# 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_7/inductor_cache/pw/cpw5jgywzg5ntkknxkt5orxsrrr5zq7a6eoteboi3ba7zrcxj2p7.py
# Topologically Sorted Source Nodes: [conv2d], Original ATen: [aten.convolution]
# Source node to ATen node mapping:
#   conv2d => convolution
# Graph fragment:
#   %convolution : [num_users=2] = call_function[target=torch.ops.aten.convolution.default](args = (%getitem, %primals_2, %primals_3, [1, 1], [1, 1], [1, 1], False, [0, 0], 1), kwargs = {})
triton_poi_fused_convolution_0 = async_compile.triton('triton_poi_fused_convolution_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_convolution_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_convolution_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = (xindex // 16) % 4
    tmp0 = tl.load(in_out_ptr0 + (x3), xmask)
    tmp1 = tl.load(in_ptr0 + (x1), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + (x3), tmp2, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/q3/cq3kbhforo3b3fwckbdclbntms4uvq3k27nhsuqzdfmodbuldqt6.py
# Topologically Sorted Source Nodes: [sub, neg, exp_1, z2, sum_1, logdet], Original ATen: [aten.sub, aten.neg, aten.exp, aten.mul, aten.sum]
# Source node to ATen node mapping:
#   exp_1 => exp_1
#   logdet => neg_1
#   neg => neg
#   sub => sub
#   sum_1 => sum_1
#   z2 => mul_1
# Graph fragment:
#   %sub : [num_users=2] = call_function[target=torch.ops.aten.sub.Tensor](args = (%getitem_1, %slice_2), kwargs = {})
#   %neg : [num_users=1] = call_function[target=torch.ops.aten.neg.default](args = (%slice_6,), kwargs = {})
#   %exp_1 : [num_users=1] = call_function[target=torch.ops.aten.exp.default](args = (%neg,), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub, %exp_1), kwargs = {})
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%slice_6, [1, 2, 3]), kwargs = {})
#   %neg_1 : [num_users=1] = call_function[target=torch.ops.aten.neg.default](args = (%sum_1,), kwargs = {})
triton_per_fused_exp_mul_neg_sub_sum_1 = async_compile.triton('triton_per_fused_exp_mul_neg_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=[4, 32],
    reduction_hint=ReductionHint.DEFAULT,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: '*fp32', 6: 'i32', 7: '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, 3, 4, 5, 7), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_exp_mul_neg_sub_sum_1', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 5, '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_exp_mul_neg_sub_sum_1(in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, out_ptr0, out_ptr1, xnumel, rnumel, XBLOCK : tl.constexpr):
    xnumel = 4
    rnumel = 32
    RBLOCK: tl.constexpr = 32
    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)
    r3 = rindex
    x0 = xindex
    r1 = rindex % 16
    r2 = (rindex // 16)
    tmp0 = tl.load(in_ptr0 + (32 + r3 + (64*x0)), xmask, other=0.0)
    tmp1 = tl.load(in_ptr1 + (r1 + (32*r2) + (64*x0)), xmask, other=0.0)
    tmp2 = tl.load(in_ptr2 + (2*r2), None, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr1 + (16 + r1 + (32*r2) + (64*x0)), xmask, other=0.0)
    tmp7 = tl.load(in_ptr2 + (1 + (2*r2)), None, eviction_policy='evict_last')
    tmp3 = tl_math.exp(tmp2)
    tmp4 = tmp1 * tmp3
    tmp5 = tmp0 - tmp4
    tmp8 = tl_math.exp(tmp7)
    tmp9 = tmp6 * tmp8
    tmp10 = -tmp9
    tmp11 = tl_math.exp(tmp10)
    tmp12 = tmp5 * tmp11
    tmp13 = tl.broadcast_to(tmp9, [XBLOCK, RBLOCK])
    tmp15 = tl.where(xmask, tmp13, 0)
    tmp16 = tl.sum(tmp15, 1)[:, None]
    tmp17 = -tmp16
    tl.store(out_ptr0 + (r3 + (32*x0)), tmp5, xmask)
    tl.store(out_ptr1 + (r3 + (32*x0)), tmp12, xmask)
    tl.debug_barrier()
    tl.store(in_out_ptr0 + (x0), tmp17, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    primals_1, primals_2, primals_3, primals_4 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (4, 2, 3, 3), (18, 9, 3, 1))
    assert_size_stride(primals_3, (4, ), (1, ))
    assert_size_stride(primals_4, (4, 1, 1), (1, 1, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        # Topologically Sorted Source Nodes: [conv2d], Original ATen: [aten.convolution]
        buf0 = extern_kernels.convolution(reinterpret_tensor(primals_1, (4, 2, 4, 4), (64, 16, 4, 1), 0), primals_2, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf0, (4, 4, 4, 4), (64, 16, 4, 1))
        buf1 = buf0; del buf0  # reuse
        # Topologically Sorted Source Nodes: [conv2d], Original ATen: [aten.convolution]
        stream0 = get_raw_stream(0)
        triton_poi_fused_convolution_0.run(buf1, primals_3, 256, grid=grid(256), stream=stream0)
        del primals_3
        buf2 = empty_strided_cuda((4, 2, 4, 4), (32, 16, 4, 1), torch.float32)
        buf3 = empty_strided_cuda((4, 2, 4, 4), (32, 16, 4, 1), torch.float32)
        buf4 = empty_strided_cuda((4, ), (1, ), torch.float32)
        buf5 = buf4; del buf4  # reuse
        # Topologically Sorted Source Nodes: [sub, neg, exp_1, z2, sum_1, logdet], Original ATen: [aten.sub, aten.neg, aten.exp, aten.mul, aten.sum]
        triton_per_fused_exp_mul_neg_sub_sum_1.run(buf5, primals_1, buf1, primals_4, buf2, buf3, 4, 32, grid=grid(4), stream=stream0)
    return (reinterpret_tensor(primals_1, (4, 2, 4, 4), (64, 16, 4, 1), 0), buf3, buf5, primals_2, primals_4, reinterpret_tensor(primals_1, (4, 2, 4, 4), (64, 16, 4, 1), 0), buf1, buf2, )


