import torch

import numpy as np

import torch.nn.functional as F

from utils import is_tensor

def cuda_dist(x, y, metric='euc'):

    x = torch.from_numpy(x).cuda()

    y = torch.from_numpy(y).cuda()

    if metric == 'cos':

        x = F.normalize(x, p=2, dim=1)  # n c p

        y = F.normalize(y, p=2, dim=1)  # n c p

    num_bin = x.size(2)

    n_x = x.size(0)

    n_y = y.size(0)

    dist = torch.zeros(n_x, n_y).cuda()

    for i in range(num_bin):

        _x = x[:, :, i]

        _y = y[:, :, i]

        if metric == 'cos':

            dist += torch.matmul(_x, _y.transpose(0, 1))

        else:

            _dist = torch.sum(_x ** 2, 1).unsqueeze(1) + torch.sum(_y ** 2, 1).unsqueeze(

                0) - 2 * torch.matmul(_x, _y.transpose(0, 1))

            dist += torch.sqrt(F.relu(_dist))

    return 1 - dist/num_bin if metric == 'cos' else dist / num_bin

def mean_iou(msk1, msk2, eps=1.0e-9):

    if not is_tensor(msk1):

        msk1 = torch.from_numpy(msk1).cuda()

    if not is_tensor(msk2):

        msk2 = torch.from_numpy(msk2).cuda()

    n = msk1.size(0)

    inter = msk1 * msk2

    union = ((msk1 + msk2) > 0.).float()

    miou = inter.view(n, -1).sum(-1) / (union.view(n, -1).sum(-1) + eps)

    return miou

def compute_ACC_mAP(distmat, q_pids, g_pids, q_views=None, g_views=None, rank=1):

    num_q, _ = distmat.shape

    # indices = np.argsort(distmat, axis=1)

    # matches = (g_pids[indices] == q_pids[:, np.newaxis]).astype(np.int32)

    all_ACC = []

    all_AP = []

    num_valid_q = 0.  # number of valid query

    for q_idx in range(num_q):

        q_idx_dist = distmat[q_idx]

        q_idx_glabels = g_pids

        if q_views is not None and g_views is not None:

            q_idx_mask = np.isin(g_views, q_views[q_idx], invert=True) | np.isin(

                g_pids, q_pids[q_idx], invert=True)

            q_idx_dist = q_idx_dist[q_idx_mask]

            q_idx_glabels = q_idx_glabels[q_idx_mask]

        assert(len(q_idx_glabels) >

               0), "No gallery after excluding identical-view cases!"

        q_idx_indices = np.argsort(q_idx_dist)

        q_idx_matches = (q_idx_glabels[q_idx_indices]

                         == q_pids[q_idx]).astype(np.int32)

        # binary vector, positions with value 1 are correct matches

        # orig_cmc = matches[q_idx]

        orig_cmc = q_idx_matches

        cmc = orig_cmc.cumsum()

        cmc[cmc > 1] = 1

        all_ACC.append(cmc[rank-1])

        # compute average precision

        # reference: https://en.wikipedia.org/wiki/Evaluation_measures_(information_retrieval)#Average_precision

        num_rel = orig_cmc.sum()

        if num_rel > 0:

            num_valid_q += 1.

            tmp_cmc = orig_cmc.cumsum()

            tmp_cmc = [x / (i + 1.) for i, x in enumerate(tmp_cmc)]

            tmp_cmc = np.asarray(tmp_cmc) * orig_cmc

            AP = tmp_cmc.sum() / num_rel

            all_AP.append(AP)

    # all_ACC = np.asarray(all_ACC).astype(np.float32)

    ACC = np.mean(all_ACC)

    mAP = np.mean(all_AP)

    return ACC, mAP

def evaluate_rank(distmat, p_lbls, g_lbls, max_rank=50):

    '''

    Copy from https://github.com/Gait3D/Gait3D-Benchmark/blob/72beab994c137b902d826f4b9f9e95b107bebd78/lib/utils/rank.py#L12-L63

    '''

    num_p, num_g = distmat.shape

    if num_g < max_rank:

        max_rank = num_g

        print('Note: number of gallery samples is quite small, got {}'.format(num_g))

    indices = np.argsort(distmat, axis=1)

    matches = (g_lbls[indices] == p_lbls[:, np.newaxis]).astype(np.int32)

