import math
import torch
import torch.nn as nn
import torch.optim
import torch.utils.data
import torch.backends.cudnn


class Attention(nn.Module):

    def __init__(self, dim, heads, max_len):
        super().__init__()
        self.q_mat = nn.Linear(dim, dim)
        self.k_mat = nn.Linear(dim, dim)
        self.v_mat = nn.Linear(dim, dim)
        self.dim = dim
        self.heads = heads
        self.max_len = max_len
        self.dk = dim // heads
        self.drop = nn.Dropout(0.1)
        self.softmax = nn.Softmax(-1)
        self.out = nn.Linear(dim, dim)

    def forward(self, x, mask=None):
        bs = x.size(0)
        q = self.q_mat(x).view(bs, -1, self.heads, self.dk)
        k = self.k_mat(x).view(bs, -1, self.heads, self.dk)
        v = self.v_mat(x).view(bs, -1, self.heads, self.dk)
        q = q.transpose(1, 2)
        k = k.transpose(1, 2)
        v = v.transpose(1, 2)
        scores = torch.matmul(q, k.transpose(2, 3)) / math.sqrt(self.dk)
        if mask is not None:
            mask = mask[:, None, None, :].float()
            scores -= 10000.0 * (1.0 - mask)
        scores = self.drop(self.softmax(scores))
        output = torch.matmul(scores, v)
        concat = output.transpose(1, 2).contiguous().view(bs, -1, self.dim)
        output = self.out(concat)
        return output


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


def get_init_inputs():
    return [[], {'dim': 4, 'heads': 4, 'max_len': 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 import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
import torch.optim
import torch.utils.data
import torch.backends.cudnn
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_0(in_ptr0, in_ptr1, out_ptr0, ynumel, xnumel, YBLOCK:
    tl.constexpr, XBLOCK: tl.constexpr):
    ynumel = 16
    xnumel = 16
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y0 = yindex % 4
    y1 = yindex // 4
    y3 = yindex
    tmp0 = tl.load(in_ptr0 + (y0 + 4 * x2 + 64 * y1), xmask & ymask)
    tmp1 = tl.load(in_ptr1 + y0, ymask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 1.0
    tmp4 = tmp2 * tmp3
    tl.store(out_ptr0 + (x2 + 16 * y3), tmp4, xmask & ymask)


@triton.jit
def triton_per_fused_1(in_ptr0, out_ptr3, xnumel, rnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 256
    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, float('-inf'))
    tmp4 = triton_helpers.max2(tmp3, 1)[:, None]
    tmp5 = tmp0 - tmp4
    tmp6 = tl_math.exp(tmp5)
    tmp7 = tl.broadcast_to(tmp6, [XBLOCK, RBLOCK])
    tmp9 = tl.where(xmask, tmp7, 0)
    tmp10 = tl.sum(tmp9, 1)[:, None]
    tmp11 = float('-inf')
    tmp12 = tmp0 == tmp11
    tmp13 = tmp12 == 0
    tmp14 = tmp13.to(tl.int64)
    tmp15 = tmp14 != 0
    tmp16 = tl.broadcast_to(tmp15, [XBLOCK, RBLOCK])
    tmp18 = tl.where(xmask, tmp16, 0)
    tmp19 = triton_helpers.any(tmp18, 1)[:, None]
    tmp20 = tmp19 == 0
    tmp21 = tmp6 / tmp10
    tmp22 = 0.0
    tmp23 = tl.where(tmp20, tmp22, tmp21)
    tl.store(out_ptr3 + (r1 + 16 * x0), tmp23, xmask)


@triton.jit
def triton_poi_fused_2(in_ptr0, in_ptr1, out_ptr0, ynumel, xnumel, YBLOCK:
    tl.constexpr, XBLOCK: tl.constexpr):
    ynumel = 16
    xnumel = 16
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y0 = yindex % 4
    y1 = yindex // 4
    y3 = yindex
    tmp0 = tl.load(in_ptr0 + (y0 + 4 * x2 + 64 * y1), xmask & ymask)
    tmp1 = tl.load(in_ptr1 + y0, ymask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(out_ptr0 + (x2 + 16 * y3), tmp2, xmask & ymask)


