# AOT ID: ['0_forward']
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/2d/c2dur4cqvjdmojoen7nz625qbl3hymirj3ihsdj2scx7nk2xzzx5.py
# Topologically Sorted Source Nodes: [logdet, log_det_W], Original ATen: [aten.eq, aten.mul]
# Source node to ATen node mapping:
#   log_det_W => mul
#   logdet => eq
# Graph fragment:
#   %eq : [num_users=2] = call_function[target=torch.ops.aten.eq.Scalar](args = (%getitem, -1.0), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%squeeze_1, 16), kwargs = {})
triton_poi_fused_eq_mul_0 = async_compile.triton('triton_poi_fused_eq_mul_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=[1], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*i1', 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': {3: 1}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=(3,))]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_eq_mul_0', 'mutated_arg_names': ['in_out_ptr0'], '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_eq_mul_0(in_out_ptr0, in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 1
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    tmp0 = tl.load(in_ptr0 + (0))
    tmp1 = tl.broadcast_to(tmp0, [XBLOCK])
    tmp4 = tl.load(in_out_ptr0 + (0))
    tmp5 = tl.broadcast_to(tmp4, [XBLOCK])
    tmp2 = -1.0
    tmp3 = tmp1 == tmp2
    tmp6 = float("nan")
    tmp7 = tl.where(tmp3, tmp6, tmp5)
    tmp8 = 16.0
    tmp9 = tmp7 * tmp8
    tl.store(out_ptr0 + (tl.full([XBLOCK], 0, tl.int32)), tmp3, None)
    tl.store(in_out_ptr0 + (tl.full([XBLOCK], 0, tl.int32)), tmp9, None)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/fv/cfvl2yytwjnvovj3pb564j4e36ftzegt465t5hi3f4s6v27gm42g.py
# Topologically Sorted Source Nodes: [z], Original ATen: [aten.convolution]
# Source node to ATen node mapping:
#   z => convolution
# Graph fragment:
#   %convolution : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%primals_1, %primals_2, None, [1], [0], [1], False, [0], 1), kwargs = {})
triton_poi_fused_convolution_1 = async_compile.triton('triton_poi_fused_convolution_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=[4, 4], tile_hint=TileHint.SQUARE,
    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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, '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_convolution_1(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr):
    ynumel = 4
    xnumel = 4
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x1 = xindex
    y0 = yindex
    tmp0 = tl.load(in_ptr0 + (y0 + (4*x1)), xmask & ymask)
    tl.store(out_ptr0 + (x1 + (4*y0)), tmp0, xmask & ymask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    primals_1, primals_2 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_2, (4, 4, 1), (1, 4, 4))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        # Topologically Sorted Source Nodes: [logdet], Original ATen: [aten._linalg_slogdet]
        buf0 = torch.ops.aten._linalg_slogdet.default(reinterpret_tensor(primals_2, (1, 4, 4), (0, 1, 4), 0))
        buf1 = buf0[0]
        buf2 = buf0[1]
        buf3 = buf0[2]
        buf4 = buf0[3]
        del buf0
        buf5 = empty_strided_cuda((1, ), (1, ), torch.bool)
        buf8 = reinterpret_tensor(buf2, (), (), 0); del buf2  # reuse
        # Topologically Sorted Source Nodes: [logdet, log_det_W], Original ATen: [aten.eq, aten.mul]
        stream0 = get_raw_stream(0)
        triton_poi_fused_eq_mul_0.run(buf8, buf1, buf5, 1, grid=grid(1), stream=stream0)
        del buf1
        buf6 = empty_strided_cuda((4, 4, 1), (4, 1, 1), torch.float32)
        # Topologically Sorted Source Nodes: [z], Original ATen: [aten.convolution]
        triton_poi_fused_convolution_1.run(primals_2, buf6, 4, 4, grid=grid(4, 4), stream=stream0)
        # Topologically Sorted Source Nodes: [z], Original ATen: [aten.convolution]
        buf7 = extern_kernels.convolution(primals_1, buf6, stride=(1,), padding=(0,), dilation=(1,), transposed=False, output_padding=(0,), groups=1, bias=None)
        assert_size_stride(buf7, (4, 4, 4), (16, 4, 1))
        del buf6
    return (buf7, buf8, primals_1, primals_2, buf3, buf4, buf5, )


def benchmark_compiled_module(times=10, repeat=10):
    from torch._dynamo.testing import rand_strided
    from torch._inductor.utils import print_performance
    primals_1 = rand_strided((4, 4, 4), (16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((4, 4, 1), (1, 4, 4), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2])
    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 as F
from torch.autograd import Variable
import torch.utils.data
import torch.nn


class Invertible1x1Conv(torch.nn.Module):
    """
    The layer outputs both the convolution, and the log determinant
    of its weight matrix.  If reverse=True it does convolution with
    inverse
    """

    def __init__(self, c):
        super(Invertible1x1Conv, self).__init__()
        self.conv = torch.nn.Conv1d(c, c, kernel_size=1, stride=1, padding=
            0, bias=False)
        W = torch.qr(torch.FloatTensor(c, c).normal_())[0]
        if torch.det(W) < 0:
            W[:, 0] = -1 * W[:, 0]
        W = W.view(c, c, 1)
        self.conv.weight.data = W

