# 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/k6/ck65c2dgolwnidsoaorjr6l3cmw4zwwifsnyth2aiafv7g2y42xb.py
# Topologically Sorted Source Nodes: [probs, sub, add, pow_1, mul, mul_1, add_1, log, mul_2, add_2, pow_2, mul_3, sub_1, mul_4, sub_2, add_3, log_1, mul_5, loss_tmp, loss_tmp_1], Original ATen: [aten.sigmoid, aten.rsub, aten.add, aten.pow, aten.mul, aten.log, aten.sub, aten.squeeze]
# Source node to ATen node mapping:
#   add => add
#   add_1 => add_1
#   add_2 => add_2
#   add_3 => add_3
#   log => log
#   log_1 => log_1
#   loss_tmp => sub_3
#   loss_tmp_1 => squeeze
#   mul => mul
#   mul_1 => mul_1
#   mul_2 => mul_2
#   mul_3 => mul_3
#   mul_4 => mul_4
#   mul_5 => mul_5
#   pow_1 => pow_1
#   pow_2 => pow_2
#   probs => sigmoid
#   sub => sub
#   sub_1 => sub_1
#   sub_2 => sub_2
# Graph fragment:
#   %sigmoid : [num_users=4] = call_function[target=torch.ops.aten.sigmoid.default](args = (%arg0_1,), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (1.0, %sigmoid), kwargs = {})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%sub, 1e-08), kwargs = {})
#   %pow_1 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%add, 2.0), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%pow_1, -4), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mul, %unsqueeze), kwargs = {})
#   %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%sigmoid, 1e-08), kwargs = {})
#   %log : [num_users=1] = call_function[target=torch.ops.aten.log.default](args = (%add_1,), kwargs = {})
#   %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mul_1, %log), kwargs = {})
#   %add_2 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%sigmoid, 1e-08), kwargs = {})
#   %pow_2 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%add_2, 2.0), kwargs = {})
#   %mul_3 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%pow_2, -3), kwargs = {})
#   %sub_1 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (1.0, %unsqueeze), kwargs = {})
#   %mul_4 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mul_3, %sub_1), kwargs = {})
#   %sub_2 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (1.0, %sigmoid), kwargs = {})
#   %add_3 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%sub_2, 1e-08), kwargs = {})
#   %log_1 : [num_users=1] = call_function[target=torch.ops.aten.log.default](args = (%add_3,), kwargs = {})
#   %mul_5 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mul_4, %log_1), kwargs = {})
#   %sub_3 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%mul_2, %mul_5), kwargs = {})
#   %squeeze : [num_users=1] = call_function[target=torch.ops.aten.squeeze.dim](args = (%sub_3, 1), kwargs = {})
triton_poi_fused_add_log_mul_pow_rsub_sigmoid_squeeze_sub_0 = async_compile.triton('triton_poi_fused_add_log_mul_pow_rsub_sigmoid_squeeze_sub_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=[1024], 
    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_add_log_mul_pow_rsub_sigmoid_squeeze_sub_0', '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_add_log_mul_pow_rsub_sigmoid_squeeze_sub_0(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 1024
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex % 256
    x0 = xindex % 64
    x2 = (xindex // 256)
    x4 = xindex
    tmp0 = tl.load(in_ptr0 + (x3), xmask, eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr1 + (x0 + (64*x2)), xmask, eviction_policy='evict_last')
    tmp1 = tl.sigmoid(tmp0)
    tmp2 = 1.0
    tmp3 = tmp2 - tmp1
    tmp4 = 1e-08
    tmp5 = tmp3 + tmp4
    tmp6 = tmp5 * tmp5
    tmp7 = -4.0
    tmp8 = tmp6 * tmp7
    tmp10 = tmp8 * tmp9
    tmp11 = tmp1 + tmp4
    tmp12 = tl_math.log(tmp11)
    tmp13 = tmp10 * tmp12
    tmp14 = tmp11 * tmp11
    tmp15 = -3.0
    tmp16 = tmp14 * tmp15
    tmp17 = tmp2 - tmp9
    tmp18 = tmp16 * tmp17
    tmp19 = tl_math.log(tmp5)
    tmp20 = tmp18 * tmp19
    tmp21 = tmp13 - tmp20
    tl.store(out_ptr0 + (x4), tmp21, 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, 4, 4, 4), (64, 16, 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)
        buf0 = empty_strided_cuda((4, 4, 4, 4, 4), (256, 64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [probs, sub, add, pow_1, mul, mul_1, add_1, log, mul_2, add_2, pow_2, mul_3, sub_1, mul_4, sub_2, add_3, log_1, mul_5, loss_tmp, loss_tmp_1], Original ATen: [aten.sigmoid, aten.rsub, aten.add, aten.pow, aten.mul, aten.log, aten.sub, aten.squeeze]
        stream0 = get_raw_stream(0)
        triton_poi_fused_add_log_mul_pow_rsub_sigmoid_squeeze_sub_0.run(arg0_1, arg1_1, buf0, 1024, grid=grid(1024), stream=stream0)
        del arg0_1
        del arg1_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)
    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


