# 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_6/inductor_cache/kt/cktr2fi4oyvatsyylkr2wbxli7afdvnkso4duaghvvhtpd2q6ygy.py
# Topologically Sorted Source Nodes: [mean, sub, pow_1, mean_1, var, log, sum_1, logdet], Original ATen: [aten.mean, aten.sub, aten.pow, aten.add, aten.log, aten.sum, aten.mul]
# Source node to ATen node mapping:
#   log => log
#   logdet => mul
#   mean => mean
#   mean_1 => mean_1
#   pow_1 => pow_1
#   sub => sub
#   sum_1 => sum_1
#   var => add
# Graph fragment:
#   %mean : [num_users=3] = call_function[target=torch.ops.aten.mean.dim](args = (%arg0_1, [0]), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%arg0_1, %mean), kwargs = {})
#   %pow_1 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%sub, 2), kwargs = {})
#   %mean_1 : [num_users=1] = call_function[target=torch.ops.aten.mean.dim](args = (%pow_1, [0]), kwargs = {})
#   %add : [num_users=3] = call_function[target=torch.ops.aten.add.Tensor](args = (%mean_1, 1e-05), kwargs = {})
#   %log : [num_users=1] = call_function[target=torch.ops.aten.log.default](args = (%add,), kwargs = {})
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.default](args = (%log,), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sum_1, -0.5), kwargs = {})
triton_per_fused_add_log_mean_mul_pow_sub_sum_0 = async_compile.triton('triton_per_fused_add_log_mean_mul_pow_sub_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_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: 'i32', 5: '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': {4: 1}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 5), equal_to_1=(4,))]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_add_log_mean_mul_pow_sub_sum_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 4, '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_add_log_mean_mul_pow_sub_sum_0(in_out_ptr0, in_ptr0, out_ptr0, out_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 + (r0), None)
    tmp1 = tl.load(in_ptr0 + (64 + r0), None)
    tmp3 = tl.load(in_ptr0 + (128 + r0), None)
    tmp5 = tl.load(in_ptr0 + (192 + r0), None)
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tmp6 = tmp4 + tmp5
    tmp7 = 4.0
    tmp8 = tmp6 / tmp7
    tmp9 = tmp0 - tmp8
    tmp10 = tmp9 * tmp9
    tmp11 = tmp1 - tmp8
    tmp12 = tmp11 * tmp11
    tmp13 = tmp10 + tmp12
    tmp14 = tmp3 - tmp8
    tmp15 = tmp14 * tmp14
    tmp16 = tmp13 + tmp15
    tmp17 = tmp5 - tmp8
    tmp18 = tmp17 * tmp17
    tmp19 = tmp16 + tmp18
    tmp20 = tmp19 / tmp7
    tmp21 = 1e-05
    tmp22 = tmp20 + tmp21
    tmp23 = tl_math.log(tmp22)
    tmp24 = tl.broadcast_to(tmp23, [XBLOCK, RBLOCK])
    tmp26 = tl.sum(tmp24, 1)[:, None]
    tmp27 = -0.5
    tmp28 = tmp26 * tmp27
    tl.store(out_ptr0 + (tl.broadcast_to(r0, [XBLOCK, RBLOCK])), tmp8, None)
    tl.store(out_ptr1 + (tl.broadcast_to(r0, [XBLOCK, RBLOCK])), tmp22, None)
    tl.debug_barrier()
    tl.store(in_out_ptr0 + (tl.full([XBLOCK, 1], 0, tl.int32)), tmp28, None)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/xm/cxmt6duqqeteu4xwsym3p3vmedzj7nayhm62z7v7nl2hxjisngyy.py
# Topologically Sorted Source Nodes: [sub_1, sqrt, u], Original ATen: [aten.sub, aten.sqrt, aten.div]
# Source node to ATen node mapping:
#   sqrt => sqrt
#   sub_1 => sub_1
#   u => div
# Graph fragment:
#   %sub_1 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%arg0_1, %mean), kwargs = {})
#   %sqrt : [num_users=1] = call_function[target=torch.ops.aten.sqrt.default](args = (%add,), kwargs = {})
#   %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%sub_1, %sqrt), kwargs = {})
triton_poi_fused_div_sqrt_sub_1 = async_compile.triton('triton_poi_fused_div_sqrt_sub_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=[256], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 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': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_div_sqrt_sub_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_div_sqrt_sub_1(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 64
    tmp0 = tl.load(in_ptr0 + (x2), xmask)
    tmp1 = tl.load(in_ptr1 + (x0), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr2 + (x0), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 - tmp1
    tmp4 = libdevice.sqrt(tmp3)
    tmp5 = tmp2 / tmp4
    tl.store(out_ptr0 + (x2), tmp5, 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, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        buf1 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        buf3 = empty_strided_cuda((), (), torch.float32)
        buf4 = buf3; del buf3  # reuse
        # Topologically Sorted Source Nodes: [mean, sub, pow_1, mean_1, var, log, sum_1, logdet], Original ATen: [aten.mean, aten.sub, aten.pow, aten.add, aten.log, aten.sum, aten.mul]
        stream0 = get_raw_stream(0)
        triton_per_fused_add_log_mean_mul_pow_sub_sum_0.run(buf4, arg0_1, buf0, buf1, 1, 64, grid=grid(1), stream=stream0)
        buf2 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [sub_1, sqrt, u], Original ATen: [aten.sub, aten.sqrt, aten.div]
        triton_poi_fused_div_sqrt_sub_1.run(arg0_1, buf0, buf1, buf2, 256, grid=grid(256), stream=stream0)
        del arg0_1
    return (buf2, buf4, buf1, buf0, )


