# 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/qy/cqymekkq7rsf7633rd2qbrxvyapcb52mdt4zzzx3nx42kjqconp2.py
# Topologically Sorted Source Nodes: [eq_1, eq, nul, a_denom, b_denom, mul, sim, sim_1, sim_2], Original ATen: [aten.eq, aten.zeros_like, aten.add, aten.mul, aten.div, aten.where]
# Source node to ATen node mapping:
#   a_denom => add
#   b_denom => add_1
#   eq => eq
#   eq_1 => eq_1
#   mul => mul
#   nul => full_default
#   sim => div
#   sim_1 => where
#   sim_2 => where_1
# Graph fragment:
#   %eq_1 : [num_users=1] = call_function[target=torch.ops.aten.eq.Scalar](args = (%expand_3, 0), kwargs = {})
#   %eq : [num_users=1] = call_function[target=torch.ops.aten.eq.Scalar](args = (%expand_2, 0), kwargs = {})
#   %full_default : [num_users=2] = call_function[target=torch.ops.aten.full.default](args = ([4, 4, 4], 0), kwargs = {dtype: torch.float32, layout: torch.strided, device: cuda:0, pin_memory: False})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%expand, 1e-09), kwargs = {})
#   %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%expand_1, 1e-09), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%add, %add_1), kwargs = {})
#   %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%bmm, %mul), kwargs = {})
#   %where : [num_users=1] = call_function[target=torch.ops.aten.where.self](args = (%eq, %full_default, %div), kwargs = {})
#   %where_1 : [num_users=1] = call_function[target=torch.ops.aten.where.self](args = (%eq_1, %full_default, %where), kwargs = {})
triton_poi_fused_add_div_eq_mul_where_zeros_like_0 = async_compile.triton('triton_poi_fused_add_div_eq_mul_where_zeros_like_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: '*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_eq_mul_where_zeros_like_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 11, '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_eq_mul_where_zeros_like_0(in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, in_ptr3, xnumel, XBLOCK : tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x4 = (xindex // 4)
    x0 = xindex % 4
    x2 = (xindex // 16)
    tmp0 = tl.load(in_out_ptr0 + (x3), xmask)
    tmp1 = tl.load(in_ptr0 + (4*x4), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (1 + (4*x4)), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (2 + (4*x4)), xmask, eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr0 + (3 + (4*x4)), xmask, eviction_policy='evict_last')
    tmp15 = tl.load(in_ptr1 + ((4*x0) + (16*x2)), xmask, eviction_policy='evict_last')
    tmp17 = tl.load(in_ptr1 + (1 + (4*x0) + (16*x2)), xmask, eviction_policy='evict_last')
    tmp20 = tl.load(in_ptr1 + (2 + (4*x0) + (16*x2)), xmask, eviction_policy='evict_last')
    tmp23 = tl.load(in_ptr1 + (3 + (4*x0) + (16*x2)), xmask, eviction_policy='evict_last')
    tmp30 = tl.load(in_ptr2 + (x0 + (4*x2)), xmask, eviction_policy='evict_last')
    tmp33 = tl.load(in_ptr3 + (x4), 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-09
    tmp14 = tmp12 + tmp13
    tmp16 = tmp15 * tmp15
    tmp18 = tmp17 * tmp17
    tmp19 = tmp16 + tmp18
    tmp21 = tmp20 * tmp20
    tmp22 = tmp19 + tmp21
    tmp24 = tmp23 * tmp23
    tmp25 = tmp22 + tmp24
    tmp26 = libdevice.sqrt(tmp25)
    tmp27 = tmp26 + tmp13
    tmp28 = tmp14 * tmp27
    tmp29 = tmp0 / tmp28
    tmp31 = 0.0
    tmp32 = tmp30 == tmp31
    tmp34 = tmp33 == tmp31
    tmp35 = tl.where(tmp34, tmp31, tmp29)
    tmp36 = tl.where(tmp32, tmp31, tmp35)
    tl.store(in_out_ptr0 + (x3), tmp36, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    arg0_1, arg1_1, arg2_1, arg3_1 = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4, 4), (16, 4, 1))
    assert_size_stride(arg1_1, (4, 4, 4), (16, 4, 1))
    assert_size_stride(arg2_1, (4, 4), (4, 1))
    assert_size_stride(arg3_1, (4, 4), (4, 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: [bmm], Original ATen: [aten.bmm]
        extern_kernels.bmm(arg0_1, reinterpret_tensor(arg1_1, (4, 4, 4), (16, 1, 4), 0), out=buf0)
        buf1 = buf0; del buf0  # reuse
        buf2 = buf1; del buf1  # reuse
        # Topologically Sorted Source Nodes: [eq_1, eq, nul, a_denom, b_denom, mul, sim, sim_1, sim_2], Original ATen: [aten.eq, aten.zeros_like, aten.add, aten.mul, aten.div, aten.where]
        stream0 = get_raw_stream(0)
        triton_poi_fused_add_div_eq_mul_where_zeros_like_0.run(buf2, arg0_1, arg1_1, arg3_1, arg2_1, 64, grid=grid(64), stream=stream0)
        del arg0_1
        del arg1_1
        del arg2_1
        del arg3_1
    return (reinterpret_tensor(buf2, (4, 1, 4, 4), (16, 16, 4, 1), 0), )


