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
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_convolution_0(in_ptr0, out_ptr0, ynumel, xnumel,
    YBLOCK: tl.constexpr, XBLOCK: tl.constexpr):
    ynumel = 200
    xnumel = 50
    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
    y0 = yindex % 50
    y1 = yindex // 50
    y3 = yindex
    tmp0 = tl.load(in_ptr0 + (y0 + 50 * x2 + 2500 * y1), xmask & ymask,
        eviction_policy='evict_last')
    tl.store(out_ptr0 + (x2 + 50 * y3), tmp0, xmask & ymask)


@triton.jit
def triton_poi_fused_convolution_relu_threshold_backward_1(in_out_ptr0,
    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)
    x3 = xindex
    x1 = xindex // 50 % 256
    tmp0 = tl.load(in_out_ptr0 + x3, None)
    tmp1 = tl.load(in_ptr0 + x1, None, 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 + x3, tmp4, None)
    tl.store(out_ptr0 + x3, tmp6, None)


def call(args):
    primals_1, primals_2, primals_3 = args
    args.clear()
    assert_size_stride(primals_1, (4, 50, 50), (2500, 50, 1))
    assert_size_stride(primals_2, (256, 50, 3), (150, 3, 1))
    assert_size_stride(primals_3, (256,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 50, 50), (2500, 50, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_convolution_0[grid(200, 50)](primals_1, buf0, 200,
            50, XBLOCK=32, YBLOCK=32, num_warps=4, num_stages=1)
        buf1 = extern_kernels.convolution(buf0, primals_2, stride=(1,),
            padding=(1,), dilation=(1,), transposed=False, output_padding=(
            0,), groups=1, bias=None)
        assert_size_stride(buf1, (4, 256, 50), (12800, 50, 1))
        del buf0
        buf2 = buf1
        del buf1
        buf3 = empty_strided_cuda((4, 256, 50), (12800, 50, 1), torch.bool)
        triton_poi_fused_convolution_relu_threshold_backward_1[grid(51200)](
            buf2, primals_3, buf3, 51200, XBLOCK=512, num_warps=4, num_stages=1
            )
        del primals_3
    return reinterpret_tensor(buf2, (4, 50, 256), (12800, 1, 50), 0
        ), primals_2, reinterpret_tensor(primals_1, (4, 50, 50), (2500, 1, 
        50), 0), buf3


class CNNNew(nn.Module):

    def __init__(self, input_size=50, hidden_size=256, dropout=0,
        kernel_size=3, padding=1, activation_function=F.relu):
        """
        Args:
            input_size: dimention of input embedding
            kernel_size: kernel_size for CNN
            padding: padding for CNN
            hidden_size: hidden size
        """
        super().__init__()
        self.conv = nn.Conv1d(input_size, hidden_size, kernel_size, padding
            =padding)
        self.act = activation_function
        self.dropout = nn.Dropout(dropout)

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


Measured latency_ms: 0.048963