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
from torch._inductor.runtime.triton_helpers import libdevice
import math
import torch.nn as nn
import torch.utils.data
import torch.onnx.operators
import torch.optim
import torch.optim.lr_scheduler
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_clamp_ge_le_logical_and_0(in_ptr0, out_ptr0, out_ptr1,
    xnumel, XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + x0, None)
    tmp1 = -1.0
    tmp2 = triton_helpers.maximum(tmp0, tmp1)
    tmp3 = 1.0
    tmp4 = triton_helpers.minimum(tmp2, tmp3)
    tmp5 = tmp0 >= tmp1
    tmp6 = tmp0 <= tmp3
    tmp7 = tmp5 & tmp6
    tl.store(out_ptr0 + x0, tmp4, None)
    tl.store(out_ptr1 + x0, tmp7, None)


@triton.jit
def triton_poi_fused_gelu_1(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)
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + x0, None)
    tmp1 = 0.5
    tmp2 = tmp0 * tmp1
    tmp3 = 0.7071067811865476
    tmp4 = tmp0 * tmp3
    tmp5 = libdevice.erf(tmp4)
    tmp6 = 1.0
    tmp7 = tmp5 + tmp6
    tmp8 = tmp2 * tmp7
    tl.store(out_ptr0 + x0, tmp8, None)


@triton.jit
def triton_poi_fused_gelu_2(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + x0, xmask)
    tmp1 = 0.5
    tmp2 = tmp0 * tmp1
    tmp3 = 0.7071067811865476
    tmp4 = tmp0 * tmp3
    tmp5 = libdevice.erf(tmp4)
    tmp6 = 1.0
    tmp7 = tmp5 + tmp6
    tmp8 = tmp2 * tmp7
    tl.store(out_ptr0 + x0, tmp8, xmask)


def call(args):
    primals_1, primals_2, primals_3 = args
    args.clear()
    assert_size_stride(primals_1, (1024, 4), (4, 1))
    assert_size_stride(primals_2, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_3, (4, 1024), (1024, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((1024, 4), (4, 1), torch.float32)
        buf7 = empty_strided_cuda((1024, 4), (4, 1), torch.bool)
        get_raw_stream(0)
        triton_poi_fused_clamp_ge_le_logical_and_0[grid(4096)](primals_1,
            buf0, buf7, 4096, XBLOCK=128, num_warps=4, num_stages=1)
        del primals_1
        buf1 = empty_strided_cuda((64, 1024), (1024, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_2, (64, 4), (4, 1), 0),
            reinterpret_tensor(buf0, (4, 1024), (1, 4), 0), out=buf1)
        buf2 = reinterpret_tensor(buf0, (1024, 4), (1, 1024), 0)
        del buf0
        buf6 = empty_strided_cuda((4, 1024), (1024, 1), torch.bool)
        triton_poi_fused_clamp_ge_le_logical_and_0[grid(4096)](primals_3,
            buf2, buf6, 4096, XBLOCK=128, num_warps=4, num_stages=1)
        del primals_3
        buf3 = empty_strided_cuda((4, 4, 4, 1024), (16384, 4096, 1024, 1),
            torch.float32)
        triton_poi_fused_gelu_1[grid(65536)](buf1, buf3, 65536, XBLOCK=256,
            num_warps=4, num_stages=1)
        buf4 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf3, (64, 1024), (1024, 1), 0
            ), buf2, out=buf4)
        buf5 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused_gelu_2[grid(256)](buf4, buf5, 256, XBLOCK=128,
            num_warps=4, num_stages=1)
    return buf5, reinterpret_tensor(primals_2, (64, 4), (4, 1), 0
        ), buf1, reinterpret_tensor(buf3, (64, 1024), (1024, 1), 0
        ), buf4, reinterpret_tensor(buf2, (4, 1024), (1024, 1), 0), buf6, buf7


class ReRegualizedLinearNACLayer(torch.nn.Module):

    def __init__(self, in_features, out_features, **kwargs):
        super().__init__()
        self.in_features = in_features
        self.out_features = out_features
        self.W = torch.nn.Parameter(torch.Tensor(out_features, in_features))
        self.register_parameter('bias', None)

    def reset_parameters(self):
        std = math.sqrt(2.0 / (self.in_features + self.out_features))
        r = min(0.5, math.sqrt(3.0) * std)
        torch.nn.init.uniform_(self.W, -r, r)

    def forward(self, input, reuse=False):
        W = torch.clamp(self.W, -1, 1)
        return torch.nn.functional.linear(input, W, self.bias)

    def extra_repr(self):
        return 'in_features={}, out_features={}'.format(self.in_features,
            self.out_features)


class ByteCombineNew(nn.Module):

    def __init__(self, input_dim, output_dim, inner_dim=1024, **kwags):
        super().__init__()
        self.layer_1 = ReRegualizedLinearNACLayer(input_dim, inner_dim)
        self.layer_2 = ReRegualizedLinearNACLayer(inner_dim, output_dim)
        self.act = nn.GELU()
        self.reset_parameters()

    def reset_parameters(self):
        self.layer_1.reset_parameters()
        self.layer_2.reset_parameters()

    def forward(self, input_0):
        primals_1 = self.layer_1.W
        primals_3 = self.layer_2.W
        primals_2 = input_0
        output = call([primals_1, primals_2, primals_3])
        return output[0]


Measured latency_ms: 0.024256