"""
Byte pair encoding utilities from GPT-2.

Original source: https://github.com/openai/gpt-2/blob/master/src/encoder.py
Original license: MIT
"""

import json
from functools import lru_cache


@lru_cache()
def bytes_to_unicode():
	"""
	Returns list of utf-8 byte and a corresponding list of unicode strings.
	The reversible bpe codes work on unicode strings.
	This means you need a large # of unicode characters in your vocab if you want to avoid UNKs.
	When you're at something like a 10B token dataset you end up needing around 5K for decent coverage.
	This is a signficant percentage of your normal, say, 32K bpe vocab.
	To avoid that, we want lookup tables between utf-8 bytes and unicode strings.
	And avoids mapping to whitespace/control characters the bpe code barfs on.
	"""
	bs = (
		list(range(ord("!"), ord("~") + 1))
		+ list(range(ord("¡"), ord("¬") + 1))
		+ list(range(ord("®"), ord("ÿ") + 1))
	)
	cs = bs[:]
	n = 0
	for b in range(2 ** 8):
		if b not in bs:
			bs.append(b)
			cs.append(2 ** 8 + n)
			n += 1
	cs = [chr(n) for n in cs]
	return dict(zip(bs, cs))


def get_pairs(word):
	"""Return set of symbol pairs in a word.
	Word is represented as tuple of symbols (symbols being variable-length strings).
	"""
	pairs = set()
	prev_char = word[0]
	for char in word[1:]:
		pairs.add((prev_char, char))
		prev_char = char
	return pairs


class Encoder:
	def __init__(self, encoder, bpe_merges, errors="replace"):
		self.encoder = encoder
		self.decoder = {v: k for k, v in self.encoder.items()}
		self.errors = errors  # how to handle errors in decoding
		self.byte_encoder = bytes_to_unicode()
		self.byte_decoder = {v: k for k, v in self.byte_encoder.items()}
		self.bpe_ranks = dict(zip(bpe_merges, range(len(bpe_merges))))
		self.cache = {}

		try:
			import regex as re

			self.re = re
		except ImportError:
			raise ImportError("Please install regex with: pip install regex")

		# Should haved added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
		self.pat = self.re.compile(
			r"""'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+"""
		)

	def bpe(self, token):
		if token in self.cache:
			return self.cache[token]
		word = tuple(token)
		pairs = get_pairs(word)

		if not pairs:
			return token

		while True:
			bigram = min(pairs, key=lambda pair: self.bpe_ranks.get(pair, float("inf")))
			if bigram not in self.bpe_ranks:
				break
			first, second = bigram
			new_word = []
			i = 0
			while i < len(word):
				try:
					j = word.index(first, i)
					new_word.extend(word[i:j])
					i = j
				except:
					new_word.extend(word[i:])
					break

				if word[i] == first and i < len(word) - 1 and word[i + 1] == second:
					new_word.append(first + second)
					i += 2
				else:
					new_word.append(word[i])
					i += 1
			new_word = tuple(new_word)
			word = new_word
			if len(word) == 1:
				break
			else:
				pairs = get_pairs(word)
		word = " ".join(word)
		self.cache[token] = word
		return word

	def encode(self, text):
		bpe_tokens = []
		for token in self.re.findall(self.pat, text):
			token = "".join(self.byte_encoder[b] for b in token.encode("utf-8"))
			bpe_tokens.extend(
				self.encoder[bpe_token] for bpe_token in self.bpe(token).split(" ")
			)
		return bpe_tokens

	def decode(self, tokens):
		text = "".join([self.decoder.get(token, token) for token in tokens])
		text = bytearray([self.byte_decoder[c] for c in text]).decode(
			"utf-8", errors=self.errors
		)
		return text


def get_encoder(encoder_json_path, vocab_bpe_path):
	with open(encoder_json_path, "r") as f:
		encoder = json.load(f)
	with open(vocab_bpe_path, "r", encoding="utf-8") as f:
		bpe_data = f.read()
	bpe_merges = [tuple(merge_str.split()) for merge_str in bpe_data.split("\n")[1:-1]]
	return Encoder(
		encoder=encoder,
		bpe_merges=bpe_merges,
	)
