# 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/2j/c2jmzl4b2ce7i3thgupjuvcfnxmj3hs2yjipasebp2ag4wqcazbi.py
# Topologically Sorted Source Nodes: [shifts], Original ATen: [aten.stack]
# Source node to ATen node mapping:
#   shifts => cat_2
# Graph fragment:
#   %cat_2 : [num_users=1] = call_function[target=torch.ops.aten.cat.default](args = ([%cat, %cat_1],), kwargs = {})
triton_poi_fused_stack_0 = async_compile.triton('triton_poi_fused_stack_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=[512], 
    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_stack_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 4, '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_stack_0(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 512
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = (xindex // 64)
    x1 = (xindex // 4) % 4
    x0 = xindex % 4
    x2 = (xindex // 16) % 4
    x4 = (xindex // 4) % 16
    x5 = xindex
    tmp0 = x3
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = x1
    tmp6 = tmp5 >= tmp1
    tmp7 = tl.full([1], 3, tl.int64)
    tmp8 = tmp5 < tmp7
    tmp9 = tmp8 & tmp4
    tmp10 = tl.load(in_ptr0 + (4 + x0 + (4*x1) + (16*x2) + (64*x3)), tmp9 & xmask, other=0.0)
    tmp11 = tmp5 >= tmp7
    tmp12 = tmp5 < tmp3
    tmp13 = tmp11 & tmp4
    tmp14 = tl.load(in_ptr0 + (x0 + (16*x2) + (64*x3)), tmp13 & xmask, eviction_policy='evict_last', other=0.0)
    tmp15 = tl.where(tmp8, tmp10, tmp14)
    tmp16 = tl.full(tmp15.shape, 0.0, tmp15.dtype)
    tmp17 = tl.where(tmp4, tmp15, tmp16)
    tmp18 = tmp0 >= tmp3
    tmp19 = tl.full([1], 8, tl.int64)
    tmp20 = tmp0 < tmp19
    tmp21 = x0
    tmp22 = tmp21 >= tmp1
    tmp23 = tmp21 < tmp7
    tmp24 = tmp23 & tmp18
    tmp25 = tl.load(in_ptr0 + (1 + (4*x4) + (64*((-4) + x3)) + x0), tmp24 & xmask, eviction_policy='evict_last', other=0.0)
    tmp26 = tmp21 >= tmp7
    tmp27 = tmp21 < tmp3
    tmp28 = tmp26 & tmp18
    tmp29 = tl.load(in_ptr0 + ((4*x4) + (64*((-4) + x3))), tmp28 & xmask, eviction_policy='evict_last', other=0.0)
    tmp30 = tl.where(tmp23, tmp25, tmp29)
    tmp31 = tl.full(tmp30.shape, 0.0, tmp30.dtype)
    tmp32 = tl.where(tmp18, tmp30, tmp31)
    tmp33 = tl.where(tmp4, tmp17, tmp32)
    tl.store(out_ptr0 + (x5), tmp33, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/rd/crdtg6bomx4n5mywhjxqfm744jp26iiz2tgfhuwahti5j44pyn54.py
# Topologically Sorted Source Nodes: [sub, tv, sum_1, mean], Original ATen: [aten.sub, aten.pow, aten.sum, aten.mean]
# Source node to ATen node mapping:
#   mean => mean
#   sub => sub
#   sum_1 => sum_1
#   tv => pow_1
# Graph fragment:
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%view, %unsqueeze), kwargs = {})
#   %pow_1 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%sub, 2), kwargs = {})
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%pow_1, [0]), kwargs = {})
#   %mean : [num_users=1] = call_function[target=torch.ops.aten.mean.default](args = (%sum_1,), kwargs = {})
triton_per_fused_mean_pow_sub_sum_1 = async_compile.triton('triton_per_fused_mean_pow_sub_sum_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.persistent_reduction(
    size_hints=[1, 256],
    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_mean_pow_sub_sum_1', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': True, 'num_load': 3, '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_mean_pow_sub_sum_1(in_out_ptr0, in_ptr0, in_ptr1, xnumel, rnumel):
    xnumel = 1
    XBLOCK: tl.constexpr = 1
    rnumel = 256
    RBLOCK: tl.constexpr = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = tl.full([1], xoffset, tl.int32)
    xmask = tl.full([RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[:]
    roffset = 0
    rmask = tl.full([RBLOCK], True, tl.int1)
    r0 = rindex
    tmp0 = tl.load(in_ptr0 + (r0), None)
    tmp1 = tl.load(in_ptr1 + (r0), None)
    tmp4 = tl.load(in_ptr0 + (256 + r0), None)
    tmp2 = tmp0 - tmp1
    tmp3 = tmp2 * tmp2
    tmp5 = tmp4 - tmp1
    tmp6 = tmp5 * tmp5
    tmp7 = tmp3 + tmp6
    tmp8 = tl.broadcast_to(tmp7, [RBLOCK])
    tmp10 = triton_helpers.promote_to_tensor(tl.sum(tmp8, 0))
    tmp11 = 256.0
    tmp12 = tmp10 / tmp11
    tl.debug_barrier()
    tl.store(in_out_ptr0 + (tl.full([1], 0, tl.int32)), tmp12, None)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    arg0_1, = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((8, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [shifts], Original ATen: [aten.stack]
        stream0 = get_raw_stream(0)
        triton_poi_fused_stack_0.run(arg0_1, buf0, 512, grid=grid(512), stream=stream0)
        buf1 = empty_strided_cuda((), (), torch.float32)
        buf2 = buf1; del buf1  # reuse
        # Topologically Sorted Source Nodes: [sub, tv, sum_1, mean], Original ATen: [aten.sub, aten.pow, aten.sum, aten.mean]
        triton_per_fused_mean_pow_sub_sum_1.run(buf2, buf0, arg0_1, 1, 256, grid=grid(1), stream=stream0)
        del arg0_1
        del buf0
    return (buf2, )