def binary_focal_loss_with_logits(input: 'torch.Tensor', target:
    'torch.Tensor', alpha: 'float'=0.25, gamma: 'float'=2.0, reduction:
    'str'='none', eps: 'float'=1e-08) ->torch.Tensor:
    """Function that computes Binary Focal loss.

    .. math::

        \\text{FL}(p_t) = -\\alpha_t (1 - p_t)^{\\gamma} \\, \\text{log}(p_t)

    where:
       - :math:`p_t` is the model's estimated probability for each class.

    Args:
        input (torch.Tensor): input data tensor with shape :math:`(N, 1, *)`.
        target (torch.Tensor): the target tensor with shape :math:`(N, 1, *)`.
        alpha (float): Weighting factor for the rare class :math:`\\alpha \\in [0, 1]`. Default: 0.25.
        gamma (float): Focusing parameter :math:`\\gamma >= 0`. Default: 2.0.
        reduction (str, optional): Specifies the reduction to apply to the. Default: 'none'.
        eps (float): for numerically stability when dividing. Default: 1e-8.

    Returns:
        torch.tensor: the computed loss.

    Examples:
        >>> num_classes = 1
        >>> kwargs = {"alpha": 0.25, "gamma": 2.0, "reduction": 'mean'}
        >>> logits = torch.tensor([[[[6.325]]],[[[5.26]]],[[[87.49]]]])
        >>> labels = torch.tensor([[[1.]],[[1.]],[[0.]]])
        >>> binary_focal_loss_with_logits(logits, labels, **kwargs)
        tensor(4.6052)
    """
    if not isinstance(input, torch.Tensor):
        raise TypeError('Input type is not a torch.Tensor. Got {}'.format(
            type(input)))
    if not len(input.shape) >= 2:
        raise ValueError('Invalid input shape, we expect BxCx*. Got: {}'.
            format(input.shape))
    if input.size(0) != target.size(0):
        raise ValueError(
            'Expected input batch_size ({}) to match target batch_size ({}).'
            .format(input.size(0), target.size(0)))
    probs = torch.sigmoid(input)
    target = target.unsqueeze(dim=1)
    loss_tmp = -alpha * torch.pow(1.0 - probs + eps, gamma
        ) * target * torch.log(probs + eps) - (1 - alpha) * torch.pow(probs +
        eps, gamma) * (1.0 - target) * torch.log(1.0 - probs + eps)
    loss_tmp = loss_tmp.squeeze(dim=1)
    if reduction == 'none':
        loss = loss_tmp
    elif reduction == 'mean':
        loss = torch.mean(loss_tmp)
    elif reduction == 'sum':
        loss = torch.sum(loss_tmp)
    else:
        raise NotImplementedError('Invalid reduction mode: {}'.format(
            reduction))
    return loss


class BinaryFocalLossWithLogits(nn.Module):
    """Criterion that computes Focal loss.

    According to :cite:`lin2017focal`, the Focal loss is computed as follows:

    .. math::

        \\text{FL}(p_t) = -\\alpha_t (1 - p_t)^{\\gamma} \\, \\text{log}(p_t)

    where:
       - :math:`p_t` is the model's estimated probability for each class.

