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
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_0(in_ptr0, out_ptr0, out_ptr1, xnumel, XBLOCK:
    tl.constexpr):
    xnumel = 16
    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 = 0.0
    tmp2 = triton_helpers.maximum(tmp0, tmp1)
    tmp3 = tmp0 >= tmp1
    tl.store(out_ptr0 + x0, tmp2, xmask)
    tl.store(out_ptr1 + x0, tmp3, xmask)


def call(args):
    primals_1, primals_2, primals_3 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4), (4, 1))
    assert_size_stride(primals_2, (4,), (1,))
    assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        buf2 = empty_strided_cuda((4, 4), (4, 1), torch.bool)
        get_raw_stream(0)
        triton_poi_fused_clamp_ge_0[grid(16)](primals_1, buf0, buf2, 16,
            XBLOCK=16, num_warps=1, num_stages=1)
        del primals_1
        buf1 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_2, reinterpret_tensor(primals_3, (64, 
            4), (4, 1), 0), reinterpret_tensor(buf0, (4, 4), (1, 4), 0),
            alpha=1, beta=1, out=buf1)
        del buf0
        del primals_2
    return reinterpret_tensor(buf1, (4, 4, 4, 4), (64, 16, 4, 1), 0
        ), reinterpret_tensor(primals_3, (64, 4), (4, 1), 0), buf2


class PositiveLinearNew(nn.Linear):
    """Applies a transformation to the incoming data of the following form: :math:`y_i = xlog(exp(A)+1)^T`
        where log and exp are elementwise operations.

        Args:
            in_features: size of each input sample
            out_features: size of each output sample
            bias: If set to False, the layer will not learn an additive bias.
                Default: ``True``

        Shape:
            - Input: :math:`(N, *, in\\_features)` where :math:`*` means any number of
              additional dimensions
            - Output: :math:`(N, *, out\\_features)` where all but the last dimension
              are the same shape as the input.

        Attributes:
            weight: the learnable weights of the module of shape
                `(out_features x in_features)`
            bias:   the learnable bias of the module of shape `(out_features)`

        Examples::

            >>> m = nn.PositiveLinear(20, 30)
            >>> input = torch.randn(128, 20)
            >>> output = m(input)
            >>> print(output.size())
        """

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


Measured latency_ms: 0.025883