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, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((4, 2, 3, 3), (18, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((4, 1, 1), (1, 1, 1), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2, primals_3, primals_4])
    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


class Gaussianize(nn.Module):
    """ Gaussianization per RealNVP sec 3.6 / fig 4b -- at each step half the variables are directly modeled as Gaussians.
    Model as Gaussians:
        x2 = z2 * exp(logs) + mu, so x2 ~ N(mu, exp(logs)^2) where mu, logs = f(x1)
    then to recover the random numbers z driving the model:
        z2 = (x2 - mu) * exp(-logs)
    Here f(x1) is a conv layer initialized to identity.
    """

    def __init__(self, n_channels):
        super().__init__()
        self.net = nn.Conv2d(n_channels, 2 * n_channels, kernel_size=3,
            padding=1)
        self.log_scale_factor = nn.Parameter(torch.zeros(2 * n_channels, 1, 1))
        self.net.weight.data.zero_()
        self.net.bias.data.zero_()

    def forward(self, x1, x2):
        h = self.net(x1) * self.log_scale_factor.exp()
        m, logs = h[:, 0::2, :, :], h[:, 1::2, :, :]
        z2 = (x2 - m) * torch.exp(-logs)
        logdet = -logs.sum([1, 2, 3])
        return z2, logdet

    def inverse(self, x1, z2):
        h = self.net(x1) * self.log_scale_factor.exp()
        m, logs = h[:, 0::2, :, :], h[:, 1::2, :, :]
        x2 = m + z2 * torch.exp(logs)
        logdet = logs.sum([1, 2, 3])
        return x2, logdet


class Split(nn.Module):
    """ Split layer; cf Glow figure 2 / RealNVP figure 4b
    Based on RealNVP multi-scale architecture: splits an input in half along the channel dim; half the vars are
    directly modeled as Gaussians while the other half undergo further transformations (cf RealNVP figure 4b).
    """

    def __init__(self, n_channels):
        super().__init__()
        self.gaussianize = Gaussianize(n_channels // 2)

    def forward(self, x):
        x1, x2 = x.chunk(2, dim=1)
        z2, logdet = self.gaussianize(x1, x2)
        return x1, z2, logdet

    def inverse(self, x1, z2):
        x2, logdet = self.gaussianize.inverse(x1, z2)
        x = torch.cat([x1, x2], dim=1)
        return x, logdet


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


def get_init_inputs():
    return [[], {'n_channels': 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
from torch._inductor.runtime.triton_helpers import 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
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor


@triton.jit
def triton_poi_fused_convolution_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl
    .constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 16 % 4
    tmp0 = tl.load(in_out_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + x3, tmp2, xmask)


