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 math
from torch import nn
from torch.nn.parameter import Parameter
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_poi_fused_add_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 4
    tmp0 = tl.load(in_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x0, 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, 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 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_3, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_1, (4, 4), (1, 4), 0), out=buf0)
        del primals_1
        buf1 = reinterpret_tensor(buf0, (4, 4, 4, 4), (64, 16, 4, 1), 0)
        del buf0
        get_raw_stream(0)
        triton_poi_fused_add_0[grid(256)](buf1, primals_2, 256, XBLOCK=256,
            num_warps=4, num_stages=1)
        del primals_2
    return buf1, reinterpret_tensor(primals_3, (64, 4), (4, 1), 0)


def linearExcitability(input, weight, excitability=None, bias=None):
    """Applies a linear transformation to the incoming data: :math:`y = c(xA^T) + b`.

    Shape:
        - input:        :math:`(N, *, in_features)`
        - weight:       :math:`(out_features, in_features)`
        - excitability: :math:`(out_features)`
        - bias:         :math:`(out_features)`
        - output:       :math:`(N, *, out_features)`
    (NOTE: `*` means any number of additional dimensions)"""
    if excitability is not None:
        output = torch.matmul(input, weight.t()) * excitability
    else:
        output = torch.matmul(input, weight.t())
    if bias is not None:
        output = output + bias
    return output


class LinearExcitabilityNew(nn.Module):
    """Module for a linear transformation with multiplicative excitability-parameter (i.e., learnable) and/or -buffer.

    Args:
        in_features:    size of each input sample
        out_features:   size of each output sample
        bias:           if 'False', layer will not learn an additive bias-parameter (DEFAULT=True)
        excitability:   if 'True', layer will learn a multiplicative excitability-parameter (DEFAULT=False)
        excit_buffer:   if 'True', layer will have excitability-buffer whose value can be set (DEFAULT=False)

    Shape:
        - input:    :math:`(N, *, in_features)` where `*` means any number of additional dimensions
        - output:   :math:`(N, *, out_features)` where all but the last dimension are the same shape as the input.

    Attributes:
        weight:         the learnable weights of the module of shape (out_features x in_features)
        excitability:   the learnable multiplication terms (out_features)
        bias:           the learnable bias of the module of shape (out_features)
        excit_buffer:   fixed multiplication variable (out_features)"""

    def __init__(self, in_features, out_features, bias=True, excitability=
        False, excit_buffer=False):
        super(LinearExcitabilityNew, self).__init__()
        self.in_features = in_features
        self.out_features = out_features
        self.weight = Parameter(torch.Tensor(out_features, in_features))
        if excitability:
            self.excitability = Parameter(torch.Tensor(out_features))
        else:
            self.register_parameter('excitability', None)
        if bias:
            self.bias = Parameter(torch.Tensor(out_features))
        else:
            self.register_parameter('bias', None)
        if excit_buffer:
            buffer = torch.ones(out_features)
            self.register_buffer('excit_buffer', buffer)
        else:
            self.register_buffer('excit_buffer', None)
        self.reset_parameters()

    def reset_parameters(self):
        """Modifies the parameters to initialize / reset them at appropriate values."""
        """Avoid using in-place init methods"""
        stdv = 1.0 / math.sqrt(self.weight.size(1))
        self.weight = Parameter(torch.distributions.Uniform(-stdv, stdv).
            sample(self.weight.size()))
        if self.excitability is not None:
            self.excitability = Parameter(torch.ones_like(self.excitability))
        if self.bias is not None:
            self.bias = Parameter(torch.distributions.Uniform(-stdv, stdv).
                sample(self.bias.size()))

    def __repr__(self):
        return self.__class__.__name__ + '(' + 'in_features=' + str(self.
            in_features) + ', out_features=' + str(self.out_features) + ')'

    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]


Measured latency_ms: 0.021855