# 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/zv/czvhqogqibwnm23q44xn6gzvpm2ac5f4wseb2e7zgfnmpvynwgoy.py
# Topologically Sorted Source Nodes: [output, mul, exp, mul_1], Original ATen: [aten.convolution, aten.mul, aten.exp]
# Source node to ATen node mapping:
#   exp => exp
#   mul => mul
#   mul_1 => mul_1
#   output => convolution
# Graph fragment:
#   %convolution : [num_users=2] = call_function[target=torch.ops.aten.convolution.default](args = (%primals_3, %primals_1, %primals_2, [1, 1], [1, 1], [1, 1], False, [0, 0], 1), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%primals_4, 3), kwargs = {})
#   %exp : [num_users=1] = call_function[target=torch.ops.aten.exp.default](args = (%mul,), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%convolution, %exp), kwargs = {})
triton_poi_fused_convolution_exp_mul_0 = async_compile.triton('triton_poi_fused_convolution_exp_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=[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_convolution_exp_mul_0', 'mutated_arg_names': ['in_out_ptr0'], '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_convolution_exp_mul_0(in_out_ptr0, in_ptr0, in_ptr1, out_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')
    tmp3 = tl.load(in_ptr1 + (x1), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp4 = 3.0
    tmp5 = tmp3 * tmp4
    tmp6 = tl_math.exp(tmp5)
    tmp7 = tmp2 * tmp6
    tl.store(in_out_ptr0 + (x3), tmp2, xmask)
    tl.store(out_ptr0 + (x3), tmp7, 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, 3, 3), (36, 9, 3, 1))
    assert_size_stride(primals_2, (4, ), (1, ))
    assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 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: [output], Original ATen: [aten.convolution]
        buf0 = extern_kernels.convolution(primals_3, primals_1, 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
        buf2 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [output, mul, exp, mul_1], Original ATen: [aten.convolution, aten.mul, aten.exp]
        stream0 = get_raw_stream(0)
        triton_poi_fused_convolution_exp_mul_0.run(buf1, primals_2, primals_4, buf2, 256, grid=grid(256), stream=stream0)
        del primals_2
    return (buf2, primals_1, primals_3, primals_4, 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((4, 4, 3, 3), (36, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((4, 4, 4, 4), (64, 16, 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 _ActNorm(nn.Module):
    """
    Activation Normalization
    Initialize the bias and scale with a given minibatch,
    so that the output per-channel have zero mean and unit variance for that.

    After initialization, `bias` and `logs` will be trained as parameters.
    """

    def __init__(self, num_features, scale=1.0):
        super().__init__()
        size = [1, num_features, 1, 1]
        self.register_parameter('bias', nn.Parameter(torch.zeros(*size)))
        self.register_parameter('logs', nn.Parameter(torch.zeros(*size)))
        self.num_features = num_features
        self.scale = float(scale)
        self.inited = False

    def _check_input_dim(self, input):
        return NotImplemented

    def initialize_parameters(self, input):
        self._check_input_dim(input)
        if not self.training:
            return
        assert input.device == self.bias.device
        with torch.no_grad():
            bias = thops.mean(input.clone(), dim=[0, 2, 3], keepdim=True
                ) * -1.0
            vars = thops.mean((input.clone() + bias) ** 2, dim=[0, 2, 3],
                keepdim=True)
            logs = torch.log(self.scale / (torch.sqrt(vars) + 1e-06))
            self.bias.data.copy_(bias.data)
            self.logs.data.copy_(logs.data)
            self.inited = True

    def _center(self, input, reverse=False):
        if not reverse:
            return input + self.bias
        else:
            return input - self.bias

    def _scale(self, input, logdet=None, reverse=False):
        logs = self.logs
        if not reverse:
            input = input * torch.exp(logs)
        else:
            input = input * torch.exp(-logs)
        if logdet is not None:
            """
            logs is log_std of `mean of channels`
            so we need to multiply pixels
            """
            dlogdet = thops.sum(logs) * thops.pixels(input)
            if reverse:
                dlogdet *= -1
            logdet = logdet + dlogdet
        return input, logdet

