# 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_8/inductor_cache/l3/cl35tzbhrd24dhunkbb6gjs54aklpyr46oikqhoylcgmkcmhujil.py
# Topologically Sorted Source Nodes: [out], Original ATen: [aten.mean]
# Source node to ATen node mapping:
#   out => mean
# Graph fragment:
#   %mean : [num_users=1] = call_function[target=torch.ops.aten.mean.dim](args = (%primals_1, [-1, -2], True), kwargs = {})
triton_per_fused_mean_0 = async_compile.triton('triton_per_fused_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.persistent_reduction(
    size_hints=[16, 16],
    reduction_hint=ReductionHint.INNER,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32', 3: '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, 3), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_mean_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 1, '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_per_fused_mean_0(in_out_ptr0, in_ptr0, xnumel, rnumel, XBLOCK : tl.constexpr):
    xnumel = 16
    rnumel = 16
    RBLOCK: tl.constexpr = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rindex = tl.arange(0, RBLOCK)[None, :]
    roffset = 0
    rmask = tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r1 = rindex
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (r1 + (16*x0)), xmask, other=0.0)
    tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
    tmp3 = tl.where(xmask, tmp1, 0)
    tmp4 = tl.sum(tmp3, 1)[:, None]
    tmp5 = 16.0
    tmp6 = tmp4 / tmp5
    tl.debug_barrier()
    tl.store(in_out_ptr0 + (x0), tmp6, 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), (4, 1))
    assert_size_stride(primals_3, (4, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 1, 1), (4, 1, 16, 16), torch.float32)
        buf1 = buf0; del buf0  # reuse
        # Topologically Sorted Source Nodes: [out], Original ATen: [aten.mean]
        stream0 = get_raw_stream(0)
        triton_per_fused_mean_0.run(buf1, primals_1, 16, 16, grid=grid(16), stream=stream0)
        del primals_1
        buf2 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [out_2], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_3, reinterpret_tensor(buf1, (4, 4), (4, 1), 0), reinterpret_tensor(primals_2, (4, 4), (1, 4), 0), alpha=1, beta=1, out=buf2)
        del primals_2
        del primals_3
    return (buf2, reinterpret_tensor(buf1, (4, 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, 4, 4, 4), (64, 16, 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, ), (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
from typing import Any
from typing import Dict
from typing import Optional
import torch.nn as nn
import torch.nn.modules as nn
import torch.optim
from torch import nn


def is_pos_int(number):
    """
    Returns True if a number is a positive integer.
    """
    return type(number) == int and number >= 0


class ClassyHead(nn.Module):
    """
    Base class for heads that can be attached to :class:`ClassyModel`.

    A head is a regular :class:`torch.nn.Module` that can be attached to a
    pretrained model. This enables a form of transfer learning: utilizing a
    model trained for one dataset to extract features that can be used for
    other problems. A head must be attached to a :class:`models.ClassyBlock`
    within a :class:`models.ClassyModel`.
    """

    def __init__(self, unique_id: 'Optional[str]'=None, num_classes:
        'Optional[int]'=None):
        """
        Constructs a ClassyHead.

        Args:
            unique_id: A unique identifier for the head. Multiple instances of
                the same head might be attached to a model, and unique_id is used
                to refer to them.

            num_classes: Number of classes for the head.
        """
        super().__init__()
        self.unique_id = unique_id or self.__class__.__name__
        self.num_classes = num_classes

    @classmethod
    def from_config(cls, config: 'Dict[str, Any]') ->'ClassyHead':
        """Instantiates a ClassyHead from a configuration.

        Args:
            config: A configuration for the ClassyHead.

        Returns:
            A ClassyHead instance.
        """
        raise NotImplementedError

    def forward(self, x):
        """
        Performs inference on the head.

        This is a regular PyTorch method, refer to :class:`torch.nn.Module` for
        more details
        """
        raise NotImplementedError


class FullyConnectedHead(ClassyHead):
    """This head defines a 2d average pooling layer
    (:class:`torch.nn.AdaptiveAvgPool2d`) followed by a fully connected
    layer (:class:`torch.nn.Linear`).
    """

    def __init__(self, unique_id: 'str', num_classes: 'int', in_plane:
        'int', zero_init_bias: 'bool'=False):
        """Constructor for FullyConnectedHead

        Args:
            unique_id: A unique identifier for the head. Multiple instances of
                the same head might be attached to a model, and unique_id is used
                to refer to them.

            num_classes: Number of classes for the head. If None, then the fully
                connected layer is not applied.

            in_plane: Input size for the fully connected layer.
        """
        super().__init__(unique_id, num_classes)
        assert num_classes is None or is_pos_int(num_classes)
        assert is_pos_int(in_plane)
        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
        self.fc = None if num_classes is None else nn.Linear(in_plane,
            num_classes)
        if zero_init_bias:
            self.fc.bias.data.zero_()

    @classmethod
    def from_config(cls, config: 'Dict[str, Any]') ->'FullyConnectedHead':
        """Instantiates a FullyConnectedHead from a configuration.

        Args:
            config: A configuration for a FullyConnectedHead.
                See :func:`__init__` for parameters expected in the config.

