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 libdevice, math as tl_math
import math
import torch.nn as nn
import torch.utils.data
import torch.jit
from torch.nn import Parameter
from torch.nn.parameter import Parameter
import torch.optim
import torch.utils.collect_env
import torch.nn.parallel
import torch.utils.data.distributed
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_clone_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 4
    x1 = xindex // 4 % 4
    x2 = xindex // 16
    x3 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 4 * x2 + 16 * x1), xmask)
    tl.store(out_ptr0 + x3, tmp0, xmask)


@triton.jit
def triton_poi_fused_mv_1(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + 4 * (x0 // 4), xmask, eviction_policy='evict_last'
        )
    tmp1 = tl.load(in_ptr1 + (4 * (x0 % 4) + 16 * (x0 // 16)), xmask,
        eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr2 + 0)
    tmp5 = tl.broadcast_to(tmp4, [XBLOCK])
    tmp7 = tl.load(in_ptr0 + (1 + 4 * (x0 // 4)), xmask, eviction_policy=
        'evict_last')
    tmp8 = tl.load(in_ptr1 + (1 + 4 * (x0 % 4) + 16 * (x0 // 16)), xmask,
        eviction_policy='evict_last')
    tmp11 = tl.load(in_ptr2 + 1)
    tmp12 = tl.broadcast_to(tmp11, [XBLOCK])
    tmp15 = tl.load(in_ptr0 + (2 + 4 * (x0 // 4)), xmask, eviction_policy=
        'evict_last')
    tmp16 = tl.load(in_ptr1 + (2 + 4 * (x0 % 4) + 16 * (x0 // 16)), xmask,
        eviction_policy='evict_last')
    tmp19 = tl.load(in_ptr2 + 2)
    tmp20 = tl.broadcast_to(tmp19, [XBLOCK])
    tmp23 = tl.load(in_ptr0 + (3 + 4 * (x0 // 4)), xmask, eviction_policy=
        'evict_last')
    tmp24 = tl.load(in_ptr1 + (3 + 4 * (x0 % 4) + 16 * (x0 // 16)), xmask,
        eviction_policy='evict_last')
    tmp27 = tl.load(in_ptr2 + 3)
    tmp28 = tl.broadcast_to(tmp27, [XBLOCK])
    tmp2 = tmp0 + tmp1
    tmp3 = libdevice.tanh(tmp2)
    tmp6 = tmp3 * tmp5
    tmp9 = tmp7 + tmp8
    tmp10 = libdevice.tanh(tmp9)
    tmp13 = tmp10 * tmp12
    tmp14 = tmp6 + tmp13
    tmp17 = tmp15 + tmp16
    tmp18 = libdevice.tanh(tmp17)
    tmp21 = tmp18 * tmp20
    tmp22 = tmp14 + tmp21
    tmp25 = tmp23 + tmp24
    tmp26 = libdevice.tanh(tmp25)
    tmp29 = tmp26 * tmp28
    tmp30 = tmp22 + tmp29
    tl.store(out_ptr0 + x0, tmp30, xmask)


@triton.jit
def triton_poi_fused__softmax_2(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x1 = xindex // 4
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp3 = triton_helpers.maximum(tmp1, tmp2)
    tmp5 = triton_helpers.maximum(tmp3, tmp4)
    tmp7 = triton_helpers.maximum(tmp5, tmp6)
    tmp8 = tmp0 - tmp7
    tmp9 = tl_math.exp(tmp8)
    tl.store(out_ptr0 + x2, tmp9, xmask)


@triton.jit
def triton_poi_fused__softmax_3(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x1 = xindex // 4
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp3 = tmp1 + tmp2
    tmp5 = tmp3 + tmp4
    tmp7 = tmp5 + tmp6
    tmp8 = tmp0 / tmp7
    tl.store(out_ptr0 + x2, tmp8, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5 = 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, (4, 4), (4, 1))
    assert_size_stride(primals_4, (4, 4), (4, 1))
    assert_size_stride(primals_5, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_clone_0[grid(64)](primals_2, buf0, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        del primals_2
        buf1 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf0, (16, 4), (4, 1), 0),
            reinterpret_tensor(primals_3, (4, 4), (1, 4), 0), out=buf1)
        del primals_3
        buf2 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        triton_poi_fused_clone_0[grid(64)](primals_1, buf2, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        buf3 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf2, (16, 4), (4, 1), 0),
            reinterpret_tensor(primals_4, (4, 4), (1, 4), 0), out=buf3)
        del primals_4
        buf4 = empty_strided_cuda((64,), (1,), torch.float32)
        triton_poi_fused_mv_1[grid(64)](buf1, buf3, primals_5, buf4, 64,
            XBLOCK=64, num_warps=1, num_stages=1)
        buf5 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        triton_poi_fused__softmax_2[grid(64)](buf4, buf5, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        buf6 = reinterpret_tensor(buf4, (4, 4, 4), (16, 4, 1), 0)
        del buf4
        triton_poi_fused__softmax_3[grid(64)](buf5, buf6, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        buf7 = buf5
        del buf5
        extern_kernels.bmm(buf6, reinterpret_tensor(primals_1, (4, 4, 4), (
            4, 16, 1), 0), out=buf7)
    return reinterpret_tensor(buf7, (4, 4, 4), (4, 16, 1), 0
        ), reinterpret_tensor(buf6, (4, 4, 4), (4, 16, 1), 0
        ), primals_5, reinterpret_tensor(buf0, (16, 4), (4, 1), 0
        ), buf1, reinterpret_tensor(buf2, (16, 4), (4, 1), 0
        ), buf3, buf6, reinterpret_tensor(primals_1, (4, 4, 4), (4, 1, 16), 0)


