import torch
from torch import nn
from torch.nn import functional as F


class MFH(nn.Module):

    def __init__(self, input_dims, output_dim, mm_dim=1200, factor=2,
        activ_input='relu', activ_output='relu', normalize=False,
        dropout_input=0.0, dropout_pre_lin=0.0, dropout_output=0.0):
        super(MFH, self).__init__()
        self.input_dims = input_dims
        self.output_dim = output_dim
        self.mm_dim = mm_dim
        self.factor = factor
        self.activ_input = activ_input
        self.activ_output = activ_output
        self.normalize = normalize
        self.dropout_input = dropout_input
        self.dropout_pre_lin = dropout_pre_lin
        self.dropout_output = dropout_output
        self.linear0_0 = nn.Linear(input_dims[0], mm_dim * factor)
        self.linear1_0 = nn.Linear(input_dims[1], mm_dim * factor)
        self.linear0_1 = nn.Linear(input_dims[0], mm_dim * factor)
        self.linear1_1 = nn.Linear(input_dims[1], mm_dim * factor)
        self.linear_out = nn.Linear(mm_dim * 2, output_dim)
        self.n_params = sum(p.numel() for p in self.parameters() if p.
            requires_grad)

    def forward(self, x):
        x0 = self.linear0_0(x[0])
        x1 = self.linear1_0(x[1])
        if self.activ_input:
            x0 = getattr(F, self.activ_input)(x0)
            x1 = getattr(F, self.activ_input)(x1)
        if self.dropout_input > 0:
            x0 = F.dropout(x0, p=self.dropout_input, training=self.training)
            x1 = F.dropout(x1, p=self.dropout_input, training=self.training)
        z_0_skip = x0 * x1
        if self.dropout_pre_lin:
            z_0_skip = F.dropout(z_0_skip, p=self.dropout_pre_lin, training
                =self.training)
        z_0 = z_0_skip.view(z_0_skip.size(0), self.mm_dim, self.factor)
        z_0 = z_0.sum(2)
        if self.normalize:
            z_0 = torch.sqrt(F.relu(z_0)) - torch.sqrt(F.relu(-z_0))
            z_0 = F.normalize(z_0, p=2)
        x0 = self.linear0_1(x[0])
        x1 = self.linear1_1(x[1])
        if self.activ_input:
            x0 = getattr(F, self.activ_input)(x0)
            x1 = getattr(F, self.activ_input)(x1)
        if self.dropout_input > 0:
            x0 = F.dropout(x0, p=self.dropout_input, training=self.training)
            x1 = F.dropout(x1, p=self.dropout_input, training=self.training)
        z_1 = x0 * x1 * z_0_skip
        if self.dropout_pre_lin > 0:
            z_1 = F.dropout(z_1, p=self.dropout_pre_lin, training=self.training
                )
        z_1 = z_1.view(z_1.size(0), self.mm_dim, self.factor)
        z_1 = z_1.sum(2)
        if self.normalize:
            z_1 = torch.sqrt(F.relu(z_1)) - torch.sqrt(F.relu(-z_1))
            z_1 = F.normalize(z_1, p=2)
        cat_dim = z_0.dim() - 1
        z = torch.cat([z_0, z_1], cat_dim)
        z = self.linear_out(z)
        if self.activ_output:
            z = getattr(F, self.activ_output)(z)
        if self.dropout_output > 0:
            z = F.dropout(z, p=self.dropout_output, training=self.training)
        return z


def get_inputs():
    return [torch.rand([4, 4, 4])]


def get_init_inputs():
    return [[], {'input_dims': [4, 4], 'output_dim': 4}]