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)
    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 numpy as np
from torch import tensor
import torch.nn as nn
import numpy.random as rng


class BaseFlow(nn.Module):
    """ """

    def __init__(self, n_inputs, **kwargs):
        super(BaseFlow, self).__init__()
        self.n_inputs = n_inputs

    def forward(self, x, **kwargs):
        raise NotImplementedError

    def generate_samples(self, n_samples=1, u=None, **kwargs):
        raise NotImplementedError

    def log_likelihood(self, x, **kwargs):
        """ Calculates log p(x) with a Gaussian base density """
        u, logdet_dudx = self.forward(x, **kwargs)
        constant = float(-0.5 * self.n_inputs * np.log(2.0 * np.pi))
        log_likelihood = constant - 0.5 * torch.sum(u ** 2, dim=1
            ) + logdet_dudx
        return u, log_likelihood

    def log_likelihood_and_score(self, x, **kwargs):
        """ Calculates log p(x) and t(x) with a Gaussian base density """
        u, log_likelihood = self.log_likelihood(x, **kwargs)
        return u, log_likelihood, None


class BatchNorm(BaseFlow):
    """BatchNorm implementation"""

    def __init__(self, n_inputs, alpha=0.1, eps=1e-05):
        super(BatchNorm, self).__init__(n_inputs)
        self.n_inputs = n_inputs
        self.alpha = alpha
        self.eps = eps
        self.calculated_running_mean = False
        self.running_mean = torch.zeros(self.n_inputs)
        self.running_var = torch.zeros(self.n_inputs)

    def forward(self, x, fixed_params=False):
        """Calculates x -> u(x) (batch norming)"""
        if fixed_params:
            mean = self.running_mean
            var = self.running_var
        else:
            mean = torch.mean(x, dim=0)
            var = torch.mean((x - mean) ** 2, dim=0) + self.eps
            if not self.calculated_running_mean:
                self.running_mean = mean
                self.running_var = var
            else:
                self.running_mean = (1.0 - self.alpha
                    ) * self.running_mean + self.alpha * mean
                self.running_var = (1.0 - self.alpha
                    ) * self.running_var + self.alpha * var
                self.calculated_running_mean = True
        u = (x - mean) / torch.sqrt(var)
        logdet = -0.5 * torch.sum(torch.log(var))
        return u, logdet

    def inverse(self, u):
        """Calculates u -> x(u) (the approximate inverse transformation based on running mean and variance)"""
        x = torch.sqrt(self.running_var) * u + self.running_mean
        return x

    def generate_samples(self, n_samples=1, u=None, **kwargs):
        if u is None:
            u = tensor(rng.randn(n_samples, self.n_inputs))
        x = torch.sqrt(self.running_var) * u + self.running_mean
        return x

    def to(self, *args, **kwargs):
        logger.debug('Transforming BatchNorm to %s', args)
        self = super()
        self.running_mean = self.running_mean
        self.running_var = self.running_var
        return self


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


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

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 numpy as np
from torch import tensor
import torch.nn as nn
import numpy.random as rng
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda


