import pdb
from os import path
import torch
import torch.distributed as dist
import torch.autograd as autograd
import torch.cuda.comm as comm
from torch.autograd.function import once_differentiable
from torch.utils.cpp_extension import load

_src_path = path.join(path.dirname(path.abspath(__file__)), "src")
_backend = load(name="inplace_abn",
                extra_cflags=["-O3"],
                sources=[path.join(_src_path, f) for f in [
                    "inplace_abn.cpp",
                    "inplace_abn_cpu.cpp",
                    "inplace_abn_cuda.cu",
                    "inplace_abn_cuda_half.cu"
                ]],
                extra_cuda_cflags=["--expt-extended-lambda"])

# Activation names
ACT_RELU = "relu"
ACT_LEAKY_RELU = "leaky_relu"
ACT_ELU = "elu"
ACT_NONE = "none"


def _check(fn, *args, **kwargs):
    success = fn(*args, **kwargs)
    if not success:
        raise RuntimeError("CUDA Error encountered in {}".format(fn))


def _broadcast_shape(x):
    out_size = []
    for i, s in enumerate(x.size()):
        if i != 1:
            out_size.append(1)
        else:
            out_size.append(s)
    return out_size


def _reduce(x):
    if len(x.size()) == 2:
        return x.sum(dim=0)
    else:
        n, c = x.size()[0:2]
        return x.contiguous().view((n, c, -1)).sum(2).sum(0)


def _count_samples(x):
    count = 1
    for i, s in enumerate(x.size()):
        if i != 1:
            count *= s
    return count


def _act_forward(ctx, x):
    if ctx.activation == ACT_LEAKY_RELU:
        _backend.leaky_relu_forward(x, ctx.slope)
    elif ctx.activation == ACT_ELU:
        _backend.elu_forward(x)
    elif ctx.activation == ACT_NONE:
        pass


def _act_backward(ctx, x, dx):
    if ctx.activation == ACT_LEAKY_RELU:
        _backend.leaky_relu_backward(x, dx, ctx.slope)
    elif ctx.activation == ACT_ELU:
        _backend.elu_backward(x, dx)
    elif ctx.activation == ACT_NONE:
        pass


class InPlaceABN(autograd.Function):
    @staticmethod
    def forward(ctx, x, weight, bias, running_mean, running_var,
                training=True, momentum=0.1, eps=1e-05, activation=ACT_LEAKY_RELU, slope=0.01):
        # Save context
        ctx.training = training
        ctx.momentum = momentum
        ctx.eps = eps
        ctx.activation = activation
        ctx.slope = slope
        ctx.affine = weight is not None and bias is not None

        # Prepare inputs
        count = _count_samples(x)
        x = x.contiguous()
        weight = weight.contiguous() if ctx.affine else x.new_empty(0)
        bias = bias.contiguous() if ctx.affine else x.new_empty(0)

        if ctx.training:
            mean, var = _backend.mean_var(x)

            # Update running stats
            running_mean.mul_((1 - ctx.momentum)).add_(ctx.momentum * mean)
            running_var.mul_((1 - ctx.momentum)).add_(ctx.momentum * var * count / (count - 1))

            # Mark in-place modified tensors
            ctx.mark_dirty(x, running_mean, running_var)
        else:
            mean, var = running_mean.contiguous(), running_var.contiguous()
            ctx.mark_dirty(x)

        # BN forward + activation
        _backend.forward(x, mean, var, weight, bias, ctx.affine, ctx.eps)
        _act_forward(ctx, x)

        # Output
        ctx.var = var
        ctx.save_for_backward(x, var, weight, bias)
        ctx.mark_non_differentiable(running_mean, running_var)
        return x, running_mean, running_var

    @staticmethod
    @once_differentiable
    def backward(ctx, dz, _drunning_mean, _drunning_var):
        z, var, weight, bias = ctx.saved_tensors
        dz = dz.contiguous()

