# 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/fh/cfhnguw4v6uy4ysjg54ojclakwi3bj2lte6oqizl4rpf4lcxpiyp.py
# Topologically Sorted Source Nodes: [emb], Original ATen: [aten.div]
# Source node to ATen node mapping:
#   emb => div
# Graph fragment:
#   %div : [num_users=2] = call_function[target=torch.ops.aten.div.Tensor](args = (%arg0_1, %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=[256], 
    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': [], '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_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x0 = xindex % 16
    x2 = (xindex // 64)
    tmp0 = tl.load(in_ptr0 + (x3), xmask)
    tmp1 = tl.load(in_ptr0 + (x0 + (64*x2)), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (16 + x0 + (64*x2)), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (32 + x0 + (64*x2)), xmask, eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr0 + (48 + x0 + (64*x2)), xmask, eviction_policy='evict_last')
    tmp2 = tmp1 * tmp1
    tmp4 = tmp3 * tmp3
    tmp5 = tmp2 + tmp4
    tmp7 = tmp6 * tmp6
    tmp8 = tmp5 + tmp7
    tmp10 = tmp9 * tmp9
    tmp11 = tmp8 + tmp10
    tmp12 = libdevice.sqrt(tmp11)
    tmp13 = 1e-12
    tmp14 = triton_helpers.maximum(tmp12, tmp13)
    tmp15 = tmp0 / tmp14
    tl.store(out_ptr0 + (x3), tmp15, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/lv/clvvd5zl3dk2doqn4hqb7xws2n2pplfd2c6z2bjtqzpgmwmcb6bi.py
# Topologically Sorted Source Nodes: [sim_mat], Original ATen: [aten.linalg_vector_norm, aten.clamp_min, aten.div, aten.mul]
# Source node to ATen node mapping:
#   sim_mat => clamp_min_1, clamp_min_2, div_1, div_2, mul, pow_3, pow_4, pow_5, pow_6, sum_2, sum_3
# Graph fragment:
#   %pow_3 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%expand_2, 2), kwargs = {})
#   %sum_2 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%pow_3, [2], True), kwargs = {})
#   %pow_4 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%sum_2, 0.5), kwargs = {})
#   %clamp_min_1 : [num_users=1] = call_function[target=torch.ops.aten.clamp_min.default](args = (%pow_4, 1e-08), kwargs = {})
#   %div_2 : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%expand_2, %clamp_min_1), kwargs = {})
#   %pow_5 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%expand_1, 2), kwargs = {})
#   %sum_3 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%pow_5, [2], True), kwargs = {})
#   %pow_6 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%sum_3, 0.5), kwargs = {})
#   %clamp_min_2 : [num_users=1] = call_function[target=torch.ops.aten.clamp_min.default](args = (%pow_6, 1e-08), kwargs = {})
#   %div_1 : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%expand_1, %clamp_min_2), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%div_2, %div_1), kwargs = {})
triton_poi_fused_clamp_min_div_linalg_vector_norm_mul_1 = async_compile.triton('triton_poi_fused_clamp_min_div_linalg_vector_norm_mul_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=[1024], 
    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_clamp_min_div_linalg_vector_norm_mul_1', 'mutated_arg_names': [], '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_clamp_min_div_linalg_vector_norm_mul_1(in_ptr0, 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)
    x4 = xindex % 64
    x0 = xindex % 16
    x6 = xindex % 256
    x2 = (xindex // 64) % 4
    x7 = xindex
    tmp0 = tl.load(in_ptr0 + (x4 + (64*x3)), xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr0 + (x0 + (64*x3)), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (16 + x0 + (64*x3)), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (32 + x0 + (64*x3)), xmask, eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr0 + (48 + x0 + (64*x3)), xmask, eviction_policy='evict_last')
    tmp16 = tl.load(in_ptr0 + (x6), xmask, eviction_policy='evict_last')
    tmp17 = tl.load(in_ptr0 + (x0 + (64*x2)), xmask, eviction_policy='evict_last')
    tmp19 = tl.load(in_ptr0 + (16 + x0 + (64*x2)), xmask, eviction_policy='evict_last')
    tmp22 = tl.load(in_ptr0 + (32 + x0 + (64*x2)), xmask, eviction_policy='evict_last')
    tmp25 = tl.load(in_ptr0 + (48 + x0 + (64*x2)), xmask, eviction_policy='evict_last')
    tmp2 = tmp1 * tmp1
    tmp4 = tmp3 * tmp3
    tmp5 = tmp2 + tmp4
    tmp7 = tmp6 * tmp6
    tmp8 = tmp5 + tmp7
    tmp10 = tmp9 * tmp9
    tmp11 = tmp8 + tmp10
    tmp12 = libdevice.sqrt(tmp11)
    tmp13 = 1e-08
    tmp14 = triton_helpers.maximum(tmp12, tmp13)
    tmp15 = tmp0 / tmp14
    tmp18 = tmp17 * tmp17
    tmp20 = tmp19 * tmp19
    tmp21 = tmp18 + tmp20
    tmp23 = tmp22 * tmp22
    tmp24 = tmp21 + tmp23
    tmp26 = tmp25 * tmp25
    tmp27 = tmp24 + tmp26
    tmp28 = libdevice.sqrt(tmp27)
    tmp29 = triton_helpers.maximum(tmp28, tmp13)
    tmp30 = tmp16 / tmp29
    tmp31 = tmp15 * tmp30
    tl.store(out_ptr0 + (x7), tmp31, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/zb/czbffbfo4mbxwezgrsoj7ucwdqx2cq7uw73rnvx3wpkm4bu6o5k4.py
