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
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_copy_0(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
    constexpr, XBLOCK: tl.constexpr):
    ynumel = 24
    xnumel = 4
    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
    y0 = yindex % 6
    x2 = xindex
    y1 = yindex // 6
    tmp0 = y0
    tmp1 = tl.full([1, 1], 5, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.broadcast_to(-4 + y0, [XBLOCK, YBLOCK])
    tmp4 = tl.full([1, 1], 1, tl.int64)
    tmp5 = tmp3 < tmp4
    tmp6 = tmp5 & tmp2
    tmp7 = tl.broadcast_to(y0, [XBLOCK, YBLOCK])
    tmp8 = tmp7 >= tmp4
    tmp9 = tmp7 < tmp1
    tmp10 = tmp8 & tmp9
    tmp11 = tmp10 & tmp6
    tmp12 = tl.load(in_ptr0 + (-4 + x2 + 4 * y0 + 16 * y1), tmp11 & xmask &
        ymask, eviction_policy='evict_last', other=0.0)
    tmp13 = float('nan')
    tmp14 = tl.where(tmp10, tmp12, tmp13)
    tmp15 = tl.full(tmp14.shape, 0.0, tmp14.dtype)
    tmp16 = tl.where(tmp6, tmp14, tmp15)
    tmp17 = tmp3 >= tmp4
    tmp18 = tmp3 < tmp1
    tmp19 = tmp17 & tmp18
    tmp20 = tmp19 & tmp2
    tmp21 = tl.load(in_ptr0 + (-20 + x2 + 4 * y0 + 16 * y1), tmp20 & xmask &
        ymask, eviction_policy='evict_last', other=0.0)
    tmp22 = tl.where(tmp19, tmp21, tmp13)
    tmp23 = tl.where(tmp5, tmp16, tmp22)
    tmp24 = tl.full(tmp23.shape, 0.0, tmp23.dtype)
    tmp25 = tl.where(tmp2, tmp23, tmp24)
    tmp26 = tmp0 < tmp4
    tmp27 = tl.broadcast_to(4 + y0, [XBLOCK, YBLOCK])
    tmp28 = tmp27 >= tmp4
    tmp29 = tmp27 < tmp1
    tmp30 = tmp28 & tmp29
    tmp31 = tmp30 & tmp26
    tmp32 = tl.load(in_ptr0 + (12 + x2 + 4 * y0 + 16 * y1), tmp31 & xmask &
        ymask, eviction_policy='evict_last', other=0.0)
    tmp33 = tl.where(tmp30, tmp32, tmp13)
    tmp34 = tl.full(tmp33.shape, 0.0, tmp33.dtype)
    tmp35 = tl.where(tmp26, tmp33, tmp34)
    tmp36 = tmp0 >= tmp4
    tmp37 = tmp0 < tmp1
    tmp38 = tmp36 & tmp37
    tmp39 = tl.load(in_ptr0 + (-4 + x2 + 4 * y0 + 16 * y1), tmp38 & xmask &
        ymask, eviction_policy='evict_last', other=0.0)
    tmp40 = tl.where(tmp38, tmp39, tmp13)
    tmp41 = tl.where(tmp26, tmp35, tmp40)
    tmp42 = tl.where(tmp2, tmp25, tmp41)
    tl.store(out_ptr0 + (y0 + 6 * x2 + 24 * y1), tmp42, xmask & ymask)


@triton.jit
def triton_poi_fused_convolution_1(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl
    .constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 4 % 4
    tmp0 = tl.load(in_out_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + x3, tmp2, xmask)


def call(args):
    primals_1, primals_2, primals_3 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_2, (4, 4, 3), (12, 3, 1))
    assert_size_stride(primals_3, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf1 = empty_strided_cuda((4, 4, 6), (24, 6, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_copy_0[grid(24, 4)](primals_1, buf1, 24, 4, XBLOCK
            =4, YBLOCK=32, num_warps=4, num_stages=1)
        del primals_1
        buf2 = extern_kernels.convolution(buf1, primals_2, stride=(1,),
            padding=(0,), dilation=(1,), transposed=False, output_padding=(
            0,), groups=1, bias=None)
        assert_size_stride(buf2, (4, 4, 4), (16, 4, 1))
        buf3 = buf2
        del buf2
        triton_poi_fused_convolution_1[grid(64)](buf3, primals_3, 64,
            XBLOCK=64, num_warps=1, num_stages=1)
        del primals_3
    return reinterpret_tensor(buf3, (4, 4, 4), (16, 1, 4), 0), primals_2, buf1


class TokenEmbeddingNew(nn.Module):

    def __init__(self, c_in, d_model):
        super(TokenEmbeddingNew, self).__init__()
        padding = 1 if torch.__version__ >= '1.5.0' else 2
        self.tokenConv = nn.Conv1d(in_channels=c_in, out_channels=d_model,
            kernel_size=3, padding=padding, padding_mode='circular')
        for m in self.modules():
            if isinstance(m, nn.Conv1d):
                nn.init.kaiming_normal_(m.weight, mode='fan_in',
                    nonlinearity='leaky_relu')

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


Measured latency_ms: 0.018377