class BahdanauAttentionNew(nn.Module):
    """
    Bahdanau Attention (https://arxiv.org/abs/1409.0473)
    Implementation is very similar to tf.contrib.seq2seq.BahdanauAttention
    """

    def __init__(self, query_size, key_size, num_units, normalize=False,
        batch_first=False, init_weight=0.1):
        """
        Constructor for the BahdanauAttention.

        :param query_size: feature dimension for query
        :param key_size: feature dimension for keys
        :param num_units: internal feature dimension
        :param normalize: whether to normalize energy term
        :param batch_first: if True batch size is the 1st dimension, if False
            the sequence is first and batch size is second
        :param init_weight: range for uniform initializer used to initialize
            Linear key and query transform layers and linear_att vector
        """
        super(BahdanauAttentionNew, self).__init__()
        self.normalize = normalize
        self.batch_first = batch_first
        self.num_units = num_units
        self.linear_q = nn.Linear(query_size, num_units, bias=False)
        self.linear_k = nn.Linear(key_size, num_units, bias=False)
        nn.init.uniform_(self.linear_q.weight.data, -init_weight, init_weight)
        nn.init.uniform_(self.linear_k.weight.data, -init_weight, init_weight)
        self.linear_att = Parameter(torch.Tensor(num_units))
        self.mask = None
        if self.normalize:
            self.normalize_scalar = Parameter(torch.Tensor(1))
            self.normalize_bias = Parameter(torch.Tensor(num_units))
        else:
            self.register_parameter('normalize_scalar', None)
            self.register_parameter('normalize_bias', None)
        self.reset_parameters(init_weight)

    def reset_parameters(self, init_weight):
        """
        Sets initial random values for trainable parameters.
        """
        stdv = 1.0 / math.sqrt(self.num_units)
        self.linear_att.data.uniform_(-init_weight, init_weight)
        if self.normalize:
            self.normalize_scalar.data.fill_(stdv)
            self.normalize_bias.data.zero_()

    def set_mask(self, context_len, context):
        """
        sets self.mask which is applied before softmax
        ones for inactive context fields, zeros for active context fields

        :param context_len: b
        :param context: if batch_first: (b x t_k x n) else: (t_k x b x n)

        self.mask: (b x t_k)
        """
        if self.batch_first:
            max_len = context.size(1)
        else:
            max_len = context.size(0)
        indices = torch.arange(0, max_len, dtype=torch.int64, device=
            context.device)
        self.mask = indices >= context_len.unsqueeze(1)

    def calc_score(self, att_query, att_keys):
        """
        Calculate Bahdanau score

        :param att_query: b x t_q x n
        :param att_keys: b x t_k x n

        returns: b x t_q x t_k scores
        """
        b, t_k, n = att_keys.size()
        t_q = att_query.size(1)
        att_query = att_query.unsqueeze(2).expand(b, t_q, t_k, n)
        att_keys = att_keys.unsqueeze(1).expand(b, t_q, t_k, n)
        sum_qk = att_query + att_keys
        if self.normalize:
            sum_qk = sum_qk + self.normalize_bias
            linear_att = self.linear_att / self.linear_att.norm()
            linear_att = linear_att * self.normalize_scalar
        else:
            linear_att = self.linear_att
        out = torch.tanh(sum_qk).matmul(linear_att)
        return out

    def forward(self, input_0, input_1):
        primals_5 = self.linear_att
        primals_3 = self.linear_q.weight
        primals_4 = self.linear_k.weight
        primals_1 = input_0
        primals_2 = input_1
        output = call([primals_1, primals_2, primals_3, primals_4, primals_5])
        return output[0], output[1]


Measured latency_ms: 0.026829