# Topologically Sorted Source Nodes: [sim_mat, truediv, sim_mat_1, mul, pos, sum_2, inner, log, loss, loss_1], Original ATen: [aten.sum, aten.div, aten.exp, aten.mul, aten.log, aten.neg, aten.mean]
# Source node to ATen node mapping:
#   inner => div_4
#   log => log
#   loss => neg
#   loss_1 => mean
#   mul => mul_1
#   pos => sum_5
#   sim_mat => sum_4
#   sim_mat_1 => exp
#   sum_2 => sum_6
#   truediv => div_3
# Graph fragment:
#   %sum_4 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%mul, [2]), kwargs = {})
#   %div_3 : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%sum_4, 0.5), kwargs = {})
#   %exp : [num_users=2] = call_function[target=torch.ops.aten.exp.default](args = (%div_3,), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%exp, %arg1_1), kwargs = {})
#   %sum_5 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%mul_1, [1]), kwargs = {})
#   %sum_6 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%exp, [1]), kwargs = {})
#   %div_4 : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%sum_5, %sum_6), kwargs = {})
#   %log : [num_users=1] = call_function[target=torch.ops.aten.log.default](args = (%div_4,), kwargs = {})
#   %neg : [num_users=1] = call_function[target=torch.ops.aten.neg.default](args = (%log,), kwargs = {})
#   %mean : [num_users=1] = call_function[target=torch.ops.aten.mean.default](args = (%neg,), kwargs = {})
triton_per_fused_div_exp_log_mean_mul_neg_sum_2 = async_compile.triton('triton_per_fused_div_exp_log_mean_mul_neg_sum_2', '''
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.INNER,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32', 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': {3: 1}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 4), equal_to_1=(3,))]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_div_exp_log_mean_mul_neg_sum_2', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 20, '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_div_exp_log_mean_mul_neg_sum_2(in_out_ptr0, in_ptr0, in_ptr1, 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)
    r0 = rindex % 16
    r1 = (rindex // 16)
    r2 = rindex
    tmp0 = tl.load(in_ptr0 + (r0 + (256*r1)), None)
    tmp1 = tl.load(in_ptr0 + (16 + r0 + (256*r1)), None)
    tmp3 = tl.load(in_ptr0 + (32 + r0 + (256*r1)), None)
    tmp5 = tl.load(in_ptr0 + (48 + r0 + (256*r1)), None)
    tmp10 = tl.load(in_ptr1 + (r0 + (64*r1)), None)
    tmp12 = tl.load(in_ptr0 + (64 + r0 + (256*r1)), None)
    tmp13 = tl.load(in_ptr0 + (80 + r0 + (256*r1)), None)
    tmp15 = tl.load(in_ptr0 + (96 + r0 + (256*r1)), None)
    tmp17 = tl.load(in_ptr0 + (112 + r0 + (256*r1)), None)
    tmp21 = tl.load(in_ptr1 + (16 + r0 + (64*r1)), None)
    tmp24 = tl.load(in_ptr0 + (128 + r0 + (256*r1)), None)
    tmp25 = tl.load(in_ptr0 + (144 + r0 + (256*r1)), None)
    tmp27 = tl.load(in_ptr0 + (160 + r0 + (256*r1)), None)
    tmp29 = tl.load(in_ptr0 + (176 + r0 + (256*r1)), None)
    tmp33 = tl.load(in_ptr1 + (32 + r0 + (64*r1)), None)
    tmp36 = tl.load(in_ptr0 + (192 + r0 + (256*r1)), None)
    tmp37 = tl.load(in_ptr0 + (208 + r0 + (256*r1)), None)
    tmp39 = tl.load(in_ptr0 + (224 + r0 + (256*r1)), None)
    tmp41 = tl.load(in_ptr0 + (240 + r0 + (256*r1)), None)
    tmp45 = tl.load(in_ptr1 + (48 + r0 + (64*r1)), None)
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tmp6 = tmp4 + tmp5
    tmp7 = 2.0
    tmp8 = tmp6 * tmp7
    tmp9 = tl_math.exp(tmp8)
    tmp11 = tmp9 * tmp10
    tmp14 = tmp12 + tmp13
    tmp16 = tmp14 + tmp15
    tmp18 = tmp16 + tmp17
    tmp19 = tmp18 * tmp7
    tmp20 = tl_math.exp(tmp19)
    tmp22 = tmp20 * tmp21
    tmp23 = tmp11 + tmp22
    tmp26 = tmp24 + tmp25
    tmp28 = tmp26 + tmp27
    tmp30 = tmp28 + tmp29
    tmp31 = tmp30 * tmp7
    tmp32 = tl_math.exp(tmp31)
    tmp34 = tmp32 * tmp33
    tmp35 = tmp23 + tmp34
    tmp38 = tmp36 + tmp37
    tmp40 = tmp38 + tmp39
    tmp42 = tmp40 + tmp41
    tmp43 = tmp42 * tmp7
    tmp44 = tl_math.exp(tmp43)
    tmp46 = tmp44 * tmp45
    tmp47 = tmp35 + tmp46
    tmp48 = tmp9 + tmp20
    tmp49 = tmp48 + tmp32
    tmp50 = tmp49 + tmp44
    tmp51 = tmp47 / tmp50
    tmp52 = tl_math.log(tmp51)
    tmp53 = -tmp52
    tmp54 = tl.broadcast_to(tmp53, [XBLOCK, RBLOCK])
    tmp56 = tl.sum(tmp54, 1)[:, None]
    tmp57 = 64.0
    tmp58 = tmp56 / tmp57
    tl.debug_barrier()
    tl.store(in_out_ptr0 + (tl.full([XBLOCK, 1], 0, tl.int32)), tmp58, None)
''', 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), (64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [emb], Original ATen: [aten.div]
        stream0 = get_raw_stream(0)
        triton_poi_fused_div_0.run(arg0_1, buf0, 256, grid=grid(256), stream=stream0)
        del arg0_1
        buf1 = empty_strided_cuda((4, 4, 4, 4, 4), (256, 64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [sim_mat], Original ATen: [aten.linalg_vector_norm, aten.clamp_min, aten.div, aten.mul]
        triton_poi_fused_clamp_min_div_linalg_vector_norm_mul_1.run(buf0, buf1, 1024, grid=grid(1024), stream=stream0)
        del buf0
        buf4 = empty_strided_cuda((), (), torch.float32)
        buf5 = buf4; del buf4  # reuse
        # Topologically Sorted Source Nodes: [sim_mat, truediv, sim_mat_1, mul, pos, sum_2, inner, log, loss, loss_1], Original ATen: [aten.sum, aten.div, aten.exp, aten.mul, aten.log, aten.neg, aten.mean]
        triton_per_fused_div_exp_log_mean_mul_neg_sum_2.run(buf5, buf1, arg1_1, 1, 64, grid=grid(1), stream=stream0)
        del arg1_1
        del buf1
    return (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)
    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
import torch.nn.functional as F


