from torch.nn import Module
import math
import torch
from torchvision.datasets import *
import torch.nn.functional as F
from torch.nn import Parameter
from torch.nn.modules.utils import _pair
from torchvision.transforms import *


class UpsampleConv2d(Module):
    """
    To avoid the checkerboard artifacts of standard Fractionally-strided Convolution,
    we adapt an integer stride convolution but producing a :math:`2\\times 2` outputs for
    each convolutional window.

    .. image:: _static/img/upconv.png
        :width: 50%
        :align: center

    Reference:
        Hang Zhang and Kristin Dana. "Multi-style Generative Network for Real-time Transfer."
        *arXiv preprint arXiv:1703.06953 (2017)*

    Args:
        in_channels (int): Number of channels in the input image
        out_channels (int): Number of channels produced by the convolution
        kernel_size (int or tuple): Size of the convolving kernel
        stride (int or tuple, optional): Stride of the convolution. Default: 1
        padding (int or tuple, optional): Zero-padding added to both sides of the input. Default: 0
        output_padding (int or tuple, optional): Zero-padding added to one side of the output.
          Default: 0
        groups (int, optional): Number of blocked connections from input channels to output
          channels. Default: 1
        bias (bool, optional): If True, adds a learnable bias to the output. Default: True
        dilation (int or tuple, optional): Spacing between kernel elements. Default: 1
        scale_factor (int): scaling factor for upsampling convolution. Default: 1

    Shape:
        - Input: :math:`(N, C_{in}, H_{in}, W_{in})`
        - Output: :math:`(N, C_{out}, H_{out}, W_{out})` where
          :math:`H_{out} = scale * (H_{in} - 1) * stride[0] - 2 * padding[0] + kernel\\_size[0] + output\\_padding[0]`
          :math:`W_{out} = scale * (W_{in} - 1) * stride[1] - 2 * padding[1] + kernel\\_size[1] + output\\_padding[1]`

    Attributes:
        weight (Tensor): the learnable weights of the module of shape
                         (in_channels, scale * scale * out_channels, kernel_size[0], kernel_size[1])
        bias (Tensor):   the learnable bias of the module of shape (scale * scale * out_channels)

    Examples:
        >>> # With square kernels and equal stride
        >>> m = nn.UpsampleCov2d(16, 33, 3, stride=2)
        >>> # non-square kernels and unequal stride and with padding
        >>> m = nn.UpsampleCov2d(16, 33, (3, 5), stride=(2, 1), padding=(4, 2))
        >>> input = autograd.Variable(torch.randn(20, 16, 50, 100))
        >>> output = m(input)
        >>> # exact output size can be also specified as an argument
        >>> input = autograd.Variable(torch.randn(1, 16, 12, 12))
        >>> downsample = nn.Conv2d(16, 16, 3, stride=2, padding=1)
        >>> upsample = nn.UpsampleCov2d(16, 16, 3, stride=2, padding=1)
        >>> h = downsample(input)
        >>> h.size()
        torch.Size([1, 16, 6, 6])
        >>> output = upsample(h, output_size=input.size())
        >>> output.size()
        torch.Size([1, 16, 12, 12])

    """

    def __init__(self, in_channels, out_channels, kernel_size, stride=1,
        padding=0, dilation=1, groups=1, scale_factor=1, bias=True):
        super(UpsampleConv2d, self).__init__()
        kernel_size = _pair(kernel_size)
        stride = _pair(stride)
        padding = _pair(padding)
        dilation = _pair(dilation)
        if in_channels % groups != 0:
            raise ValueError('in_channels must be divisible by groups')
        if out_channels % groups != 0:
            raise ValueError('out_channels must be divisible by groups')
        self.in_channels = in_channels
        self.out_channels = out_channels
        self.kernel_size = kernel_size
        self.stride = stride
        self.padding = padding
        self.dilation = dilation
        self.groups = groups
        self.scale_factor = scale_factor
        self.weight = Parameter(torch.Tensor(out_channels * scale_factor *
            scale_factor, in_channels // groups, *kernel_size))
        if bias:
            self.bias = Parameter(torch.Tensor(out_channels * scale_factor *
                scale_factor))
        else:
            self.register_parameter('bias', None)
        self.reset_parameters()

    def reset_parameters(self):
        n = self.in_channels
        for k in self.kernel_size:
            n *= k
        stdv = 1.0 / math.sqrt(n)
        self.weight.data.uniform_(-stdv, stdv)
        if self.bias is not None:
            self.bias.data.uniform_(-stdv, stdv)

    def forward(self, input):
        out = F.conv2d(input, self.weight, self.bias, self.stride, self.
            padding, self.dilation, self.groups)
        return F.pixel_shuffle(out, self.scale_factor)


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


def get_init_inputs():
    return [[], {'in_channels': 4, 'out_channels': 4, 'kernel_size': 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.nn import Module
import math
from torchvision.datasets import *
from torch.nn import Parameter
from torch.nn.modules.utils import _pair
from torchvision.transforms import *
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor


