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_per_fused_cat_max_0(in_ptr0, out_ptr1, out_ptr2, xnumel, rnumel,
    XBLOCK: tl.constexpr):
    xnumel = 16
    RBLOCK: tl.constexpr = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rindex = tl.arange(0, RBLOCK)[None, :]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r1 = rindex
    x0 = xindex
    x2 = xindex % 4
    x3 = xindex // 4
    tmp0 = tl.load(in_ptr0 + (r1 + 16 * x0), xmask, other=0.0)
    tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
    tmp3 = tl.where(xmask, tmp1, float('-inf'))
    tmp4 = triton_helpers.max2(tmp3, 1)[:, None]
    tl.store(out_ptr1 + (r1 + 16 * x2 + 256 * x3), tmp0, xmask)
    tl.store(out_ptr2 + (r1 + 16 * x2 + 256 * x3), tmp4, xmask)


@triton.jit
def triton_poi_fused_cat_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 % 4
    x4 = xindex // 16
    x3 = xindex // 64
    x5 = xindex % 64
    tmp0 = tl.load(in_ptr0 + (x0 + 16 * x4), xmask, eviction_policy=
        'evict_last')
    tmp1 = tl.load(in_ptr0 + (4 + x0 + 16 * x4), xmask, eviction_policy=
        'evict_last')
    tmp3 = tl.load(in_ptr0 + (8 + x0 + 16 * x4), xmask, eviction_policy=
        'evict_last')
    tmp5 = tl.load(in_ptr0 + (12 + x0 + 16 * x4), xmask, eviction_policy=
        'evict_last')
    tmp2 = triton_helpers.maximum(tmp0, tmp1)
    tmp4 = triton_helpers.maximum(tmp2, tmp3)
    tmp6 = triton_helpers.maximum(tmp4, tmp5)
    tl.store(out_ptr0 + (x5 + 256 * x3), tmp6, xmask)


@triton.jit
def triton_poi_fused_cat_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
    x3 = xindex // 4
    x2 = xindex // 64
    x4 = xindex % 64
    tmp0 = tl.load(in_ptr0 + 4 * x3, xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr0 + (1 + 4 * x3), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (2 + 4 * x3), xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr0 + (3 + 4 * x3), xmask, eviction_policy='evict_last')
    tmp2 = triton_helpers.maximum(tmp0, tmp1)
    tmp4 = triton_helpers.maximum(tmp2, tmp3)
    tmp6 = triton_helpers.maximum(tmp4, tmp5)
    tl.store(out_ptr0 + (x4 + 256 * x2), tmp6, 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, 16), (16, 1))
    assert_size_stride(primals_3, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf6 = empty_strided_cuda((4, 16, 4, 4), (256, 16, 4, 1), torch.float32
            )
        buf2 = reinterpret_tensor(buf6, (4, 4, 4, 4), (256, 16, 4, 1), 0)
        buf5 = reinterpret_tensor(buf6, (4, 4, 4, 4), (256, 16, 4, 1), 192)
        get_raw_stream(0)
        triton_per_fused_cat_max_0[grid(16)](primals_1, buf2, buf5, 16, 16,
            XBLOCK=1, num_warps=2, num_stages=1)
        buf3 = reinterpret_tensor(buf6, (4, 4, 4, 4), (256, 16, 4, 1), 64)
        triton_poi_fused_cat_1[grid(256)](primals_1, buf3, 256, XBLOCK=256,
            num_warps=4, num_stages=1)
        buf4 = reinterpret_tensor(buf6, (4, 4, 4, 4), (256, 16, 4, 1), 128)
        triton_poi_fused_cat_2[grid(256)](primals_1, buf4, 256, XBLOCK=256,
            num_warps=4, num_stages=1)
        del primals_1
        buf7 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_3, reinterpret_tensor(buf6, (64, 16),
            (16, 1), 0), reinterpret_tensor(primals_2, (16, 4), (1, 16), 0),
            alpha=1, beta=1, out=buf7)
        del primals_2
        del primals_3
    return reinterpret_tensor(buf7, (4, 4, 4, 4), (64, 1, 16, 4), 0
        ), reinterpret_tensor(buf6, (64, 16), (16, 1), 0)


class ActionPool(nn.Module):
    """
    Basic pooling operations. 
    """

    def __init__(self, axis, function='mean', expand=True):
        super(ActionPool, self).__init__()
        self.expand = expand
        self._function_name = function
        self._axis_name = axis
        if isinstance(axis, str):
            self.dim = {'row': 0, 'column': 1, 'both': None}[axis]
        else:
            self.dim = axis
        if function == 'max':
            self.function = lambda x, dim: torch.max(x, dim=dim)[0]
        elif function == 'sum':
            self.function = lambda x, dim: torch.sum(x, dim=dim)
        elif function == 'mean':
            self.function = torch.mean
        else:
            raise ValueError('Unrecognised function: %s' % function)

    def forward(self, input):
        if self.dim is None:
            n, p, i, j = input.size()
            input = input.contiguous()
            reshaped = input.view(n, p, i * j)
            output = self.function(reshaped, dim=2)
            if self.expand:
                output = output.unsqueeze(2).unsqueeze(2)
        else:
            output = self.function(input, dim=self.dim + 2)
            if self.expand:
                output = output.unsqueeze(self.dim + 2)
        if self.expand:
            return output.expand_as(input)
        else:
            return output


class MatrixLinear(nn.Linear):
    """
    Matrix-based linear feed-forward layer. Think of it as the
    matrix analog to a feed-forward layer in an MLP.
    """

    def forward(self, input):
        n, p, i, j = input.size()
        input.permute(0, 2, 3, 1)
        state = input.view((n * i * j, p))
        output = super(MatrixLinear, self).forward(state)
        output = output.view((n, i, j, self.out_features)).permute(0, 3, 1, 2)
        return output


class MatrixLayerNew(nn.Module):
    """
    Set layers are linear layers with pooling. Pooling operations
    are defined above.
    """

    def __init__(self, in_features, out_features, pooling='max', axes=[
        'row', 'column', 'both'], name='', debug=False, dropout=0.0):
        super(MatrixLayerNew, self).__init__()
        pool = []
        for axis in axes:
            pool.append(ActionPool(axis, pooling, expand=True))
        self.pool = pool
        self.dropout = dropout
        self.linear = MatrixLinear(in_features * (1 + len(pool)), out_features)
        if dropout > 0.0:
            self.dropoutlayer = nn.Dropout2d(dropout)
        self.name = name
        self.debug = debug

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


Measured latency_ms: 0.015252