class NTXent(nn.Module):

    def __init__(self, metric: 'str'='CosineSimilarity', temperature:
        'float'=0.5, reduction: 'str'='mean'):
        super().__init__()
        if metric not in ['CosineSimilarity']:
            raise ValueError('Undefined metric!')
        if reduction not in ['AvgNonInf', 'mean', 'sum', 'none']:
            raise ValueError('Undefined reduction!')
        self.metric = metric
        self.t = temperature
        self.reduction = reduction

    def forward(self, embedding, label):
        if self.metric == 'CosineSimilarity':
            emb = F.normalize(embedding, dim=1)
            sim_mat = F.cosine_similarity(emb.unsqueeze(1), emb.unsqueeze(0
                ), dim=2)
        sim_mat = torch.exp(sim_mat / self.t)
        pos = torch.sum(sim_mat * label, dim=1)
        inner = pos / torch.sum(sim_mat, dim=1)
        loss = -torch.log(inner)
        if self.reduction == 'AvgNonInf':
            non_inf = inner > 0
            loss = loss * non_inf
            loss = loss.sum() / torch.sum(non_inf) if torch.sum(non_inf
                ) > 0 else loss.mean()
        elif self.reduction == 'mean':
            loss = loss.mean()
        elif self.reduction == 'sum':
            loss = loss.sum()
        elif self.reduction == 'none':
            pass
        return loss