    def forward(self, z):
        batch_size, _group_size, n_of_groups = z.size()
        W = self.conv.weight.squeeze()
        log_det_W = batch_size * n_of_groups * torch.logdet(W.unsqueeze(0).
            float()).squeeze()
        z = self.conv(z)
        return z, log_det_W

    def infer(self, z):
        _batch_size, _group_size, _n_of_groups = z.size()
        W = self.conv.weight.squeeze()
        if not hasattr(self, 'W_inverse'):
            W_inverse = W.float().inverse()
            W_inverse = Variable(W_inverse[..., None])
            if z.type() == 'torch.cuda.HalfTensor' or z.type(
                ) == 'torch.HalfTensor':
                W_inverse = W_inverse.half()
            self.W_inverse = W_inverse
        z = F.conv1d(z, self.W_inverse, bias=None, stride=1, padding=0)
        return z


def get_inputs():
    return [torch.rand([4, 4, 4])]


def get_init_inputs():
    return [[], {'c': 4}]

import torch
from torch._inductor.select_algorithm import extern_kernels
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
import torch.nn.functional as F
from torch.autograd import Variable
import torch.utils.data
import torch.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_eq_mul_0(in_out_ptr0, in_ptr0, out_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    tmp0 = tl.load(in_ptr0 + 0)
    tmp1 = tl.broadcast_to(tmp0, [XBLOCK])
    tmp4 = tl.load(in_out_ptr0 + 0)
    tmp5 = tl.broadcast_to(tmp4, [XBLOCK])
    tmp2 = -1.0
    tmp3 = tmp1 == tmp2
    tmp6 = float('nan')
    tmp7 = tl.where(tmp3, tmp6, tmp5)
    tmp8 = 16.0
    tmp9 = tmp7 * tmp8
    tl.store(out_ptr0 + tl.full([XBLOCK], 0, tl.int32), tmp3, None)
    tl.store(in_out_ptr0 + tl.full([XBLOCK], 0, tl.int32), tmp9, None)


@triton.jit
def triton_poi_fused_convolution_1(in_ptr0, out_ptr0, ynumel, xnumel,
    YBLOCK: tl.constexpr, XBLOCK: tl.constexpr):
    ynumel = 4
    xnumel = 4
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x1 = xindex
    y0 = yindex
    tmp0 = tl.load(in_ptr0 + (y0 + 4 * x1), xmask & ymask)
    tl.store(out_ptr0 + (x1 + 4 * y0), tmp0, xmask & ymask)


def call(args):
    primals_1, primals_2 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_2, (4, 4, 1), (1, 4, 4))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = torch.ops.aten._linalg_slogdet.default(reinterpret_tensor(
            primals_2, (1, 4, 4), (0, 1, 4), 0))
        buf1 = buf0[0]
        buf2 = buf0[1]
        buf3 = buf0[2]
        buf4 = buf0[3]
        del buf0
        buf5 = empty_strided_cuda((1,), (1,), torch.bool)
        buf8 = reinterpret_tensor(buf2, (), (), 0)
        del buf2
        get_raw_stream(0)
        triton_poi_fused_eq_mul_0[grid(1)](buf8, buf1, buf5, 1, XBLOCK=1,
            num_warps=1, num_stages=1)
        del buf1
        buf6 = empty_strided_cuda((4, 4, 1), (4, 1, 1), torch.float32)
        triton_poi_fused_convolution_1[grid(4, 4)](primals_2, buf6, 4, 4,
            XBLOCK=4, YBLOCK=4, num_warps=1, num_stages=1)
        buf7 = extern_kernels.convolution(primals_1, buf6, stride=(1,),
            padding=(0,), dilation=(1,), transposed=False, output_padding=(
            0,), groups=1, bias=None)
        assert_size_stride(buf7, (4, 4, 4), (16, 4, 1))
        del buf6
    return buf7, buf8, primals_1, primals_2, buf3, buf4, buf5


class Invertible1x1ConvNew(torch.nn.Module):
    """
    The layer outputs both the convolution, and the log determinant
    of its weight matrix.  If reverse=True it does convolution with
    inverse
    """

    def __init__(self, c):
        super(Invertible1x1ConvNew, self).__init__()
        self.conv = torch.nn.Conv1d(c, c, kernel_size=1, stride=1, padding=
            0, bias=False)
        W = torch.qr(torch.FloatTensor(c, c).normal_())[0]
        if torch.det(W) < 0:
            W[:, 0] = -1 * W[:, 0]
        W = W.view(c, c, 1)
        self.conv.weight.data = W

    def infer(self, z):
        _batch_size, _group_size, _n_of_groups = z.size()
        W = self.conv.weight.squeeze()
        if not hasattr(self, 'W_inverse'):
            W_inverse = W.float().inverse()
            W_inverse = Variable(W_inverse[..., None])
            if z.type() == 'torch.cuda.HalfTensor' or z.type(
                ) == 'torch.HalfTensor':
                W_inverse = W_inverse.half()
            self.W_inverse = W_inverse
        z = F.conv1d(z, self.W_inverse, bias=None, stride=1, padding=0)
        return z

    def forward(self, input_0):
        primals_2 = self.conv.weight
        primals_1 = input_0
        output = call([primals_1, primals_2])
        return output[0], output[1]
