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_unsqueeze_0(
    in_out_ptr0, in_ptr0, out_ptr0, out_ptr1, ynumel, xnumel, YBLOCK: tl.
    constexpr, XBLOCK: tl.constexpr):
    ynumel = 256
    xnumel = 64
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y3 = yindex
    y0 = yindex % 64
    y1 = yindex // 64
    tmp0 = tl.load(in_out_ptr0 + (x2 + 64 * y3), xmask & ymask,
        eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr0 + y0, ymask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1, 1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp5 = 0.0
    tmp6 = tmp4 <= tmp5
    tl.store(out_ptr0 + (y0 + 64 * x2 + 4096 * y1), tmp4, xmask & ymask)
    tl.store(out_ptr1 + (x2 + 64 * y3), tmp6, xmask & ymask)


@triton.jit
def triton_poi_fused_max_pool2d_with_indices_1(in_ptr0, out_ptr0, out_ptr1,
    ynumel, xnumel, YBLOCK: tl.constexpr, XBLOCK: tl.constexpr):
    ynumel = 128
    xnumel = 64
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x1 = xindex
    y0 = yindex
    y2 = yindex % 32
    y3 = yindex // 32
    tmp0 = tl.load(in_ptr0 + (x1 + 128 * y0), xmask & ymask,
        eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr0 + (64 + x1 + 128 * y0), xmask & ymask,
        eviction_policy='evict_last')
    tmp2 = tmp1 > tmp0
    tmp3 = tl.full([1, 1], 1, tl.int8)
    tmp4 = tl.full([1, 1], 0, tl.int8)
    tmp5 = tl.where(tmp2, tmp3, tmp4)
    tmp6 = triton_helpers.maximum(tmp1, tmp0)
    tl.store(out_ptr0 + (x1 + 64 * y0), tmp5, xmask & ymask)
    tl.store(out_ptr1 + (y2 + 32 * x1 + 2048 * y3), tmp6, xmask & ymask)


@triton.jit
def triton_poi_fused_convolution_relu_threshold_backward_unsqueeze_2(
    in_out_ptr0, in_ptr0, out_ptr0, out_ptr1, ynumel, xnumel, YBLOCK: tl.
    constexpr, XBLOCK: tl.constexpr):
    ynumel = 512
    xnumel = 32
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y3 = yindex
    y0 = yindex % 128
    y1 = yindex // 128
    tmp0 = tl.load(in_out_ptr0 + (x2 + 32 * y3), xmask & ymask,
        eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr0 + y0, ymask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1, 1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp5 = 0.0
    tmp6 = tmp4 <= tmp5
    tl.store(out_ptr0 + (y0 + 128 * x2 + 4096 * y1), tmp4, xmask & ymask)
    tl.store(out_ptr1 + (x2 + 32 * y3), tmp6, xmask & ymask)


@triton.jit
def triton_poi_fused_max_pool2d_with_indices_3(in_ptr0, out_ptr0, out_ptr1,
    ynumel, xnumel, YBLOCK: tl.constexpr, XBLOCK: tl.constexpr):
    ynumel = 64
    xnumel = 128
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x1 = xindex
    y0 = yindex
    y2 = yindex % 16
    y3 = yindex // 16
    tmp0 = tl.load(in_ptr0 + (x1 + 256 * y0), xmask & ymask,
        eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr0 + (128 + x1 + 256 * y0), xmask & ymask,
        eviction_policy='evict_last')
    tmp2 = tmp1 > tmp0
    tmp3 = tl.full([1, 1], 1, tl.int8)
    tmp4 = tl.full([1, 1], 0, tl.int8)
    tmp5 = tl.where(tmp2, tmp3, tmp4)
    tmp6 = triton_helpers.maximum(tmp1, tmp0)
    tl.store(out_ptr0 + (x1 + 128 * y0), tmp5, xmask & ymask)
    tl.store(out_ptr1 + (y2 + 16 * x1 + 2048 * y3), tmp6, xmask & ymask)