    Args:
        alpha (float): Weighting factor for the rare class :math:`\\alpha \\in [0, 1]`.
        gamma (float): Focusing parameter :math:`\\gamma >= 0`.
        reduction (str, optional): Specifies the reduction to apply to the
         output: ‘none’ | ‘mean’ | ‘sum’. ‘none’: no reduction will be applied,
         ‘mean’: the sum of the output will be divided by the number of elements
         in the output, ‘sum’: the output will be summed. Default: ‘none’.

    Shape:
        - Input: :math:`(N, 1, *)`.
        - Target: :math:`(N, 1, *)`.

    Examples:
        >>> N = 1  # num_classes
        >>> kwargs = {"alpha": 0.25, "gamma": 2.0, "reduction": 'mean'}
        >>> loss = BinaryFocalLossWithLogits(**kwargs)
        >>> input = torch.randn(1, N, 3, 5, requires_grad=True)
        >>> target = torch.empty(1, 3, 5, dtype=torch.long).random_(N)
        >>> output = loss(input, target)
        >>> output.backward()
    """

    def __init__(self, alpha: 'float', gamma: 'float'=2.0, reduction: 'str'
        ='none') ->None:
        super(BinaryFocalLossWithLogits, self).__init__()
        self.alpha: 'float' = alpha
        self.gamma: 'float' = gamma
        self.reduction: 'str' = reduction
        self.eps: 'float' = 1e-08

    def forward(self, input: 'torch.Tensor', target: 'torch.Tensor'
        ) ->torch.Tensor:
        return binary_focal_loss_with_logits(input, target, self.alpha,
            self.gamma, self.reduction, self.eps)


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


def get_init_inputs():
    return [[], {'alpha': 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.triton_helpers import math as tl_math
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_poi_fused_add_log_mul_pow_rsub_sigmoid_squeeze_sub_0(in_ptr0,
    in_ptr1, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 1024
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex % 256
    x0 = xindex % 64
    x2 = xindex // 256
    x4 = xindex
    tmp0 = tl.load(in_ptr0 + x3, xmask, eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr1 + (x0 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp1 = tl.sigmoid(tmp0)
    tmp2 = 1.0
    tmp3 = tmp2 - tmp1
    tmp4 = 1e-08
    tmp5 = tmp3 + tmp4
    tmp6 = tmp5 * tmp5
    tmp7 = -4.0
    tmp8 = tmp6 * tmp7
    tmp10 = tmp8 * tmp9
    tmp11 = tmp1 + tmp4
    tmp12 = tl_math.log(tmp11)
    tmp13 = tmp10 * tmp12
    tmp14 = tmp11 * tmp11
    tmp15 = -3.0
    tmp16 = tmp14 * tmp15
    tmp17 = tmp2 - tmp9
    tmp18 = tmp16 * tmp17
    tmp19 = tl_math.log(tmp5)
    tmp20 = tmp18 * tmp19
    tmp21 = tmp13 - tmp20
    tl.store(out_ptr0 + x4, tmp21, xmask)


def call(args):
    arg0_1, arg1_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))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4, 4, 4), (256, 64, 16, 4, 1),
            torch.float32)
        get_raw_stream(0)
        triton_poi_fused_add_log_mul_pow_rsub_sigmoid_squeeze_sub_0[grid(1024)
            ](arg0_1, arg1_1, buf0, 1024, XBLOCK=128, num_warps=4, num_stages=1
            )
        del arg0_1
        del arg1_1
    return buf0,


def binary_focal_loss_with_logits(input: 'torch.Tensor', target:
    'torch.Tensor', alpha: 'float'=0.25, gamma: 'float'=2.0, reduction:
    'str'='none', eps: 'float'=1e-08) ->torch.Tensor:
    """Function that computes Binary Focal loss.

    .. math::

        \\text{FL}(p_t) = -\\alpha_t (1 - p_t)^{\\gamma} \\, \\text{log}(p_t)

    where:
       - :math:`p_t` is the model's estimated probability for each class.

