# 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_6/inductor_cache/du/cduvmb5hpbjgwnwykzo2hhalu225cbhq36a4xnrmox4sitjisfkt.py
# Topologically Sorted Source Nodes: [pow_1, ne_1, sum_1, total_sents, gt], Original ATen: [aten.pow, aten.ne, aten.sum, aten._to_copy, aten.gt]
# Source node to ATen node mapping:
#   gt => gt
#   ne_1 => ne_1
#   pow_1 => pow_1
#   sum_1 => sum_1
#   total_sents => convert_element_type_1
# Graph fragment:
#   %pow_1 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%arg2_1, 2), kwargs = {})
#   %ne_1 : [num_users=1] = call_function[target=torch.ops.aten.ne.Scalar](args = (%arg2_1, 0), kwargs = {})
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.default](args = (%ne_1,), kwargs = {})
#   %convert_element_type_1 : [num_users=2] = call_function[target=torch.ops.prims.convert_element_type.default](args = (%sum_1, torch.float32), kwargs = {})
#   %gt : [num_users=1] = call_function[target=torch.ops.aten.gt.Scalar](args = (%convert_element_type_1, 0), kwargs = {})
triton_per_fused__to_copy_gt_ne_pow_sum_0 = async_compile.triton('triton_per_fused__to_copy_gt_ne_pow_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.persistent_reduction(
    size_hints=[1, 256],
    reduction_hint=ReductionHint.INNER,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*i1', 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, 5), equal_to_1=(4,))]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused__to_copy_gt_ne_pow_sum_0', 'mutated_arg_names': [], 'no_x_dim': True, '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__to_copy_gt_ne_pow_sum_0(in_ptr0, out_ptr0, out_ptr2, out_ptr3, xnumel, rnumel):
    xnumel = 1
    XBLOCK: tl.constexpr = 1
    rnumel = 256
    RBLOCK: tl.constexpr = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = tl.full([1], xoffset, tl.int32)
    xmask = tl.full([RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[:]
    roffset = 0
    rmask = tl.full([RBLOCK], True, tl.int1)
    r0 = rindex
    tmp0 = tl.load(in_ptr0 + (r0), None)
    tmp1 = tmp0 * tmp0
    tmp2 = 0.0
    tmp3 = tmp0 != tmp2
    tmp4 = tmp3.to(tl.int64)
    tmp5 = tl.broadcast_to(tmp4, [RBLOCK])
    tmp7 = triton_helpers.promote_to_tensor(tl.sum(tmp5, 0))
    tmp8 = tmp7.to(tl.float32)
    tmp9 = tmp8 > tmp2
    tl.store(out_ptr0 + (tl.broadcast_to(r0, [RBLOCK])), tmp1, None)
    tl.store(out_ptr2 + (tl.full([1], 0, tl.int32)), tmp8, None)
    tl.store(out_ptr3 + (tl.full([1], 0, tl.int32)), tmp9, None)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/vo/cvoi446gy7yflmv6g5aq7ogsszwzcewz6p6hjw7csauq7kcquqp3.py
# Topologically Sorted Source Nodes: [ne, labels_1s, labels_masked, predictions_masked], Original ATen: [aten.ne, aten._to_copy, aten.mul]
# Source node to ATen node mapping:
#   labels_1s => convert_element_type
#   labels_masked => mul_1
#   ne => ne
#   predictions_masked => mul
# Graph fragment:
#   %ne : [num_users=1] = call_function[target=torch.ops.aten.ne.Scalar](args = (%arg0_1, -1), kwargs = {})
#   %convert_element_type : [num_users=2] = call_function[target=torch.ops.prims.convert_element_type.default](args = (%ne, torch.float32), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%arg0_1, %convert_element_type), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%arg1_1, %convert_element_type), kwargs = {})
triton_poi_fused__to_copy_mul_ne_1 = async_compile.triton('triton_poi_fused__to_copy_mul_ne_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: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '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, 4), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__to_copy_mul_ne_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, '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__to_copy_mul_ne_1(in_ptr0, in_ptr1, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (x0), xmask)
    tmp5 = tl.load(in_ptr1 + (x0), xmask)
    tmp1 = -1.0
    tmp2 = tmp0 != tmp1
    tmp3 = tmp2.to(tl.float32)
    tmp4 = tmp0 * tmp3
    tmp6 = tmp5 * tmp3
    tl.store(out_ptr0 + (x0), tmp4, xmask)
    tl.store(out_ptr1 + (x0), tmp6, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    arg0_1, arg1_1, arg2_1 = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(arg2_1, (4, 4, 4, 4), (64, 16, 4, 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)
        buf4 = empty_strided_cuda((), (), torch.float32)
        buf5 = empty_strided_cuda((), (), torch.bool)
        # Topologically Sorted Source Nodes: [pow_1, ne_1, sum_1, total_sents, gt], Original ATen: [aten.pow, aten.ne, aten.sum, aten._to_copy, aten.gt]
        stream0 = get_raw_stream(0)
        triton_per_fused__to_copy_gt_ne_pow_sum_0.run(arg2_1, buf0, buf4, buf5, 1, 256, grid=grid(1), stream=stream0)
        del arg2_1
        buf1 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        buf2 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [ne, labels_1s, labels_masked, predictions_masked], Original ATen: [aten.ne, aten._to_copy, aten.mul]
        triton_poi_fused__to_copy_mul_ne_1.run(arg0_1, arg1_1, buf1, buf2, 256, grid=grid(256), stream=stream0)
        del arg0_1
        del arg1_1
    return (buf0, buf4, buf1, buf2, buf5, )


