module DNN
  module Models

    # This class is used to hold multiple layers in an array.
    class LayersList < Array
      def self.from_hash_list(hash_list)
        layers_list = new
        hash_list.each do |hash|
          obj_class = DNN.const_get(hash[:class])
          obj = obj_class.allocate
          if obj.is_a?(Chain)
            obj = obj_class.new
            obj.load_hash(hash)
          else
            obj = Layers::Layer.from_hash(hash)
          end
          layers_list << obj
        end
        layers_list
      end

      def to_hash_list
        map(&:to_hash)
      end

      # Get the all layers.
      # @return [Array] All layers array.
      def layers
        layers_array = []
        each do |layer|
          if layer.is_a?(Layers::Layer)
            layers_array << layer
          elsif layer.is_a?(Chain) || layer.is_a?(LayersList)
            layers_array.concat(layer.layers)
          end
        end
        layers_array
      end
    end

    class Chain
      def initialize
        @layers_cache = nil
      end

      # Forward propagation.
      # @param [Tensor] input_tensors Input tensors.
      # @return [Tensor] Output tensor.
      def forward(input_tensors)
        raise NotImplementedError, "Class '#{self.class.name}' has implement method 'forward'"
      end

      # Forward propagation and create a link.
      # @param [Tensor | Array] input_tensors Input tensors.
      # @return [Tensor] Output tensor.
      def call(input_tensors)
        forward(input_tensors)
      end

      # Get the all layers.
      # @return [Array] All layers array.
      def layers
        return @layers_cache if @layers_cache
        layers_array = []
        instance_variables.sort.each do |ivar|
          obj = instance_variable_get(ivar)
          if obj.is_a?(Layers::Layer)
            layers_array << obj
          elsif obj.is_a?(Chain) || obj.is_a?(LayersList)
            layers_array.concat(obj.layers)
          end
        end
        @layers_cache = layers_array
      end
    end

    # This class deals with the model of the network.
    class Model < Chain
      attr_accessor :optimizer
      attr_accessor :loss_func
      attr_reader :last_log

      # Load marshal model.
      # @param [String] file_name File name of marshal model to load.
      # @return [DNN::Models::Model] Return the loaded model.
      def self.load(file_name)
        model = self.allocate
        loader = Loaders::MarshalLoader.new(model)
        loader.load(file_name)
        model
      end

      def initialize
        super
        @optimizer = nil
        @loss_func = nil
        @built = false
        @callbacks = []
        @last_log = {}
      end

      def call(input_tensors)
        output_tensor = forward(input_tensors)
        @built = true unless @built
        output_tensor
      end

      # Set optimizer and loss_func to model.
      # @param [DNN::Optimizers::Optimizer] optimizer Optimizer to use for learning.
      # @param [DNN::Losses::Loss] loss_func Loss function to use for learning.
      def setup(optimizer, loss_func)
        unless optimizer.is_a?(Optimizers::Optimizer)
          raise TypeError, "optimizer:#{optimizer.class} is not an instance of DNN::Optimizers::Optimizer class."
        end
        unless loss_func.is_a?(Losses::Loss)
          raise TypeError, "loss_func:#{loss_func.class} is not an instance of DNN::Losses::Loss class."
        end
        @optimizer = optimizer
        @loss_func = loss_func
      end

      # Start training.
      # Setup the model before use this method.
      # @param [Numo::SFloat] x Input training data.
      # @param [Numo::SFloat] y Output training data.
      # @param [Integer] epochs Number of training.
      # @param [Integer] batch_size Batch size used for one training.
      # @param [Integer] initial_epoch Initial epoch.
      # @param [Array | NilClass] test If you to test the model for every 1 epoch,
      #                                specify [x_test, y_test]. Don't test to the model, specify nil.
      # @param [Boolean] verbose Set true to display the log. If false is set, the log is not displayed.
      def train(x, y, epochs,
                batch_size: 1,
                initial_epoch: 1,
                test: nil,
                verbose: true)
        check_xy_type(x, y)
        train_iterator = Iterator.new(x, y)
        train_by_iterator(train_iterator, epochs,
                          batch_size: batch_size,
                          initial_epoch: initial_epoch,
                          test: test,
                          verbose: verbose)
      end