    Args:
        input (torch.Tensor): input data tensor with shape :math:`(N, 1, *)`.
        target (torch.Tensor): the target tensor with shape :math:`(N, 1, *)`.
        alpha (float): Weighting factor for the rare class :math:`\\alpha \\in [0, 1]`. Default: 0.25.
        gamma (float): Focusing parameter :math:`\\gamma >= 0`. Default: 2.0.
        reduction (str, optional): Specifies the reduction to apply to the. Default: 'none'.
        eps (float): for numerically stability when dividing. Default: 1e-8.

    Returns:
        torch.tensor: the computed loss.

    Examples:
        >>> num_classes = 1
        >>> kwargs = {"alpha": 0.25, "gamma": 2.0, "reduction": 'mean'}
        >>> logits = torch.tensor([[[[6.325]]],[[[5.26]]],[[[87.49]]]])
        >>> labels = torch.tensor([[[1.]],[[1.]],[[0.]]])
        >>> binary_focal_loss_with_logits(logits, labels, **kwargs)
        tensor(4.6052)
    """
    if not isinstance(input, torch.Tensor):
        raise TypeError('Input type is not a torch.Tensor. Got {}'.format(
            type(input)))
    if not len(input.shape) >= 2:
        raise ValueError('Invalid input shape, we expect BxCx*. Got: {}'.
            format(input.shape))
    if input.size(0) != target.size(0):
        raise ValueError(
            'Expected input batch_size ({}) to match target batch_size ({}).'
            .format(input.size(0), target.size(0)))
    probs = torch.sigmoid(input)
    target = target.unsqueeze(dim=1)
    loss_tmp = -alpha * torch.pow(1.0 - probs + eps, gamma
        ) * target * torch.log(probs + eps) - (1 - alpha) * torch.pow(probs +
        eps, gamma) * (1.0 - target) * torch.log(1.0 - probs + eps)
    loss_tmp = loss_tmp.squeeze(dim=1)
    if reduction == 'none':
        loss = loss_tmp
    elif reduction == 'mean':
        loss = torch.mean(loss_tmp)
    elif reduction == 'sum':
        loss = torch.sum(loss_tmp)
    else:
        raise NotImplementedError('Invalid reduction mode: {}'.format(
            reduction))
    return loss


class BinaryFocalLossWithLogitsNew(nn.Module):
    """Criterion that computes Focal loss.

    According to :cite:`lin2017focal`, the Focal loss is computed as follows:

    .. math::

        \\text{FL}(p_t) = -\\alpha_t (1 - p_t)^{\\gamma} \\, \\text{log}(p_t)

    where:
       - :math:`p_t` is the model's estimated probability for each class.

    Args:
        alpha (float): Weighting factor for the rare class :math:`\\alpha \\in [0, 1]`.
        gamma (float): Focusing parameter :math:`\\gamma >= 0`.
        reduction (str, optional): Specifies the reduction to apply to the
         output: ‘none’ | ‘mean’ | ‘sum’. ‘none’: no reduction will be applied,
         ‘mean’: the sum of the output will be divided by the number of elements
         in the output, ‘sum’: the output will be summed. Default: ‘none’.

    Shape:
        - Input: :math:`(N, 1, *)`.
        - Target: :math:`(N, 1, *)`.

    Examples:
        >>> N = 1  # num_classes
        >>> kwargs = {"alpha": 0.25, "gamma": 2.0, "reduction": 'mean'}
        >>> loss = BinaryFocalLossWithLogits(**kwargs)
        >>> input = torch.randn(1, N, 3, 5, requires_grad=True)
        >>> target = torch.empty(1, 3, 5, dtype=torch.long).random_(N)
        >>> output = loss(input, target)
        >>> output.backward()
    """

    def __init__(self, alpha: 'float', gamma: 'float'=2.0, reduction: 'str'
        ='none') ->None:
        super(BinaryFocalLossWithLogitsNew, self).__init__()
        self.alpha: 'float' = alpha
        self.gamma: 'float' = gamma
        self.reduction: 'str' = reduction
        self.eps: 'float' = 1e-08

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