# 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/ug/cugldwv35g53yxideoh7o6qqdvauiciej3welxrwa4qfta7lmlax.py
# Topologically Sorted Source Nodes: [sum_1, add, tensor_a, cdf_tensor_a], Original ATen: [aten.sum, aten.add, aten.div, aten.cumsum]
# Source node to ATen node mapping:
#   add => add
#   cdf_tensor_a => cumsum
#   sum_1 => sum_1
#   tensor_a => div
# Graph fragment:
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%arg0_1, [-1], True), kwargs = {})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%sum_1, 1e-14), kwargs = {})
#   %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%arg0_1, %add), kwargs = {})
#   %cumsum : [num_users=1] = call_function[target=torch.ops.aten.cumsum.default](args = (%div, -1), kwargs = {})
triton_per_fused_add_cumsum_div_sum_0 = async_compile.triton('triton_per_fused_add_cumsum_div_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.jit
def _triton_helper_fn_add0(arg0_0, arg1_0):
    tmp0 = arg0_0 + arg1_0
    return tmp0

@triton_heuristics.persistent_reduction(
    size_hints=[64, 4],
    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_per_fused_add_cumsum_div_sum_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}
)
@triton.jit
def triton_per_fused_add_cumsum_div_sum_0(in_ptr0, out_ptr0, xnumel, rnumel, XBLOCK : tl.constexpr):
    xnumel = 64
    rnumel = 4
    RBLOCK: tl.constexpr = 4
    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 + (4*x0)), xmask, other=0.0)
    tmp1 = tl.load(in_ptr0 + (4*x0), xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr0 + (1 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr0 + (2 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (3 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp3 = tmp1 + tmp2
    tmp5 = tmp3 + tmp4
    tmp7 = tmp5 + tmp6
    tmp8 = 1e-14
    tmp9 = tmp7 + tmp8
    tmp10 = tmp0 / tmp9
    tmp11 = tmp10.to(tl.float32)
    tmp12 = tl.broadcast_to(tmp11, [XBLOCK, RBLOCK])
    tmp13, = tl.associative_scan((tmp12,), 1, _triton_helper_fn_add0)
    tl.store(out_ptr0 + (r1 + (4*x0)), tmp13, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/o7/co7to4ycybqjfm36u63qemdpnbojs735ow643af46n6zxahrimbt.py
# Topologically Sorted Source Nodes: [sub, abs_1, cdf_distance, cdf_loss], Original ATen: [aten.sub, aten.abs, aten.sum, aten.mean]
# Source node to ATen node mapping:
#   abs_1 => abs_1
#   cdf_distance => sum_3
#   cdf_loss => mean
#   sub => sub
# Graph fragment:
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%cumsum, %cumsum_1), kwargs = {})
#   %abs_1 : [num_users=1] = call_function[target=torch.ops.aten.abs.default](args = (%sub,), kwargs = {})
#   %sum_3 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%abs_1, [-1]), kwargs = {})
#   %mean : [num_users=1] = call_function[target=torch.ops.aten.mean.default](args = (%sum_3,), kwargs = {})
triton_per_fused_abs_mean_sub_sum_1 = async_compile.triton('triton_per_fused_abs_mean_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, 64],
    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_abs_mean_sub_sum_1', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 8, '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_abs_mean_sub_sum_1(in_out_ptr0, in_ptr0, in_ptr1, 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
    tmp0 = tl.load(in_ptr0 + (4*r0), None, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + (4*r0), None, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr0 + (1 + (4*r0)), None, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr1 + (1 + (4*r0)), None, eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr0 + (2 + (4*r0)), None, eviction_policy='evict_last')
    tmp10 = tl.load(in_ptr1 + (2 + (4*r0)), None, eviction_policy='evict_last')
    tmp14 = tl.load(in_ptr0 + (3 + (4*r0)), None, eviction_policy='evict_last')
    tmp15 = tl.load(in_ptr1 + (3 + (4*r0)), None, eviction_policy='evict_last')
    tmp2 = tmp0 - tmp1
    tmp3 = tl_math.abs(tmp2)
    tmp6 = tmp4 - tmp5
    tmp7 = tl_math.abs(tmp6)
    tmp8 = tmp3 + tmp7
    tmp11 = tmp9 - tmp10
    tmp12 = tl_math.abs(tmp11)
    tmp13 = tmp8 + tmp12
    tmp16 = tmp14 - tmp15
    tmp17 = tl_math.abs(tmp16)
    tmp18 = tmp13 + tmp17
    tmp19 = tl.broadcast_to(tmp18, [XBLOCK, RBLOCK])
    tmp21 = tl.sum(tmp19, 1)[:, None]
    tmp22 = 64.0
    tmp23 = tmp21 / tmp22
    tl.debug_barrier()
    tl.store(in_out_ptr0 + (tl.full([XBLOCK, 1], 0, tl.int32)), tmp23, 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: [sum_1, add, tensor_a, cdf_tensor_a], Original ATen: [aten.sum, aten.add, aten.div, aten.cumsum]
        stream0 = get_raw_stream(0)
        triton_per_fused_add_cumsum_div_sum_0.run(arg0_1, buf0, 64, 4, grid=grid(64), stream=stream0)
        del arg0_1
        buf1 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [sum_2, add_1, tensor_b, cdf_tensor_b], Original ATen: [aten.sum, aten.add, aten.div, aten.cumsum]
        triton_per_fused_add_cumsum_div_sum_0.run(arg1_1, buf1, 64, 4, grid=grid(64), stream=stream0)
        del arg1_1
        buf2 = empty_strided_cuda((), (), torch.float32)
        buf3 = buf2; del buf2  # reuse
        # Topologically Sorted Source Nodes: [sub, abs_1, cdf_distance, cdf_loss], Original ATen: [aten.sub, aten.abs, aten.sum, aten.mean]
        triton_per_fused_abs_mean_sub_sum_1.run(buf3, buf0, buf1, 1, 64, 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


