# encoding: utf-8

import copy
import itertools
from collections import defaultdict
from typing import Optional, List

import numpy as np
from torch.utils.data.sampler import Sampler

import random
import torch

from fastreid.utils import comm


def no_index(a, b):
    assert isinstance(a, list)
    return [i for i, j in enumerate(a) if j != b]


def reorder_index(batch_indices, world_size):
    r"""Reorder indices of samples to align with DataParallel training.
    In this order, each process will contain all images for one ID, triplet loss
    can be computed within each process, and BatchNorm will get a stable result.
    Args:
        batch_indices: A batched indices generated by sampler
        world_size: number of process
    Returns:

    """
    mini_batchsize = len(batch_indices) // world_size
    reorder_indices = []
    for i in range(0, mini_batchsize):
        for j in range(0, world_size):
            reorder_indices.append(batch_indices[i + j * mini_batchsize])
    return reorder_indices


class BalancedIdentitySampler(Sampler):
    def __init__(self, data_source: List, mini_batch_size: int, num_instances: int, seed: Optional[int] = None):
        self.data_source = data_source
        self.num_instances = num_instances
        self.num_pids_per_batch = mini_batch_size // self.num_instances

        self._rank = comm.get_rank()
        self._world_size = comm.get_world_size()
        self.batch_size = mini_batch_size * self._world_size

        self.index_pid = dict()
        self.pid_cam = defaultdict(list)
        self.pid_index = defaultdict(list)

        for index, info in enumerate(data_source):
            pid = info[1]
            camid = info[2]
            self.index_pid[index] = pid
            self.pid_cam[pid].append(camid)
            self.pid_index[pid].append(index)

        self.pids = sorted(list(self.pid_index.keys()))
        self.num_identities = len(self.pids)

        if seed is None:
            seed = comm.shared_random_seed()
        self._seed = int(seed)

        self._rank = comm.get_rank()
        self._world_size = comm.get_world_size()

    def __iter__(self):
        start = self._rank
        yield from itertools.islice(self._infinite_indices(), start, None, self._world_size)

    def _infinite_indices(self):
        np.random.seed(self._seed)
        while True:
            # Shuffle identity list
            identities = np.random.permutation(self.num_identities)

            # If remaining identities cannot be enough for a batch,
            # just drop the remaining parts
            drop_indices = self.num_identities % (self.num_pids_per_batch * self._world_size)
            if drop_indices: identities = identities[:-drop_indices]

            batch_indices = []
            for kid in identities:
                i = np.random.choice(self.pid_index[self.pids[kid]])
                _, i_pid, i_cam = self.data_source[i]
                batch_indices.append(i)
                pid_i = self.index_pid[i]
                cams = self.pid_cam[pid_i]
                index = self.pid_index[pid_i]
                select_cams = no_index(cams, i_cam)

                if select_cams:
                    if len(select_cams) >= self.num_instances:
                        cam_indexes = np.random.choice(select_cams, size=self.num_instances - 1, replace=False)
                    else:
                        cam_indexes = np.random.choice(select_cams, size=self.num_instances - 1, replace=True)
                    for kk in cam_indexes:
                        batch_indices.append(index[kk])
                else:
                    select_indexes = no_index(index, i)
                    if not select_indexes:
                        # Only one image for this identity
                        ind_indexes = [0] * (self.num_instances - 1)
                    elif len(select_indexes) >= self.num_instances:
                        ind_indexes = np.random.choice(select_indexes, size=self.num_instances - 1, replace=False)
                    else:
                        ind_indexes = np.random.choice(select_indexes, size=self.num_instances - 1, replace=True)

                    for kk in ind_indexes:
                        batch_indices.append(index[kk])

                if len(batch_indices) == self.batch_size:
                    yield from reorder_index(batch_indices, self._world_size)
                    batch_indices = []


class SetReWeightSampler(Sampler):
    def __init__(self, data_source: str, mini_batch_size: int, num_instances: int, set_weight: list,
                 seed: Optional[int] = None):
        self.data_source = data_source
        self.num_instances = num_instances
        self.num_pids_per_batch = mini_batch_size // self.num_instances

        self.set_weight = set_weight

        self._rank = comm.get_rank()
        self._world_size = comm.get_world_size()
        self.batch_size = mini_batch_size * self._world_size

        assert self.batch_size % (sum(self.set_weight) * self.num_instances) == 0 and \
               self.batch_size > sum(
            self.set_weight) * self.num_instances, "Batch size must be divisible by the sum set weight"

        self.index_pid = dict()
        self.pid_cam = defaultdict(list)
        self.pid_index = defaultdict(list)

        self.cam_pid = defaultdict(list)

        for index, info in enumerate(data_source):
            pid = info[1]
            camid = info[2]
            self.index_pid[index] = pid
            self.pid_cam[pid].append(camid)
            self.pid_index[pid].append(index)
            self.cam_pid[camid].append(pid)

