import torch
import torch.nn as nn


def truncated_normal_(tensor, mean=0, std=1):
    size = tensor.shape
    tmp = tensor.new_empty(size + (4,)).normal_()
    valid = (tmp < 2) & (tmp > -2)
    ind = valid.max(-1, keepdim=True)[1]
    tensor.data.copy_(tmp.gather(-1, ind).squeeze(-1))
    tensor.data.mul_(std).add_(mean)


def init_weights_orthogonal_normal(m):
    if type(m) == nn.Conv2d or type(m) == nn.ConvTranspose2d:
        nn.init.orthogonal_(m.weight)
        truncated_normal_(m.bias, mean=0, std=0.001)


def weights_init(m):
    classname = m.__class__.__name__
    if type(m) == nn.Conv2d or type(m) == nn.ConvTranspose2d:
        m.weight.data.normal_(0.0, 0.02)
    if classname.find('Linear') != -1:
        nn.init.xavier_uniform_(m.weight.data)
        m.bias.data.fill_(0)


class Noise_injector(nn.Module):

    def __init__(self, n_hidden, z_dim, num_channels, n_channels_out,
        device='cpu'):
        super(Noise_injector, self).__init__()
        self.num_channels = num_channels
        self.n_channels_out = n_channels_out
        self.n_hidden = n_hidden
        self.z_dim = z_dim
        self.device = device
        self.residual = nn.Linear(self.z_dim, self.n_hidden)
        self.scale = nn.Linear(self.z_dim, self.n_hidden)
        self.last_layer = nn.Conv2d(self.n_hidden, self.n_channels_out,
            kernel_size=1)
        self.residual.apply(weights_init)
        self.scale.apply(weights_init)
        self.last_layer.apply(init_weights_orthogonal_normal)

    def forward(self, feature_map, z):
        """
        Z is B x Z_dim and feature_map is B x C x H x W.
        So broadcast Z to batch_sizexlatent_dimxHxW. Behavior is exactly the same as tf.tile (verified)
        """
        residual = self.residual(z).view(z.shape[0], self.n_hidden, 1, 1)
        scale = self.scale(z).view(z.shape[0], self.n_hidden, 1, 1)
        feature_map = (feature_map + residual) * (scale + 1e-05)
        return self.last_layer(feature_map)


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


def get_init_inputs():
    return [[], {'n_hidden': 4, 'z_dim': 4, 'num_channels': 4,
        'n_channels_out': 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_poi_fused_add_mul_0(in_ptr0, in_ptr1, in_ptr2, 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 % 16
    x1 = xindex // 16
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr2 + x1, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp4 = 1e-05
    tmp5 = tmp3 + tmp4
    tmp6 = tmp2 * tmp5
    tl.store(out_ptr0 + x2, tmp6, xmask)


@triton.jit
def triton_poi_fused_convolution_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
    x3 = xindex
    x1 = xindex // 16 % 4
    tmp0 = tl.load(in_out_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + x3, tmp2, xmask)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7, primals_8) = 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, 1))
    assert_size_stride(primals_4, (4, 4), (4, 1))
    assert_size_stride(primals_5, (4,), (1,))
    assert_size_stride(primals_6, (4, 4), (4, 1))
    assert_size_stride(primals_7, (4, 4, 1, 1), (4, 1, 1, 1))
    assert_size_stride(primals_8, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_2, primals_3, reinterpret_tensor(
            primals_1, (4, 4), (1, 4), 0), alpha=1, beta=1, out=buf0)
        del primals_1
        del primals_2
        buf1 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_5, primals_3, reinterpret_tensor(
            primals_4, (4, 4), (1, 4), 0), alpha=1, beta=1, out=buf1)
        del primals_4
        del primals_5
        buf2 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_add_mul_0[grid(256)](primals_6, buf0, buf1, buf2, 
            256, XBLOCK=128, num_warps=4, num_stages=1)
        buf3 = extern_kernels.convolution(buf2, primals_7, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf3, (4, 4, 4, 4), (64, 16, 4, 1))
        buf4 = buf3
        del buf3
        triton_poi_fused_convolution_1[grid(256)](buf4, primals_8, 256,
            XBLOCK=256, num_warps=4, num_stages=1)
        del primals_8
    return buf4, primals_3, primals_6, primals_7, buf0, buf1, buf2


def truncated_normal_(tensor, mean=0, std=1):
    size = tensor.shape
    tmp = tensor.new_empty(size + (4,)).normal_()
    valid = (tmp < 2) & (tmp > -2)
    ind = valid.max(-1, keepdim=True)[1]
    tensor.data.copy_(tmp.gather(-1, ind).squeeze(-1))
    tensor.data.mul_(std).add_(mean)


def init_weights_orthogonal_normal(m):
    if type(m) == nn.Conv2d or type(m) == nn.ConvTranspose2d:
        nn.init.orthogonal_(m.weight)
        truncated_normal_(m.bias, mean=0, std=0.001)


def weights_init(m):
    classname = m.__class__.__name__
    if type(m) == nn.Conv2d or type(m) == nn.ConvTranspose2d:
        m.weight.data.normal_(0.0, 0.02)
    if classname.find('Linear') != -1:
        nn.init.xavier_uniform_(m.weight.data)
        m.bias.data.fill_(0)


class Noise_injectorNew(nn.Module):

    def __init__(self, n_hidden, z_dim, num_channels, n_channels_out,
        device='cpu'):
        super(Noise_injectorNew, self).__init__()
        self.num_channels = num_channels
        self.n_channels_out = n_channels_out
        self.n_hidden = n_hidden
        self.z_dim = z_dim
        self.device = device
        self.residual = nn.Linear(self.z_dim, self.n_hidden)
        self.scale = nn.Linear(self.z_dim, self.n_hidden)
        self.last_layer = nn.Conv2d(self.n_hidden, self.n_channels_out,
            kernel_size=1)
        self.residual.apply(weights_init)
        self.scale.apply(weights_init)
        self.last_layer.apply(init_weights_orthogonal_normal)

    def forward(self, input_0, input_1):
        primals_1 = self.residual.weight
        primals_2 = self.residual.bias
        primals_3 = self.scale.weight
        primals_5 = self.scale.bias
        primals_7 = self.last_layer.weight
        primals_8 = self.last_layer.bias
        primals_4 = input_0
        primals_6 = input_1
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7, primals_8])
        return output[0]