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)
    fn = lambda: call([arg0_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 numpy as np
import torch.nn as nn


class TVLoss(nn.Module):

    def __init__(self, norm=2):
        super().__init__()
        self.norm = norm

    def forward(self, x):
        rank = len(x.shape[2:])
        shift_h = torch.cat([x[:, :, 1:], x[:, :, :1]], dim=2)
        shift_w = torch.cat([x[:, :, :, 1:], x[:, :, :, :1]], dim=3)
        shifts = [shift_h, shift_w]
        if rank > 2:
            shift_z = torch.cat([x[..., 1:], x[..., :1]], dim=-1)
            shifts.append(shift_z)
        shifts = torch.stack(shifts, dim=0)
        x = x[np.newaxis]
        if self.norm == 1:
            tv = abs(shifts - x)
        elif self.norm % 2 == 0:
            tv = (shifts - x).pow(self.norm)
        else:
            tv = abs(shifts - x).pow(self.norm)
        return tv.sum(dim=0).mean()

    def extra_repr(self):
        return f'norm={self.norm}'


def get_inputs():
    return [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
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_stack_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 512
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex // 64
    x1 = xindex // 4 % 4
    x0 = xindex % 4
    x2 = xindex // 16 % 4
    x4 = xindex // 4 % 16
    x5 = xindex
    tmp0 = x3
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = x1
    tmp7 = tl.full([1], 3, tl.int64)
    tmp8 = tmp5 < tmp7
    tmp9 = tmp8 & tmp4
    tmp10 = tl.load(in_ptr0 + (4 + x0 + 4 * x1 + 16 * x2 + 64 * x3), tmp9 &
        xmask, other=0.0)
    tmp11 = tmp5 >= tmp7
    tmp13 = tmp11 & tmp4
    tmp14 = tl.load(in_ptr0 + (x0 + 16 * x2 + 64 * x3), tmp13 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp15 = tl.where(tmp8, tmp10, tmp14)
    tmp16 = tl.full(tmp15.shape, 0.0, tmp15.dtype)
    tmp17 = tl.where(tmp4, tmp15, tmp16)
    tmp18 = tmp0 >= tmp3
    tl.full([1], 8, tl.int64)
    tmp21 = x0
    tmp23 = tmp21 < tmp7
    tmp24 = tmp23 & tmp18
    tmp25 = tl.load(in_ptr0 + (1 + 4 * x4 + 64 * (-4 + x3) + x0), tmp24 &
        xmask, eviction_policy='evict_last', other=0.0)
    tmp26 = tmp21 >= tmp7
    tmp28 = tmp26 & tmp18
    tmp29 = tl.load(in_ptr0 + (4 * x4 + 64 * (-4 + x3)), tmp28 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp30 = tl.where(tmp23, tmp25, tmp29)
    tmp31 = tl.full(tmp30.shape, 0.0, tmp30.dtype)
    tmp32 = tl.where(tmp18, tmp30, tmp31)
    tmp33 = tl.where(tmp4, tmp17, tmp32)
    tl.store(out_ptr0 + x5, tmp33, xmask)


@triton.jit
def triton_per_fused_mean_pow_sub_sum_1(in_out_ptr0, in_ptr0, in_ptr1,
    xnumel, rnumel):
    XBLOCK: tl.constexpr = 1
    RBLOCK: tl.constexpr = 256
    xoffset = tl.program_id(0) * XBLOCK
    tl.full([1], xoffset, tl.int32)
    tl.full([RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[:]
    tl.full([RBLOCK], True, tl.int1)
    r0 = rindex
    tmp0 = tl.load(in_ptr0 + r0, None)
    tmp1 = tl.load(in_ptr1 + r0, None)
    tmp4 = tl.load(in_ptr0 + (256 + r0), None)
    tmp2 = tmp0 - tmp1
    tmp3 = tmp2 * tmp2
    tmp5 = tmp4 - tmp1
    tmp6 = tmp5 * tmp5
    tmp7 = tmp3 + tmp6
    tmp8 = tl.broadcast_to(tmp7, [RBLOCK])
    tmp10 = triton_helpers.promote_to_tensor(tl.sum(tmp8, 0))
    tmp11 = 256.0
    tmp12 = tmp10 / tmp11
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([1], 0, tl.int32), tmp12, None)


def call(args):
    arg0_1, = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((8, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_stack_0[grid(512)](arg0_1, buf0, 512, XBLOCK=128,
            num_warps=4, num_stages=1)
        buf1 = empty_strided_cuda((), (), torch.float32)
        buf2 = buf1
        del buf1
        triton_per_fused_mean_pow_sub_sum_1[grid(1)](buf2, buf0, arg0_1, 1,
            256, num_warps=2, num_stages=1)
        del arg0_1
        del buf0
    return buf2,


class TVLossNew(nn.Module):

    def __init__(self, norm=2):
        super().__init__()
        self.norm = norm

    def extra_repr(self):
        return f'norm={self.norm}'

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