# 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/sf/csftwll4atghxcbstukhqz22ldgctgbneow377ww7pr5wztahw63.py
# Topologically Sorted Source Nodes: [exp, mul, z, wrapped_mul, pow_1, mul_1, add_1, sum_1, log_p], Original ATen: [aten.exp, aten.mul, aten.add, aten.pow, aten.sum, aten.sub]
# Source node to ATen node mapping:
#   add_1 => add_1
#   exp => exp
#   log_p => sub
#   mul => mul
#   mul_1 => mul_2
#   pow_1 => pow_1
#   sum_1 => sum_1
#   wrapped_mul => full_default
#   z => add
# Graph fragment:
#   %exp : [num_users=1] = call_function[target=torch.ops.aten.exp.default](args = (%primals_2,), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%exp, %randn), kwargs = {})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%primals_1, %mul), kwargs = {})
#   %full_default : [num_users=1] = call_function[target=torch.ops.aten.full.default](args = ([], -3.6757541328186907), kwargs = {dtype: torch.float64, layout: torch.strided, device: cpu, pin_memory: False})
#   %pow_1 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%randn, 2), kwargs = {})
#   %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%pow_1, 0.5), kwargs = {})
#   %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%primals_2, %mul_2), kwargs = {})
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%add_1, [1]), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%full_default, %sum_1), kwargs = {})
triton_per_fused_add_exp_mul_pow_sub_sum_0 = async_compile.triton('triton_per_fused_add_exp_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, 4],
    reduction_hint=ReductionHint.INNER,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: 'i32', 6: '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': {5: 1}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4), equal_to_1=(5,))]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_add_exp_mul_pow_sub_sum_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 3, '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_exp_mul_pow_sub_sum_0(in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel, rnumel, XBLOCK : tl.constexpr):
    xnumel = 1
    rnumel = 4
    RBLOCK: tl.constexpr = 4
    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_ptr1 + (r0), None)
    tmp3 = tl.load(in_ptr2 + (r0), None)
    tmp2 = tl_math.exp(tmp1)
    tmp4 = tmp2 * tmp3
    tmp5 = tmp0 + tmp4
    tmp6 = tmp3 * tmp3
    tmp7 = 0.5
    tmp8 = tmp6 * tmp7
    tmp9 = tmp1 + tmp8
    tmp10 = tl.broadcast_to(tmp9, [XBLOCK, RBLOCK])
    tmp12 = tl.sum(tmp10, 1)[:, None]
    tmp13 = -3.6757541328186907
    tmp14 = tmp13 - tmp12
    tl.store(out_ptr0 + (tl.broadcast_to(r0, [XBLOCK, RBLOCK])), tmp5, None)
    tl.debug_barrier()
    tl.store(in_out_ptr0 + (tl.full([XBLOCK, 1], 0, tl.int32)), tmp14, None)
''', 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, (1, 4), (4, 1))
    assert_size_stride(primals_2, (1, 4), (4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        # Topologically Sorted Source Nodes: [eps], Original ATen: [aten.randn]
        buf0 = torch.ops.aten.randn.default([1, 4], device=device(type='cuda', index=0), pin_memory=False)
        buf1 = buf0
        del buf0
        buf2 = empty_strided_cuda((1, 4), (4, 1), torch.float32)
        buf3 = empty_strided_cuda((1, ), (1, ), torch.float32)
        buf4 = buf3; del buf3  # reuse
        # Topologically Sorted Source Nodes: [exp, mul, z, wrapped_mul, pow_1, mul_1, add_1, sum_1, log_p], Original ATen: [aten.exp, aten.mul, aten.add, aten.pow, aten.sum, aten.sub]
        stream0 = get_raw_stream(0)
        triton_per_fused_add_exp_mul_pow_sub_sum_0.run(buf4, primals_1, primals_2, buf1, buf2, 1, 4, grid=grid(1), stream=stream0)
        del primals_1
    return (buf2, buf4, primals_2, buf1, )


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((1, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((1, 4), (4, 1), 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 numpy as np
import torch.nn as nn
import torch.utils.data


class BaseDistribution(nn.Module):
    """
    Base distribution of a flow-based model
    Parameters do not depend of target variable (as is the case for a VAE encoder)
    """

