"""
Modified from DETR https://github.com/facebookresearch/detr
Misc functions.
Mostly copy-paste from torchvision references.
"""
import pickle
from typing import Optional, List
from collections import OrderedDict, defaultdict, deque
import time
import datetime

import torch
import torch.distributed as dist
from torch import Tensor
from torchvision.ops.boxes import box_area
import numpy as np

from rich.progress import Progress, BarColumn, TextColumn, TimeRemainingColumn
import matplotlib.pyplot as plt

# needed due to empty tensor bug in pytorch and torchvision 0.5
import torchvision
if float(torchvision.__version__.split(".")[1]) < 7.0:
    from torchvision.ops import _new_empty_tensor
    from torchvision.ops.misc import _output_size


def all_gather(data):
    """
    Run all_gather on arbitrary picklable data (not necessarily tensors)
    Args:
        data: any picklable object
    Returns:
        list[data]: list of data gathered from each rank
    """
    world_size = get_world_size()
    if world_size == 1:
        return [data]

    # serialized to a Tensor
    buffer = pickle.dumps(data)
    storage = torch.ByteStorage.from_buffer(buffer)
    tensor = torch.ByteTensor(storage).to("cuda")

    # obtain Tensor size of each rank
    local_size = torch.tensor([tensor.numel()], device="cuda")
    size_list = [torch.tensor([0], device="cuda") for _ in range(world_size)]
    dist.all_gather(size_list, local_size)
    size_list = [int(size.item()) for size in size_list]
    max_size = max(size_list)

    # receiving Tensor from all ranks
    # we pad the tensor because torch all_gather does not support
    # gathering tensors of different shapes
    tensor_list = []
    for _ in size_list:
        tensor_list.append(torch.empty((max_size,), dtype=torch.uint8, device="cuda"))
    if local_size != max_size:
        padding = torch.empty(size=(max_size - local_size,), dtype=torch.uint8, device="cuda")
        tensor = torch.cat((tensor, padding), dim=0)
    dist.all_gather(tensor_list, tensor)

    data_list = []
    for size, tensor in zip(size_list, tensor_list):
        buffer = tensor.cpu().numpy().tobytes()[:size]
        data_list.append(pickle.loads(buffer))

    return data_list


def reduce_dict(input_dict, average=True):
    """
    Args:
        input_dict (dict): all the values will be reduced
        average (bool): whether to do average or sum
    Reduce the values in the dictionary from all processes so that all processes
    have the averaged results. Returns a dict with the same fields as
    input_dict, after reduction.
    """
    world_size = get_world_size()
    if world_size < 2:
        return input_dict
    with torch.no_grad():
        names = []
        values = []
        # sort the keys so that they are consistent across processes
        for k in sorted(input_dict.keys()):
            names.append(k)
            values.append(input_dict[k])
        values = torch.stack(values, dim=0)
        dist.all_reduce(values)
        if average:
            values /= world_size
        reduced_dict = {k: v for k, v in zip(names, values)}
    return reduced_dict


def _max_by_axis(the_list):
    # type: (List[List[int]]) -> List[int]
    maxes = the_list[0]
    for sublist in the_list[1:]:
        for index, item in enumerate(sublist):
            maxes[index] = max(maxes[index], item)
    return maxes


class NestedTensor(object):
    def __init__(self, tensors, mask: Optional[Tensor]):
        self.tensors = tensors
        self.mask = mask

    def to(self, device):
        cast_tensor = self.tensors.to(device)
        mask = self.mask
        if mask is not None:
            assert mask is not None
            cast_mask = mask.to(device)
        else:
            cast_mask = None
        return NestedTensor(cast_tensor, cast_mask)

    @property
    def device(self):
        return self.tensors.device

    def decompose(self):
        return self.tensors, self.mask

    def __repr__(self):
        return str(self.tensors)


def nested_tensor_from_tensor_list(tensor_list: List[Tensor], size_divisibility=1, split=True):
    """
    This function receives a list of image tensors and returns a NestedTensor of the padded images, along with their
    padding masks (true for padding areas, false otherwise).
    """
    # make this more general
    # if image tensor is stacked as [T*3, H, W], then use split
    if split:
        tensor_list = [tensor.split(3,dim=0) for tensor in tensor_list]
        tensor_list = [item for sublist in tensor_list for item in sublist]
        # list[tensor], length = batch_size x time

    if tensor_list[0].ndim == 3:
        # make it support different-sized images
        max_size = _max_by_axis([list(img.shape) for img in tensor_list])

        if size_divisibility > 1: # so that the mask dowmsample can be matched
            stride = size_divisibility
            # the last two dims are [H, W], both subject to divisibility requirement
            max_size[-2] = (max_size[-2] + (stride - 1)) // stride * stride
            max_size[-1] = (max_size[-1] + (stride - 1)) // stride * stride