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 import 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_cat_0(in_ptr0, in_ptr1, in_ptr2, in_ptr3, out_ptr0,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 9600
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 2400
    x1 = xindex // 2400
    x2 = xindex
    tmp0 = x0
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 1200, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (2 * x0 + 2400 * x1), tmp4 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp6 = tl.full([1], 0, tl.int32)
    tmp7 = triton_helpers.maximum(tmp6, tmp5)
    tmp8 = tl.load(in_ptr1 + (2 * x0 + 2400 * x1), tmp4 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp9 = triton_helpers.maximum(tmp6, tmp8)
    tmp10 = tmp7 * tmp9
    tmp11 = tl.load(in_ptr0 + (1 + 2 * x0 + 2400 * x1), tmp4 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp12 = triton_helpers.maximum(tmp6, tmp11)
    tmp13 = tl.load(in_ptr1 + (1 + 2 * x0 + 2400 * x1), tmp4 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp14 = triton_helpers.maximum(tmp6, tmp13)
    tmp15 = tmp12 * tmp14
    tmp16 = tmp10 + tmp15
    tmp17 = tl.full(tmp16.shape, 0.0, tmp16.dtype)
    tmp18 = tl.where(tmp4, tmp16, tmp17)
    tmp19 = tmp0 >= tmp3
    tl.full([1], 2400, tl.int64)
    tmp22 = tl.load(in_ptr2 + (2 * (-1200 + x0) + 2400 * x1), tmp19 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp23 = triton_helpers.maximum(tmp6, tmp22)
    tmp24 = tl.load(in_ptr3 + (2 * (-1200 + x0) + 2400 * x1), tmp19 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp25 = triton_helpers.maximum(tmp6, tmp24)
    tmp26 = tmp23 * tmp25
    tmp27 = tl.load(in_ptr0 + (2 * (-1200 + x0) + 2400 * x1), tmp19 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp28 = triton_helpers.maximum(tmp6, tmp27)
    tmp29 = tl.load(in_ptr1 + (2 * (-1200 + x0) + 2400 * x1), tmp19 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp30 = triton_helpers.maximum(tmp6, tmp29)
    tmp31 = tmp28 * tmp30
    tmp32 = tmp26 * tmp31
    tmp33 = tl.load(in_ptr2 + (1 + 2 * (-1200 + x0) + 2400 * x1), tmp19 &
        xmask, eviction_policy='evict_last', other=0.0)
    tmp34 = triton_helpers.maximum(tmp6, tmp33)
    tmp35 = tl.load(in_ptr3 + (1 + 2 * (-1200 + x0) + 2400 * x1), tmp19 &
        xmask, eviction_policy='evict_last', other=0.0)
    tmp36 = triton_helpers.maximum(tmp6, tmp35)
    tmp37 = tmp34 * tmp36
    tmp38 = tl.load(in_ptr0 + (1 + 2 * (-1200 + x0) + 2400 * x1), tmp19 &
        xmask, eviction_policy='evict_last', other=0.0)
    tmp39 = triton_helpers.maximum(tmp6, tmp38)
    tmp40 = tl.load(in_ptr1 + (1 + 2 * (-1200 + x0) + 2400 * x1), tmp19 &
        xmask, eviction_policy='evict_last', other=0.0)
    tmp41 = triton_helpers.maximum(tmp6, tmp40)
    tmp42 = tmp39 * tmp41
    tmp43 = tmp37 * tmp42
    tmp44 = tmp32 + tmp43
    tmp45 = tl.full(tmp44.shape, 0.0, tmp44.dtype)
    tmp46 = tl.where(tmp19, tmp44, tmp45)
    tmp47 = tl.where(tmp4, tmp18, tmp46)
    tl.store(out_ptr0 + x2, tmp47, xmask)


