# 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_1/inductor_cache/3r/c3runacu4pkgvdlmsngxxodg4pf6xmzvzxpf7xzbkjc3ay27rdj3.py
# Topologically Sorted Source Nodes: [x], Original ATen: [aten.convolution]
# Source node to ATen node mapping:
#   x => convolution
# Graph fragment:
#   %convolution : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%primals_1, %primals_2, %primals_3, [1, 1], [1, 1], [1, 1], False, [0, 0], 1), kwargs = {})
triton_poi_fused_convolution_0 = async_compile.triton('triton_poi_fused_convolution_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=[256], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', '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_convolution_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = (xindex // 16) % 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)
''', 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, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (4, 4, 3, 3), (36, 9, 3, 1))
    assert_size_stride(primals_3, (4, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        # Topologically Sorted Source Nodes: [x], Original ATen: [aten.convolution]
        buf0 = extern_kernels.convolution(primals_1, primals_2, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf0, (4, 4, 4, 4), (64, 16, 4, 1))
        buf1 = buf0; del buf0  # reuse
        # Topologically Sorted Source Nodes: [x], Original ATen: [aten.convolution]
        stream0 = get_raw_stream(0)
        triton_poi_fused_convolution_0.run(buf1, primals_3, 256, grid=grid(256), stream=stream0)
        del primals_3
    return (buf1, primals_1, primals_2, )


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, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((4, 4, 3, 3), (36, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((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)

import torch
import torch.utils.data
import torch.nn as nn


def _get_activation(activation):
    valid = ['relu', 'leaky_relu', 'lrelu', 'tanh', 'sigmoid']
    assert activation in valid, 'activation should be one of {}'.format(valid)
    if activation == 'relu':
        return nn.ReLU(inplace=True)
    if activation == 'leaky_relu' or activation == 'lrelu':
        return nn.LeakyReLU(inplace=True)
    if activation == 'sigmoid':
        return nn.Sigmoid()
    if activation == 'tanh':
        return nn.Tanh()
    return None


def _init_fc_or_conv(fc_conv, activation):
    gain = 1.0
    if activation is not None:
        gain = nn.init.calculate_gain(activation)
    nn.init.xavier_uniform_(fc_conv.weight, gain)
    if fc_conv.bias is not None:
        nn.init.constant_(fc_conv.bias, 0.0)


def _get_norm_layer(norm_layer, channels):
    valid = ['instance', 'batch']
    assert norm_layer in valid, 'norm_layer should be one of {}'.format(valid)
    if norm_layer == 'instance':
        layer = nn.InstanceNorm2d(channels, affine=True)
    elif norm_layer == 'batch':
        layer = nn.BatchNorm2d(channels, affine=True)
    nn.init.constant_(layer.bias, 0.0)
    nn.init.constant_(layer.weight, 1.0)
    return layer


class ConvModule(nn.Module):
    """Basic convolution module with conv + norm(optional) + activation(optional).

    Args:
      n_in(int): number of input channels.
      n_out(int): number of output channels.
      ksize(int): size of the convolution kernel (square).
      stride(int): downsampling factor
      pad(bool): if True, pad the convolutions to maintain a constant size.
      padding_mode(str): 'zero' or 'reflection'.
      activation(str): nonlinear activation function between convolutions.
      norm_layer(str): normalization to apply between the convolution modules.
    """

    def __init__(self, n_in, n_out, ksize=3, stride=1, pad=True,
        padding_mode='zero', activation=None, norm_layer=None):
        super(ConvModule, self).__init__()
        assert isinstance(n_in, int
            ) and n_in > 0, 'Input channels should be a positive integer got {}'.format(
            n_in)
        assert isinstance(n_out, int
            ) and n_out > 0, 'Output channels should be a positive integer got {}'.format(
            n_out)
        assert isinstance(ksize, int
            ) and ksize > 0, 'Kernel size should be a positive integer got {}'.format(
            ksize)
        assert padding_mode in ['zero', 'reflection'], 'Invalid padding mode'
        padding = (ksize - 1) // 2 if pad else 0
        use_bias_in_conv = norm_layer is None
        conv_pad = padding
        if padding_mode == 'reflection' and padding > 0:
            self.add_module('reflection_pad', nn.ReflectionPad2d(padding))
            conv_pad = 0
        else:
            self.add_module('no-op', nn.Identity())
        self.add_module('conv', nn.Conv2d(n_in, n_out, ksize, stride=stride,
            padding=conv_pad, bias=use_bias_in_conv))
        if norm_layer is not None:
            self.add_module('norm', _get_norm_layer(norm_layer, n_out))
        if activation is not None:
            self.add_module('activation', _get_activation(activation))
        _init_fc_or_conv(self.conv, activation)

    def forward(self, x):
        for c in self.children():
            x = c(x)
        return x