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), (16, 4, 1), device='cuda:0', dtype=torch.float32)
    arg1_1 = rand_strided((4, 4, 4), (16, 4, 1), device='cuda:0', dtype=torch.float32)
    arg2_1 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    arg3_1 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([arg0_1, arg1_1, arg2_1, arg3_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.utils.data


class SimilarityMatrix(torch.nn.Module):

    def __init__(self, padding=0):
        super().__init__()
        self.padding = padding

    def forward(self, query_embed, doc_embed, query_tok, doc_tok):
        simmat = []
        assert type(query_embed) == type(doc_embed)
        if not isinstance(query_embed, list):
            query_embed, doc_embed = [query_embed], [doc_embed]
        for a_emb, b_emb in zip(query_embed, doc_embed):
            BAT, A, B = a_emb.shape[0], a_emb.shape[1], b_emb.shape[1]
            if a_emb is None and b_emb is None:
                sim = query_tok.reshape(BAT, A, 1).expand(BAT, A, B
                    ) == doc_tok.reshape(BAT, 1, B).expand(BAT, A, B).float()
            else:
                a_denom = a_emb.norm(p=2, dim=2).reshape(BAT, A, 1).expand(BAT,
                    A, B) + 1e-09
                b_denom = b_emb.norm(p=2, dim=2).reshape(BAT, 1, B).expand(BAT,
                    A, B) + 1e-09
                perm = b_emb.permute(0, 2, 1)
                sim = a_emb.bmm(perm) / (a_denom * b_denom)
            nul = torch.zeros_like(sim)
            sim = torch.where(query_tok.reshape(BAT, A, 1).expand(BAT, A, B
                ) == self.padding, nul, sim)
            sim = torch.where(doc_tok.reshape(BAT, 1, B).expand(BAT, A, B) ==
                self.padding, nul, sim)
            simmat.append(sim)
        return torch.stack(simmat, dim=1)


def get_inputs():
    return [torch.rand([4, 4, 4]), torch.rand([4, 4, 4]), 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.triton_helpers import libdevice
import torch.utils.data
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_add_div_eq_mul_where_zeros_like_0(in_out_ptr0, in_ptr0,
    in_ptr1, in_ptr2, in_ptr3, xnumel, XBLOCK: tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x4 = xindex // 4
    x0 = xindex % 4
    x2 = xindex // 16
    tmp0 = tl.load(in_out_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr0 + 4 * x4, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (1 + 4 * x4), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (2 + 4 * x4), xmask, eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr0 + (3 + 4 * x4), xmask, eviction_policy='evict_last')
    tmp15 = tl.load(in_ptr1 + (4 * x0 + 16 * x2), xmask, eviction_policy=
        'evict_last')
    tmp17 = tl.load(in_ptr1 + (1 + 4 * x0 + 16 * x2), xmask,
        eviction_policy='evict_last')
    tmp20 = tl.load(in_ptr1 + (2 + 4 * x0 + 16 * x2), xmask,
        eviction_policy='evict_last')
    tmp23 = tl.load(in_ptr1 + (3 + 4 * x0 + 16 * x2), xmask,
        eviction_policy='evict_last')
    tmp30 = tl.load(in_ptr2 + (x0 + 4 * x2), xmask, eviction_policy=
        'evict_last')
    tmp33 = tl.load(in_ptr3 + x4, 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-09
    tmp14 = tmp12 + tmp13
    tmp16 = tmp15 * tmp15
    tmp18 = tmp17 * tmp17
    tmp19 = tmp16 + tmp18
    tmp21 = tmp20 * tmp20
    tmp22 = tmp19 + tmp21
    tmp24 = tmp23 * tmp23
    tmp25 = tmp22 + tmp24
    tmp26 = libdevice.sqrt(tmp25)
    tmp27 = tmp26 + tmp13
    tmp28 = tmp14 * tmp27
    tmp29 = tmp0 / tmp28
    tmp31 = 0.0
    tmp32 = tmp30 == tmp31
    tmp34 = tmp33 == tmp31
    tmp35 = tl.where(tmp34, tmp31, tmp29)
    tmp36 = tl.where(tmp32, tmp31, tmp35)
    tl.store(in_out_ptr0 + x3, tmp36, xmask)


def call(args):
    arg0_1, arg1_1, arg2_1, arg3_1 = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4, 4), (16, 4, 1))
    assert_size_stride(arg1_1, (4, 4, 4), (16, 4, 1))
    assert_size_stride(arg2_1, (4, 4), (4, 1))
    assert_size_stride(arg3_1, (4, 4), (4, 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(arg0_1, reinterpret_tensor(arg1_1, (4, 4, 4), (
            16, 1, 4), 0), out=buf0)
        buf1 = buf0
        del buf0
        buf2 = buf1
        del buf1
        get_raw_stream(0)
        triton_poi_fused_add_div_eq_mul_where_zeros_like_0[grid(64)](buf2,
            arg0_1, arg1_1, arg3_1, arg2_1, 64, XBLOCK=64, num_warps=1,
            num_stages=1)
        del arg0_1
        del arg1_1
        del arg2_1
        del arg3_1
    return reinterpret_tensor(buf2, (4, 1, 4, 4), (16, 16, 4, 1), 0),


class SimilarityMatrixNew(torch.nn.Module):

    def __init__(self, padding=0):
        super().__init__()
        self.padding = padding

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