@triton.jit
def triton_poi_fused_relu_threshold_backward_1(in_out_ptr0, in_ptr0,
    out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 4
    tmp0 = tl.load(in_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp5 = 0.0
    tmp6 = tmp4 <= tmp5
    tl.store(in_out_ptr0 + x2, tmp4, xmask)
    tl.store(out_ptr0 + x2, tmp6, xmask)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7, primals_8, primals_9, primals_10, primals_11) = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_2, (2400, 4), (4, 1))
    assert_size_stride(primals_3, (2400,), (1,))
    assert_size_stride(primals_4, (2400, 4), (4, 1))
    assert_size_stride(primals_5, (2400,), (1,))
    assert_size_stride(primals_6, (2400, 4), (4, 1))
    assert_size_stride(primals_7, (2400,), (1,))
    assert_size_stride(primals_8, (2400, 4), (4, 1))
    assert_size_stride(primals_9, (2400,), (1,))
    assert_size_stride(primals_10, (4, 2400), (2400, 1))
    assert_size_stride(primals_11, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 2400), (2400, 1), torch.float32)
        extern_kernels.addmm(primals_3, reinterpret_tensor(primals_1, (4, 4
            ), (4, 1), 0), reinterpret_tensor(primals_2, (4, 2400), (1, 4),
            0), alpha=1, beta=1, out=buf0)
        del primals_2
        del primals_3
        buf1 = empty_strided_cuda((4, 2400), (2400, 1), torch.float32)
        extern_kernels.addmm(primals_5, reinterpret_tensor(primals_1, (4, 4
            ), (4, 1), 16), reinterpret_tensor(primals_4, (4, 2400), (1, 4),
            0), alpha=1, beta=1, out=buf1)
        del primals_4
        del primals_5
        buf2 = empty_strided_cuda((4, 2400), (2400, 1), torch.float32)
        extern_kernels.addmm(primals_7, reinterpret_tensor(primals_1, (4, 4
            ), (4, 1), 0), reinterpret_tensor(primals_6, (4, 2400), (1, 4),
            0), alpha=1, beta=1, out=buf2)
        del primals_6
        del primals_7
        buf3 = empty_strided_cuda((4, 2400), (2400, 1), torch.float32)
        extern_kernels.addmm(primals_9, reinterpret_tensor(primals_1, (4, 4
            ), (4, 1), 16), reinterpret_tensor(primals_8, (4, 2400), (1, 4),
            0), alpha=1, beta=1, out=buf3)
        del primals_8
        del primals_9
        buf4 = empty_strided_cuda((4, 2400), (2400, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_cat_0[grid(9600)](buf0, buf1, buf2, buf3, buf4, 
            9600, XBLOCK=128, num_warps=4, num_stages=1)
        buf5 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.mm(buf4, reinterpret_tensor(primals_10, (2400, 4), (
            1, 2400), 0), out=buf5)
        buf6 = buf5
        del buf5
        buf7 = empty_strided_cuda((4, 4), (4, 1), torch.bool)
        triton_poi_fused_relu_threshold_backward_1[grid(16)](buf6,
            primals_11, buf7, 16, XBLOCK=16, num_warps=1, num_stages=1)
        del primals_11
    return buf6, reinterpret_tensor(primals_1, (4, 4), (4, 1), 0
        ), buf0, reinterpret_tensor(primals_1, (4, 4), (4, 1), 16
        ), buf1, buf2, buf3, buf4, buf7, primals_10


class MFHNew(nn.Module):

    def __init__(self, input_dims, output_dim, mm_dim=1200, factor=2,
        activ_input='relu', activ_output='relu', normalize=False,
        dropout_input=0.0, dropout_pre_lin=0.0, dropout_output=0.0):
        super(MFHNew, self).__init__()
        self.input_dims = input_dims
        self.output_dim = output_dim
        self.mm_dim = mm_dim
        self.factor = factor
        self.activ_input = activ_input
        self.activ_output = activ_output
        self.normalize = normalize
        self.dropout_input = dropout_input
        self.dropout_pre_lin = dropout_pre_lin
        self.dropout_output = dropout_output
        self.linear0_0 = nn.Linear(input_dims[0], mm_dim * factor)
        self.linear1_0 = nn.Linear(input_dims[1], mm_dim * factor)
        self.linear0_1 = nn.Linear(input_dims[0], mm_dim * factor)
        self.linear1_1 = nn.Linear(input_dims[1], mm_dim * factor)
        self.linear_out = nn.Linear(mm_dim * 2, output_dim)
        self.n_params = sum(p.numel() for p in self.parameters() if p.
            requires_grad)

    def forward(self, input_0):
        primals_2 = self.linear0_0.weight
        primals_3 = self.linear0_0.bias
        primals_4 = self.linear1_0.weight
        primals_5 = self.linear1_0.bias
        primals_6 = self.linear0_1.weight
        primals_7 = self.linear0_1.bias
        primals_8 = self.linear1_1.weight
        primals_9 = self.linear1_1.bias
        primals_10 = self.linear_out.weight
        primals_11 = self.linear_out.bias
        primals_1 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7, primals_8, primals_9,
            primals_10, primals_11])
        return output[0]
