# AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _align as align
from torch import device, empty_strided
from torch._inductor.async_compile import AsyncCompile
from torch._inductor.select_algorithm import extern_kernels
from torch._inductor.codegen.multi_kernel import MultiKernelCall
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph
from torch._C import _cuda_getCurrentRawStream as get_raw_stream

aten = torch.ops.aten
inductor_ops = torch.ops.inductor
_quantized = torch.ops._quantized
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
alloc_from_pool = torch.ops.inductor._alloc_from_pool
async_compile = AsyncCompile()


# kernel path: runs/run_shard_7/inductor_cache/wi/cwiyl3lwwtancorrifw77xt3aqb4lermdintht45zvkj3bg54nbl.py
# Topologically Sorted Source Nodes: [mul_1], Original ATen: [aten.mul]
# Source node to ATen node mapping:
#   mul_1 => mul_1
# Graph fragment:
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%primals_2, 0.5), kwargs = {})
triton_poi_fused_mul_0 = async_compile.triton('triton_poi_fused_mul_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties

@triton_heuristics.pointwise(
    size_hints=[16], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_mul_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_mul_0(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 16
    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 = 0.5
    tmp2 = tmp0 * tmp1
    tl.store(out_ptr0 + (x0), tmp2, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/2o/c2oqkq7zaubqmw7vuixxlseb2ff5jzqqbyczicxlmsahuxwdpdyp.py
# Topologically Sorted Source Nodes: [bias], Original ATen: [aten.mul]
# Source node to ATen node mapping:
#   bias => mul
# Graph fragment:
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%primals_1, 1), kwargs = {})
triton_poi_fused_mul_1 = async_compile.triton('triton_poi_fused_mul_1', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties

@triton_heuristics.pointwise(
    size_hints=[4], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_mul_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_mul_1(in_ptr0, 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 = 1.0
    tmp2 = tmp0 * tmp1
    tl.store(out_ptr0 + (x0), tmp2, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    primals_1, primals_2, primals_3 = args
    args.clear()
    assert_size_stride(primals_1, (4, ), (1, ))
    assert_size_stride(primals_2, (4, 4), (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, 1), torch.float32)
        # Topologically Sorted Source Nodes: [mul_1], Original ATen: [aten.mul]
        stream0 = get_raw_stream(0)
        triton_poi_fused_mul_0.run(primals_2, buf0, 16, grid=grid(16), stream=stream0)
        del primals_2
        buf1 = empty_strided_cuda((4, ), (1, ), torch.float32)
        # Topologically Sorted Source Nodes: [bias], Original ATen: [aten.mul]
        triton_poi_fused_mul_1.run(primals_1, buf1, 4, grid=grid(4), stream=stream0)
        del primals_1
        buf2 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [bias, out], Original ATen: [aten.mul, aten.addmm]
        extern_kernels.addmm(buf1, reinterpret_tensor(primals_3, (64, 4), (4, 1), 0), reinterpret_tensor(buf0, (4, 4), (1, 4), 0), alpha=1, beta=1, out=buf2)
        del buf0
        del buf1
    return (reinterpret_tensor(buf2, (4, 4, 4, 4), (64, 16, 4, 1), 0), reinterpret_tensor(primals_3, (64, 4), (4, 1), 0), )


def benchmark_compiled_module(times=10, repeat=10):
    from torch._dynamo.testing import rand_strided
    from torch._inductor.utils import print_performance
    primals_1 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2, primals_3])
    return print_performance(fn, times=times, repeat=repeat)


if __name__ == "__main__":
    from torch._inductor.wrapper_benchmark import compiled_module_main
    compiled_module_main('None', benchmark_compiled_module)

from torch.autograd import Function
import math
import torch
from torch import nn as nn
from torch.nn import functional as F
from torch.nn import init as init
from torchvision.models import vgg as vgg
import torch.utils.data
from torch.utils import data as data
from torch import autograd as autograd


def fused_leaky_relu(input, bias, negative_slope=0.2, scale=2 ** 0.5):
    return FusedLeakyReLUFunction.apply(input, bias, negative_slope, scale)


class FusedLeakyReLUFunctionBackward(Function):

