import logging
import tensorflow

from .config import config, DATA_SET_SIZE
from .database import mongo_reviews_collection_from_config, sample_reviews_polar, sample_reviews_varied
from .analysis.nltk_sentiment import NLTKSentimentAnalyzer
from .analysis.tf_text import TensorflowSentimentAnalyzer
from .tokenizer import NLTKWordTokenizer, PottsTokenizer, PottsTokenizerWithNegation, LowercaseTokenizer
from .log import install_log_handler

log = logging.getLogger(__name__)


def main():
    if len(tensorflow.config.list_physical_devices(device_type="GPU")) == 0:
        log.warning("Tensorflow reports no GPU acceleration available.")
    else:
        log.debug("Tensorflow successfully found GPU acceleration!")

    for dataset_func in [sample_reviews_polar, sample_reviews_varied]:
        # Tensorflow-based
        for Tokenizer in [
            LowercaseTokenizer
        ]:
            tokenizer = Tokenizer()
            model = TensorflowSentimentAnalyzer()

            with mongo_reviews_collection_from_config() as collection:
                ...

        # NLTK-based
        for Tokenizer in [
            NLTKWordTokenizer,
            PottsTokenizer,
            PottsTokenizerWithNegation,
            LowercaseTokenizer,
        ]:
            tokenizer = Tokenizer()
            model = NLTKSentimentAnalyzer(tokenizer=tokenizer)

            with mongo_reviews_collection_from_config() as collection:
                reviews_training = dataset_func(collection=collection, amount=DATA_SET_SIZE.__wrapped__)
                reviews_evaluation = dataset_func(collection=collection, amount=DATA_SET_SIZE.__wrapped__)

                log.info("Training model %s", model)
                model.train(reviews_training)
                log.info("Evaluating model %s", model)
                correct, evaluated = model.evaluate(reviews_evaluation)
                log.info("%d evaluated, %d correct, %0.2d %% accuracy", evaluated, correct, correct / evaluated * 100)

            # try:
            #     print("Manual testing for %s" % model)
            #     print("Input an empty string to continue to the next model.")
            #     while inp := input():
            #         print(model.use(inp))
            # except KeyboardInterrupt:
            #     pass


if __name__ == "__main__":
    install_log_handler()
    config.proxies.resolve()
    main()