@triton.jit
def triton_poi_fused_convolution_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl
    .constexpr):
    xnumel = 16
    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
    tl.store(in_out_ptr0 + x2, 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 = extern_kernels.convolution(primals_3, primals_1, stride=(1, 
            1), padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf0, (4, 4, 1, 1), (4, 1, 1, 1))
        buf1 = reinterpret_tensor(buf0, (4, 4, 1, 1), (4, 1, 16, 16), 0)
        del buf0
        get_raw_stream(0)
        triton_poi_fused_convolution_0[grid(16)](buf1, primals_2, 16,
            XBLOCK=16, num_warps=1, num_stages=1)
        del primals_2
    return reinterpret_tensor(buf1, (4, 4, 1, 1), (4, 1, 1, 1), 0
        ), primals_1, primals_3


class UpsampleConv2dNew(Module):
    """
    To avoid the checkerboard artifacts of standard Fractionally-strided Convolution,
    we adapt an integer stride convolution but producing a :math:`2\\times 2` outputs for
    each convolutional window.

    .. image:: _static/img/upconv.png
        :width: 50%
        :align: center

    Reference:
        Hang Zhang and Kristin Dana. "Multi-style Generative Network for Real-time Transfer."
        *arXiv preprint arXiv:1703.06953 (2017)*

    Args:
        in_channels (int): Number of channels in the input image
        out_channels (int): Number of channels produced by the convolution
        kernel_size (int or tuple): Size of the convolving kernel
        stride (int or tuple, optional): Stride of the convolution. Default: 1
        padding (int or tuple, optional): Zero-padding added to both sides of the input. Default: 0
        output_padding (int or tuple, optional): Zero-padding added to one side of the output.
          Default: 0
        groups (int, optional): Number of blocked connections from input channels to output
          channels. Default: 1
        bias (bool, optional): If True, adds a learnable bias to the output. Default: True
        dilation (int or tuple, optional): Spacing between kernel elements. Default: 1
        scale_factor (int): scaling factor for upsampling convolution. Default: 1

    Shape:
        - Input: :math:`(N, C_{in}, H_{in}, W_{in})`
        - Output: :math:`(N, C_{out}, H_{out}, W_{out})` where
          :math:`H_{out} = scale * (H_{in} - 1) * stride[0] - 2 * padding[0] + kernel\\_size[0] + output\\_padding[0]`
          :math:`W_{out} = scale * (W_{in} - 1) * stride[1] - 2 * padding[1] + kernel\\_size[1] + output\\_padding[1]`

    Attributes:
        weight (Tensor): the learnable weights of the module of shape
                         (in_channels, scale * scale * out_channels, kernel_size[0], kernel_size[1])
        bias (Tensor):   the learnable bias of the module of shape (scale * scale * out_channels)

    Examples:
        >>> # With square kernels and equal stride
        >>> m = nn.UpsampleCov2d(16, 33, 3, stride=2)
        >>> # non-square kernels and unequal stride and with padding
        >>> m = nn.UpsampleCov2d(16, 33, (3, 5), stride=(2, 1), padding=(4, 2))
        >>> input = autograd.Variable(torch.randn(20, 16, 50, 100))
        >>> output = m(input)
        >>> # exact output size can be also specified as an argument
        >>> input = autograd.Variable(torch.randn(1, 16, 12, 12))
        >>> downsample = nn.Conv2d(16, 16, 3, stride=2, padding=1)
        >>> upsample = nn.UpsampleCov2d(16, 16, 3, stride=2, padding=1)
        >>> h = downsample(input)
        >>> h.size()
        torch.Size([1, 16, 6, 6])
        >>> output = upsample(h, output_size=input.size())
        >>> output.size()
        torch.Size([1, 16, 12, 12])

    """

    def __init__(self, in_channels, out_channels, kernel_size, stride=1,
        padding=0, dilation=1, groups=1, scale_factor=1, bias=True):
        super(UpsampleConv2dNew, self).__init__()
        kernel_size = _pair(kernel_size)
        stride = _pair(stride)
        padding = _pair(padding)
        dilation = _pair(dilation)
        if in_channels % groups != 0:
            raise ValueError('in_channels must be divisible by groups')
        if out_channels % groups != 0:
            raise ValueError('out_channels must be divisible by groups')
        self.in_channels = in_channels
        self.out_channels = out_channels
        self.kernel_size = kernel_size
        self.stride = stride
        self.padding = padding
        self.dilation = dilation
        self.groups = groups
        self.scale_factor = scale_factor
        self.weight = Parameter(torch.Tensor(out_channels * scale_factor *
            scale_factor, in_channels // groups, *kernel_size))
        if bias:
            self.bias = Parameter(torch.Tensor(out_channels * scale_factor *
                scale_factor))
        else:
            self.register_parameter('bias', None)
        self.reset_parameters()

    def reset_parameters(self):
        n = self.in_channels
        for k in self.kernel_size:
            n *= k
        stdv = 1.0 / math.sqrt(n)
        self.weight.data.uniform_(-stdv, stdv)
        if self.bias is not None:
            self.bias.data.uniform_(-stdv, stdv)

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