# AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _align as align
from torch import device, empty_strided
from torch._inductor.async_compile import AsyncCompile
from torch._inductor.select_algorithm import extern_kernels
from torch._inductor.codegen.multi_kernel import MultiKernelCall
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph
from torch._C import _cuda_getCurrentRawStream as get_raw_stream

aten = torch.ops.aten
inductor_ops = torch.ops.inductor
_quantized = torch.ops._quantized
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
alloc_from_pool = torch.ops.inductor._alloc_from_pool
async_compile = AsyncCompile()


# kernel path: runs/run_shard_7/inductor_cache/2p/c2pz7hk7ojfdgnk4ip5f32yhhrwsy5z4dgkcle3scbax3xbada7z.py
# Topologically Sorted Source Nodes: [mul, sum_1, mean_emb], Original ATen: [aten.mul, aten.sum, aten.div]
# Source node to ATen node mapping:
#   mean_emb => div
#   mul => mul
#   sum_1 => sum_1
# Graph fragment:
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%primals_2, %unsqueeze), kwargs = {})
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%mul, [1]), kwargs = {})
#   %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%sum_1, %unsqueeze_1), kwargs = {})
triton_poi_fused_div_mul_sum_0 = async_compile.triton('triton_poi_fused_div_mul_sum_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties

@triton_heuristics.pointwise(
    size_hints=[256], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_div_mul_sum_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 8, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@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)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/tf/ctfw3infhg572qltjdhl4t446ht5hhkkxggakqt7t537x5viyosq.py
# Topologically Sorted Source Nodes: [argmax, delta], Original ATen: [aten.argmax, aten.sub]
# Source node to ATen node mapping:
#   argmax => argmax
#   delta => sub
# Graph fragment:
#   %argmax : [num_users=1] = call_function[target=torch.ops.aten.argmax.default](args = (%view_1, -1), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%argmax, 50.0), kwargs = {})
triton_per_fused_argmax_sub_1 = async_compile.triton('triton_per_fused_argmax_sub_1', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties

@triton_heuristics.persistent_reduction(
    size_hints=[64, 128],
    reduction_hint=ReductionHint.INNER,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_argmax_sub_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 1, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}
)
@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, :]
    roffset = 0
    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)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

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)
        # Topologically Sorted Source Nodes: [mul, sum_1, mean_emb], Original ATen: [aten.mul, aten.sum, aten.div]
        stream0 = get_raw_stream(0)
        triton_poi_fused_div_mul_sum_0.run(primals_2, primals_1, buf0, 256, grid=grid(256), stream=stream0)
        del primals_1
        del primals_2
        buf1 = empty_strided_cuda((64, 100), (100, 1), torch.float32)
        # Topologically Sorted Source Nodes: [logits], Original ATen: [aten.addmm]
        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)
        # Topologically Sorted Source Nodes: [argmax, delta], Original ATen: [aten.argmax, aten.sub]
        triton_per_fused_argmax_sub_1.run(buf1, buf3, 64, 100, grid=grid(64), stream=stream0)
    return (reinterpret_tensor(buf1, (4, 4, 4, 100), (1600, 400, 100, 1), 0), buf3, reinterpret_tensor(buf0, (64, 4), (4, 1), 0), )


def benchmark_compiled_module(times=10, repeat=10):
    from torch._dynamo.testing import rand_strided
    from torch._inductor.utils import print_performance
    primals_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((100, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((100, ), (1, ), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2, primals_3, primals_4])
    return print_performance(fn, times=times, repeat=repeat)


if __name__ == "__main__":
    from torch._inductor.wrapper_benchmark import compiled_module_main
    compiled_module_main('None', benchmark_compiled_module)

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]
