# 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/qz/cqza6p5fjiie2hfiu5dfjqqugrnzziwuwxzlhzy2aa7khopxjbym.py
# Topologically Sorted Source Nodes: [prob], Original ATen: [aten._softmax]
# Source node to ATen node mapping:
#   prob => amax, exp, sub
# Graph fragment:
#   %amax : [num_users=1] = call_function[target=torch.ops.aten.amax.default](args = (%arg0_1, [1], True), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%arg0_1, %amax), kwargs = {})
#   %exp : [num_users=2] = call_function[target=torch.ops.aten.exp.default](args = (%sub,), kwargs = {})
triton_poi_fused__softmax_0 = async_compile.triton('triton_poi_fused__softmax_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=[256], 
    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__softmax_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 5, '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__softmax_0(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x0 = xindex % 16
    x2 = (xindex // 64)
    tmp0 = tl.load(in_ptr0 + (x3), xmask)
    tmp1 = tl.load(in_ptr0 + (x0 + (64*x2)), xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr0 + (16 + x0 + (64*x2)), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr0 + (32 + x0 + (64*x2)), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (48 + x0 + (64*x2)), xmask, eviction_policy='evict_last')
    tmp3 = triton_helpers.maximum(tmp1, tmp2)
    tmp5 = triton_helpers.maximum(tmp3, tmp4)
    tmp7 = triton_helpers.maximum(tmp5, tmp6)
    tmp8 = tmp0 - tmp7
    tmp9 = tl_math.exp(tmp8)
    tl.store(out_ptr0 + (x3), tmp9, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/33/c33emzxioz4aendulyfvlgxfkusqaq7xtawak6kw2hep7kizcwyw.py
# Topologically Sorted Source Nodes: [prob, prediction, y_pred_f, intersection, sum_1, sum_2, sum_3, intersection_1, sum_4, sum_5, sum_6, intersection_2, sum_7, sum_8, sum_9, dices], Original ATen: [aten._softmax, aten.argmax, aten._to_copy, aten.mul, aten.sum, aten.stack]
# Source node to ATen node mapping:
#   dices => cat
#   intersection => mul
#   intersection_1 => mul_3
#   intersection_2 => mul_6
#   prediction => argmax
#   prob => div, sum_1
#   sum_1 => sum_2
#   sum_2 => sum_3
#   sum_3 => sum_4
#   sum_4 => sum_5
#   sum_5 => sum_6
#   sum_6 => sum_7
#   sum_7 => sum_8
#   sum_8 => sum_9
#   sum_9 => sum_10
#   y_pred_f => convert_element_type
# Graph fragment:
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%exp, [1], True), kwargs = {})
#   %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%exp, %sum_1), kwargs = {})
#   %argmax : [num_users=1] = call_function[target=torch.ops.aten.argmax.default](args = (%div, 1), kwargs = {})
#   %convert_element_type : [num_users=6] = call_function[target=torch.ops.prims.convert_element_type.default](args = (%view, torch.float32), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%view_1, %convert_element_type), kwargs = {})
#   %sum_2 : [num_users=1] = call_function[target=torch.ops.aten.sum.default](args = (%mul,), kwargs = {})
#   %sum_3 : [num_users=1] = call_function[target=torch.ops.aten.sum.default](args = (%view_1,), kwargs = {})
#   %sum_4 : [num_users=1] = call_function[target=torch.ops.aten.sum.default](args = (%convert_element_type,), kwargs = {})
#   %mul_3 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%view_2, %convert_element_type), kwargs = {})
#   %sum_5 : [num_users=1] = call_function[target=torch.ops.aten.sum.default](args = (%mul_3,), kwargs = {})
#   %sum_6 : [num_users=1] = call_function[target=torch.ops.aten.sum.default](args = (%view_2,), kwargs = {})
#   %sum_7 : [num_users=1] = call_function[target=torch.ops.aten.sum.default](args = (%convert_element_type,), kwargs = {})
#   %mul_6 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%view_3, %convert_element_type), kwargs = {})
#   %sum_8 : [num_users=1] = call_function[target=torch.ops.aten.sum.default](args = (%mul_6,), kwargs = {})
#   %sum_9 : [num_users=1] = call_function[target=torch.ops.aten.sum.default](args = (%view_3,), kwargs = {})
#   %sum_10 : [num_users=1] = call_function[target=torch.ops.aten.sum.default](args = (%convert_element_type,), kwargs = {})
#   %cat : [num_users=1] = call_function[target=torch.ops.aten.cat.default](args = ([%unsqueeze, %unsqueeze_1, %unsqueeze_2],), kwargs = {})
triton_per_fused__softmax__to_copy_argmax_mul_stack_sum_1 = async_compile.triton('triton_per_fused__softmax__to_copy_argmax_mul_stack_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, 64],
    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': {5: 1}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 4, 6), equal_to_1=(5,))]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused__softmax__to_copy_argmax_mul_stack_sum_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 7, 'num_reduction': 9, '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__to_copy_argmax_mul_stack_sum_1(in_ptr0, in_ptr1, out_ptr10, out_ptr11, out_ptr12, xnumel, rnumel, XBLOCK : tl.constexpr):
    xnumel = 1
    rnumel = 64
    RBLOCK: tl.constexpr = 64
    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 % 16
    r1 = (rindex // 16)
    r2 = rindex
    tmp0 = tl.load(in_ptr0 + (r0 + (64*r1)), None)
    tmp1 = tl.load(in_ptr0 + (16 + r0 + (64*r1)), None)
    tmp3 = tl.load(in_ptr0 + (32 + r0 + (64*r1)), None)
    tmp5 = tl.load(in_ptr0 + (48 + r0 + (64*r1)), None)
    tmp54 = tl.load(in_ptr1 + (16 + r0 + (64*r1)), None)
    tmp63 = tl.load(in_ptr1 + (32 + r0 + (64*r1)), None)
    tmp68 = tl.load(in_ptr1 + (48 + r0 + (64*r1)), None)
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tmp6 = tmp4 + tmp5
    tmp7 = tmp0 / tmp6
    tmp8 = tmp1 / tmp6
    tmp9 = tmp7 > tmp8
    tmp10 = tmp7 == tmp8
    tmp11 = tmp7 != tmp7
    tmp12 = tmp8 != tmp8
    tmp13 = tmp11 > tmp12
    tmp14 = tmp9 | tmp13
    tmp15 = tmp11 & tmp12
    tmp16 = tmp10 | tmp15
    tmp17 = tl.full([1, 1], 0, tl.int64)
    tmp18 = tl.full([1, 1], 1, tl.int64)
    tmp19 = tmp17 < tmp18
    tmp20 = tmp16 & tmp19
    tmp21 = tmp14 | tmp20
    tmp22 = tl.where(tmp21, tmp7, tmp8)
    tmp23 = tl.where(tmp21, tmp17, tmp18)
    tmp24 = tmp3 / tmp6
    tmp25 = tmp22 > tmp24
    tmp26 = tmp22 == tmp24
    tmp27 = tmp22 != tmp22
    tmp28 = tmp24 != tmp24
    tmp29 = tmp27 > tmp28
    tmp30 = tmp25 | tmp29
    tmp31 = tmp27 & tmp28
    tmp32 = tmp26 | tmp31
    tmp33 = tl.full([1, 1], 2, tl.int64)
    tmp34 = tmp23 < tmp33
    tmp35 = tmp32 & tmp34
    tmp36 = tmp30 | tmp35
    tmp37 = tl.where(tmp36, tmp22, tmp24)
    tmp38 = tl.where(tmp36, tmp23, tmp33)
    tmp39 = tmp5 / tmp6
    tmp40 = tmp37 > tmp39
    tmp41 = tmp37 == tmp39
    tmp42 = tmp37 != tmp37
    tmp43 = tmp39 != tmp39
    tmp44 = tmp42 > tmp43
    tmp45 = tmp40 | tmp44
    tmp46 = tmp42 & tmp43
    tmp47 = tmp41 | tmp46
    tmp48 = tl.full([1, 1], 3, tl.int64)
    tmp49 = tmp38 < tmp48
    tmp50 = tmp47 & tmp49
    tmp51 = tmp45 | tmp50
    tmp52 = tl.where(tmp51, tmp37, tmp39)
    tmp53 = tl.where(tmp51, tmp38, tmp48)
    tmp55 = tmp53.to(tl.float32)
    tmp56 = tmp54 * tmp55
    tmp57 = tl.broadcast_to(tmp56, [XBLOCK, RBLOCK])
    tmp59 = tl.sum(tmp57, 1)[:, None]
    tmp60 = tl.broadcast_to(tmp55, [XBLOCK, RBLOCK])
    tmp62 = tl.sum(tmp60, 1)[:, None]
    tmp64 = tmp63 * tmp55
    tmp65 = tl.broadcast_to(tmp64, [XBLOCK, RBLOCK])
    tmp67 = tl.sum(tmp65, 1)[:, None]
    tmp69 = tmp68 * tmp55
