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]


Measured latency_ms: 0.023359