import re
import spacy
import tiktoken
from lemminflect import getLemma

class AdvancedPromptOptimizer:
    def __init__(self):
        # For NER, consider using en_core_web_md for better accuracy
        self.nlp = spacy.load("en_core_web_sm")
        self.nlp.Defaults.stop_words -= {"not", "no", "never"}
        self.tokenizer = tiktoken.get_encoding("cl100k_base")
        self.negation_words = {"not", "no", "never", "without", "except"}

    def _mask_spans(self, s):
        masks = {}
        # triple backticks
        s, n = re.subn(r"```.*?```", lambda m: masks.setdefault(f"<CODE{len(masks)}>", m.group(0)) or list(masks.keys())[-1], s, flags=re.S)
        # inline code
        s = re.sub(r"`[^`]+`", lambda m: masks.setdefault(f"<IC{len(masks)}>", m.group(0)) or list(masks.keys())[-1], s)
        # urls
        s = re.sub(r"https?://\S+", lambda m: masks.setdefault(f"<URL{len(masks)}>", m.group(0)) or list(masks.keys())[-1], s)
        # comparators
        s = re.sub(r"\b(less than|at least|no more than)\b", lambda m: masks.setdefault(f"<CMP{len(masks)}>", m.group(0)) or list(masks.keys())[-1], s, flags=re.I)
        return s, masks

    def _unmask_spans(self, s, masks):
        for k, v in masks.items():
            s = s.replace(k, v)
        return s

    def optimize(self, prompt: str, aggressiveness: float = 0.7) -> tuple:
        """Optimize prompt with token counting"""
        masked_prompt, masks = self._mask_spans(prompt)
        optimized = self._apply_rules(masked_prompt, aggressiveness)
        optimized = self._linguistic_optimize(optimized, aggressiveness)
        optimized = self._unmask_spans(optimized, masks)
        optimized = re.sub(r"\s+", " ", optimized).strip()

        try:
            orig_tokens = len(self.tokenizer.encode(prompt))
            new_tokens = len(self.tokenizer.encode(optimized))
        except:
            orig_tokens = len(prompt.split())
            new_tokens = len(optimized.split())

        return optimized, orig_tokens, new_tokens

    def _apply_rules(self, text: str, aggressiveness: float) -> str:
        # Apply safer rules first
        rules = [
            (r"\s{2,}", " ", 0.0),
            (r"\b(\w+)\s+\1\b", r"\1", 0.0),
            (r"\b(advantages and disadvantages)\b", "pros/cons", 0.5),
            (r"\b(in a detailed manner|in a detailed way)\b", "", 0.7),
            (r"\b(I want to|I need to|I would like to)\b", "", 0.7),
            (r"\b(for example|e\.g\.|such as|i\.e\.)\b", "e.g.", 0.8),
            (r"\b(please\s+)?(kindly\s+)?(carefully|very|extremely|really|quite)\b", "", 0.8),
            (r"\b(can you|could you|would you)\b", "", 0.9),
            (r"\b(output|provide|give|return)\s+in\s+(JSON|json)\s+format\b", "JSON:", 1.0),
        ]
        for pattern, repl, priority in rules:
            if aggressiveness >= priority:
                text = re.sub(pattern, repl, text, flags=re.IGNORECASE)
        return text

    def _linguistic_optimize(self, text: str, aggressiveness: float) -> str:
        if not text.strip():
            return text
        doc = self.nlp(text)
        out = []
        for token in doc:
            # Guard important labels
            if token.text.lower() in ["deliverables:", "constraints:", "metrics:"] and token.is_sent_start:
                out.append(token.text)
                continue

            if token.pos_ in ("PUNCT", "SPACE"): continue
            if token.like_num or token.ent_type_ or token.dep_ == "neg" or token.text.lower() in self.negation_words:
                out.append(token.text)
                continue
            if token.pos_ in ("PROPN", "NUM", "NOUN", "ADJ"):
                out.append(token.text)
                continue
            if token.pos_ == "VERB":
                if aggressiveness >= 0.8:
                    lemma = getLemma(token.text, upos="VERB") or [token.lemma_]
                    out.append(lemma[0])
                else:
                    out.append(token.text)
                continue
            if token.pos_ in ("ADV", "DET", "PRON"):
                if aggressiveness < 0.6:
                    out.append(token.text)
                # else drop
                continue
            out.append(token.text)
        return " ".join(out)