# 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_4/inductor_cache/si/csi7zswrgkt5rqoak2nctfstk2b7vqihfvbcs5nlpzurs2rijsjs.py
# Topologically Sorted Source Nodes: [cat, smooth_translation_loss_1, smooth_translation_loss_2, smooth_translation_loss_3, smooth_translation_loss_4], Original ATen: [aten.cat, aten.sum, aten.mul, aten.mean]
# Source node to ATen node mapping:
#   cat => cat
#   smooth_translation_loss_1 => sum_2
#   smooth_translation_loss_2 => mul
#   smooth_translation_loss_3 => mul_1
#   smooth_translation_loss_4 => mean
# Graph fragment:
#   %cat : [num_users=1] = call_function[target=torch.ops.aten.cat.default](args = ([%full_default, %sum_1],), kwargs = {})
#   %sum_2 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%cat, [-1]), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sum_2, 1000.0), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mul, 1.0), kwargs = {})
#   %mean : [num_users=1] = call_function[target=torch.ops.aten.mean.default](args = (%mul_1,), kwargs = {})
triton_per_fused_cat_mean_mul_sum_0 = async_compile.triton('triton_per_fused_cat_mean_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.persistent_reduction(
    size_hints=[1, 64],
    reduction_hint=ReductionHint.DEFAULT,
    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': {2: 1}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 3), equal_to_1=(2,))]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_cat_mean_mul_sum_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 8, '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_cat_mean_mul_sum_0(in_out_ptr0, in_ptr0, xnumel, rnumel, XBLOCK : tl.constexpr):
    xnumel = 1
    rnumel = 64
    RBLOCK: tl.constexpr = 64
    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)
    r1 = (rindex // 16)
    r0 = rindex % 16
    r2 = rindex
    tmp0 = r1
    tmp1 = tl.full([1, 1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1, 1], 1, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = 0.0
    tmp6 = tl.full(tmp5.shape, 0.0, tmp5.dtype)
    tmp7 = tl.where(tmp4, tmp5, tmp6)
    tmp8 = tmp0 >= tmp3
    tmp9 = tl.full([1, 1], 4, tl.int64)
    tmp10 = tmp0 < tmp9
    tmp11 = tl.load(in_ptr0 + (tl.broadcast_to(64 + (4*r0) + (64*((-1) + r1)), [XBLOCK, RBLOCK])), tmp8, eviction_policy='evict_last', other=0.0)
    tmp12 = tl.load(in_ptr0 + (tl.broadcast_to((4*r0) + (64*((-1) + r1)), [XBLOCK, RBLOCK])), tmp8, eviction_policy='evict_last', other=0.0)
    tmp13 = tmp11 - tmp12
    tmp14 = tl_math.abs(tmp13)
    tmp15 = tl.load(in_ptr0 + (tl.broadcast_to(65 + (4*r0) + (64*((-1) + r1)), [XBLOCK, RBLOCK])), tmp8, eviction_policy='evict_last', other=0.0)
    tmp16 = tl.load(in_ptr0 + (tl.broadcast_to(1 + (4*r0) + (64*((-1) + r1)), [XBLOCK, RBLOCK])), tmp8, eviction_policy='evict_last', other=0.0)
    tmp17 = tmp15 - tmp16
    tmp18 = tl_math.abs(tmp17)
    tmp19 = tmp14 + tmp18
    tmp20 = tl.load(in_ptr0 + (tl.broadcast_to(66 + (4*r0) + (64*((-1) + r1)), [XBLOCK, RBLOCK])), tmp8, eviction_policy='evict_last', other=0.0)
    tmp21 = tl.load(in_ptr0 + (tl.broadcast_to(2 + (4*r0) + (64*((-1) + r1)), [XBLOCK, RBLOCK])), tmp8, eviction_policy='evict_last', other=0.0)
    tmp22 = tmp20 - tmp21
    tmp23 = tl_math.abs(tmp22)
    tmp24 = tmp19 + tmp23
    tmp25 = tl.load(in_ptr0 + (tl.broadcast_to(67 + (4*r0) + (64*((-1) + r1)), [XBLOCK, RBLOCK])), tmp8, eviction_policy='evict_last', other=0.0)
    tmp26 = tl.load(in_ptr0 + (tl.broadcast_to(3 + (4*r0) + (64*((-1) + r1)), [XBLOCK, RBLOCK])), tmp8, eviction_policy='evict_last', other=0.0)
    tmp27 = tmp25 - tmp26
    tmp28 = tl_math.abs(tmp27)
    tmp29 = tmp24 + tmp28
    tmp30 = tl.full(tmp29.shape, 0.0, tmp29.dtype)
    tmp31 = tl.where(tmp8, tmp29, tmp30)
    tmp32 = tl.where(tmp4, tmp7, tmp31)
    tmp33 = 1000.0
    tmp34 = tmp32 * tmp33
    tmp35 = 1.0
    tmp36 = tmp34 * tmp35
    tmp37 = tl.broadcast_to(tmp36, [XBLOCK, RBLOCK])
    tmp39 = tl.sum(tmp37, 1)[:, None]
    tmp40 = 64.0
    tmp41 = tmp39 / tmp40
    tl.debug_barrier()
    tl.store(in_out_ptr0 + (tl.full([XBLOCK, 1], 0, tl.int32)), tmp41, None)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    arg0_1, = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf1 = empty_strided_cuda((), (), torch.float32)
        buf2 = buf1; del buf1  # reuse
        # Topologically Sorted Source Nodes: [cat, smooth_translation_loss_1, smooth_translation_loss_2, smooth_translation_loss_3, smooth_translation_loss_4], Original ATen: [aten.cat, aten.sum, aten.mul, aten.mean]
        stream0 = get_raw_stream(0)
        triton_per_fused_cat_mean_mul_sum_0.run(buf2, arg0_1, 1, 64, grid=grid(1), stream=stream0)
        del arg0_1
    return (buf2, )


