"""
Formatting utilities for GAIA implementation.

This module provides functions for extracting, formatting, and validating text content,
including answers, dates, numbers, and structured data.
"""

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

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

# Constants for formatting types
FORMAT_TYPES = {
    "TEXT": "text",              # Free-form text
    "NUMBER": "number",          # Numerical answer
    "DATE": "date",              # Date or time
    "BOOLEAN": "boolean",        # Yes/No or True/False
    "LIST": "list",              # Comma-separated list
    "ENTITY": "entity",          # Named entity (person, place, etc.)
    "STRUCTURED": "structured"   # JSON or structured data
}

def extract_answer(response: str, expected_format: Optional[str] = None) -> str:
    """
    Extract the answer from a response.
    
    Args:
        response: The full response text
        expected_format: Optional expected format type
        
    Returns:
        The extracted answer
    """
    if len(response.split()) < 20:
        return response.strip()
    
    answer_patterns = [
        r'(?:The\s+answer\s+is:?\s+)([^.]+)',
        r'(?:In\s+conclusion:?\s+)([^.]+)',
        r'(?:To\s+summarize:?\s+)([^.]+)',
        r'(?:Therefore:?\s+)([^.]+)',
        r'(?:Thus:?\s+)([^.]+)',
        r'(?:In\s+summary:?\s+)([^.]+)',
        r'(?:The\s+result\s+is:?\s+)([^.]+)'
    ]
    
    for pattern in answer_patterns:
        match = re.search(pattern, response, re.IGNORECASE)
        if match:
            return match.group(1).strip()
    
    if expected_format:
        if expected_format == FORMAT_TYPES["NUMBER"]:
            return extract_number(response)
        elif expected_format == FORMAT_TYPES["DATE"]:
            return extract_date(response)
        elif expected_format == FORMAT_TYPES["BOOLEAN"]:
            return extract_boolean(response)
        elif expected_format == FORMAT_TYPES["LIST"]:
            return extract_list(response)
        elif expected_format == FORMAT_TYPES["ENTITY"]:
            return extract_entity(response)
        elif expected_format == FORMAT_TYPES["STRUCTURED"]:
            return extract_structured_data(response)
    
    sentences = re.split(r'[.!?]\s+', response)
    if sentences:
        return sentences[-1].strip()
    
    return response.strip()

def extract_number(text: str) -> str:
    """
    Extract a numerical answer from text.
    
    Args:
        text: The text to extract from
        
    Returns:
        The extracted number as a string
    """
    number_pattern = r'(?:[\$€£¥])?(?:[\-+])?(?:\d{1,3}(?:,\d{3})+|\d+)(?:\.\d+)?(?:\s*%)?'
    
    answer_number_patterns = [
        rf'(?:The\s+(?:answer|result|number|value|amount)\s+is:?\s+)({number_pattern})',
        rf'(?:equals\s+)({number_pattern})',
        rf'(?:approximately\s+)({number_pattern})',
        rf'(?:about\s+)({number_pattern})'
    ]
    
    for pattern in answer_number_patterns:
        match = re.search(pattern, text, re.IGNORECASE)
        if match:
            return match.group(1).strip()
    
    matches = re.findall(number_pattern, text)
    if matches:
        return matches[-1].strip()
    
    return ""

def extract_date(text: str) -> str:
    """
    Extract a date or time from text.
    
    Args:
        text: The text to extract from
        
    Returns:
        The extracted date as a string
    """
    date_patterns = [
        r'\d{1,2}[-/]\d{1,2}[-/]\d{2,4}',  # MM/DD/YYYY or DD/MM/YYYY
        r'\d{4}[-/]\d{1,2}[-/]\d{1,2}',    # YYYY/MM/DD
        r'(?:Jan|Feb|Mar|Apr|May|Jun|Jul|Aug|Sep|Oct|Nov|Dec)[a-z]*\s+\d{1,2}(?:st|nd|rd|th)?,?\s+\d{4}',  # Month DD, YYYY
        r'\d{1,2}(?:st|nd|rd|th)?\s+(?:of\s+)?(?:Jan|Feb|Mar|Apr|May|Jun|Jul|Aug|Sep|Oct|Nov|Dec)[a-z]*,?\s+\d{4}',  # DD Month YYYY
        r'\d{1,2}:\d{2}(?::\d{2})?\s*(?:AM|PM|am|pm)?'  # HH:MM:SS AM/PM
    ]
    