    tmp70 = tl.broadcast_to(tmp69, [XBLOCK, RBLOCK])
    tmp72 = tl.sum(tmp70, 1)[:, None]
    tmp73 = tl.broadcast_to(tmp54, [XBLOCK, RBLOCK])
    tmp75 = tl.sum(tmp73, 1)[:, None]
    tmp76 = tl.broadcast_to(tmp63, [XBLOCK, RBLOCK])
    tmp78 = tl.sum(tmp76, 1)[:, None]
    tmp79 = tl.broadcast_to(tmp68, [XBLOCK, RBLOCK])
    tmp81 = tl.sum(tmp79, 1)[:, None]
    tmp82 = 2.0
    tmp83 = tmp72 * tmp82
    tmp84 = tmp81 + tmp62
    tmp85 = 1e-15
    tmp86 = tmp84 + tmp85
    tmp87 = tmp83 / tmp86
    tmp88 = 100.0
    tmp89 = tmp87 * tmp88
    tmp90 = tmp67 * tmp82
    tmp91 = tmp78 + tmp62
    tmp92 = tmp91 + tmp85
    tmp93 = tmp90 / tmp92
    tmp94 = tmp93 * tmp88
    tmp95 = tmp59 * tmp82
    tmp96 = tmp75 + tmp62
    tmp97 = tmp96 + tmp85
    tmp98 = tmp95 / tmp97
    tmp99 = tmp98 * tmp88
    tl.store(out_ptr10 + (tl.full([XBLOCK, 1], 0, tl.int32)), tmp89, None)
    tl.store(out_ptr11 + (tl.full([XBLOCK, 1], 0, tl.int32)), tmp94, None)
    tl.store(out_ptr12 + (tl.full([XBLOCK, 1], 0, tl.int32)), tmp99, None)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/s7/cs7iq6m5475mzwweftxc7y5pb74lnv7wdmyryfqnistrkyccwipi.py
# Topologically Sorted Source Nodes: [mean], Original ATen: [aten.mean]
# Source node to ATen node mapping:
#   mean => mean
# Graph fragment:
#   %mean : [num_users=1] = call_function[target=torch.ops.aten.mean.default](args = (%cat,), kwargs = {})
triton_per_fused_mean_2 = async_compile.triton('triton_per_fused_mean_2', '''
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, 4],
    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), equal_to_1=(2,))]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_mean_2', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 1, '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_2(in_out_ptr0, in_ptr0, xnumel, rnumel, XBLOCK : tl.constexpr):
    xnumel = 1
    rnumel = 3
    RBLOCK: tl.constexpr = 4
    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 = rindex < rnumel
    r0 = rindex
    tmp0 = tl.load(in_ptr0 + (r0), rmask, other=0.0)
    tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
    tmp3 = tl.where(rmask, tmp1, 0)
    tmp4 = tl.sum(tmp3, 1)[:, None]
    tmp5 = 3.0
    tmp6 = tmp4 / tmp5
    tl.debug_barrier()
    tl.store(in_out_ptr0 + (tl.full([XBLOCK, 1], 0, tl.int32)), tmp6, 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, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [prob], Original ATen: [aten._softmax]
        stream0 = get_raw_stream(0)
        triton_poi_fused__softmax_0.run(arg0_1, buf0, 256, grid=grid(256), stream=stream0)
        del arg0_1
        buf14 = empty_strided_cuda((3, ), (1, ), torch.float32)
        buf13 = reinterpret_tensor(buf14, (1, ), (1, ), 2)  # alias
        buf12 = reinterpret_tensor(buf14, (1, ), (1, ), 1)  # alias
        buf11 = reinterpret_tensor(buf14, (1, ), (1, ), 0)  # alias
        # Topologically Sorted Source Nodes: [prob, prediction, y_pred_f, intersection, sum_1, sum_2, sum_3, intersection_1, sum_4, sum_5, sum_6, intersection_2, sum_7, sum_8, sum_9, dices], Original ATen: [aten._softmax, aten.argmax, aten._to_copy, aten.mul, aten.sum, aten.stack]
        triton_per_fused__softmax__to_copy_argmax_mul_stack_sum_1.run(buf0, arg1_1, buf13, buf12, buf11, 1, 64, grid=grid(1), stream=stream0)
        del arg1_1
        del buf0
        buf15 = empty_strided_cuda((), (), torch.float32)
        buf16 = buf15; del buf15  # reuse
        # Topologically Sorted Source Nodes: [mean], Original ATen: [aten.mean]
        triton_per_fused_mean_2.run(buf16, buf14, 1, 3, grid=grid(1), stream=stream0)
        del buf11
        del buf12
        del buf13
        del buf14
    return (buf16, )


