import math
import torch
import torch.nn.functional as F
import torch.utils.data
from torch.nn import Parameter
import torch.onnx.operators
from torch.nn.parameter import Parameter
from torch.nn import init
import torch.optim
import torch.optim.lr_scheduler


class WDV29Linear(torch.nn.Module):
    """Applies a linear transformation to the incoming data: :math:`y = xA^T + b`

    Args:
        in_features: size of each input sample
        out_features: size of each output sample
        bias: If set to ``False``, the layer will not learn an additive bias.
            Default: ``True``

    Shape:
        - Input: :math:`(N, *, H_{in})` where :math:`*` means any number of
          additional dimensions and :math:`H_{in} = \\text{in\\_features}`
        - Output: :math:`(N, *, H_{out})` where all but the last dimension
          are the same shape as the input and :math:`H_{out} = \\text{out\\_features}`.

    Attributes:
        weight: the learnable weights of the module of shape
            :math:`(\\text{out\\_features}, \\text{in\\_features})`. The values are
            initialized from :math:`\\mathcal{U}(-\\sqrt{k}, \\sqrt{k})`, where
            :math:`k = \\frac{1}{\\text{in\\_features}}`
        bias:   the learnable bias of the module of shape :math:`(\\text{out\\_features})`.
                If :attr:`bias` is ``True``, the values are initialized from
                :math:`\\mathcal{U}(-\\sqrt{k}, \\sqrt{k})` where
                :math:`k = \\frac{1}{\\text{in\\_features}}`

    Examples::

        >>> m = nn.Linear(20, 30)
        >>> input = torch.randn(128, 20)
        >>> output = m(input)
        >>> print(output.size())
        torch.Size([128, 30])
    """
    __constants__ = ['bias', 'in_features', 'out_features']

    def __init__(self, in_features, out_features, wd_require_gradient=False,
        bias=True):
        super(WDV29Linear, self).__init__()
        self.in_features = in_features
        self.out_features = out_features
        self.weight = Parameter(torch.Tensor(out_features, in_features),
            requires_grad=wd_require_gradient)
        self.tanh_weight_weight = Parameter(torch.Tensor(out_features,
            in_features))
        self.tanh_bias_weight = Parameter(torch.Tensor(out_features,
            in_features))
        if bias:
            self.bias = Parameter(torch.Tensor(out_features), requires_grad
                =wd_require_gradient)
            self.tanh_weight_bias = Parameter(torch.Tensor(out_features))
            self.tanh_bias_bias = Parameter(torch.Tensor(out_features))
        else:
            self.register_parameter('bias', None)
        self.reset_parameters()

    def reset_parameters(self):
        torch.nn.init.constant_(self.tanh_weight_weight, 1.0)
        torch.nn.init.constant_(self.tanh_weight_bias, 1.0)
        init.kaiming_uniform_(self.weight, a=math.sqrt(5))
        if self.bias is not None:
            torch.nn.init.constant_(self.tanh_bias_weight, 0.0)
            torch.nn.init.constant_(self.tanh_bias_bias, 0.0)
            fan_in, _ = init._calculate_fan_in_and_fan_out(self.weight)
            bound = 1 / math.sqrt(fan_in)
            init.uniform_(self.bias, -bound, bound)

    def forward(self, input):
        weight = self.weight
        weight = self.tanh_weight_weight * torch.tanh(weight
            ) + self.tanh_bias_weight
        bias = self.bias
        bias = self.tanh_weight_bias * torch.tanh(bias) + self.tanh_bias_bias
        return F.linear(input, weight, bias)

    def extra_repr(self):
        return 'in_features={}, out_features={}, bias={}'.format(self.
            in_features, self.out_features, self.bias is not None)


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


def get_init_inputs():
    return [[], {'in_features': 4, 'out_features': 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 torch._inductor.runtime.triton_helpers import libdevice
import math
import torch.utils.data
from torch.nn import Parameter
import torch.onnx.operators
from torch.nn.parameter import Parameter
from torch.nn import init
import torch.optim
import torch.optim.lr_scheduler
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_mul_tanh_0(in_ptr0, in_ptr1, in_ptr2, out_ptr0,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + x0, xmask)
    tmp1 = tl.load(in_ptr1 + x0, xmask)
    tmp4 = tl.load(in_ptr2 + x0, xmask)
    tmp2 = libdevice.tanh(tmp1)
    tmp3 = tmp0 * tmp2
    tmp5 = tmp3 + tmp4
    tl.store(out_ptr0 + x0, tmp5, xmask)


