import torch
import torch.nn as nn


class Conv2dZeroInit(nn.Conv2d):

    def __init__(self, channels_in, channels_out, filter_size, stride=1,
        padding=0, logscale=3.0):
        super().__init__(channels_in, channels_out, filter_size, stride=
            stride, padding=padding)
        self.register_parameter('logs', nn.Parameter(torch.zeros(
            channels_out, 1, 1)))
        self.logscale_factor = logscale

    def reset_parameters(self):
        self.weight.data.zero_()
        self.bias.data.zero_()

    def forward(self, input):
        out = super().forward(input)
        return out * torch.exp(self.logs * self.logscale_factor)


def get_inputs():
    return [torch.rand([4, 4, 4, 4])]


def get_init_inputs():
    return [[], {'channels_in': 4, 'channels_out': 4, 'filter_size': 4}]

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
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda


@triton.jit
def triton_poi_fused_convolution_exp_mul_0(in_out_ptr0, in_ptr0, in_ptr1,
    out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 4
    tmp0 = tl.load(in_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp4 = 3.0
    tmp5 = tmp3 * tmp4
    tmp6 = tl_math.exp(tmp5)
    tmp7 = tmp2 * tmp6
    tl.store(in_out_ptr0 + x2, tmp2, xmask)
    tl.store(out_ptr0 + x2, tmp7, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (4,), (1,))
    assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_4, (4, 1, 1), (1, 1, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = extern_kernels.convolution(primals_3, primals_1, stride=(1, 
            1), padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf0, (4, 4, 1, 1), (4, 1, 1, 1))
        buf1 = buf0
        del buf0
        buf2 = empty_strided_cuda((4, 4, 1, 1), (4, 1, 1, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_convolution_exp_mul_0[grid(16)](buf1, primals_2,
            primals_4, buf2, 16, XBLOCK=16, num_warps=1, num_stages=1)
        del primals_2
    return buf2, primals_1, primals_3, primals_4, buf1


class Conv2dZeroInitNew(nn.Conv2d):

    def __init__(self, channels_in, channels_out, filter_size, stride=1,
        padding=0, logscale=3.0):
        super().__init__(channels_in, channels_out, filter_size, stride=
            stride, padding=padding)
        self.register_parameter('logs', nn.Parameter(torch.zeros(
            channels_out, 1, 1)))
        self.logscale_factor = logscale

    def reset_parameters(self):
        self.weight.data.zero_()
        self.bias.data.zero_()

    def forward(self, input_0):
        primals_1 = self.weight
        primals_2 = self.bias
        primals_4 = self.logs
        primals_3 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4])
        return output[0]
