import torch
import torch.nn as nn


class Biaffine(nn.Module):
    """
    Biaffine layer for first-order scoring.

    This function has a tensor of weights :math:`W` and bias terms if needed.
    The score :math:`s(x, y)` of the vector pair :math:`(x, y)` is computed as :math:`x^T W y`,
    in which :math:`x` and :math:`y` can be concatenated with bias terms.

    References:
        - Timothy Dozat and Christopher D. Manning. 2017.
          `Deep Biaffine Attention for Neural Dependency Parsing`_.

    Args:
        n_in (int):
            The size of the input feature.
        n_out (int):
            The number of output channels.
        bias_x (bool):
            If ``True``, adds a bias term for tensor :math:`x`. Default: ``True``.
        bias_y (bool):
            If ``True``, adds a bias term for tensor :math:`y`. Default: ``True``.

    .. _Deep Biaffine Attention for Neural Dependency Parsing:
        https://openreview.net/forum?id=Hk95PK9le
    """

    def __init__(self, n_in, n_out=1, bias_x=True, bias_y=True):
        super().__init__()
        self.n_in = n_in
        self.n_out = n_out
        self.bias_x = bias_x
        self.bias_y = bias_y
        self.weight = nn.Parameter(torch.Tensor(n_out, n_in + bias_x, n_in +
            bias_y))
        self.reset_parameters()

    def __repr__(self):
        s = f'n_in={self.n_in}, n_out={self.n_out}'
        if self.bias_x:
            s += f', bias_x={self.bias_x}'
        if self.bias_y:
            s += f', bias_y={self.bias_y}'
        return f'{self.__class__.__name__}({s})'

    def reset_parameters(self):
        nn.init.zeros_(self.weight)

    def forward(self, x, y):
        """
        Args:
            x (torch.Tensor): ``[batch_size, seq_len, n_in]``.
            y (torch.Tensor): ``[batch_size, seq_len, n_in]``.

        Returns:
            ~torch.Tensor:
                A scoring tensor of shape ``[batch_size, n_out, seq_len, seq_len]``.
                If ``n_out=1``, the dimension for ``n_out`` will be squeezed automatically.
        """
        if self.bias_x:
            x = torch.cat((x, torch.ones_like(x[..., :1])), -1)
        if self.bias_y:
            y = torch.cat((y, torch.ones_like(y[..., :1])), -1)
        s = torch.einsum('bxi,oij,byj->boxy', x, self.weight, y)
        s = s.squeeze(1)
        return s


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


def get_init_inputs():
    return [[], {'n_in': 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
import torch.nn as 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_poi_fused_cat_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 80
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 5
    x1 = xindex // 5
    x2 = xindex
    tmp0 = x0
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (4 * x1 + x0), tmp4 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp6 = tmp0 >= tmp3
    tl.full([1], 5, tl.int64)
    tmp9 = 1.0
    tmp10 = tl.full(tmp9.shape, 0.0, tmp9.dtype)
    tmp11 = tl.where(tmp6, tmp9, tmp10)
    tmp12 = tl.where(tmp4, tmp5, tmp11)
    tl.store(out_ptr0 + x2, tmp12, xmask)


def call(args):
    primals_1, primals_2, primals_3 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_2, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_3, (1, 5, 5), (25, 5, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 5), (20, 5, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_cat_0[grid(80)](primals_1, buf0, 80, XBLOCK=128,
            num_warps=4, num_stages=1)
        del primals_1
        buf1 = empty_strided_cuda((1, 16, 5), (80, 5, 1), torch.float32)
        extern_kernels.bmm(reinterpret_tensor(buf0, (1, 16, 5), (0, 5, 1), 
            0), primals_3, out=buf1)
        del primals_3
        buf2 = empty_strided_cuda((4, 4, 5), (20, 5, 1), torch.float32)
        triton_poi_fused_cat_0[grid(80)](primals_2, buf2, 80, XBLOCK=128,
            num_warps=4, num_stages=1)
        del primals_2
        buf3 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        extern_kernels.bmm(buf2, reinterpret_tensor(buf1, (4, 5, 4), (20, 1,
            5), 0), out=buf3)
        del buf1
    return reinterpret_tensor(buf3, (4, 4, 4), (16, 1, 4), 0
        ), reinterpret_tensor(buf2, (4, 5, 4), (20, 1, 5), 0
        ), reinterpret_tensor(buf0, (1, 5, 16), (80, 1, 5), 0)


class BiaffineNew(nn.Module):
    """
    Biaffine layer for first-order scoring.

    This function has a tensor of weights :math:`W` and bias terms if needed.
    The score :math:`s(x, y)` of the vector pair :math:`(x, y)` is computed as :math:`x^T W y`,
    in which :math:`x` and :math:`y` can be concatenated with bias terms.

    References:
        - Timothy Dozat and Christopher D. Manning. 2017.
          `Deep Biaffine Attention for Neural Dependency Parsing`_.

    Args:
        n_in (int):
            The size of the input feature.
        n_out (int):
            The number of output channels.
        bias_x (bool):
            If ``True``, adds a bias term for tensor :math:`x`. Default: ``True``.
        bias_y (bool):
            If ``True``, adds a bias term for tensor :math:`y`. Default: ``True``.

    .. _Deep Biaffine Attention for Neural Dependency Parsing:
        https://openreview.net/forum?id=Hk95PK9le
    """

    def __init__(self, n_in, n_out=1, bias_x=True, bias_y=True):
        super().__init__()
        self.n_in = n_in
        self.n_out = n_out
        self.bias_x = bias_x
        self.bias_y = bias_y
        self.weight = nn.Parameter(torch.Tensor(n_out, n_in + bias_x, n_in +
            bias_y))
        self.reset_parameters()

    def __repr__(self):
        s = f'n_in={self.n_in}, n_out={self.n_out}'
        if self.bias_x:
            s += f', bias_x={self.bias_x}'
        if self.bias_y:
            s += f', bias_y={self.bias_y}'
        return f'{self.__class__.__name__}({s})'

    def reset_parameters(self):
        nn.init.zeros_(self.weight)

    def forward(self, input_0, input_1):
        primals_3 = self.weight
        primals_1 = input_0
        primals_2 = input_1
        output = call([primals_1, primals_2, primals_3])
        return output[0]
