# 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/4z/c4z2pzq3lsrzow5vs7cjekzn2ekbosokmllvu2kdaggxwyxu2icb.py
# Topologically Sorted Source Nodes: [conv3d], Original ATen: [aten.convolution]
# Source node to ATen node mapping:
#   conv3d => convolution
# Graph fragment:
#   %convolution : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%unsqueeze, %primals_1, %primals_2, [1, 1, 1], [4, 4, 0], [1, 1, 1], False, [0, 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=[2048], 
    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_convolution_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, '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_convolution_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 1296
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x1 = (xindex // 324)
    tmp0 = tl.load(in_out_ptr0 + (x2), xmask)
    tmp1 = tl.load(in_ptr0 + (x1), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + (x2), 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, 1), (64, 16, 4, 1, 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)
        # Topologically Sorted Source Nodes: [conv3d], Original ATen: [aten.convolution]
        buf0 = extern_kernels.convolution(reinterpret_tensor(primals_3, (1, 4, 4, 4, 4), (256, 64, 16, 4, 1), 0), primals_1, stride=(1, 1, 1), padding=(4, 4, 0), dilation=(1, 1, 1), transposed=False, output_padding=(0, 0, 0), groups=1, bias=None)
        assert_size_stride(buf0, (1, 4, 9, 9, 4), (1296, 324, 36, 4, 1))
        buf1 = buf0; del buf0  # reuse
        # Topologically Sorted Source Nodes: [conv3d], Original ATen: [aten.convolution]
        stream0 = get_raw_stream(0)
        triton_poi_fused_convolution_0.run(buf1, primals_2, 1296, grid=grid(1296), stream=stream0)
        del primals_2
    return (reinterpret_tensor(buf1, (4, 9, 9, 4), (324, 36, 4, 1), 0), primals_1, reinterpret_tensor(primals_3, (1, 4, 4, 4, 4), (256, 64, 16, 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, 4, 4, 4, 1), (64, 16, 4, 1, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((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 _paritybench_helpers import _mock_config
import torch
import torch.nn as nn
import torch.nn.functional as f


class ConvLayer(nn.Conv3d):

    def __init__(self, network_config, config, name, in_shape, groups=1):
        self.name = name
        self.layer_config = config
        self.network_config = network_config
        self.type = config['type']
        in_features = config['in_channels']
        out_features = config['out_channels']
        kernel_size = config['kernel_size']
        if 'padding' in config:
            padding = config['padding']
        else:
            padding = 0
        if 'stride' in config:
            stride = config['stride']
        else:
            stride = 1
        if 'dilation' in config:
            dilation = config['dilation']
        else:
            dilation = 1
        if 'weight_scale' in config:
            weight_scale = config['weight_scale']
        else:
            weight_scale = 1
        if type(kernel_size) == int:
            kernel = kernel_size, kernel_size, 1
        elif len(kernel_size) == 2:
            kernel = kernel_size[0], kernel_size[1], 1
        else:
            raise Exception(
                'kernelSize can only be of 1 or 2 dimension. It was: {}'.
                format(kernel_size.shape))
        if type(stride) == int:
            stride = stride, stride, 1
        elif len(stride) == 2:
            stride = stride[0], stride[1], 1
        else:
            raise Exception(
                'stride can be either int or tuple of size 2. It was: {}'.
                format(stride.shape))
        if type(padding) == int:
            padding = padding, padding, 0
        elif len(padding) == 2:
            padding = padding[0], padding[1], 0
        else:
            raise Exception(
                'padding can be either int or tuple of size 2. It was: {}'.
                format(padding.shape))
        if type(dilation) == int:
            dilation = dilation, dilation, 1
        elif len(dilation) == 2:
            dilation = dilation[0], dilation[1], 1
        else:
            raise Exception(
                'dilation can be either int or tuple of size 2. It was: {}'
                .format(dilation.shape))
        super(ConvLayer, self).__init__(in_features, out_features, kernel,
            stride, padding, dilation, groups, bias=True)
        nn.init.normal_(self.weight)
        nn.init.zeros_(self.bias)
        self.weight = torch.nn.Parameter(weight_scale * self.weight,
            requires_grad=True)
        self.bias = torch.nn.Parameter(weight_scale * self.bias,
            requires_grad=True)
        self.in_shape = in_shape
        self.out_shape = [out_features, int((in_shape[1] + 2 * padding[0] -
            kernel[0]) / stride[0] + 1), int((in_shape[2] + 2 * padding[1] -
            kernel[1]) / stride[1] + 1)]
        None
        None
        None
        None
        None

    def forward(self, x):
        return f.conv3d(x, self.weight, self.bias, self.stride, self.
            padding, self.dilation, self.groups)

    def get_parameters(self):
        return [self.weight, self.bias]

    def forward_pass(self, x, epoch):
        y = self.forward(x)
        shape = x.shape
        if shape[4] > shape[0] * 10:
            y = TSSLBP.PSP_spike_long_time.apply(y, self.network_config,
                self.layer_config, self.name)
        else:
            y = TSSLBP.PSP_spike_large_batch.apply(y, self.network_config,
                self.layer_config, self.name)
        return y

    def weight_clipper(self):
        w = self.weight.data
        b = self.bias.data
        w = w.clamp(-4, 4)
        b = b.clamp(-4, 4)
        self.weight.data = w
        self.bias.data = b