def get_inputs():
    return [torch.rand([4, 4, 4, 4]), 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 import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, 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_div_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x0 = xindex % 16
    x2 = xindex // 64
    tmp0 = tl.load(in_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr0 + (x0 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp3 = tl.load(in_ptr0 + (16 + x0 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp6 = tl.load(in_ptr0 + (32 + x0 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp9 = tl.load(in_ptr0 + (48 + x0 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp2 = tmp1 * tmp1
    tmp4 = tmp3 * tmp3
    tmp5 = tmp2 + tmp4
    tmp7 = tmp6 * tmp6
    tmp8 = tmp5 + tmp7
    tmp10 = tmp9 * tmp9
    tmp11 = tmp8 + tmp10
    tmp12 = libdevice.sqrt(tmp11)
    tmp13 = 1e-12
    tmp14 = triton_helpers.maximum(tmp12, tmp13)
    tmp15 = tmp0 / tmp14
    tl.store(out_ptr0 + x3, tmp15, xmask)


@triton.jit
def triton_poi_fused_clamp_min_div_linalg_vector_norm_mul_1(in_ptr0,
    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
    x4 = xindex % 64
    x0 = xindex % 16
    x6 = xindex % 256
    x2 = xindex // 64 % 4
    x7 = xindex
    tmp0 = tl.load(in_ptr0 + (x4 + 64 * x3), xmask, eviction_policy=
        'evict_last')
    tmp1 = tl.load(in_ptr0 + (x0 + 64 * x3), xmask, eviction_policy=
        'evict_last')
    tmp3 = tl.load(in_ptr0 + (16 + x0 + 64 * x3), xmask, eviction_policy=
        'evict_last')
    tmp6 = tl.load(in_ptr0 + (32 + x0 + 64 * x3), xmask, eviction_policy=
        'evict_last')
    tmp9 = tl.load(in_ptr0 + (48 + x0 + 64 * x3), xmask, eviction_policy=
        'evict_last')
    tmp16 = tl.load(in_ptr0 + x6, xmask, eviction_policy='evict_last')
    tmp17 = tl.load(in_ptr0 + (x0 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp19 = tl.load(in_ptr0 + (16 + x0 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp22 = tl.load(in_ptr0 + (32 + x0 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp25 = tl.load(in_ptr0 + (48 + x0 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp2 = tmp1 * tmp1
    tmp4 = tmp3 * tmp3
    tmp5 = tmp2 + tmp4
    tmp7 = tmp6 * tmp6
    tmp8 = tmp5 + tmp7
    tmp10 = tmp9 * tmp9
    tmp11 = tmp8 + tmp10
    tmp12 = libdevice.sqrt(tmp11)
    tmp13 = 1e-08
    tmp14 = triton_helpers.maximum(tmp12, tmp13)
    tmp15 = tmp0 / tmp14
    tmp18 = tmp17 * tmp17
    tmp20 = tmp19 * tmp19
    tmp21 = tmp18 + tmp20
    tmp23 = tmp22 * tmp22
    tmp24 = tmp21 + tmp23
    tmp26 = tmp25 * tmp25
    tmp27 = tmp24 + tmp26
    tmp28 = libdevice.sqrt(tmp27)
    tmp29 = triton_helpers.maximum(tmp28, tmp13)
    tmp30 = tmp16 / tmp29
    tmp31 = tmp15 * tmp30
    tl.store(out_ptr0 + x7, tmp31, xmask)


