import torch
from torch import Tensor
from torch import nn


class ScaledConv2d(nn.Conv2d):

    def __init__(self, *args, initial_scale: float=1.0, initial_speed:
        float=1.0, **kwargs):
        super(ScaledConv2d, self).__init__(*args, **kwargs)
        initial_scale = torch.tensor(initial_scale).log()
        self.weight_scale = nn.Parameter(initial_scale.clone().detach())
        if self.bias is not None:
            self.bias_scale = nn.Parameter(initial_scale.clone().detach())
        else:
            self.register_parameter('bias_scale', None)
        self._reset_parameters(initial_speed)

    def _reset_parameters(self, initial_speed: 'float'):
        std = 0.1 / initial_speed
        a = 3 ** 0.5 * std
        nn.init.uniform_(self.weight, -a, a)
        if self.bias is not None:
            nn.init.constant_(self.bias, 0.0)
        fan_in = self.weight.shape[1] * self.weight[0][0].numel()
        scale = fan_in ** -0.5
        with torch.no_grad():
            self.weight_scale += torch.tensor(scale / std).log()

    def get_weight(self):
        return self.weight * self.weight_scale.exp()

    def get_bias(self):
        return None if self.bias is None else self.bias * self.bias_scale.exp()

    def _conv_forward(self, input, weight):
        F = torch.nn.functional
        if self.padding_mode != 'zeros':
            return F.conv2d(F.pad(input, self.
                _reversed_padding_repeated_twice, mode=self.padding_mode),
                weight, self.get_bias(), self.stride, _pair(0), self.
                dilation, self.groups)
        return F.conv2d(input, weight, self.get_bias(), self.stride, self.
            padding, self.dilation, self.groups)

    def forward(self, input: 'Tensor') ->Tensor:
        return self._conv_forward(input, self.get_weight())


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._inductor.runtime.triton_helpers import math as tl_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_exp_mul_0(in_ptr0, in_ptr1, 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 = tl.load(in_ptr1 + 0)
    tmp2 = tl.broadcast_to(tmp1, [XBLOCK])
    tmp3 = tl_math.exp(tmp2)
    tmp4 = tmp0 * tmp3
    tl.store(out_ptr0 + x0, tmp4, xmask)


@triton.jit
def triton_poi_fused_convolution_exp_mul_1(in_ptr0, in_ptr1, out_ptr0,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 4
    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 = tl.load(in_ptr1 + 0)
    tmp2 = tl.broadcast_to(tmp1, [XBLOCK])
    tmp3 = tl_math.exp(tmp2)
    tmp4 = tmp0 * tmp3
    tl.store(out_ptr0 + x0, tmp4, xmask)


@triton.jit
def triton_poi_fused_convolution_exp_mul_2(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, primals_4, primals_5 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (), ())
    assert_size_stride(primals_3, (4,), (1,))
    assert_size_stride(primals_4, (), ())
    assert_size_stride(primals_5, (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_exp_mul_0[grid(256)](primals_1, primals_2, buf0, 
            256, XBLOCK=256, num_warps=4, num_stages=1)
        buf1 = empty_strided_cuda((4,), (1,), torch.float32)
        triton_poi_fused_convolution_exp_mul_1[grid(4)](primals_3,
            primals_4, buf1, 4, XBLOCK=4, num_warps=1, num_stages=1)
        buf2 = extern_kernels.convolution(primals_5, buf0, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf2, (4, 4, 1, 1), (4, 1, 1, 1))
        buf3 = buf2
        del buf2
        triton_poi_fused_convolution_exp_mul_2[grid(16)](buf3, buf1, 16,
            XBLOCK=16, num_warps=1, num_stages=1)
        del buf1
    return buf3, primals_1, primals_2, primals_3, primals_4, primals_5, buf0


class ScaledConv2dNew(nn.Conv2d):

    def __init__(self, *args, initial_scale: float=1.0, initial_speed:
        float=1.0, **kwargs):
        super(ScaledConv2dNew, self).__init__(*args, **kwargs)
        initial_scale = torch.tensor(initial_scale).log()
        self.weight_scale = nn.Parameter(initial_scale.clone().detach())
        if self.bias is not None:
            self.bias_scale = nn.Parameter(initial_scale.clone().detach())
        else:
            self.register_parameter('bias_scale', None)
        self._reset_parameters(initial_speed)

    def _reset_parameters(self, initial_speed: 'float'):
        std = 0.1 / initial_speed
        a = 3 ** 0.5 * std
        nn.init.uniform_(self.weight, -a, a)
        if self.bias is not None:
            nn.init.constant_(self.bias, 0.0)
        fan_in = self.weight.shape[1] * self.weight[0][0].numel()
        scale = fan_in ** -0.5
        with torch.no_grad():
            self.weight_scale += torch.tensor(scale / std).log()

    def get_weight(self):
        return self.weight * self.weight_scale.exp()

    def get_bias(self):
        return None if self.bias is None else self.bias * self.bias_scale.exp()

    def _conv_forward(self, input, weight):
        F = torch.nn.functional
        if self.padding_mode != 'zeros':
            return F.conv2d(F.pad(input, self.
                _reversed_padding_repeated_twice, mode=self.padding_mode),
                weight, self.get_bias(), self.stride, _pair(0), self.
                dilation, self.groups)
        return F.conv2d(input, weight, self.get_bias(), self.stride, self.
            padding, self.dilation, self.groups)

    def forward(self, input_0):
        primals_1 = self.weight
        primals_3 = self.bias
        primals_2 = self.weight_scale
        primals_4 = self.bias_scale
        primals_5 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4, primals_5])
        return output[0]
