import torch
from torch import nn
import en_core_web_sm
from transformers import AutoModel, TrainingArguments, Trainer, RobertaTokenizer, RobertaModel
from transformers import AutoTokenizer

model_checkpoint = "ehsanaghaei/SecureBERT"
device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")

tokenizer = AutoTokenizer.from_pretrained(model_checkpoint, add_prefix_space=True)
roberta_model = RobertaModel.from_pretrained(model_checkpoint).to(device)

event_nugget_list = ['B-Phishing',
 'I-Phishing',
 'O',
 'B-DiscoverVulnerability',
 'B-Ransom',
 'I-Ransom',
 'B-Databreach',
 'I-DiscoverVulnerability',
 'B-PatchVulnerability',
 'I-PatchVulnerability',
 'I-Databreach']

nlp = en_core_web_sm.load()
pos_spacy_tag_list = ["ADJ","ADP","ADV","AUX","CCONJ","DET","INTJ","NOUN","NUM","PART","PRON","PROPN","PUNCT","SCONJ","SYM","VERB","SPACE","X"]
ner_spacy_tag_list = [bio + entity for entity in list(nlp.get_pipe('ner').labels) for bio in ["B-", "I-"]] + ["O"]
dep_spacy_tag_list = list(nlp.get_pipe("parser").labels)

class CustomRobertaWithPOS(nn.Module):
    def __init__(self, num_classes_realis):
        super(CustomRobertaWithPOS, self).__init__()
        self.num_classes_realis = num_classes_realis
        self.pos_embed = nn.Embedding(len(pos_spacy_tag_list), 16)
        self.ner_embed = nn.Embedding(len(ner_spacy_tag_list), 8)
        self.dep_embed = nn.Embedding(len(dep_spacy_tag_list), 8)
        self.depth_embed = nn.Embedding(17, 8)
        self.nugget_embed = nn.Embedding(len(event_nugget_list), 8)
        self.roberta = roberta_model
        self.dropout1 = nn.Dropout(0.2)
        self.fc1 = nn.Linear(self.roberta.config.hidden_size + 48, self.num_classes_realis)

    def forward(self, input_ids, attention_mask, pos_spacy, ner_spacy, dep_spacy, depth_spacy, ner_tags):
        outputs = self.roberta(input_ids=input_ids, attention_mask=attention_mask)
        last_hidden_output = outputs.last_hidden_state

        pos_mask = pos_spacy != -100
        pos_embed_masked = self.pos_embed(pos_spacy[pos_mask])
        pos_embed = torch.zeros((pos_spacy.shape[0], pos_spacy.shape[1], 16), dtype=torch.float).to(device)
        pos_embed[pos_mask] = pos_embed_masked

        ner_mask = ner_spacy != -100
        ner_embed_masked = self.ner_embed(ner_spacy[ner_mask])
        ner_embed = torch.zeros((ner_spacy.shape[0], ner_spacy.shape[1], 8), dtype=torch.float).to(device)
        ner_embed[ner_mask] = ner_embed_masked

        dep_mask = dep_spacy != -100
        dep_embed_masked = self.dep_embed(dep_spacy[dep_mask])
        dep_embed = torch.zeros((dep_spacy.shape[0], dep_spacy.shape[1], 8), dtype=torch.float).to(device)
        dep_embed[dep_mask] = dep_embed_masked

        depth_mask = depth_spacy != -100
        depth_embed_masked = self.depth_embed(depth_spacy[depth_mask])
        depth_embed = torch.zeros((depth_spacy.shape[0], depth_spacy.shape[1], 8), dtype=torch.float).to(device)
        depth_embed[dep_mask] = depth_embed_masked

        nugget_mask = ner_tags != -100
        nugget_embed_masked = self.nugget_embed(ner_tags[nugget_mask])
        nugget_embed = torch.zeros((ner_tags.shape[0], ner_tags.shape[1], 8), dtype=torch.float).to(device)
        nugget_embed[dep_mask] = nugget_embed_masked

