# 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_7/inductor_cache/zj/czjd4gk7xzwy4hojfvo35lgyxe4nkfjguyrajnu2f2jvryoevie7.py
# Topologically Sorted Source Nodes: [phase, setitem], Original ATen: [aten.atan2, aten.lift_fresh, aten.index_put]
# Source node to ATen node mapping:
#   phase => atan2
#   setitem => full_default, index_put
# Graph fragment:
#   %atan2 : [num_users=2] = call_function[target=torch.ops.aten.atan2.default](args = (%squeeze_1, %squeeze), kwargs = {})
#   %full_default : [num_users=1] = call_function[target=torch.ops.aten.full.default](args = ([], 0.0), kwargs = {dtype: torch.float32, layout: torch.strided, device: cpu, pin_memory: False})
#   %index_put : [num_users=1] = call_function[target=torch.ops.aten.index_put_.default](args = (%atan2, [%ne], %full_default), kwargs = {})
triton_poi_fused_atan2_index_put_lift_fresh_0 = async_compile.triton('triton_poi_fused_atan2_index_put_lift_fresh_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=[4], 
    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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_atan2_index_put_lift_fresh_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, '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_atan2_index_put_lift_fresh_0(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 4
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (1 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr0 + (4*x0), xmask, eviction_policy='evict_last')
    tmp2 = libdevice.atan2(tmp0, tmp1)
    tmp3 = tmp2 != tmp2
    tmp4 = 0.0
    tmp5 = tl.where(tmp3, tmp4, tmp2)
    tl.store(out_ptr0 + (x0), tmp5, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/wh/cwh3pilr24tt4wzehv4kii4m2ehr4pwbvjawqndhgx5gjo2ympl5.py
# Topologically Sorted Source Nodes: [x, setitem_1], Original ATen: [aten.stack, aten.lift_fresh, aten.index_put]
# Source node to ATen node mapping:
#   setitem_1 => full_default_1, index_put_1
#   x => cat
# Graph fragment:
#   %cat : [num_users=2] = call_function[target=torch.ops.aten.cat.default](args = ([%unsqueeze, %unsqueeze_1], 1), kwargs = {})
#   %full_default_1 : [num_users=1] = call_function[target=torch.ops.aten.full.default](args = ([], 0.0), kwargs = {dtype: torch.float32, layout: torch.strided, device: cpu, pin_memory: False})
#   %index_put_1 : [num_users=2] = call_function[target=torch.ops.aten.index_put_.default](args = (%cat, [%ne_1], %full_default_1), kwargs = {})
triton_poi_fused_index_put_lift_fresh_stack_1 = async_compile.triton('triton_poi_fused_index_put_lift_fresh_stack_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.pointwise(
    size_hints=[8], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 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_poi_fused_index_put_lift_fresh_stack_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 3, '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_index_put_lift_fresh_stack_1(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 8
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 2
    x1 = (xindex // 2)
    x2 = xindex
    tmp0 = x0
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 1, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (4*x1), tmp4 & xmask, eviction_policy='evict_last', other=0.0)
    tmp6 = tmp5 * tmp5
    tmp7 = tl.load(in_ptr0 + (1 + (4*x1)), tmp4 & xmask, eviction_policy='evict_last', other=0.0)
    tmp8 = tmp7 * tmp7
    tmp9 = tmp6 + tmp8
    tmp10 = libdevice.sqrt(tmp9)
    tmp11 = tl.full(tmp10.shape, 0.0, tmp10.dtype)
    tmp12 = tl.where(tmp4, tmp10, tmp11)
    tmp13 = tmp0 >= tmp3
    tmp14 = tl.full([1], 2, tl.int64)
    tmp15 = tmp0 < tmp14
    tmp16 = tl.load(in_ptr1 + (x1), tmp13 & xmask, eviction_policy='evict_last', other=0.0)
    tmp17 = tl.where(tmp4, tmp12, tmp16)
    tmp18 = tmp17 != tmp17
    tmp19 = 0.0
    tmp20 = tl.where(tmp18, tmp19, tmp17)
    tl.store(out_ptr0 + (x2), tmp20, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/bm/cbm36xvh2kbgn5p5eb6lqhj2dneqtr2zmw22ow42nd34c4vqxrka.py
# Topologically Sorted Source Nodes: [x_1], Original ATen: [aten.stack]
# Source node to ATen node mapping:
#   x_1 => cat_1
# Graph fragment:
#   %cat_1 : [num_users=1] = call_function[target=torch.ops.aten.cat.default](args = ([%unsqueeze_2, %unsqueeze_3], 1), kwargs = {})
triton_poi_fused_stack_2 = async_compile.triton('triton_poi_fused_stack_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.pointwise(
    size_hints=[8], 
    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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_stack_2', '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_2(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 8
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 2
    x1 = (xindex // 2)
    x2 = xindex
    tmp0 = x0
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 1, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (2*x1), tmp4 & xmask, eviction_policy='evict_last', other=0.0)
    tmp6 = tl.load(in_ptr0 + (1 + (2*x1)), tmp4 & xmask, eviction_policy='evict_last', other=0.0)
    tmp7 = tl_math.cos(tmp6)
    tmp8 = tmp5 * tmp7
    tmp9 = tl.full(tmp8.shape, 0.0, tmp8.dtype)
    tmp10 = tl.where(tmp4, tmp8, tmp9)
    tmp11 = tmp0 >= tmp3
    tmp12 = tl.full([1], 2, tl.int64)
    tmp13 = tmp0 < tmp12
    tmp14 = tl.load(in_ptr0 + (2*x1), tmp11 & xmask, eviction_policy='evict_last', other=0.0)
    tmp15 = tl.load(in_ptr0 + (1 + (2*x1)), tmp11 & xmask, eviction_policy='evict_last', other=0.0)
    tmp16 = tl_math.sin(tmp15)
    tmp17 = tmp14 * tmp16
    tmp18 = tl.full(tmp17.shape, 0.0, tmp17.dtype)
    tmp19 = tl.where(tmp11, tmp17, tmp18)
    tmp20 = tl.where(tmp4, tmp10, tmp19)
    tl.store(out_ptr0 + (x2), tmp20, xmask)
''', 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, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, ), (1, ), torch.float32)
        # Topologically Sorted Source Nodes: [phase, setitem], Original ATen: [aten.atan2, aten.lift_fresh, aten.index_put]
        stream0 = get_raw_stream(0)
        triton_poi_fused_atan2_index_put_lift_fresh_0.run(arg0_1, buf0, 4, grid=grid(4), stream=stream0)
        buf1 = empty_strided_cuda((4, 2), (2, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x, setitem_1], Original ATen: [aten.stack, aten.lift_fresh, aten.index_put]
        triton_poi_fused_index_put_lift_fresh_stack_1.run(arg0_1, buf0, buf1, 8, grid=grid(8), stream=stream0)
        del arg0_1
        del buf0
        buf2 = empty_strided_cuda((4, 2), (2, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_1], Original ATen: [aten.stack]
        triton_poi_fused_stack_2.run(buf1, buf2, 8, grid=grid(8), stream=stream0)
        del buf1
    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, 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 torch.nn as nn


