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 import triton_helpers
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
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_relu_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + x0, xmask)
    tmp1 = tl.full([1], 0, tl.int32)
    tmp2 = triton_helpers.maximum(tmp1, tmp0)
    tl.store(out_ptr0 + x0, tmp2, xmask)


@triton.jit
def triton_poi_fused_relu_threshold_backward_1(in_out_ptr0, in_ptr0,
    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
    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
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp5 = 0.0
    tmp6 = tmp4 <= tmp5
    tl.store(in_out_ptr0 + x2, tmp4, xmask)
    tl.store(out_ptr0 + x2, tmp6, xmask)


@triton.jit
def triton_poi_fused_add_mul_2(in_out_ptr0, in_ptr0, in_ptr1, 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_ptr0 + x2, xmask)
    tmp1 = tl.load(in_out_ptr0 + x2, xmask)
    tmp2 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
    tmp3 = tmp1 + tmp2
    tmp4 = 1.0
    tmp5 = tmp3 * tmp4
    tmp6 = tmp0 + tmp5
    tl.store(in_out_ptr0 + x2, tmp6, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4), (4, 1))
    assert_size_stride(primals_2, (4, 4, 1), (4, 1, 1))
    assert_size_stride(primals_3, (4,), (1,))
    assert_size_stride(primals_4, (4, 4, 1), (4, 1, 1))
    assert_size_stride(primals_5, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_relu_0[grid(16)](primals_1, buf0, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        buf1 = extern_kernels.convolution(reinterpret_tensor(buf0, (1, 4, 4
            ), (0, 4, 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 = reinterpret_tensor(buf1, (4, 4), (4, 1), 0)
        del buf1
        buf5 = empty_strided_cuda((4, 4), (4, 1), torch.bool)
        triton_poi_fused_relu_threshold_backward_1[grid(16)](buf2,
            primals_3, buf5, 16, XBLOCK=16, num_warps=1, num_stages=1)
        del primals_3
        buf3 = extern_kernels.convolution(reinterpret_tensor(buf2, (1, 4, 4
            ), (0, 4, 1), 0), primals_4, stride=(1,), padding=(0,),
            dilation=(1,), transposed=False, output_padding=(0,), groups=1,
            bias=None)
        assert_size_stride(buf3, (1, 4, 4), (16, 4, 1))
        buf4 = reinterpret_tensor(buf3, (4, 4), (4, 1), 0)
        del buf3
        triton_poi_fused_add_mul_2[grid(16)](buf4, primals_1, primals_5, 16,
            XBLOCK=16, num_warps=1, num_stages=1)
        del primals_1
        del primals_5
    return buf4, primals_2, primals_4, reinterpret_tensor(buf0, (1, 4, 4),
        (16, 4, 1), 0), reinterpret_tensor(buf2, (1, 4, 4), (16, 4, 1), 0
        ), buf5


class EqualizedLR(nn.Module):

    def __init__(self, module):
        super().__init__()
        self.module = module
        self._make_params()

    def _make_params(self):
        weight = self.module.weight
        height = weight.data.shape[0]
        width = weight.view(height, -1).data.shape[1]
        del self.module._parameters['weight']
        self.module.weight = None
        self.weight = nn.Parameter(weight.data)
        self.factor = np.sqrt(2 / width)
        nn.init.normal_(self.weight)
        self.bias = self.module.bias
        self.module.bias = None
        if self.bias is not None:
            del self.module._parameters['bias']
            nn.init.zeros_(self.bias)

    def forward(self, *args, **kwargs):
        self.module.weight = self.factor * self.weight
        if self.bias is not None:
            self.module.bias = 1.0 * self.bias
        out = self.module.forward(*args, **kwargs)
        self.module.weight = None
        self.module.bias = None
        return out


class ResnetBlockPointwiseNew(nn.Module):

    def __init__(self, f_in, f_out=None, f_hidden=None, is_bias=True, actvn
        =F.relu, factor=1.0, eq_lr=False):
        super().__init__()
        if f_out is None:
            f_out = f_in
        if f_hidden is None:
            f_hidden = min(f_in, f_out)
        self.f_in = f_in
        self.f_hidden = f_hidden
        self.f_out = f_out
        self.factor = factor
        self.eq_lr = eq_lr
        self.actvn = actvn
        self.conv_0 = nn.Conv1d(f_in, f_hidden, 1)
        self.conv_1 = nn.Conv1d(f_hidden, f_out, 1, bias=is_bias)
        if self.eq_lr:
            self.conv_0 = EqualizedLR(self.conv_0)
            self.conv_1 = EqualizedLR(self.conv_1)
        if f_in == f_out:
            self.shortcut = nn.Sequential()
        else:
            self.shortcut = nn.Conv1d(f_in, f_out, 1, bias=False)
            if self.eq_lr:
                self.shortcut = EqualizedLR(self.shortcut)
        nn.init.zeros_(self.conv_1.weight)

    def forward(self, input_0):
        primals_2 = self.conv_0.weight
        primals_3 = self.conv_0.bias
        primals_4 = self.conv_1.weight
        primals_5 = self.conv_1.bias
        primals_1 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4, primals_5])
        return output[0]


Measured latency_ms: 0.067522