import copy
import math

import torch
from torch import nn


class Transformer(nn.Module):
    def __init__(self,
                 input_dim,
                 emb_size,
                 max_position_size,
                 dropout,
                 n_layer,
                 intermediate_size,
                 num_attention_heads,
                 attention_probs_dropout,
                 hidden_dropout,
                 ):
        super().__init__()
        self.emb = Embeddings(input_dim,
                              emb_size,
                              max_position_size,
                              dropout)
        self.encoder = MultiLayeredEncoder(n_layer,
                                           emb_size,
                                           intermediate_size,
                                           num_attention_heads,
                                           attention_probs_dropout,
                                           hidden_dropout)

    def forward(self, v):
        e = v[0].long()
        e_mask = v[1].long()
        ex_e_mask = e_mask.unsqueeze(1).unsqueeze(2)
        ex_e_mask = (1.0 - ex_e_mask) * -10000.0

        emb = self.emb(e)
        encoded_layers = self.encoder(emb.float(), ex_e_mask.float())
        return encoded_layers[:, 0]


class LayerNorm(nn.Module):
    def __init__(self, hidden_size, variance_epsilon=1e-12):
        super(LayerNorm, self).__init__()
        self.gamma = nn.Parameter(torch.ones(hidden_size))
        self.beta = nn.Parameter(torch.zeros(hidden_size))
        self.variance_epsilon = variance_epsilon

    def forward(self, x):
        u = x.mean(-1, keepdim=True)
        s = (x - u).pow(2).mean(-1, keepdim=True)
        x = (x - u) / torch.sqrt(s + self.variance_epsilon)
        return self.gamma * x + self.beta


class Embeddings(nn.Module):
    """Construct the embeddings from protein/target, position embeddings.
    """

    def __init__(self, vocab_size, hidden_size, max_position_size, dropout_rate):
        super(Embeddings, self).__init__()
        self.word_embeddings = nn.Embedding(vocab_size, hidden_size)
        self.position_embeddings = nn.Embedding(max_position_size, hidden_size)

        self.LayerNorm = LayerNorm(hidden_size)
        self.dropout = nn.Dropout(dropout_rate)

    def forward(self, input_ids):
        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)

        words_embeddings = self.word_embeddings(input_ids)
        position_embeddings = self.position_embeddings(position_ids)

        embeddings = words_embeddings + position_embeddings
        embeddings = self.LayerNorm(embeddings)
        embeddings = self.dropout(embeddings)
        return embeddings


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

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

        self.dropout = nn.Dropout(attention_probs_dropout_prob)

    def transpose_for_scores(self, x):
        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, hidden_states, attention_mask):
        mixed_query_layer = self.query(hidden_states)
        mixed_key_layer = self.key(hidden_states)
        mixed_value_layer = self.value(hidden_states)

        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))
        attention_scores = attention_scores / math.sqrt(self.attention_head_size)

        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.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)
        return context_layer


class SelfOutput(nn.Module):
    def __init__(self, hidden_size, hidden_dropout_prob):
        super(SelfOutput, self).__init__()
        self.dense = nn.Linear(hidden_size, hidden_size)
        self.LayerNorm = LayerNorm(hidden_size)
        self.dropout = nn.Dropout(hidden_dropout_prob)

    def forward(self, hidden_states, input_tensor):
        hidden_states = self.dense(hidden_states)
        hidden_states = self.dropout(hidden_states)
        hidden_states = self.LayerNorm(hidden_states + input_tensor)
        return hidden_states
    

class Attention(nn.Module):
    def __init__(self, hidden_size, num_attention_heads, attention_probs_dropout_prob, hidden_dropout_prob):
        super(Attention, self).__init__()
        self.self = SelfAttention(hidden_size, num_attention_heads, attention_probs_dropout_prob)
        self.output = SelfOutput(hidden_size, hidden_dropout_prob)

    def forward(self, input_tensor, attention_mask):
        self_output = self.self(input_tensor, attention_mask)
        attention_output = self.output(self_output, input_tensor)
        return attention_output


class Intermediate(nn.Module):
    def __init__(self, hidden_size, intermediate_size):
        super(Intermediate, self).__init__()
        self.dense = nn.Linear(hidden_size, intermediate_size)

    def forward(self, hidden_states):
        hidden_states = self.dense(hidden_states)
        hidden_states = nn.functional.relu(hidden_states)
        return hidden_states


class Output(nn.Module):
    def __init__(self, intermediate_size, hidden_size, hidden_dropout_prob):
        super(Output, self).__init__()
        self.dense = nn.Linear(intermediate_size, hidden_size)
        self.LayerNorm = LayerNorm(hidden_size)
        self.dropout = nn.Dropout(hidden_dropout_prob)

    def forward(self, hidden_states, input_tensor):
        hidden_states = self.dense(hidden_states)
        hidden_states = self.dropout(hidden_states)
        hidden_states = self.LayerNorm(hidden_states + input_tensor)
        return hidden_states


class Encoder(nn.Module):
    def __init__(self, hidden_size, intermediate_size, num_attention_heads, attention_probs_dropout_prob,
                 hidden_dropout_prob):
        super(Encoder, self).__init__()
        self.attention = Attention(hidden_size, num_attention_heads, attention_probs_dropout_prob, hidden_dropout_prob)
        self.intermediate = Intermediate(hidden_size, intermediate_size)
        self.output = Output(intermediate_size, hidden_size, hidden_dropout_prob)

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


class MultiLayeredEncoder(nn.Module):
    def __init__(self, n_layer, hidden_size, intermediate_size, num_attention_heads, attention_probs_dropout_prob,
                 hidden_dropout_prob):
        super(MultiLayeredEncoder, self).__init__()
        layer = Encoder(hidden_size, intermediate_size, num_attention_heads, attention_probs_dropout_prob,
                        hidden_dropout_prob)
        self.layer = nn.ModuleList([copy.deepcopy(layer) for _ in range(n_layer)])

    def forward(self, hidden_states, attention_mask):
        for layer_module in self.layer:
            hidden_states = layer_module(hidden_states, attention_mask)
        return hidden_states