#ifndef TH_GENERIC_FILE
#define TH_GENERIC_FILE "generic/HardTanh.c"
#else

static int nn_(HardTanh_updateOutput)(lua_State *L)
{
  THTensor *input = luaT_checkudata(L, 2, torch_Tensor);
  THTensor *output = luaT_getfieldcheckudata(L, 1, "output", torch_Tensor);

  THTensor_(resizeAs)(output, input);
  
  if (input->nDimension == 1 || !THTensor_(isContiguous)(input) || !THTensor_(isContiguous)(output))
  {
    TH_TENSOR_APPLY2(real, output, real, input,     \
         if(*input_data < -1)     \
           *output_data = -1;   \
         else if(*input_data <= 1)    \
           *output_data = *input_data;  \
         else       \
           *output_data = 1;);
  }
  else
  {
    real* output_data = THTensor_(data)(output);
    real* input_data  = THTensor_(data)(input);
    long k;


#pragma omp parallel for private(k)
    for (k = 0; k < input->size[0]; k++)
    {
      real* ptr_output = output_data + k*input->stride[0];
      real* ptr_input  = input_data  + k*input->stride[0];
      long i;
      for (i = 0; i < input->stride[0]; i++)
      {
        if(ptr_input[i] < -1)
          ptr_output[i] = -1;
        else if (ptr_input[i] <= 1)
          ptr_output[i] = ptr_input[i];
        else
          ptr_output[i] = 1;
      }
    }
  }
  
  return 1;
}

static int nn_(HardTanh_updateGradInput)(lua_State *L)
{
  THTensor *input = luaT_checkudata(L, 2, torch_Tensor);
  THTensor *gradOutput = luaT_checkudata(L, 3, torch_Tensor);
  THTensor *gradInput = luaT_getfieldcheckudata(L, 1, "gradInput", torch_Tensor);

  THTensor_(resizeAs)(gradInput, input);

  if (input->nDimension == 1 || 
      !THTensor_(isContiguous)(input) || 
      !THTensor_(isContiguous)(gradOutput) ||
      !THTensor_(isContiguous)(gradInput))
  {
    TH_TENSOR_APPLY3(real, gradInput, real, gradOutput, real, input,  \
         if(*input_data < -1 || *input_data > 1)    \
           *gradInput_data = 0;                             \
         else           \
           *gradInput_data = *gradOutput_data;);
  }
  else
  {
    real* gradOutput_data = THTensor_(data)(gradOutput);
    real* gradInput_data  = THTensor_(data)(gradInput);
    real* input_data      = THTensor_(data)(input);
    long k;

#pragma omp parallel for private(k)
    for (k = 0; k < input->size[0]; k++)
    {
      real* ptr_gradOutput = gradOutput_data + k*input->stride[0];
      real* ptr_gradInput  = gradInput_data  + k*input->stride[0];
      real* ptr_input      = input_data      + k*input->stride[0];
      long i;
      for (i = 0; i < input->stride[0]; i++)
      {
        if(ptr_input[i] < -1 || ptr_input[i] > 1)
          ptr_gradInput[i] = 0;
        else
          ptr_gradInput[i] = ptr_gradOutput[i];
      }
    }
  }
  return 1;
}

static const struct luaL_Reg nn_(HardTanh__) [] = {
  {"HardTanh_updateOutput", nn_(HardTanh_updateOutput)},
  {"HardTanh_updateGradInput", nn_(HardTanh_updateGradInput)},
  {NULL, NULL}
};

static void nn_(HardTanh_init)(lua_State *L)
{
  luaT_pushmetatable(L, torch_Tensor);
  luaT_registeratname(L, nn_(HardTanh__), "nn");
  lua_pop(L,1);
}

#endif