# 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/in/cinvfeqy6ry6tu24qtajhlnsldmnir4gax6ikx6vnnqb4avr5ien.py
# Topologically Sorted Source Nodes: [w_scale, truediv, sigmoid, weight], Original ATen: [aten.norm, aten.div, aten.sigmoid, aten.mul]
# Source node to ATen node mapping:
#   sigmoid => sigmoid
#   truediv => div
#   w_scale => abs_1, pow_2, sum_1
#   weight => mul
# Graph fragment:
#   %abs_1 : [num_users=1] = call_function[target=torch.ops.aten.abs.default](args = (%view,), kwargs = {})
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%abs_1, [1]), kwargs = {})
#   %pow_2 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%sum_1, 1.0), kwargs = {})
#   %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%primals_1, %view_1), kwargs = {})
#   %sigmoid : [num_users=1] = call_function[target=torch.ops.aten.sigmoid.default](args = (%primals_2,), kwargs = {})
#   %mul : [num_users=2] = call_function[target=torch.ops.aten.mul.Tensor](args = (%div, %sigmoid), kwargs = {})
triton_per_fused_div_mul_norm_sigmoid_0 = async_compile.triton('triton_per_fused_div_mul_norm_sigmoid_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=[4, 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': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 5), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_div_mul_norm_sigmoid_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, '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_div_mul_norm_sigmoid_0(in_out_ptr0, in_ptr0, in_ptr1, out_ptr0, xnumel, rnumel, XBLOCK : tl.constexpr):
    xnumel = 4
    rnumel = 64
    RBLOCK: tl.constexpr = 64
    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)
    r1 = rindex
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (r1 + (64*x0)), xmask, other=0.0)
    tmp7 = tl.load(in_ptr1 + (x0), xmask, eviction_policy='evict_last')
    tmp1 = tl_math.abs(tmp0)
    tmp2 = tl.broadcast_to(tmp1, [XBLOCK, RBLOCK])
    tmp4 = tl.where(xmask, tmp2, 0)
    tmp5 = tl.sum(tmp4, 1)[:, None]
    tmp6 = tmp0 / tmp5
    tmp8 = tl.sigmoid(tmp7)
    tmp9 = tmp6 * tmp8
    tl.debug_barrier()
    tl.store(in_out_ptr0 + (x0), tmp5, xmask)
    tl.store(out_ptr0 + (r1 + (64*x0)), tmp9, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/2i/c2ia6clymej2axaxwh5dhlf5hhex6emmkbazo7542zo3gcyaffyw.py
# Topologically Sorted Source Nodes: [conv2d], Original ATen: [aten.convolution]
# Source node to ATen node mapping:
#   conv2d => convolution
# Graph fragment:
#   %convolution : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%primals_4, %mul, %primals_3, [1, 1], [4, 4], [1, 1], False, [0, 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=[2048], 
    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_1', '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_1(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 1296
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = (xindex // 81) % 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')


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, 1, 1, 1), (1, 1, 1, 1))
    assert_size_stride(primals_3, (4, ), (1, ))
    assert_size_stride(primals_4, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, ), (1, ), torch.float32)
        buf1 = buf0; del buf0  # reuse
        buf2 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [w_scale, truediv, sigmoid, weight], Original ATen: [aten.norm, aten.div, aten.sigmoid, aten.mul]
        stream0 = get_raw_stream(0)
        triton_per_fused_div_mul_norm_sigmoid_0.run(buf1, primals_1, primals_2, buf2, 4, 64, grid=grid(4), stream=stream0)
        # Topologically Sorted Source Nodes: [conv2d], Original ATen: [aten.convolution]
        buf3 = extern_kernels.convolution(primals_4, buf2, stride=(1, 1), padding=(4, 4), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf3, (4, 4, 9, 9), (324, 81, 9, 1))
        buf4 = buf3; del buf3  # reuse
        # Topologically Sorted Source Nodes: [conv2d], Original ATen: [aten.convolution]
        triton_poi_fused_convolution_1.run(buf4, primals_3, 1296, grid=grid(1296), stream=stream0)
        del primals_3
    return (buf4, primals_1, primals_2, primals_4, reinterpret_tensor(buf1, (4, 1, 1, 1), (1, 1, 1, 1), 0), 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, 1, 1, 1), (1, 1, 1, 1), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((4, 4, 4, 4), (64, 16, 4, 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
from torch import nn
import torch.nn.functional as F
import torch.utils.data.distributed


