import torch
from transformers.file_utils import is_tf_available, is_torch_available, is_torch_tpu_available
from transformers import BertTokenizerFast, BertForSequenceClassification
from transformers import Trainer, TrainingArguments
import numpy as np
import random
from sklearn.metrics import accuracy_score
import pandas as pd


class Dataset(torch.utils.data.Dataset):
    def __init__(self, encodings, labels):
        self.encodings = encodings
        self.labels = labels

    def __getitem__(self, idx):
        item = {k: torch.tensor(v[idx]) for k, v in self.encodings.items()}
        item["labels"] = torch.tensor([self.labels[idx]])
        return item

    def __len__(self):
        return len(self.labels)


def set_seed(seed: int):
    random.seed(seed)
    np.random.seed(seed)
    if is_torch_available():
        torch.manual_seed(seed)
        torch.cuda.manual_seed_all(seed)
    if is_tf_available():
        import tensorflow as tf

        tf.random.set_seed(seed)


def compute_metrics(pred):
    labels = pred.label_ids
    preds = pred.predictions.argmax(-1)
    acc = accuracy_score(labels, preds)
    return {
        'accuracy': acc,
    }


def get_prediction(text):
    inputs = tokenizer(text, padding=True, truncation=True, max_length=max_length, return_tensors="pt").to("cuda")
    outputs = model(**inputs)
    probs = outputs[0].softmax(1)
    return probs.argmax()


set_seed(1)

train_texts = \
    pd.read_csv('train/in.tsv.xz', compression='xz', sep='\t', header=None, error_bad_lines=False, quoting=3)[0].tolist()
train_labels = pd.read_csv('train/expected.tsv', sep='\t', header=None, quoting=3)[0].tolist()
dev_texts = pd.read_csv('dev-0/in.tsv.xz', compression='xz', sep='\t', header=None, quoting=3)[0].tolist()
dev_labels = pd.read_csv('dev-0/expected.tsv', sep='\t', header=None, quoting=3)[0].tolist()
# test_texts = pd.read_table('test-A/in.tsv.xz', compression='xz', sep='\t', header=None, quoting=3)

model_name = "bert-base-uncased"
max_length = 512
tokenizer = BertTokenizerFast.from_pretrained(model_name, do_lower_case=True)

train_encodings = tokenizer(train_texts, truncation=True, padding=True, max_length=max_length)
valid_encodings = tokenizer(dev_texts, truncation=True, padding=True, max_length=max_length)

train_dataset = Dataset(train_encodings, train_labels)
valid_dataset = Dataset(valid_encodings, dev_labels)

model = BertForSequenceClassification.from_pretrained(
    model_name, num_labels=len(pd.unique(train_labels))).to("cuda")

training_args = TrainingArguments(
    output_dir='./results',          # output directory
    num_train_epochs=3,              # total number of training epochs
    per_device_train_batch_size=1,  # batch size per device during training
    per_device_eval_batch_size=1,   # batch size for evaluation
    warmup_steps=500,                # number of warmup steps for learning rate scheduler
    weight_decay=0.01,               # strength of weight decay
    logging_dir='./logs',            # directory for storing logs
    load_best_model_at_end=True,     # load the best model when finished training (default metric is loss)
    # but you can specify `metric_for_best_model` argument to change to accuracy or other metric
    logging_steps=200,               # log & save weights each logging_steps
    evaluation_strategy="steps",     # evaluate each `logging_steps`
)

trainer = Trainer(
    model=model,                         # the instantiated Transformers model to be trained
    args=training_args,                  # training arguments, defined above
    train_dataset=train_dataset,         # training dataset
    eval_dataset=valid_dataset,          # evaluation dataset
    compute_metrics=compute_metrics,     # the callback that computes metrics of interest
)

trainer.train()

trainer.evaluate()

model_path = "bert-base-uncased"
model.save_pretrained(model_path)
tokenizer.save_pretrained(model_path)