from __future__ import absolute_import
from __future__ import division

from collections import defaultdict
import numpy as np
import copy
import random

import torch
from torch.utils.data.sampler import Sampler


class RandomIdentitySampler(Sampler):
    """
    Randomly sample N identities, then for each identity,
    randomly sample K instances, therefore batch size is N*K.

    Args:
    - data_source (Dataset): dataset to sample from.
    - num_instances (int): number of instances per identity.
    """
    def __init__(self, data_source, num_instances=4):
        self.data_source = data_source
        self.num_instances = num_instances
        self.index_dic = defaultdict(list)
        for index, (_, pid, _) in enumerate(data_source):
            self.index_dic[pid].append(index)
        self.pids = list(self.index_dic.keys())
        self.num_identities = len(self.pids)

        # compute number of examples in an epoch
        self.length = 0
        for pid in self.pids:
            idxs = self.index_dic[pid]
            num = len(idxs)
            if num < self.num_instances:
                num = self.num_instances
            self.length += num - num % self.num_instances

    def __iter__(self):
        list_container = []

        for pid in self.pids:
            idxs = copy.deepcopy(self.index_dic[pid])
            if len(idxs) < self.num_instances:
                idxs = np.random.choice(idxs, size=self.num_instances, replace=True)
            random.shuffle(idxs)
            batch_idxs = []
            for idx in idxs:
                batch_idxs.append(idx)
                if len(batch_idxs) == self.num_instances:
                    list_container.append(batch_idxs)
                    batch_idxs = []

        random.shuffle(list_container)

        ret = []
        for batch_idxs in list_container:
            ret.extend(batch_idxs)

        return iter(ret)

    def __len__(self):
        return self.length