# 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_6/inductor_cache/tb/ctbghj42v5gqfb4hjrtagncr6rwduqbnmvrhbowejkjnd2qzwaye.py
# Topologically Sorted Source Nodes: [x_3], Original ATen: [aten.mean]
# Source node to ATen node mapping:
#   x_3 => mean
# Graph fragment:
#   %mean : [num_users=1] = call_function[target=torch.ops.aten.mean.dim](args = (%view, [1]), kwargs = {})
triton_red_fused_mean_0 = async_compile.triton('triton_red_fused_mean_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.reduction(
    size_hints=[8192, 512],
    reduction_hint=ReductionHint.DEFAULT,
    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': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_red_fused_mean_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 0, '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_red_fused_mean_0(in_ptr0, out_ptr0, xnumel, rnumel, XBLOCK : tl.constexpr, RBLOCK : tl.constexpr):
    xnumel = 5776
    rnumel = 484
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rbase = tl.arange(0, RBLOCK)[None, :]
    x0 = xindex % 1444
    x1 = (xindex // 1444)
    _tmp11 = tl.full([XBLOCK, RBLOCK], 0, tl.float32)
    for roffset in range(0, rnumel, RBLOCK):
        rindex = roffset + rbase
        rmask = rindex < rnumel
        r2 = rindex
        tmp0 = (11*(x0 // 38)) + ((r2 // 11) % 11)
        tmp1 = tmp0.to(tl.float32)
        tmp2 = 0.009569377990430622
        tmp3 = tmp1 * tmp2
        tmp4 = tmp3.to(tl.int32)
        tmp5 = (11*(x0 % 38)) + (r2 % 11)
        tmp6 = tmp5.to(tl.float32)
        tmp7 = tmp6 * tmp2
        tmp8 = tmp7.to(tl.int32)
        tmp9 = tl.load(in_ptr0 + (tmp8 + (4*tmp4) + (16*(r2 // 121)) + (64*x1)), rmask & xmask, eviction_policy='evict_last', other=0.0)
        tmp10 = tl.broadcast_to(tmp9, [XBLOCK, RBLOCK])
        tmp12 = _tmp11 + tmp10
        _tmp11 = tl.where(rmask & xmask, tmp12, _tmp11)
    tmp11 = tl.sum(_tmp11, 1)[:, None]
    tl.store(out_ptr0 + (x0 + (1472*x1)), tmp11, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/q7/cq7ks2q74csjgm3oa6yac2keazqoatflpcmxe7azl6dscnvlb422.py
# Topologically Sorted Source Nodes: [x_3, gt, x_4, x_8, gt_1, y, eq, float_5, acc], Original ATen: [aten.mean, aten.gt, aten._to_copy, aten.eq]
# Source node to ATen node mapping:
#   acc => mean_2
#   eq => eq
#   float_5 => convert_element_type_10
#   gt => gt
#   gt_1 => gt_1
#   x_3 => mean
#   x_4 => convert_element_type_4
#   x_8 => mean_1
#   y => convert_element_type_9
# Graph fragment:
#   %mean : [num_users=1] = call_function[target=torch.ops.aten.mean.dim](args = (%view, [1]), kwargs = {})
#   %gt : [num_users=1] = call_function[target=torch.ops.aten.gt.Scalar](args = (%mean, 0.25), kwargs = {})
#   %convert_element_type_4 : [num_users=1] = call_function[target=torch.ops.prims.convert_element_type.default](args = (%gt, torch.float32), kwargs = {})
#   %mean_1 : [num_users=1] = call_function[target=torch.ops.aten.mean.dim](args = (%view_1, [1]), kwargs = {})
#   %gt_1 : [num_users=1] = call_function[target=torch.ops.aten.gt.Scalar](args = (%mean_1, 0.25), kwargs = {})
#   %convert_element_type_9 : [num_users=1] = call_function[target=torch.ops.prims.convert_element_type.default](args = (%gt_1, torch.float32), kwargs = {})
#   %eq : [num_users=1] = call_function[target=torch.ops.aten.eq.Tensor](args = (%convert_element_type_4, %convert_element_type_9), kwargs = {})
#   %convert_element_type_10 : [num_users=1] = call_function[target=torch.ops.prims.convert_element_type.default](args = (%eq, torch.float32), kwargs = {})
#   %mean_2 : [num_users=1] = call_function[target=torch.ops.aten.mean.default](args = (%convert_element_type_10,), kwargs = {})
triton_red_fused__to_copy_eq_gt_mean_1 = async_compile.triton('triton_red_fused__to_copy_eq_gt_mean_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.reduction(
    size_hints=[1, 8192],
    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_red_fused__to_copy_eq_gt_mean_1', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, '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_red_fused__to_copy_eq_gt_mean_1(in_out_ptr0, in_ptr0, in_ptr1, xnumel, rnumel, XBLOCK : tl.constexpr, RBLOCK : tl.constexpr):
    xnumel = 1
    rnumel = 5776
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = tl.full([XBLOCK, RBLOCK], True, tl.int1)
    rbase = tl.arange(0, RBLOCK)[None, :]
    _tmp13 = tl.full([XBLOCK, RBLOCK], 0, tl.float32)
    for roffset in range(0, rnumel, RBLOCK):
        rindex = roffset + rbase
        rmask = rindex < rnumel
        r0 = rindex % 1444
        r1 = (rindex // 1444)
        tmp0 = tl.load(in_ptr0 + (r0 + (1472*r1)), rmask, eviction_policy='evict_first', other=0.0)
        tmp6 = tl.load(in_ptr1 + (r0 + (1472*r1)), rmask, eviction_policy='evict_first', other=0.0)
        tmp1 = 484.0
        tmp2 = tmp0 / tmp1
        tmp3 = 0.25
        tmp4 = tmp2 > tmp3
        tmp5 = tmp4.to(tl.float32)
        tmp7 = tmp6 / tmp1
        tmp8 = tmp7 > tmp3
        tmp9 = tmp8.to(tl.float32)
        tmp10 = tmp5 == tmp9
        tmp11 = tmp10.to(tl.float32)
        tmp12 = tl.broadcast_to(tmp11, [XBLOCK, RBLOCK])
        tmp14 = _tmp13 + tmp12
        _tmp13 = tl.where(rmask, tmp14, _tmp13)
    tmp13 = tl.sum(_tmp13, 1)[:, None]
    tmp15 = 5776.0
    tmp16 = tmp13 / tmp15
    tl.debug_barrier()
    tl.store(in_out_ptr0 + (tl.full([XBLOCK, 1], 0, tl.int32)), tmp16, 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, 1444), (1472, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_3], Original ATen: [aten.mean]
        stream0 = get_raw_stream(0)
        triton_red_fused_mean_0.run(arg0_1, buf0, 5776, 484, grid=grid(5776), stream=stream0)
        del arg0_1
        buf1 = empty_strided_cuda((4, 1444), (1472, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_8], Original ATen: [aten.mean]
        triton_red_fused_mean_0.run(arg1_1, buf1, 5776, 484, grid=grid(5776), stream=stream0)
        del arg1_1
        buf2 = empty_strided_cuda((), (), torch.float32)
        buf3 = buf2; del buf2  # reuse
        # Topologically Sorted Source Nodes: [x_3, gt, x_4, x_8, gt_1, y, eq, float_5, acc], Original ATen: [aten.mean, aten.gt, aten._to_copy, aten.eq]
        triton_red_fused__to_copy_eq_gt_mean_1.run(buf3, buf0, buf1, 1, 5776, grid=grid(1), stream=stream0)
        del buf0
        del buf1
    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, 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
from torch import Tensor
from torch import nn