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
import torch.nn.functional
import torch.nn as nn
import torch.nn.functional as F


def centercrop(image, w, h):
    _nt, _ct, ht, wt = image.size()
    padw, padh = (wt - w) // 2, (ht - h) // 2
    if padw > 0 and padh > 0:
        image = image[:, :, padh:-padh, padw:-padw]
    return image


def flatten(x):
    x_flat = x.clone()
    x_flat = x_flat.view(x.shape[0], -1)
    return x_flat


class Dice(nn.Module):

    def __init__(self, bback_ignore=True):
        super(Dice, self).__init__()
        self.bback_ignore = bback_ignore

    def forward(self, y_pred, y_true):
        eps = 1e-15
        _n, ch, h, w = y_pred.size()
        y_true = centercrop(y_true, w, h)
        prob = F.softmax(y_pred, dim=1)
        prob = prob.data
        prediction = torch.argmax(prob, dim=1)
        y_pred_f = flatten(prediction).float()
        dices = []
        for c in range(int(self.bback_ignore), ch):
            y_true_f = flatten(y_true[:, c, ...]).float()
            intersection = y_true_f * y_pred_f
            dice = 2.0 * torch.sum(intersection) / (torch.sum(y_true_f) +
                torch.sum(y_pred_f) + eps) * 100
            dices.append(dice)
        dices = torch.stack(dices)
        return dices.mean()


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._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn.functional
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_poi_fused__softmax_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x0 = xindex % 16
    x2 = xindex // 64
    tmp0 = tl.load(in_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr0 + (x0 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp2 = tl.load(in_ptr0 + (16 + x0 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp4 = tl.load(in_ptr0 + (32 + x0 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp6 = tl.load(in_ptr0 + (48 + x0 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp3 = triton_helpers.maximum(tmp1, tmp2)
    tmp5 = triton_helpers.maximum(tmp3, tmp4)
    tmp7 = triton_helpers.maximum(tmp5, tmp6)
    tmp8 = tmp0 - tmp7
    tmp9 = tl_math.exp(tmp8)
    tl.store(out_ptr0 + x3, tmp9, xmask)