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

    def forward(self, num_samples=1):
        """
        Samples from base distribution and calculates log probability
        :param num_samples: Number of samples to draw from the distriubtion
        :return: Samples drawn from the distribution, log probability
        """
        raise NotImplementedError

    def log_prob(self, z):
        """
        Calculate log probability of batch of samples
        :param z: Batch of random variables to determine log probability for
        :return: log probability for each batch element
        """
        raise NotImplementedError


class DiagGaussian(BaseDistribution):
    """
    Multivariate Gaussian distribution with diagonal covariance matrix
    """

    def __init__(self, d):
        """
        Constructor
        :param d: Dimension of Gaussian distribution
        """
        super().__init__()
        self.d = d
        self.loc = nn.Parameter(torch.zeros(1, self.d))
        self.log_scale = nn.Parameter(torch.zeros(1, self.d))

    def forward(self, num_samples=1):
        eps = torch.randn((num_samples, self.d), device=self.loc.device)
        z = self.loc + torch.exp(self.log_scale) * eps
        log_p = -0.5 * self.d * np.log(2 * np.pi) - torch.sum(self.
            log_scale + 0.5 * torch.pow(eps, 2), 1)
        return z, log_p

    def log_prob(self, z):
        log_p = -0.5 * self.d * np.log(2 * np.pi) - torch.sum(self.
            log_scale + 0.5 * torch.pow((z - self.loc) / torch.exp(self.
            log_scale), 2), 1)
        return log_p


def get_inputs():
    return []


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

import torch
from torch import device
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 numpy as np
import torch.nn as nn
import torch.utils.data
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_exp_mul_pow_sub_sum_0(in_out_ptr0, in_ptr0,
    in_ptr1, in_ptr2, out_ptr0, xnumel, rnumel, XBLOCK: tl.constexpr):
    RBLOCK: tl.constexpr = 4
    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_ptr1 + r0, None)
    tmp3 = tl.load(in_ptr2 + r0, None)
    tmp2 = tl_math.exp(tmp1)
    tmp4 = tmp2 * tmp3
    tmp5 = tmp0 + tmp4
    tmp6 = tmp3 * tmp3
    tmp7 = 0.5
    tmp8 = tmp6 * tmp7
    tmp9 = tmp1 + tmp8
    tmp10 = tl.broadcast_to(tmp9, [XBLOCK, RBLOCK])
    tmp12 = tl.sum(tmp10, 1)[:, None]
    tmp13 = -3.6757541328186907
    tmp14 = tmp13 - tmp12
    tl.store(out_ptr0 + tl.broadcast_to(r0, [XBLOCK, RBLOCK]), tmp5, None)
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp14, None)


def call(args):
    primals_1, primals_2 = args
    args.clear()
    assert_size_stride(primals_1, (1, 4), (4, 1))
    assert_size_stride(primals_2, (1, 4), (4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = torch.ops.aten.randn.default([1, 4], device=device(type=
            'cuda', index=0), pin_memory=False)
        buf1 = buf0
        del buf0
        buf2 = empty_strided_cuda((1, 4), (4, 1), torch.float32)
        buf3 = empty_strided_cuda((1,), (1,), torch.float32)
        buf4 = buf3
        del buf3
        get_raw_stream(0)
        triton_per_fused_add_exp_mul_pow_sub_sum_0[grid(1)](buf4, primals_1,
            primals_2, buf1, buf2, 1, 4, XBLOCK=1, num_warps=2, num_stages=1)
        del primals_1
    return buf2, buf4, primals_2, buf1


class BaseDistribution(nn.Module):
    """
    Base distribution of a flow-based model
    Parameters do not depend of target variable (as is the case for a VAE encoder)
    """

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

    def forward(self, num_samples=1):
        """
        Samples from base distribution and calculates log probability
        :param num_samples: Number of samples to draw from the distriubtion
        :return: Samples drawn from the distribution, log probability
        """
        raise NotImplementedError

    def log_prob(self, z):
        """
        Calculate log probability of batch of samples
        :param z: Batch of random variables to determine log probability for
        :return: log probability for each batch element
        """
        raise NotImplementedError


class DiagGaussianNew(BaseDistribution):
    """
    Multivariate Gaussian distribution with diagonal covariance matrix
    """

    def __init__(self, d):
        """
        Constructor
        :param d: Dimension of Gaussian distribution
        """
        super().__init__()
        self.d = d
        self.loc = nn.Parameter(torch.zeros(1, self.d))
        self.log_scale = nn.Parameter(torch.zeros(1, self.d))

    def log_prob(self, z):
        log_p = -0.5 * self.d * np.log(2 * np.pi) - torch.sum(self.
            log_scale + 0.5 * torch.pow((z - self.loc) / torch.exp(self.
            log_scale), 2), 1)
        return log_p

    def forward(self):
        primals_1 = self.loc
        primals_2 = self.log_scale
        output = call([primals_1, primals_2])
        return output[0], output[1]
