from torch.autograd import Function
import torch
import torch.nn as nn
from typing import Tuple
from typing import Optional
from typing import Any
import torch.utils.data


class GradientReverseFunction(Function):

    @staticmethod
    def forward(ctx: 'Any', input: 'torch.Tensor', coeff: 'Optional[float]'=1.0
        ) ->torch.Tensor:
        ctx.coeff = coeff
        output = input * 1.0
        return output

    @staticmethod
    def backward(ctx: 'Any', grad_output: 'torch.Tensor') ->Tuple[torch.
        Tensor, Any]:
        return grad_output.neg() * ctx.coeff, None


class GradientReverseLayer(nn.Module):

    def __init__(self):
        super(GradientReverseLayer, self).__init__()

    def forward(self, *input):
        return GradientReverseFunction.apply(*input)


class Theta(nn.Module):
    """
    maximize loss respect to :math:`	heta`
    minimize loss respect to features
    """

    def __init__(self, dim: 'int'):
        super(Theta, self).__init__()
        self.grl1 = GradientReverseLayer()
        self.grl2 = GradientReverseLayer()
        self.layer1 = nn.Linear(dim, dim)
        nn.init.eye_(self.layer1.weight)
        nn.init.zeros_(self.layer1.bias)

    def forward(self, features: 'torch.Tensor') ->torch.Tensor:
        features = self.grl1(features)
        return self.grl2(self.layer1(features))


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


def get_init_inputs():
    return [[], {'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.autograd import Function
import torch.nn as nn
from typing import Tuple
from typing import Optional
from typing import Any
import torch.utils.data
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_mul_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
    tmp0 = tl.load(in_ptr0 + x0, xmask)
    tmp1 = 1.0
    tmp2 = tmp0 * tmp1
    tl.store(out_ptr0 + x0, tmp2, xmask)


@triton.jit
def triton_poi_fused_mul_1(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
    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 = 1.0
    tmp4 = tmp2 * tmp3
    tl.store(in_out_ptr0 + x2, tmp4, 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, 1))
    assert_size_stride(primals_3, (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_mul_0[grid(256)](primals_1, buf0, 256, XBLOCK=128,
            num_warps=4, num_stages=1)
        del primals_1
        buf1 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf0, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_2, (4, 4), (1, 4), 0), out=buf1)
        del primals_2
        buf2 = reinterpret_tensor(buf1, (4, 4, 4, 4), (64, 16, 4, 1), 0)
        del buf1
        triton_poi_fused_mul_1[grid(256)](buf2, primals_3, 256, XBLOCK=128,
            num_warps=4, num_stages=1)
        del primals_3
    return buf2, reinterpret_tensor(buf0, (64, 4), (4, 1), 0)


class GradientReverseFunction(Function):

    @staticmethod
    def forward(ctx: 'Any', input: 'torch.Tensor', coeff: 'Optional[float]'=1.0
        ) ->torch.Tensor:
        ctx.coeff = coeff
        output = input * 1.0
        return output

    @staticmethod
    def backward(ctx: 'Any', grad_output: 'torch.Tensor') ->Tuple[torch.
        Tensor, Any]:
        return grad_output.neg() * ctx.coeff, None


class GradientReverseLayer(nn.Module):

    def __init__(self):
        super(GradientReverseLayer, self).__init__()

    def forward(self, *input):
        return GradientReverseFunction.apply(*input)


class ThetaNew(nn.Module):
    """
    maximize loss respect to :math:`	heta`
    minimize loss respect to features
    """

    def __init__(self, dim: 'int'):
        super(ThetaNew, self).__init__()
        self.grl1 = GradientReverseLayer()
        self.grl2 = GradientReverseLayer()
        self.layer1 = nn.Linear(dim, dim)
        nn.init.eye_(self.layer1.weight)
        nn.init.zeros_(self.layer1.bias)

    def forward(self, input_0):
        primals_2 = self.layer1.weight
        primals_3 = self.layer1.bias
        primals_1 = input_0
        output = call([primals_1, primals_2, primals_3])
        return output[0]
