from smolagents.tools import Tool
import pronouncing
import json
import string

class ParodyWordSuggestionTool(Tool):
    name = "parody_word_suggester"
    description = "Suggests rhyming funny words using CMU dictionary pronunciations."
    inputs = {'target': {'type': 'string', 'description': 'The word you want to find rhyming alternatives for'}, 'word_list_str': {'type': 'string', 'description': 'JSON string of word list (e.g. \'["word1", "word2"]\')'}, 'min_similarity': {'type': 'string', 'description': 'Minimum similarity threshold (0.0-1.0)', 'nullable': True, 'default': '0.5'}, 'custom_phones': {'type': 'object', 'description': 'Optional dictionary of custom word pronunciations', 'nullable': True, 'default': None}}
    output_type = "string"
    RHYME_WEIGHT = 0.5
    PHONE_SEQUENCE_WEIGHT = 0.3
    LENGTH_WEIGHT = 0.2

    def _get_word_phones(self, word, custom_phones=None):
        """Get phones for a word, checking custom dictionary first."""
        if custom_phones and word in custom_phones:
            return custom_phones[word]["primary_phones"]
        
        import pronouncing
        phones = pronouncing.phones_for_word(word)
        return phones[0] if phones else None


    def _get_primary_vowel(self, phones: list) -> str:
        """Get the primary stressed vowel from phone list."""
        v = ""
        for phone in phones:
            if '1' in phone and any(v in phone for v in 'AEIOU'):
                return phone.rstrip('012')
        return None


    def _calculate_phone_sequence_similarity(self, phones1: list, phones2: list) -> float:
        """Calculate similarity based on matching phones in sequence."""
        p = ""
        if not phones1 or not phones2:
            return 0.0
    
        # Strip stress markers for comparison
        clean_phones1 = [p.rstrip('012') for p in phones1]
        clean_phones2 = [p.rstrip('012') for p in phones2]
    
        matches = 0
        total_comparisons = max(len(clean_phones1), len(clean_phones2))
    
        # Compare phones in sequence
        for i in range(min(len(clean_phones1), len(clean_phones2))):
            if clean_phones1[i] == clean_phones2[i]:
                matches += 1
            
        return matches / total_comparisons if total_comparisons > 0 else 0.0


    def _calculate_length_similarity(self, phones1: list, phones2: list) -> float:
        """Calculate similarity based on phone length."""
        max_length = max(len(phones1), len(phones2))
        length_diff = abs(len(phones1) - len(phones2))
        return 1.0 - (length_diff / max_length) if max_length > 0 else 0.0


    def _calculate_similarity(self, word1, phones1, word2, phones2):
        """Calculate similarity based on multiple factors."""
        # Initialize phone lists
        phone_list1 = phones1.split()
        phone_list2 = phones2.split()
    
        # 1. Rhyme score (50%) - based on primary vowel
        vowel1 = self._get_primary_vowel(phone_list1)
        vowel2 = self._get_primary_vowel(phone_list2)
        rhyme_score = 1.0 if vowel1 and vowel2 and vowel1 == vowel2 else 0.0
    
        # 2. Phone sequence similarity (30%)
        phone_sequence_score = self._calculate_phone_sequence_similarity(phone_list1, phone_list2)
    
        # 3. Length similarity (20%)
        length_score = self._calculate_length_similarity(phone_list1, phone_list2)
    
        # Combined weighted score
        similarity = (
            (rhyme_score * self.RHYME_WEIGHT) +
            (phone_sequence_score * self.PHONE_SEQUENCE_WEIGHT) +
            (length_score * self.LENGTH_WEIGHT)
        )
    
        return {
            "similarity": round(similarity, 3),
            "rhyme_score": round(rhyme_score, 3),
            "phone_sequence_score": round(phone_sequence_score, 3),
            "length_score": round(length_score, 3),
            "details": {
                "primary_vowel1": vowel1,
                "primary_vowel2": vowel2,
                "phone_count1": len(phone_list1),
                "phone_count2": len(phone_list2),
                "matching_phones": round(phone_sequence_score * len(phone_list1))
            }
        }


    def forward(self, target: str, word_list_str: str, min_similarity: str = "0.5", custom_phones: dict = None) -> str:
        import pronouncing
        import string
        import json
    
        # Initialize variables
        target = target.lower().strip(string.punctuation)
        min_similarity = float(min_similarity)
        suggestions = []
        valid_words = []
        invalid_words = []
    
        # Parse JSON string to list
        try:
            words = json.loads(word_list_str)
        except json.JSONDecodeError:
            return json.dumps({
                "error": "Invalid JSON string for word_list_str",
                "suggestions": []
            }, indent=2)
        
        # Get target pronunciation
        target_phones = self._get_word_phones(target, custom_phones)
        if not target_phones:
            return json.dumps({
                "error": f"Target word '{target}' not found in dictionary or custom phones",
                "suggestions": []
            }, indent=2)
    
        # Filter word list
        for word in words:
            word = word.lower().strip(string.punctuation)
            if self._get_word_phones(word, custom_phones):
                valid_words.append(word)
            else:
                invalid_words.append(word)
    
        if not valid_words:
            return json.dumps({
                "error": "No valid words found in dictionary or custom phones",
                "invalid_words": invalid_words,
                "suggestions": []
            }, indent=2)
    
        # Check each word
        for word in valid_words:
            word_phones = self._get_word_phones(word, custom_phones)
            if word_phones:
                similarity_result = self._calculate_similarity(word, word_phones, target, target_phones)
            
                if similarity_result["similarity"] >= min_similarity:
                    suggestions.append({
                        "word": word,
                        "similarity": similarity_result["similarity"],
                        "rhyme_score": similarity_result["rhyme_score"],
                        "phone_sequence_score": similarity_result["phone_sequence_score"],
                        "length_score": similarity_result["length_score"],
                        "phones": word_phones,
                        "is_custom": word in custom_phones if custom_phones else False,
                        "details": similarity_result["details"]
                    })
    
        # Sort by similarity score descending
        suggestions.sort(key=lambda x: x["similarity"], reverse=True)
    
        result = {
            "target": target,
            "target_phones": target_phones,
            "invalid_words": invalid_words,
            "suggestions": suggestions
        }
    
        return json.dumps(result, indent=2)


    def __init__(self, *args, **kwargs):
        self.is_initialized = False