# 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_6/inductor_cache/bd/cbdotac6ukup5jfyef3gol4xzuff4mzk4u5pqfhzizchbce25ivd.py
# Topologically Sorted Source Nodes: [x_1], Original ATen: [aten.leaky_relu]
# Source node to ATen node mapping:
#   x_1 => gt, mul, where
# Graph fragment:
#   %gt : [num_users=2] = call_function[target=torch.ops.aten.gt.Scalar](args = (%view_1, 0), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%view_1, 0.1), kwargs = {})
#   %where : [num_users=1] = call_function[target=torch.ops.aten.where.self](args = (%gt, %view_1, %mul), kwargs = {})
triton_poi_fused_leaky_relu_0 = async_compile.triton('triton_poi_fused_leaky_relu_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: '*i1', 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_leaky_relu_0', 'mutated_arg_names': [], '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_leaky_relu_0(in_ptr0, in_ptr1, out_ptr0, out_ptr1, 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 % 4
    tmp0 = tl.load(in_ptr0 + (x2), xmask)
    tmp1 = tl.load(in_ptr1 + (x0), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tmp5 = 0.1
    tmp6 = tmp2 * tmp5
    tmp7 = tl.where(tmp4, tmp2, tmp6)
    tl.store(out_ptr0 + (x2), tmp4, xmask)
    tl.store(out_ptr1 + (x2), tmp7, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    primals_1, primals_2, primals_3 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4), (4, 1))
    assert_size_stride(primals_2, (4, ), (1, ))
    assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(reinterpret_tensor(primals_3, (64, 4), (4, 1), 0), reinterpret_tensor(primals_1, (4, 4), (1, 4), 0), out=buf0)
        del primals_1
        buf1 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
        buf2 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_1], Original ATen: [aten.leaky_relu]
        stream0 = get_raw_stream(0)
        triton_poi_fused_leaky_relu_0.run(buf0, primals_2, buf1, buf2, 256, grid=grid(256), stream=stream0)
        del buf0
        del primals_2
    return (buf2, reinterpret_tensor(primals_3, (64, 4), (4, 1), 0), 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), (4, 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)
    fn = lambda: call([primals_1, primals_2, primals_3])
    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 SharedDropout(nn.Module):
    """
    SharedDropout differs from the vanilla dropout strategy in that the dropout mask is shared across one dimension.

    Args:
        p (float):
            The probability of an element to be zeroed. Default: 0.5.
        batch_first (bool):
            If ``True``, the input and output tensors are provided as ``[batch_size, seq_len, *]``.
            Default: ``True``.

    Examples:
        >>> x = torch.ones(1, 3, 5)
        >>> nn.Dropout()(x)
        tensor([[[0., 2., 2., 0., 0.],
                 [2., 2., 0., 2., 2.],
                 [2., 2., 2., 2., 0.]]])
        >>> SharedDropout()(x)
        tensor([[[2., 0., 2., 0., 2.],
                 [2., 0., 2., 0., 2.],
                 [2., 0., 2., 0., 2.]]])
    """

    def __init__(self, p=0.5, batch_first=True):
        super().__init__()
        self.p = p
        self.batch_first = batch_first

    def __repr__(self):
        s = f'p={self.p}'
        if self.batch_first:
            s += f', batch_first={self.batch_first}'
        return f'{self.__class__.__name__}({s})'

    def forward(self, x):
        """
        Args:
            x (~torch.Tensor):
                A tensor of any shape.
        Returns:
            The returned tensor is of the same shape as `x`.
        """
        if self.training:
            if self.batch_first:
                mask = self.get_mask(x[:, 0], self.p).unsqueeze(1)
            else:
                mask = self.get_mask(x[0], self.p)
            x = x * mask
        return x

    @staticmethod
    def get_mask(x, p):
        return x.new_empty(x.shape).bernoulli_(1 - p) / (1 - p)


class MLP(nn.Module):
    """
    Applies a linear transformation together with a non-linear activation to the incoming tensor:
    :math:`y = \\mathrm{Activation}(x A^T + b)`

    Args:
        n_in (~torch.Tensor):
            The size of each input feature.
        n_out (~torch.Tensor):
            The size of each output feature.
        dropout (float):
            If non-zero, introduces a :class:`SharedDropout` layer on the output with this dropout ratio. Default: 0.
        activation (bool):
            Whether to use activations. Default: True.
    """

    def __init__(self, n_in, n_out, dropout=0, activation=True):
        super().__init__()
        self.n_in = n_in
        self.n_out = n_out
        self.linear = nn.Linear(n_in, n_out)
        self.activation = nn.LeakyReLU(negative_slope=0.1
            ) if activation else nn.Identity()
        self.dropout = SharedDropout(p=dropout)
        self.reset_parameters()

    def __repr__(self):
        s = f'n_in={self.n_in}, n_out={self.n_out}'
        if self.dropout.p > 0:
            s += f', dropout={self.dropout.p}'
        return f'{self.__class__.__name__}({s})'

    def reset_parameters(self):
        nn.init.orthogonal_(self.linear.weight)
        nn.init.zeros_(self.linear.bias)

    def forward(self, x):
        """
        Args:
            x (~torch.Tensor):
                The size of each input feature is `n_in`.

