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


class LayerNorm(nn.Module):

    def __init__(self, features, eps=1e-06):
        super(LayerNorm, self).__init__()
        self.gamma = nn.Parameter(torch.ones(features))
        self.beta = nn.Parameter(torch.zeros(features))
        self.eps = eps

    def forward(self, x):
        mean = x.mean(-1, keepdim=True)
        std = x.std(-1, keepdim=True)
        return self.gamma * (x - mean) / (std + self.eps) + self.beta


class normrelu(nn.Module):

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

    def forward(self, x):
        dim = 1
        x = F.relu(x) / torch.max(x, dim, keepdim=True)[0]
        return x


class LinearBlock(nn.Module):

    def __init__(self, pre_dim, dim, activation='none', dropout_rate=0,
        use_batch_norm=False, use_layer_norm=False):
        self.linear = None
        self.bn = None
        self.ln = None
        self.act = None
        self.dropout_layer = None
        super(LinearBlock, self).__init__()
        if activation == 'relu':
            self.act = nn.ReLU()
        elif activation == 'tanh':
            self.act = nn.Tanh()
        elif activation == 'sigmoid':
            self.act = nn.Sigmoid()
        elif activation == 'normrelu':
            self.act = normrelu()
        elif activation == 'none':
            self.act = None
        else:
            None
        if use_batch_norm:
            self.linear = nn.Linear(pre_dim, dim, bias=False)
            self.bn = nn.BatchNorm1d(dim, momentum=0.05)
        else:
            self.linear = nn.Linear(pre_dim, dim)
        if use_layer_norm:
            self.ln = LayerNorm(dim)
        if dropout_rate > 0.0001:
            self.dropout_layer = nn.Dropout(p=dropout_rate)

    def forward(self, x):
        if self.linear is not None:
            x = self.linear(x)
        if self.bn is not None:
            x = self.bn(x)
        if self.ln is not None:
            x = self.ln(x)
        if self.act is not None:
            x = self.act(x)
        if self.dropout_layer is not None:
            x = self.dropout_layer(x)
        return x


class FinalLayer(nn.Module):
    """
    final classification and bounding box regression layer for RPN KWS
    """

    def __init__(self, input_dim, num_class):
        super(FinalLayer, self).__init__()
        self.linear = LinearBlock(input_dim, input_dim, activation='relu')
        self.cls_score_KWS = nn.Linear(input_dim, num_class, bias=True)
        self.bbox_score_KWS = nn.Linear(input_dim, 2, bias=True)

    def forward(self, x):
        x = self.linear(x)
        kws_cls_score = self.cls_score_KWS(x)
        kws_bbox_pred = self.bbox_score_KWS(x)
        return kws_cls_score, kws_bbox_pred


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


def get_init_inputs():
    return [[], {'input_dim': 4, 'num_class': 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
import torch.nn.functional as F
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_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 = 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) = 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, (4, 4), (4, 1))
    assert_size_stride(primals_5, (4,), (1,))
    assert_size_stride(primals_6, (2, 4), (4, 1))
    assert_size_stride(primals_7, (2,), (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_relu_threshold_backward_0[grid(256)](buf1,
            primals_2, buf4, 256, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_2
        buf2 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_5, reinterpret_tensor(buf1, (64, 4), (
            4, 1), 0), reinterpret_tensor(primals_4, (4, 4), (1, 4), 0),
            alpha=1, beta=1, out=buf2)
        del primals_5
        buf3 = empty_strided_cuda((64, 2), (2, 1), torch.float32)
        extern_kernels.addmm(primals_7, reinterpret_tensor(buf1, (64, 4), (
            4, 1), 0), reinterpret_tensor(primals_6, (4, 2), (1, 4), 0),
            alpha=1, beta=1, out=buf3)
        del primals_7
    return reinterpret_tensor(buf2, (4, 4, 4, 4), (64, 16, 4, 1), 0
        ), reinterpret_tensor(buf3, (4, 4, 4, 2), (32, 8, 2, 1), 0
        ), reinterpret_tensor(primals_3, (64, 4), (4, 1), 0
        ), reinterpret_tensor(buf1, (64, 4), (4, 1), 0
        ), primals_6, primals_4, buf4


class LayerNorm(nn.Module):

    def __init__(self, features, eps=1e-06):
        super(LayerNorm, self).__init__()
        self.gamma = nn.Parameter(torch.ones(features))
        self.beta = nn.Parameter(torch.zeros(features))
        self.eps = eps

    def forward(self, x):
        mean = x.mean(-1, keepdim=True)
        std = x.std(-1, keepdim=True)
        return self.gamma * (x - mean) / (std + self.eps) + self.beta


class normrelu(nn.Module):

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

    def forward(self, x):
        dim = 1
        x = F.relu(x) / torch.max(x, dim, keepdim=True)[0]
        return x


class LinearBlock(nn.Module):

    def __init__(self, pre_dim, dim, activation='none', dropout_rate=0,
        use_batch_norm=False, use_layer_norm=False):
        self.linear = None
        self.bn = None
        self.ln = None
        self.act = None
        self.dropout_layer = None
        super(LinearBlock, self).__init__()
        if activation == 'relu':
            self.act = nn.ReLU()
        elif activation == 'tanh':
            self.act = nn.Tanh()
        elif activation == 'sigmoid':
            self.act = nn.Sigmoid()
        elif activation == 'normrelu':
            self.act = normrelu()
        elif activation == 'none':
            self.act = None
        else:
            None
        if use_batch_norm:
            self.linear = nn.Linear(pre_dim, dim, bias=False)
            self.bn = nn.BatchNorm1d(dim, momentum=0.05)
        else:
            self.linear = nn.Linear(pre_dim, dim)
        if use_layer_norm:
            self.ln = LayerNorm(dim)
        if dropout_rate > 0.0001:
            self.dropout_layer = nn.Dropout(p=dropout_rate)

    def forward(self, x):
        if self.linear is not None:
            x = self.linear(x)
        if self.bn is not None:
            x = self.bn(x)
        if self.ln is not None:
            x = self.ln(x)
        if self.act is not None:
            x = self.act(x)
        if self.dropout_layer is not None:
            x = self.dropout_layer(x)
        return x


class FinalLayerNew(nn.Module):
    """
    final classification and bounding box regression layer for RPN KWS
    """

    def __init__(self, input_dim, num_class):
        super(FinalLayerNew, self).__init__()
        self.linear = LinearBlock(input_dim, input_dim, activation='relu')
        self.cls_score_KWS = nn.Linear(input_dim, num_class, bias=True)
        self.bbox_score_KWS = nn.Linear(input_dim, 2, bias=True)

    def forward(self, input_0):
        primals_1 = self.linear.linear.weight
        primals_2 = self.linear.linear.bias
        primals_4 = self.cls_score_KWS.weight
        primals_5 = self.cls_score_KWS.bias
        primals_6 = self.bbox_score_KWS.weight
        primals_7 = self.bbox_score_KWS.bias
        primals_3 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7])
        return output[0], output[1]