def cylindricalToPolarConversion(input1, input2=None):
    if input2 is None:
        """input1 is tensor of [B,C,H,W,D,2] contains both real and imaginary channels
         in the last dims"""
        ndims = input1.ndimension()
        real_input = input1.narrow(ndims - 1, 0, 1).squeeze(ndims - 1)
        imag_input = input1.narrow(ndims - 1, 1, 1).squeeze(ndims - 1)
        mag = (real_input ** 2 + imag_input ** 2) ** 0.5
        phase = torch.atan2(imag_input, real_input)
        phase[phase.ne(phase)] = 0.0
        return torch.stack((mag, phase), dim=input1.ndimension() - 1)
    else:
        """input1 is real part and input2 is imaginary part; both of size [B,C,H,W,D]"""
        mag = (input1 ** 2 + input2 ** 2) ** 0.5
        phase = torch.atan2(input2, input1)
        phase[phase.ne(phase)] = 0.0
        return mag, phase


def polarToCylindricalConversion(input1, input2=None):
    if input2 is None:
        """input1 is tensor of [B,C,H,W,D,2] contains both magnitude and phase channels
         in the last dims"""
        ndims = input1.ndimension()
        mag_input = input1.narrow(ndims - 1, 0, 1).squeeze(ndims - 1)
        phase_input = input1.narrow(ndims - 1, 1, 1).squeeze(ndims - 1)
        real = mag_input * torch.cos(phase_input)
        imag = mag_input * torch.sin(phase_input)
        return torch.stack((real, imag), dim=input1.ndimension() - 1)
    else:
        """input1 is magnitude part and input2 is phase part; both of size [B,C,H,W,D]"""
        real = input1 * torch.cos(input2)
        imag = input1 * torch.sin(input2)
        return real, imag


