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.utils
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_threshold_backward_0(in_out_ptr0, in_ptr0,
    out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 8
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x1 = xindex // 2
    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_max_pool2d_with_indices_1(in_ptr0, out_ptr0, out_ptr1,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 4
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + 2 * x0, xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr0 + (1 + 2 * x0), 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)
    tl.store(out_ptr0 + x0, tmp5, xmask)
    tl.store(out_ptr1 + x0, tmp6, xmask)


def call(args):
    primals_1, primals_2, primals_3 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 5), (20, 5, 1))
    assert_size_stride(primals_2, (4,), (1,))
    assert_size_stride(primals_3, (4, 4), (4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = extern_kernels.convolution(reinterpret_tensor(primals_3, (1,
            4, 4), (16, 4, 1), 0), primals_1, stride=(1,), padding=(1,),
            dilation=(1,), transposed=False, output_padding=(0,), groups=1,
            bias=None)
        assert_size_stride(buf0, (1, 4, 2), (8, 2, 1))
        buf1 = reinterpret_tensor(buf0, (4, 2), (2, 1), 0)
        del buf0
        buf4 = empty_strided_cuda((4, 2), (2, 1), torch.bool)
        get_raw_stream(0)
        triton_poi_fused_relu_threshold_backward_0[grid(8)](buf1, primals_2,
            buf4, 8, XBLOCK=8, num_warps=1, num_stages=1)
        del primals_2
        buf2 = empty_strided_cuda((4, 1, 1), (1, 1, 1), torch.int8)
        buf3 = empty_strided_cuda((4, 1, 1), (1, 1, 1), torch.float32)
        triton_poi_fused_max_pool2d_with_indices_1[grid(4)](buf1, buf2,
            buf3, 4, XBLOCK=4, num_warps=1, num_stages=1)
    return reinterpret_tensor(buf3, (4,), (1,), 0
        ), primals_1, reinterpret_tensor(primals_3, (1, 4, 4), (16, 4, 1), 0
        ), reinterpret_tensor(buf1, (4, 1, 2), (2, 2, 1), 0), buf2, buf4


class CNNNew(nn.Module):
    """
    Convolutional layer of a character-based convolutional encoder that outputs word embeddings.
    """

    def __init__(self, char_embed_size: 'int', word_embed_size: 'int',
        kernel_size: 'int'=5, padding: 'int'=1):
        """ Init CNN

        @param char_embed_size (int): size of the character embedding vector; in_channels (dimensionality)
        @param word_embed_size (int): size of the word embedding vector; out_channels (dimensionality)
        @param kernel_size (int): kernel size of the convolutional layer
        @param padding (int): padding size for convolutional layer
        """
        super(CNNNew, self).__init__()
        self.conv = nn.Conv1d(char_embed_size, word_embed_size, kernel_size
            =kernel_size, padding=padding)

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


Measured latency_ms: 0.041626