# 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_0/inductor_cache/rq/crqom5wr56ilbnvvekic54ftgpfauuywdgkv4ufa7zpsh2g4oazw.py
# Topologically Sorted Source Nodes: [softmax, mul, expected_x, mul_1, expected_y, expected_xy], Original ATen: [aten._softmax, aten.mul, aten.sum, aten.stack]
# Source node to ATen node mapping:
#   expected_x => sum_2
#   expected_xy => cat
#   expected_y => sum_3
#   mul => mul
#   mul_1 => mul_1
#   softmax => amax, div, exp, sub, sum_1
# Graph fragment:
#   %amax : [num_users=1] = call_function[target=torch.ops.aten.amax.default](args = (%view, [-1], True), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%view, %amax), kwargs = {})
#   %exp : [num_users=2] = call_function[target=torch.ops.aten.exp.default](args = (%sub,), kwargs = {})
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%exp, [-1], True), kwargs = {})
#   %div : [num_users=2] = call_function[target=torch.ops.aten.div.Tensor](args = (%exp, %sum_1), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%arg1_1, %div), kwargs = {})
#   %sum_2 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%mul, [-1]), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%arg2_1, %div), kwargs = {})
#   %sum_3 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%mul_1, [-1]), kwargs = {})
#   %cat : [num_users=1] = call_function[target=torch.ops.aten.cat.default](args = ([%unsqueeze, %unsqueeze_1], 2), kwargs = {})
triton_per_fused__softmax_mul_stack_sum_0 = async_compile.triton('triton_per_fused__softmax_mul_stack_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=[16, 16],
    reduction_hint=ReductionHint.INNER,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: 'i32', 6: '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, 5, 6), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused__softmax_mul_stack_sum_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 3, 'num_reduction': 4, '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__softmax_mul_stack_sum_0(in_ptr0, in_ptr1, in_ptr2, out_ptr4, out_ptr5, xnumel, rnumel, XBLOCK : tl.constexpr):
    xnumel = 16
    rnumel = 16
    RBLOCK: tl.constexpr = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rindex = tl.arange(0, RBLOCK)[None, :]
    roffset = 0
    rmask = tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r1 = rindex
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (r1 + (16*x0)), xmask, other=0.0)
    tmp11 = tl.load(in_ptr1 + (r1), None, eviction_policy='evict_last')
    tmp18 = tl.load(in_ptr2 + (r1), None, eviction_policy='evict_last')
    tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
    tmp3 = tl.where(xmask, tmp1, float("-inf"))
    tmp4 = triton_helpers.max2(tmp3, 1)[:, None]
    tmp5 = tmp0 - tmp4
    tmp6 = tl_math.exp(tmp5)
    tmp7 = tl.broadcast_to(tmp6, [XBLOCK, RBLOCK])
    tmp9 = tl.where(xmask, tmp7, 0)
    tmp10 = tl.sum(tmp9, 1)[:, None]
    tmp12 = tmp6 / tmp10
    tmp13 = tmp11 * tmp12
    tmp14 = tl.broadcast_to(tmp13, [XBLOCK, RBLOCK])
    tmp16 = tl.where(xmask, tmp14, 0)
    tmp17 = tl.sum(tmp16, 1)[:, None]
    tmp19 = tmp18 * tmp12
    tmp20 = tl.broadcast_to(tmp19, [XBLOCK, RBLOCK])
    tmp22 = tl.where(xmask, tmp20, 0)
    tmp23 = tl.sum(tmp22, 1)[:, None]
    tmp24 = -tmp17
    tmp25 = 1.0
    tmp26 = tmp24 + tmp25
    tmp27 = 0.5
    tmp28 = tmp26 * tmp27
    tl.store(out_ptr4 + (2*x0), tmp28, xmask)
    tl.store(out_ptr5 + (2*x0), tmp23, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    arg0_1, arg1_1, arg2_1 = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(arg1_1, (16, ), (1, ))
    assert_size_stride(arg2_1, (16, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf6 = empty_strided_cuda((4, 4, 2), (8, 2, 1), torch.float32)
        buf4 = reinterpret_tensor(buf6, (4, 4, 1), (8, 2, 1), 0)  # alias
        buf5 = reinterpret_tensor(buf6, (4, 4, 1), (8, 2, 1), 1)  # alias
        # Topologically Sorted Source Nodes: [softmax, mul, expected_x, mul_1, expected_y, expected_xy], Original ATen: [aten._softmax, aten.mul, aten.sum, aten.stack]
        stream0 = get_raw_stream(0)
        triton_per_fused__softmax_mul_stack_sum_0.run(arg0_1, arg1_1, arg2_1, buf4, buf5, 16, 16, grid=grid(16), stream=stream0)
        del arg0_1
        del arg1_1
        del arg2_1
    return (buf6, )