@triton.jit
def triton_per_fused_add_log_mean_mul_pow_sub_sum_0(in_out_ptr0, in_ptr0,
    out_ptr0, out_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 + r0, None)
    tmp1 = tl.load(in_ptr0 + (64 + r0), None)
    tmp3 = tl.load(in_ptr0 + (128 + r0), None)
    tmp5 = tl.load(in_ptr0 + (192 + r0), None)
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tmp6 = tmp4 + tmp5
    tmp7 = 4.0
    tmp8 = tmp6 / tmp7
    tmp9 = tmp0 - tmp8
    tmp10 = tmp9 * tmp9
    tmp11 = tmp1 - tmp8
    tmp12 = tmp11 * tmp11
    tmp13 = tmp10 + tmp12
    tmp14 = tmp3 - tmp8
    tmp15 = tmp14 * tmp14
    tmp16 = tmp13 + tmp15
    tmp17 = tmp5 - tmp8
    tmp18 = tmp17 * tmp17
    tmp19 = tmp16 + tmp18
    tmp20 = tmp19 / tmp7
    tmp21 = 1e-05
    tmp22 = tmp20 + tmp21
    tmp23 = tl_math.log(tmp22)
    tmp24 = tl.broadcast_to(tmp23, [XBLOCK, RBLOCK])
    tmp26 = tl.sum(tmp24, 1)[:, None]
    tmp27 = -0.5
    tmp28 = tmp26 * tmp27
    tl.store(out_ptr0 + tl.broadcast_to(r0, [XBLOCK, RBLOCK]), tmp8, None)
    tl.store(out_ptr1 + tl.broadcast_to(r0, [XBLOCK, RBLOCK]), tmp22, None)
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp28, None)


@triton.jit
def triton_poi_fused_div_sqrt_sub_1(in_ptr0, in_ptr1, in_ptr2, out_ptr0,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 64
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr2 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 - tmp1
    tmp4 = libdevice.sqrt(tmp3)
    tmp5 = tmp2 / tmp4
    tl.store(out_ptr0 + x2, tmp5, xmask)


def call(args):
    arg0_1, = args
    args.clear()
    assert_size_stride(arg0_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), (16, 4, 1), torch.float32)
        buf1 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        buf3 = empty_strided_cuda((), (), torch.float32)
        buf4 = buf3
        del buf3
        get_raw_stream(0)
        triton_per_fused_add_log_mean_mul_pow_sub_sum_0[grid(1)](buf4,
            arg0_1, buf0, buf1, 1, 64, XBLOCK=1, num_warps=2, num_stages=1)
        buf2 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused_div_sqrt_sub_1[grid(256)](arg0_1, buf0, buf1, buf2,
            256, XBLOCK=256, num_warps=4, num_stages=1)
        del arg0_1
    return buf2, buf4, buf1, buf0


class BaseFlow(nn.Module):
    """ """

    def __init__(self, n_inputs, **kwargs):
        super(BaseFlow, self).__init__()
        self.n_inputs = n_inputs

    def forward(self, x, **kwargs):
        raise NotImplementedError

    def generate_samples(self, n_samples=1, u=None, **kwargs):
        raise NotImplementedError

    def log_likelihood(self, x, **kwargs):
        """ Calculates log p(x) with a Gaussian base density """
        u, logdet_dudx = self.forward(x, **kwargs)
        constant = float(-0.5 * self.n_inputs * np.log(2.0 * np.pi))
        log_likelihood = constant - 0.5 * torch.sum(u ** 2, dim=1
            ) + logdet_dudx
        return u, log_likelihood

    def log_likelihood_and_score(self, x, **kwargs):
        """ Calculates log p(x) and t(x) with a Gaussian base density """
        u, log_likelihood = self.log_likelihood(x, **kwargs)
        return u, log_likelihood, None


class BatchNormNew(BaseFlow):
    """BatchNorm implementation"""

    def __init__(self, n_inputs, alpha=0.1, eps=1e-05):
        super(BatchNormNew, self).__init__(n_inputs)
        self.n_inputs = n_inputs
        self.alpha = alpha
        self.eps = eps
        self.calculated_running_mean = False
        self.running_mean = torch.zeros(self.n_inputs)
        self.running_var = torch.zeros(self.n_inputs)

    def inverse(self, u):
        """Calculates u -> x(u) (the approximate inverse transformation based on running mean and variance)"""
        x = torch.sqrt(self.running_var) * u + self.running_mean
        return x

    def generate_samples(self, n_samples=1, u=None, **kwargs):
        if u is None:
            u = tensor(rng.randn(n_samples, self.n_inputs))
        x = torch.sqrt(self.running_var) * u + self.running_mean
        return x

    def to(self, *args, **kwargs):
        logger.debug('Transforming BatchNorm to %s', args)
        self = super()
        self.running_mean = self.running_mean
        self.running_var = self.running_var
        return self

    def forward(self, input_0):
        arg0_1 = input_0
        output = call([arg0_1])
        return output[0], output[1]