    @staticmethod
    def forward(ctx, grad_output, out, negative_slope, scale):
        ctx.save_for_backward(out)
        ctx.negative_slope = negative_slope
        ctx.scale = scale
        empty = grad_output.new_empty(0)
        grad_input = fused_act_ext.fused_bias_act(grad_output, empty, out, 
            3, 1, negative_slope, scale)
        dim = [0]
        if grad_input.ndim > 2:
            dim += list(range(2, grad_input.ndim))
        grad_bias = grad_input.sum(dim).detach()
        return grad_input, grad_bias

    @staticmethod
    def backward(ctx, gradgrad_input, gradgrad_bias):
        out, = ctx.saved_tensors
        gradgrad_out = fused_act_ext.fused_bias_act(gradgrad_input,
            gradgrad_bias, out, 3, 1, ctx.negative_slope, ctx.scale)
        return gradgrad_out, None, None, None


class FusedLeakyReLUFunction(Function):

    @staticmethod
    def forward(ctx, input, bias, negative_slope, scale):
        empty = input.new_empty(0)
        out = fused_act_ext.fused_bias_act(input, bias, empty, 3, 0,
            negative_slope, scale)
        ctx.save_for_backward(out)
        ctx.negative_slope = negative_slope
        ctx.scale = scale
        return out

    @staticmethod
    def backward(ctx, grad_output):
        out, = ctx.saved_tensors
        grad_input, grad_bias = FusedLeakyReLUFunctionBackward.apply(
            grad_output, out, ctx.negative_slope, ctx.scale)
        return grad_input, grad_bias, None, None


class EqualLinear(nn.Module):
    """Equalized Linear as StyleGAN2.

    Args:
        in_channels (int): Size of each sample.
        out_channels (int): Size of each output sample.
        bias (bool): If set to ``False``, the layer will not learn an additive
            bias. Default: ``True``.
        bias_init_val (float): Bias initialized value. Default: 0.
        lr_mul (float): Learning rate multiplier. Default: 1.
        activation (None | str): The activation after ``linear`` operation.
            Supported: 'fused_lrelu', None. Default: None.
    """

    def __init__(self, in_channels, out_channels, bias=True, bias_init_val=
        0, lr_mul=1, activation=None):
        super(EqualLinear, self).__init__()
        self.in_channels = in_channels
        self.out_channels = out_channels
        self.lr_mul = lr_mul
        self.activation = activation
        if self.activation not in ['fused_lrelu', None]:
            raise ValueError(
                f"Wrong activation value in EqualLinear: {activation}Supported ones are: ['fused_lrelu', None]."
                )
        self.scale = 1 / math.sqrt(in_channels) * lr_mul
        self.weight = nn.Parameter(torch.randn(out_channels, in_channels).
            div_(lr_mul))
        if bias:
            self.bias = nn.Parameter(torch.zeros(out_channels).fill_(
                bias_init_val))
        else:
            self.register_parameter('bias', None)

    def forward(self, x):
        if self.bias is None:
            bias = None
        else:
            bias = self.bias * self.lr_mul
        if self.activation == 'fused_lrelu':
            out = F.linear(x, self.weight * self.scale)
            out = fused_leaky_relu(out, bias)
        else:
            out = F.linear(x, self.weight * self.scale, bias=bias)
        return out

    def __repr__(self):
        return (
            f'{self.__class__.__name__}(in_channels={self.in_channels}, out_channels={self.out_channels}, bias={self.bias is not None})'
            )


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


def get_init_inputs():
    return [[], {'in_channels': 4, 'out_channels': 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.autograd import Function
import math
from torch import nn as nn
from torch.nn import init as init
from torchvision.models import vgg as vgg
import torch.utils.data
from torch.utils import data as data
from torch import autograd as autograd
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_mul_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 16
    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 = 0.5
    tmp2 = tmp0 * tmp1
    tl.store(out_ptr0 + x0, tmp2, xmask)