@triton.jit
def triton_per_fused_exp_mul_neg_sub_sum_1(in_out_ptr0, in_ptr0, in_ptr1,
    in_ptr2, out_ptr0, out_ptr1, xnumel, rnumel, XBLOCK: tl.constexpr):
    xnumel = 4
    RBLOCK: tl.constexpr = 32
    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)
    r3 = rindex
    x0 = xindex
    r1 = rindex % 16
    r2 = rindex // 16
    tmp0 = tl.load(in_ptr0 + (32 + r3 + 64 * x0), xmask, other=0.0)
    tmp1 = tl.load(in_ptr1 + (r1 + 32 * r2 + 64 * x0), xmask, other=0.0)
    tmp2 = tl.load(in_ptr2 + 2 * r2, None, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr1 + (16 + r1 + 32 * r2 + 64 * x0), xmask, other=0.0)
    tmp7 = tl.load(in_ptr2 + (1 + 2 * r2), None, eviction_policy='evict_last')
    tmp3 = tl_math.exp(tmp2)
    tmp4 = tmp1 * tmp3
    tmp5 = tmp0 - tmp4
    tmp8 = tl_math.exp(tmp7)
    tmp9 = tmp6 * tmp8
    tmp10 = -tmp9
    tmp11 = tl_math.exp(tmp10)
    tmp12 = tmp5 * tmp11
    tmp13 = tl.broadcast_to(tmp9, [XBLOCK, RBLOCK])
    tmp15 = tl.where(xmask, tmp13, 0)
    tmp16 = tl.sum(tmp15, 1)[:, None]
    tmp17 = -tmp16
    tl.store(out_ptr0 + (r3 + 32 * x0), tmp5, xmask)
    tl.store(out_ptr1 + (r3 + 32 * x0), tmp12, xmask)
    tl.debug_barrier()
    tl.store(in_out_ptr0 + x0, tmp17, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (4, 2, 3, 3), (18, 9, 3, 1))
    assert_size_stride(primals_3, (4,), (1,))
    assert_size_stride(primals_4, (4, 1, 1), (1, 1, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = extern_kernels.convolution(reinterpret_tensor(primals_1, (4,
            2, 4, 4), (64, 16, 4, 1), 0), primals_2, stride=(1, 1), padding
            =(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 
            0), groups=1, bias=None)
        assert_size_stride(buf0, (4, 4, 4, 4), (64, 16, 4, 1))
        buf1 = buf0
        del buf0
        get_raw_stream(0)
        triton_poi_fused_convolution_0[grid(256)](buf1, primals_3, 256,
            XBLOCK=256, num_warps=4, num_stages=1)
        del primals_3
        buf2 = empty_strided_cuda((4, 2, 4, 4), (32, 16, 4, 1), torch.float32)
        buf3 = empty_strided_cuda((4, 2, 4, 4), (32, 16, 4, 1), torch.float32)
        buf4 = empty_strided_cuda((4,), (1,), torch.float32)
        buf5 = buf4
        del buf4
        triton_per_fused_exp_mul_neg_sub_sum_1[grid(4)](buf5, primals_1,
            buf1, primals_4, buf2, buf3, 4, 32, XBLOCK=1, num_warps=2,
            num_stages=1)
    return reinterpret_tensor(primals_1, (4, 2, 4, 4), (64, 16, 4, 1), 0
        ), buf3, buf5, primals_2, primals_4, reinterpret_tensor(primals_1,
        (4, 2, 4, 4), (64, 16, 4, 1), 0), buf1, buf2


class Gaussianize(nn.Module):
    """ Gaussianization per RealNVP sec 3.6 / fig 4b -- at each step half the variables are directly modeled as Gaussians.
    Model as Gaussians:
        x2 = z2 * exp(logs) + mu, so x2 ~ N(mu, exp(logs)^2) where mu, logs = f(x1)
    then to recover the random numbers z driving the model:
        z2 = (x2 - mu) * exp(-logs)
    Here f(x1) is a conv layer initialized to identity.
    """

    def __init__(self, n_channels):
        super().__init__()
        self.net = nn.Conv2d(n_channels, 2 * n_channels, kernel_size=3,
            padding=1)
        self.log_scale_factor = nn.Parameter(torch.zeros(2 * n_channels, 1, 1))
        self.net.weight.data.zero_()
        self.net.bias.data.zero_()

    def forward(self, x1, x2):
        h = self.net(x1) * self.log_scale_factor.exp()
        m, logs = h[:, 0::2, :, :], h[:, 1::2, :, :]
        z2 = (x2 - m) * torch.exp(-logs)
        logdet = -logs.sum([1, 2, 3])
        return z2, logdet

    def inverse(self, x1, z2):
        h = self.net(x1) * self.log_scale_factor.exp()
        m, logs = h[:, 0::2, :, :], h[:, 1::2, :, :]
        x2 = m + z2 * torch.exp(logs)
        logdet = logs.sum([1, 2, 3])
        return x2, logdet


class SplitNew(nn.Module):
    """ Split layer; cf Glow figure 2 / RealNVP figure 4b
    Based on RealNVP multi-scale architecture: splits an input in half along the channel dim; half the vars are
    directly modeled as Gaussians while the other half undergo further transformations (cf RealNVP figure 4b).
    """

    def __init__(self, n_channels):
        super().__init__()
        self.gaussianize = Gaussianize(n_channels // 2)

    def inverse(self, x1, z2):
        x2, logdet = self.gaussianize.inverse(x1, z2)
        x = torch.cat([x1, x2], dim=1)
        return x, logdet

    def forward(self, input_0):
        primals_4 = self.gaussianize.log_scale_factor
        primals_2 = self.gaussianize.net.weight
        primals_3 = self.gaussianize.net.bias
        primals_1 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4])
        return output[0], output[1], output[2]