        # Get sampler prob for each cam
        self.set_pid_prob = defaultdict(list)
        for camid, pid_list in self.cam_pid.items():
            index_per_pid = []
            for pid in pid_list:
                index_per_pid.append(len(self.pid_index[pid]))
            cam_image_number = sum(index_per_pid)
            prob = [i / cam_image_number for i in index_per_pid]
            self.set_pid_prob[camid] = prob

        self.pids = sorted(list(self.pid_index.keys()))
        self.num_identities = len(self.pids)

        if seed is None:
            seed = comm.shared_random_seed()
        self._seed = int(seed)

        self._rank = comm.get_rank()
        self._world_size = comm.get_world_size()

    def __iter__(self):
        start = self._rank
        yield from itertools.islice(self._infinite_indices(), start, None, self._world_size)

    def _infinite_indices(self):
        np.random.seed(self._seed)
        while True:
            batch_indices = []
            for camid in range(len(self.cam_pid.keys())):
                select_pids = np.random.choice(self.cam_pid[camid], size=self.set_weight[camid], replace=False,
                                               p=self.set_pid_prob[camid])
                for pid in select_pids:
                    index_list = self.pid_index[pid]
                    if len(index_list) > self.num_instances:
                        select_indexs = np.random.choice(index_list, size=self.num_instances, replace=False)
                    else:
                        select_indexs = np.random.choice(index_list, size=self.num_instances, replace=True)

                    batch_indices += select_indexs
            np.random.shuffle(batch_indices)

            if len(batch_indices) == self.batch_size:
                yield from reorder_index(batch_indices, self._world_size)


class NaiveIdentitySampler(Sampler):
    """
    Randomly sample N identities, then for each identity,
    randomly sample K instances, therefore batch size is N*K.
    Args:
    - data_source (list): list of (img_path, pid, camid).
    - num_instances (int): number of instances per identity in a batch.
    - batch_size (int): number of examples in a batch.
    """

    def __init__(self, data_source: str, mini_batch_size: int, num_instances: int, seed: Optional[int] = None):
        self.data_source = data_source
        self.num_instances = num_instances
        self.num_pids_per_batch = mini_batch_size // self.num_instances

        self._rank = comm.get_rank()
        self._world_size = comm.get_world_size()
        self.batch_size = mini_batch_size * self._world_size

        self.pid_index = defaultdict(list)

        for index, info in enumerate(data_source):
            pid = info[1]
            self.pid_index[pid].append(index)

        self.pids = sorted(list(self.pid_index.keys()))
        self.num_identities = len(self.pids)

        if seed is None:
            seed = comm.shared_random_seed()
        self._seed = int(seed)

    def __iter__(self):
        start = self._rank
        yield from itertools.islice(self._infinite_indices(), start, None, self._world_size)

    def _infinite_indices(self):
        np.random.seed(self._seed)
        while True:
            avl_pids = copy.deepcopy(self.pids)
            batch_idxs_dict = {}

            batch_indices = []
            while len(avl_pids) >= self.num_pids_per_batch:
                selected_pids = np.random.choice(avl_pids, self.num_pids_per_batch, replace=False).tolist()
                for pid in selected_pids:
                    # Register pid in batch_idxs_dict if not
                    if pid not in batch_idxs_dict:
                        idxs = copy.deepcopy(self.pid_index[pid])
                        if len(idxs) < self.num_instances:
                            idxs = np.random.choice(idxs, size=self.num_instances, replace=True).tolist()
                        np.random.shuffle(idxs)
                        batch_idxs_dict[pid] = idxs

                    avl_idxs = batch_idxs_dict[pid]
                    for _ in range(self.num_instances):
                        batch_indices.append(avl_idxs.pop(0))

                    if len(avl_idxs) < self.num_instances: avl_pids.remove(pid)

                if len(batch_indices) == self.batch_size:
                    yield from reorder_index(batch_indices, self._world_size)
                    batch_indices = []


def set_seed(seed):
    random.seed(seed)
    np.random.seed(seed)
    torch.manual_seed(seed)
    torch.cuda.manual_seed_all(seed)
    torch.backends.cudnn.deterministic = True
    torch.backends.cudnn.benchmark = False

