"""
Question parsing utilities for GAIA implementation.

This module provides functions for extracting key information from questions,
identifying required tools, and determining question complexity.
"""

import re
import logging
from typing import Dict, Any, List, Optional, Union, Tuple, Set
import json

logger = logging.getLogger("gaia_agent.utils.parsing")

COMPLEXITY_LEVELS = {
    "SIMPLE": 1,  # Single fact or straightforward question
    "MODERATE": 2,  # Multiple facts or moderate reasoning
    "COMPLEX": 3,  # Complex reasoning or multiple steps
    "VERY_COMPLEX": 4  # Advanced reasoning, multiple domains, or specialized knowledge
}

def extract_key_information(question: str) -> Dict[str, Any]:
    """
    Extract key information from a question.
    
    Args:
        question: The question to analyze
        
    Returns:
        Dictionary containing extracted information
    """
    
    result = {
        "question_text": question,
        "entities": [],
        "keywords": [],
        "question_type": "",
        "constraints": [],
        "temporal_references": [],
        "expected_answer_type": "unknown"
    }
    
    capitalized_words = re.findall(r'\b[A-Z][a-zA-Z]*\b', question)
    result["entities"] = [word for word in capitalized_words if len(word) > 1]
    
    stop_words = {"a", "an", "the", "is", "are", "was", "were", "be", "been", 
                 "being", "to", "of", "and", "or", "in", "on", "at", "by", 
                 "for", "with", "about", "against", "between", "into", 
                 "through", "during", "before", "after", "above", "below", 
                 "from", "up", "down", "out", "off", "over", "under", "again", 
                 "further", "then", "once", "here", "there", "when", "where", 
                 "why", "how", "all", "any", "both", "each", "few", "more", 
                 "most", "other", "some", "such", "no", "nor", "not", "only", 
                 "own", "same", "so", "than", "too", "very", "s", "t", "can", 
                 "will", "just", "don", "should", "now", "d", "ll", "m", "o", 
                 "re", "ve", "y", "ain", "aren", "couldn", "didn", "doesn", 
                 "hadn", "hasn", "haven", "isn", "ma", "mightn", "mustn", 
                 "needn", "shan", "shouldn", "wasn", "weren", "won", "wouldn"}
    
    words = re.findall(r'\b[a-zA-Z]+\b', question.lower())
    result["keywords"] = [word for word in words if word not in stop_words and len(word) > 2]
    
    if re.search(r'\b(what|who|where|when|which)\b', question.lower()):
        result["question_type"] = "factual"
    elif re.search(r'\b(why|how)\b', question.lower()):
        result["question_type"] = "explanatory"
    elif re.search(r'\b(is|are|was|were|do|does|did|can|could|will|would|should|has|have)\b', question.lower()):
        result["question_type"] = "yes_no"
    elif re.search(r'\b(list|name|give|enumerate)\b', question.lower()):
        result["question_type"] = "list"
    elif re.search(r'\b(compare|contrast|difference|similarities)\b', question.lower()):
        result["question_type"] = "comparative"
    else:
        result["question_type"] = "other"
    
    time_patterns = [
        r'\b(today|yesterday|tomorrow|now|current|latest|recent)\b',
        r'\b(in\s+\d{4})\b',  # in 2023
        r'\b(\d{4}s)\b',  # 1990s
        r'\b(last|this|next)\s+(day|week|month|year|decade)\b',
        r'\b(january|february|march|april|may|june|july|august|september|october|november|december)\b',
        r'\b(jan|feb|mar|apr|jun|jul|aug|sep|oct|nov|dec)\b'
    ]
    
    for pattern in time_patterns:
        matches = re.findall(pattern, question.lower())
        if matches:
            result["temporal_references"].extend(matches)
    
    if re.search(r'\b(who|person)\b', question.lower()):
        result["expected_answer_type"] = "person"
    elif re.search(r'\b(where|location|place)\b', question.lower()):
        result["expected_answer_type"] = "location"
    elif re.search(r'\b(when|date|time|year)\b', question.lower()):
        result["expected_answer_type"] = "time"
    elif re.search(r'\b(how\s+many|count|number|sum|total)\b', question.lower()):
        result["expected_answer_type"] = "number"
    elif re.search(r'\b(why|reason|cause)\b', question.lower()):
        result["expected_answer_type"] = "reason"
    elif re.search(r'\b(how|process|steps|procedure)\b', question.lower()):
        result["expected_answer_type"] = "process"
    elif re.search(r'\b(list|examples|types)\b', question.lower()):
        result["expected_answer_type"] = "list"
    
    constraint_patterns = [
        r'(only|just)\s+([^.,;!?]*)',
        r'(at\s+least|at\s+most|more\s+than|less\s+than|exactly)\s+(\d+)',
        r'(between)\s+(\d+)\s+and\s+(\d+)',
        r'(not|except|excluding)\s+([^.,;!?]*)',
        r'(from|in)\s+([^.,;!?]*)',
        r'(before|after|during)\s+([^.,;!?]*)'
    ]
    
    for pattern in constraint_patterns:
        matches = re.findall(pattern, question.lower())
        if matches:
            result["constraints"].extend([' '.join(match) for match in matches])
    
    return result

def identify_required_tools(question: str) -> List[str]:
    """
    Identify tools required to answer a question.
    
