# 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/dw/cdwyx742hy7mnf755bi5wotiz44x7xxsxrkqosaq6ntl5d5ccmx7.py
# Topologically Sorted Source Nodes: [diag_s, add, cost_s, cost_s_1, sum_1, diag_im, add_1, cost_im, cost_im_1, sum_2, add_2], Original ATen: [aten.diag_embed, aten.add, aten.clamp, aten.sub, aten.sum]
# Source node to ATen node mapping:
#   add => add
#   add_1 => add_1
#   add_2 => add_2
#   cost_im => clamp_min_1
#   cost_im_1 => sub_3
#   cost_s => clamp_min
#   cost_s_1 => sub_2
#   diag_im => eq_1, full_default_1, iota_2, where_1
#   diag_s => eq, full_default, iota, where
#   sum_1 => sum_1
#   sum_2 => sum_2
# Graph fragment:
#   %iota : [num_users=1] = call_function[target=torch.ops.prims.iota.default](args = (4,), kwargs = {start: 0, step: 1, dtype: torch.int64, device: cuda:0, requires_grad: False})
#   %eq : [num_users=1] = call_function[target=torch.ops.aten.eq.Tensor](args = (%iota, %unsqueeze_1), kwargs = {})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%expand, %mm), kwargs = {})
#   %clamp_min : [num_users=2] = call_function[target=torch.ops.aten.clamp_min.default](args = (%add, 0), kwargs = {})
#   %full_default : [num_users=1] = call_function[target=torch.ops.aten.full.default](args = ([], 0.0), kwargs = {dtype: torch.float32, layout: torch.strided, device: cuda:0, pin_memory: False})
#   %where : [num_users=1] = call_function[target=torch.ops.aten.where.self](args = (%eq, %permute_1, %full_default), kwargs = {})
#   %sub_2 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%clamp_min, %where), kwargs = {})
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.default](args = (%sub_2,), kwargs = {})
#   %iota_2 : [num_users=1] = call_function[target=torch.ops.prims.iota.default](args = (4,), kwargs = {start: 0, step: 1, dtype: torch.int64, device: cuda:0, requires_grad: False})
#   %eq_1 : [num_users=1] = call_function[target=torch.ops.aten.eq.Tensor](args = (%iota_2, %unsqueeze_3), kwargs = {})
#   %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%expand_1, %mm), kwargs = {})
#   %clamp_min_1 : [num_users=2] = call_function[target=torch.ops.aten.clamp_min.default](args = (%add_1, 0), kwargs = {})
#   %full_default_1 : [num_users=1] = call_function[target=torch.ops.aten.full.default](args = ([], 0.0), kwargs = {dtype: torch.float32, layout: torch.strided, device: cuda:0, pin_memory: False})
#   %where_1 : [num_users=1] = call_function[target=torch.ops.aten.where.self](args = (%eq_1, %permute_2, %full_default_1), kwargs = {})
#   %sub_3 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%clamp_min_1, %where_1), kwargs = {})
#   %sum_2 : [num_users=1] = call_function[target=torch.ops.aten.sum.default](args = (%sub_3,), kwargs = {})
#   %add_2 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%sum_1, %sum_2), kwargs = {})
triton_per_fused_add_clamp_diag_embed_sub_sum_0 = async_compile.triton('triton_per_fused_add_clamp_diag_embed_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, 16],
    reduction_hint=ReductionHint.INNER,
    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_add_clamp_diag_embed_sub_sum_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 3, 'num_reduction': 2, '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_clamp_diag_embed_sub_sum_0(in_out_ptr0, in_ptr0, xnumel, rnumel, XBLOCK : tl.constexpr):
    xnumel = 1
    rnumel = 16
    RBLOCK: tl.constexpr = 16
    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 % 4
    r2 = rindex
    r1 = (rindex // 4)
    tmp0 = tl.load(in_ptr0 + (5*r0), None, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (r2), None)
    tmp17 = tl.load(in_ptr0 + (5*r1), None, eviction_policy='evict_last')
    tmp1 = 0.2
    tmp2 = tmp1 - tmp0
    tmp4 = tmp2 + tmp3
    tmp5 = 0.0
    tmp6 = triton_helpers.maximum(tmp4, tmp5)
    tmp7 = r0
    tmp8 = r1
    tmp9 = tmp7 == tmp8
    tmp10 = tmp2 + tmp0
    tmp11 = triton_helpers.maximum(tmp10, tmp5)
    tmp12 = tl.where(tmp9, tmp11, tmp5)
    tmp13 = tmp6 - tmp12
    tmp14 = tl.broadcast_to(tmp13, [XBLOCK, RBLOCK])
    tmp16 = tl.sum(tmp14, 1)[:, None]
    tmp18 = tmp1 - tmp17
    tmp19 = tmp18 + tmp3
    tmp20 = triton_helpers.maximum(tmp19, tmp5)
    tmp21 = tmp20 - tmp12
    tmp22 = tl.broadcast_to(tmp21, [XBLOCK, RBLOCK])
    tmp24 = tl.sum(tmp22, 1)[:, None]
    tmp25 = tmp16 + tmp24
    tl.debug_barrier()
    tl.store(in_out_ptr0 + (tl.full([XBLOCK, 1], 0, tl.int32)), tmp25, 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, 1))
    assert_size_stride(arg1_1, (4, 4), (4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [scores], Original ATen: [aten.mm]
        extern_kernels.mm(arg1_1, reinterpret_tensor(arg0_1, (4, 4), (1, 4), 0), out=buf0)
        del arg0_1
        del arg1_1
        buf1 = empty_strided_cuda((), (), torch.float32)
        buf3 = buf1; del buf1  # reuse
        # Topologically Sorted Source Nodes: [diag_s, add, cost_s, cost_s_1, sum_1, diag_im, add_1, cost_im, cost_im_1, sum_2, add_2], Original ATen: [aten.diag_embed, aten.add, aten.clamp, aten.sub, aten.sum]
        stream0 = get_raw_stream(0)
        triton_per_fused_add_clamp_diag_embed_sub_sum_0.run(buf3, buf0, 1, 16, grid=grid(1), stream=stream0)
        del buf0
    return (buf3, )


