# Source code for block_zoo.ConvPooling

    # Copyright (c) Microsoft Corporation. All rights reserved.
    # Licensed under the MIT license.

    import torch
    import torch.nn as nn
    import torch.nn.functional as F
    from collections import OrderedDict

    from block_zoo.BaseLayer import BaseLayer, BaseConf
    from utils.DocInherit import DocInherit

    class ConvPoolingConf(BaseConf):
        """ Configuration of Conv + Pooling architecture

        Args:
            stride (int): the stride of the convolving kernel. Can be a single number or a tuple (sH, sW). Default: 1
            padding (int): implicit zero paddings on both sides of the input. Can be a single number or a tuple (padH, padW). Default: 0
            window_sizes (list): for each window_size, the actual window size is (window_size, feature_dim), because for NLP tasks, 1d convolution is more commonly used.
            input_channel_num (int): for NLP tasks, input_channel_num would always be 1
            output_channel_num (int): number of feature maps
            batch_norm (bool): If True, apply batch normalization before activation
            activation (string): activation functions, e.g. ReLU

        """
        def __int__(self, **kwargs):
            super(ConvPoolingConf, self).__init__(**kwargs)

        @DocInherit
        def default(self):
            self.stride = 1
            self.padding = 0
            self.window_sizes = [1, 2, 3]
            self.input_channel_num = 1      # for NLP tasks, input_channel_num would always be 1
            self.output_channel_num = 16
            self.batch_norm = True
            self.activation = 'ReLU'
            self.pool_type = 'max'  # Supported: ['max', mean']
            self.pool_axis = 1

        @DocInherit
        def declare(self):
            self.num_of_inputs = 1
            self.input_ranks = [3]

        @DocInherit
        def inference(self):
            self.output_dim = [-1]
            self.output_dim.append(self.output_channel_num * len(self.window_sizes))

            super(ConvPoolingConf, self).inference()  # PUT THIS LINE AT THE END OF inference()

        @DocInherit
        def verify_before_inference(self):
            super(ConvPoolingConf, self).verify_before_inference()
            necessary_attrs_for_user = ['output_channel_num']
            for attr in necessary_attrs_for_user:
                self.add_attr_exist_assertion_for_user(attr)

        @DocInherit
        def verify(self):
            super(ConvPoolingConf, self).verify()

            necessary_attrs_for_user = ['stride', 'padding', 'window_sizes', 'input_channel_num', 'output_channel_num', 'activation', 'pool_type', 'pool_axis']
            for attr in necessary_attrs_for_user:
                self.add_attr_exist_assertion_for_user(attr)

    class ConvPooling(BaseLayer):
        """ Convolution along just 1 direction

        Args:
            layer_conf (ConvConf): configuration of a layer
        """
        def __init__(self, layer_conf):
            super(ConvPooling, self).__init__(layer_conf)
            self.layer_conf = layer_conf

            self.filters = nn.ParameterList()
            if layer_conf.batch_norm:
                self.batch_norms = nn.ModuleList()
            else:
                self.batch_norms = None

            #device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
            for i in range(len(layer_conf.window_sizes)):
                self.filters.append(nn.Parameter(torch.randn(layer_conf.output_channel_num, layer_conf.input_channel_num, layer_conf.window_sizes[i], layer_conf.input_dims[0][2], requires_grad=True).float()))
                if layer_conf.batch_norm:
                    self.batch_norms.append(nn.BatchNorm2d(layer_conf.output_channel_num))

            if layer_conf.activation:
                self.activation = eval("nn." + self.layer_conf.activation)()
            else:
                self.activation = None

        def forward(self, string, string_len=None):
            """ process inputs

            Args:
                string (Tensor): tensor with shape: [batch_size, seq_len, feature_dim]
                string_len (Tensor):  [batch_size]

            Returns:
                Tensor: shape: [batch_size, (seq_len - conv_window_size) // stride + 1, output_channel_num]

            """
            if string_len is not None:
                string_len_val = string_len.cpu().data.numpy()
                masks = []
                for i in range(len(string_len)):
                    masks.append(torch.cat([torch.ones(string_len_val[i]), torch.zeros(string.shape[1] - string_len_val[i])]))
                masks = torch.stack(masks).view(string.shape[0], string.shape[1], 1).expand_as(string)
                if self.is_cuda():
                    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
                    masks = masks.to(device)
                string = string * masks

            string = torch.unsqueeze(string, 1)     # [batch_size, input_channel_num=1, seq_len, feature_dim]

            outputs = []
            for idx, (filter, window_size) in enumerate(zip(self.filters, self.layer_conf.window_sizes)):
                string_out = F.conv2d(string, filter, stride=self.layer_conf.stride, padding=self.layer_conf.padding)
                if hasattr(self, 'batch_norms') and self.batch_norms:     #hasattr(self, 'batch_norms') enable NB to be compatible to models trained previously
                    string_out = self.batch_norms[idx](string_out)
                string_out = torch.squeeze(string_out, 3).permute(0, 2, 1)
                if self.activation:
                    string_out = self.activation(string_out)

                if string_len is not None:
                    string_len_out = (string_len - window_size) // self.layer_conf.stride + 1
                else:
                    string_len_out = None

                if self.layer_conf.pool_type == "mean":
                    assert not string_len_out is None, "Parameter string_len should not be None!"
                    string_out = torch.sum(string_out, self.layer_conf.pool_axis).squeeze(self.layer_conf.pool_axis)
                    #if not isinstance(string_len_out, torch.Tensor):
                    if not torch.is_tensor(string_len_out):
                        string_len_out = torch.FloatTensor(string_len_out)
                    string_len_out = string_len_out.unsqueeze(1)
                    if self.is_cuda():
                        device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
                        string_len_out = string_len_out.to(device)
                    output = string_out / string_len_out.expand_as(string_out)
                elif self.layer_conf.pool_type == "max":
                    output = torch.max(string_out, self.layer_conf.pool_axis)[0]

                outputs.append(output)

            if len(outputs) > 1:
                string_output = torch.cat(outputs, 1)
            else:
                string_output = outputs[0]

            return string_output, None