    Args:
        question: The question to analyze
        
    Returns:
        List of required tool names
    """
    
    question_lower = question.lower()
    required_tools = set()
    
    if any(term in question_lower for term in ["search", "find", "look up", "latest", "current", "news", "information about"]):
        required_tools.add("web_search")
    
    if any(term in question_lower for term in ["website", "webpage", "url", "link", "extract", "content"]):
        required_tools.add("web_content")
    
    if any(term in question_lower for term in ["reason", "explain", "why", "how", "analyze", "understand", "interpret"]):
        required_tools.add("reasoning")
    
    if any(term in question_lower for term in ["calculate", "compute", "solve", "equation", "math", "formula", "number"]):
        required_tools.add("math")
    
    if any(term in question_lower for term in ["verify", "check", "fact", "true", "false", "accurate", "correct"]):
        required_tools.add("fact_verification")
    
    if any(term in question_lower for term in ["image", "picture", "photo", "describe", "visual", "see", "look at"]):
        required_tools.add("image_analysis")
    
    if any(term in question_lower for term in ["chart", "graph", "plot", "diagram", "visualization", "trend", "data"]):
        required_tools.add("chart_interpretation")
    
    if any(term in question_lower for term in ["document", "pdf", "docx", "text", "extract", "parse", "read"]):
        required_tools.add("document_parsing")
    
    if not required_tools:
        required_tools.add("reasoning")
        required_tools.add("web_search")
    
    return list(required_tools)

def determine_question_complexity(question: str) -> Dict[str, Any]:
    """
    Determine the complexity of a question.
    
    Args:
        question: The question to analyze
        
    Returns:
        Dictionary containing complexity assessment
    """
    
    question_lower = question.lower()
    
    complexity_score = 1  # Start with SIMPLE
    
    
    if len(question.split()) > 15:
        complexity_score += 1
    if len(question.split()) > 30:
        complexity_score += 1
    
    if question.count("?") > 1:
        complexity_score += 1
    
    complex_reasoning_terms = [
        "why", "how", "explain", "analyze", "compare", "contrast", 
        "evaluate", "assess", "interpret", "synthesize", "relationship",
        "impact", "effect", "cause", "implication", "consequence"
    ]
    if any(term in question_lower for term in complex_reasoning_terms):
        complexity_score += 1
    
    domains = {
        "science": ["physics", "chemistry", "biology", "scientific", "experiment"],
        "math": ["math", "equation", "calculation", "formula", "compute"],
        "history": ["history", "historical", "ancient", "century", "era", "period"],
        "geography": ["geography", "country", "region", "map", "location"],
        "literature": ["book", "author", "novel", "character", "literary"],
        "arts": ["art", "painting", "music", "artist", "composition"],
        "technology": ["technology", "computer", "software", "hardware", "digital"],
        "business": ["business", "economy", "market", "finance", "industry"],
        "politics": ["politics", "government", "policy", "election", "law"]
    }
    
    domain_count = 0
    for domain, terms in domains.items():
        if any(term in question_lower for term in terms):
            domain_count += 1
    
    if domain_count > 1:
        complexity_score += 1
    if domain_count > 2:
        complexity_score += 1
    
    temporal_terms = [
        "before", "after", "during", "while", "simultaneously",
        "previously", "subsequently", "meanwhile", "throughout",
        "initially", "finally", "eventually", "ultimately"
    ]
    if any(term in question_lower for term in temporal_terms):
        complexity_score += 1
    
    conditional_terms = [
        "if", "unless", "assuming", "provided that", "in case",
        "depending on", "subject to", "given that", "on condition"
    ]
    if any(term in question_lower for term in conditional_terms):
        complexity_score += 1
    
    complexity_score = min(complexity_score, 4)
    
    complexity_level = ""
    for level, score in COMPLEXITY_LEVELS.items():
        if complexity_score == score:
            complexity_level = level
    
    return {
        "complexity_level": complexity_level,
        "complexity_score": complexity_score,
        "requires_multiple_tools": len(identify_required_tools(question)) > 1,
        "requires_complex_reasoning": any(term in question_lower for term in complex_reasoning_terms),
        "multi_domain": domain_count > 1,
        "domains_involved": [domain for domain, terms in domains.items() if any(term in question_lower for term in terms)]
    }

def parse_question(question: str) -> Dict[str, Any]:
    """
    Comprehensive parsing of a question.
    
    Args:
        question: The question to parse
        
    Returns:
        Dictionary containing all parsed information
    """
    
    result = {
        "question": question,
        "key_information": extract_key_information(question),
        "required_tools": identify_required_tools(question),
        "complexity": determine_question_complexity(question)
    }
    
    return result