    answer_date_patterns = [
        rf'(?:The\s+(?:date|time)\s+is:?\s+)({")|(".join(date_patterns)})',
        rf'(?:occurred\s+on\s+)({")|(".join(date_patterns)})',
        rf'(?:happened\s+on\s+)({")|(".join(date_patterns)})',
        rf'(?:scheduled\s+for\s+)({")|(".join(date_patterns)})'
    ]
    
    for pattern in answer_date_patterns:
        match = re.search(pattern, text, re.IGNORECASE)
        if match:
            return match.group(1).strip()
    
    for pattern in date_patterns:
        match = re.search(pattern, text)
        if match:
            return match.group(0).strip()
    
    return ""

def extract_boolean(text: str) -> str:
    """
    Extract a boolean answer (yes/no, true/false) from text.
    
    Args:
        text: The text to extract from
        
    Returns:
        The extracted boolean as a string
    """
    text_lower = text.lower()
    
    yes_patterns = [
        r'^yes',
        r'^affirmative',
        r'^correct',
        r'^true',
        r'the answer is yes',
        r'the answer is affirmative',
        r'the answer is true',
        r'the answer is correct'
    ]
    
    no_patterns = [
        r'^no',
        r'^negative',
        r'^incorrect',
        r'^false',
        r'the answer is no',
        r'the answer is negative',
        r'the answer is false',
        r'the answer is incorrect'
    ]
    
    for pattern in yes_patterns:
        if re.search(pattern, text_lower):
            return "Yes"
    
    for pattern in no_patterns:
        if re.search(pattern, text_lower):
            return "No"
    
    positive_terms = ["can", "does", "is", "will", "should", "would", "could", "positive", "affirmative"]
    negative_terms = ["cannot", "can't", "doesn't", "does not", "isn't", "is not", "won't", "will not", 
                     "shouldn't", "should not", "wouldn't", "would not", "couldn't", "could not", 
                     "negative", "never"]
    
    positive_count = sum(1 for term in positive_terms if term in text_lower)
    negative_count = sum(1 for term in negative_terms if term in text_lower)
    
    if positive_count > negative_count:
        return "Yes"
    elif negative_count > positive_count:
        return "No"
    
    return "Unknown"

def extract_list(text: str) -> str:
    """
    Extract a comma-separated list from text.
    
    Args:
        text: The text to extract from
        
    Returns:
        The extracted list as a comma-separated string
    """
    list_patterns = [
        r'(?:The\s+list\s+is:?\s+)(.*?)(?:\.|\n|$)',
        r'(?:The\s+items\s+are:?\s+)(.*?)(?:\.|\n|$)',
        r'(?:The\s+elements\s+are:?\s+)(.*?)(?:\.|\n|$)',
        r'(?:They\s+are:?\s+)(.*?)(?:\.|\n|$)'
    ]
    
    for pattern in list_patterns:
        match = re.search(pattern, text, re.IGNORECASE)
        if match:
            items = match.group(1).strip()
            items = re.sub(r'\s*,\s*', ', ', items)  # Normalize commas
            items = re.sub(r'\s+and\s+', ', ', items)  # Replace "and" with comma
            items = re.sub(r'\s*;\s*', ', ', items)  # Replace semicolons with commas
            return items
    
    bullet_pattern = r'(?:^|\n)(?:\d+\.|[-•*])\s*(.*?)(?:$|\n)'
    bullet_matches = re.findall(bullet_pattern, text)
    if bullet_matches:
        return ', '.join(item.strip() for item in bullet_matches)
    
    comma_pattern = r'(?:^|:)\s*((?:\w+(?:\s+\w+)*,\s*){2,}(?:\w+(?:\s+\w+)*))'
    comma_match = re.search(comma_pattern, text)
    if comma_match:
        return comma_match.group(1).strip()
    
    return ""

def extract_entity(text: str) -> str:
    """
    Extract a named entity (person, place, organization) from text.
    
