# AOT ID: ['0_forward']
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/hp/chptg55ofbvcnhmdfgvcjqku736b3xcyxeahxk2eb2jpz7f2wey2.py
# Topologically Sorted Source Nodes: [prototypes_1], Original ATen: [aten.div]
# Source node to ATen node mapping:
#   prototypes_1 => div
# Graph fragment:
#   %div : [num_users=2] = call_function[target=torch.ops.aten.div.Tensor](args = (%bmm, %expand), kwargs = {})
triton_poi_fused_div_0 = async_compile.triton('triton_poi_fused_div_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=[64], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_div_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 5, '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_div_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = (xindex // 4) % 4
    x2 = (xindex // 16)
    tmp0 = tl.load(in_out_ptr0 + (x3), xmask)
    tmp1 = tl.load(in_ptr0 + (x1 + (16*x2)), xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr0 + (4 + x1 + (16*x2)), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr0 + (8 + x1 + (16*x2)), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (12 + x1 + (16*x2)), xmask, eviction_policy='evict_last')
    tmp3 = tmp1 + tmp2
    tmp5 = tmp3 + tmp4
    tmp7 = tmp5 + tmp6
    tmp8 = tmp0 / tmp7
    tl.store(in_out_ptr0 + (x3), tmp8, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/sa/csatjxzzljvzrbsywuhl5u6duvaywsdqxfdcvhr7vxe6wtpp43gn.py
# Topologically Sorted Source Nodes: [neg, add, mul_2, dist, dist_1, scores_cls], Original ATen: [aten.neg, aten.add, aten.mul, aten.sub, aten.div]
# Source node to ATen node mapping:
#   add => add
#   dist => sub
#   dist_1 => div_1
#   mul_2 => mul_2
#   neg => neg
#   scores_cls => mul_3
# Graph fragment:
#   %neg : [num_users=1] = call_function[target=torch.ops.aten.neg.default](args = (%primals_4,), kwargs = {})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%expand_1, %expand_2), kwargs = {})
#   %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%bmm_1, 2), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%add, %mul_2), kwargs = {})
#   %div_1 : [num_users=2] = call_function[target=torch.ops.aten.div.Tensor](args = (%sub, 4), kwargs = {})
#   %mul_3 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%neg, %div_1), kwargs = {})
triton_poi_fused_add_div_mul_neg_sub_1 = async_compile.triton('triton_poi_fused_add_div_mul_neg_sub_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=[64], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 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': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_div_mul_neg_sub_1', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 10, '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_div_mul_neg_sub_1(in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x4 = (xindex // 4)
    x0 = xindex % 4
    x2 = (xindex // 16)
    x3 = xindex
    tmp0 = tl.load(in_ptr0 + (4*x4), xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr0 + (1 + (4*x4)), xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr0 + (2 + (4*x4)), xmask, eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr0 + (3 + (4*x4)), xmask, eviction_policy='evict_last')
    tmp11 = tl.load(in_ptr1 + ((4*x0) + (16*x2)), xmask, eviction_policy='evict_last')
    tmp13 = tl.load(in_ptr1 + (1 + (4*x0) + (16*x2)), xmask, eviction_policy='evict_last')
    tmp16 = tl.load(in_ptr1 + (2 + (4*x0) + (16*x2)), xmask, eviction_policy='evict_last')
    tmp19 = tl.load(in_ptr1 + (3 + (4*x0) + (16*x2)), xmask, eviction_policy='evict_last')
    tmp23 = tl.load(in_out_ptr0 + (x3), xmask)
    tmp29 = tl.load(in_ptr2 + (0))
    tmp30 = tl.broadcast_to(tmp29, [XBLOCK])
    tmp1 = tmp0 * tmp0
    tmp3 = tmp2 * tmp2
    tmp4 = tmp1 + tmp3
    tmp6 = tmp5 * tmp5
    tmp7 = tmp4 + tmp6
    tmp9 = tmp8 * tmp8
    tmp10 = tmp7 + tmp9
    tmp12 = tmp11 * tmp11
    tmp14 = tmp13 * tmp13
    tmp15 = tmp12 + tmp14
    tmp17 = tmp16 * tmp16
    tmp18 = tmp15 + tmp17
    tmp20 = tmp19 * tmp19
    tmp21 = tmp18 + tmp20
    tmp22 = tmp10 + tmp21
    tmp24 = 2.0
    tmp25 = tmp23 * tmp24
    tmp26 = tmp22 - tmp25
    tmp27 = 0.25
    tmp28 = tmp26 * tmp27
    tmp31 = -tmp30
    tmp32 = tmp31 * tmp28
    tl.store(in_out_ptr0 + (x3), tmp28, xmask)
    tl.store(out_ptr0 + (x3), tmp32, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    primals_1, primals_2, primals_3, primals_4 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_2, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_3, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_4, (1, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [prototypes], Original ATen: [aten.bmm]
        extern_kernels.bmm(reinterpret_tensor(primals_2, (4, 4, 4), (16, 1, 4), 0), primals_1, out=buf0)
        del primals_1
        buf1 = buf0; del buf0  # reuse
        # Topologically Sorted Source Nodes: [prototypes_1], Original ATen: [aten.div]
        stream0 = get_raw_stream(0)
        triton_poi_fused_div_0.run(buf1, primals_2, 64, grid=grid(64), stream=stream0)
        del primals_2
        buf2 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [AB], Original ATen: [aten.bmm]
        extern_kernels.bmm(primals_3, reinterpret_tensor(buf1, (4, 4, 4), (16, 1, 4), 0), out=buf2)
        buf3 = buf2; del buf2  # reuse
        buf4 = buf3; del buf3  # reuse
        buf5 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [neg, add, mul_2, dist, dist_1, scores_cls], Original ATen: [aten.neg, aten.add, aten.mul, aten.sub, aten.div]
        triton_poi_fused_add_div_mul_neg_sub_1.run(buf4, primals_3, buf1, primals_4, buf5, 64, grid=grid(64), stream=stream0)
        del buf1
        del primals_3
        del primals_4
    return (buf5, buf4, )