def _max_except_dim(input, dim):
    maxed = input
    for axis in range(input.ndimension() - 1, dim, -1):
        maxed, _ = maxed.max(axis, keepdim=True)
    for axis in range(dim - 1, -1, -1):
        maxed, _ = maxed.max(axis, keepdim=True)
    return maxed


def _norm_except_dim(w, norm_type, dim):
    if norm_type == 1 or norm_type == 2:
        return torch.norm_except_dim(w, norm_type, dim)
    elif norm_type == float('inf'):
        return _max_except_dim(w, dim)


def operator_norm_settings(domain, codomain):
    if domain == 1 and codomain == 1:
        max_across_input_dims = True
        norm_type = 1
    elif domain == 1 and codomain == 2:
        max_across_input_dims = True
        norm_type = 2
    elif domain == 1 and codomain == float('inf'):
        max_across_input_dims = True
        norm_type = float('inf')
    elif domain == 2 and codomain == float('inf'):
        max_across_input_dims = False
        norm_type = 2
    elif domain == float('inf') and codomain == float('inf'):
        max_across_input_dims = False
        norm_type = 1
    else:
        raise ValueError('Unknown combination of domain "{}" and codomain "{}"'
            .format(domain, codomain))
    return max_across_input_dims, norm_type


def _logit(p):
    p = torch.max(torch.ones(1) * 0.1, torch.min(torch.ones(1) * 0.9, p))
    return torch.log(p + 1e-10) + torch.log(1 - p + 1e-10)


class LipNormConv2d(nn.Conv2d):
    """Lipschitz constant defined using operator norms."""

    def __init__(self, in_channels, out_channels, kernel_size, stride,
        padding, bias=True, coeff=0.97, domain=float('inf'), codomain=float
        ('inf'), local_constraint=True, **unused_kwargs):
        del unused_kwargs
        super(LipNormConv2d, self).__init__(in_channels, out_channels,
            kernel_size, stride, padding, bias)
        self.coeff = coeff
        self.domain = domain
        self.codomain = codomain
        self.local_constraint = local_constraint
        max_across_input_dims, self.norm_type = operator_norm_settings(self
            .domain, self.codomain)
        self.max_across_dim = 1 if max_across_input_dims else 0
        with torch.no_grad():
            w_scale = _norm_except_dim(self.weight, self.norm_type, dim=
                self.max_across_dim)
            if not self.local_constraint:
                w_scale = w_scale.max()
            self.scale = nn.Parameter(_logit(w_scale / self.coeff))

    def compute_weight(self):
        w_scale = _norm_except_dim(self.weight, self.norm_type, dim=self.
            max_across_dim)
        if not self.local_constraint:
            w_scale = w_scale.max()
        return self.weight / w_scale * torch.sigmoid(self.scale)

    def forward(self, input):
        weight = self.compute_weight()
        return F.conv2d(input, weight, self.bias, self.stride, self.padding,
            1, 1)

    def extra_repr(self):
        s = super(LipNormConv2d, self).extra_repr()
        return s + ', coeff={}, domain={}, codomain={}, local={}'.format(self
            .coeff, self.domain, self.codomain, self.local_constraint)


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


def get_init_inputs():
    return [[], {'in_channels': 4, 'out_channels': 4, 'kernel_size': 4,
        'stride': 1, 'padding': 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
from torch import nn
import torch.utils.data.distributed
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_per_fused_div_mul_norm_sigmoid_0(in_out_ptr0, in_ptr0, in_ptr1,
    out_ptr0, xnumel, rnumel, XBLOCK: tl.constexpr):
    xnumel = 4
    RBLOCK: tl.constexpr = 64
    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)
    r1 = rindex
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (r1 + 64 * x0), xmask, other=0.0)
    tmp7 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last')
    tmp1 = tl_math.abs(tmp0)
    tmp2 = tl.broadcast_to(tmp1, [XBLOCK, RBLOCK])
    tmp4 = tl.where(xmask, tmp2, 0)
    tmp5 = tl.sum(tmp4, 1)[:, None]
    tmp6 = tmp0 / tmp5
    tmp8 = tl.sigmoid(tmp7)
    tmp9 = tmp6 * tmp8
    tl.debug_barrier()
    tl.store(in_out_ptr0 + x0, tmp5, xmask)
    tl.store(out_ptr0 + (r1 + 64 * x0), tmp9, xmask)


