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
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor


@triton.jit
def triton_poi_fused_convolution_relu_threshold_backward_0(in_ptr0, in_ptr1,
    out_ptr0, out_ptr1, xnumel, XBLOCK: tl.constexpr):
    xnumel = 4240
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x4 = xindex
    x1 = xindex // 53 % 20
    x2 = xindex // 1060
    x3 = xindex % 1060
    tmp0 = tl.load(in_ptr0 + x4, xmask)
    tmp1 = tl.load(in_ptr1 + 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(out_ptr0 + (x3 + 1088 * x2), tmp4, xmask)
    tl.store(out_ptr1 + (x3 + 1152 * x2), tmp6, xmask)


@triton.jit
def triton_poi_fused_max_pool2d_with_indices_1(in_ptr0, out_ptr0, out_ptr1,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 800
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 10
    x1 = xindex // 10 % 20
    x2 = xindex // 200
    x3 = xindex
    tmp0 = tl.load(in_ptr0 + (5 * x0 + 53 * x1 + 1088 * x2), xmask,
        eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr0 + (1 + 5 * x0 + 53 * x1 + 1088 * x2), xmask,
        eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr0 + (2 + 5 * x0 + 53 * x1 + 1088 * x2), xmask,
        eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr0 + (3 + 5 * x0 + 53 * x1 + 1088 * x2), xmask,
        eviction_policy='evict_last')
    tmp17 = tl.load(in_ptr0 + (4 + 5 * x0 + 53 * x1 + 1088 * x2), xmask,
        eviction_policy='evict_last')
    tmp2 = tmp1 > tmp0
    tmp3 = tl.full([1], 1, tl.int8)
    tmp4 = tl.full([1], 0, tl.int8)
    tmp5 = tl.where(tmp2, tmp3, tmp4)
    tmp6 = triton_helpers.maximum(tmp1, tmp0)
    tmp8 = tmp7 > tmp6
    tmp9 = tl.full([1], 2, tl.int8)
    tmp10 = tl.where(tmp8, tmp9, tmp5)
    tmp11 = triton_helpers.maximum(tmp7, tmp6)
    tmp13 = tmp12 > tmp11
    tmp14 = tl.full([1], 3, tl.int8)
    tmp15 = tl.where(tmp13, tmp14, tmp10)
    tmp16 = triton_helpers.maximum(tmp12, tmp11)
    tmp18 = tmp17 > tmp16
    tmp19 = tl.full([1], 4, tl.int8)
    tmp20 = tl.where(tmp18, tmp19, tmp15)
    tmp21 = triton_helpers.maximum(tmp17, tmp16)
    tl.store(out_ptr0 + x3, tmp20, xmask)
    tl.store(out_ptr1 + x3, tmp21, xmask)


@triton.jit
def triton_poi_fused_relu_threshold_backward_2(in_out_ptr0, in_ptr0,
    out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 32
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 8
    tmp0 = tl.load(in_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x0, 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)


def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5 = args
    args.clear()
    assert_size_stride(primals_1, (4, 2, 51), (102, 51, 1))
    assert_size_stride(primals_2, (20, 2, 3), (6, 3, 1))
    assert_size_stride(primals_3, (20,), (1,))
    assert_size_stride(primals_4, (8, 200), (200, 1))
    assert_size_stride(primals_5, (8,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = extern_kernels.convolution(primals_1, primals_2, stride=(1,),
            padding=(2,), dilation=(1,), transposed=False, output_padding=(
            0,), groups=1, bias=None)
        assert_size_stride(buf0, (4, 20, 53), (1060, 53, 1))
        buf1 = empty_strided_cuda((4, 20, 53), (1088, 53, 1), torch.float32)
        buf7 = empty_strided_cuda((4, 20, 53), (1152, 53, 1), torch.bool)
        get_raw_stream(0)
        triton_poi_fused_convolution_relu_threshold_backward_0[grid(4240)](buf0
            , primals_3, buf1, buf7, 4240, XBLOCK=128, num_warps=4,
            num_stages=1)
        del buf0
        del primals_3
        buf2 = empty_strided_cuda((4, 20, 1, 10), (200, 10, 10, 1), torch.int8)
        buf3 = empty_strided_cuda((4, 20, 1, 10), (200, 10, 10, 1), torch.
            float32)
        triton_poi_fused_max_pool2d_with_indices_1[grid(800)](buf1, buf2,
            buf3, 800, XBLOCK=256, num_warps=4, num_stages=1)
        buf4 = empty_strided_cuda((4, 8), (8, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf3, (4, 200), (200, 1), 0),
            reinterpret_tensor(primals_4, (200, 8), (1, 200), 0), out=buf4)
        buf5 = buf4
        del buf4
        buf6 = empty_strided_cuda((4, 8), (8, 1), torch.bool)
        triton_poi_fused_relu_threshold_backward_2[grid(32)](buf5,
            primals_5, buf6, 32, XBLOCK=32, num_warps=1, num_stages=1)
        del primals_5
    return buf5, primals_2, primals_1, reinterpret_tensor(buf1, (4, 20, 1, 
        53), (1088, 53, 0, 1), 0), buf2, reinterpret_tensor(buf3, (4, 200),
        (200, 1), 0), buf6, primals_4, buf7


class SummaryNet_largeNew(nn.Module):

    def __init__(self):
        super().__init__()
        self.conv1 = nn.Conv1d(in_channels=2, out_channels=20, kernel_size=
            3, padding=2)
        self.pool = nn.MaxPool1d(kernel_size=5, stride=5)
        self.fc = nn.Linear(in_features=20 * 5 * 2, out_features=8)

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


Measured latency_ms: 0.066944