import datetime
import io
import os
import math
import time
import json
import argparse
from collections import defaultdict, deque
import datetime
import numpy as np
from timm.utils import get_state_dict

from pathlib import Path

import torch
import torch.distributed as dist

from torch.utils.tensorboard import SummaryWriter
import builtins

import sh
import glob


def init_distributed_mode(args):
    if 'RANK' in os.environ and 'WORLD_SIZE' in os.environ:
        args.rank = int(os.environ["RANK"])
        args.world_size = int(os.environ['WORLD_SIZE'])
        args.gpu = int(os.environ['LOCAL_RANK'])
    else:
        print('Not using distributed mode')
        args.distributed = False
        return

    args.distributed = True

    torch.cuda.set_device(args.gpu)
    args.dist_backend = 'nccl'
    print('| distributed init (rank {}): {}, gpu {}'.format(
        args.rank, args.dist_url, args.gpu), flush=True)
    torch.distributed.init_process_group(
        backend=args.dist_backend,
        init_method=args.dist_url,
        world_size=args.world_size,
        rank=args.rank,
        timeout=datetime.timedelta(0, 7200)
    )
    torch.distributed.barrier()
    setup_for_distributed(args.rank == 0)

def Print(s):
    if is_main_process(): print(s)


def setup_for_distributed(is_master):
    """
    This function disables printing when not in master process
    """
    builtin_print = builtins.print

    def print(*args, **kwargs):
        force = kwargs.pop('force', False)
        # force = force or (get_world_size() > 8)   # uncomment this to print all ranks stdouts
        if is_master or force:
            now = datetime.datetime.now().time()
            builtin_print('[{}] '.format(now), end='')  # print with timestamp (MAE)
            builtin_print(*args, **kwargs)

    builtins.print = print


def is_dist_avail_and_initialized():
    if not dist.is_available():
        return False
    if not dist.is_initialized():
        return False
    return True


def get_world_size():
    if not is_dist_avail_and_initialized():
        return 1
    return dist.get_world_size()


def get_rank():
    if not is_dist_avail_and_initialized():
        return 0
    return dist.get_rank()


def trivial_batch_collator(batch):
    """
    A batch collator that does nothing.
    """
    return batch


def create_ds_config(args):
    args.deepspeed_config = os.path.join(args.output_dir, "ds_config.json")
    with open(args.deepspeed_config, mode="w") as writer:
        ds_config = {
            "train_micro_batch_size_per_gpu": args.batch_size,
            "gradient_accumulation_steps": args.update_freq,
            "optimizer": {
                "type": "Adam",
                "params": {
                    "lr": args.lr,
                    "betas": [args.opt_betas[0], args.opt_betas[1]],
                    "eps": args.opt_eps,
                    "weight_decay": args.weight_decay,
                }
            },
            "scheduler": {
                "type": "WarmupDecayLR",
                "params": {
                    "total_num_steps": args.epochs * args.steps_per_epoch,
                    "warmup_min_lr": 0,
                    "warmup_max_lr": args.lr,
                    "warmup_num_steps": 100,
                    "warmup_type": "linear",
                },
            },
            "fp16": {
                "enabled": args.precision == "fp16",
            },
            "bf16": {
                "enabled": args.precision == "bf16",
            },
            "gradient_clipping": 1.0,
            "zero_optimization": {
                "stage": 2,
                "contiguous_gradients": True,
                "overlap_comm": True,
                "reduce_scatter": True,
                "reduce_bucket_size": 5e8,
                "allgather_bucket_size": 5e8,
            },
        }

        writer.write(json.dumps(ds_config, indent=2))


def auto_load_model(args, model, model_without_ddp, optimizer,):
    output_dir = Path(args.output_dir)
    if not args.enable_deepspeed:     # torch amp
        if args.auto_resume and len(args.resume) == 0:
            import glob
            all_checkpoints = glob.glob(os.path.join(output_dir, 'checkpoint-*.pth'))
            latest_ckpt = -1
            for ckpt in all_checkpoints:
                t = ckpt.split('-')[-1].split('.')[0]
                if t.isdigit():
                    latest_ckpt = max(int(t), latest_ckpt)
            if latest_ckpt >= 0:
                args.resume = os.path.join(output_dir, 'checkpoint-%d.pth' % latest_ckpt)
            print("Auto resume checkpoint: %s" % args.resume)

        if args.resume:
            checkpoint = torch.load(args.resume, map_location='cpu')
            model_without_ddp.load_state_dict(checkpoint['model'])
            print("Resume checkpoint %s" % args.resume)
            if 'optimizer' in checkpoint and 'epoch' in checkpoint:
                optimizer.load_state_dict(checkpoint['optimizer'])
                args.start_epoch = checkpoint['epoch'] + 1
                print("With optim & sched!")
    else:   # ds
        if args.auto_resume:
            import glob
            all_checkpoints = glob.glob(os.path.join(output_dir, 'checkpoint-*'))
            latest_ckpt = -1
            for ckpt in all_checkpoints:
                t = ckpt.split('-')[-1].split('.')[0]
                if t.isdigit(): latest_ckpt = max(int(t), latest_ckpt)
            if latest_ckpt >= 0:
                args.resume = os.path.join(output_dir, 'checkpoint-%d' % latest_ckpt)
                print("Auto resume checkpoint: %d" % latest_ckpt)
                _, client_states = model.load_checkpoint(args.output_dir, tag='checkpoint-%d' % latest_ckpt)
                args.start_epoch = client_states['epoch'] + 1
        elif args.resume:
            args.resume = os.path.join(output_dir, args.resume)
            print("Resume checkpoint %s" % args.resume)
            _, client_states = model.load_checkpoint(args.output_dir, tag=args.resume)
            args.start_epoch = client_states['epoch'] + 1

