# AOT ID: ['0_inference']
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/bl/cbl2rxdmd7vl7atdeosqikyevrmaoinhggmygw2rjrqvkexjpxgt.py
# Topologically Sorted Source Nodes: [neg, sort, sort_1, x_len], Original ATen: [aten.neg, aten.sort, aten.index]
# Source node to ATen node mapping:
#   neg => neg
#   sort => sort
#   sort_1 => getitem_3, sort_1
#   x_len => index
# Graph fragment:
#   %neg : [num_users=1] = call_function[target=torch.ops.aten.neg.default](args = (%arg0_1,), kwargs = {})
#   %sort : [num_users=1] = call_function[target=torch.ops.aten.sort.default](args = (%neg,), kwargs = {})
#   %sort_1 : [num_users=1] = call_function[target=torch.ops.aten.sort.default](args = (%getitem_1,), kwargs = {})
#   %getitem_3 : [num_users=1] = call_function[target=operator.getitem](args = (%sort_1, 1), kwargs = {})
#   %index : [num_users=1] = call_function[target=torch.ops.aten.index.Tensor](args = (%arg0_1, [%getitem_1]), kwargs = {})
triton_per_fused_index_neg_sort_0 = async_compile.triton('triton_per_fused_index_neg_sort_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.persistent_reduction(
    size_hints=[1, 4],
    reduction_hint=ReductionHint.INNER,
    filename=__file__,
    triton_meta={'signature': {0: '*i64', 1: '*i16', 2: '*i64', 3: '*i64', 4: 'i32', 5: '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': {4: 1}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=(4,))]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_index_neg_sort_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, '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}
)
@triton.jit
def triton_per_fused_index_neg_sort_0(in_ptr0, out_ptr0, out_ptr2, out_ptr3, xnumel, rnumel, XBLOCK : tl.constexpr):
    xnumel = 1
    rnumel = 4
    RBLOCK: tl.constexpr = 4
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = tl.full([XBLOCK, RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[None, :]
    roffset = 0
    rmask = tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r0 = rindex
    tmp0 = tl.load(in_ptr0 + (r0), None)
    tmp1 = -tmp0
    tmp2 = r0
    tmp3 = tmp2.to(tl.int16)
    tmp4 = tl.broadcast_to(tmp1, [XBLOCK, RBLOCK])
    tmp5 = tl.broadcast_to(tmp3, [XBLOCK, RBLOCK])
    tmp6, tmp7, = triton_helpers.sort_with_index(tmp4, tmp5, None, 1, stable=False, descending=False)
    tmp8 = tmp7.to(tl.int64)
    tmp9 = tl.broadcast_to(tmp8, [XBLOCK, RBLOCK])
    tmp10, tmp11, = triton_helpers.sort_with_index(tmp9, tmp5, None, 1, stable=False, descending=False)
    tmp12 = tmp11.to(tl.int64)
    tmp13 = tl.full([XBLOCK, RBLOCK], 4, tl.int32)
    tmp14 = tmp8 + tmp13
    tmp15 = tmp8 < 0
    tmp16 = tl.where(tmp15, tmp14, tmp8)
    tl.device_assert((0 <= tmp16) & (tmp16 < 4), "index out of bounds: 0 <= tmp16 < 4")
    tmp18 = tl.load(in_ptr0 + (tmp16), None, eviction_policy='evict_last')
    tl.store(out_ptr0 + (tl.broadcast_to(r0, [XBLOCK, RBLOCK])), tmp7, None)
    tl.store(out_ptr2 + (tl.broadcast_to(r0, [XBLOCK, RBLOCK])), tmp12, None)
    tl.store(out_ptr3 + (tl.broadcast_to(r0, [XBLOCK, RBLOCK])), tmp18, None)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/lq/clqymvokcnia6n7yv7kbq5esk6ua7uet4ifi3agynd3zh6rr47mg.py
# Topologically Sorted Source Nodes: [x], Original ATen: [aten.index]
# Source node to ATen node mapping:
#   x => index_1
# Graph fragment:
#   %index_1 : [num_users=1] = call_function[target=torch.ops.aten.index.Tensor](args = (%arg1_1, [%getitem_1]), kwargs = {})
triton_poi_fused_index_1 = async_compile.triton('triton_poi_fused_index_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.pointwise(
    size_hints=[256], 
    filename=__file__,
    triton_meta={'signature': {0: '*i16', 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_index_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, '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_index_1(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
    x1 = (xindex // 64)
    x0 = xindex % 64
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (x1), xmask, eviction_policy='evict_last')
    tmp1 = tmp0.to(tl.int64)
    tmp2 = tl.full([XBLOCK], 4, tl.int32)
    tmp3 = tmp1 + tmp2
    tmp4 = tmp1 < 0
    tmp5 = tl.where(tmp4, tmp3, tmp1)
    tl.device_assert(((0 <= tmp5) & (tmp5 < 4)) | ~(xmask), "index out of bounds: 0 <= tmp5 < 4")
    tmp7 = tl.load(in_ptr1 + (x0 + (64*tmp5)), xmask)
    tl.store(out_ptr0 + (x2), tmp7, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    arg0_1, arg1_1 = args
    args.clear()
    assert_size_stride(arg0_1, (4, ), (1, ))
    assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf1 = empty_strided_cuda((4, ), (1, ), torch.int16)
        buf4 = empty_strided_cuda((4, ), (1, ), torch.int64)
        buf6 = empty_strided_cuda((4, ), (1, ), torch.int64)
        # Topologically Sorted Source Nodes: [neg, sort, sort_1, x_len], Original ATen: [aten.neg, aten.sort, aten.index]
        stream0 = get_raw_stream(0)
        triton_per_fused_index_neg_sort_0.run(arg0_1, buf1, buf4, buf6, 1, 4, grid=grid(1), stream=stream0)
        del arg0_1
        buf5 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x], Original ATen: [aten.index]
        triton_poi_fused_index_1.run(buf1, arg1_1, buf5, 256, grid=grid(256), stream=stream0)
        del arg1_1
        del buf1
    buf7 = empty_strided_cpu((4, ), (1, ), torch.int64)
    buf7.copy_(buf6)
    return (buf5, buf7, buf4, )