@triton.jit
def triton_poi_fused_add_mul_tanh_1(in_ptr0, in_ptr1, in_ptr2, out_ptr0,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 4
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + x0, xmask)
    tmp1 = tl.load(in_ptr1 + x0, xmask)
    tmp4 = tl.load(in_ptr2 + x0, xmask)
    tmp2 = libdevice.tanh(tmp1)
    tmp3 = tmp0 * tmp2
    tmp5 = tmp3 + tmp4
    tl.store(out_ptr0 + x0, tmp5, xmask)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7) = args
    args.clear()
    assert_size_stride(primals_1, (4, 4), (4, 1))
    assert_size_stride(primals_2, (4, 4), (4, 1))
    assert_size_stride(primals_3, (4, 4), (4, 1))
    assert_size_stride(primals_4, (4,), (1,))
    assert_size_stride(primals_5, (4,), (1,))
    assert_size_stride(primals_6, (4,), (1,))
    assert_size_stride(primals_7, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_add_mul_tanh_0[grid(16)](primals_2, primals_1,
            primals_3, buf0, 16, XBLOCK=16, num_warps=1, num_stages=1)
        del primals_2
        del primals_3
        buf1 = empty_strided_cuda((4,), (1,), torch.float32)
        triton_poi_fused_add_mul_tanh_1[grid(4)](primals_5, primals_4,
            primals_6, buf1, 4, XBLOCK=4, num_warps=1, num_stages=1)
        del primals_5
        del primals_6
        buf2 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.addmm(buf1, reinterpret_tensor(primals_7, (64, 4), (
            4, 1), 0), reinterpret_tensor(buf0, (4, 4), (1, 4), 0), alpha=1,
            beta=1, out=buf2)
        del buf0
        del buf1
    return reinterpret_tensor(buf2, (4, 4, 4, 4), (64, 16, 4, 1), 0
        ), primals_1, primals_4, reinterpret_tensor(primals_7, (64, 4), (4,
        1), 0)


class WDV29LinearNew(torch.nn.Module):
    """Applies a linear transformation to the incoming data: :math:`y = xA^T + b`

    Args:
        in_features: size of each input sample
        out_features: size of each output sample
        bias: If set to ``False``, the layer will not learn an additive bias.
            Default: ``True``

    Shape:
        - Input: :math:`(N, *, H_{in})` where :math:`*` means any number of
          additional dimensions and :math:`H_{in} = \\text{in\\_features}`
        - Output: :math:`(N, *, H_{out})` where all but the last dimension
          are the same shape as the input and :math:`H_{out} = \\text{out\\_features}`.

    Attributes:
        weight: the learnable weights of the module of shape
            :math:`(\\text{out\\_features}, \\text{in\\_features})`. The values are
            initialized from :math:`\\mathcal{U}(-\\sqrt{k}, \\sqrt{k})`, where
            :math:`k = \\frac{1}{\\text{in\\_features}}`
        bias:   the learnable bias of the module of shape :math:`(\\text{out\\_features})`.
                If :attr:`bias` is ``True``, the values are initialized from
                :math:`\\mathcal{U}(-\\sqrt{k}, \\sqrt{k})` where
                :math:`k = \\frac{1}{\\text{in\\_features}}`

    Examples::

        >>> m = nn.Linear(20, 30)
        >>> input = torch.randn(128, 20)
        >>> output = m(input)
        >>> print(output.size())
        torch.Size([128, 30])
    """
    __constants__ = ['bias', 'in_features', 'out_features']

    def __init__(self, in_features, out_features, wd_require_gradient=False,
        bias=True):
        super(WDV29LinearNew, self).__init__()
        self.in_features = in_features
        self.out_features = out_features
        self.weight = Parameter(torch.Tensor(out_features, in_features),
            requires_grad=wd_require_gradient)
        self.tanh_weight_weight = Parameter(torch.Tensor(out_features,
            in_features))
        self.tanh_bias_weight = Parameter(torch.Tensor(out_features,
            in_features))
        if bias:
            self.bias = Parameter(torch.Tensor(out_features), requires_grad
                =wd_require_gradient)
            self.tanh_weight_bias = Parameter(torch.Tensor(out_features))
            self.tanh_bias_bias = Parameter(torch.Tensor(out_features))
        else:
            self.register_parameter('bias', None)
        self.reset_parameters()

    def reset_parameters(self):
        torch.nn.init.constant_(self.tanh_weight_weight, 1.0)
        torch.nn.init.constant_(self.tanh_weight_bias, 1.0)
        init.kaiming_uniform_(self.weight, a=math.sqrt(5))
        if self.bias is not None:
            torch.nn.init.constant_(self.tanh_bias_weight, 0.0)
            torch.nn.init.constant_(self.tanh_bias_bias, 0.0)
            fan_in, _ = init._calculate_fan_in_and_fan_out(self.weight)
            bound = 1 / math.sqrt(fan_in)
            init.uniform_(self.bias, -bound, bound)

    def extra_repr(self):
        return 'in_features={}, out_features={}, bias={}'.format(self.
            in_features, self.out_features, self.bias is not None)

    def forward(self, input_0):
        primals_1 = self.weight
        primals_2 = self.tanh_weight_weight
        primals_3 = self.tanh_bias_weight
        primals_4 = self.bias
        primals_5 = self.tanh_weight_bias
        primals_6 = self.tanh_bias_bias
        primals_7 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7])
        return output[0]
