import logging
import os
import time
from typing import List

import torch
from eval import verification
from torch import distributed
from torch.utils.tensorboard import SummaryWriter
from utils.utils_logging import AverageMeter


class CallBackVerification(object):
    def __init__(self, val_targets, rec_prefix, summary_writer=None, image_size=(112, 112), wandb_logger=None):
        self.rank: int = distributed.get_rank()
        self.highest_acc: float = 0.0
        self.highest_acc_list: List[float] = [0.0] * len(val_targets)
        self.ver_list: List[object] = []
        self.ver_name_list: List[str] = []
        if self.rank is 0:
            self.init_dataset(val_targets=val_targets, data_dir=rec_prefix, image_size=image_size)

        self.summary_writer = summary_writer
        self.wandb_logger = wandb_logger

    def ver_test(self, backbone: torch.nn.Module, global_step: int):
        results = []
        for i in range(len(self.ver_list)):
            acc1, std1, acc2, std2, xnorm, embeddings_list = verification.test(self.ver_list[i], backbone, 10, 10)
            logging.info("[%s][%d]XNorm: %f" % (self.ver_name_list[i], global_step, xnorm))
            logging.info("[%s][%d]Accuracy-Flip: %1.5f+-%1.5f" % (self.ver_name_list[i], global_step, acc2, std2))

            self.summary_writer: SummaryWriter
            self.summary_writer.add_scalar(
                tag=self.ver_name_list[i],
                scalar_value=acc2,
                global_step=global_step,
            )
            if self.wandb_logger:
                import wandb

                self.wandb_logger.log(
                    {
                        f"Acc/val-Acc1 {self.ver_name_list[i]}": acc1,
                        f"Acc/val-Acc2 {self.ver_name_list[i]}": acc2,
                        # f'Acc/val-std1 {self.ver_name_list[i]}': std1,
                        # f'Acc/val-std2 {self.ver_name_list[i]}': acc2,
                    }
                )

            if acc2 > self.highest_acc_list[i]:
                self.highest_acc_list[i] = acc2
            logging.info(
                "[%s][%d]Accuracy-Highest: %1.5f" % (self.ver_name_list[i], global_step, self.highest_acc_list[i])
            )
            results.append(acc2)

    def init_dataset(self, val_targets, data_dir, image_size):
        for name in val_targets:
            path = os.path.join(data_dir, name + ".bin")
            if os.path.exists(path):
                data_set = verification.load_bin(path, image_size)
                self.ver_list.append(data_set)
                self.ver_name_list.append(name)

    def __call__(self, num_update, backbone: torch.nn.Module):
        if self.rank is 0 and num_update > 0:
            backbone.eval()
            self.ver_test(backbone, num_update)
            backbone.train()


class CallBackLogging(object):
    def __init__(self, frequent, total_step, batch_size, start_step=0, writer=None):
        self.frequent: int = frequent
        self.rank: int = distributed.get_rank()
        self.world_size: int = distributed.get_world_size()
        self.time_start = time.time()
        self.total_step: int = total_step
        self.start_step: int = start_step
        self.batch_size: int = batch_size
        self.writer = writer

        self.init = False
        self.tic = 0

    def __call__(
        self,
        global_step: int,
        loss: AverageMeter,
        epoch: int,
        fp16: bool,
        learning_rate: float,
        grad_scaler: torch.cuda.amp.GradScaler,
    ):
        if self.rank == 0 and global_step > 0 and global_step % self.frequent == 0:
            if self.init:
                try:
                    speed: float = self.frequent * self.batch_size / (time.time() - self.tic)
                    speed_total = speed * self.world_size
                except ZeroDivisionError:
                    speed_total = float("inf")

                # time_now = (time.time() - self.time_start) / 3600
                # time_total = time_now / ((global_step + 1) / self.total_step)
                # time_for_end = time_total - time_now
                time_now = time.time()
                time_sec = int(time_now - self.time_start)
                time_sec_avg = time_sec / (global_step - self.start_step + 1)
                eta_sec = time_sec_avg * (self.total_step - global_step - 1)
                time_for_end = eta_sec / 3600
                if self.writer is not None:
                    self.writer.add_scalar("time_for_end", time_for_end, global_step)
                    self.writer.add_scalar("learning_rate", learning_rate, global_step)
                    self.writer.add_scalar("loss", loss.avg, global_step)
                if fp16:
                    msg = (
                        "Speed %.2f samples/sec   Loss %.4f   LearningRate %.6f   Epoch: %d   Global Step: %d   "
                        "Fp16 Grad Scale: %2.f   Required: %1.f hours"
                        % (
                            speed_total,
                            loss.avg,
                            learning_rate,
                            epoch,
                            global_step,
                            grad_scaler.get_scale(),
                            time_for_end,
                        )
                    )
                else:
                    msg = (
                        "Speed %.2f samples/sec   Loss %.4f   LearningRate %.6f   Epoch: %d   Global Step: %d   "
                        "Required: %1.f hours"
                        % (speed_total, loss.avg, learning_rate, epoch, global_step, time_for_end)
                    )
                logging.info(msg)
                loss.reset()
                self.tic = time.time()
            else:
                self.init = True
                self.tic = time.time()