import torch
import torch.quantization
from torch import nn


class PointerHead(nn.Module):
    """Head for pointer ordering task."""

    def __init__(self, embed_dim, bias=True):
        super().__init__()
        self.embed_dim = embed_dim
        self.scaling = self.embed_dim ** -0.5
        self.k_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
        self.q_proj = nn.Linear(embed_dim, embed_dim, bias=bias)

    def _shape(self, tensor, seq_len, bsz):
        return tensor.contiguous().view(seq_len, bsz, self.embed_dim
            ).transpose(0, 1)

    def forward(self, query, key):
        """Input shape: Time(SeqLen) x Batch x Channel"""
        tgt_len, bsz, embed_dim = query.size()
        assert embed_dim == self.embed_dim
        assert list(query.size()) == [tgt_len, bsz, embed_dim]
        q = self.q_proj(query) * self.scaling
        k = self.k_proj(key)
        q = self._shape(q, tgt_len, bsz)
        k = self._shape(k, -1, bsz)
        assert k is not None
        assert q is not None
        src_len = k.size(1)
        attn_weights = torch.bmm(q, k.transpose(1, 2))
        assert attn_weights.size() == (bsz, tgt_len, src_len)
        return attn_weights


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


def get_init_inputs():
    return [[], {'embed_dim': 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.quantization
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_poi_fused_mul_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 64
    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
    tmp3 = 0.5
    tmp4 = tmp2 * tmp3
    tl.store(in_out_ptr0 + x2, tmp4, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5, primals_6 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_2, (4, 4), (4, 1))
    assert_size_stride(primals_3, (4,), (1,))
    assert_size_stride(primals_4, (4, 4), (4, 1))
    assert_size_stride(primals_5, (4,), (1,))
    assert_size_stride(primals_6, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_1, (16, 4), (4, 1), 0),
            reinterpret_tensor(primals_2, (4, 4), (1, 4), 0), out=buf0)
        del primals_2
        buf1 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_5, reinterpret_tensor(primals_6, (64, 
            4), (4, 1), 0), reinterpret_tensor(primals_4, (4, 4), (1, 4), 0
            ), alpha=1, beta=1, out=buf1)
        del primals_4
        del primals_5
        buf2 = reinterpret_tensor(buf0, (4, 4, 4), (16, 4, 1), 0)
        del buf0
        get_raw_stream(0)
        triton_poi_fused_mul_0[grid(64)](buf2, primals_3, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        del primals_3
        buf3 = empty_strided_cuda((4, 4, 16), (64, 16, 1), torch.float32)
        extern_kernels.bmm(reinterpret_tensor(buf2, (4, 4, 4), (4, 16, 1), 
            0), reinterpret_tensor(buf1, (4, 4, 16), (4, 1, 16), 0), out=buf3)
    return buf3, reinterpret_tensor(primals_1, (16, 4), (4, 1), 0
        ), reinterpret_tensor(primals_6, (64, 4), (4, 1), 0
        ), reinterpret_tensor(buf2, (4, 4, 4), (4, 1, 16), 0
        ), reinterpret_tensor(buf1, (4, 16, 4), (4, 16, 1), 0)


class PointerHeadNew(nn.Module):
    """Head for pointer ordering task."""

    def __init__(self, embed_dim, bias=True):
        super().__init__()
        self.embed_dim = embed_dim
        self.scaling = self.embed_dim ** -0.5
        self.k_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
        self.q_proj = nn.Linear(embed_dim, embed_dim, bias=bias)

    def _shape(self, tensor, seq_len, bsz):
        return tensor.contiguous().view(seq_len, bsz, self.embed_dim
            ).transpose(0, 1)

    def forward(self, input_0, input_1):
        primals_2 = self.k_proj.weight
        primals_3 = self.k_proj.bias
        primals_4 = self.q_proj.weight
        primals_5 = self.q_proj.bias
        primals_1 = input_0
        primals_6 = input_1
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6])
        return output[0]