@triton.jit
def triton_poi_fused_mul_1(in_ptr0, 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 = 1.0
    tmp2 = tmp0 * tmp1
    tl.store(out_ptr0 + x0, tmp2, xmask)


def call(args):
    primals_1, primals_2, primals_3 = args
    args.clear()
    assert_size_stride(primals_1, (4,), (1,))
    assert_size_stride(primals_2, (4, 4), (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, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_mul_0[grid(16)](primals_2, buf0, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        del primals_2
        buf1 = empty_strided_cuda((4,), (1,), torch.float32)
        triton_poi_fused_mul_1[grid(4)](primals_1, buf1, 4, XBLOCK=4,
            num_warps=1, num_stages=1)
        del primals_1
        buf2 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.addmm(buf1, reinterpret_tensor(primals_3, (64, 4), (
            4, 1), 0), reinterpret_tensor(buf0, (4, 4), (1, 4), 0), alpha=1,
            beta=1, out=buf2)
        del buf0
        del buf1
    return reinterpret_tensor(buf2, (4, 4, 4, 4), (64, 16, 4, 1), 0
        ), reinterpret_tensor(primals_3, (64, 4), (4, 1), 0)


def fused_leaky_relu(input, bias, negative_slope=0.2, scale=2 ** 0.5):
    return FusedLeakyReLUFunction.apply(input, bias, negative_slope, scale)


class FusedLeakyReLUFunctionBackward(Function):

    @staticmethod
    def forward(ctx, grad_output, out, negative_slope, scale):
        ctx.save_for_backward(out)
        ctx.negative_slope = negative_slope
        ctx.scale = scale
        empty = grad_output.new_empty(0)
        grad_input = fused_act_ext.fused_bias_act(grad_output, empty, out, 
            3, 1, negative_slope, scale)
        dim = [0]
        if grad_input.ndim > 2:
            dim += list(range(2, grad_input.ndim))
        grad_bias = grad_input.sum(dim).detach()
        return grad_input, grad_bias

    @staticmethod
    def backward(ctx, gradgrad_input, gradgrad_bias):
        out, = ctx.saved_tensors
        gradgrad_out = fused_act_ext.fused_bias_act(gradgrad_input,
            gradgrad_bias, out, 3, 1, ctx.negative_slope, ctx.scale)
        return gradgrad_out, None, None, None


class FusedLeakyReLUFunction(Function):

    @staticmethod
    def forward(ctx, input, bias, negative_slope, scale):
        empty = input.new_empty(0)
        out = fused_act_ext.fused_bias_act(input, bias, empty, 3, 0,
            negative_slope, scale)
        ctx.save_for_backward(out)
        ctx.negative_slope = negative_slope
        ctx.scale = scale
        return out

    @staticmethod
    def backward(ctx, grad_output):
        out, = ctx.saved_tensors
        grad_input, grad_bias = FusedLeakyReLUFunctionBackward.apply(
            grad_output, out, ctx.negative_slope, ctx.scale)
        return grad_input, grad_bias, None, None


class EqualLinearNew(nn.Module):
    """Equalized Linear as StyleGAN2.

    Args:
        in_channels (int): Size of each sample.
        out_channels (int): Size of each output sample.
        bias (bool): If set to ``False``, the layer will not learn an additive
            bias. Default: ``True``.
        bias_init_val (float): Bias initialized value. Default: 0.
        lr_mul (float): Learning rate multiplier. Default: 1.
        activation (None | str): The activation after ``linear`` operation.
            Supported: 'fused_lrelu', None. Default: None.
    """

    def __init__(self, in_channels, out_channels, bias=True, bias_init_val=
        0, lr_mul=1, activation=None):
        super(EqualLinearNew, self).__init__()
        self.in_channels = in_channels
        self.out_channels = out_channels
        self.lr_mul = lr_mul
        self.activation = activation
        if self.activation not in ['fused_lrelu', None]:
            raise ValueError(
                f"Wrong activation value in EqualLinear: {activation}Supported ones are: ['fused_lrelu', None]."
                )
        self.scale = 1 / math.sqrt(in_channels) * lr_mul
        self.weight = nn.Parameter(torch.randn(out_channels, in_channels).
            div_(lr_mul))
        if bias:
            self.bias = nn.Parameter(torch.zeros(out_channels).fill_(
                bias_init_val))
        else:
            self.register_parameter('bias', None)

    def __repr__(self):
        return (
            f'{self.__class__.__name__}(in_channels={self.in_channels}, out_channels={self.out_channels}, bias={self.bias is not None})'
            )

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