# coding=utf-8
# Copyright 2025-present, the HuggingFace Inc. Team and AIRAS Inc. Team. All rights reserved.
#
# 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.
from typing import List, Optional, Tuple, Union
from transformers.tokenization_utils import PreTrainedTokenizer
from transformers import AutoTokenizer
import json
import regex as re
from pathlib import Path
from typing import Dict, List, Optional, Union

BYTES_TO_UNICODE_REGEX = re.compile(r"'([^']+)':\s*([0-9]+)")

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 SapnousTokenizer(PreTrainedTokenizer):
    model_input_names = ["input_ids", "attention_mask"]

    def __init__(
        self,
        vocab_file: str,
        merges_file: Optional[str] = None,
        unk_token: str = "<|endoftext|>",
        bos_token: str = "<|startoftext|>",
        eos_token: str = "<|endoftext|>",
        pad_token: str = "<|pad|>",
        vision_start_token: str = "<|vision_start|>",
        vision_end_token: str = "<|vision_end|>",
        image_token: str = "<|image|>",
        video_token: str = "<|video|>",
        add_prefix_space: bool = False,
        **kwargs
    ):
        super().__init__(
            unk_token=unk_token,
            bos_token=bos_token,
            eos_token=eos_token,
            pad_token=pad_token,
            **kwargs,
        )

        self.vocab_file = vocab_file
        self.merges_file = merges_file
        self.add_prefix_space = add_prefix_space

        self.special_tokens = {
            "unk_token": unk_token,
            "bos_token": bos_token,
            "eos_token": eos_token,
            "pad_token": pad_token,
            "vision_start_token": vision_start_token,
            "vision_end_token": vision_end_token,
            "image_token": image_token,
            "video_token": video_token,
        }

        with Path(vocab_file).open(encoding="utf-8") as f:
            self.encoder = json.load(f)
        self.decoder = {v: k for k, v in self.encoder.items()}

        if merges_file:
            with Path(merges_file).open(encoding="utf-8") as f:
                bpe_merges = f.read().strip().split('\n')[1:]
            bpe_merges = [tuple(merge.split()) for merge in bpe_merges]
            self.bpe_ranks = dict(zip(bpe_merges, range(len(bpe_merges))))
        else:
            self.bpe_ranks = {}

        self.byte_encoder = bytes_to_unicode()
        self.byte_decoder = {v: k for k, v in self.byte_encoder.items()}
        self.pat = re.compile(r"""'s|'t|'re|'ve|'m|'ll|'d| ?\w+| ?\d+| ?[^\s\w\d]+|\s+(?!\S)|\s+""")

    def bpe(self, token: str) -> str:
        if token in self.special_tokens.values():
            return 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])
                    if word[j + 1] == second:
                        new_word.append(first + second)
                        i = j + 2
                    else:
                        new_word.append(word[j])
                        i = j + 1
                except ValueError:
                    new_word.extend(word[i:])
                    break
            word = tuple(new_word)
            if len(word) == 1:
                break
            pairs = get_pairs(word)
        return ' '.join(word)

    def _tokenize(self, text: str) -> List[str]:
        if self.add_prefix_space:
            text = ' ' + text

        bpe_tokens = []
        for token in re.findall(self.pat, text):
            token = ''.join(self.byte_encoder[ord(b)] for b in token)
            bpe_tokens.extend(bpe_token for bpe_token in self.bpe(token).split(' '))
        return bpe_tokens

    def _convert_token_to_id(self, token: str) -> int:
        return self.encoder.get(token, self.encoder.get(self.unk_token))

    def _convert_id_to_token(self, index: int) -> str:
        return self.decoder.get(index, self.unk_token)

    def convert_tokens_to_string(self, tokens: List[str]) -> str:
        text = ''.join(tokens)
        text = bytearray([self.byte_decoder[c] for c in text]).decode('utf-8', errors='replace')
        return text

    def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str, str]:
        if not filename_prefix:
            filename_prefix = ""

        vocab_file = Path(save_directory) / f"{filename_prefix}vocab.json"
        merge_file = Path(save_directory) / f"{filename_prefix}merges.txt"

        with vocab_file.open('w', encoding='utf-8') as f:
            json.dump(self.encoder, f, ensure_ascii=False)

        if self.merges_file:
            with merge_file.open('w', encoding='utf-8') as f:
                for merge in self.bpe_ranks:
                    f.write(f"{merge[0]} {merge[1]}\n")
            return str(vocab_file), str(merge_file)

        return str(vocab_file)

    def prepare_for_vision(self, text: str) -> str:
        """Prepare text for vision tasks by adding special tokens."""
        return f"{self.vision_start_token}{text}{self.vision_end_token}"

    def prepare_for_image(self, text: str) -> str:
        """Prepare text for image tasks."""
        return f"{self.image_token}{text}"

    def prepare_for_video(self, text: str) -> str:
        """Prepare text for video tasks."""
        return f"{self.video_token}{text}"

    @property
    def vocab_size(self) -> int:
        return len(self.encoder)

    def get_vocab(self) -> Dict[str, int]:
        return self.encoder.copy()

# Register the tokenizer
AutoTokenizer.register(SapnousTokenizer, "sapnous")