#!/usr/bin/env python3
# -*- coding:utf-8 -*-

import time
from contextlib import contextmanager
from copy import deepcopy
import torch
import torch.distributed as dist
import torch.nn as nn
import torch.nn.functional as F
from yolov6.utils.events import LOGGER

try:
    import thop  # for FLOPs computation
except ImportError:
    thop = None


@contextmanager
def torch_distributed_zero_first(local_rank: int):
    """
    Decorator to make all processes in distributed training wait for each local_master to do something.
    """
    if local_rank not in [-1, 0]:
        dist.barrier(device_ids=[local_rank])
    yield
    if local_rank == 0:
        dist.barrier(device_ids=[0])


def time_sync():
    '''Waits for all kernels in all streams on a CUDA device to complete if cuda is available.'''
    if torch.cuda.is_available():
        torch.cuda.synchronize()
    return time.time()


def initialize_weights(model):
    for m in model.modules():
        t = type(m)
        if t is nn.Conv2d:
            pass
        elif t is nn.BatchNorm2d:
            m.eps = 1e-3
            m.momentum = 0.03
        elif t in [nn.Hardswish, nn.LeakyReLU, nn.ReLU, nn.ReLU6, nn.SiLU]:
            m.inplace = True


def fuse_conv_and_bn(conv, bn):
    '''Fuse convolution and batchnorm layers https://tehnokv.com/posts/fusing-batchnorm-and-conv/.'''
    fusedconv = (
        nn.Conv2d(
            conv.in_channels,
            conv.out_channels,
            kernel_size=conv.kernel_size,
            stride=conv.stride,
            padding=conv.padding,
            groups=conv.groups,
            bias=True,
        )
        .requires_grad_(False)
        .to(conv.weight.device)
    )

    # prepare filters
    w_conv = conv.weight.clone().view(conv.out_channels, -1)
    w_bn = torch.diag(bn.weight.div(torch.sqrt(bn.eps + bn.running_var)))
    fusedconv.weight.copy_(torch.mm(w_bn, w_conv).view(fusedconv.weight.shape))

    # prepare spatial bias
    b_conv = (
        torch.zeros(conv.weight.size(0), device=conv.weight.device)
        if conv.bias is None
        else conv.bias
    )
    b_bn = bn.bias - bn.weight.mul(bn.running_mean).div(
        torch.sqrt(bn.running_var + bn.eps)
    )
    fusedconv.bias.copy_(torch.mm(w_bn, b_conv.reshape(-1, 1)).reshape(-1) + b_bn)

    return fusedconv


def fuse_model(model):
    '''Fuse convolution and batchnorm layers of the model.'''
    from yolov6.layers.common import Conv, SimConv, Conv_C3

    for m in model.modules():
        if (type(m) is Conv or type(m) is SimConv or type(m) is Conv_C3) and hasattr(m, "bn"):
            m.conv = fuse_conv_and_bn(m.conv, m.bn)  # update conv
            delattr(m, "bn")  # remove batchnorm
            m.forward = m.forward_fuse  # update forward
    return model


def get_model_info(model, img_size=640,cfg = None):
    """Get model Params and GFlops.
    Code base on https://github.com/Megvii-BaseDetection/YOLOX/blob/main/yolox/utils/model_utils.py
    """
    from thop import profile
    stride = 64
    img = torch.zeros((1, 3, stride, stride), device=next(model.parameters()).device)

    flops, params = profile(deepcopy(model), inputs=(img,), verbose=False)
    params /= 1e6
    flops /= 1e9
    img_size = img_size if isinstance(img_size, list) else [img_size, img_size]
    flops *= img_size[0] * img_size[1] / stride / stride * 2  # Gflops
    info = "Params: {:.2f}M, Gflops: {:.2f} for {}x{}".format(params, flops,img_size[0],img_size[1])
    return info