    def forward(self, input, logdet=None, reverse=False):
        if not self.inited:
            self.initialize_parameters(input)
        self._check_input_dim(input)
        if not reverse:
            input = self._center(input, reverse)
            input, logdet = self._scale(input, logdet, reverse)
        else:
            input, logdet = self._scale(input, logdet, reverse)
            input = self._center(input, reverse)
        return input, logdet


class ActNorm2d(_ActNorm):

    def __init__(self, num_features, scale=1.0):
        super().__init__(num_features, scale)

    def _check_input_dim(self, input):
        assert len(input.size()) == 4
        assert input.size(1
            ) == self.num_features, '[ActNorm]: input should be in shape as `BCHW`, channels should be {} rather than {}'.format(
            self.num_features, input.size())


class Conv2d(nn.Conv2d):
    pad_dict = {'same': lambda kernel, stride: [(((k - 1) * s + 1) // 2) for
        k, s in zip(kernel, stride)], 'valid': lambda kernel, stride: [(0) for
        _ in kernel]}

    @staticmethod
    def get_padding(padding, kernel_size, stride):
        if isinstance(padding, str):
            if isinstance(kernel_size, int):
                kernel_size = [kernel_size, kernel_size]
            if isinstance(stride, int):
                stride = [stride, stride]
            padding = padding.lower()
            try:
                padding = Conv2d.pad_dict[padding](kernel_size, stride)
            except KeyError:
                raise ValueError('{} is not supported'.format(padding))
        return padding

    def __init__(self, in_channels, out_channels, kernel_size=[3, 3],
        stride=[1, 1], padding='same', do_actnorm=True, weight_std=0.05):
        padding = Conv2d.get_padding(padding, kernel_size, stride)
        super().__init__(in_channels, out_channels, kernel_size, stride,
            padding, bias=not do_actnorm)
        self.weight.data.normal_(mean=0.0, std=weight_std)
        if not do_actnorm:
            self.bias.data.zero_()
        else:
            self.actnorm = ActNorm2d(out_channels)
        self.do_actnorm = do_actnorm

    def forward(self, input):
        x = super().forward(input)
        if self.do_actnorm:
            x, _ = self.actnorm(x)
        return x


class Conv2dZeros(nn.Conv2d):

    def __init__(self, in_channels, out_channels, kernel_size=[3, 3],
        stride=[1, 1], padding='same', logscale_factor=3):
        padding = Conv2d.get_padding(padding, kernel_size, stride)
        super().__init__(in_channels, out_channels, kernel_size, stride,
            padding)
        self.logscale_factor = logscale_factor
        self.register_parameter('logs', nn.Parameter(torch.zeros(
            out_channels, 1, 1)))
        self.weight.data.zero_()
        self.bias.data.zero_()

    def forward(self, input):
        output = super().forward(input)
        return output * torch.exp(self.logs * self.logscale_factor)


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


def get_init_inputs():
    return [[], {'in_channels': 4, 'out_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


@triton.jit
def triton_poi_fused_convolution_exp_mul_0(in_out_ptr0, in_ptr0, in_ptr1,
    out_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')
    tmp3 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp4 = 3.0
    tmp5 = tmp3 * tmp4
    tmp6 = tl_math.exp(tmp5)
    tmp7 = tmp2 * tmp6
    tl.store(in_out_ptr0 + x3, tmp2, xmask)
    tl.store(out_ptr0 + x3, tmp7, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 3, 3), (36, 9, 3, 1))
    assert_size_stride(primals_2, (4,), (1,))
    assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 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(primals_3, primals_1, 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
        buf2 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_convolution_exp_mul_0[grid(256)](buf1, primals_2,
            primals_4, buf2, 256, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_2
    return buf2, primals_1, primals_3, primals_4, buf1


class _ActNorm(nn.Module):
    """
    Activation Normalization
    Initialize the bias and scale with a given minibatch,
    so that the output per-channel have zero mean and unit variance for that.