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)
    fn = lambda: call([arg0_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
import torch.cuda.comm


class SmoothTranslationLoss(nn.Module):
    """Smooth loss for translations.

    Args:
        reduction (str, optional): The method that reduces the loss to a
            scalar. Options are "none", "mean" and "sum".
        loss_weight (float, optional): The weight of the loss. Defaults to 1.0
    """

    def __init__(self, reduction='mean', loss_weight=1.0):
        super().__init__()
        assert reduction in (None, 'none', 'mean', 'sum')
        self.reduction = reduction
        self.loss_weight = loss_weight

    def forward(self, translation, loss_weight_override=None,
        reduction_override=None):
        """Forward function of loss.

        Args:
            translation (torch.Tensor): The body translation parameters
            loss_weight_override (float, optional): The weight of loss used to
                override the original weight of loss
            reduction_override (str, optional): The reduction method used to
                override the original reduction method of the loss.
                Defaults to None
        Returns:
            torch.Tensor: The calculated loss
        """
        assert reduction_override in (None, 'none', 'mean', 'sum')
        reduction = (reduction_override if reduction_override else self.
            reduction)
        loss_weight = (loss_weight_override if loss_weight_override is not
            None else self.loss_weight)
        translation_diff = translation[1:] - translation[:-1]
        smooth_translation_loss = translation_diff.abs().sum(dim=-1,
            keepdim=True)
        smooth_translation_loss = torch.cat([torch.zeros_like(
            smooth_translation_loss)[:1], smooth_translation_loss]).sum(dim=-1)
        smooth_translation_loss *= 1000.0
        smooth_translation_loss = loss_weight * smooth_translation_loss
        if reduction == 'mean':
            smooth_translation_loss = smooth_translation_loss.mean()
        elif reduction == 'sum':
            smooth_translation_loss = smooth_translation_loss.sum()
        return smooth_translation_loss