def normalizeComplexBatch_byMagnitudeOnly(x, polar=False):
    """ normalize the complex batch by making the magnitude of mean 1 and std 1, and keep the phase as it is"""
    ndims = x.ndimension()
    shift_mean = 1
    if not polar:
        x = cylindricalToPolarConversion(x)
    if ndims == 4:
        mag = x[:, :, :, 0]
        mdims = mag.ndimension()
        mag_shaped = mag.reshape((mag.shape[0], mag.shape[1], mag.shape[2]))
        normalized_mag = (mag - torch.mean(mag_shaped, mdims - 1, keepdim=
            True).unsqueeze(mdims)) / torch.std(mag_shaped, mdims - 1,
            keepdim=True).unsqueeze(mdims) + shift_mean
        x = torch.stack([normalized_mag, x[:, :, :, :, 1]], dim=3)
    elif ndims == 5:
        mag = x[:, :, :, :, 0]
        mdims = mag.ndimension()
        mag_shaped = mag.reshape((mag.shape[0], mag.shape[1], mag.shape[2] *
            mag.shape[3]))
        normalized_mag = (mag - torch.mean(mag_shaped, mdims - 2, keepdim=
            True).unsqueeze(mdims - 1)) / torch.std(mag_shaped, mdims - 2,
            keepdim=True).unsqueeze(mdims - 1) + shift_mean
        x = torch.stack([normalized_mag, x[:, :, :, :, 1]], dim=4)
    elif ndims == 6:
        mag = x[:, :, :, :, :, 0]
        mdims = mag.ndimension()
        mag_shaped = mag.reshape((mag.shape[0], mag.shape[1], mag.shape[2] *
            mag.shape[3] * mag.shape[4]))
        normalized_mag = (mag - torch.mean(mag_shaped, mdims - 3, keepdim=
            True).unsqueeze(mdims - 2)) / torch.std(mag_shaped, mdims - 3,
            keepdim=True).unsqueeze(mdims - 2) + shift_mean
        x = torch.stack([normalized_mag, x[:, :, :, :, :, 1]], dim=5)
    x[x.ne(x)] = 0.0
    if not polar:
        x = polarToCylindricalConversion(x)
    return x


class ComplexBatchNormalize(nn.Module):

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

    def forward(self, input):
        return normalizeComplexBatch_byMagnitudeOnly(input)


def get_inputs():
    return [torch.rand([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 libdevice, math as tl_math
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_atan2_index_put_lift_fresh_0(in_ptr0, out_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 4
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (1 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr0 + 4 * x0, xmask, eviction_policy='evict_last')
    tmp2 = libdevice.atan2(tmp0, tmp1)
    tmp3 = tmp2 != tmp2
    tmp4 = 0.0
    tmp5 = tl.where(tmp3, tmp4, tmp2)
    tl.store(out_ptr0 + x0, tmp5, xmask)


@triton.jit
def triton_poi_fused_index_put_lift_fresh_stack_1(in_ptr0, in_ptr1,
    out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 8
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 2
    x1 = xindex // 2
    x2 = xindex
    tmp0 = x0
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 1, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + 4 * x1, tmp4 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp6 = tmp5 * tmp5
    tmp7 = tl.load(in_ptr0 + (1 + 4 * x1), tmp4 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp8 = tmp7 * tmp7
    tmp9 = tmp6 + tmp8
    tmp10 = libdevice.sqrt(tmp9)
    tmp11 = tl.full(tmp10.shape, 0.0, tmp10.dtype)
    tmp12 = tl.where(tmp4, tmp10, tmp11)
    tmp13 = tmp0 >= tmp3
    tl.full([1], 2, tl.int64)
    tmp16 = tl.load(in_ptr1 + x1, tmp13 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp17 = tl.where(tmp4, tmp12, tmp16)
    tmp18 = tmp17 != tmp17
    tmp19 = 0.0
    tmp20 = tl.where(tmp18, tmp19, tmp17)
    tl.store(out_ptr0 + x2, tmp20, xmask)


@triton.jit
def triton_poi_fused_stack_2(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 8
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 2
    x1 = xindex // 2
    x2 = xindex
    tmp0 = x0
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 1, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + 2 * x1, tmp4 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp6 = tl.load(in_ptr0 + (1 + 2 * x1), tmp4 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp7 = tl_math.cos(tmp6)
    tmp8 = tmp5 * tmp7
    tmp9 = tl.full(tmp8.shape, 0.0, tmp8.dtype)
    tmp10 = tl.where(tmp4, tmp8, tmp9)
    tmp11 = tmp0 >= tmp3
    tl.full([1], 2, tl.int64)
    tmp14 = tl.load(in_ptr0 + 2 * x1, tmp11 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp15 = tl.load(in_ptr0 + (1 + 2 * x1), tmp11 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp16 = tl_math.sin(tmp15)
    tmp17 = tmp14 * tmp16
    tmp18 = tl.full(tmp17.shape, 0.0, tmp17.dtype)
    tmp19 = tl.where(tmp11, tmp17, tmp18)
    tmp20 = tl.where(tmp4, tmp10, tmp19)
    tl.store(out_ptr0 + x2, tmp20, xmask)


