# Copyright (c) OpenMMLab. All rights reserved.
"""CLIP tokenizer.

Copied from https://github.com/openai/CLIP. Originally MIT License, Copyright
(c) 2021 OpenAI.
"""
import gzip
import html
import os
from functools import lru_cache
from typing import List, Union

import ftfy
import regex as re
import torch

os.environ['TOKENIZERS_PARALLELISM'] = 'false'


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


@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 significant 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


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)
    text = text.strip()
    return text


class SimpleTokenizer:

    def __init__(self, bpe_path: str = default_bpe(), special_tokens=None):
        self.byte_encoder = bytes_to_unicode()
        self.byte_decoder = {v: k for k, v in self.byte_encoder.items()}
        merges = gzip.open(bpe_path).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))
        if not special_tokens:
            special_tokens = ['<start_of_text>', '<end_of_text>']
        else:
            special_tokens = ['<start_of_text>', '<end_of_text>'
                              ] + special_tokens
        vocab.extend(special_tokens)
        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 = {t: t for t in special_tokens}
        special = '|'.join(special_tokens)
        self.pat = re.compile(
            special +
            r"""|'s|'t|'re|'ve|'m|'ll|'d|[\p{L}]+|[\p{N}]|[^\s\p{L}\p{N}]+""",
            re.IGNORECASE)

        self.vocab_size = len(self.encoder)
        self.all_special_ids = [self.encoder[t] for t in special_tokens]

    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:  # noqa: E722, E261
                    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 = []
        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

    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').replace('</w>', ' ')
        return text


_tokenizer = SimpleTokenizer()


def decode(output_ids: torch.Tensor):
    output_ids = output_ids.cpu().numpy()
    return _tokenizer.decode(output_ids)


def tokenize(texts: Union[str, List[str]],
             context_length: int = 77) -> torch.LongTensor:
    """Returns the tokenized representation of given input string(s)

    Parameters
    ----------
    texts : Union[str, List[str]]
        An input string or a list of input strings to tokenize
    context_length : int
        The context length to use; all CLIP models use 77 as the context length

    Returns
    -------
    A two-dimensional tensor containing the resulting tokens,
    shape = [number of input strings, context_length]
    """
    if isinstance(texts, str):
        texts = [texts]

    sot_token = _tokenizer.encoder['<start_of_text>']
    eot_token = _tokenizer.encoder['<end_of_text>']
    all_tokens = [[sot_token] + _tokenizer.encode(text) + [eot_token]
                  for text in texts]
    result = torch.zeros(len(all_tokens), context_length, dtype=torch.long)

    for i, tokens in enumerate(all_tokens):
        if len(tokens) > context_length:
            tokens = tokens[:context_length]  # Truncate
            tokens[-1] = eot_token
        result[i, :len(tokens)] = torch.tensor(tokens)

    return result


class HFTokenizer:
    """HuggingFace tokenizer wrapper."""

    def __init__(self, tokenizer_name: str):
        from transformers import AutoTokenizer
        self.tokenizer = AutoTokenizer.from_pretrained(tokenizer_name)

    def save_pretrained(self, dest):
        self.tokenizer.save_pretrained(dest)

    def __call__(self,
                 texts: Union[str, List[str]],
                 context_length: int = 77) -> torch.Tensor:
        # same cleaning as for default tokenizer, except lowercasing
        # adding lower (for case-sensitive tokenizers) will make it
        # more robust but less sensitive to nuance
        if isinstance(texts, str):
            texts = [texts]
        texts = [whitespace_clean(basic_clean(text)) for text in texts]
        input_ids = self.tokenizer(
            texts,
            return_tensors='pt',
            max_length=context_length,
            padding='max_length',
            truncation=True,
        ).input_ids
        return input_ids