        # Undo activation
        _act_backward(ctx, z, dz)

        if ctx.training:
            edz, eydz = _backend.edz_eydz(z, dz, weight, bias, ctx.affine, ctx.eps)
        else:
            # TODO: implement simplified CUDA backward for inference mode
            edz = dz.new_zeros(dz.size(1))
            eydz = dz.new_zeros(dz.size(1))

        dx = _backend.backward(z, dz, var, weight, bias, edz, eydz, ctx.affine, ctx.eps)
        # dweight = eydz * weight.sign() if ctx.affine else None
        dweight = eydz if ctx.affine else None
        if dweight is not None:
            dweight[weight < 0] *= -1
        dbias = edz if ctx.affine else None

        return dx, dweight, dbias, None, None, None, None, None, None, None


class InPlaceABNSync(autograd.Function):
    @classmethod
    def forward(cls, ctx, x, weight, bias, running_mean, running_var,
                training=True, momentum=0.1, eps=1e-05, activation=ACT_LEAKY_RELU, slope=0.01, equal_batches=True):
        # Save context
        ctx.training = training
        ctx.momentum = momentum
        ctx.eps = eps
        ctx.activation = activation
        ctx.slope = slope
        ctx.affine = weight is not None and bias is not None

        # Prepare inputs
        ctx.world_size = dist.get_world_size() if dist.is_initialized() else 1

        # count = _count_samples(x)
        batch_size = x.new_tensor([x.shape[0]], dtype=torch.long)

        x = x.contiguous()
        weight = weight.contiguous() if ctx.affine else x.new_empty(0)
        bias = bias.contiguous() if ctx.affine else x.new_empty(0)

        if ctx.training:
            mean, var = _backend.mean_var(x)
            if ctx.world_size > 1:
                # get global batch size
                if equal_batches:
                    batch_size *= ctx.world_size
                else:
                    dist.all_reduce(batch_size, dist.ReduceOp.SUM)

                ctx.factor = x.shape[0] / float(batch_size.item())

                mean_all = mean.clone() * ctx.factor
                dist.all_reduce(mean_all, dist.ReduceOp.SUM)

                var_all = (var + (mean - mean_all) ** 2) * ctx.factor
                dist.all_reduce(var_all, dist.ReduceOp.SUM)

                mean = mean_all
                var = var_all

            # Update running stats
            running_mean.mul_((1 - ctx.momentum)).add_(ctx.momentum * mean)
            count = batch_size.item() * x.view(x.shape[0], x.shape[1], -1).shape[-1]
            running_var.mul_((1 - ctx.momentum)).add_(ctx.momentum * var * (float(count) / (count - 1)))

            # Mark in-place modified tensors
            ctx.mark_dirty(x, running_mean, running_var)
        else:
            mean, var = running_mean.contiguous(), running_var.contiguous()
            ctx.mark_dirty(x)

        # BN forward + activation
        _backend.forward(x, mean, var, weight, bias, ctx.affine, ctx.eps)
        _act_forward(ctx, x)

        # Output
        ctx.var = var
        ctx.save_for_backward(x, var, weight, bias)
        ctx.mark_non_differentiable(running_mean, running_var)
        return x, running_mean, running_var

    @staticmethod
    @once_differentiable
    def backward(ctx, dz, _drunning_mean, _drunning_var):
        z, var, weight, bias = ctx.saved_tensors
        dz = dz.contiguous()

        # Undo activation
        _act_backward(ctx, z, dz)

        if ctx.training:
            edz, eydz = _backend.edz_eydz(z, dz, weight, bias, ctx.affine, ctx.eps)
            edz_local = edz.clone()
            eydz_local = eydz.clone()

            if ctx.world_size > 1:
                edz *= ctx.factor
                dist.all_reduce(edz, dist.ReduceOp.SUM)

                eydz *= ctx.factor
                dist.all_reduce(eydz, dist.ReduceOp.SUM)
        else:
            edz_local = edz = dz.new_zeros(dz.size(1))
            eydz_local = eydz = dz.new_zeros(dz.size(1))

        dx = _backend.backward(z, dz, var, weight, bias, edz, eydz, ctx.affine, ctx.eps)
        # dweight = eydz_local * weight.sign() if ctx.affine else None
        dweight = eydz_local if ctx.affine else None
        if dweight is not None:
            dweight[weight < 0] *= -1
        dbias = edz_local if ctx.affine else None

        return dx, dweight, dbias, None, None, None, None, None, None, None


inplace_abn = InPlaceABN.apply
inplace_abn_sync = InPlaceABNSync.apply

__all__ = ["inplace_abn", "inplace_abn_sync", "ACT_RELU", "ACT_LEAKY_RELU", "ACT_ELU", "ACT_NONE"]