    After initialization, `bias` and `logs` will be trained as parameters.
    """

    def __init__(self, num_features, scale=1.0):
        super().__init__()
        size = [1, num_features, 1, 1]
        self.register_parameter('bias', nn.Parameter(torch.zeros(*size)))
        self.register_parameter('logs', nn.Parameter(torch.zeros(*size)))
        self.num_features = num_features
        self.scale = float(scale)
        self.inited = False

    def _check_input_dim(self, input):
        return NotImplemented

    def initialize_parameters(self, input):
        self._check_input_dim(input)
        if not self.training:
            return
        assert input.device == self.bias.device
        with torch.no_grad():
            bias = thops.mean(input.clone(), dim=[0, 2, 3], keepdim=True
                ) * -1.0
            vars = thops.mean((input.clone() + bias) ** 2, dim=[0, 2, 3],
                keepdim=True)
            logs = torch.log(self.scale / (torch.sqrt(vars) + 1e-06))
            self.bias.data.copy_(bias.data)
            self.logs.data.copy_(logs.data)
            self.inited = True

    def _center(self, input, reverse=False):
        if not reverse:
            return input + self.bias
        else:
            return input - self.bias

    def _scale(self, input, logdet=None, reverse=False):
        logs = self.logs
        if not reverse:
            input = input * torch.exp(logs)
        else:
            input = input * torch.exp(-logs)
        if logdet is not None:
            """
            logs is log_std of `mean of channels`
            so we need to multiply pixels
            """
            dlogdet = thops.sum(logs) * thops.pixels(input)
            if reverse:
                dlogdet *= -1
            logdet = logdet + dlogdet
        return input, logdet

    def forward(self, input, logdet=None, reverse=False):
        if not self.inited:
            self.initialize_parameters(input)
        self._check_input_dim(input)
        if not reverse:
            input = self._center(input, reverse)
            input, logdet = self._scale(input, logdet, reverse)
        else:
            input, logdet = self._scale(input, logdet, reverse)
            input = self._center(input, reverse)
        return input, logdet


class ActNorm2d(_ActNorm):

    def __init__(self, num_features, scale=1.0):
        super().__init__(num_features, scale)

    def _check_input_dim(self, input):
        assert len(input.size()) == 4
        assert input.size(1
            ) == self.num_features, '[ActNorm]: input should be in shape as `BCHW`, channels should be {} rather than {}'.format(
            self.num_features, input.size())


class Conv2d(nn.Conv2d):
    pad_dict = {'same': lambda kernel, stride: [(((k - 1) * s + 1) // 2) for
        k, s in zip(kernel, stride)], 'valid': lambda kernel, stride: [(0) for
        _ in kernel]}

    @staticmethod
    def get_padding(padding, kernel_size, stride):
        if isinstance(padding, str):
            if isinstance(kernel_size, int):
                kernel_size = [kernel_size, kernel_size]
            if isinstance(stride, int):
                stride = [stride, stride]
            padding = padding.lower()
            try:
                padding = Conv2d.pad_dict[padding](kernel_size, stride)
            except KeyError:
                raise ValueError('{} is not supported'.format(padding))
        return padding

    def __init__(self, in_channels, out_channels, kernel_size=[3, 3],
        stride=[1, 1], padding='same', do_actnorm=True, weight_std=0.05):
        padding = Conv2d.get_padding(padding, kernel_size, stride)
        super().__init__(in_channels, out_channels, kernel_size, stride,
            padding, bias=not do_actnorm)
        self.weight.data.normal_(mean=0.0, std=weight_std)
        if not do_actnorm:
            self.bias.data.zero_()
        else:
            self.actnorm = ActNorm2d(out_channels)
        self.do_actnorm = do_actnorm

    def forward(self, input):
        x = super().forward(input)
        if self.do_actnorm:
            x, _ = self.actnorm(x)
        return x


class Conv2dZerosNew(nn.Conv2d):

    def __init__(self, in_channels, out_channels, kernel_size=[3, 3],
        stride=[1, 1], padding='same', logscale_factor=3):
        padding = Conv2d.get_padding(padding, kernel_size, stride)
        super().__init__(in_channels, out_channels, kernel_size, stride,
            padding)
        self.logscale_factor = logscale_factor
        self.register_parameter('logs', nn.Parameter(torch.zeros(
            out_channels, 1, 1)))
        self.weight.data.zero_()
        self.bias.data.zero_()

    def forward(self, input_0):
        primals_1 = self.weight
        primals_2 = self.bias
        primals_4 = self.logs
        primals_3 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4])
        return output[0]