def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5 = args
    args.clear()
    assert_size_stride(primals_1, (64, 1, 3), (3, 3, 1))
    assert_size_stride(primals_2, (64,), (1,))
    assert_size_stride(primals_3, (4, 1, 64), (64, 64, 1))
    assert_size_stride(primals_4, (128, 64, 3), (192, 3, 1))
    assert_size_stride(primals_5, (128,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = extern_kernels.convolution(primals_3, primals_1, stride=(1,),
            padding=(1,), dilation=(1,), transposed=False, output_padding=(
            0,), groups=1, bias=None)
        assert_size_stride(buf0, (4, 64, 64), (4096, 64, 1))
        buf1 = buf0
        del buf0
        buf2 = empty_strided_cuda((4, 64, 1, 64), (4096, 1, 4096, 64),
            torch.float32)
        buf11 = empty_strided_cuda((4, 64, 64), (4096, 64, 1), torch.bool)
        get_raw_stream(0)
        triton_poi_fused_convolution_relu_threshold_backward_unsqueeze_0[grid
            (256, 64)](buf1, primals_2, buf2, buf11, 256, 64, XBLOCK=32,
            YBLOCK=32, num_warps=4, num_stages=1)
        del primals_2
        buf3 = empty_strided_cuda((4, 64, 1, 32), (2048, 1, 2048, 64),
            torch.int8)
        buf4 = empty_strided_cuda((4, 64, 1, 32), (2048, 32, 32, 1), torch.
            float32)
        triton_poi_fused_max_pool2d_with_indices_1[grid(128, 64)](buf2,
            buf3, buf4, 128, 64, XBLOCK=64, YBLOCK=4, num_warps=4, num_stages=1
            )
        buf5 = extern_kernels.convolution(reinterpret_tensor(buf4, (4, 64, 
            32), (2048, 32, 1), 0), primals_4, stride=(1,), padding=(1,),
            dilation=(1,), transposed=False, output_padding=(0,), groups=1,
            bias=None)
        assert_size_stride(buf5, (4, 128, 32), (4096, 32, 1))
        buf6 = buf5
        del buf5
        buf7 = reinterpret_tensor(buf1, (4, 128, 1, 32), (4096, 1, 4096, 
            128), 0)
        del buf1
        buf10 = empty_strided_cuda((4, 128, 32), (4096, 32, 1), torch.bool)
        triton_poi_fused_convolution_relu_threshold_backward_unsqueeze_2[grid
            (512, 32)](buf6, primals_5, buf7, buf10, 512, 32, XBLOCK=32,
            YBLOCK=32, num_warps=4, num_stages=1)
        del buf6
        del primals_5
        buf8 = empty_strided_cuda((4, 128, 1, 16), (2048, 1, 2048, 128),
            torch.int8)
        buf9 = empty_strided_cuda((4, 128, 1, 16), (2048, 16, 16, 1), torch
            .float32)
        triton_poi_fused_max_pool2d_with_indices_3[grid(64, 128)](buf7,
            buf8, buf9, 64, 128, XBLOCK=128, YBLOCK=2, num_warps=4,
            num_stages=1)
    return reinterpret_tensor(buf9, (4, 128, 16), (2048, 16, 1), 0
        ), primals_1, primals_3, primals_4, buf2, buf3, reinterpret_tensor(buf4
        , (4, 64, 32), (2048, 32, 1), 0), buf7, buf8, buf10, buf11


class EncoderNew(nn.Module):

    def __init__(self):
        super(EncoderNew, self).__init__()
        self.conv1 = torch.nn.Conv1d(1, 64, 3, padding=1)
        self.maxp1 = torch.nn.MaxPool1d(2, padding=0)
        self.conv2 = torch.nn.Conv1d(64, 128, 3, padding=1)
        self.maxp2 = torch.nn.MaxPool1d(2, padding=0)

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


Measured latency_ms: 0.23494