@triton.jit
def triton_poi_fused_convolution_1(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl
    .constexpr):
    xnumel = 1296
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 81 % 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)


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, 1, 1, 1), (1, 1, 1, 1))
    assert_size_stride(primals_3, (4,), (1,))
    assert_size_stride(primals_4, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4,), (1,), torch.float32)
        buf1 = buf0
        del buf0
        buf2 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_per_fused_div_mul_norm_sigmoid_0[grid(4)](buf1, primals_1,
            primals_2, buf2, 4, 64, XBLOCK=1, num_warps=2, num_stages=1)
        buf3 = extern_kernels.convolution(primals_4, buf2, stride=(1, 1),
            padding=(4, 4), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf3, (4, 4, 9, 9), (324, 81, 9, 1))
        buf4 = buf3
        del buf3
        triton_poi_fused_convolution_1[grid(1296)](buf4, primals_3, 1296,
            XBLOCK=128, num_warps=4, num_stages=1)
        del primals_3
    return buf4, primals_1, primals_2, primals_4, reinterpret_tensor(buf1,
        (4, 1, 1, 1), (1, 1, 1, 1), 0), buf2


def _max_except_dim(input, dim):
    maxed = input
    for axis in range(input.ndimension() - 1, dim, -1):
        maxed, _ = maxed.max(axis, keepdim=True)
    for axis in range(dim - 1, -1, -1):
        maxed, _ = maxed.max(axis, keepdim=True)
    return maxed


def _norm_except_dim(w, norm_type, dim):
    if norm_type == 1 or norm_type == 2:
        return torch.norm_except_dim(w, norm_type, dim)
    elif norm_type == float('inf'):
        return _max_except_dim(w, dim)


def operator_norm_settings(domain, codomain):
    if domain == 1 and codomain == 1:
        max_across_input_dims = True
        norm_type = 1
    elif domain == 1 and codomain == 2:
        max_across_input_dims = True
        norm_type = 2
    elif domain == 1 and codomain == float('inf'):
        max_across_input_dims = True
        norm_type = float('inf')
    elif domain == 2 and codomain == float('inf'):
        max_across_input_dims = False
        norm_type = 2
    elif domain == float('inf') and codomain == float('inf'):
        max_across_input_dims = False
        norm_type = 1
    else:
        raise ValueError('Unknown combination of domain "{}" and codomain "{}"'
            .format(domain, codomain))
    return max_across_input_dims, norm_type


def _logit(p):
    p = torch.max(torch.ones(1) * 0.1, torch.min(torch.ones(1) * 0.9, p))
    return torch.log(p + 1e-10) + torch.log(1 - p + 1e-10)


class LipNormConv2dNew(nn.Conv2d):
    """Lipschitz constant defined using operator norms."""

    def __init__(self, in_channels, out_channels, kernel_size, stride,
        padding, bias=True, coeff=0.97, domain=float('inf'), codomain=float
        ('inf'), local_constraint=True, **unused_kwargs):
        del unused_kwargs
        super(LipNormConv2dNew, self).__init__(in_channels, out_channels,
            kernel_size, stride, padding, bias)
        self.coeff = coeff
        self.domain = domain
        self.codomain = codomain
        self.local_constraint = local_constraint
        max_across_input_dims, self.norm_type = operator_norm_settings(self
            .domain, self.codomain)
        self.max_across_dim = 1 if max_across_input_dims else 0
        with torch.no_grad():
            w_scale = _norm_except_dim(self.weight, self.norm_type, dim=
                self.max_across_dim)
            if not self.local_constraint:
                w_scale = w_scale.max()
            self.scale = nn.Parameter(_logit(w_scale / self.coeff))

    def compute_weight(self):
        w_scale = _norm_except_dim(self.weight, self.norm_type, dim=self.
            max_across_dim)
        if not self.local_constraint:
            w_scale = w_scale.max()
        return self.weight / w_scale * torch.sigmoid(self.scale)

    def extra_repr(self):
        s = super(LipNormConv2dNew, self).extra_repr()
        return s + ', coeff={}, domain={}, codomain={}, local={}'.format(self
            .coeff, self.domain, self.codomain, self.local_constraint)

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