    Args:
        text: The text to extract from
        
    Returns:
        The extracted entity as a string
    """
    entity_patterns = [
        r'(?:The\s+(?:person|individual)\s+is:?\s+)([A-Z][a-z]+(?:\s+[A-Z][a-z]+)*)',
        r'(?:The\s+(?:place|location)\s+is:?\s+)([A-Z][a-z]+(?:\s+[A-Z][a-z]+)*)',
        r'(?:The\s+(?:organization|company)\s+is:?\s+)([A-Z][a-z]+(?:\s+[A-Z][a-z]+)*)',
        r'(?:The\s+(?:entity)\s+is:?\s+)([A-Z][a-z]+(?:\s+[A-Z][a-z]+)*)'
    ]
    
    for pattern in entity_patterns:
        match = re.search(pattern, text)
        if match:
            return match.group(1).strip()
    
    proper_noun_pattern = r'([A-Z][a-z]+(?:\s+[A-Z][a-z]+)*)'
    matches = re.findall(proper_noun_pattern, text)
    
    if matches:
        common_words = {"The", "A", "An", "This", "That", "These", "Those", "It", "Its", "I", "My", "Me", "You", "Your"}
        filtered_matches = [match for match in matches if match not in common_words]
        
        if filtered_matches:
            return filtered_matches[0].strip()
    
    return ""

def extract_structured_data(text: str) -> str:
    """
    Extract structured data (JSON) from text.
    
    Args:
        text: The text to extract from
        
    Returns:
        The extracted structured data as a string
    """
    json_pattern = r'```(?:json)?\s*({[\s\S]*?})```'
    match = re.search(json_pattern, text)
    if match:
        json_str = match.group(1).strip()
        try:
            parsed = json.loads(json_str)
            return json.dumps(parsed, indent=2)
        except json.JSONDecodeError:
            logger.warning("Found JSON-like block but it's not valid JSON")
    
    curly_pattern = r'({[\s\S]*?})'
    matches = re.findall(curly_pattern, text)
    for match in matches:
        try:
            parsed = json.loads(match)
            return json.dumps(parsed, indent=2)
        except json.JSONDecodeError:
            continue
    
    return ""

def format_answer(answer: str, format_type: str) -> str:
    """
    Format an answer according to the specified format type.
    
    Args:
        answer: The answer to format
        format_type: The desired format type
        
    Returns:
        The formatted answer
    """
    
    if format_type == FORMAT_TYPES["TEXT"]:
        return answer.strip()
    
    elif format_type == FORMAT_TYPES["NUMBER"]:
        number_str = extract_number(answer)
        if not number_str:
            return answer.strip()
        
        clean_number = re.sub(r'[^\d\.\-\+]', '', number_str)
        try:
            num = float(clean_number)
            if num.is_integer():
                return str(int(num))
            return str(num)
        except ValueError:
            return number_str
    
    elif format_type == FORMAT_TYPES["DATE"]:
        date_str = extract_date(answer)
        if date_str:
            return date_str
        return answer.strip()
    
    elif format_type == FORMAT_TYPES["BOOLEAN"]:
        bool_str = extract_boolean(answer)
        if bool_str in ["Yes", "No", "Unknown"]:
            return bool_str
        return answer.strip()
    
    elif format_type == FORMAT_TYPES["LIST"]:
        list_str = extract_list(answer)
        if list_str:
            items = [item.strip() for item in list_str.split(',')]
            return ', '.join(items)
        return answer.strip()
    
    elif format_type == FORMAT_TYPES["ENTITY"]:
        entity_str = extract_entity(answer)
        if entity_str:
            return entity_str
        return answer.strip()
    
    elif format_type == FORMAT_TYPES["STRUCTURED"]:
        json_str = extract_structured_data(answer)
        if json_str:
            return json_str
        return answer.strip()
    
    return answer.strip()

def validate_answer_format(answer: str, expected_format: str) -> Dict[str, Any]:
    """
    Validate that an answer matches the expected format.
    
