# 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_1/inductor_cache/y2/cy25s5y43zkbgrbnvhjujpfwhgrcmw47z3si4uiyfobzuys3jnse.py
# Topologically Sorted Source Nodes: [sigmoid], Original ATen: [aten.sigmoid]
# Source node to ATen node mapping:
#   sigmoid => sigmoid
# Graph fragment:
#   %sigmoid : [num_users=1] = call_function[target=torch.ops.aten.sigmoid.default](args = (%primals_1,), kwargs = {})
triton_poi_fused_sigmoid_0 = async_compile.triton('triton_poi_fused_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.pointwise(
    size_hints=[4], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_sigmoid_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', '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_sigmoid_0(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 4
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (x0), xmask)
    tmp1 = tl.sigmoid(tmp0)
    tl.store(out_ptr0 + (x0), tmp1, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    primals_1, = args
    args.clear()
    assert_size_stride(primals_1, (1, 4), (4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((1, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [sigmoid], Original ATen: [aten.sigmoid]
        stream0 = get_raw_stream(0)
        triton_poi_fused_sigmoid_0.run(primals_1, buf0, 4, grid=grid(4), stream=stream0)
        del primals_1
    return (buf0, buf0, )


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)
    fn = lambda: call([primals_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
import torch.nn as nn
import torch.nn.init as weight_init
from torch.nn import Parameter


class ArchSampler(nn.Module):

    def __init__(self, distrib_dim, all_same, deter_eval, var_names=None, *
        args, **kwargs):
        super().__init__()
        self.distrib_dim = distrib_dim
        self.all_same = all_same
        self.deter_eval = deter_eval
        self.frozen = False
        self.log_probas = None
        self.distrib_entropies = None
        self._seq_probas = None
        if var_names is not None:
            assert len(var_names) == self.distrib_dim
        self.var_names = var_names

    def freeze(self):
        self.frozen = True

    def start_new_sequence(self):
        self.log_probas = []
        self.distrib_entropies = []
        self._seq_probas = []

    def nodes_to_prune(self, treshold):
        nodes = []
        for node, weight in zip(self.var_names, self().squeeze().unbind()):
            if weight < treshold:
                nodes.append(node)
        return nodes

    def sample_archs(self, batch_size, probas):
        """
        Hook called by pytorch before each forward
        :param: Current module
        :param input: Input given to the module's forward
        :return:
        """
        self._check_probas(probas, self.all_same)
        if probas.size(0) != batch_size:
            if probas.size(0) != 1:
                raise ValueError(
                    "Sampling probabilities dimensions {} doesn't match with batch size {}."
                    .format(probas.size(), batch_size))
            if not self.all_same:
                probas = probas.expand(batch_size, -1)
        distrib = torch.distributions.Bernoulli(probas)
        if not self.training and self.deter_eval:
            samplings = (probas > 0.5).float()
        else:
            samplings = distrib.sample()
        if self.all_same:
            samplings = samplings.expand(batch_size, -1)
        self._seq_probas.append(probas)
        self.distrib_entropies.append(distrib.entropy())
        self.log_probas.append(distrib.log_prob(samplings))
        return samplings

    def _check_probas(self, probas, all_same):
        """
        :param probas: B_size*N_nodes Tensor containing the probability of each
         arch being sampled in the nex forward.
        :param all_same: if True, the same sampling will be used for the whole
        batch in the next forward.
        :return:
        """
        if probas.dim() != 2 or all_same and probas.size(0) != 1:
            raise ValueError(
                'probas params has wrong dimension: {} (all_same={})'.
                format(probas.size(), all_same))
        if probas.size(-1) != self.distrib_dim:
            raise ValueError(
                'Should have exactly as many probas as the number of stochastic nodes({}), got {} instead.'
                .format(self.distrib_dim, probas.size(-1)))

    @property
    def last_arch_probas(self):
        return self.probas

    @property
    def last_sequence_probas(self):
        """
        :return: The probabilities of each arch for the last sequence in
        format (seq_len*batch_size*n_sampling_params)
        """
        return torch.stack(self._seq_probas)


class StaticArchGenerator(ArchSampler):

    def __init__(self, initial_p, *args, **kwargs):
        super().__init__(*args, **kwargs)
        self.params = Parameter(torch.Tensor(1, self.distrib_dim))
        logit = np.log(initial_p / (1 - initial_p)
            ) if initial_p < 1 else float('inf')
        weight_init.constant_(self.params, logit)

    def forward(self, z=None):
        if self.frozen and self.training:
            raise RuntimeError(
                'Trying to sample from a frozen distrib gen in training mode')
        return self.params.sigmoid()

    def entropy(self):
        distrib = torch.distributions.Bernoulli(self.params.sigmoid())
        return distrib.entropy()

