import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data
import torch.nn.init


class Hidden2Discrete(nn.Module):

    def __init__(self, input_size, y_size, k_size, is_lstm=False, has_bias=True
        ):
        super(Hidden2Discrete, self).__init__()
        self.y_size = y_size
        self.k_size = k_size
        latent_size = self.k_size * self.y_size
        if is_lstm:
            self.p_h = nn.Linear(input_size, latent_size, bias=has_bias)
            self.p_c = nn.Linear(input_size, latent_size, bias=has_bias)
        else:
            self.p_h = nn.Linear(input_size, latent_size, bias=has_bias)
        self.is_lstm = is_lstm

    def forward(self, inputs):
        """
        :param inputs: batch_size x input_size
        :return:
        """
        if self.is_lstm:
            h, c = inputs
            if h.dim() == 3:
                h = h.squeeze(0)
                c = c.squeeze(0)
            logits = self.p_h(h) + self.p_c(c)
        else:
            logits = self.p_h(inputs)
        logits = logits.view(-1, self.k_size)
        log_qy = F.log_softmax(logits, dim=1)
        return logits, log_qy


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


def get_init_inputs():
    return [[], {'input_size': 4, 'y_size': 4, 'k_size': 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 import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
import torch.utils.data
import torch.nn.init
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__log_softmax_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 1024
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x1 = xindex // 4
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp3 = triton_helpers.maximum(tmp1, tmp2)
    tmp5 = triton_helpers.maximum(tmp3, tmp4)
    tmp7 = triton_helpers.maximum(tmp5, tmp6)
    tmp8 = tmp0 - tmp7
    tl.store(out_ptr0 + x2, tmp8, xmask)


@triton.jit
def triton_poi_fused__log_softmax_1(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 1024
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x1 = xindex // 4
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp2 = tl_math.exp(tmp1)
    tmp4 = tl_math.exp(tmp3)
    tmp5 = tmp2 + tmp4
    tmp7 = tl_math.exp(tmp6)
    tmp8 = tmp5 + tmp7
    tmp10 = tl_math.exp(tmp9)
    tmp11 = tmp8 + tmp10
    tmp12 = tl_math.log(tmp11)
    tmp13 = tmp0 - tmp12
    tl.store(out_ptr0 + x2, tmp13, xmask)


def call(args):
    primals_1, primals_2, primals_3 = args
    args.clear()
    assert_size_stride(primals_1, (16, 4), (4, 1))
    assert_size_stride(primals_2, (16,), (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, 16), (16, 1), torch.float32)
        extern_kernels.addmm(primals_2, reinterpret_tensor(primals_3, (64, 
            4), (4, 1), 0), reinterpret_tensor(primals_1, (4, 16), (1, 4), 
            0), alpha=1, beta=1, out=buf0)
        del primals_1
        del primals_2
        buf1 = empty_strided_cuda((256, 4), (4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused__log_softmax_0[grid(1024)](buf0, buf1, 1024,
            XBLOCK=256, num_warps=4, num_stages=1)
        buf2 = empty_strided_cuda((256, 4), (4, 1), torch.float32)
        triton_poi_fused__log_softmax_1[grid(1024)](buf1, buf2, 1024,
            XBLOCK=256, num_warps=4, num_stages=1)
        del buf1
    return reinterpret_tensor(buf0, (256, 4), (4, 1), 0
        ), buf2, reinterpret_tensor(primals_3, (64, 4), (4, 1), 0), buf2


class Hidden2DiscreteNew(nn.Module):

    def __init__(self, input_size, y_size, k_size, is_lstm=False, has_bias=True
        ):
        super(Hidden2DiscreteNew, self).__init__()
        self.y_size = y_size
        self.k_size = k_size
        latent_size = self.k_size * self.y_size
        if is_lstm:
            self.p_h = nn.Linear(input_size, latent_size, bias=has_bias)
            self.p_c = nn.Linear(input_size, latent_size, bias=has_bias)
        else:
            self.p_h = nn.Linear(input_size, latent_size, bias=has_bias)
        self.is_lstm = is_lstm

    def forward(self, input_0):
        primals_1 = self.p_h.weight
        primals_2 = self.p_h.bias
        primals_3 = input_0
        output = call([primals_1, primals_2, primals_3])
        return output[0], output[1]