def benchmark_compiled_module(times=10, repeat=10):
    from torch._dynamo.testing import rand_strided
    from torch._inductor.utils import print_performance
    primals_1 = rand_strided((4, 4, 4), (16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((4, 4, 4), (16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((4, 4, 4), (16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((1, ), (1, ), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2, primals_3, primals_4])
    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
import torch.optim
import torch.nn.parallel


def L2SquareDist(A, B, average=True):
    assert A.dim() == 3
    assert B.dim() == 3
    assert A.size(0) == B.size(0) and A.size(2) == B.size(2)
    nB = A.size(0)
    Na = A.size(1)
    Nb = B.size(1)
    nC = A.size(2)
    AB = torch.bmm(A, B.transpose(1, 2))
    AA = (A * A).sum(dim=2, keepdim=True).view(nB, Na, 1)
    BB = (B * B).sum(dim=2, keepdim=True).view(nB, 1, Nb)
    dist = AA.expand_as(AB) + BB.expand_as(AB) - 2 * AB
    if average:
        dist = dist / nC
    return dist


class PrototypicalNetwork(nn.Module):

    def __init__(self, opt):
        super(PrototypicalNetwork, self).__init__()
        scale_cls = opt['scale_cls'] if 'scale_cls' in opt else 1.0
        self.scale_cls = nn.Parameter(torch.FloatTensor(1).fill_(scale_cls),
            requires_grad=True)

    def forward(self, features_test, features_train, labels_train):
        """Recognize novel categories based on the Prototypical Nets approach.

        Classify the test examples (i.e., `features_test`) using the available
        training examples (i.e., `features_test` and `labels_train`) using the
        Prototypical Nets approach.

        Args:
            features_test: A 3D tensor with shape
                [batch_size x num_test_examples x num_channels] that represents
                the test features of each training episode in the batch.
            features_train: A 3D tensor with shape
                [batch_size x num_train_examples x num_channels] that represents
                the train features of each training episode in the batch.
            labels_train: A 3D tensor with shape
                [batch_size x num_train_examples x nKnovel] that represents
                the train labels (encoded as 1-hot vectors) of each training
                episode in the batch.

        Return:
            scores_cls: A 3D tensor with shape
                [batch_size x num_test_examples x nKnovel] that represents the
                classification scores of the test feature vectors for the
                nKnovel novel categories.
        """
        assert features_train.dim() == 3
        assert labels_train.dim() == 3
        assert features_test.dim() == 3
        assert features_train.size(0) == labels_train.size(0)
        assert features_train.size(0) == features_test.size(0)
        assert features_train.size(1) == labels_train.size(1)
        assert features_train.size(2) == features_test.size(2)
        labels_train_transposed = labels_train.transpose(1, 2)
        prototypes = torch.bmm(labels_train_transposed, features_train)
        prototypes = prototypes.div(labels_train_transposed.sum(dim=2,
            keepdim=True).expand_as(prototypes))
        scores_cls = -self.scale_cls * L2SquareDist(features_test, prototypes)
        return scores_cls


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


def get_init_inputs():
    return [[], {'opt': _mock_config(scale_cls=1.0)}]

import torch
from torch._inductor.select_algorithm import extern_kernels
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
import torch.nn as nn
import torch.optim
import torch.nn.parallel
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor


@triton.jit
def triton_poi_fused_div_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 4 % 4
    x2 = xindex // 16
    tmp0 = tl.load(in_out_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr0 + (x1 + 16 * x2), xmask, eviction_policy=
        'evict_last')
    tmp2 = tl.load(in_ptr0 + (4 + x1 + 16 * x2), xmask, eviction_policy=
        'evict_last')
    tmp4 = tl.load(in_ptr0 + (8 + x1 + 16 * x2), xmask, eviction_policy=
        'evict_last')
    tmp6 = tl.load(in_ptr0 + (12 + x1 + 16 * x2), xmask, eviction_policy=
        'evict_last')
    tmp3 = tmp1 + tmp2
    tmp5 = tmp3 + tmp4
    tmp7 = tmp5 + tmp6
    tmp8 = tmp0 / tmp7
    tl.store(in_out_ptr0 + x3, tmp8, xmask)