      alias fit train

      # Start training by iterator.
      # Setup the model before use this method.
      # @param [DNN::Iterator] train_iterator Iterator used for training.
      # @param [Integer] epochs Number of training.
      # @param [Integer] batch_size Batch size used for one training.
      # @param [Integer] initial_epoch Initial epoch.
      # @param [Array | NilClass] test If you to test the model for every 1 epoch,
      #                                specify [x_test, y_test]. Don't test to the model, specify nil.
      # @param [Boolean] verbose Set true to display the log. If false is set, the log is not displayed.
      def train_by_iterator(train_iterator, epochs,
                            batch_size: 1,
                            initial_epoch: 1,
                            test: nil,
                            verbose: true)
        raise DNNError, "The model is not optimizer setup complete." unless @optimizer
        raise DNNError, "The model is not loss_func setup complete." unless @loss_func

        num_train_datas = train_iterator.num_datas
        num_train_datas = num_train_datas / batch_size * batch_size if train_iterator.last_round_down

        stopped = catch(:stop) do
          (initial_epoch..epochs).each do |epoch|
            @last_log[:epoch] = epoch
            call_callbacks(:before_epoch)
            puts "【 epoch #{epoch}/#{epochs} 】" if verbose

            train_iterator.foreach(batch_size) do |x_batch, y_batch, index|
              train_step_met = train_step(x_batch, y_batch)
              num_trained_datas = (index + 1) * batch_size
              num_trained_datas = num_trained_datas > num_train_datas ? num_train_datas : num_trained_datas
              log = "\r"
              40.times do |i|
                if i < num_trained_datas * 40 / num_train_datas
                  log << "="
                elsif i == num_trained_datas * 40 / num_train_datas
                  log << ">"
                else
                  log << "_"
                end
              end

              log << "  #{num_trained_datas}/#{num_train_datas} "
              log << metrics_to_str(train_step_met)
              print log if verbose
            end

            if test
              acc, loss = if test.is_a?(Array)
                            evaluate(test[0], test[1], batch_size: batch_size)
                          else
                            evaluate_by_iterator(test, batch_size: batch_size)
                          end
              print "  " + metrics_to_str({ accuracy: acc, test_loss: loss }) if verbose
            end
            puts "" if verbose
            call_callbacks(:after_epoch)
          end
          nil
        end

        if stopped
          puts "\n#{stopped}" if verbose
        end
      end

      alias fit_by_iterator train_by_iterator

      # Implement the training process to be performed in one step.
      # @param [Numo::SFloat] x Input training data.
      # @param [Numo::SFloat] y Output training data.
      # @return [Hash] Hash of contents to be output to log.
      private def train_step(x, y)
        loss_value = train_on_batch(x, y)
        { loss: loss_value }
      end

      # Training once.
      # Setup the model before use this method.
      # @param [Numo::SFloat] x Input training data.
      # @param [Numo::SFloat] y Output training data.
      # @return [Float | Numo::SFloat] Return loss value in the form of Float or Numo::SFloat.
      def train_on_batch(x, y)
        raise DNNError, "The model is not optimizer setup complete." unless @optimizer
        raise DNNError, "The model is not loss_func setup complete." unless @loss_func
        check_xy_type(x, y)
        call_callbacks(:before_train_on_batch)
        DNN.learning_phase = true
        out = call(Tensor.convert(x))
        loss = @loss_func.loss(out, Tensor.convert(y), layers)
        loss.link.backward(Xumo::SFloat.ones(y[0...1, false].shape[0], 1))
        @optimizer.update(get_all_trainable_params)
        @last_log[:train_loss] = loss.data
        call_callbacks(:after_train_on_batch)
        loss.data
      end

      # Evaluate model and get accuracy and loss of test data.
      # @param [Numo::SFloat] x Input test data.
      # @param [Numo::SFloat] y Output test data.
      # @param [Integer] batch_size Batch size used for one test.
      # @return [Array] Returns the test data accuracy and mean loss in the form [accuracy, mean_loss].
      def evaluate(x, y, batch_size: 100)
        check_xy_type(x, y)
        evaluate_by_iterator(Iterator.new(x, y, random: false), batch_size: batch_size)
      end