    def remove_var(self, name):
        assert self.var_names
        self.distrib_dim -= 1
        remove_idx = self.var_names.index(name)
        self.var_names.remove(name)
        all_idx = torch.ones_like(self.params).bool()
        all_idx[0, remove_idx] = 0
        self.params = nn.Parameter(self.params[all_idx].unsqueeze(0))

    def is_deterministic(self):
        distrib = self()
        return torch.equal(distrib, distrib ** 2)


def get_inputs():
    return []


def get_init_inputs():
    return [[], {'initial_p': 4, 'distrib_dim': 4, 'all_same': 4,
        'deter_eval': 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
import numpy as np
import torch.nn as nn
import torch.nn.init as weight_init
from torch.nn import Parameter
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_sigmoid_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 4
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + x0, xmask)
    tmp1 = tl.sigmoid(tmp0)
    tl.store(out_ptr0 + x0, tmp1, xmask)


def call(args):
    primals_1, = args
    args.clear()
    assert_size_stride(primals_1, (1, 4), (4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((1, 4), (4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_sigmoid_0[grid(4)](primals_1, buf0, 4, XBLOCK=4,
            num_warps=1, num_stages=1)
        del primals_1
    return buf0, buf0


class ArchSampler(nn.Module):

    def __init__(self, distrib_dim, all_same, deter_eval, var_names=None, *
        args, **kwargs):
        super().__init__()
        self.distrib_dim = distrib_dim
        self.all_same = all_same
        self.deter_eval = deter_eval
        self.frozen = False
        self.log_probas = None
        self.distrib_entropies = None
        self._seq_probas = None
        if var_names is not None:
            assert len(var_names) == self.distrib_dim
        self.var_names = var_names

    def freeze(self):
        self.frozen = True

    def start_new_sequence(self):
        self.log_probas = []
        self.distrib_entropies = []
        self._seq_probas = []

    def nodes_to_prune(self, treshold):
        nodes = []
        for node, weight in zip(self.var_names, self().squeeze().unbind()):
            if weight < treshold:
                nodes.append(node)
        return nodes

    def sample_archs(self, batch_size, probas):
        """
        Hook called by pytorch before each forward
        :param: Current module
        :param input: Input given to the module's forward
        :return:
        """
        self._check_probas(probas, self.all_same)
        if probas.size(0) != batch_size:
            if probas.size(0) != 1:
                raise ValueError(
                    "Sampling probabilities dimensions {} doesn't match with batch size {}."
                    .format(probas.size(), batch_size))
            if not self.all_same:
                probas = probas.expand(batch_size, -1)
        distrib = torch.distributions.Bernoulli(probas)
        if not self.training and self.deter_eval:
            samplings = (probas > 0.5).float()
        else:
            samplings = distrib.sample()
        if self.all_same:
            samplings = samplings.expand(batch_size, -1)
        self._seq_probas.append(probas)
        self.distrib_entropies.append(distrib.entropy())
        self.log_probas.append(distrib.log_prob(samplings))
        return samplings

    def _check_probas(self, probas, all_same):
        """
        :param probas: B_size*N_nodes Tensor containing the probability of each
         arch being sampled in the nex forward.
        :param all_same: if True, the same sampling will be used for the whole
        batch in the next forward.
        :return:
        """
        if probas.dim() != 2 or all_same and probas.size(0) != 1:
            raise ValueError(
                'probas params has wrong dimension: {} (all_same={})'.
                format(probas.size(), all_same))
        if probas.size(-1) != self.distrib_dim:
            raise ValueError(
                'Should have exactly as many probas as the number of stochastic nodes({}), got {} instead.'
                .format(self.distrib_dim, probas.size(-1)))

    @property
    def last_arch_probas(self):
        return self.probas

    @property
    def last_sequence_probas(self):
        """
        :return: The probabilities of each arch for the last sequence in
        format (seq_len*batch_size*n_sampling_params)
        """
        return torch.stack(self._seq_probas)


class StaticArchGeneratorNew(ArchSampler):

    def __init__(self, initial_p, *args, **kwargs):
        super().__init__(*args, **kwargs)
        self.params = Parameter(torch.Tensor(1, self.distrib_dim))
        logit = np.log(initial_p / (1 - initial_p)
            ) if initial_p < 1 else float('inf')
        weight_init.constant_(self.params, logit)

    def entropy(self):
        distrib = torch.distributions.Bernoulli(self.params.sigmoid())
        return distrib.entropy()

    def remove_var(self, name):
        assert self.var_names
        self.distrib_dim -= 1
        remove_idx = self.var_names.index(name)
        self.var_names.remove(name)
        all_idx = torch.ones_like(self.params).bool()
        all_idx[0, remove_idx] = 0
        self.params = nn.Parameter(self.params[all_idx].unsqueeze(0))

    def is_deterministic(self):
        distrib = self()
        return torch.equal(distrib, distrib ** 2)

    def forward(self):
        primals_1 = self.params
        output = call([primals_1])
        return output[0]
