from config import MODEL, TEST
import pickle
from datasets import load_dataset, Dataset
from transformers import AutoTokenizer
from tqdm import tqdm
from sklearn.preprocessing import MinMaxScaler
import numpy as np

values = ('year_start_float', 'year_end_float', 'year_middle_float', 'year_middle_int', 'text')

tokenizer = AutoTokenizer.from_pretrained(MODEL)
def tokenize_function(examples):
    t = tokenizer(examples["text"], padding="max_length", truncation=True, max_length=512)
    return t

def get_dataset_dict(dataset):
    with open(dataset) as f_in:
        next(f_in)
        d = dict()
        for v in values:
            d[v] = list()
        for l in f_in:
            args = l.rstrip().split('\t')
            for v, a in zip(values, args):
                d[v].append(a)
    return d

train_dataset = Dataset.from_dict(get_dataset_dict('train_huggingface_format.csv')).map(tokenize_function, batched=True).shuffle(seed=42)
eval_dataset_full = Dataset.from_dict(get_dataset_dict('dev-0_huggingface_format.csv')).map(tokenize_function, batched=True)
eval_dataset_small = eval_dataset_full.shuffle(seed=42).select(range(2000))
test_dataset_A = Dataset.from_dict(get_dataset_dict('test-A_huggingface_format.csv')).map(tokenize_function, batched=True)

if TEST:
    train_dataset = train_dataset.select(range(500))
    eval_dataset_full = eval_dataset_full.select(range(400))
    eval_dataset_small = eval_dataset_small.select(range(50))
    test_dataset_A = test_dataset_A.select(range(200))


scalers = dict()
values_to_scale = ('year_start_float', 'year_end_float', 'year_middle_float')
for v in values_to_scale:
    scalers[v] = MinMaxScaler().fit(np.array(train_dataset[v]).reshape(-1, 1))

def add_scaled(example):
    for factor in values_to_scale:
        example[factor + '_scaled'] = scalers[factor].transform(np.array(example[factor]).reshape(-1,1)).reshape(1,-1)[0].item()
    return example


train_dataset = train_dataset.map(add_scaled)
eval_dataset_full = eval_dataset_full.map(add_scaled)
eval_dataset_small = eval_dataset_small.map(add_scaled)
test_dataset_A = test_dataset_A.map(add_scaled)


with open('train_dataset.pickle', 'wb') as f_p:
    pickle.dump(train_dataset, f_p)

with open('eval_dataset_small.pickle', 'wb') as f_p:
    pickle.dump(eval_dataset_small, f_p)

with open('eval_dataset_full.pickle', 'wb') as f_p:
    pickle.dump(eval_dataset_full, f_p)

with open('test_dataset_A.pickle', 'wb') as f_p:
    pickle.dump(test_dataset_A, f_p)

with open('scalers.pickle', 'wb') as f_p:
    pickle.dump(scalers, f_p)