from collections import Counter

import pandas as pd
import torch
from torchtext.vocab import Vocab


class NERModel(torch.nn.Module):
    def __init__(self, ):
        super(NERModel, self).__init__()
        self.emb = torch.nn.Embedding(23628, 200)
        self.fc1 = torch.nn.Linear(600, 9)

    def forward(self, x):
        x = self.emb(x)
        x = x.reshape(600)
        x = self.fc1(x)
        return x


def data_process(dt):
    return [torch.tensor([vocab['<bos>']] + [vocab[token] for token in document] + [vocab['<eos>']], dtype=torch.long)
            for document in dt]


def build_vocab(dataset):
    counter = Counter()
    for document in dataset:
        counter.update(document)
    return Vocab(counter, specials=['<unk>', '<pad>', '<bos>', '<eos>'])


def labels_process(dt):
    return [torch.tensor([0] + document + [0], dtype=torch.long) for document in dt]


def predict(input_tokens, labels):
    results = []
    for i in range(len(input_tokens)):
        line_results = []
        for j in range(1, len(input_tokens[i]) - 1):
            x = input_tokens[i][j - 1: j + 2].to(device)
            predicted = ner_model(x.long())
            result = torch.argmax(predicted)
            label = labels[result]
            line_results.append(label)
        results.append(line_results)

    return results


def features(data):
    featurez = []
    for sentence in data["tokens"]:
        t_sentence = torch.tensor(())
        for word in sentence:
            temp = torch.tensor([word[0].isupper(), len(word)])
            t_sentence = torch.cat((t_sentence, temp))
        featurez.append(t_sentence)

    return featurez


def merge_features(token_ids, tensors_list):
    return [torch.cat((token, tensors_list[i])) for i, token in enumerate(token_ids)]


def process_output(lines):
    result = []
    for line in lines:
        last_label = None
        new_line = []
        for label in line:
            if label != "O" and label[0:2] == "I-":
                if last_label is None or last_label == "O":
                    label = label.replace('I-', 'B-')
                else:
                    label = "I-" + last_label[2:]
            last_label = label
            new_line.append(label)
        result.append(" ".join(new_line))
    return result


def infer(path_in, path_out, labels):
    df = pd.read_csv(path_in, sep='\t', names=['tokens'])
    df_token_ids = data_process(df["tokens"].apply(lambda x: x.split()))
    df_infer = merge_features(df_token_ids, features(df))
    infers = predict(df_infer, labels)
    infers_processed = process_output(infers)
    with open(path_out, "w") as file_out:
        for inf in infers_processed:
            file_out.write(inf + "\n")


labels = ['O', 'B-LOC', 'I-LOC', 'B-MISC', 'I-MISC', 'B-ORG', 'I-ORG', 'B-PER', 'I-PER']

df = pd.read_csv('train/train.tsv.xz', compression='xz', sep='\t', names=['iob', 'tokens'])
df["iob"] = df["iob"].apply(lambda x: [labels.index(y) for y in x.split()])
df["tokens"] = df["tokens"].apply(lambda x: x.split())


vocab = build_vocab(df['tokens'])

device = torch.device("cuda:0")
ner_model = NERModel().to(device)
criterion = torch.nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(ner_model.parameters())

train_labels = labels_process(df['iob'])
train_tokens_ids = data_process(df['tokens'])
df_features = features(df)
train_tensors = merge_features(train_tokens_ids, df_features)

for epoch in range(5):
    acc_score = 0
    prec_score = 0
    selected_items = 0
    recall_score = 0
    relevant_items = 0
    items_total = 0
    ner_model.train()
    for i in range(len(train_labels)):
        for j in range(1, len(train_labels[i]) - 1):
            X = train_tensors[i][j - 1: j + 2].to(device)
            Y = train_labels[i][j: j + 1].to(device)
            Y_predictions = ner_model(X.long())

            acc_score += int(torch.argmax(Y_predictions) == Y)
            if torch.argmax(Y_predictions) != 0:
                selected_items += 1
            if torch.argmax(Y_predictions) != 0 and torch.argmax(Y_predictions) == Y.item():
                prec_score += 1
            if Y.item() != 0:
                relevant_items += 1
            if Y.item() != 0 and torch.argmax(Y_predictions) == Y.item():
                recall_score += 1

            items_total += 1
            optimizer.zero_grad()
            loss = criterion(Y_predictions.unsqueeze(0), Y)
            loss.backward()
            optimizer.step()

    precision = prec_score / selected_items
    recall = recall_score / relevant_items
    f1_score = (2 * precision * recall) / (precision + recall)
    print(f'epoch: {epoch}')
    print(f'f1: {f1_score}')
    print(f'acc: {acc_score / items_total}')

infer('dev-0/in.tsv', 'dev-0/out.tsv', labels=labels)
infer('test-A/in.tsv', 'test-A/out.tsv', labels=labels)