from flair.data import Sentence
from flair.models import SequenceTagger
from tqdm import tqdm

# --------------------- VARIABLES ---------------------- #



# ----------------- DATA PREPROCESSING ----------------- #

# def convert_to_BIO_format(directory):
# 	"""
# 	Read data in BIO format.
# 	"""
# 	_in = open(directory + "/in.tsv", "r").read().split(" ")
# 	_expected = open(directory + "/expected.tsv", "r").read().split(" ")

# 	_lines = list(map(lambda x: f"{x[0]} {x[1]}", zip(_in, _expected)))
# 	_lines = list(map(lambda x: "" if x.startswith("</S>") else x, _lines))

# 	open(directory + "/in_bio.tsv", "w").write("\n".join(_lines))

# convert_to_BIO_format("dev-0")

# ---------------------- GENERATE PREDICTIONS ---------------------- #
DIRECTORY = "dev-0"

# load tagger
tagger = SequenceTagger.load("flair/ner-english")
batches = open(f"{DIRECTORY}/in.tsv", "r").read().split("\n")[:-1]
results = []

with tqdm(total=len(batches)) as pbar:
	for batch in batches:
		batch_result = []

		for sentence in batch.split("</S>"):
			raw_sentence = sentence
			sentence = sentence.strip()

			if sentence == "":
				continue

			sentence = Sentence(sentence, use_tokenizer=False)

			tagger.predict(sentence)

			out = ["O"] * len(sentence)

			for entity in sentence.get_spans('ner'):

				is_first = True
				for token in entity:
					if is_first:
						out[token.idx - 1] = f"B-{entity.tag}"
					else:
						out[token.idx - 1] = f"I-{entity.tag}"

					is_first = False
			batch_result.append(" ".join(out))
	
		results.append(" O ".join(batch_result) + " O")

		pbar.update(1)

open(f"{DIRECTORY}/out.tsv", "w").write("\n".join(results))

# 456