@triton.jit
def triton_per_fused__softmax__to_copy_argmax_mul_stack_sum_1(in_ptr0,
    in_ptr1, out_ptr10, out_ptr11, out_ptr12, xnumel, rnumel, XBLOCK: tl.
    constexpr):
    RBLOCK: tl.constexpr = 64
    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 % 16
    r1 = rindex // 16
    tmp0 = tl.load(in_ptr0 + (r0 + 64 * r1), None)
    tmp1 = tl.load(in_ptr0 + (16 + r0 + 64 * r1), None)
    tmp3 = tl.load(in_ptr0 + (32 + r0 + 64 * r1), None)
    tmp5 = tl.load(in_ptr0 + (48 + r0 + 64 * r1), None)
    tmp54 = tl.load(in_ptr1 + (16 + r0 + 64 * r1), None)
    tmp63 = tl.load(in_ptr1 + (32 + r0 + 64 * r1), None)
    tmp68 = tl.load(in_ptr1 + (48 + r0 + 64 * r1), None)
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tmp6 = tmp4 + tmp5
    tmp7 = tmp0 / tmp6
    tmp8 = tmp1 / tmp6
    tmp9 = tmp7 > tmp8
    tmp10 = tmp7 == tmp8
    tmp11 = tmp7 != tmp7
    tmp12 = tmp8 != tmp8
    tmp13 = tmp11 > tmp12
    tmp14 = tmp9 | tmp13
    tmp15 = tmp11 & tmp12
    tmp16 = tmp10 | tmp15
    tmp17 = tl.full([1, 1], 0, tl.int64)
    tmp18 = tl.full([1, 1], 1, tl.int64)
    tmp19 = tmp17 < tmp18
    tmp20 = tmp16 & tmp19
    tmp21 = tmp14 | tmp20
    tmp22 = tl.where(tmp21, tmp7, tmp8)
    tmp23 = tl.where(tmp21, tmp17, tmp18)
    tmp24 = tmp3 / tmp6
    tmp25 = tmp22 > tmp24
    tmp26 = tmp22 == tmp24
    tmp27 = tmp22 != tmp22
    tmp28 = tmp24 != tmp24
    tmp29 = tmp27 > tmp28
    tmp30 = tmp25 | tmp29
    tmp31 = tmp27 & tmp28
    tmp32 = tmp26 | tmp31
    tmp33 = tl.full([1, 1], 2, tl.int64)
    tmp34 = tmp23 < tmp33
    tmp35 = tmp32 & tmp34
    tmp36 = tmp30 | tmp35
    tmp37 = tl.where(tmp36, tmp22, tmp24)
    tmp38 = tl.where(tmp36, tmp23, tmp33)
    tmp39 = tmp5 / tmp6
    tmp40 = tmp37 > tmp39
    tmp41 = tmp37 == tmp39
    tmp42 = tmp37 != tmp37
    tmp43 = tmp39 != tmp39
    tmp44 = tmp42 > tmp43
    tmp45 = tmp40 | tmp44
    tmp46 = tmp42 & tmp43
    tmp47 = tmp41 | tmp46
    tmp48 = tl.full([1, 1], 3, tl.int64)
    tmp49 = tmp38 < tmp48
    tmp50 = tmp47 & tmp49
    tmp51 = tmp45 | tmp50
    tl.where(tmp51, tmp37, tmp39)
    tmp53 = tl.where(tmp51, tmp38, tmp48)
    tmp55 = tmp53.to(tl.float32)
    tmp56 = tmp54 * tmp55
    tmp57 = tl.broadcast_to(tmp56, [XBLOCK, RBLOCK])
    tmp59 = tl.sum(tmp57, 1)[:, None]
    tmp60 = tl.broadcast_to(tmp55, [XBLOCK, RBLOCK])
    tmp62 = tl.sum(tmp60, 1)[:, None]
    tmp64 = tmp63 * tmp55
    tmp65 = tl.broadcast_to(tmp64, [XBLOCK, RBLOCK])
    tmp67 = tl.sum(tmp65, 1)[:, None]
    tmp69 = tmp68 * tmp55
    tmp70 = tl.broadcast_to(tmp69, [XBLOCK, RBLOCK])
    tmp72 = tl.sum(tmp70, 1)[:, None]
    tmp73 = tl.broadcast_to(tmp54, [XBLOCK, RBLOCK])
    tmp75 = tl.sum(tmp73, 1)[:, None]
    tmp76 = tl.broadcast_to(tmp63, [XBLOCK, RBLOCK])
    tmp78 = tl.sum(tmp76, 1)[:, None]
    tmp79 = tl.broadcast_to(tmp68, [XBLOCK, RBLOCK])
    tmp81 = tl.sum(tmp79, 1)[:, None]
    tmp82 = 2.0
    tmp83 = tmp72 * tmp82
    tmp84 = tmp81 + tmp62
    tmp85 = 1e-15
    tmp86 = tmp84 + tmp85
    tmp87 = tmp83 / tmp86
    tmp88 = 100.0
    tmp89 = tmp87 * tmp88
    tmp90 = tmp67 * tmp82
    tmp91 = tmp78 + tmp62
    tmp92 = tmp91 + tmp85
    tmp93 = tmp90 / tmp92
    tmp94 = tmp93 * tmp88
    tmp95 = tmp59 * tmp82
    tmp96 = tmp75 + tmp62
    tmp97 = tmp96 + tmp85
    tmp98 = tmp95 / tmp97
    tmp99 = tmp98 * tmp88
    tl.store(out_ptr10 + tl.full([XBLOCK, 1], 0, tl.int32), tmp89, None)
    tl.store(out_ptr11 + tl.full([XBLOCK, 1], 0, tl.int32), tmp94, None)
    tl.store(out_ptr12 + tl.full([XBLOCK, 1], 0, tl.int32), tmp99, None)


