import torch
import torch.nn as nn
import torch.nn.functional as F

import os
import math
import copy

def gelu(x):
    """Implementation of the gelu activation function.
        For information: OpenAI GPT's gelu is slightly different
        (and gives slightly different results):
        0.5 * x * (1 + torch.tanh(math.sqrt(2 / math.pi) *
        (x + 0.044715 * torch.pow(x, 3))))
        Also see https://arxiv.org/abs/1606.08415
    """
    return x * 0.5 * (1.0 + torch.erf(x / math.sqrt(2.0)))


def swish(x):
    return x * torch.sigmoid(x)


ACT2FN = {"gelu": gelu, "relu": F.relu, "swish": swish}


class LayerNorm(nn.Module):
    def __init__(self, hidden_size, eps=1e-12):
        """Construct a layernorm module in the TF style (epsilon inside the square root).
        """
        super(LayerNorm, self).__init__()
        self.weight = nn.Parameter(torch.ones(hidden_size))
        self.bias = nn.Parameter(torch.zeros(hidden_size))
        self.variance_epsilon = eps

    def forward(self, x):
        u = x.mean(-1, keepdim=True)
        # ipdb.set_trace()
        s = (x - u).pow(2).mean(-1, keepdim=True)
        x = (x - u) / torch.sqrt(s + self.variance_epsilon)
        return self.weight * x + self.bias


class Attention(nn.Module):
    def __init__(self, args):
        super().__init__()
        self.hidden_dim = args.hidden_size
        self.projection = nn.Sequential(nn.Linear(args.hidden_size, 64),
                                        nn.ReLU(), nn.Linear(64, 1))

    def forward(self, input_tensor):
        batch_size = input_tensor.size(0)
        # [B L 5 H] -> [B L 5 1]
        energy = self.projection(input_tensor)
        # [B L 5]
        weights = F.softmax(energy.squeeze(-1),
                            dim=-2)  # 可以返回weight 看attention的情况
        # [B L 5 H] * [B L 5 1] -> [B L 5 H]
        # 5个向量都已经乘过了权重 直接sum
        outputs = (input_tensor * weights.unsqueeze(-1)).sum(dim=-2)
        return outputs


def kmax_pooling(x, dim, k):
    index = x.topk(k, dim=dim)[1].sort(dim=dim)[0]
    return x.gather(dim, index).squeeze(dim)


def avg_pooling(x, dim):
    return x.sum(dim=dim) / x.size(1)


def get_item_audience():
    import pickle
    with open('item_audience_distribute.pkl') as fin:
        item_audience = pickle.load(fin)
    return item_audience


class Embeddings(nn.Module):
    """Construct the embeddings from item, position, attribute.
    """
    def __init__(self, args):
        super(Embeddings, self).__init__()

        self.item_embeddings = nn.Embedding(
            args.item_size, args.hidden_size)  # 不要乱用padding_idx
        self.position_embeddings = nn.Embedding(args.max_seq_length,
                                                args.hidden_size)

        self.LayerNorm = LayerNorm(args.hidden_size, eps=1e-12)
        self.dropout = nn.Dropout(args.hidden_dropout_prob)

        self.args = args

    def forward(self, input_ids, use_cuda=False):
        seq_length = input_ids.size(1)

        position_ids = torch.arange(seq_length,
                                    dtype=torch.long,
                                    device=input_ids.device)
        position_ids = position_ids.unsqueeze(0).expand_as(input_ids)
        # bug原因：inputs-ids有个item-size的id
        items_embeddings = self.item_embeddings(input_ids)
        position_embeddings = self.position_embeddings(position_ids)

        embeddings = items_embeddings + position_embeddings
        embeddings = self.LayerNorm(embeddings)
        embeddings = self.dropout(embeddings)

        return embeddings


class SelfAttention(nn.Module):
    def __init__(self, args):
        super(SelfAttention, self).__init__()
        if args.hidden_size % args.num_attention_heads != 0:
            raise ValueError(
                "The hidden size (%d) is not a multiple of the number of attention "
                "heads (%d)" % (args.hidden_size, args.num_attention_heads))
        self.num_attention_heads = args.num_attention_heads
        self.attention_head_size = int(args.hidden_size /
                                       args.num_attention_heads)
        self.all_head_size = self.num_attention_heads * self.attention_head_size

        self.query = nn.Linear(args.hidden_size, self.all_head_size)
        self.key = nn.Linear(args.hidden_size, self.all_head_size)
        self.value = nn.Linear(args.hidden_size, self.all_head_size)

        self.attn_dropout = nn.Dropout(args.attention_probs_dropout_prob)