        Returns:
            A FullyConnectedHead instance.
        """
        num_classes = config.get('num_classes', None)
        in_plane = config['in_plane']
        return cls(config['unique_id'], num_classes, in_plane,
            zero_init_bias=config.get('zero_init_bias', False))

    def forward(self, x):
        out = self.avgpool(x)
        out = out.flatten(start_dim=1)
        if self.fc is not None:
            out = self.fc(out)
        return out


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


def get_init_inputs():
    return [[], {'unique_id': 4, 'num_classes': 4, 'in_plane': 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 typing import Any
from typing import Dict
from typing import Optional
import torch.nn as nn
import torch.nn.modules as nn
import torch.optim
from torch import nn
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_per_fused_mean_0(in_out_ptr0, in_ptr0, xnumel, rnumel, XBLOCK:
    tl.constexpr):
    xnumel = 16
    RBLOCK: tl.constexpr = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rindex = tl.arange(0, RBLOCK)[None, :]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r1 = rindex
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (r1 + 16 * x0), xmask, other=0.0)
    tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
    tmp3 = tl.where(xmask, tmp1, 0)
    tmp4 = tl.sum(tmp3, 1)[:, None]
    tmp5 = 16.0
    tmp6 = tmp4 / tmp5
    tl.debug_barrier()
    tl.store(in_out_ptr0 + x0, tmp6, 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), (4, 1))
    assert_size_stride(primals_3, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 1, 1), (4, 1, 16, 16), torch.float32)
        buf1 = buf0
        del buf0
        get_raw_stream(0)
        triton_per_fused_mean_0[grid(16)](buf1, primals_1, 16, 16, XBLOCK=8,
            num_warps=2, num_stages=1)
        del primals_1
        buf2 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_3, reinterpret_tensor(buf1, (4, 4), (4,
            1), 0), reinterpret_tensor(primals_2, (4, 4), (1, 4), 0), alpha
            =1, beta=1, out=buf2)
        del primals_2
        del primals_3
    return buf2, reinterpret_tensor(buf1, (4, 4), (4, 1), 0)


def is_pos_int(number):
    """
    Returns True if a number is a positive integer.
    """
    return type(number) == int and number >= 0


class ClassyHead(nn.Module):
    """
    Base class for heads that can be attached to :class:`ClassyModel`.

    A head is a regular :class:`torch.nn.Module` that can be attached to a
    pretrained model. This enables a form of transfer learning: utilizing a
    model trained for one dataset to extract features that can be used for
    other problems. A head must be attached to a :class:`models.ClassyBlock`
    within a :class:`models.ClassyModel`.
    """

    def __init__(self, unique_id: 'Optional[str]'=None, num_classes:
        'Optional[int]'=None):
        """
        Constructs a ClassyHead.

        Args:
            unique_id: A unique identifier for the head. Multiple instances of
                the same head might be attached to a model, and unique_id is used
                to refer to them.

            num_classes: Number of classes for the head.
        """
        super().__init__()
        self.unique_id = unique_id or self.__class__.__name__
        self.num_classes = num_classes

    @classmethod
    def from_config(cls, config: 'Dict[str, Any]') ->'ClassyHead':
        """Instantiates a ClassyHead from a configuration.

        Args:
            config: A configuration for the ClassyHead.

        Returns:
            A ClassyHead instance.
        """
        raise NotImplementedError

    def forward(self, x):
        """
        Performs inference on the head.

        This is a regular PyTorch method, refer to :class:`torch.nn.Module` for
        more details
        """
        raise NotImplementedError


class FullyConnectedHeadNew(ClassyHead):
    """This head defines a 2d average pooling layer
    (:class:`torch.nn.AdaptiveAvgPool2d`) followed by a fully connected
    layer (:class:`torch.nn.Linear`).
    """

    def __init__(self, unique_id: 'str', num_classes: 'int', in_plane:
        'int', zero_init_bias: 'bool'=False):
        """Constructor for FullyConnectedHead

        Args:
            unique_id: A unique identifier for the head. Multiple instances of
                the same head might be attached to a model, and unique_id is used
                to refer to them.

            num_classes: Number of classes for the head. If None, then the fully
                connected layer is not applied.

            in_plane: Input size for the fully connected layer.
        """
        super().__init__(unique_id, num_classes)
        assert num_classes is None or is_pos_int(num_classes)
        assert is_pos_int(in_plane)
        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
        self.fc = None if num_classes is None else nn.Linear(in_plane,
            num_classes)
        if zero_init_bias:
            self.fc.bias.data.zero_()

    @classmethod
    def from_config(cls, config: 'Dict[str, Any]') ->'FullyConnectedHead':
        """Instantiates a FullyConnectedHead from a configuration.

        Args:
            config: A configuration for a FullyConnectedHead.
                See :func:`__init__` for parameters expected in the config.

        Returns:
            A FullyConnectedHead instance.
        """
        num_classes = config.get('num_classes', None)
        in_plane = config['in_plane']
        return cls(config['unique_id'], num_classes, in_plane,
            zero_init_bias=config.get('zero_init_bias', False))

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