/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
 
 Licensed under the Apache License, Version 2.0 (the "License");
 you may not use this file except in compliance with the License.
 You may obtain a copy of the License at
 
 http://www.apache.org/licenses/LICENSE-2.0
 
 Unless required by applicable law or agreed to in writing, software
 distributed under the License is distributed on an "AS IS" BASIS,
 WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 See the License for the specific language governing permissions and
 limitations under the License. */

import Foundation

class ConvAddPreluParam<P: PrecisionType>: OpParam {
  //typealias ParamPrecisionType = P
  required init(opDesc: OpDesc, inScope: Scope) throws {
    do {
      filter = try ConvAddPreluParam.inputFilter(paraInputs: opDesc.paraInputs, from: inScope)
      input = try ConvAddPreluParam.input(inputs: opDesc.inputs, from: inScope)
      output = try ConvAddPreluParam.outputOut(outputs: opDesc.outputs, from: inScope)
      stride = try ConvAddPreluParam.getAttr(key: "strides", attrs: opDesc.attrs)
      paddings = try ConvAddPreluParam.getAttr(key: "paddings", attrs: opDesc.attrs)
      dilations = try ConvAddPreluParam.getAttr(key: "dilations", attrs: opDesc.attrs)
      groups = try ConvAddPreluParam.getAttr(key: "groups", attrs: opDesc.attrs)
      alpha = try ConvAddPreluParam.paramInputAlpha(inputs: opDesc.paraInputs, from: inScope)
      mode = try ConvAddPreluParam.getAttr(key: "mode", attrs: opDesc.attrs)
      y = try ConvAddPreluParam.inputY(inputs: opDesc.paraInputs, from: inScope)
    } catch let error {
      throw error
    }
  }
  
  let input: Texture
  let y: Tensor<P>
  let filter: Tensor<P>
  let mode: String
  let alpha: Tensor<P>
  var output: Texture
  let stride: [Int32]
  let paddings: [Int32]
  let dilations: [Int32]
  let groups: Int
}

class ConvAddPreluOp<P: PrecisionType>: Operator<ConvAddPreluKernel<P>, ConvAddPreluParam<P>>, Runable, Creator, InferShaperable, Fusion{
  typealias OpType = ConvAddPreluOp<P>
  
  static func fusionNode() -> Node {
    let beginNode = Node.init(inType: gConvType)
    _ = beginNode
      --> Node.init(inType: gElementwiseAddType) --> Node.init(inType: gPreluType)
    return beginNode
  }
  
  static func change() -> [String : [(from: String, to: String)]] {
    return [:]
  }
  
  static func fusionType() -> String {
    return gConvAddPreluType
  }
  
  func inferShape() {
    let inDims = para.input.dim
    let filterDim = para.filter.dim
    let strides = para.stride
    let paddings = para.paddings
    let dilations = para.dilations
    
    var outDim = [inDims[0]]
    for i in 0..<strides.count {
      let dilation: Int = Int(dilations[i])
      let filterSize: Int = filterDim[i + 1]
      let inputSize: Int = inDims[i + 1]
      let padding: Int = Int(paddings[i])
      let stride: Int = Int(strides[i])
      let dKernel = dilation * (filterSize - 1) + 1
      let outputSize = (inputSize + 2 * padding - dKernel) / stride + 1
      outDim.append(outputSize)
    }
    outDim.append(filterDim[0])
    para.output.dim = Dim.init(inDim: outDim)
  }
  
  func runImpl(device: MTLDevice, buffer: MTLCommandBuffer) throws {
    do {
      try kernel.compute(commandBuffer: buffer, param: para)
    } catch let error {
      throw error
    }
  }
  
  func delogOutput() {
    print(" \(type) output: ")
    print(para.output.metalTexture.toTensor(dim: (n: para.output.tensorDim[0], c: para.output.tensorDim[1], h: para.output.tensorDim[2], w: para.output.tensorDim[3])).strideArray())
  }
  
}