      # Evaluate model by iterator.
      # @param [DNN::Iterator] test_iterator Iterator used for testing.
      # @param [Integer] batch_size Batch size used for one test.
      # @return [Array] Returns the test data accuracy and mean loss in the form [accuracy, mean_loss].
      def evaluate_by_iterator(test_iterator, batch_size: 100)
        num_test_datas = test_iterator.num_datas
        batch_size = batch_size >= num_test_datas ? num_test_datas : batch_size
        total_correct = 0
        sum_loss = 0
        max_steps = (num_test_datas.to_f / batch_size).ceil
        test_iterator.foreach(batch_size) do |x_batch, y_batch|
          correct, loss_value = test_on_batch(x_batch, y_batch)
          total_correct += correct
          sum_loss += loss_value
        end
        mean_loss = sum_loss / max_steps
        acc = total_correct.to_f / num_test_datas
        @last_log[:test_loss] = mean_loss
        @last_log[:test_accuracy] = acc
        [acc, mean_loss]
      end

      # Evaluate once.
      # @param [Numo::SFloat] x Input test data.
      # @param [Numo::SFloat] y Output test data.
      # @return [Array] Returns the test data accuracy and mean loss in the form [accuracy, mean_loss].
      def test_on_batch(x, y)
        call_callbacks(:before_test_on_batch)
        DNN.learning_phase = false
        out = call(Tensor.convert(x))
        correct = accuracy(out.data, y)
        loss = @loss_func.(out, Tensor.convert(y))
        call_callbacks(:after_test_on_batch)
        [correct, loss.data]
      end

      # Implement the process to accuracy this model.
      # @param [Numo::SFloat] x Input test data.
      # @param [Numo::SFloat] y Output test data.
      # @return [Integer] Returns the test data accuracy.
      private def accuracy(x, y)
        if x.shape[1..-1] == [1]
          correct = 0
          x.shape[0].times do |i|
            if @loss_func.is_a?(Losses::SigmoidCrossEntropy)
              correct += 1 if (x[i, 0] < 0 && y[i, 0] < 0.5) || (x[i, 0] >= 0 && y[i, 0] >= 0.5)
            else
              correct += 1 if (x[i, 0] < 0 && y[i, 0] < 0) || (x[i, 0] >= 0 && y[i, 0] >= 0)
            end
          end
        else
          correct = x.max_index(axis: 1).eq(y.max_index(axis: 1)).count
        end
        correct
      end

      # Predict data.
      # @param [Numo::SFloat] x Input data.
      # @param [Boolean] use_loss_activation Use loss activation when loss has an activation.
      def predict(x, use_loss_activation: true)
        check_xy_type(x)
        DNN.learning_phase = false
        out = call(Tensor.convert(x))
        y = out.data
        if use_loss_activation && @loss_func.class.respond_to?(:activation)
          y = @loss_func.class.activation(y)
        end
        y
      end

      # Predict one data.
      # @param [Numo::SFloat] x Input data. However, x is single data.
      def predict1(x, use_loss_activation: true)
        check_xy_type(x)
        input = if x.is_a?(Array)
                  x.map { |v| v.reshape(1, *v.shape) }
                else
                  x.reshape(1, *x.shape)
                end
        predict(input, use_loss_activation: use_loss_activation)[0, false]
      end

      # Add callback function.
      # @param [Callback] callback Callback object.
      def add_callback(callback)
        callback.model = self
        @callbacks << callback
      end

      # Clear the callback function registered for each event.
      def clear_callbacks
        @callbacks = []
      end

      # Load marshal params.
      # @param [String] file_name File name of marshal model to load.
      def load_params(file_name)
        loader = Loaders::MarshalLoader.new(self)
        loader.load(file_name)
      end

      # Save the model in marshal format.
      # @param [String] file_name Name to save model.
      def save(file_name)
        saver = Savers::MarshalSaver.new(self, include_model: true)
        saver.save(file_name)
      end

      # Save the params in marshal format.
      # @param [String] file_name Name to save model.
      def save_params(file_name)
        saver = Savers::MarshalSaver.new(self, include_model: false)
        saver.save(file_name)
      end