def torch_cdf_loss(tensor_a, tensor_b, p=1):
    tensor_a = tensor_a / (torch.sum(tensor_a, dim=-1, keepdim=True) + 1e-14)
    tensor_b = tensor_b / (torch.sum(tensor_b, dim=-1, keepdim=True) + 1e-14)
    cdf_tensor_a = torch.cumsum(tensor_a, dim=-1)
    cdf_tensor_b = torch.cumsum(tensor_b, dim=-1)
    if p == 1:
        cdf_distance = torch.sum(torch.abs(cdf_tensor_a - cdf_tensor_b), dim=-1
            )
    elif p == 2:
        cdf_distance = torch.sqrt(torch.sum(torch.pow(cdf_tensor_a -
            cdf_tensor_b, 2), dim=-1))
    else:
        cdf_distance = torch.pow(torch.sum(torch.pow(torch.abs(cdf_tensor_a -
            cdf_tensor_b), p), dim=-1), 1 / p)
    cdf_loss = cdf_distance.mean()
    return cdf_loss


def torch_wasserstein_loss(tensor_a, tensor_b):
    return torch_cdf_loss(tensor_a, tensor_b, p=1)


class WassersteinLoss(torch.nn.Module):

    def __init__(self):
        super().__init__()

    def forward(self, tensor_a, tensor_b):
        return torch_wasserstein_loss(tensor_a, tensor_b)


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.triton_helpers import math as tl_math
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda


@triton.jit
def _triton_helper_fn_add0(arg0_0, arg1_0):
    tmp0 = arg0_0 + arg1_0
    return tmp0


@triton.jit
def triton_per_fused_add_cumsum_div_sum_0(in_ptr0, out_ptr0, xnumel, rnumel,
    XBLOCK: tl.constexpr):
    xnumel = 64
    RBLOCK: tl.constexpr = 4
    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 + 4 * x0), xmask, other=0.0)
    tmp1 = tl.load(in_ptr0 + 4 * x0, xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr0 + (1 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr0 + (2 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (3 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp3 = tmp1 + tmp2
    tmp5 = tmp3 + tmp4
    tmp7 = tmp5 + tmp6
    tmp8 = 1e-14
    tmp9 = tmp7 + tmp8
    tmp10 = tmp0 / tmp9
    tmp11 = tmp10.to(tl.float32)
    tmp12 = tl.broadcast_to(tmp11, [XBLOCK, RBLOCK])
    tmp13, = tl.associative_scan((tmp12,), 1, _triton_helper_fn_add0)
    tl.store(out_ptr0 + (r1 + 4 * x0), tmp13, xmask)


