import torch
import torch.nn as nn


def act_layer(act):
    if act == 'relu':
        return nn.ReLU()
    elif act == 'lrelu':
        return nn.LeakyReLU(LRELU_SLOPE)
    elif act == 'elu':
        return nn.ELU()
    elif act == 'tanh':
        return nn.Tanh()
    elif act == 'prelu':
        return nn.PReLU()
    else:
        raise ValueError('%s not recognized.' % act)


def norm_layer2d(norm, channels):
    if norm == 'batch':
        return nn.BatchNorm2d(channels)
    elif norm == 'instance':
        return nn.InstanceNorm2d(channels, affine=True)
    elif norm == 'layer':
        return nn.GroupNorm(1, channels, affine=True)
    elif norm == 'group':
        return nn.GroupNorm(4, channels, affine=True)
    else:
        raise ValueError('%s not recognized.' % norm)


class Conv2DBlock(nn.Module):

    def __init__(self, in_channels, out_channels, kernel_sizes, strides,
        norm=None, activation=None, padding_mode='replicate'):
        super(Conv2DBlock, self).__init__()
        padding = kernel_sizes // 2 if isinstance(kernel_sizes, int) else (
            kernel_sizes[0] // 2, kernel_sizes[1] // 2)
        self.conv2d = nn.Conv2d(in_channels, out_channels, kernel_sizes,
            strides, padding=padding, padding_mode=padding_mode)
        if activation is None:
            nn.init.xavier_uniform_(self.conv2d.weight, gain=nn.init.
                calculate_gain('linear'))
            nn.init.zeros_(self.conv2d.bias)
        elif activation == 'tanh':
            nn.init.xavier_uniform_(self.conv2d.weight, gain=nn.init.
                calculate_gain('tanh'))
            nn.init.zeros_(self.conv2d.bias)
        elif activation == 'lrelu':
            nn.init.kaiming_uniform_(self.conv2d.weight, a=LRELU_SLOPE,
                nonlinearity='leaky_relu')
            nn.init.zeros_(self.conv2d.bias)
        elif activation == 'relu':
            nn.init.kaiming_uniform_(self.conv2d.weight, nonlinearity='relu')
            nn.init.zeros_(self.conv2d.bias)
        else:
            raise ValueError()
        self.activation = None
        self.norm = None
        if norm is not None:
            self.norm = norm_layer2d(norm, out_channels)
        if activation is not None:
            self.activation = act_layer(activation)

    def forward(self, x):
        x = self.conv2d(x)
        x = self.norm(x) if self.norm is not None else x
        x = self.activation(x) if self.activation is not None else x
        return x


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


def get_init_inputs():
    return [[], {'in_channels': 4, 'out_channels': 4, 'kernel_sizes': 4,
        'strides': 1}]

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


@triton.jit
def triton_poi_fused_replication_pad2d_0(in_ptr0, out_ptr0, xnumel, XBLOCK:
    tl.constexpr):
    xnumel = 1024
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 8
    x1 = xindex // 8 % 8
    x2 = xindex // 64
    x3 = xindex
    tmp0 = tl.load(in_ptr0 + (4 * (3 * (3 <= 0 * (0 >= -2 + x1) + (-2 + x1) *
        (-2 + x1 > 0)) + (0 * (0 >= -2 + x1) + (-2 + x1) * (-2 + x1 > 0)) *
        (0 * (0 >= -2 + x1) + (-2 + x1) * (-2 + x1 > 0) < 3)) + 16 * x2 + (
        3 * (3 <= 0 * (0 >= -2 + x0) + (-2 + x0) * (-2 + x0 > 0)) + (0 * (0 >=
        -2 + x0) + (-2 + x0) * (-2 + x0 > 0)) * (0 * (0 >= -2 + x0) + (-2 +
        x0) * (-2 + x0 > 0) < 3))), xmask, eviction_policy='evict_last')
    tl.store(out_ptr0 + x3, tmp0, xmask)


@triton.jit
def triton_poi_fused_convolution_1(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl
    .constexpr):
    xnumel = 400
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 25 % 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 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (4,), (1,))
    assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 8, 8), (256, 64, 8, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_replication_pad2d_0[grid(1024)](primals_3, buf0, 
            1024, XBLOCK=128, num_warps=4, num_stages=1)
        del primals_3
        buf1 = extern_kernels.convolution(buf0, primals_1, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf1, (4, 4, 5, 5), (100, 25, 5, 1))
        buf2 = buf1
        del buf1
        triton_poi_fused_convolution_1[grid(400)](buf2, primals_2, 400,
            XBLOCK=256, num_warps=4, num_stages=1)
        del primals_2
    return buf2, primals_1, buf0


def act_layer(act):
    if act == 'relu':
        return nn.ReLU()
    elif act == 'lrelu':
        return nn.LeakyReLU(LRELU_SLOPE)
    elif act == 'elu':
        return nn.ELU()
    elif act == 'tanh':
        return nn.Tanh()
    elif act == 'prelu':
        return nn.PReLU()
    else:
        raise ValueError('%s not recognized.' % act)


def norm_layer2d(norm, channels):
    if norm == 'batch':
        return nn.BatchNorm2d(channels)
    elif norm == 'instance':
        return nn.InstanceNorm2d(channels, affine=True)
    elif norm == 'layer':
        return nn.GroupNorm(1, channels, affine=True)
    elif norm == 'group':
        return nn.GroupNorm(4, channels, affine=True)
    else:
        raise ValueError('%s not recognized.' % norm)


class Conv2DBlockNew(nn.Module):

    def __init__(self, in_channels, out_channels, kernel_sizes, strides,
        norm=None, activation=None, padding_mode='replicate'):
        super(Conv2DBlockNew, self).__init__()
        padding = kernel_sizes // 2 if isinstance(kernel_sizes, int) else (
            kernel_sizes[0] // 2, kernel_sizes[1] // 2)
        self.conv2d = nn.Conv2d(in_channels, out_channels, kernel_sizes,
            strides, padding=padding, padding_mode=padding_mode)
        if activation is None:
            nn.init.xavier_uniform_(self.conv2d.weight, gain=nn.init.
                calculate_gain('linear'))
            nn.init.zeros_(self.conv2d.bias)
        elif activation == 'tanh':
            nn.init.xavier_uniform_(self.conv2d.weight, gain=nn.init.
                calculate_gain('tanh'))
            nn.init.zeros_(self.conv2d.bias)
        elif activation == 'lrelu':
            nn.init.kaiming_uniform_(self.conv2d.weight, a=LRELU_SLOPE,
                nonlinearity='leaky_relu')
            nn.init.zeros_(self.conv2d.bias)
        elif activation == 'relu':
            nn.init.kaiming_uniform_(self.conv2d.weight, nonlinearity='relu')
            nn.init.zeros_(self.conv2d.bias)
        else:
            raise ValueError()
        self.activation = None
        self.norm = None
        if norm is not None:
            self.norm = norm_layer2d(norm, out_channels)
        if activation is not None:
            self.activation = act_layer(activation)

    def forward(self, input_0):
        primals_1 = self.conv2d.weight
        primals_2 = self.conv2d.bias
        primals_3 = input_0
        output = call([primals_1, primals_2, primals_3])
        return output[0]
