Support any LLMLingua-2-style token classifier (detect subword style from the tokenizer)

[Dario A (riceharvest)]

is_begin_of_new_word and get_pure_token pick their word-merging rule by matching substrings of
model_name (bert-base-multilingual-cased, tinybert, mobilebert, xlm-roberta-large, slingua).
Any other checkpoint raises NotImplementedError (see #168, #221), even when it follows the
LLMLingua-2 format exactly (2-label token classifier, label 1 = keep).

This PR detects the subword style once from the tokenizer (## WordPiece, ▁ SentencePiece, Ġ
byte-level BPE) and uses it whenever no model-name rule matches.

• Backward compatible: the existing name-based branches run first and are unchanged. Outputs of
  microsoft/llmlingua-2-bert-base-multilingual-cased-meetingbank are byte-identical before and after
  (checked on 15 mixed prompts).
• For SentencePiece and byte-level tokenizers, a new word starts only where the tokenizer saw
  whitespace. Identifiers such as ord_8f3a2c91 or 10.0.4.17:5432 therefore stay one word instead of
  becoming ord _ 8f3a2c91.
• The two lstrip("▁") blocks gated on xlm-roberta-large now also apply to detected SentencePiece
  and byte-level styles.

Example of a new checkpoint that works with this change: darioooooo0o/ctxprune-small (mmBERT-small,
Apache-2.0, trained for agent tool outputs).

from llmlingua import PromptCompressor
pc = PromptCompressor("darioooooo0o/ctxprune-small", use_llmlingua2=True)
pc.compress_prompt(text, rate=0.5, force_tokens=["\n", "?"])

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