        features_concat = torch.cat((last_hidden_output, pos_embed, ner_embed, dep_embed, depth_embed, nugget_embed), 2).to(device)
        features_concat = self.dropout1(features_concat)
        features_concat = self.dropout1(features_concat)

        logits = self.fc1(features_concat)

        return logits

    
def get_entity_for_realis_from_idx(start_idx, end_idx, event_nuggets):
    event_nuggets_idxs = [(nugget["startOffset"], nugget["endOffset"]) for nugget in event_nuggets]
    for idx, (nugget_start, nugget_end) in enumerate(event_nuggets_idxs):
        if (start_idx == nugget_start and end_idx == nugget_end) or (start_idx == nugget_start and end_idx <= nugget_end) or (start_idx == nugget_start and end_idx > nugget_end) or (end_idx == nugget_end and start_idx < nugget_start) or (start_idx <= nugget_start and end_idx <= nugget_end and end_idx > nugget_start):
            return "B-" + event_nuggets[idx]["subtype"]
        elif (start_idx > nugget_start and end_idx <= nugget_end) or (start_idx > nugget_start and start_idx < nugget_end):
            return "I-" + event_nuggets[idx]["subtype"]
    return "O"

def tokenize_and_align_labels_with_pos_ner_realis(examples, tokenizer, ner_names, label_all_tokens = True):
    tokenized_inputs = tokenizer(examples["tokens"], padding='max_length', truncation=True, is_split_into_words=True)
    #tokenized_inputs.pop('input_ids')
    labels = []
    nuggets = []
    ner_spacy = []
    pos_spacy = []
    dep_spacy = []
    depth_spacy = []

    for i, (nugget, pos, ner, dep, depth) in enumerate(zip(examples["ner_tags"], examples["pos_spacy"], examples["ner_spacy"], examples["dep_spacy"], examples["depth_spacy"])):
        word_ids = tokenized_inputs.word_ids(batch_index=i)
        previous_word_idx = None
        nugget_ids = []
        ner_spacy_ids = []
        pos_spacy_ids = []
        dep_spacy_ids = []
        depth_spacy_ids = []

        for word_idx in word_ids:
            # Special tokens have a word id that is None. We set the label to -100 so they are automatically
            # ignored in the loss function.
            if word_idx is None:
                nugget_ids.append(-100)
                ner_spacy_ids.append(-100)
                pos_spacy_ids.append(-100)
                dep_spacy_ids.append(-100)
                depth_spacy_ids.append(-100)
            # We set the label for the first token of each word.
            elif word_idx != previous_word_idx:
                nugget_ids.append(nugget[word_idx])
                ner_spacy_ids.append(ner[word_idx])
                pos_spacy_ids.append(pos[word_idx])
                dep_spacy_ids.append(dep[word_idx])
                depth_spacy_ids.append(depth[word_idx])
            # For the other tokens in a word, we set the label to either the current label or -100, depending on
            # the label_all_tokens flag.
            else:
                nugget_ids.append(nugget[word_idx] if label_all_tokens else -100)
                ner_spacy_ids.append(ner[word_idx] if label_all_tokens else -100)
                pos_spacy_ids.append(pos[word_idx] if label_all_tokens else -100)
                dep_spacy_ids.append(dep[word_idx] if label_all_tokens else -100)
                depth_spacy_ids.append(depth[word_idx] if label_all_tokens else -100)
            previous_word_idx = word_idx

        nuggets.append(nugget_ids)
        ner_spacy.append(ner_spacy_ids)
        pos_spacy.append(pos_spacy_ids)
        dep_spacy.append(dep_spacy_ids)
        depth_spacy.append(depth_spacy_ids)

    tokenized_inputs["ner_tags"] = nuggets
    tokenized_inputs["pos_spacy"] = pos_spacy
    tokenized_inputs["ner_spacy"] = ner_spacy
    tokenized_inputs["dep_spacy"] = dep_spacy
    tokenized_inputs["depth_spacy"] = depth_spacy
    return tokenized_inputs