def is_main_process():
    return get_rank() == 0

def save_on_master(*args, **kwargs):
    if is_main_process():
        torch.save(*args, **kwargs)


def save_model(args, epoch, model, model_without_ddp, optimizer):
    output_dir = Path(args.output_dir)
    epoch_name = str(epoch)
    if not args.enable_deepspeed:
        checkpoint_paths = [output_dir / ('checkpoint-%s.pth' % epoch_name)]
        for checkpoint_path in checkpoint_paths:
            to_save = {
                'model': model_without_ddp.state_dict(),
                'optimizer': optimizer.state_dict(),
                'epoch': epoch,
                'args': args,
            }

            save_on_master(to_save, checkpoint_path)
    else:
        client_state = {'epoch': epoch}
        model.save_checkpoint(save_dir=args.output_dir, tag="checkpoint-%s" % epoch_name, client_state=client_state)


class SmoothedValue(object):
    """Track a series of values and provide access to smoothed values over a
    window or the global series average.
    """

    def __init__(self, window_size=20, fmt=None):
        if fmt is None:
            fmt = "{median:.4f} ({global_avg:.4f})"
        self.deque = deque(maxlen=window_size)
        self.total = 0.0
        self.count = 0
        self.fmt = fmt

    def update(self, value, n=1):
        self.deque.append(value)
        self.count += n
        self.total += value * n

    def synchronize_between_processes(self):
        """
        Warning: does not synchronize the deque!
        """
        if not is_dist_avail_and_initialized():
            return
        t = torch.tensor([self.count, self.total], dtype=torch.float64, device='cuda')
        dist.barrier()
        dist.all_reduce(t)
        t = t.tolist()
        self.count = int(t[0])
        self.total = t[1]

    @property
    def median(self):
        d = torch.tensor(list(self.deque))
        return d.median().item()

    @property
    def avg(self):
        d = torch.tensor(list(self.deque), dtype=torch.float32)
        return d.mean().item()

    @property
    def global_avg(self):
        return self.total / self.count

    @property
    def max(self):
        return max(self.deque)

    @property
    def value(self):
        return self.deque[-1]

    def __str__(self):
        return self.fmt.format(
            median=self.median,
            avg=self.avg,
            global_avg=self.global_avg,
            max=self.max,
            value=self.value)


class MetricLogger(object):
    def __init__(self, delimiter="\t"):
        self.meters = defaultdict(SmoothedValue)
        self.delimiter = delimiter

    def update(self, **kwargs):
        for k, v in kwargs.items():
            if v is None:
                continue
            if isinstance(v, torch.Tensor):
                v = v.item()
            assert isinstance(v, (float, int))
            self.meters[k].update(v)

    def __getattr__(self, attr):
        if attr in self.meters:
            return self.meters[attr]
        if attr in self.__dict__:
            return self.__dict__[attr]
        raise AttributeError("'{}' object has no attribute '{}'".format(
            type(self).__name__, attr))

    def __str__(self):
        loss_str = []
        for name, meter in self.meters.items():
            loss_str.append(
                "{}: {}".format(name, str(meter))
            )
        return self.delimiter.join(loss_str)

    def synchronize_between_processes(self):
        for meter in self.meters.values():
            meter.synchronize_between_processes()

    def add_meter(self, name, meter):
        self.meters[name] = meter

    def log_every(self, iterable, print_freq, header=None):
        i = 0
        if not header:
            header = ''
        start_time = time.time()
        end = time.time()
        iter_time = SmoothedValue(fmt='{avg:.4f}')
        data_time = SmoothedValue(fmt='{avg:.4f}')
        space_fmt = ':' + str(len(str(len(iterable)))) + 'd'
        log_msg = [
            header,
            '[{0' + space_fmt + '}/{1}]',
            'eta: {eta}',
            '{meters}',
            'time: {time}',
            'data: {data}'
        ]
        if torch.cuda.is_available():
            log_msg.append('max mem: {memory:.0f}')
        log_msg = self.delimiter.join(log_msg)
        MB = 1024.0 * 1024.0
        for obj in iterable:
            data_time.update(time.time() - end)
            yield obj
            iter_time.update(time.time() - end)
            if i % print_freq == 0 or i == len(iterable) - 1:
                eta_seconds = iter_time.global_avg * (len(iterable) - i)
                eta_string = str(datetime.timedelta(seconds=int(eta_seconds)))
                if torch.cuda.is_available():
                    print(log_msg.format(
                        i, len(iterable), eta=eta_string,
                        meters=str(self),
                        time=str(iter_time), data=str(data_time),
                        memory=torch.cuda.max_memory_allocated() / MB))
                else:
                    print(log_msg.format(
                        i, len(iterable), eta=eta_string,
                        meters=str(self),
                        time=str(iter_time), data=str(data_time)))
            i += 1
            end = time.time()
        total_time = time.time() - start_time
        total_time_str = str(datetime.timedelta(seconds=int(total_time)))
        print('{} Total time: {} ({:.4f} s / it)'.format(
            header, total_time_str, total_time / len(iterable)))

class TensorboardLogger(object):
    def __init__(self, log_dir):
        self.writer = SummaryWriter(log_dir=log_dir)
        self.step = 0

    def set_step(self, step=None):
        if step is not None:
            self.step = step
        else:
            self.step += 1

    def update(self, head='scalar', step=None, **kwargs):
        for k, v in kwargs.items():
            if v is None:
                continue
            if isinstance(v, torch.Tensor):
                v = v.item()
            assert isinstance(v, (float, int))
            self.writer.add_scalar(head + "/" + k, v, self.step if step is None else step)

    def flush(self):
        self.writer.flush()