import torch
from torch.nn import functional as F
from torch import nn
from torchvision import models as models
import torch.onnx
import torch.nn


class LengthPredictionLoss(nn.Module):

    def __init__(self, max_delta=50):
        super().__init__()
        self.max_delta = max_delta

    def forward(self, logits, src_mask, tgt_mask):
        src_lens, tgt_lens = src_mask.sum(1), tgt_mask.sum(1)
        delta = (tgt_lens - src_lens + self.max_delta).clamp(0, self.
            max_delta * 2 - 1).long()
        loss = F.cross_entropy(logits, delta, reduction='mean')
        return {'length_prediction_loss': loss}


class LengthPredictor(nn.Module):

    def __init__(self, hidden_size, max_delta=50):
        super().__init__()
        self.hidden_size = hidden_size
        self.max_delta = max_delta
        self._init_modules()
        self._init_loss()

    def forward(self, src, src_mask, tgt_len=None):
        src_mean = self._compute_mean_emb(src, src_mask)
        logits, delta = self._predict_delta(src_mean)
        return logits, delta

    def _predict_delta(self, src):
        logits = self.length_predictor(src)
        delta = logits.argmax(-1) - float(self.max_delta)
        return logits, delta

    def _compute_mean_emb(self, src, src_mask):
        mean_emb = (src * src_mask[:, :, None]).sum(1) / src_mask.sum(1)[:,
            None]
        return mean_emb

    def _init_modules(self):
        self.length_predictor = nn.Linear(self.hidden_size, self.max_delta * 2)

    def _init_loss(self):
        self.loss = LengthPredictionLoss(self.max_delta)


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


def get_init_inputs():
    return [[], {'hidden_size': 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.nn import functional as F
from torch import nn
from torchvision import models as models
import torch.onnx
import torch.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_div_mul_sum_0(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
    x3 = xindex % 64
    x0 = xindex % 16
    x2 = xindex // 64
    x4 = xindex
    tmp0 = tl.load(in_ptr0 + x3, xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + (x0 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp3 = tl.load(in_ptr0 + (64 + x3), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr1 + (16 + x0 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp7 = tl.load(in_ptr0 + (128 + x3), xmask, eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr1 + (32 + x0 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp11 = tl.load(in_ptr0 + (192 + x3), xmask, eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr1 + (48 + x0 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp2 = tmp0 * tmp1
    tmp5 = tmp3 * tmp4
    tmp6 = tmp2 + tmp5
    tmp9 = tmp7 * tmp8
    tmp10 = tmp6 + tmp9
    tmp13 = tmp11 * tmp12
    tmp14 = tmp10 + tmp13
    tmp15 = tmp1 + tmp4
    tmp16 = tmp15 + tmp8
    tmp17 = tmp16 + tmp12
    tmp18 = tmp14 / tmp17
    tl.store(out_ptr0 + x4, tmp18, xmask)


@triton.jit
def triton_per_fused_argmax_sub_1(in_ptr0, out_ptr1, xnumel, rnumel, XBLOCK:
    tl.constexpr):
    xnumel = 64
    rnumel = 100
    RBLOCK: tl.constexpr = 128
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rindex = tl.arange(0, RBLOCK)[None, :]
    rmask = rindex < rnumel
    r1 = rindex
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (r1 + 100 * x0), rmask & xmask, other=0.0)
    tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
    tmp3 = tl.where(rmask & xmask, tmp1, float('-inf'))
    tmp4 = tl.broadcast_to(rindex, tmp3.shape)
    _, tmp2_tmp = triton_helpers.max_with_index(tmp3, tmp4, 1)
    tmp2 = tmp2_tmp[:, None]
    tmp5 = tmp2.to(tl.float32)
    tmp6 = 50.0
    tmp7 = tmp5 - tmp6
    tl.store(out_ptr1 + x0, tmp7, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_3, (100, 4), (4, 1))
    assert_size_stride(primals_4, (100,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_div_mul_sum_0[grid(256)](primals_2, primals_1,
            buf0, 256, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_1
        del primals_2
        buf1 = empty_strided_cuda((64, 100), (100, 1), torch.float32)
        extern_kernels.addmm(primals_4, reinterpret_tensor(buf0, (64, 4), (
            4, 1), 0), reinterpret_tensor(primals_3, (4, 100), (1, 4), 0),
            alpha=1, beta=1, out=buf1)
        del primals_3
        del primals_4
        buf3 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        triton_per_fused_argmax_sub_1[grid(64)](buf1, buf3, 64, 100, XBLOCK
            =8, num_warps=8, num_stages=1)
    return reinterpret_tensor(buf1, (4, 4, 4, 100), (1600, 400, 100, 1), 0
        ), buf3, reinterpret_tensor(buf0, (64, 4), (4, 1), 0)


class LengthPredictionLoss(nn.Module):

    def __init__(self, max_delta=50):
        super().__init__()
        self.max_delta = max_delta

    def forward(self, logits, src_mask, tgt_mask):
        src_lens, tgt_lens = src_mask.sum(1), tgt_mask.sum(1)
        delta = (tgt_lens - src_lens + self.max_delta).clamp(0, self.
            max_delta * 2 - 1).long()
        loss = F.cross_entropy(logits, delta, reduction='mean')
        return {'length_prediction_loss': loss}


class LengthPredictorNew(nn.Module):

    def __init__(self, hidden_size, max_delta=50):
        super().__init__()
        self.hidden_size = hidden_size
        self.max_delta = max_delta
        self._init_modules()
        self._init_loss()

    def _predict_delta(self, src):
        logits = self.length_predictor(src)
        delta = logits.argmax(-1) - float(self.max_delta)
        return logits, delta

    def _compute_mean_emb(self, src, src_mask):
        mean_emb = (src * src_mask[:, :, None]).sum(1) / src_mask.sum(1)[:,
            None]
        return mean_emb

    def _init_modules(self):
        self.length_predictor = nn.Linear(self.hidden_size, self.max_delta * 2)

    def _init_loss(self):
        self.loss = LengthPredictionLoss(self.max_delta)

    def forward(self, input_0, input_1):
        primals_3 = self.length_predictor.weight
        primals_4 = self.length_predictor.bias
        primals_1 = input_0
        primals_2 = input_1
        output = call([primals_1, primals_2, primals_3, primals_4])
        return output[0], output[1]