        # min_size = tuple(min(s) for s in zip(*[img.shape for img in tensor_list]))
        batch_shape = [len(tensor_list)] + max_size
        b, c, h, w = batch_shape
        dtype = tensor_list[0].dtype
        device = tensor_list[0].device
        tensor = torch.zeros(batch_shape, dtype=dtype, device=device)
        mask = torch.ones((b, h, w), dtype=torch.bool, device=device)
        for img, pad_img, m in zip(tensor_list, tensor, mask):
            pad_img[: img.shape[0], : img.shape[1], : img.shape[2]].copy_(img)
            m[: img.shape[1], :img.shape[2]] = False # valid locations
    else:
        raise ValueError('not supported')
    return NestedTensor(tensor, mask)


def nested_tensor_from_videos_list(videos_list: List[Tensor], size_divisibility=1):
    """
    This function receives a list of videos (each of shape [T, C, H, W]) and returns a NestedTensor of the padded
    videos (shape [B, T, C, PH, PW], along with their padding masks (true for padding areas, false otherwise, of shape
    [B, T, PH, PW].
    """
    max_size = _max_by_axis([list(img.shape) for img in videos_list])

    if size_divisibility > 1: # so that the mask dowmsample can be matched
        stride = size_divisibility
        # the last two dims are [H, W], both subject to divisibility requirement
        max_size[-2] = (max_size[-2] + (stride - 1)) // stride * stride
        max_size[-1] = (max_size[-1] + (stride - 1)) // stride * stride

    padded_batch_shape = [len(videos_list)] + max_size
    b, t, c, h, w = padded_batch_shape
    dtype = videos_list[0].dtype
    device = videos_list[0].device
    padded_videos = torch.zeros(padded_batch_shape, dtype=dtype, device=device)
    videos_pad_masks = torch.ones((b, t, h, w), dtype=torch.bool, device=device)
    # generate a bigger template, put fg features into template, other places/pad mask convert to True.
    # TODO: currently padding blank on bottom right. roialign only need to consider use initial h w to rescale.
    for vid_frames, pad_vid_frames, vid_pad_m in zip(videos_list, padded_videos, videos_pad_masks):
        pad_vid_frames[:vid_frames.shape[0], :, :vid_frames.shape[2], :vid_frames.shape[3]].copy_(vid_frames)
        vid_pad_m[:vid_frames.shape[0], :vid_frames.shape[2], :vid_frames.shape[3]] = False
    return NestedTensor(padded_videos, videos_pad_masks)


def setup_for_distributed(is_master):
    """
    This function disables printing when not in master process
    """
    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 box_xyxy_to_cxcywh(x):
    x0, y0, x1, y1 = x.unbind(-1)
    b = [(x0 + x1) / 2, (y0 + y1) / 2,
         (x1 - x0), (y1 - y0)]
    return torch.stack(b, dim=-1)

def interpolate(input, size=None, scale_factor=None, mode="nearest", align_corners=None):
    # type: (Tensor, Optional[List[int]], Optional[float], str, Optional[bool]) -> Tensor
    """
    Equivalent to nn.functional.interpolate, but with support for empty batch sizes.
    This will eventually be supported natively by PyTorch, and this
    class can go away.
    """
    if float(torchvision.__version__.split(".")[1]) < 7.0:
        if input.numel() > 0:
            return torch.nn.functional.interpolate(
                input, size, scale_factor, mode, align_corners
            )

        output_shape = _output_size(2, input, size, scale_factor)
        output_shape = list(input.shape[:-2]) + list(output_shape)
        return _new_empty_tensor(input, output_shape)
    else:
        return torchvision.ops.misc.interpolate(input, size, scale_factor, mode, align_corners)

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 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'
        if torch.cuda.is_available():
            log_msg = self.delimiter.join([
                header,
                '[{0' + space_fmt + '}/{1}]',
                'eta: {eta}',
                '{meters}',
                'time: {time}',
                'data: {data}',
                'max mem: {memory:.0f}'
            ])
        else:
            log_msg = self.delimiter.join([
                header,
                '[{0' + space_fmt + '}/{1}]',
                'eta: {eta}',
                '{meters}',
                'time: {time}',
                'data: {data}'
            ])
        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)))

def clip_iou(boxes1,boxes2):
    area1 = box_area(boxes1)
    area2 = box_area(boxes2)
    lt = torch.max(boxes1[:, :2], boxes2[:, :2])
    rb = torch.min(boxes1[:, 2:], boxes2[:, 2:])
    wh = (rb - lt).clamp(min=0)
    inter = wh[:,0] * wh[:,1]
    union = area1 + area2 - inter
    iou = (inter + 1e-6) / (union+1e-6)
    return iou

def multi_iou(boxes1, boxes2):
    lt = torch.max(boxes1[...,:2], boxes2[...,:2])
    rb = torch.min(boxes1[...,2:], boxes2[...,2:])
    wh = (rb - lt).clamp(min=0)
    wh_1 = boxes1[...,2:] - boxes1[...,:2]
    wh_2 = boxes2[...,2:] - boxes2[...,:2]
    inter = wh[...,0] * wh[...,1]
    union = wh_1[...,0] * wh_1[...,1] + wh_2[...,0] * wh_2[...,1] - inter
    iou = (inter + 1e-6) / (union + 1e-6)
    return iou

def box_cxcywh_to_xyxy(x):
    x_c, y_c, w, h = x.unbind(-1)
    b = [(x_c - 0.5 * w), (y_c - 0.5 * h),
         (x_c + 0.5 * w), (y_c + 0.5 * h)]
    return torch.stack(b, dim=-1)

