import torch
import torch.nn as nn
import torch.cuda
import torch.distributed


class SimpleFusionGenerator(nn.Module):

    def __init__(self, decoder_input_size, lm_input_size, output_size):
        super(SimpleFusionGenerator, self).__init__()
        self.decoder_linear = nn.Linear(decoder_input_size, output_size)
        self.lm_linear = nn.Linear(lm_input_size, output_size, bias=False)
        self.gen_func = nn.LogSoftmax(dim=-1)

    def forward(self, decoder_hidden, lm_hidden):
        """
        Compute a distribution over the target dictionary
        extended by the dynamic dictionary implied by copying
        source words.

        Args:
           decoder_hidden (FloatTensor): hidden outputs ``(batch x tlen, input_size)``
           lm_hidden (FloatTensor): hidden outputs ``(batch x tlen, input_size)``
        """
        decoder_logits = self.decoder_linear(decoder_hidden)
        lm_logits = self.lm_linear(lm_hidden)
        logits = (decoder_logits + lm_logits).float()
        log_probs = self.gen_func(logits)
        return log_probs


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


def get_init_inputs():
    return [[], {'decoder_input_size': 4, 'lm_input_size': 4, 'output_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.cuda
import torch.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_poi_fused__log_softmax_add_0(in_ptr0, in_ptr1, in_ptr2, out_ptr0,
    out_ptr1, xnumel, XBLOCK: tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + 4 * x0, xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + 0)
    tmp2 = tl.broadcast_to(tmp1, [XBLOCK])
    tmp4 = tl.load(in_ptr2 + 4 * x0, xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (1 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr1 + 1)
    tmp8 = tl.broadcast_to(tmp7, [XBLOCK])
    tmp10 = tl.load(in_ptr2 + (1 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp13 = tl.load(in_ptr0 + (2 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp14 = tl.load(in_ptr1 + 2)
    tmp15 = tl.broadcast_to(tmp14, [XBLOCK])
    tmp17 = tl.load(in_ptr2 + (2 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp20 = tl.load(in_ptr0 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp21 = tl.load(in_ptr1 + 3)
    tmp22 = tl.broadcast_to(tmp21, [XBLOCK])
    tmp24 = tl.load(in_ptr2 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp3 = tmp0 + tmp2
    tmp5 = tmp3 + tmp4
    tmp9 = tmp6 + tmp8
    tmp11 = tmp9 + tmp10
    tmp12 = triton_helpers.maximum(tmp5, tmp11)
    tmp16 = tmp13 + tmp15
    tmp18 = tmp16 + tmp17
    tmp19 = triton_helpers.maximum(tmp12, tmp18)
    tmp23 = tmp20 + tmp22
    tmp25 = tmp23 + tmp24
    tmp26 = triton_helpers.maximum(tmp19, tmp25)
    tmp27 = tmp5 - tmp26
    tmp28 = tl_math.exp(tmp27)
    tmp29 = tmp11 - tmp26
    tmp30 = tl_math.exp(tmp29)
    tmp31 = tmp28 + tmp30
    tmp32 = tmp18 - tmp26
    tmp33 = tl_math.exp(tmp32)
    tmp34 = tmp31 + tmp33
    tmp35 = tmp25 - tmp26
    tmp36 = tl_math.exp(tmp35)
    tmp37 = tmp34 + tmp36
    tl.store(out_ptr0 + x0, tmp26, xmask)
    tl.store(out_ptr1 + x0, tmp37, xmask)


@triton.jit
def triton_poi_fused__log_softmax_add_1(in_out_ptr0, in_ptr0, in_ptr1,
    in_ptr2, in_ptr3, 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
    x1 = xindex // 4
    tmp0 = tl.load(in_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr1 + x2, xmask)
    tmp5 = tl.load(in_ptr2 + x1, xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr3 + x1, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tmp6 = tmp4 - tmp5
    tmp8 = tl_math.log(tmp7)
    tmp9 = tmp6 - tmp8
    tl.store(in_out_ptr0 + x2, tmp9, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5 = 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))
    assert_size_stride(primals_4, (4, 4), (4, 1))
    assert_size_stride(primals_5, (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((64, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_5, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_4, (4, 4), (1, 4), 0), out=buf1)
        del primals_4
        buf2 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 64), torch.float32)
        buf3 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 64), torch.float32)
        get_raw_stream(0)
        triton_poi_fused__log_softmax_add_0[grid(64)](buf0, primals_2, buf1,
            buf2, buf3, 64, XBLOCK=64, num_warps=1, num_stages=1)
        buf4 = reinterpret_tensor(buf0, (4, 4, 4, 4), (64, 16, 4, 1), 0)
        del buf0
        triton_poi_fused__log_softmax_add_1[grid(256)](buf4, primals_2,
            buf1, buf2, buf3, 256, XBLOCK=128, num_warps=4, num_stages=1)
        del buf1
        del buf2
        del buf3
        del primals_2
    return buf4, reinterpret_tensor(primals_3, (64, 4), (4, 1), 0
        ), reinterpret_tensor(primals_5, (64, 4), (4, 1), 0), buf4


class SimpleFusionGeneratorNew(nn.Module):

    def __init__(self, decoder_input_size, lm_input_size, output_size):
        super(SimpleFusionGeneratorNew, self).__init__()
        self.decoder_linear = nn.Linear(decoder_input_size, output_size)
        self.lm_linear = nn.Linear(lm_input_size, output_size, bias=False)
        self.gen_func = nn.LogSoftmax(dim=-1)

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