# 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/2l/c2lpbmkah6k7ad7ej2t4cu6gqp3tcq5mzhelfntwzyst66pi6yw6.py
# Topologically Sorted Source Nodes: [softplus, sigma2, log, mul, add_2, sub, pow_1, sub_1, pow_2, delta_t_delta, truediv, add_3, loss, sum_1], Original ATen: [aten.softplus, aten.add, aten.log, aten.mul, aten.sub, aten.pow, aten.div, aten.sum]
# Source node to ATen node mapping:
#   add_2 => add_2
#   add_3 => add_3
#   delta_t_delta => add_1
#   log => log
#   loss => mul_1
#   mul => mul
#   pow_1 => pow_1
#   pow_2 => pow_2
#   sigma2 => add
#   softplus => exp, gt, log1p, where
#   sub => sub
#   sub_1 => sub_1
#   sum_1 => sum_1
#   truediv => div
# Graph fragment:
#   %gt : [num_users=1] = call_function[target=torch.ops.aten.gt.Scalar](args = (%arg0_1, 20), kwargs = {})
#   %exp : [num_users=1] = call_function[target=torch.ops.aten.exp.default](args = (%arg0_1,), kwargs = {})
#   %log1p : [num_users=1] = call_function[target=torch.ops.aten.log1p.default](args = (%exp,), kwargs = {})
#   %where : [num_users=1] = call_function[target=torch.ops.aten.where.self](args = (%gt, %arg0_1, %log1p), kwargs = {})
#   %add : [num_users=2] = call_function[target=torch.ops.aten.add.Tensor](args = (%where, 4), kwargs = {})
#   %log : [num_users=1] = call_function[target=torch.ops.aten.log.default](args = (%add,), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%log, 2), kwargs = {})
#   %add_2 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul, 1.8378770664093453), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%arg1_1, %arg2_1), kwargs = {})
#   %pow_1 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%sub, 2), kwargs = {})
#   %sub_1 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%arg3_1, %arg4_1), kwargs = {})
#   %pow_2 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%sub_1, 2), kwargs = {})
#   %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%pow_1, %pow_2), kwargs = {})
#   %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%add_1, %add), kwargs = {})
#   %add_3 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%add_2, %div), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%add_3, 0.5), kwargs = {})
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.default](args = (%mul_1,), kwargs = {})
triton_per_fused_add_div_log_mul_pow_softplus_sub_sum_0 = async_compile.triton('triton_per_fused_add_div_log_mul_pow_softplus_sub_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: '*fp32', 4: '*fp32', 5: '*fp32', 6: 'i32', 7: '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': {6: 1}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5, 7), equal_to_1=(6,))]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_add_div_log_mul_pow_softplus_sub_sum_0', 'mutated_arg_names': [], 'no_x_dim': True, 'num_load': 5, '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_add_div_log_mul_pow_softplus_sub_sum_0(in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, out_ptr0, 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)
    tmp13 = tl.load(in_ptr1 + (r0), None)
    tmp14 = tl.load(in_ptr2 + (r0), None)
    tmp17 = tl.load(in_ptr3 + (r0), None)
    tmp18 = tl.load(in_ptr4 + (r0), None)
    tmp1 = 20.0
    tmp2 = tmp0 > tmp1
    tmp3 = tl_math.exp(tmp0)
    tmp4 = libdevice.log1p(tmp3)
    tmp5 = tl.where(tmp2, tmp0, tmp4)
    tmp6 = 4.0
    tmp7 = tmp5 + tmp6
    tmp8 = tl_math.log(tmp7)
    tmp9 = 2.0
    tmp10 = tmp8 * tmp9
    tmp11 = 1.8378770664093453
    tmp12 = tmp10 + tmp11
    tmp15 = tmp13 - tmp14
    tmp16 = tmp15 * tmp15
    tmp19 = tmp17 - tmp18
    tmp20 = tmp19 * tmp19
    tmp21 = tmp16 + tmp20
    tmp22 = tmp21 / tmp7
    tmp23 = tmp12 + tmp22
    tmp24 = 0.5
    tmp25 = tmp23 * tmp24
    tmp26 = tl.broadcast_to(tmp25, [RBLOCK])
    tmp28 = triton_helpers.promote_to_tensor(tl.sum(tmp26, 0))
    tl.store(out_ptr0 + (tl.full([1], 0, tl.int32)), tmp28, None)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    arg0_1, arg1_1, arg2_1, arg3_1, arg4_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))
    assert_size_stride(arg3_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(arg4_1, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((), (), torch.float32)
        # Topologically Sorted Source Nodes: [softplus, sigma2, log, mul, add_2, sub, pow_1, sub_1, pow_2, delta_t_delta, truediv, add_3, loss, sum_1], Original ATen: [aten.softplus, aten.add, aten.log, aten.mul, aten.sub, aten.pow, aten.div, aten.sum]
        stream0 = get_raw_stream(0)
        triton_per_fused_add_div_log_mul_pow_softplus_sub_sum_0.run(arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, buf0, 1, 256, grid=grid(1), stream=stream0)
        del arg0_1
        del arg1_1
        del arg2_1
        del arg3_1
        del arg4_1
    return (buf0, )