@triton.jit
def triton_poi_fused_clone_3(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
    constexpr, XBLOCK: tl.constexpr):
    ynumel = 64
    xnumel = 4
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y0 = yindex % 16
    y1 = yindex // 16
    y3 = yindex
    tmp0 = tl.load(in_ptr0 + (y0 + 16 * x2 + 64 * y1), xmask & ymask,
        eviction_policy='evict_last')
    tl.store(out_ptr0 + (x2 + 4 * y3), tmp0, xmask & ymask)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7, primals_8, primals_9) = 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,))
    assert_size_stride(primals_4, (4, 4), (4, 1))
    assert_size_stride(primals_5, (4,), (1,))
    assert_size_stride(primals_6, (4, 4), (4, 1))
    assert_size_stride(primals_7, (4,), (1,))
    assert_size_stride(primals_8, (4, 4), (4, 1))
    assert_size_stride(primals_9, (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_1, (64, 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.mm(reinterpret_tensor(primals_1, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_4, (4, 4), (1, 4), 0), out=buf1)
        del primals_4
        buf2 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_1, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_6, (4, 4), (1, 4), 0), out=buf2)
        del primals_6
        buf3 = empty_strided_cuda((4, 4, 16, 1), (64, 16, 1, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_0[grid(16, 16)](buf0, primals_3, buf3, 16, 16,
            XBLOCK=16, YBLOCK=16, num_warps=4, num_stages=1)
        del primals_3
        buf4 = reinterpret_tensor(buf0, (4, 4, 1, 16), (64, 16, 16, 1), 0)
        del buf0
        triton_poi_fused_0[grid(16, 16)](buf1, primals_5, buf4, 16, 16,
            XBLOCK=16, YBLOCK=16, num_warps=4, num_stages=1)
        del primals_5
        buf5 = empty_strided_cuda((16, 16, 16), (256, 16, 1), torch.float32)
        extern_kernels.bmm(reinterpret_tensor(buf3, (16, 16, 1), (16, 1, 0),
            0), reinterpret_tensor(buf4, (16, 1, 16), (16, 0, 1), 0), out=buf5)
        buf9 = empty_strided_cuda((4, 4, 16, 16), (1024, 256, 16, 1), torch
            .float32)
        triton_per_fused_1[grid(256)](buf5, buf9, 256, 16, XBLOCK=32,
            num_warps=4, num_stages=1)
        del buf5
        buf10 = reinterpret_tensor(buf1, (4, 4, 16, 1), (64, 16, 1, 1), 0)
        del buf1
        triton_poi_fused_2[grid(16, 16)](buf2, primals_7, buf10, 16, 16,
            XBLOCK=16, YBLOCK=16, num_warps=4, num_stages=1)
        del primals_7
        buf11 = reinterpret_tensor(buf2, (16, 16, 1), (16, 1, 1), 0)
        del buf2
        extern_kernels.bmm(reinterpret_tensor(buf9, (16, 16, 16), (256, 16,
            1), 0), reinterpret_tensor(buf10, (16, 16, 1), (16, 1, 0), 0),
            out=buf11)
        buf12 = empty_strided_cuda((4, 16, 4, 1), (64, 4, 1, 1), torch.float32)
        triton_poi_fused_clone_3[grid(64, 4)](buf11, buf12, 64, 4, XBLOCK=4,
            YBLOCK=32, num_warps=4, num_stages=1)
        buf13 = reinterpret_tensor(buf11, (64, 4), (4, 1), 0)
        del buf11
        extern_kernels.addmm(primals_9, reinterpret_tensor(buf12, (64, 4),
            (4, 1), 0), reinterpret_tensor(primals_8, (4, 4), (1, 4), 0),
            alpha=1, beta=1, out=buf13)
        del primals_9
    return reinterpret_tensor(buf13, (4, 16, 4), (64, 4, 1), 0
        ), reinterpret_tensor(primals_1, (64, 4), (4, 1), 0
        ), buf9, reinterpret_tensor(buf10, (16, 1, 16), (16, 1, 1), 0
        ), reinterpret_tensor(buf3, (16, 1, 16), (16, 1, 1), 0
        ), reinterpret_tensor(buf4, (16, 16, 1), (16, 1, 16), 0
        ), reinterpret_tensor(buf12, (64, 4), (4, 1), 0), primals_8


class AttentionNew(nn.Module):

    def __init__(self, dim, heads, max_len):
        super().__init__()
        self.q_mat = nn.Linear(dim, dim)
        self.k_mat = nn.Linear(dim, dim)
        self.v_mat = nn.Linear(dim, dim)
        self.dim = dim
        self.heads = heads
        self.max_len = max_len
        self.dk = dim // heads
        self.drop = nn.Dropout(0.1)
        self.softmax = nn.Softmax(-1)
        self.out = nn.Linear(dim, dim)

    def forward(self, input_0):
        primals_2 = self.q_mat.weight
        primals_3 = self.q_mat.bias
        primals_4 = self.k_mat.weight
        primals_5 = self.k_mat.bias
        primals_6 = self.v_mat.weight
        primals_7 = self.v_mat.bias
        primals_8 = self.out.weight
        primals_9 = self.out.bias
        primals_1 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7, primals_8, primals_9])
        return output[0]
