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
import torch.nn.functional as F
import torch.nn as nn
import torch.jit
import torch.onnx.operators
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_add_max_mean_relu_0(in_ptr0, in_ptr1, out_ptr0,
    out_ptr1, xnumel, XBLOCK: tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 4
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr0 + (16 + x2), xmask)
    tmp23 = tl.load(in_ptr0 + (32 + x2), xmask)
    tmp40 = tl.load(in_ptr0 + (48 + x2), xmask)
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp6 = tmp5 + tmp1
    tmp7 = triton_helpers.maximum(tmp3, tmp6)
    tmp8 = tmp4 > tmp7
    tmp9 = tmp4 == tmp7
    tmp10 = tmp4 != tmp4
    tmp11 = tmp7 != tmp7
    tmp12 = tmp10 > tmp11
    tmp13 = tmp8 | tmp12
    tmp14 = tmp10 & tmp11
    tmp15 = tmp9 | tmp14
    tmp16 = tl.full([1], 0, tl.int64)
    tmp17 = tl.full([1], 1, tl.int64)
    tmp18 = tmp16 < tmp17
    tmp19 = tmp15 & tmp18
    tmp20 = tmp13 | tmp19
    tmp21 = tl.where(tmp20, tmp4, tmp7)
    tmp22 = tl.where(tmp20, tmp16, tmp17)
    tmp24 = tmp23 + tmp1
    tmp25 = triton_helpers.maximum(tmp3, tmp24)
    tmp26 = tmp21 > tmp25
    tmp27 = tmp21 == tmp25
    tmp28 = tmp21 != tmp21
    tmp29 = tmp25 != tmp25
    tmp30 = tmp28 > tmp29
    tmp31 = tmp26 | tmp30
    tmp32 = tmp28 & tmp29
    tmp33 = tmp27 | tmp32
    tmp34 = tl.full([1], 2, tl.int64)
    tmp35 = tmp22 < tmp34
    tmp36 = tmp33 & tmp35
    tmp37 = tmp31 | tmp36
    tmp38 = tl.where(tmp37, tmp21, tmp25)
    tmp39 = tl.where(tmp37, tmp22, tmp34)
    tmp41 = tmp40 + tmp1
    tmp42 = triton_helpers.maximum(tmp3, tmp41)
    tmp43 = tmp38 > tmp42
    tmp44 = tmp38 == tmp42
    tmp45 = tmp38 != tmp38
    tmp46 = tmp42 != tmp42
    tmp47 = tmp45 > tmp46
    tmp48 = tmp43 | tmp47
    tmp49 = tmp45 & tmp46
    tmp50 = tmp44 | tmp49
    tmp51 = tl.full([1], 3, tl.int64)
    tmp52 = tmp39 < tmp51
    tmp53 = tmp50 & tmp52
    tmp54 = tmp48 | tmp53
    tl.where(tmp54, tmp38, tmp42)
    tmp56 = tl.where(tmp54, tmp39, tmp51)
    tmp57 = tmp4 + tmp7
    tmp58 = tmp57 + tmp25
    tmp59 = tmp58 + tmp42
    tmp60 = 4.0
    tmp61 = tmp59 / tmp60
    tmp62 = triton_helpers.maximum(tmp4, tmp7)
    tmp63 = triton_helpers.maximum(tmp62, tmp25)
    tmp64 = triton_helpers.maximum(tmp63, tmp42)
    tmp65 = tmp61 + tmp64
    tl.store(out_ptr0 + x2, tmp56, xmask)
    tl.store(out_ptr1 + x2, tmp65, xmask)


@triton.jit
def triton_poi_fused_relu_threshold_backward_1(in_ptr0, in_ptr1, 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
    x0 = xindex % 4
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp5 = 0.0
    tmp6 = tmp4 <= tmp5
    tl.store(out_ptr0 + x2, tmp6, 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, 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,))
    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((4, 4), (4, 1), torch.int64)
        buf2 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_add_max_mean_relu_0[grid(16)](buf0, primals_3,
            buf1, buf2, 16, XBLOCK=16, num_warps=1, num_stages=1)
        buf3 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_5, buf2, reinterpret_tensor(primals_4,
            (4, 4), (1, 4), 0), alpha=1, beta=1, out=buf3)
        del primals_5
        buf4 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.bool)
        triton_poi_fused_relu_threshold_backward_1[grid(64)](buf0,
            primals_3, buf4, 64, XBLOCK=64, num_warps=1, num_stages=1)
        del buf0
        del primals_3
    return buf3, reinterpret_tensor(primals_1, (16, 4), (4, 1), 0
        ), buf2, primals_4, reinterpret_tensor(buf1, (1, 4, 4), (16, 4, 1), 0
        ), buf4


class WordPredictorNew(nn.Module):

    def __init__(self, encoder_output_dim, hidden_dim, output_dim,
        topk_labels_per_source_token=None, use_self_attention=False):
        super().__init__()
        self.encoder_output_dim = encoder_output_dim
        self.hidden_dim = hidden_dim
        self.output_dim = output_dim
        self.topk_labels_per_source_token = topk_labels_per_source_token
        self.use_self_attention = use_self_attention
        if self.use_self_attention:
            self.init_layer = nn.Linear(encoder_output_dim, encoder_output_dim)
            self.attn_layer = nn.Linear(2 * encoder_output_dim, 1)
            self.hidden_layer = nn.Linear(2 * encoder_output_dim, hidden_dim)
            self.output_layer = nn.Linear(hidden_dim, output_dim)
        else:
            self.hidden_layer = nn.Linear(encoder_output_dim, hidden_dim)
            self.output_layer = nn.Linear(hidden_dim, output_dim)

    def _get_init_state(self, encoder_hiddens):
        x = torch.mean(encoder_hiddens, 0)
        x = F.relu(self.init_layer(x))
        return x

    def _attention(self, encoder_hiddens, init_state):
        init_state = init_state.unsqueeze(0).expand_as(encoder_hiddens)
        attn_input = torch.cat([init_state, encoder_hiddens], 2)
        attn_scores = F.relu(self.attn_layer(attn_input))
        attn_scores = F.softmax(attn_scores, 0)
        return attn_scores

    def get_normalized_probs(self, net_output, log_probs):
        """Get normalized probabilities (or log probs) from a net's output."""
        logits = net_output
        if log_probs:
            return F.log_softmax(logits, dim=1)
        else:
            return F.softmax(logits, dim=1)

    def get_topk_predicted_tokens(self, net_output, src_tokens, log_probs:
        'bool'):
        """
        Get self.topk_labels_per_source_token top predicted words for vocab
        reduction (per source token).
        """
        assert isinstance(self.topk_labels_per_source_token, int
            ) and self.topk_labels_per_source_token > 0, 'topk_labels_per_source_token must be a positive int, or None'
        k = src_tokens.size(1) * self.topk_labels_per_source_token
        probs = self.get_normalized_probs(net_output, log_probs)
        _, topk_indices = torch.topk(probs, k, dim=1)
        return topk_indices

    def forward(self, input_0):
        primals_2 = self.hidden_layer.weight
        primals_3 = self.hidden_layer.bias
        primals_4 = self.output_layer.weight
        primals_5 = self.output_layer.bias
        primals_1 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4, primals_5])
        return output[0]


Measured latency_ms: 0.047475