from _paritybench_helpers import _mock_config
import torch
import torch.nn.functional as F
import torch.nn as nn
from torch.nn import init
import torch.nn.parallel


class PEM(torch.nn.Module):

    def __init__(self, opt):
        super(PEM, self).__init__()
        self.feat_dim = opt['pem_feat_dim']
        self.batch_size = opt['pem_batch_size']
        self.hidden_dim = opt['pem_hidden_dim']
        self.u_ratio_m = opt['pem_u_ratio_m']
        self.u_ratio_l = opt['pem_u_ratio_l']
        self.output_dim = 1
        self.pem_best_loss = 1000000
        self.fc1 = torch.nn.Linear(in_features=self.feat_dim, out_features=
            self.hidden_dim, bias=True)
        self.fc2 = torch.nn.Linear(in_features=self.hidden_dim,
            out_features=self.output_dim, bias=True)
        self.reset_params()

    @staticmethod
    def weight_init(m):
        if isinstance(m, nn.Conv2d):
            init.xavier_uniform_(m.weight)
            init.constant(m.bias, 0)

    def reset_params(self):
        for i, m in enumerate(self.modules()):
            self.weight_init(m)

    def forward(self, x):
        x = F.relu(0.1 * self.fc1(x))
        x = torch.sigmoid(0.1 * self.fc2(x))
        return x


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


def get_init_inputs():
    return [[], {'opt': _mock_config(pem_feat_dim=4, pem_batch_size=4,
        pem_hidden_dim=4, pem_u_ratio_m=4, pem_u_ratio_l=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
import torch.nn as nn
from torch.nn import init
import torch.nn.parallel
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_relu_threshold_backward_0(in_out_ptr0, 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
    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 = 0.1
    tmp4 = tmp2 * tmp3
    tmp5 = tl.full([1], 0, tl.int32)
    tmp6 = triton_helpers.maximum(tmp5, tmp4)
    tmp7 = 0.0
    tmp8 = tmp6 <= tmp7
    tl.store(in_out_ptr0 + x2, tmp6, xmask)
    tl.store(out_ptr0 + x2, tmp8, xmask)


@triton.jit
def triton_poi_fused_mul_sigmoid_1(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl
    .constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_out_ptr0 + x0, xmask)
    tmp1 = tl.load(in_ptr0 + 0)
    tmp2 = tl.broadcast_to(tmp1, [XBLOCK])
    tmp3 = tmp0 + tmp2
    tmp4 = 0.1
    tmp5 = tmp3 * tmp4
    tmp6 = tl.sigmoid(tmp5)
    tl.store(in_out_ptr0 + x0, tmp6, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4), (4, 1))
    assert_size_stride(primals_2, (4,), (1,))
    assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_4, (1, 4), (4, 1))
    assert_size_stride(primals_5, (1,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_3, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_1, (4, 4), (1, 4), 0), out=buf0)
        del primals_1
        buf1 = reinterpret_tensor(buf0, (4, 4, 4, 4), (64, 16, 4, 1), 0)
        del buf0
        buf4 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
        get_raw_stream(0)
        triton_poi_fused_mul_relu_threshold_backward_0[grid(256)](buf1,
            primals_2, buf4, 256, XBLOCK=128, num_warps=4, num_stages=1)
        del primals_2
        buf2 = empty_strided_cuda((64, 1), (1, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf1, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_4, (4, 1), (1, 4), 0), out=buf2)
        buf3 = reinterpret_tensor(buf2, (4, 4, 4, 1), (16, 4, 1, 1), 0)
        del buf2
        triton_poi_fused_mul_sigmoid_1[grid(64)](buf3, primals_5, 64,
            XBLOCK=64, num_warps=1, num_stages=1)
        del primals_5
    return buf3, reinterpret_tensor(primals_3, (64, 4), (4, 1), 0
        ), reinterpret_tensor(buf1, (64, 4), (4, 1), 0), buf3, primals_4, buf4


class PEMNew(torch.nn.Module):

    def __init__(self, opt):
        super(PEMNew, self).__init__()
        self.feat_dim = opt['pem_feat_dim']
        self.batch_size = opt['pem_batch_size']
        self.hidden_dim = opt['pem_hidden_dim']
        self.u_ratio_m = opt['pem_u_ratio_m']
        self.u_ratio_l = opt['pem_u_ratio_l']
        self.output_dim = 1
        self.pem_best_loss = 1000000
        self.fc1 = torch.nn.Linear(in_features=self.feat_dim, out_features=
            self.hidden_dim, bias=True)
        self.fc2 = torch.nn.Linear(in_features=self.hidden_dim,
            out_features=self.output_dim, bias=True)
        self.reset_params()

    @staticmethod
    def weight_init(m):
        if isinstance(m, nn.Conv2d):
            init.xavier_uniform_(m.weight)
            init.constant(m.bias, 0)

    def reset_params(self):
        for i, m in enumerate(self.modules()):
            self.weight_init(m)

    def forward(self, input_0):
        primals_1 = self.fc1.weight
        primals_2 = self.fc1.bias
        primals_4 = self.fc2.weight
        primals_5 = self.fc2.bias
        primals_3 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4, primals_5])
        return output[0]