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, 1), device='cuda:0', dtype=torch.float32)
    arg1_1 = rand_strided((4, 4), (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


class ContrastiveLoss(nn.Module):

    def __init__(self, margin=0.2):
        super(ContrastiveLoss, self).__init__()
        self.margin = margin

    def forward(self, imgs, caps):
        scores = torch.mm(imgs, caps.t())
        diag = scores.diag()
        cost_s = torch.clamp((self.margin - diag).expand_as(scores) +
            scores, min=0)
        cost_im = torch.clamp((self.margin - diag.view(-1, 1)).expand_as(
            scores) + scores, min=0)
        diag_s = torch.diag(cost_s.diag())
        diag_im = torch.diag(cost_im.diag())
        cost_s = cost_s - diag_s
        cost_im = cost_im - diag_im
        return cost_s.sum() + cost_im.sum()


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


def get_init_inputs():
    return [[], {}]

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
from torch._inductor.runtime import triton_helpers
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
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor


@triton.jit
def triton_per_fused_add_clamp_diag_embed_sub_sum_0(in_out_ptr0, in_ptr0,
    xnumel, rnumel, XBLOCK: tl.constexpr):
    RBLOCK: tl.constexpr = 16
    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 % 4
    r2 = rindex
    r1 = rindex // 4
    tmp0 = tl.load(in_ptr0 + 5 * r0, None, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + r2, None)
    tmp17 = tl.load(in_ptr0 + 5 * r1, None, eviction_policy='evict_last')
    tmp1 = 0.2
    tmp2 = tmp1 - tmp0
    tmp4 = tmp2 + tmp3
    tmp5 = 0.0
    tmp6 = triton_helpers.maximum(tmp4, tmp5)
    tmp7 = r0
    tmp8 = r1
    tmp9 = tmp7 == tmp8
    tmp10 = tmp2 + tmp0
    tmp11 = triton_helpers.maximum(tmp10, tmp5)
    tmp12 = tl.where(tmp9, tmp11, tmp5)
    tmp13 = tmp6 - tmp12
    tmp14 = tl.broadcast_to(tmp13, [XBLOCK, RBLOCK])
    tmp16 = tl.sum(tmp14, 1)[:, None]
    tmp18 = tmp1 - tmp17
    tmp19 = tmp18 + tmp3
    tmp20 = triton_helpers.maximum(tmp19, tmp5)
    tmp21 = tmp20 - tmp12
    tmp22 = tl.broadcast_to(tmp21, [XBLOCK, RBLOCK])
    tmp24 = tl.sum(tmp22, 1)[:, None]
    tmp25 = tmp16 + tmp24
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp25, None)


def call(args):
    arg0_1, arg1_1 = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4), (4, 1))
    assert_size_stride(arg1_1, (4, 4), (4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.mm(arg1_1, reinterpret_tensor(arg0_1, (4, 4), (1, 4),
            0), out=buf0)
        del arg0_1
        del arg1_1
        buf1 = empty_strided_cuda((), (), torch.float32)
        buf3 = buf1
        del buf1
        get_raw_stream(0)
        triton_per_fused_add_clamp_diag_embed_sub_sum_0[grid(1)](buf3, buf0,
            1, 16, XBLOCK=1, num_warps=2, num_stages=1)
        del buf0
    return buf3,


class ContrastiveLossNew(nn.Module):

    def __init__(self, margin=0.2):
        super(ContrastiveLossNew, self).__init__()
        self.margin = margin

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