import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.nn
import torch.nn.functional
import torch.jit
import torch.utils.data
import torch.nn.utils
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor


@triton.jit
def triton_poi_fused_constant_pad_nd_0(in_ptr0, out_ptr0, xnumel, XBLOCK:
    tl.constexpr):
    xnumel = 28
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 7
    x1 = xindex // 7
    x2 = xindex
    tmp0 = -3 + x0
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.load(in_ptr0 + (-3 + x0 + 4 * x1), tmp2 & xmask, other=0.0)
    tl.store(out_ptr0 + x2, tmp3, xmask)


@triton.jit
def triton_poi_fused_convolution_1(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl
    .constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x1 = xindex // 4
    tmp0 = tl.load(in_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + x2, tmp2, xmask)


def call(args):
    primals_1, primals_2, primals_3 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4), (4, 1))
    assert_size_stride(primals_2, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_3, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 7), (7, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_constant_pad_nd_0[grid(28)](primals_1, buf0, 28,
            XBLOCK=32, num_warps=1, num_stages=1)
        del primals_1
        buf1 = extern_kernels.convolution(reinterpret_tensor(buf0, (1, 4, 7
            ), (0, 7, 1), 0), primals_2, stride=(1,), padding=(0,),
            dilation=(1,), transposed=False, output_padding=(0,), groups=1,
            bias=None)
        assert_size_stride(buf1, (1, 4, 4), (16, 4, 1))
        buf2 = buf1
        del buf1
        triton_poi_fused_convolution_1[grid(16)](buf2, primals_3, 16,
            XBLOCK=16, num_warps=1, num_stages=1)
        del primals_3
    return reinterpret_tensor(buf2, (4, 4), (4, 1), 0
        ), primals_2, reinterpret_tensor(buf0, (1, 4, 7), (28, 7, 1), 0)


class VariationalHidDropout(nn.Module):

    def __init__(self, dropout=0.0):
        """
        Hidden-to-hidden (VD-based) dropout that applies the same mask at every time step and every layer of TrellisNet
        :param dropout: The dropout rate (0 means no dropout is applied)
        :param temporal: Whether the dropout mask is the same across the temporal dimension (or only the depth dimension)
        """
        super(VariationalHidDropout, self).__init__()
        self.dropout = dropout
        self.mask = None

    def reset_mask(self, x):
        dropout = self.dropout
        m = torch.zeros_like(x[:, :, :1]).bernoulli_(1 - dropout)
        mask = m.requires_grad_(False) / (1 - dropout)
        self.mask = mask
        return mask

    def forward(self, x):
        if not self.training or self.dropout == 0:
            return x
        assert self.mask is not None, 'You need to reset mask before using VariationalHidDropout'
        mask = self.mask.expand_as(x)
        return mask * x


class WeightShareConv1dNew(nn.Module):

    def __init__(self, n_hid, n_out, kernel_size, dropouth=0.0):
        """
        The weight-tied 1D convolution used in TrellisNet.
        :param n_hid: The dim of hidden input
        :param n_out: The dim of the pre-activation (i.e. convolutional) output
        :param kernel_size: The size of the convolutional kernel
        :param dropouth: Hidden-to-hidden dropout
        """
        super(WeightShareConv1dNew, self).__init__()
        self.kernel_size = kernel_size
        conv = nn.Conv1d(n_hid, n_out, kernel_size)
        self.weight = conv.weight
        self.bias = conv.bias
        self.init_weights()
        self.dict = dict()
        self.drop = VariationalHidDropout(dropout=dropouth)

    def init_weights(self):
        bound = 0.01
        self.weight.data.normal_(0, bound)
        self.bias.data.normal_(0, bound)

    def copy(self, func):
        self.weight.data = func.weight.data.clone().detach()
        self.bias.data = func.bias.data.clone().detach()
        self.drop.mask = func.drop.mask.clone().detach()

    def forward(self, input_0):
        primals_2 = self.weight
        primals_3 = self.bias
        primals_1 = input_0
        output = call([primals_1, primals_2, primals_3])
        return output[0]


Measured latency_ms: 0.036883