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 math
import torch as th
from torch.nn import Parameter
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_clone_0(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 % 16
    x1 = xindex // 16 % 4
    x2 = xindex // 64
    x3 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 16 * x2 + 64 * x1), xmask)
    tl.store(out_ptr0 + x3, tmp0, xmask)


@triton.jit
def triton_poi_fused_clone_1(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 % 64
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tl.store(out_ptr0 + x2, tmp0, xmask)


@triton.jit
def triton_poi_fused_add_2(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x4 = xindex
    x0 = xindex % 4
    x2 = xindex // 16 % 4
    tmp0 = tl.load(in_out_ptr0 + x4, xmask)
    tmp1 = tl.load(in_ptr0 + (x0 + 4 * x2), xmask, eviction_policy='evict_last'
        )
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + x4, tmp2, 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, 4, 4), (16, 4, 1))
    assert_size_stride(primals_3, (4, 4), (4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_clone_0[grid(256)](primals_1, buf0, 256, XBLOCK=
            256, num_warps=4, num_stages=1)
        del primals_1
        buf1 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused_clone_1[grid(256)](primals_2, buf1, 256, XBLOCK=
            256, num_warps=4, num_stages=1)
        del primals_2
        buf2 = empty_strided_cuda((16, 4, 4), (16, 4, 1), torch.float32)
        extern_kernels.bmm(reinterpret_tensor(buf0, (16, 4, 4), (16, 4, 1),
            0), reinterpret_tensor(buf1, (16, 4, 4), (16, 4, 1), 0), out=buf2)
        del buf1
        buf3 = reinterpret_tensor(buf2, (4, 4, 4, 4), (64, 16, 4, 1), 0)
        del buf2
        triton_poi_fused_add_2[grid(256)](buf3, primals_3, 256, XBLOCK=256,
            num_warps=4, num_stages=1)
        del primals_3
    return reinterpret_tensor(buf3, (4, 4, 4, 4), (16, 64, 4, 1), 0
        ), reinterpret_tensor(buf0, (16, 4, 4), (16, 1, 4), 0)


def functional_linear3d(input, weight, bias=None):
    """
    Apply a linear transformation to the incoming data: :math:`y = xA^T + b`.

    Shape:
        - Input: :math:`(N, *, in\\_features)` where `*` means any number of
          additional dimensions
        - Weight: :math:`(out\\_features, in\\_features)`
        - Bias: :math:`(out\\_features)`
        - Output: :math:`(N, *, out\\_features)`
    """
    output = input.transpose(0, 1).matmul(weight)
    if bias is not None:
        output += bias.unsqueeze(1)
    return output.transpose(0, 1)


class Linear3DNew(th.nn.Module):
    """Applies a linear transformation to the incoming data: :math:`y = Ax + b`.

    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.Linear(3, 20, 30)
        >>> input = torch.randn(128, 20)
        >>> output = m(input)
        >>> print(output.size())
    """

    def __init__(self, channels, in_features, out_features, batch_size=-1,
        bias=True, noise=False):
        super(Linear3DNew, self).__init__()
        self.in_features = in_features
        self.out_features = out_features
        self.channels = channels
        if noise:
            self.in_features += 1
        self.weight = Parameter(th.Tensor(channels, self.in_features,
            out_features))
        if bias:
            self.bias = Parameter(th.Tensor(channels, out_features))
        else:
            self.register_parameter('bias', None)
        if noise:
            self.register_buffer('noise', th.Tensor(batch_size, channels, 1))
        self.reset_parameters()

    def reset_parameters(self):
        stdv = 1.0 / math.sqrt(self.weight.size(1))
        self.weight.data.uniform_(-stdv, stdv)
        if self.bias is not None:
            self.bias.data.uniform_(-stdv, stdv)

    def extra_repr(self):
        return 'in_features={}, out_features={}, bias={}'.format(self.
            in_features, self.out_features, self.bias is not None)

    def apply_filter(self, permutation_matrix):
        transpose_weight = self.weight.transpose(1, 2) @ permutation_matrix
        self.weight = Parameter(transpose_weight.transpose(1, 2))

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


Measured latency_ms: 0.023081