        Returns:
            A tensor with the size of each output feature `n_out`.
        """
        x = self.linear(x)
        x = self.activation(x)
        x = self.dropout(x)
        return x


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


def get_init_inputs():
    return [[], {'n_in': 4, 'n_out': 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
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_leaky_relu_0(in_ptr0, in_ptr1, out_ptr0, out_ptr1,
    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 % 4
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tmp5 = 0.1
    tmp6 = tmp2 * tmp5
    tmp7 = tl.where(tmp4, tmp2, tmp6)
    tl.store(out_ptr0 + x2, tmp4, xmask)
    tl.store(out_ptr1 + x2, tmp7, xmask)


def call(args):
    primals_1, primals_2, primals_3 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4), (4, 1))
    assert_size_stride(primals_2, (4,), (1,))
    assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_3, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_1, (4, 4), (1, 4), 0), out=buf0)
        del primals_1
        buf1 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
        buf2 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_leaky_relu_0[grid(256)](buf0, primals_2, buf1,
            buf2, 256, XBLOCK=256, num_warps=4, num_stages=1)
        del buf0
        del primals_2
    return buf2, reinterpret_tensor(primals_3, (64, 4), (4, 1), 0), buf1


class SharedDropout(nn.Module):
    """
    SharedDropout differs from the vanilla dropout strategy in that the dropout mask is shared across one dimension.

    Args:
        p (float):
            The probability of an element to be zeroed. Default: 0.5.
        batch_first (bool):
            If ``True``, the input and output tensors are provided as ``[batch_size, seq_len, *]``.
            Default: ``True``.

    Examples:
        >>> x = torch.ones(1, 3, 5)
        >>> nn.Dropout()(x)
        tensor([[[0., 2., 2., 0., 0.],
                 [2., 2., 0., 2., 2.],
                 [2., 2., 2., 2., 0.]]])
        >>> SharedDropout()(x)
        tensor([[[2., 0., 2., 0., 2.],
                 [2., 0., 2., 0., 2.],
                 [2., 0., 2., 0., 2.]]])
    """

    def __init__(self, p=0.5, batch_first=True):
        super().__init__()
        self.p = p
        self.batch_first = batch_first

    def __repr__(self):
        s = f'p={self.p}'
        if self.batch_first:
            s += f', batch_first={self.batch_first}'
        return f'{self.__class__.__name__}({s})'

    def forward(self, x):
        """
        Args:
            x (~torch.Tensor):
                A tensor of any shape.
        Returns:
            The returned tensor is of the same shape as `x`.
        """
        if self.training:
            if self.batch_first:
                mask = self.get_mask(x[:, 0], self.p).unsqueeze(1)
            else:
                mask = self.get_mask(x[0], self.p)
            x = x * mask
        return x

    @staticmethod
    def get_mask(x, p):
        return x.new_empty(x.shape).bernoulli_(1 - p) / (1 - p)


class MLPNew(nn.Module):
    """
    Applies a linear transformation together with a non-linear activation to the incoming tensor:
    :math:`y = \\mathrm{Activation}(x A^T + b)`

    Args:
        n_in (~torch.Tensor):
            The size of each input feature.
        n_out (~torch.Tensor):
            The size of each output feature.
        dropout (float):
            If non-zero, introduces a :class:`SharedDropout` layer on the output with this dropout ratio. Default: 0.
        activation (bool):
            Whether to use activations. Default: True.
    """

    def __init__(self, n_in, n_out, dropout=0, activation=True):
        super().__init__()
        self.n_in = n_in
        self.n_out = n_out
        self.linear = nn.Linear(n_in, n_out)
        self.activation = nn.LeakyReLU(negative_slope=0.1
            ) if activation else nn.Identity()
        self.dropout = SharedDropout(p=dropout)
        self.reset_parameters()

    def __repr__(self):
        s = f'n_in={self.n_in}, n_out={self.n_out}'
        if self.dropout.p > 0:
            s += f', dropout={self.dropout.p}'
        return f'{self.__class__.__name__}({s})'

    def reset_parameters(self):
        nn.init.orthogonal_(self.linear.weight)
        nn.init.zeros_(self.linear.bias)

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