# 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/jd/cjdlhq4n2k6jvqgazpf26tgmhut4bhxemh4iahfzgmxvymcjvdd5.py
# Topologically Sorted Source Nodes: [x, x_1], Original ATen: [aten.convolution, aten.mean]
# Source node to ATen node mapping:
#   x => convolution
#   x_1 => mean
# Graph fragment:
#   %convolution : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%primals_3, %primals_1, %primals_2, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
#   %mean : [num_users=1] = call_function[target=torch.ops.aten.mean.dim](args = (%convolution, [-1, -2], True), kwargs = {})
triton_red_fused_convolution_mean_0 = async_compile.triton('triton_red_fused_convolution_mean_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.reduction(
    size_hints=[4, 4096],
    reduction_hint=ReductionHint.INNER,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32', 4: '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, 4), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_red_fused_convolution_mean_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 1, '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}
)
@triton.jit
def triton_red_fused_convolution_mean_0(in_out_ptr0, in_ptr0, in_ptr1, xnumel, rnumel, XBLOCK : tl.constexpr, RBLOCK : tl.constexpr):
    xnumel = 4
    rnumel = 4096
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rbase = tl.arange(0, RBLOCK)[None, :]
    x0 = xindex
    tmp1 = tl.load(in_ptr1 + (0))
    tmp2 = tl.broadcast_to(tmp1, [XBLOCK, RBLOCK])
    _tmp5 = tl.full([XBLOCK, RBLOCK], 0, tl.float32)
    for roffset in range(0, rnumel, RBLOCK):
        rindex = roffset + rbase
        rmask = rindex < rnumel
        r1 = rindex
        tmp0 = tl.load(in_ptr0 + (r1 + (4096*x0)), rmask & xmask, eviction_policy='evict_first', other=0.0)
        tmp3 = tmp0 + tmp2
        tmp4 = tl.broadcast_to(tmp3, [XBLOCK, RBLOCK])
        tmp6 = _tmp5 + tmp4
        _tmp5 = tl.where(rmask & xmask, tmp6, _tmp5)
    tmp5 = tl.sum(_tmp5, 1)[:, None]
    tmp7 = 4096.0
    tmp8 = tmp5 / tmp7
    tl.debug_barrier()
    tl.store(in_out_ptr0 + (x0), tmp8, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5 = args
    args.clear()
    assert_size_stride(primals_1, (1, 3, 1, 1), (3, 1, 1, 1))
    assert_size_stride(primals_2, (1, ), (1, ))
    assert_size_stride(primals_3, (4, 3, 64, 64), (12288, 4096, 64, 1))
    assert_size_stride(primals_4, (1000, 1), (1, 1))
    assert_size_stride(primals_5, (1000, ), (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_3, primals_1, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf0, (4, 1, 64, 64), (4096, 4096, 64, 1))
        buf1 = empty_strided_cuda((4, 1, 1, 1), (1, 4, 4, 4), torch.float32)
        buf2 = buf1; del buf1  # reuse
        # Topologically Sorted Source Nodes: [x, x_1], Original ATen: [aten.convolution, aten.mean]
        stream0 = get_raw_stream(0)
        triton_red_fused_convolution_mean_0.run(buf2, buf0, primals_2, 4, 4096, grid=grid(4), stream=stream0)
        del buf0
        del primals_2
        buf3 = empty_strided_cuda((4, 1000), (1000, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_3], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_5, reinterpret_tensor(buf2, (4, 1), (1, 0), 0), reinterpret_tensor(primals_4, (1, 1000), (1, 1), 0), alpha=1, beta=1, out=buf3)
        del primals_5
    return (buf3, primals_1, primals_3, reinterpret_tensor(buf2, (4, 1), (1, 1), 0), primals_4, )


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((1, 3, 1, 1), (3, 1, 1, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((1, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((4, 3, 64, 64), (12288, 4096, 64, 1), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((1000, 1), (1, 1), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((1000, ), (1, ), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5])
    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
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.nn import Module
import torch.nn as nn
from typing import Any
from torch.nn.modules import Module
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_red_fused_convolution_mean_0(in_out_ptr0, in_ptr0, in_ptr1,
    xnumel, rnumel, XBLOCK: tl.constexpr, RBLOCK: tl.constexpr):
    xnumel = 4
    rnumel = 4096
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rbase = tl.arange(0, RBLOCK)[None, :]
    x0 = xindex
    tmp1 = tl.load(in_ptr1 + 0)
    tmp2 = tl.broadcast_to(tmp1, [XBLOCK, RBLOCK])
    _tmp5 = tl.full([XBLOCK, RBLOCK], 0, tl.float32)
    for roffset in range(0, rnumel, RBLOCK):
        rindex = roffset + rbase
        rmask = rindex < rnumel
        r1 = rindex
        tmp0 = tl.load(in_ptr0 + (r1 + 4096 * x0), rmask & xmask,
            eviction_policy='evict_first', other=0.0)
        tmp3 = tmp0 + tmp2
        tmp4 = tl.broadcast_to(tmp3, [XBLOCK, RBLOCK])
        tmp6 = _tmp5 + tmp4
        _tmp5 = tl.where(rmask & xmask, tmp6, _tmp5)
    tmp5 = tl.sum(_tmp5, 1)[:, None]
    tmp7 = 4096.0
    tmp8 = tmp5 / tmp7
    tl.debug_barrier()
    tl.store(in_out_ptr0 + x0, tmp8, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5 = args
    args.clear()
    assert_size_stride(primals_1, (1, 3, 1, 1), (3, 1, 1, 1))
    assert_size_stride(primals_2, (1,), (1,))
    assert_size_stride(primals_3, (4, 3, 64, 64), (12288, 4096, 64, 1))
    assert_size_stride(primals_4, (1000, 1), (1, 1))
    assert_size_stride(primals_5, (1000,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = extern_kernels.convolution(primals_3, primals_1, stride=(1, 
            1), padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf0, (4, 1, 64, 64), (4096, 4096, 64, 1))
        buf1 = empty_strided_cuda((4, 1, 1, 1), (1, 4, 4, 4), torch.float32)
        buf2 = buf1
        del buf1
        get_raw_stream(0)
        triton_red_fused_convolution_mean_0[grid(4)](buf2, buf0, primals_2,
            4, 4096, XBLOCK=1, RBLOCK=2048, num_warps=16, num_stages=1)
        del buf0
        del primals_2
        buf3 = empty_strided_cuda((4, 1000), (1000, 1), torch.float32)
        extern_kernels.addmm(primals_5, reinterpret_tensor(buf2, (4, 1), (1,
            0), 0), reinterpret_tensor(primals_4, (1, 1000), (1, 1), 0),
            alpha=1, beta=1, out=buf3)
        del primals_5
    return buf3, primals_1, primals_3, reinterpret_tensor(buf2, (4, 1), (1,
        1), 0), primals_4


class ClassificationTestModelNew(Module):

    def __init__(self, in_chans: 'int'=3, num_classes: 'int'=1000, **kwargs:
        Any) ->None:
        super().__init__()
        self.conv1 = nn.Conv2d(in_channels=in_chans, out_channels=1,
            kernel_size=1)
        self.pool = nn.AdaptiveAvgPool2d((1, 1))
        self.fc = nn.Linear(1, num_classes)

    def forward(self, input_0):
        primals_1 = self.conv1.weight
        primals_2 = self.conv1.bias
        primals_4 = self.fc.weight
        primals_5 = self.fc.bias
        primals_3 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4, primals_5])
        return output[0]