@triton.jit
def triton_poi_fused_add_div_mul_neg_sub_1(in_out_ptr0, in_ptr0, in_ptr1,
    in_ptr2, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x4 = xindex // 4
    x0 = xindex % 4
    x2 = xindex // 16
    x3 = xindex
    tmp0 = tl.load(in_ptr0 + 4 * x4, xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr0 + (1 + 4 * x4), xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr0 + (2 + 4 * x4), xmask, eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr0 + (3 + 4 * x4), xmask, eviction_policy='evict_last')
    tmp11 = tl.load(in_ptr1 + (4 * x0 + 16 * x2), xmask, eviction_policy=
        'evict_last')
    tmp13 = tl.load(in_ptr1 + (1 + 4 * x0 + 16 * x2), xmask,
        eviction_policy='evict_last')
    tmp16 = tl.load(in_ptr1 + (2 + 4 * x0 + 16 * x2), xmask,
        eviction_policy='evict_last')
    tmp19 = tl.load(in_ptr1 + (3 + 4 * x0 + 16 * x2), xmask,
        eviction_policy='evict_last')
    tmp23 = tl.load(in_out_ptr0 + x3, xmask)
    tmp29 = tl.load(in_ptr2 + 0)
    tmp30 = tl.broadcast_to(tmp29, [XBLOCK])
    tmp1 = tmp0 * tmp0
    tmp3 = tmp2 * tmp2
    tmp4 = tmp1 + tmp3
    tmp6 = tmp5 * tmp5
    tmp7 = tmp4 + tmp6
    tmp9 = tmp8 * tmp8
    tmp10 = tmp7 + tmp9
    tmp12 = tmp11 * tmp11
    tmp14 = tmp13 * tmp13
    tmp15 = tmp12 + tmp14
    tmp17 = tmp16 * tmp16
    tmp18 = tmp15 + tmp17
    tmp20 = tmp19 * tmp19
    tmp21 = tmp18 + tmp20
    tmp22 = tmp10 + tmp21
    tmp24 = 2.0
    tmp25 = tmp23 * tmp24
    tmp26 = tmp22 - tmp25
    tmp27 = 0.25
    tmp28 = tmp26 * tmp27
    tmp31 = -tmp30
    tmp32 = tmp31 * tmp28
    tl.store(in_out_ptr0 + x3, tmp28, xmask)
    tl.store(out_ptr0 + x3, tmp32, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_2, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_3, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_4, (1,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        extern_kernels.bmm(reinterpret_tensor(primals_2, (4, 4, 4), (16, 1,
            4), 0), primals_1, out=buf0)
        del primals_1
        buf1 = buf0
        del buf0
        get_raw_stream(0)
        triton_poi_fused_div_0[grid(64)](buf1, primals_2, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        del primals_2
        buf2 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        extern_kernels.bmm(primals_3, reinterpret_tensor(buf1, (4, 4, 4), (
            16, 1, 4), 0), out=buf2)
        buf3 = buf2
        del buf2
        buf4 = buf3
        del buf3
        buf5 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        triton_poi_fused_add_div_mul_neg_sub_1[grid(64)](buf4, primals_3,
            buf1, primals_4, buf5, 64, XBLOCK=64, num_warps=1, num_stages=1)
        del buf1
        del primals_3
        del primals_4
    return buf5, buf4


def L2SquareDist(A, B, average=True):
    assert A.dim() == 3
    assert B.dim() == 3
    assert A.size(0) == B.size(0) and A.size(2) == B.size(2)
    nB = A.size(0)
    Na = A.size(1)
    Nb = B.size(1)
    nC = A.size(2)
    AB = torch.bmm(A, B.transpose(1, 2))
    AA = (A * A).sum(dim=2, keepdim=True).view(nB, Na, 1)
    BB = (B * B).sum(dim=2, keepdim=True).view(nB, 1, Nb)
    dist = AA.expand_as(AB) + BB.expand_as(AB) - 2 * AB
    if average:
        dist = dist / nC
    return dist


class PrototypicalNetworkNew(nn.Module):

    def __init__(self, opt):
        super(PrototypicalNetworkNew, self).__init__()
        scale_cls = opt['scale_cls'] if 'scale_cls' in opt else 1.0
        self.scale_cls = nn.Parameter(torch.FloatTensor(1).fill_(scale_cls),
            requires_grad=True)

    def forward(self, input_0, input_1, input_2):
        primals_4 = self.scale_cls
        primals_1 = input_0
        primals_2 = input_1
        primals_3 = input_2
        output = call([primals_1, primals_2, primals_3, primals_4])
        return output[0]
