# 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_6/inductor_cache/zt/cztkrjzjfnkenqtkvtpoo63d7nucri4mdum3kkn2uzy44lvpt2ft.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, [4, 4, 4], [0, 0, 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=[2097152], 
    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 = 1572864
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = (xindex // 4096) % 96
    tmp0 = tl.load(in_out_ptr0 + (x3), None)
    tmp1 = tl.load(in_ptr0 + (x1), None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + (x3), tmp2, None)
''', 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, 64, 64, 64), (262144, 262144, 4096, 64, 1))
    assert_size_stride(primals_2, (96, 1, 4, 4, 4), (64, 64, 16, 4, 1))
    assert_size_stride(primals_3, (96, ), (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=(4, 4, 4), padding=(0, 0, 0), dilation=(1, 1, 1), transposed=False, output_padding=(0, 0, 0), groups=1, bias=None)
        assert_size_stride(buf0, (4, 96, 16, 16, 16), (393216, 4096, 256, 16, 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, 1572864, grid=grid(1572864), 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, 1, 64, 64, 64), (262144, 262144, 4096, 64, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((96, 1, 4, 4, 4), (64, 64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((96, ), (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.nn.functional as F
import torch.nn as nn
import torch._C
import torch.serialization


class PatchEmbed(nn.Module):
    """ Image to Patch Embedding

    Args:
        patch_size (tuple[int]): Patch token size. Default: (4, 4, 4).
        in_chans (int): Number of input image channels. Default: 1.
        embed_dim (int): Number of linear projection output channels. Default: 96.
        norm_layer (nn.Module, optional): Normalization layer. Default: None
    """

    def __init__(self, patch_size=(4, 4, 4), in_chans=1, embed_dim=96,
        norm_layer=None, use_spectral_aggregation='None'):
        super().__init__()
        self._patch_size = patch_size
        self.in_chans = in_chans
        self.embed_dim = embed_dim
        self.proj = nn.Conv3d(in_chans, embed_dim, kernel_size=patch_size,
            stride=patch_size)
        if norm_layer is not None:
            self.norm = norm_layer(embed_dim)
        else:
            self.norm = None
        self.use_spectral_aggregation = use_spectral_aggregation
        if self.use_spectral_aggregation == 'token':
            self.spectral_aggregation_token = nn.Parameter(data=torch.empty
                (embed_dim), requires_grad=True)
            trunc_normal_(self.spectral_aggregation_token, std=0.02)

    def forward(self, x):
        """Forward function."""
        if self.use_spectral_aggregation != 'None':
            x = F.instance_norm(x)
            x = torch.unsqueeze(x, 1)
        _, _, S, H, W = x.size()
        if W % self._patch_size[2] != 0:
            x = F.pad(x, [0, self._patch_size[2] - W % self._patch_size[2]])
        if H % self._patch_size[1] != 0:
            x = F.pad(x, [0, 0, 0, self._patch_size[1] - H % self.
                _patch_size[1]])
        if S % self._patch_size[0] != 0:
            x = F.pad(x, [0, 0, 0, 0, 0, self._patch_size[0] - S % self.
                _patch_size[0]])
        x = self.proj(x)
        if self.use_spectral_aggregation == 'token':
            _b, _c, _s, _h, _w = x.shape
            token = self.spectral_aggregation_token.view(1, -1, 1, 1, 1
                ).repeat(_b, 1, 1, _h, _w)
            x = torch.cat((token, x), dim=2)
        if self.norm is not None:
            Ws, Wh, Ww = x.size(2), x.size(3), x.size(4)
            x = x.flatten(2).transpose(1, 2)
            x = self.norm(x)
            x = x.transpose(1, 2).view(-1, self.embed_dim, Ws, Wh, Ww)
        return x


def get_inputs():
    return [torch.rand([4, 1, 64, 64, 64])]


def get_init_inputs():
    return [[], {}]

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
import torch._C
import torch.serialization
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):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = xindex // 4096 % 96
    tmp0 = tl.load(in_out_ptr0 + x3, None)
    tmp1 = tl.load(in_ptr0 + x1, None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + x3, tmp2, None)


def call(args):
    primals_1, primals_2, primals_3 = args
    args.clear()
    assert_size_stride(primals_1, (4, 1, 64, 64, 64), (262144, 262144, 4096,
        64, 1))
    assert_size_stride(primals_2, (96, 1, 4, 4, 4), (64, 64, 16, 4, 1))
    assert_size_stride(primals_3, (96,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = extern_kernels.convolution(primals_1, primals_2, stride=(4, 
            4, 4), padding=(0, 0, 0), dilation=(1, 1, 1), transposed=False,
            output_padding=(0, 0, 0), groups=1, bias=None)
        assert_size_stride(buf0, (4, 96, 16, 16, 16), (393216, 4096, 256, 
            16, 1))
        buf1 = buf0
        del buf0
        get_raw_stream(0)
        triton_poi_fused_convolution_0[grid(1572864)](buf1, primals_3, 
            1572864, XBLOCK=1024, num_warps=4, num_stages=1)
        del primals_3
    return buf1, primals_1, primals_2


class PatchEmbedNew(nn.Module):
    """ Image to Patch Embedding

    Args:
        patch_size (tuple[int]): Patch token size. Default: (4, 4, 4).
        in_chans (int): Number of input image channels. Default: 1.
        embed_dim (int): Number of linear projection output channels. Default: 96.
        norm_layer (nn.Module, optional): Normalization layer. Default: None
    """

    def __init__(self, patch_size=(4, 4, 4), in_chans=1, embed_dim=96,
        norm_layer=None, use_spectral_aggregation='None'):
        super().__init__()
        self._patch_size = patch_size
        self.in_chans = in_chans
        self.embed_dim = embed_dim
        self.proj = nn.Conv3d(in_chans, embed_dim, kernel_size=patch_size,
            stride=patch_size)
        if norm_layer is not None:
            self.norm = norm_layer(embed_dim)
        else:
            self.norm = None
        self.use_spectral_aggregation = use_spectral_aggregation
        if self.use_spectral_aggregation == 'token':
            self.spectral_aggregation_token = nn.Parameter(data=torch.empty
                (embed_dim), requires_grad=True)
            trunc_normal_(self.spectral_aggregation_token, std=0.02)

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