import json
import re
import unicodedata
from functools import lru_cache
import wget
import os
from constants import *
import nltk

@lru_cache()
def bytes_to_unicode():
    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):
    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', tokenize=None):
        self.encoder = encoder
        self.decoder = {v:k for k,v in self.encoder.items()}
        self.errors = errors
        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 = {}
        if tokenize is None:
            self.pat = re.compile(r"""'s|'t|'re|'ve|'m|'ll|'d| ?\w+| ?[^\s\w]+|\s+(?!\S)|\s+""", re.UNICODE)
            self.tokenize = lambda text: re.findall(self.pat, text)
        else:
            self.tokenize = tokenize

    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 ValueError:
                    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 = []
        normalized_text = unicodedata.normalize('NFKC', text)
        normalized_text = ''.join(c for c in normalized_text if c.isascii() and c != '\t')
        normalized_text = ''.join(c for c in normalized_text if not unicodedata.category(c).startswith('C'))
        for token in self.tokenize(normalized_text):
            token = ''.join(self.byte_encoder[b] for b in token.encode('utf-8', errors='ignore'))
            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[token] for token in tokens])
        text = bytearray([self.byte_decoder[c] for c in text]).decode('utf-8', errors='replace')
        decoded_text = text.replace(" .", ".").replace(" ,", ",").replace(" '", "'").replace(" ?", "?").replace(" !", "!").replace(" :", ":").replace('\n', '<br>')
        sentences = nltk.sent_tokenize(decoded_text)
        return ' '.join(sentences).replace("<br>", "<br>\n")

def get_encoder_gpt2():
    encoder_path = os.path.join(GPT2_FOLDER, ENCODER_FILE)
    vocab_path = os.path.join(GPT2_FOLDER, VOCAB_FILE)
    if not os.path.exists(GPT2_FOLDER):
        os.makedirs(GPT2_FOLDER)
    if not os.path.exists(encoder_path):
        wget.download(ENCODER_URL, out=encoder_path)
    if not os.path.exists(vocab_path):
        wget.download(VOCAB_URL, out=vocab_path)

    with open(encoder_path, 'r') as f:
        encoder = json.load(f)
    with open(vocab_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]]
    encoder_obj = Encoder(encoder=encoder, bpe_merges=bpe_merges)
    encoder_obj.encoder[END_OF_TEXT_TOKEN] = len(encoder_obj.encoder)
    encoder_obj.decoder[len(encoder_obj.decoder)] = END_OF_TEXT_TOKEN
    return encoder_obj

def get_codegen_tokenizer_pure(vocab_file, merges_file):
    vocab = json.load(open(vocab_file))
    merges = open(merges_file, 'r', encoding="utf-8").read().split('\n')[1:-1]
    bpe_merges = [tuple(m.split()) for m in merges]
    byte_encoder = bytes_to_unicode()
    byte_decoder = {v: k for k, v in byte_encoder.items()}
    tokenizer_regex = re.compile(r'''<\|endoftext\|>|'s|'t|'re|'ve|'m|'ll|'d|[\p{L}]+|[\p{N}]| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+''')
    tokenize = lambda text: re.findall(tokenizer_regex, text)
    encoder_obj = Encoder(
        encoder=vocab,
        bpe_merges=bpe_merges,
        byte_encoder=byte_encoder,
        byte_decoder=byte_decoder,
        tokenize=tokenize
    )
    return encoder_obj

def codegen_tokenize(text, tokenizer):
    return tokenizer.encode(text)

def codegen_decode(tokens, tokenizer):
    return tokenizer.decode(tokens)