from sentence_transformers import models, losses
from sentence_transformers import LoggingHandler, SentenceTransformer, util, InputExample
from sentence_transformers.evaluation import EmbeddingSimilarityEvaluator
import logging
from datetime import datetime
import sys
import os
import gzip
import csv
from MultiDatasetDataLoader import MultiDatasetDataLoader

#### Just some code to print debug information to stdout
logging.basicConfig(
    format="%(asctime)s - %(message)s", datefmt="%Y-%m-%d %H:%M:%S", level=logging.INFO, handlers=[LoggingHandler()]
)
#### /print debug information to stdout

model_name = "distilroberta-base"
num_epochs = 1
sts_dataset_path = "data-eval/stsbenchmark.tsv.gz"
batch_size_pairs = 384
batch_size_triplets = 256
max_seq_length = 128
use_amp = True  # Set to False, if you use a CPU or your GPU does not support FP16 operations
evaluation_steps = 500
warmup_steps = 500

#####

if not os.path.exists(sts_dataset_path):
    util.http_get("https://sbert.net/datasets/stsbenchmark.tsv.gz", sts_dataset_path)


# Save path of the model
model_save_path = (
    "output/training_paraphrases_" + model_name.replace("/", "-") + "-" + datetime.now().strftime("%Y-%m-%d_%H-%M-%S")
)


## SentenceTransformer model
word_embedding_model = models.Transformer(model_name, max_seq_length=max_seq_length)
pooling_model = models.Pooling(word_embedding_model.get_word_embedding_dimension())
model = SentenceTransformer(modules=[word_embedding_model, pooling_model])

dataset_list = []
for filepath in sys.argv[1:]:
    dataset = []
    with_guid = "with-guid" in filepath  # Some datasets have a guid in the first column

    with gzip.open(filepath, "rt", encoding="utf8") as fIn:
        for line in fIn:
            splits = line.strip().split("\t")
            if with_guid:
                guid = splits[0]
                texts = splits[1:]
            else:
                guid = None
                texts = splits

            dataset.append(InputExample(texts=texts, guid=guid))

    dataset_list.append(dataset)


train_dataloader = MultiDatasetDataLoader(
    dataset_list, batch_size_pairs=batch_size_pairs, batch_size_triplets=batch_size_triplets
)


# Our training loss
train_loss = losses.MultipleNegativesRankingLoss(model)


# Read STSbenchmark dataset and use it as development set
logging.info("Read STSbenchmark dev dataset")
dev_samples = []
with gzip.open(sts_dataset_path, "rt", encoding="utf8") as fIn:
    reader = csv.DictReader(fIn, delimiter="\t", quoting=csv.QUOTE_NONE)
    for row in reader:
        if row["split"] == "dev":
            score = float(row["score"]) / 5.0  # Normalize score to range 0 ... 1
            dev_samples.append(InputExample(texts=[row["sentence1"], row["sentence2"]], label=score))

dev_evaluator = EmbeddingSimilarityEvaluator.from_input_examples(dev_samples, name="sts-dev")

# Configure the training
logging.info("Warmup-steps: {}".format(warmup_steps))

# Train the model
model.fit(
    train_objectives=[(train_dataloader, train_loss)],
    evaluator=dev_evaluator,
    epochs=num_epochs,
    evaluation_steps=evaluation_steps,
    warmup_steps=warmup_steps,
    output_path=model_save_path,
    use_amp=use_amp,
    checkpoint_path=model_save_path,
    checkpoint_save_steps=1000,
    checkpoint_save_total_limit=3,
)