def call(args):
    arg0_1, = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4), (4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4,), (1,), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_atan2_index_put_lift_fresh_0[grid(4)](arg0_1, buf0,
            4, XBLOCK=4, num_warps=1, num_stages=1)
        buf1 = empty_strided_cuda((4, 2), (2, 1), torch.float32)
        triton_poi_fused_index_put_lift_fresh_stack_1[grid(8)](arg0_1, buf0,
            buf1, 8, XBLOCK=8, num_warps=1, num_stages=1)
        del arg0_1
        del buf0
        buf2 = empty_strided_cuda((4, 2), (2, 1), torch.float32)
        triton_poi_fused_stack_2[grid(8)](buf1, buf2, 8, XBLOCK=8,
            num_warps=1, num_stages=1)
        del buf1
    return buf2,


def cylindricalToPolarConversion(input1, input2=None):
    if input2 is None:
        """input1 is tensor of [B,C,H,W,D,2] contains both real and imaginary channels
         in the last dims"""
        ndims = input1.ndimension()
        real_input = input1.narrow(ndims - 1, 0, 1).squeeze(ndims - 1)
        imag_input = input1.narrow(ndims - 1, 1, 1).squeeze(ndims - 1)
        mag = (real_input ** 2 + imag_input ** 2) ** 0.5
        phase = torch.atan2(imag_input, real_input)
        phase[phase.ne(phase)] = 0.0
        return torch.stack((mag, phase), dim=input1.ndimension() - 1)
    else:
        """input1 is real part and input2 is imaginary part; both of size [B,C,H,W,D]"""
        mag = (input1 ** 2 + input2 ** 2) ** 0.5
        phase = torch.atan2(input2, input1)
        phase[phase.ne(phase)] = 0.0
        return mag, phase


def polarToCylindricalConversion(input1, input2=None):
    if input2 is None:
        """input1 is tensor of [B,C,H,W,D,2] contains both magnitude and phase channels
         in the last dims"""
        ndims = input1.ndimension()
        mag_input = input1.narrow(ndims - 1, 0, 1).squeeze(ndims - 1)
        phase_input = input1.narrow(ndims - 1, 1, 1).squeeze(ndims - 1)
        real = mag_input * torch.cos(phase_input)
        imag = mag_input * torch.sin(phase_input)
        return torch.stack((real, imag), dim=input1.ndimension() - 1)
    else:
        """input1 is magnitude part and input2 is phase part; both of size [B,C,H,W,D]"""
        real = input1 * torch.cos(input2)
        imag = input1 * torch.sin(input2)
        return real, imag


def normalizeComplexBatch_byMagnitudeOnly(x, polar=False):
    """ normalize the complex batch by making the magnitude of mean 1 and std 1, and keep the phase as it is"""
    ndims = x.ndimension()
    shift_mean = 1
    if not polar:
        x = cylindricalToPolarConversion(x)
    if ndims == 4:
        mag = x[:, :, :, 0]
        mdims = mag.ndimension()
        mag_shaped = mag.reshape((mag.shape[0], mag.shape[1], mag.shape[2]))
        normalized_mag = (mag - torch.mean(mag_shaped, mdims - 1, keepdim=
            True).unsqueeze(mdims)) / torch.std(mag_shaped, mdims - 1,
            keepdim=True).unsqueeze(mdims) + shift_mean
        x = torch.stack([normalized_mag, x[:, :, :, :, 1]], dim=3)
    elif ndims == 5:
        mag = x[:, :, :, :, 0]
        mdims = mag.ndimension()
        mag_shaped = mag.reshape((mag.shape[0], mag.shape[1], mag.shape[2] *
            mag.shape[3]))
        normalized_mag = (mag - torch.mean(mag_shaped, mdims - 2, keepdim=
            True).unsqueeze(mdims - 1)) / torch.std(mag_shaped, mdims - 2,
            keepdim=True).unsqueeze(mdims - 1) + shift_mean
        x = torch.stack([normalized_mag, x[:, :, :, :, 1]], dim=4)
    elif ndims == 6:
        mag = x[:, :, :, :, :, 0]
        mdims = mag.ndimension()
        mag_shaped = mag.reshape((mag.shape[0], mag.shape[1], mag.shape[2] *
            mag.shape[3] * mag.shape[4]))
        normalized_mag = (mag - torch.mean(mag_shaped, mdims - 3, keepdim=
            True).unsqueeze(mdims - 2)) / torch.std(mag_shaped, mdims - 3,
            keepdim=True).unsqueeze(mdims - 2) + shift_mean
        x = torch.stack([normalized_mag, x[:, :, :, :, :, 1]], dim=5)
    x[x.ne(x)] = 0.0
    if not polar:
        x = polarToCylindricalConversion(x)
    return x


class ComplexBatchNormalizeNew(nn.Module):

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

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