class KaggleAccuracy(nn.Module):

    def __init__(self, threshold: 'float'=0.25, num_patches: 'int'=38, size:
        'int'=418) ->None:
        super().__init__()
        self.threshold = threshold
        self.num_patches = num_patches
        self.patch_size = size // num_patches
        self.resize = nn.Upsample(size=size)
        self.unfold = nn.Unfold(kernel_size=self.patch_size, stride=self.
            patch_size)

    def forward(self, x: 'Tensor', y: 'Tensor') ->Tensor:
        x = self.to_patch_value(x)
        y = self.to_patch_value(y)
        acc = torch.mean((x == y).float())
        return acc

    def to_patch_value(self, x: 'Tensor') ->Tensor:
        x = x.float()
        x = self.resize(x)
        x = self.unfold(x)
        x = x.mean(dim=1)
        return (x > self.threshold).float()


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 import Tensor
from torch import 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_red_fused_mean_0(in_ptr0, out_ptr0, xnumel, rnumel, XBLOCK: tl.
    constexpr, RBLOCK: tl.constexpr):
    xnumel = 5776
    rnumel = 484
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rbase = tl.arange(0, RBLOCK)[None, :]
    x0 = xindex % 1444
    x1 = xindex // 1444
    _tmp11 = tl.full([XBLOCK, RBLOCK], 0, tl.float32)
    for roffset in range(0, rnumel, RBLOCK):
        rindex = roffset + rbase
        rmask = rindex < rnumel
        r2 = rindex
        tmp0 = 11 * (x0 // 38) + r2 // 11 % 11
        tmp1 = tmp0.to(tl.float32)
        tmp2 = 0.009569377990430622
        tmp3 = tmp1 * tmp2
        tmp4 = tmp3.to(tl.int32)
        tmp5 = 11 * (x0 % 38) + r2 % 11
        tmp6 = tmp5.to(tl.float32)
        tmp7 = tmp6 * tmp2
        tmp8 = tmp7.to(tl.int32)
        tmp9 = tl.load(in_ptr0 + (tmp8 + 4 * tmp4 + 16 * (r2 // 121) + 64 *
            x1), rmask & xmask, eviction_policy='evict_last', other=0.0)
        tmp10 = tl.broadcast_to(tmp9, [XBLOCK, RBLOCK])
        tmp12 = _tmp11 + tmp10
        _tmp11 = tl.where(rmask & xmask, tmp12, _tmp11)
    tmp11 = tl.sum(_tmp11, 1)[:, None]
    tl.store(out_ptr0 + (x0 + 1472 * x1), tmp11, xmask)


@triton.jit
def triton_red_fused__to_copy_eq_gt_mean_1(in_out_ptr0, in_ptr0, in_ptr1,
    xnumel, rnumel, XBLOCK: tl.constexpr, RBLOCK: tl.constexpr):
    rnumel = 5776
    xoffset = tl.program_id(0) * XBLOCK
    xoffset + tl.arange(0, XBLOCK)[:, None]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    rbase = tl.arange(0, RBLOCK)[None, :]
    _tmp13 = tl.full([XBLOCK, RBLOCK], 0, tl.float32)
    for roffset in range(0, rnumel, RBLOCK):
        rindex = roffset + rbase
        rmask = rindex < rnumel
        r0 = rindex % 1444
        r1 = rindex // 1444
        tmp0 = tl.load(in_ptr0 + (r0 + 1472 * r1), rmask, eviction_policy=
            'evict_first', other=0.0)
        tmp6 = tl.load(in_ptr1 + (r0 + 1472 * r1), rmask, eviction_policy=
            'evict_first', other=0.0)
        tmp1 = 484.0
        tmp2 = tmp0 / tmp1
        tmp3 = 0.25
        tmp4 = tmp2 > tmp3
        tmp5 = tmp4.to(tl.float32)
        tmp7 = tmp6 / tmp1
        tmp8 = tmp7 > tmp3
        tmp9 = tmp8.to(tl.float32)
        tmp10 = tmp5 == tmp9
        tmp11 = tmp10.to(tl.float32)
        tmp12 = tl.broadcast_to(tmp11, [XBLOCK, RBLOCK])
        tmp14 = _tmp13 + tmp12
        _tmp13 = tl.where(rmask, tmp14, _tmp13)
    tmp13 = tl.sum(_tmp13, 1)[:, None]
    tmp15 = 5776.0
    tmp16 = tmp13 / tmp15
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp16, 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, 1444), (1472, 1), torch.float32)
        get_raw_stream(0)
        triton_red_fused_mean_0[grid(5776)](arg0_1, buf0, 5776, 484, XBLOCK
            =64, RBLOCK=64, num_warps=16, num_stages=1)
        del arg0_1
        buf1 = empty_strided_cuda((4, 1444), (1472, 1), torch.float32)
        triton_red_fused_mean_0[grid(5776)](arg1_1, buf1, 5776, 484, XBLOCK
            =64, RBLOCK=64, num_warps=16, num_stages=1)
        del arg1_1
        buf2 = empty_strided_cuda((), (), torch.float32)
        buf3 = buf2
        del buf2
        triton_red_fused__to_copy_eq_gt_mean_1[grid(1)](buf3, buf0, buf1, 1,
            5776, XBLOCK=1, RBLOCK=2048, num_warps=16, num_stages=1)
        del buf0
        del buf1
    return buf3,


class KaggleAccuracyNew(nn.Module):

    def __init__(self, threshold: 'float'=0.25, num_patches: 'int'=38, size:
        'int'=418) ->None:
        super().__init__()
        self.threshold = threshold
        self.num_patches = num_patches
        self.patch_size = size // num_patches
        self.resize = nn.Upsample(size=size)
        self.unfold = nn.Unfold(kernel_size=self.patch_size, stride=self.
            patch_size)

    def to_patch_value(self, x: 'Tensor') ->Tensor:
        x = x.float()
        x = self.resize(x)
        x = self.unfold(x)
        x = x.mean(dim=1)
        return (x > self.threshold).float()

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