# Copyright 2022 Cerebras Systems.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

"""Byte pair encoding/decoding utilities

Modified from the GPT-2 codebase: https://github.com/openai/gpt-2
"""

import json
from functools import lru_cache

import regex as re


@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 BPETokenizer:
    def __init__(
        self, vocab_file, encoder_file, errors='replace', special_tokens=None
    ):
        with open(vocab_file, '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]
        ]

        with open(encoder_file, 'r') as f:
            self.encoder = json.load(f)
        self.decoder = {v: k for k, v in self.encoder.items()}
        # Assert encoder file is 1-1
        assert len(self.encoder) == len(self.decoder), (
            f"BPETokenizer: Length mismatch."
            f" This can happen when multiple words in the encoder"
            f" are mapped to the same id."
        )
        if special_tokens:
            for t in special_tokens:
                self.add_token(t)

        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 = {}

        # Should haved added re.IGNORECASE so BPE merges can happen for
        # capitalized versions of contractions
        self.pat = 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 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=self.errors
        )
        return text

    def get_token_id(self, token):
        if token in self.encoder:
            return self.encoder[token]

    def add_token(self, token):
        if token in self.encoder:
            print(
                f"BPETokenizer: {token} already exists in tokenizer"
                f" with id {self.encoder[token]}."
            )
        else:
            token_id = max(self.decoder.keys()) + 1
            self.encoder[token] = token_id
            self.decoder[token_id] = token
            print(f"BPETokenizer: {token} added with token_id {token_id}")