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


def get_init_inputs():
    return [[], {'n_in': 4, 'n_out': 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 torch.utils.data
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride


@triton.jit
def triton_poi_fused_convolution_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl
    .constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 16 % 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, 4, 3, 3), (36, 9, 3, 1))
    assert_size_stride(primals_3, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = extern_kernels.convolution(primals_1, primals_2, stride=(1, 
            1), padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf0, (4, 4, 4, 4), (64, 16, 4, 1))
        buf1 = buf0
        del buf0
        get_raw_stream(0)
        triton_poi_fused_convolution_0[grid(256)](buf1, primals_3, 256,
            XBLOCK=128, num_warps=4, num_stages=1)
        del primals_3
    return buf1, primals_1, primals_2


def _get_activation(activation):
    valid = ['relu', 'leaky_relu', 'lrelu', 'tanh', 'sigmoid']
    assert activation in valid, 'activation should be one of {}'.format(valid)
    if activation == 'relu':
        return nn.ReLU(inplace=True)
    if activation == 'leaky_relu' or activation == 'lrelu':
        return nn.LeakyReLU(inplace=True)
    if activation == 'sigmoid':
        return nn.Sigmoid()
    if activation == 'tanh':
        return nn.Tanh()
    return None


def _init_fc_or_conv(fc_conv, activation):
    gain = 1.0
    if activation is not None:
        gain = nn.init.calculate_gain(activation)
    nn.init.xavier_uniform_(fc_conv.weight, gain)
    if fc_conv.bias is not None:
        nn.init.constant_(fc_conv.bias, 0.0)


def _get_norm_layer(norm_layer, channels):
    valid = ['instance', 'batch']
    assert norm_layer in valid, 'norm_layer should be one of {}'.format(valid)
    if norm_layer == 'instance':
        layer = nn.InstanceNorm2d(channels, affine=True)
    elif norm_layer == 'batch':
        layer = nn.BatchNorm2d(channels, affine=True)
    nn.init.constant_(layer.bias, 0.0)
    nn.init.constant_(layer.weight, 1.0)
    return layer


class ConvModuleNew(nn.Module):
    """Basic convolution module with conv + norm(optional) + activation(optional).

    Args:
      n_in(int): number of input channels.
      n_out(int): number of output channels.
      ksize(int): size of the convolution kernel (square).
      stride(int): downsampling factor
      pad(bool): if True, pad the convolutions to maintain a constant size.
      padding_mode(str): 'zero' or 'reflection'.
      activation(str): nonlinear activation function between convolutions.
      norm_layer(str): normalization to apply between the convolution modules.
    """

    def __init__(self, n_in, n_out, ksize=3, stride=1, pad=True,
        padding_mode='zero', activation=None, norm_layer=None):
        super(ConvModuleNew, self).__init__()
        assert isinstance(n_in, int
            ) and n_in > 0, 'Input channels should be a positive integer got {}'.format(
            n_in)
        assert isinstance(n_out, int
            ) and n_out > 0, 'Output channels should be a positive integer got {}'.format(
            n_out)
        assert isinstance(ksize, int
            ) and ksize > 0, 'Kernel size should be a positive integer got {}'.format(
            ksize)
        assert padding_mode in ['zero', 'reflection'], 'Invalid padding mode'
        padding = (ksize - 1) // 2 if pad else 0
        use_bias_in_conv = norm_layer is None
        conv_pad = padding
        if padding_mode == 'reflection' and padding > 0:
            self.add_module('reflection_pad', nn.ReflectionPad2d(padding))
            conv_pad = 0
        else:
            self.add_module('no-op', nn.Identity())
        self.add_module('conv', nn.Conv2d(n_in, n_out, ksize, stride=stride,
            padding=conv_pad, bias=use_bias_in_conv))
        if norm_layer is not None:
            self.add_module('norm', _get_norm_layer(norm_layer, n_out))
        if activation is not None:
            self.add_module('activation', _get_activation(activation))
        _init_fc_or_conv(self.conv, activation)

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