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


class Mutan(nn.Module):

    def __init__(self, input_dims, output_dim, mm_dim=1600, rank=15, shared
        =False, normalize=False, dropout_input=0.0, dropout_pre_lin=0.0,
        dropout_output=0.0):
        super(Mutan, self).__init__()
        self.input_dims = input_dims
        self.shared = shared
        self.mm_dim = mm_dim
        self.rank = rank
        self.output_dim = output_dim
        self.dropout_input = dropout_input
        self.dropout_pre_lin = dropout_pre_lin
        self.dropout_output = dropout_output
        self.normalize = normalize
        self.linear0 = nn.Linear(input_dims[0], mm_dim)
        self.merge_linear0 = nn.Linear(mm_dim, mm_dim * rank)
        if self.shared:
            self.linear1 = self.linear0
            self.merge_linear1 = self.merge_linear0
        else:
            self.linear1 = nn.Linear(input_dims[1], mm_dim)
            self.merge_linear1 = nn.Linear(mm_dim, mm_dim * rank)
        self.linear_out = nn.Linear(mm_dim, output_dim)
        self.n_params = sum(p.numel() for p in self.parameters() if p.
            requires_grad)

    def forward(self, x):
        x0 = self.linear0(x[0])
        x1 = self.linear1(x[1])
        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)
        m0 = self.merge_linear0(x0)
        m1 = self.merge_linear1(x1)
        m = m0 * m1
        m = m.view(-1, self.rank, self.mm_dim)
        z = torch.sum(m, 1)
        if self.normalize:
            z = torch.sqrt(F.relu(z)) - torch.sqrt(F.relu(-z))
            z = F.normalize(z, p=2)
        if self.dropout_pre_lin > 0:
            z = F.dropout(z, p=self.dropout_pre_lin, training=self.training)
        z = self.linear_out(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, 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
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_sum_0(in_ptr0, in_ptr1, out_ptr0, xnumel, rnumel,
    XBLOCK: tl.constexpr):
    xnumel = 25600
    rnumel = 15
    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, :]
    rmask = rindex < rnumel
    r2 = rindex
    x0 = xindex % 1600
    x1 = xindex // 1600
    x3 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 1600 * r2 + 24000 * x1), rmask & xmask,
        other=0.0)
    tmp1 = tl.load(in_ptr1 + (x0 + 1600 * r2 + 24000 * x1), rmask & xmask,
        other=0.0)
    tmp2 = tmp0 * tmp1
    tmp3 = tl.broadcast_to(tmp2, [XBLOCK, RBLOCK])
    tmp5 = tl.where(rmask & xmask, tmp3, 0)
    tmp6 = tl.sum(tmp5, 1)[:, None]
    tl.store(out_ptr0 + x3, 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, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (1600, 4), (4, 1))
    assert_size_stride(primals_3, (1600,), (1,))
    assert_size_stride(primals_4, (1600, 4), (4, 1))
    assert_size_stride(primals_5, (1600,), (1,))
    assert_size_stride(primals_6, (24000, 1600), (1600, 1))
    assert_size_stride(primals_7, (24000,), (1,))
    assert_size_stride(primals_8, (24000, 1600), (1600, 1))
    assert_size_stride(primals_9, (24000,), (1,))
    assert_size_stride(primals_10, (4, 1600), (1600, 1))
    assert_size_stride(primals_11, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((16, 1600), (1600, 1), torch.float32)
        extern_kernels.addmm(primals_3, reinterpret_tensor(primals_1, (16, 
            4), (4, 1), 0), reinterpret_tensor(primals_2, (4, 1600), (1, 4),
            0), alpha=1, beta=1, out=buf0)
        del primals_2
        del primals_3
        buf1 = empty_strided_cuda((16, 1600), (1600, 1), torch.float32)
        extern_kernels.addmm(primals_5, reinterpret_tensor(primals_1, (16, 
            4), (4, 1), 64), reinterpret_tensor(primals_4, (4, 1600), (1, 4
            ), 0), alpha=1, beta=1, out=buf1)
        del primals_4
        del primals_5
        buf2 = empty_strided_cuda((16, 24000), (24000, 1), torch.float32)
        extern_kernels.addmm(primals_7, buf0, reinterpret_tensor(primals_6,
            (1600, 24000), (1, 1600), 0), alpha=1, beta=1, out=buf2)
        del primals_7
        buf3 = empty_strided_cuda((16, 24000), (24000, 1), torch.float32)
        extern_kernels.addmm(primals_9, buf1, reinterpret_tensor(primals_8,
            (1600, 24000), (1, 1600), 0), alpha=1, beta=1, out=buf3)
        del primals_9
        buf4 = empty_strided_cuda((16, 1600), (1600, 1), torch.float32)
        get_raw_stream(0)
        triton_per_fused_sum_0[grid(25600)](buf2, buf3, buf4, 25600, 15,
            XBLOCK=32, num_warps=4, num_stages=1)
        buf5 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_11, buf4, reinterpret_tensor(
            primals_10, (1600, 4), (1, 1600), 0), alpha=1, beta=1, out=buf5)
        del primals_11
    return buf5, reinterpret_tensor(primals_1, (16, 4), (4, 1), 0
        ), reinterpret_tensor(primals_1, (16, 4), (4, 1), 64
        ), buf0, buf2, buf1, buf3, buf4, primals_10, primals_8, primals_6


class MutanNew(nn.Module):

    def __init__(self, input_dims, output_dim, mm_dim=1600, rank=15, shared
        =False, normalize=False, dropout_input=0.0, dropout_pre_lin=0.0,
        dropout_output=0.0):
        super(MutanNew, self).__init__()
        self.input_dims = input_dims
        self.shared = shared
        self.mm_dim = mm_dim
        self.rank = rank
        self.output_dim = output_dim
        self.dropout_input = dropout_input
        self.dropout_pre_lin = dropout_pre_lin
        self.dropout_output = dropout_output
        self.normalize = normalize
        self.linear0 = nn.Linear(input_dims[0], mm_dim)
        self.merge_linear0 = nn.Linear(mm_dim, mm_dim * rank)
        if self.shared:
            self.linear1 = self.linear0
            self.merge_linear1 = self.merge_linear0
        else:
            self.linear1 = nn.Linear(input_dims[1], mm_dim)
            self.merge_linear1 = nn.Linear(mm_dim, mm_dim * rank)
        self.linear_out = nn.Linear(mm_dim, 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.weight
        primals_3 = self.linear0.bias
        primals_6 = self.merge_linear0.weight
        primals_7 = self.merge_linear0.bias
        primals_4 = self.linear1.weight
        primals_5 = self.linear1.bias
        primals_8 = self.merge_linear1.weight
        primals_9 = self.merge_linear1.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]
