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.triton_helpers import math as tl_math
from torch import 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_diag_embed_view_0(in_ptr0, out_ptr0, xnumel, XBLOCK:
    tl.constexpr):
    xnumel = 1024
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 4
    x2 = xindex
    x1 = xindex // 4
    tmp3 = tl.load(in_ptr0 + (x0 + 4 * (x1 // 4)), xmask)
    tmp0 = x0
    tmp1 = x2 // 4 % 4
    tmp2 = tmp0 == tmp1
    tmp4 = 0.0
    tmp5 = tl.where(tmp2, tmp3, tmp4)
    tl.store(out_ptr0 + x2, tmp5, xmask)


@triton.jit
def triton_poi_fused_cat_1(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 15872
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 62
    x1 = xindex // 62
    x2 = xindex
    tmp0 = x0
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 1, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (62 * x1 + x0), tmp4 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp6 = tl.load(in_ptr1 + x0, tmp4 & xmask, eviction_policy='evict_last',
        other=0.0)
    tmp7 = tmp5 + tmp6
    tmp8 = tl.full(tmp7.shape, 0.0, tmp7.dtype)
    tmp9 = tl.where(tmp4, tmp7, tmp8)
    tmp10 = tmp0 >= tmp3
    tl.full([1], 62, tl.int64)
    tmp13 = tl.load(in_ptr0 + (1 + 62 * x1 + (-1 + x0)), tmp10 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp14 = tl.load(in_ptr1 + (1 + (-1 + x0)), tmp10 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp15 = tmp13 + tmp14
    tmp16 = tl_math.sin(tmp15)
    tmp17 = tl.full(tmp16.shape, 0.0, tmp16.dtype)
    tmp18 = tl.where(tmp10, tmp16, tmp17)
    tmp19 = tl.where(tmp4, tmp9, tmp18)
    tl.store(out_ptr0 + x2, tmp19, xmask)


@triton.jit
def triton_poi_fused_cos_2(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 15616
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 61
    x1 = xindex // 61
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (1 + x0 + 62 * x1), xmask)
    tmp1 = tl.load(in_ptr1 + (1 + x0), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl_math.cos(tmp2)
    tl.store(out_ptr0 + x2, tmp3, xmask)


def call(args):
    primals_1, primals_2, primals_3 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (4, 62), (62, 1))
    assert_size_stride(primals_3, (62,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((256, 4), (4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_diag_embed_view_0[grid(1024)](primals_1, buf0, 
            1024, XBLOCK=128, num_warps=4, num_stages=1)
        del primals_1
        buf1 = empty_strided_cuda((256, 62), (62, 1), torch.float32)
        extern_kernels.mm(buf0, primals_2, out=buf1)
        del primals_2
        buf2 = empty_strided_cuda((4, 4, 4, 4, 62), (3968, 992, 248, 62, 1),
            torch.float32)
        triton_poi_fused_cat_1[grid(15872)](buf1, primals_3, buf2, 15872,
            XBLOCK=256, num_warps=4, num_stages=1)
        buf3 = empty_strided_cuda((4, 4, 4, 4, 61), (3904, 976, 244, 61, 1),
            torch.float32)
        triton_poi_fused_cos_2[grid(15616)](buf1, primals_3, buf3, 15616,
            XBLOCK=128, num_warps=4, num_stages=1)
        del buf1
        del primals_3
    return reinterpret_tensor(buf2, (4, 4, 992), (3968, 992, 1), 0
        ), buf3, reinterpret_tensor(buf0, (4, 256), (1, 4), 0)


class Time2VecNew(nn.Module):

    def __init__(self, input_dim=4, embed_dim=248, act_function=torch.sin):
        assert embed_dim % input_dim == 0
        super(Time2VecNew, self).__init__()
        self.enabled = embed_dim > 0
        if self.enabled:
            self.embed_dim = embed_dim // input_dim
            self.input_dim = input_dim
            self.embed_weight = nn.parameter.Parameter(torch.randn(self.
                input_dim, self.embed_dim))
            self.embed_bias = nn.parameter.Parameter(torch.randn(self.
                embed_dim))
            self.act_function = act_function

    def forward(self, input_0):
        primals_2 = self.embed_weight
        primals_3 = self.embed_bias
        primals_1 = input_0
        output = call([primals_1, primals_2, primals_3])
        return output[0]


Measured latency_ms: 0.048121