/* 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
import MetalPerformanceShaders

struct ConvBNReluTestParam: TestParam {
  let inputTexture: MTLTexture
  let outputTexture: MTLTexture
  var metalParam: MetalConvParam
  let filterBuffer: MTLBuffer
  let biaseBuffer: MTLBuffer
  let newScaleBuffer: MTLBuffer
  let newBiaseBuffer: MTLBuffer
  let filterSize: (width: Int, height: Int, channel: Int)
  init(inInputTexture: MTLTexture, inOutputTexture: MTLTexture, inMetalParam: MetalConvParam, inFilterBuffer: MTLBuffer, inBiaseBuffer: MTLBuffer, inNewScaleBuffer: MTLBuffer, inNewBiaseBuffer: MTLBuffer, inFilterSize: (width: Int, height: Int, channel: Int)) {
    
    inputTexture = inInputTexture
    outputTexture = inOutputTexture
    metalParam = inMetalParam
    filterBuffer = inFilterBuffer
    biaseBuffer = inBiaseBuffer
    newScaleBuffer = inNewScaleBuffer
    newBiaseBuffer = inNewBiaseBuffer
    filterSize = inFilterSize
  }
}

class ConvBNReluKernel<P: PrecisionType>: Kernel, Computable, Testable {
  required init(device: MTLDevice, testParam: ConvBNReluTestParam) {
    if testParam.filterSize.width == 1 && testParam.filterSize.height == 1 {
      super.init(device: device, inFunctionName: "conv_batch_norm_relu_1x1")
    } else if testParam.filterSize.channel == 1 {
      super.init(device: device, inFunctionName: "depthwise_conv_batch_norm_relu_3x3")
    } else {
      super.init(device: device, inFunctionName: "conv_batch_norm_relu_3x3")
    }
  }
  
  var metalParam: MetalConvParam!
  
  required init(device: MTLDevice, param: ConvBNReluParam<P>) {
    
    if param.filter.width == 1 && param.filter.height == 1 {
      super.init(device: device, inFunctionName: "conv_batch_norm_relu_1x1")
    } else if param.filter.channel == 1 {
      super.init(device: device, inFunctionName: "depthwise_conv_batch_norm_relu_3x3")
    } else {
      super.init(device: device, inFunctionName: "conv_batch_norm_relu_3x3")
    }
    param.output.initTexture(device: device, inTranspose: [0, 2, 3, 1])
    param.filter.initBuffer(device: device, precision: Tensor.BufferPrecision.Float32)
    
    param.variance.initBuffer(device: device)
    param.mean.initBuffer(device: device)
    param.scale.initBuffer(device: device)
    param.bias.initBuffer(device: device)
    
    let offsetX = param.filter.width/2 - Int(param.paddings[0])
    let offsetY = param.filter.height/2 - Int(param.paddings[1])
    
    print(" param filter width: \(param.filter.width)")
    print(" param filter height: \(param.filter.height)")
    
    print(" param paddings: \(param.paddings)")
    
    print("ConvBNReluKernel offset x: \(offsetX)")
    print("ConvBNReluKernel offset y: \(offsetY)")
    
    let offsetZ = 0.0
    
    print(" fuck ")
    metalParam = MetalConvParam.init(offsetX: Int16(offsetX), offsetY: Int16(offsetY), offsetZ: Int16(offsetZ), strideX: UInt16(param.stride[0]), strideY: UInt16(param.stride[1]), paddedZ: UInt16(param.input.metalTexture.arrayLength * 4 - param.input.dim[3]))
    
    var invs: [P] = []
    let varianceContents = param.variance.buffer.contents().assumingMemoryBound(to: P.self)
    
    for i in 0..<param.variance.buffer.length/MemoryLayout<P>.stride {
      let inv = 1.0/pow(Float32.init(varianceContents[i]) + param.epsilon, 0.5)
      invs.append(P(inv))
    }
    
    let newScale: UnsafeMutablePointer<P> = UnsafeMutablePointer<P>.allocate(capacity: param.scale.buffer.length)
    let newBiase: UnsafeMutablePointer<P> = UnsafeMutablePointer<P>.allocate(capacity: param.bias.buffer.length)
    
    let scaleContents = param.scale.buffer.contents().assumingMemoryBound(to: P.self)
    let biaseContents = param.bias.buffer.contents().assumingMemoryBound(to: P.self)
    let meanContents = param.mean.buffer.contents().assumingMemoryBound(to: P.self)
    for i in 0..<param.scale.buffer.length/MemoryLayout<P>.stride {
      newScale[i] = invs[i] * scaleContents[i]
      newBiase[i] = biaseContents[i] - meanContents[i] * invs[i] * scaleContents[i]
    }
    
    param.newBiase = device.makeBuffer(bytes: newBiase, length: param.bias.buffer.length)
    param.newScale = device.makeBuffer(bytes: newScale, length: param.scale.buffer.length)
    
    newScale.deinitialize(count: param.scale.buffer.length)
    newScale.deallocate()
    
    newBiase.deinitialize(count: param.bias.buffer.length)
    newBiase.deallocate()
  }
  
  func compute(commandBuffer: MTLCommandBuffer, param: ConvBNReluParam<P>) throws {
    guard let encoder = commandBuffer.makeComputeCommandEncoder() else {
      throw PaddleMobileError.predictError(message: " encode is nil")
    }
    
    encoder.setTexture(param.input.metalTexture, index: 0)
    encoder.setTexture(param.output.metalTexture, index: 1)
    encoder.setBytes(&metalParam, length: MemoryLayout<MetalConvParam>.size, index: 0)
    encoder.setBuffer(param.filter.buffer, offset: 0, index: 1)
    encoder.setBuffer(param.newScale!, offset: 0, index: 2)
    encoder.setBuffer(param.newBiase!, offset: 0, index: 3)
    encoder.dispatch(computePipline: pipline, outTexture: param.output.metalTexture)
    encoder.endEncoding()
  }
  
  public func test(commandBuffer: MTLCommandBuffer, param: ConvBNReluTestParam) {
    guard let encoder = commandBuffer.makeComputeCommandEncoder() else {
      fatalError()
    }
    
    encoder.setTexture(param.inputTexture, index: 0)
    encoder.setTexture(param.outputTexture, index: 1)
    var inMetalParam = param.metalParam
    encoder.setBytes(&inMetalParam, length: MemoryLayout<MetalConvParam>.size, index: 0)
    encoder.setBuffer(param.filterBuffer, offset: 0, index: 1)
    encoder.setBuffer(param.newScaleBuffer, offset: 0, index: 2)
    encoder.setBuffer(param.newBiaseBuffer, offset: 0, index: 3)
    encoder.dispatch(computePipline: pipline, outTexture: param.outputTexture)
    encoder.endEncoding()
  }
}