      # @return [DNN::Models::Model] Return the copy this model.
      def copy
        Marshal.load(Marshal.dump(self))
      end

      # Get the all trainable layers.
      # @return [Array] All has param layers array.
      def trainable_layers
        layers.select { |layer| layer.is_a?(Layers::TrainableLayer) }
      end

      # Get the layer that the model has.
      # @param [Symbol] name The name of the layer to get.
      # @return [DNN::Layers::Layer] Return the layer.
      def get_layer(name)
        layer = instance_variable_get("@#{name}")
        return layer if layer.is_a?(Layers::Layer) || layer.is_a?(Chain) || layer.is_a?(LayersList)
        nil
      end

      # @return [Boolean] If model have already been built then return true.
      def built?
        @built
      end

      # Clean all layers.
      def clean_layers
        layers.each(&:clean)
        @loss_func.clean
        @layers_cache = nil
      end

      # Get parameter data of all layers.
      # @return [Array] Parameter data.
      def get_all_params_data
        trainable_layers.map do |layer|
          layer.get_params.to_h do |key, param|
            [key, param.data]
          end
        end
      end

      # Set parameter data of all layers.
      # @param [Array] params_data Parameter data obtained by get_all_params_data.
      def set_all_params_data(params_data)
        trainable_layers.each.with_index do |layer, i|
          params_data[i].each do |(key, data)|
            layer.get_params[key].data = data
          end
        end
      end

      private

      def get_all_trainable_params
        layers.select { |layer| layer.is_a?(Layers::TrainableLayer) && layer.trainable }
              .map { |layer| layer.get_params.values }.flatten.compact
              .select(&:grad)
      end

      def call_callbacks(event)
        @callbacks.each do |callback|
          callback.send(event) if callback.respond_to?(event)
        end
      end

      def metrics_to_str(mertics)
        mertics.map { |key, num| "#{key}: #{sprintf('%.4f', num)}" }.join(", ")
      end

      def check_xy_type(x, y = nil)
        if !x.is_a?(Xumo::SFloat) && !x.is_a?(Array)
          raise TypeError, "x:#{x.class.name} is not an instance of #{Xumo::SFloat.name} class or Array class."
        end
        if y && !y.is_a?(Xumo::SFloat) && !y.is_a?(Array)
          raise TypeError, "y:#{y.class.name} is not an instance of #{Xumo::SFloat.name} class or Array class."
        end
      end
    end

    class Sequential < Model
      attr_reader :stack

      # @param [Array] stack All layers possessed by the model.
      def initialize(stack = [])
        super()
        @stack = LayersList.new
        stack.each do |layer|
          add(layer)
        end
      end

      # Add layer to the model.
      # @param [DNN::Layers::Layer] layer Layer to add to the model.
      # @return [DNN::Models::Model] Return self.
      def add(layer)
        if layer.is_a?(Layers::MergeLayer)
          raise TypeError, "layer: #{layer.class.name} should not be a DNN::Layers::MergeLayer class."
        end
        unless layer.is_a?(Layers::Layer) || layer.is_a?(Model)
          raise TypeError, "layer: #{layer.class.name} is not an instance of the DNN::Layers::Layer class or DNN::Models::Model class."
        end
        @stack << layer
        self
      end

      alias << add

      # Insert layer to the model by index position.
      # @param [DNN::Layers::Layer] layer Layer to add to the model.
      # @return [DNN::Models::Model] Return self.
      def insert(index, layer)
        if layer.is_a?(Layers::MergeLayer)
          raise TypeError, "layer: #{layer.class.name} should not be a DNN::Layers::MergeLayer class."
        end
        unless layer.is_a?(Layers::Layer) || layer.is_a?(Model)
          raise TypeError, "layer: #{layer.class.name} is not an instance of the DNN::Layers::Layer class or DNN::Models::Model class."
        end
        @stack.insert(index, layer)
      end

      # Remove layer to the model.
      # @param [DNN::Layers::Layer] layer Layer to remove to the model.
      # @return [Boolean] Return true if success for remove layer.
      def remove(layer)
        @stack.delete(layer) ? true : false
      end

      def forward(x)
        @stack.each do |layer|
          x = layer.(x)
        end
        x
      end
    end

  end
end