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


def get_init_inputs():
    return [[], {'network_config': _mock_config(), 'config': _mock_config(
        type=4, in_channels=4, out_channels=4, kernel_size=4, padding=4,
        stride=1, dilation=1, weight_scale=1.0), 'name': 4, 'in_shape': [4,
        4, 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.nn as nn
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 = 1296
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x1 = xindex // 324
    tmp0 = tl.load(in_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x1, 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, 1), (64, 16, 4, 1, 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(reinterpret_tensor(primals_3, (1,
            4, 4, 4, 4), (256, 64, 16, 4, 1), 0), primals_1, stride=(1, 1, 
            1), padding=(4, 4, 0), dilation=(1, 1, 1), transposed=False,
            output_padding=(0, 0, 0), groups=1, bias=None)
        assert_size_stride(buf0, (1, 4, 9, 9, 4), (1296, 324, 36, 4, 1))
        buf1 = buf0
        del buf0
        get_raw_stream(0)
        triton_poi_fused_convolution_0[grid(1296)](buf1, primals_2, 1296,
            XBLOCK=128, num_warps=4, num_stages=1)
        del primals_2
    return reinterpret_tensor(buf1, (4, 9, 9, 4), (324, 36, 4, 1), 0
        ), primals_1, reinterpret_tensor(primals_3, (1, 4, 4, 4, 4), (256, 
        64, 16, 4, 1), 0)


class ConvLayerNew(nn.Conv3d):

    def __init__(self, network_config, config, name, in_shape, groups=1):
        self.name = name
        self.layer_config = config
        self.network_config = network_config
        self.type = config['type']
        in_features = config['in_channels']
        out_features = config['out_channels']
        kernel_size = config['kernel_size']
        if 'padding' in config:
            padding = config['padding']
        else:
            padding = 0
        if 'stride' in config:
            stride = config['stride']
        else:
            stride = 1
        if 'dilation' in config:
            dilation = config['dilation']
        else:
            dilation = 1
        if 'weight_scale' in config:
            weight_scale = config['weight_scale']
        else:
            weight_scale = 1
        if type(kernel_size) == int:
            kernel = kernel_size, kernel_size, 1
        elif len(kernel_size) == 2:
            kernel = kernel_size[0], kernel_size[1], 1
        else:
            raise Exception(
                'kernelSize can only be of 1 or 2 dimension. It was: {}'.
                format(kernel_size.shape))
        if type(stride) == int:
            stride = stride, stride, 1
        elif len(stride) == 2:
            stride = stride[0], stride[1], 1
        else:
            raise Exception(
                'stride can be either int or tuple of size 2. It was: {}'.
                format(stride.shape))
        if type(padding) == int:
            padding = padding, padding, 0
        elif len(padding) == 2:
            padding = padding[0], padding[1], 0
        else:
            raise Exception(
                'padding can be either int or tuple of size 2. It was: {}'.
                format(padding.shape))
        if type(dilation) == int:
            dilation = dilation, dilation, 1
        elif len(dilation) == 2:
            dilation = dilation[0], dilation[1], 1
        else:
            raise Exception(
                'dilation can be either int or tuple of size 2. It was: {}'
                .format(dilation.shape))
        super(ConvLayerNew, self).__init__(in_features, out_features,
            kernel, stride, padding, dilation, groups, bias=True)
        nn.init.normal_(self.weight)
        nn.init.zeros_(self.bias)
        self.weight = torch.nn.Parameter(weight_scale * self.weight,
            requires_grad=True)
        self.bias = torch.nn.Parameter(weight_scale * self.bias,
            requires_grad=True)
        self.in_shape = in_shape
        self.out_shape = [out_features, int((in_shape[1] + 2 * padding[0] -
            kernel[0]) / stride[0] + 1), int((in_shape[2] + 2 * padding[1] -
            kernel[1]) / stride[1] + 1)]
        None
        None
        None
        None
        None

    def get_parameters(self):
        return [self.weight, self.bias]

    def forward_pass(self, x, epoch):
        y = self.forward(x)
        shape = x.shape
        if shape[4] > shape[0] * 10:
            y = TSSLBP.PSP_spike_long_time.apply(y, self.network_config,
                self.layer_config, self.name)
        else:
            y = TSSLBP.PSP_spike_large_batch.apply(y, self.network_config,
                self.layer_config, self.name)
        return y

    def weight_clipper(self):
        w = self.weight.data
        b = self.bias.data
        w = w.clamp(-4, 4)
        b = b.clamp(-4, 4)
        self.weight.data = w
        self.bias.data = b

    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]
