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, math as tl_math
import math
import torch.nn as nn
from torch.nn import Parameter
import torch.optim.lr_scheduler
import torch.utils.data
import torch.onnx.operators
import torch.optim
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_per_fused_linalg_vector_norm_0(in_ptr0, out_ptr0, xnumel, rnumel,
    XBLOCK: tl.constexpr):
    RBLOCK: tl.constexpr = 4
    xoffset = tl.program_id(0) * XBLOCK
    xoffset + tl.arange(0, XBLOCK)[:, None]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[None, :]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r0 = rindex
    tmp0 = tl.load(in_ptr0 + r0, None)
    tmp1 = tmp0 * tmp0
    tmp2 = tl.broadcast_to(tmp1, [XBLOCK, RBLOCK])
    tmp4 = tl.sum(tmp2, 1)[:, None]
    tl.store(out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp4, None)


@triton.jit
def triton_poi_fused_add_tanh_1(in_out_ptr0, in_ptr0, in_ptr1, 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 + x2, xmask)
    tmp3 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tmp5 = libdevice.tanh(tmp4)
    tl.store(in_out_ptr0 + x2, tmp5, xmask)


@triton.jit
def triton_poi_fused__softmax_div_linalg_vector_norm_mul_sum_2(in_out_ptr0,
    in_ptr0, in_ptr1, in_ptr2, in_ptr3, xnumel, XBLOCK: tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 4
    x2 = xindex // 16
    x4 = xindex % 16
    x3 = xindex
    tmp0 = tl.load(in_ptr0 + 0)
    tmp1 = tl.broadcast_to(tmp0, [XBLOCK])
    tmp2 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr2 + 0)
    tmp5 = tl.broadcast_to(tmp4, [XBLOCK])
    tmp8 = tl.load(in_ptr3 + (x4 + 64 * x2), xmask)
    tmp10 = tl.load(in_ptr3 + (16 + x4 + 64 * x2), xmask)
    tmp13 = tl.load(in_ptr3 + (32 + x4 + 64 * x2), xmask)
    tmp16 = tl.load(in_ptr3 + (48 + x4 + 64 * x2), xmask)
    tmp3 = tmp1 * tmp2
    tmp6 = libdevice.sqrt(tmp5)
    tmp7 = tmp3 / tmp6
    tmp9 = tmp7 * tmp8
    tmp11 = tmp7 * tmp10
    tmp12 = tmp9 + tmp11
    tmp14 = tmp7 * tmp13
    tmp15 = tmp12 + tmp14
    tmp17 = tmp7 * tmp16
    tmp18 = tmp15 + tmp17
    tmp19 = tmp18 - tmp18
    tmp20 = tl_math.exp(tmp19)
    tmp21 = tmp20 / tmp20
    tl.store(in_out_ptr0 + x3, tmp21, xmask)


@triton.jit
def triton_poi_fused_mul_sum_3(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK:
    tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 16
    x2 = xindex // 64
    x3 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 16 * x2), xmask, eviction_policy=
        'evict_last')
    tmp1 = tl.load(in_ptr1 + x3, xmask)
    tmp2 = tmp0 * tmp1
    tl.store(out_ptr0 + x3, tmp2, 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, 4), (64, 16, 4, 1))
    assert_size_stride(primals_3, (4, 4), (4, 1))
    assert_size_stride(primals_4, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_5, (1,), (1,))
    assert_size_stride(primals_6, (4,), (1,))
    assert_size_stride(primals_7, (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_2, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_1, (4, 4), (1, 4), 0), out=buf0)
        del primals_1
        buf1 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_4, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_3, (4, 4), (1, 4), 0), out=buf1)
        del primals_3
        buf2 = empty_strided_cuda((), (), torch.float32)
        get_raw_stream(0)
        triton_per_fused_linalg_vector_norm_0[grid(1)](primals_6, buf2, 1, 
            4, XBLOCK=1, num_warps=2, num_stages=1)
        buf3 = reinterpret_tensor(buf0, (1, 4, 4, 4, 4), (256, 64, 16, 4, 1), 0
            )
        del buf0
        triton_poi_fused_add_tanh_1[grid(256)](buf3, buf1, primals_7, 256,
            XBLOCK=256, num_warps=4, num_stages=1)
        del primals_7
        buf4 = empty_strided_cuda((1, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        buf5 = buf4
        del buf4
        triton_poi_fused__softmax_div_linalg_vector_norm_mul_sum_2[grid(64)](
            buf5, primals_5, primals_6, buf2, buf3, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        del buf2
        buf6 = reinterpret_tensor(buf1, (4, 4, 4, 4), (64, 16, 4, 1), 0)
        del buf1
        triton_poi_fused_mul_sum_3[grid(256)](buf5, primals_4, buf6, 256,
            XBLOCK=256, num_warps=4, num_stages=1)
    return buf6, buf5, primals_4, primals_5, primals_6, reinterpret_tensor(
        primals_2, (64, 4), (4, 1), 0), buf3, buf5


class BaseAttention(nn.Module):
    """Base class for attention layers."""

    def __init__(self, query_dim, value_dim, embed_dim=None):
        super().__init__()
        self.query_dim = query_dim
        self.value_dim = value_dim
        self.embed_dim = embed_dim
        self.onnx_trace = False

    def prepare_for_onnx_export_(self):
        self.onnx_trace = True

    def reset_parameters(self):
        pass

    def forward(self, query, value, key_padding_mask=None, state=None):
        raise NotImplementedError


class BahdanauAttentionNew(BaseAttention):
    """ Bahdanau Attention."""

    def __init__(self, query_dim, value_dim, embed_dim, normalize=True):
        super().__init__(query_dim, value_dim, embed_dim)
        self.query_proj = nn.Linear(self.query_dim, embed_dim, bias=False)
        self.value_proj = nn.Linear(self.value_dim, embed_dim, bias=False)
        self.v = Parameter(torch.Tensor(embed_dim))
        self.normalize = normalize
        if self.normalize:
            self.b = Parameter(torch.Tensor(embed_dim))
            self.g = Parameter(torch.Tensor(1))
        self.reset_parameters()

    def reset_parameters(self):
        self.query_proj.weight.data.uniform_(-0.1, 0.1)
        self.value_proj.weight.data.uniform_(-0.1, 0.1)
        nn.init.uniform_(self.v, -0.1, 0.1)
        if self.normalize:
            nn.init.constant_(self.b, 0.0)
            nn.init.constant_(self.g, math.sqrt(1.0 / self.embed_dim))

    def forward(self, input_0, input_1):
        primals_6 = self.v
        primals_7 = self.b
        primals_5 = self.g
        primals_1 = self.query_proj.weight
        primals_3 = self.value_proj.weight
        primals_2 = input_0
        primals_4 = input_1
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7])
        return output[0], output[1], output[2]


Measured latency_ms: 0.013794