import math
import torch
import torch.nn.functional as F
from torch import nn


class EqualConvTranspose2d(nn.Module):

    def __init__(self, in_channel, out_channel, kernel_size, stride=1,
        padding=0, bias=True):
        super().__init__()
        self.weight = nn.Parameter(torch.randn(in_channel, out_channel,
            kernel_size, kernel_size))
        self.scale = 1 / math.sqrt(in_channel * kernel_size ** 2)
        self.stride = stride
        self.padding = padding
        if bias:
            self.bias = nn.Parameter(torch.zeros(out_channel))
        else:
            self.bias = None

    def forward(self, input):
        out = F.conv_transpose2d(input, self.weight * self.scale, bias=self
            .bias, stride=self.stride, padding=self.padding)
        return out

    def __repr__(self):
        return (
            f'{self.__class__.__name__}({self.weight.shape[0]}, {self.weight.shape[1]}, {self.weight.shape[2]}, stride={self.stride}, padding={self.padding})'
            )


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


def get_init_inputs():
    return [[], {'in_channel': 4, 'out_channel': 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
import math
from torch import 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_mul_0(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
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + x0, xmask)
    tmp1 = 0.125
    tmp2 = tmp0 * tmp1
    tl.store(out_ptr0 + x0, tmp2, xmask)


@triton.jit
def triton_poi_fused_convolution_1(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl
    .constexpr):
    xnumel = 784
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 49 % 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, 4, 4), (64, 16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_mul_0[grid(256)](primals_1, buf0, 256, XBLOCK=128,
            num_warps=4, num_stages=1)
        del primals_1
        buf1 = extern_kernels.convolution(primals_3, buf0, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=True,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf1, (4, 4, 7, 7), (196, 49, 7, 1))
        buf2 = buf1
        del buf1
        triton_poi_fused_convolution_1[grid(784)](buf2, primals_2, 784,
            XBLOCK=256, num_warps=4, num_stages=1)
        del primals_2
    return buf2, primals_3, buf0


class EqualConvTranspose2dNew(nn.Module):

    def __init__(self, in_channel, out_channel, kernel_size, stride=1,
        padding=0, bias=True):
        super().__init__()
        self.weight = nn.Parameter(torch.randn(in_channel, out_channel,
            kernel_size, kernel_size))
        self.scale = 1 / math.sqrt(in_channel * kernel_size ** 2)
        self.stride = stride
        self.padding = padding
        if bias:
            self.bias = nn.Parameter(torch.zeros(out_channel))
        else:
            self.bias = None

    def __repr__(self):
        return (
            f'{self.__class__.__name__}({self.weight.shape[0]}, {self.weight.shape[1]}, {self.weight.shape[2]}, stride={self.stride}, padding={self.padding})'
            )

    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]
