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 import triton_helpers
import torch as th
from torch.distributions import Categorical
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_relu_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 32
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 8
    tmp0 = tl.load(in_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tl.store(in_out_ptr0 + x2, tmp4, xmask)


@triton.jit
def triton_poi_fused_repeat_1(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 16
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tl.store(out_ptr0 + x2, tmp0, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5, primals_6 = args
    args.clear()
    assert_size_stride(primals_1, (8, 4), (4, 1))
    assert_size_stride(primals_2, (8,), (1,))
    assert_size_stride(primals_3, (4, 4), (4, 1))
    assert_size_stride(primals_4, (4, 8), (8, 1))
    assert_size_stride(primals_5, (4,), (1,))
    assert_size_stride(primals_6, (4, 4), (4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 8), (8, 1), torch.float32)
        extern_kernels.mm(primals_3, reinterpret_tensor(primals_1, (4, 8),
            (1, 4), 0), out=buf0)
        del primals_1
        buf1 = buf0
        del buf0
        get_raw_stream(0)
        triton_poi_fused_relu_0[grid(32)](buf1, primals_2, 32, XBLOCK=32,
            num_warps=1, num_stages=1)
        del primals_2
        buf2 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_5, buf1, reinterpret_tensor(primals_4,
            (8, 4), (1, 8), 0), alpha=1, beta=1, out=buf2)
        del primals_5
        buf3 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        triton_poi_fused_repeat_1[grid(64)](primals_6, buf3, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        del primals_6
        buf4 = empty_strided_cuda((4, 4, 1), (4, 1, 1), torch.float32)
        extern_kernels.bmm(buf3, reinterpret_tensor(buf2, (4, 4, 1), (4, 1,
            1), 0), out=buf4)
        del buf2
    return reinterpret_tensor(buf4, (4, 4), (4, 1), 0
        ), primals_3, buf1, reinterpret_tensor(buf3, (4, 4, 4), (16, 1, 4), 0
        ), primals_4


class DotSelectorNew(nn.Module):

    def __init__(self, input_shape, args):
        super(DotSelectorNew, self).__init__()
        self.args = args
        self.epsilon_start = self.args.epsilon_start
        self.epsilon_finish = self.args.role_epsilon_finish
        self.epsilon_anneal_time = self.args.epsilon_anneal_time
        self.epsilon_anneal_time_exp = self.args.epsilon_anneal_time_exp
        self.delta = (self.epsilon_start - self.epsilon_finish
            ) / self.epsilon_anneal_time
        self.role_action_spaces_update_start = (self.args.
            role_action_spaces_update_start)
        self.epsilon_start_t = 0
        self.epsilon_reset = True
        self.fc1 = nn.Linear(args.rnn_hidden_dim, 2 * args.rnn_hidden_dim)
        self.fc2 = nn.Linear(2 * args.rnn_hidden_dim, args.action_latent_dim)
        self.epsilon = 0.05

    def select_role(self, role_qs, hp, test_mode=False, t_env=None):
        self.epsilon = self.epsilon_schedule(t_env)
        self.calc_roleQ_by_human(hp)
        if test_mode:
            self.epsilon = 0.0
        masked_q_values = role_qs.detach().clone()
        random_numbers = th.rand_like(role_qs[:, 0])
        pick_random = (random_numbers < self.epsilon).long()
        random_roles = Categorical(th.ones(role_qs.shape).float()).sample(
            ).long()
        picked_roles = pick_random * random_roles + (1 - pick_random
            ) * masked_q_values.max(dim=1)[1]
        return picked_roles

    def calc_roleQ_by_human(self, hp):
        human_roleQ = []
        for index1 in range(len(hp)):
            for index2 in range(len(hp[index1])):
                cur_hp = hp[index1, index2]
                if cur_hp >= 0.85:
                    human_roleQ.append(th.FloatTensor([0, 0.8, 0.1, 0.1]))
                elif cur_hp >= 0.7 and cur_hp < 0.85:
                    human_roleQ.append(th.FloatTensor([0, 0.5, 0.5, 0]))
                elif cur_hp < 0.7 and cur_hp >= 0.2:
                    human_roleQ.append(th.FloatTensor([0.5, 0.2, 0.2, 0.1]))
                else:
                    human_roleQ.append(th.FloatTensor([0.6, 0.2, 0.1, 0.1]))
        human_roleQ = th.stack(human_roleQ, dim=0)
        return human_roleQ

    def epsilon_schedule(self, t_env):
        if t_env is None:
            return 0.05
        if t_env > self.role_action_spaces_update_start and self.epsilon_reset:
            self.epsilon_reset = False
            self.epsilon_start_t = t_env
            self.epsilon_anneal_time = self.epsilon_anneal_time_exp
            self.delta = (self.epsilon_start - self.epsilon_finish
                ) / self.epsilon_anneal_time
        if t_env - self.epsilon_start_t > self.epsilon_anneal_time:
            epsilon = self.epsilon_finish
        else:
            epsilon = self.epsilon_start - (t_env - self.epsilon_start_t
                ) * self.delta
        return epsilon

    def forward(self, input_0, input_1):
        primals_1 = self.fc1.weight
        primals_2 = self.fc1.bias
        primals_4 = self.fc2.weight
        primals_5 = self.fc2.bias
        primals_3 = input_0
        primals_6 = input_1
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6])
        return output[0]


Measured latency_ms: 0.366281