@triton.jit
def triton_per_fused_mean_2(in_out_ptr0, in_ptr0, xnumel, rnumel, XBLOCK:
    tl.constexpr):
    rnumel = 3
    RBLOCK: tl.constexpr = 4
    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, :]
    rmask = rindex < rnumel
    r0 = rindex
    tmp0 = tl.load(in_ptr0 + r0, rmask, other=0.0)
    tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
    tmp3 = tl.where(rmask, tmp1, 0)
    tmp4 = tl.sum(tmp3, 1)[:, None]
    tmp5 = 3.0
    tmp6 = tmp4 / tmp5
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp6, 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, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused__softmax_0[grid(256)](arg0_1, buf0, 256, XBLOCK=
            256, num_warps=4, num_stages=1)
        del arg0_1
        buf14 = empty_strided_cuda((3,), (1,), torch.float32)
        buf13 = reinterpret_tensor(buf14, (1,), (1,), 2)
        buf12 = reinterpret_tensor(buf14, (1,), (1,), 1)
        buf11 = reinterpret_tensor(buf14, (1,), (1,), 0)
        triton_per_fused__softmax__to_copy_argmax_mul_stack_sum_1[grid(1)](buf0
            , arg1_1, buf13, buf12, buf11, 1, 64, XBLOCK=1, num_warps=2,
            num_stages=1)
        del arg1_1
        del buf0
        buf15 = empty_strided_cuda((), (), torch.float32)
        buf16 = buf15
        del buf15
        triton_per_fused_mean_2[grid(1)](buf16, buf14, 1, 3, XBLOCK=1,
            num_warps=2, num_stages=1)
        del buf11
        del buf12
        del buf13
        del buf14
    return buf16,


def centercrop(image, w, h):
    _nt, _ct, ht, wt = image.size()
    padw, padh = (wt - w) // 2, (ht - h) // 2
    if padw > 0 and padh > 0:
        image = image[:, :, padh:-padh, padw:-padw]
    return image


def flatten(x):
    x_flat = x.clone()
    x_flat = x_flat.view(x.shape[0], -1)
    return x_flat


class DiceNew(nn.Module):

    def __init__(self, bback_ignore=True):
        super(DiceNew, self).__init__()
        self.bback_ignore = bback_ignore

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