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_leaky_relu_0(in_ptr0, in_ptr1, out_ptr0, out_ptr1,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 4
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tmp5 = 0.1
    tmp6 = tmp2 * tmp5
    tmp7 = tl.where(tmp4, tmp2, tmp6)
    tl.store(out_ptr0 + x2, tmp4, xmask)
    tl.store(out_ptr1 + x2, tmp7, 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((64, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_3, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_1, (4, 4), (1, 4), 0), out=buf0)
        del primals_1
        buf1 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
        buf2 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_leaky_relu_0[grid(256)](buf0, primals_2, buf1,
            buf2, 256, XBLOCK=256, num_warps=4, num_stages=1)
        del buf0
        del primals_2
    return buf2, reinterpret_tensor(primals_3, (64, 4), (4, 1), 0), buf1


class SharedDropout(nn.Module):
    """
    SharedDropout differs from the vanilla dropout strategy in that
    the dropout mask is shared across one dimension.

    Args:
        p (float):
            The probability of an element to be zeroed. Default: 0.5.
        batch_first (bool):
            If ``True``, the input and output tensors are provided as ``[batch_size, seq_len, *]``.
            Default: ``True``.

    Examples:
        >>> x = torch.ones(1, 3, 5)
        >>> nn.Dropout()(x)
        tensor([[[0., 2., 2., 0., 0.],
                 [2., 2., 0., 2., 2.],
                 [2., 2., 2., 2., 0.]]])
        >>> SharedDropout()(x)
        tensor([[[2., 0., 2., 0., 2.],
                 [2., 0., 2., 0., 2.],
                 [2., 0., 2., 0., 2.]]])
    """

    def __init__(self, p=0.5, batch_first=True):
        super().__init__()
        self.p = p
        self.batch_first = batch_first

    def __repr__(self):
        s = f'p={self.p}'
        if self.batch_first:
            s += f', batch_first={self.batch_first}'
        return f'{self.__class__.__name__}({s})'

    def forward(self, x):
        """
        Args:
            x (~torch.Tensor):
                A tensor of any shape.
        Returns:
            The returned tensor is of the same shape as `x`.
        """
        if self.training:
            if self.batch_first:
                mask = self.get_mask(x[:, 0], self.p).unsqueeze(1)
            else:
                mask = self.get_mask(x[0], self.p)
            x = x * mask
        return x

    @staticmethod
    def get_mask(x, p):
        return x.new_empty(x.shape).bernoulli_(1 - p) / (1 - p)


class MLPNew(nn.Module):
    """
    Applies a linear transformation together with a non-linear activation to the incoming tensor:
    :math:`y = \\mathrm{Activation}(x A^T + b)`

    Args:
        n_in (~torch.Tensor):
            The size of each input feature.
        n_out (~torch.Tensor):
            The size of each output feature.
        dropout (float):
            If non-zero, introduce a :class:`SharedDropout` layer on the output with this dropout ratio. Default: 0.
        activation (bool):
            Whether to use activations. Default: True.
    """

    def __init__(self, n_in, n_out, dropout=0, activation=True):
        super().__init__()
        self.n_in = n_in
        self.n_out = n_out
        self.linear = nn.Linear(n_in, n_out)
        self.activation = nn.LeakyReLU(negative_slope=0.1
            ) if activation else nn.Identity()
        self.dropout = SharedDropout(p=dropout)
        self.reset_parameters()

    def __repr__(self):
        s = f'n_in={self.n_in}, n_out={self.n_out}'
        if self.dropout.p > 0:
            s += f', dropout={self.dropout.p}'
        return f'{self.__class__.__name__}({s})'

    def reset_parameters(self):
        nn.init.orthogonal_(self.linear.weight)
        nn.init.zeros_(self.linear.bias)

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


Measured latency_ms: 0.063915