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 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_relu_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + x0, None)
    tmp1 = tl.full([1], 0, tl.int32)
    tmp2 = triton_helpers.maximum(tmp1, tmp0)
    tl.store(out_ptr0 + x0, tmp2, None)


@triton.jit
def triton_poi_fused_convolution_relu_1(in_out_ptr0, in_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x0 = xindex
    tmp0 = tl.load(in_out_ptr0 + x0, None)
    tmp1 = tl.load(in_ptr0 + 0)
    tmp2 = tl.broadcast_to(tmp1, [XBLOCK])
    tmp3 = tmp0 + tmp2
    tmp4 = tl.full([1], 0, tl.int32)
    tmp5 = triton_helpers.maximum(tmp4, tmp3)
    tl.store(in_out_ptr0 + x0, tmp5, None)


@triton.jit
def triton_poi_fused_add_convolution_2(in_out_ptr0, in_ptr0, in_ptr1,
    xnumel, XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x0 = xindex
    tmp0 = tl.load(in_out_ptr0 + x0, None)
    tmp1 = tl.load(in_ptr0 + 0)
    tmp2 = tl.broadcast_to(tmp1, [XBLOCK])
    tmp4 = tl.load(in_ptr1 + x0, None)
    tmp3 = tmp0 + tmp2
    tmp5 = tmp3 + tmp4
    tl.store(in_out_ptr0 + x0, tmp5, None)


def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5 = args
    args.clear()
    assert_size_stride(primals_1, (4, 1, 64, 64), (4096, 4096, 64, 1))
    assert_size_stride(primals_2, (1, 1, 3, 3), (9, 9, 3, 1))
    assert_size_stride(primals_3, (1,), (1,))
    assert_size_stride(primals_4, (1, 1, 3, 3), (9, 9, 3, 1))
    assert_size_stride(primals_5, (1,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 1, 64, 64), (4096, 4096, 64, 1),
            torch.float32)
        get_raw_stream(0)
        triton_poi_fused_relu_0[grid(16384)](primals_1, buf0, 16384, XBLOCK
            =128, num_warps=4, num_stages=1)
        buf1 = extern_kernels.convolution(buf0, primals_2, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf1, (4, 1, 64, 64), (4096, 4096, 64, 1))
        buf2 = buf1
        del buf1
        triton_poi_fused_convolution_relu_1[grid(16384)](buf2, primals_3, 
            16384, XBLOCK=128, num_warps=4, num_stages=1)
        del primals_3
        buf3 = extern_kernels.convolution(buf2, primals_4, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf3, (4, 1, 64, 64), (4096, 4096, 64, 1))
        buf4 = buf3
        del buf3
        triton_poi_fused_add_convolution_2[grid(16384)](buf4, primals_5,
            primals_1, 16384, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_1
        del primals_5
    return buf4, primals_2, primals_4, buf0, buf2


class ImpalaResidualNew(nn.Module):
    """
    A residual block for an IMPALA CNN.
    """

    def __init__(self, depth):
        super().__init__()
        self.conv1 = nn.Conv2d(depth, depth, 3, padding=1)
        self.conv2 = nn.Conv2d(depth, depth, 3, padding=1)

    def forward(self, input_0):
        primals_2 = self.conv1.weight
        primals_3 = self.conv1.bias
        primals_4 = self.conv2.weight
        primals_5 = self.conv2.bias
        primals_1 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4, primals_5])
        return output[0]


Measured latency_ms: 0.046718