    Args:
        answer: The answer to validate
        expected_format: The expected format type
        
    Returns:
        Dictionary with validation results
    """
    
    result = {
        "is_valid": False,
        "formatted_answer": answer,
        "error": None
    }
    
    if expected_format == FORMAT_TYPES["NUMBER"]:
        clean_answer = re.sub(r'[^\d\.\-\+]', '', answer)
        try:
            float(clean_answer)
            result["is_valid"] = True
            result["formatted_answer"] = clean_answer
        except ValueError:
            result["is_valid"] = False
            result["error"] = "Answer is not a valid number"
    
    elif expected_format == FORMAT_TYPES["DATE"]:
        date_patterns = [
            r'\d{1,2}[-/]\d{1,2}[-/]\d{2,4}',
            r'\d{4}[-/]\d{1,2}[-/]\d{1,2}',
            r'(?:Jan|Feb|Mar|Apr|May|Jun|Jul|Aug|Sep|Oct|Nov|Dec)[a-z]*\s+\d{1,2}(?:st|nd|rd|th)?,?\s+\d{4}',
            r'\d{1,2}(?:st|nd|rd|th)?\s+(?:of\s+)?(?:Jan|Feb|Mar|Apr|May|Jun|Jul|Aug|Sep|Oct|Nov|Dec)[a-z]*,?\s+\d{4}'
        ]
        if any(re.search(pattern, answer) for pattern in date_patterns):
            result["is_valid"] = True
        else:
            result["is_valid"] = False
            result["error"] = "Answer is not a valid date"
    
    elif expected_format == FORMAT_TYPES["BOOLEAN"]:
        bool_answer = extract_boolean(answer)
        if bool_answer in ["Yes", "No"]:
            result["is_valid"] = True
            result["formatted_answer"] = bool_answer
        else:
            result["is_valid"] = False
            result["error"] = "Answer is not a clear Yes/No"
    
    elif expected_format == FORMAT_TYPES["LIST"]:
        list_items = [item.strip() for item in answer.split(',')]
        if len(list_items) > 1:
            result["is_valid"] = True
            result["formatted_answer"] = ', '.join(list_items)
        else:
            result["is_valid"] = False
            result["error"] = "Answer is not a comma-separated list"
    
    elif expected_format == FORMAT_TYPES["ENTITY"]:
        if re.match(r'^[A-Z][a-z]+(?:\s+[A-Z][a-z]+)*$', answer):
            result["is_valid"] = True
        else:
            result["is_valid"] = False
            result["error"] = "Answer is not a valid named entity"
    
    elif expected_format == FORMAT_TYPES["STRUCTURED"]:
        try:
            json.loads(answer)
            result["is_valid"] = True
        except json.JSONDecodeError:
            result["is_valid"] = False
            result["error"] = "Answer is not valid JSON"
    
    else:  # FORMAT_TYPES["TEXT"] or any other
        result["is_valid"] = True
    
    return result

def process_answer(response: str, expected_format: Optional[str] = None) -> Dict[str, Any]:
    """
    Process an answer: extract, format, and validate.
    
    Args:
        response: The full response text
        expected_format: Optional expected format type
        
    Returns:
        Dictionary with processed answer information
    """
    
    format_type = expected_format if expected_format else FORMAT_TYPES["TEXT"]
    
    extracted_answer = extract_answer(response, format_type)
    formatted_answer = format_answer(extracted_answer, format_type)
    validation_result = validate_answer_format(formatted_answer, format_type)
    
    return {
        "original_response": response,
        "extracted_answer": extracted_answer,
        "formatted_answer": formatted_answer,
        "format_type": format_type,
        "is_valid": validation_result["is_valid"],
        "error": validation_result["error"]
    }