2-D feature matrix
        input2 (torch.Tensor): 2-D feature matrix

    Returns:
        distmat (numpy.ndarray): distance matrix
    """
    m, n = input1.size(0), input2.size(0)
    distmat = torch.pow(input1, 2).sum(dim=1, keepdim=True).expand(m, n) + \
              torch.pow(input2, 2).sum(dim=1, keepdim=True).expand(n, m).t()
    distmat.addmm_(1, -2, input1, input2.t())
    return distmat.numpy()


def cosine_distance(input1, input2):
    """
    Args:
        input1 (torch.Tensor): 2-D feature matrix
        input2 (torch.Tensor): 2-D feature matrix

    Returns:
        distmat (numpy.ndarray): distance matrix
    """
    input1_normed = F.normalize(input1, p=2, dim=1)
    input2_normed = F.normalize(input2, p=2, dim=1)
    distmat = 1 - torch.mm(input1_normed, input2_normed.t())
    return distmat.numpy()