# modified from torchvision to also return the union
def box_iou(boxes1, boxes2):
    area1 = box_area(boxes1)
    area2 = box_area(boxes2)

    lt = torch.max(boxes1[:, None, :2], boxes2[:, :2])  # [N,M,2]
    rb = torch.min(boxes1[:, None, 2:], boxes2[:, 2:])  # [N,M,2]

    wh = (rb - lt).clamp(min=0)  # [N,M,2]
    inter = wh[:, :, 0] * wh[:, :, 1]  # [N,M]

    union = area1[:, None] + area2 - inter

    iou = (inter+1e-6) / (union+1e-6)
    return iou, union

def generalized_box_iou(boxes1, boxes2):
    """
    Generalized IoU from https://giou.stanford.edu/

    The boxes should be in [x0, y0, x1, y1] format

    Returns a [N, M] pairwise matrix, where N = len(boxes1)
    and M = len(boxes2)
    """
    # degenerate boxes gives inf / nan results
    # so do an early check
    assert (boxes1[:, 2:] >= boxes1[:, :2]).all()
    assert (boxes2[:, 2:] >= boxes2[:, :2]).all()
    iou, union = box_iou(boxes1, boxes2)

    lt = torch.min(boxes1[:, None, :2], boxes2[:, :2])
    rb = torch.max(boxes1[:, None, 2:], boxes2[:, 2:])

    wh = (rb - lt).clamp(min=0)  # [N,M,2]
    area = wh[:, :, 0] * wh[:, :, 1]

    return iou - ((area - union) + 1e-6) / (area + 1e-6)

def inverse_sigmoid(x, eps=1e-5):
    x = x.clamp(min=0, max=1)
    x1 = x.clamp(min=eps)
    x2 = (1 - x).clamp(min=eps)
    return torch.log(x1/x2)

def masks_to_boxes(masks):
    """Compute the bounding boxes around the provided masks

    The masks should be in format [N, H, W] where N is the number of masks, (H, W) are the spatial dimensions.

    Returns a [N, 4] tensors, with the boxes in xyxy format
    """
    if masks.numel() == 0:
        return torch.zeros((0, 4), device=masks.device)

    h, w = masks.shape[-2:]

    y = torch.arange(0, h, dtype=torch.float)
    x = torch.arange(0, w, dtype=torch.float)
    y, x = torch.meshgrid(y, x)

    x_mask = (masks * x.unsqueeze(0))
    x_max = x_mask.flatten(1).max(-1)[0]
    x_min = x_mask.masked_fill(~(masks.bool()), 1e8).flatten(1).min(-1)[0]

    y_mask = (masks * y.unsqueeze(0))
    y_max = y_mask.flatten(1).max(-1)[0]
    y_min = y_mask.masked_fill(~(masks.bool()), 1e8).flatten(1).min(-1)[0]

    return torch.stack([x_min, y_min, x_max, y_max], 1)


def clean_state_dict(state_dict):
    new_state_dict = OrderedDict()
    for k, v in state_dict.items():
        if k[:7] == 'module.':
            k = k[7:]  # remove `module.`
        new_state_dict[k] = v
    return new_state_dict

def get_batch_observer(cur_epoch, total_epochs, batch_num, disable=False):
    batch_ob = Progress(
        TextColumn("[bold cyan]Epoch:{task.fields[epoch]}/{task.fields[total_epoch]}"),
        BarColumn(bar_width=40),
        "{task.completed}/{task.total}",
        "•", TimeRemainingColumn(),
        "•", TextColumn("[bold red]loss={task.fields[loss]:.4f}"),
        "•", TextColumn("[bold deep_sky_blue1]cls={task.fields[cls]:.4f}"),
        "•", TextColumn("[bold magenta]bbox={task.fields[bbox]:.4f}"),
        "•", TextColumn("[bold magenta]giou={task.fields[giou]:.4f}"),
        "•", TextColumn("[bold gold1]mask={task.fields[mask]:.4f}"),
        "•", TextColumn("[bold gold1]dice={task.fields[dice]:.4f}"),
        "•", TextColumn("[bold gold1]proj={task.fields[proj]:.4f}"),
        disable=disable,
    )
    pg = batch_ob.add_task(description="Training Observer", total=batch_num, epoch=cur_epoch, total_epoch=total_epochs,
                           loss=0, cls=0, bbox=0, giou=0, mask=0, dice=0, proj=0)
    return batch_ob, pg

def colormap(rgb=False):
    # Choose a matplotlib colormap
    # cmap = plt.cm.tab10
    cmap = plt.cm.Set1

    # Number of colors you want to extract from the colormap
    num_colors = 10

    # Extract the colors from the colormap
    color_list = cmap(np.linspace(0, 1, num_colors))[:, :3] * 255
    if not rgb:
        color_list = color_list[:, ::-1]
    return color_list