@triton.jit
def triton_per_fused_div_exp_log_mean_mul_neg_sum_2(in_out_ptr0, in_ptr0,
    in_ptr1, 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)
    r0 = rindex % 16
    r1 = rindex // 16
    tmp0 = tl.load(in_ptr0 + (r0 + 256 * r1), None)
    tmp1 = tl.load(in_ptr0 + (16 + r0 + 256 * r1), None)
    tmp3 = tl.load(in_ptr0 + (32 + r0 + 256 * r1), None)
    tmp5 = tl.load(in_ptr0 + (48 + r0 + 256 * r1), None)
    tmp10 = tl.load(in_ptr1 + (r0 + 64 * r1), None)
    tmp12 = tl.load(in_ptr0 + (64 + r0 + 256 * r1), None)
    tmp13 = tl.load(in_ptr0 + (80 + r0 + 256 * r1), None)
    tmp15 = tl.load(in_ptr0 + (96 + r0 + 256 * r1), None)
    tmp17 = tl.load(in_ptr0 + (112 + r0 + 256 * r1), None)
    tmp21 = tl.load(in_ptr1 + (16 + r0 + 64 * r1), None)
    tmp24 = tl.load(in_ptr0 + (128 + r0 + 256 * r1), None)
    tmp25 = tl.load(in_ptr0 + (144 + r0 + 256 * r1), None)
    tmp27 = tl.load(in_ptr0 + (160 + r0 + 256 * r1), None)
    tmp29 = tl.load(in_ptr0 + (176 + r0 + 256 * r1), None)
    tmp33 = tl.load(in_ptr1 + (32 + r0 + 64 * r1), None)
    tmp36 = tl.load(in_ptr0 + (192 + r0 + 256 * r1), None)
    tmp37 = tl.load(in_ptr0 + (208 + r0 + 256 * r1), None)
    tmp39 = tl.load(in_ptr0 + (224 + r0 + 256 * r1), None)
    tmp41 = tl.load(in_ptr0 + (240 + r0 + 256 * r1), None)
    tmp45 = tl.load(in_ptr1 + (48 + r0 + 64 * r1), None)
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tmp6 = tmp4 + tmp5
    tmp7 = 2.0
    tmp8 = tmp6 * tmp7
    tmp9 = tl_math.exp(tmp8)
    tmp11 = tmp9 * tmp10
    tmp14 = tmp12 + tmp13
    tmp16 = tmp14 + tmp15
    tmp18 = tmp16 + tmp17
    tmp19 = tmp18 * tmp7
    tmp20 = tl_math.exp(tmp19)
    tmp22 = tmp20 * tmp21
    tmp23 = tmp11 + tmp22
    tmp26 = tmp24 + tmp25
    tmp28 = tmp26 + tmp27
    tmp30 = tmp28 + tmp29
    tmp31 = tmp30 * tmp7
    tmp32 = tl_math.exp(tmp31)
    tmp34 = tmp32 * tmp33
    tmp35 = tmp23 + tmp34
    tmp38 = tmp36 + tmp37
    tmp40 = tmp38 + tmp39
    tmp42 = tmp40 + tmp41
    tmp43 = tmp42 * tmp7
    tmp44 = tl_math.exp(tmp43)
    tmp46 = tmp44 * tmp45
    tmp47 = tmp35 + tmp46
    tmp48 = tmp9 + tmp20
    tmp49 = tmp48 + tmp32
    tmp50 = tmp49 + tmp44
    tmp51 = tmp47 / tmp50
    tmp52 = tl_math.log(tmp51)
    tmp53 = -tmp52
    tmp54 = tl.broadcast_to(tmp53, [XBLOCK, RBLOCK])
    tmp56 = tl.sum(tmp54, 1)[:, None]
    tmp57 = 64.0
    tmp58 = tmp56 / tmp57
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp58, None)


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), (64, 16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_div_0[grid(256)](arg0_1, buf0, 256, XBLOCK=128,
            num_warps=4, num_stages=1)
        del arg0_1
        buf1 = empty_strided_cuda((4, 4, 4, 4, 4), (256, 64, 16, 4, 1),
            torch.float32)
        triton_poi_fused_clamp_min_div_linalg_vector_norm_mul_1[grid(1024)](
            buf0, buf1, 1024, XBLOCK=128, num_warps=4, num_stages=1)
        del buf0
        buf4 = empty_strided_cuda((), (), torch.float32)
        buf5 = buf4
        del buf4
        triton_per_fused_div_exp_log_mean_mul_neg_sum_2[grid(1)](buf5, buf1,
            arg1_1, 1, 64, XBLOCK=1, num_warps=2, num_stages=1)
        del arg1_1
        del buf1
    return buf5,


class NTXentNew(nn.Module):

    def __init__(self, metric: 'str'='CosineSimilarity', temperature:
        'float'=0.5, reduction: 'str'='mean'):
        super().__init__()
        if metric not in ['CosineSimilarity']:
            raise ValueError('Undefined metric!')
        if reduction not in ['AvgNonInf', 'mean', 'sum', 'none']:
            raise ValueError('Undefined reduction!')
        self.metric = metric
        self.t = temperature
        self.reduction = reduction

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