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)
    arg3_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    arg4_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, arg3_1, arg4_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 math
import torch
import torch.utils.data
import torch.nn.functional as F
from torch import nn


class IIDIsotropicGaussianUVLoss(nn.Module):
    """
    Loss for the case of iid residuals with isotropic covariance:
    $Sigma_i = sigma_i^2 I$
    The loss (negative log likelihood) is then:
    $1/2 sum_{i=1}^n (log(2 pi) + 2 log sigma_i^2 + ||delta_i||^2 / sigma_i^2)$,
    where $delta_i=(u - u', v - v')$ is a 2D vector containing UV coordinates
    difference between estimated and ground truth UV values
    For details, see:
    N. Neverova, D. Novotny, A. Vedaldi "Correlated Uncertainty for Learning
    Dense Correspondences from Noisy Labels", p. 918--926, in Proc. NIPS 2019
    """

    def __init__(self, sigma_lower_bound: 'float'):
        super(IIDIsotropicGaussianUVLoss, self).__init__()
        self.sigma_lower_bound = sigma_lower_bound
        self.log2pi = math.log(2 * math.pi)

    def forward(self, u: 'torch.Tensor', v: 'torch.Tensor', sigma_u:
        'torch.Tensor', target_u: 'torch.Tensor', target_v: 'torch.Tensor'):
        sigma2 = F.softplus(sigma_u) + self.sigma_lower_bound
        delta_t_delta = (u - target_u) ** 2 + (v - target_v) ** 2
        loss = 0.5 * (self.log2pi + 2 * torch.log(sigma2) + delta_t_delta /
            sigma2)
        return loss.sum()


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


def get_init_inputs():
    return [[], {'sigma_lower_bound': 4}]

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
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import math
import torch.utils.data
from torch import 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_add_div_log_mul_pow_softplus_sub_sum_0(in_ptr0,
    in_ptr1, in_ptr2, in_ptr3, in_ptr4, out_ptr0, 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)
    tmp13 = tl.load(in_ptr1 + r0, None)
    tmp14 = tl.load(in_ptr2 + r0, None)
    tmp17 = tl.load(in_ptr3 + r0, None)
    tmp18 = tl.load(in_ptr4 + r0, None)
    tmp1 = 20.0
    tmp2 = tmp0 > tmp1
    tmp3 = tl_math.exp(tmp0)
    tmp4 = libdevice.log1p(tmp3)
    tmp5 = tl.where(tmp2, tmp0, tmp4)
    tmp6 = 4.0
    tmp7 = tmp5 + tmp6
    tmp8 = tl_math.log(tmp7)
    tmp9 = 2.0
    tmp10 = tmp8 * tmp9
    tmp11 = 1.8378770664093453
    tmp12 = tmp10 + tmp11
    tmp15 = tmp13 - tmp14
    tmp16 = tmp15 * tmp15
    tmp19 = tmp17 - tmp18
    tmp20 = tmp19 * tmp19
    tmp21 = tmp16 + tmp20
    tmp22 = tmp21 / tmp7
    tmp23 = tmp12 + tmp22
    tmp24 = 0.5
    tmp25 = tmp23 * tmp24
    tmp26 = tl.broadcast_to(tmp25, [RBLOCK])
    tmp28 = triton_helpers.promote_to_tensor(tl.sum(tmp26, 0))
    tl.store(out_ptr0 + tl.full([1], 0, tl.int32), tmp28, None)


def call(args):
    arg0_1, arg1_1, arg2_1, arg3_1, arg4_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))
    assert_size_stride(arg3_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(arg4_1, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((), (), torch.float32)
        get_raw_stream(0)
        triton_per_fused_add_div_log_mul_pow_softplus_sub_sum_0[grid(1)](arg0_1
            , arg1_1, arg2_1, arg3_1, arg4_1, buf0, 1, 256, num_warps=2,
            num_stages=1)
        del arg0_1
        del arg1_1
        del arg2_1
        del arg3_1
        del arg4_1
    return buf0,


class IIDIsotropicGaussianUVLossNew(nn.Module):
    """
    Loss for the case of iid residuals with isotropic covariance:
    $Sigma_i = sigma_i^2 I$
    The loss (negative log likelihood) is then:
    $1/2 sum_{i=1}^n (log(2 pi) + 2 log sigma_i^2 + ||delta_i||^2 / sigma_i^2)$,
    where $delta_i=(u - u', v - v')$ is a 2D vector containing UV coordinates
    difference between estimated and ground truth UV values
    For details, see:
    N. Neverova, D. Novotny, A. Vedaldi "Correlated Uncertainty for Learning
    Dense Correspondences from Noisy Labels", p. 918--926, in Proc. NIPS 2019
    """

    def __init__(self, sigma_lower_bound: 'float'):
        super(IIDIsotropicGaussianUVLossNew, self).__init__()
        self.sigma_lower_bound = sigma_lower_bound
        self.log2pi = math.log(2 * math.pi)

    def forward(self, input_0, input_1, input_2, input_3, input_4):
        arg0_1 = input_0
        arg1_1 = input_1
        arg2_1 = input_2
        arg3_1 = input_3
        arg4_1 = input_4
        output = call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1])
        return output[0]
