"""

中文数据：clue superclue
英文数据：glue cnn_dailymail gigaword
代码数据:
数字：

## 参考
- https://github.com/baichuan-inc/Baichuan-7B  记录了不同分词器的压缩率
  - 指标：猜测是 n_tokens/n_chars  (baichuan小，说明百川token少，压缩率高)
  - Baichuan 0.73; llama 1.31;
- https://github.com/QwenLM/Qwen/blob/main/tech_memo.md  记录了不同分词器的压缩率
  - 以 XLM-RoBERTa为基准 (Unsupervised Cross-lingual Representation Learning at Scale ) ，
  - Qwen-7B 在很多语言上压缩率都较高压缩率 (high compression rate)
  - 中文： llama7b 2.2; baichuan7b 1.1; chatglm2-6b 0.9;  qwen7b 0.95
  - 英文：
  - 指标：猜测是 n_tokens / n_tokens_xlmR
- https://github.com/hpcaitech/ColossalAI/blob/4b8312c08e8d05a5f41453d63c8671aab601ed1c/applications/Colossal-LLaMA-2/prepare_pretrain_dataset.py#L134
  - 有压缩率的计算方式
  - https://github.com/hpcaitech/ColossalAI/blob/main/applications/Colossal-LLaMA-2/README.md#tokenizer
  - 记录了不同分词器的压缩率
  - 指标：
- https://github.com/AUGMXNT/shisa/blob/6a823d77a71acbd18ab8f68a6b02f4b87ec9dddd/eval/tokenizer-efficiency-ja.py#L24
  - 有压缩率的计算方式 = {n_chars} / {n_tokens}
  -
- https://github.com/huggingface/transformers/blob/cec773345aeffce3c04e8891303a3f748de7141e/src/transformers/models/whisper/generation_whisper.py#L354
  - 这个可能不是
- https://github.com/bojone/bytepiece/blob/main/README_en.md
  - "bytes/token": the average number of bytes per token
- Getting the most out of your tokenizer for pre-training and domain adaptation 👍
  - 定义：
    - NSL: 两个分词器的编码长度 比例，通常以 llama为基准
    - average number of bytes per token. {n_bytes} / {n_tokens}
  - higher compression rate  --
- *** https://github.com/microsoft/LLMLingua/blob/main/llmlingua/prompt_compressor.py
  - 定义：{Compressed Size}/{Raw Size}, 来自论文 Language modeling is compression. 数值<=1.0，用 % 来表示。也有>1的情况。
    -
    - {Compressed Size} 指的是？
  - 这里的压缩指的是 模型参数相关的。
"""

import json
import os
import pandas as pd
from datasets import load_dataset
from utils.log_util import logger
from vocab import load_tokener

CURRENT_DIR = os.path.dirname(os.path.abspath(__file__))


def get_n_bytes_of_string(string_text):
    n_bytes = len(string_text.encode("utf-8"))
    return n_bytes


def unit_convertor(stat, unit):
    n_tokens = stat["n_tokens"]
    n_chars = stat["n_chars"]
    n_bytes = stat["n_bytes"]

    n_tokens_in_billion = n_tokens / (1000 * 1000 * 1000)
    n_tokens_in_trillion = n_tokens / (1000 * 1000 * 1000 * 1000)
    n_bytes_in_mb = n_bytes / (1024 * 1024)
    n_bytes_in_gb = n_bytes_in_mb / 1024
    n_bytes_in_tb = n_bytes_in_gb / 1024
    # n_chars_in_billion = n_chars / (1000 * 1000 * 1000)

    if unit == "n_tokens/n_bytes":
        value = n_tokens / n_bytes
    elif unit == "n_chars/n_tokens":  # 重要：平均一个token包含多少个字符。
        value = n_chars / n_tokens
    elif unit == "n_tokens/n_chars":  # 一个中文汉字需要几个token？
        value = n_tokens / n_chars
    elif unit == "g_bytes/b_tokens":
        value = n_bytes_in_gb / n_tokens_in_billion
    elif unit == "t_bytes/t_tokens":  # 重要：
        value = n_bytes_in_tb / n_tokens_in_trillion
    elif unit == "b_tokens/g_bytes":
        value = n_tokens_in_billion / n_bytes_in_gb
    else:
        raise "measure not support"
    return round(value, 2)


all_units = ["g_bytes/b_tokens", "t_bytes/t_tokens", "b_tokens/g_bytes"]


def pprint(stats):
    table = []
    for tokenizer_name, stat in stats.items():
        columns = {"tokenizer": tokenizer_name, "vocab_size": stat["vocab_size"]}
        for unit in all_units:
            if unit not in stat:
                columns[unit] = unit_convertor(stat, unit)
            else:
                pass

        table.append(columns)
    df = pd.DataFrame(table)
    # print(df.to_markdown(index=False, tablefmt='fancy_grid'))
    logger.info(df.to_markdown(index=False))
    return


cache = {}


def tokenize_corpus(tokenizer, lang, cache_dir="stats/compress_rate"):
    """
    这个要独立的cache，因为速度慢。
    :param tokenizer:
    :param lang:
    :param cache_dir:
    :return:
    """

    def _tokenize(tokenizer, dataset):
        n_tokens = 0
        n_chars = 0
        n_bytes = 0
        for item in dataset:
            text = item["text"]
            n_bytes += get_n_bytes_of_string(text)
            n_chars += len(text)
            encodings = tokenizer.encode(text)
            n_tokens += len(encodings)
        stat = {
            "vocab_size": tokenizer.vocab_size,
            "n_bytes": n_bytes,
            "n_tokens": n_tokens,
            "n_chars": n_chars,
        }
        return stat

    tokenizer_name = tokenizer.alias
    lang = lang.replace("cc100-", "")
    cache_id = f"{tokenizer_name}.{lang}"
    # L1: in-memory cache
    if cache_id in cache:
        logger.info(f"loading {cache_id} from in-memory cache")
        return cache[cache_id]

    # L2: file cache
    cache_dir = os.path.join(CURRENT_DIR, f"../{cache_dir}")
    os.makedirs(cache_dir, exist_ok=True)
    cache_path = os.path.join(cache_dir, f"{cache_id}.json")
    if os.path.exists(cache_path):
        logger.info(f"loading {cache_id} from file cache")
        stat = json.load(open(cache_path, "r", encoding="utf-8"))
        cache[cache_id] = stat
        return stat

    # tokenize corpus
    dataset = load_dataset("eson/cc100-samples", lang, split="train")
    stat = _tokenize(tokenizer, dataset)
    logger.info(f"saving {cache_id} to {cache_path}")
    json.dump(stat, open(cache_path, "w", encoding="utf-8"))
    logger.info(f"saving {cache_id} to in-memory cache")
    cache[cache_id] = stat
    return stat


def main():
    from vocab import all_tokenizers
    stats = {}
    for lang in ["en", "zh-Hans"]:
        print("###" * 10 + lang)

        for tokenizer_name in ['llama', 'llama2', 'llama3']:
            # for tokenizer_name in all_tokenizers:
            tokenizer = load_tokener(tokenizer_name)
            stat = tokenize_corpus(tokenizer, lang)
            # ["qwen1_5_14b_chat", "gpt_35_turbo",]:
            stats[tokenizer_name] = stat

        pprint(stats)


if __name__ == "__main__":
    main()