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


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.triton_helpers import math as tl_math
import torch.nn as nn
import torch.cuda.comm
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_cat_mean_mul_sum_0(in_out_ptr0, in_ptr0, xnumel,
    rnumel, XBLOCK: tl.constexpr):
    RBLOCK: tl.constexpr = 64
    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)
    r1 = rindex // 16
    r0 = rindex % 16
    tmp0 = r1
    tl.full([1, 1], 0, tl.int64)
    tmp3 = tl.full([1, 1], 1, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = 0.0
    tmp6 = tl.full(tmp5.shape, 0.0, tmp5.dtype)
    tmp7 = tl.where(tmp4, tmp5, tmp6)
    tmp8 = tmp0 >= tmp3
    tl.full([1, 1], 4, tl.int64)
    tmp11 = tl.load(in_ptr0 + tl.broadcast_to(64 + 4 * r0 + 64 * (-1 + r1),
        [XBLOCK, RBLOCK]), tmp8, eviction_policy='evict_last', other=0.0)
    tmp12 = tl.load(in_ptr0 + tl.broadcast_to(4 * r0 + 64 * (-1 + r1), [
        XBLOCK, RBLOCK]), tmp8, eviction_policy='evict_last', other=0.0)
    tmp13 = tmp11 - tmp12
    tmp14 = tl_math.abs(tmp13)
    tmp15 = tl.load(in_ptr0 + tl.broadcast_to(65 + 4 * r0 + 64 * (-1 + r1),
        [XBLOCK, RBLOCK]), tmp8, eviction_policy='evict_last', other=0.0)
    tmp16 = tl.load(in_ptr0 + tl.broadcast_to(1 + 4 * r0 + 64 * (-1 + r1),
        [XBLOCK, RBLOCK]), tmp8, eviction_policy='evict_last', other=0.0)
    tmp17 = tmp15 - tmp16
    tmp18 = tl_math.abs(tmp17)
    tmp19 = tmp14 + tmp18
    tmp20 = tl.load(in_ptr0 + tl.broadcast_to(66 + 4 * r0 + 64 * (-1 + r1),
        [XBLOCK, RBLOCK]), tmp8, eviction_policy='evict_last', other=0.0)
    tmp21 = tl.load(in_ptr0 + tl.broadcast_to(2 + 4 * r0 + 64 * (-1 + r1),
        [XBLOCK, RBLOCK]), tmp8, eviction_policy='evict_last', other=0.0)
    tmp22 = tmp20 - tmp21
    tmp23 = tl_math.abs(tmp22)
    tmp24 = tmp19 + tmp23
    tmp25 = tl.load(in_ptr0 + tl.broadcast_to(67 + 4 * r0 + 64 * (-1 + r1),
        [XBLOCK, RBLOCK]), tmp8, eviction_policy='evict_last', other=0.0)
    tmp26 = tl.load(in_ptr0 + tl.broadcast_to(3 + 4 * r0 + 64 * (-1 + r1),
        [XBLOCK, RBLOCK]), tmp8, eviction_policy='evict_last', other=0.0)
    tmp27 = tmp25 - tmp26
    tmp28 = tl_math.abs(tmp27)
    tmp29 = tmp24 + tmp28
    tmp30 = tl.full(tmp29.shape, 0.0, tmp29.dtype)
    tmp31 = tl.where(tmp8, tmp29, tmp30)
    tmp32 = tl.where(tmp4, tmp7, tmp31)
    tmp33 = 1000.0
    tmp34 = tmp32 * tmp33
    tmp35 = 1.0
    tmp36 = tmp34 * tmp35
    tmp37 = tl.broadcast_to(tmp36, [XBLOCK, RBLOCK])
    tmp39 = tl.sum(tmp37, 1)[:, None]
    tmp40 = 64.0
    tmp41 = tmp39 / tmp40
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp41, None)


def call(args):
    arg0_1, = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf1 = empty_strided_cuda((), (), torch.float32)
        buf2 = buf1
        del buf1
        get_raw_stream(0)
        triton_per_fused_cat_mean_mul_sum_0[grid(1)](buf2, arg0_1, 1, 64,
            XBLOCK=1, num_warps=2, num_stages=1)
        del arg0_1
    return buf2,


class SmoothTranslationLossNew(nn.Module):
    """Smooth loss for translations.

    Args:
        reduction (str, optional): The method that reduces the loss to a
            scalar. Options are "none", "mean" and "sum".
        loss_weight (float, optional): The weight of the loss. Defaults to 1.0
    """

    def __init__(self, reduction='mean', loss_weight=1.0):
        super().__init__()
        assert reduction in (None, 'none', 'mean', 'sum')
        self.reduction = reduction
        self.loss_weight = loss_weight

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