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((16, ), (1, ), device='cuda:0', dtype=torch.float32)
    arg2_1 = rand_strided((16, ), (1, ), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([arg0_1, arg1_1, arg2_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 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 math as tl_math
import numpy as np
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__softmax_mul_stack_sum_0(in_ptr0, in_ptr1, in_ptr2,
    out_ptr4, out_ptr5, xnumel, rnumel, XBLOCK: tl.constexpr):
    xnumel = 16
    RBLOCK: tl.constexpr = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rindex = tl.arange(0, RBLOCK)[None, :]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r1 = rindex
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (r1 + 16 * x0), xmask, other=0.0)
    tmp11 = tl.load(in_ptr1 + r1, None, eviction_policy='evict_last')
    tmp18 = tl.load(in_ptr2 + r1, None, eviction_policy='evict_last')
    tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
    tmp3 = tl.where(xmask, tmp1, float('-inf'))
    tmp4 = triton_helpers.max2(tmp3, 1)[:, None]
    tmp5 = tmp0 - tmp4
    tmp6 = tl_math.exp(tmp5)
    tmp7 = tl.broadcast_to(tmp6, [XBLOCK, RBLOCK])
    tmp9 = tl.where(xmask, tmp7, 0)
    tmp10 = tl.sum(tmp9, 1)[:, None]
    tmp12 = tmp6 / tmp10
    tmp13 = tmp11 * tmp12
    tmp14 = tl.broadcast_to(tmp13, [XBLOCK, RBLOCK])
    tmp16 = tl.where(xmask, tmp14, 0)
    tmp17 = tl.sum(tmp16, 1)[:, None]
    tmp19 = tmp18 * tmp12
    tmp20 = tl.broadcast_to(tmp19, [XBLOCK, RBLOCK])
    tmp22 = tl.where(xmask, tmp20, 0)
    tmp23 = tl.sum(tmp22, 1)[:, None]
    tmp24 = -tmp17
    tmp25 = 1.0
    tmp26 = tmp24 + tmp25
    tmp27 = 0.5
    tmp28 = tmp26 * tmp27
    tl.store(out_ptr4 + 2 * x0, tmp28, xmask)
    tl.store(out_ptr5 + 2 * x0, tmp23, xmask)


def call(args):
    arg0_1, arg1_1, arg2_1 = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(arg1_1, (16,), (1,))
    assert_size_stride(arg2_1, (16,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf6 = empty_strided_cuda((4, 4, 2), (8, 2, 1), torch.float32)
        buf4 = reinterpret_tensor(buf6, (4, 4, 1), (8, 2, 1), 0)
        buf5 = reinterpret_tensor(buf6, (4, 4, 1), (8, 2, 1), 1)
        get_raw_stream(0)
        triton_per_fused__softmax_mul_stack_sum_0[grid(16)](arg0_1, arg1_1,
            arg2_1, buf4, buf5, 16, 16, XBLOCK=1, num_warps=2, num_stages=1)
        del arg0_1
        del arg1_1
        del arg2_1
    return buf6,


class SpatialSoftmaxBZNew(torch.nn.Module):
    """
    IMPORTANT:
    i in [0, 1], where 0 is at the bottom, 1 is at the top
    j in [-1, 1]
    """

    def __init__(self, height, width):
        super().__init__()
        self.height = height
        self.width = width
        pos_x, pos_y = np.meshgrid(np.linspace(-1.0, 1.0, self.height), np.
            linspace(-1.0, 1.0, self.width))
        self.pos_x = torch.from_numpy(pos_x).reshape(-1).float()
        self.pos_x = torch.nn.Parameter(self.pos_x, requires_grad=False)
        self.pos_y = torch.from_numpy(pos_y).reshape(-1).float()
        self.pos_y = torch.nn.Parameter(self.pos_y, requires_grad=False)

    def forward(self, input_0):
        arg1_1 = self.pos_x
        arg2_1 = self.pos_y
        arg0_1 = input_0
        output = call([arg0_1, arg1_1, arg2_1])
        return output[0]