        # 做完self-attention 做一个前馈全连接 LayerNorm 输出
        self.dense = nn.Linear(args.hidden_size, args.hidden_size)
        self.LayerNorm = LayerNorm(args.hidden_size, eps=1e-12)
        self.out_dropout = nn.Dropout(args.hidden_dropout_prob)

    def transpose_for_scores(self, x):
        '''
            args:
            x:
                (bs, seq_len, all_head_size)
            return:
            x.permute(0, 2, 1, 3):
                (bs, num_heads, seq_len, head_size)
        '''
        new_x_shape = x.size()[:-1] + (self.num_attention_heads,
                                       self.attention_head_size)
        x = x.view(*new_x_shape)

        return x.permute(0, 2, 1, 3)

    def forward(self, input_tensor, attention_mask):
        mixed_query_layer = self.query(input_tensor)
        mixed_key_layer = self.key(input_tensor)
        mixed_value_layer = self.value(input_tensor)

        query_layer = self.transpose_for_scores(mixed_query_layer)
        key_layer = self.transpose_for_scores(mixed_key_layer)
        value_layer = self.transpose_for_scores(mixed_value_layer)
        # Take the dot product between "query" and "key" to get the raw attention scores.
        attention_scores = torch.matmul(query_layer, key_layer.transpose(
            -1, -2))  # (bs, num_heads, seq_len, seq_len)

        attention_scores = attention_scores / math.sqrt(
            self.attention_head_size)
        # Apply the attention mask is (precomputed for all layers in BertModel forward() function)

        # [batch_size heads seq_len seq_len] scores
        # [batch_size 1 1 seq_len]
        attention_scores = attention_scores + attention_mask

        # Normalize the attention scores to probabilities.
        attention_probs = nn.Softmax(dim=-1)(attention_scores)

        # This is actually dropping out entire tokens to attend to, which might
        # seem a bit unusual, but is taken from the original Transformer paper.
        attention_probs = self.attn_dropout(attention_probs)

        context_layer = torch.matmul(attention_probs, value_layer)
        context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
        new_context_layer_shape = context_layer.size()[:-2] + (
            self.all_head_size, )
        context_layer = context_layer.view(*new_context_layer_shape)

        hidden_states = self.dense(context_layer)
        hidden_states = self.out_dropout(hidden_states)
        hidden_states = self.LayerNorm(hidden_states + input_tensor)

        return hidden_states


class Intermediate(nn.Module):
    def __init__(self, args):
        super(Intermediate, self).__init__()
        self.dense_1 = nn.Linear(args.hidden_size, args.hidden_size * 4)
        if isinstance(args.hidden_act, str):
            self.intermediate_act_fn = ACT2FN[args.hidden_act]
        else:
            self.intermediate_act_fn = args.hidden_act

        self.dense_2 = nn.Linear(args.hidden_size * 4, args.hidden_size)
        self.LayerNorm = LayerNorm(args.hidden_size, eps=1e-12)
        self.dropout = nn.Dropout(args.hidden_dropout_prob)

    def forward(self, input_tensor):

        hidden_states = self.dense_1(input_tensor)
        hidden_states = self.intermediate_act_fn(hidden_states)

        hidden_states = self.dense_2(hidden_states)
        hidden_states = self.dropout(hidden_states)
        hidden_states = self.LayerNorm(hidden_states + input_tensor)
        return hidden_states


class Layer(nn.Module):
    def __init__(self, args):
        super(Layer, self).__init__()
        self.attention = SelfAttention(args)
        self.intermediate = Intermediate(args)

    def forward(self, hidden_states, attention_mask):
        attention_output = self.attention(hidden_states, attention_mask)
        intermediate_output = self.intermediate(attention_output)
        return intermediate_output


class Encoder(nn.Module):
    def __init__(self, args):
        super(Encoder, self).__init__()
        layer = Layer(args)
        self.layer = nn.ModuleList(
            [copy.deepcopy(layer) for _ in range(args.num_hidden_layers)])

    def forward(self,
                hidden_states,
                attention_mask,
                output_all_encoded_layers=True):
        all_encoder_layers = []
        for layer_module in self.layer:
            hidden_states = layer_module(hidden_states, attention_mask)
            if output_all_encoded_layers:
                all_encoder_layers.append(hidden_states)
        if not output_all_encoded_layers:
            all_encoder_layers.append(hidden_states)
        return all_encoder_layers