    # compute cmc curve for each probe

    all_cmc = []

    all_AP = []

    all_INP = []

    num_valid_p = 0.  # number of valid probe

    for p_idx in range(num_p):

        # compute cmc curve

        # binary vector, positions with value 1 are correct matches

        raw_cmc = matches[p_idx]

        if not np.any(raw_cmc):

            # this condition is true when probe identity does not appear in gallery

            continue

        cmc = raw_cmc.cumsum()

        pos_idx = np.where(raw_cmc == 1)    # 返回坐标，此处raw_cmc为一维矩阵，所以返回相当于index

        max_pos_idx = np.max(pos_idx)

        inp = cmc[max_pos_idx] / (max_pos_idx + 1.0)

        all_INP.append(inp)

        cmc[cmc > 1] = 1

        all_cmc.append(cmc[:max_rank])

        num_valid_p += 1.

        # compute average precision

        # reference: https://en.wikipedia.org/wiki/Evaluation_measures_(information_retrieval)#Average_precision

        num_rel = raw_cmc.sum()

        tmp_cmc = raw_cmc.cumsum()

        tmp_cmc = [x / (i + 1.) for i, x in enumerate(tmp_cmc)]

        tmp_cmc = np.asarray(tmp_cmc) * raw_cmc

        AP = tmp_cmc.sum() / num_rel

        all_AP.append(AP)

    assert num_valid_p > 0, 'Error: all probe identities do not appear in gallery'

    all_cmc = np.asarray(all_cmc).astype(np.float32)

    all_cmc = all_cmc.sum(0) / num_valid_p

    return all_cmc, all_AP, all_INP

def evaluate_many(distmat, q_pids, g_pids, q_camids, g_camids, max_rank=50):

    num_q, num_g = distmat.shape

    if num_g < max_rank:

        max_rank = num_g

        print("Note: number of gallery samples is quite small, got {}".format(num_g))

    indices = np.argsort(distmat, axis=1)   # 对应位置变成从小到大的序号

    matches = (g_pids[indices] == q_pids[:, np.newaxis]).astype(

        np.int32)  # 根据indices调整顺序 g_pids[indices]

    # print(matches)

    # compute cmc curve for each query

    all_cmc = []

    all_AP = []

    all_INP = []

    num_valid_q = 0.

    for q_idx in range(num_q):

        # get query pid and camid

        q_pid = q_pids[q_idx]

        q_camid = q_camids[q_idx]

        # remove gallery samples that have the same pid and camid with query

        order = indices[q_idx]

        remove = (g_pids[order] == q_pid) & (g_camids[order] == q_camid)

        keep = np.invert(remove)

        # compute cmc curve

        # binary vector, positions with value 1 are correct matches

        orig_cmc = matches[q_idx][keep]

        if not np.any(orig_cmc):

            # this condition is true when query identity does not appear in gallery

            continue

        cmc = orig_cmc.cumsum()

        pos_idx = np.where(orig_cmc == 1)

        max_pos_idx = np.max(pos_idx)

        inp = cmc[max_pos_idx] / (max_pos_idx + 1.0)

        all_INP.append(inp)

        cmc[cmc > 1] = 1

        all_cmc.append(cmc[:max_rank])

        num_valid_q += 1.

        # compute average precision

        # reference: https://en.wikipedia.org/wiki/Evaluation_measures_(information_retrieval)#Average_precision

        num_rel = orig_cmc.sum()

        tmp_cmc = orig_cmc.cumsum()

        tmp_cmc = [x / (i+1.) for i, x in enumerate(tmp_cmc)]

        tmp_cmc = np.asarray(tmp_cmc) * orig_cmc

        AP = tmp_cmc.sum() / num_rel

        all_AP.append(AP)

    assert num_valid_q > 0, "Error: all query identities do not appear in gallery"

    all_cmc = np.asarray(all_cmc).astype(np.float32)

    all_cmc = all_cmc.sum(0) / num_valid_q

    mAP = np.mean(all_AP)

    mINP = np.mean(all_INP)

    return all_cmc, mAP, mINP