@triton.jit
def triton_per_fused_abs_mean_sub_sum_1(in_out_ptr0, in_ptr0, in_ptr1,
    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
    tmp0 = tl.load(in_ptr0 + 4 * r0, None, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + 4 * r0, None, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr0 + (1 + 4 * r0), None, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr1 + (1 + 4 * r0), None, eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr0 + (2 + 4 * r0), None, eviction_policy='evict_last')
    tmp10 = tl.load(in_ptr1 + (2 + 4 * r0), None, eviction_policy='evict_last')
    tmp14 = tl.load(in_ptr0 + (3 + 4 * r0), None, eviction_policy='evict_last')
    tmp15 = tl.load(in_ptr1 + (3 + 4 * r0), None, eviction_policy='evict_last')
    tmp2 = tmp0 - tmp1
    tmp3 = tl_math.abs(tmp2)
    tmp6 = tmp4 - tmp5
    tmp7 = tl_math.abs(tmp6)
    tmp8 = tmp3 + tmp7
    tmp11 = tmp9 - tmp10
    tmp12 = tl_math.abs(tmp11)
    tmp13 = tmp8 + tmp12
    tmp16 = tmp14 - tmp15
    tmp17 = tl_math.abs(tmp16)
    tmp18 = tmp13 + tmp17
    tmp19 = tl.broadcast_to(tmp18, [XBLOCK, RBLOCK])
    tmp21 = tl.sum(tmp19, 1)[:, None]
    tmp22 = 64.0
    tmp23 = tmp21 / tmp22
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp23, 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_per_fused_add_cumsum_div_sum_0[grid(64)](arg0_1, buf0, 64, 4,
            XBLOCK=8, num_warps=2, num_stages=1)
        del arg0_1
        buf1 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_per_fused_add_cumsum_div_sum_0[grid(64)](arg1_1, buf1, 64, 4,
            XBLOCK=8, num_warps=2, num_stages=1)
        del arg1_1
        buf2 = empty_strided_cuda((), (), torch.float32)
        buf3 = buf2
        del buf2
        triton_per_fused_abs_mean_sub_sum_1[grid(1)](buf3, buf0, buf1, 1, 
            64, XBLOCK=1, num_warps=2, num_stages=1)
        del buf0
        del buf1
    return buf3,


def torch_cdf_loss(tensor_a, tensor_b, p=1):
    tensor_a = tensor_a / (torch.sum(tensor_a, dim=-1, keepdim=True) + 1e-14)
    tensor_b = tensor_b / (torch.sum(tensor_b, dim=-1, keepdim=True) + 1e-14)
    cdf_tensor_a = torch.cumsum(tensor_a, dim=-1)
    cdf_tensor_b = torch.cumsum(tensor_b, dim=-1)
    if p == 1:
        cdf_distance = torch.sum(torch.abs(cdf_tensor_a - cdf_tensor_b), dim=-1
            )
    elif p == 2:
        cdf_distance = torch.sqrt(torch.sum(torch.pow(cdf_tensor_a -
            cdf_tensor_b, 2), dim=-1))
    else:
        cdf_distance = torch.pow(torch.sum(torch.pow(torch.abs(cdf_tensor_a -
            cdf_tensor_b), p), dim=-1), 1 / p)
    cdf_loss = cdf_distance.mean()
    return cdf_loss


def torch_wasserstein_loss(tensor_a, tensor_b):
    return torch_cdf_loss(tensor_a, tensor_b, p=1)


class WassersteinLossNew(torch.nn.Module):

    def __init__(self):
        super().__init__()

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