def benchmark_compiled_module(times=10, repeat=10):
    from torch._dynamo.testing import rand_strided
    from torch._inductor.utils import print_performance
    arg0_1 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.int64)
    arg1_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([arg0_1, arg1_1])
    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
import torch.nn as nn


class SqueezeEmbedding(nn.Module):
    """
    Squeeze sequence embedding length to the longest one in the batch
    """

    def __init__(self, batch_first=True):
        super(SqueezeEmbedding, self).__init__()
        self.batch_first = batch_first

    def forward(self, x, x_len):
        """
        sequence -> sort -> pad and pack -> unpack ->unsort
        :param x: sequence embedding vectors
        :param x_len: numpy/tensor list
        :return:
        """
        """sort"""
        x_sort_idx = torch.sort(-x_len)[1].long()
        x_unsort_idx = torch.sort(x_sort_idx)[1].long()
        x_len = x_len[x_sort_idx]
        x = x[x_sort_idx]
        """pack"""
        x_emb_p = torch.nn.utils.rnn.pack_padded_sequence(x, x_len.cpu(),
            batch_first=self.batch_first)
        """unpack: out"""
        out = torch.nn.utils.rnn.pad_packed_sequence(x_emb_p, batch_first=
            self.batch_first)
        out = out[0]
        """unsort"""
        out = out[x_unsort_idx]
        return out


def get_inputs():
    return [torch.rand([4, 4, 4, 4]), torch.ones([4], dtype=torch.int64)]


def get_init_inputs():
    return [[], {}]

import torch
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 as nn
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


