import gzip
import html
import os
from functools import lru_cache
import ftfy
import numpy as np
import regex as re
from PIL import Image


@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


def basic_clean(text):
    text = ftfy.fix_text(text)
    text = html.unescape(html.unescape(text))
    return text.strip()


def whitespace_clean(text):
    text = re.sub(r"\s+", " ", text)
    return text.strip()


class SimpleTokenizer(object):
    def __init__(self, bpe_path: str):
        self.byte_encoder = bytes_to_unicode()
        self.byte_decoder = {v: k for k, v in self.byte_encoder.items()}

        merges = gzip.open(bpe_path, "rb").read().decode("utf-8").split("\n")
        merges = merges[1:49152 - 256 - 2 + 1]
        merges = [tuple(merge.split()) for merge in merges]

        vocab = list(bytes_to_unicode().values())
        vocab = vocab + [v + "</w>" for v in vocab]
        for merge in merges:
            vocab.append("".join(merge))
        vocab.extend(["<|startoftext|>", "<|endoftext|>"])
        self.encoder = dict(zip(vocab, range(len(vocab))))
        self.decoder = {v: k for k, v in self.encoder.items()}
        self.bpe_ranks = dict(zip(merges, range(len(merges))))
        self.cache = {"<|startoftext|>": "<|startoftext|>",
                      "<|endoftext|>": "<|endoftext|>"}
        self.pat = re.compile(
            r"""<\|startoftext\|>|<\|endoftext\|>|'s|'t|'re|'ve|'m|'ll|'d|[\p{L}]+|[\p{N}]|[^\s\p{L}\p{N}]+""",
            re.IGNORECASE,
        )

    def bpe(self, token):
        if token in self.cache:
            return self.cache[token]
        word = tuple(token[:-1]) + (token[-1] + "</w>",)
        pairs = get_pairs(word)
        if not pairs:
            return token + "</w>"
        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
            word = tuple(new_word)
            if len(word) == 1:
                break
            pairs = get_pairs(word)
        word = " ".join(word)
        self.cache[token] = word
        return word

    def encode(self, text):
        bpe_tokens = []
        text = whitespace_clean(basic_clean(text)).lower()
        for token in 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


@lru_cache()
def default_bpe_path():
    return os.path.join(os.path.dirname(os.path.abspath(__file__)), "bpe_simple_vocab_16e6.txt.gz")


def tokenize(texts, context_length=77, truncate=False):
    if isinstance(texts, str):
        texts = [texts]
    tokenizer = SimpleTokenizer(default_bpe_path())

    sot_token = tokenizer.encoder["<|startoftext|>"]
    eot_token = tokenizer.encoder["<|endoftext|>"]

    all_tokens = [[sot_token] +
                  tokenizer.encode(text) + [eot_token] for text in texts]
    result = np.zeros((len(all_tokens), context_length), dtype=np.int64)

    for i, tokens in enumerate(all_tokens):
        if len(tokens) > context_length:
            if truncate:
                tokens = tokens[:context_length]
                tokens[-1] = eot_token
            else:
                raise RuntimeError(
                    f"Input {texts[i]} is too long for context length {context_length}")
        result[i, :len(tokens)] = np.array(tokens, dtype=np.int64)

    return result


def preprocess(image: Image.Image, image_resolution: int = 224) -> np.ndarray:
    image = image.convert("RGB")
    width, height = image.size
    size = image_resolution
    if width < height:
        new_width = size
        new_height = int(round(size * height / width))
    else:
        new_height = size
        new_width = int(round(size * width / height))

    image = image.resize((new_width, new_height), Image.BICUBIC)
    left = (new_width - size) // 2
    top = (new_height - size) // 2
    image = image.crop((left, top, left + size, top + size))

    arr = np.array(image).astype(np.float32) / 255.0
    mean = np.array([0.48145466, 0.4578275, 0.40821073], dtype=np.float32)
    std = np.array([0.26862954, 0.26130258, 0.27577711], dtype=np.float32)
    arr = (arr - mean) / std
    arr = arr.transpose(2, 0, 1)
    return arr[np.newaxis, ...].astype(np.float32)