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, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    arg1_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    arg2_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([arg0_1, arg1_1, arg2_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)

from _paritybench_helpers import _mock_config
import torch
import torch.nn as nn


class L1DistanceLoss(nn.Module):
    """Custom L1 loss for distance matrices."""

    def __init__(self, args):
        super(L1DistanceLoss, self).__init__()
        self.args = args
        self.word_pair_dims = 1, 2

    def forward(self, predictions, label_batch, length_batch):
        """ Computes L1 loss on distance matrices.

    Ignores all entries where label_batch=-1
    Normalizes first within sentences (by dividing by the square of the sentence length)
    and then across the batch.

    Args:
      predictions: A pytorch batch of predicted distances
      label_batch: A pytorch batch of true distances
      length_batch: A pytorch batch of sentence lengths

    Returns:
      A tuple of:
        batch_loss: average loss in the batch
        total_sents: number of sentences in the batch
    """
        labels_1s = (label_batch != -1).float()
        predictions_masked = predictions * labels_1s
        labels_masked = label_batch * labels_1s
        total_sents = torch.sum(length_batch != 0).float()
        squared_lengths = length_batch.pow(2).float()
        if total_sents > 0:
            loss_per_sent = torch.sum(torch.abs(predictions_masked -
                labels_masked), dim=self.word_pair_dims)
            normalized_loss_per_sent = loss_per_sent / squared_lengths
            batch_loss = torch.sum(normalized_loss_per_sent) / total_sents
        else:
            batch_loss = torch.tensor(0.0, device=self.args['device'])
        return batch_loss, total_sents


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


def get_init_inputs():
    return [[], {'args': _mock_config()}]

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_cuda = torch._C._dynamo.guards._empty_strided_cuda


@triton.jit
def triton_per_fused__to_copy_gt_ne_pow_sum_0(in_ptr0, out_ptr0, out_ptr2,
    out_ptr3, xnumel, rnumel):
    XBLOCK: tl.constexpr = 1
    RBLOCK: tl.constexpr = 256
    xoffset = tl.program_id(0) * XBLOCK
    tl.full([1], xoffset, tl.int32)
    tl.full([RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[:]
    tl.full([RBLOCK], True, tl.int1)
    r0 = rindex
    tmp0 = tl.load(in_ptr0 + r0, None)
    tmp1 = tmp0 * tmp0
    tmp2 = 0.0
    tmp3 = tmp0 != tmp2
    tmp4 = tmp3.to(tl.int64)
    tmp5 = tl.broadcast_to(tmp4, [RBLOCK])
    tmp7 = triton_helpers.promote_to_tensor(tl.sum(tmp5, 0))
    tmp8 = tmp7.to(tl.float32)
    tmp9 = tmp8 > tmp2
    tl.store(out_ptr0 + tl.broadcast_to(r0, [RBLOCK]), tmp1, None)
    tl.store(out_ptr2 + tl.full([1], 0, tl.int32), tmp8, None)
    tl.store(out_ptr3 + tl.full([1], 0, tl.int32), tmp9, None)


@triton.jit
def triton_poi_fused__to_copy_mul_ne_1(in_ptr0, in_ptr1, out_ptr0, out_ptr1,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + x0, xmask)
    tmp5 = tl.load(in_ptr1 + x0, xmask)
    tmp1 = -1.0
    tmp2 = tmp0 != tmp1
    tmp3 = tmp2.to(tl.float32)
    tmp4 = tmp0 * tmp3
    tmp6 = tmp5 * tmp3
    tl.store(out_ptr0 + x0, tmp4, xmask)
    tl.store(out_ptr1 + x0, tmp6, xmask)


def call(args):
    arg0_1, arg1_1, arg2_1 = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(arg2_1, (4, 4, 4, 4), (64, 16, 4, 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)
        buf4 = empty_strided_cuda((), (), torch.float32)
        buf5 = empty_strided_cuda((), (), torch.bool)
        get_raw_stream(0)
        triton_per_fused__to_copy_gt_ne_pow_sum_0[grid(1)](arg2_1, buf0,
            buf4, buf5, 1, 256, num_warps=2, num_stages=1)
        del arg2_1
        buf1 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        buf2 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused__to_copy_mul_ne_1[grid(256)](arg0_1, arg1_1, buf1,
            buf2, 256, XBLOCK=256, num_warps=4, num_stages=1)
        del arg0_1
        del arg1_1
    return buf0, buf4, buf1, buf2, buf5


class L1DistanceLossNew(nn.Module):
    """Custom L1 loss for distance matrices."""

    def __init__(self, args):
        super(L1DistanceLossNew, self).__init__()
        self.args = args
        self.word_pair_dims = 1, 2

    def forward(self, input_0, input_1, input_2):
        arg0_1 = input_0
        arg1_1 = input_1
        arg2_1 = input_2
        output = call([arg0_1, arg1_1, arg2_1])
        return output[0], output[1]
