# Copyright (c) 2015-present, Facebook, Inc.
# All rights reserved.
import io
import os
import time
from collections import defaultdict, deque
import datetime
import warnings

import torch
from torch import nn
import torch.distributed as dist


class SmoothedValue(object):

    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 update_dict(self, d):
        for k, v in d.items():
            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}'
        ]
        log_msg = self.delimiter.join(log_msg)
        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)))
                else:
                    print(log_msg.format(
                        i, len(iterable), eta=eta_string,
                        meters=str(self)))
            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)))


def _load_checkpoint_for_ema(model_ema, checkpoint):
    mem_file = io.BytesIO()
    torch.save(checkpoint, mem_file)
    mem_file.seek(0)
    model_ema._load_checkpoint(mem_file)


def setup_for_distributed(is_master):
    import builtins as __builtin__
    builtin_print = __builtin__.print

    def print(*args, **kwargs):
        force = kwargs.pop('force', False)
        if is_master or force:
            builtin_print(*args, **kwargs)

    __builtin__.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 is_main_process():
    return get_rank() == 0


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


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'])
    elif 'SLURM_PROCID' in os.environ:
        args.rank = int(os.environ['SLURM_PROCID'])
        args.gpu = args.rank % torch.cuda.device_count()
    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 {}): {}'.format(
        args.rank, args.dist_url), flush=True)
    torch.distributed.init_process_group(backend=args.dist_backend, init_method=args.dist_url,
                                         world_size=args.world_size, rank=args.rank)
    torch.distributed.barrier()
    setup_for_distributed(args.rank == 0)


def load_first_task_model(model_without_ddp, loss_scaler, task_id, args):
    strict = False

    if args.resume.startswith('https'):
        checkpoint = torch.hub.load_state_dict_from_url(
            args.resume, map_location='cpu', check_hash=True)
    elif os.path.isdir(args.resume):
        path = os.path.join(args.resume, f"checkpoint_{task_id}.pth")
        checkpoint = torch.load(path, map_location='cpu')
    else:
        checkpoint = torch.load(args.resume, map_location='cpu')

    model_ckpt = checkpoint['model']

    if not strict:
        for i in range(1, 6):
            k = f"head.fcs.{i}.weight"
            if k in model_ckpt: del model_ckpt[k]
            k = f"head.fcs.{i}.bias"
            if k in model_ckpt: del model_ckpt[k]
    model_without_ddp.load_state_dict(model_ckpt, strict=strict)
    if not args.eval and 'optimizer' in checkpoint and 'lr_scheduler' in checkpoint and 'epoch' in checkpoint:
        # optimizer.load_state_dict(checkpoint['optimizer'])
        # lr_scheduler.load_state_dict(checkpoint['lr_scheduler'])
        # args.start_epoch = checkpoint['epoch'] + 1
        # if args.model_ema:
        #    utils._load_checkpoint_for_ema(model_ema, checkpoint['model_ema'])
        if 'scaler' in checkpoint:
            try:
                loss_scaler.load_state_dict(checkpoint['scaler'])
            except:
                warnings.warn("Could not reload loss scaler, probably because of amp/noamp mismatch")


def change_pos_embed_size(pos_embed, new_size=32, patch_size=16, old_size=224):
    nb_patches = (new_size // patch_size) ** 2
    new_pos_embed = torch.randn(1, nb_patches + 1, pos_embed.shape[2])
    new_pos_embed[0, 0] = pos_embed[0, 0]

    lo_idx = 1
    for i in range(nb_patches):
        hi_idx = lo_idx + old_size // nb_patches
        new_pos_embed[0, i] = pos_embed[0, lo_idx:hi_idx].mean(dim=0)
        lo_idx = hi_idx

    return torch.nn.Parameter(new_pos_embed)


def freeze_parameters(m, requires_grad=False):
    if m is None:
        return

    if isinstance(m, nn.Parameter):
        m.requires_grad = requires_grad
    else:
        for p in m.parameters():
            p.requires_grad = requires_grad