@triton.jit
def triton_per_fused_index_neg_sort_0(in_ptr0, out_ptr0, out_ptr2, out_ptr3,
    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
    tmp2 = r0
    tmp3 = tmp2.to(tl.int16)
    tmp4 = tl.broadcast_to(tmp1, [XBLOCK, RBLOCK])
    tmp5 = tl.broadcast_to(tmp3, [XBLOCK, RBLOCK])
    _tmp6, tmp7 = triton_helpers.sort_with_index(tmp4, tmp5, None, 1,
        stable=False, descending=False)
    tmp8 = tmp7.to(tl.int64)
    tmp9 = tl.broadcast_to(tmp8, [XBLOCK, RBLOCK])
    _tmp10, tmp11 = triton_helpers.sort_with_index(tmp9, tmp5, None, 1,
        stable=False, descending=False)
    tmp12 = tmp11.to(tl.int64)
    tmp13 = tl.full([XBLOCK, RBLOCK], 4, tl.int32)
    tmp14 = tmp8 + tmp13
    tmp15 = tmp8 < 0
    tmp16 = tl.where(tmp15, tmp14, tmp8)
    tl.device_assert((0 <= tmp16) & (tmp16 < 4),
        'index out of bounds: 0 <= tmp16 < 4')
    tmp18 = tl.load(in_ptr0 + tmp16, None, eviction_policy='evict_last')
    tl.store(out_ptr0 + tl.broadcast_to(r0, [XBLOCK, RBLOCK]), tmp7, None)
    tl.store(out_ptr2 + tl.broadcast_to(r0, [XBLOCK, RBLOCK]), tmp12, None)
    tl.store(out_ptr3 + tl.broadcast_to(r0, [XBLOCK, RBLOCK]), tmp18, None)


@triton.jit
def triton_poi_fused_index_1(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
    x1 = xindex // 64
    x0 = xindex % 64
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
    tmp1 = tmp0.to(tl.int64)
    tmp2 = tl.full([XBLOCK], 4, tl.int32)
    tmp3 = tmp1 + tmp2
    tmp4 = tmp1 < 0
    tmp5 = tl.where(tmp4, tmp3, tmp1)
    tl.device_assert((0 <= tmp5) & (tmp5 < 4) | ~xmask,
        'index out of bounds: 0 <= tmp5 < 4')
    tmp7 = tl.load(in_ptr1 + (x0 + 64 * tmp5), xmask)
    tl.store(out_ptr0 + x2, tmp7, xmask)


def call(args):
    arg0_1, arg1_1 = args
    args.clear()
    assert_size_stride(arg0_1, (4,), (1,))
    assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf1 = empty_strided_cuda((4,), (1,), torch.int16)
        buf4 = empty_strided_cuda((4,), (1,), torch.int64)
        buf6 = empty_strided_cuda((4,), (1,), torch.int64)
        get_raw_stream(0)
        triton_per_fused_index_neg_sort_0[grid(1)](arg0_1, buf1, buf4, buf6,
            1, 4, XBLOCK=1, num_warps=2, num_stages=1)
        del arg0_1
        buf5 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused_index_1[grid(256)](buf1, arg1_1, buf5, 256, XBLOCK
            =128, num_warps=4, num_stages=1)
        del arg1_1
        del buf1
    buf7 = empty_strided_cpu((4,), (1,), torch.int64)
    buf7.copy_(buf6)
    return buf5, buf7, buf4


class SqueezeEmbeddingNew(nn.Module):
    """
    Squeeze sequence embedding length to the longest one in the batch
    """

    def __init__(self, batch_first=True):
        super(SqueezeEmbeddingNew, self).__init__()
        self.batch_first = batch_first

    def forward(self, input_0, input_1):
        arg1_1 = input_0
        arg0_1 = input_1
        output = call([arg0_1, arg1_1])
        return output[0]
